62699 WORLD DEVELOPMENT INDICATORS INCOME MAP The world by income Low income Honduras Grenada Hong Kong SAR, China Afghanistan India Iran, Islamic Rep. Hungary Bangladesh Indonesia Jamaica Iceland Benin Iraq Kazakhstan Ireland Burkina Faso Jordan Lebanon Isle of Man Burundi Kiribati Libya Israel Cambodia Kosovo Lithuania Italy Central African Republic Lesotho Macedonia, FYR Japan Chad Maldives Malaysia Korea, Rep. Comoros Marshall Islands Mauritius Kuwait Congo, Dem. Rep. Micronesia, Fed. Sts. Mayotte Latvia Eritrea Moldova Mexico Liechtenstein Ethiopia Mongolia Montenegro Luxembourg Gambia, The Morocco Namibia Macao SAR, China Ghana Nicaragua Palau Malta Guinea Nigeria Panama Monaco Guinea-Bissau Pakistan Peru Netherlands Haiti Papua New Guinea Romania Netherlands Antilles Kenya Paraguay Russian Federation New Caledonia Korea, Dem. Rep. Philippines Serbia New Zealand Kyrgyz Republic Samoa Seychelles Northern Mariana Islands Lao PDR São Tomé and Principe South Africa Norway Liberia Senegal St. Kitts and Nevis Oman Madagascar Sri Lanka St. Lucia Poland Malawi Sudan St. Vincent and the Portugal Mali Swaziland Grenadines Puerto Rico Mauritania Syrian Arab Republic Suriname Qatar Mozambique Thailand Turkey San Marino Myanmar Timor-Leste Uruguay Saudi Arabia Nepal Tonga Venezuela, RB Singapore Niger Tunisia Slovak Republic Rwanda Turkmenistan High income Slovenia Sierra Leone Tuvalu Andorra Spain Solomon Islands Ukraine Aruba Sweden Somalia Uzbekistan Australia Switzerland Tajikistan Vanuatu Austria Trinidad and Tobago Tanzania Vietnam Bahamas, The Turks and Caicos Islands Togo West Bank and Gaza Bahrain United Arab Emirates Uganda Yemen, Rep. Barbados United Kingdom Zambia Belgium United States Zimbabwe Upper middle income Bermuda Virgin Islands (U.S.) Albania Brunei Darussalam Lower middle income Algeria Canada Angola American Samoa Cayman Islands Armenia Antigua and Barbuda Channel Islands Belize Argentina Croatia Bhutan Azerbaijan Cyprus Bolivia Belarus Czech Republic Cameroon Bosnia and Herzegovina Denmark Cape Verde Botswana Equatorial Guinea China Brazil Estonia Congo, Rep. Bulgaria Faeroe Islands Côte d'Ivoire Chile Finland Djibouti Colombia France Ecuador Costa Rica French Polynesia Egypt, Arab Rep. Cuba Germany El Salvador Dominica Gibraltar Georgia Dominican Republic Greece Guatemala Fiji Greenland Guyana Gabon Guam Designed and edited by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London 2011 WORLD DEVELOPMENT INDICATORS Copyright 2011 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2011 This volume is a product of the staff of the Development Data Group of the World Bank’s Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank’s Board of Execu- tive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsi- bility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth’s surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemina- tion of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: Front cover, Curt Carnemark/World Bank; page xxiv, Curt Carnemark/World Bank; page 30, Trevor Samson/World Bank; page 122, Curt Carnemark/World Bank; page 188, Curt Carnemark/World Bank; page 262, Ray Witlin/World Bank; page 318, Curt Carnemark/World Bank. If you have questions or comments about this product, please contact: Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or data.worldbank.org ISBN 978-0-8213-8709-2 ECO-AUDIT Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2011 on recycled paper with 50 percent post-consumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www. greenpressinitiative.org. Saved: 91 trees 29 million Btu of total energy 8,609 pounds of net greenhouse gases 41,465 gallons of waste water 2,518 pounds of solid waste 2011 WORLD DEVELOPMENT INDICATORS PREFACE World Development Indicators 2011, the 15th edition in its current format, aims to provide relevant, high-quality, inter- nationally comparable statistics about development and the quality of people’s lives around the globe. This latest printed volume is one of a group of products; others include an online dataset, accessible at http://data.worldbank. org; the popular Little Data Book series; and DataFinder, a data query and charting application for mobile devices. Fifteen years ago, World Development Indicators was overhauled and redesigned, organizing the data to present an integrated view of development, with the goal of putting these data in the hands of policymakers, development spe- cialists, students, and the public in a way that makes the data easy to use. Although there have been small changes, the format has stood the test of time, and this edition employs the same sections as the first one: world view, people, environment, economy, states and markets, and global links. Technical innovation and the rise of connected computing devices have gradually changed the way users obtain and consume the data in the World Development Indicators database. Last year saw a more abrupt change: the decision in April 2010 to make the dataset freely available resulted in a large, immediate increase in the use of the on-line resources. Perhaps more important has been the shift in how the data are used. Software developers are now free to use the data in applications they develop—and they are doing just that. We applaud and encourage all efforts to use the World Bank’s databases in creative ways to solve the world’s most pressing development challenges. This edition of World Development Indicators focuses on the impact of the decision to make data freely available under an open license and with better online tools. To help those who wish to use and reuse the data in these new ways, the section introductions discuss key issues in measuring the economic and social phenomena described in the tables and charts and introduce new sources of data. World Development Indicators is possible only through the excellent collaboration of many partners who provide the data that form part of this collection, and we thank them all: the United Nations family, the International Monetary Fund, the World Trade Organization, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others who make this unique product possible. As always, we welcome your ideas for making the data in World Development Indicators useful and relevant for improving the lives of people around the world. Shaida Badiee Director Development Economics Data Group 2011 World Development Indicators v ACKNOWLEDGMENTS This book was prepared by a team led by Soong Sup Lee under the management of Neil Fantom and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Maja Bresslauer, David Cieslikowski, Mahyar Eshragh- Tabary, Shota Hatakeyama, Masako Hiraga, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Levent, Johan Mistiaen, Sulekha Patel, William Prince, Premi Rathan Raj, Evis Rucaj, Eric Swanson, Jomo Tariku, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. World Development Indicators electronic products were prepared by a team led by Reza Farivari, consisting of Ramvel Chandrasekaran, Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Shelley Fu, Gytis Kanchas, Ugendran Makhachkala, Vilas Mandlekar, Nacer Megherbi, Parastoo Oloumi, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuable advice was provided by Shahrokh Fardoust. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank’s four thematic networks—Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Financial and Private Sector Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substan- tial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated (CDI) provided editorial services, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott. Jomo Tariku designed the cover, Deborah Arroyo and Elaine Wilson typeset the book, and Katrina Van Duyn provided proofreading. Azita Amjadi and Alison Kwong oversaw the production process. Staff from External Affairs Office of the Publisher oversaw printing and dissemination of the book. 2011 World Development Indicators vii TABLE OF CONTENTS FRONT 2. PEOPLE Preface v Introduction 31 Acknowledgments vii Tables Partners xii Users guide xxii 2.1 Population dynamics 36 2.2 Labor force structure 40 2.3 Employment by economic activity 44 1. WORLD VIEW 2.4 Decent work and productive employment 48 2.5 Unemployment 52 2.6 Children at work 56 2.7 Poverty rates at national poverty lines 60 Introduction 1 2.8 Poverty rates at international poverty lines 63 Tables 2.9 Distribution of income or consumption 68 1.1 Size of the economy 10 2.10 Assessing vulnerability and security 72 1.2 Millennium Development Goals: eradicating poverty and 2.11 Education inputs 76 saving lives 14 2.12 Participation in education 80 1.3 Millennium Development Goals: protecting our common 2.13 Education efficiency 84 environment 18 2.14 Education completion and outcomes 88 1.4 Millennium Development Goals: overcoming obstacles 22 2.15 Education gaps by income and gender 92 1.5 Women in development 24 2.16 Health systems 94 1.6 Key indicators for other economies 28 2.17 Health information 98 2.18 Disease prevention coverage and quality 102 Text figures, tables, and boxes 2.19 Reproductive health 106 1a Use of World Bank data has risen with the launch of the 2.20 Nutrition 110 Open Data Initiative 1 2.21 Health risk factors and future challenges 114 1b Terms of use for World Bank data 2 2.22 Mortality 118 1c Access to information at the World Bank 3 1d Progress toward eradicating poverty 4 Text figures, tables, and boxes 1e Progress toward universal primary education completion 4 2a Maternal mortality ratios have declined in all developing 1f Progress toward gender parity 4 country regions since 1990 31 1g Progress toward reducing child mortality 5 2b Maternal mortality ratios have declined fastest 1h Progress toward improving maternal health 5 among low- and lower middle-income countries but remain high 31 1i HIV incidence is remaining stable or decreasing in many 2c The births of many children in Asia and Africa go unregistered 32 developing countries, but many lack data 5 2d In Nigeria, children’s births are more likely to be unregistered 1j Progress on access to an improved water source 6 in rural areas . . . 33 1k Progress on access to improved sanitation 6 2e . . . in poor households . . . 33 1l Official development assistance provided by Development 2f . . . and where the mother has a lower education level 33 Assistance Committee members 7 2g Most people live in countries with low-quality cause of death 1.2a Location of indicators for Millennium Development Goals 1–4 17 statistics 34 1.3a Location of indicators for Millennium Development Goals 5–7 21 2h More countries used surveys for mortality statistics, but civil 1.4a Location of indicators for Millennium Development Goal 8 23 registration did not expand 34 2i Estimates of infant mortality in the Philippines differ by source 35 2.6a The largest sector for child labor remains agriculture, and the majority of children work as unpaid family members 59 2.8a 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 65 2.8b Poverty rates have begun to fall 65 2.8c Regional poverty estimates 66 2.13a There are more overage children among the poor in primary school in Zambia 87 2.17a South Asia has the highest number of unregistered births 101 viii 2011 World Development Indicators 3. ENVIRONMENT Introduction 123 3.4a At least 33 percent of assessed species are estimated to be threatened 141 Tables 3.1 Rural population and land use 126 3.5a Agriculture is still the largest user of water, accounting for some 70 percent of global withdrawals . . . 145 3.2 Agricultural inputs 130 3.5b . . . and approaching 90 percent in some developing regions 145 3.3 Agricultural output and productivity 134 3.6a Emissions of organic water pollutants vary among countries 3.4 Deforestation and biodiversity 138 from 1990 to 2007 149 3.5 Freshwater 142 3.7a A person in a high-income economy uses more than 14 times 3.6 Water pollution 146 as much energy on average as a person in a low-income economy in 3.7 Energy production and use 150 2008 153 3.8 Energy dependency and efficiency and carbon dioxide emissions 154 3.7b Fossil fuels are still the primary global energy source in 2008 153 3.9 Trends in greenhouse gas emissions 158 3.8a High-income economies depend on imported energy 157 3.10 Sources of electricity 162 3.9a The six largest contributors to methane emissions account 3.11 Urbanization 166 for about 50 percent of emissions 161 3.12 Urban housing conditions 170 3.9b The five largest contributors to nitrous oxide emissions 3.13 Traffic and congestion 174 account for about 50 percent of emissions 161 3.14 Air pollution 178 3.10a More than 50 percent of electricity in Latin America is 3.15 Government commitment 180 produced by hydropower 165 3.16 Contribution of natural resources to gross domestic product 184 3.10b Lower middle-income countries produce the majority of their Text figures, tables, and boxes power from coal 165 3a The 10 countries with the highest natural resource rents are 3.11a Urban population is increasing in developing economies, primarily oil and gas producers 124 especially in low and lower middle-income economies 169 3b Countries with negative adjusted net savings are depleting 3.11b Latin America and Caribbean has the greatest share of natural capital without replacing it and are becoming poorer 124 urban population, even greater than the high-income 3.1a What is rural? Urban? 129 economies in 2009 169 3.2a Nearly 40 percent of land globally is devoted to agriculture 133 3.12a Selected housing indicators for smaller economies 173 3.2b Rainfed agriculture plays a significant role in Sub-Saharan 3.13a Biogasoline consumption as a share of total agriculture where about 95 percent of cropland depends on consumption is highest in Brazil . . . 177 precipitation, 2008 133 3.13b . . . but the United States consumes the most biogasoline 177 3.3a The food production index has increased steadily since early 3.16a Oil dominates the contribution of natural resources in the 1960, and the index for low-income economies has been Middle East and North Africa 187 higher than the world average since early 2000 137 3.16b Upper middle-income countries have the highest contribution 3.3b Cereal yield in Sub-Saharan Africa increased between 1990 of natural resources to GDP 187 and 2009 but still is the lowest among the regions 137 2011 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 189 Introduction 263 Tables Tables 4.a Recent economic performance 192 5.1 Private sector in the economy 266 4.1 Growth of output 194 5.2 Business environment: Enterprise Surveys 270 4.2 Structure of output 198 5.3 Business environment: Doing Business indicators 274 4.3 Structure of manufacturing 202 5.4 Stock markets 278 4.4 Structure of merchandise exports 206 5.5 Financial access, stability, and efficiency 282 4.5 Structure of merchandise imports 210 5.6 Tax policies 286 4.6 Structure of service exports 214 5.7 Military expenditures and arms transfers 290 4.7 Structure of service imports 218 5.8 Fragile situations 294 4.8 Structure of demand 222 5.9 Public policies and institutions 298 4.9 Growth of consumption and investment 226 5.10 Transport services 302 4.10 Toward a broader measure of national income 230 5.11 Power and communications 306 4.11 Toward a broader measure of saving 234 5.12 The information age 310 4.12 Central government finances 238 5.13 Science and technology 314 4.13 Central government expenses 242 Text figures, tables, and boxes 4.14 Central government revenues 246 5a The average business in Latin America and the Caribbean 4.15 Monetary indicators 250 spends about 400 hours a year in preparing, filing, and 4.16 Exchange rates and prices 254 paying business taxes, 2009 264 4.17 Balance of payments current account 258 5b Firms in East Asia and the Pacific have the lowest business Text figures, tables, and boxes tax rate, 2010 264 4a Differences in GDP growth among developing country regions 189 5c Two approaches to collecting business environment data: 4b Developing countries are contributing more to global growth 189 Doing Business and Enterprise Surveys 265 4c Economies—both developing and high income—rebounded 5d People living in developing countries of East Asia and Pacific in 2010 190 have more commercial bank accounts than those in other 4d Revisions to GDP decline over time, and GDP data become developing country regions, 2009 265 more stable on average 190 4e Ghana’s revised GDP was 60 percent higher in the new base year, 2006 190 4f Revised data for Ghana show a larger share of services in GDP 190 4g Commission on the Measurement of Economic and Social Progress 191 4.3a Manufacturing continues to show strong growth in East Asia and Pacific through 2009 205 4.4a Developing economies’ share of world merchandise exports continues to expand 209 4.5a Top 10 developing economy exporters of merchandise goods in 2009 213 4.6a Top 10 developing economy exporters of commercial services in 2009 217 4.7a The mix of commercial service imports by developing economies is changing 221 4.9a GDP per capita is still lagging in some regions 229 4.10a GDP and adjusted net national income in Sub-Saharan Africa, 2000–09 233 4.12a Twenty selected economies had a central government debt to GDP ratio of 65 percent or higher 241 4.13a Interest payments are a large part of government expenses for some developing economies 245 4.14a Rich economies rely more on direct taxes 249 4.17a Top 15 economies with the largest reserves in 2009 261 x 2011 World Development Indicators 6. GLOBAL LINKS Introduction 319 Text figures, tables, and boxes Tables 6a Source of data for bilateral trade flows 320 6.1 Integration with the global economy 324 6b Trade in professional services faces the highest barriers 320 6.2 Growth of merchandise trade 328 6c Discrepancies persist in measures of FDI net flows 321 6.3 Direction and growth of merchandise trade 332 6d Source of data on FDI 322 6.4 High-income economy trade with low- and middle-income 6e At least 30 percent of remittance inflows go unrecorded by the sending economies 323 economies 335 6.5 Direction of trade of developing economies 338 6f Migrants originating from low- and middle-income economies and residing in high-income economies rose fivefold over 6.6 Primary commodity prices 341 1960–2000 323 6.7 Regional trade blocs 344 6g The ratio of central government debt to GDP has increased 6.8 Tariff barriers 348 for most economies, 2007–10 323 6.9 Trade facilitation 352 6.3a More than half of the world’s merchandise trade takes place 6.10 External debt 356 between high-income economies. But low- and middle-income 6.11 Ratios for external debt 360 economies’ participation in the global trade has increased in 6.12 Global private financial flows 364 the past 15 years 334 6.13 Net official financial flows 368 6.4a Low-income economies have a small market share in the 6.14 Financial flows from Development Assistance Committee global market of various commodities 337 members 372 6.15 Allocation of bilateral aid from Development Assistance 6.5a Developing economies are trading more with other developing economies 340 Committee members 374 6.16 Aid dependency 376 6.6a Primary commodity prices soared again in 2010 343 6.17 Distribution of net aid by Development Assistance 6.7a Global Preferential Trade Agreements Database 347 Committee members 380 6.11a Ratio of debt services to exports for middle-income economies have sharply increased in 2009 as export revenues declined 363 6.18 Movement of people across borders 384 6.16a Official development assistance from non-DAC donors, 6.19 Travel and tourism 388 2005–09 379 6.17a Beyond the DAC: The role of other providers of development assistance 383 BACK Primary data documentation 393 Statistical methods 404 Credits 406 Bibliography 408 Index of indicators 418 2011 World Development Indicators xi 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 organiza- tions and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2011. Information about the World Bank is also provided. International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation 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 ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbon dioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation, and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/. Deutsche Gesellschaft für Internationale Zusammenarbeit The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GIZ’s aim is to positively shape politi- cal, economic, ecological, and social development in partner countries, thereby improving people’s living conditions and prospects. For more information, see www.giz.de/. xii 2011 World Development Indicators Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/. Internal Displacement Monitoring Centre The Internal Displacement Monitoring Centre was established in 1998 by the Norwegian Refugee Council and is the leading international body monitoring conflict-induced internal displacement worldwide. The center contributes to improving national and international capacities to protect and assist the millions of people around the globe who have been displaced within their own country as a result of conflicts or human rights violations. For more information, see www.internal-displacement.org/. International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is respon- sible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO’s strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/. International Energy Agency The International Energy Agency (IEA) was founded in 1973/74 with a mandate to facilitate cooperation among the IEA member countries to increase energy efficiency, promoting use of clean energy and technol- ogy, and diversify their energy sources while protecting the environment. IEA publishes annual and quarterly statistical publications covering both OECD and non-OECD countries’ statistics on oil, gas, coal, electricity and renewable sources of energy, energy supply and consumption, and energy prices and taxes. IEA also con- tributes in analysis of all aspects of sustainable development globally and provides policy recommendations. For more information, see www.iea.org/. International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people 2011 World Development Indicators xiii PARTNERS a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/. International Monetary Fund The International Monetary Fund (IMF) is an international organization of 187 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expan- sion 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 communica- tion technologies. ITU’s mission is to enable the growth and sustained development of telecommunications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promoting adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation. For more information, see www.itu.int/. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF’s goals—discovery, learning, research infrastructure, and stewardship—provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation’s research capabil- ity 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/. xiv 2011 World Development Indicators Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 34 member countries shar- ing 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’ eco- nomic 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 sup- port 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 under- standing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI’s main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/. Understanding Children’s Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labour Organization, the United Nations Children’s Fund (UNICEF), and the World Bank initiated the joint interagency research program “Understanding Children’s Work and Its Impact” in December 2000. The Understanding Children’s Work (UCW) project was located at UNICEF’s Innocenti Research Centre in Florence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW’s online data- base contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/. United Nations The United Nations currently has 192 member states. The purposes of the United Nations, as set forth in its charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promot- ing 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/. 2011 World Development Indicators xv PARTNERS United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity-building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/. United Nations Children’s Fund The United Nations Children’s Fund (UNICEF) works with other UN bodies and with governments and non- governmental organizations to improve children’s lives in more than 190 countries through various programs in education and health. UNICEF focuses primarily on five areas: child survival and development, basic education and gender equality (including girls’ education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/. United Nations Department of Peacekeeping Operations The United Nations Department of Peacekeeping Operations contributes to the most important function of the United Nations—maintaining international peace and security. The department helps countries torn by conflict to create the conditions for lasting peace. The first peacekeeping mission was established in 1948 and has evolved to meet the demands of different conflicts and a changing political landscape. Today’s peacekeepers undertake a wide variety of complex tasks, from helping build sustainable institutions of gov- ernance, to monitoring human rights, to assisting in security sector reform, to disarmaming, demobilizing, and reintegrating former combatants. For more information, see www.un.org/en/peacekeeping/. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members in education, science, culture, and communications. The UNESCO Institute for Statistics is the organization’s xvi 2011 World Development Indicators 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 partner- ship in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/. United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/. United Nations Office on Drugs and Crime The United Nations Office on Drugs and Crime was established in 1977 and is a global leader in the fight against illicit drugs and international crime. The office assists member states in their struggle against illicit drugs, crime, and terrorism by helping build capacity, conducting research and analytical work, and assist- ing in the ratification and implementation of relevant international treaties and domestic legislation related to drugs, crime, and terrorism. For more information, see www.unodc.org/. The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org/. Upsalla Conflict Data Program The Upsalla Conflict Data Program has collected information on armed violence since 1946 and is one of the most accurate and well used data sources on global armed conflicts. Its definition of armed conflict is becoming a standard in how conflicts are systematically defined and studied. In addition to data collection on armed violence, its researchers conduct theoretically and empirically based analyses of the causes, escalation, spread, prevention, and resolution of armed conflict. For more information, see www.pcr.uu.se/research/UCDP/. 2011 World Development Indicators xvii PARTNERS World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 187 member countries—the International Bank for Reconstruction and Development (IBRD)  and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank’s work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see http://data.worldbank.org/. World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int/. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativ- ity, 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 promot- ing 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/. xviii 2011 World Development Indicators World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as pos- sible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues—through technical assistance and training programs—and cooperating with other international organizations. At the heart of the system—known as the multilateral trading system—are the WTO’s agreements, negotiated and signed by a large majority of the world’s trading nations and ratified by their parliaments. For more information, see www.wto.org/. Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/. DHL DHL provides shipping and customized transportation solutions for customers in more than 220 countries and territories. It offers expertise in express, air, and ocean freight; overland transport; contract logistics solutions; and international mail services. For more information, see www.dhl.com/. International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/. International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org/. 2011 World Development Indicators xix PARTNERS Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, script- ing 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 stake- holder issues. For more information, see www.pwc.com/. Standard & Poor’s Standard & Poor’s is the world’s foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P’s Global Stock Markets Factbook draws on data from S&P’s Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indexes to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com/. World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world’s living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world’s living resources. For more information, see www.unep-wcmc.org/. xx 2011 World Development Indicators World Economic Forum The World Economic Forum (WEF) is an independent international organization committed to improving the state of the world by engaging leaders in partnerships to shape global, regional, and industry agendas. Economic research at the WEF—led by the Global Competitiveness Programme—focuses on identifying the impediments to growth so that strategies to achieve sustainable economic progress, reduce poverty, and increase prosperity can be developed. The WEF’s competitiveness reports range from global coverage, such as Global Competitiveness Report, to regional and topical coverage, such as Africa Competitiveness Report, The Lisbon Review, and Global Information Technology Report. For more information, see www.weforum.org/. World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides—and helps other institutions provide— objective information and practical proposals for policy and institutional change that will foster environmen- tally 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/. 2011 World Development Indicators xxi USERS GUIDE Tables gap-filled estimates for missing data and by an s, for complex technical and conceptual problems that can- The tables are numbered by section and display the simple totals, where they do not), median values (m), not be resolved unequivocally. Data coverage may identifying icon of the section. Countries and econo- weighted averages (w), or simple averages (u). Gap not be complete because of special circumstances mies are listed alphabetically (except for Hong Kong filling of amounts not allocated to countries may result affecting the collection and reporting of data, such SAR, China, which appears after China). Data are in discrepancies between subgroup aggregates and as problems stemming from conflicts. shown for 155 economies with populations of more overall totals. For further discussion of aggregation For these reasons, although data are drawn from than 1 million, as well as for Taiwan, China, in selected methods, see Statistical methods. sources thought to be the most authoritative, they tables. Table 1.6 presents selected indicators for 58 should be construed only as indicating trends and other economies—small economies with populations Aggregate measures for regions characterizing major differences among economies between 30,000 and 1 million and smaller econo- The aggregate measures for regions include only rather than as offering precise quantitative mea- mies if they are members of the International Bank low- and middle-income economies including econo- sures of those differences. Discrepancies in data for Reconstruction and Development (IBRD) or, as it mies with populations of less than 1 million listed presented in different editions of World Development is commonly known, the World Bank. Data for these in table 1.6. Indicators reflect updates by countries as well as economies are included on the World Development The country composition of regions is based on the revisions to historical series and changes in meth- Indicators CD-ROM and the World Bank’s Open Data World Bank’s analytical regions and may differ from odology. Thus readers are advised not to compare website at data.worldbank.org/. common geographic usage. For regional classifica- data series between editions of World Development The term country, used interchangeably with tions, see the map on the inside back cover and the Indicators or between different World Bank publica- economy, does not imply political independence, but list on the back cover flap. For further discussion of tions. Consistent time-series data for 1960–2009 refers to any territory for which authorities report aggregation methods, see Statistical methods. are available on the World Development Indicators separate social or economic statistics. When avail- CD-ROM and at data.worldbank.org/. able, aggregate measures for income and regional Statistics Except where otherwise noted, growth rates are groups appear at the end of each table. Data are shown for economies as they were con- in real terms. (See Statistical methods for information Indicators are shown for the most recent year or stituted in 2009, and historical data are revised to on the methods used to calculate growth rates.) Data period for which data are available and, in most tables, reflect current political arrangements. Exceptions are for some economic indicators for some economies for an earlier year or period (usually 1990 or 1995 in noted throughout the tables. are presented in fiscal years rather than calendar this edition). Time-series data for all 213 economies Additional information about the data is provided years; see Primary data documentation. All dollar fig- are available on the World Development Indicators CD- in Primary data documentation. That section sum- ures are current U.S. dollars unless otherwise stated. ROM and at data.worldbank.org/. marizes national and international efforts to improve The methods used for converting national currencies Known deviations from standard definitions or basic data collection and gives country-level informa- are described in Statistical methods. breaks in comparability over time or across countries tion on primary sources, census years, fiscal years, are either footnoted in the tables or noted in About statistical methods and concepts used, and other Country notes the data. When available data are deemed to be background information. Statistical methods provides • Unless otherwise noted, data for China do not too weak to provide reliable measures of levels and technical information on some of the general calcula- include data for Hong Kong SAR, China; Macao trends or do not adequately adhere to international tions and formulas used throughout the book. SAR, China; or Taiwan, China. standards, the data are not shown. • Data for Indonesia include Timor-Leste through Data consistency, reliability, and comparability 1999 unless otherwise noted. Aggregate measures for income groups Considerable effort has been made to standardize • Montenegro declared independence from Serbia The aggregate measures for income groups include the data, but full comparability cannot be assured, and Montenegro on June 3, 2006. Where avail- 213 economies (the economies listed in the main and care must be taken in interpreting the indicators. able, data for each country are shown separately. tables plus those in table 1.6) whenever data are Many factors affect data availability, comparability, However, for the Serbia listing, some indicators available. To maintain consistency in the aggregate and reliability: statistical systems in many develop- continue to include data for Montenegro through measures over time and between tables, missing ing economies are still weak; statistical methods, 2005; these data are footnoted in the tables. data are imputed where possible. The aggregates coverage, practices, and definitions differ widely; and Moreover, data from 1999 onward for Serbia for are totals (designated by a t if the aggregates include cross-country and intertemporal comparisons involve most indicators exclude data for Kosovo, 1999 xxii 2011 World Development Indicators being the year when Kosovo became a territory more. The 17 participating member countries of the under international administration pursuant to Euro area are presented as a subgroup under high- UN Security Council Resolution 1244 (1999); any income economies. Estonia joined the Euro area on exceptions are noted. Kosovo became a World January 1, 2011. Bank member on June 29, 2009; available data are shown separately for Kosovo in the main tables. Symbols • Netherlands Antilles ceased to exist on October .. 10, 2010. Curaçao and St. Maarten became means that data are not available or that aggregates countries within the Kingdom of the Netherlands. cannot be calculated because of missing data in the Bonaire, St. Eustatius, and Saba became special years shown. municipalities of the Netherlands. 0 or 0.0 Classification of economies means zero or small enough that the number would For operational and analytical purposes the World round to zero at the displayed number of decimal Bank’s main criterion for classifying economies is places. gross national income (GNI) per capita (calculated by the World Bank Atlas method). Every economy / is classified as low income, middle income (subdi- in dates, as in 2003/04, means that the period of vided into lower middle and upper middle), or high time, usually 12 months, straddles two calendar income. For income classifications see the map on years and refers to a crop year, a survey year, or a the inside front cover and the list on the front cover fiscal year. flap. Low- and middle-income economies are some- times referred to as developing economies. The term $ is used for convenience; it is not intended to imply means current U.S. dollars unless otherwise noted. that all economies in the group are experiencing similar development or that other economies have > reached a preferred or final stage of development. means more than. Note that classification by income does not neces- sarily reflect development status. Because GNI per < capita changes over time, the country composition means less than. of income groups may change from one edition of World Development Indicators to the next. Once the Data presentation conventions classification is fixed for an edition, based on GNI • A blank means not applicable or, for an aggre- per capita in the most recent year for which data are gate, not analytically meaningful. available (2009 in this edition), all historical data • A billion is 1,000 million. presented are based on the same country grouping. • A trillion is 1,000 billion. Low-income economies are those with a GNI per • Figures in italics refer to years or periods other capita of $995 or less in 2009. Middle-income econ- than those specified or to growth rates calculated omies are those with a GNI per capita of more than for less than the full period specified. $995 but less than $12,196. Lower middle-income • Data for years that are more than three years and upper middle-income economies are separated from the range shown are footnoted. at a GNI per capita of $3,945. High-income econo- mies are those with a GNI per capita of $12,196 or The cutoff date for data is February 1, 2011. 2011 World Development Indicators xxiii WORLD VIEW Introduction 1 “Our aim is for open data, open knowledge, and open solutions.” —Robert Zoellick, Georgetown University, September 2010 W orld Development Indicators provides a comprehensive selection of national and international data that focus attention on critical development issues, facilitate research, encourage debate and analysis of policy options, and monitor prog- ress toward development goals. Organized around six themes—world view, people, environment, economy, states and markets, and global links—the book contains more than 800 indicators for 155 economies with a population of 1 million people or more, together with relevant aggregates. The online database includes more than 1,100 indicators for 213 economies, with many time series extending back to 1960. In 2010, to improve the impact of the indicators and org—has recorded well over 20 million page views. to provide a platform for others to use the data to And at the time of printing this edition of World De- solve pressing development challenges, the World velopment Indicators, it provides data to more than Development Indicators database and many other 100,000 unique visitors each week, three times as public databases maintained by the World Bank many as before (figure 1a). were made available as open data: free of charge, Making the World Development Indicators and in accessible nonproprietary formats on the World other databases free was only the first step in creat- Wide Web. This year, the first part of the introduc- ing an open data environment. Open data should tion to the World View section provides an overview mean that users can access and search public of the initiative, the impact of moving to an open datasets at no cost, combine data from different data platform, a brief survey of the global open data sources, add data and select data records to include movement, and an examination of its relevance to or exclude in derived works, change the format or development. The second part reviews progress structure of the data, and give away or sell any prod- toward the Millennium Development Goals—whose ucts they create. For the World Bank, this required target date of 2015 is now just four years away. designing new user interfaces and developing new search tools to more easily find and report the data. The World Bank Open Data Initiative It also required a new license defining the terms of The Open Data Initiative is a new strategy for reach- ing data users and a major change in the Bank’s Use of World Bank data has risen with business model for data, which had previously been the launch of the Open Data Initiative 1a a subscription-based model for licensing data ac- Weekly unique visitors to http://data.worldbank.org (thousands) cess and use, using a network of university librar- 125 April 2010 ies, development agencies, and private firms, and 100 Launch of the Open Data Initiative free access provided through the World Bank’s Public Information Centers and depository libraries. 75 Recess period for At the time of the open data announcement there 50 US and European academic teaching were around 140,000 regular users of the subscrip- institutions 25 tion database annually—a substantial number for a highly specialized data product. But providing free 0 January April July October January and easier access to the databases has had an im- 2010 2010 2010 2010 2011 mediate and lasting impact on data use. Since April Source: World Bank staff calculations from Omniture data. 2010 the new data website—http://data.worldbank. 2011 World Development Indicators 1 Terms of use for World Bank data 1b enabling citizens to access and create value through the reuse of public sector information” Why do open data need to be licensed? Because a license conveys certain rights to the (Rahemtulla 2011). licensee—in this case, the data user—while protecting the interests of the licensor. If there is no explicit license attached to a dataset, users may be uncertain of their rights. Can they The Sunlight Foundation, a U.S.-based civil republish these data? Can they include them in a new dataset along with data from other society organization, describes its goals as sources? Can they give them away or resell them? “improving access to government information Intellectual property laws differ by country. In an international environment where data are published on the World Wide Web, it may not be clear what law applies. Lacking a license, by making it available online, indeed redefining a cautious data user would assume that he or she should seek permission of the dataset ‘public’ information as meaning ‘online,’ and . . . owner or publisher, creating a real or imagined impediment to using the data. A license can creating new tools and websites to enable indi- help encourage data use by making clear exactly what is permitted, true even for free data. Use of data in the World Bank’s Data Catalog is governed by the Terms of Use of Datasets viduals and communities to better access that posted at http://data.worldbank.org. The terms follow the general model of the Creative information and put it to use. . . . We want to Commons Attribution License (http://creativecommons.org/licenses/by/3.0) and the Open catalyze greater government transparency by Data Commons Attribution License (www.opendatacommons.org/licenses/by/1.0). These licenses require users to acknowledge the original source when they publish or reuse the engaging individual citizens and communities— data, particularly important for World Development Indicators, where many datasets are technologists, policy wonks, open government obtained from sources such as specialized UN and international agencies. The terms of advocates, and ordinary citizens —demanding use impose some further limitations, still within the spirit of an open data license: users may not claim endorsement by the World Bank or use its name or logos without permission. policies that will enable all of us to hold govern- Acknowledging data sources is good practice, regardless of the terms of a license. Iden- ment accountable” (http://sunlightfoundation. tifying sources makes it possible for others to locate the same or similar data. And credit com/about/). to data producers or publisher recognizes their effort and encourages them to continue. The World Bank’s Terms of Use for Datasets provide a suggested form of attribution: Digital information and communication The World Bank: Dataset name: Data source. technologies permitting dissemination of The information for completing this form of attribution is available in the metadata sup- large amounts of data at little or no cost have plied with data downloaded from http://data.worldbank.org. strengthened the argument for providing free access to public sector information. Pollock use for data (box 1b). And it required new think- (2010) estimates the direct benefit to the U.K. ing to promote the use and reuse of data. To public of providing free access to public sec- reach out to new audiences and communities of tor information that was previously sold to be data users, the World Bank organized a global £1.6–£6 billion, 4–15 times the forgone sales “Apps for Development” competition—one of revenues of £400 million. Additional indirect the first of its kind—inviting developers to cre- benefits come from new products and services ate new applications for desktop computers using open datasets or complementary prod- or mobile devices using World Bank datasets, ucts and services and from reducing the trans- including World Development Indicators data. action costs to data users and reusers. Open data and open government initiatives Open data and open government have progressed farther in rich countries than in Advocates of greater transparency in public developing ones. This may reflect a lack of polit- agencies—the open government movement— ical will or popular demand, but it often reflects have been among the most vocal proponents a lack of technical capacity and resources to of open data. Likewise, those seeking data- make data available in accessible formats. A bases to build new applications have supported study commissioned by the Transparency and freedom of information laws and unrestricted Accountability Initiative (Hogge 2010) identified access to data created by public agencies. three drivers behind the success of the U.K. Opening public databases empowers people and U.S. data.gov initiatives: because data are essential for monitoring the • Civil society, particularly a small and moti- performance of governments and the impact of vated group of “civic hackers” responsible public policies on citizens. for developing grassroots political engage- For advocates of open data, governments ment websites. are vast repositories of statistical and nonsta- • An engaged and well resourced “middle tistical information with unrealized potential for layer” of skilled government bureaucrats. creative applications. The political, philosophi- • A top-level mandate, motivated by an out- cal, and economic impulses for open data and side force (in the United Kingdom) or a open government are often linked. One advo- refreshed political administration hungry cate of open data writes, “The term ‘Open Data’ for change (in the United States). refers to the philosophical and methodologi- Statistical offices exemplify the “middle cal approach to the democratization of data layer” of a government bureaucracy, uniquely 2 2011 World Development Indicators WORLD VIEW skilled in collecting and organizing large data- Access to information at the World Bank 1c sets. But even they may lack the motivation or Opening the World Bank’s databases is part of a broader effort to introduce greater transpar- resources to make their products freely avail- ency in the World Bank’s operations, and a new policy on information disclosure went into able to the public unless they enjoy full support effect on July 1, 2010. Besides formalizing the Open Data Initiative, the Access to Informa- from the top. tion Policy (www.worldbank.org/wbaccess) establishes the principle that the World Bank will disclose any information in its possession that is not on a specific list of exceptions. In developing countries aid donors can act In the past, only documents selected for disclosure were available to the public. The new as fourth driver by providing technical assis- policy reverses the process and presumes that most information is disclosable. Exceptions tance and funding for open data projects and by include personal information and staff records, internal deliberations and administrative matters, and information received in confidence from clients and third parties. Some docu- modeling transparency in their own practices. ments with restricted access are subject to a declassification schedule, ensuring that they The International Aid Transparency Initiative— will become available to the public in due course. A process for requesting documents has the World Bank is a founding member—aims to also been established that allows users to search for documents by country and topic in seven languages. create a global repository of information on aid flows, starting from the commitment of fund- ing from donors and continuing through its dis- the data become “local” and much more acces- bursement to recipient countries, the allocation sible and relevant to project stakeholders. The of aid money in national budgets, the procure- data are open and available directly to software ment of goods and services, and the measure- developers though an application programming ment of results. interface and through an interactive web-based To fulfill the initiative’s goal of providing a application called Mapping for Results (http:// complete accounting of aid to the citizens of maps.worldbank.org). donor and developing countries will require In keeping with the philosophy of the Open cooperation among donors and recipients. Data Initiative, the Mapping for Results appli- Terminology and coding systems must be cation uses the dataset of geo-located project standardized and agreements reached on activities and combines the data with sub- everything from the timing of reports to the national human and social development indica- mechanisms for posting and accessing the tors, such as child mortality rates, poverty inci- datasets. In many cases donor governments dence, malnutrition, and population measures. and international agencies will have to change But even more value may lie in what other their rules on access to information to provide researchers and software developers might do full transparency to their aid programs (box 1c). with the data, combining them with their own For more information on the initiative, see www. data or with data from other sources, perform- aidtransparency.net. ing their own analysis, or providing applications that help citizens and beneficiaries connect Mapping for results—making data directly with the project during implementation, not just accessible but useful through feedback or other mechanisms. The new Access to Information Policy and the Open Data Initiative provide much greater ac- Countdown to the Millennium cess to the World Bank Group’s knowledge Development Goals in 2015 resources than before. But accessible informa- There are four years to the target date for the tion is not the same as usable information. Proj- Millennium Development Goals (MDGs). The ect documents contain a wealth of data about MDGs have focused the world’s attention on planned activities—for instance, on their loca- the living conditions of billions of people who tion. But it may be difficult for many interested live in poor and developing countries and on parties, such as project beneficiaries, citizen the need to improve the quality, frequency, and groups, and civil society organizations, to ex- timeliness of the statistics used to track their tract and visualize relevant data from long texts progress. Progress toward the MDGs has been or tables. marked by slow changes in outcome indicators To help solve this problem, the World Bank, and by improvements in data availability. on a pilot basis, has started to provide geo- World Development Indicators has moni- location codes along with data and information tored global and regional trends in poverty about the projects that it supports. The objec- reduction, education, health, and the envi- tive is to improve aid effectiveness through ronment since 1997. After the UN Millennium enhanced transparency and accountability of Summit in 2000, World Development Indicators project activities. Location information makes began closely tracking the progress of countries 2011 World Development Indicators 3 Progress toward against the targets selected for the MDGs. The eradicating poverty 1d MDGs highlight important outcomes, but the Share of countries focus on this limited set of indicators should making progress toward Reached target On track reducing extreme poverty Off track Insufficient not obscure the fact that development is a com- by half (percent) Seriously off track data 100 plex process whose course is determined in part by geographic location, historical circum- stances, institutional capacity, and uncontrol- 50 lable events such as weather and natural disas- ters. Success or failure, while not arbitrary or 0 entirely accidental, still has a large component of chance. 50 This review employs the same assessment method that World Development Indicators has 100 used since 2004 to track progress of countries 2004 2011 toward the time-bound and quantified targets 140 countries 144 countries Source: World Bank staff estimates. of the MDGs. Countries are “on track” if their past progress equals or exceeds the rate of change necessary to reach an MDG target. A few countries have already reached their tar- Progress toward universal primary education completion 1e gets. They are counted as having achieved the goal, although some may slip back. Countries Share of countries making progress toward Reached target On track making less than necessary progress are “off full completion of primary Off track Insufficient education (percent) Seriously off track data track,” or “seriously off track” if their past rate 100 progress would not allow them to reach the tar- get even in another 25 years. The remaining 50 countries do not have sufficient data to evalu- ate their progress—in some cases because 0 there are no data for the benchmark period of 1990–99 and in others because more recent data are missing. But the situation is improv- 50 ing: starting from the earliest World Develop- ment Indicators progress assessments in 2004 100 2004 2011 (based on data for 1990–2002), the number 140 countries 144 countries of countries with insufficient data has fallen, Source: World Bank staff estimates. enhancing our picture of progress toward the MDGs. For more information on the work of the Progress toward World Bank and its partners to achieve the gender parity 1f MDGs, see www.worldbank.org/mdgs, which Share of countries making Reached On track includes a link to the World Bank’s MDG eAtlas. progress toward gender target Off track parity in primary and Seriously off track secondary education (percent) Insufficient data 100 Goal 1. Eradicate extreme poverty and hunger The number of people living on less than $1.25 a day fell from 1.8 billion in 1990 to 1.4 billion 50 in 2005. New global and regional estimates, to become available later in 2011, are likely to 0 show a continuation of past trends, although the financial crisis of 2008 and the recent surge 50 in food prices will have slowed progress in some countries. Because household income and ex- 100 penditure surveys are expensive and time con- 2004 2011 suming, they are not conducted frequently and 140 countries 144 countries Source: World Bank staff estimates. there are often difficulties in making reliable comparisons over time or across countries. 4 2011 World Development Indicators WORLD VIEW For 140 developing countries, figure 1d com- Progress toward pares the progress assessments in 2005 and reducing child mortality 1g in 2011, based on available data. Forty-three Share of countries making Reached On track progress toward reducing target Off track countries are on track or have reached the tar- under-five child mortality by Seriously off track two-thirds (percent) Insufficient data get of cutting the extreme poverty rate in half, 100 twice as many as in 2005. They include China, Brazil, and the Russian Federation. India, with 50 more than 400 million people living in poverty lags behind, but with faster economic growth may well reach the 2015 target. 0 Goal 2. Achieve universal primary education 50 The goal of providing universal primary educa- tion has proved surprisingly hard to achieve. 100 Completion rates measure the proportion of 2004 2011 140 countries 144 countries children enrolled in the final year of primary ed- Source: World Bank staff estimates. ucation after adjusting for repetition. In 2011, 49 countries had achieved or were on track to achieve 100 percent primary completion rates, Progress toward only three more than in 2004, and the number improving maternal health 1h of countries seriously off track has increased, Share of countries making Reached On track especially in Sub-Saharan Africa (figure 1e). progress toward providing target Off track skilled attendants at births Seriously off track There are more and better data, but the goal (percent) Insufficient data remains elusive. 100 Goal 3. Promote gender equality 50 Gender equality and empowering women foster progress toward all the Millennium Development 0 Goals. Equality of educational opportunities, measured by the ratio of girls’ to boys’ enroll- 50 ments in primary and secondary education, is a starting point. Since the 2004 assessment, the number of countries on track to reach the tar- 100 2004 2011 get has increased steadily, driven by rising en- 140 countries 144 countries rollments of girls, and the number of countries Source: World Bank staff estimates. without sufficient data to measure progress has dropped (figure 1f). HIV incidence is remaining stable Goal 4. Reduce child mortality or decreasing in many developing countries, but many lack data 1i Of 144 countries with data in February 2011, 11 had achieved a two-thirds reduction in their Change in HIV incidence rate, 2001–09 (number of developing countries) under-five child mortality rate, and another 25 100 were on track to do so (figure 1g). This is re- markable progress since 2004, but more than 75 100 countries remain off track, and only a few of them are likely to reach the MDG target by 50 2015. Measuring child mortality is the product of a successful collaboration of international 25 statisticians. By bringing together the most reliable data from multiple sources and apply- 0 ing appropriate estimation methods, consis- Incidence Stable Incidence No data increased by decreased by tent time series comparable across countries more than 25% more than 25% are available for monitoring this important in- Source: Joint United Nations Programme on HIV/AIDS. dicator. More information about data sources 2011 World Development Indicators 5 Progress on access to Indicators. While the number of countries se- an improved water source 1j riously off track has increased, the number Share of countries reducing Reached On track without adequate data has decreased, and the proportion of population target Off track without access to an improved Seriously off track number providing skilled attendants at birth has water source by half (percent) Insufficient data 100 risen 35 percent. Goal 6. Combat HIV/AIDS, 50 malaria, and other diseases When the MDGs were formulated, the HIV/AIDS 0 epidemic was spreading rapidly, engulfing many poor countries in Southern Africa. Data on the 50 extent of the epidemic were derived from sen- tinel sites and limited reporting through health 100 systems. The goal refers to halting and reversing 2004 2011 the spread of HIV/AIDS. Under the circumstanc- 140 countries 144 countries Source: World Bank staff estimates. es it was impossible to set time-bound quan- tified targets. Now the statistical record is be- ginning to improve. UNAIDS, in its 2010 Report on the Global AIDS Epidemic, estimates that the Progress on access to improved sanitation 1k annual number of new HIV infections has fallen 21 percent since its peak in 1997 (figure 1i). But Share of countries making progress toward Reached target On track reliable estimates of incidence are available for improved sanitation Off track Insufficient (percent) Seriously off track data only 60 developing countries and do not include 100 Brazil, China, and the Russian Federation. 50 Goal 7. Ensure environmental sustainability Reversing environmental losses and ensuring 0 a sustainable flow of services from the Earth’s resources have many dimensions: preserving forests, protecting plant and animal species, 50 reducing carbon emissions, and limiting and adapting to the effects of climate change. Im- 100 2004 2011 proving the built environment is also important. 140 countries 144 countries The MDGs set targets for reducing the propor- Source: World Bank staff estimates. tion of people without access to safe water and sanitation by half. The ability to measure prog- ress toward both targets has improved signifi - and estimation methods is available at www. cantly since 2004, and almost half the develop- childmortality.org. ing countries with sufficient data are on track to meet the water target (figure 1j). Progress in Goal 5. Improve maternal health providing access to sanitation has been slower: Reliable measurements of maternal mortality almost half the countries are seriously off track are difficult to obtain. Many national estimates (figure 1k). are not comparable over time or across coun- tries because of differences in methods and Goal 8. Develop a global estimation techniques. Consistently modeled partnership for development estimates that became available only recently Partnership between high-income and develop- show that 30 countries are on track to achieve ing economies, fundamental to achieving the a three-quarter reduction in their maternal mor- MDGs, rests on four pillars: reducing external tality ratio and that 94 are off track or seriously debt of developing countries, increasing their off track. Figure 1h compares the availability of access to markets in OECD countries, realizing skilled birth attendants, a critical factor for re- the benefits of new technologies and essential ducing maternal and infant deaths, using data drugs, and providing financing for development from the 2004 and 2011 World Development programs in the poorest countries. Following 6 2011 World Development Indicators WORLD VIEW the adoption of the MDGs, the International Official development assistance provided by Conference on Financing for Development in Development Assistance Committee members 1l 2002 urged developed countries “to make con- Official development 0.7% GNI or more assistance provided, 0.3% to <0.7% GNI crete efforts toward the target of 0.7 percent of by share of GNI 0.2% to <0.3% GNI (2009 $ billions) <0.2% GNI gross national income [GNI] as official develop- 150 ment assistance to developing countries.” Since then many countries have increased their official development assistance, but few 100 have reached the target of 0.7 percent (fig- ure 1l). In 2009, five countries provided more than 0.7 percent of their GNI as aid, but their 50 share of total aid was only 15 percent. The larg- est share of total aid was provided by 10 donors that gave 0.3–0.7 percent of their GNI. The larg- 0 est single donor, the United States, provided 2000 2009 0.21 percent of its GNI as official development Source: World Bank staff estimates. assistance. 2011 World Development Indicators 7 Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of 1.1 Proportion of population below $1 purchasing power people whose income is less than $1 a day parity (PPP) a day1 1.2 Poverty gap ratio [incidence × depth of poverty] 1.3 Share of poorest quintile in national consumption Target 1.B Achieve full and productive employment and decent 1.4 Growth rate of GDP per person employed work for all, including women and young people 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 (PPP) a day 1.7 Proportion of own-account and contributing family workers in total employment Target 1.C Halve, between 1990 and 2015, the proportion of 1.8 Prevalence of underweight children under five years of age people who suffer from hunger 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and 2.1 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 2.2 Proportion of pupils starting grade 1 who reach last primary schooling grade of primary education 2.3 Literacy rate of 15- to 24-year-olds, women and men Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary 3.1 Ratios of girls to boys in primary, secondary, and tertiary education, preferably by 2005, and in all levels of education education no later than 2015 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 Target 4.A Reduce by two-thirds, between 1990 and 2015, the 4.1 Under-five mortality rate under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, 5.1 Maternal mortality ratio the maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel Target 5.B Achieve by 2015 universal access to reproductive 5.3 Contraceptive prevalence rate health 5.4 Adolescent birth rate 5.5 Antenatal care coverage (at least one visit and at least four visits) 5.6 Unmet need for family planning Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the 6.1 HIV prevalence among population ages 15–24 years spread of HIV/AIDS 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 years Target 6.B Achieve by 2010 universal access to treatment for 6.5 Proportion of population with advanced HIV infection with HIV/AIDS for all those who need it access to antiretroviral drugs Target 6.C Have halted by 2015 and begun to reverse the 6.6 Incidence and death rates associated with malaria incidence of malaria and other major diseases 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should be disaggregated by sex and urban-rural location as far as possible. 8 2011 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development 7.1 Proportion of land area covered by forest into country policies and programs and reverse the 7.2 Carbon dioxide emissions, total, per capita and loss of environmental resources per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances Target 7.B Reduce biodiversity loss, achieving, by 2010, a 7.4 Proportion of fish stocks within safe biological limits significant reduction in the rate of loss 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 Target 7.C Halve by 2015 the proportion of people without 7.8 Proportion of population using an improved drinking water sustainable access to safe drinking water and basic source sanitation 7.9 Proportion of population using an improved sanitation facility Target 7.D Achieve by 2020 a significant improvement in the 7.10 Proportion of urban population living in slums2 lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored separately nondiscriminatory trading and financial system 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 Official development assistance (ODA) nationally and internationally.) 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 Target 8.B Address the special needs of the least developed education, primary health care, nutrition, safe water, and countries sanitation) 8.3 Proportion of bilateral official development assistance of (Includes tariff and quota-free access for the least OECD/DAC donors that is untied developed countries’ exports; enhanced program of 8.4 ODA received in landlocked developing countries as a debt relief for heavily indebted poor countries (HIPC) proportion of their gross national incomes and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a generous ODA for countries committed to poverty proportion of their gross national incomes 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, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 8.F In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population 1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2011 World Development Indicators 9 1.1 Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 Afghanistan 30 652 46 9.1 125 310 207 25.1a 860a 201 40.8 37.1 Albania 3 29 115 12.6 114 4,000 116 27.3 8,640 106 2.5 2.1 Algeria 35 2,382 15 154.2 49 4,420 112 283.2a 8,110a 110 2.1 0.6 Angola 18 1,247 15 69.4 63 3,750 123 96.1 5,190 131 0.7 –1.9 Argentina 40 2,780 15 304.1 29 7,550 85 567.5 14,090 76 0.9 –0.1 Armenia 3 30 108 9.5 124 3,100 131 16.7 5,410 128 –14.4 –14.6 Australia 22 7,741 3 957.5 15 43,770 23 842.3 38,510 24 1.3 –0.8 Austria 8 84 101 388.5 25 46,450 17 321.3 38,410 25 –3.9 –4.2 Azerbaijan 9 87 106 42.5 76 4,840 106 79.2 9,020 101 9.3 8.0 Bangladesh 162 144 1,246 93.5 57 580 189 250.6 1,550 181 5.7 4.3 Belarus 10 208 48 53.7 68 5,560 100 123.1 12,740 88 1.4 1.6 Belgium 11 31 356 488.4 19 45,270 20 395.0 36,610 32 –2.8 –3.5 Benin 9 113 81 6.7 138 750 182 13.5 1,510 183 3.8 0.6 Bolivia 10 1,099 9 16.1 105 1,630 155 41.9 4,250 146 3.4 1.6 Bosnia and Herzegovina 4 51 74 17.7 103 4,700 107 33.0 8,770 105 –2.9 –2.7 Botswana 2 582 3 12.2 117 6,260 92 25.0 12,840 87 –3.7 –5.1 Brazil 194 8,515 23 1,564.2 8 8,070 83 1,968.0 10,160 98 –0.6 –1.5 Bulgaria 8 111 70 46.0 73 6,060 95 100.6 13,260 84 –4.9 –4.5 Burkina Faso 16 274 58 8.0 133 510 190 18.4 1,170 193 3.5 0.1 Burundi 8 28 323 1.2 186 150 213 3.3 390 211 3.5 0.6 Cambodia 15 181 84 9.7 123 650 185 27.0 1,820 176 –1.9 –3.5 Cameroon 20 475 41 23.2 93 1,190 162 42.8 2,190 169 2.0 –0.3 Canada 34 9,985 4 1,416.4 10 41,980 28 1,257.7 37,280 29 –2.5 –3.7 Central African Republic 4 623 7 2.0 177 450 195 3.3 750 207 2.4 0.5 Chad 11 1,284 9 6.7 139 600 187 13.0 1,160 194 –1.6 –4.2 Chile 17 756 23 160.7 48 9,470 75 227.7 13,420 81 –1.5 –2.5 China 1,331 9,600 143 4,856.2 3 3,650 125 9,170.1 6,890 119 9.1 8.5 Hong Kong SAR, China 7 1 6,721 221.1 37 31,570 40 311.9 44,540 18 –2.8 –3.1 Colombia 46 1,142 41 227.8 36 4,990 103 392.5 8,600 107 0.8 –0.6 Congo, Dem. Rep. 66 2,345 29 10.6 121 160 211 19.6 300 212 2.7 0.0 Congo, Rep. 4 342 11 7.7 135 2,080 147 11.2 3,040 157 7.6 5.6 Costa Rica 5 51 90 28.7 86 6,260 92 50.0a 10,930a 95 –1.5 –2.8 Côte d’Ivoire 21 322 66 22.5 95 1,070 168 34.5 1,640 179 3.6 1.2 Croatia 4 57 79 61.0 66 13,770 65 85.1 19,200 65 –5.8 –5.8 Cuba 11 110 105 62.2 65 5,550 98 .. .. 4.3 4.3 Czech Republic 10 79 136 181.6 43 17,310 57 251.1 23,940 59 –4.2 –4.8 Denmark 6 43 130 326.5 28 59,060 9 214.4 38,780 23 –4.9 –5.5 Dominican Republic 10 49 209 45.9 74 4,550 110 81.9a 8,110a 110 3.5 2.0 Ecuador 14 256 55 54.1 67 3,970 b 118 110.4 8,100 112 0.4 –0.7 Egypt, Arab Rep. 83 1,001 83 172.1 45 2,070 148 471.2 5,680 126 4.6 2.8 El Salvador 6 21 297 20.8 100 3,370 127 39.6a 6,420a 121 –3.5 –4.0 Eritrea 5 118 50 1.6 180 320 207 2.9a 580a 210 3.6 0.6 Estonia 1 45 32 18.9 102 14,060 63 25.6 19,120 66 –14.1 –14.1 Ethiopia 83 1,104 83 27.2 89 330 206 77.3 930 200 8.7 5.9 Finland 5 338 18 245.3 33 45,940 19 188.3 35,280 34 –8.0 –8.4 France 63c 549c 114 c 2,750.9 5 42,620 25 2,191.2 33,950 36 –2.6 –3.2 Gabon 1 268 6 10.9 120 7,370 86 18.4 12,450 89 –1.0 –2.7 Gambia, The 2 11 171 0.7 196 440 196 2.3 1,330 186 4.6 1.8 Georgia 4 70 61 11.1d 118 2,530 d 140 20.6d 4,700 d 137 –3.9d –4.0 d Germany 82 357 235 3,476.1 4 42,450 26 3,017.3 36,850 31 –4.7 –4.5 Ghana 24 239 105 28.4 87 1,190e 162 36.6 1,530 182 4.7 2.5 Greece 11 132 88 327.7 27 29,040 42 325.0 28,800 46 –2.0 –2.4 Guatemala 14 109 131 37.2 81 2,650 138 64.1a 4,570a 139 0.6 –1.9 Guinea 10 246 41 3.8 162 370 202 9.5 940 199 –0.3 –2.6 Guinea-Bissau 2 36 57 0.8 194 510 190 1.7 1,060 196 3.0 0.7 Haiti 10 28 364 .. ..f .. .. 2.9 1.3 Honduras 7 112 67 13.5 111 1,800 153 27.7a 3,710a 148 –1.9 –3.8 10 2011 World Development Indicators 1.1 WORLD VIEW Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 Hungary 10 93 112 130.1 51 12,980 66 191.3 19,090 67 –6.3 –6.2 India 1,155 3,287 389 1,405.7 11 1,220 160 3,786.3 3,280 154 9.1 7.7 Indonesia 230 1,905 127 471.0 20 2,050 149 855.0 3,720 147 4.5 3.4 Iran, Islamic Rep. 73 1,745 45 330.6 26 4,530 111 836.5 11,470 94 1.8 0.5 Iraq 31 438 72 69.7 62 2,210 146 105.0 3,330 151 4.2 1.6 Ireland 4 70 65 197.1 39 44,280 22 147.0 33,040 38 –7.1 –7.6 Israel 7 22 344 192.0 40 25,790 46 201.0 27,010 52 0.8 –1.0 Italy 60 301 205 2,114.5 7 35,110 35 1,919.2 31,870 41 –5.0 –5.7 Jamaica 3 11 249 12.4 116 4,590 109 19.5a 7,230a 117 –3.0 –3.5 Japan 128 378 350 4,857.2 2 38,080 32 4,265.3 33,440 37 –5.2 –5.1 Jordan 6 89 67 23.7 92 3,980 b 117 34.1 5,730 125 2.3 –0.1 Kazakhstan 16 2,725 6 110.0 55 6,920 89 164.0 10,320 97 1.2 –0.2 Kenya 40 580 70 30.3 84 760 181 62.5 1,570 180 2.6 –0.1 Korea, Dem. Rep. 24 121 199 .. ..f .. .. .. .. Korea, Rep. 49 100 503 966.6 13 19,830 54 1,328.0 27,240 51 0.2 –0.1 Kosovo 2 11 166 5.9 143 3,240 129 .. .. 4.0 3.4 Kuwait 3 18 157 117.0 50 43,930 10 143.5 53,890 6 4.4 1.9 Kyrgyz Republic 5 200 28 4.6 153 870 179 11.7 2,200 167 2.3 1.5 Lao PDR 6 237 27 5.6 146 880 178 13.9 2,200 167 6.4 4.5 Latvia 2 65 36 27.9 88 12,390 68 39.7 17,610 71 –18.0 –17.6 Lebanon 4 10 413 34.1 82 8,060 84 56.6 13,400 82 9.0 8.2 Lesotho 2 30 68 2.0 175 980 b 175 3.7 1,800 178 0.9 0.0 Liberia 4 111 41 0.7 197 160 211 1.2 290 213 4.6 0.3 Libya 6 1,760 4 77.2 61 12,020 71 105.3a 16,400a 74 2.1 0.1 Lithuania 3 65 53 38.1 80 11,410 72 57.8 17,310 72 –15.0 –14.6 Macedonia, FYR 2 26 81 9.0 128 4,400 113 22.2 10,880 96 –0.7 –0.8 Madagascar 20 587 34 8.5 131 430 200 19.5 990 197 –3.7 –6.2 Malawi 15 118 162 4.4 156 290 210 11.9 780 206 7.6 4.7 Malaysia 27 331 84 201.8 38 7,350 87 376.6 13,710 78 –1.7 –3.3 Mali 13 1,240 11 8.9 129 680 184 15.4 1,190 189 4.3 1.9 Mauritania 3 1,031 3 3.3 166 990 174 6.4 1,940 173 –1.1 –3.3 Mauritius 1 2 628 9.2 127 7,250 88 16.9 13,270 83 2.1 1.6 Mexico 107 1,964 55 962.1 14 8,960 78 1,506.3 14,020 77 –6.5 –7.5 Moldova 4 34 110 5.6g 145 1,560 g 157 10.7g 3,010 g 158 –6.5g –6.4g Mongolia 3 1,564 2 4.4 157 1,630 155 8.9 3,330 151 –1.6 –2.7 Morocco 32 447 72 89.9h 58 2,770h 136 143.1h 4,400h 143 4.9h 3.6h Mozambique 23 799 29 10.0 122 440 196 20.1 880 201 6.3 4.0 Myanmar 50 677 77 .. ..f .. .. .. .. Namibia 2 824 3 9.3 126 4,270 114 13.8 6,350 122 –0.8 –2.7 Nepal 29 147 205 13.0 113 440 196 34.7 1,180 191 4.7 2.8 Netherlands 17 42 490 801.1 16 48,460 15 657.0 39,740 22 –4.0 –4.5 New Zealand 4 268 16 124.3 53 28,810 43 120.0 27,790 48 –0.4 –1.5 Nicaragua 6 130 48 5.7 144 1,000 171 14.6a 2,540a 163 –5.6 –6.9 Niger 15 1,267 12 5.2 148 340 204 10.3 680 209 1.0 –2.9 Nigeria 155 924 170 184.7 42 1,190 162 321.0 2,070 170 5.6 3.2 Norway 5 324 16 408.5 24 84,640 3 267.5 55,420 8 –1.6 –2.8 Oman 3 310 9 49.8 69 17,890 56 68.3 24,530 54 12.8 10.4 Pakistan 170 796 220 169.8 46 1,000 171 454.7 2,680 162 3.6 1.4 Panama 3 75 46 22.7 94 6,570 91 42.1a 12,180a 91 2.4 0.8 Papua New Guinea 7 463 15 7.9 134 1,180 165 15.2a 2,260a 166 4.5 2.1 Paraguay 6 407 16 14.3 108 2,250 145 28.1 4,430 142 –3.8 –5.5 Peru 29 1,285 23 122.4 54 4,200 115 236.7 8,120 109 0.9 –0.3 Philippines 92 300 308 164.6 47 1,790 154 325.6 3,540 149 1.1 –0.7 Poland 38 313 125 467.6 21 12,260 69 697.9 18,290 69 1.7 1.6 Portugal 11 92 116 232.9 35 21,910 51 256.1 24,080 57 –2.6 –2.7 Puerto Rico 4 9 447 .. ..i .. .. .. .. Qatar 1 12 122 .. ..i .. .. 8.6 –1.3 2011 World Development Indicators 11 1.1 Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 Romania 21 238 93 178.9 44 8,330 81 312.4 14,540 75 –8.5 –8.4 Russian Federation 142 17,098 9 1,324.4 12 9,340 76 2,599.4 18,330 68 –7.9 –7.8 Rwanda 10 26 405 4.9 150 490 193 11.3 1,130 195 4.1 1.2 Saudi Arabia 25 2,000j 13 436.9 23 17,210 58 609.8 24,020 58 0.6 –1.7 Senegal 13 197 65 13.1 112 1,040 170 22.7 1,810 177 2.2 –0.4 Serbia 7 88 83 43.9 75 6,000 96 85.6 11,700 93 –3.0 –2.6 Sierra Leone 6 72 80 1.9 178 340 204 4.5 790 205 4.0 1.5 Singapore 5 1 7,125 185.7 41 37,220 33 248.3 49,780 11 –1.3 –4.2 Slovak Republic 5 49 113 87.4 60 16,130 60 119.8 22,110 63 –6.2 –6.4 Slovenia 2 20 101 48.1 72 23,520 49 54.1 26,470 53 –7.8 –8.8 Somalia 9 638 15 .. ..f .. .. .. .. South Africa 49 1,219 41 284.3 31 5,760 97 495.6 10,050 99 –1.8 –2.8 Spain 46 505 92 1,476.2 9 32,120 39 1,447.2 31,490 43 –3.6 –4.5 Sri Lanka 20 66 324 40.4 77 1,990 151 95.8 4,720 136 3.5 2.8 Sudan 42 2,506 18 51.5 70 1,220 160 84.1 1,990 171 4.5 2.2 Swaziland 1 17 69 2.9 167 2,470 143 5.7 4,790 134 1.2 –0.3 Sweden 9 450 23 454.4 22 48,840 14 353.9 38,050 28 –5.1 –6.0 Switzerland 8 41 193 505.8 18 65,430 8 364.1 47,100 14 –1.9 –3.0 Syrian Arab Republic 21 185 115 50.9 71 2,410 144 97.3 4,620 138 4.0 1.5 Tajikistan 7 143 50 4.8 151 700 183 13.5 1,950 172 3.4 1.7 Tanzania 44 947 49 21.4k 97 500k 192 57.9k 1,360k 184 6.0k 3.0k Thailand 68 513 133 254.7 32 3,760 122 517.5 7,640 115 –2.2 –2.8 Timor-Leste 1 15 76 2.7 169 2,460 141 5.2a 4,730a 133 1.9 –1.3 Togo 7 57 122 2.9 168 440 196 5.6 850 203 2.5 0.0 Trinidad and Tobago 1 5 261 22.4 96 16,700 59 33.4 a 24,970a 55 –3.0 –3.4 Tunisia 10 164 67 38.9 78 3,720 124 81.4 7,810 113 3.1 2.1 Turkey 75 784 97 652.4 17 8,720 79 1,009.8 13,500 80 –4.7 –5.8 Turkmenistan 5 488 11 17.5 104 3,420 126 35.7a 6,980a 118 8.0 6.6 Uganda 33 241 166 15.2 106 460 194 39.0 1,190 189 7.1 3.6 Ukraine 46 604 79 128.9 52 2,800 135 284.4 6,180 123 –15.1 –14.6 United Arab Emirates 5 84 55 .. ..i .. .. –0.7 –3.2 United Kingdom 62 244 256 2,558.1 6 41,370 29 2,217.4 35,860 33 –4.9 –5.6 United States 307 9,832 34 14,233.5 1 46,360 18 14,011.0 45,640 16 –2.6 –3.5 Uruguay 3 176 19 30.2 85 9,010 77 43.1 12,900 86 2.9 2.5 Uzbekistan 28 447 65 30.6 83 1,100 167 80.9a 2,910a 159 8.1 6.3 Venezuela, RB 28 912 32 286.4 30 10,090 74 346.9 12,220 90 –3.3 –4.8 Vietnam 87 331 281 87.7 59 1,000 b 171 243.6 2,790 161 5.3 4.0 West Bank and Gaza 4 6 672 .. ..l .. .. .. .. Yemen, Rep. 24 528 45 25.0 90 1,060 169 55.0 2,330 165 3.8 0.8 Zambia 13 753 17 12.5 115 960 176 16.5 1,280 187 6.4 3.8 Zimbabwe 13 391 32 4.6 154 360 203 .. .. 5.7 5.2 World 6,775 s 134,123 s 52 w 59,162.8 t 8,732 w 71,774.4 t 10,594 w –1.9 w –3.0 w Low income 846 17,838 49 431.0 509 1,032.5 1,220 4.6 2.4 Middle income 4,813 80,558 61 16,346.7 3,397 30,653.8 6,370 2.6 1.5 Lower middle income 3,811 31,898 124 8,845.9 2,321 18,229.1 4,784 7.1 5.9 Upper middle income 1,002 48,659 21 7,515.1 7,502 12,461.9 12,440 –2.6 –3.4 Low & middle income 5,659 98,396 59 16,792.6 2,968 31,684.3 5,599 2.7 1.4 East Asia & Pacific 1,944 16,302 123 6,148.6 3,163 11,712.8 6,026 7.4 6.6 Europe & Central Asia 404 23,549 18 2,745.8 6,793 5,097.0 12,609 –5.8 –6.1 Latin America & Carib. 572 20,394 28 4,011.3 7,007 5,888.7 10,286 –1.9 –3.0 Middle East & N. Africa 331 8,778 38 1,190.2 3,597 2,617.6 7,911 3.4 1.6 South Asia 1,568 5,131 329 1,735.4 1,107 4,658.7 2,972 8.1 6.5 Sub-Saharan Africa 840 24,242 36 944.2 1,125 1,722.2 2,051 1.7 –0.7 High income 1,117 35,727 33 42,417.7 37,990 40,433.9 36,213 –3.3 –3.9 Euro area 327 2,583 128 12,723.2 38,872 11,127.6 33,997 –4.1 –4.5 a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for lower middle-income economies based on earlier data. c. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. d. Excludes Abkhazia and South Ossetia. e. Included in the aggregates for low-income economies based on earlier data. f. Estimated to be low income ($995 or less). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Estimated to be high income ($12,196 or more). j. Provisional estimate. k. Covers mainland Tanzania only. l. Estimated to be lower middle income ($996–$3,945). 12 2011 World Development Indicators 1.1 WORLD VIEW Size of the economy About the data Definitions Population, land area, income, and output are basic conventional price indexes allow comparison of real • Population is based on the de facto definition of measures of the size of an economy. They also values over time. population, which counts all residents regardless of provide a broad indication of actual and potential PPP rates are calculated by simultaneously com- legal status or citizenship—except for refugees not resources. Population, land area, income (as mea- paring the prices of similar goods and services permanently settled in the country of asylum, who sured by gross national income, GNI), and output among a large number of countries. In the most are generally considered part of the population of (as measured by gross domestic product, GDP) are recent round of price surveys conducted by the Inter- their country of origin. The values shown are midyear therefore used throughout World Development Indica- national Comparison Program (ICP), 146 countries estimates. See also table 2.1. •  Surface area is tors to normalize other indicators. and territories participated in the data collection, a country’s total area, including areas under inland Population estimates are generally based on including China for the first time, India for the first bodies of water and some coastal waterways. • Pop- extrapolations from the most recent national cen- time since 1985, and almost all African countries. ulation density is midyear population divided by land sus. For further discussion of the measurement of The PPP conversion factors presented in the table area in square kilometers. • Gross national income population and population growth, see About the data come from three sources. For 45 high- and upper (GNI) is the sum of value added by all resident pro- for table 2.1. middle-income countries conversion factors are ducers plus any product taxes (less subsidies) not The surface area of an economy includes inland provided by Eurostat and the Organisation for Eco- included in the valuation of output plus net receipts bodies of water and some coastal waterways. Sur- nomic Co-operation and Development (OECD), with of primary income (compensation of employees and face area thus differs from land area, which excludes PPP estimates for 34 European countries incorpo- property income) from abroad. Data are in current bodies of water, and from gross area, which may rating new price data collected since 2005. For the U.S. dollars converted using the World Bank Atlas include offshore territorial waters. Land area is par- remaining 2005 ICP countries the PPP estimates are method (see Statistical methods). • GNI per capita is ticularly important for understanding an economy’s extrapolated from the 2005 ICP benchmark results, GNI divided by midyear population. GNI per capita in agricultural capacity and the environmental effects which account for relative price changes between U.S. dollars is converted using the World Bank Atlas of human activity. (For measures of land area and each economy and the United States. For countries method. • Purchasing power parity (PPP) GNI is GNI data on rural population density, land use, and agri- that did not participate in the 2005 ICP round, the converted to international dollars using PPP rates. An cultural productivity, see tables 3.1–3.3.) Innova- PPP estimates are imputed using a statistical model. international dollar has the same purchasing power tions in satellite mapping and computer databases More information on the results of the 2005 ICP over GNI that a U.S. dollar has in the United States. have resulted in more precise measurements of land is available at www.worldbank.org/data/icp. • Gross domestic product (GDP) is the sum of value and water areas. All 213 economies shown in World Development added by all resident producers plus any product GNI measures total domestic and foreign value Indicators are ranked by size, including those that taxes (less subsidies) not included in the valuation added claimed by residents. GNI comprises GDP appear in table 1.6. The ranks are shown only in of output. Growth is calculated from constant price plus net receipts of primary income (compensation table 1.1. No rank is shown for economies for which GDP data in local currency. • GDP per capita is GDP of employees and property income) from nonresident numerical estimates of GNI per capita are not pub- divided by midyear population. sources. The World Bank uses GNI per capita in U.S. lished. Economies with missing data are included in dollars to classify countries for analytical purposes the ranking at their approximate level, so that the rel- and to determine borrowing eligibility. For definitions ative order of other economies remains consistent. of the income groups in World Development Indica- tors, see Users guide. For discussion of the useful- ness of national income and output as measures of productivity or welfare, see About the data for tables Data sources 4.1 and 4.2. When calculating GNI in U.S. dollars from GNI Population estimates are prepared by World Bank reported in national currencies, the World Bank fol- staff from a variety of sources (see Data sources lows the World Bank Atlas conversion method, using for table 2.1). Data on surface and land area are a three-year average of exchange rates to smooth from the Food and Agriculture Organization (see the effects of transitory fluctuations in exchange Data sources for table 3.1). GNI, GNI per capita, rates. (For further discussion of the World Bank Atlas GDP growth, and GDP per capita growth are esti- method, see Statistical methods.) mated by World Bank staff based on national Because exchange rates do not always refl ect accounts data collected by World Bank staff during differences in price levels between countries, economic missions or reported by national statis- the table also converts GNI and GNI per capita tical offices to other international organizations estimates into international dollars using purchas- such as the OECD. PPP conversion factors are ing power parity (PPP) rates. PPP rates provide estimates by Eurostat/OECD and by World Bank a standard measure allowing comparison of real staff based on data collected by the ICP. levels of expenditure between countries, just as 2011 World Development Indicators 13 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary education mortality rate % % of total employment under age 5 % % per 1,000 1995– 2009a,b 1990 2008 1990 2004–09a 1991 2009c 1991 2009c 1990 2009 Afghanistan 9.0 .. .. .. 32.9 28 .. 54 62 250 199 Albania 8.1 .. .. .. 6.6 .. 90 96 100 51 15 Algeria 6.9 .. .. 9.2 3.7 80 91 83 .. 61 32 Angola 2.0 d .. .. .. .. 33 .. .. .. 258 161 Argentina 4.1d .. 20e .. 2.3 .. 102 .. 105 28 14 Armenia 8.8 .. .. .. 4.2 .. 98 .. 103 56 22 Australia .. 10 9 .. .. .. .. 100 97 9 5 Austria 8.6 .. 9 .. .. .. 99 95 97 9 4 Azerbaijan 8.0 .. 53 .. 8.4 95 92 100 102 98 34 Bangladesh 9.4 .. .. 61.5 41.3 41 61 75 108 148 52 Belarus 9.2 .. .. .. 1.3 94 96 .. 101 24 12 Belgium 8.5 16 10 .. .. 79 86 101 98 10 5 Benin 6.9 .. .. .. 20.2 22 62 .. .. 184 118 Bolivia 2.8 40e .. 9.7 4.5 71 99 .. 99 122 51 Bosnia and Herzegovina 6.7 .. .. .. 1.6 .. .. .. 102 23 14 Botswana .. .. .. .. .. 90 95 109 100 60 57 Brazil 3.3 29e 27 .. 2.2 93 .. .. 103 56 21 Bulgaria 5.0 .. 9 .. 1.6 90 90 99 97 18 10 Burkina Faso 7.0 .. .. 29.6 26.0 20 43 .. 86 201 166 Burundi 9.0 .. .. 30.2 .. 46 52 82 93 189 166 Cambodia 6.6 .. .. .. 28.8 .. 83 .. 90 117 88 Cameroon 5.6 .. .. 18.0 16.6 53 73 83 86 148 154 Canada 7.2 .. 10e .. .. .. .. 99 .. 8 6 Central African Republic 5.2 .. .. .. .. 28 38 61 69 175 171 Chad 6.3 94 .. .. 33.9 18 33 41 64 201 209 Chile 8.6 .. 25 .. 0.5 .. 95 100 99 22 9 China 5.7 .. .. 12.6 4.5 107 .. 86 105 46 19 Hong Kong SAR, China 5.3 6 7e .. .. 102 93 .. 102 .. .. Colombia 2.5 28e 41 8.8 5.1 73 115 108 105 35 19 Congo, Dem. Rep. 5.5 .. .. .. 28.2 48 56 70 77 199 199 Congo, Rep. 5.0 .. .. 21.1 11.8 54 74 89 .. 104 128 Costa Rica 4.2 25 20 2.5 .. 79 96 101 102 18 11 Côte d’Ivoire 5.6 .. .. .. 16.7 42 46 .. .. 152 119 Croatia 8.1 .. 22 f .. 1.0 .. 100 103 102 13 5 Cuba .. .. .. .. .. 99 98 106 99 14 6 Czech Republic 10.2 7 13 0.9 .. 92 95 98 101 12 4 Denmark 8.3 7 5 .. .. 98 101 101 102 9 4 Dominican Republic 4.4 39 42 8.4 3.4 .. 90 .. 97 62 32 Ecuador 4.2 36e 34e .. 6.2 .. 103 100 103 53 24 Egypt, Arab Rep. 9.0 28e 25 10.5 6.8 .. 95 81 .. 90 21 El Salvador 4.3 35 36 11.1 .. 65 89 101 98 62 17 Eritrea .. .. .. 36.9 .. .. 48 82 77 150 55 Estonia 6.8 2e 6e .. .. .. 100 103 101 17 6 Ethiopia 9.3 .. 52e .. 34.6 23 55 68 88 210 104 Finland 9.6 .. 9 .. .. 97 98 109 102 7 3 France 7.2 11 6 .. .. 106 .. 102 100 9 4 Gabon 6.1 48 .. .. .. 62 .. 96 .. 93 69 Gambia, The 4.8 .. .. .. 15.8 45 79 65 102 153 103 Georgia 5.3 .. 62 .. 2.3 .. 107 98 96 47 29 Germany 8.5 .. 7 .. 1.1 100 104 99 98 9 4 Ghana 5.2 .. .. 24.1 14.3 64 83 78 95 120 69 Greece 6.7 40e 27 .. .. 99 101 99 97 11 3 Guatemala 3.4 .. .. 27.8 .. .. 80 87 94 76 40 Guinea 6.4 .. .. .. 20.8 17 62 45 77 231 142 Guinea-Bissau 7.2 .. .. .. 17.4 .. .. 55 .. 240 193 Haiti 2.5 .. .. 23.7 18.9 27 .. .. .. 152 87 Honduras 2.0 49e .. 15.8 8.6 64 90 104 107 55 30 14 2011 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary education mortality rate % % of total employment under age 5 % % per 1,000 1995– 2009a,b 1990 2008 1990 2004–09a 1991 2009c 1991 2009c 1990 2009 Hungary 8.4 7e 7 2.3 .. 82 95 100 98 17 6 India 8.1 .. .. 59.5 43.5 .. 95 73 92 118 66 Indonesia 7.6 .. 63 31.0 17.5g 93 109 93 98 86 39 Iran, Islamic Rep. 6.4 .. 43 .. .. 88 101 85 97 73 31 Iraq .. .. .. 10.4 7.1 58 64 79 81 53 44 Ireland 7.4 20 12 .. .. 103 99 104 103 9 4 Israel 5.7 .. 7 .. .. .. 99 105 101 11 4 Italy 6.5 27 19 .. .. 98 104 100 99 10 4 Jamaica 5.2 42 35 4.0 2.2 94 89 103 100 33 31 Japan .. 19 11 .. .. 102 101 101 100 6 3 Jordan 7.2 .. .. 4.8 1.9 101 100 101 102 39 25 Kazakhstan 8.7 .. .. .. 4.9 .. 106 .. 99 60 29 Kenya 4.7 .. .. 20.1 16.4 .. .. .. 95 99 84 Korea, Dem. Rep. .. .. .. .. 20.6 .. .. .. .. 45 33 Korea, Rep. 7.9 .. 25 .. .. 99 99 99 97 9 5 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. 1.7 57 93 100 101 17 10 Kyrgyz Republic 8.8 .. 47 .. 2.7 .. 94 102 101 75 37 Lao PDR 7.6 .. .. 39.8 31.6 41 75 77 87 157 59 Latvia 6.8 .. 7 .. .. .. 95 101 100 16 8 Lebanon .. .. .. .. 4.2 .. 85 101 104 40 12 Lesotho 3.0 38 .. 13.8 16.6 59 70 124 107 93 84 Liberia 6.4 .. .. .. 20.4 .. 58 .. .. 247 112 Libya .. .. .. .. 5.6 .. .. .. .. 36 19 Lithuania 6.6 .. 9 .. .. .. 92 96 100 15 6 Macedonia, FYR 5.4 .. 22 .. 1.8 98 92 99 98 36 11 Madagascar 6.2 84 .. 35.5 36.8 36 79 96 97 167 58 Malawi 7.0 .. .. 24.4 15.5 31 59 82 100 218 110 Malaysia 4.5 29 22 22.1 .. 91 97 101 103 18 6 Mali 6.5 .. .. 29.0 27.9 .. 59 58 78 250 191 Mauritania 6.2 .. .. 43.3 16.7 33 64 71 103 129 117 Mauritius .. 12 17 .. .. 115 89 102 101 24 17 Mexico 3.9 26 30 13.9 3.4 88 104 97 102 45 17 Moldova 6.8 .. 32 .. 3.2 .. 93 105 101 37 17 Mongolia 7.1 .. .. 10.8 5.3 .. 93 109 103 101 29 Morocco 6.5 .. 51 8.1 9.9 48 80 70 88 89 38 Mozambique 5.2 .. .. .. .. 26 57 71 88 232 142 Myanmar .. .. .. 28.8 .. .. 99 95 100 118 71 Namibia .. .. .. 21.5 17.5 74 87 106 104 73 48 Nepal 6.1 .. .. .. 38.8 51 .. 59 .. 142 48 Netherlands 7.6 8 9 .. .. .. .. 97 98 8 4 New Zealand 6.4 13 12 .. .. .. .. 100 103 11 6 Nicaragua 3.8 .. 45 9.6 4.3 42 75 119 102 68 26 Niger 8.3 .. .. 41.0 39.9 17 40 53 75 305 160 Nigeria 5.1 .. .. 35.1 26.7 .. 79 77 85 212 138 Norway 9.6 .. 6 .. .. 100 98 102 99 9 3 Oman .. .. .. 21.4 .. 74 80 89 97 48 12 Pakistan 9.0 .. 62 39.0 .. .. 61 48 82 130 87 Panama 3.6 34 28 .. .. 86 102 99 101 31 23 Papua New Guinea 4.5 .. .. .. 18.1 46 .. 80 .. 91 68 Paraguay 3.8 23e 47 2.8 .. 68 94 98 100 42 23 Peru 3.9 36e 40e 8.8 5.4 .. 101 96 99 78 21 Philippines 5.6 .. 45e 29.8 .. 88 94 99 102 59 33 Poland 7.6 28e 19 .. .. 96 96 101 99 17 7 Portugal 5.8 25e 19 .. .. .. .. 103 100 15 4 Puerto Rico .. .. .. .. .. .. .. .. 102 .. .. Qatar 3.9 .. .. .. .. 71 108 98 120 19 11 2011 World Development Indicators 15 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary education mortality rate % % of total employment under age 5 % % per 1,000 1995– 2009a,b 1990 2008 1990 2004–09a 1991 2009c 1991 2009c 1990 2009 Romania 8.1 27e 31 5.0 .. 96 96 99 99 32 12 Russian Federation 6.0 1 6 .. .. .. 95 105 98 27 12 Rwanda 4.2 .. .. 24.3 18.0 50 54 95 100 171 111 Saudi Arabia .. .. .. .. 5.3 .. 93 .. 91 43 21 Senegal 6.2 83 .. 19.0 14.5 39 57 69 95 151 93 Serbia 9.1 .. 23 .. 1.8 .. 96 .. 101 29 7 Sierra Leone 6.1 .. .. 25.4 21.3 .. 88 64 84 285 192 Singapore 5.0 8 10 .. .. .. .. .. .. 8 3 Slovak Republic 8.8 .. 11 .. .. 95 96 102 100 15 7 Slovenia 8.2 12e 11 .. .. 95 96 103 99 10 3 Somalia .. .. .. .. 32.8 .. .. .. 53 180 180 South Africa 3.1 .. 3 .. .. 76 93 104 99 62 62 Spain 7.0 22e 12 .. .. 104 100 104 103 9 4 Sri Lanka 6.9 .. 41e 29.3 21.6 101 97 102 .. 28 15 Sudan .. .. .. 31.8 31.7 .. 57 78 89 124 108 Swaziland 4.5 .. .. .. 6.1 61 72 .. 92 92 73 Sweden 9.1 .. 7 .. .. 96 94 102 99 7 3 Switzerland 7.6 9 10 .. .. 53 94 97 97 8 4 Syrian Arab Republic 7.7 .. .. 11.5 10.0 89 112 85 97 36 16 Tajikistan 9.3 .. .. .. 14.9 .. 98 .. 91 117 61 Tanzania 6.8 .. 88e 25.1 16.7 55 102 97 96 162 108 Thailand 3.9 70 53 16.3 7.0 .. .. 99 103 32 14 Timor-Leste 9.0 .. .. .. .. .. 80 .. .. 184 56 Togo 5.4 .. .. 21.2 22.3 35 61 59 75 150 98 Trinidad and Tobago .. 22 .. 4.7 .. 102 93 101 101 34 35 Tunisia 5.9 .. .. 8.5 3.3 74 93 86 103 50 21 Turkey 5.7 .. 35 8.7 3.5 90 93 81 93 84 20 Turkmenistan 6.0 .. .. .. .. .. .. .. .. 99 45 Uganda 5.8 .. .. 19.7 16.4 .. 72 77 99 184 128 Ukraine 9.4 .. .. .. .. 92 95 102 99 21 15 United Arab Emirates .. .. .. .. .. 103 99 104 100 17 7 United Kingdom 6.1 10 11 .. .. .. .. 102 101 10 6 United States 5.4 .. .. .. 1.3 .. 95 100 100 11 8 Uruguay 5.6 .. 25e 6.5 6.0 94 106 .. 104 24 13 Uzbekistan 7.1 .. .. .. 4.4 .. 92 .. 99 74 36 Venezuela, RB 4.9 .. 30 6.7 3.7 81 95 105 102 32 18 Vietnam 7.3 .. .. 40.7 20.2 .. .. .. .. 55 24 West Bank and Gaza .. .. 36 .. 2.2 .. 82 .. 104 43 30 Yemen, Rep. 7.2 .. .. 29.6 .. .. 61 .. .. 125 66 Zambia 3.6 65 .. 21.2 14.9 .. 87 .. 96 179 141 Zimbabwe 4.6 .. .. 8.0 14.0 97 .. 92 97 81 90 World .. w .. w .. w 21.3 w 79 w 88 w 87 w 96 w 92 w 61 w Low income .. .. .. 27.7 44 63 80 91 171 118 Middle income .. .. 31.7 20.8 83 92 85 97 85 51 Lower middle income .. .. 33.5 24.0 82 90 81 95 93 57 Upper middle income .. 26 .. .. 88 100 98 101 51 22 Low & middle income .. .. 32.5 22.4 78 87 84 96 100 66 East Asia & Pacific .. .. 18.0 8.8 101 99 89 102 55 26 Europe & Central Asia .. 19 .. .. 92 96 98 97 52 21 Latin America & Carib. .. 30 .. 3.8 84 101 99 102 52 23 Middle East & N. Africa .. 37 .. 6.8 .. 95 80 96 76 33 South Asia .. .. 57.2 42.5 62 79 69 91 125 71 Sub-Saharan Africa .. .. .. 24.7 51 64 82 88 181 130 High income .. 12 .. .. .. 98 100 99 12 7 Euro area .. 11 .. .. 101 .. .. .. 9 4 a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Covers urban areas only. e. Limited coverage. f. Data are for 2009. g. Data are for 2010. 16 2011 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives About the data Definitions Tables 1.2–1.4 present indicators for 17 of the 21 nutrients, and undernourished mothers who give • Share of poorest quintile in national consump- targets specified by the Millennium Development birth to underweight children. tion or income is the share of the poorest 20 per- Goals. Each of the eight goals includes one or more Progress toward universal primary education is cent of the population in consumption or, in some targets, and each target has several associated measured by the primary completion rate. Because cases, income. • Vulnerable employment is the sum indicators for monitoring progress toward the target. many school systems do not record school comple- of unpaid family workers and own-account workers Most of the targets are set as a value of a specific tion on a consistent basis, it is estimated from the as a percentage of total employment. • Prevalence indicator to be attained by a certain date. In some gross enrollment rate in the final grade of primary of malnutrition is the percentage of children under cases the target value is set relative to a level in education, adjusted for repetition. Offi cial enroll- age 5 whose weight for age is more than two stan- 1990. In others it is set at an absolute level. Some ments sometimes differ signifi cantly from atten- dard deviations below the median for the interna- of the targets for goals 7 and 8 have not yet been dance, and even school systems with high average tional reference population ages 0–59 months. The quantified. enrollment ratios may have poor completion rates. data are based on the new international child growth The indicators in this table relate to goals 1–4. Eliminating gender disparities in education would standards for infants and young children, called the Goal 1 has three targets between 1990 and 2015: help increase the status and capabilities of women. Child Growth Standards, released in 2006 by the to halve the proportion of people whose income is The ratio of female to male enrollments in primary World Health Organization. • Primary completion less than $1.25 a day, to achieve full and productive and secondary education provides an imperfect mea- rate is the percentage of students completing the employment and decent work for all, and to halve the sure of the relative accessibility of schooling for girls. last year of primary education. It is calculated as proportion of people who suffer from hunger. Esti- The targets for reducing under-five mortality rates the total number of students in the last grade of mates of poverty rates are in tables 2.7 and 2.8. are among the most challenging. Under-five mortal- primary education, minus the number of repeaters The indicator shown here, the share of the poorest ity rates are harmonized estimates produced by a in that grade, divided by the total number of children quintile in national consumption or income, is a dis- weighted least squares regression model and are of official graduation age. • Ratio of girls to boys tributional measure. Countries with more unequal available at regular intervals for most countries. enrollments in primary and secondary education distributions of consumption (or income) have a Most of the 60 indicators relating to the Millennium is the ratio of the female to male gross enrollment higher rate of poverty for a given average income. Development Goals can be found in World Develop- rate in primary and secondary education. • Under- Vulnerable employment measures the portion of the ment Indicators. Table 1.2a shows where to find the five mortality rate is the probability that a newborn labor force that receives the lowest wages and least indicators for the first four goals. For more informa- baby will die before reaching age five, if subject to security in employment. No single indicator captures tion about data collection methods and limitations, current age-specific mortality rates. The probability the concept of suffering from hunger. Child malnutri- see About the data for the tables listed there. For is expressed as a rate per 1,000. tion is a symptom of inadequate food supply, lack information about the indicators for goals 5–8, see of essential nutrients, illnesses that deplete these About the data for tables 1.3 and 1.4. Location of indicators for Millennium Development Goals 1–4 1.2a Goal 1. Eradicate extreme poverty and hunger Table 1.1 Proportion of population below $1.25 a day 2.8 1.2 Poverty gap ratio 2.7, 2.8 1.3 Share of poorest quintile in national consumption 1.2, 2.9 1.4 Growth rate of GDP per person employed 2.4 1.5 Employment to population ratio 2.4 1.6 Proportion of employed people living below $1 per day — 1.7 Proportion of own-account and unpaid family workers in total employment 1.2, 2.4 1.8 Prevalence of underweight in children under age five 1.2, 2.20 1.9 Proportion of population below minimum level of dietary energy consumption 2.20 Goal 2. Achieve universal primary education Data sources 2.1 Net enrollment ratio in primary education 2.12 The indicators here and throughout this book have 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.13 2.3 Literacy rate of 15- to 24-year-olds 2.14 been compiled by World Bank staff from primary Goal 3. Promote gender equality and empower women and secondary sources. Efforts have been made 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 1.2, 2.12* to harmonize the data series used to compile this 3.2 Share of women in wage employment in the nonagricultural sector 1.5, 2.3* table with those published on the United Nations 3.3 Proportion of seats held by women in national parliament 1.5 Millennium Development Goals Web site (www. Goal 4. Reduce child mortality 4.1 Under-five mortality rate 1.2, 2.22 un.org/millenniumgoals), but some differences in 4.2 Infant mortality rate 2.22 timing, sources, and definitions remain. For more 4.3 Proportion of one-year-old children immunized against measles 2.18 information see the data sources for the indica- — No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in table 1.2a. 2011 World Development Indicators 17 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15–49 ages 15–49 people metric tons % % of population peoplea 2008 1990 2004–09b 2009 2009 1990 2007 2008 1990 2008 2009 Afghanistan 1,400 .. 15 .. 189 0.1 0.0 0.7 .. 37 3.4 Albania 31 .. 69 .. 15 2.3 1.4 1.5 .. 98 41.2 Algeria 120 47 61 0.1 59 3.1 4.1 2.1 88 95 13.5 Angola 610 .. .. 2.0 298 0.4 1.4 1.4 25 57 3.3 Argentina 70 .. 78 0.5 28 3.5 4.6 1.9 90 90 30.4 Armenia 29 .. 53 0.1 73 1.1 1.6 0.9 .. 90 6.8 Australia 8 .. .. 0.1 6 17.2 17.7 4.7 100 100 72.0 Austria 5 .. .. 0.3 11 7.9 8.3 1.9 100 100 73.5 Azerbaijan 38 .. 51 0.1 110 6.0 3.7 0.8 .. 45 42.0 Bangladesh 340 40 53 <0.1 225 0.1 0.3 1.9 39 53 0.4 Belarus 15 .. 73 0.3 39 9.6 6.9 0.7 .. 93 45.9 Belgium 5 78 75 0.2 9 10.8 9.7 1.3 100 100 75.2 Benin 410 .. 17 1.2 93 0.1 0.5 1.5 5 12 2.2 Bolivia 180 30 61 0.2 140 0.8 1.4 0.8 19 25 11.2 Bosnia and Herzegovina 9 .. 36 .. 50 1.2 7.7 13.1 .. 95 37.7 Botswana 190 33 53 24.8 694 1.6 2.6 0.5 36 60 6.2 Brazil 58 59 81 .. 45 1.4 1.9 1.3 69 80 39.2 Bulgaria 13 .. .. 0.1 41 8.8 6.8 1.1 99 100 44.8 Burkina Faso 560 .. 17 1.2 215 0.1 0.1 1.0 6 11 1.1 Burundi 970 .. 9 3.3 348 0.1 0.0 1.5 44 46 0.8 Cambodia 290 .. 40 0.5 442 0.0 0.3 29.8 9 29 0.5 Cameroon 600 16 29 5.3 182 0.1 0.3 5.4 47 47 3.8 Canada 12 .. .. 0.2 5 16.2 16.9 1.8 100 100 77.7 Central African Republic 850 .. 19 4.7 327 0.1 0.1 0.6 11 34 0.5 Chad 1,200 .. 3 3.4 283 0.0 0.0 1.0 6 9 1.7 Chile 26 56 58 0.4 11 2.6 4.3 2.4 84 96 34.0 China 38 85 85 0.1c 96 2.2 5.0 2.4 41 55 28.8 Hong Kong SAR, China .. 86 .. .. 82 4.8 5.8 13.2 .. .. 61.4 Colombia 85 66 78 0.5 35 1.7 1.4 1.2 68 74 45.5 Congo, Dem. Rep. 670 8 21 .. 372 0.1 0.0 2.5 9 23 0.6 Congo, Rep. 580 .. 44 3.4 382 0.5 0.4 1.0 .. 30 6.7 Costa Rica 44 .. 80 0.3 10 1.0 1.8 1.9 93 95 34.5 Côte d’Ivoire 470 .. 13 3.4 399 0.5 0.3 3.9 20 23 4.6 Croatia 14 .. .. <0.1 25 3.8 5.6 1.8 .. 99 50.4 Cuba 53 .. 78 0.1 6 3.1 2.4 4.2 80 91 14.3 Czech Republic 8 78 .. <0.1 9 13.5 12.1 1.5 100 98 63.7 Denmark 5 78 .. 0.2 7 9.8 9.1 1.6 100 100 85.9 Dominican Republic 100 56 73 0.9 70 1.3 2.1 2.1 73 83 26.8 Ecuador 140 53 73 0.4 68 1.6 2.2 10.4 69 92 15.1 Egypt, Arab Rep. 82 47 60 <0.1 19 1.3 2.3 4.1 72 94 20.0 El Salvador 110 47 73 0.8 30 0.5 1.1 1.8 75 87 14.4 Eritrea 280 .. .. 0.8 99 .. 0.1 15.0 9 14 4.9 Estonia 12 .. .. 1.2 30 16.3 15.2 0.6 .. 95 72.3 Ethiopia 470 4 15 .. 359 0.1 0.1 1.3 4 12 0.5 Finland 8 77 .. 0.1 9 10.2 12.1 1.3 100 100 83.9 France 8 81 71 0.4 6 7.0 6.0 2.5 100 100 71.3 Gabon 260 .. .. 5.2 501 6.6 1.4 2.1 .. 33 6.7 Gambia, The 400 12 .. 2.0 269 0.2 0.2 2.2 .. 67 7.6 Georgia 48 .. 47 0.1 107 2.9 1.4 1.0 96 95 30.5 Germany 7 75 .. 0.1 5 12.0 9.6 2.2 100 100 79.5 Ghana 350 13 24 1.8 201 0.3 0.4 3.7 7 13 5.4 Greece 2 .. .. 0.1 5 7.2 8.8 2.1 97 98 44.1 Guatemala 110 .. 54 0.8 62 0.6 1.0 2.4 65 81 16.3 Guinea 680 .. 9 1.3 318 0.2 0.1 2.2 9 19 0.9 Guinea-Bissau 1,000 .. 10 2.5 229 0.2 0.2 2.4 .. 21 2.3 Haiti 300 10 32 1.9 238 0.1 0.2 2.3 26 17 10.0 Honduras 110 47 65 0.8 58 0.5 1.2 3.5 44 71 9.8 18 2011 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15–49 ages 15–49 people metric tons % % of population peoplea 2008 1990 2004–09b 2009 2009 1990 2007 2008 1990 2008 2009 Hungary 13 .. .. <0.1 16 6.1 5.6 1.8 100 100 61.6 India 230 43 54 0.3 168 0.8 1.4 3.3 18 31 5.3 Indonesia 240 50 57 0.2 189 0.8 1.8 3.4 33 52 8.7 Iran, Islamic Rep. 30 49 79 0.2 19 4.2 7.0 1.0 83 .. 38.3 Iraq 75 14 50 .. 64 2.8 3.3 11.0 .. 73 1.0 Ireland 3 60 89 0.2 9 8.6 10.2 1.8 99 99 68.4 Israel 7 68 .. 0.2 5 7.2 9.3 4.3 100 100 49.7 Italy 5 .. .. 0.3 6 7.5 7.7 2.2 .. .. 48.5 Jamaica 89 55 .. 1.7 7 3.3 5.2 7.7 83 83 58.6 Japan 6 58 54 <0.1 21 9.3 9.8 4.9 100 100 77.7 Jordan 59 40 59 .. 6 3.3 3.8 3.4 .. 98 29.3 Kazakhstan 45 .. 51 0.1 163 15.9 14.7 1.1 96 97 33.4 Kenya 530 27 46 6.3 305 0.2 0.3 3.9 26 31 10.0 Korea, Dem. Rep. 250 62 .. .. 345 12.1 3.0 1.3 .. .. 0.0 Korea, Rep. 18 79 80 <0.1 90 5.6 10.4 1.7 100 100 80.9 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 9 .. .. .. 35 19.2 32.3 6.3 100 100 39.4 Kyrgyz Republic 81 .. 48 0.3 159 2.4 1.2 0.8 .. 93 41.2 Lao PDR 580 .. 38 0.2 89 0.1 0.3 1.2 .. 53 4.7 Latvia 20 .. .. 0.7 45 5.1 3.4 1.4 .. 78 66.7 Lebanon 26 .. 58 0.1 15 3.1 3.2 1.2 .. .. 23.7 Lesotho 530 23 47 23.6 634 .. .. 0.6 32 29 3.7 Liberia 990 .. 11 1.5 288 0.2 0.2 3.8 11 17 0.5 Libya 64 .. .. .. 40 9.2 9.3 1.6 97 97 5.5 Lithuania 13 .. .. 0.1 71 6.0 4.5 0.9 .. .. 58.8 Macedonia, FYR 9 .. 14 .. 23 5.6 5.5 0.9 .. 89 51.8 Madagascar 440 17 40 0.2 261 0.1 0.1 6.4 8 11 1.6 Malawi 510 13 41 11.0 304 0.1 0.1 3.3 42 56 4.7 Malaysia 31 50 .. 0.5 83 3.1 7.3 6.9 84 96 57.6 Mali 830 .. 8 1.0 324 0.0 0.0 1.0 26 36 1.9 Mauritania 550 3 9 0.7 330 1.3 0.6 2.9 16 26 2.3 Mauritius 36 75 .. 1.0 22 1.4 3.1 24.3 91 91 22.7 Mexico 85 .. 73 0.3 17 4.3 4.5 3.2 66 85 26.5 Moldova 32 .. 68 0.4 178 4.8 1.3 1.3 .. 79 35.9 Mongolia 65 .. 55 <0.1 224 4.5 4.0 1.1 .. 50 13.1 Morocco 110 42 63 0.1 92 0.9 1.5 1.9 53 69 32.2 Mozambique 550 .. 16 11.5 409 0.1 0.1 2.9 11 17 2.7 Myanmar 240 17 41 0.6 404 0.1 0.3 2.7 .. 81 0.2 Namibia 180 29 55 13.1 727 0.0 1.5 2.1 25 33 5.9 Nepal 380 23 48 0.4 163 0.0 0.1 1.1 11 31 2.1 Netherlands 9 76 69 0.2 8 11.0 10.6 1.3 100 100 90.0 New Zealand 14 .. .. 0.1 8 6.9 7.7 5.1 .. .. 83.4 Nicaragua 100 .. 72 0.2 44 0.6 0.8 1.3 43 52 3.5 Niger 820 4 11 0.8 181 0.1 0.1 1.0 5 9 0.8 Nigeria 840 6 15 3.6 295 0.5 0.6 4.3 37 32 28.4 Norway 7 74 88 0.1 6 7.4 9.1 1.5 100 100 91.8 Oman 20 9 .. 0.1 13 5.6 13.7 4.2 85 .. 43.5 Pakistan 260 15 30 0.1 231 0.6 1.0 1.7 28 45 12.0 Panama 71 .. .. 0.9 48 1.3 2.2 2.9 58 69 27.8 Papua New Guinea 250 .. 32 0.9 250 0.5 0.5 3.6 47 45 1.9 Paraguay 95 48 79 0.3 47 0.5 0.7 0.5 37 70 15.8 Peru 98 59 73 0.4 113 1.0 1.5 2.8 54 68 27.7 Philippines 94 36 51 <0.1 280 0.7 0.8 6.6 58 76 6.5 Poland 6 49 .. 0.1 24 9.1 8.3 1.2 .. 90 58.8 Portugal 7 .. 67 0.6 30 4.5 5.5 2.8 92 100 48.6 Puerto Rico 18 .. .. .. 2 .. .. 3.6 .. .. 25.2 Qatar 8 .. .. 0.1 49 25.2 55.4 .. 100 100 28.3 2011 World Development Indicators 19 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15–49 ages 15–49 people metric tons % % of population peoplea 2008 1990 2004–09b 2009 2009 1990 2007 2008 1990 2008 2009 Romania 27 .. 70 0.1 125 6.8 4.4 1.6 71 72 36.2 Russian Federation 39 34 80 1.0 106 13.9 10.8 1.3 87 87 42.1 Rwanda 540 21 36 2.9 376 0.1 0.1 1.6 23 54 4.5 Saudi Arabia 24 .. 24 .. 18 13.2 16.6 3.8 .. .. 38.6 Senegal 410 .. 12 0.9 282 0.4 0.5 2.2 38 51 7.4 Serbia 8 .. 41 0.1 21 .. .. .. .. 92 56.1 Sierra Leone 970 .. 8 1.6 644 0.1 0.2 3.2 .. 13 0.3 Singapore 9 65 .. 0.1 36 15.4 11.8 9.7 99 100 73.3 Slovak Republic 6 74 .. <0.1 9 8.6 6.8 1.1 100 100 75.0 Slovenia 18 .. .. <0.1 12 6.2 7.5 2.1 100 100 63.6 Somalia 1,200 1 15 0.7 285 0.0 0.1 3.2 .. 23 1.2 South Africa 410 57 .. 17.8 971 9.5 9.0 1.6 69 77 9.0 Spain 6 .. 66 0.4 17 5.9 8.0 3.8 100 100 61.2 Sri Lanka 39 .. 68 <0.1 66 0.2 0.6 14.0 70 91 8.7 Sudan 750 9 8 1.1 119 0.2 0.3 2.4 34 34 9.9 Swaziland 420 20 51 25.9 1,257 0.5 0.9 0.8 .. 55 7.6 Sweden 5 .. .. 0.1 6 6.0 5.4 1.4 100 100 90.3 Switzerland 10 .. .. 0.4 5 6.4 5.0 1.4 100 100 70.9 Syrian Arab Republic 46 .. 58 .. 21 2.9 3.5 2.0 83 96 18.7 Tajikistan 64 .. 37 0.2 202 3.9 1.1 0.8 .. 94 10.1 Tanzania 790 10 26 5.6 183 0.1 0.1 5.1 24 24 1.5 Thailand 48 .. 77 1.3 137 1.7 4.1 3.4 80 96 25.8 Timor-Leste 370 .. 22d .. 498 .. 0.2 .. .. 50 .. Togo 350 34 17 3.2 446 0.2 0.2 1.2 13 12 5.4 Trinidad and Tobago 55 .. 43 1.5 23 13.9 27.9 1.7 93 92 36.2 Tunisia 60 50 60 <0.1 24 1.6 2.3 2.1 74 85 33.5 Turkey 23 63 73 <0.1 29 2.7 4.0 1.4 84 90 35.3 Turkmenistan 77 .. 48 .. 67 7.2 9.2 10.7 98 98 1.6 Uganda 430 5 24 6.5 293 0.0 0.1 2.5 39 48 9.8 Ukraine 26 .. 67 1.1 101 11.7 6.8 1.1 95 95 33.3 United Arab Emirates 10 .. .. .. 4 29.3 31.0 14.1 97 97 82.2 United Kingdom 12 .. .. 0.2 12 10.0 8.8 2.8 100 100 83.2 United States 24 71 .. 0.6 4 19.5 19.3 5.7 100 100 78.1 Uruguay 27 .. 78 0.5 22 1.3 1.9 2.6 94 100 55.5 Uzbekistan 30 .. 65 0.1 128 5.3 4.3 1.0 84 100 16.9 Venezuela, RB 68 .. .. .. 33 6.2 6.0 1.1 82 .. 31.2 Vietnam 56 53 80 0.4 200 0.3 1.3 3.5 35 75 27.5 West Bank and Gaza .. .. 50 .. 19 .. 0.6 .. .. 89 8.8 Yemen, Rep. 210 10 28 .. 54 0.8 1.0 12.6 18 52 1.8 Zambia 470 15 41 13.5 433 0.3 0.2 0.7 46 49 6.3 Zimbabwe 790 43 65 14.3 742 1.5 0.8 0.9 43 44 11.4 World 260 w 57 w 61 w 0.8 w 137 w 4.3e w 4.6e w   52 w 61 w 27.1 w Low income 580 23 33 2.7 294 0.7 0.3   23 35 2.7 Middle income 200 58 66 0.6 138 2.6 3.3   45 57 20.9 Lower middle income 230 60 63 0.4 147 1.6 2.8   37 50 17.2 Upper middle income 82 52 75 1.4 101 6.1 5.3   78 84 34.6 Low & middle income 290 54 61 0.9 161 2.4 2.9   43 54 18.1 East Asia & Pacific 89 75 77 0.2 136 1.9 4.0   42 59 24.1 Europe & Central Asia 32 .. 69 0.6 89 10.7 7.2   87 89 36.4 Latin America & Carib. 86 .. 75 0.5 45 2.3 2.7   69 79 31.5 Middle East & N. Africa 88 42 62 0.1 39 2.5 3.7   73 84 21.5 South Asia 290 40 51 0.3 180 0.7 1.2   22 36 5.5 Sub-Saharan Africa 650 15 21 5.4 342 0.9 0.8   27 31 8.8 High income 15 70 .. 0.3 14 11.9 12.5 100 99 72.3 Euro area 7 .. .. 0.3 9 8.6 8.2 100 100 67.3 a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Hong Kong SAR, China. d. Data are for 2010. e. Includes emissions not allocated to specific countries. 20 2011 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment About the data Definitions The Millennium Development Goals address con- between contraction of the virus and the appearance • Maternal mortality ratio is the number of women cerns common to all economies. Diseases and envi- of symptoms, or malaria, which has periods of dor- who die from pregnancy-related causes during preg- ronmental degradation do not respect national bound- mancy, can be particularly difficult. The table shows nancy and childbirth, per 100,000 live births. Data aries. Epidemic diseases, wherever they occur, pose the estimated prevalence of HIV among adults ages are from various years and adjusted to a common a threat to people everywhere. And environmental 15–49. Prevalence among older populations can be 2008 base year. The values are modeled estimates damage in one location may affect the well-being of affected by life-prolonging treatment. The incidence of (see About the data for table 2.19). • Contraceptive plants, animals, and humans far away. The indicators tuberculosis is based on case notifications and esti- prevalence rate is the percentage of women ages in the table relate to goals 5, 6, and 7 and the targets mates of cases detected in the population. 15–49 married or in union who are practicing, or of goal 8 that address access to new technologies. Carbon dioxide emissions are the primary source whose sexual partners are practicing, any form of For the other targets of goal 8, see table 1.4. of greenhouse gases, which contribute to global contraception. • HIV prevalence is the percentage The target of achieving universal access to repro- warming, threatening human and natural habitats. of people ages 15–49 who are infected with HIV. ductive health has been added to goal 5 to address In recognition of the vulnerability of animal and plant • Incidence of tuberculosis is the estimated number the importance of family planning and health ser- species, a new target of reducing biodiversity loss of new tuberculosis cases (pulmonary, smear posi- vices in improving maternal health and preventing has been added to goal 7. tive, and extrapulmonary). • Carbon dioxide emis- maternal death. Women with multiple pregnancies Access to reliable supplies of safe drinking water and sions are those stemming from the burning of fossil are more likely to die in childbirth. Access to contra- sanitary disposal of excreta are two of the most impor- ception is an important way to limit and space births. tant means of improving human health and protecting fuels and the manufacture of cement. They include Measuring disease prevalence or incidence can be the environment. Improved sanitation facilities prevent emissions produced during consumption of solid, difficult. Most developing economies lack reporting human, animal, and insect contact with excreta. liquid, and gas fuels and gas flaring (see table 3.8). systems for monitoring diseases. Estimates are often Internet use includes narrowband and broadband • Proportion of species threatened with extinction derived from survey data and report data from sentinel Internet. Narrowband is often limited to basic appli- is the total number of threatened mammal (exclud- sites, extrapolated to the general population. Tracking cations; broadband is essential to promote e-busi- ing whales and porpoises), bird, and higher native, diseases such as HIV/AIDS, which has a long latency ness, e-learning, e-government, and e-health. vascular plant species as a percentage of the total number of known species of the same categories. Location of indicators for Millennium Development Goals 5–7 1.3a •  Access to improved sanitation facilities is the percentage of the population with at least adequate Goal 5. Improve maternal health Table access to excreta disposal facilities (private or 5.1 Maternal mortality ratio 1.3, 2.19 shared, but not public) that can effectively prevent 5.2 Proportion of births attended by skilled health personnel 2.19 human, animal, and insect contact with excreta 5.3 Contraceptive prevalence rate 1.3, 2.19 (facilities do not have to include treatment to ren- 5.4 Adolescent fertility rate 2.19 5.5 Antenatal care coverage 1.5, 2.19 der sewage outflows innocuous). Improved facilities 5.6 Unmet need for family planning 2.19 range from simple but protected pit latrines to flush Goal 6. Combat HIV/AIDS, malaria, and other diseases toilets with a sewerage connection. To be effective, 6.1 HIV prevalence among pregnant women ages 15–24 1.3*, 2.21* facilities must be correctly constructed and properly 6.2 Condom use at last high-risk sex 2.21* maintained. • Internet users are people with access 6.3 Proportion of population ages 15–24 with comprehensive, correct knowledge — to the worldwide network. 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 2.18 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 2.18 6.9 Incidence, prevalence, and death rates associated with tuberculosis 1.3, 2.21 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course 2.18 Data sources Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 3.1 The indicators here and throughout this book have 7.2 Carbon dioxide emissions, total, per capita and per $1 purchasing power parity been compiled by World Bank staff from primary GDP 3.8 7.3 Consumption of ozone-depleting substances 3.9* and secondary sources. Efforts have been made 7.4 Proportion of fish stocks within safe biological limits — to harmonize the data series used to compile this 7.5 Proportion of total water resources used 3.5 table with those published on the United Nations 7.6 Proportion of terrestrial and marine areas protected — Millennium Development Goals Web site (www. 7.7 Proportion of species threatened with extinction 1.3 7.8 Proportion of population using an improved drinking water source 1.3, 2.18, 3.5 un.org/millenniumgoals), but some differences in 7.9 Proportion of population using an improved sanitation facility 1.3, 2.18, 3.11 timing, sources, and definitions remain. For more Proportion of urban population living in slums — information see the data sources for the indica- — No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in tables 1.3a and 1.4a. 2011 World Development Indicators 21 1.4 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Official development Least developed countries’ access Support to assistance (ODA) to high-income markets agriculture by donor For basic Goods Net social services a (excluding arms) Average tariff on exports of disbursements % of total sector- admitted free of tariffs least developed countries % of donor allocable ODA % of exports from least % GNI commitments developed countries Agricultural products Textiles Clothing % of GDP 2009 2009 2002 2008 2002 2008 2002 2008 2002 2008 2009b Australia 0.29 14.5 95.9 100.0 0.2 0.0 5.1 0.0 19.7 0.0 0.15 Canada 0.30 25.5 67.2 100.0 0.3 0.1 5.7 0.2 17.9 1.7 0.75 European Union 97.0 98.7 1.8 0.9 0.1 0.1 1.2 1.2 0.84 Austria 0.30 6.3 Belgium 0.55 12.7 Denmark 0.88 21.3 Finland 0.54 5.8 France 0.46 8.8 Germany 0.35 8.7 Greece 0.19 11.2 Ireland 0.54 32.1 Italy 0.16 12.9 Luxembourg 1.04 35.4 Netherlands 0.82 11.9 Portugal 0.23 3.6 Spain 0.46 24.2 Sweden 1.12 10.8 United Kingdom 0.52 21.4 Japan 0.18 18.6 33.2 99.6 4.8 1.4 2.8 2.6 0.1 0.1 1.11 Korea, Rep.c 0.10 6.7 14.6 57.7 26.1 28.5 11.4 4.0 12.5 3.7 2.44 New Zealandc 0.28 27.7 98.0 98.2 3.1 0.0 0.3 0.0 0.3 0.0 0.20 Norway 1.06 21.9 97.9 99.9 3.8 18.0 3.1 0.0 1.3 1.0 1.07 Switzerland 0.45 9.5 93.4 100.0 5.1 0.1 0.0 0.0 0.0 0.0 1.37 United States 0.21 31.7 61.7 83.8 6.3 5.8 6.6 5.7 12.5 11.3 0.87 Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC MDRI HIPC HIPC HIPC MDRI decision completion Initiative assistance decision completion Initiative assistance pointd pointd assistance pointd pointd assistance end-2009 end-2009 net present value net present value $ millions $ millions Afghanistan Jul. 2007 Jan. 2010 654 20 Haiti Nov. 2006 Jun. 2009 164 665 Benin Jul. 2000 Mar. 2003 385 754 Honduras Jul. 2000 Apr. 2005 816 1,893 Boliviae Feb. 2000 Jun. 2001 1,949 1,953 Liberia Mar. 2008 Jun. 2010 2,958 243 Burkina Fasoe,f Jul. 2000 Apr. 2002 812 764 Madagascar Dec. 2000 Oct. 2004 1,228 1,598 Burundi Aug. 2005 Jan. 2009 1,009 58 Malawif Dec. 2000 Aug. 2006 1,379 898 Cameroon Oct. 2000 Apr. 2006 1,861 646 Malie Sep. 2000 Mar. 2003 792 1,308 Central African Republic Sep. 2007 Jun. 2009 675 435 Mauritania Feb. 2000 Jun. 2002 913 558 Chad May 2001 Floating 241 .. Mozambiquee Apr. 2000 Sep. 2001 3,147 1,322 Comoros Jun. 2010 Floating 151 .. Nicaragua Dec. 2000 Jan. 2004 4,861 1,191 Congo, Dem. Rep. Jul. 2003 Jul. 2010 9,493 515 Niger f Dec. 2000 Apr. 2004 947 651 Congo, Rep. Mar. 2006 Jan. 2010 1,906 120 Rwandaf Dec. 2000 Apr. 2005 956 283 Côte d’Ivoire Mar. 2009 Floating 3,245 .. São Tomé & Principef Dec. 2000 Mar. 2007 172 34 Ethiopiaf Nov. 2001 Apr. 2004 2,735 1,862 Senegal Jun. 2000 Apr. 2004 717 1,661 Gambia, The Dec. 2000 Dec. 2007 98 232 Sierra Leone Mar. 2002 Dec. 2006 919 465 Ghana Feb. 2002 Jul. 2004 3,091 2,570 Tanzania Apr. 2000 Nov. 2001 2,977 2,517 Guinea Dec. 2000 Floating 801 .. Togo Nov. 2008 Dec. 2010 305 463 Guinea-Bissau Dec. 2000 Dec. 2010 746 77 Ugandae Feb. 2000 May 2000 1,509 2,245 Guyanae Nov. 2000 Dec. 2003 897 493 Zambia Dec. 2000 Apr. 2005 3,672 1,962 a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Provisional data. c. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development’s Trade Analysis and Information Systems database. d. Refers to the Enhanced HIPC Initiative. e. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. f. Assistance includes topping up at completion point. 22 2011 World Development Indicators 1.4 WORLD VIEW Millennium Development Goals: overcoming obstacles About the data Definitions Achieving the Millennium Development Goals lines with “international peaks”). The averages in •  Official development assistance (ODA) net dis- requires an open, rule-based global economy in the table include ad valorem duties and equivalents. bursements are grants and loans (net of repayments of which all countries, rich and poor, participate. Many Subsidies to agricultural producers and exporters principal) that meet the DAC definition of ODA and are poor countries, lacking the resources to finance in OECD countries are another barrier to developing made to countries on the DAC list of recipients. • ODA for basic social services is aid commitments by DAC development, burdened by unsustainable debt, and economies’ exports. Agricultural subsidies in OECD donors for basic education, primary health care, nutri- unable to compete globally, need assistance from economies are estimated at $384 billion in 2009. tion, population policies and programs, reproductive rich countries. For goal 8—develop a global partner- The Debt Initiative for Heavily Indebted Poor Coun- health, and water and sanitation services. • Goods ship for development—many indicators therefore tries (HIPCs), an important step in placing debt relief admitted free of tariffs are exports of goods (excluding monitor the actions of members of the Organisa- within the framework of poverty reduction, is the first arms) from least developed countries admitted without tion for Economic Co-operation and Development’s comprehensive approach to reducing the external tariff. • Average tariff is the unweighted average of (OECD) Development Assistance Committee (DAC). debt of the world’s poorest, most heavily indebted the effectively applied rates for all products subject to Official development assistance (ODA) has risen countries. A 1999 review led to an enhancement of tariffs. • Agricultural products are plant and animal in recent years as a share of donor countries’ gross the framework. In 2005, to further reduce the debt products, including tree crops but excluding timber and national income (GNI), but the poorest economies of HIPCs and provide resources for meeting the Mil- fish products. • Textiles and clothing are natural and need additional assistance to achieve the Millen- lennium Development Goals, the Multilateral Debt synthetic fibers and fabrics and articles of clothing nium Development Goals. In 2009 total net ODA from Relief Initiative (MDRI), proposed by the Group of made from them. • Support to agriculture is the value OECD DAC members rose 0.7 percent in real terms Eight countries, was launched. of gross transfers from taxpayers and consumers aris- to $119.6 billion, representing 0.31 percent of DAC Under the MDRI four multilateral institutions—the ing from policy measures, net of associated budgetary members’ combined gross national income. International Development Association (IDA), Inter- receipts, regardless of their objectives and impacts on farm production and income or consumption of farm One important action that high-income economies national Monetary Fund (IMF), African Development products. • HIPC decision point is the date when a can take is to reduce barriers to exports from low- Fund (AfDF), and Inter-American Development Bank heavily indebted poor country with an established and middle- income economies. The European Union (IDB)—provide 100 percent debt relief on eligible track record of good performance under adjustment has begun to eliminate tariffs on exports of “every- debts due to them from countries having completed programs supported by the IMF and the World Bank thing but arms” from least developed countries, and the HIPC Initiative process. Data in the table refer commits to additional reforms and a poverty reduc- the United States offers special concessions to Sub- to status as of March 2011 and might not show tion strategy and starts receiving debt relief. • HIPC Saharan African exports. However, these programs countries that have since reached the decision or completion point is the date when a country success- still have many restrictions. completion point. Debt relief under the HIPC Initia- fully completes the key structural reforms agreed on Average tariffs in the table refl ect high-income tive has reduced future debt payments by $59 bil- at the decision point, including implementing a poverty OECD member tariff schedules for exports of coun- lion (in end-2009 net present value terms) for 36 reduction strategy. The country then receives full debt tries designated least developed countries by the countries that have reached the decision point. And relief under the HIPC Initiative without further policy United Nations. Although average tariffs have been 32 countries that have reached the completion point conditions. • HIPC Initiative assistance is the debt falling, averages may disguise high tariffs on specific have received additional assistance of $30 billion (in relief committed as of the decision point (assuming full goods (see table 6.8 for each country’s share of tariff end-2009 net present value terms) under the MDRI. participation of creditors). Topping-up assistance and assistance provided under the original HIPC Initiative Location of indicators for Millennium Development Goal 8 1.4a were committed in net present value terms as of the decision point and are converted to end-2009 terms. Goal8. Develop a global partnership for development Table • MDRI assistance is 100 percent debt relief on eli- 8.1 Net ODA as a percentage of DAC donors’ gross national income 1.4, 6.14 8.2 Proportion of ODA for basic social services 1.4 gible debt from IDA, IMF, AfDF, and IDB, delivered in full 8.3 Proportion of ODA that is untied 6.15b to countries having reached the HIPC completion point. 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 Data sources developed countries admitted free of duty 1.4 Data on ODA are from the OECD. Data on goods 8.7 Average tariffs imposed by developed countries on agricultural products and admitted free of tariffs and average tariffs are textiles and clothing from least developed countries 1.4, 6.8* 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.4 from the World Trade Organization, in collabora- 8.9 Proportion of ODA provided to help build trade capacity — tion with the United Nations Conference on Trade 8.10 Number of countries reaching HIPC decision and completion points 1.4 and Development and the International Trade Cen- 8.11 Debt relief committed under new HIPC initiative 1.4 tre. These data are available at www.mdg-trade. 8.12 Debt services as a percentage of exports of goods and services 6.11* 8.13 Proportion of population with access to affordable, essential drugs on a org. Data on subsidies to agriculture are from sustainable basis — the OECD’s Producer and Consumer Support Esti- 8.14 Telephone lines per 100 people 1.3*, 5.11 mates, OECD Database 1986–2009. Data on the 8.15 Cellular subscribers per 100 people 1.3*, 5.11 HIPC Initiative and MDRI are from the World Bank’s 8.16 Internet users per 100 people 5.12 Economic Policy and Debt Department. — No data are available in the World Development Indicators database. * Table shows information on related indicators. 2011 World Development Indicators 23 1.5 Women in development Female Life expectancy Pregnant Teenage Women in wage Unpaid Female Ratio Women in population at birth women mothers employment in family workers part-time of female parliaments receiving nonagricultural employment to male prenatal sector wages in care manufacturing % of nonagricultural Male Female years % of women wage % of male % of female % of % % of total Male Female % ages 15–19 employment employment employment % of total total seats 2009 2009 2009 2004–09a 2004–09a 2008 2008 2008 2004–09a 2004–09a 1990 2010 Afghanistan  48.2 44 44 36 .. .. .. .. .. .. 4 28 Albania 50.6 74 80 97 .. .. .. .. .. .. 29 16 Algeria 49.5 71 74 89 .. 13 .. .. .. .. 2 8 Angola 50.7 46 50 80 29 .. .. .. .. .. 15 39 Argentina 51.0 72 79 99 .. 45 0.7b 1.6 b 61b .. 6 39 Armenia 53.4 71 77 93 5 45 .. .. .. .. 36 9 Australia 50.3 79 84 .. .. 47 0.2 0.4 71b 90 6 25 Austria 51.2 77 83 .. .. 47 2.0 2.7 81 .. 12 28 Azerbaijan 51.1 68 73 77 6 44 0.0 0.0 .. .. .. 11 Bangladesh 49.4 66 68 51 33 .. .. .. .. .. 10 19 Belarus 53.5 65 76 99 .. 56 .. .. .. .. .. 35 Belgium 51.0 78 84 .. .. 47 0.4 2.2 81 86 9 39 Benin 49.5 61 63 84 21 .. .. .. .. .. 3 11 Bolivia 50.1 64 68 86 .. 38 .. .. .. .. 9 25 Bosnia and Herzegovina  51.9 73 78 99 .. 36 2.0 8.9 .. .. .. 19 Botswana 50.0 55 55 94 .. 43 .. .. .. 66 5 8 Brazil 50.8 69 76 97 .. 42 4.6 8.1 .. .. 5 9 Bulgaria 51.7 70 77 .. .. 51 0.6 1.5 54 69 21 21 Burkina Faso 50.1 52 55 85 .. .. .. .. .. .. .. 15 Burundi 51.0 49 52 92 .. .. .. .. .. .. .. 32 Cambodia 51.1 60 63 83b 8 .. .. .. .. .. .. 21 Cameroon 50.0 51 52 82 28 .. .. .. .. .. 14 14 Canada 50.5 79 84 .. .. 50 0.1 0.2 68b .. 13 22 Central African Republic 50.9 46 49 69 .. .. .. .. .. .. 4 10 Chad 50.3 48 50 39 37 .. .. .. .. .. .. 5 Chile 50.5 76 82 .. .. 36 0.9 2.8 56 .. .. 14 China 48.1c 72c 75c 91 .. .. .. .. .. .. 21 21 Hong Kong SAR, China 52.6 80 86 .. .. 49 0.1b 1.1b .. 59 .. .. Colombia 50.8 70 77 94 21 48 3.2 6.1 .. 60 5 8 Congo, Dem. Rep. 50.4 46 49 85 24 .. .. .. .. .. 5 8 Congo, Rep. 50.1 53 55 86 27 .. .. .. .. .. 14 7 Costa Rica 49.2 77 82 90 .. 42 1.3 2.8 .. 70 11 39 Côte d’Ivoire 49.1 57 59 85 .. .. .. .. .. .. 6 9 Croatia 51.8 73 80 100 b 4 45d 0.9d 3.9d 59 77 .. 24 Cuba 49.9 77 81 100 .. 43 .. .. .. .. 34 43 Czech Republic 50.9 74 80 .. .. 46 0.3 1.0 69 .. .. 22 Denmark 50.4 77 81 .. .. 49 0.3 0.5 62 87 31 38 Dominican Republic 49.8 70 76 99 21 39 2.9 3.4 .. .. 8 21 Ecuador 49.9 72 78 84 19 39 4.4b 11.1b .. .. 5 32 Egypt, Arab Rep. 49.7 69 72 74 10 19 8.6 32.6 .. 76 4 2 El Salvador 52.8 67 76 94 .. 48 8.8 9.9 .. 85 12 19 Eritrea 50.8 58 62 .. .. .. .. .. .. .. .. 22 Estonia 53.9 70 80 .. .. 52 0.0 b 0.0 b 68 .. .. 23 Ethiopia 50.3 54 57 28 17 47 7.8b 12.7b 56b .. .. 28 Finland 51.0 77 83 .. .. 51 0.6 0.4 64 84 32 40 France  51.4 78 85 .. .. 49 0.3 0.9 80 82 7 19 Gabon 50.0 60 62 .. .. .. .. .. .. .. 13 15 Gambia, The 50.4 55 58 98 .. .. .. .. .. .. 8 8 Georgia 53.0 68 75 94 10 46 .. .. 56 61 .. 7 Germany 51.0 77 83 .. .. 47 0.4 1.5 80 74 .. 33 Ghana 49.3 56 58 90 13 .. .. .. .. .. .. 8 Greece 50.4 78 83 .. .. 42 3.4 9.8 68 .. 7 17 Guatemala 51.3 67 74 .. .. 43 .. .. .. .. 7 12 Guinea 49.5 56 60 88 32 .. .. .. .. .. .. 19 Guinea-Bissau 50.5 47 50 78 .. .. .. .. .. .. 20 10 Haiti 50.6 60 63 85 14 .. .. .. .. .. .. 4 Honduras 50.0 70 75 92 22 34 .. .. .. .. 10 18 24 2011 World Development Indicators 1.5 WORLD VIEW Women in development Female Life expectancy Pregnant Teenage Women in wage Unpaid Female Ratio Women in population at birth women mothers employment in family workers part-time of female parliaments receiving nonagricultural employment to male prenatal sector wages in care manufacturing % of nonagricultural Male Female years % of women wage % of male % of female % of % % of total Male Female % ages 15–19 employment employment employment % of total total seats 2009 2009 2009 2004–09a 2004–09a 2008 2008 2008 2004–09a 2004–09a 1990 2010 Hungary 52.5 70 78 .. .. 48 0.3 0.5 65 77 21 9 India 48.3 63 66 75 16 .. .. .. .. .. 5 11 Indonesia 50.1 69 73 93 9 32 7.8 33.6 .. .. 12 18 Iran, Islamic Rep. 49.2 70 73 98 .. .. 5.4 32.7 .. .. 2 3 Iraq 49.4 65 72 84 .. 12 .. .. .. .. 11 25 Ireland 49.9 77 82 .. .. 49 0.6 0.8 77 .. 8 14 Israel 50.4 80 84 .. .. 49 0.1 0.4 73 .. 7 18 Italy 51.4 79 84 .. .. 44 1.2 2.5 78 .. 13 21 Jamaica  51.1 69 75 91 .. 48 0.5 2.2 .. .. 5 13 Japan 51.3 80 86 .. .. 42 1.1 7.3 70 60 1 11 Jordan 48.7 71 75 99 4 16 .. .. .. 61 0 6 Kazakhstan 52.4 64 74 100 7 50 .. .. .. 70 .. 18 Kenya 50.0 54 55 92 .. .. .. .. .. .. 1 10 Korea, Dem. Rep. 50.6 65 70 .. .. .. .. .. .. .. 21 16 Korea, Rep. 50.5 77 84 .. .. 42 1.2 12.7 59 57 2 15 Kosovo .. 68 72 .. .. .. .. .. .. .. .. .. Kuwait 40.5 76 80 .. .. .. .. .. .. .. .. 8 Kyrgyz Republic 50.7 62 72 97 .. 51 8.8 19.3 .. .. .. 26 Lao PDR 50.1 64 67 35 17 .. 26.4 64.2 .. .. 6 25 Latvia 53.9 68 78 .. .. 53 1.4 1.2 59 77 .. 22 Lebanon 51.0 70 74 96 .. .. .. .. .. .. 0 3 Lesotho 52.8 45 46 92 20 .. .. .. .. .. .. 24 Liberia 50.3 57 60 79 38 .. .. .. .. .. .. 13 Libya  48.3 72 77 .. .. .. .. .. .. .. .. 8 Lithuania 53.2 68 79 .. .. 53 1.0 2.0 60 71 .. 19 Macedonia, FYR 50.1 72 77 94 .. 42 7.0 14.9 47 .. .. 33 Madagascar 50.2 59 62 86 34 .. .. .. .. .. 7 8 Malawi 50.3 53 55 92 34 .. .. .. .. .. 10 21 Malaysia 49.2 72 77 79 .. 39 2.7 8.8 .. .. 5 10 Mali 50.6 48 50 70 36 .. .. .. .. .. .. 10 Mauritania  49.3 55 59 75 .. .. .. .. .. .. .. 22 Mauritius 50.4 69 76 .. .. 37 0.9 4.7 44 .. 7 19 Mexico 50.8 73 78 94 .. 39 4.9 10.0 65 70 12 26 Moldova 52.5 65 72 98 6 54 1.3 3.4 .. .. .. 24 Mongolia 50.5 64 70 100 .. 51 .. .. .. 77 25 4 Morocco 50.9 69 74 68 7 21 16.5 51.8 .. .. 0 11 Mozambique 51.4 47 49 89 .. .. .. .. .. .. 16 39 Myanmar 51.2 60 64 80 .. .. .. .. .. 89 .. .. Namibia 50.7 61 62 95 15 .. 0.9 1.1 .. .. 7 24 Nepal 50.3 66 68 44 19 .. .. .. .. .. 6 33 Netherlands 50.4 79 83 .. .. 48 0.2 0.8 75 82 21 41 New Zealand 50.6 78 82 .. .. 48 0.8 1.5 72b 82 14 34 Nicaragua 50.5 70 77 90 25 38 12.2 9.1 .. .. 15 21 Niger 49.9 51 53 46 39 36 .. .. .. .. 5 12 Nigeria 49.9 48 49 58 23 .. .. .. .. .. .. 7 Norway 50.3 79 83 .. .. 49 0.2 0.4 71 89 36 40 Oman 43.6 75 78 .. .. 22 .. .. .. .. .. 0 Pakistan 48.5 67 67 61 9 13 18.6 61.9 .. .. 10 22 Panama 49.6 73 79 .. .. 42 2.3 4.0 47 95 8 9 Papua New Guinea 49.2 59 64 79 .. .. .. .. .. .. 0 1 Paraguay 49.5 70 74 96 13 40 10.8 8.9 .. .. 6 13 Peru 49.9 71 76 94 26 38 4.7b 9.9 b .. .. 6 28 Philippines 49.6 70 74 91 10 42 9.0 b 18.0 b .. 91 9 21 Poland 51.8 72 80 .. .. 47 2.7 5.9 68 .. 14 20 Portugal 51.6 76 82 .. .. 48 0.7 1.2 68 69 8 27 Puerto Rico 52.0 75 83 .. .. 42 0.0 0.0 .. .. .. .. Qatar 24.6 75 77 .. .. 13 .. .. .. 142 .. 0 2011 World Development Indicators 25 1.5 Women in development Female Life expectancy Pregnant Teenage Women in wage Unpaid Female Ratio Women in population at birth women mothers employment in family workers part-time of female parliaments receiving nonagricultural employment to male prenatal sector wages in care manufacturing % of nonagricultural Male Female years % of women wage % of male % of female % of % % of total Male Female % ages 15–19 employment employment employment % of total total seats 2009 2009 2009 2004–09a 2004–09a 2008 2008 2008 2004–09a 2004–09a 1990 2010 Romania 51.4 70 77 94 .. 46 6.0 18.9 49 74 34 11 Russian Federation 53.8 63 75 .. .. 51 0.1 0.1 62 .. .. 14 Rwanda 51.6 49 52 96 4 .. .. .. .. .. 17 56 Saudi Arabia 44.8 73 74 .. .. 15 .. .. .. .. .. 0 Senegal 50.4 54 57 94 18 .. .. .. .. .. 13 23 Serbia 50.5 71 76 98 .. 44 3.1 11.9 .. .. .. 22 Sierra Leone 51.3 47 49 87 34 .. .. .. .. .. .. 13 Singapore 49.8 79 84 .. .. 46 0.4b 1.3b .. 65 5 23 Slovak Republic 51.5 71 79 .. .. 48 0.1 0.2 59 .. .. 15 Slovenia 51.2 76 82 .. .. 47 3.2 5.4 57 .. .. 14 Somalia 50.4 49 52 26 .. .. .. .. .. .. 4 7 South Africa 50.7 50 53 .. .. 44 0.3 0.6 .. .. 3 45 Spain 50.7 79 85 .. .. 45 0.8 1.4 79 .. 15 37 Sri Lanka 50.8 71 78 99 .. 31 4.4b 21.7b .. 93 5 5 Sudan 49.6 57 60 64 .. .. .. .. .. .. .. 26 Swaziland 51.1 47 46 85 23 .. .. .. .. .. 4 14 Sweden 50.4 79 83 .. .. 50 0.2 0.3 64 90 38 45 Switzerland  51.2 80 84 .. .. 48 1.7b 3.2b 81 77 14 29 Syrian Arab Republic 49.5 73 76 84 .. 16 .. .. .. .. 9 12 Tajikistan 50.6 64 70 80 .. 37 .. .. .. .. .. 19 Tanzania 50.1 56 57 76 26 31 9.7 13.0 .. .. .. 31 Thailand 50.8 66 72 98 .. 45 14.0 29.9 .. .. 3 13 Timor-Leste 49.1 61 63 .. .. .. .. .. .. .. .. 29 Togo 50.5 61 65 84 .. .. .. .. .. .. 5 11 Trinidad and Tobago 51.4 66 73 96 .. .. .. .. .. .. 17 29 Tunisia 49.7 73 77 96 .. .. .. .. .. .. 4 28 Turkey 49.8 70 75 95 .. 22 5.3 37.7 58 .. 1 9 Turkmenistan 50.7 61 69 99 .. .. .. .. .. .. 26 17 Uganda 49.9 53 54 94 25 .. .. .. .. .. 12 32 Ukraine 53.9 64 75 99 4 55 0.4 0.3 .. 71 .. 8 United Arab Emirates 32.7 77 79 .. .. 20 .. .. .. .. 0 23 United Kingdom 50.9 78 82 .. .. 52 0.2 0.5 76 80 6 22 United States 50.7 76 81 .. .. 48 0.1 0.1 67b .. 7 17 Uruguay 51.7 73 80 96 .. 46 0.9 b 3.0 b 59b .. 6 15 Uzbekistan 50.3 65 71 99 .. 39 .. .. .. .. .. 22 Venezuela, RB 49.8 71 77 .. .. 42 0.6 1.6 .. .. 10 19 Vietnam 50.6 73 77 91 .. .. .. .. .. .. 18 26 West Bank and Gaza 49.1 72 75 99 .. 18 6.6 31.5 .. 53 .. .. Yemen, Rep. 49.4 62 65 47 .. 6 .. .. .. .. 4 0 Zambia 50.1 46 47 94 28 .. .. .. .. .. 7 14 Zimbabwe  51.7 45 46 93 21 .. .. .. .. .. 11 15 World 49.6 w 67 w 71 w 82 w   .. w .. w .. w .. w 71 m 13 w 19 w Low income 50.1 56 59 67    ..  .. .. .. 89 .. 19 Middle income 49.3 67 71 85   .. .. .. .. 71 13 18 Lower middle income 48.8 66 70 83   .. .. .. .. 85 13 17 Upper middle income 50.9 69 75 95   43 3.3 7.2 .. 70 12 19 Low & middle income 49.4 65 69 82   .. .. .. .. 71 13 18 East Asia & Pacific 48.8 71 74 91   .. .. .. .. 91 17 19 Europe & Central Asia 52.2 66 75 ..   48 1.9 5.3 .. 71 .. 15 Latin America & Carib. 50.6 71 77 95   41 4.0 7.5 .. 70 12 24 Middle East & N. Africa 49.6 69 73 83   .. .. .. .. 53 4 9 South Asia 48.5 63 66 70   .. .. .. .. 93 6 19 Sub-Saharan Africa 50.2 51 54 71   .. .. .. .. 66 .. 20 High income 50.6 77 83 ..   46 0.6 2.4 71 71 12 23 Euro area 51.1 78 83 ..   47 0.8 1.8 78 73 12 26 a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2009. 26 2011 World Development Indicators 1.5 WORLD VIEW Women in development About the data Definitions Despite much progress in recent decades, gender in non-agricultural wage employment. The indicator • Female population is the percentage of the popu- inequalities remain pervasive in many dimensions of does not reveal any differences in the quality of the lation that is female. • Life expectancy at birth is life—worldwide. But while disparities exist through- different types of non-agricultural wage employment, the number of years a newborn infant would live if out the world, they are most prevalent in developing regarding earnings, conditions of work, or the legal prevailing patterns of mortality at the time of its birth countries. Gender inequalities in the allocation of and social protection, which they offer. The indica- were to stay the same throughout its life. • Pregnant such resources as education, health care, nutrition, tor cannot reflect whether women are able to reap women receiving prenatal care are the percentage and political voice matter because of the strong the economic benefits of such employment, either. of women attended at least once during pregnancy association with well-being, productivity, and eco- Finally it should be noted that the female employ- by skilled health personnel for reasons related to nomic growth. These patterns of inequality begin at ment of any kind tends to be underreported in all pregnancy. • Teenage mothers are the percentage of an early age, with boys routinely receiving a larger kinds of surveys. In addition, the employment share women ages 15–19 who already have children or are share of education and health spending than do girls, of the agricultural sector, for both men and women, currently pregnant. • Women in wage employment for example. is severely underreported. in nonagricultural sector are female wage employ- Because of biological differences girls are Women’s wage work is important for economic ees in the nonagricultural sector as a percentage expected to experience lower infant and child mor- growth and the well-being of families. But women of total nonagricultural wage employment. • Unpaid tality rates and to have a longer life expectancy than often face such obstacles as restricted access to family workers are those who work without pay in a boys. This biological advantage may be overshad- credit markets, capital, land, training, and educa- market-oriented establishment or activity operated owed, however, by gender inequalities in nutrition tion, time constraints due to their traditional family by a related person living in the same household. and medical interventions and by inadequate care responsibilities, and labor market bias and discrimi- • Part-time employment, female is a female share during pregnancy and delivery, so that female rates nation. These obstacles force women to limit their of total part-time workers. Part-time worker is an of illness and death sometimes exceed male rates. full participation in paid economic activities, and to employed person whose normal hours of work are These gender bias can be seen in the child mortal- be less productive and to receive lower wages. More less than those of comparable full-time workers. Defi - ity rates (table 2.22) or life expectancy by gender. women than men are found in unpaid family employ- nition of part-time varies across countries. • Ratio of Female child mortality rates that are as high as or ment and part time employment. The gender wage female to male wages in manufacturing is a ratio of higher than male child mortality rates may indicate gap in manufacturing remains an unfortunate reality women’s wage to men’s in manufacturing. • Women discrimination against girls. of almost all countries of the world, even though the in parliaments are the percentage of parliamentary Having a child during the teenage years limits girls’ gap may not be attributed entirely to discrimination. seats in a single or lower chamber held by women. opportunities for better education, jobs, and income. Women are vastly underrepresented in decision- Data sources Pregnancy is more likely to be unintended during making positions in government, although there is the teenage years, and births are more likely to be some evidence of recent improvement. Gender parity Data on female population are from the United premature and are associated with greater risks of in parliamentary representation is still far from being Nations Population Division’s World Population complications during delivery and of death. In many realized. In 2010 women accounted for 19 percent Prospects: The 2008 Revision, and data on life countries maternal mortality (tables 1.3 and 2.19) is of parliamentarians worldwide, compared with 9 per- expectancy for more than half the countries in the a leading cause of death among women of reproduc- cent in 1987. Without representation at this level, it table (most of them developing countries) are from tive age, although most of them are preventable. is difficult for women to influence policy. its World Population Prospects: The 2008 Revision, Women in wage employment in nonagricultural sec- For information on other aspects of gender, see with additional data from census reports, other tor shows the extent that women have access to paid tables 1.2 (Millennium Development Goals: eradicat- statistical publications from national statistical employment, which will affect their integration into ing poverty and saving lives), 1.3 (Millennium Devel- offices, Eurostat’s Demographic Statistics, the the monetary economy. It also indicates the degree opment Goals: protecting our common environment), Secretariat of the Pacific Community’s Statistics that labour markets are open to women in industry 2.3 (Employment by economic activity), 2.4 (Decent and Demography Programme, and the U.S. Bureau and services sectors which affects not only equal work and productive employment), 2.5 (Unemploy- of the Census International Data Base. Data on employment opportunity for women, but also eco- ment), 2.6 (Children at work), 2.10 (Assessing vulner- pregnant women receiving prenatal care are from nomic efficiency through flexibility of the labor market ability and security), 2.13 (Education efficiency), 2.14 UNICEF’s The State of the World’s Children 2010 and the economy’s capacity to adapt to changes over (Education completion and outcomes), 2.15 (Educa- based on household surveys including Demo- Data sources time. In many developing countries, non-agricultural tion gaps by income and gender), 2.19 (Reproductive graphic and Health Surveys by Macro International wage employment represents only a small portion health), 2.21 (Health risk factors and future chal- and Multiple Indicator Cluster Surveys by UNICEF. of total employment. As a result the contribution of lenges), and 2.22 (Mortality). Data on teenage mothers are from Demographic women to the national economy is underestimated and Health Surveys by Macro International. Data and therefore misrepresented. The indicator is dif- on labor force, employment and wage are from the ficult to interpret, unless additional information is International Labour Organization’s Key Indicators available on the share of women in total employ- of the Labour Market, 6th edition. Data on women in ment, which would allow an assessment to be made parliaments are from the Inter-Parliamentary Union. of whether women are under- or over-represented 2011 World Development Indicators 27 1.6 Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at birth rate emissions Purchasing Atlas method power parity thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 2009 2005–09a 2007 American Samoa 67 0.2 336 .. ..b .. .. .. .. .. .. .. Andorra 85 0.5 181 3,447 41,130 .. .. 3.6 1.6 .. .. 539 Antigua and Barbuda 88 0.4 199 1,062 12,130 1,548 c 17,670 c –8.5 –9.5 .. 99 436 Aruba 107 0.2 592 .. ..d .. .. .. .. 75 98 2,396 Bahamas, The 342 13.9 34 7,136 21,390 .. .. 2.8 1.5 74 .. 2,147 Bahrain 791 0.8 1,041 19,712 25,420 26,130 33,690 6.3 4.1 76 91 22,446 Barbados 256 0.4 595 .. ..d .. .. .. .. 77 .. 1,345 Belize 333 23.0 15 1,205 3,740 1,929c 5,990 c 0.0 –3.4 77 .. 425 Bermuda 64 0.1 1,288 .. ..d .. .. –8.1 –8.4 79 .. 513 Bhutan 697 38.4 18 1,405 2,020 3,692 5,290 7.4 5.8 67 53 579 Brunei Darussalam 400 5.8 76 .. ..d 19,706 51,200 0.6 –1.3 78 95 7,599 Cape Verde 506 4.0 125 1,520 3,010 1,783 3,530 2.8 1.4 71 84 308 Cayman Islands 55 0.3 229 .. ..d .. .. .. .. .. 99 539 Channel Islands 150 0.2 789 10,242 68,610 .. .. 5.9 5.7 79 .. .. Comoros 659 1.9 354 531 810 779 1,180 1.8 –0.6 66 74 121 Cyprus 871 9.3 94 24,400e 30,480e 24,250e 30,290e –1.0e –1.9e 80 98 8,193 Djibouti 864 23.2 37 1,106 1,280 2,140 2,480 5.0 3.2 56 .. 487 Dominica 74 0.8 98 360 4,900 623c 8,460 c –0.8 –1.3 .. .. 121 Equatorial Guinea 676 28.1 24 8,398 12,420 13,069 19,330 –5.4 –7.8 51 93 4,793 Faeroe Islands 49 1.4 35 .. ..d .. .. .. .. 80 .. 696 Fiji 849 18.3 46 3,259 3,840 f 3,850 4,530 –3.0 –3.6 69 .. 1,458 French Polynesia 269 4.0 74 .. ..d .. .. .. .. 75 .. 806 Gibraltar 31 0.0 3,105 .. ..d .. .. .. .. .. .. 407 Greenland 56 410.5 0g 1,467 26,160 .. .. –5.4 –5.0 68 .. 520 Grenada 104 0.3 306 580 5,580 802c 7,710 c –6.8 –7.1 75 .. 242 Guam 178 0.5 329 .. ..d .. .. .. .. 76 .. .. Guyana 762 215.0 4 2,026 2,660 2,491c 3,270 c 3.3 3.4 68 .. 1,506 Iceland 319 103.0 3 13,858 43,430 10,478 32,840 –6.5 –7.0 81 .. 2,338 Isle of Man 80 0.6 141 3,972 49,310 .. .. 7.5 7.4 .. .. .. About the data Definitions The table shows data for economies with populations • Population is based on the de facto definition of included in the valuation of output plus net receipts between 30,000 and 1 million and for smaller econo- population, which counts all residents regardless of of primary income (compensation of employees mies if they are members of the World Bank. Where legal status or citizenship—except for refugees not and property income) from abroad. Data are in cur- data on gross national income (GNI) per capita are permanently settled in the country of asylum, who rent U.S. dollars converted using the World Bank not available, the estimated range is given. For more are generally considered part of the population of Atlas method (see Statistical methods). • Purchasing information on the calculation of GNI and purchasing their country of origin. The values shown are midyear power parity (PPP) GNI is GNI converted to interna- power parity (PPP) conversion factors, see About the estimates. For more information, see About the data tional dollars using PPP rates. An international dollar data for table 1.1. Additional data for the economies for table 2.1. •  Surface area is a country’s total has the same purchasing power over GNI that a U.S. in the table are available on the World Development area, including areas under inland bodies of water dollar has in the United States. • GNI per capita is Indicators CD-ROM or in WDI Online. and some coastal waterways. • Population density GNI divided by midyear population. • Gross domes- is midyear population divided by land area in square tic product (GDP) is the sum of value added by all kilometers. •  Gross national income (GNI), Atlas resident producers plus any product taxes (less sub- method, is the sum of value added by all resident sidies) not included in the valuation of output. Growth producers plus any product taxes (less subsidies) not is calculated from constant price GDP data in local 28 2011 World Development Indicators 1.6 WORLD VIEW Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at birth rate emissions Purchasing Atlas method power parity thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2009 2009 2009 2009 2009 2009 2009 2008–09 2008–09 2009 2005–09a 2007 Kiribati 98 0.8 121 180 1,830 324 c 3,310 c –0.7 –2.2 .. .. 33 Liechtenstein 36 0.2 224 4,906 136,630 .. .. –1.2 –1.9 83 .. .. Luxembourg 498 2.6 192 38,188 76,710 29,669 59,590 –4.1 –5.8 80 .. 10,834 Macao SAR, China 538 0.0 19,213 21,275 39,550 30,874 57,390 1.3 –0.9 81 93 1,554 Maldives 309 0.3 1,031 1,229 3,970h 1,625 5,250 –3.0 –4.4 72 98 898 Malta 415 0.3 1,297 7,621 18,360 9,616 23,170 –2.1 –2.8 80 92 2,722 Marshall Islands 61 0.2 339 186 3,060 .. .. 0.0 –2.2 .. .. 99 Mayotte 197 0.4 531 .. ..b .. .. .. .. 76 .. .. Micronesia, Fed. Sts. 111 0.7 158 277 2,500 359c 3,240 c –1.5 –1.8 69 .. 62 Monaco 33 0.0 16,406 6,483 197,590 .. .. –2.6 –2.9 .. .. .. Montenegro 624 13.8 46 4,149 6,650 8,183 13,110 –5.7 –6.0 74 .. .. Netherlands Antilles 198 0.8 248 .. ..d .. .. .. .. 76 96 6,232 New Caledonia 250 18.6 14 .. ..d .. .. .. .. 77 96 2,847 Northern Mariana Islands 87 0.5 189 .. ..d .. .. .. .. .. .. .. Palau 20 0.5 44 127 6,220 .. .. –2.1 –2.7 .. .. 213 Samoa 179 2.8 63 508 2,840 763c 4,270 c –5.5 –5.5 72 99 161 San Marino 31 0.1 524 1,572 50,670 .. .. 1.9 0.4 83 .. .. Sao Tome and Principe 163 1.0 170 185 1,130 301 1,850 4.0 2.4 66 88 128 Seychelles 88 0.5 191 746 8,480 1,477c 16,790 c –7.6 –8.7 74 92 623 Solomon Islands 523 28.9 19 477 910 974 c 1,860 c –2.2 –4.5 67 .. 198 St. Kitts and Nevis 50 0.3 191 503 10,150 676c 13,640 c –8.0 –8.8 .. .. 249 St. Lucia 172 0.6 282 894 5,190 1,525c 8,860 c –3.8 –4.9 .. .. 381 St. Vincent and the Grena- 109 0.4 280 560 5,130 964 c 8,830 c –2.8 –2.8 72 .. 202 dines Suriname 520 163.8 3 2,454 4,760 3,469c 6,730 c 5.1 4.2 69 95 2,437 Tonga 104 0.8 144 339 3,260 475c 4,570 c –0.4 –0.8 72 99 176 Turks and Caicos Islands 33 1.0 35 .. ..d .. .. .. .. .. .. 158 Tuvalu .. 0.0 .. .. ..i .. .. .. .. .. .. .. Vanuatu 240 12.2 20 627 2,620 1,028 c 4,290 c 4.0 1.4 71 81 103 Virgin Islands (U.S.) 110 0.4 314 .. ..d .. .. .. .. 79 .. .. a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,946–$12,195). c. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. d. Estimated to be high income ($12,196 or more). e. Data are for the area controlled by the government of the Republic of Cyprus. f. Included in the aggregates for upper middle-income economies based on earlier data. g. Less than 0.5. h. Included in the aggregates for lower middle-income economies based on earlier data. i. Estimated to be lower middle income ($996–$3,945). currency. • GDP per capita is GDP divided by midyear population. • Life expectancy at birth is the number of years a newborn infant would live if prevailing pat- terns of mortality at the time of its birth were to stay the same throughout its life. • Adult literacy rate is Data sources the percentage of adults ages 15 and older who can, with understanding, read and write a short, simple The indicators here and throughout the book statement about their everyday life. • Carbon dioxide are compiled by World Bank staff from primary emissions are those stemming from the burning of and secondary sources. More information about fossil fuels and the manufacture of cement. They the indicators and their sources can be found in include carbon dioxide produced during consumption the About the data, Definitions, and Data sources of solid, liquid, and gas fuels and gas flaring. entries that accompany each table in subsequent sections. 2011 World Development Indicators 29 Text figures, tables, and boxes PEOPLE Introduction S 2 ustainable development is about improving the quality of peoples’ lives and expanding their abilities to shape their futures. This generally calls for higher per capita incomes, but also for human capital development through improvements in health and education. Although developing countries have made large investments in human capital, good health and basic education remain elusive to many. This limits people’s ability to take advantage of employment opportunities and work their way out of poverty. The tables in this section review the achievements for which estimates are available since 2000. But countries have made in improving the welfare of their the full range of these poverty estimates can be people. They show the levels of poverty prevalent in accessed through the Bank’s Open Data Initiative countries, the distribution of income, and the preva- (data.worldbank.org), and the entire database of lence of child labour—which while it reduces house- $1.25 and $2 a day purchasing power parity poverty hold poverty, is always at the expense of children’s rate and poverty gap estimates will also be available education and future human capital. The section through PovcalNet. also looks at investments in health and education In addition, several new indicators have been and their impact on the worst aspects of nonincome added to existing tables. Data on children’s learn- poverty by reducing hunger and malnutrition, lowering ing assessment, from the Programme for Interna- mortality rates, and improving education outcomes. tional Student Assessment, have been added to This year’s national and international poverty table 2.14, and the lifetime risk of maternal death estimates were prepared by the World Bank’s Global has been added to table 2.19. The new maternal Poverty Working Group, recently established by the mortality ratio, estimated by the Inter-Agency group, Poverty Board. The results of their work are evident is now available in a consistent time series for the in tables 2.7–2.9. The baseline database, with esti- first time, and data for 1990 and the most recent mates for 231 data points (country and year com- year are presented in table 2.19. The entire time binations) covering 104 countries, was updated series can be accessed through data.worldbank.org; to include estimates for 577 data points covering regional and income group aggregates for maternal 115 countries. Because of space restrictions in the mortality ratios are in figures 2a and 2b. printed edition, this report cannot include estimates The next sections look at civil registration, high- for all countries. Thus, it includes only countries lighting the problems countries face in planning for Maternal mortality ratios have declined in Maternal mortality ratios have declined fastest among all developing country regions since 1990 2a low- and lower middle-income countries but remain high 2b Maternal mortality ratio by region Maternal mortality ratio by income group Maternal mortality ratio, modeled estimates (per 100,000 live births) Maternal mortality ratio, modeled estimates (per 100,000 live births) 1,000 1,000 Sub-Saharan Africa 750 750 South Asia Low income 500 500 Latin America and Caribbean Lower middle income Middle East and East Asia 250 North Africa and Pacific 250 Europe and Central Asia Upper middle income High income 0 0 1990 1995 2000 2005 2008 1990 1995 2000 2005 2008 Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008. Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008. 2011 World Development Indicators 31 the welfare of their people. Countries need to provide detailed characteristics of the popula- know, at a minimum, how many people are born tion recorded by censuses and civil registration and die each year. In most developing countries systems. Administrative records from health this is not easy. The discussion highlights the and education systems add further information obstacles countries must surmount in record- to manage those services and—combined with ing births and deaths and the interim measures census, survey, and vital statistics —are used they have adopted, and it indicates the way to plan for future needs. forward for countries and their development Civil registration has two functions: partners. administrative —providing legal documenta- tion that protects identities, citizenship, prop- Civil registration, the missing pillar erty, and other economic, social, and human In 2009 the births of 50 million children went rights—and statistical—providing regular, fre- unrecorded. They entered the world with no quent, and timely information on the dynamics proof of age, citizenship, or parentage. That of population growth, size, and distribution and same year 40 million people died unnoted ex- on records of births and deaths by age, sex, and cept by family or friends. There are no records cause at the national and subnational levels. of where they died, when they died, and more Vital statistics from civil registration systems importantly how they died. are essential for planning basic social services In most high-income countries these vital and infrastructure development and for under- events (births and deaths) are recorded by civil standing and monitoring health status and registration systems, which also record mar- health issues in the country. riages, adoptions, and divorces. But in many A complete civil registration system has developing countries registration systems are three strengths: it costs less than conducting a incomplete or absent. In South Asia only 1 per- census or survey, data are based on a record of cent of the population is covered by complete events rather than recall, and information can vital registration records (at least 90 percent be made available at low cost. In a well function- coverage for births and deaths), and in Sub- ing civil registration system a family member or Saharan Africa only 2 percent (UN, Population caretaker reports births and deaths at the reg- and Vital Statistics Report, 2011). Lacking effec- istration office in the local area and receives tive registration systems, countries must rely appropriate legal documentation. Medical cer- on infrequent and expensive censuses and sur- tification of death from a health care provider veys to estimate the vital statistics needed to identifies the cause of death. support the core functions of government and To be considered complete, civil registration to plan for the future. systems must collect information on at least 90 A state-of-the-art statistical system has percent of vital events. Systems in most devel- three pillars: censuses and surveys, administra- oping country regions fall well short of that stan- tive records, and civil registration, each with an dard. So today, most people in Africa and South important and complementary role. Censuses Asia are born and die without a trace in any give benchmark estimates that provide a base legal record or official statistic (figure 2c), caus- for and a check on vital statistics, and surveys ing a vicious cycle. These are the regions where most premature deaths occur and where the The births of many children in Asia and Africa go unregistered 2c need for robust information for planning is most Children under age 5 whose births are unregistered, 2007 (percent) critical. Roughly half the countries claim to have 75 complete registration of births and deaths (UN, Population and Vital Statistics Report, 2011), 50 leaving nearly 40 percent of births and 70 per- cent of deaths unregistered (WHO 2007). In many countries vital events are unre- 25 ported or only partially reported for certain areas, ages, or populations for a variety of rea- 0 CEE/CISa East Asia Latin America Middle East & South Sub-Saharan sons. People may not know their responsibility & Pacificb & Caribbean North Africa Asia Africa to register events or where to register. They may a. Central and Eastern Europe and Commonwealth of Independent States. choose not to register because of the distance b. Excludes China. Source: UNICEF Childinfo (www.childinfo.org/birth_registration_progress.html). to the registration offices or for cultural reasons. 32 2011 World Development Indicators PEOPLE Or they cannot afford the registration costs. impact of major diseases in developing coun- Data from Nigeria show that most unregistered tries can be estimated using only models or births are found among the rural poor, for whom intuition and educated guesses rather than a significant barrier may be the distance to the facts (Cooper and others 1998). Without data nearest registration facility, and among poorly on the cause of death, verbal autopsy (an inter- educated mothers (figures 2d–2f). view with caregivers or family members after Where many infants die young, parents may a death to establish probable cause of death) be reluctant to go through the formalities of can be used. In Tanzania several districts imple- registration until they have some confidence in mented sentinel demographic surveillance sys- the child’s survival or need a birth certificate tems that provided routine monitoring of vital for administrative purposes. In many cultures, events and data for cause of death derived especially in Western Africa, a child’s death from a validated set of core verbal autopsy pro- before age 2 is generally not registered. In cedures. District councils used this information Burkina Faso, for example, there are different words to express or describe death. The word In Nigeria, children’s births are more likely for infant death among the Mossi is lebame, to be unregistered in rural areas . . . 2d which translates literally to “s/he went back,” Registered births, by area, Nigeria 2007 (percent) which is different from kiime, which is used for 50 a teenager or adult who has died (private con- 40 versation). Reporting is lower for deaths than for births because people perceive death as a 30 private, sad event and because there are fewer 20 incentives associated with registering a death, 10 especially where formal inheritance is rare. Such recording lapses have consequences 0 Urban Rural for data quality. Even where there is complete Source: Multiple Indicator Cluster Survey 2007. registration, births and deaths may be recorded as need arises, rather than when they occur, reducing the timeliness and relevance of data. . . . in poor households . . . 2e Not all administrative levels have the same Registered births, by wealth quintile, Nigeria 2007 (percent) capacity to maintain registers, resulting in omis- 60 sions that may be difficult to quantify and there- fore rectify, since underregistration cannot be 40 assumed to be uniform across the population. Correct information on cause of death is critical for guiding policies and priorities for the 20 health system. Routine data from civil registra- tion in the United Kingdom helped identify the 0 Poorest Secondary Middle Fourth Richest causal association between smoking and lung cancer in the 1950s. But even when deaths Source: Multiple Indicator Cluster Survey 2007. are recorded, age or cause of death may be misreported or miscoded. Correct reporting of cause of death is particularly difficult in devel- . . . and where the mother has a lower education level 2f oping countries, where many deaths occur at Registered births, by mother’s education, Nigeria 2007 (percent) home without medical care or certification. In 60 Myanmar only 10 percent of deaths occur in the hospital (Mahar 2010). More than two-thirds of 40 people live in countries where cause of death statistics are partially reported and therefore of 20 limited use or where deaths are not reported at all (table 2g; Mahapatra and others 2007). 0 Because of the lack of reliable vital sta- Nonstandard curriculum None Primary Secondary tistics from civil registration systems, the Source: Multiple Indicator Cluster Survey 2007. long-term social, economic, and demographic 2011 World Development Indicators 33 Most people live in countries with Other obstacles relate to the need for low-quality cause of death statistics 2g human and physical infrastructure to set up Classification of countries based on the quality of cause of death statistics reported to the and maintain a civil registration system. While World Health Organization, 2007 technical assistance and development grants Quality Number of countries Percent of global population can finance fixed costs and provide initial staff High 31 13 training, countries need to finance recurring costs to run a civil registration system effi - Medium 50 15 ciently. Because many developing countries Low 26 7 have enormous economic and social develop- Limited use 17 41 ment needs, this would claim low priority. A No report 68 24 first and inexpensive step is adequate legis- Total 192 100 lation. But while most countries have legisla- Source: Mahapatra and others 2007. tion requiring registration of vital events, many have not established organizational arrange- ments to direct, coordinate, and supervise the to identify disease burdens, set priorities, and operation. allocate resources (Setel 2007). But verbal autopsy is often limited to small areas, such as Interim approaches sample vital registration and demographic sur- Because of the time and expense of building veillance systems, because it is expensive, and complete civil registration systems, many coun- accuracy depends on family members’ knowl- tries have adopted alternative approaches to edge of events leading to the death, the skill of measure and monitor vital events and related interviewers, and the competence of physicians sociodemographic information. But as depen- who do the diagnosis and coding. dence on these measures (often intended as interim) grows, national authorities have fewer Why civil registration incentives to invest in complete civil registra- fails to develop tion systems (figure 2h; Setel and others 2007). Good civil registration systems require long- These alternative approaches—notably term political commitment, a supportive legal censuses, demographic household surveys, framework, allocation of roles and responsi- sample registration systems with verbal autop- bilities among stakeholders, mobilization of sies, demographic surveillance sites, and financial and human resources, and most criti- facility -based information—effectively fill data cally, the trust of citizens (AbouZahr and others gaps with up-to-date information in many devel- 2007). Although establishing civil registration oping countries. Figure 2i illustrates the high systems takes time, there is no substitute in underreporting of deaths in the civil registration the long run. But when civil registration systems system in the Philippines, based on calcula- lack a sponsor or key stakeholder, or citizens tions by the Inter-agency Group for Child Mortal- lack incentives to participate, and when high ity Estimation, using surveys and other sources initial costs deter investments, civil registration of mortality data. fails to take root. No single blueprint for establishing and More countries used surveys for mortality maintaining civil registration systems ensures statistics, but civil registration did not expand 2h the availability of timely and sound vital statis- Collection and reporting of data for mortality by sources in 57 low-income countries, 1980–2004 (number of countries) tics. Each country faces different challenges, 50 and strategies must be tailored accordingly. Surveys 40 Some obstacles to a viable civil registration system can be removed only through long-term 30 social and economic development. These gen- 20 erally relate to geography and population dis- 10 tribution, with widely dispersed populations Civil register requiring transportation to registration cen- 0 1980–84 1985–89 1990–94 1995–99 2000–04 ters. And a largely illiterate population may be unaware of the need to comply with the law or Source: Boerma and Stansfield 2007. be unmotivated to do so. 34 2011 World Development Indicators PEOPLE These interim approaches also produce Estimates of infant mortality supplemental information that is not col- in the Philippines differ by source 2i lected through civil registration, such as socio- Infant mortality rate (per 1,000 live births) economic information, risk factors, and health 80 Estimate by Inter-Agency Group status. But these approaches are not a com- 70 for Child Mortality Estimation 60 plete or permanent solution. Censuses and 50 surveys are expensive, and developing coun- 40 tries often require international technical and 30 financial assistance. They must be repeated World Health Organization vital registration 20 regularly to yield useful data. And they must 10 be supplemented or adjusted to produce sat- 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2009 isfactory estimates. Burkina Faso, which has Note: Dotted lines are Demographic and Health Surveys, World Fertility Surveys, and Family Planning Surveys for partial coverage of civil registration (birth regis- various years. Source: Inter-agency Group for Child Mortality Estimation (www.childmortality.org). tration coverage is 60 percent), has conducted four censuses (1975, 1985, 1996, 2006), five Demographic and Health Surveys (1991, 1993, aims to ensure consistency and comparability 1998, 2003, 2010), two Multiple Indicator Clus- of statistics across countries and over time. ter Surveys (1996, 2006), and a migration and Used correctly, these principles and guide- urbanization survey (1993). lines improve data quality, as in Chile and Tan- zania (Setel and others 2007), but in reality How to build a good civil few countries have pursued or attained most registration system recommendations. Over the years, international and development The WHO’s International Classification of agencies have tried to identify the strengths Diseases and Related Health Problems has and weaknesses of national civil registration improved the comparability of cause of death systems and assess the quality of the data data. Still, there are substantial differences in they produce. In 2001 the United Nations up- interpretation and application of these codes. dated the Principles and Recommendations In 2007 only 31 of 192 WHO member countries for a Vital Statistics System, fi rst published (13 percent of the world’s population) reported in 1973, to offer best practice guidelines for reliable cause-of-death statistics to the WHO, establishing a civil registration system and most of them high-income countries (WHO producing timely, complete, and accurate sta- 2007). tistics. Regional initiatives by the United Na- tions include the 1994 African Workshop on International support Strategies for Accelerating the Improvement of The international community can continue its Civil Registration and Vital Statistics Systems. strong supportive rule by setting standards and In 2005 the World Health Organization (WHO) guidelines for collecting and validating systems established the Health Metrics Network, which and data, publicizing the importance of civil recommends an integrated approach for de- registration, and providing comprehensive and veloping health information systems, includ- integrated technical and financial assistance. ing civil registration. Some 85 countries have Since no single UN agency has a clear mandate used the network’s Framework and Standards for guidance and technical support for civil reg- for Country Health Information Systems, which istration, good coordination is key. 2011 World Development Indicators 35 Tables 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0–14 15–64 65+ Young Old people people 1990 2009 2015 1990–2009 2009–15 2009 2009 2009 2009 2009 2009 2009 Afghanistan 18.6 29.8 35.0 2.5 2.7 46 52 2 89 4 19 46 Albania 3.3 3.2 3.3 –0.2 0.5 24 67 10 35 14 6 15 Algeria 25.3 34.9 38.1 1.7 1.4 27 68 5 40 7 5 21 Angola 10.7 18.5 21.7 2.9 2.6 45 53 2 86 5 16 42 Argentina 32.5 40.3 42.4 1.1 0.9 25 64 11 39 16 8 17 Armenia 3.5 3.1 3.1 –0.7 0.2 20 68 11 30 16 9 15 Australia 17.1 21.9 23.4 1.3 1.2 19 67 14 28 20 6 14 Austria 7.7 8.4 8.4 0.4 0.1 15 68 17 22 26 9 9 Azerbaijan 7.2 8.8 9.4 1.1 1.1 24 69 7 35 10 6 17 Bangladesh 115.6 162.2 176.3 1.8 1.4 31 65 4 49 6 6 21 Belarus 10.2 9.7 9.4 –0.3 –0.4 15 72 14 21 19 14 12 Belgium 10.0 10.8 11.0 0.4 0.3 17 66 17 25 26 10 12 Benin 4.8 8.9 10.6 3.3 2.9 43 54 3 80 6 9 39 Bolivia 6.7 9.9 10.8 2.1 1.6 36 59 5 61 8 7 27 Bosnia and Herzegovina 4.3 3.8 3.7 –0.7 –0.2 15 71 14 22 20 10 9 Botswana 1.4 1.9 2.1 1.9 1.3 33 63 4 53 6 12 24 Brazil 149.6 193.7 202.4 1.4 0.7 26 67 7 39 10 6 16 Bulgaria 8.7 7.6 7.3 –0.7 –0.6 13 69 17 19 25 14 11 Burkina Faso 8.8 15.8 19.0 3.1 3.1 46 52 2 90 4 13 47 Burundi 5.7 8.3 9.4 2.0 2.1 38 59 3 65 5 14 34 Cambodia 9.7 14.8 16.4 2.2 1.7 33 63 3 53 6 8 25 Cameroon 12.2 19.5 22.2 2.5 2.1 41 56 4 74 6 14 36 Canada 27.8 33.7 35.7 1.0 0.9 17 70 14 24 20 7 11 Central African Republic 2.9 4.4 4.9 2.2 1.8 41 55 4 73 7 17 35 Chad 6.1 11.2 13.1 3.2 2.6 46 51 3 89 6 16 45 Chile 13.2 17.0 17.9 1.3 0.9 23 68 9 33 13 5 15 China 1,135.2 1,331.5 1,377.7 0.8 0.6 20a 72a 8a 28a 11a 7 12 Hong Kong SAR, China 5.7 7.0 7.3 1.1 0.8 12 75 13 16 17 6 12 Colombia 33.2 45.7 49.3 1.7 1.3 29 65 5 45 8 6 20 Congo, Dem. Rep. 37.0 66.0 77.4 3.0 2.6 47 51 3 92 5 17 44 Congo, Rep. 2.4 3.7 4.2 2.2 2.3 40 56 4 73 7 13 34 Costa Rica 3.1 4.6 4.9 2.1 1.3 26 68 6 38 9 4 16 Côte d’Ivoire 12.6 21.1 24.2 2.7 2.3 41 55 4 73 7 11 34 Croatia 4.8 4.4 4.4 –0.4 –0.2 15 68 17 22 25 12 10 Cuba 10.6 11.2 11.2 0.3 0.0 18 70 12 25 17 7 10 Czech Republic 10.4 10.5 10.6 0.1 0.2 14 71 15 20 21 10 11 Denmark 5.1 5.5 5.6 0.4 0.2 18 65 16 28 25 10 11 Dominican Republic 7.4 10.1 10.8 1.7 1.1 31 63 6 50 10 6 22 Ecuador 10.3 13.6 14.6 1.5 1.1 31 62 7 50 10 5 20 Egypt, Arab Rep. 57.8 83.0 91.7 1.9 1.7 32 63 5 51 7 6 24 El Salvador 5.3 6.2 6.4 0.8 0.6 32 61 7 53 12 7 20 Eritrea 3.2 5.1 6.0 2.5 2.8 42 56 2 74 4 8 36 Estonia 1.6 1.3 1.3 –0.8 –0.1 15 68 17 22 25 12 12 Ethiopia 48.3 82.8 96.2 2.8 2.5 44 53 3 82 6 12 38 Finland 5.0 5.3 5.4 0.4 0.3 17 67 17 25 25 9 11 Franceb 56.7 62.6 63.9 0.5 0.3 18 65 17 28 26 9 13 Gabon 0.9 1.5 1.6 2.4 1.8 36 60 4 61 7 10 27 Gambia, The 0.9 1.7 2.0 3.4 2.5 42 55 3 77 5 11 36 Georgia 5.5 4.3 4.1 –1.3 –0.7 17 69 14 24 21 12 12 Germany 79.4 81.9 80.6 0.2 –0.3 14 66 20 20 31 10 8 Ghana 15.0 23.8 26.6 2.4 1.8 38 58 4 66 6 11 32 Greece 10.2 11.3 11.4 0.6 0.2 14 68 18 21 27 10 11 Guatemala 8.9 14.0 16.2 2.4 2.4 42 54 4 78 8 6 32 Guinea 6.1 10.1 11.8 2.6 2.7 43 54 3 79 6 11 39 Guinea-Bissau 1.0 1.6 1.8 2.4 2.3 43 54 3 79 6 17 41 Haiti 7.1 10.0 10.7 1.8 1.1 36 59 4 61 7 9 27 Honduras 4.9 7.5 8.4 2.2 1.9 37 58 4 64 7 5 27 36 2011 World Development Indicators 2.1 PEOPLE Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0–14 15–64 65+ Young Old people people 1990 2009 2015 1990–2009 2009–15 2009 2009 2009 2009 2009 2009 2009 Hungary 10.4 10.0 9.9 –0.2 –0.2 15 69 16 22 24 13 10 India 849.5 1,155.3 1,246.9 1.6 1.3 31 64 5 49 8 7 22 Indonesia 177.4 230.0 247.5 1.4 1.2 27 67 6 40 9 6 18 Iran, Islamic Rep. 54.4 72.9 78.6 1.5 1.2 24 71 5 34 7 6 19 Iraq 18.9 31.5 36.3 2.7 2.4 41 56 3 74 6 6 31 Ireland 3.5 4.5 4.8 1.3 1.1 21 68 11 30 16 7 17 Israel 4.7 7.4 8.2 2.5 1.6 28 62 10 45 16 5 22 Italy 56.7 60.2 60.8 0.3 0.1 14 66 20 22 31 10 10 Jamaica 2.4 2.7 2.8 0.6 0.4 29 63 8 47 12 7 16 Japan 123.5 127.6 125.3 0.2 –0.3 13 65 22 21 34 9 9 Jordan 3.2 6.0 6.8 3.3 2.2 34 62 4 56 6 4 25 Kazakhstan 16.3 15.9 16.9 –0.2 1.0 24 69 7 34 10 9 22 Kenya 23.4 39.8 46.4 2.8 2.6 43 55 3 78 5 11 38 Korea, Dem. Rep. 20.1 23.9 24.4 0.9 0.3 22 69 10 32 14 10 14 Korea, Rep. 42.9 48.7 49.3 0.7 0.2 17 73 11 23 15 5 10 Kosovo 1.9 1.8 1.9 –0.2 0.6 .. .. .. .. .. 7 19 Kuwait 2.1 2.8 3.2 1.4 2.1 23 74 2 31 3 2 17 Kyrgyz Republic 4.4 5.3 5.7 1.0 1.3 29 65 5 45 8 7 25 Lao PDR 4.2 6.3 7.0 2.1 1.8 38 59 4 64 6 7 27 Latvia 2.7 2.3 2.2 –0.9 –0.5 14 69 17 20 25 13 10 Lebanon 3.0 4.2 4.4 1.8 0.8 25 67 7 38 11 7 16 Lesotho 1.6 2.1 2.2 1.3 0.8 39 56 5 69 8 17 29 Liberia 2.2 4.0 4.8 3.2 3.2 43 54 3 79 6 10 38 Libya 4.4 6.4 7.2 2.0 1.8 30 66 4 46 6 4 23 Lithuania 3.7 3.3 3.2 –0.5 –0.7 15 69 16 22 23 13 11 Macedonia, FYR 1.9 2.0 2.0 0.4 0.0 18 70 12 26 17 9 11 Madagascar 11.3 19.6 22.8 2.9 2.5 43 54 3 79 6 9 35 Malawi 9.5 15.3 18.0 2.5 2.7 46 51 3 91 6 12 40 Malaysia 18.1 27.5 30.0 2.2 1.5 29 66 5 45 7 5 20 Mali 8.7 13.0 15.4 2.1 2.8 44 54 2 83 4 15 42 Mauritania 2.0 3.3 3.7 2.7 2.1 39 58 3 68 5 10 33 Mauritius 1.1 1.3 1.3 1.0 0.4 23 70 7 32 10 7 12 Mexico 83.2 107.4 113.1 1.3 0.9 28 65 6 44 10 5 18 Moldova 4.4 3.6 3.5 –1.0 –0.7 17 72 11 23 15 13 12 Mongolia 2.2 2.7 2.9 1.0 1.1 26 70 4 37 6 7 19 Morocco 24.8 32.0 34.3 1.3 1.2 28 66 5 43 8 6 20 Mozambique 13.5 22.9 25.9 2.8 2.1 44 53 3 83 6 16 38 Myanmar 40.8 50.0 53.0 1.1 1.0 27 68 5 40 8 10 20 Namibia 1.4 2.2 2.4 2.2 1.7 37 60 4 62 6 8 27 Nepal 19.1 29.3 32.5 2.3 1.7 37 59 4 62 7 6 25 Netherlands 15.0 16.5 16.8 0.5 0.3 18 67 15 26 22 8 11 New Zealand 3.4 4.3 4.6 1.2 1.0 20 67 13 31 19 7 15 Nicaragua 4.1 5.7 6.3 1.7 1.4 35 60 5 58 7 5 24 Niger 7.9 15.3 19.1 3.5 3.7 50 48 2 104 4 15 53 Nigeria 97.3 154.7 178.7 2.4 2.4 43 54 3 78 6 16 39 Norway 4.2 4.8 5.1 0.7 0.8 19 66 15 29 22 9 13 Oman 1.8 2.8 3.2 2.3 1.9 31 66 3 48 5 3 22 Pakistan 108.0 169.7 193.5 2.4 2.2 37 59 4 63 7 7 30 Panama 2.4 3.5 3.8 1.9 1.5 29 64 7 46 10 5 20 Papua New Guinea 4.1 6.7 7.7 2.6 2.2 40 58 2 69 4 8 31 Paraguay 4.2 6.3 7.0 2.1 1.6 34 61 5 56 8 6 24 Peru 21.8 29.2 31.2 1.5 1.1 30 64 6 48 9 5 21 Philippines 62.4 92.0 102.7 2.0 1.8 34 62 4 55 7 5 24 Poland 38.1 38.1 38.0 0.0 –0.1 15 72 13 21 19 10 11 Portugal 9.9 10.6 10.7 0.4 0.0 15 67 18 23 26 10 9 Puerto Rico 3.5 4.0 4.0 0.6 0.3 20 66 14 31 21 8 12 Qatar 0.5 1.4 1.6 5.8 c 2.4 16 83 1 19 1 2 12 2011 World Development Indicators 37 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0–14 15–64 65+ Young Old people people 1990 2009 2015 1990–2009 2009–15 2009 2009 2009 2009 2009 2009 2009 Romania 23.2 21.5 21.0 –0.4 –0.4 15 70 15 22 21 12 10 Russian Federation 148.3 141.9 139.0 –0.2 –0.3 15 72 13 21 18 14 12 Rwanda 7.2 10.0 11.7 1.8 2.7 42 55 2 77 5 14 41 Saudi Arabia 16.3 25.4 28.6 2.3 2.0 32 65 3 50 5 4 24 Senegal 7.5 12.5 14.5 2.7 2.4 44 54 2 81 4 11 38 Serbia 7.6 7.3 7.2 –0.2 –0.3 18d 68d 14 d 26d 21d 14 10 Sierra Leone 4.1 5.7 6.6 1.8 2.3 43 55 2 79 3 15 40 Singapore 3.0 5.0 5.4 2.6 1.2 16 74 10 22 13 4 10 Slovak Republic 5.3 5.4 5.4 0.1 0.1 15 73 12 21 17 10 11 Slovenia 2.0 2.0 2.1 0.1 0.3 14 70 16 20 23 9 11 Somalia 6.6 9.1 10.7 1.7 2.7 45 52 3 86 5 16 44 South Africa 35.2 49.3 51.1 1.8 0.6 31 65 4 47 7 15 22 Spain 38.8 46.0 47.9 0.9 0.7 15 68 17 22 25 8 11 Sri Lanka 17.1 20.3 21.2 0.9 0.7 24 68 7 36 11 5 19 Sudan 27.1 42.3 47.7 2.3 2.0 39 57 4 68 6 10 31 Swaziland 0.9 1.2 1.3 1.7 1.4 39 57 3 69 6 15 30 Sweden 8.6 9.3 9.6 0.4 0.5 17 65 18 25 28 10 12 Switzerland 6.7 7.7 7.9 0.7 0.4 15 68 17 23 25 8 10 Syrian Arab Republic 12.7 21.1 24.1 2.7 2.2 35 62 3 57 5 3 27 Tajikistan 5.3 7.0 7.8 1.4 1.8 37 59 4 62 6 6 28 Tanzania 25.5 43.7 52.1 2.8 2.9 45 52 3 86 6 11 41 Thailand 56.7 67.8 69.9 0.9 0.5 22 71 8 31 11 9 14 Timor-Leste 0.7 1.1 1.4 2.2 3.3 45 52 3 86 6 8 40 Togo 3.9 6.6 7.6 2.7 2.3 40 57 4 71 6 8 32 Trinidad and Tobago 1.2 1.3 1.4 0.5 0.3 21 73 7 28 9 8 15 Tunisia 8.2 10.4 11.1 1.3 1.1 23 70 7 33 10 6 18 Turkey 56.1 74.8 79.9 1.5 1.1 27 67 6 40 9 6 18 Turkmenistan 3.7 5.1 5.5 1.7 1.2 29 66 4 45 6 8 22 Uganda 17.7 32.7 39.7 3.2 3.2 49 49 3 101 5 12 46 Ukraine 51.9 46.0 44.4 –0.6 –0.6 14 70 16 20 22 15 11 United Arab Emirates 1.9 4.6 5.2 4.7 2.0 19 80 1 24 1 2 14 United Kingdom 57.2 61.8 63.8 0.4 0.5 17 66 16 26 25 9 13 United States 249.6 307.0 323.5 1.1 0.9 20 67 13 30 19 8 14 Uruguay 3.1 3.3 3.4 0.4 0.2 23 63 14 36 22 9 15 Uzbekistan 20.5 27.8 30.2 1.6 1.4 29 66 4 44 7 5 22 Venezuela, RB 19.8 28.4 31.0 1.9 1.5 30 65 5 46 8 5 21 Vietnam 66.2 87.3 92.8 1.5 1.0 26 68 6 38 9 5 17 West Bank and Gaza 2.0 4.0 4.8 3.8 2.8 45 52 3 86 6 3 35 Yemen, Rep. 12.3 23.6 27.8 3.4 2.7 44 54 2 81 4 7 36 Zambia 7.9 12.9 15.0 2.6 2.4 46 51 3 91 6 17 42 Zimbabwe 10.5 12.5 14.0 0.9 1.9 40 56 4 71 7 15 30 World 5,278.9 s 6,775.2 s 7,241.9 s 1.3 w 1.1 w 27 w 65 w 8w 42 w 12 w 8w 20 w Low income 547.3 846.1 962.6 2.3 2.1 39 57 4 69 6 11 34 Middle income 3,751.3 4,812.5 5,131.2 1.3 1.1 27 66 6 41 10 8 19 Lower middle income 2,930.9 3,810.8 4,084.9 1.4 1.2 28 66 6 42 9 8 20 Upper middle income 820.3 1,001.7 1,046.3 1.1 0.7 25 68 8 36 11 8 17 Low & middle income 4,298.6 5,658.7 6,093.8 1.4 1.2 29 65 6 45 9 8 21 East Asia & Pacific 1,599.6 1,943.8 2,035.8 1.0 0.8 23 70 7 32 11 7 14 Europe & Central Asia 392.4 404.2 409.0 0.2 0.2 19 70 11 28 16 11 15 Latin America & Carib. 435.6 572.5 606.9 1.4 1.0 28 65 7 43 10 6 18 Middle East & N. Africa 227.4 330.9 366.1 2.0 1.7 31 64 4 48 7 6 24 South Asia 1,128.7 1,567.7 1,706.5 1.7 1.4 32 63 5 51 7 7 24 Sub-Saharan Africa 514.9 839.6 969.5 2.6 2.4 43 54 3 78 6 14 38 High income 980.4 1,116.6 1,148.0 0.7 0.5 17 67 15 26 23 8 12 Euro area 301.6 327.3 332.3 0.4 0.3 15 66 18 23 27 9 10 a. Includes Taiwan, China. b. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo. 38 2011 World Development Indicators 2.1 PEOPLE Population dynamics About the data Definitions Population estimates are usually based on national Dependency ratios capture variations in the propor- • Population is based on the de facto definition of popu- population censuses. Estimates for the years before tions of children, elderly people, and working-age peo- lation, which counts all residents regardless of legal sta- and after the census are interpolations or extrapola- ple in the population that imply the dependency burden tus or citizenship—except for refugees not permanently tions based on demographic models. Errors and under- that the working-age population bears in relation to settled in the country of asylum, who are generally con- counting occur even in high income countries; in devel- children and the elderly. But dependency ratios show sidered part of the population of their country of origin. oping countries errors may be substantial because only the age composition of a population, not economic The values shown are midyear estimates for 1990 and of limits in the transport, communications, and other dependency. Some children and elderly people are part 2009 and projections for 2015. • Average annual popu- resources required to conduct and analyze a full census. of the labor force, and many working-age people are not. lation growth is the exponential change for the period The quality and reliability of official demographic Vital rates are based on data from birth and death indicated. See Statistical methods for more information. data are also affected by public trust in the govern- registration systems, censuses, and sample surveys • Population age composition is the percentage of the ment, government commitment to full and accurate by national statistical offices and other organiza- total population that is in specific age groups. • Depen- enumeration, confidentiality and protection against tions, or on demographic analysis. Data for 2009 dency ratio is the ratio of dependents—people younger misuse of census data, and census agencies’ inde- for most high-income countries are provisional esti- than 15 or older than 64—to the working age popula- pendence from political influence. Moreover, compara- mates based on vital registers. The estimates for tion—those ages 15–64. • Crude death rate and crude bility of population indicators is limited by differences many countries are projections based on extrapo- birth rate are the number of deaths and the number of in the concepts, definitions, collection procedures, lations of levels and trends from earlier years or live births occurring during the year, per 1,000 people, and estimation methods used by national statistical interpolations of population estimates and projec- estimated at midyear. Subtracting the crude death rate agencies and other organizations that collect the data. tions from the United Nations Population Division. from the crude birth rate provides the rate of natural Of the 155 economies in the table and the 55 econo- Vital registers are the preferred source for these increase, which is equal to the population growth rate in mies in table 1.6, 180 (about 86 percent) conducted a data, but in many developing countries systems for the absence of migration. census during the 2000 census round (1995–2004). registering births and deaths are absent or incomplete As of January 2011, 119 countries have completed because of deficiencies in the coverage of events or a census for the 2010 census round (2005–14). geographic areas. Many developing countries carry out The currentness of a census and the availability of special household surveys that ask respondents about complementary data from surveys or registration recent births and deaths. Estimates derived in this systems are objective ways to judge demographic way are subject to sampling errors and recall errors. data quality. Some European countries’ registration The United Nations Statistics Division monitors the systems offer complete information on population in completeness of vital registration systems. Progress the absence of a census. See table 2.17 and Primary has been made over the past 60 years in some coun- data documentation for the most recent census or tries. But many countries still have deficiencies in civil survey year and for the completeness of registration. registration systems. For example, only 60 percent of Current population estimates for developing coun- countries and areas register at least 90 percent of tries that lack recent census data and pre- and post- births, and only 47 percent register at least 90 percent census estimates for countries with census data are of deaths. Some of the most populous developing coun- Data sources provided by the United Nations Population Division and tries—Bangladesh, Brazil, India, Indonesia, Nigeria, The World Bank’s population estimates are compiled other agencies. The cohort component method—a Pakistan—lack complete vital registration systems. and produced by its Development Data Group in con- standard method for estimating and projecting popu- International migration is the only other factor sultation with its Human Development Network, oper- lation— requires fertility, mortality, and net migration besides birth and death rates that directly deter- ational staff, and country offices. The United Nations data, often collected from sample surveys, which can mines a country’s population growth. From 1990 to Population Division’s World Population Prospects: The be small or limited in coverage. Population estimates 2005 the number of migrants in high-income coun- 2008 Revision is a source of the demographic data for are from demographic modeling and so are susceptible tries rose 40 million. About 195 million people (3 more than half the countries, most of them developing to biases and errors from shortcomings in the model percent of the world population) live outside their countries, and the source of data on age composi- and in the data. Because the five-year age group is the home country. Estimating migration is difficult. At tion and dependency ratios for all countries. Other cohort unit and five-year period data are used, interpo- any time many people are located outside their important sources are census reports and other sta- lations to obtain annual data or single age structure home country as tourists, workers, or refugees or tistical publications from national statistical offices; may not reflect actual events or age composition. for other reasons. Standards for the duration and household surveys conducted by national agencies, The growth rate of the total population conceals purpose of international moves that qualify as migra- Macro International, and the U.S. Centers for Disease age-group differences in growth rates. In many tion vary, and estimates require information on flows Control and Prevention; Eurostat’s Demographic Sta- developing countries the once rapidly growing under- into and out of countries that is difficult to collect. tistics; Secretariat of the Pacific Community, Statistics 15 population is shrinking. Previously high fertility and Demography Programme; and U.S. Bureau of the rates and declining mortality rates are now reflected Census, International Data Base. in the larger share of the working-age population. 2011 World Development Indicators 39 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2009 1990 2009 1990 2009 1990–2009 1990 2009 Afghanistan 84 85 32 33 5.9 9.6 2.5 26.2 26.6 Albania 74 70 51 49 1.4 1.4 0.2 39.9 42.5 Algeria 75 80 23 37 7.0 14.8 3.9 23.4 31.6 Angola 90 88 74 75 4.6 8.3 3.1 46.3 46.9 Argentina 78 78 43 52 13.5 19.6 1.9 36.9 41.6 Armenia 78 75 61 60 1.7 1.6 –0.2 46.3 49.6 Australia 76 72 52 58 8.5 11.5 1.6 41.3 45.4 Austria 70 68 43 53 3.5 4.3 1.0 40.9 45.5 Azerbaijan 74 67 59 60 3.1 4.2 1.5 46.8 49.5 Bangladesh 89 83 61 59 49.5 78.6 2.4 39.9 41.2 Belarus 75 67 60 55 5.3 5.0 –0.3 48.9 49.5 Belgium 61 61 36 47 3.9 4.8 1.0 39.0 44.9 Benin 89 78 57 67 1.9 3.7 3.5 41.1 46.2 Bolivia 82 82 59 62 2.8 4.5 2.6 43.1 43.8 Bosnia and Herzegovina 67 68 53 55 2.0 1.9 0.0 45.2 47.1 Botswana 82 81 64 72 0.5 1.0 3.2 45.5 47.4 Brazil 85 82 45 60 62.6 101.5 2.5 35.1 43.7 Bulgaria 63 61 55 48 4.1 3.6 –0.7 47.9 46.1 Burkina Faso 91 91 77 78 3.9 7.1 3.2 48.0 47.1 Burundi 90 88 91 91 2.8 4.6 2.6 52.5 52.6 Cambodia 84 86 78 74 4.3 7.8 3.1 52.8 48.3 Cameroon 83 81 48 54 4.4 7.7 3.0 37.5 40.1 Canada 76 73 58 63 14.7 19.1 1.4 44.1 47.0 Central African Republic 87 87 69 72 1.3 2.1 2.5 45.6 46.5 Chad 81 78 65 63 2.4 4.3 3.1 45.6 45.2 Chile 77 73 32 42 5.0 7.5 2.1 30.5 37.2 China 85 80 73 67 643.9 783.2 1.0 44.8 44.6 Hong Kong SAR, China 80 69 47 52 2.9 3.7 1.4 36.3 46.3 Colombia 78 78 29 41 11.2 19.0 2.8 28.2 35.8 Congo, Dem. Rep. 85 86 53 57 13.4 24.9 3.3 39.9 40.6 Congo, Rep. 84 83 59 63 1.0 1.6 2.6 42.1 43.6 Costa Rica 84 80 33 45 1.2 2.1 3.2 27.4 35.5 Côte d’Ivoire 88 82 43 51 4.7 8.4 3.1 30.1 36.9 Croatia 69 60 47 46 2.2 2.0 –0.4 42.7 45.8 Cuba 73 67 36 41 4.4 5.0 0.6 33.0 38.1 Czech Republic 71 68 52 49 4.9 5.2 0.3 44.4 43.2 Denmark 75 71 62 60 2.9 3.0 0.1 46.1 46.9 Dominican Republic 85 80 43 51 2.9 4.5 2.3 33.2 38.8 Ecuador 78 78 33 47 3.5 5.9 2.7 29.5 38.0 Egypt, Arab Rep. 74 75 27 22 16.8 27.4 2.6 26.6 23.0 El Salvador 83 77 41 46 1.9 2.5 1.4 35.2 41.9 Eritrea 84 83 55 63 1.2 2.2 3.2 41.4 44.5 Estonia 77 69 63 55 0.8 0.7 –1.0 49.5 49.1 Ethiopia 91 90 72 81 21.5 40.0 3.3 45.1 47.9 Finland 72 65 59 57 2.6 2.7 0.2 47.1 48.1 France 65 62 46 51 25.0 28.7 0.7 43.3 46.8 Gabon 83 81 63 70 0.4 0.7 3.1 44.2 46.7 Gambia, The 86 85 71 71 0.4 0.8 3.4 46.2 46.2 Georgia 78 74 60 55 2.8 2.3 –1.2 46.9 46.8 Germany 73 67 45 53 38.8 42.3 0.5 40.7 45.6 Ghana 73 75 70 74 6.0 11.0 3.2 48.9 49.1 Greece 67 65 36 43 4.2 5.2 1.1 36.2 40.5 Guatemala 88 88 39 48 3.1 5.5 3.0 31.0 37.9 Guinea 90 89 79 79 2.9 4.8 2.7 46.8 46.9 Guinea-Bissau 81 84 59 60 0.4 0.7 2.4 43.0 42.4 Haiti 81 83 57 58 2.8 4.5 2.5 43.0 42.3 Honduras 88 80 41 40 1.7 2.8 2.6 32.3 33.9 40 2011 World Development Indicators 2.2 PEOPLE Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2009 1990 2009 1990 2009 1990–2009 1990 2009 Hungary 65 59 46 43 4.5 4.3 –0.3 44.5 45.1 India 84 81 34 33 317.8 457.5 1.9 27.1 27.6 Indonesia 81 86 50 52 74.9 115.6 2.3 38.4 38.1 Iran, Islamic Rep. 80 73 22 32 15.5 29.2 3.3 20.1 29.8 Iraq 73 69 11 14 4.3 7.7 3.0 13.1 16.7 Ireland 71 73 35 54 1.3 2.2 2.7 33.9 43.0 Israel 64 63 42 52 1.7 3.1 3.1 40.6 46.5 Italy 66 61 35 38 23.7 25.4 0.4 36.5 40.5 Jamaica 80 74 65 56 1.1 1.2 0.5 46.6 44.9 Japan 77 72 50 48 63.9 65.8 0.2 40.7 41.6 Jordan 71 74 15 23 0.7 1.9 5.0 16.2 23.0 Kazakhstan 78 76 62 66 7.8 8.6 0.5 47.0 49.8 Kenya 90 88 75 76 9.8 18.7 3.4 46.0 46.7 Korea, Dem. Rep. 80 78 55 55 10.0 12.4 1.1 42.6 42.7 Korea, Rep. 73 72 47 50 19.2 24.7 1.3 39.7 41.9 Kosovo .. .. .. .. .. .. .. .. .. Kuwait 82 83 36 45 0.9 1.5 2.8 22.4 25.0 Kyrgyz Republic 74 79 58 55 1.8 2.5 1.7 46.1 42.3 Lao PDR 83 79 80 78 1.9 3.1 2.5 49.8 50.4 Latvia 77 70 63 54 1.4 1.2 –1.0 49.6 48.3 Lebanon 72 72 20 22 0.9 1.5 2.8 23.3 25.0 Lesotho 83 78 68 71 0.7 0.9 1.9 51.7 52.4 Liberia 78 76 65 67 0.8 1.6 3.4 46.7 47.6 Libya 75 79 15 25 1.2 2.4 3.7 14.8 22.5 Lithuania 74 62 59 50 1.9 1.6 –1.0 48.1 48.7 Macedonia, FYR 68 65 46 43 0.8 0.9 0.6 40.7 40.1 Madagascar 89 89 83 84 5.4 9.7 3.1 48.4 49.2 Malawi 80 79 76 75 3.9 6.3 2.5 50.7 49.8 Malaysia 80 79 43 44 7.0 12.0 2.8 34.5 35.4 Mali 68 67 37 38 2.5 3.8 2.2 36.1 37.3 Mauritania 82 81 53 59 0.7 1.4 3.3 39.8 42.0 Mauritius 81 75 38 41 0.4 0.6 1.3 32.1 36.1 Mexico 84 81 34 43 29.9 47.2 2.4 30.0 36.2 Moldova 74 53 61 47 2.1 1.5 –1.8 48.7 49.9 Mongolia 77 78 63 68 0.9 1.4 2.5 45.6 47.4 Morocco 81 80 25 26 7.8 12.0 2.2 23.7 25.8 Mozambique 88 87 85 85 6.3 11.0 3.0 53.2 52.0 Myanmar 89 85 71 63 20.7 27.0 1.4 45.3 44.2 Namibia 64 63 48 52 0.4 0.8 3.0 44.9 46.5 Nepal 85 80 52 63 7.5 13.3 3.0 38.0 45.4 Netherlands 70 73 43 60 6.9 9.0 1.4 38.8 45.7 New Zealand 74 76 54 62 1.7 2.4 1.7 43.0 46.1 Nicaragua 85 78 39 47 1.4 2.3 2.8 32.3 38.7 Niger 91 88 27 39 2.3 4.8 3.8 24.7 31.6 Nigeria 76 73 36 39 29.4 50.0 2.8 33.0 35.1 Norway 73 71 57 63 2.2 2.6 0.9 44.7 47.7 Oman 80 77 19 25 0.6 1.1 3.4 13.7 18.8 Pakistan 85 85 14 22 31.0 58.1 3.3 12.7 19.4 Panama 79 81 39 48 0.9 1.6 2.8 32.4 37.4 Papua New Guinea 74 74 71 72 1.8 3.0 2.8 46.9 48.9 Paraguay 87 87 47 57 1.7 3.0 3.1 34.9 39.4 Peru 75 76 49 58 8.3 13.6 2.6 39.7 43.6 Philippines 83 79 48 49 24.1 38.8 2.5 36.5 38.6 Poland 72 62 55 46 18.1 17.4 –0.2 45.4 45.0 Portugal 73 69 49 56 4.7 5.6 0.9 42.4 46.9 Puerto Rico 61 58 31 36 1.2 1.5 1.2 35.8 40.8 Qatar 94 93 40 50 0.3 1.0 6.9 13.5 11.9 2011 World Development Indicators 41 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2009 1990 2009 1990 2009 1990–2009 1990 2009 Romania 73 60 60 45 11.8 9.5 –1.1 46.3 45.0 Russian Federation 76 69 60 58 76.8 75.9 –0.1 48.6 50.1 Rwanda 89 85 87 87 3.2 5.0 2.3 52.1 52.8 Saudi Arabia 80 74 15 17 5.0 8.6 2.8 11.5 14.9 Senegal 90 89 62 65 3.0 5.4 3.0 40.8 43.3 Serbia .. .. .. .. .. .. .. .. .. Sierra Leone 68 68 66 65 1.6 2.1 1.6 50.9 51.4 Singapore 79 76 51 54 1.6 2.7 2.9 39.1 41.5 Slovak Republic 72 69 59 51 2.6 2.7 0.3 46.8 44.7 Slovenia 59 65 47 53 0.8 1.0 1.2 46.8 46.2 Somalia 84 85 58 57 2.6 3.5 1.6 41.8 40.9 South Africa 62 63 36 47 10.4 18.8 3.1 37.5 43.7 Spain 67 69 34 49 15.6 22.9 2.0 34.8 42.8 Sri Lanka 79 75 37 34 6.8 8.3 1.1 31.8 32.4 Sudan 79 74 27 31 8.0 13.5 2.7 26.0 29.5 Swaziland 81 75 45 53 0.3 0.5 2.7 41.2 43.4 Sweden 72 69 63 61 4.7 5.0 0.3 47.7 47.4 Switzerland 81 74 57 61 3.8 4.4 0.7 42.9 46.8 Syrian Arab Republic 81 80 18 21 3.3 6.9 4.0 18.3 20.9 Tajikistan 80 78 59 57 2.1 2.9 1.8 43.3 43.9 Tanzania 91 91 87 86 12.3 21.4 2.9 49.8 49.4 Thailand 87 81 75 66 32.1 38.7 1.0 47.0 46.1 Timor-Leste 82 83 58 59 0.3 0.4 1.8 40.4 40.9 Togo 87 86 56 64 1.5 3.0 3.5 40.1 43.5 Trinidad and Tobago 76 78 39 55 0.5 0.7 2.3 35.0 43.3 Tunisia 76 71 21 26 2.4 3.8 2.4 21.6 26.7 Turkey 81 70 34 24 20.7 25.6 1.1 29.7 25.7 Turkmenistan 72 74 58 62 1.4 2.4 2.9 46.1 47.1 Uganda 91 91 81 78 7.9 14.1 3.0 47.7 46.5 Ukraine 71 65 56 52 25.5 23.0 –0.5 49.2 49.0 United Arab Emirates 92 92 25 42 1.0 2.9 5.8 9.8 15.7 United Kingdom 74 70 52 55 29.0 31.8 0.5 43.2 45.7 United States 76 72 57 58 129.2 159.0 1.1 44.4 46.0 Uruguay 76 76 48 54 1.4 1.7 0.9 40.8 44.1 Uzbekistan 68 71 53 58 7.3 12.7 2.9 45.5 45.9 Venezuela, RB 81 80 36 52 7.2 13.1 3.2 30.5 39.3 Vietnam 82 76 74 68 31.1 46.6 2.1 50.7 48.6 West Bank and Gaza 66 68 11 17 0.4 1.0 4.4 13.8 19.0 Yemen, Rep. 74 74 16 20 2.6 6.2 4.5 18.0 21.1 Zambia 79 79 61 60 3.0 4.8 2.5 44.3 43.4 Zimbabwe 80 74 67 60 4.1 5.0 1.0 46.3 47.5 World 81 w 78 w 52 w 52 w 2,342.6 t 3,175.8 t 1.6 w 39.4 w 40.1 w Low income 86 84 65 66 232.9 384.5 2.6 43.8 44.6 Middle income 82 79 52 50 1,646.7 2,244.8 1.6 38.1 38.4 Lower middle income 83 80 54 50 1,317.1 1,786.5 1.6 38.2 37.7 Upper middle income 78 75 45 48 329.6 458.2 1.7 37.6 40.8 Low & middle income 83 80 53 52 1,879.5 2,629.2 1.8 38.8 39.3 East Asia & Pacific 84 80 69 64 853.5 1,090.7 1.3 44.2 43.9 Europe & Central Asia 75 69 56 50 180.3 187.2 0.2 45.8 45.5 Latin America & Carib. 82 80 40 52 169.1 269.3 2.4 33.8 40.5 Middle East & N. Africa 77 75 22 26 63.3 115.2 3.2 22.0 25.7 South Asia 85 82 35 35 418.8 625.9 2.1 27.8 29.0 Sub-Saharan Africa 82 81 57 61 194.6 341.0 3.0 42.0 43.6 High income 73 70 49 52 463.0 546.6 0.9 41.6 43.9 Euro area 69 65 42 49 135.2 158.5 0.8 39.8 44.4 42 2011 World Development Indicators 2.2 PEOPLE Labor force structure About the data Definitions The labor force is the supply of labor available for pro- information on source, reference period, or defini- • Labor force participation rate is the proportion ducing goods and services in an economy. It includes tion, consult the original source. of the population ages 15 and older that engages people who are currently employed and people who The labor force participation rates in the table are actively in the labor market, either by working or are unemployed but seeking work as well as first-time from the ILO’s Key Indicators of the Labour Market, looking for work during a reference period. • Total job-seekers. Not everyone who works is included, 6th edition, database. These harmonized estimates labor force is people ages 15 and older who engage however. Unpaid workers, family workers, and stu- use strict data selection criteria and enhanced actively in the labor market, either by working or look- dents are often omitted, and some countries do not methods to ensure comparability across countries ing for work during a reference period. It includes count members of the armed forces. Labor force size and over time, including collection and tabulation both the employed and the unemployed. • Average tends to vary during the year as seasonal workers methodologies and methods applied to such country- annual percentage growth of the labor force is cal- enter and leave. specific factors as military service requirements. culated using the exponential endpoint method (see Data on the labor force are compiled by the Inter- Estimates are based mainly on labor force surveys, Statistical methods for more information). • Female national Labour Organization (ILO) from labor force with other sources (population censuses and nation- labor force as a percentage of the labor force shows surveys, censuses, establishment censuses and ally reported estimates) used only when no survey the extent to which women are active in the labor surveys, and administrative records such as employ- data are available. force. ment exchange registers and unemployment insur- The labor force estimates in the table were calcu- ance schemes. For some countries a combination lated by applying labor force participation rates from of these sources is used. Labor force surveys are the ILO database to World Bank population estimates the most comprehensive source for internationally to create a series consistent with these population comparable labor force data. They can cover all estimates. This procedure sometimes results in noninstitutionalized civilians, all branches and sec- labor force estimates that differ slightly from those tors of the economy, and all categories of workers, in the ILO’s Yearbook of Labour Statistics and its including people holding multiple jobs. By contrast, database Key Indicators of the Labour Market. labor force data from population censuses are often Estimates of women in the labor force and employ- based on a limited number of questions on the eco- ment are generally lower than those of men and are nomic characteristics of individuals, with little scope not comparable internationally, reflecting that demo- to probe. The resulting data often differ from labor graphic, social, legal, and cultural trends and norms force survey data and vary considerably by country, determine whether women’s activities are regarded depending on the census scope and coverage. Estab- as economic. In many countries many women work lishment censuses and surveys provide data only on farms or in other family enterprises without pay, on the employed population, not unemployed work- and others work in or near their homes, mixing work ers, workers in small establishments, or workers in and family activities during the day. the informal sector (ILO, Key Indicators of the Labour Market 2001–2002). The reference period of a census or survey is another important source of differences: in some countries data refer to people’s status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In devel- 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. Data sources Differing definitions of employment age also affect comparability. For most countries the working age is Data on labor force participation rates are from 15 and older, but in some countries children younger the ILO’s Key Indicators of the Labour Market, 6th than 15 work full- or part-time and are included in the edition, database. Labor force numbers were cal- estimates. Similarly, some countries have an upper culated by World Bank staff, applying labor force age limit. As a result, calculations may systemati- participation rates from the ILO database to popu- cally over- or underestimate actual rates. For further lation estimates. 2011 World Development Indicators 43 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b,c 1c 0 b,c 0 b,c 40 c 33c 18 c 11c 59c 66c 81c 89c Armenia .. 46 .. 46 .. 21 .. 10 .. 33 .. 45 Australia 6 4 4 2 32 31 12 9 61 64 84 89 Austria 6 6 8 6 47 37 20 12 46 57 72 82 Azerbaijan .. 40 .. 38 .. 17 .. 9 .. 44 .. 53 Bangladesh 54 42 85 68 16 15 9 13 25 43 2 19 Belarus .. 15 .. 9 .. 33 .. 24 .. 37 .. 64 Belgium 3 2 2 1 41 36 16 11 56 61 81 88 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia .. .. .. .. .. .. .. .. .. .. .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 35 .. 24 .. 19 .. 11 .. 46 .. 65 Brazil 31c 23 25c 15 27c 28 10 c 13 43c 50 65c 72 Bulgaria .. 9 .. 6 .. 42 .. 29 .. 49 .. 65 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 6c 3c 2c 2c 31c 32c 11c 11c 64 c 65c 87c 88 c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 16 6 6 32 31 15 11 45 53 79 84 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 1c 0 b,c 0 b,c 0 b,c 37c 21c 27c 6c 63c 78 c 73c 94 c Colombia .. 27 .. 6 .. 22 .. 16 .. 51 .. 78 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 18 5 5 27 28 25 13 41 54 69 82 Côte d’Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 13d .. 15d .. 39d .. 15d .. 48d .. 69d Cuba .. 25 .. 9 .. 22 .. 12 .. 54 .. 79 Czech Republic .. 4 .. 2 .. 51 .. 27 .. 45 .. 71 Denmark 7 4 3 1 37 32 16 12 56 64 82 86 Dominican Republic 26 21 3 2 23 26 21 14 52 53 76 84 Ecuador 10 c 11c 2c 4c 29c 28 c 17c 13c 62c 61c 81c 83c Egypt, Arab Rep. 35 28 52 43 25 26 10 6 41 46 37 51 El Salvador 48 29 15 5 23 26 23 19 29 45 63 76 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 5 13 2 42 48 30 23 36 46 57 75 Ethiopia .. 9c,d .. 10 c,d .. 25c,d .. 20 c,d .. 76c,d .. 64 c,d Finland 11 6 6 3 38 39 15 11 51 54 78 86 France 7 4 5 2 39 34 17 11 54 61 78 86 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 51 .. 57 .. 17 .. 4 .. 33 .. 39 Germany 4 3 4 2 50 41 24 16 46 56 73 83 Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 .. Greece 20 11 26 12 29 30 17 9 51 59 57 79 Guatemala .. 44 .. 16 .. 24 .. 21 .. 32 .. 63 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 76 .. 50 .. 9 .. 9 .. 13 .. 38 .. Honduras 53c 51c 6c 13c 18 c 20 c 25c 23c 29c 29c 69c 63c 44 2011 World Development Indicators 2.3 PEOPLE Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a Hungary 19 6 13 2 43 42 29 21 38 52 58 77 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 54 41 57 41 15 21 13 15 31 38 31 44 Iran, Islamic Rep. .. 21 .. 33 .. 33 .. 29 .. 47 .. 38 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 19 9 3 2 33 38 18 10 48 53 78 88 Israel 5 3 2 1 38 32 15 11 57 65 83 88 Italy 8 5 9 3 41 39 23 16 52 57 68 81 Jamaica 36 26 16 8 25 27 12 5 39 47 72 87 Japan 6 4 7 4 40 35 27 17 54 59 65 77 Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 14 7 18 8 40 33 28 16 46 60 54 76 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 37 .. 35 .. 26 .. 11 .. 37 .. 54 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 10 .. 6 .. 40 .. 17 .. 49 .. 77 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 10 .. 6 .. 41 .. 19 .. 49 .. 75 Macedonia, FYR .. 19 .. 17 .. 33 .. 29 .. 48 .. 54 Madagascar .. 82 .. 83 .. 5 .. 2 .. 13 .. 16 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 18 20 10 31 32 32 23 46 51 48 67 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15 10 13 8 36 36 48 26 48 54 39 66 Mexico 34 19 11 4 25 31 19 18 41 50 70 77 Moldova .. 36 .. 30 .. 25 .. 12 .. 39 .. 58 Mongolia .. 41 .. 35 .. 21 .. 15 .. 39 .. 50 Morocco .. 35 .. 60 .. 24 .. 15 .. 41 .. 25 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 23 52 8 21 24 8 9 34 24 40 63 Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 .. Netherlands 5 3 2 2 33 27 10 8 60 63 81 85 New Zealand 13c 9 8c 5 31c 32 13c 10 56c 58 79c 85 Nicaragua .. 42 .. 8 .. 20 .. 18 .. 38 .. 73 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 4 3 1 34 33 10 8 58 63 86 90 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 45 36 69 72 20 23 15 13 35 41 16 15 Panama 35 21 3 3 20 25 11 10 45 54 85 87 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay .. 33 .. 24 .. 24 .. 9 .. 43 .. 68 Peru 1c 12c 0 b,c 6c 30 c 41c 13c 43c 69c 46c 87c 51c Philippines 53c 42d 32c 23d 17c 18d 14 c 10 d 29c 41d 55c 68d Poland .. 15c .. 14 c .. 41c .. 18 c .. 44 c .. 68 c Portugal 10 11 13 12 39 40 24 17 51 49 63 71 Puerto Rico 5 2 0b 0b 27 26 19 10 67 72 80 89 Qatar .. 4 .. 0 .. 48 .. 4 .. 48 .. 96 2011 World Development Indicators 45 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a 1990–92a 2005–08a Romania 29 27 38 30 44 38 30 24 28 35 33 46 Russian Federation .. 11 .. 7 .. 38 .. 20 .. 51 .. 73 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5d .. 0 b,d .. 23d .. 2d .. 72d .. 98d Senegal .. 34 .. 33 .. 20 .. 5 .. 33 .. 42 Serbia .. 22 .. 20 .. 37 .. 20 .. 42 .. 61 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 2 0b 1 36 26 32 18 63 72 68 82 Slovak Republic .. 6 .. 2 .. 52 .. 24 .. 43 .. 74 Slovenia .. 10 c .. 10 c .. 44 c .. 23c .. 45c .. 65c Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 5d .. 3d .. 31d .. 13d .. 57d .. 79d Spain 11 6 8 3 41 40 17 11 49 55 75 86 Sri Lanka .. 28 c .. 37c .. 26c .. 27c .. 41c .. 34 c Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5c 3c 2c 1c 40 c 33c 12c 9c 55c 64 c 86c 90 c Switzerland 5 5 4 3 39 34 15 12 57 62 81 86 Syrian Arab Republic 23 .. 54 .. 28 .. 8 .. 49 .. 38 .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania .. 71 .. 78 .. 7 .. 3 .. 22 .. 19 Thailand 59 43 62 40 17 22 13 19 24 35 25 41 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 6 6 2 34 41 14 16 51 52 80 82 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 18d 72 42d 26 21d 11 15d 41 53d 17 43d Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. 6 .. 0b .. 45 .. 6 .. 49 .. 92 United Kingdom 3 2 1 1 41 32 16 9 55 66 82 90 United States 4 2 1 1 34 30 14 9 62 68 85 90 Uruguay .. 16c .. 5c .. 29c .. 13c .. 56c .. 83c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 13 2 2 32 30 16 12 52 56 82 86 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 11 .. 36 .. 27 .. 10 .. 61 .. 53 Yemen, Rep. 44 .. 83 .. 14 .. 2 .. 38 .. 13 .. Zambia 47 .. 56 .. 15 .. 3 .. 22 .. 18 .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w . w. .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 17 .. 12 .. 32 .. 20 .. 50 .. 68 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 18 .. 18 .. 34 .. 20 .. 48 .. 63 Latin America & Carib. .. 20 .. 9 .. 29 .. 16 .. 51 .. 75 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 3 38 34 19 13 55 61 76 84 Euro area 7 5 6 3 42 38 20 13 50 57 73 83 Note: Data across sectors may not sum to 100 percent because of workers not classified by sector. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. d. Data are for 2009. 46 2011 World Development Indicators 2.3 PEOPLE Employment by economic activity About the data Definitions The International Labour Organization (ILO) classi- Such broad classification may obscure fundamental • Agriculture corresponds to division 1 (ISIC revi- fies economic activity using the International Stan- shifts within countries’ industrial patterns. A slight sion 2) or tabulation categories A and B (ISIC revi- dard Industrial Classification (ISIC) of All Economic majority of countries report economic activity accord- sion 3) and includes hunting, forestry, and fishing. Activities, revision 2 (1968) and revision 3 (1990). ing to the ISIC revision 2 instead of revision 3. The • Industry corresponds to divisions 2–5 (ISIC revi- Because this classification is based on where work use of one classification or the other should not have sion 2) or tabulation categories C–F (ISIC revision is performed (industry) rather than type of work per- a significant impact on the information for the three 3) and includes mining and quarrying (including oil formed (occupation), all of an enterprise’s employees broad sectors presented in the table. production), manufacturing, construction, and public are classified under the same industry, regardless The distribution of economic wealth in the world utilities (electricity, gas, and water). • Services corre- of their trade or occupation. The categories should remains strongly correlated with employment by spond to divisions 6–9 (ISIC revision 2) or tabulation sum to 100 percent. Where they do not, the differ- economic activity. The wealthier economies are categories G–P (ISIC revision 3) and include whole- ences are due to workers who cannot be classified those with the largest share of total employment in sale and retail trade and restaurants and hotels; by economic activity. services, whereas the poorer economies are largely transport, storage, and communications; financing, Data on employment are drawn from labor force agriculture based. insurance, real estate, and business services; and surveys, household surveys, official estimates, cen- The distribution of economic activity by gender community, social, and personal services. suses and administrative records of social insurance reveals some clear patterns. Men still make up the schemes, and establishment surveys when no other majority of people employed in all three sectors, but information is available. The concept of employment the gender gap is biggest in industry. Employment in generally refers to people above a certain age who agriculture is also male-dominated, although not as worked, or who held a job, during a reference period. much as industry. Segregating one sex in a narrow Employment data include both full-time and part-time range of occupations significantly reduces economic workers. efficiency by reducing labor market flexibility and thus There are many differences in how countries define the economy’s ability to adapt to change. This seg- and measure employment status, particularly mem- regation is particularly harmful for women, who have bers of the armed forces, self-employed workers, and a much narrower range of labor market choices and unpaid family workers. Where members of the armed lower levels of pay than men. But it is also detri- forces are included, they are allocated to the service mental to men when job losses are concentrated sector, causing that sector to be somewhat over- in industries dominated by men and job growth is stated relative to the service sector in economies centered in service occupations, where women have where they are excluded. Where data are obtained better chances, as has been the recent experience from establishment surveys, data cover only employ- in many countries. ees; thus self-employed and unpaid family workers There are several explanations for the rising impor- are excluded. In such cases the employment share tance of service jobs for women. Many service jobs— of the agricultural sector is severely underreported. such as nursing and social and clerical work—are Caution should be also used where the data refer considered “feminine” because of a perceived simi- only to urban areas, which record little or no agricul- larity to women’s traditional roles. Women often do tural work. Moreover, the age group and area covered not receive the training needed to take advantage of could differ by country or change over time within a changing employment opportunities. And the greater country. For detailed information on breaks in series, availability of part-time work in service industries consult the original source. may lure more women, although it is unclear whether Countries also take different approaches to the this is a cause or an effect. treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribu- tion of employment by economic activity may not be fully comparable across countries. Data sources The ILO reports data by major divisions of the ISIC revision 2 or revision 3. In the table the reported Data on employment are from the ILO’s Key Indica- divisions or categories are aggregated into three tors of the Labour Market, 6th edition, database. broad groups: agriculture, industry, and services. 2011 World Development Indicators 47 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2009a 1990 2008 1990 2008 1990–92 2005–08 Afghanistan 54 55 45 47 16 44 .. .. .. .. .. .. Albania 49 46 37 36 89 72 .. .. .. .. –17.5 6.1 Algeria 39 49 25 31 60 .. .. .. .. .. –4.0 –0.7 Angola 77 76 71 69 12 .. .. .. .. .. –5.0 14.6 Argentina 53 57 42 36 74 85 .. 22b .. 17b 9.0 3.7 Armenia 38 38 24 25 .. 93 .. .. .. .. –24.8 12.2 Australia 56 59 58 64 132 149 12 11 9 7 3.3 0.7 Austria 52 55 61 53 102 100 .. 9 .. 9 0.7 0.4 Azerbaijan 57 60 38 39 88 99 .. 41 .. 66 –12.6 21.4 Bangladesh 74 68 66 56 18 42 .. .. .. .. 1.9 4.0 Belarus 58 52 40 35 93 95 .. .. .. .. –4.0 8.7 Belgium 44 47 31 27 101 108 17 11 15 9 1.6 0.7 Benin 70 72 64 59 .. .. .. .. .. .. .. .. Bolivia 61 71 48 49 .. 81 32b .. 50 b .. 2.6 1.8 Bosnia and Herzegovina 42 42 17 18 .. 91 .. .. .. .. –14.8 1.6 Botswana 47 46 34 27 49 82 .. .. .. .. .. .. Brazil 56 64 54 53 .. 101 29b 30 30 b 24 –0.3 3.2 Bulgaria 45 46 27 27 98 89 .. 10 .. 8 3.1 3.0 Burkina Faso 82 82 77 74 7 20 .. .. .. .. 1.3 1.3 Burundi 85 84 74 73 5 21 .. .. .. .. .. .. Cambodia 77 75 66 68 25 40 .. .. .. .. 4.0 6.5 Cameroon 59 59 37 33 26 41 .. .. .. .. –6.7 1.0 Canada 58 61 57 61 101 .. .. 12b .. 9b 0.8 0.2 Central African Republic 73 73 59 58 12 14 .. .. .. .. .. .. Chad 67 70 51 50 6 24 .. .. .. .. .. .. Chile 51 50 34 24 97 90 .. 25 .. 24 6.6 0.2 China 75 71 71 55 41 78 .. .. .. .. 6.8 10.6 Hong Kong SAR, China 62 57 54 38 .. 82 .. 10 b .. 4b 5.3 3.0 Colombia 52 62 38 43 53 95 30 b 41 26 b 41 –0.7 4.8 Congo, Dem. Rep. 68 67 60 62 21 37 .. .. .. .. –12.9 2.9 Congo, Rep. 66 65 49 46 46 .. .. .. .. .. .. .. Costa Rica 56 57 48 43 45 96 26 20 21 20 2.4 1.9 Côte d’Ivoire 63 60 52 45 .. .. .. .. .. .. –3.6 –0.7 Croatia 50 46 27 29 83 90 .. 23c .. 20 c –7.7 2.8 Cuba 52 54 40 32 94 90 .. .. .. .. .. .. Czech Republic 58 54 48 29 91 95 .. 15 .. 9 –5.2 3.4 Denmark 59 60 65 61 109 119 7 7 6 3 2.5 –0.7 Dominican Republic 44 53 28 34 .. 77 42 49 30 30 0.7 5.4 Ecuador 52 61 39 40 55 81 33b 29 b 41b 41b –0.1 0.5 Egypt, Arab Rep. 43 43 22 23 69 .. .. 20 .. 44 2.1 4.4 El Salvador 59 54 42 39 38 64 .. 29 .. 44 .. .. Eritrea 66 66 60 54 11 32 .. .. .. .. .. .. Estonia 61 55 43 29 100 99 2b 8b 3b 4b –9.4 2.4 Ethiopia 71 81 64 74 14 34 .. 48b .. 56 b –8.4 7.4 Finland 57 55 45 44 116 110 .. 11 .. 7 1.4 1.5 France 47 48 28 29 100 113 11 7 10 5 1.4 0.6 Gabon 58 58 37 33 40 .. .. .. .. .. .. .. Gambia, The 73 72 59 55 19 51 .. .. .. .. .. .. Georgia 57 54 28 22 95 108 .. .. .. .. –25.3 10.1 Germany 54 52 58 44 98 102 .. 7 .. 6 3.7 0.9 Ghana 68 65 40 40 35 57 .. .. .. .. 2.8 3.7 Greece 44 48 31 28 94 102 .. 27 .. 27 2.4 2.4 Guatemala 55 62 50 52 23 57 .. .. .. .. 1.0 1.4 Guinea 82 81 75 73 11 37 .. .. .. .. .. .. Guinea-Bissau 66 67 57 63 5 .. .. .. .. .. .. .. Haiti 56 55 37 47 .. .. .. .. .. .. .. .. Honduras 59 56 49 43 33 65 48b .. 50 b .. .. .. 48 2011 World Development Indicators 2.4 PEOPLE Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2009a 1990 2008 1990 2008 1990–92 2005–08 Hungary 48 45 37 20 86 97 8b 8 7b 6 0.3 2.0 India 58 56 46 40 46 60 .. .. .. .. 1.0 5.9 Indonesia 63 62 46 41 46 79 .. 60 .. 68 6.2 3.8 Iran, Islamic Rep. 46 49 33 36 53 83 .. 40 .. 56 6.5 1.8 Iraq 37 37 27 23 40 51 .. .. .. .. –33.6 1.9 Ireland 44 58 38 44 100 115 25 17 9 5 2.4 0.7 Israel 45 50 25 27 92 90 .. 9 .. 5 0.0 1.3 Italy 43 44 30 25 79 101 29 21 24 15 0.6 –0.3 Jamaica 61 56 40 29 70 91 46 38 37 31 0.7 –2.2 Japan 61 54 43 40 97 101 15 10 26 12 0.7 1.2 Jordan 36 38 25 20 82 88 .. .. .. .. –5.5 2.5 Kazakhstan 63 64 46 42 98 99 .. .. .. .. –15.1 4.8 Kenya 73 73 62 59 .. 59 .. .. .. .. –3.9 2.5 Korea, Dem. Rep. 62 64 46 39 .. .. .. .. .. .. .. .. Korea, Rep. 59 58 36 28 91 97 .. 23 .. 28 5.0 3.1 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 62 65 29 30 53 90 .. .. .. .. –0.2 3.2 Kyrgyz Republic 58 58 41 40 100 84 .. 47 .. 47 –13.1 4.3 Lao PDR 80 78 74 64 21 44 .. .. .. .. .. .. Latvia 58 55 43 35 92 98 .. 8 .. 6 –19.6 2.9 Lebanon 44 46 31 29 61 82 .. .. .. .. .. .. Lesotho 48 54 40 40 24 45 .. .. .. .. .. .. Liberia 66 66 57 57 .. .. .. .. .. .. .. .. Libya 45 49 28 27 .. .. .. .. .. .. .. .. Lithuania 54 50 36 18 92 99 .. 11 .. 8 –13.9 5.2 Macedonia, FYR 37 35 17 13 76 84 .. 24 .. 20 –5.6 1.2 Madagascar 79 83 65 71 19 32 .. .. .. .. –5.9 2.2 Malawi 72 72 48 49 17 30 .. .. .. .. –1.9 5.6 Malaysia 60 61 47 45 57 69 31 23 25 21 6.0 3.1 Mali 49 47 40 35 7 38 .. .. .. .. 0.4 1.9 Mauritania 67 47 54 23 13 24 .. .. .. .. .. .. Mauritius 56 54 45 37 55 87 13 18 7 15 .. .. Mexico 57 57 50 42 54 90 29 28 15 32 1.0 1.0 Moldova 58 45 39 17 90 88 .. 35 .. 30 –22.0 6.9 Mongolia 50 52 39 35 82 92 .. .. .. .. .. .. Morocco 46 46 40 35 36 56 .. 46 .. 65 –1.7 2.8 Mozambique 80 78 67 66 7 23 .. .. .. .. –3.0 5.5 Myanmar 74 74 62 53 23 53 .. .. .. .. 2.0 5.8 Namibia 45 43 24 14 43 66 .. .. .. .. .. .. Nepal 60 62 52 46 34 .. .. .. .. .. .. .. Netherlands 51 59 55 67 120 121 7 10 10 8 0.4 1.0 New Zealand 55 63 55 56 92 119 15 14 10 10 0.5 –0.3 Nicaragua 57 58 46 48 43 68 .. 45 .. 46 .. .. Niger 59 60 50 52 7 12 .. .. .. .. –5.7 2.3 Nigeria 53 52 29 24 24 30 .. .. .. .. –2.9 3.3 Norway 58 62 49 56 103 112 .. 8 .. 3 3.9 –1.1 Oman 53 51 30 29 45 91 .. .. .. .. 0.2 3.7 Pakistan 48 52 38 44 23 33 .. 58 .. 75 6.5 2.5 Panama 50 59 33 40 62 73 44 30 19 24 .. .. Papua New Guinea 70 70 57 54 12 .. .. .. .. .. .. .. Paraguay 61 73 51 58 31 67 17b 45 31b 50 .. .. Peru 53 69 34 53 67 89 30 b 33b 46b 47b –0.8 0.2 Philippines 59 60 42 39 70 82 .. 44b .. 47b –3.3 3.9 Poland 53 48 31 27 87 100 .. 20 .. 18 2.8 1.9 Portugal 58 56 53 35 66 104 22 18 30 19 2.2 0.9 Puerto Rico 37 41 21 29 .. 84 .. .. .. .. .. .. Qatar 73 77 35 47 84 85 .. .. .. .. 0.1 13.3 2011 World Development Indicators 49 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15–24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2009a 1990 2008 1990 2008 1990–92 2005–08 Romania 56 48 42 24 92 92 21 31 33 32 –9.3 6.5 Russian Federation 57 57 34 33 93 85 1 6 1 6 –7.9 6.4 Rwanda 87 80 79 64 18 27 .. .. .. .. .. .. Saudi Arabia 50 48 26 13 .. 97 .. .. .. .. 4.9 0.7 Senegal 67 66 60 55 15 30 77 .. 91 .. –1.0 0.9 Serbia 49d 44 d 28d 21d .. 91 .. 25 .. 20 .. .. Sierra Leone 64 65 38 42 16 35 .. .. .. .. .. .. Singapore 64 62 56 38 .. .. 10 12 6 7 1.5 –1.8 Slovak Republic 55 53 43 30 88 92 .. 14 .. 6 –0.8 6.1 Slovenia 55 54 38 32 89 97 .. 12 .. 10 –2.3 3.0 Somalia 66 67 59 58 .. 8 .. .. .. .. .. .. South Africa 39 41 19 15 69 94 .. 2 .. 3 –4.5 3.7 Spain 41 49 36 37 105 120 20 b 13 24b 10 2.4 0.7 Sri Lanka 51 55 31 36 72 .. .. 39 b .. 44b 5.5 9.3 Sudan 46 47 29 23 20 38 .. .. .. .. –1.3 7.5 Swaziland 54 50 34 26 49 53 .. .. .. .. .. .. Sweden 62 58 59 45 90 103 .. 9 .. 4 1.9 0.6 Switzerland 65 61 69 63 98 96 8 10 11 11 –0.6 1.0 Syrian Arab Republic 47 45 38 32 48 75 .. .. .. .. 6.5 0.3 Tajikistan 54 55 36 38 102 84 .. .. .. .. –20.4 6.3 Tanzania 87 78 79 70 5 27 .. 82b .. 93b –2.4 4.5 Thailand 77 72 70 46 31 76 67 51 74 56 6.8 2.7 Timor-Leste 64 67 51 58 .. 51 .. .. .. .. .. .. Togo 66 65 58 53 20 41 .. .. .. .. .. .. Trinidad and Tobago 45 61 33 46 82 89 22 .. 21 .. –3.5 5.4 Tunisia 41 41 29 22 45 92 .. .. .. .. 2.6 2.7 Turkey 53 42 48 31 48 82 .. 30 .. 49 1.0 2.6 Turkmenistan 56 58 35 34 .. .. .. .. .. .. –13.0 7.9 Uganda 82 83 73 75 10 27 .. .. .. .. –1.1 6.1 Ukraine 57 54 37 34 94 94 .. .. .. .. –7.9 5.9 United Arab Emirates 71 76 43 46 68 95 .. .. .. .. –3.9 0.7 United Kingdom 56 56 66 56 87 99 13 14 6 7 2.0 2.2 United States 59 59 56 51 92 94 .. .. .. .. 1.7 1.4 Uruguay 53 56 42 39 84 88 .. 26 b .. 24b 5.2 4.9 Uzbekistan 54 58 36 39 99 104 .. .. .. .. –7.8 5.9 Venezuela, RB 51 61 35 40 56 82 .. 28 .. 33 4.5 4.3 Vietnam 75 69 75 51 35 .. .. .. .. .. 4.6 5.6 West Bank and Gaza 30 30 19 15 .. 87 .. 34 .. 44 .. .. Yemen, Rep. 38 39 23 22 .. .. .. .. .. .. 0.9 –0.8 Zambia 57 61 40 46 21 49 56 .. 81 .. –2.5 3.9 Zimbabwe 70 65 48 50 49 .. .. .. .. .. –4.7 –7.7 World 62 w 60 w 52 w 45 w 50 w 67 w .. w .. w .. w .. w 0.7 w 3.1 w Low income 71 70 60 58 26 38 .. .. .. .. –3.2 4.4 Middle income 63 61 52 42 47 68 .. .. .. .. 1.3 6.2 Lower middle income 65 62 55 44 42 63 .. .. .. .. 3.2 7.4 Upper middle income 53 56 41 38 67 88 .. 26 .. 26 –2.3 3.6 Low & middle income 63 62 53 45 44 63 .. .. .. .. 1.1 6.1 East Asia & Pacific 73 69 67 51 41 74 .. .. .. .. 6.5 8.7 Europe & Central Asia 55 53 38 33 85 89 .. 19 .. 19 –9.1 5.8 Latin America & Carib. 55 61 46 45 57 89 .. 30 .. 30 1.8 2.6 Middle East & N. Africa 43 45 29 29 54 73 .. 33 .. 52 1.4 2.2 South Asia 59 57 48 42 37 52 .. .. .. .. 3.1 5.5 Sub-Saharan Africa 64 64 50 49 22 34 .. .. .. .. –5.3 4.1 High income 55 55 47 43 91 100 .. 13 .. 11 2.3 1.2 Euro area 48 50 41 37 .. .. .. 12 .. 9 2.4 0.7 a. Provisional data. b. Limited coverage. c. Data are for 2009. d. Includes Montenegro. 50 2011 World Development Indicators 2.4 PEOPLE Decent work and productive employment About the data Definitions Four targets were added to the UN Millennium Dec- Data on employment by status are drawn from • Employment to population ratio is the proportion laration at the 2005 World Summit High-Level Ple- labor force surveys and household surveys, supple- of a country’s population that is employed. People nary Meeting of the 60th Session of the UN General mented by offi cial estimates and censuses for a ages 15 and older are generally considered the Assembly. One was full and productive employment small group of countries. The labor force survey is working-age population. People ages 15–24 are and decent work for all, which is seen as the main the most comprehensive source for internationally generally considered the youth population. • Gross route for people to escape poverty. The four indi- comparable employment, but there are still some enrollment ratio, secondary, is the ratio of total cators for this target have an economic focus, and limitations for comparing data across countries and enrollment in secondary education, regardless of three of them are presented in the table. over time even within a country. Information from age, to the population of the age group that officially The employment to population ratio indicates how labor force surveys is not always consistent in what corresponds to secondary education. • Vulnerable efficiently an economy provides jobs for people who is included in employment. For example, informa- employment is unpaid family workers and own- want to work. A high ratio means that a large pro- tion provided by the Organisation for Economic account workers as a percentage of total employ- portion of the population is employed. But a lower Co-operation and Development relates only to civil- ment. •  Labor productiv ity is the growth rate employment to population ratio can be seen as a ian employment, which can result in an underesti- of gross domestic product (GDP) divided by the num- positive sign, especially for young people, if it is mation of “employees” and “workers not classified ber of people engaged in the production of goods caused by an increase in their education. This indi- by status,” especially in countries with large armed and services. cator has a gender bias because women who do not forces. While the categories of unpaid family work- consider their work employment or who are not per- ers and self-employed workers, which include own- ceived as working tend to be undercounted. This bias account workers, would not be affected, their relative has different effects across countries and reflects shares would be. Geographic coverage is another demographic, social, legal, and cultural trends and factor that can limit cross-country comparisons. The norms. employment by status data for many Latin Ameri- Comparability of employment ratios across coun- can countries covers urban areas only. Similarly, in tries is also affected by variations in definitions of some countries in Sub-Saharan Africa, where limited employment and population (see About the data for information is available anyway, the members of pro- table 2.3). The biggest difference results from the ducer cooperatives are usually excluded from the age range used to define labor force activity. The self-employed category. For detailed information on population base for employment ratios can also vary definitions and coverage, consult the original source. (see table 2.1). Most countries use the resident, Labor productivity is used to assess a country’s noninstitutionalized population of working age living economic ability to create and sustain decent in private households, which excludes members of employment opportunities with fair and equitable the armed forces and individuals residing in men- remuneration. Productivity increases obtained tal, penal, or other types of institutions. But some through investment, trade, technological progress, or countries include members of the armed forces in changes in work organization can increase social pro- the population base of their employment ratio while tection and reduce poverty, which in turn reduce vul- excluding them from employment data (International nerable employment and working poverty. Productiv- Labour Organization, Key Indicators of the Labour ity increases do not guarantee these improvements, Market, 6th edition). but without them—and the economic growth they The proportion of unpaid family workers and bring—improvements are highly unlikely. For compa- own-account workers in total employment is derived rability of individual sectors labor productivity is esti- from information on status in employment. Each mated according to national accounts conventions. status group faces different economic risks, and However, there are still significant limitations on the unpaid family workers and own-account workers availability of reliable data. Information on consis- are the most vulnerable—and therefore the most tent series of output in both national currencies and Data sources likely to fall into poverty. They are the least likely to purchasing power parity dollars is not easily avail- have formal work arrangements, are the least likely able, especially in developing countries, because the Data on employment to population ratio, vulner- to have social protection and safety nets to guard definition, coverage, and methodology are not always able employment, and labor productivity are from against economic shocks, and often are incapable of consistent across countries. For example, countries the ILO’s Key Indicators of the Labour Market, generating sufficient savings to offset these shocks. employ different methodologies for estimating the 6th edition, database. Data on gross enrollment A high proportion of unpaid family workers in a coun- missing values for the nonmarket service sectors ratios are from the United Nations Educational, try indicates weak development, little job growth, and and use different definitions of the informal sector. Scientific, and Cultural Organization Institute for often a large rural economy. Statistics. 2011 World Development Indicators 51 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990–92a 2006–09a 1990–92a 2006–09a 1990–92a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 12.7 .. .. .. .. .. .. .. .. .. .. Algeria 23.0 11.3 24.2 11.0 20.3 10.1 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.7b 8.6b 6.4b 7.8b 7.0 b 9.8b .. .. .. 48.1b 36.7b 15.3b Armenia .. 28.6b .. 21.9b .. 35.0 b .. .. .. 5.2 83.0 11.9 Australia 10.8 5.6b 11.4 5.7b 10.0 5.4b 14.7b 15.0 b 14.4b 48.0 34.1 17.9 Austria 3.6 4.8 3.5 5.0 3.8 4.5 20.3 19.7 21.0 37.9b 52.1b 10.0 b Azerbaijan .. 6.1 .. 7.1 .. 4.9 .. .. .. 6.3 78.9 14.9 Bangladesh 1.9 .. 2.0 .. 1.9 .. .. .. .. .. .. .. Belarus .. .. .. .. .. .. .. .. .. 10.8 38.6 50.6 Belgium 6.7 7.9 4.8 7.7 9.5 8.1 44.2 43.5 45.0 42.1 38.2 19.7 Benin 1.5 .. 2.2 .. 0.6 .. .. .. .. .. .. .. Bolivia 5.5b 5.2b 5.5b 4.5b 5.6b 6.0 b .. .. .. .. .. .. Bosnia and Herzegovina 17.6 23.9 15.5 21.8 21.6 27.1 .. .. .. 95.7 .. 4.0 Botswana 13.8 17.6b 11.7 15.3b 17.2 19.9b .. .. .. .. .. .. Brazil 6.4b 8.3 5.4b 6.1 7.9b 11.0 .. .. .. 51.6 33.6 3.6 Bulgaria .. 6.8 .. 7.0 .. 6.6 43.3 40.7 46.4 41.8 49.7 8.6 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi 0.5 .. 0.7 .. 0.3 .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. 2.9 .. 2.5 .. 3.3 .. .. .. .. .. .. Canada 11.2b 8.3b 12.0 b 9.4b 10.2b 7.0 b 7.8b 8.1b 7.4b 27.7b 41.1b 31.2b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 4.4 9.7 3.9 9.1 5.3 10.7 .. .. .. 17.8 58.5 23.5 China 2.3b 4.3 .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 2.0 b 5.2b 2.0 b 6.0 b 1.9b 4.3b .. .. .. 40.8b 41.4b 16.6b Colombia 9.5b 12.0 6.8b 9.3 13.0 b 15.8 .. .. .. 76.6 .. 20.6 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 4.1 4.9 3.5 4.1 5.4 6.2 .. .. .. 65.2 27.3 6.4 Côte d’Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. .. Croatia 11.1 9.1 11.1 8.0 11.2 10.2 56.2 50.8 61.0 16.0 70.4 11.6 Cuba .. 1.6 .. 1.4 .. 2.0 .. .. .. 43.0 52.4 4.6 Czech Republic 2.3 6.7 2.4 5.8 2.1 7.7 31.2 29.0 33.4 26.8 68.8 4.3 Denmark 9.0 6.0 8.3 6.5 9.9 5.4 9.1 8.9 9.4 35.9 35.1 23.0 Dominican Republic 20.7 14.2 12.0 8.5 35.2 22.8 .. .. .. 35.0 44.5 16.4 Ecuador 8.9b 6.5 6.0 b 5.2 13.2b 8.4 .. .. .. 74.0 b .. 23.6b Egypt, Arab Rep. .. 9.4 .. 5.2 .. 22.9 .. .. .. .. .. .. El Salvador 7.9b 5.9 8.4b 7.5 7.2b 3.6 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.7b 13.7 3.9b 17.0 3.5b 10.8 27.4 26.8 28.4 23.1b 57.8b 16.6b Ethiopia 1.3 20.5b 1.1 12.1b 1.6 29.9b .. .. .. .. .. .. Finland 11.6 8.2 13.3 8.9 9.6 7.5 16.6 18.2 14.7 35.5 45.9 18.6 France 10.2 9.1 8.1 8.9 12.8 9.3 35.4 35.6 35.3 39.9 39.6 19.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 16.5 .. 16.8 .. 16.1 .. .. .. 5.1b 52.5b 42.3b Germany 6.6 7.7 5.3 8.1 8.4 7.3 45.5 44.4 47.0 33.1 56.3 10.6 Ghana 4.7 .. 3.7 .. 5.5 .. .. .. .. .. .. .. Greece 7.8 9.5 4.9 6.9 12.9 13.1 40.8 34.4 45.6 29.3b 48.4b 21.8b Guatemala .. 1.8 .. 1.5 .. 2.4 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12.7 .. 11.9 .. 13.8 .. .. .. .. .. .. .. Honduras 3.2b 2.9b 3.3b 2.9b 3.0 b 2.9b .. .. .. .. .. .. 52 2011 World Development Indicators 2.5 PEOPLE Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990–92a 2006–09a 1990–92a 2006–09a 1990–92a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a Hungary 9.9 10.0 11.0 10.3 8.7 9.7 42.6 42.4 42.8 33.1b 58.7b 8.1b India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 2.8 7.9 2.7 7.5 3.0 8.5 .. .. .. 44.4 40.7 9.6 Iran, Islamic Rep. 11.1 10.5 9.5 9.1 24.4 16.8 .. .. .. .. .. .. Iraq .. 17.5 .. 16.2 .. 22.5 .. .. .. .. .. .. Ireland 15.0 11.7 14.9 14.7 15.2 8.0 29.0 32.1 21.7 39.8 37.2 18.2 Israel 11.2 7.6 9.2 7.6 13.9 7.6 28.6 32.3 25.0 12.2 12.8 72.5 Italy 9.3 7.8 6.7 6.8 13.9 9.3 44.4 42.0 46.9 46.5 40.6 11.3 Jamaica 15.4 11.4 9.4 8.5 22.2 14.8 .. .. .. 9.7 4.3 8.4 Japan 2.2 5.0 2.1 5.3 2.2 4.7 28.5 34.8 18.8 67.2 .. 32.8 Jordan .. 12.9 .. 10.3 .. 24.1 .. .. .. .. .. .. Kazakhstan .. 6.6 .. 5.6 .. 7.5 .. .. .. .. .. .. Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.5b 3.6b 2.8b 4.1b 2.1b 3.0 b 0.5 0.6 0.3 15.2 49.7 35.2 Kosovo .. 45.4 .. 40.7 .. 56.4 81.7 82.8 79.8 64.0 46.0 15.0 Kuwait .. .. .. .. .. .. .. .. .. 19.4 41.4 9.6 Kyrgyz Republic .. 8.2 .. 7.3 .. 9.4 .. .. .. 13.3 77.1 9.6 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 17.1 .. 20.4 .. 14.0 26.7 27.1 26.0 24.3b 59.9b 14.6b Lebanon .. 9.0 .. 8.6 .. 10.1 .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. 5.6 .. 6.8 .. 4.2 .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 13.7 .. 17.1 .. 10.4 23.2 21.0 26.8 14.2b 70.4b 15.4b Macedonia, FYR .. 32.2 .. 31.7 .. 33.0 81.6 82.2 80.6 .. .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 3.7 3.7 .. 3.2 .. 3.7 .. .. .. 13.3 61.6 25.1 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.3 7.3 3.2 4.4 3.6 12.3 .. .. .. 44.2 48.5 6.4 Mexico 3.1 5.2 2.7 5.4 4.0 4.8 1.9 1.8 2.1 50.7 24.5 22.9 Moldova .. 6.4 .. 7.8 .. 4.9 .. .. .. .. .. .. Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 16.0 b 10.0 13.0 b 9.8 25.3b 10.5 .. .. .. .. .. .. Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.0 .. 4.7 .. 8.8 .. .. .. .. .. .. .. Namibia 19.0 37.6 20.0 32.5 19.0 43.0 .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5.6 3.4 4.0 3.4 7.8 3.5 24.8 23.7 26.1 41.3 39.7 17.0 New Zealand 10.6b 6.1b 11.4b 6.1b 9.7b 6.1b 6.3b 6.3b 6.4b 30.6 38.8 26.9 Nicaragua 14.4 5.0 11.3 4.9 19.5 5.1 .. .. .. 72.8 2.1 18.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 5.9 3.2 6.6 3.6 5.1 2.6 7.7 7.5 8.0 25.4 49.2 20.6 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 5.2 5.0 3.8 4.0 14.0 8.7 .. .. .. 14.3 11.4 26.0 Panama 14.7 5.9 10.8 4.6 22.3 7.9 .. .. .. 36.0 39.6 24.0 Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. .. Paraguay 5.0 b 5.6 6.0 b 4.4 3.7b 7.5 .. .. .. 49.9 38.0 9.9 Peru 9.4b 6.8b 7.5b 5.4b 12.5b 8.3b .. .. .. 30.0 b 31.9b 37.6b Philippines 8.6b 7.5 7.9b 7.5 9.9b 7.4 .. .. .. 13.8 45.2 41.1 Poland 13.3 8.2 12.2 7.8 14.7 8.7 25.2 23.3 27.3 16.4b 73.2b 10.4b Portugal 4.1b 9.5 3.5b 8.9 5.0 b 10.1 44.2 40.8 47.5 68.1b 15.4b 13.2b Puerto Rico 16.9 13.4 19.1 14.9 13.3 11.6 .. .. .. .. .. .. Qatar .. 0.5 .. 0.2 .. 2.6 .. .. .. .. .. .. 2011 World Development Indicators 53 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990–92a 2006–09a 1990–92a 2006–09a 1990–92a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a 2006–09a Romania .. 6.9 .. 7.7 .. 5.8 31.6 32.2 30.6 25.8 66.3 6.1 Russian Federation 5.2 8.2 5.2 8.4 5.2 7.9 35.7 33.3 38.4 13.7 54.2 32.1 Rwanda 0.3 .. 0.6 .. 0.2 .. .. .. .. .. .. .. Saudi Arabia .. 5.4 .. 3.5 .. 15.9 .. .. .. 7.5 48.6 43.6 Senegal .. 10.0 .. 7.9 .. 13.6 .. .. .. 40.2 6.9 2.5 Serbia .. 16.6 .. 15.3 .. 18.4 71.1 70.1 72.1 20.3 68.4 11.2 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7b 5.9 2.7b 5.4 2.6b 6.5 .. .. .. 31.0 25.6 43.2 Slovak Republic .. 12.1 .. 11.4 .. 12.9 50.9 47.8 54.4 29.2 65.3 5.3 Slovenia 7.1 5.9 8.1 5.9 6.0 5.8 30.1 28.3 32.1 25.0 b 60.4b 12.5b Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 23.8 .. 22.0 .. 25.9 14.4 .. .. 36.2 56.3 4.5 Spain 18.1 18.0 13.9 17.7 25.8 18.4 30.2 26.9 34.4 54.8b 23.6b 20.4b Sri Lanka 14.2b 7.6 .. 7.2 .. 8.1 .. .. .. 45.4b 22.0 b 32.6b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5.7 8.3 6.7 8.6 4.6 8.0 12.8 13.1 12.4 32.2b 46.0 b 17.1b Switzerland 2.8 4.1 2.3 3.7 3.5 4.5 30.0 26.4 33.6 28.8 53.2 17.9 Syrian Arab Republic 6.8 8.4 5.2 5.2 14.0 25.7 .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. 66.5 28.8 4.6 Tanzania 3.6b 4.3 2.8b 2.8 4.3b 5.8 .. .. .. .. .. .. Thailand 1.4 1.2 1.3 1.2 1.5 1.1 .. .. .. 40.5 45.5 0.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 19.6 5.3 17.0 3.5 23.9 6.2 .. .. .. .. .. .. Tunisia .. 14.2 .. .. .. .. .. .. .. .. .. .. Turkey 8.5 14.0 8.8 13.9 7.8 14.3 25.3 22.6 32.2 52.3 28.2 12.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 1.0 .. 1.3 .. 0.6 .. .. .. .. .. .. .. Ukraine .. 8.8 .. 6.6 .. 6.1 .. .. .. 8.5 52.2 39.3 United Arab Emirates .. 4.0 .. 2.0 .. 12.0 .. .. .. .. .. .. United Kingdom 9.7 7.7 11.5 8.8 7.3 6.4 24.6 26.5 21.5 37.3 47.7 14.3 United States 7.5b 9.3b 7.9b 10.3b 7.0 b 8.1b 16.3b 16.4b 16.1b 18.7 35.5 45.7 Uruguay 9.0 b 7.3 6.8b 5.3 11.8b 9.7 .. .. .. 59.1b 27.0 b 13.8b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7.7 7.6 8.2 7.2 6.8 8.1 .. .. .. .. .. .. Vietnam .. 2.4 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 24.5 .. 17.7 .. 38.6 .. .. .. 54.3 14.2 23.5 Yemen, Rep. .. 15.0 .. 11.5 .. 40.9 .. .. .. .. .. .. Zambia 18.9 .. 16.3 .. 22.4 .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income 6.7 9.1 6.4 8.5 7.4 10.3 .. .. .. 43.4 40.9 14.3 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 2.5 4.6 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 9.2 .. 9.9 .. 8.6 .. .. .. 26.7 50.2 24.1 Latin America & Carib. 6.6 7.9 5.4 6.6 8.4 9.8 .. .. .. 50.8 34.9 12.3 Middle East & N. Africa .. 10.6 .. 8.9 .. 16.7 .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.5 8.1 7.1 8.4 8.0 7.7 24.8 25.3 23.8 33.9 43.7 25.7 Euro area 9.1 9.4 7.2 9.2 11.9 9.6 38.2 36.7 39.8 41.3 43.0 14.9 a. Data are for the most recent year available. b. Limited coverage. 54 2011 World Development Indicators 2.5 PEOPLE Unemployment About the data 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, par- Data sources ticularly people seeking work for the first time. These surveys generally use a definition of unemployment Data on unemployment are from the ILO’s Key Indi- that follows the international recommendations more cators of the Labour Market, 6th edition, database. closely than that used by other sources and therefore 2011 World Development Indicators 55 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7–14 % of children ages 7–14 % of children ages 7–14 % of children in employment in employment in employment ages 7–14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2005 25.0 18.8 22.0 6.7 93.3 .. .. .. .. 1.4 94.5 Algeria .. .. .. .. .. .. .. .. .. .. Angolab 2001 30.1 30.0 30.1 26.6 73.4 .. .. .. .. 6.2 80.1 Argentina 2004 12.9 15.7 9.8 4.8 95.2 .. .. .. 34.2 8.1 56.2 Armenia .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2005 5.2 5.8 4.5 6.3 93.7 91.7 0.7 7.4 4.1 3.8 92.1 Bangladesh 2006 16.2 25.7 6.4 37.8 62.2 .. .. .. - 17.0 77.8 Belarus 2005 11.7 12.1 11.2 0.0 100.0 .. .. .. .. 9.2 78.8 Belgium .. .. .. .. .. .. .. .. .. .. .. Benin 2006 74.4 72.8 76.1 36.1 63.9 .. .. .. .. .. .. Bolivia 2008 32.1 33.0 31.1 5.2 94.8 73.2 6.1 19.2 0.9 9.2 89.9 Bosnia and Herzegovina 2006 10.6 11.7 9.5 0.1 99.9 .. .. .. .. 1.6 92.1 Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2008 5.2 6.9 3.5 4.8 95.2 54..7 7.6 34.6 5.5 24.7 69.8 c Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 2006 42.1 49.0 34.5 67.7 32.3 70.9 1.4 24.9 1.9 2.2 95.8 Burundi 2005 11.7 12.5 11.0 38.9 61.1 .. .. .. .. 25.9 68.6 Cambodiad 2003/04 48.9 49.6 48.1 13.8 86.2 82.3 4.2 12.9 6.0 4.1 89.4 Cameroon 2007 43.4 43.5 43.4 21.9 78.1 88.5 3.1 8.2 2.5 9.5 87.6 Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. 2.0 56.4 Chad 2004 60.4 64.4 56.2 49.1 50.9 .. .. .. .. 1.8 77.2 Chile 2003 4.1 5.1 3.1 3.2 96.8 24.1 6.9 66.9 .. .. .. China .. .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2007 3.9 5.3 2.3 24.8 75.2 41.2 10.8 46.1 22.7 29.1 45.6 Congo, Dem. Rep.d 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. 6.6 76.7 Congo, Rep 2005 30.1 29.9 30.2 9.9 90.1 .. .. .. .. 4.2 84.5 Costa Ricad 2004 5.7 8.1 3.5 44.6 55.4 40.3 9.5 49.0 15.8 57.7 26.6 Côte d’Ivoire 2006 45.7 47.7 43.6 46.8 53.2 .. .. .. .. 2.4 88.0 Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republicd 2005 5.8 9.0 2.7 6.2 93.8 18.5 9.8 57.5 23.8 19.5 56.2e Ecuador 2006 14.3 16.9 11.6 21.0 79.0 69.3 6.3 22.8 3.6 15.2 81.2 Egypt, Arab Rep. 2005 7.9 11.5 4.3 21.0 79.0 .. .. .. 11.4 87.4 El Salvador 2007 7.1 10.1 3.8 24.9 75.1 50.1 13.3 35.2 2.2 23.6 74.2 Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2005 56.0 64.3 47.1 69.4 30.6 94.6 1.5 3.7 1.7 2.4 95.8 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2005 43.5 33.9 52.3 32.1 67.9 .. .. .. .. 1.1 87.3 Georgia 2006 31.8 33.6 29.9 1.0 99.0 .. .. .. .. 4.3 77.0 Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2006 48.9 49.9 48.0 18.7 81.3 .. .. .. .. 6.1 76.2 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2006 18.2 24.5 11.7 28.4 71.6 63.7 9.7 24.7 2.0 18.8 79.2 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2006 50.5 52.8 48.1 36.4 63.6 .. .. .. .. 4.0 87.7 Haiti 2005 33.4 37.3 29.6 17.7 82.3 .. .. .. .. 1.8 79.4 Honduras 2007 8.7 13.3 4.1 45.1 54.9 61.6 10.4 25.1 3.5 23.0 73.5 56 2011 World Development Indicators 2.6 PEOPLE Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7–14 % of children ages 7–14 % of children ages 7–14 % of children in employment in employment in employment ages 7–14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Hungary .. .. .. .. .. .. .. .. .. .. .. India 2004/05 4.2 4.2 4.2 84.9 15.2 69.4 16.0 12.4 7.1 6.8 59.3 Indonesia 2000 8.9 8.8 9.1 24.9 75.1 .. .. .. .. 17.8 75.8e Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq 2006 14.7 17.9 11.3 32.4 67.6 .. .. .. .. 7.0 85.3 Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica 2005 9.8 11.3 8.3 2.5 97.5 .. .. .. .. 16.3 74.9 Japan .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 2006 3.6 4.4 2.8 1.6 98.4 .. .. .. - 4.0 75.0 Kenya 2000 37.7 40.1 35.2 14.1 85.9 .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2006 5.2 5.8 4.6 7.9 92.1 .. .. .. - 3.7 81.9 Lao PDR .. .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. .. Lesotho 2002 2.6 4.0 1.3 74.4 25.6 58.0 0.0 10.4 3.7 36.6 59.7c Liberia 2007 37.4 37.8 37.1 45.0 55.0 .. .. .. .. 1.7 79.3 Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 2005 11.8 14.8 8.6 2.8 97.2 .. .. .. .. 3.9 89.5 Madagascar 2007 26.0 27.7 24.2 40.9 59.1 87.6 2.9 8.2 0.1 10.0 89.9 Malawi 2006 40.3 41.3 39.4 10.5 89.5 .. .. .. .. 6.7 75.5 Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2006 49.5 55.0 44.1 59.5 40.5 .. .. .. .. 1.6 80.4 Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexicof 2009 12.2 16.5 7.6 22.6 77.4 38.2 11.7 47.0 2.7 34.3 63.1 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. 2.9 82.0 Mongolia 2006/07 10.1 11.4 8.6 16.4 83.6 91.3 0.3 6.3 5.1 0.1 94.7 Morocco 1998/99 13.2 13.5 12.8 93.2 6.8 60.6 8.3 10.1 2.1 10.0 81.7 Mozambiqued 1996 1.8 1.9 1.7 100.0 0.0 .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 0.4 8.0 0.1 4.5 95.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 87.0 1.4 11.1 4.2 3.3 92.4 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2005 10.1 16.2 3.9 30.8 69.2 70.5 9.7 19.3 1.2 13.8 85.0 c Niger 2006 47.1 49.2 45.0 66.5 33.5 .. .. .. 4.8 74.5 Nigeria .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. .. Panama 2008 8.9 12.1 5.4 14.6 85.4 73.3 2.9 22.9 12.6 11.3 76.1c Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguayc 2005 15.3 22.6 7.7 24.2 75.7 60.8 6.2 32.1 9.3 24.8 65.8 Peru 2007 42.2 44.8 39.5 4.0 96.0 62.6 5.0 31.1 3.8 7.6 88.6 Philippines 2001 13.3 16.3 10.0 14.8 85.2 64.3 4.1 30.6 4.1 22.8 73.1 Poland .. .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 48.5 11.2 33.3 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. 2011 World Development Indicators 57 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7–14 % of children ages 7–14 % of children ages 7–14 % of children in employment in employment in employment ages 7–14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Romania 2000 1.4 1.7 1.1 20.7 79.3 97.1 0.0 2.3 4.5 .. 92.9e Russian Federation .. .. .. .. .. .. .. .. .. .. .. Rwanda 2008 7.5 8.0 7.0 18.5 81.5 85.5 0.7 10.5 14.8 12.8 72.3 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. Senegal 2005 18.5 24.4 12.6 61.9 38.1 79.1 5.0 14.0 6.3 4.4 84.1 Serbia 2005 6.9 7.2 6.6 2.1 97.9 .. .. .. .. 5.2 89.4 Sierra Leone 2007 14.9 14.9 14.9 57.7 42.3 83.8 0.8 13.4 9.7 0.9 87.8 Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia 2006 43.5 45.5 41.5 53.5 46.5 .. .. .. .. 1.6 94.8 South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. 7.1 7.1 85.8 Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 1999 17.0 20.4 13.4 5.4 94.6 71.2 13.1 15.0 2.9 8.3 88.0 Sudang 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. 7.3 81.3 Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. 10.4 85.9 Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2006 6.6 8.8 4.3 34.6 65.4 .. .. .. .. 21.5 68.8 Tajikistan 2005 8.9 8.7 9.1 9.0 91.0 .. .. .. .. 24.2 71.3 Tanzaniah 2005/06 31.1 35.0 27.1 28.2 71.8 85.3 0.7 14.0 56.3 0.9 42.8e Thailand 2005 15.1 15.7 14.4 4.2 95.8 .. .. .. .. 13.5 80.0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 2006 38.7 39.8 37.4 29.8 70.2 82.9 1.3 15.1 5.0 1.6 93.4 Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. 29.8 64.9 Tunisia .. .. .. .. .. .. .. .. .. .. Turkeyi 2006 2.6 3.3 1.8 38.8 61.2 57.1 14.3 27.1 2.1 34.1 63.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 2005/06 38.2 39.8 36.5 7.7 92.3 95.5 1.4 3.0 1.4 1.5 97.1 Ukraine 2005 17.3 18.0 16.6 0.1 99.9 .. .. .. .. 3.1 79.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2005 5.1 5.3 4.9 1.0 99.0 .. .. .. .. 3.8 78.6 Venezuela, RBd 2006 5.1 6.9 3.3 19.8 80.2 32.3 7.2 55.7 31.6 33.1 35.3 Vietnam 2006 21.3 21.0 21.6 11.9 88.1 .. .. .. .. 5.9 91.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2006 18.3 20.7 15.9 30.9 69.1 .. .. .. .. 6.1 86.1 Zambia 2008 34.4 35.4 33.3 18.6 81.4 91.9 0.7 7.0 2.9 3.9 93.1 Zimbabwe 1999 14.3 15.3 13.3 12.0 88.0 .. .. .. 3.4 28.4 68.2 a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Refers to unpaid workers, regardless of whether they are family workers. d. Covers children ages 10–14. e. Refers to family workers, regardless of whether they are paid. f. Covers children ages 12–14. g. Northern Sudan only. h. Refers mainly to work on own shamba. i. Estimates are for children ages 6–14. 58 2011 World Development Indicators 2.6 PEOPLE Children at work About the data Definitions The data in the table refer to children’s work in the data on children in employment and in the sampling •  Survey year is the year in which the underlying sense of “economic activity”—that is, children in design underlying the surveys. Differences exist data were collected. • Children in employment are employment, a broader concept than child labor not only across different household surveys in the children involved in any economic activity for at least (see ILO 2009a for details on this distinction). same country but also across the same type of sur- one hour in the reference week of the survey. • Work In line with the definition of economic activity vey carried out in different countries, so estimates only refers to children who are employed and not adopted by the 13th International Conference of of working children are not fully comparable across attending school. • Study and work refer to children Labour Statisticians, the threshold for classifying a countries. attending school in combination with employment. person as employed is to have been engaged at least The table aggregates the distribution of children in • Employment by economic activity is the distribu- one hour in any activity during the reference period employment by the industrial categories of the Inter- tion of children in employment by the major industrial relating to the production of goods and services national Standard Industrial Classifi cation (ISIC): categories (ISIC revision 2 or revision 3). • Agricul- set by the 1993 UN System of National Accounts. agriculture, manufacturing, and services. A residual ture corresponds to division 1 (ISIC revision  2) or Children seeking work are thus excluded. Economic category—which includes mining and quarrying; categories A and B (ISIC revision  3) and includes activity covers all market production and certain non- electricity, gas, and water; construction; extraterri- agriculture and hunting, forestry and logging, and market production, including production of goods for torial organization; and other inadequately defined fishing. • Manufacturing corresponds to division 3 own use. It excludes unpaid household services (com- activities—is not presented. Both ISIC revision 2 and (ISIC revision 2) or category D (ISIC revision 3). • Ser- monly called “household chores”)—that is, the pro- revision 3 are used, depending on the country’s codi- vices correspond to divisions 6–9 (ISIC revision duction of domestic and personal services by house- fication for describing economic activity. This does 2) or categories G–P (ISIC revision  3) and include hold members for own-household consumption. not affect the definition of the groups in the table. wholesale and retail trade, hotels and restaurants, Data are from household surveys conducted by The table also aggregates the distribution of transport, financial intermediation, real estate, pub- the International Labor Organization (ILO), the United children in employment by status in employment, lic administration, education, health and social work, Nations Children’s Fund (UNICEF), the World Bank, based on the International Classification of Status in other community services, and private household and national statistical offices. The surveys yield data Employment (1993), which shows the distribution in activity. • Self-employed workers are people whose on education, employment, health, expenditure, and employment by three major categories: selfemployed remuneration depends directly on the profits derived consumption indicators related to children’s work. workers, wage workers (also known as employees), from the goods and services they produce, with or Household survey data generally include information and unpaid family workers. A residual category— without other employees, and include employers, on work type—for example, whether a child is working which includes those not classifiable by status—is own-account workers, and members of produc- for payment in cash or in kind or is involved in unpaid not presented. ers cooperatives. • Wage workers (also known as work, working for someone who is not a member of the In most countries more boys are involved in employ- employees) are people who hold explicit (written or household, or involved in any type of family work (on the ment or the gender difference is small. However, girls oral) or implicit employment contracts that provide farm or in a business). Country surveys define the ages are often more present in hidden or under-reported basic remuneration that does not depend directly on for child labor as 5–17. The data in the table have been forms of employment such as domestic service, and the revenue of the unit for which they work. • Unpaid recalculated to present statistics for children ages 7–14. in almost all societies girls bear greater responsibil- family workers are people who work without pay in a Although efforts are made to harmonize the defini- ity for household chores in their own homes, work market-oriented establishment operated by a related tion of employment and the questions on employ- that lies outside the System of National Accounts person living in the same household. ment in survey questionnaires, signifi cant differ- production boundary and is thus not considered in ences remain in the survey instruments that collect estimates of children’s employment. Data sources The largest sector for child labor remains agriculture, and the majority of children Data on children at work are estimates produced work as unpaid family members 2.6a by the Understanding Children’s Work project based on household survey data sets made avail- Child labor by sector Child labor by status in employment (% of children ages 5–17), 2004–08 (% of children ages 5–17), 2004–08 able by the ILO’s International Programme on the Elimination of Child Labour under its Statistical Not Not defined defined Monitoring Programme on Child Labour, UNICEF 6% Industry 7% Self-employment 5% 7% under its Multiple Indicator Cluster Survey pro- gram, the World Bank under its Living Standards Paid Measurement Study program, and national sta- employment Service 21% 26% Agriculture Unpaid tistical offices. Information on how the data were 60% family workers collected and some indication of their reliability 68% can be found at www.ilo.org/public/english/ standards/ipec/simpoc/, www.childinfo.org, and www.worldbank.org/lsms. Detailed country statis- Source: Accelerating Action Against Child Labour, ILO, Geneva 2010. tics can be found at www.ucw-project.org. 2011 World Development Indicators 59 2.7 Poverty rates at national poverty lines Population below national poverty linea Poverty gap at national poverty linea Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year b % % % year b % % % year b % % % Afghanistanc .. .. .. 2008d 37.5 29.0 36.0 2008d 8.3 6.2 7.9 Albaniac 2005 24.2 11.2 18.5 2008 14.6 10.1 12.4 2008 2.6 1.9 2.3 Angola .. .. .. 2000 d .. 62.3 .. .. .. .. Argentina 2008e .. 15.3 .. 2009e .. 13.2 .. .. .. .. Armeniac 2008 22.9 23.8 23.5 2009 25.5 26.9 26.5 2009 .. .. 4.9 Azerbaijanc 2001 42.5 55.7 49.6 2008 18.5 14.8 15.8 2008 .. .. 2.0 Bangladesh 2000 52.3 35.2 48.9 2005 43.8 28.4 40.0 2005 9.8 6.5 9.0 Belarus 2008 .. .. 6.1 2009 .. .. 5.4 .. .. .. Benin .. .. .. 2003d 46.0 29.0 39.0 2003d 14.0 8.0 12.0 Bhutan .. .. .. 2007d 30.9 1.7 23.2 2007d 8.1 0.4 6.1 Bolivia 2006e 76.5 50.3 59.9 2007e 77.3 50.9 60.1 .. .. .. Bosnia and Herzegovinac 2004 22.0 11.3 17.7 2007 17.8 8.2 14.0 .. .. .. Botswana 1993 40.4 24.7 32.9 2003 44.8 19.4 30.6 2003 18.4 6.5 11.7 Brazil 2008e .. .. 22.6 2009e .. .. 21.4 .. .. .. Bulgariac 1997 .. .. 36.0 2001 .. .. 12.8 2001 .. .. 4.2 Burkina Faso .. .. .. 2003d 52.4 19.2 46.4 2003d 17.6 5.1 15.3 Burundi .. .. .. 2006d 68.9 34.0 66.9 2006d 24.2 10.3 23.4 Cambodiac 2004 37.8 17.6 34.7 2007 34.5 11.8 30.1 2007 8.3 2.8 7.2 Cameroon .. .. .. 2007d 55.0 12.2 39.9 2007d 17.5 2.8 12.3 Cape Verde .. .. .. 2007d 44.3 13.2 26.6 2007d 14.3 3.3 8.1 Central African Republic .. .. .. 2008d 69.4 49.6 62.0 2008d 35.0 29.8 33.1 Chad .. .. .. 2003d 58.6 24.6 55.0 2003d 23.3 7.4 21.6 Chile 2006e 12.3 13.9 13.7 2009e 12.9 15.5 15.1 .. .. .. China 2004 e 2.8 .. .. 2005e 2.5 .. .. .. .. .. Colombia 2008e 65.2 39.8 46.0 2009e 64.3 39.6 45.5 .. .. .. Comoros .. .. .. 2004 d 48.7 34.5 44.8 2004 d 17.8 12.1 16.3 Congo, Dem. Rep. .. .. .. 2005 75.7 61.5 71.3 2005 34.9 26.2 32.2 Congo, Rep. .. .. .. 2005 57.7 .. 50.1 2005 20.6 .. 18.9 Costa Rica 2008e 22.2 19.5 20.7 2009e .. .. 21.7 .. .. .. Croatia 2002 .. .. 11.2 2004 .. .. 11.1 2004 .. .. 2.6 Côte d’Ivoirec 2002 45.8 32.3 40.2 2008 54.2 29.4 42.7 2008 20.3 9.5 15.3 Dominican Republic 2005e 60.2 49.9 53.5 2006e 57.1 45.3 49.4 .. .. .. Ecuador 2008e 59.7 22.6 35.1 2009e 57.5 25.0 36.0 .. .. .. Egypt, Arab Rep. 2005 26.8 10.1 19.6 2008 30.0 10.6 22.0 .. .. .. El Salvador 2007e,f 43.8 29.8 34.6 2008e,f 49.0 35.7 40.0 .. .. .. Ethiopia 1999 45.4 36.9 44.2 2004 39.3 35.1 38.9 2004 8.5 7.7 8.3 Fiji 2003 40.0 28.0 35.0 2009 43.3 18.6 31.0 2009 14.8 5.4 10.1 Gabon .. .. .. 2005 44.6 29.8 32.7 2005 16.0 8.5 10.0 Gambia, Thec .. .. .. 2003d 67.8 39.6 58.0 2003d 30.5 14.8 25.1 Georgiac .. .. .. 2007 29.7 18.3 23.6 2007 9.2 5.3 7.2 Ghana 1998 49.6 19.4 39.5 2006 39.2 10.8 28.5 2006 13.5 3.1 9.6 Guatemala 2000e 74.5 27.1 56.2 2006e 70.5 30.0 51.0 .. .. .. Guinea .. .. .. 2007d 63.0 30.5 53.0 2007d 22.0 7.7 17.6 Guinea-Bissau .. .. .. 2002 69.1 51.6 64.7 2002 27.8 16.9 25.0 Haiti .. .. .. 2001e 88.0 45.0 77.0 .. .. .. Honduras 2008e,f 64.1 55.0 59.6 2009e,f 64.4 52.8 58.8 .. .. .. India 1994 37.3 32.4 36.0 2005 28.3 25.7 27.5 .. .. .. Indonesia 2009 17.4 10.7 14.2 2010 16.6 9.9 13.3 2010 2.8 1.6 2.2 Iraq .. .. .. 2007 39.3 16.1 22.9 2007 9.0 2.7 4.5 Jamaica 2006e .. .. 14.3 2007e .. .. 9.9 .. .. .. Jordan 2002 18.7 12.9 14.2 2006 19.0 12.0 13.0 2006 .. .. 2.8 Kazakhstanc 2001 23.2 13.0 17.6 2002 21.7 10.2 15.4 2002 4.5 2.0 3.1 Kenya .. .. .. 2005d 49.1 33.7 45.9 2005d 17.5 11.4 16.3 Kosovoc 2005 37.2 30.3 34.8 2006 49.2 37.4 45.0 2006 14.3 11.3 13.3 Kyrgyz Republicc 2003 57.5 35.7 49.9 2005 50.8 29.8 43.1 2005 12.0 7.0 10.0 Lao PDRc 2003 .. .. 33.5 2008 31.7 17.4 27.6 .. .. .. Latviac 2002 11.6 .. 7.5 2004 12.7 .. 5.9 2004 .. .. 1.2 60 2011 World Development Indicators 2.7 PEOPLE Poverty rates at national poverty lines Population below national poverty linea Poverty gap at national poverty linea Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year b % % % year b % % % year b % % % Lesothoc 1994 68.9 36.7 66.6 2003 60.5 41.5 56.6 .. .. .. Liberiac .. .. .. 2007 67.7 55.1 63.8 2007 26.3 20.2 24.4 Macedonia, FYRc 2005 21.2 19.8 20.4 2006 21.3 17.7 19.0 2006 7.7 6.9 7.2 Madagascar 2004 77.3 53.7 72.1 2005 73.5 52.0 68.7 2005 28.9 19.3 26.8 Malawi 1998 58.1 18.5 54.1 2004 55.9 25.4 52.4 2004 19.2 7.1 17.8 Malaysiac 2007 7.1 2.0 3.6 2009 8.2 1.7 3.8 2009 1.8 0.3 0.8 Mali .. .. .. 2006d 57.6 25.5 47.4 2006d .. .. 16.7 Mauritania .. .. .. 2000 d 61.2 25.4 46.3 2000 d 24.1 6.3 17.0 Mexico 2006e 54.7 35.6 42.6 2008e 60.8 39.8 47.4 .. .. .. Moldovac 2004 .. .. 26.5 2005 .. .. 29.0 .. .. .. Mongolia .. .. .. 2008d 46.6 26.9 35.2 2008d 13.4 7.7 10.1 Montenegro 2007 12.0 5.5 8.0 2008 8.9 2.4 4.9 2008 1.4 0.6 0.9 Morocco .. .. .. 2001 25.1 7.6 15.3 .. .. .. Mozambique 2002 55.3 51.5 54.1 2008 56.9 49.6 54.7 2008 22.2 19.1 21.2 Namibia .. .. .. 2003d 49.0 17.0 38.0 2003d 16.0 6.0 13.0 Nepal 1996 43.3 21.6 41.8 2004 34.6 9.6 30.9 2004 8.5 2.2 7.5 Nicaragua 2001e 67.8 30.1 45.8 2005e 67.9 29.1 46.2 .. .. .. Niger .. .. .. 2007d 63.9 36.7 59.5 2007d 21.2 11.3 19.6 Nigeria .. .. .. 2004 d 63.8 43.1 54.7 2004 d 26.6 16.2 22.8 Pakistan 2005 28.1 14.9 23.9 2006 27.0 13.1 22.3 .. .. .. Panama 2003 62.7 20.0 36.8 2008 59.8 17.7 32.7 .. .. .. Paraguay 2008e 48.8 30.2 37.9 2009e 49.8 24.7 35.1 .. .. .. Peru 2008 59.8 23.5 36.2 2009 60.3 21.1 34.8 .. .. .. Philippines 2006 .. .. 26.4 2009 .. .. 26.5 2009 .. .. 2.7 Polandc 2001 .. .. 15.6 2002 .. .. 16.6 .. .. .. Romaniac 2005 23.5 8.1 15.1 2006 22.3 6.8 13.8 2006 5.3 1.4 3.2 Russian Federationc 2005 22.7 8.1 11.9 2006 21.2 7.4 11.1 2006 5.5 1.7 2.7 Rwanda .. .. .. 2006d 64.2 23.2 58.5 2006d 26.0 8.0 24.0 São Tomé and Príncipe .. .. .. 2001 64.9 45.0 53.8 2001 24.7 14.9 19.2 Senegalc .. .. .. 2005d 61.9 35.1 50.8 2005d 21.5 9.3 16.4 Serbiac 2006 13.9 5.2 9.0 2007 9.8 4.3 6.6 2007 2.0 0.8 1.3 Sierra Leone .. .. .. 2003d 78.5 47.0 66.4 2003d 34.6 16.3 27.5 South Africa 2000 .. .. 38.0 2005 .. .. 23.0 2005 .. .. 7.0 Sri Lanka 2002 24.7 7.9 22.7 2007 15.7 6.7 15.2 2007 3.2 1.3 3.1 Swaziland .. .. .. 2001d 75.0 49.0 69.2 2001d 37.0 20.0 32.9 Tajikistanc 2007 54.4 49.3 53.1 2009 49.2 41.8 47.2 .. .. .. Tanzania 2000 38.6 23.1 35.6 2007 37.4 21.8 33.4 2007 11.0 6.5 9.9 Thailand 2008 11.5 3.0 9.0 2009 10.4 3.0 8.1 .. .. .. Timor-Leste 2001 .. .. 39.7 2007 .. .. 49.9 .. .. .. Togo .. .. .. 2006 74.3 36.8 61.7 2006 29.3 10.3 22.9 Turkey 2008 34.6 9.4 17.1 2009 38.7 8.9 18.1 .. .. .. Uganda 2005 34.2 13.7 31.1 2009 27.2 9.1 24.5 2009 7.6 1.8 6.8 Ukrainec 2004 18.1 12.0 14.0 2005 11.3 6.3 7.9 2005 2.3 1.1 1.5 Uruguay 2007e 29.4 25.5 26.0 2008e 22.2 20.3 20.5 .. .. .. Venezuela, RB 2008e .. .. 32.6 2009e .. .. 29.0 .. .. .. Vietnam 2006 20.4 3.9 16.0 2008 18.7 3.3 14.5 2008 4.6 0.5 3.5 West Bank and Gaza 2007 .. .. 31.2 2009 .. .. 21.9 2009 .. .. 4.9 Yemen, Rep. 1998 42.5 32.3 40.1 2005 40.1 20.7 34.8 2005 10.6 4.5 8.9 Zambia 2004 77.3 29.1 58.4 2006 76.8 26.7 59.3 2006 38.8 9.4 28.5 Zimbabwe .. .. .. 2003d .. .. 72.0 .. .. .. a. Based on per capita consumption estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. World Bank estimates. d. Estimates based on survey data from earlier year(s) are available, but are not comparable with the most recent year reported here; these are available online at http://data.worldbank.org. e. Based on income per capita estimated from household survey data. f. Measured as a share of households. 2011 World Development Indicators 61 2.7 Poverty rates at national poverty lines About the data Definitions Estimates of poverty rates and gaps at national pov- As with any indicator measured from household • Survey year is the year in which the underlying erty lines are useful for comparing poverty across surveys, data quality issues can affect the precision household survey data were collected; when the data time within but not across countries. Table 2.8 shows of poverty estimates and their comparability over collection period bridged two calendar years, the year poverty indicators at international poverty lines that time. These include selective survey nonresponse, in which most of the data were collected is reported. allow for comparisons across countries. seasonality effects, differences in the number of • Population below national poverty line is the per- For countries with an active poverty monitoring pro- income or consumption items in the questionnaire, centage of the rural, urban, and national population gram, the World Bank—in collaboration with national and the time period over which respondents are living below the corresponding rural, urban, national institutions, other development agencies, and civil asked to recall their expenditures. poverty line, based on consumption estimated from society—periodically prepares poverty assessments household survey data, unless otherwise noted. and other analytical reports to assess the extent National poverty lines • Poverty gap at national poverty line is the mean and causes of poverty. These reports review levels National poverty lines are the benchmark for esti- shortfall from the rural, urban, or national poverty and changes in poverty indicators over time and mating poverty indicators that are consistent with line (counting the nonpoor as having zero shortfall) across regions within countries, assess the impact the country’s specific economic and social circum- as a percentage of the corresponding rural, urban, of growth and public policy on poverty and inequal- stances. National poverty lines reflect local percep- or national poverty line, based on consumption esti- ity, review the adequacy of monitoring and evalua- tions of the level and composition of consumption or mated from household survey data, unless otherwise tion, and contain detailed technical overviews of income needed to be nonpoor. The perceived bound- noted. This measure reflects the depth of poverty as the underlying household survey data and poverty ary between poor and nonpoor typically rises with the well as its incidence. measurement methods used. The reports are a key average income of a country and thus does not pro- source of comprehensive information on poverty indi- vide a uniform measure for comparing poverty rates cators at national poverty lines and generally feed across countries. While poverty rates at national into country-owned processes to reduce poverty, poverty lines should not be used for comparing pov- build in-country capacity, and support joint work. erty rates across countries, they are appropriate for An increasing number of countries have their guiding and monitoring the results of country-specific own national programs to monitor and disseminate national poverty reduction strategies. official poverty estimates at national poverty lines Almost all national poverty lines are anchored to along with well documented household survey data the cost of a food bundle—based on the prevailing sources and estimation methodology. Estimates national diet of the poor—that provides adequate from national poverty monitoring programs and the nutrition for good health and normal activity, plus underlying methods used are periodically reviewed by an allowance for nonfood spending. National poverty the World Bank and included in the table. lines must be adjusted for inflation between survey The complete online database of poverty estimates years to remain constant in real terms and thus allow at national poverty lines (available at http://data. for meaningful comparisons of poverty over time. worldbank.org) is regularly updated and may con- Because diets and consumption baskets change tain more recent data or revisions not incorporated over time, countries periodically recalculate the pov- in the table. It is maintained by the Global Poverty erty line based on new survey data. In such cases Working Group, a team of poverty experts from the the new poverty lines should be deflated to obtain Poverty Reduction and Equity Network, the Develop- comparable poverty estimates from earlier years. ment Research Group, and the Development Data The table reports indicators based on the two most Group, which recently updated the database to cover recent years for which survey data is available. Coun- 115 countries and more than 575 sets of poverty tries for which the most recent indicators reported Data sources estimates at national poverty lines for 1974−2010. are not comparable to those based on survey data Poverty rates at national poverty lines are com- from an earlier year are footnoted in the table. piled by the Global Poverty Working Group, based Data quality on data from World Bank’s country poverty assess- Poverty estimates at national poverty lines are com- ments and analytical reports as well as country puted from household survey data collected from Poverty Reduction Strategies and official poverty nationally representative samples of households. estimates. Further documentation of the data, These data must contain sufficiently detailed infor- measurement methods and tools, and research, mation to compute a comprehensive estimate of as well as poverty assessments and analytical total household income or consumption (including reports, are available at http://data.worldbank. consumption or income from own production), from org, www.worldbank.org/poverty, and http://econ. which it is possible to construct a correctly weighted worldbank.org. distribution of per capita consumption or income. 62 2011 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty Population below International poverty linea line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year b % % % % year b % % % % Albania 75.5 120.8 2005 <2 <0.5 7.9 1.5 2008 <2 <0.5 4.3 0.9 Algeria 48.4 c 77.5c 1988 6.6 1.8 23.8 6.6 1995 6.8 1.4 23.6 6.5 Angola 88.1 141.0   ..  ..   ..  .. 2000 d 54.3 29.9 70.2 42.4 Argentina 1.7 2.7 2006d,e 2.8 0.6 8.0 2.4 2009d,e <2 <0.5 <2 <0.5 Armenia 245.2 392.4 2003 10.6 1.9 43.5 11.3 2008 <2 <0.5 12.4 2.3 Azerbaijan 2,170.9 3,473.5 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 7.8 1.5 Bangladesh 31.9 51.0 2000 f 57.8 17.3 85.4 38.8 2005f 49.6 13.1 81.3 33.8 Belarus 949.5 1,519.2 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Belize 1.8 c 2.9c 1995 14.0 5.4 23.6 10.5 1999e 12.1 4.7 23.9 9.7 Benin 344.0 550.4   ..  ..   .. ..  2003 47.3 15.7 75.3 33.5 Bhutan 23.1 36.9   ..  ..  ..  ..  2003 26.2 7.0 49.5 18.8 Bolivia 3.2 5.1 2005e 19.6 9.7 30.4 15.5 2007e 14.0 5.8 24.7 10.9 Bosnia and Herzegovina 1.1 1.7 2004 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 Botswana 4.2 6.8 1986 35.6 13.8 54.7 25.8 1994 31.2 11.0 49.4 22.3 Brazil 2.0 3.1 2008e 4.3 1.4 10.4 3.6 2009e 3.8 1.1 9.9 3.2 Bulgaria 0.9 1.5 2003 <2 <0.5 2.4 0.9 2007 <2 <0.5 7.3 1.5 Burkina Faso 303.0 484.8 1998 70.0 30.2 87.6 49.1 2003 56.5 20.3 81.2 39.3 Burundi 558.8 894.1 1998 86.4 47.3 95.4 64.1 2006 81.3 36.4 93.5 56.1 Cambodia 2,019.1 3,230.6 2004 40.2 11.3 68.2 28.0 2007 28.3 6.1 56.5 20.2 Cameroon 368.1 589.0 2001 32.8 10.2 57.7 23.7 2007 9.6 1.2 30.8 8.4 Cape Verde 97.7 156.3   ..  ..  ..   .. 2001 20.6 5.9 40.3 14.9 Central African Republic 384.3 614.9 1993 82.8 57.0 90.8 68.4 2003 62.4 28.3 81.9 45.3 Chad 409.5 655.1    ..  ..  .. ..  2003 61.9 25.6 83.3 43.9 Chile 484.2 774.7 2006e <2 <0.5 2.4 <0.5 2009e <2 <0.5 <2 <0.5 China 5.1g 8.2g 2002h 28.4 8.7 51.1 20.6 2005h 15.9 4.0 36.3 12.2 Colombia 1,489.7 2,383.5 2003e 15.4 6.1 26.3 10.9 2006e 16.0 5.7 27.9 11.9 Comoros 368.0 588.8  ..  ..  .. ..  2004 46.1 20.8 65.0 34.2 Congo, Dem. Rep. 395.3 632.5  ..  ..  .. ..  2006 59.2 25.3 79.6 42.4 Congo, Rep. 469.5 751.1  ..  ..  .. ..  2005 54.1 22.8 74.4 38.8 Costa Rica 348.7c 557.9c 2005e 2.4 <0.5 8.6 2.3 2009e <2 <0.5 4.8 0.9 Croatia 5.6 8.9 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Czech Republic 19.0 30.4 1993e <2 <0.5 <2 <0.5 1996e <2 <0.5 <2 <0.5 Côte d’Ivoire 407.3 651.6 2002 23.3 6.8 46.8 17.6 2008 23.8 7.5 46.0 17.9 Djibouti 134.8 215.6 1996 4.8 1.6 15.1 4.5 2002 18.8 5.3 41.2 14.6 Dominican Republic 25.5c 40.8 c 2006e 4.0 0.7 13.5 3.7 2007e 4.3 0.9 13.6 3.9 Ecuador 0.6 1.0 2007e 4.7 1.2 12.8 4.0 2009e 5.1 1.6 13.4 4.4 Egypt, Arab Rep. 2.5 4.0 2000 <2 <0.5 19.4 3.5 2005 <2 <0.5 18.5 3.5 El Salvador 6.0 c 9.6c 2005e 11.0 4.8 20.5 8.9 2008e 5.1 1.1 15.2 4.5 Estonia 11.0 17.7 2003 <2 <0.5 2.7 0.9 2004 <2 <0.5 <2 <0.5 Ethiopia 3.4 5.5 2000 55.6 16.2 86.4 37.9 2005 39.0 9.6 77.6 28.9 Gabon 554.7 887.5    ..  ..  .. ..  2005 4.8 0.9 19.6 5.0 Gambia, The 12.9 20.7 1998 66.7 34.7 82.0 50.0 2003 34.3 12.1 56.7 24.9 Georgia 1.0 1.6 2005 13.4 4.4 30.4 10.9 2008 14.7 4.6 32.6 11.8 Ghana 5,594.8 8,951.6 1998 39.1 14.4 63.3 28.5 2006 30.0 10.5 53.6 22.3 Guatemala 5.7c 9.1c 2002e 16.9 6.5 29.8 12.9 2006e 12.7 3.8 25.7 9.6 Guinea 1,849.5 2,959.1 2003 70.1 32.2 87.2 50.3 2007 43.8 15.2 70.0 31.3 Guinea-Bissau 355.3 568.6 1993 52.1 20.6 75.7 37.4 2002 48.8 16.5 77.9 34.8 Guyana 131.5c 210.3c 1993e 5.8 2.6 15.0 5.4 1998 e 7.7 3.9 16.8 6.9 Haiti 24.2c 38.7c    ..  ..  .. ..  2001e 54.9 28.2 72.2 41.8 Honduras 12.1c 19.3c 2006e 18.2 8.2 29.7 14.2 2007e 23.2 11.3 35.6 18.1 Hungary 171.9 275.0 2004 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 India 19.5i 31.2i 1994h 49.4 14.4 81.7 35.3 2005h 41.6 10.8 75.6 30.4 Indonesia 5,241.0i 8,385.7i 2005h 21.4 4.6 53.8 17.3 2009h 18.7 3.6 50.7 15.5 Iraq 799.8 1,279.7    ..  ..  .. ..  2007 4.0 0.6 25.3 5.6 Jamaica 54.2c 86.7c 2002 <2 <0.5 8.7 1.6 2004 <2 <0.5 5.9 0.9 Jordan 0.6 1.0 2003 <2 <0.5 11.0 2.1 2006 <2 <0.5 3.5 0.6 Kazakhstan 81.2 129.9 2003 3.1 <0.5 17.2 3.9 2007 <2 <0.5 <2 <0.5 2011 World Development Indicators 63 2.8 Poverty rates at international poverty lines International poverty Population below International poverty linea line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year b % % % % year b % % % % Kenya 40.9 65.4 1997 19.6 4.6 42.7 14.7 2005 19.7 6.1 39.9 15.1 Kyrgyz Republic 16.2 26.0 2004 21.8 4.4 51.9 16.8 2007 <2 <0.5 29.4 5.5 Lao PDR 4,677.0 7,483.2 2002 44.0 12.1 76.9 31.1 2008 33.9 9.0 66.0 24.8 Latvia 0.4 0.7 2004 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Lesotho 4.3 6.9 1995 47.6 26.7 61.1 37.3 2003 43.4 20.8 62.3 33.1 Liberia 0.6 1.0  .. ..   .. ..  2007 83.7 40.8 94.8 59.5 Lithuania 2.1 3.3 2004 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Macedonia, FYR 29.5 47.2 2003 <2 <0.5 3.2 0.7 2008 <2 <0.5 4.3 0.7 Madagascar 945.5 1,512.8 2001 76.3 41.4 88.8 57.2 2005 67.8 26.5 89.6 46.9 Malawi 71.2 113.8 1998 83.1 46.0 93.5 62.3 2004 73.9 32.3 90.5 51.8 Malaysia 2.6 4.2 2004 e <2 <0.5 7.8 1.4 2009e <2 <0.5 2.3 <0.5 Maldives 12.2 19.5    .. ..   .. ..  2004 <2 <0.5 12.2 2.5 Mali 362.1 579.4 2001 61.2 25.8 82.0 43.6 2006 51.4 18.8 77.1 36.5 Mauritania 157.1 251.3 1996 23.4 7.1 48.3 17.8 2000 21.2 5.7 44.1 15.9 Mexico 9.6 15.3 2006 <2 <0.5 4.8 1.0 2008 <2 <0.5 8.6 2.0 Micronesia, Fed. Sts. 0.8 c 1.3c    .. ..   .. ..  2000 31.1 16.3 44.7 24.5 Moldova 6.0 9.7 2004 8.1 1.7 29.0 7.9 2008 <2 <0.5 12.5 2.6 Mongolia 653.1 1,045.0  ..  ..  .. ..  2002 15.5 3.6 38.9 12.3 Montenegro 0.6 1.0    .. ..   .. ..  2008 <2 <0.5 <2 <0.5 Morocco 6.9 11.0 2001 6.3 0.9 24.3 6.3 2007 2.5 0.5 14.0 3.2 Mozambique 14,532.1 23,251.4 2003 74.7 35.4 90.0 53.6 2008 60.0 25.2 81.6 42.9 Namibia 6.3 10.1    .. ..   .. ..  1993e 49.1 24.6 62.2 36.5 Nepal 33.1 52.9 1996 68.4 26.7 88.1 46.8 2004 55.1 19.7 77.6 37.8 Nicaragua 9.1c 14.6c 2001e 19.4 6.7 37.5 14.5 2005e 15.8 5.2 31.9 12.3 Niger 334.2 534.7 2005 65.9 28.1 85.6 46.7 2007 43.1 11.9 75.9 30.6 Nigeria 98.2 157.2 1996 68.5 32.1 86.4 49.7 2004 64.4 29.6 83.9 46.9 Pakistan 25.9 41.4 2005 22.6 4.4 60.3 18.7 2006 22.6 4.1 61.0 18.8 Panama 0.8c 1.2c 2006e 9.5 3.1 17.9 7.1 2009e 2.4 <0.5 9.5 2.4 Papua New Guinea 2.1c 3.4 c    .. ..   .. ..  1996 35.8 12.3 57.4 25.5 Paraguay 2,659.7 4,255.6 2007e 6.5 2.7 14.2 5.5 2008e 5.1 1.5 13.2 4.3 Peru 2.1 3.3 2006e 7.9 1.9 18.5 6.0 2009e 5.9 1.4 14.7 4.7 Philippines 30.2 48.4 2003 22.0 5.5 43.8 16.0 2006 22.6 5.5 45.0 16.4 Poland 2.7 4.3 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Romania 2.1 3.4 2005 <2 <0.5 3.4 0.9 2008 <2 <0.5 <2 0.5 Russian Federation 16.7 26.8 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Rwanda 295.9 473.5 2000 76.6 38.2 90.3 55.7 2005 76.8 40.9 89.6 57.2 São Tomé and Príncipe 7,953.9 12,726.3    .. ..   .. ..  2001 28.6 8.2 57.3 21.6 Senegal 372.8 596.5 2001 44.2 14.3 71.3 31.2 2005 33.5 10.8 60.4 24.7 Serbia 42.9 68.6    .. ..   .. ..  2008 <2 <0.5 <2 <0.5 Seychelles 5.6c 9.0 c 2000 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5 Sierra Leone 1,745.3 2,792.4 1990 62.8 44.8 75.0 54.0 2003 53.4 20.3 76.1 37.5 Slovak Republic 23.5 37.7 1992e <2 <0.5 <2 <0.5 1996e <2 <0.5 <2 <0.5 Slovenia 198.2 317.2 2002 <2 <0.5 <2 <0.5 2004 <2 <0.5 <2 <0.5 South Africa 5.7 9.1 1995 21.4 5.2 39.9 15.0 2000 26.2 8.2 42.9 18.3 Sri Lanka 50.0 80.1 2002 14.0 2.6 39.7 11.9 2007 7.0 1.0 29.1 7.4 St. Lucia 2.4 c 3.8 c    .. ..   .. ..  1995e 20.9 7.2 40.6 15.5 Suriname 2.3c 3.7c    .. ..   .. ..  1999e 15.5 5.9 27.2 11.7 Swaziland 4.7 7.5 1995 78.6 47.7 89.3 61.7 2001 62.9 29.4 81.0 45.8 Syrian Arab Republic 30.8 49.3    .. ..   .. ..  2004 <2 <0.5 16.9 3.3 Tajikistan 1.2 1.9 2003 36.3 10.3 68.8 26.7 2004 21.5 5.1 50.9 16.8 Tanzania 603.1 964.9 2000 88.5 46.8 96.6 64.4 2007 67.9 28.1 87.9 47.5 Thailand 21.8 34.9 2004 <2 <0.5 11.5 2.0 2009 12.8 2.4 26.5 8.3 Timor-Leste 0.6c 1.0 c 2001 52.9 19.1 77.5 37.1 2007 37.4 8.9 72.8 27.0 Togo 352.8 564.5    .. ..   .. ..  2006 38.7 11.4 69.3 27.9 Trinidad and Tobago 5.8 c 9.2c 1988e <2 <0.5 8.6 1.9 1992e 4.2 1.1 13.5 3.9 Tunisia 0.9 1.4 1995 6.5 1.3 20.4 5.8 2000 2.6 <0.5 12.8 3.0 64 2011 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty Population below International poverty linea line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year b % % % % year b % % % % Turkmenistan 5,961.1c 9,537.7c 1993e 63.5 25.8 85.7 44.9 1998 24.8 7.0 49.7 18.4 Uganda 930.8 1,489.2 2005 51.5 19.1 75.6 36.4 2009 37.7 12.1 64.5 27.2 Ukraine 2.1 3.4 2005 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5 Uruguay 19.1 30.6 2006e <2 <0.5 4.2 0.6 2009e <2 <0.5 <2 <0.5 Uzbekistan 470.1c 752.1c 2002 42.3 12.4 75.6 30.6 2003 46.3 15.0 76.7 33.2 Venezuela, RB 1,563.9 2,502.2 2005e 10.0 4.5 19.8 8.4 2006e 3.5 1.1 10.2 3.2 Vietnam 7,399.9 11,839.8 2006 21.5 4.6 48.4 16.2 2008 13.1 2.3 38.4 10.8 Yemen, Rep. 113.8 182.1 1998 12.9 3.0 36.4 11.1 2005 17.5 4.2 46.6 14.8 Zambia 3,537.9 5,660.7 2003 64.6 27.1 85.2 45.8 2004 64.3 32.8 81.5 48.3 a. Based on nominal per capita consumption averages and distributions estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Urban areas only. e. Based on per capita income averages and distribution data estimated from household survey data. f. Adjusted by spatial consumer price index data. g. PPP conversion factor based on urban prices. h. Population-weighted average of urban and rural estimates. i. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and rural areas. Regional poverty estimates and progress toward 84 percent to 16 percent, leaving 620 million fewer developing countries in 2005 was $2.00 a day. The the Millennium Development Goals people in poverty. poverty rate for all developing countries measured Global poverty measured at the $1.25 a day pov- Over the same period the poverty rate in South Asia at this line fell from nearly 70 percent in 1981 to 47 erty line has been decreasing since the 1980s. The fell from 59 percent to 40 percent (table 2.8c). In con- percent in 2005, but the number of people living on share of population living on less than $1.25 a day trast, the poverty rate fell only slightly in Sub- Saharan less than $2.00 a day has remained nearly constant fell 10 percentage points, to 42 percent, in 1990 and Africa—from less than 54 percent in 1981 to more at 2.5 billion. The largest decrease, both in number then fell nearly 17 percentage points between 1990 than 58 percent in 1999 then down to 51 percent and proportion, occurred in East Asia and Pacific, led and 2005. The number of people living in extreme in 2005. But the number of people living below the by China. Elsewhere, the number of people living on poverty fell from 1.9 billion in 1981 to 1.8 billion poverty line has nearly doubled. Only East Asia and less than $2.00 a day increased, and the number of in 1990 to about 1.4 billion in 2005 (figure 2.8a). Pacific is consistently on track to meet the Millennium people living between $1.25 and $2.00 a day nearly This substantial reduction in extreme poverty over Development Goal target of reducing 1990 poverty doubled, to 1.2 billion. the past quarter century, however, disguises large rates by half by 2015. A slight acceleration over his- Once household survey data collected after 2005 regional differences. torical growth rates could lift Latin America and the in large countries—such as China and India, as well The greatest reduction in poverty occurred in East Caribbean and South Asia to the target. However, the as some countries in Sub-Saharan Africa and the Asia and Pacific, where the poverty rate declined recent slowdown in the global economy may leave Middle East and North Africa—become available, from 78 percent in 1981 to 17 percent in 2005 and these regions and many countries short of the target. the World Bank’s Development Research Group will the number of people living on less than $1.25 a day Most of the people who have escaped extreme update regional poverty estimates at international dropped more than 750 million (figure 2.8b). Much poverty remain very poor by the standards of mid- poverty lines; see http://iresearch.worldbank.org/ of this decline was in China, where poverty fell from dle- income economies. The median poverty line for povcalnet/. While the number of people living on less than $1.25 a day has Poverty rates fallen, the number living on $1.25–$2.00 a day has increased 2.8a have begun to fall 2.8b People living in poverty (billions) Share of population living on less than $1.25 a day, by region (percent) 3.0 80 2.5 People living on more than $1.25 and less than $2.00 Sub-Saharan Africa People living on less than a day, all developing regions 60 2.0 $1.25 a day, other developing regions 1.5 40 People living on less than South Asia $1.25 a day, East Asia & Pacific 1.0 East Asia Europe & Central Asia & Pacific 20 People living on less than Middle East & North Africa 0.5 Latin America & Caribbean $1.25 a day, South Asia People living on less than $1.25 a day, Sub-Saharan Africa 0 0 1981 1984 1987 1990 1993 1996 1999 2002 2005 1981 1984 1987 1990 1993 1996 1999 2002 2005 Source: World Bank PovcalNet. Source: World Bank PovcalNet. 2011 World Development Indicators 65 2.8 Poverty rates at international poverty lines Regional poverty estimates 2.8c Region or country 1981 1984 1987 1990 1993 1996 1999 2002 2005 People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific 1,072 947 822 873 845 622 635 507 316 China 835 720 586 683 633 443 447 363 208 Europe & Central Asia 7 6 5 9 20 22 24 22 17 Latin America & Caribbean 47 59 57 50 47 53 55 57 45 Middle East & North Africa 14 12 12 10 10 11 12 10 11 South Asia 548 548 569 579 559 594 589 616 596 India 420 416 428 436 444 442 447 460 456 Sub-Saharan Africa 211 242 258 297 317 356 383 390 388 Total 1,900 1,814 1,723 1,818 1,799 1,658 1,698 1,601 1,374 Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific 77.7 65.5 54.2 54.7 50.8 36.0 35.5 27.6 16.8 China 84.0 69.4 54.0 60.2 53.7 36.4 35.6 28.4 15.9 Europe & Central Asia 1.7 1.3 1.1 2.0 4.3 4.6 5.1 4.6 3.7 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 South Asia 59.4 55.6 54.2 51.7 46.9 47.1 44.1 43.8 40.3 India 59.8 55.5 53.6 51.3 49.4 46.6 44.8 43.9 41.6 Sub-Saharan Africa 53.4 55.8 54.5 57.6 56.9 58.8 58.4 55.0 50.9 Total 51.9 46.7 41.9 41.7 39.2 34.5 33.7 30.5 25.2 People living on less than 2005 PPP $2.00 a day (millions) East Asia & Pacific 1,278 1,280 1,238 1,274 1,262 1,108 1,105 954 729 China 972 963 907 961 926 792 770 655 474 Europe & Central Asia 35 28 25 32 49 56 68 57 42 Latin America & Caribbean 90 110 103 96 96 107 111 114 94 Middle East & North Africa 46 44 47 44 48 52 52 51 51 South Asia 799 836 881 926 950 1,009 1,031 1,084 1,092 India 609 635 669 702 735 757 783 813 828 Sub-Saharan Africa 294 328 351 393 423 471 509 536 556 Total 2,542 2,625 2,646 2,765 2,828 2,803 2,875 2,795 2,564 Share of people living on less than 2005 PPP $2.00 a day (percent) East Asia & Pacific 92.6 88.5 81.6 79.8 75.8 64.1 61.8 51.9 38.7 China 97.8 92.9 83.7 84.6 78.6 65.1 61.4 51.2 36.3 Europe & Central Asia 8.3 6.5 5.6 6.9 10.3 11.9 14.3 12.0 8.9 Latin America & Caribbean 24.6 28.1 24.9 21.9 20.7 22.0 21.8 21.6 17.1 Middle East & North Africa 26.7 23.1 22.7 19.7 19.8 20.2 19.0 17.6 16.9 South Asia 86.5 84.8 83.9 82.7 79.7 79.9 77.2 77.1 73.9 India 86.6 84.8 83.8 82.6 81.7 79.8 78.4 77.6 75.6 Sub-Saharan Africa 73.8 75.5 74.0 76.1 75.9 77.9 77.6 75.6 72.9 Total 69.4 67.7 64.3 63.4 61.6 58.3 57.1 53.3 47.0 Source: World Bank PovcalNet. 66 2011 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti- The statistics reported here are based on consump- PPP rates were designed for comparing aggregates from mates for developing countries for World Development tion data or, when unavailable, on income surveys. national accounts, not for making international poverty Report 1990: Poverty using household survey data for Analysis of some 20 countries for which income and comparisons. As a result, there is no certainty that an 22 countries (Ravallion, Datt, and van de Walle 1991). consumption expenditure data were both available from international poverty line measures the same degree Since then there has been considerable expansion in the same surveys found income to yield a higher mean of need or deprivation across countries. So-called pov- the number of countries that field household income than consumption but also higher inequality. When pov- erty PPPs, designed to compare the consumption of and expenditure surveys. The World Bank’s poverty erty measures based on consumption and income were the poorest people in the world, might provide a better monitoring database now includes more than 600 compared, the two effects roughly cancelled each other basis for comparison of poverty across countries. Work surveys representing 115 developing countries. More out: there was no significant statistical difference. on these measures is ongoing. than 1.2 million randomly sampled households were Definitions interviewed in these surveys, representing 96 percent International poverty lines of the population of developing countries. International comparisons of poverty estimates entail • International poverty line in local currency is the both conceptual and practical problems. Countries have international poverty lines of $1.25 and $2.00 a day in Data availability different definitions of poverty, and consistent compari- 2005 prices, converted to local currency using the PPP The number of data sets within two years of any given sons across countries can be difficult. Local poverty conversion factors estimated by the International Com- year rose dramatically, from 13 between 1978 and lines tend to have higher purchasing power in rich coun- parison Program. • Survey year is the year in which the 1982 to 158 between 2001 and 2006. Data cover- tries, where more generous standards are used, than underlying household survey data were collected; when age is improving in all regions, but the Middle East in poor countries. the data collection period bridged two calendar years, and North Africa and Sub-Saharan Africa continue to Poverty measures based on an international poverty the year in which most of the data were collected is lag. A complete database of estimates, maintained line attempt to hold the real value of the poverty line con- reported. • Population below $1.25 a day and popula- by a team in the World Bank’s Development Research stant across countries, as is done when making com- tion below $2 a day are the percentages of the popula- Group, is updated annually as new survey data parisons over time. Since World Development Report tion living on less than $1.25 a day and $2.00 a day at become available, and a major reassessment of prog- 1990 the World Bank has aimed to apply a common 2005 international prices based on nominal per capita ress against poverty is made about every three years. standard in measuring extreme poverty, anchored to consumption averages and distributions estimated from The most recent estimates and a complete overview what poverty means in the world’s poorest countries. household survey data, unless otherwise noted. As a of data availability by year and country are available The welfare of people living in different countries can result of revisions in PPP exchange rates, poverty rates at http://iresearch.worldbank.org/povcalnet/. be measured on a common scale by adjusting for dif- for individual countries cannot be compared with pov- ferences in the purchasing power of currencies. The erty rates reported in earlier editions. • Poverty gap Data quality commonly used $1 a day standard, measured in 1985 is the mean shortfall from the poverty line (counting Besides the frequency and timeliness of survey data, international prices and adjusted to local currency using the nonpoor as having zero shortfall), expressed as a other data quality issues arise in measuring household purchasing power parities (PPPs), was chosen for World percentage of the poverty line. This measure reflects living standards. The surveys ask detailed questions on Development Report 1990 because it was typical of the the depth of poverty as well as its incidence. sources of income and how it was spent, which must poverty lines in low-income countries at the time. be carefully recorded by trained personnel. Income is Early editions of World Development Indicators used Data sources generally more difficult to measure accurately, and PPPs from the Penn World Tables to convert values in The poverty measures are prepared by the World consumption comes closer to the notion of living stan- local currency to equivalent purchasing power measured Bank’s Development Research Group. The interna- dards. And income can vary over time even if living in U.S dollars. Later editions used 1993 consumption tional poverty lines are based on nationally repre- standards do not. But consumption data are not always PPP estimates produced by the World Bank. Interna- sentative primary household surveys conducted by available: the latest estimates reported here use con- tional poverty lines were recently revised using the new national statistical offices or by private agencies sumption for about two-thirds of countries. data on PPPs compiled in the 2005 round of the Inter- under the supervision of government or interna- However, even similar surveys may not be strictly national Comparison Program, along with data from an tional agencies and obtained from government comparable because of differences in timing or in expanded set of household income and expenditure statistical offices and World Bank Group country the quality and training of enumerators. Comparisons surveys. The new extreme poverty line is set at $1.25 departments. The World Bank Group has pre- of countries at different levels of development also a day in 2005 PPP terms, which represents the mean pared an annual review of its poverty work since pose a potential problem because of differences in of the poverty lines found in the poorest 15 countries 1993. For details on data sources and methods the relative importance of the consumption of nonmar- ranked by per capita consumption. The new poverty line used to derive the World Bank’s latest estimates, ket goods. The local market value of all consumption maintains the same standard for extreme poverty— further discussion of the results, and related in kind (including own production, particularly impor- the poverty line typical of the poorest countries in the publications, see http://iresearch.worldbank.org/ tant in underdeveloped rural economies) should be world—but updates it using the latest information on povcalnet/ and Shaohua Chen and Martin Rav- included in total consumption expenditure, but may the cost of living in developing countries. allion’s “The Developing World Is Poorer Than not be. Most survey data now include valuations for PPP exchange rates are used to estimate global pov- We Thought, but No Less Successful in the Fight consumption or income from own production, but valu- erty, because they take into account the local prices against Poverty” (2008). ation methods vary. of goods and services not traded internationally. But 2011 World Development Indicators 67 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan 2008b 29.4 3.8 9.0 13.1 16.9 22.3 38.7 24.0 Albania 2008b 34.5 3.5 8.1 12.1 15.9 20.9 43.0 29.0 Algeria 1995b 35.3 2.8 6.9 11.5 16.3 22.8 42.4 26.9 Angolac 2000 b 58.6 0.6 2.0 5.7 10.8 19.7 61.9 44.7 Argentinac 2009d 45.8 1.5 4.1 8.9 14.3 22.2 50.5 33.6 Armenia 2008b 30.9 3.7 8.8 12.8 16.7 21.9 39.8 25.4 Australia 1994 d 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 d 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2008b 33.7 3.4 8.0 12.1 16.2 21.7 42.1 27.4 Bangladesh 2005b 31.0 4.3 9.4 12.6 16.1 21.1 40.8 26.6 Belarus 2008b 27.2 3.8 9.2 13.8 17.8 22.9 36.4 21.9 Belgium 2000 d 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Belize 1999d 54.4 1.2 3.4 7.2 11.9 19.1 58.5 43.5 Benin 2003b 38.6 2.9 6.9 10.9 15.1 21.2 45.9 31.0 Bolivia 2007d 57.3 1.0 2.8 6.4 11.1 18.8 61.0 45.4 Bosnia and Herzegovina 2007b 36.2 2.7 6.7 11.3 16.1 22.7 43.2 27.3 Botswana 1994b 61.0 1.3 3.1 5.8 9.6 16.4 65.0 51.2 Brazil 2009d 53.9 1.2 3.3 7.2 11.9 19.5 58.1 42.5 Bulgaria 2007b 45.3 2.0 5.0 9.1 13.9 21.0 51.0 35.2 Burkina Faso 2003b 39.6 3.0 7.0 10.6 14.7 20.6 47.1 32.4 Burundi 2006b 33.3 4.1 9.0 11.9 15.4 21.0 42.8 28.0 Cambodia 2007b 44.4 3.0 6.6 9.4 13.1 19.2 51.7 37.3 Cameroon 2001b 44.6 2.4 5.6 9.3 13.7 20.5 50.9 35.5 Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 2003b 43.6 2.1 5.2 9.4 14.3 21.7 49.4 33.0 Chad 2003b 39.8 2.6 6.3 10.4 15.0 21.8 46.6 30.8 Chile 2009d 22.6 3.1 8.6 15.5 20.2 24.7 30.9 16.5 China 2005d 41.5 2.4 5.7 9.8 14.7 22.0 47.8 31.4 Hong Kong SAR, China 1996d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2006d 58.5 0.9 2.5 6.0 10.7 18.7 62.1 46.2 Congo, Dem. Rep. 2006b 44.4 2.3 5.5 9.2 13.8 20.9 50.6 34.7 Congo, Rep. 2005b 47.3 2.1 5.0 8.4 13.0 20.5 53.1 37.1 Costa Rica 2009d 50.3 1.7 4.2 7.8 12.5 20.1 55.4 39.4 Côte d’Ivoire 2008b 41.5 2.2 5.6 10.1 14.9 21.8 47.6 31.8 Croatia 2008b 33.7 3.3 8.1 12.2 16.2 21.6 42.0 27.5 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996d 25.8 4.3 10.2 14.3 17.5 21.7 36.2 22.7 Denmark 1997d 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2007d 48.4 1.7 4.4 8.4 13.1 20.5 53.6 37.8 Ecuador 2009d 49.0 1.6 4.2 8.3 13.2 20.4 53.9 38.3 Egypt, Arab Rep. 2005b 32.1 3.9 9.0 12.6 16.1 20.9 41.5 27.6 El Salvador 2007d 46.9 1.6 4.3 9.0 13.9 20.9 51.9 36.3 Eritrea .. .. .. .. .. .. .. .. Estonia 2004b 36.0 2.7 6.8 11.6 16.2 22.5 43.0 27.7 Ethiopia 2005b 29.8 4.1 9.3 13.2 16.8 21.4 39.4 25.6 Finland 2000 d 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995d 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon 2005b 41.5 2.5 6.1 10.1 14.6 21.2 47.9 32.7 Gambia, The 2003b 47.3 2.0 4.8 8.6 13.2 20.6 52.8 36.9 Georgia 2008b 41.3 2.0 5.3 10.3 15.2 22.1 47.2 31.3 Germany 2000 d 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 2006b 42.8 1.9 5.2 9.8 14.8 21.9 48.3 32.5 Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2006d 53.7 1.3 3.4 7.2 12.0 19.5 57.8 42.4 Guinea 2007b 39.4 2.7 6.4 10.5 15.1 21.9 46.2 30.3 Guinea-Bissau 2002b 35.5 2.9 7.2 11.6 16.0 22.1 43.0 28.0 Haiti 2001d 59.5 0.9 2.5 5.9 10.5 18.1 63.0 47.8 Honduras 2007d 57.7 0.6 2.0 6.0 11.3 20.0 60.8 43.8 68 2011 World Development Indicators 2.9 PEOPLE Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Hungary 2007b 31.2 3.5 8.4 12.9 16.9 22.0 39.9 25.4 India 2005b 36.8 3.6 8.1 11.3 14.9 20.4 45.3 31.1 Indonesia 2009b 36.8 3.3 7.6 11.3 15.1 21.1 44.9 29.9 Iran, Islamic Rep. 2005b 38.3 2.6 6.4 10.9 15.6 22.2 45.0 29.6 Iraq .. .. .. .. .. .. .. .. Ireland 2000 d 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001d 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 d 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2004b 45.5 2.1 5.2 9.0 13.8 20.9 51.2 35.6 Japan 1993d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2006b 37.7 3.0 7.2 11.1 15.2 21.1 45.4 30.7 Kazakhstan 2007b 30.9 3.8 8.7 12.8 16.7 22.0 39.9 25.2 Kenya 2005b 47.7 1.8 4.7 8.8 13.3 20.3 53.0 37.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kosovo .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2007b 33.4 4.1 8.8 11.8 15.5 21.2 42.8 27.9 Lao PDR 2008b 36.7 3.3 7.6 11.3 15.3 20.9 44.8 30.3 Latvia 2008b 35.7 2.7 6.8 11.7 16.3 22.4 42.9 27.6 Lebanon .. .. .. .. .. .. .. .. Lesotho 2003b 52.5 1.0 3.0 7.2 12.5 21.0 56.4 39.4 Liberia 2007b 52.6 2.4 6.4 11.4 15.7 21.6 45.0 30.1 Libya .. .. .. .. .. .. .. .. Lithuania 2008b 37.6 2.6 6.6 11.1 15.7 22.1 44.4 29.1 Macedonia, FYR 2008b 44.2 2.2 5.4 9.3 14.0 21.0 50.3 34.5 Madagascar 2005b 47.2 2.6 6.2 9.6 13.1 17.7 53.5 41.5 Malawi 2004b 39.0 2.9 7.0 10.8 14.9 20.9 46.4 31.7 Malaysia 2009d 46.2 1.8 4.5 8.7 13.7 21.6 51.5 34.7 Maldives 2004b 37.4 2.7 6.5 10.9 15.7 22.7 44.2 28.0 Mali 2006b 39.0 2.7 6.5 10.7 15.2 21.6 46.0 30.5 Mauritania 2000 b 39.0 2.5 6.2 10.5 15.4 22.3 45.7 29.6 Mauritius .. .. .. .. .. .. .. .. Mexico 2008d 51.7 1.5 3.9 7.9 12.5 19.4 56.2 41.4 Micronesia 2000 b 61.1 0.4 1.6 5.2 10.2 19.1 64.0 47.1 Moldova 2008b 38.0 2.9 6.8 10.9 15.4 21.7 45.3 29.8 Mongolia 2008b 36.5 3.0 7.1 11.2 15.6 22.1 44.0 28.4 Montenegro 2008b 30.0 3.6 8.5 13.1 17.2 22.4 38.8 24.1 Morocco 2007b 40.9 2.7 6.5 10.5 14.5 20.6 47.9 33.2 Mozambique 2008b 45.6 1.9 5.2 9.5 13.7 20.1 51.5 36.7 Myanmar .. .. .. .. .. .. .. .. Namibia 1993d 74.3 0.6 1.5 2.8 5.5 12.0 78.3 65.0 Nepal 2004b 47.3 2.7 6.1 8.9 12.5 18.4 54.2 40.4 Netherlands 1999d 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997d 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2005d 52.3 1.4 3.8 7.7 12.3 19.4 56.9 41.8 Niger 2007b 34.0 3.7 8.3 12.0 15.8 21.1 42.8 28.5 Nigeria 2004b 42.9 2.0 5.1 9.7 14.7 21.9 48.6 32.4 Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2006b 32.7 4.0 9.0 12.4 15.8 20.7 42.1 28.3 Panama 2009d 52.3 1.3 3.6 7.4 12.2 20.1 56.8 40.6 Papua New Guinea 1996b 50.9 1.9 4.5 7.7 12.1 19.3 56.4 40.9 Paraguay 2008d 52.0 1.4 3.8 7.7 12.4 19.7 56.5 41.0 Peru 2009d 48.0 1.4 3.9 8.4 13.6 21.5 52.6 35.9 Philippines 2006b 44.0 2.4 5.6 9.1 13.7 21.2 50.4 33.9 Poland 2008b 34.2 3.2 7.6 12.0 16.3 22.0 42.2 27.2 2011 World Development Indicators 69 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Portugal 1997d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. Qatar 2007b 41.1 1.3 3.9 .. .. .. 52.0 35.9 Romania 2008 b 31.2 3.3 8.1 12.8 17.1 22.7 39.3 24.5 Russian Federation 2008b 42.3 2.6 6.0 9.8 14.3 20.9 48.9 33.5 Rwanda 2005b 53.1 1.7 4.2 7.7 11.7 18.2 58.2 44.0 São Tomé & Príncipe 2000 b 50.8 2.2 5.2 8.5 12.2 17.7 56.4 43.6 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 2005b 39.2 2.5 6.2 10.6 15.3 22.0 45.9 30.1 Serbia 2008b 28.2 3.9 9.1 13.5 17.5 22.5 37.4 22.8 Seychelles 2007b 19.0 4.7 10.8 15.7 19.9 24.2 29.4 15.4 Sierra Leone 2003b 42.5 2.6 6.1 9.7 14.0 20.9 49.3 33.6 Singapore 1998d 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996d 25.8 3.1 8.8 14.9 18.6 22.9 34.8 20.8 Slovenia 2004b 31.2 3.4 8.2 12.8 17.0 22.6 39.4 24.6 Somalia .. .. .. .. .. .. .. .. South Africa 2000 b 57.8 1.3 3.1 5.6 9.9 18.8 62.7 44.9 Spain 2000 d 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 2007b 40.3 3.1 6.9 10.4 14.4 20.5 47.8 32.9 Sudan .. .. .. .. .. .. .. .. Swaziland 2001b 50.7 1.8 4.5 8.0 12.3 19.4 55.9 40.8 Sweden 2000 d 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 d 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic 2004b 35.8 3.4 7.7 11.4 15.5 21.4 43.9 28.9 Tajikistan 2007b 29.4 4.0 9.3 13.4 16.7 21.5 39.0 25.2 Tanzania 2007b 37.6 2.8 6.8 11.1 15.6 21.7 44.8 29.6 Thailand 2009b 53.6 1.6 3.9 7.0 11.4 19.2 58.6 42.6 Timor-Leste 2007b 31.9 4.0 9.0 12.5 16.1 21.2 41.3 27.0 Togo 2006b 34.4 2.0 5.4 10.3 15.2 22.0 47.1 31.3 Trinidad and Tobago 1992d 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2000 b 40.8 2.4 5.9 10.2 14.9 21.8 47.2 31.6 Turkey 2008b 39.7 2.1 5.7 10.8 15.6 22.1 45.8 30.3 Turkmenistan 1998b 40.8 2.5 6.0 10.2 14.9 21.7 47.2 31.8 Uganda 2009b 44.3 2.4 5.8 9.6 13.8 20.0 50.7 36.1 Ukraine 2008 b 27.5 4.1 9.4 13.6 17.5 22.5 37.1 22.6 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999d 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000d 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay 2009d 42.4 2.3 5.6 9.8 14.5 21.4 48.6 32.9 Uzbekistan 2003b 36.7 2.9 7.1 11.5 15.7 21.5 44.2 29.5 Venezuela, RB 2006d 43.5 1.9 4.9 9.6 14.7 21.8 49.0 33.0 Vietnam 2008b 37.6 3.2 7.3 10.9 15.1 21.3 45.4 30.2 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 2005b 37.7 2.9 7.2 11.3 15.3 21.0 45.3 30.8 Zambia 2004b 50.7 1.3 3.6 7.8 12.8 20.6 55.2 38.9 Zimbabwe 1995b 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Covers urban areas only. d. Refers to income shares by percentiles of population, ranked by per capita income. 70 2011 World Development Indicators 2.9 PEOPLE Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected • Survey year is the year in which the underlying data in the percentage shares of income or consumption were collected. •  Gini index measures the extent accruing to portions of the population ranked by to which the distribution of income (or consump- income or consumption levels. The portions ranked tion expenditure) among individuals or households lowest by personal income receive the smallest within an economy deviates from a perfectly equal shares of total income. The Gini index provides a con- distribution. A Lorenz curve plots the cumulative venient summary measure of the degree of inequal- percentages of total income received against the ity. Data on the distribution of income or consump- cumulative number of recipients, starting with the tion come from nationally representative household poorest individual. The Gini index measures the area surveys. Where the original data from the house- between the Lorenz curve and a hypothetical line of hold survey were available, they have been used to absolute equality, expressed as a percentage of the directly calculate the income or consumption shares maximum area under the line. Thus a Gini index of by quintile. Otherwise, shares have been estimated 0 represents perfect equality, while an index of 100 from the best available grouped data. implies perfect inequality. •  Percentage share of The distribution data have been adjusted for income or consumption is the share of total income household size, providing a more consistent measure or consumption that accrues to subgroups of popula- of per capita income or consumption. No adjustment tion indicated by deciles or quintiles. has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle- income economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achiev- ing strict comparability is still impossible (see About the data for tables 2.7 and 2.8). Two sources of non-comparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or con- sumption expenditure as the living standard indi- cator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (num- ber of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure Data sources that the data are as comparable as possible. Wher- ever possible, consumption has been used rather Data on distribution are compiled by the World than income. Income distribution and Gini indexes for Bank’s Development Research Group using pri- high-income economies are calculated directly from mary household survey data obtained from govern- the Luxembourg Income Study database, using an ment statistical agencies and World Bank country estimation method consistent with that applied for departments. Data for high-income economies are developing countries. from the Luxembourg Income Study database. 2011 World Development Indicators 71 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15–24 ages 15–24 total % of labor age % of average 2006–09a 2006–09a 2006–09a Year force population Year GDP Year wage Afghanistan .. .. .. 2005 .. 2.2 2005 0.5   .. Albania .. .. .. 2007 51.1 34.7 2009 6.1   .. Algeria .. .. .. 2002 36.7 22.1 2002 3.2   .. Angola .. .. 25   .. ..   ..   .. Argentina 19b 25b 34 2008 41.9 31.3 2007 8.0 2000 43.8 Armenia 47b 69b .. 2008 39.2 23.9 2008 4.3 2007 20.3 Australia 13b 10 b .. 2005 92.6 69.6 2005 3.5   .. Austria 10 9 .. 2005 96.4 68.7 2005 12.6   .. Azerbaijan 19 10 25 2007 35.4 24.7 2007 3.8 2006 24.3 Bangladesh .. .. 13 2004 2.8 2.1 2006 0.3   .. Belarus .. .. .. 2008 93.5 66.8 2008 10.2 2002 41.6 Belgium 21 22 .. 2005 94.2 61.6 2005 9.0   .. Benin .. .. 23   .. .. 2006 1.5   .. Bolivia .. .. .. 2008 11.4 8.9 2000 4.5   .. Bosnia and Herzegovina 45 52 .. 2009 70.2 28.7 2009 9.4   .. Botswana .. .. .. 2006 9.0 7.3   ..   .. Brazil 14 23 .. 2008 53.8 41.7 2004 12.6   .. Bulgaria 18 14 .. 2008 72.7 49.6 2007 9.8 2004 42.9 Burkina Faso .. .. .. 2004 1.2 1.0   ..   .. Burundi .. .. ..   .. ..   ..   .. Cambodia .. .. ..   .. ..   ..   .. Cameroon .. .. ..   .. .. 2001 0.8   .. Canada 18b 12b .. 2007 66.9 53.6 2005 4.1   .. Central African Republic .. .. .. 2004 1.5 1.3 2004 0.8   .. Chad .. .. ..   .. ..   ..   .. Chile 21 24 .. 2008 53.8 36.2 2001 2.9 2006 53.5 China .. .. .. 2007 19.3 15.9   ..   .. Hong Kong SAR, China 15b 10 b .. 2008 .. 55.6   ..   .. Colombia 18 30 19 2008 31.3 20.0 2008 3.0   .. Congo, Dem. Rep. .. .. 21   .. ..   ..   .. Congo, Rep. .. .. ..   .. .. 2004 0.9   .. Costa Rica 10 13 .. 2004 55.3 37.6 2006 2.4   .. Côte d’Ivoire .. .. ..   .. ..   ..   .. Croatia 19 27 24 2010 82.9 52.6 2009 10.3 2005 32.4 Cuba 3 4 46   .. ..   ..   .. Czech Republic 17 17 .. 2007 84.5 67.3 2007 8.5 2005 40.7 Denmark 12 10 .. 2007 94.4 86.9 2005 5.4   .. Dominican Republic 21 45 35 2008 21.0 15.2 2000 0.8   .. Ecuador 12b 18b .. 2004 31.6 21.1 2002 2.5   .. Egypt, Arab Rep. 17 48 .. 2009 57.0 31.0 2004 4.1   .. El Salvador 13 8 .. 2008 23.9 16.2 2006 1.9   .. Eritrea .. .. ..   .. .. 2001 0.3   .. Estonia 32 21 .. 2004 95.2 68.6 2007 10.9 2007 35.4 Ethiopia 20 b 29b ..   .. .. 2006 0.3   .. Finland 22 19 .. 2005 88.7 67.2 2005 8.4   .. France 23 22 .. 2005 89.9 61.4 2005 12.4   .. Gabon .. .. ..   .. ..   ..   .. Gambia, The .. .. .. 2006 2.7 2.2   ..   .. Georgia 32 41 .. 2004 29.9 22.7 2004 3.0 2003 13.0 Germany 12 10 .. 2005 88.2 65.5 2005 11.4   .. Ghana .. .. 34 2004 9.1 7.1 2002 1.3   .. Greece 19 34 .. 2005 85.2 58.5 2005 11.5   .. Guatemala .. .. .. 2008 20.3 14.7 2005 1.0   .. Guinea .. .. .. 1993 1.5 1.8   ..   .. Guinea-Bissau .. .. .. 2004 1.9 1.5 2005 2.1   .. Haiti .. .. 44   .. ..   ..   .. Honduras .. .. 26 2008 18.7 12.6   ..   .. 72 2011 World Development Indicators 2.10 PEOPLE Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15–24 ages 15–24 total % of labor age % of average 2006–09a 2006–09a 2006–09a Year force population Year GDP Year wage Hungary 28 24 .. 2008 92.0 56.7 2008 10.5 2005 39.8 India .. .. 14 2006 10.3 6.4 2007 2.2   .. Indonesia 22 23 13 2008 11.7 8.7   ..   .. Iran, Islamic Rep. 20 34 .. 2001 35.1 20.0 2000 1.1   .. Iraq .. .. 11 2009 16.8 15.2 2009 3.9   .. Ireland 31 17 .. 2005 88.0 63.9 2005 3.4   .. Israel 16 14 ..   .. ..   ..   .. Italy 23 29 .. 2005 92.4 58.4 2005 14.0   .. Jamaica 22 33 .. 2004 17.4 12.6   ..   .. Japan 10 8 .. 2005 95.3 75.0 2005 8.7   .. Jordan 23 46 10 2006 38.4 19.9 2001 2.2   .. Kazakhstan 7 8 .. 2004 34.4 26.5 2009 3.2 2003 24.9 Kenya .. .. .. 2006 7.5 6.5 2003 1.1   .. Korea, Dem. Rep. .. .. ..   .. ..   ..   .. Korea, Rep. 12b 9b .. 2005 49.5 34.3 2005 1.6   .. Kosovo .. .. ..   .. .. 2007 2.7c   .. Kuwait .. .. ..   .. .. ..   .. Kyrgyz Republic 14 16 25 2006 42.2 28.9 2010 2.7 2003 27.5 Lao PDR .. .. ..   .. ..   ..   .. Latvia 38 28 .. 2003 92.4 66.5 2009 8.5 2005 33.1 Lebanon 22 22 .. 2003 33.1 19.9 2003 2.1   .. Lesotho .. .. .. 2005 5.7 3.6   ..   .. Liberia 6b 4b 31   .. ..   ..   .. Libya .. .. .. 2004 65.5 38.1 2001 2.1   .. Lithuania 35 22 .. 2007 99.3 68.7 2009 8.9 2005 30.9 Macedonia, FYR 53 59 8 2008 47.9 30.4 2008 9.4 2006 55.0 Madagascar .. .. ..   .. ..   ..   .. Malawi .. .. ..   .. ..   ..   .. Malaysia 10 12 .. 2008 49.0 32.5   ..   .. Mali .. .. 12   .. ..   ..   .. Mauritania .. .. ..   .. ..   ..   .. Mauritius 18 26 .. 2000 51.4 33.6   ..   .. Mexico 10 11 .. 2008 30.3 20.6 2005 1.3   .. Moldova 16 15 .. 2009 58.7 32.1 2009 9.1 2003 20.9 Mongolia .. .. 29 2005 27.9 21.3 2007 6.5d   .. Morocco 23 19 .. 2007 23.8 13.6 2003 1.9   .. Mozambique .. .. ..   .. ..   ..   .. Myanmar .. .. ..   .. ..   ..   .. Namibia .. .. 44   .. ..   ..   .. Nepal .. .. 23 2008 3.4 2.6 2006 0.2   .. Netherlands 7 6 .. 2005 90.7 70.7 2005 5.0e   .. New Zealand 16b 17b .. 2003 92.7 72.3 2005 4.4 e   .. Nicaragua 8 10 .. 2008 21.7 14.6   ..   .. Niger .. .. 19 2006 1.9 1.2 2006 0.7   .. Nigeria .. .. .. 2004 1.9 1.1   ..   .. Norway 10 8 .. 2005 93.2 75.2 2005 4.8e   .. Oman .. .. ..   .. ..   ..   .. Pakistan 7 10 10 2008 3.9 2.2 2004 0.5   .. Panama 12 21 ..   .. ..   ..   .. Papua New Guinea .. .. ..   .. ..   ..   .. Paraguay 9 17 .. 2004 11.6 9.1 2001 1.2   .. Peru 13b 16b 22 2008 19.1 13.9 2000 2.6   .. Philippines 16 19 19 2007 25.0 17.0   ..   .. Poland 20 21 .. 2005 83.8 54.7 2009 10.0 2007 47.1 Portugal 19 22 .. 2005 92.0 71.6 2005 10.2e   .. Puerto Rico 29 b 22b ..   .. ..   ..   .. Qatar 1 7 ..   .. ..   ..   .. 2011 World Development Indicators 73 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15–24 ages 15–24 total % of labor age % of average 2006–09a 2006–09a 2006–09a Year force population Year GDP Year wage Romania 21 20 .. 2007 54.8 36.4 2009 8.3 2005 41.5 Russian Federation 18 19 .. 2007 67.0 50.0 2007 4.7 2003 29.2 Rwanda .. .. .. 2004 4.6 4.1   ..   .. Saudi Arabia 24 46 ..   .. ..   ..   .. Senegal 12 20 .. 2003 5.1 4.1 2003 1.3   .. Serbia 31 41 29 2003 45.0 35.4 2010 14.0   .. Sierra Leone .. .. .. 2004 5.5 3.8   ..   .. Singapore 10 17 .. 2008 61.7 45.3   ..   .. Slovak Republic 28 27 .. 2003 78.9 55.3 2007 9.3e 2005 44.7 Slovenia 14 13 .. 2008 87.4 63.2 2007 12.7 2005 44.3 Somalia .. .. ..   .. ..   ..   .. South Africa 45 53 .. 2007 6.5 3.7 2006 1.2   .. Spain 39 36 .. 2005 69.4 48.7 2005 8.1e 2006 58.6 Sri Lanka 17 28 .. 2006 24.1 14.9 2007 2.0   .. Sudan .. .. 19   .. ..   ..   .. Swaziland .. .. 48   .. ..   ..   .. Sweden 26 24 .. 2005 88.8 72.2 2005 7.7e   .. Switzerland 8 9 .. 2005 95.4 78.7 2005 6.8e 2000 40.0 Syrian Arab Republic 13 49 .. 2008 26.8 13.8 2004 1.3   .. Tajikistan .. .. ..   .. .. .. 2003 25.7 Tanzania 7 10 .. 2006 4.3 4.0 2006 0.9   .. Thailand 4 5 30 2008 23.0 18.6   ..   .. Timor-Leste .. .. ..   .. ..   ..   .. Togo .. .. ..   .. ..   ..   .. Trinidad and Tobago 9 13 .. 2008 76.4 54.2   ..   .. Tunisia .. .. .. 2004 48.6 25.5 2003 4.3   .. Turkey 25 25 .. 2007 60.3 31.0 2008 6.2 2007 61.3 Turkmenistan .. .. ..   .. .. ..   .. Uganda .. .. 30 2004 10.3 9.2 2003 0.3   .. Ukraine .. .. 49 2010 65.3 52.3 2010 17.8 2007 48.3 United Arab Emirates 8 22 ..   .. ..   ..   .. United Kingdom 22 16 .. 2005 93.2 71.5 2005 5.7   .. United States 20 b 15b .. 2005 92.2 71.5 2005 6.0e 2006 29.2 Uruguay 16 25 .. 2007 72.7 56.9 2007 10.0e   .. Uzbekistan .. .. .. 2005 86.1 57.5 2005 6.5 2005 40.0 Venezuela, RB 12 16 .. 2008 32.1 22.7 2001 2.7   .. Vietnam .. .. .. 2008 19.3 15.2 ..   .. West Bank and Gaza 39 47 .. 2009 18.5 8.0 2009 4.0   .. Yemen, Rep. .. .. .. 2006 10.4 5.0 ..   .. Zambia .. .. 24 2006 10.9 8.0 2008 1.0   .. Zimbabwe .. .. 38   .. .. 2002 2.3   .. World .. w .. w                 Low income .. ..   Middle income .. ..                 Lower middle income .. ..                 Upper middle income 19 23                 Low & middle income .. ..                 East Asia & Pacific .. ..                 Europe & Central Asia 17 18                 Latin America & Carib. 12 18                 Middle East & N. Africa 18 37               South Asia .. ..                 Sub-Saharan Africa .. ..                 High income 19 16                 Euro area 21 21               a. Data are for the most recent year available. b. Limited coverage. c. Includes only expenditure on social pensions. d. Includes old-age, survivors, disability, military, work accident or disease pensions. e. Includes only expenditures on old-age and survivors’ benefi ts. 74 2011 World Development Indicators 2.10 PEOPLE Assessing vulnerability and security About the data Definitions As traditionally measured, poverty is a static con- citizenship, residency, or income status. In contri- • Youth unemployment is the share of the labor force cept, and vulnerability a dynamic one. Vulnerabil- bution-related schemes, however, eligibility is usually ages 15–24 without work but available for and seek- ity reflects a household’s resilience in the face of restricted to individuals who have contributed for a ing employment. • Female-headed households are shocks and the likelihood that a shock will lead to a minimum number of years. Definitional issues—relat- the percentage of households with a female head. decline in well-being. Thus, it depends primarily on ing to the labor force, for example—may arise in •  Pension contributors are the share of the labor the household’s assets and insurance mechanisms. comparing coverage by contribution-related schemes force or working-age population (here defined as Because poor people have fewer assets and less over time and across countries (for country-specific ages 15 and older) covered by a pension scheme. diversified sources of income than do the better-off, information, see Hinz and others 2011). The share • Public expenditure on pensions is all government fluctuations in income affect them more. of the labor force covered by a pension scheme may expenditures on cash transfers to the elderly, the Enhancing security for poor people means reduc- be overstated in countries that do not try to count disabled, and survivors and the administrative costs ing their vulnerability to such risks as ill health, pro- informal sector workers as part of the labor force. of these programs. • Average pension is the aver- viding them the means to manage risk themselves, Public interventions and institutions can provide age pension payment of all pensioners of the main and strengthening market or public institutions for services directly to poor people, although whether pension schemes (including old-age, survivors, dis- managing risk. Tools include microfinance programs, these interventions and institutions work well for the ability, military, and work accident or disease pen- public provision of education and basic health care, poor is debated. State action is often ineffective, sions) divided by the average wage of all formal sec- and old age assistance (see tables 2.11 and 2.16). in part because governments can influence only a tor workers. Poor households face many risks, and vulnerability few of the many sources of well-being and in part is thus multidimensional. The indicators in the table because of difficulties in delivering goods and ser- focus on individual risks—youth unemployment, vices. The effectiveness of public provision is further female-headed households, income insecurity in constrained by the fiscal resources at governments’ old age—and the extent to which publicly provided disposal and the fact that state institutions may not services may be capable of mitigating some of these be responsive to the needs of poor people. risks. Poor people face labor market risks, often hav- The data on public pension spending cover the ing to take up precarious, low-quality jobs and to pension programs of the social insurance schemes increase their household’s labor market participa- for which contributions had previously been made. tion by sending their children to work (see tables In many cases noncontributory pensions or social 2.4 and 2.6). Income security is a prime concern assistance targeted to the elderly and disabled are for the elderly. also included. A country’s pattern of spending is cor- Youth unemployment is an important policy issue related with its demographic structure—spending for many economies. Experiencing unemployment increases as the population ages. may permanently impair a young person’s produc- 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 dif- fer 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 limita- tions on comparability as the general unemployment Data sources rate. For further information, see About the data for table 2.5 and the original source. Data on youth unemployment are from the ILO’s The definition of female-headed household differs Key Indicators of the Labour Market, 6th edition, greatly across countries, making cross-country com- database. Data on female-headed households are parison difficult. In some cases it is assumed that a from Macro International Demographic and Health woman cannot be the head of any household with an Surveys. Data on pension contributors and pen- adult male, because of sex-biased stereotype. Cau- sion spending are from Hinz and others’ Interna- tion should be used in interpreting the data. tional Patterns of Pension Provision II: Facts and Pension scheme coverage may be broad or Figures of the 2000s (2011). even universal where eligibility is determined by 2011 World Development Indicators 75 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil–teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2009a 1999 2009a 1999 2009a 2009a 2009a 2009a 2009a Afghanistan .. .. .. .. .. .. .. .. .. 43 Albania .. .. .. .. .. .. .. .. .. 20 Algeria 12.0 .. .. .. .. .. 4.3 20.3 .. 23 Angola .. .. .. .. .. .. .. .. .. .. Argentina 12.9 14.7 18.2 21.9 17.7 15.6 4.9 13.5 .. 16 Armenia .. 11.0 .. 18.8 .. 6.8 3.0 15.0 .. 19 Australia 16.4 16.4 15.0 14.5 26.6 20.2 4.5 .. .. .. Austria 25.1 23.3 30.2 26.7 52.1 47.6 5.4 11.1 .. 12 Azerbaijan 6.9 .. 17.0 .. 19.1 15.6 2.8 9.1 99.9 11 Bangladesh .. 10.7 12.5 14.9 50.7 39.8 2.4 14.0 58.4 44 Belarus .. .. .. .. .. 15.0 4.5 10.6 99.9 15 Belgium 18.2 20.5 23.8 33.3 38.3 35.3 6.0 12.4 .. 11 Benin 12.1 .. 24.6 .. 212.7 .. 3.5 15.9 40.4 45 Bolivia 14.2 .. 11.7 .. 44.1 .. .. .. .. 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 12.4 .. 37.6 .. 251.5 8.9 22.0 97.4 25 Brazil 10.8 17.3 9.5 18.0 57.1 29.6 5.1 16.1 .. 23 Bulgaria 15.5 23.5 18.8 22.3 17.9 20.1 4.1 10.0 .. 16 Burkina Faso .. 29.0 .. 30.2 .. 307.1 4.6 21.8 86.1 49 Burundi 14.7 21.1 .. 59.4 1,051.5 520.4 8.3 23.4 91.2 51 Cambodia 5.9 .. 11.5 .. 43.6 .. 2.1 12.4 99.5 49 Cameroon .. 7.4 .. 30.7 .. 35.8 3.7 19.2 .. 46 Canada .. .. .. .. 44.0 .. 4.9 .. .. .. Central African Republic .. 4.5 .. 16.1 .. 124.1 1.3 11.7 .. 95 Chad .. 12.7 .. 24.1 .. 217.8 3.2 12.6 34.6 61 Chile 14.4 14.7 14.8 16.0 19.4 12.1 4.0 18.2 .. 25 China .. .. 11.5 .. 90.0 .. .. .. .. 18 Hong Kong SAR, China 12.4 13.8 17.7 16.7 .. 56.2 4.5 24.1 95.1 16 Colombia 15.2 15.9 16.1 15.4 37.7 27.4 4.8 14.9 100.0 29 Congo, Dem. Rep. .. .. .. .. .. .. .. .. 93.4 37 Congo, Rep. .. .. .. .. .. .. .. .. .. 64 Costa Rica 15.5 14.6 21.4 14.4 .. .. 6.3 37.7 87.6 18 Côte d’Ivoire 14.8 .. 42.8 .. 146.3 119.1 4.6 24.6 100.0 42 Croatia .. 21.8 .. 25.2 35.8 26.2 4.6 10.4 100.0 11 Cuba 27.8 44.7 41.2 51.9 86.2 58.8 13.6 17.5 100.0 9 Czech Republic 11.2 13.0 21.7 22.0 33.7 30.5 4.2 9.9 .. 18 Denmark 24.6 24.5 38.1 32.2 65.9 53.8 7.8 15.4 .. .. Dominican Republic 7.2 7.3 .. 7.4 .. .. 2.3 12.0 83.6 25 Ecuador 4.4 .. 9.6 .. .. .. .. .. 82.6 17 Egypt, Arab Rep. .. .. .. .. .. .. 3.8 11.9 .. 27 El Salvador 8.6 8.5 7.5 9.1 8.9 13.7 3.6 13.1 93.2 31 Eritrea 15.0 .. 37.3 .. 429.6 .. .. .. 92.2 38 Estonia 20.9 20.0 27.2 23.9 31.8 20.8 4.8 13.9 .. 12 Ethiopia .. 12.4 .. 8.9 .. 642.9 5.5 23.3 84.6 58 Finland 17.4 17.5 25.8 30.8 40.4 31.7 5.9 12.5 .. 14 France 17.3 17.7 28.5 26.4 29.7 34.8 5.6 10.7 .. 19 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. 34 Georgia .. 14.5 .. 15.2 .. 11.2 3.2 7.7 94.6 9 Germany .. 15.7 .. 21.8 .. .. 4.5 10.3 .. 13 Ghana .. .. .. .. .. .. .. .. 47.6 33 Greece 11.7 .. 15.5 .. 26.2 .. .. .. .. 10 Guatemala 6.7 10.5 4.3 6.2 .. 19.0 3.2 .. .. 29 Guinea .. 7.1 .. 6.3 .. 102.3 2.4 19.2 73.1 44 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. Honduras .. .. .. .. .. .. .. .. 36.4 33 76 2011 World Development Indicators 2.11 PEOPLE Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil–teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2009a 1999 2009a 1999 2009a 2009a 2009a 2009a 2009a Hungary 18.0 24.9 19.1 23.1 34.2 23.8 5.2 10.4 .. 10 India 11.9 .. 24.7 .. 95.0 .. .. .. .. .. Indonesia .. 11.0 .. 12.5 .. 16.2 2.8 17.9 .. 17 Iran, Islamic Rep. 9.1 15.1 9.9 21.0 34.8 22.2 4.7 20.9 98.4 20 Iraq .. .. .. .. .. .. .. .. .. 17 Ireland 11.0 15.7 16.8 23.2 28.6 26.2 4.9 13.8 .. 16 Israel 20.5 19.4 21.9 19.0 30.9 22.7 5.9 13.1 .. 13 Italy 24.0 22.6 27.7 25.2 27.6 22.1 4.3 9.0 .. 10 Jamaica 13.4 15.8 21.0 26.8 70.4 42.4 5.8 .. .. .. Japan 21.1 21.7 20.9 22.4 15.1 20.1 3.5 9.4 .. 18 Jordan 13.7 12.7 15.8 16.3 .. .. .. .. .. .. Kazakhstan .. .. .. .. .. 7.9 2.8 .. .. 16 Kenya 21.5 .. 14.5 .. 209.0 .. .. .. 96.8 47 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 18.4 17.0 15.7 22.2 8.4 9.0 4.2 14.8 .. 24 Kosovo .. .. .. .. .. .. 4.3 17.4 .. .. Kuwait 19.2 10.9 .. 14.9 .. .. .. .. 100.0 9 Kyrgyz Republic .. .. .. .. 24.3 17.3 5.9 19.0 65.7 24 Lao PDR 2.3 .. 4.5 .. 68.6 .. 2.3 12.2 96.9 29 Latvia 19.5 23.3 23.7 24.1 27.9 16.3 5.0 13.9 .. 11 Lebanon .. .. .. .. 13.9 10.2 1.8 7.2 .. 14 Lesotho 34.5 22.6 76.7 50.8 875.4 .. 12.4 23.7 57.6 37 Liberia .. 5.7 .. 8.4 .. .. 2.8 12.1 40.2 24 Libya .. .. .. .. 23.9 .. .. .. .. .. Lithuania .. 15.8 .. 20.1 34.2 17.1 4.7 13.4 .. 13 Macedonia, FYR .. .. .. .. .. .. .. .. .. 17 Madagascar 5.7 7.1 .. 10.5 .. 132.4 3.0 13.4 .. 48 Malawi 14.0 .. 10.0 .. 2,613.3 .. .. .. .. .. Malaysia 12.5 14.3 21.7 12.4 81.1 34.0 4.1 17.2 .. 15 Mali 14.3 13.0 56.1 32.6 241.3 117.7 4.4 22.3 50.0 50 Mauritania 11.4 .. 35.9 .. 79.0 .. .. .. 100.0 39 Mauritius 9.3 9.3 14.2 15.1 25.4 16.7 3.2 11.4 100.0 22 Mexico 11.7 13.3 14.2 13.4 47.8 37.0 4.8 .. 95.4 28 Moldova .. 42.4 .. 40.3 .. 46.1 9.6 21.0 .. 16 Mongolia .. 16.2 .. .. .. .. 5.6 14.6 100.0 30 Morocco 17.2 16.1 45.1 38.7 96.2 71.1 5.6 25.7 100.0 27 Mozambique .. .. .. .. 1,412.2 .. .. .. 71.2 61 Myanmar .. .. 6.9 .. 28.0 .. .. .. 98.9 29 Namibia 21.4 15.6 35.2 15.8 152.2 .. 6.4 22.4 95.6 30 Nepal 9.1 17.6 13.1 11.3 141.6 55.5 4.6 19.5 66.4 33 Netherlands 15.2 16.9 22.2 24.5 47.4 40.2 5.3 11.7 .. .. New Zealand 20.2 17.6 24.1 19.6 40.1 28.6 6.1 .. .. 15 Nicaragua .. .. .. .. .. .. .. .. 72.7 29 Niger .. 28.3 .. 56.6 .. 429.3 4.5 19.3 98.0 39 Nigeria .. .. .. .. .. .. .. .. .. 46 Norway 21.8 18.5 30.4 26.5 45.8 47.3 6.8 16.5 .. .. Oman 11.2 .. 21.8 .. .. .. .. .. 100.0 12 Pakistan .. .. .. .. .. .. 2.7 11.2 85.2 40 Panama 13.7 7.5 19.1 9.9 33.6 21.6 3.8 .. 91.5 24 Papua New Guinea .. .. .. .. .. .. .. .. .. .. Paraguay 13.6 10.8 18.5 16.3 58.9 26.0 4.0 11.9 .. 26 Peru 7.6 8.1 10.8 9.9 21.2 .. 2.7 20.7 .. 21 Philippines 12.8 9.0 11.0 9.1 15.4 9.6 2.8 16.9 .. 34 Poland .. 24.3 10.9 22.0 21.1 16.6 4.9 11.7 .. 10 Portugal 19.5 .. 27.5 .. 28.1 .. .. .. .. 11 Puerto Rico .. .. .. .. .. .. .. .. 6.6 12 Qatar .. 9.2 .. 9.8 .. 337.7 .. .. 48.9 11 2011 World Development Indicators 77 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil–teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2009a 1999 2009a 1999 2009a 2009a 2009a 2009a 2009a Romania .. 20.0 .. 16.6 32.6 26.2 4.3 11.8 .. 16 Russian Federation .. .. .. .. 10.9 .. .. .. .. 17 Rwanda 11.0 8.2 41.9 34.3 1,206.8 222.8 4.1 20.4 93.9 68 Saudi Arabia .. 18.4 .. 18.3 .. .. 5.6 19.3 91.5 11 Senegal 14.1 20.9 .. 25.7 .. 191.5 5.8 19.0 .. 35 Serbia .. 56.9 .. 13.6 .. 40.1 4.7 9.3 94.2 16 Sierra Leone .. 7.1 .. 18.0 .. .. 4.3 18.1 49.4 44 Singapore .. 10.5 .. 15.7 .. 27.3 3.0 11.6 94.3 19 Slovak Republic 10.2 15.6 18.4 14.7 32.9 19.5 3.6 10.5 .. 17 Slovenia 26.3 .. 25.7 .. 27.9 .. .. .. .. 17 Somalia .. .. .. .. .. .. .. .. .. 36 South Africa 14.2 15.1 20.0 17.7 .. .. 5.4 16.9 87.4 31 Spain 18.0 19.4 24.4 24.1 19.6 25.1 4.3 11.1 .. 12 Sri Lanka .. .. .. .. .. .. .. .. .. 23 Sudan .. .. .. .. .. .. .. .. 59.7 38 Swaziland 8.5 13.0 23.7 36.2 444.5 .. 7.8 21.6 94.0 32 Sweden 22.5 25.0 26.2 30.6 52.1 38.3 6.6 12.7 .. 10 Switzerland 22.7 22.5 27.3 25.2 53.8 46.7 5.2 16.1 .. .. Syrian Arab Republic 11.2 18.3 21.7 15.5 .. .. 4.9 16.7 .. 18 Tajikistan .. .. .. .. .. 21.8 3.5 18.7 88.3 23 Tanzania .. 22.1 .. 18.8 .. .. 6.8 27.5 100.0 54 Thailand 17.8 24.0 15.9 9.1 36.0 22.3 4.1 20.3 .. 16 Timor-Leste .. 27.6 .. .. .. 92.7 16.8 15.5 .. 29 Togo 8.5 13.0 30.3 19.1 .. 155.2 4.6 17.6 14.6 41 Trinidad and Tobago 11.5 9.0 12.2 9.9 148.7 .. .. .. 88.0 17 Tunisia 15.6 .. 27.1 .. 89.4 54.5 7.1 22.4 .. 17 Turkey 9.8 .. 9.6 .. 33.5 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 7.3 .. 21.2 .. 105.4 3.2 15.0 89.4 49 Ukraine .. .. .. .. 36.5 25.1 5.3 20.2 99.9 16 United Arab Emirates 8.7 4.9 11.6 6.7 41.4 15.5 1.2 23.4 100.0 16 United Kingdom 13.9 23.0 23.8 28.2 25.6 24.4 5.5 11.7 .. 18 United States 17.9 22.0 22.5 24.2 27.0 21.7 5.5 14.1 .. 14 Uruguay 7.2 .. 9.9 .. .. .. .. .. .. 15 Uzbekistan .. .. .. .. .. .. .. .. 100.0 17 Venezuela, RB .. 9.2 .. 8.2 .. .. 3.7 .. 86.3 16 Vietnam .. 19.7 .. 17.3 .. 61.7 5.3 19.8 99.6 20 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 28 Yemen, Rep. .. .. .. .. .. .. 5.2 16.0 .. .. Zambia 7.2 .. 19.4 .. 164.6 .. 1.3 .. .. 61 Zimbabwe 12.7 .. 19.3 .. 193.0 .. .. .. .. .. World .. m .. m .. m .. m .. m .. m 4.5 m .. m .. m 24 w Low income .. .. .. .. .. .. 3.7 .. 80.4 46 Middle income .. .. .. .. .. .. 4.1 .. .. 23 Lower middle income .. .. .. .. .. .. .. .. .. 23 Upper middle income 12.0 13.8 16.4 17.0 .. .. 4.5 13.5 .. 21 Low & middle income .. .. .. .. .. .. .. .. .. 26 East Asia & Pacific .. .. .. .. 38.2 .. 3.5 15.9 .. 18 Europe & Central Asia .. .. .. .. .. .. 4.2 13.4 .. 17 Latin America & Carib. 12.7 12.2 13.7 13.4 .. .. 4.0 .. .. 24 Middle East & N. Africa .. .. .. .. .. .. 4.6 18.0 .. 23 South Asia .. .. 13.6 .. 90.8 .. 2.9 .. .. .. Sub-Saharan Africa .. .. .. .. .. .. 3.8 .. .. 45 High income 18.0 19.4 22.5 23.9 31.4 25.2 5.1 12.5 .. 15 Euro area 17.4 17.6 25.1 24.8 29.1 28.9 5.2 11.1 .. 15 a. Provisional data. 78 2011 World Development Indicators 2.11 PEOPLE Education inputs About the data Definitions Data on education are collected by the United The primary school pupil–teacher ratio reflects the • Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- average number of pupils per teacher at the specified and capital spending on education divided by the zation (UNESCO) Institute for Statistics from official level of education. It differs from the average class number of students by level as a percentage of gross responses to its annual education survey. The data size because of the different practices countries domestic product (GDP) per capita. • Public expen- are used for monitoring, policymaking, and resource employ, such as part-time teachers, school shifts, diture on education is current and capital expendi- allocation. While international standards ensure and multigrade classes. The comparability of pupil– tures on education by local, regional, and national comparable datasets, data collection methods may teacher ratios across countries is affected by the governments, including municipalities. • Trained vary by country and within countries over time. definition of teachers and by differences in class size teachers in primary education are the percentage For most countries the data on education spend- by grade and in the number of hours taught, as well of primary school teachers who have received the ing in the table refer to public spending—total gov- as the different practices mentioned above. More- minimum organized teacher training (pre-service or ernment spending on education at all levels plus over, the underlying enrollment levels are subject to in-service) required for teaching at the specified level subsidies provided to households and other private a variety of reporting errors (for further discussion of of education in their country. • Primary school pupil– entities—and generally exclude the part of foreign enrollment data, see About the data for table 2.12). teacher ratio is the number of pupils enrolled in pri- aid for education that is not included in the govern- While the pupil–teacher ratio is often used to com- mary school divided by the number of primary school ment budget. The data may also exclude spending pare the quality of schooling across countries, it is teachers (regardless of their teaching assignment). by religious schools, which play a significant role in often weakly related to student learning and quality many developing countries. Data are gathered from of education. ministries of education and from other ministries or All education data published by the UNESCO Insti- agencies involved in education spending. tute for Statistics are mapped to the International The share of public expenditure devoted to educa- Standard Classification of Education 1997 (ISCED tion allows an assessment of the priority a govern- 1997). This classification system ensures the com- ment assigns to education relative to other public parability of education programs at the international investments, as well as a government’s commitment level. UNESCO developed the ISCED to facilitate to investing in human capital development. However, comparisons of education statistics and indicators returns on investment to education, especially pri- of different countries on the basis of uniform and mary and lower secondary education, cannot be internationally agreed definitions. First developed in understood simply by comparing current education the 1970s, the current version was formally adopted indicators with national income. It takes a long time in November 1997. before currently enrolled children can productively The reference years shown in the table reflect the contribute to the national economy (Hanushek school year for which the data are presented. In 2002). some countries the school year spans two calendar High-quality data on education finance are scarce. years (for example, from September 2009 to June Improving the quality of education finance data is a 2010); in these cases the reference year refers to priority of the UNESCO Institute for Statistics. Addi- the year in which the school year ended (2010 in the tional resources are being allocated for technical previous example). assistance to countries in need, especially those in Sub-Saharan Africa. Interagency partnerships and collaborations with national ministries in charge of education finance data are improving, and actual expenditure data are increasingly being collected. Tracking private educational spending is still a chal- lenge for all countries. The share of trained teachers in primary educa- tion reveals a country’s commitment to invest in the development of its human capital engaged in teaching, but it does not take into account differ- ences in teachers’ experiences and status, teaching methods, teaching materials, and classroom condi- Data sources tions—all factors that affect the quality of teaching and learning. Some teachers without this formal Data on education inputs are from the UNESCO training may have acquired equivalent pedagogical Institute for Statistics (www.uis.unesco.org). skills through professional experience. 2011 World Development Indicators 79 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio rate enrollment rate, school primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2009a 2009a 2009a 2009a 1991 2009a 1999 2009a 2009a 2009a 2009a 2009a Afghanistan .. 104 44 4 28 .. .. 27 .. .. .. .. Albania 58 119 72 .. .. 85 70 .. 86 84 15 16 Algeria 23 108 .. 31 89 94 .. .. 96 94 59 82 Angola 40 128 .. .. .. .. .. .. .. .. .. .. Argentina 69 116 85 68 .. .. 76 79 .. .. .. .. Armenia 33 99 93 50 .. 84 86 87 92 94 5 3 Australia 82 106 149 77 98 97 90 88 97 98 33 22 Austria 95 100 100 55 90 .. .. .. .. .. .. .. Azerbaijan 24 95 99 19 89 85 75 93 86 85 38 37 Bangladesh 10 95 42 8 64 86 40 41 86 93 1,234 575 Belarus 102 99 95 77 .. 94 82 87 94 96 12 7 Belgium 122 103 108 63 96 98 .. .. 98 99 6 4 Benin 14 122 .. .. 51 95 18 .. 99 86 7 91 Bolivia 47 107 81 38 .. 91 68 69 92 92 58 53 Bosnia and Herzegovina 15 109 91 37 .. 87 .. .. 86 88 11 9 Botswana 17 109 82 .. 89 87 54 60 86 88 21 18 Brazil 65 120 90 38 .. 95 66 52 96 94 289 393 Bulgaria 81 101 89 51 .. 96 85 83 97 98 4 3 Burkina Faso 3 78 20 3 27 63 9 15 68 60 392 473 Burundi 10 147 21 3 50 99 .. 9 98 100 9 1 Cambodia 19 116 40 10 .. 95 15 34 90 87 99 131 Cameroon 26 114 41 9 69 92 .. .. 97 86 38 210 Canada 71 98 .. .. 98 .. 95 .. .. .. .. .. Central African Republic 5 89 14 2 53 67 .. 10 77 57 78 149 Chad 1 90 24 2 .. .. 7 .. .. .. .. .. Chile 55 106 90 55 .. 95 .. 85 96 95 35 41 China 47 113 78 25 97 .. .. .. .. .. .. .. Hong Kong SAR, China 121 104 82 57 .. 94 74 75 97 100 6 0b Colombia 51 120 95 37 71 90 56 74 93 93 155 152 Congo, Dem. Rep. 4 90 37 6 56 .. .. .. .. .. .. .. Congo, Rep. 13 120 .. 6 .. .. .. .. .. .. .. .. Costa Rica 70 110 96 .. 87 .. .. .. .. .. .. .. Côte d’Ivoire 4 74 .. 8 46 57 19 .. 62 52 609 774 Croatia 60 94 90 51 .. 91 81 .. 91 92 8 8 Cuba 105 104 90 118 94 99 73 83 100 99 2 2 Czech Republic 111 103 95 58 .. .. 81 .. .. .. .. .. Denmark 96 98 119 78 98 95 88 90 94 97 12 7 Dominican Republic 37 106 77 .. .. 87 38 61 96 89 23 70 Ecuador 131 117 81 42 .. 97 46 59 .. .. .. .. Egypt, Arab Rep. 16 100 .. 28 .. 94 71 .. 97 93 137 324 El Salvador 60 115 64 25 .. 94 47 55 95 96 23 15 Eritrea 13 48 32 2 20 36 17 27 39 34 190 202 Estonia 95 100 99 64 .. 94 84 89 96 97 1 1 Ethiopia 4 102 34 4 30 83 12 .. 86 81 929 1,255 Finland 65 97 110 94 99 96 95 96 96 96 7 7 France 110 110 113 55 100 98 94 98 99 99 18 15 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 22 86 51 5 50 69 26 42 69 74 40 33 Georgia 63 108 108 25 .. 100 76 81 96 93 6 10 Germany 109 105 102 .. 84 98 .. .. .. .. .. .. Ghana 70 105 57 9 .. 76 33 46 76 77 430 398 Greece 69 101 102 91 95 99 82 91 99 100 2 0b Guatemala 29 114 57 18 .. 95 24 40 98 95 23 55 Guinea 12 90 37 9 27 73 12 29 78 68 174 244 Guinea-Bissau .. .. .. .. .. .. 10 .. .. .. .. .. Haiti .. .. .. .. 21 .. .. .. .. .. .. .. Honduras 40 116 65 19 88 97 .. .. 96 96 22 9 80 2011 World Development Indicators 2.12 PEOPLE Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio rate enrollment rate, school primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2009a 2009a 2009a 2009a 1991 2009a 1999 2009a 2009a 2009a 2009a 2009a Hungary 87 99 97 65 .. 90 82 91 95 95 9 9 India 54 117 60 13 .. 91 .. .. 91 88 5,543 7,112 Indonesia 50 121 79 24 95 95 50 69 .. .. .. .. Iran, Islamic Rep. 40 103 83 36 97 99 .. .. .. .. .. .. Iraq 6 103 51 .. 76 88 30 43 93 82 176 415 Ireland .. 105 115 58 90 97 84 88 96 98 9 5 Israel 97 111 90 60 .. 97 86 86 97 98 13 9 Italy 100 103 101 67 .. 98 88 95 100 99 5 15 Jamaica 86 93 91 24 97 80 83 77 82 79 31 35 Japan 89 102 101 58 100 100 99 98 .. .. .. .. Jordan 36 97 88 41 .. 89 79 82 93 94 30 23 Kazakhstan 52 108 99 41 .. 89 87 89 89 90 52 42 Kenya 51 113 59 4 .. 83 33 50 83 84 532 497 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 111 105 97 98 99 99 97 95 100 98 4 31 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 76 95 90 29 47 88 89 80 94 93 6 8 Kyrgyz Republic 18 95 84 51 .. 84 .. 79 91 91 19 18 Lao PDR 22 121 44 13 59 93c 26 36 84 81 65 76 Latvia 89 98 98 69 .. .. .. .. .. .. .. .. Lebanon 77 103 82 53 .. 90 .. 75 92 90 19 21 Lesotho .. 104 45 .. 72 73 17 29 71 76 54 45 Liberia 145 91 .. .. .. .. 20 .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 72 96 99 77 .. 92 90 92 96 96 3 3 Macedonia, FYR 23 88 84 40 .. 86 79 .. 91 92 6 5 Madagascar 10 160 32 4 72 98 .. 26 99 100 16 3 Malawi .. 119 30 0 .. 91 29 25 89 94 152 85 Malaysia 71 95 69 36 .. 94 65 68 94 94 97 95 Mali 4 95 38 6 .. 73 .. 30 84 70 165 304 Mauritania .. 104 24 4 .. 76 14 16 74 79 66 51 Mauritius 98 100 87 26 93 94 67 .. 93 95 4 3 Mexico 114 114 90 27 98 98 56 72 99 100 39 23 Moldova 74 94 88 38 .. 88 79 80 91 90 8 8 Mongolia 59 110 92 53 .. 90 58 82 99 99 1 1 Morocco 57 107 56 13 56 90 30 .. 92 88 154 203 Mozambique .. 114 23 .. 42 91 3 15 93 88 149 264 Myanmar 7 116 53 11 .. .. 31 50 .. .. .. .. Namibia .. 112 66 9 82 89 39 54 88 92 22 14 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 100 107 121 61 95 99 91 88 99 99 4 9 New Zealand 94 101 119 78 100 99 90 .. 99 100 1 0b Nicaragua 56 117 68 .. 70 92 35 45 93 94 29 24 Niger 3 62 12 1 23 54 6 9 60 48 511 637 Nigeria 16 93 30 .. .. 61 .. 26 66 60 4,023 4,626 Norway 95 99 112 73 100 99 96 96 99 99 3 3 Oman 38 84 91 26 69 77 65 82 82 81 33 34 Pakistan .. 85 33 6 .. 66 .. 33 72 60 3,108 4,191 Panama 66 109 73 45 92 97 59 66 98 97 4 6 Papua New Guinea .. .. .. .. 65 .. .. .. .. .. .. .. Paraguay 109 102 67 29 94 87 46 59 88 88 52 50 Peru 72 109 89 .. 86 94 62 71 97 98 54 43 Philippines 49 110 82 29 96 92 50 61 91 93 555 407 Poland 62 97 100 69 .. 95 90 94 95 95 62 55 Portugal 81 115 104 60 98 99 82 88 99 99 2 4 Puerto Rico 154 91 84 78 .. .. .. .. .. .. .. .. Qatar 53 106 85 10 89 93 74 77 98 98 1 1 2011 World Development Indicators 81 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio rate enrollment rate, school primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2009a 2009a 2009a 2009a 1991 2009a 1999 2009a 2009a 2009a 2009a 2009a Romania 73 100 92 66 73 90 75 73 96 97 16 14 Russian Federation 90 97 85 77 .. .. .. .. .. .. .. .. Rwanda 17 151 27 5 .. 96 .. .. 95 97 38 22 Saudi Arabia 11 99 97 37 .. 86 .. 72 88 85 205 244 Senegal 12 84 30 8 45 73 .. .. 74 76 262 232 Serbia 51 98 91 50 .. 94 .. 90 96 96 5 6 Sierra Leone 5 158 35 .. .. .. .. 25 .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 94 103 92 54 .. .. .. .. .. .. .. .. Slovenia 83 97 97 87 .. 97 90 91 98 97 1 1 Somalia .. 33 8 .. .. .. .. .. .. .. .. .. South Africa 64 101 94 .. 90 85 63 72 89 91 385 331 Spain 126 107 120 71 100 100 88 95 100 100 1 3 Sri Lanka .. 97 .. .. .. 95 .. .. 95 96 45 36 Sudan 28 74 38 .. .. .. .. .. .. .. .. .. Swaziland .. 108 53 .. 74 83 32 29 82 84 19 18 Sweden 102 95 103 71 100 95 96 99 95 94 16 17 Switzerland 102 103 96 49 84 94 84 85 99 99 3 1 Syrian Arab Republic 9 122 75 .. 91 .. 36 69 .. .. .. .. Tajikistan 9 102 84 20 .. 97 63 83 99 96 2 15 Tanzania 33 105 27 .. 51 96 5 .. 96 97 160 107 Thailand 92 91 76 45 .. 90 .. 71 91 89 281 305 Timor-Leste .. 113 51 15 .. 82 23 .. 84 82 15 17 Togo 7 115 41 5 65 94 20 .. 98 89 10 56 Trinidad and Tobago 81 104 89 .. 90 93 70 74 97 94 2 4 Tunisia .. 107 92 34 94 98 63 71 99 100 6 0b Turkey 18 99 82 38 89 95 62 74 96 94 147 214 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 12 122 27 4 .. 92 8 22 91 94 310 213 Ukraine 101 98 94 79 .. 89 91 85 89 90 89 81 United Arab Emirates 94 105 95 30 97 90 69 83 98 97 2 4 United Kingdom 81 106 99 57 97 100 95 93 100 100 5 1 United States 58 99 94 83 97 92 88 88 93 94 944 770 Uruguay 86 114 88 65 91 99 .. 70 99 99 1 2 Uzbekistan 26 92 104 10 .. 87 .. 92 91 89 101 119 Venezuela, RB 77 103 82 79 .. 92 47 71 94 94 108 96 Vietnam .. .. .. .. .. .. 59 .. .. .. .. .. West Bank and Gaza 34 79 87 46 .. 75 77 85 78 77 57 55 Yemen, Rep. .. 85 .. 10 .. 73 32 .. 80 66 395 641 Zambia .. 113 49 .. .. 91 17 46 91 94 112 78 Zimbabwe .. .. .. 3 .. .. 40 .. .. .. .. .. World 44 w 107 w 67 w 26 w .. w 88 w 52 w 59 w 91 w 89 w Low income 15 104 38 6 .. 80 .. .. 83 79 Middle income 46 109 68 24 .. 88 .. .. 92 90 Lower middle income 42 107 63 19 .. 87 .. .. 91 88 Upper middle income 63 111 88 42 .. 93 67 75 94 94 Low & middle income 40 107 63 21 .. 87 .. 55 90 88 East Asia & Pacific 44 111 74 .. 96 .. .. .. .. .. Europe & Central Asia 55 99 89 55 90 92 79 81 94 94 Latin America & Carib. 68 116 89 35 .. 94 59 73 95 95 Middle East & N. Africa 20 105 73 27 .. 89 60 64 92 89 South Asia .. 108 52 11 68 86 .. .. 92 88 Sub-Saharan Africa 17 100 34 6 .. 75 .. .. 78 75 High income 77 101 100 67 95 95 88 90 95 96 Euro area 110 .. .. .. .. .. .. .. .. .. a. Provisional data. b. Less than 0.5. c. Data are for 2010. 82 2011 World Development Indicators 2.12 PEOPLE Participation in education About the data Definitions School enrollment data are reported to the United children of primary age enrolled in preprimary edu- • Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organi- cation) are compiled from administrative data. Large ment, regardless of age, to the population of the age zation (UNESCO) Institute for Statistics by national numbers of children out of school create pressure group that officially corresponds to the level of educa- education authorities and statistical offices. Enroll- to enroll children and provide classrooms, teachers, tion shown. • Preprimary education (ISCED O) refers ment indicators help monitor whether a country is on and educational materials, a task made difficult in to programs at the initial stage of organized instruc- track to achieve the Millennium Development Goal of many countries by limited education budgets. How- tion, designed primarily to introduce very young chil- universal primary education by 2015, and whether ever, getting children into school is a high priority for dren, usually from age 3, to a school-type environment an education system has the capacity to meet the countries and crucial for achieving the Millennium and to provide a bridge between the home and school. needs of universal primary education. Development Goal of universal primary education. On completing these programs, children continue their Enrollment indicators are based on annual school In 2006 the UNESCO Institute for Statistics education at the primary level. • Primary education surveys but do not necessarily reflect actual atten- changed its convention for citing the reference year. (ISCED 1) refers to programs normally designed to dance or dropout rates during the year. Also, the For more information, see About the data for table give students a sound basic education in reading, length of primary education differs across coun- 2.11. writing, and mathematics along with an elementary tries and can influence enrollment rates and ratios, understanding of other subjects such as history, although the International Standard Classification of geography, natural science, social science, art, and Education (ISCED) tries to minimize the difference. music. Religious instruction may also be featured. It A shorter duration for primary education tends to is sometimes called elementary education. • Sec- increase the ratio; a longer one to decrease it (in ondary education refers to programs of lower (ISCED part because older children are more at risk of drop- 2) and upper (ISCED 3) secondary education. Lower ping out). secondary education continues the basic programs Over- or under-age enrollments are frequent, par- of the primary level, but the teaching is typically more ticularly when parents prefer children to start school subject focused, requiring more specialized teachers at other than the official age. Age at enrollment may for each subject area. In upper secondary educa- be inaccurately estimated or misstated, especially tion, instruction is often organized even more along in communities where registration of births is not subject lines, and teachers typically need a higher or strictly enforced. more subject-specific qualification. • Tertiary educa- Population data used to calculate population- tion refers to a wide range of programs with more based indicators are drawn from the United Nations advanced educational content. The first stage of ter- Population Division. Using a single source for popula- tiary education (ISECD 5) refers to theoretically based tion data standardizes definitions, estimations, and programs intended to provide sufficient qualifications interpolation methods, ensuring a consistent meth- to enter advanced research programs or professions odology across countries and minimizing potential with high-skill requirements and programs that are enumeration problems in national censuses. practical, technical, or occupationally specific. The Gross enrollment ratios indicate the capacity of second stage of tertiary education (ISCED 6) refers each level of the education system, but a high ratio to programs devoted to advanced study and original may reflect a substantial number of over-age children research and leading to the award of an advanced enrolled in each grade because of repetition or late research qualification. • Net enrollment rate is the entry, rather than a successful education system. ratio of total enrollment of children of official school The net enrollment rate excludes over- and under- age to the population of the age group that offi - age students and more accurately captures the sys- cially corresponds to the level of education shown. tem’s coverage and internal efficiency. Differences • Adjusted net enrollment rate, primary, is the ratio between the gross enrollment ratio and net enroll- of total enrollment of children of official school age ment rate show the incidence of over- and under-age for primary education who are enrolled in primary or enrollments. secondary education to the total primary school-age The adjusted net enrollment rate in primary educa- population. • Children out of school are the number tion captures primary-school-age children who have of primary-school-age children not enrolled in primary progressed to secondary education faster than their or secondary school. peers and who would not be counted in the tradi- Data sources tional net enrollment rate. Data on children out of school (primary-school- Data on participation in education are from age children not enrolled in primary or secondary the UNESCO Institute for Statistics, www.uis. school—dropouts, children never enrolled, and unesco.org. 2011 World Development Indicators 83 2.13 Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in first grade of survival rate primary education secondary education primary education % of grade 1 students % of relevant Reaching Reaching last grade of % of age group grade 5 primary education enrollment % Male Female Male Female Male Female Male Female Male Female 2009a 2009a 1991 2008a 1991 2008a 2008a 2008a 2009a 2009a 2008a 2008a Afghanistan 129 93 89 .. 89 .. .. .. .. .. .. .. Albania 89 82 .. .. .. .. .. .. 2 1 .. .. Algeria 101 99 82 94 79 95 91 95 13 8 90 92 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 111 111 .. 95 .. 98 93 97 7 5 93 96 Armenia 86 89 .. .. .. .. 98 97 0b 0b 100 98 Australia .. .. 98 .. 99 .. .. .. .. .. .. .. Austria 104 100 .. .. .. .. 96 99 0 0 100 99 Azerbaijan 95 94 .. .. .. .. 100 97 0b 0b 100 98 Bangladesh 101 105 .. 67 .. 66 67 66 14 13 .. .. Belarus 97 102 .. .. .. .. 99 99 0b 0b 100 100 Belgium 97 98 87 90 90 92 86 88 4 3 100 99 Benin 161 152 30 .. 31 .. .. .. 14 14 .. .. Bolivia 114 113 57 86 51 85 85 82 1 1 96 94 Bosnia and Herzegovina 89 92 .. .. .. .. .. .. 0b 0b .. .. Botswana 114 112 73 .. 81 .. .. .. 6 4 98 97 Brazil .. .. .. .. .. .. .. .. .. .. .. .. Bulgaria 107 108 .. .. .. .. 93 94 2 1 95 95 Burkina Faso 90 83 61 73c 58 78 c 61c 67c 11 11 56c 51c Burundi 152 146 66 62 61 68 56 64 32 32 48 23 Cambodia 158 157 .. 68 .. 71 60 63 10 8 80 81 Cameroon 134 117 67 76 66 79 68 69 15 14 42 45 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 110 86 52 58 39 48 51 41 24 24 45 45 Chad 131 98 43 .. 22 .. .. .. 22 24 64 65 Chile 101 98 .. 96 .. 97 .. .. 3 2 86 100 China 94 98 .. .. .. .. .. .. 0b 0b .. .. Hong Kong SAR, China 117 124 .. 100 .. 100 100 100 1 1 100 100 Colombia 118 114 53 82 59 89 82 89 2 2 100 100 Congo, Dem. Rep. 119 106 66 78 55 77 78 73 15 16 83 76 Congo, Rep. 115 112 66 75 68 79 71 71 21 19 65 62 Costa Rica 98 96 70 95 73 97 93 96 6 4 97 91 Côte d’Ivoire 77 67 68 66 61 66 62 59 19 19 47 45 Croatia 95 94 .. .. .. .. 97 99 0b 0b 100 99 Cuba 100 102 .. 96 .. 96 96 95 1 0b 99 98 Czech Republic 109 107 .. 99 .. 99 99 99 1 1 99 99 Denmark 98 99 98 100 99 99 99 99 0 0 95 98 Dominican Republic 109 90 .. .. .. .. .. .. 9 5 88 92 Ecuador 119 124 .. 80 .. 83 79 82 6 5 81 77 Egypt, Arab Rep. 98 96 .. .. .. .. .. .. 4 2 .. .. El Salvador 123 119 54 78 57 82 74 78 7 5 92 92 Eritrea 45 39 .. 74 .. 72 74 72 14 13 85 81 Estonia 102 102 .. 99 .. 98 99 98 1 0b 97 99 Ethiopia 158 141 .. 43 .. 49 35 41 6 5 84 87 Finland 100 98 96 99 97 100 99 100 1 0b 100 100 France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. 47 .. 46 .. .. .. .. .. .. .. Gambia, The 91 96 59 71 53 72 68 72 6 5 83 83 Georgia 107 112 .. 96 .. 95 95 94 0b 0b 99 99 Germany 100 99 .. .. .. .. 95 96 1 1 99 99 Ghana 109 111 72 80 65 78 75 71 7 6 91 92 Greece 102 103 .. 98 .. 97 98 97 1 1 .. .. Guatemala 123 121 .. 71 .. 70 65 64 13 11 93 90 Guinea 106 96 43 72 35 64 68 57 15 16 50 40 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. 47 .. 46 .. .. .. .. .. .. .. Honduras 126 122 50 75 43 80 74 79 6 5 82 86 84 2011 World Development Indicators 2.13 PEOPLE Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in first grade of survival rate primary education secondary education primary education % of grade 1 students % of relevant Reaching Reaching last grade of % of age group grade 5 primary education enrollment % Male Female Male Female Male Female Male Female Male Female 2009a 2009a 1991 2008a 1991 2008a 2008a 2008a 2009a 2009a 2008a 2008a Hungary 103 103 .. .. .. .. 99 99 2 1 99 99 India 132 124 .. 67 .. 70 67 70 3 3 81 81 Indonesia 125 122 .. 83 .. 89 77 83 4 3 91 93 Iran, Islamic Rep. 100 100 75 94 67 94 94 95 2 2 96 97 Iraq 105 103 75 .. 70 .. .. .. 19 14 .. .. Ireland 99 101 .. 98 .. 100 .. .. 1 1 .. .. Israel 96 98 .. 100 .. 98 99 98 2 1 71 70 Italy 102 101 .. 99 .. 100 99 100 0b 0b 100 100 Jamaica 90 86 92 .. 94 .. .. .. 3 3 .. .. Japan 102 102 100 100 100 100 100 100 0 0 .. .. Jordan 99 99 93 .. 89 .. .. .. 1 1 99 98 Kazakhstan 105 106 .. .. .. .. 98 c 99c 0b 0b 100 c 100 c Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 106 104 92 98 92 99 98 99 0b 0b 100 100 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 95 93 .. 95 .. 96 95 96 1 1 99 100 Kyrgyz Republic 97 97 .. .. .. .. 96 97 0b 0b 99 100 Lao PDR 124 115 34 66 32 68 66 68 15d 13d 80 77 Latvia 104 105 .. 98 .. 94 97 94 5 2 92 97 Lebanon 100 105 .. 94 .. 96 90 93 11 7 84 89 Lesotho 106 98 53 56 77 69 38 56 23 16 68 66 Liberia 117 107 .. 64 .. 56 49 43 6 7 64 60 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 97 94 .. .. .. .. 98 98 1 1 99 99 Macedonia, FYR 92 93 .. .. .. .. 98 97 0b 0b 99 100 Madagascar 198 196 31 48 31 50 48 50 21 20 57 55 Malawi 136 144 37 51 33 50 42 42 19 18 75 74 Malaysia 89 89 86 96 87 97 96 96 .. .. 100 99 Mali 102 89 48 88 42 85 81 77 13 14 72 68 Mauritania 112 119 52 48 47 51 40 42 2 2 38 31 Mauritius 99 99 .. 96 .. 99 94 98 4 3 64 75 Mexico 122 122 81 93 82 95 90 93 4 3 94 93 Moldova 94 93 .. .. .. .. 95 96 0b 0b 99 98 Mongolia 147 142 .. 94 .. 95 94 95 0b 0b 96 99 Morocco 107 106 70 84 64 85 78 78 13 9 80 78 Mozambique 163 156 42 56c 34 51c 37c 34 c 7 7 52c 55c Myanmar 140 135 .. 70 .. 69 70 69 0b 0b 74 73 Namibia 98 99 52 90 57 93 80 85 18 14 80 83 Nepal .. .. 44 60 32 64 60 64 17 17 81 81 Netherlands 101 101 .. 99 .. 100 .. .. .. .. .. .. New Zealand .. .. 96 .. 95 .. .. .. .. .. .. .. Nicaragua 158 148 39 48 48 55 45 52 13 9 .. .. Niger 97 83 68 66c 65 62c 63c 60 c 5 5 56c 62c Nigeria 102 83 .. .. .. .. .. .. .. .. 44 44 Norway 97 99 99 99 100 100 99 99 .. .. 100 100 Oman 88 86 77 .. 78 .. .. .. 1 2 .. .. Pakistan 111 96 .. 61 .. 60 61 60 3 3 73 72 Panama 105 103 .. 88 .. 91 86 88 6 4 96 97 Papua New Guinea .. .. 55 .. 52 .. .. .. .. .. .. .. Paraguay 101 97 58 82 60 85 77 81 5 3 88 89 Peru 100 100 .. 87 .. 88 82 84 7 7 94 93 Philippines 139 130 .. 75 .. 82 71 80 3 2 100 98 Poland .. .. .. .. .. .. .. .. 2 1 .. .. Portugal 107 103 .. .. .. .. .. .. .. .. .. .. Puerto Rico 97 94 .. .. .. .. .. .. .. .. .. .. Qatar 103 108 98 92 99 99 91 97 0b 0b 100 100 2011 World Development Indicators 85 2.13 Education efficiency Gross intake ratio Cohort Repeaters in Transition rate to in first grade of survival rate primary education secondary education primary education % of grade 1 students % of relevant Reaching Reaching last grade of % of age group grade 5 primary education enrollment % Male Female Male Female Male Female Male Female Male Female 2009a 2009a 1991 2008a 1991 2008a 2008a 2008a 2009a 2009a 2008a 2008a Romania 101 99 .. .. .. .. 93 94 2 1 97 97 Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 194 189 49 46 51 51 .. .. 15 14 .. .. Saudi Arabia 102 101 80 99 76 93 98 91 4 4 93 100 Senegal 96 102 78 69 68 71 56 59 8 7 62 57 Serbia 95 94 .. .. .. .. 99 97 1 1 100 99 Sierra Leone 201 182 .. .. .. .. .. .. 10 10 .. .. Singapore .. .. .. 99 .. 99 99 99 0b 0b 86 92 Slovak Republic 100 99 .. .. .. .. 97 98 3 3 97 97 Slovenia 97 97 .. .. .. .. .. .. 1 0b .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 92 87 61 .. 67 .. .. .. 8 8 90 91 Spain 105 106 .. 99 .. 100 99 100 3 2 .. .. Sri Lanka 92 93 97 88 98 89 88 89 1 1 95 97 Sudan 86 76 .. 89 .. 100 86 98 4 4 90 98 Swaziland 105 101 58 75 64 86 70 74 21 15 .. .. Sweden 104 103 99 100 99 100 100 100 0 0 100 100 Switzerland 93 96 72 .. 72 .. .. .. 2 1 99 100 Syrian Arab Republic 117 113 87 .. 85 .. 93 94 9 7 94 96 Tajikistan 106 101 .. .. .. .. .. .. 0b 0b 98 98 Tanzania 99 100 69 79 71 83 71 77 2 2 40 32 Thailand .. .. .. .. .. .. .. .. 12 6 85 89 Timor-Leste 142 134 .. 72 .. 80 68 78 21 18 86 88 Togo 105 102 55 80 38 71 76 62 23 22 66 58 Trinidad and Tobago 102 100 98 97 99 95 93 93 7 5 86 92 Tunisia 106 107 76 96 70 96 94 95 10 6 79 86 Turkey 101 98 93 94 92 94 94 94 2 2 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 140 143 .. 57 .. 58 54 53 14 14 58 55 Ukraine 100 100 .. .. .. .. 96 98 0b 0b 100 100 United Arab Emirates 113 113 78 97 80 97 97 97 2 2 98 99 United Kingdom .. .. .. .. .. .. .. .. 0 0 .. .. United States 103 109 .. .. .. .. .. .. 0 0 .. .. Uruguay 101 111 98 93 100 96 93 96 8 5 81 93 Uzbekistan 94 91 .. .. .. .. 98 99 0b 0b 100 99 Venezuela, RB 101 98 69 92 80 96 89 95 4 3 97 97 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 77 77 .. .. .. .. 99 97 0 0 97 97 Yemen, Rep. 110 98 .. .. .. .. .. .. 6 5 .. .. Zambia 116 119 .. 71 .. 70 55 52 6 6 66 67 Zimbabwe .. .. 70 .. 72 .. .. .. .. .. .. .. World 114 w 110 w .. w .. w .. w .. w .. w .. w 5w 4w .. w .. w Low income 133 126 .. .. .. .. .. .. 11 11 .. ..  Middle income 114 110 .. .. .. .. .. .. 4 3 .. .. Lower middle income 115 110 .. .. .. .. .. .. 4 3 .. .. Upper middle income .. .. .. .. .. .. .. .. .. .. .. .. Low & middle income 115 111 .. .. .. .. .. .. 5 4 .. .. East Asia & Pacific 105 107 .. .. .. .. .. .. 1 1 .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. .. .. .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 104 101 .. .. .. .. .. .. 9 5 .. .. South Asia 126 117 .. 68 .. 70 68 70 4 4 80 80 Sub-Saharan Africa 121 113 .. .. .. .. .. .. 10 10 66 65 High income 102 104 .. .. .. .. .. .. 1 1 .. .. Euro area 102 101 .. .. .. .. 98 99 2 1 .. .. a. Provisional data. b. Less than 0.5. c. Data are for 2009. d. Data are for 2010. 86 2011 World Development Indicators 2.13 PEOPLE Education efficiency About the data Definitions The United Nations Educational, Scientific, and Cul- data on repeaters by grade for the most recent of • Gross intake ratio in first grade of primary edu- tural Organization (UNESCO) Institute for Statistics those two years to reflect current patterns of grade cation is the number of new entrants in grade 1, calculates indicators of students’ progress through transition. Rates approaching 100 percent indicate regardless of age, expressed as a percentage of the school. These indicators measure an education sys- high retention and low dropout levels. population of the official school age. • Cohort sur- tem’s success in reaching students, efficiently mov- Data on repeaters are often used to indicate an vival rate is the percentage of children enrolled in ing students from one grade to the next, and trans- education system’s internal efficiency. Repeaters not the first grade of primary education who eventually mitting knowledge at a particular level of education. only increase the cost of education for the family reach grade 5 or the last grade of primary education. The gross intake ratio to the first grade of primary and the school system, but also use limited school The estimate is based on the reconstructed cohort education indicates the level of access to primary resources. Country policies on repetition and promo- method (see About the data). • Repeaters in primary education and the education system’s capacity to tion differ. In some cases the number of repeaters education are the number of students enrolled in the provide access to primary education. A low gross is controlled because of limited capacity. In other same grade as in the previous year as a percentage intake ratio in grade 1 reflects the fact that many chil- cases the number of repeaters is almost 0 because of all students enrolled in primary school. • Transi- dren do not enter primary school even though school of automatic promotion—suggesting a system that tion rate to secondary education is the number of attendance, at least through the primary level, is is highly efficient but that may not be endowing stu- new entrants to the first grade of secondary edu- mandatory in most countries. Because the gross dents with enough cognitive skills. cation (general programs only) in a given year as a intake ratio includes all new entrants regardless of The transition rate from primary to secondary percentage of the number of pupils enrolled in the age, it can exceed 100 percent in some situations, school conveys the degree of access or transition final grade of primary education in the previous year. such as immediately after fees have been abolished between the two levels. As completing primary edu- or when the number of reenrolled children is large. cation is a prerequisite for participating in lower The indicator is not calculated when new entrants secondary school, growing numbers of primary and repeaters are not correctly distinguished in completers will inevitably create pressure for more grade 1. available places at the secondary level. A low transi- The survival rate to grade 5 and to the last grade tion rate can signal such problems as an inadequate of primary education shows the percentage of stu- examination and promotion system or insufficient dents entering primary school who are expected to secondary school capacity. The quality of data on reach the specified grade. It measures an education the transition rate is affected when new entrants and system’s holding power and internal efficiency. Sur- repeaters are not correctly distinguished in the first vival rates are calculated based on the reconstructed grade of secondary school. Students who interrupt cohort method, which uses data on enrollment by their studies after completing primary school could grade for the two most recent consecutive years and also affect data quality. In 2006 the UNESCO Institute for Statistics There are more overage children changed its convention for citing the reference year. among the poor in primary For more information, see About the data for table school in Zambia 2.13a 2.11. Percent of total Overage children enrollment Underage children On-time Children 100 75 50 25 0 Poorest Middle Richest wealth wealth Data sources quintile quintile Data on education efficiency are from the UNESCO Source: World Bank, EdStats. Institute for Statistics, www.uis.unesco.org. 2011 World Development Indicators 87 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy PISA rate rate rate mathematics literacy % ages 15 % of relevant age group % ages 15–24 and older Mean Total Male Female Male Female Total score 1991 2009a 1991 2009a 1991 2009a 1990 2005–09b 1990 2005–09b 2005–09b 2009 Afghanistan 28 .. 41 .. 14 .. .. .. .. .. .. .. Albania .. 90 .. 90 .. 89 .. 99 .. 99 96 377 Algeria 80 91 86 90 73 91 86 94 62 89 73 .. Angola 33 .. .. .. .. .. .. 81 .. 66 70 .. Argentina 100 102 .. 100 .. 104 .. 99 .. 99 98 388 Armenia 105 98 .. 96 .. 100 100 100 100 100 100 .. Australia .. .. .. .. .. .. .. .. .. .. .. 514 Austria .. 99 .. 99 .. 98 .. .. .. .. .. 496 Azerbaijan 95 92 96 92 94 91 .. 100 .. 100 100 431 Bangladesh 41 61 .. 58 .. 63 .. 74 .. 77 56 .. Belarus 94 96 95 93 95 92 100 100 100 100 100 .. Belgium 79 86 76 84 82 88 .. .. .. .. .. 515 Benin 22 62 30 71 14 53 .. 65 .. 43 42 .. Bolivia 71 99 78 99 64 98 .. 99 .. 99 91 .. Bosnia and Herzegovina .. .. .. .. .. .. .. 100 .. 100 98 .. Botswana 90 95 83 93 98 97 .. 94 .. 97 84 .. Brazil 93 .. .. .. .. .. .. 97 .. 99 90 386 Bulgaria 90 90 88 91 92 89 .. 98 .. 97 98 428 Burkina Faso 20 43 25 46 15 40 .. 47 .. 33 29 .. Burundi 46 52 49 54 43 51 59 77 48 76 67 .. Cambodia 45 83 .. 83 .. 84 .. 89 .. 86 78 .. Cameroon 53 73 57 80 49 67 .. 89 .. 77 71 .. Canada .. .. .. .. .. .. .. .. .. .. .. 527 Central African Republic 28 38 37 47 20 29 63 72 35 57 55 .. Chad 18 33 29 42 7 24 26 54 9 39 34 .. Chile .. 95 .. 101 .. 88 .. 99 .. 99 99 421 China 107 .. .. .. .. .. 97 99 91 99 94 .. Hong Kong SAR, China 102 93 .. 92 .. 93 .. .. .. .. .. 555 Colombia 73 115 70 113 76 117 .. 97 .. 98 93 381 Congo, Dem. Rep. 48 56 61 66 36 46 .. 73 .. 62 67 .. Congo, Rep. 54 74 59 77 49 72 .. 87 .. 78 .. .. Costa Rica 79 96 77 95 81 97 .. 98 .. 99 96 .. Côte d’Ivoire 42 46 53 54 32 39 60 72 38 61 55 .. Croatia 85 100 .. 99 .. 100 .. 100 .. 100 99 460 Cuba 99 98 .. 98 .. 98 .. 100 .. 100 100 .. Czech Republic 92 95 91 95 93 95 .. .. .. .. .. 493 Denmark 98 101 98 100 98 101 .. .. .. .. .. 503 Dominican Republic 61 90 .. 90 .. 89 .. 95 .. 97 88 .. Ecuador 91 103 91 101 92 104 97 97 96 97 84 .. Egypt, Arab Rep. .. 95 .. 97 .. 93 71 88 54 82 66 .. El Salvador 65 89 64 88 66 91 .. 95 .. 95 84 .. Eritrea 18 48 21 52 15 43 .. 92 .. 86 67 .. Estonia .. 100 .. 100 .. 101 100 100 100 100 100 512 Ethiopia 23 55 28 57 18 53 .. 56 .. 33 30 .. Finland 97 98 98 99 97 97 .. .. .. .. .. 541 France 106 .. .. .. .. .. .. .. .. .. .. 497 Gabon 62 .. 59 .. 65 .. .. 99 .. 97 88 .. Gambia, The 45 79 56 76 34 83 .. 71 .. 60 46 .. Georgia .. 107 .. 110 .. 104 .. 100 .. 100 100 .. Germany 100 104 99 103 100 104 .. .. .. .. .. 513 Ghana 64 83 71 85 56 81 .. 81 .. 79 67 .. Greece 99 101 99 102 98 101 .. 99 .. 99 97 466 Guatemala .. 80 .. 83 .. 77 .. 89 .. 84 74 .. Guinea 17 62 24 71 9 53 .. 68 .. 54 39 .. Guinea-Bissau 5 .. 7 .. 3 .. .. 78 .. 64 52 .. Haiti 27 .. 29 .. 26 .. .. .. .. .. 49 .. Honduras 64 90 67 87 61 93 .. 93 .. 95 84 .. 88 2011 World Development Indicators 2.14 PEOPLE Education completion and outcomes Primary completion Youth literacy Adult literacy PISA rate rate rate mathematics literacy % ages 15 % of relevant age group % ages 15–24 and older Mean Total Male Female Male Female Total score 1991 2009a 1991 2009a 1991 2009a 1990 2005–09b 1990 2005–09b 2005–09b 2009 Hungary 82 95 89 97 90 94 .. 99 .. 99 99 490 India 64 95 76 95 52 94 .. 88 .. 74 63 .. Indonesia 93 109 .. 109 .. 110 97 100 95 99 92 371 Iran, Islamic Rep. 88 101 93 101 82 101 85 99 66 99 85 .. Iraq 58 64 63 73 52 54 .. 85 .. 80 78 .. Ireland 103 99 103 99 103 99 .. .. .. .. .. 487 Israel .. 99 .. 99 .. 100 .. .. .. .. .. 447 Italy 98 104 98 104 97 104 .. 100 .. 100 99 483 Jamaica 94 89 90 88 98 90 .. 92 .. 98 86 .. Japan 102 101 102 100 102 101 .. .. .. .. .. 529 Jordan 101 100 101 99 101 100 .. 99 .. 99 92 387 Kazakhstan 103 106 103 106 103 106 100 100 100 100 100 405 Kenya .. .. .. .. .. .. .. 92 .. 94 87 .. Korea, Dem. Rep. .. .. .. .. .. .. .. 100 .. 100 100 .. Korea, Rep. 99 99 99 100 100 97 .. .. .. .. .. 546 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 57 93 58 94 56 93 91 99 84 99 94 .. Kyrgyz Republic .. 94 .. 94 .. 95 .. 100 .. 100 99 331 Lao PDR 41 75 46 78 36 71 .. 89 .. 79 73 .. Latvia .. 95 .. 97 .. 93 100 100 100 100 100 482 Lebanon .. 85 .. 83 .. 87 .. 98 .. 99 90 .. Lesotho 59 70 42 60 76 81 .. 86 .. 98 90 .. Liberia .. 58 .. 63 .. 53 .. 70 .. 81 59 .. Libya .. .. .. .. .. .. .. 100 .. 100 89 .. Lithuania .. 92 .. 92 .. 92 100 100 100 100 100 477 Macedonia, FYR 98 92 .. 91 .. 93 .. 99 .. 99 97 .. Madagascar 36 79 35 79 37 79 .. 66 .. 64 64 .. Malawi 31 59 35 58 27 60 70 87 49 86 74 .. Malaysia 91 97 91 97 91 97 .. 98 .. 99 92 .. Mali 9 59 12 67 7 52 .. 47 .. 31 26 .. Mauritania 33 64 39 63 26 66 .. 71 .. 64 57 .. Mauritius 115 89 115 89 115 90 91 96 92 98 88 .. Mexico 88 104 91 104 92 105 96 99 95 98 93 419 Moldova .. 93 .. 94 .. 91 100 99 100 100 98 .. Mongolia .. 93 .. 94 .. 92 .. 95 .. 97 97 .. Morocco 48 80 57 84 39 77 .. 87 .. 72 56 .. Mozambique 26 57 32 63 21 51 .. 78 .. 64 55 .. Myanmar .. 99 .. 98 .. 100 .. 96 .. 95 92 .. Namibia 74 87 67 83 81 91 .. 91 .. 95 89 .. Nepal 51 .. 70 .. 41 .. .. 87 .. 77 59 .. Netherlands .. .. .. .. .. .. .. .. .. .. .. 526 New Zealand .. .. .. .. .. .. .. .. .. .. .. 519 Nicaragua 42 75 43 71 53 78 .. 85 .. 89 78 .. Niger 17 40 21 47 13 34 .. 52 .. 23 29 .. Nigeria .. 79 .. 84 .. 74 .. 78 .. 65 61 .. Norway 100 98 100 98 100 97 .. .. .. .. .. 498 Oman 74 80 78 80 70 79 .. 98 .. 98 87 .. Pakistan .. 61 .. 68 .. 54 .. 79 .. 61 56 .. Panama 86 102 86 102 86 101 95 97 95 96 94 360 Papua New Guinea 46 .. 51 .. 42 .. .. 65 .. 70 60 .. Paraguay 68 94 68 93 69 95 .. 99 .. 99 95 .. Peru .. 101 .. 101 .. 101 .. 98 .. 97 90 365 Philippines 88 94 85 91 86 97 96 97 97 98 95 .. Poland 96 96 .. .. .. .. .. 100 .. 100 100 495 Portugal .. .. .. .. .. .. .. 100 .. 100 95 487 Puerto Rico .. .. .. .. .. .. 92 87 94 88 90 .. Qatar 71 108 71 109 72 106 89 98 91 98 95 368 2011 World Development Indicators 89 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy PISA rate rate rate mathematics literacy % ages 15 % of relevant age group % ages 15–24 and older Mean Total Male Female Male Female Total score 1991 2009a 1991 2009a 1991 2009a 1990 2005–09b 1990 2005–09b 2005–09b 2009 Romania 96 96 96 96 96 96 .. 97 .. 98 98 427 Russian Federation 92 95 92 .. 93 .. 100 100 100 100 100 468 Rwanda 50 54 51 52 50 56 .. 77 .. 77 71 .. Saudi Arabia .. 93 .. 95 .. 90 .. 99 .. 97 86 .. Senegal 39 57 48 56 31 57 49 74 28 56 50 .. Serbia .. 96 .. 97 .. 96 .. .. .. .. .. 442 Sierra Leone .. 88 .. 101 .. 75 .. 68 .. 48 41 .. Singapore .. .. .. .. .. .. 99 100 99 100 95 562 Slovak Republic 95 96 95 96 96 96 .. .. .. .. .. 497 Slovenia 95 96 .. 97 .. 96 .. 100 .. 100 100 501 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 76 93 72 93 80 94 .. 97 .. 98 89 .. Spain 104 100 104 100 103 100 .. 100 .. 100 98 483 Sri Lanka 101 97 101 97 101 98 .. 97 .. 99 91 .. Sudan .. 57 .. 53 .. 47 .. 89 .. 83 70 .. Swaziland 61 72 57 75 64 69 83 92 84 95 87 .. Sweden 96 94 96 95 96 94 .. .. .. .. .. 494 Switzerland 53 94 53 93 54 95 .. .. .. .. .. 534 Syrian Arab Republic 89 112 94 113 84 111 .. 96 .. 93 84 .. Tajikistan .. 98 .. 97 .. 93 100 100 100 100 100 .. Tanzania 55 102 56 102 55 102 86 78 78 76 73 .. Thailand .. .. .. .. .. .. .. 98 .. 98 94 419 Timor-Leste .. 80 .. 80 .. 79 .. .. .. .. 51 .. Togo 35 61 48 71 22 52 .. 85 .. 68 57 .. Trinidad and Tobago 102 93 99 93 105 93 99 100 99 100 99 414 Tunisia 74 93 79 93 70 93 .. 98 .. 96 78 371 Turkey 90 93 93 95 86 92 97 99 88 97 91 445 Turkmenistan .. .. .. .. .. .. .. 100 .. 100 100 .. Uganda .. 72 .. 72 .. 73 .. 90 c .. 85c 73c .. Ukraine 92 95 99 98 99 99 .. 100 .. 100 100 .. United Arab Emirates 103 99 104 100 103 98 81 94 85 97 90 .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. 492 United States .. 95 .. 94 .. 97 .. .. .. .. .. 487 Uruguay 94 106 91 104 96 108 98 98 99 100 98 427 Uzbekistan 80 92 .. 93 .. 91 .. 100 .. 100 99 .. Venezuela, RB 81 95 76 94 86 96 95 98 96 99 95 .. Vietnam .. .. .. .. .. .. 94 97 93 96 93 .. West Bank and Gaza .. 82 .. 82 .. 81 .. 99 .. 99 95 .. Yemen, Rep. .. 61 .. 72 .. 49 .. 96 .. 72 62 .. Zambia .. 87 .. 92 .. 82 67 82 66 67 71 .. Zimbabwe 97 .. 99 .. 96 .. .. 98 .. 99 92 .. World 79 w 88 w 86 w 90 w 75 w 87 w 87 w 92 w 78 w 87 w 84 w   Low income 44 63 .. 66 .. 60 66 76 52 69 62   Middle income 83 92 89 93 77 91 88 94 78 88 83   Lower middle income 82 90 89 92 74 89 87 93 74 86 80   Upper middle income 88 100 89 100 88 100 94 98 92 97 92   Low & middle income 78 87 85 89 73 85 86 91 75 85 80   East Asia & Pacific 101 99 105 98 97 100 96 99 91 99 94   Europe & Central Asia 92 96 93 97 92 95 99 99 98 99 98   Latin America & Carib. 84 101 84 100 85 102 91 97 92 97 91   Middle East & N. Africa .. 95 .. 97 .. 92 84 93 67 87 74   South Asia 62 79 75 82 52 76 71 85 47 72 61   Sub-Saharan Africa 51 64 57 69 47 60 73 77 58 67 62   High income .. 98 .. 98 .. 98 99 99 99 99 98   Euro area 101 .. 100 .. 100 .. .. .. .. .. ..   a. Provisional data. b. Data are for the most recent year available. c. Data are for 2010. 90 2011 World Development Indicators 2.14 PEOPLE Education completion and outcomes About the data Definitions Many governments publish statistics that indicate Many countries estimate the number of literate • Primary completion rate is approximated by the how their education systems are working and devel- people from self-reported data. Some use educa- gross intake ratio to last grade of primary educa- oping—statistics on enrollment and such efficiency tional attainment data as a proxy but apply different tion, which is the total number of new entrants in indicators as repetition rates, pupil–teacher ratios, lengths of school attendance or levels of completion. the last grade of primary education, regardless of and cohort progression. The World Bank and the Because definitions and methodologies of data col- age, expressed as a percentage of the population United Nations Educational, Scientific, and Cultural lection differ across countries, data should be used at the entrance age to the last grade of primary. Organization (UNESCO) Institute for Statistics jointly cautiously. • Youth literacy rate is the percentage of the popula- developed the primary completion rate indicator. The reported literacy data are compiled by the tion ages 15–24 that can, with understanding, both Increasingly used as a core indicator of an educa- UNESCO Institute for Statistics based on national cen- read and write a short simple statement on their tion system’s performance, it reflects an education suses and household surveys during 1985–2009. For everyday life. • Adult literacy rate is the percentage system’s coverage and the educational attainment countries without recent literacy data, the UNESCO of the population ages 15 and older that can, with of students. The indicator is a key measure of edu- Institute for Statistics estimates literacy rates with understanding, both read and write a short simple cation outcome at the primary level and of progress the Global Age-specific Literacy Projections Model statement on their everyday life. • PISA mathemat- toward the Millennium Development Goals and the (GALP). For detailed information on sources, defini- ics literacy is the country’s mean mathematics Education for All initiative. However, a high primary tions, and methodology, consult www.uis.unesco.org. score from the Programme for International Student completion rate does not necessarily mean high lev- Literacy statistics for most countries cover the Assessment (PISA). els of student learning. population ages 15 and older, but some include The primary completion rate reflects the primary younger ages or are confined to age ranges that tend cycle as defined by the International Standard Classi- to inflate literacy rates. The youth literacy rate for fication of Education (ISCED 97), ranging from three or ages 15–24 reflects recent progress in education: it four years of primary education (in a very small num- measures the accumulated outcomes of primary edu- ber of countries) to five or six years (in most coun- cation over the previous 10 years or so by indicating tries) and seven (in a small number of countries). the proportion of people who have passed through The table shows the primary completion rate, also the primary education system and acquired basic called the gross intake ratio to last grade of primary literacy and numeracy skills. Generally, literacy also education. It is the total number of new entrants in encompasses numeracy, the ability to make simple the last grade of primary education, regardless of age, arithmetic calculations. expressed as a percentage of the population at the In many countries national assessments enable entrance age to the last grade of primary education. ministries of education to monitor progress in learn- Data limitations preclude adjusting for students who ing outcomes. Of the handful of internationally or drop out during the final year of primary education. regionally comparable assessments, one of the Thus, this rate is a proxy that should be taken as an largest is the Programme for International Student upper estimate of the actual primary completion rate. Assessment (PISA). Coordinated by the Organisation There are many reasons why the primary comple- for Economic Co-operation and Development (OECD), tion rate can exceed 100 percent. The numerator it measures the knowledge and skills of 15-year-olds, may include late entrants and overage children the age at which students in most countries are near- who have repeated one or more grades of primary ing the end of their compulsory time in school. The education as well as children who entered school assessment tests reading, mathematical, and sci- early, while the denominator is the number of chil- entific literacy in terms of general competencies— dren at the entrance age to the last grade of primary that is, how well students can apply the knowledge education. and skills they have learned at school to real-life Basic student outcomes include achievements in challenges. It does not test how well a student has reading and mathematics judged against established mastered a school’s specific curriculum. standards. The UNESCO Institute for Statistics has The table presents the mean PISA mathematical established literacy as an outcome indicator based literacy score, as demonstrated through students’ on an internationally agreed definition. The literacy ability to analyze, reason, and communicate effec- rate is the percentage of the population who can, tively while posing, solving, and interpreting math- with understanding, both read and write a short, ematical problems that involve quantitative, spatial, Data sources simple statement about their everyday life. In prac- probabilistic, or other mathematical concepts. The Data on education completion and outcomes are tice, literacy is diffi cult to measure. To estimate average score in 2009 was 496. Because the figures from the UNESCO Institute for Statistics. Data literacy using such a definition requires census or are derived from samples, the scores reflect a small on PISA mathematics literacy are from the OECD. survey measurements under controlled conditions. measure of statistical uncertainty. 2011 World Development Indicators 91 2.15 Education gaps by income and gender Survey Gross intake Gross primary Average years Primary Children year rate in grade 1 participation rate of schooling completion rate out of school % of relevant % of relevant % of relevant % of relevant age group age group Ages 15–19 age group age group Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile Male Female quintile quintile Armenia 2005 93 80 106 102 9 10 119 116 113 112 2 1 Azerbaijan 2006 92 118 100 108 9 11 94 109 103 105 20 11 Bangladesh 2006 144 147 96 105 8 13 65 97 83 86 12 6 Belize 2006 80 89 106 113 8 11 59 130 107 72 5 7 Benin 2006 67 107 61 114 6 8 31 95 67 52 57 12 Bolivia 2003 92 95 108 129 6 9 76 98 90 81 22 5 Burundi 2005 201 191 91 144 4 7 20 70 44 39 5 3 Cambodia 2005 208 151 113 134 5 8 42 121 88 85 37 13 Cameroon 2006 108 75 93 116 6 14 43 111 90 74 3 2 Colombia 2005 161 84 127 99 6 10 94 109 100 103 11 2 Côte d’Ivoire 2006 51 77 57 110 5 8 47 127 88 71 4 3 Dominican Republic 2007 130 112 113 107 7 11 69 109 88 106 12 4 Egypt, Arab Rep. 2005 107 97 95 99 9 12 84 92 92 88 12 1 Ethiopia 2005 86 124 47 112 3 6 14 90 46 33 74 30 Georgia 2006 90 104 101 103 15 14 102 102 106 104 2 1 Ghana 2006 107 121 81 117 5 8 62 88 93 86 22 12 Guatemala 2000 176 124 81 114 4 8 15 80 34 36 7 3 Guinea 2005 55 119 52 121 5 7 32 93 76 48 60 16 Guinea-Bissau 2006 135 184 94 166 4 7 34 125 80 54 12 11 Guyana 2006 74 76 105 101 10 10 109 118 91 112 2 1 Haiti 2005 177 188 87 159 4 7 31 136 73 82 69 24 Kazakhstan 2006 118 101 106 103 9 9 102 115 102 97 0 1 Kenya 2003 134 125 92 106 6 9 40 76 71 72 38 11 Kosovo 2000 104 119 95 104 9 11 82 94 98 83 1 4 Lesotho 2004 169 111 116 124 5 8 36 122 69 85 18 3 Macedonia, FYR 2005 102 190 89 97 8 10 120 119 133 78 0 0 Madagascar 2003/04 250 153 118 145 3 8 42 141 77 77 33 3 Malawi 2004 235 145 98 122 5 8 24 81 47 35 23 4 Malawi 2006 234 207 133 169 5 7 30 80 49 52 0 0 Mali 2006 41 98 46 110 5 8 36 79 55 41 67 20 Mauritania 2007 67 96 62 116 5 9 17 89 48 52 2 2 Moldova 2005 96 84 99 95 9 12 97 100 96 98 2 1 Mozambique 2003 128 143 75 143 3 6 13 100 57 43 46 7 Namibia 2006 112 104 118 109 7 10 81 109 94 90 11 2 Nepal 2001 184 141 109 139 5 8 49 96 69 62 33 6 Nicaragua 2001 149 106 85 105 4 9 34 124 78 83 40 4 Niger 2006 50 90 35 89 4 7 31 71 60 30 74 28 Nigeria 2003 78 101 70 108 7 10 48 71 70 54 52 6 Panama 2003 125 116 108 102 7 11 100 94 105 88 1 1 Peru 2004 121 90 118 96 7 11 106 99 100 97 6 1 Rwanda 2005 274 195 131 151 3 5 31 88 48 42 13 8 Serbia 2005 90 98 98 100 9 10 86 96 94 89 1 0 Somalia 2005 13 44 8 93 8 10 2 58 26 20 87 46 Swaziland 2006 147 117 117 114 6 9 69 110 85 98 17 4 Syrian Arab Republic 2006 110 149 102 107 7 8 92 93 93 92 0 0 Tanzania 2004 123 123 82 119 5 7 32 108 58 60 44 15 Togo 2006 115 148 99 128 6 7 40 82 67 56 1 1 Turkey 2003 108 111 97 97 6 7 95 85 100 81 20 5 Uganda 2006 180 144 107 124 5 8 27 68 50 42 25 7 Vietnam 2006 99 100 108 100 .. .. 99 104 96 103 3 2 Yemen, Rep. 2006 66 109 50 101 7 10 25 103 84 31 2 2 Zambia 2007 135 123 105 112 5 9 50 101 88 73 22 3 Zimbabwe 1999 106 111 144 144 7 10 36 80 51 57 22 8 92 2011 World Development Indicators 2.15 PEOPLE Education gaps by income and gender About the data Definitions The data in the table describe basic information on exclusion. To that extent the index provides only a • Survey year is the year in which the underlying school participation and educational attainment partial view of the multidimensional concepts of pov- data were collected. • Gross intake rate in grade 1 by individuals in different socioeconomic groups erty, inequality, and inequity. is the number of students in the first grade of pri- within countries. The data are from Demographic Creating one index that includes all asset indica- mary education regardless of age as a percentage and Health Surveys (DHS) conducted by Macro tors limits the types of analysis that can be per- of the population of the offi cial primary school International with the support of the U.S. Agency for formed. In particular, the use of a unified index does entrance age. These data may differ from those in International Development, Multiple Indicator Clus- not permit a disaggregated analysis to examine table 2.13. • Gross primary participation rate is ter Surveys (MICS) conducted by the United Nations which asset indicators have a more or less important the ratio of total students attending primary school Children’s Fund (UNICEF), and Living Standards association with education status. In addition, some regardless of age to the population of the age group Measurement Study conducted by the World Bank asset indicators may reflect household wealth better that offi cially corresponds to primary education. Development Economics Research Group. These in some countries than in others—or reflect differ- • Average years of schooling are the years of for- large-scale household sample surveys, conducted ent degrees of wealth in different countries. Taking mal schooling received, on average, by youths and periodically in developing countries, collect infor- such information into account and creating country adults ages 15–19. • Primary completion rate is mation on a large number of health, nutrition, and specific asset indexes with country-specific choices the total number of students regardless of age in the population measures as well as on respondents’ of asset indicators might produce a more effective last grade of primary school, minus the number of social, demographic, and economic characteristics and accurate index for each country. The asset index repeaters in that grade, divided by the total number using detailed questionnaires. The data presented used in the table does not have this flexibility. of children of official graduation age. These data dif- here draw on responses to individual and household The analysis was carried out for around 80 coun- fer from those in table 2.14 because the source is questionnaires. tries. The table only shows the estimates for the different. • Children out of school are the number Typically, those surveys collect basic information poorest and richest quintiles, gender, and latest of children in the official primary school ages who on educational attainment and enrollment levels data; the full set of estimates for all indicators, other are not attending primary or secondary education, from every household member ages 5 or 6 and older subgroups including urban and rural areas, and older expressed as a percentage of children of the official as part of the household’s socioeconomic charac- data are available in the country reports (see Data primary school ages. Children in the official primary teristics. The surveys are not intended for the col- sources). The data in the table differ from data for school age, who are attending pre-primary education, lection of detailed education data. As a result, the similar indicators in preceding tables either because are considered out-of-school. These data differ from education section of the surveys does not replace the indicator refers to a period a few years preceding those in table 2.12 because the source is different. education flows, nor are as detailed as, for instance, the survey date or because the indicator definition the health section for the case of the DHS and MICS. or methodology is different. Findings should be used Still, the education data are very useful for providing with caution because of measurement error inherent micro-level information on education that cannot be in the use of survey data. obtained from administrative data, such as informa- tion on children not attending school. Socioeconomic status as displayed in the table is based on a household’s assets, including ownership of consumer items, features of the household’s dwell- ing, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principal- component analysis which was then used to create Data sources break-points defining wealth quintiles, expressed as quintiles of individuals in the population. Data on education gaps by income and gender are The selection of the asset index for defining socio- from an analysis of Demographic and Health Sur- economic status was based on pragmatic rather than veys by Macro International, Multiple Indicators conceptual considerations: Demographic and Health Cluster surveys by UNICEF, and Living Standards Surveys do not collect consumption data but do have Measurement Study by World Bank, and these detailed information on households’ ownership of sources are analyzed by the EdStats team of the consumer goods and access to a variety of goods World Bank Human Development Network Edu- and services. Like income or consumption, the asset cation using ADePT Education. Country reports, index defines disparities primarily in economic terms. further updates, and ADePT Education software It therefore excludes other possibilities of disparities are available at www.worldbank.org/education/ among groups, such as those based on gender, edu- edstats/. cation, ethnic background, or other facets of social 2011 World Development Indicators 93 2.16 Health systems Health Health workers Hospital Outpatient expenditure beds visits Out of External per 1,000 people Total Public pocket resources Per capita Nurses and per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives people per capita 2008 2008 2008 2008 2008 2008 2004–09a 2004–09a 2004–09a 2000–09a Afghanistan 7.4b 21.5b 77.7b 17.3b 47b 57b 0.2 0.5 0.4 .. Albania 6.8 39.4 58.6 2.1 281 569 1.1 4.0 2.9 1.5 Algeria 5.4 86.1 13.2 0.0 272 437 1.2 2.0 1.7 .. Angola 3.3c 85.0 c 15.0 c 3.0 c 148 c 183c 0.1 1.4 0.8 .. Argentina 8.4 62.6 22.2 0.0 695 1,062 3.2 0.5 4.0 .. Armenia 3.8 44.5 51.8 10.4 143 224 3.7 4.9 4.1 2.8 Australia 8.5d 65.4 d 17.9d 0.0 d 4,180 d 3,365d 3.0 9.6 3.8 6.2 Austria 10.5 73.7 15.1 0.0 5,201 4,150 4.7 7.8 7.7 6.7 Azerbaijan 4.3 19.3 73.3 0.6 240 395 3.8 8.4 7.9 4.6 Bangladesh 3.3 31.4 66.2 5.8 17 44 0.3 0.3 0.4 .. Belarus 5.6 72.2 19.9 0.2 351 688 5.1 12.6 11.2 13.2 Belgium 11.1 66.8 20.5 0.0 5,243 4,096 3.0 0.3 6.6 7.0 Benin 4.1 51.7 44.7 17.7 32 61 0.1 0.8 0.5 .. Bolivia 4.4 63.1 30.1 9.1 75 187 .. .. 1.1 .. Bosnia and Herzegovina 10.3 58.2 41.8 1.3 506 937 1.4 4.7 3.0 3.3 Botswana 7.6 78.2 7.2 4.2 530 1,053 0.3 2.8 1.8 .. Brazil 8.4 44.0 31.9 0.0 721 875 1.7 6.5 2.4 .. Bulgaria 7.1 57.8 36.5 0.0 482 974 3.6 4.7 6.5 .. Burkina Faso 5.9 59.1 38.1 29.2 37 82 0.1 0.7 0.9 .. Burundi 13.0 c 40.0 c 38.1c 34.5c 19c 50 c 0.0 0.2 0.7 .. Cambodia 5.7 23.8 64.4 17.1 43 118 0.2 0.8 0.1 .. Cameroon 5.3c 22.7c 73.5c 5.5c 65c 117c 0.2 1.6 1.5 .. Canada 9.8 69.5 15.5 0.0 4,445 3,867 1.9 10.1 3.4 6.3 Central African Republic 4.3 39.3 57.7 31.5 20 32 0.1 0.4 1.2 .. Chad 6.4 50.6 47.8 5.3 49 86 0.0 0.3 0.4 .. Chile 7.5 44.0 36.5 0.0 762 1,088 1.3 .. 2.1 .. China 4.3 47.3 43.5 0.2 146 265 1.4 1.4 4.1 .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 5.9 83.9 7.9 0.1 317 517 1.4 .. 1.0 .. Congo, Dem. Rep. 7.3 54.2 39.2 18.8 13 23 0.1 0.5 0.8 .. Congo, Rep. 2.7 49.9 50.1 4.7 81 108 0.1 0.8 1.6 .. Costa Rica 9.4 66.9 29.3 0.1 618 1,059 .. .. 1.2 .. Côte d’Ivoire 5.4 16.9 75.6 5.9 61 88 0.1 0.5 0.4 .. Croatia 7.8 84.9 14.5 0.0 1,230 1,553 2.7 5.6 5.5 6.4 Cuba 12.0 95.5 4.1 0.2 672 495 6.4 8.6 5.9 .. Czech Republic 7.1 80.1 15.7 0.0 1,469 1,830 3.6 8.6 7.2 15.0 Denmark 9.9 80.1 13.6 0.0 6,133 3,814 3.4 14.5 3.6 4.1 Dominican Republic 5.7 37.1 41.8 1.6 261 465 .. .. 1.0 .. Ecuador 5.3 42.3 50.4 1.1 216 466 .. .. 1.5 .. Egypt, Arab Rep. 4.8 42.2 56.5 0.6 97 261 2.8 3.5 1.7 .. El Salvador 6.0 59.6 35.8 3.5 217 410 1.6 0.4 1.1 .. Eritrea 3.1c 44.9c 55.1c 60.8 c 10 c 18 c 0.1 0.6 1.2 .. Estonia 6.1 77.8 19.7 1.5 1,074 1,325 3.4 6.8 5.7 6.9 Ethiopia 4.3 51.9 38.5 40.7 14 37 0.0 0.2 0.2 .. Finland 8.8 70.7 18.5 0.0 4,481 3,299 2.7 15.5 6.5 4.3 France 11.2 75.9 7.4 0.0 4,966 3,851 3.5 8.9 7.1 6.9 Gabon 2.6c 43.7c 56.3c 2.3c 264 c 384 c 0.3 5.0 1.3 .. Gambia, The 5.5 48.1 25.1 38.0 27 75 0.0 0.6 1.1 .. Georgia 8.7 30.9 66.5 10.5 258 433 4.5 3.9 3.3 2.2 Germany 10.5 74.6 11.8 0.0 4,720 3,922 3.5 10.8 8.2 7.0 Ghana 7.8 50.0 39.4 14.0 55 114 0.1 1.1 0.9 .. Greece 10.1 60.9 37.0 0.0 3,110 3,010 6.0 3.7 4.8 .. Guatemala 6.5 35.7 57.4 1.8 184 308 .. .. 0.6 .. Guinea 5.5 13.6 85.9 10.1 21 58 0.1 0.0 0.3 .. Guinea-Bissau 6.0 c 26.0 c 40.7c 77.3c 17c 32c 0.0 0.6 1.0 .. Haiti 6.1 22.1 47.4 34.7 40 69 .. .. 1.3 .. Honduras 6.3 58.6 34.5 10.4 121 248 .. .. 0.8 .. 94 2011 World Development Indicators 2.16 PEOPLE Health systems Health Health workers Hospital Outpatient expenditure beds visits Out of External per 1,000 people Total Public pocket resources Per capita Nurses and per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives people per capita 2008 2008 2008 2008 2008 2008 2004–09a 2004–09a 2004–09a 2000–09a Hungary 7.2 68.9 23.9 0.0 1,119 1,506 3.1 6.3 7.0 12.9 India 4.2 32.4 50.3 1.6 45 122 0.6 1.3 0.9 .. Indonesia 2.3 54.4 32.1 1.7 51 91 0.3 2.0 .. .. Iran, Islamic Rep. 5.5 42.4 55.6 0.0 254 613 0.9 1.6 1.4 .. Iraq 3.2c,e 70.2c,e 29.8 c,e 8.2c,e 109c,e 107c,e 0.7 1.4 1.3 .. Ireland 8.7 76.9 14.4 0.0 5,253 3,796 3.2 15.7 5.2 .. Israel 7.6 58.4 30.5 0.0 2,093 2,093 3.6 6.2 5.8 7.1 Italy 8.7 76.3 20.2 0.0 3,343 2,836 4.2 6.5 3.7 6.1 Jamaica 4.8 50.4 35.2 1.5 256 364 .. .. 1.7 .. Japan 8.3 80.5 14.5 0.0 3,190 2,817 2.1 4.1 13.8 14.4 Jordan 9.4f 62.7f 30.8f 1.8f 325f 496f 2.5 4.0 1.8 .. Kazakhstan 3.9 58.5 41.0 0.2 333 444 3.8 7.8 7.6 6.7 Kenya 4.2 36.3 49.2 26.8 33 66 0.1 .. 1.4 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 6.5 53.9 35.0 0.0 1,245 1,806 2.0 5.3 12.3 .. Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 2.0 76.3 21.7 0.0 990 932 1.8 4.6 1.8 .. Kyrgyz Republic 5.7 48.4 45.0 12.6 54 123 2.3 5.7 5.1 3.6 Lao PDR 4.0 17.6 62.6 16.1 34 84 0.3 1.0 1.2 .. Latvia 6.6 60.0 38.7 0.0 979 1,206 3.0 4.8 6.4 5.5 Lebanon 8.5 48.3 40.7 4.8 604 1,009 3.5 2.2 3.5 .. Lesotho 7.6 63.3 25.3 19.3 60 119 .. .. 1.3 .. Liberia 11.9 33.0 35.0 47.0 26 46 0.0 0.3 0.7 .. Libya 3.0 c 70.3c 29.7c 0.1c 458 c 502c 1.9 6.8 3.7 .. Lithuania 6.6 68.3 26.8 1.1 931 1,318 3.7 7.3 6.8 6.6 Macedonia, FYR 6.8 68.2 31.6 1.8 328 738 2.5 4.3 4.6 6.0 Madagascar 4.4 70.2 20.2 16.1 22 46 0.2 0.3 0.3 0.5 Malawi 6.5 59.4 11.6 87.0 18 50 0.0 0.3 1.1 .. Malaysia 4.3 44.1 40.9 0.0 353 621 0.9 2.7 1.8 .. Mali 5.6 47.1 52.6 22.2 39 65 0.0 0.3 0.6 .. Mauritania 2.6c 61.4 c 38.6c 27.4 c 27c 54 c 0.1 0.7 0.4 .. Mauritius 5.5 34.8 57.8 2.0 402 681 1.1 3.7 3.3 .. Mexico 5.9 46.9 49.3 0.0 588 837 2.9 4.0 1.6 2.5 Moldova 10.7g 50.6g 48.3g 4.7g 181g 320 g 2.7 6.7 6.1 6.0 Mongolia 3.8 81.4 14.6 7.5 73 131 2.8 3.5 5.9 .. Morocco 5.3 36.3 55.0 0.2 149 231 0.6 0.9 1.1 .. Mozambique 4.7 75.2 7.0 80.8 21 39 0.0 0.3 0.8 .. Myanmar 2.0 8.8 87.1 10.7 10 23 0.5 0.8 0.6 .. Namibia 6.9 54.6 8.1 21.4 284 440 0.4 2.8 2.7 .. Nepal 6.0 37.7 45.1 11.0 24 66 0.2 0.5 5.0 .. Netherlands 9.9 75.3 5.7 0.0 5,243 4,233 3.9 0.2 4.3 5.4 New Zealand 9.7 80.2 14.0 0.0 2,917 2,655 2.4 10.9 .. 4.4 Nicaragua 9.4 54.6 41.8 10.3 105 251 .. .. 0.9 .. Niger 5.9 57.7 40.7 26.3 21 40 0.0 0.1 0.3 .. Nigeria 5.2c 36.7c 60.4 c 4.6c 73c 113c 0.4 1.6 0.5 .. Norway 8.5 78.6 15.5 0.0 8,019 5,207 4.1 14.8 3.5 .. Oman 2.1 76.4 14.4 0.0 454 593 1.9 4.1 1.9 .. Pakistan 2.6 32.3 53.7 4.8 22 62 0.8 0.6 0.6 .. Panama 7.2 69.3 25.7 0.2 493 924 .. .. 2.2 .. Papua New Guinea 3.2 80.1 8.2 20.6 39 70 0.1 0.5 .. .. Paraguay 6.0 40.1 52.8 1.6 161 281 .. .. 1.3 .. Peru 4.5 59.4 30.6 0.8 200 381 0.9 1.3 1.5 .. Philippines 3.7 34.7 53.9 1.5 68 129 1.2 6.0 0.5 .. Poland 7.0 67.4 22.4 0.0 971 1,271 2.1 5.7 6.6 6.1 Portugal 10.6 67.4 22.1 0.0 2,434 2,578 3.8 5.3 3.4 3.9 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar 2.1 79.8 14.8 0.0 1,775 1,689 2.8 7.4 1.4 .. 2011 World Development Indicators 95 2.16 Health systems Health Health workers Hospital Outpatient expenditure beds visits Out of External per 1,000 people Total Public pocket resources Per capita Nurses and per 1,000 % of GDP % of total % of total % of total $ PPP $ Physicians midwives people per capita 2008 2008 2008 2008 2008 2008 2004–09a 2004–09a 2004–09a 2000–09a Romania 5.4 78.9 17.6 0.0 517 840 1.9 4.2 6.5 5.6 Russian Federation 4.8 64.3 29.1 0.0 568 985 4.3 8.5 9.7 9.0 Rwanda 9.4 47.8 23.2 42.6 45 102 0.0 0.5 1.6 .. Saudi Arabia 3.6 68.2 17.0 0.0 676 831 0.9 2.1 2.2 .. Senegal 5.7 55.4 35.0 11.4 62 102 0.1 0.4 0.3 .. Serbia 10.0 62.5 35.5 0.4 499 867 2.0 4.4 5.4 .. Sierra Leone 13.3 6.5 83.7 17.0 47 104 0.0 0.2 0.4 .. Singapore 3.3 34.1 62.1 0.0 1,404 1,833 1.8 5.9 3.1 .. Slovak Republic 8.0 67.1 24.9 0.0 1,395 1,849 3.0 6.6 6.6 12.5 Slovenia 8.3 68.6 12.8 0.0 2,238 2,420 2.5 8.2 4.7 6.6 Somalia .. .. .. .. .. .. 0.0 0.1 .. .. South Africa 8.2 39.7 17.9 1.2 459 843 0.8 4.1 2.8 .. Spain 9.0 69.7 20.7 0.0 3,132 2,941 3.7 5.2 3.2 9.5 Sri Lanka 4.1 43.7 48.8 1.8 83 187 0.5 1.9 3.1 .. Sudan 6.9 33.1 64.1 4.3 97 147 0.3 0.8 0.7 .. Swaziland 5.8 60.8 16.6 11.1 141 287 0.2 6.3 2.1 .. Sweden 9.4 78.1 15.6 0.0 4,858 3,622 3.6 11.6 .. 2.8 Switzerland 10.7 59.1 30.8 0.0 6,988 4,815 4.1 16.0 5.3 .. Syrian Arab Republic 3.1 38.8 61.2 0.5 71 123 1.5 1.9 1.5 .. Tajikistan 5.0 27.7 68.8 10.5 37 95 2.0 5.0 5.4 8.3 Tanzania 4.5 71.9 18.3 59.2 22 57 0.0 0.2 1.1 .. Thailand 4.1 74.3 17.5 0.3 164 328 0.3 1.5 .. .. Timor-Leste 13.8 73.4 6.8 21.8 71 126 0.1 2.2 .. .. Togo 5.9 24.5 63.5 14.1 38 70 0.1 0.3 0.9 .. Trinidad and Tobago 4.7 48.9 41.8 0.3 908 1,237 1.2 3.6 2.5 .. Tunisia 6.2 54.1 40.0 0.5 248 501 1.2 3.3 2.1 .. Turkey 6.1 73.1 17.4 0.0 623 845 1.6 1.9 2.4 3.1 Turkmenistan 2.2c 49.1c 50.9c 0.3c 82c 146c 2.4 4.5 4.1 3.7 Uganda 8.4 17.4 54.0 27.9 44 112 0.1 1.3 0.4 .. Ukraine 6.8 55.9 40.9 0.4 268 502 3.1 8.5 8.7 10.8 United Arab Emirates 2.5 67.1 21.7 0.0 1,427 868 1.9 4.1 1.9 .. United Kingdom 8.7 82.6 11.1 0.0 3,771 3,222 2.7 10.3 3.4 4.9 United States 15.2 47.8 12.7 0.0 7,164 7,164 2.7 9.8 3.1 9.0 Uruguay 7.8 63.1 12.1 0.2 725 982 3.7 5.6 2.9 .. Uzbekistan 4.9 50.5 48.5 2.4 51 134 2.6 10.8 4.8 8.7 Venezuela, RB 5.4 44.9 49.3 0.0 597 683 .. .. 1.3 .. Vietnam 7.2 38.5 55.5 1.7 76 201 1.2 1.0 2.9 .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 5.3 30.1 68.9 4.6 67 137 0.3 0.7 0.7 .. Zambia 5.9 62.0 28.3 38.4 68 80 0.1 0.7 1.9 .. Zimbabwe .. .. .. .. .. .. 0.2 0.7 3.0 .. World 9.4 w 60.5 w 17.9 w 0.2 w 857 w 901 w 1.4 w 3.0 w 2.9 w .. w Low income 5.3 41.9 47.9 24.2 25 55 0.2 0.5 .. .. Middle income 5.3 51.4 37.0 0.6 186 314 1.3 2.3 2.4 .. Lower middle income 4.3 45.5 45.0 1.1 95 188 1.0 1.7 1.9 .. Upper middle income 6.3 55.4 31.4 0.2 531 792 2.3 4.8 4.5 .. Low & middle income 5.3 51.2 37.2 1.1 163 277 1.1 2.0 2.3 .. East Asia & Pacific 4.2 48.2 42.2 0.5 125 231 1.2 1.7 4.0 .. Europe & Central Asia 5.4 65.4 28.2 0.3 448 738 3.2 6.8 7.3 7.6 Latin America & Carib. 7.2 50.3 34.3 0.2 542 733 2.2 4.8 .. .. Middle East & N. Africa 5.0 53.0 44.3 1.0 176 350 1.5 2.2 1.6 .. South Asia 4.0 32.6 51.5 2.4 40 106 0.6 1.1 0.9 .. Sub-Saharan Africa 6.1 42.9 36.5 9.3 74 132 0.2 1.0 .. .. High income 11.0 62.2 14.2 0.0 4,455 4,136 2.9 7.9 6.1 8.5 Euro area 10.0 73.7 14.2 0.0 4,132 3,458 3.8 7.5 5.8 6.8 a. Data are for the most recent year available. b. GDP includes measures of illicit activities such as opium production. Government expenditures include external assistance (external budget). c. Derived from incomplete data. d. Excludes expenditure in residential facilities for care of the aged. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine. g. Excludes Transdniestria. 96 2011 World Development Indicators 2.16 PEOPLE Health systems About the data Definitions Health systems—the combined arrangements of this reason, data for this indicator should not be • Total health expenditure is the sum of public and institutions and actions whose primary purpose compared across editions. private health expenditure. It covers the provision is to promote, restore, or maintain health (World External resources for health are disbursements of health services (preventive and curative), family Health Organization, World Health Report 2000)— to recipient countries as reported by donors, lagged planning and nutrition activities, and emergency aid are increasingly being recognized as key to com- one year to account for the delay between disburse- for health but excludes provision of water and sani- bating disease and improving the health status of ment and expenditure. Disbursement data are not tation. • Public health expenditure is recurrent and populations. The World Bank’s Healthy Develop- available before 2002, so commitments are used. capital spending from central and local governments, ment: Strategy for Health, Nutrition, and Population Except where a reliable full national health account external borrowing and grants (including donations Results emphasizes the need to strengthen health study has been done, most data are from the Organ- from international agencies and nongovernmental systems, which are weak in many countries, in order isation for Economic Co-operation and Development organizations), and social (or compulsory) health to increase the effectiveness of programs aimed at Development Assistance Committee’s Creditor insurance funds. • Out-of-pocket health expendi- reducing specific diseases and further reduce mor- Reporting System database, which compiles data ture is the percentage of total expenditure that is bidity and mortality (World Bank 2007). To evaluate from government expenditure accounts, government direct household outlays, including gratuities and health systems, the World Health Organization (WHO) records on external assistance, routine surveys of in-kind payments, for health practitioners and phar- has recommended that key components—such as external financing assistance, and special services. maceutical suppliers, therapeutic appliances, and financing, service delivery, workforce, governance, Because of the variety of sources, care should be other goods and services whose primary intent is and information—be monitored using several key taken in interpreting the data. to restore or enhance health. • External resources indicators (WHO 2008b). The data in the table are In countries where the fiscal year spans two cal- for health are funds or services in kind that are pro- a subset of the first four indicators. Monitoring endar years, expenditure data have been allocated vided by entities not part of the country in ques- health systems allows the effectiveness, efficiency, to the later year (for example, 2008 data cover fis- tion. The resources may come from international and equity of different health system models to be cal year 2007/08). Many low-income countries use organizations, other countries through bilateral compared. Health system data also help identify Demographic and Health Surveys or Multiple Indica- arrangements, or foreign nongovernmental orga- weaknesses and strengths and areas that need tor Cluster Surveys funded by donors to obtain health nizations and are part of public and private health investment, such as additional health facilities, system data. expenditure. • Health expenditure per capita is better health information systems, or better trained Data on health worker (physicians, nurses, and total health expenditure divided by population in human resources. midwives) density show the availability of medical U.S. dollars and in international dollars converted Health expenditure data are broken down into pub- personnel. The WHO estimates that at least 2.5 using 2005 purchasing power parity (PPP) rates from lic and private expenditures. In general, low-income physicians, nurses, and midwives per 1,000 people the World Bank’s International Comparison Project. economies have a higher share of private health are needed to provide adequate coverage with pri- • Physicians include generalist and specialist medi- expenditure than do middle- and high-income coun- mary care interventions associated with achieving cal practitioners. • Nurses and midwives include pro- tries, and out-of-pocket expenditure (direct payments the Millennium Development Goals (WHO, World fessional nurses and midwives, auxiliary nurses and by households to providers) makes up the largest Health Report 2006). The WHO compiles data from midwives, enrolled nurses and midwives, and other proportion of private expenditure. High out-of-pocket household and labor force surveys, censuses, and personnel, such as dental nurses and primary care expenditures may discourage people from access- administrative records. Data comparability is limited nurses. • Hospital beds are inpatient beds for both ing preventive or curative care and can impoverish by differences in definitions and training of medical acute and chronic care available in public, private, households that cannot afford needed care. Health personnel varies. In addition, human resources tend general, and specialized hospitals and rehabilita- financing data are collected through national health to be concentrated in urban areas, so that average tion centers. • Outpatient visits per capita are the accounts, which systematically, comprehensively, densities do not provide a full picture of health per- number of visits to health care facilities per capita, and consistently monitoring health system resource sonnel available to the entire population. including repeat visits. flows. To establish a national health account, coun- Availability and use of health services, shown by tries must define the boundaries of the health system hospital beds per 1,000 people and outpatient visits and classify health expenditure information along per capita, reflect both demand- and supply-side fac- several dimensions, including sources of financing, tors. In the absence of a consistent definition these Data sources providers of health services, functional use of health are crude indicators of the extent of physical, finan- Data on health expenditures are from the WHO’s expenditures, and beneficiaries of expenditures. The cial, and other barriers to health care. National Health Account database (latest updates accounting system can then provide an accurate pic- are available at www.who.int/nha/), supple- ture of resource envelopes and financial flows and mented by country data. Data on physicians, and allow analysis of the equity and efficiency of financing nurses and midwives, are from WHO’s Global Atlas to inform policy. of the Health Workforce. For the latest updates and This year’s table presents out-of-pocket expendi- metadata, see http://apps.who.int/globalatlas/. ture as a percentage of total health expenditure; pre- Data on hospital beds and outpatient visits are vious editions presented out-of-pocket expenditure from the WHO, supplemented by country data. as a percentage of private health expenditure. For 2011 World Development Indicators 97 2.17 Health information Year last national Number of Year Year Completeness health account national health of last of last completed accounts health survey census completed % Birth Infant death Total death registration reporting reporting 1995–2009 2001–11 2004–09a 2004–09a 2004–09a Afghanistan 0 2003 .. .. .. Albania 2009 3 2008/09 2001 99 28 76 Algeria 2003 3 2006 2008 99 .. 90 Angola 0 2006/07 .. .. .. Argentina 1997 1 2010 91 100 100 Armenia 2009 6 2005 2001 96 38 100 Australia 2007 13 2006 .. 100 96 Austria 2008 14 2001 .. 90 100 Azerbaijan 0 2006 2009 94 24 100 Bangladesh 2008 13 2007 2001 10 .. .. Belarus 0 2005 2009 .. 55 96 Belgium 2008 6 2001 .. 100 97 Benin 2008 4 2006 2002 60 .. .. Bolivia 2007 13 2008 2001 .. .. 30 Bosnia and Herzegovina 2009 6 2006 100 54 92 Botswana 2002 3 2000 2001 72 35 47 Brazil 2006 7 1996 2010 91 48 87 Bulgaria 2007 6 2001 .. 79 100 Burkina Faso 2008 6 2006 2006 64 29 88 Burundi 2007 1 2005 2008 60 .. .. Cambodia 0 2005 2008 66 0 100 Cameroon 1995 1 2006 2005 70 .. .. Canada 2009 15 2006 .. 100 98 Central African Republic 0 2006 2003 49 .. .. Chad 0 2004 2009 9 .. .. Chile 2008 5 2002 99 100 100 China 2007 13 2010 .. .. 99 Hong Kong SAR, China 0 2006 .. 66 91 Colombia 2003 9 2005 2006 90 52 71 Congo, Dem. Rep. 2009 7 2010 31 .. .. Congo, Rep. 2005 1 2009 2007 81 .. .. Costa Rica 2003 2 1993 2000 .. 90 98 Côte d’Ivoire 2008 2 2006 55 .. .. Croatia 0 2001 .. 75 100 Cuba 0 2006 2002 100 99 100 Czech Republic 2008 14 1993 2001 .. 84 94 Denmark 2007 13 2001 .. 97 97 Dominican Republic 2008 8 2007 2010 78 1 54 Ecuador 2008 7 2004 2010 85 58 86 Egypt, Arab Rep. 2008 3 2008 2006 99 47 97 El Salvador 2009 14 2008 2007 99 36 75 Eritrea 0 2002 .. .. .. Estonia 2008 10 2000 .. 68 94 Ethiopia 2008 4 2005 2007 7 .. 88 Finland 2008 14 2010 .. 84 98 France 2008 14 2006 .. 95 100 Gabon 0 2000 2003 .. .. .. Gambia, The 2004 3 2005/06 2003 55 .. .. Georgia 2009 9 2005 2002 92 54 83 Germany 2008 14 .. 96 99 Ghana 2002 1 2008 2010 71 95 .. Greece 0 2001 .. 78 95 Guatemala 2008 14 2002 2002 .. 62 93 Guinea 0 2005 43 .. .. Guinea-Bissau 0 2010 2009 39 .. .. Haiti 2006 1 2005/06 2003 81 .. .. Honduras 2005 3 2005/06 2001 94 100 99 98 2011 World Development Indicators 2.17 PEOPLE Health information Year last national Number of Year Year Completeness health account national health of last of last completed accounts health survey census completed % Birth Infant death Total death registration reporting reporting 1995–2009 2001–11 2004–09a 2004–09a 2004–09a Hungary 2008 14 2001 .. 84 97 India 2004 2 2005/06 2001 41 .. .. Indonesia 2008 8 2007 2010 53 .. .. Iran, Islamic Rep. 2007 4 2000 2006 .. .. 99 Iraq 0 2006 95 100 100 Ireland 2008 14 2006 .. 75 99 Israel 2006 1 2009 .. 90 99 Italy 2008 4 2001 .. 99 98 Jamaica 2000 1 2005 2001 89 76 68 Japan 2007 13 2010 .. 88 98 Jordan 2008 5 2009 2004 .. .. 76 Kazakhstan 2007 1 2006 2009 99 95 82 Kenya 2006 2 2008/09 2009 60 37 39 Korea, Dem. Rep. 0 2010 2008 .. 43 91 Korea, Rep. 2008 14 2005 .. 80 92 Kosovo 0 .. .. .. Kuwait 0 1996 2010 .. 100 100 Kyrgyz Republic 2009 5 2005/06 2009 94 78 95 Lao PDR 0 2006 2005 72 .. .. Latvia 2007 5 2000 .. 79 96 Lebanon 2005 4 2000 .. .. 72 Lesotho 0 2009/10 2006 26 .. .. Liberia 2008 1 2009 2008 4 .. .. Libya 0 2000 2006 .. .. .. Lithuania 2008 7 2001 .. 68 95 Macedonia, FYR 0 2005 2002 94 87 99 Madagascar 2007 2 2008/09 75 .. .. Malawi 2006 5 2006 2008 .. .. 75 Malaysia 2006 10 2010 .. 62 100 Mali 2004 6 2006 2009 53 .. .. Mauritania 0 2007 2000 56 .. .. Mauritius 2004 2 2000 .. 80 97 Mexico 2009 15 1995 2010 .. 89 100 Moldova 0 2005 2004 .. 62 89 Mongolia 2003 5 2005 2010 98 60 96 Morocco 2006 3 2006 2004 .. .. .. Mozambique 2006 4 2009 2007 31 .. .. Myanmar 2007 10 2000 .. 56 55 Namibia 2008 11 2006/07 2001 67 .. 100 Nepal 2005 5 2006 2001 35 .. .. Netherlands 2008 14 2001 .. 84 97 New Zealand 2008 14 2006 .. 100 98 Nicaragua 2008 14 2006/07 2005 .. 66 68 Niger 2006 4 2006 2001 32 .. .. Nigeria 2005 8 2008 2006 30 .. 1 Norway 2008 12 2001 .. 97 100 Oman 1998 1 1995 2010 .. 100 97 Pakistan 2006 1 2006/07 27 85 84 Panama 2003 1 2003 2010 .. 70 88 Papua New Guinea 2000 3 1996 2000 .. .. .. Paraguay 2008 13 2004 2002 .. 34 71 Peru 2005 11 2008 2007 93 41 70 Philippines 2007 13 2008 2010 .. 39 100 Poland 2008 14 2002 .. 95 100 Portugal 2007 8 2001 .. 85 95 Puerto Rico 0 1996 2010 .. 100 95 Qatar 0 2010 .. 95 77 2011 World Development Indicators 99 2.17 Health information Year last national Number of Year Year Completeness health account national health of last of last completed accounts health survey census completed % Birth Infant death Total death registration reporting reporting 1995–2009 2001–11 2004–09a 2004–09a 2004–09a Romania 2006 9 1999 2002 .. 76 96 Russian Federation 2007 13 1996 2010 .. 80 95 Rwanda 2006 5 2007 2002 82 .. .. Saudi Arabia 0 2007 2010 .. 94 100 Senegal 2005 2 2008/09 2002 55 .. .. Serbia 2009 7 2005/06 2002 99 38 90 Sierra Leone 2006 3 2008 2004 51 .. .. Singapore 0 2005 2010 .. 93 72 Slovak Republic 2008 12 2001 .. 93 98 Slovenia 2008 14 2002 .. 72 96 Somalia 0 2006 3 .. .. South Africa 1998 3 2003 2001 92 81 81 Spain 2008 14 2001 .. 99 100 Sri Lanka 2006 12 2006/07 2001 97 63 91 Sudan 2008 1 2006 2008 33 .. .. Swaziland 0 2006/07 2007 30 .. .. Sweden 2008 8 .. 83 99 Switzerland 2009 15 2010 .. 100 99 Syrian Arab Republic 0 2006 2004 95 .. 100 Tajikistan 2008 2 2005 2010 88 19 69 Tanzania 2006 3 2007/08 2002 22 .. .. Thailand 2007 13 2005/06 2010 99 86 65 Timor-Leste 0 2009 2010 .. .. .. Togo 2002 1 2006 2010 78 .. .. Trinidad and Tobago 2000 1 2006 2000 96 50 94 Tunisia 2005 5 2006 2004 .. .. 98 Turkey 2005 8 2003 2000 94 56 100 Turkmenistan 0 2006 96 .. .. Uganda 2006 6 2009/10 2002 21 .. .. Ukraine 2008 6 2007 2001 100 90 100 United Arab Emirates 0 2010 .. 75 100 United Kingdom 2008 12 2001 .. 100 95 United States 2009 15 2009 2010 .. 100 100 Uruguay 2008 13 2004 .. 78 100 Uzbekistan 0 2006 100 .. .. Venezuela, RB 0 2000 2001 .. 62 84 Vietnam 2007 10 2006 2009 88 72 83 West Bank and Gaza 1 2006 2007 96 31 66 Yemen, Rep. 2007 4 2006 2004 22 .. 15 Zambia 2006 11 2007 2000 14 .. .. Zimbabwe 2001 3 2005/06 2002 74 .. .. a. Data are for the most recent year available. 100 2011 World Development Indicators 2.17 PEOPLE Health information About the data Definitions According to the World Health Organization (WHO), the institutional frameworks needed to ensure data • Year last national health account completed is the health information systems are crucial for moni- quality, including independence, transparency, and latest year for which the health expenditure data are toring and evaluating health systems, which are access. Benchmarks include the availability of inde- available using the national health account approach. increasingly recognized as important for combating pendent coordination mechanisms and micro- and • Number of national health accounts completed is disease and improving health status. Health informa- meta-data (WHO 2008a). the number of national health accounts completed tion systems underpin decisionmaking through four The indicators in the table are all related to data between 1995 and 2008. • Year of last health sur- data functions: generation, compilation, analysis and generation, including the years the last national vey is the latest year the national survey that collects synthesis, and communication and use. The health health account, last health survey, and latest popu- health information was conducted. • Year of last cen- information system collects data from the health sec- lation census were completed. Frequency of data col- sus is the latest year a census was conducted in the tor and other relevant sectors; analyzes the data and lection, a benchmark of data generation, is shown last 10 years. • Completeness of birth registration is ensures their overall quality, relevance, and timeli- as the number of years for which a national health the percentage of children under age 5 whose births ness; and converts data into information for health- account was completed between 1995 and 2009. were registered at the time of the survey. The numera- related decisionmaking (WHO 2008b). National health account data may be collected tor of completeness of birth registration includes chil- Numerous indicators have been proposed to using different approaches such as Organisation for dren whose birth certificate was seen by the interviewer assess a country’s health information system. Economic Co-operation and Development (OECD) or whose mother or caretaker says the birth has been They can be grouped into two broad types: indica- System of Health Accounts, WHO National Health registered. • Completeness of infant death reporting tors related to data generation using core sources Account producers guide approach, local national is the number of infant deaths reported by national and methods (health surveys, civil registration, cen- health accounting methods, or Pan American statistical authorities to the United Nations Statistics suses, facility reporting, health system resource Health Organization/WHO satellite health accounts Division’s Demographic Yearbook divided by the number tracking) and indicators related to capacity for approach. of infant deaths estimated by the United Nations Popu- data synthesis, analysis, and validation. Indicators Indicators related to data generation include com- lation Division. • Completeness of total death report- related to data generation reflect a country’s capac- pleteness of birth registration, infant death report- ing is the number of total deaths from civil registration ity to collect relevant data at suitable intervals using ing, and total death reporting. system reported by national statistical authorities to the most appropriate data sources. Benchmarks the United Nations Statistics Division’s Demographic include periodicity, timeliness, contents, and avail- Yearbook divided by the number of total deaths esti- ability. Indicators related to capacity for synthesis, mated by the United Nations Population Division. analysis, and validation measure the dimensions of Data sources Data on year last national health account completed South Asia has the highest number of unregistered births 2.17a and number of national health accounts completed were compiled by staff in the World Health Organiza- tion’s Health Financing Department and the World Number of unregistered births, 2007 (millions) Bank’s Health, Nutrition, and Population Unit using data on the health expenditures reported by the Latin America and Caribbean 1.3 CEE/CIS 0.4 WHO and OECD and consultation with colleagues Middle East and North Africa 2.4 from countries and other international organizations. East Asia Data on year of last health survey are from Macro and Pacific, excluding China 3.5 International and the United Nations Children’s Fund (UNICEF). Data on year of last census are from Eastern and Southern Africa South Asia United Nations Statistics Division’s 2011 World 9.7 24.3 Population and Housing Census Program (http:// unstats.un.org/unsd/demographic/sources/cen- West and Central Africa sus/2010_PHC/default.htm.) Data on completeness 9.8 of birth registration are compiled by UNICEF in State of the World’s Children 2010 based mostly on house- hold surveys and ministry of health data. Data used to calculate completeness of infant death reporting Too many people, especially poor, are never counted. They are born, live, and die uncounted and and total death reporting are from the United Nations ignored. Around 50 million, or 40 percent of children born in 2007, have not been registered. Statistics Division’s Population and Vital Statistics Report and the United Nations Population Division’s Source: United Nations Children’s Fund Childinfo. World Population Prospects: The 2008 Revision. 2011 World Development Indicators 101 2.18 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12–23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2008 1990 2008 2009 2009 2004–09c 2004–09c 2004–09c 2004–09c 2008 2009 Afghanistan .. 48 .. 37 76 83 .. .. .. .. 88 48 Albania .. 97 .. 98 97 98 70 63 .. .. 91 94 Algeria 94 83 88 95 88 93 53 24 .. .. 90 100 Angola 36 50 25 57 77 73 .. .. 17.7 29.3 70 75 Argentina 94 97 90 90 99 94 .. .. .. .. 44 67 Armenia .. 96 .. 90 96 93 36 59 .. .. 73 70 Australia 100 100 100 100 94 92 .. .. .. .. 80 89 Austria 100 100 100 100 83 83 .. .. .. .. 47 48 Azerbaijan 70 80 .. 45 67 73 33 31 .. .. 56 75 Bangladesh 78 80 39 53 89 94 37 68 .. .. 91 44 Belarus 100 100 .. 93 99 96 90 54 .. .. 71 140 Belgium 100 100 100 100 94 99 .. .. .. .. 76 88 Benin 56 75 5 12 72 83 36 42 20.1 54.0 89 47 Bolivia 70 86 19 25 86 85 51 .. .. .. 84 64 Bosnia and Herzegovina .. 99 .. 95 93 90 91 53 .. .. 92 91 Botswana 93 95 36 60 94 96 .. .. .. .. 65 62 Brazil 88 97 69 80 99 99 50 .. .. .. 71 86 Bulgaria 100 100 99 100 96 94 .. .. .. .. 85 86 Burkina Faso 41 76 6 11 75 82 39 42 9.6 48.0 76 14 Burundi 70 72 44 46 91 92 38 23 8.3 30.0 90 25 Cambodia 35 61 9 29 92 94 48 50 4.2 0.2 95 60 Cameroon 50 74 47 47 74 80 35 22 13.1 57.8 76   70 Canada 100 100 100 100 93 80 .. .. .. .. 78 93 Central African Republic 58 67 11 34 62 54 32 47 15.1 57.0 71 60 Chad 38 50 6 9 23 23 12 27 .. 53.0 54   26 Chile 90 96 84 96 96 97 .. .. .. .. 72 130 China 67 89 41 55 94 97 .. .. .. .. 94 75 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. 68 89 Colombia 88 92 68 74 95 92 62 39 .. .. 76 70 Congo, Dem. Rep. 45 46 9 23 76 77 42 42 5.8 29.8 87 46 Congo, Rep. .. 71 .. 30 76 91 48 39 6.1 48.0 76 69 Costa Rica 93 97 93 95 81 86 .. .. .. .. 89 93 Côte d’Ivoire 76 80 20 23 67 81 35 45 3.0 36.0 76 27 Croatia .. 99 .. 99 98 96 .. .. .. .. 58 76 Cuba 82 94 80 91 96 96 .. .. .. .. 88 120 Czech Republic 100 100 100 98 98 99 .. .. .. .. 68 70 Denmark 100 100 100 100 84 89 .. .. .. .. 41 79 Dominican Republic 88 86 73 83 79 82 70 55 .. 0.6 75 60 Ecuador 72 94 69 92 66 75 .. .. .. .. 78 51 Egypt, Arab Rep. 90 99 72 94 95 97 73 19 .. .. 89 63 El Salvador 74 87 75 87 95 91 67 .. .. .. 91 92 Eritrea 43 61 9 14 95 99 .. .. .. .. 76 58 Estonia 98 98 .. 95 95 95 .. .. .. .. 60 89 Ethiopia 17 38 4 12 75 79 19 15 33.1 9.5 84 50 Finland 100 100 100 100 98 99 .. .. .. .. 72 110 France 100 100 100 100 90 99 .. .. .. .. .. 77 Gabon .. 87 .. 33 55 45 .. .. .. .. 53 42 Gambia, The 74 92 .. 67 96 98 69 38 49.0 62.6 84 47 Georgia 81 98 96 95 83 88 74 37 .. .. 73 100 Germany 100 100 100 100 96 93 .. .. .. .. 68 91 Ghana 54 82 7 13 93 94 51 45 28.2 43.0 86 31 Greece 96 100 97 98 99 99 .. .. .. .. .. 92 Guatemala 82 94 65 81 92 92 .. .. .. .. 83 33 Guinea 52 71 9 19 51 57 42 38 4.5 43.5 78 26 Guinea-Bissau .. 61 .. 21 76 68 57 25 39.0 45.7 70 59 Haiti 47 63 26 17 59 59 31 43 .. 5.1 82   60   Honduras 72 86 44 71 99 98 56 49 .. 0.5 85 68 102 2011 World Development Indicators 2.18 PEOPLE Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12–23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2008 1990 2008 2009 2009 2004–09c 2004–09c 2004–09c 2004–09c 2008 2009 Hungary 96 100 100 100 99 99 .. .. .. .. 53 82 India 72 88 18 31 71 66 69 33 .. 8.2 87 67 Indonesia 71 80 33 52 82 82 66 54 3.3 0.8 91 67 Iran, Islamic Rep. 91 .. 83 .. 99 99 .. .. .. .. 83 74 Iraq 81 79 .. 73 69 65 82 64 .. .. 88 48 Ireland 100 100 99 99 89 93 .. .. .. .. 76 89 Israel 100 100 100 100 96 93 .. .. .. .. 81 89 Italy 100 100 .. .. 91 96 .. .. .. .. .. 66 Jamaica 93 94 83 83 88 90 75 39 .. .. 64 78 Japan 100 100 100 100 94 98 .. .. .. .. 48 89 Jordan 97 96 .. 98 95 98 75 32 .. .. 84 100 Kazakhstan 96 95 96 97 99 98 71 48 .. .. 64 80 Kenya 43 59 26 31 74 75 56 .. 46.1 23.2 85 85 Korea, Dem. Rep. 100 100 .. .. 98 93 93 .. .. .. 89 93 Korea, Rep. .. 98 100 100 93 94 .. .. .. .. 84 89 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 99 99 100 100 97 98 .. .. .. .. 80 89 Kyrgyz Republic .. 90 .. 93 99 95 62 22 .. .. 84 66 Lao PDR .. 57 .. 53 59 57 32 49 40.5 8.2 93 68 Latvia 99 99 .. 78 96 95 .. .. .. .. 33 94 Lebanon 100 100 .. .. 53 74 .. .. .. .. 77 78 Lesotho 61 85 32 29 85 83 66 53 .. .. 73 93 Liberia 58 68 11 17 64 64 62 47 26.4 67.2 79 52 Libya 54 .. 97 97 98 98 .. .. .. .. 69 82 Lithuania .. .. .. .. 96 98 .. .. .. .. 82 81 Macedonia, FYR .. 100 .. 89 96 96 93 45 .. .. 89 98 Madagascar 31 41 8 11 64 78 42 47 45.8 19.7 81 44 Malawi 40 80 42 56 92 93 52 27 24.7 24.9 87 49 Malaysia 88 100 84 96 95 95 .. .. .. .. 78 76 Mali 29 56 26 36 71 74 38 38 27.1 31.7 82 16 Mauritania 30 49 16 26 59 64 45 32 2.1 20.7 68 24 Mauritius 99 99 91 91 99 99 .. .. .. .. 87 41 Mexico 85 94 66 85 95 89 .. .. .. .. 85 99 Moldova .. 90 .. 79 90 85 60 48 .. .. 62 68 Mongolia 58 76 .. 50 94 95 63 47 .. .. 87 75 Morocco 74 81 53 69 98 99 38 46 .. .. 85 93 Mozambique 36 47 11 17 77 76 65 47 22.8 36.7 84 46 Myanmar 57 71 .. 81 87 90 .. .. .. .. 85 64 Namibia 64 92 25 33 76 83 72 48 10.5 9.8 82 76 Nepal 76 88 11 31 79 82 43 37 .. 0.1 89 73 Netherlands 100 100 100 100 96 97 .. .. .. .. 85 89 New Zealand 100 100 .. .. 89 92 .. .. .. .. 73 89 Nicaragua 74 85 43 52 99 98 .. .. .. .. 89 90 Niger 35 48 5 9 73 70 47 34 42.8 33.0 81 36 Nigeria 47 58 37 32 41 42 45 25 5.5 33.2 78 19 Norway 100 100 100 100 92 92