World Development Indicators 2012

Page 1

12

WORLD DEVELOPMENT INDICATORS


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

Kosovo Lao PDR Lesotho Marshall Islands Mauritania Micronesia, Fed. Sts. Moldova Mongolia Morocco Nicaragua Nigeria Pakistan Papua New Guinea Paraguay Philippines Samoa São Tomé and Príncipe Senegal Solomon Islands Sri Lanka South Sudan Sudan Swaziland Syrian Arab Republic Timor-Leste Tonga Turkmenistan Tuvalu Ukraine Uzbekistan Vanuatu Vietnam West Bank and Gaza Yemen, Rep. Zambia Upper middle income Albania Algeria American Samoa Antigua and Barbuda Argentina Azerbaijan Belarus Bosnia and Herzegovina Botswana Brazil Bulgaria Chile China Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador Gabon Grenada Iran, Islamic Rep. Jamaica

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

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

INCOME MAP

The world by income


The world by income Low ($1,005 or less)

Classified according to World Bank estimates of 2010 GNI per capita

Lower middle ($1,006–$3,975) Upper middle ($3,976–$12,275) High ($12,276 or more) No data

Greenland (Den)

Iceland

Norway

Faeroe Islands (Den)

Sweden

Finland Russian Federation

The Netherlands

Estonia Denmark Russian Latvia Fed. Lithuania United Belarus Germany Poland Kingdom Belgium Ukraine Moldova Romania France Italy

Isle of Man (UK)

Canada

Ireland Channel Islands (UK)

Luxembourg Liechtenstein Switzerland Andorra United States

Kazakhstan

Bulgaria

Portugal

Spain Monaco

Turkey

Greece

Gibraltar (UK) Bermuda (UK)

Tunisia

Cyprus Lebanon Israel

Syrian Arab Rep.

West Bank and Gaza

Jordan

Malta

Morocco

Georgia Armenia

Azerbaijan

Cayman Is.(UK)

Libya

Former Spanish Sahara

Saudi Arabia

Cuba Belize Jamaica Guatemala Honduras El Salvador Nicaragua Costa Rica

Mauritania

Haiti

Cape Verde

Mali

Senegal The Gambia Guinea-Bissau Guinea

Panama

R.B. de Venezuela

Guyana Suriname

Sierra Leone Liberia

French Guiana (Fr)

Colombia

Niger

Benin Côte Ghana d’Ivoire

Cameroon

Congo

Malawi

Zimbabwe

Tonga

Namibia Paraguay Germany

Antigua and Barbuda

U.S. Virgin Islands (US)

St. Kitts and Nevis

Curaçao (Neth)

Argentina

Dominica St. Lucia

St. Vincent and the Grenadines

Barbados Grenada Trinidad and Tobago

Guam (US)

Philippines

Federated States of Micronesia

Brunei Darussalam Malaysia

Marshall Islands

Palau Nauru

Singapore

Botswana

South Africa

Poland

Kiribati

Comoros

Solomon Islands

Papua New Guinea

Indonesia

Tuvalu

Mayotte (Fr)

Madagascar

Vanuatu

Fiji

Mauritius Réunion (Fr)

Australia

New Caledonia (Fr)

Lesotho

Czech Republic Ukraine Slovak Republic Austria

Guadeloupe (Fr)

Martinique (Fr) Aruba (Neth)

Chile

Uruguay

N. Mariana Islands (US)

Seychelles

Mozambique

Swaziland St. Maarten (Neth)

Lao P.D.R.

Maldives

Kenya

Rwanda Dem.Rep.of Burundi Congo Tanzania

Zambia

St. Martin (Fr)

Myanmar

Sri Lanka Somalia

Uganda Gabon

Bolivia

Puerto Rico (US)

India

Vietnam Cambodia

South Sudan

Angola

American Samoa (US)

Bangladesh

Thailand

Brazil

Peru

Bhutan

Nepal

Rep. of Yemen

Ethiopia

Central African Republic

Japan

Timor-Leste

French Polynesia (Fr)

Dominican Republic

Pakistan

Djibouti Nigeria

Kiribati

Samoa

Eritrea

Sudan

Burkina Faso

Togo Equatorial Guinea São Tomé and Príncipe

Ecuador

Chad

Rep.of Korea

China

Afghanistan

United Arab Emirates Oman

Turks and Caicos Is. (UK)

Mexico

Fiji

Arab Rep. of Egypt

Dem.People’s Rep.of Korea

Tajikistan

Bahrain Qatar

Algeria The Bahamas

Turkmenistan

Islamic Rep. of Iran Kuwait

Iraq

Mongolia Kyrgyz Rep.

Uzbekistan

Romania

Bosnia and Herzegovina San Marino Italy Montenegro Vatican City

New Zealand

Hungary

Slovenia Croatia

Serbia

Kosovo Bulgaria FYR Macedonia Albania Greece

R.B. de Venezuela

Antarctica

IBRD 39125 MARCH 2012

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


2012

WORLD DEVELOPMENT INDICATORS


Copyright 2012 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 2012 This volume is a product of the staff of the Development Data Group of the World Bank’s Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank’s Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth’s surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: World Bank photo library, except page 282, David Cieslikowski/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 978-0-8213-8985-0 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 2012 on recycled paper with 50 percent postconsumer 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: 64 trees 26 million Btu of total energy 6,503 pounds of net greenhouse gases 29,321 gallons of waste water 1,859 pounds of solid waste


2012

WORLD DEVELOPMENT INDICATORS



PREFACE World Development Indicators 2012 is a compilation of relevant, high-quality, and internationally comparable statistics about development and the quality of people’s lives. Organized around six themes—world view, people, the environment, the economy, states and markets, and global links—it aims to put data into the hands of policy makers, development specialists, students, and the public. We encourage and applaud the use of the data presented here to help reduce poverty and to solve the world’s most pressing development challenges. The full dataset used to produce World Development Indicators contains more than 1,000 indicators for 216 economies, with many time series extending back to 1960. Highly visual, interactive, and multilingual presentations of the data are available at the popular website http://data.worldbank.org and through the DataFinder application for mobile devices. And, as a major part of the World Bank’s Open Data Initiative, the data are freely available for use and reuse under an open license. A companion printed volume, The Little Data Book 2012, presents a selection of indicators for each economy, and the biennial Statistics for Small States presents data for less-populated developing countries. This 16th edition of World Development Indicators relies heavily on statistics produced by national authorities and agencies. Since the first edition in 1997, there has been a substantial increase in the availability and quality of the data, thanks to improvements in statistical capacity in many countries. More remains to be done: the capacity to use statistical data remains weak; demand is growing for greater disaggregation of indicators (for instance by sex, age, or geography); and data in some key areas, such as agriculture, are often missing or outdated. A new global statistical action plan (www.paris21.org/busan-action-plan), endorsed in November 2011 at the highest political levels at the Fourth High Level Forum on Aid Effectiveness in Busan, Republic of Korea, provides an important framework to address remaining challenges, to integrate statistics into decision making, to promote open access to data and improve their use, and to increase resources for statistical systems. World Development Indicators is possible only through the excellent collaboration of many partners who provide the data for this collection, and I would like to thank them all: the United Nations family, the International Monetary Fund, the International Telecommunication Union, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others whose support and advice have made 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

2012 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, Azita Amjadi, Maja Bresslauer, David Cieslikowski, Liu Cui, Mahyar Eshragh-Tabary, Shota Hatakeyama, Masako Hiraga, Wendy Ven-dee Huang, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Levent, Hiroko Maeda, Johan Mistiaen, Maurice Nsabimana, Sulekha Patel, Beatriz Prieto-Oramas, William Prince, Premi Rathan Raj, Evis Rucaj, Emi Suzuki, 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 and comprising Ramvel Chandrasekaran, Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Federico Escaler, Shelley Fu, Gytis Kanchas, Ugendran Makhachkala, Vilas Mandlekar, Nacer Megherbi, Shanmugam Natarajan, Parastoo Oloumi, Atsushi Shimo, Maryna Taran, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuable advice was provided by Zia M. Qureshi and David Rosenblatt. 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—Financial and Private Sector Development, Human Development, Poverty Reduction and Economic Management, and Sustainable Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott and assisted by Rob Elson. Elaine Wilson created the cover and graphics and typeset the book. Joseph Caponio provided production assistance. Peter Grundy, of Peter Grundy Art & Design, designed the report. Staff from External Affairs oversaw printing and dissemination of the book.

2012 World Development Indicators

vii


TABLE OF CONTENTS FRONT Preface Acknowledgments Partners Users guide

v vii xii xxii

1. WORLD VIEW Introduction

1.4 1.5 1.6

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

1a 1b 1c 1d 1e 1f 1g 1h 1i 1j 1k 1l 1m 1n 1o 1p 1q 1r 1s

Text figures, tables, and boxes Poverty rates fell sharply in the new millennium Fewer people living in extreme poverty Progress toward poverty reduction Progress toward reducing undernourishment More and less income equality Many children remain malnourished The last step toward education for all 64 million children out of school Progress toward education for all The missing enrollments How much schooling Increasing participation by girls at all levels of education Progress toward gender equality in education Women have become a larger part of the workforce More women decisionmakers A slim lead for girls Still far to go in reducing under-five mortality Most deaths occur in the first year of life Progress toward reducing child mortality

1.1 1.2 1.3

viii

2012 World Development Indicators

1

20 24 28 32 34 38

2 2 3 3 3 3 4 4 5 5 5 6 7 7 7 7 8 8 9

1t 1u 1v 1w 1x 1y 1z 1aa 1bb 1cc 1dd 1ee 1ff 1gg 1hh 1ii 1jj 1kk 1ll 1mm 1nn 1oo 1pp 1qq 1rr 1.2a 1.3a 1.4a

Preventing childhood diseases For some, better than expected improvements Maternal mortality rates have been falling but large regional differences persist The 12 countries with highest lifetime risk of maternal death Progress in reducing maternal mortality Planning for motherhood Fewer young women giving birth Help for mothers Bringing HIV/AIDS under control Millions of people still afflicted with HIV/AIDS Progress toward reversing the HIV epidemic Turning the tide of tuberculosis Protecting children from malaria Carbon dioxide emissions continue to rise Forest losses and gains Progress toward improved sanitation Progress toward improved water sources Many still lack access to sanitation Water demand strains supplies Most donors have maintained their aid levels But their domestic subsidies to agricultural are greater Developing countries have easier access to Organisation for Economic Co-operation and Development markets Cellular phones are connecting developing countries Debt service burdens have been falling A more connected world Location of indicators for Millennium Development Goals 1–4 Location of indicators for Millennium Development Goals 5–7 Location of indicators for Millennium Development Goal 8

9 9 10 10 11 11 11 11 12 12 13 13 13 14 14 15 15 15 15 16 16 17 17 17 17 27 31 33


2. PEOPLE Introduction

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2.23 2.24 2.8a 2.8b 2.8c

Tables Population dynamics Labor force structure Employment by economic activity Decent work and productive employment Unemployment Children at work Poverty rates at national poverty lines Poverty rates at international poverty lines Distribution of income or consumption Assessing vulnerability and security Education inputs Participation in education Education efficiency Education completion and outcomes Education gaps by income and gender Health systems Health information Disease prevention coverage and quality Reproductive health Nutrition and growth Nutrition intake and supplements Health risk factors and future challenges Mortality Health gaps by income Text figures, tables, and boxes While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2 a day has increased Poverty rates are falling in all developing regions Regional poverty estimates

3. ENVIRONMENT 41

42 46 50 54 58 62 66 72 74 78 82 86 90 94 98 100 104 108 112 116 120 124 128 132

Introduction

137

3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18

Tables Rural population and land use Agricultural inputs Agricultural output and productivity Deforestation and biodiversity Freshwater Water pollution Energy production and use Electricity production, sources, and access Energy dependency and efficiency and carbon dioxide emissions Trends in greenhouse gas emissions Carbon dioxide emissions by sector Climate variability, exposure to impact, and resilience Urbanization Urban housing conditions Traffic and congestion Air pollution Government commitment Contribution of natural resources to gross domestic product

170 174 178 182 186 190 194 198 200 204

3.1a

Text figures, tables, and boxes What is rural? Urban?

141

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9

138 142 146 150 154 158 162 166

71 71 72

2012 World Development Indicators

ix


TABLE OF CONTENTS 4. ECONOMY 4.a 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17

x

5. STATES AND MARKETS

Introduction

209

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

210 214 218 222 226 230 234 238 242 246 250 254 258 262 266 270 274 278

2012 World Development Indicators

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13

Introduction

283

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

284 288 292 296 300 304 308 312 316 320 324 328 332


6. GLOBAL LINKS Introduction

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.6a 6.13a

Tables Growth of merchandise trade Direction and growth of merchandise trade High-income economy trade with low- and middle-income economies Direction of trade of developing economies Primary commodity prices Regional trade blocs Tariff barriers Trade facilitation External debt Global private financial flows Net official financial flows Aid dependency Distribution of net aid by Development Assistance Committee members Movement of people across borders Travel and tourism Text ďŹ gures, tables, and boxes Global Preferential Trade Agreement Database Official development assistance from non-DAC donors, 2006–10

BACK 337

338 342

Primary data documentation Statistical methods Credits Bibliography Index of indicators

391 402 404 406 414

344 346 349 352 354 358 362 366 370 374 378 382 386

353 381

2012 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 organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2012. Information about the World Bank is also provided.

International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and information analysis center of the U.S. Department of Energy. The CDIAC’s scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere, the role of the terrestrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbon dioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation, and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.ornl.gov. Centre for Research on the Epidemiology of Disasters Since 1988 the World Health Organization Collaborating Centre for Research on the Epidemiology of Disasters has maintained the Emergency Events Database, which was created with support from the Belgian government. The main objective of the database is to serve the purposes of humanitarian action at the national and international levels. It aims to rationalize decisionmaking for disaster preparedness and provide an objective base for vulnerability assessment and priority setting. The database contains essential core data—compiled from various sources, including UN agencies, nongovernmental organizations, insurance companies, research institutes, and press agencies—on the occurrence and effects of more than 18,000 mass disasters since 1900. For more information, see www.emdat.be.

xii

2012 World Development Indicators


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

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

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

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

International Energy Agency Founded in 1974, the International Energy Agency’s (IEA) mandate is to facilitate cooperation among member countries in order to increase energy efficiency, promote use of clean energy and technology, and diversify energy sources while protecting the environment. The IEA publishes annual and quarterly statistical publications covering both Organisation for Economic Co-operation and Development (OECD) and non-OECD countries’ data on oil, gas, coal, electricity, and renewable sources of energy; energy supply and consumption; and energy prices and taxes. The IEA also analyzes all aspects of sustainable development globally and provides policy recommendations. For more information, see www.iea.org.

2012 World Development Indicators

xiii


PARTNERS International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org.

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

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

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

xiv

2012 World Development Indicators


The Office of U.S. Foreign Disaster Assistance On November 3, 1961, U.S. President John F. Kennedy established the U.S. Agency for International Development (USAID), the first U.S. foreign assistance organization whose primary emphasis was long-range economic and social development assistance to foreign countries. The Office of U.S. Foreign Disaster Assistance is the office within USAID responsible for providing nonfood humanitarian assistance in response to international crises and disasters. The USAID administrator is designated as the president’s special coordinator for international disaster assistance, which the Office of U.S. Foreign Disaster Assistance assists in coordinating. For more information see www.globalcorps.com/ofda.html and www.usaid.gov/our_work/humanitarian_ assistance/disaster_assistance.

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

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

Understanding Children’s Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labour Organization, the United Nations Children’s Fund (UNICEF), and the World Bank initiated the joint interagency research program “Understanding Children’s Work and Its Impact” in December 2000. The Understanding Children’s Work (UCW) project was located at UNICEF’s Innocenti Research Centre in Florence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW’s online database contains data by country on child labor and the status of children. For more information, see www.ucw-project.org. 2012 World Development Indicators

xv


PARTNERS United Nations The United Nations currently has 193 member states. The purposes of the United Nations, as set forth in its charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promoting respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org.

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

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

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

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

xvi

2012 World Development Indicators


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

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

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

United Nations International Strategy for Disaster Reduction Created in December 1999 as the successor to the International Decade for Natural Disaster Reduction, the mandate of the secretariat of the United Nations International Strategy for Disaster Reduction is to serve as the focal point in the UN system for coordination of disaster reduction and to ensure synergies among disaster relief activities. For more information, see www.unisdr.org.

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

United Nations Office of the High Commissioner for Refugees The United Nations Office of the High Commissioner for Refugees (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.

2012 World Development Indicators

xvii


PARTNERS 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.

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 evidencebased policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int.

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

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

xviii

2012 World Development Indicators


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

Private and nongovernmental organizations Center for International Earth Science Information Network The Center for International Earth Science Information Network, a center within the Earth Institute at Columbia University, works at the intersection of the social, natural, and information sciences and specializes in online data and information management, spatial data integration and training, and interdisciplinary research related to human interactions in the environment. For more information, see www.ciesin.org. 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.

2012 World Development Indicators

xix


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

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

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

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

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

xx

2012 World Development Indicators


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

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

2012 World Development Indicators

xxi


USERS GUIDE Tables

are totals (designated by a t if the aggregates include

cross-country and intertemporal comparisons involve

The tables are numbered by section and display

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

complex technical and conceptual problems that can-

the identifying icon of the section. Countries and

for simple totals, where they do not), median values

not be resolved unequivocally. Data coverage may

economies are listed alphabetically (except for Hong

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

not be complete because of special circumstances

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

Gap filling of amounts not allocated to countries may

affecting the collection and reporting of data, such

are shown for 158 economies with a population of

result in discrepancies between subgroup aggregates

as problems stemming from conflicts.

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

and overall totals. For further discussion of aggrega-

selected tables. Table 1.6 presents selected indi-

tion methods, see Statistical methods.

cators for 58 other economies—small economies

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

with a population between 30,000 and 1 million and

Aggregate measures for regions

characterizing major differences among economies

smaller economies if they are members of the Inter-

The aggregate measures for regions cover only low-

rather than as offering precise quantitative mea-

national Bank for Reconstruction and Development

and middle-income economies, including econo-

sures of those differences. Discrepancies in data

or, as it is commonly known, the World Bank. Data for

mies with populations of less than 1 million listed

presented in different editions of World Development

these economies are included on the World Develop-

in table 1.6.

Indicators reflect updates by countries as well as revi-

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

The country composition of regions is based on

sions to historical series and changes in methodol-

Data website (http://data.worldbank.org). The term

the World Bank’s analytical regions and may differ

ogy. Thus readers are advised not to compare data

country, used interchangeably with economy, does

from common geographic usage. For regional clas-

series across editions of World Development Indica-

not imply political independence but refers to any

sifications, see the map on the inside back cover and

tors or across World Bank publications. Consistent

territory for which authorities report separate social

the list on the back cover flap. For further discussion

time series data for 1960–2010 are available on

or economic statistics. When available, aggregate

of aggregation methods, see Statistical methods.

the World Development Indicators CD-ROM and the

measures for income and regional groups appear at the end of each table.

World Bank’s Open Data website (http://data.world-

Statistics

bank.org).

Indicators are shown for the most recent year

Data are shown for economies as they were con-

Except where otherwise noted, growth rates are

or period for which data are available and, in most

stituted in 2010, and historical data are revised to

in real terms. (See Statistical methods for information

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

reflect current political arrangements. Exceptions are

on the methods used to calculate growth rates.) Data

2000 in this edition). Time series data for all 216

noted throughout the tables.

for some economic indicators for some economies

economies are available on the World Development

Additional information about the data is provided

are presented in fiscal years rather than calendar

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

in Primary data documentation, which summarizes

years; see Primary data documentation. The methods

website (http://data.worldbank.org).

national and international efforts to improve basic

used for converting national currencies are described

Known deviations from standard definitions or

data collection and gives country-level information

in Statistical methods.

breaks in comparability over time or across countries

on primary sources, census years, fiscal years,

are either footnoted in the tables or noted in About

statistical methods and concepts used, and other

Country notes

the data. When available data are deemed to be

background information. Statistical methods provides

too weak to provide reliable measures of levels and

technical information on some of the general calcula-

trends or do not adequately adhere to international

tions and formulas used throughout the book.

standards, the data are not shown.

Unless otherwise noted, data for China do not include data for Hong Kong SAR, China; Macao SAR, China; or Taiwan, China.

Data for Indonesia include Timor-Leste through

Montenegro declared independence from Ser-

Data consistency, reliability, and comparability

1999 unless otherwise noted.

Aggregate measures for income groups

Considerable effort has been made to standardize

The aggregate measures for income groups include

the data, but full comparability cannot be assured,

bia and Montenegro on June 3, 2006. When

216 economies (the economies listed in the main

and care must be taken in interpreting the indicators.

available, data for each country are shown

tables plus those in table 1.6) whenever data are

Many factors affect data availability, comparability,

separately. However, some indicators for Serbia

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 for most indicators from 1999

xxii

2012 World Development Indicators


onward for Serbia exclude data for Kosovo, which

Low-income economies are those with a GNI per

in 1999 became a territory under international

capita of $1,005 or less in 2010. Middle-income

administration pursuant to UN Security Council

economies are those with a GNI per capita of $1,006–

Resolution 1244 (1999); any exceptions are

$12,275. Lower middle-income and upper middle-

noted. Kosovo became a World Bank member

income economies are separated at a GNI per capita

on June 29, 2009, and its data are shown in the

of $3,976. High-income economies are those with a

tables when available.

GNI per capita of $12,276 or more. The 17 participat-

Netherlands Antilles, for which data were listed in

ing member countries of the euro area are presented

previous editions, ceased to exist on October 10,

as a subgroup under high-income economies.

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

The cutoff date for data is February 1, 2012.

2010. Data for Curaçao and Sint Maarten, which

became countries within the Kingdom of the

Symbols

Netherlands, are now listed separately. Data for

..

Bonaire, Saba, and St. Eustatius, which became

means that data are not available or that aggregates

special municipalities of the Netherlands, are

cannot be calculated because of missing data in the

included in data for the Netherlands.

years shown.

South Sudan declared its independence on July 9, 2011. When available, data are shown

0 or 0.0

separately for South Sudan; data for Sudan

means zero or small enough that the number would

include South Sudan unless otherwise noted.

round to zero at the displayed number of decimal places.

Classification of economies For operational and analytical purposes the World

/

Bank’s main criterion for classifying economies is

in dates, as in 2009/10, means that the period of

gross national income (GNI) per capita (calculated

time, usually 12 months, straddles two calendar

by the World Bank Atlas method). Every economy

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

is classified as low income, middle income (subdi-

fiscal year.

vided into lower middle and upper middle), or high income. For income classifications see the map on

$

the inside front cover and the list on the front cover

means current U.S. dollars unless otherwise noted.

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

>

is used for convenience; it is not intended to imply

means more than.

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

<

reached a preferred or final stage of development.

means less than.

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

Data presentation conventions

capita changes over time, the country composition

of income groups may change from one edition of

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

World Development Indicators to the next. Once the

A billion is 1,000 million.

classification is fixed for an edition, based on GNI

A trillion is 1,000 billion.

per capita in the most recent year for which data are

Figures in italics refer to years or periods other

available (2010 in this edition), all historical data

than those specified or to growth rates calculated

presented are based on the same country grouping.

for less than the full period specified.

2012 World Development Indicators

xxiii


WORLD VIEW


W

e now have data to monitor the first 10 years of the Millennium Development Goals. Results are starting to appear, and we have a better view of where we will be in 2015. We will not achieve all the targets we set for ourselves, but progress measured against 1990 benchmarks accelerated in the last decade, lifting millions of people out of poverty, enrolling millions of children in school, and sharply reducing loss of life from preventable causes. We know this because we have access to greatly improved statistics. The need for reliable and timely statistics was recognized long before the Millennium Development Goals were proposed in 2000, but the widespread attention given to their quantitative targets has increased demand for regular and uniform reporting of key indicators. The International Development Goals proposed by the Organisation for Economic Co-operation and Development in 1996 (OECD DAC 1996) included 21 indicators under seven headings that anticipated the Millennium Development Goals. The World Bank’s 1992 Poverty Reduction Handbook (World Bank 1992) noted the need for an overall strategy for country statistical capacity and institution building. Faced with large gaps in the international database, the Partnership in Statistics for Development in the 21st Century (PARIS21) was established in 1999 to coordinate efforts to increase developing countries’ statistical capacity. In 2004 the Second Roundtable on Managing for Development Results endorsed the Marrakech Action Plan for Statistics, creating an international agenda for support to statistics in developing countries. Subsequently the Accra Agenda for Action made broad commitments on behalf of donors and developing countries to strengthen national statistical systems; provide more data disaggregated by sex, region, and economic status; and “invest in strengthening developing countries’ national statistical

capacity and information systems, including those for managing aid” (OECD 2008a). More recently, the 2009 Dakar Declaration on the Development of Statistics reaffirmed that “concerted and co-ordinated actions are required to make more effective use of statistical data to support poverty reduction policies and programs and to strengthen and sustain the capacity of statistical systems especially in developing countries” (PARIS21 2009a). Much progress has been made. When the current round of censuses concludes in 2014, 98 percent of the world’s population will have been counted. Since donors began reporting support for statistical capacity development in 2008, financial commitments to statistics have increased 60 percent to $1.6 billion over 2008– 10. More than 55 developing countries have improved the data collection, management, and dissemination of household surveys. The United Nations Inter-Agency and Expert Group on the Millennium Development Goal Indicators has conducted a series of regional workshops aimed at improving the MDG monitoring and has reported annually on progress. The quality of statistics as measured by the World Bank’s statistical capacity indicator has improved from its benchmark level of 54 in 1999 to 67 in 2011. The availability of data for monitoring the Millennium Development Goals has improved commensurately: in 2003 only 4 countries had two data points for at least 16 of 22 principal Millennium Development Goals indicators; by 2009 this had risen to 118 countries (PARIS21 2009b). Any assessment of the Millennium Development Goals must acknowledge that amid all the signs of progress, there are gaps. Shortcomings. Disappointments. Some targets will not be reached in this decade or the next. Likewise the statistical record is still incomplete, Continuing progress will require renewed commitment and careful monitoring.

1

2012 World Development Indicators

1


Eradicate extreme poverty and hunger

Goal 1

P

overty and hunger remain, but fewer people live in extreme poverty. The proportion of people living on less than $1.25 a day fell from 43.1 percent in 1990 to 22.2Â percent in 2008. While the food, fuel, and financial crises over the past four years have worsened the situation of vulnerable populations and slowed poverty reduction in some countries, global poverty rates have continued to fall. Between 2005 and 2008 both the poverty rate and the number of people living in extreme poverty fell in all six developing country regions, the first time that has happened. Preliminary estimates for 2010 show that the extreme poverty rate fell further, reaching the global target of the Millennium Development Goals of halving world poverty five years early. Further progress is possible and likely before the 2015 target date of the Millennium Development Goals, if developing countries maintain the robust growth rates achieved over much of the past decade. But even then, hundreds of millions of people will remain mired in poverty, especially in Sub-Saharan Africa and South Asia and wherever poor health and lack of education deprive people of productive employment; environmental resources have been depleted or spoiled; and corruption, conflict, and misgovernance waste public resources and discourage private investment.

1a

Poverty rates fell sharply in the new millennium Poverty rate at $1.25 a day (percent)

1b

Fewer people living in extreme poverty People living on $1.25 a day or less (billions) 2.0

75

Europe & Central Asia Middle East & North Africa Sub-Saharan Africa

1.5

Latin America & Caribbean

50 South Asia

East Asia & Pacific

1.0 25

Sub-Saharan Africa

East Asia & Pacific

0.5 Latin America & Caribbean Middle East & North Africa

0 1993

1996

1999

2002

2005

2008

Source: World Bank staff estimates.

The most rapid decline in poverty occurred in East Asia and the Pacific, where extreme poverty in China fell from 60 percent in 1990 to 13 percent. In developing countries outside China, the poverty rate fell from 37 percent to 25 percent. Poverty remains widespread in Sub-Saharan Africa and South Asia, but progress has been substantial. In South Asia the poverty rate fell from 54 percent in 1990 to 36 percent in 2008. And over 2005– 2008 the poverty rate in Sub-Saharan Africa fell 4.8 percentage points to less than 50 percent, the largest drop in Sub-Saharan Africa since international poverty rates have been computed.

2

2012 World Development Indicators

South Asia

0

Europe & Central Asia

1990

1990

1993

1996

1999

2002

2005

2008

Source: World Bank staff estimates.

In 2008, 1.28 billion people lived on less than $1.25 a day. Since 1990 the number of people living in extreme poverty has fallen in all regions except Sub-Saharan Africa, where the rate of population growth exceeded the rate of poverty reduction, increasing the number of extremely poor people from 290Â million in 1990 to 356 million in 2008. The largest number of poor people remain in South Asia, where 571 million people live on less than $1.25 a day, down from a peak of 641 million in 2002.


WORLD VIEW

1c

Progress toward poverty reduction Share of countries making progress toward poverty reduction (percent)

Reached target Seriously off track

On track Off track Insufficient data

100

1e

More and less income equality Low income Upper middle income

Gini coefficient (most recent value, 2000–09)

Lower middle income No change

70 Less equal 60

50 50 0 40 50 30 More equal 100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

30

40 50 60 Gini coefficient (most recent value, 1990–99)

70

Source: World Development Indicators database.

Individual country progress is assessed by comparing the rate of poverty reduction with the average rate required to achieve a 50 percent reduction in 25 years. Countries that have already reached the target are listed as “achieved.” Those matching the required rate are listed as “on track.” Countries that will take longer but could reach the target by 2040, based on past performance, are listed as “off track.” And those that would need still longer or where poverty rates have increased are listed as “seriously off track.” 1d

Progress toward reducing undernourishment Share of countries making progress toward reducing undernourishment (percent)

20 20

Reached target Seriously off track

On track Off track Insufficient data

Is income inequality improving or getting worse? At any level of income per person, the less equal the distribution of income the greater the poverty rate. The Gini coefficient is a common measure of inequality. A higher value indicates greater inequality. Poor countries often have less equal distributions of income than do rich countries, but there are significant regional differences as well. Data for 81 countries with values measured before and after 2000 show Gini coefficients fell in 44, including many low-income economies. 1f

Many children remain malnourished Malnutrition prevalence, weight for age (percent of children under age 5)

Low income Lower middle income Upper middle income

50

100 40 50 30 0 20 50

10

0

100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

Undernourishment measures the availability of food to meet people’s basic energy needs. The Millennium Development Goals call for halving the proportion of undernourished people, but few countries will reach that target by 2015. Rising agricultural production has kept ahead of population growth, but increasing food prices and the diversion of food crops to fuel production have reversed the declining rate of undernourishment since 2004–06. The Food and Agriculture Organization estimated that there were 739 million people without adequate daily food intake in 2008.

1990

2010

Source: World Health Organization and World Development Indicators database.

Malnutrition rates have dropped substantially since 1990, but more than 100 million children under age 5 remain malnourished. Only 40 of 90 countries with adequate data to monitor trends are on track to reach the Millennium Development Goal target of halving the number of people who suffer from hunger. Malnutrition in children often begins at birth, when poorly nourished mothers give birth to underweight babies. Malnourished children develop more slowly, enter school later, and perform less well. Programs to encourage breastfeeding and improve the diets of mothers and children can help. 2012 World Development Indicators

3


Achieve universal primary education

Goal 2

T

he commitment to provide primary education to every child is the oldest of the Millennium Development Goals, having been set at the first Education for All conference in Jomtien, Thailand, more than 20 years ago. Achieving this goal has often seemed tantalizingly near, but only Latin America and the Caribbean has reached the goal, although East Asia and Pacific and Europe and Central Asia are close. Progress among the poorest countries, slow in the 1990s, has accelerated since 2000, particularly in South Asia and Sub-Saharan Africa, but full enrollment remains elusive. Many children start school but drop out before completing the primary stage, discouraged by cost, distance, physical danger, and failure to progress. Even as countries approach the target of Millennium Development Goal 2, the education demands of modern economies expand. In the 21st century primary education will be of value only as a stepping stone toward secondary and higher education.

1g

The last step toward education for all Primary school completion rate (percent)

1h

64 million children out of school Number of children not attending primary school (millions)

1999

2009

50

110 East Asia & Pacific, actual

East Asia & Pacific, target

Latin America & Caribbean, actual

100

40

Europe & Central Asia, target Europe & Central Asia, actual

90 80

Latin America & Caribbean, target

70

South Asia, target

Middle East & North Africa, target

Middle East & North Africa, actual

30

20

South Asia, actual

10 60 Sub-Saharan Africa, target

50 1991

1995

0

Sub-Saharan Africa, actual

2000

2005

2010

2015

Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.

In 2009, 87 percent of children in developing countries completed primary school. In most regions school enrollment picked up after the Millennium Development Goals were promulgated in 2000, when the completion rate was 80 percent. Sub-Saharan Africa and South Asia, which started out farthest behind, have made substantial progress but will still fall short of the goal. The Middle East and North Africa has stalled at completion rates of around 90 percent, while Europe and Central Asia and East Asia and Pacific are within striking distance but have made little progress in the last five years.

4

2012 World Development Indicators

East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics.

Many children enroll in primary school but attend intermittently or drop out entirely. This is particularly the case for girls whose work is needed at home. In rural areas the work of children of both sexes may be needed during planting and harvest. Other obstacles, including the lack of suitable facilities, absence of teachers, and school fees, discourage parents from sending their children to school. The problem is worst in South Asia and Sub-Saharan Africa, where more than 48 million children of primary school age are not in school.


WORLD VIEW

1i

Progress toward education for all Share of countries making progress toward universal primary education(percent)

Reached target Seriously off track

On track Off track Insufficient data

1k

How much schooling Madagascar, 2008 Share of people ages 15–19 completing each year of schooling, by year and wealth quintile (percent)

100

100

50

75 Second quintile

0

50

50

25

Richest quintile

Third quintile

Poorest quintile

100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Fourth quintile

0 Year 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

Year 8

Year 9

Source: World Bank staff calculations.

Sixty developing countries, half the countries for which adequate data are available, have achieved or are on track to achieve the Millennium Development Goal target of a full course of primary schooling for all children. Twelve more will miss the 2015 deadline. At their current rate of progress they will achieve full enrollment sometime after 2015. That leaves at least 48 countries seriously off track, making little or no progress, 30 of them in Sub-Saharan Africa.

Bangladesh, 2007 Share of people ages 15–19 completing each year of schooling, by sex and location (percent) 100

75 Urban, girls Urban, boys

Rural, girls

50 Rural, boys

25

0 Year 1

1j

The missing enrollments

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

Year 8

Year 9

Cambodia, 2005 Share of people ages 15–19 completing each year of schooling, by parents’ education level (percent)

Ratio of girls’ to boys’ primary school enrollment (percent)

100 Boys underenrolled

100 Girls underenrolled

75

Secondary Some higher

90 50

Incomplete secondary

80

Primary

25 70 30

Incomplete primary No education

50

70

90

100

Net primary enrollment rate (percent) Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.

A major obstacle to achieving universal primary education is the shortfall in girls’ enrollments. Almost all school systems with low enrollment rates show underenrollment of girls in primary school. In only a few places are boys’ enrollment rates lower than girls’. Starting at such a disadvantage, most girls will never catch up. Achieving the Millennium Development Goal target to enroll and keep girls in school is essential.

0 Year 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

Year 8

Year 9

Source: Demographic and Health Surveys.

Many factors affect how long students stay in school. Children from poor families are less likely to attend or stay in school. In most countries girls and children from rural areas are also less likely to attend, but Bangladesh has used targeted incentives to raise girls’ attendance rates. In Cambodia and everywhere else parents with lower levels of education are less likely to keep their children in school. Achieving the Millennium Development Goal target will require breaking the cycle of lack of education– poverty– low enrollment.

2012 World Development Indicators

5


Promote gender equity and empower women

Goal 3

W

omen are making progress along the three dimensions of gender equality and women’s empowerment that the Millennium Development Goals monitor: education, employment, and participation in public decisionmaking. These are important, but there are others. Efforts are under way to improve monitoring of women’s access to financial services, entrepreneurship, and migration and remittances as well as of violence against women. Time-use surveys, for example, can illuminate differences in the roles of women and men within the household and the workplace. Disaggregating other statistical indicators by sex can also reveal patterns of disadvantage or, occasionally, advantage for women. Whatever the case, women make important contributions to economic and social development. Expanding opportunities for them in the public and private sectors is a core development strategy. And good statistics are essential for developing policies that effectively promote gender equity and increase the welfare and productivity of women.

1l

Increasing participation by girls at all levels of education Ratio of girls’ to boys’ enrollment rate, 2009 (percent) 150 125 100 75 50 25 0 Primary Secondary Tertiary

Primary Secondary Tertiary

Primary Secondary Tertiary

Primary Secondary Tertiary

Primarya Secondary Tertiarya

Primary Secondary Tertiary

East Asia & Pacific

Europe & Central Asia

Latin America & Caribbean

Middle East & North Africa

South Asia

Sub-Saharan Africa

a. Data are for 2008. Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.

Girls have made substantial gains in primary and secondary school enrollment. In many countries girls’ enrollment rates outnumber boys’, particularly in secondary school. And more girls are staying in school. In 1991 only 73 percent of girls in developing countries finished primary school; by 2010 the completion rate stood at 86 percent. But this comparison obscures the underlying problem of underenrollment. Girls are still less likely to enroll in primary school or to stay through the end of primary

6

2012 World Development Indicators

school. In some countries the situation changes at the secondary level. Girls who complete primary school may be more likely to stay in school, while boys drop out. In Europe and Central Asia and Latin American and the Caribbean the differences in higher education enrollment are substantial. This is an unsatisfactory path to equity. Rapid growth and poverty reduction truly require education for all.


WORLD VIEW

1m

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

Reached target Seriously off track

On track Off track Insufficient data

1o

More women decisionmakers Proportion of seats held by women in national parliaments (percent) 25

Latin America & Caribbean

100

High income

20

South Asia

East Asia & Pacific

50 15 Europe & Central Asia

Sub-Saharan Africa

0

10 Middle East & North Africa

50

5

0

100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Source: Inter-Parliamentary Union and World Development Indicators database.

Substantial progress has been made toward increasing the proportion of girls enrolled in primary and secondary education. By the end of the 2009/10 school year, 96 countries had achieved equality in enrollment rates and 7 more were on track to do so by 2015. That leaves only 27 countries off track or seriously off track, mostly low- and lower middle-income economies in the Middle East and North Africa, South Asia, and Sub-Saharan Africa. Fourteen countries lacked adequate data to assess progress. 1n

Women have become a larger part of the workforce Share of women employed in the nonagricultural sector (percent of total nonagricultural employment)

1990

Most recent year

The proportion of parliamentary seats held by women has increased everywhere. In Latin America and the Caribbean women now hold 24 percent of all parliamentary seats. The most impressive gains have been made in South Asia, where the number of seats held by women tripled between 1999 and 2000. In Sub-Saharan Africa Rwanda leads the way, making history in 2008 when it elected a parliament composed 56 percent of women. The Middle East and North Africa lags far behind.

Underweight children under age 5, most recent year available, 2005–11 (percent)

50

50

40

40

30

30

20

20

10

10

Female

Male

Note: Insufficient data are available for Sub-Saharan Africa. Source: International Labour Organization and World Development Indicators database.

Women’s share in paid employment in the nonagricultural sector has risen marginally but remains less than 20 percent in the Middle East and North Africa and South Asia. In many countries the majority of women who work hold insecure jobs outside the formal sector. Overall labor force participation rates of women follow a similar pattern, but they are highest in Sub-Saharan Africa, where 60 percent of women ages 15 and older are in the labor force, although many are employed as unpaid family workers.

Dj ib ou ti

PD R La o

Su da n

So m al ia

South Asia

Et hi op ia

Middle East & N. Africa

Ne pa l

Latin America & Carib.

Ni ge r

Europe & Central Asia

In di a Ba ng la de sh

0 East Asia & Pacific

Ti m or -Le st e

0

1p

A slim lead for girls

Source: World Health Organization and World Bank staff calculations.

Girls are less likely to attend school, have secure jobs, or hold public office. But by most measures, they have an advantage in one area: malnutrition. Out of 99 countries with data for 2005–11, 19 had a larger proportion of underweight girls than of underweight boys; 74 had a larger proportion of underweight boys than of underweight girls, and 6 had no difference. The chart shows the 10 countries with the highest proportion of underweight children during the period.

2012 World Development Indicators

7


Reduce child mortality

Goal 4

I

n 1990, 12 million children died before their fifth birthday. By 1999 there were fewer than 10 million child deaths, and the number has continued to fall to just over 7.5 million in 2010. That is good news, but the ambitious Millennium Development Goal target of a two-thirds reduction in the under-five mortality rate will be met by no more than 40 countries. Only Latin America and the Caribbean and upper middle-income economies as a whole will, on average, reach the target. Most children die from causes that are readily preventable or curable with existing interventions, such as acute respiratory infections, diarrhea, measles, and malaria. And most die in the first year of life. Rapid improvements prior to 1990 in a few countries gave hope that mortality rates for infants and children could be cut further in the following 25 years, but progress slowed almost everywhere after 1990, leaving most countries far behind the target before the goals were announced. Was the target too challenging? Perhaps. But the more important question is whether it encouraged countries to use their resources wisely to achieve the fastest possible progress.

1q

Still far to go in reducing under-ďŹ ve mortality Under-five mortality rate (per 1,000 live births) 200

1r

Most deaths occur in the ďŹ rst year of life Deaths, 2010 (millions) 4

Children ages 1–5

Infants

Sub-Saharan Africa

3

150 South Asia

2

100 Middle East & North Africa East Asia & Pacific

50

Europe & Central Asia

1

Latin America & Caribbean High income

0

0 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Source: World Health Organization and World Development Indicators database.

Mortality rates have been falling everywhere. In developing countries the mortality rate fell from an average of 98 per 1,000 live births in 1990 to 63 in 2010. But rates remain much higher in many countries, especially in Sub-Saharan Africa and parts of South Asia. In Sub-Saharan Africa one child in eight dies before his or her fifth birthday. The odds are somewhat better in South Asia, where 1 child in 15 dies. But even in these regions there are countries exhibiting rapid progress.

8

2012 World Development Indicators

East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Health Organization and World Development Indicators database.

Almost 70 percent of deaths of children under age 5 occur in the first year of life, and half in the first month. Therefore, reducing child mortality requires addressing the causes of neonatal and infant deaths: inadequate care at birth and afterward, malnutrition, poor sanitation, and exposure to acute and chronic disease. Improvements in infant and child mortality are, in turn, the largest contributors to increased life expectancy in most countries.


WORLD VIEW

1s

Progress toward reducing child mortality Share of countries making progress toward reducing child mortality (percent)

Reached target Seriously off track

On track Off track Insufficient data

100

50

0

50

100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

A concerted effort by academic researchers and international statistical agencies has greatly improved measurement of infant and child mortality. Therefore, few countries lack estimates of child mortality rates, although many are derived from statistical models. Ten countries have already achieved a two-thirds reduction in under-five mortality rates since 1990, and 26 are on track to do so by 2015. But that leaves 105 countries, with half of developing countries’ population, off track or seriously off track. 1t

Preventing childhood diseases Children ages 12–23 months immunized against measles (percent)

1u

For some, better than expected improvements Reduction since 1990

2010, actual

2010, target

2010, predicted

Seychelles Mauritius Botswana Cape Verde South Africa Namibia Zimbabwe Lesotho Gabon São Tomé and Príncipe Swaziland Kenya Congo, Rep. Ghana Mauritania Sudan Comoros Cameroon Senegal Eritrea Togo Côte d’Ivoire Tanzania Madagascar Rwanda Central African Republic Gambia, The Uganda Benin Somalia Congo, Dem. Rep. Zambia Burundi Ethiopia Equatorial Guinea Burkina Faso Chad Guinea-Bissau Nigeria Mozambique Malawi Liberia Guinea Angola Mali Sierra Leone Niger

100 0

High income

100

200

300

400

Under-five mortality rate (deaths per 1,000 live births) Upper middle income

Source: World Bank staff calculations.

75 Lower middle income Low income

50

25

0 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Source: World Health Organization and World Development Indicators database.

Illnesses that could be prevented by early childhood vaccinations still account for many child deaths. Despite years of vaccination campaigns, many children in low- and lower middle-income economies remain unprotected. Measles is one example. Other recommended immunizations include diphtheria, pertussis, tetanus, and the BCG immunization for tuberculosis. To be successful, vaccination campaigns must reach all children and be sustained over time.

In 1990 the under-five mortality rate in Niger stood at 311 per 1,000 live births, the worst in the world. In the same year, Seychelles, with an under-five mortality rate of 16, was the best in Sub-Saharan Africa. How have they fared since? In the 20 years from the Millennium Development Goals baseline, Niger’s mortality rate fell by 168, the greatest in the region, while Seychelles’s fell by 3. In proportional terms Niger experienced a 54 percent reduction—second greatest in the region—and Seychelles a 16 percent reduction. Both are short of the Millennium Development Goal target, but Niger, having started in last place, has progressed faster. Has this been the general rule? On average, countries in Sub-Saharan Africa that started in worse positions have done better. But experience has been mixed: conflict- affected countries such as the Democratic Republic of the Congo and Somalia have made almost no progress, while similarly situated countries such as Uganda and Zambia have done much better. Two countries, Madagascar and Malawi, are on track to achieve the Millennium Development Goal target. Several others, including Eritrea, Niger, and Tanzania, are close. Only one country, Zimbabwe, moved backward from 1990 to 2010. 2012 World Development Indicators

9


Reduce maternal mortality

Goal 5

A

n estimated 358,000 maternal deaths occurred worldwide in 2008, a 34 percent decrease since 1990. The Millennium Development Goals call for reducing the maternal mortality ratio by 75 percent between 1990 and 2015, but few countries and no developing country region on average will achieve this target. What makes maternal mortality such a compelling problem is that it strikes young women experiencing a natural life event. They die because they are poor. Malnourished. Weakened by disease. They die because they lack access to trained health care workers and modern medical facilities. And because women in poor countries have more children, their lifetime risk of maternal death may be more than 200 times greater than for women in Western Europe and North America. Reducing maternal mortality requires a comprehensive approach to women’s reproductive health, starting with family planning and access to contraception. Many health problems among pregnant women are preventable or treatable through visits with trained health workers before childbirth. Good nutrition, vaccinations, and treatment of infections can improve outcomes for mother and child. Skilled attendants at time of delivery and access to hospital treatments are essential for dealing with life-threatening emergencies such as severe bleeding and hypertensive disorders.

Maternal mortality rates have been falling, but large regional differences persist Maternal mortality ratio, modeled estimate (per 100,000 live births) 1,000

The 12 countries with highest lifetime risk of maternal death

1v 1990 2005

1995 2008

2000

1w

Lifetime risk of maternal death, 2008 (percent) 10

8

750

6

4

500

2 250

South Asia

Sub-Saharan Africa

Source: World Health Organization and World Development Indicators database.

About half of all maternal deaths occur in Sub-Saharan Africa and a third in South Asia, but mothers in other regions face substantial risks as well. The maternal mortality ratio may be many times higher in fragile and conflict-afflicted states than in countries with strong institutions and well organized health systems.

10

2012 World Development Indicators

So m al ia

Ni ge r

M al i

Ch ad Af gh an is ta n

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

Le on e Li be Gu ria in ea -B is sa u

East Asia & Pacific

Si er ra

0

Co Bu ng ru nd o, i De m .R ep . Ni ge ria Ta nz an ia

0

Source: World Health Organization and World Development Indicators database.

In high-fertility countries women are repeatedly exposed to the risk of maternal mortality. In Afghanistan in 2008, where the lifetime risk of maternal death was over 9 percent, one woman in 11 is expected to die in childbirth; in Burundi one woman in 25 is at risk. In high-income economies the lifetime risk is less than 0.03 percent, or less than 1 woman in 3,500.


WORLD VIEW

1x

Progress in reducing maternal mortality Share of countries making progress toward reducing maternal mortality (percent)

1z

Fewer young women giving birth Adolescent fertility rate (births per 1,000 women ages 15–19)

Reached target Seriously off track

On track Off track Insufficient data

150 Sub-Saharan Africa

100

100

50

South Asia

Latin America & Caribbean

0 50 50

Middle East & North Africa

Europe & Central Asia East Asia & Pacific

0

100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Source: World Health Organization and World Development Indicators database.

Progress in reducing maternal mortality ratios has been slow, far slower than imagined by the Millennium Development Goal target of a 75 percent reduction from 1990 levels. Only four countries have achieved this target, and five more are on track. Accurately measuring maternal mortality is difficult and requires specialized surveys and good reporting of vital events. Recent efforts by statisticians have improved estimates, but for many countries the need for improved monitoring of maternal health will continue long past 2015. 1y

Planning for motherhood Contraceptive prevalence, 2010 (percent of women ages 15–49)

The adolescent fertility rate is highest in Sub- Saharan Africa and is declining slowly. Women who give birth at an early age are likely to bear more children and are at greater risk of death or serious complications from pregnancy. In many developing countries the number of women ages 15–19 is still increasing. Preventing unintended pregnancies and delaying childbirth among young women increase the chances of their attending school and eventually obtaining paid employment.

1aa

Help for mothers Births attended by skilled health staff, 2010 (percent)

80

100

60

75

40

50

20

25

0

0 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Health Organization and World Development Indicators database.

Contraceptive use has increased in most developing countries for which data are available. In almost all regions more than half of women who are married or in union use some method of birth control. More than 200 million women want to delay or cease childbearing, and a substantial proportion say that their last birth was unwanted or mistimed. Worldwide an estimated 120 million women have an unmet need for family planning.

East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Health Organization and World Development Indicators database.

In South Asia and Sub- Saharan Africa less than half of all births are attended by doctors, nurses, or trained midwives. Having skilled health workers present for deliveries is key to reducing maternal mortality. In many places women have only untrained caregivers or family members to attend them during childbirth. Skilled health workers are trained to give necessary care before, during, and after delivery; they can conduct deliveries on their own, summon additional help in emergencies, and provide care for newborns.

2012 World Development Indicators

11


Combat HIV/AIDS, malaria, and other diseases

Goal 6

E

pidemic diseases exact a huge toll in human suffering and lost opportunities for development. Poverty, armed conflict, and natural disasters contribute to the spread of disease and are made worse by it. In Africa the spread of HIV/AIDS has reversed decades of improvement in life expectancy and left millions of children orphaned. It is draining the supply of teachers and eroding the quality of education. There are 300–500 million cases of malaria each year, leading to more than 1 million deaths. Malaria is a disease of poverty. Nearly all the cases occur in Sub-Saharan Africa, where the most lethal form of the malaria parasite is abundant. Most deaths from malaria are among children under age 5, but the disease can be debilitating in adults as well. Tuberculosis kills some 2 million people a year, most of them ages 15–45. The disease, once controlled by antibiotics, is spreading again because of the emergence of drug-resistant strains. People living with HIV/AIDS, which reduces resistance to tuberculosis, are particularly vulnerable, as are refugees, displaced persons, and prisoners living in close quarters and unsanitary conditions. Well managed medical intervention using appropriate drug therapy is the key to stopping the spread of tuberculosis.

1bb

Bringing HIV/AIDS under control Prevalence of HIV (percent of population ages 15–49)

1cc

Millions of people still afflicted with HIV/AIDS Population living with HIV, 2009 (millions) 6

7.5

5 Sub-Saharan Africa

4 5.0 3 2 2.5 1

1992

1994

1996

1998

2000

2002

2004

2006

2009

Source: World Health Organization, Joint United Nations Programme on HIV/AIDS, and World Development Indicators database.

Sub-Saharan Africa remains the center of the HIV/AIDS epidemic, but the proportion of adults living with AIDS has begun to fall even as the survival rate of those with access to antiretroviral drugs has increased. In Africa 58 percent of adults with HIV/AIDS are women. Among young people ages 15–24, the prevalence rate among women is more than twice that among men. Latin America and the Caribbean, where 0.5 percent of adults are infected, has the next highest prevalence rate.

12

2012 World Development Indicators

Ke ny a

In di a Ni ge So ria ut h Af ric a

1990

Ch in a M al aw Fe i de ra tio n Za m bi a Ug an da Zi m ba bw M oz e am bi qu e Ta nz an ia

0.0

0

Ru ss ia n

Latin America & Caribbean

Other developing regions

Ca m er oo n

High income

Source: World Health Organization, Joint United Nations Programme on HIV/AIDS, and World Development Indicators database.

In 2009, 31–33 million people were living with HIV/AIDS, and approximately 1.5 million of them were under age 15. Another 16.9 million children, 14.8 million of them in Sub-Saharan Africa, have lost one or both parents to AIDS. By the end of 2009, 5.25 million people were receiving antiretroviral drugs, or 36 percent of the population for which the World Health Organization recommends treatment.


WORLD VIEW

1dd

Progress toward reversing the HIV epidemic Share of countries making progress against HIV/AIDS (percent)

Halted and reversed Stable low prevalence

Halted or reversed Not improving No data

100

Protecting children from malaria

1ff

First observation (2000 or earlier)

Most recent observation (2006 or later)

Swaziland Mauritania Côte d’Ivoire Guinea

50

Congo, Rep. Comoros 0

Burkina Faso Chad Somalia

50

Cameroon Central African Republic

100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

Zimbabwe Angola Benin Mozambique

The Millennium Development Goals call for halting and then reversing the spread of HIV/AIDS by 2015. The progress assessment shown here is based on prevalence rates for adults ages 15–49. Countries that have a declining prevalence rate since 2005 are assessed to have halted the epidemic; those that have a prevalence rate less than their earliest measured rate have reversed the epidemic. Countries that have a prevalence rate of less than 0.2 percent are considered stable. Countries that have a prevalence rate greater than 0.2 percent and that have neither halted nor reversed the epidemic are shown as not improving.

Sudan Sierra Leone Liberia Ghana Nigeria Senegal Uganda Ethiopia Namibia Guinea-Bissau Congo, Dem. Rep. Burundi Madagascar

1ee

Turning the tide of tuberculosis

Kenya Eritrea

Tuberculosis incidence and prevalence rates (per 100,000 people)

Gambia, The

500

Low income, prevalence

Zambia Lower middle income, prevalence

Gabon

400

São Tomé and Príncipe Malawi

300

Low income, incidence

Togo Tanzania

Lower middle income, incidence

200

Niger

Upper middle income, prevalence

Rwanda

100 Upper middle income, incidence

0

0 1990

Mali

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

25

50

75

Use of insecticide-treated nets (percent of population under age 5) Source: World Health Organization and World Development Indicators database.

Source: World Health Organization and World Development Indicators database.

Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. The data shown here illustrate the association of tuberculosis with poverty. The incidence rate is three times higher in low-income economies than in upper middle-income economies. The number of new tuberculosis cases peaked in 2004, and prevalence rates are also declining, but the targets of halving the 1990 prevalence and death rates by 2015 are unlikely to be met.

Malaria is endemic in most tropical and subtropical regions, but 90 percent of malaria deaths occur in Sub-Saharan Africa. Those most severely affected are children under age 5. Even those who survive malaria do not escape unharmed. Repeated episodes of fever and anemia take a toll on mental and physical development. Insecticide-treated nets have proved to be an effective preventative, and their use has grown rapidly. Between 2008 and 2010, 290 million nets were distributed in Sub-Saharan Africa. But coverage remains uneven. In some countries with large numbers of reported cases, use of nets for children remains at less than 20 percent. 2012 World Development Indicators

13


Ensure environmental sustainability

Goal 7

S

ustainable development can be ensured only by protecting the environment and using its resources wisely. Poor people, often dependent on natural resources for their livelihood, are the most affected by environmental degradation and natural disasters (fires, storms, earthquakes), whose effects are worsened by environmental mismanagement. Poor people also suffer from shortcomings in the built environment: whether in urban or rural areas, they are more likely to live in substandard housing, lack basic services, and be exposed to unhealthy living conditions. The seventh goal of the Millennium Development Goals is the most wide ranging but perhaps the least well specified. Among its 10 indicators, only 3 are associated with quantified and timebound targets. For others we can only monitor trends. Most countries have adopted principles of sustainable development and agreed to international accords on protecting the environment. But the failure to reach a comprehensive agreement on limiting greenhouse gas emissions leaves billions of people and future generations vulnerable to the impacts of climate change. Growing populations put more pressure on marginal lands and expose more people to hazardous conditions that will be exacerbated by global warming.

1gg

Carbon dioxide emissions continue to rise

1hh

Forest losses and gains

Average annual change in forest area, 2000–10 (thousands of square kilometers)

Carbon dioxide emissions (millions of metric tons)

25

30 High income

0

20

Upper middle income

–25

10

Lower middle income Low income

0 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

–50 East Asia Europe & Latin & Pacific Central Asia America & Carib.

Middle East & N. Africa

South Asia

Sub-Saharan High Africa income

Source: Carbon Dioxide Information Analysis Center and World Development Indicators database.

Source: Food and Agriculture Organization and World Development Indicators database.

Annual emissions of carbon dioxide reached 32 million metric tons in 2008 and are still rising. High-income economies remain the largest emitters, but the rapidly growing upper middleincome economies are not far behind. Measured by emissions per capita, however, emissions by high-income economies are more than three times higher than average emissions by lowand middle-income economies.

Loss of forests threatens the livelihood of poor people, destroys habitat that harbors biodiversity, and eliminates an important carbon sink that helps moderate climate change. Net losses since 1990 have been substantial, especially in Latin American and the Caribbean and Sub-Saharan Africa, and only partially compensated for by net gains in Asia and high-income economies.

14

2012 World Development Indicators


WORLD VIEW

1ii

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

1kk

Many still lack access to sanitation Share of population with access to improved sanitation, 2008 (percent)

Reached target Seriously off track

On track Off track Insufficient data

Rural

Urban

100

100 75 50 50 0 25 50 0 100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Source: World Bank staff calculations.

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Health Organization and World Development Indicators database.

The Millennium Development Goals call for cutting the proportion of the population without access to improved sanitation and water sources in half by 2015. By 2010, 2.7 billion people still lacked access to improved sanitation, and more than 1 billion people practiced open defecation, posing enormous health risks. At the present pace only 37 countries are likely to reach the target—an increase of two since 2008. East Asia and Pacific and Middle East and North Africa are the only developing regions on track to reach the target by 2015. 1jj

Progress toward improved water sources Share of countries making progress toward universal access to an improved water source (percent)

East Asia & Pacific

Sub-Saharan Africa

In 1990, 63 percent of the people living in low- and middleincome economies lacked access to a flush toilet or other form of improved sanitation. By 2010 the access rate had improved 19 percentage points to 44 percent. The situation is worse in rural areas, where 57 percent of the population lack access to improved sanitation. The large urban–rural disparity, especially in Sub-Saharan Africa and South Asia, is the principal reason the sanitation target of the Millennium Development Goals will not be reached. 1ll

Water demand strains supplies Annual freshwater withdrawals, 2009 (percent of internal resources)

Reached target Seriously off track

On track Off track Insufficient data

75

100

50

50

0 25 50 0 100 East Asia & Pacific

Europe & Latin America Middle East Central Asia & Carib. & N. Africa

South Asia

Sub-Saharan Africa

Source: World Bank staff calculations.

In 1990 more than 1 billion people lacked access to drinking water from a convenient, protected source, but the situation is improving. The proportion of people in developing countries with access to an improved water source increased from 71 percent in 1990 to 86 percent in 2008, reaching the Millennium Development Goal target of halving the proportion of people without access to an improved water source. Seventy-three countries have reached or are on track to reach the target. At this rate, only Middle East and North Africa and Sub-Saharan Africa will fall short.

East Asia Europe & Latin & Pacific Central Asia America & Carib.

Middle East & N. Africa

South Asia

Sub-Saharan High Africa income

Source: Food and Agriculture Organization and World Development Indicators database.

Worldwide more than 70 percent of freshwater withdrawals go to agricultural and 20 percent to industrial uses. Only 10 percent go to households, many of which are underserved. The potential impacts of global warming and increased demand for water will require more efficient use of available resources. As pressure grows on internal water resources, conflicts over shared, external resources may also increase.

2012 World Development Indicators

15


Develop a global partnership for development

Goal 8

T

he eighth and final goal distinguishes the Millennium Development Goals from previous sets of resolutions and targeted programs. It recognizes the multidimensional nature of development and the need for wealthy countries and developing countries to work together to create an environment in which rapid, sustainable development is possible. Following the Millennium Summit, world leaders meeting at Monterrey, Mexico, in 2002 agreed to provide financing for development through a coherent process that recognized the need for domestic as well as international resources. Subsequent highlevel meetings expanded on these commitments. Along with increased aid flows and debt relief for the poorest, highly indebted countries, goal 8 recognizes the need to reduce barriers to trade and to share the benefits of new medical and communication technologies. Goal 8 also reminds us that development challenges differ for large and small countries and for those that are landlocked or isolated by large expanses of ocean. Building and sustaining a partnership is an ongoing process that does not stop at a specific date or when a target is reached. However it is measured, a strong commitment to partnership should be the continuing legacy of the Millennium Development Goals.

1mm

Most donors have maintained their aid levels

1nn

But their domestic subsidies to agricultural are greater Agricultural support (percent of GDP) 9

Official development assistance (percent of GNI) 1.25 Norway

Korea, Rep.

1.00 6 0.75 United Kingdom

0.50

Norway Germany

3 Switzerland Japan

0.25

Canada Japan

United States All DAC donors

0.00 1990

1992

1994

1996

1998

2000

2002

0 2004

2006

2008

2010

Source: Organisation for Economic Co-operation and Development.

The financial crisis that began in 2008 and fiscal austerity in many high-income economies have threatened to undermine commitments to increase official development assistance. So far leading donors have maintained their level of support. Total disbursements by members of the Organisation for Economic Co-operation and Development’s Development Assistance Committee reached $130 billion in 2010, a real increase of 4.3 percent over 2008.

16

2012 World Development Indicators

United States All DAC donors (ODA as share of GNI)

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Source: Organisation for Economic Co-operation and Development.

Organisation for Economic Co-operation and Development (OECD) members (which include some upper middle-income economies such as Chile and Mexico) spend more on support to domestic agricultural producers than on official development assistance. In 2010 the OECD producer support estimate was $227Â billion, down about 10 percent from the previous three years.


WORLD VIEW

Developing countries have easier access to OECD markets

1oo

1qq

Debt service burdens have been falling Total debt service (percent of exports of goods, services, and income)

Share of total imports from least developed countries admitted free of duty, excluding arms (percent) 100

50

European Union

Latin America & Caribbean United States

40

75

South Asia

30

Europe & Central Asia

Japan

50

Middle East & North Africa

20

25

East Asia & Pacific

10

Sub-Saharan Africa

0 0

1990 1996

1998

2000

2002

2004

2006

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2009

Source: World Trade Organization, International Trade Center, and United Nations Conference on Trade and Development.

Source: World Development Indicators database.

Many rich countries have pledged to admit the exports of the least developed countries duty free. However, arcane rules of origin and phytosanitary standards keep many countries from qualifying for duty-free access. And uncertainty over market access may inhibit development of export industries. Compared with the European Union, the large U.S. market retains many barriers to the exports of the poorest countries.

1pp

Cellular phones are connecting developing countries

Growing economies, better debt management, and debt relief for the poorest countries have allowed developing countries to substantially reduce their debt burdens. Despite the financial crisis and a 2.3 percent contraction in the global economy in 2009, debt service ratios continued to fall in most developing country regions. Only in Europe and Central Asia has the ratio of debt service to exports risen since 2008, although rising export earnings have helped moderate the trend.

1rr

A more connected world Internet users (per 100 people) 100

Fixed telephones and mobile cellular subscriptions (per 100 people) 125 High income, mobile subscriptions

100 Upper middle income, mobile subscriptions

High income

75

75 Lower middle income, mobile subscriptions

High income, fixed telephones

50

50 Europe & Central Asia

Upper middle income, fixed telephones

25

Low income, mobile subscriptions

Latin America & Caribbean

25

East Asia & Pacific Middle East & North Africa

Lower middle income, fixed telephones

Sub-Saharan Africa

0 1990

Low income, fixed telephones

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Source: International Telecommunication Union and World Development Indicators database.

Telecommunications is an essential tool for development, and new technologies are creating new opportunities everywhere. The growth of fixed-telephone systems has peaked in highincome economies and will never reach the same level of use in developing countries, where mobile cellular subscriptions continue to grow rapidly. In high-income economies, with more than one subscription per person, the pace of growth appears to be slowing. Gradually the world is becoming more connected.

South Asia

0 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Source: International Telecommunication Union and World Development Indicators database.

In 2000 Internet use was spreading rapidly in high-income economies but was barely under way in developing country regions. Now developing countries are beginning to catch up. By 2010 there were an average of 34 internet users per 100 people in upper middle-income economies. Like telephones, Internet use is strongly correlated with income. In low-income economies there were only 5.4 users per 100 people in 2010. But growth is picking up.

2012 World Development Indicators

17


Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day

1.1

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

1.2 1.3 1.4 1.5 1.6 1.7

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

1.8 1.9

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

2.1 2.2 2.3

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

3.1 3.2 3.3

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

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

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

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

4.1 4.2 4.3

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

5.1 5.2 5.3 5.4 5.5

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

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

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

6.1 6.2 6.3

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

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 November 14, 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.

18

2012 World Development Indicators


Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources Target 7.B Reduce biodiversity loss, achieving, by 2010, a significant reduction in the rate of loss

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

7.1 7.2

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

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

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

Target 8.B

Target 8.C

Target 8.D

Target 8.E

Target 8.F

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

a. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. b. 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. 2012 World Development Indicators

19


1.1

Size of the economy Population Surface Population area density

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

20

millions 2010

thousand sq. km 2010

34 3 35 19 40 3 22 8 9 1 149 9 11 9 10 4 2 195 8 16 8 14 20 34 4 11 17 1,338 7 46 66 4 5 20 4 11 1 11 6 10 14 81 6 5 1 83 5 65 2 2 4d 82 24 11 14 10 2 10

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

2012 World Development Indicators

people per sq. km 2010

53 117 15 15 15 109 3 102 110 1,661 1,142 47 360 80 9 74 4 23 69 60 326 80 41 4 7 9 23 143 6,783 42 29 12 91 62 79 106 119 136 131 205 58 81 299 52 32 83 18 118 6 173 78d 235 107 88 134 41 54 363

Gross national income, Atlas method

Gross national income per capita, Atlas method

Purchasing power parity gross national income

$ billions 2010

Rank 2010

$ 2010

Rank 2010

$ billions 2010

14.3 12.7 155.7 75.2 348.4 9.9 1,030.3 394.6 48.3 19.7 104.7 56.5 499.5 7.0 17.9 17.9 13.6 1,830.4 47.3 9.0 1.4 10.6 23.2 1,475.9 2.1 6.9 173.2 5,720.8 231.7 255.3 12.0 8.7 31.7 23.0 61.4 62.2 23.7c 188.3 329.5 49.9 55.7 196.2 21.0 1.8 19.4 32.4 255.2 2,749.8 11.7 0.8 12.0 d 3,522.0 30.1 305.0 39.4 4.0 0.9 6.6

109 117 49 62 26 127 13 25 74 99 57 67 20 139 104 105 113 8 75 131 184 121 95 10 177 140 47 2 37 34 119 135 87 96 66 64 93 43 29 72 69 41 97 180 101 85 35 5 120 198 118 4 88 31 79 160 194 141

410 3,960 b 4,390 3,940 8,620 3,200 46,200 47,030 5,330 18,730 700 5,950 45,840 780 1,810 4,770 6,790 9,390 6,280 550 170 750 1,180 43,250 470 620 10,120 4,270 32,780 5,510 180 2,150 6,810 1,160 13,890 5,520 29,430 c 17,890 59,400 5,030 3,850 b 2,420 3,380 340 14,460 390 47,570 42,370 7,740 450 2,690 d 43,070 1,230 26,950 2,740 400 590 670

204 124 119 125 85 135 20 18 109 62 187 104 21 184 158 112 95 82 101 193 215 185 168 23 199 189 78 121 36 108 214 152 94 171 67 107 40 59 10 110 126 148 131 209 66 207 16 26 89 201 145 25 166 44 142 206 191 188

36.5a 27.3 287.2a 103.1 629.3 17.5 823.0 333.9 83.9 26.0 269.7 129.0 417.3 14.0 46.0 33.5 27.5 2,144.9 101.2 20.7 3.4 29.4 44.4 1,309.5 3.5 13.7 250.5 10,221.7 335.6 419.6 21.4 13.0 52.5a 35.8 83.4 .. 24.4 c 241.0 228.0 89.6a 114.0 491.3 40.6a 2.8a 26.5 86.1 198.9 2,254.9 19.8 2.2 22.2d 3,115.4 40.5 312.7 66.8a 10.2 1.8 11.6a

Per capita $ 2010

1,060a 8,520 8,100a 5,410 15,570 5,660 36,910 39,790 9,270 24,710 1,810 13,590 38,290 1,590 4,640 8,910 13,700 11,000 13,440 1,250 400 2,080 2,270 38,370 790 1,220 14,640 7,640 47,480 9,060 320 3,220 11,270a 1,810 18,890 .. 30,300 c 22,910 41,100 9,030a 7,880 6,060 6,550a 540a 19,810 1,040 37,070 34,750 13,180 1,300 4,990 d 38,100 1,660 27,630 4,650a 1,020 1,180 1,180a

Gross domestic product

Rank 2010

% growth 2009–10

Per capita % growth 2009–10

199 114 117 135 78 133 32 24 108 55 182 88 28 186 142 113 86 98 89 190 213 174 170 27 209 193 80 120 15 109 215 160 96 182 73 42 63 23 111 118 129 126 212 68 200 31 34 90 189 139 29 185 51 141 202 196 195

8.2 3.5 3.3 2.3 9.2 2.1 2.3 2.3 5.0 6.3 6.1 7.6 2.3 3.0 4.1 0.8 7.2 7.5 0.2 9.2 3.9 6.0 2.6 3.2 3.3 4.3 5.2 10.4 7.0 4.3 7.2 8.8 4.2 3.0 –1.2 4.3 1.0 c 2.3 1.3 7.8 3.6 5.1 1.4 2.2 3.1 10.1 3.7 1.5 5.7 5.0 6.4 d 3.7 6.6 –3.5 2.8 1.9 3.5 –5.1

5.2 3.1 1.8 3.0 8.2 1.9 0.7 2.0 3.8 –6.5 4.9 7.8 1.3 0.1 2.5 1.0 5.9 6.6 0.9 6.0 1.3 4.8 1.0 2.0 1.4 1.6 4.2 9.8 6.0 2.9 4.3 6.0 2.7 1.0 –0.9 4.3 0.6c 2.0 0.9 6.3 2.1 3.3 0.9 –0.8 3.1 7.8 3.2 0.9 3.8 2.1 5.4 d 3.8 5.2 –3.8 0.2 –0.3 1.4 –6.3


Population Surface Population area density

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

Gross national income, Atlas method

Gross national income per capita, Atlas method

Purchasing power parity gross national income

1.1

WORLD VIEW

Size of the economy

Gross domestic product

millions 2010

thousand sq. km 2010

people per sq. km 2010

$ billions 2010

Rank 2010

$ 2010

Rank 2010

$ billions 2010

Per capita $ 2010

Rank 2010

% growth 2009–10

Per capita % growth 2009–10

8 10 1,225 240 74 32 4 8 60 3 127 6 16 41 24 49 2 3 5 6 2 4 2 4 6 3 2 21 15 28 15 3 1 113 4g 3 32 23 48 2 30 17 4 6 16 158 5 3 174 4 7 6 29 93 38 11 4 2

112 93 3,287 1,905 1,745 435 70 22 301 11 378 89 2,725 580 121 100 11 18 200 237 65 10 30 111 1,760 65 26 587 118 331 1,240 1,031 2 1,964 34 1,564 447 799 677 824 147 42 268 130 1,267 924 324 310 796 75 463 407 1,285 300 313 92 9 12

68 110 412 132 45 74 65 352 206 249 350 68 6 71 202 503 167 154 28 27 36 413 72 41 4 52 82 36 158 86 13 3 631 58 124g 2 72 30 73 3 209 493 17 48 12 174 16 9 225 47 15 16 23 313 126 116 448 152

14.2 128.6 1,553.9 599.2 330.4 74.9 187.1 207.2 2,159.3 13.0 5,334.4 26.3 123.8 31.8 .. 972.3 6.0 .. 4.5 6.5 26.1 37.5 2.3 0.8 77.1 37.8 9.4 8.8 4.9 220.4 9.2 3.6 9.9 1,008.0 6.5g 5.2 92.6h 10.3 .. 10.3 14.5 814.8 124.2 6.4 5.7 186.4 427.1 49.5 182.8 24.5 8.9 17.6 136.7 192.2 474.9 232.7 61.7 ..

110 53 9 18 27 63 44 39 7 115 3 90 56 86 15 147 157 144 91 82 174 197 61 81 128 134 156 38 130 162 126 14 145 153 59 123 124 108 16 54 146 149 45 24 71 46 92 133 106 52 42 21 36 65

1,870 12,860 1,270 2,500 4,520 2,340 41,820 27,180 35,700 4,800 41,850 4,340 7,580 790 ..e 19,890 3,290 ..f 830 1,050 11,640 8,880 1,040 200 12,320 11,510 4,570 430 330 7,760 600 1,030 7,750 8,890 1,810 g 1,870 2,850h 440 ..e 4,510 490 49,030 28,770 1,110 370 1,180 87,350 18,260 1,050 6,970 1,300 2,720 4,700 2,060 12,440 21,870 15,500 ..f

155 69 164 147 116 149 29 43 33 111 28 120 90 183 55 132 181 176 74 84 178 213 72 76 115 203 211 87 190 179 88 83 158 155 140 202 118 197 14 42 173 208 168 4 58 176 92 161 144 114 153 71 48 63

28.6a 195.5 4,159.7 1,008.2 840.0 108.1 150.1 210.8 1,923.7 19.7a 4,411.7 35.1 175.7 68.1 .. 1,422.7 .. .. 11.3 15.3 36.7 59.5 4.3 1.4 105.7a 59.4 22.5 19.9 12.7 403.9 15.8 6.6 17.9 1,627.0 12.0 g 10.1 149.3h 21.7 93.5a 14.7 36.2 694.7 121.3 16.1a 11.2 344.2 286.3 68.3 484.4 44.9a 16.6a 32.8 259.6 370.7 731.5 261.6 .. ..

3,770a 19,550 3,400 4,200 11,490 3,370 33,540 27,660 31,810 7,310a 34,610 5,800 10,770 1,680 .. 29,110 .. .. 2,070 2,460 16,380 14,090 1,960 340 16,880a 18,060 10,920 960 850 14,220 1,030 1,910 13,960 14,340 3,360 g 3,670 4,600h 930 1,950a 6,420 1,210 41,810 28,100 2,790a 720 2,170 58,570 25,190 2,790 12,770a 2,420a 5,080 8,930 3,980 19,160 24,590 .. ..

151 69 157 148 95 158 37 50 39 122 35 130 103 184 43 175 168 76 84 177 214 75 74 100 204 207 83 201 180 85 81 159 152 143 205 178 127 194 22 46 164 210 172 6 56 164 91 169 137 112 149 71 59

2.8 1.3 8.8 6.1 1.8 0.8 –0.4 4.7 1.5 –0.6 4.0 3.1 7.3 5.3 .. 6.2 4.0 .. –1.4 9.4 –0.3 7.0 3.3 5.5 2.1 1.3 1.8 1.6 7.1 7.2 4.5 5.0 4.0 5.4 6.9g 6.4 3.7h 7.2 10.4 4.8 4.6 1.7 –0.5 7.6 8.8 7.9 0.7 1.1 4.1 4.8 8.0 15.0 8.8 7.6 3.9 1.4 –2.1 8.6

0.7 1.5 7.3 5.0 0.6 –2.1 –0.8 2.8 1.1 –0.8 4.1 0.9 5.8 2.8 .. 5.9 3.4 .. –2.5 7.9 0.4 6.2 2.6 1.3 0.3 2.9 1.6 –1.3 3.8 5.5 1.4 2.7 3.7 4.1 7.1g 4.7 2.6h 4.8 9.6 2.9 2.7 1.2 –1.6 6.1 5.0 5.2 –0.6 –1.7 2.3 3.2 5.6 13.0 7.6 5.8 3.9 1.3 –2.3 –5.1

2012 World Development Indicators

21


1.1

Size of the economy Population Surface Population area density

millions 2010

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

thousand sq. km 2010

people per sq. km 2010

Gross national income, Atlas method

$ billions 2010

21 238 93 168.2 142 17,098 9 1,403.9 11 26 431 5.5 13 434.1 27 2,150i 12 197 65 13.5 7 88 83 41.0 6 72 82 2.0 5 1 7,253 203.4 5 49 113 91.5 2 20 102 49.0 9 638 15 .. 50 1,219 41 304.6 .. .. .. .. 46 505 92 1,462.9 21 66 333 46.7 44 2,506 18 55.3 1 17 61 3.1 9 450 23 469.8 8 41 196 559.7 20 185 111 56.3 7 143 49 5.5 45 947 51 23.4k 69 513 135 286.6 1 15 76 2.5 6 57 111 3.0 1 5 261 20.6 11 164 68 43.9 73 784 95 719.9 5 488 11 19.1 33 242 167 16.6 46 604 79 137.8 8 84 90 290.9 62 244 257 2,377.2 309 9,832 34 14,645.6 3 176 19 34.3 28 447 66 36.1 29 912 33 334.1 87 331 280 101.1 4 6 690 .. 24 528 46 28.1 13 753 17 13.8 13 391 32 5.8 6,895 s 134,222 s 53 w 62,525.2 t 796 15,551 53 420.2 4,971 82,896 62 18,508.7 2,519 23,568 111 4,077.7 2,452 59,328 42 14,429.0 5,767 98,447 60 18,948.9 1,962 16,302 124 7,249.4 405 23,603 18 2,946.7 583 20,394 29 4,505.0 331 8,775 38 1,283.5 1,633 5,131 342 1,920.1 853 24,243 36 1,003.6 1,127 35,774 33 43,682.7 332 2,628 130 12,794.4

Gross national income per capita, Atlas method

Purchasing power parity gross national income

Gross domestic product

Rank 2010

$ 2010

Rank 2010

$ billions 2010

Per capita $ 2010

Rank 2010

% growth 2009–10

Per capita % growth 2009–10

48 12 151 23 114 78 178 40 60 73 32 11 76 70 168 22 19 68 152 94 33 171 169 98 77 17 103 107 51 30 6 1 84 83 28 58

7,850 9,900 520 16,190 1,090 5,630 340 40,070 16,840 23,900 ..e 6,090 ..j 31,750 2,240 1,270 2,950 50,100 71,520 2,750 800 530k 4,150 2,220 490 15,380 4,160 9,890 3,790 500 3,000 41,930 38,200 47,340 10,230 1,280 11,590 1,160 ..j 1,170 1,070 460 9,069 w 528 3,723 1,619 5,884 3,286 3,696 7,272 7,733 3,874 1,176 1,176 38,745 38,565

86 79 195 61 174 106 209 30 60 47 102 39 150 164 138 13 7 141 182 194 123 151 197 64 122 80 128 196 136 27 31 17 77 162 75 171 170 175 200

306.4 2,726.8 12.3 609.8 23.8 80.8 4.9 283.3 124.8 54.4 .. 517.9 .. 1,465.2 104.6 88.6 5.7 372.6 391.0 104.6 14.7 62.6k 565.8 4.0a 5.4 32.3a 95.6 1,129.9 37.8a 41.8 303.8 351.0 2,230.6 14,635.6 45.7 87.7a 350.2 267.0 .. 60.1 17.8 .. 76,295.6 t 1,040.5 33,538.4 9,147.8 24,447.4 34,577.9 13,058.0 5,428.2 6,365.1 2,627.3 5,101.4 1,833.4 42,072.5 11,399.6

14,290 19,240 1,150 22,750 1,910 11,090 830 55,790 22,980 26,530 .. 10,360 .. 31,800 5,010 2,030 5,430 39,730 49,960 5,120 2,140 1,430k 8,190 3,600a 890 24,050a 9,060 15,530 7,490a 1,250 6,620 50,580 35,840 47,310 13,620 3,110a 12,150 3,070 .. 2,500 1,380 .. 11,066 w 1,307 6,747 3,632 9,970 5,996 6,657 13,396 10,926 8,068 3,124 2,148 37,317 34,360

82 70 197 64 180 97 208 7 62 54 105 40 138 176 134 25 14 136 173 187 115 153 206 60 109 79 121 190 125 11 33 16 87 161 93 162 166 188

0.9 4.0 7.5 3.8 4.2 1.0 4.9 14.5 4.2 1.4 .. 2.8 .. –0.1 8.0 4.5 1.1 5.6 2.7 3.2 3.8 7.0k 7.8 7.4 3.4 0.1 3.7 9.0 9.2 5.2 4.2 1.4 2.1 3.0 8.5 8.5 –1.5 6.8 .. 8.0 7.6 9.0 4.2 w 5.9 7.6 6.9 7.8 7.6 9.7 5.7 6.2 4.3 8.1 4.8 3.1 2.0

1.1 4.1 4.3 1.3 1.4 1.4 2.7 12.5 4.0 0.9 .. 1.5 .. –0.5 7.0 1.9 0.8 4.7 1.6 1.1 2.4 3.9k 7.2 5.1 1.2 –0.2 2.6 7.6 7.9 1.9 4.6 –6.3 1.4 2.1 8.1 6.7 –3.0 5.7 .. 4.8 5.9 8.2 3.0 w 3.7 6.4 5.3 7.1 6.2 8.9 5.3 5.0 2.5 6.6 2.3 2.5 1.6

89 111 148

a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for upper middle-income economies based on earlier data. c. Refers to the area controlled by the government of the Republic of Cyprus. d. Excludes Abkhazia and South Ossetia. e. Estimated to be low income ($1,005 or less). f. Estimated to be high income ($12,276 or more). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Provisional estimate. j. Estimated to be lower middle income ($1,006–$3,975). k. Covers mainland Tanzania only.

22

2012 World Development Indicators


About the data

1.1

WORLD VIEW

Size of the economy Definitions

Population, land area, income, and output are basic

conventional price indexes allow comparison of real

• Population is based on the de facto definition of

measures of the size of an economy. They also

values over time.

population, which counts all residents regardless of

provide a broad indication of actual and potential

PPP rates are calculated by simultaneously compar-

legal status or citizenship—except for refugees not

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

ing the prices of similar goods and services among a

permanently settled in the country of asylum, who

sured by gross national income, GNI), and output

large number of countries. In the most recent round

are generally considered part of the population of

(as measured by gross domestic product, GDP) are

of price surveys conducted by the International Com-

their country of origin. The values shown are midyear

therefore used throughout World Development Indica-

parison Program (ICP), 146 countries and territories

estimates. See also table 2.1. • Surface area is

tors to normalize other indicators.

participated in the data collection, including China

a country’s total area, including areas under inland

Population estimates are generally based on

for the first time, India for the first time since 1985,

bodies of water and some coastal waterways. • Pop-

extrapolations from the most recent national cen-

and almost all African countries. The PPP conver-

ulation density is midyear population divided by land

sus. For further discussion of the measurement of

sion factors presented in the table come from three

area in square kilometers. • Gross national income

population and population growth, see About the data

sources. For 47 high- and upper middle-income

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

for table 2.1.

countries conversion factors are provided by Euro-

ducers plus any product taxes (less subsidies) not

The surface area of an economy includes inland

stat and the Organisation for Economic Co-operation

included in the valuation of output plus net receipts

bodies of water and some coastal waterways. Sur-

and Development (OECD), with PPP estimates for

of primary income (compensation of employees and

face area thus differs from land area, which excludes

35 European countries incorporating new price data

property income) from abroad. Data are in current

bodies of water, and from gross area, which may

collected since 2005. For the remaining 2005 ICP

U.S. dollars converted using the World Bank Atlas

include offshore territorial waters. Land area is par-

countries the PPP estimates are extrapolated from

method (see Statistical methods). • Gross national

ticularly important for understanding an economy’s

the 2005 ICP benchmark results, which account for

income per capita is GNI divided by midyear popula-

agricultural capacity and the environmental effects

relative price changes between each economy and

tion. GNI per capita in U.S. dollars is converted using

of human activity. (For measures of land area and

the United States. For countries that did not partici-

the World Bank Atlas method. • Purchasing power

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

pate in the 2005 ICP round, the PPP estimates are

parity (PPP) gross national income is GNI converted

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

imputed using a statistical model. More information

to international dollars using PPP rates. An interna-

tions in satellite mapping and computer databases

on the results of the 2005 ICP is available at www.

tional dollar has the same purchasing power over GNI

have resulted in more precise measurements of land

worldbank.org/data/icp.

that a U.S. dollar has in the United States. • Gross

and water areas.

All 216 economies shown in World Development

domestic product (GDP) is the sum of value added

GNI measures total domestic and foreign value

Indicators are ranked by size, including those that

by all resident producers plus any product taxes (less

added claimed by residents. GNI comprises GDP

appear in table 1.6. The ranks are shown only in

subsidies) not included in the valuation of output.

plus net receipts of primary income (compensation

table 1.1. No rank is shown for economies for which

Growth is calculated from constant price GDP data in

of employees and property income) from nonresident

numerical estimates of GNI per capita are not pub-

local currency. • Gross domestic product per capita

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

lished. Economies with missing data are included in

is GDP divided by midyear population.

dollars to classify countries for analytical purposes

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

and to determine borrowing eligibility. For definitions

ative order of other economies remains consistent.

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

Data sources

When calculating GNI in U.S. dollars from GNI

Population estimates are prepared by World Bank

reported in national currencies, the World Bank fol-

staff from a variety of sources (see Data sources

lows the World Bank Atlas conversion method, using

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

a three-year average of exchange rates to smooth

from the Food and Agriculture Organization (see

the effects of transitory fluctuations in exchange

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

rates. (For further discussion of the World Bank Atlas

GDP growth, and GDP per capita growth are esti-

method, see Statistical methods.)

mated by World Bank staff based on national

Because exchange rates do not always refl ect

accounts data collected by World Bank staff during

differences in price levels between countries,

economic missions or reported by national statis-

the table also converts GNI and GNI per capita

tical offices to other international organizations

estimates into international dollars using purchas-

such as the OECD. PPP conversion factors are

ing power parity (PPP) rates. PPP rates provide

estimates by Eurostat/OECD and by World Bank

a standard measure allowing comparison of real

staff based on data collected by the ICP.

levels of expenditure between countries, just as

2012 World Development Indicators

23


1.2

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

Share of Vulnerable poorest quintile employment in national consumption Unpaid family workers and own-account workers or income % of total employment % a,b 2000–11 1990 2007–10a

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

24

9.4 8.1 .. 2.0 d 4.4 d 8.8 .. 8.6 8.0 .. 8.9 9.2 8.5 7.0 2.1 6.7 .. 2.9 8.5 6.7 9.0 7.5 6.7 7.2 3.4 6.3 4.3 5.0 .. 3.0 5.5 5.0 3.9 5.6 8.1 .. .. .. .. 4.7 4.3 9.2 3.7 .. 6.8 9.3 9.6 .. 6.2 4.8 5.3 8.5 5.2 6.7 3.1 6.4 7.3 2.4

2012 World Development Indicators

.. .. .. .. 26e .. 10 .. .. .. .. .. 17 .. 40e .. .. 29e .. .. .. .. .. .. .. 94 .. .. 6 28 .. .. 25 .. .. .. .. 7 7 39 36e 28 35 .. 2 92 .. 11 48 .. .. 5 .. 42 .. .. .. ..

.. .. .. .. 20e 38 9 9 55 2 .. .. 10 .. 57 .. .. 25 9 .. .. 83 .. .. .. .. 26 .. 7 49 .. .. 20 .. 18 .. 14 14 5 42 43 27 38 .. 5 .. 9 7 .. .. 63 7 .. 28 .. .. .. ..

Prevalence of malnutrition Underweight % of children under age 5 1990 2005–10a

.. .. 9.2 .. .. .. .. .. .. 6.3 61.5 .. .. .. 9.7 .. .. .. .. 29.6 30.2 .. 18 .. .. .. .. 12.6 .. 8.8 .. 21.1 2.5 .. .. .. .. 0.9 .. 8.4 .. 10.5 11.1 36.9 .. .. .. .. .. .. .. .. 24.1 .. 27.8 .. .. 23.7

.. 6.3 3.7 15.6 2.3 4.2 .. .. 8.4 .. 41.3 1.3 .. 20.2 4.5 1.6 11.2 2.2 .. 26.0 35.2 28.8 16.6 .. .. .. 0.5 3.4 .. 3.4 28.2 11.8 1.1 29.4 1.0 .. .. .. .. 3.4 .. 6.8 6.6 .. .. 34.6 .. .. .. 15.8 1.1 1.1 14.3 .. 13.0 20.8 17.2 18.9

Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate % 1991 2010

Ratio of girls to boys enrollments in primary and secondary education % 1991 2010c

Under-fi ve mortality rate per 1,000 live births 1990 2010

28 .. 80 33 100 105 .. .. 95 97 41 94 79 22 71 .. 90 93 90 20 46 45 53 .. 28 18 .. 107 102 73 48 54 79 42 85 99 90 92 98 61 91 .. 65 18 .. 23 97 106 62 45 .. 100 64 99 .. 17 5 27

.. 86 96 47 106 101 .. 99 90 .. 65 103 90 63 99 70 94 .. 95 45 56 87 79 .. 41 33 96 .. 96 114 59 71 96 59 f 95 98 103 101 97 92 106 98 96 40 98 72 98 .. .. 71 116 100 94f 101 84 64 68 ..

54 96 83 .. 107 .. 100 95 100 101 75 .. 101 .. .. .. 109 .. 99 .. 82 .. 83 99 61 41 100 86 102 108 70 89 101 .. 103 106 100 98 101 .. 100 81 101 82 103 68 109 102 96 65 98 99 78 99 87 45 55 ..

64 98 98 79 104 102 98 97 99 .. 107 101 98 .. 99 102 100 102 97 89 f 94 94 85 99 69 65 99 103 102 104 79 .. 102 .. 102 98 100 101 101 97 103 .. 98 80 101 89 102 100 .. 99 97 96 96f 97 95 77 .. ..

209 41 68 243 27 55 9 9 93 17 143 17 10 178 121 19 59 59 22 205 183 121 137 8 165 207 19 48 .. 37 181 116 17 151 13 13 11 14 9 62 52 94 62 141 21 184 7 9 93 165 47 9 122 13 78 229 210 151

149 18 36 161 14 20 5 4 46 10 48 6 4 115 54 8 48 19 13 176 142 51 136 6 159 173 9 18 .. 22 170 93 10 123 6 6 4 4 4 27 20 22 16 61 5 106 3 4 74 98 22 4 74 4 32 130 150 165


Eradicate extreme poverty and hunger

Share of Vulnerable poorest quintile employment in national consumption Unpaid family workers and own-account workers or income % of total employment % a,b 2000–11 1990 2007–10a

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

2.0 8.4 8.6 8.3 6.4 8.7 7.4 5.7 6.5 5.4 .. 7.7 9.1 4.8 .. .. .. .. 6.8 7.6 6.6 .. 3.0 6.4 .. 6.6 5.1 5.4 7.0 4.5 8.0 6.0 .. 4.4 7.8 7.1 6.5 5.2 .. 3.2 8.3 .. .. 6.2 8.1 4.4 9.6 .. 9.6 3.3 .. 3.3 3.9 6.0 7.7 .. .. 3.9

49e 7 .. .. .. .. 20 .. 27 42 19 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 84 84 29 .. .. 12 26 .. .. .. .. .. .. .. 9 13 43e .. .. .. .. .. 34 .. 23e 36e .. 28 24 .. ..

50 7 .. 64 42 .. 12 7 19 37 10 10 32 .. .. 24 .. .. .. .. 8 28 .. 79 .. 9 23 .. .. 22 83 .. 16 30 29 58 51 .. .. .. .. 11 11 45 .. .. 6 .. 63 31 .. 45 40e 44 19 18 .. 0

Prevalence of malnutrition Underweight % of children under age 5 1990 2005–10a

15.8 2.3 59.5 31.0 .. 10.4 .. .. .. 4.0 .. 4.8 .. 20.1 .. .. .. .. .. 39.8 .. .. 13.8 .. .. .. .. 35.5 24.4 22.1 29.0 43.3 .. 13.9 .. 10.8 8.1 .. 28.8 21.5 .. .. .. 9.6 41.0 35.1 .. 21.4 39.0 .. .. 2.8 8.8 29.8 .. .. .. ..

8.6 .. 43.5 17.5 .. 7.1 .. .. .. 1.9 .. 1.9 4.9 16.4 18.8 .. .. 1.7 2.7 31.6 .. .. 13.5 20.4 5.6 .. 1.8 .. 13.8 12.9 27.9 15.9 .. 3.4 3.2 5.3 .. 18.3 .. 17.5 38.8 .. .. 5.7 39.9 26.7 .. 8.6 .. .. 18.1 3.4 4.5 20.7 .. .. .. ..

1.2

WORLD VIEW

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

Promote gender equality

Reduce child mortality

Primary completion rate % 1991 2010

Ratio of girls to boys enrollments in primary and secondary education % 1991 2010c

Under-fi ve mortality rate per 1,000 live births 1990 2010

64 82 64 93 88 58 103 .. 98 94 102 101 103 .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 9 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71

99 98 96 105 104 65 .. 103 103 73 102 101 116f .. .. 101 .. 112 97 79 92 87 70 62 .. 96 92 72 67 .. 55f 75 96 104 93 108 85 61 104 84 .. .. .. 81 46f 74 100 101 67 97 .. 94 102 92 95 .. .. 100

104 100 73 93 85 79 104 105 100 103 101 101 .. 92 .. 99 .. 100 102 77 101 101 124 .. .. 96 99 96 82 101 58 71 102 97 105 109 70 71 95 106 59 97 100 119 53 77 102 89 48 99 80 98 96 99 101 103 .. 98

107 99 92 101 96 81 103 101 99 99 100 102 98f 95 .. 99 .. 105 99 87 98 104 106 .. .. 99 99 97 101 .. 83f 101 100 102 101 103 89 89 102 104 .. 99 103 102 78 90 99 98 80 101 .. 100 99 101 99 100 104 109

58 19 115 85 65 46 9 12 10 38 6 38 57 99 45 8 .. 15 72 145 21 38 89 227 45 17 39 159 222 18 255 124 24 49 37 107 86 219 112 73 141 8 11 68 311 213 9 47 124 33 90 50 78 59 17 15 .. 21

2012 World Development Indicators

24 6 63 35 26 39 4 5 4 24 3 22 33 85 33 5 .. 11 38 54 10 22 85 103 17 7 12 62 92 6 178 111 15 17 19 32 36 135 66 40 50 4 6 27 143 143 3 9 87 20 61 25 19 29 6 4 .. 8

25


1.2

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

Share of Vulnerable poorest quintile employment in national consumption Unpaid family workers and own-account workers or income % of total employment % a,b 2000–11 1990 2007–10a

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

8.3 6.5 5.2 .. 6.2 8.9 6.1 .. 10.1 8.2 .. 2.7 .. 7.0 6.9 6.8 4.1 9.1 7.6 7.7 8.3 6.8 6.7 9.0 7.6 .. 5.9 5.7 .. 5.8 9.7 .. .. 5.4 4.9 7.1 4.3 7.4 7.4 7.2 3.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

27 1 .. .. 83 .. .. 8 .. 12 .. .. .. 23 43e .. .. .. 9 .. .. 91e 70 .. .. 22 .. .. .. .. .. .. 10 .. .. .. .. .. .. .. 65 .. .. w .. .. .. .. .. .. .. 29 .. .. .. .. 15

33 6 .. .. .. 28 .. 10 12 14 .. 10 .. 11 40e .. .. 7 9 33 .. .. 53 .. .. .. .. 34 .. .. .. 1 11 .. 23e .. 31 .. 28 .. .. .. .. w .. .. 73 .. .. .. 19 31 36 81 .. .. 11

Prevalence of malnutrition Underweight % of children under age 5 1990 2005–10a

5.0 .. 24.3 .. 19.0 .. 25.4 .. .. .. .. .. .. .. 29.3 31.8 .. .. .. 11.5 .. 25.1 16.3 .. 21.2 4.7 8.5 8.7 .. 19.7 .. .. .. .. 6.5 .. 6.7 40.7 .. 29.6 21.2 8.0 .. w 40.2 .. 38.1 12.5 28.7 20.4 8.5 7.5 11.8 52.2 29.0 .. ..

.. .. 18.0 5.3 14.5 1.8 21.3 .. .. .. 32.8 8.7 .. .. 21.6 31.7 7.3 .. .. 10.1 15.0 16.2 7.0 45.3 20.5 .. 3.3 .. .. 16.4 .. .. .. .. .. 4.4 3.7 20.2 2.2 .. 14.9 14.0 .. w 23.0 .. 24.6 3.0 17.9 5.8 1.9 3.0 7.9 32.9 22.0 .. ..

Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate % 1991 2010

Ratio of girls to boys enrollments in primary and secondary education % 1991 2010c

Under-fi ve mortality rate per 1,000 live births 1990 2010

96 92 50 .. 39 .. .. .. 95 95 .. 76 .. 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 80 81 .. .. .. .. 97 79 w 44 83 68 97 78 101 92 84 .. 62 51 .. 101

91 98 70 93 59 96 74f .. 98 95 .. .. .. 102 101 58 77 94 95 104 104 90 .. 65 74 91 91 99 .. 57 98 .. .. 104 106 93f 94 .. 95 63 103 .. 88 w 65 92 88 98 87 97 95 102 88 86 67 97 101

99 105 95 .. 69 .. 64 .. 102 103 .. 104 .. 104 102 78 .. 102 97 85 .. 97 99 .. 59 101 86 81 .. 77 102 104 102 100 107 .. 105 .. .. .. 87 92 87 w 80 85 81 98 84 89 98 99 80 69 82 100 ..

99 98 102 97 100 101 .. .. 101 99 53 99 .. 102 .. 90 94 99 98 99 90 96 103 98 75 101 101 95 .. 99 99 .. 101 100 104 98f 102 102 104 75 96 .. 96 w 91 97 93 103 96 103 97 102 93 92 89 100 99

37 27 163 45 139 29 276 8 18 10 180 60 .. 11 32 125 96 7 8 38 116 155 32 169 147 37 49 80 98 175 21 22 9 11 23 77 33 51 45 128 183 78 90 w 165 85 113 49 98 56 51 54 74 120 175 12 10

14 12 91 18 75 7 174 3 8 3 180 57 .. 5 17 103 78 3 5 16 63 92 13 81 103 27 16 18 56 99 13 7 5 8 11 52 18 23 22 77 111 80 58 w 108 51 69 20 63 24 23 23 34 67 121 6 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 2011.

26

2012 World Development Indicators


About the data

1.2

WORLD VIEW

Millennium Development Goals: eradicating poverty and saving lives Definitions

Tables 1.2–1.4 present indicators for 17 of the 21

nutrients, and undernourished mothers who give

• Share of poorest quintile in national consump-

targets specified by the Millennium Development

birth to underweight children.

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

Goals. Each of the eight goals includes one or more

Progress toward universal primary education is

cent of the population in consumption or, in some

targets, and each target has several associated

measured by the primary completion rate. Because

cases, income. • Vulnerable employment is the sum

indicators for monitoring progress toward the target.

many school systems do not record school comple-

of unpaid family workers and own-account workers

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

tion on a consistent basis, it is estimated from the

as a percentage of total employment. • Prevalence

indicator to be attained by a certain date. In some

gross enrollment rate in the final grade of primary

of malnutrition is the percentage of children under

cases the target value is set relative to a level in

education, adjusted for repetition. Offi cial enroll-

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

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

ments sometimes differ signifi cantly from atten-

dard deviations below the median for the interna-

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

dance, and even school systems with high average

tional reference population ages 0–59 months. The

quantified.

enrollment ratios may have poor completion rates.

data are based on the new international child growth

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

Eliminating gender disparities in education would

standards for infants and young children, called the

Goal 1 has three targets between 1990 and 2015:

help increase the status and capabilities of women.

Child Growth Standards, released in 2006 by the

to halve the proportion of people whose income is

The ratio of female to male enrollments in primary

World Health Organization. • Primary completion

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

and secondary education provides an imperfect mea-

rate is the percentage of students completing the

employment and decent work for all, and to halve the

sure of the relative accessibility of schooling for girls.

last year of primary education. It is calculated as

proportion of people who suffer from hunger. Esti-

The targets for reducing under-five mortality rates

the total number of students in the last grade of

mates of poverty rates are in tables 2.7 and 2.8.

are among the most challenging. Under-five mortal-

primary education, minus the number of repeaters

The indicator shown here, the share of the poorest

ity rates are harmonized estimates produced by a

in that grade, divided by the total number of children

quintile in national consumption or income, is a dis-

weighted least squares regression model and are

of official graduation age. • Ratio of girls to boys

tributional measure. Countries with more unequal

available at regular intervals for most countries.

enrollments in primary and secondary education is

distributions of consumption (or income) have a

Most of the 60 indicators relating to the Millennium

the ratio of the female to male gross enrollment rate

higher rate of poverty for a given average income.

Development Goals can be found in World Develop-

in primary and secondary education. • Under-five

Vulnerable employment measures the portion of the

ment Indicators. Table 1.2a shows where to find the

mortality rate is the probability of a child born in

labor force that receives the lowest wages and least

indicators for the first four goals. For more informa-

a specific year dying before reaching age 5, if sub-

security in employment. No single indicator captures

tion about data collection methods and limitations,

ject to the age-specific mortality rate of that year.

the concept of suffering from hunger. Child malnutri-

see About the data for the tables listed there. For

The probability is derived from life tables and is

tion is a symptom of inadequate food supply, lack

information about the indicators for goals 5–8, see

expressed as a rate per 1,000 live births.

of essential nutrients, illnesses that deplete these

About the data for tables 1.3 and 1.4.

1.2a

Location of indicators for Millennium Development Goals 1–4 Goal 1. Eradicate extreme poverty and hunger 1.1 Proportion of population below $1.25 a day 1.2 Poverty gap ratio 1.3 Share of poorest quintile in national consumption 1.4 Growth rate of GDP per person employed 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 per day 1.7 Proportion of own-account and unpaid family workers in total employment 1.8 Prevalence of underweight in children under age 5 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2. Achieve universal primary education 2.1 Net enrollment ratio in primary education 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.3 Literacy rate of 15- to 24-year-olds Goal 3. Promote gender equality and empower women 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament Goal 4. Reduce child mortality 4.1 Under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles

Table 2.8 2.8 1.2, 2.9 2.4 2.4 — 1.2, 2.4 1.2, 2.20 2.20 Data sources 2.12 2.13 2.14

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

1.2, 2.12* 1.5, 2.3* 1.5

to harmonize the data series used to compile this

1.2, 2.23, 5.8 2.23 2.18

un.org/millenniumgoals), but some differences in

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

table with those published on the United Nations Millennium Development Goals Web site (www. timing, sources, and definitions remain. For more information see the data sources for the indicators listed in table 1.2a.

2012 World Development Indicators

27


1.3

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

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

28

1,400 31 120 610 70 29 8 5 38 19 340 15 5 410 180 9 190 58 13 560 970 290 600 12 850 1,200 26 38 .. 85 670 580 44 470 14 53 10 8 5 100 140 82 110 280 12 470 8 8 260 400 48 7 350 2 110 680 1,000 300

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2005–10b

.. .. 51 .. .. 56 .. .. .. 54 40 .. 78 .. 30 .. 33 59 .. .. .. .. 16 .. .. .. 56 85 86 66 8 .. .. .. .. .. .. 78 78 56 53 48 47 .. .. 5 77 81 .. 12 .. 70 17 .. .. .. .. 10

2012 World Development Indicators

23 69 61 .. 78 55 .. .. 51 .. 53 73 .. 17 61 36 53 81 .. 17 22 51 29 .. 19 5 58 85 80 79 17 44 80 13 .. 78 .. .. .. 73 .. 60 73 .. .. 15 .. 71 .. .. 53 .. 24 .. 54 9 14 32

HIV Incidence prevalence of tuberculosis % of population per 100,000 people ages 15–49 2009 2010

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

189 14 90 304 27 73 6 5 110 23 225 70 9 94 135 50 503 43 40 55 129 437 177 5 319 276 19 78 80 34 327 372 13 139 21 9 4 7 6 67 65 18 28 100 25 261 7 9 553 273 107 5 86 5 62 334 233 230

Ensure environmental sustainability

Carbon dioxide emissions per capita metric tons 1990 2008

0.1 2.3 3.1 0.4 3.4 .. 16.8 7.9 .. 24.1 0.1 .. 10.9 0.1 0.8 .. 1.6 1.4 8.9 0.1 0.1 0.0 0.1 16.2 0.1 0.0 2.6 2.2 4.8 1.7 0.1 0.5 1.0 0.5 .. 3.2 6.1 .. 9.8 1.3 1.6 1.3 0.5 .. .. 0.1 10.2 6.9 5.2 0.2 .. .. 0.3 7.2 0.6 0.2 0.2 0.1

0.0 1.3 3.2 1.4 4.8 1.8 18.6 8.1 5.4 21.4 0.3 6.5 9.8 0.5 1.3 8.3 2.5 2.1 6.6 0.1 0.0 0.3 0.3 16.3 0.1 0.0 4.4 5.3 5.5 1.5 0.0 0.5 1.8 0.4 5.3 2.8 7.9 11.2 8.4 2.2 1.9 2.7 1.0 0.1 13.6 0.1 10.6 5.9 1.7 0.3 1.2 9.6 0.4 8.7 0.9 0.1 0.2 0.3

Nationally protected terrestrial and marine areas % of total land area 2010

0.4 8.4 6.2 12.1 5.3 8.0 12.5 22.9 7.1 0.7 1.6 7.2 13.2 23.3 18.5 0.6 30.9 26.0 8.9 14.2 4.8 23.4 9.0 6.2 17.7 9.4 13.3 16.0 41.8 20.5 10.0 9.7 17.6 21.8 9.5 5.3 4.5 15.1 4.1 24.1 38.0 6.1 1.4 3.8 22.6 18.4 8.5 17.1 14.6 1.3 3.4 42.3 14.0 9.9 29.5 6.4 26.9 0.1

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2010

.. 76 88 29 90 .. 100 100 .. .. 39 93 100 5 18 .. 38 68 99 8 44 9 48 100 11 8 84 24 .. 67 9 .. 93 20 99 80 100 100 100 73 69 72 75 9 95 3 100 100 .. .. 96 100 7 97 62 10 .. 26

37 94 95 58 .. 90 100 100 82 .. 56 93 100 13 27 95 62 79 100 17 46 31 49 100 34 13 96 64 .. 77 24 18 95 24 99 91 100 98 100 83 92 95 87 .. 95 21 100 100 33 68 95 100 14 98 78 18 20 17

Internet usersa per 100 people 2010

3.7 45.0 12.5 10.0 36.0 44.0 75.9 72.7 46.7 55.0 3.7 32.1 73.7 3.1 20.0 52.0 6.0 40.7 46.0 1.4 2.1 1.3 4.0 81.3 2.3 1.7 45.0 34.4 71.8 36.5 0.7 5.0 36.5 2.6 60.1 15.9 53.0 68.6 88.8 39.5 29.0 26.7 15.9 5.4 74.2 0.7 86.9 77.5 7.2 9.2 26.3 82.5 9.5 44.6 10.5 1.0 2.5 8.4


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

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

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

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2005–10b

47 .. 45 50 49 14 .. 68 .. 55 58 40 .. 27 62 79 .. .. .. .. .. .. 23 .. .. .. .. 17 13 50 .. 4 75 63 .. .. 42 .. 17 41 24 76 .. .. 4 6 74 9 15 .. .. 48 59 36 73 .. .. ..

65 .. 54 56 79 50 65 .. .. 72 54 59 51 46 .. 80 .. .. 48 38 .. .. 47 11 .. .. 14 40 41 .. 8 9 .. 73 68 55 .. 16 41 55 48 69 .. 72 18 15 88 24 27 52 32 79 74 51 .. 67 .. ..

HIV Incidence prevalence of tuberculosis % of population per 100,000 people ages 15–49 2009 2010

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

51 15 185 189 17 64 8 5 5 7 21 5 151 298 345 97 .. 41 159 90 39 17 633 293 40 69 21 266 219 82 68 337 22 16 182 224 91 544 384 603 163 7 8 42 185 133 6 13 231 48 303 46 106 275 23 29 2 38

1.3

Ensure environmental sustainability

Carbon dioxide emissions per capita metric tons 1990 2008

0.5 6.1 0.8 0.8 4.1 2.9 8.7 7.2 7.5 3.3 8.9 3.3 .. 0.2 .. 5.7 .. 21.8 .. 0.1 .. 3.1 .. 0.2 9.3 .. .. 0.1 0.1 3.1 0.0 1.3 1.4 3.9 .. 4.6 1.0 0.1 0.1 0.0 0.0 11.0 7.0 0.6 0.1 0.5 7.4 5.5 0.6 1.3 0.5 0.5 1.0 0.7 9.6 4.4 .. 24.9

1.2 5.4 1.5 1.7 7.4 3.4 9.9 5.2 7.4 4.5 9.5 3.7 15.1 0.3 3.2 10.5 .. 30.1 1.2 0.3 3.3 4.1 .. 0.2 9.5 4.5 5.8 0.1 0.1 7.6 0.0 0.6 3.1 4.3 1.3 4.1 1.5 0.1 0.3 1.8 0.1 10.6 7.8 0.8 0.1 0.6 10.5 17.3 1.0 2.0 0.3 0.7 1.4 0.9 8.3 5.3 .. 49.1

Nationally protected terrestrial and marine areas % of total land area 2010

13.9 5.1 4.8 6.4 6.9 0.1 1.2 15.1 15.9 7.3 10.9 1.9 2.5 11.7 3.9 3.0 .. 1.1 6.9 16.6 16.4 0.4 0.5 1.6 0.1 14.4 4.9 2.5 15.0 13.7 2.4 1.1 0.7 11.9 1.4 13.4 1.5 14.8 5.2 14.7 17.0 15.2 20.0 36.8 7.1 12.6 10.9 9.3 9.8 11.5 1.4 5.4 13.1 5.0 21.8 6.1 4.4 1.4

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2010

50 100 18 32 79 .. 99 100 .. 80 100 97 96 25 .. 100 .. 100 .. .. .. .. .. .. 97 .. .. 9 39 84 15 16 89 64 .. .. 53 11 .. 24 10 100 .. 43 5 37 100 82 27 58 47 37 54 57 .. 92 .. 100

WORLD VIEW

Millennium Development Goals: protecting our common environment

Internet usersa per 100 people 2010

77 100 34 54 100 73 99 100 .. 80 100 98 97 32 80 100 .. 100 93 63 .. .. 26 18 97 .. 88 15 51 96 22 26 89 85 85 51 70 18 76 32 31 100 .. 52 9 31 100 99 48 69 45 71 71 74 90 100 .. 100

2012 World Development Indicators

11.1 65.2 7.5 9.9 13.0 2.5 69.8 65.4 53.7 26.5 77.6 38.9 33.4 25.9 0.0 82.5 .. 38.3 19.6 7.0 71.5 31.0 3.9 7.0 14.0 62.8 51.9 1.7 2.3 56.3 2.7 3.0 28.7 31.1 40.1 12.9 49.0 4.2 0.2 6.5 7.9 90.7 83.0 10.0 0.8 28.4 93.3 62.0 16.8 42.7 1.3 19.8 34.3 25.0 62.5 51.3 42.7 81.6

29


1.3

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

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

27 39 540 24 410 8 970 9 6 18 1,200 410 .. 6 39 750 420 5 10 46 64 790 48 370 350 55 60 23 77 430 26 10 12 24 27 30 68 56 .. 210 470 790 260 w 590 210 300 60 290 89 34 86 88 290 650 15 7

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2005–10b

.. .. 21 .. .. .. 3 65 74 .. .. 57 .. .. .. 9 20 .. .. .. .. 10 66 25 34 .. 50 63 .. 5 .. .. 70 71 .. .. .. 53 .. 10 15 43 58 w 22 59 40 75 56 75 .. 58 42 42 15 70 ..

.. 80 52 24 12 41 8 .. .. .. 15 .. .. 66 68 8 49 .. .. 54 37 34 80 22 15 43 60 73 48 24 67 .. 84 79 78 65 .. 80 50 28 41 59d 62 w 34 65 50 81 61 78 69 75 62 51 22 .. ..

HIV Incidence prevalence of tuberculosis % of population per 100,000 people ages 15–49 2009 2010

0.1 1.0 2.9 .. 0.9 0.1 1.6 0.1 <0.1 <0.1 0.7 17.8 .. 0.4 <0.1 1.1 25.9 0.1 0.4 .. 0.2 5.6 1.3 .. 3.2 1.5 <0.1 <0.1 .. 6.5 1.1 .. 0.2 0.6 0.5 0.1 .. 0.4 .. .. 13.5 14.3 0.8 w 2.6 0.7 0.7 0.7 0.9 0.2 0.6 0.5 0.1 0.3 5.5 0.3 0.3

116 106 106 18 288 18 682 35 8 11 286 981 .. 16 66 119 1,287 7 8 20 206 177 137 498 455 19 25 28 66 209 101 3 13 4 21 128 33 199 5 49 462 633 128 w 264 132 174 89 150 123 90 43 42 192 271 14 ..

Ensure environmental sustainability

Carbon dioxide emissions per capita metric tons 1990 2008

6.8 .. 0.1 13.3 0.4 .. 0.1 15.4 .. .. 0.0 9.5 .. 5.9 0.2 0.2 0.5 6.0 6.4 3.0 .. 0.1 1.7 .. 0.2 14.0 1.6 2.8 .. 0.0 .. 28.8 10.0 19.5 1.3 .. 6.2 0.3 .. 0.8 0.3 1.5 4.2e w .. 2.4 1.1 3.6 2.2 1.8 9.8 2.3 2.6 0.7 0.9 11.8 8.9

4.4 12.0 0.1 16.6 0.4 6.8 0.2 6.7 6.9 8.5 0.1 8.9 .. 7.2 0.6 0.3 1.1 5.3 5.3 3.6 0.5 0.2 4.2 0.2 0.2 37.4 2.4 4.0 9.7 0.1 7.0 25.0 8.5 18.0 2.5 4.6 6.1 1.5 0.5 1.0 0.2 0.7 4.8e w 0.3 3.4 1.5 5.3 3.0 4.3 7.8 2.8 3.8 1.2 0.8 11.9 8.0

Nationally protected terrestrial and marine areas % of total land area 2010

7.8 9.2 10.0 29.9 23.5 6.0 4.3 3.4 23.2 13.1 0.5 6.9 .. 7.6 15.0 4.2 3.0 10.0 24.9 0.6 4.1 26.9 17.3 6.4 11.0 9.6 1.3 1.9 3.0 10.3 3.6 4.7 18.1 13.7 0.3 2.3 50.2 4.6 0.6 0.7 36.0 28.0 11.9 w 10.6 11.9 8.5 13.2 11.6 13.3 7.7 19.8 4.0 5.6 11.6 12.7 16.5

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2010

71 74 36 .. 38 .. 11 99 100 100 .. 71 .. 100 70 27 48 100 100 85 .. 7 84 .. 13 93 74 84 98 27 .. 97 100 100 94 84 82 37 .. 24 46 41 47 w 21 39 29 46 37 30 80 68 73 22 26 100 100

.. 70 55 .. 52 92 13 100 100 100 23 79 .. 100 92 26 57 100 100 95 94 10 96 47 13 92 .. 90 98 34 94 98 100 100 100 100 .. 76 92 53 48 40 62 w 37 59 47 73 56 66 84 79 88 38 31 100 100

Internet usersa per 100 people 2010

40.0 43.4 13.0 41.0 16.0 43.1 0.3 71.1 79.9 69.3 1.2 12.3 .. 65.8 12.0 10.2 9.0 90.0 82.2 20.7 11.5 11.0 21.2 0.2 5.4 48.5 36.6 39.8 2.2 12.5 44.6 78.0 84.7 74.2 47.9 19.4 35.9 27.9 36.4 12.3 10.1 11.5 30.2 w 5.6 23.8 13.5 34.1 21.5 29.8 39.3 34.0 20.9 8.1 11.3 73.4 71.2

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators 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 2011. e. Includes emissions not allocated to specific countries.

30

2012 World Development Indicators


About the data

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

1.3

WORLD VIEW

Millennium Development Goals: protecting our common environment Definitions

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

1.3a

Location of indicators for Millennium Development Goals 5–7 Goal 5. Improve maternal health 5.1 Maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel 5.3 Contraceptive prevalence rate 5.4 Adolescent fertility rate 5.5 Antenatal care coverage 5.6 Unmet need for family planning Goal 6. Combat HIV/AIDS, malaria, and other diseases 6.1 HIV prevalence among pregnant women ages 15–24 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 7.2 Carbon dioxide emissions, total, per capita, and per $1 purchasing power parity GDP 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility 7.10 Proportion of urban population living in slums

Table 1.3, 2.19 2.19 1.3, 2.19 2.19 1.5, 2.19 2.19 1.3*, 2.22* — —

• Maternal mortality ratio is the number of women who die from pregnancy-related causes during pregnancy and childbirth, per 100,000 live births. Data are from various years and adjusted to a common 2008 base year. The values are modeled estimates (see About the data for table 2.19). • Contraceptive prevalence rate is the percentage of women ages 15–49 married or in union who are practicing, or whose sexual partners are practicing, any form of contraception. • HIV prevalence is the percentage of people ages 15–49 who are infected with HIV. • Incidence of tuberculosis is the estimated number of new tuberculosis cases (pulmonary, smear positive, and extrapulmonary). • Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include emissions produced during consumption of solid, liquid, and gas fuels and gas flaring (see table 3.9). • Nationally protected terrestrial and marine areas are terrestrial and marine protected areas as a percentage of total territorial area, where all nationally designated protected areas with known location and extent are included. All overlaps between different designations and categories, buffered points, and polygons are removed, and all the undated protected areas are dated. • Access to improved sanitation facilities is the percentage of the population with at least adequate access to excreta disposal facilities (private or shared, but not public) that can effectively prevent human, animal, and insect contact with excreta (facilities do not have to include treatment to render sewage outfl ows innocuous). Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained. • Internet users are people with access to the worldwide network.

— 2.22 — 2.18 2.18 1.3, 2.22 2.18 3.1

Data sources

3.9 3.10* — 3.5 1.3 — 2.18, 3.5 1.3, 2.18, 3.13 —

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

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

2012 World Development Indicators

31


1.4

Millennium Development Goals: overcoming obstacles

Development Assistance Committee members Net official development assistance (ODA) by donor

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

% of donor GNI 2010

For basic social servicesa % of total sector-allocable ODA 2010

0.32 0.33 0.32 0.64 0.90 0.55 0.50 0.38 0.17 0.53 0.15 1.09 0.81 0.29 0.43 0.97 0.56 0.20 0.12 0.26 1.10 0.41 0.21

14.6 18.1 3.1 11.7 10.4 8.4 8.6 5.9 6.6 22.9 12.5 34.5 7.6 6.6 13.6 12.4 12.7 2.5 4.3 16.6 11.1 11.0 31.0

Least developed countries’ access to high-income markets Goods (excluding arms) admitted free of tariffs % of exports from least developed countries 2003 2009

Support to agriculture

Average tariff on exports of least developed countries % Agricultural products 2003 2009

99.9 97.5 96.6

100.0 100.0 98.4

0.2 0.2 1.8

44.8 25.1c 97.7 98.6 96.3 67.2

99.4 48.2 98.8 100.0 100.0 75.8

4.7 27.6c 4.0 4.7 5.4 6.3

Textiles

% of GDP 2010

2009

2003

2009

0.1 0.2 0.1

0.0 0.2 0.1

1.2 1.7 1.2

0.0 1.7 1.2

0.12 0.67 0.72

2.7 10.9c 0.3 0.0 0.0 6.4

2.6 6.4 0.0 0.0 0.0 5.7

0.1 11.3c 0.4 1.2 0.0 12.3

0.1 6.2 0.0 1.0 0.0 11.3

1.09 2.01 0.23 0.99 1.11 0.91

0.0 0.1 0.5

1.2 50.2 0.0 0.2 0.0 5.7

Clothing

2003

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

MDRI assistance

HIPC HIPC HIPC decision completion Initiative pointd pointd assistance

end-2010 net present value

end-2010 net present value

$ millions

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

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

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

653 384 1,948 810 1,008 1,856 674 240 150 9,474 1,903 3,243 2,728 98 3,083 799 744 894

MDRI assistance

$ millions

20 756 1,956 765 91 885 231 669 45 528 130 1,095 1,865 244 2,549 862 79 488

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

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

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

163 814 2,957 1,224 1,375 789 911 3,140 4,847 944 953 171 715 917 2,969 305 1,505 3,662

674 1,884 241 1,584 914 1,313 563 1,318 1,178 646 286 38 1,696 423 2,503 466 2,263 1,871

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. 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. c. Data are for 2004. 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.

32

2012 World Development Indicators


About the data

1.4

WORLD VIEW

Millennium Development Goals: overcoming obstacles Definitions

Achieving the Millennium Development Goals

lines with “international peaks”). The averages in

• Net official development assistance (ODA) is grants

requires an open, rule-based global economy in

the table include ad valorem duties and equivalents.

and loans (net of repayments of principal) that meet

which all countries, rich and poor, participate. Many

Subsidies to agricultural producers and exporters

the DAC definition of ODA and are made to countries

poor countries, lacking the resources to finance

in OECD countries are another barrier to developing

on the DAC list of recipients. • ODA for basic social

development, burdened by unsustainable debt, and

economies’ exports. Agricultural subsidies in OECD

services is aid commitments by DAC donors for basic

unable to compete globally, need assistance from

economies are estimated at $366 billion in 2010.

education, primary health care, nutrition, population

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

The Debt Initiative for Heavily Indebted Poor Coun-

policies and programs, reproductive health, and water

ship for development—many indicators therefore

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

and sanitation services. • Goods admitted free of

monitor the actions of members of the Organisa-

within the framework of poverty reduction, is the first

tariffs are exports of goods (excluding arms) from

tion for Economic Co-operation and Development’s

comprehensive approach to reducing the external

least developed countries admitted without tariff.

(OECD) Development Assistance Committee (DAC).

debt of the world’s poorest, most heavily indebted

• Average tariff is the unweighted average of the

Official development assistance (ODA) has risen

countries. A 1999 review led to an enhancement of

effectively applied rates for all products subject to

in recent years as a share of donor countries’ gross

the framework. In 2005, to further reduce the debt

tariffs. • Agricultural products are plant and animal

national income (GNI), but the poorest economies

of HIPCs and provide resources for meeting the Mil-

products, including tree crops but excluding timber

need additional assistance to achieve the Millennium

lennium Development Goals, the Multilateral Debt

and fish products. • Textiles and clothing are natu-

Development Goals. In 2010 net ODA from OECD

Relief Initiative (MDRI), proposed by the Group of

ral and synthetic fibers and fabrics and articles of

DAC members rose 3.2 percent in real terms, to

Eight countries, was launched.

clothing made from them. • Support to agriculture is

$131.1 billion or 0.33 percent of OECD DAC mem-

Under the MDRI four multilateral institutions—the

gross transfers from taxpayers and consumers arising

International Development Association (IDA), Inter-

from policy measures, net of associated budgetary

One important action that high-income economies

national Monetary Fund (IMF), African Development

receipts, regardless of their objectives and impacts on

can take is to reduce barriers to exports from low-

Fund (AfDF), and Inter-American Development Bank

farm production and income or consumption of farm

and middle- income economies. The European Union

(IDB)—provide 100 percent debt relief on eligible

products. • HIPC decision point is when a heavily

has begun to eliminate tariffs on imports of “every-

debts due to them from countries having completed

indebted poor country with an established track record

thing but arms” from least developed countries,

the HIPC Initiative process. Data in the table refer

of good performance under adjustment programs sup-

and the United States offers special concessions to

to status as of September 2011 and might not show

ported by the IMF and the World Bank commits to addi-

imports from Sub-Saharan Africa. However, these

countries that have since reached the decision or

tional reforms and a poverty reduction strategy and

programs still have many restrictions.

completion point. Debt relief under the HIPC Initiative

starts receiving debt relief. • HIPC completion point is

Average tariffs in the table refl ect high-income

has reduced future debt payments by $59 billion (in

when a country successfully completes the key struc-

OECD member tariff schedules for exports of coun-

end-2010 net present value terms) for 36 countries

tural reforms agreed on at the decision point, includ-

tries designated least developed countries by the

that have reached the decision point. And 32 coun-

ing implementing a poverty reduction strategy. The

United Nations. Although average tariffs have been

tries that have reached the completion point have

country then receives full debt relief under the HIPC

falling, averages may disguise high tariffs on specific

received additional assistance of $33 billion (in end-

Initiative without further policy conditions. • HIPC Ini-

goods (see table 6.7 for each country’s share of tariff

2010 net present value terms) under the MDRI.

bers’ combined GNI.

tiative assistance is the debt relief committed as of the decision point (assuming full participation of credi-

1.4a

Location of indicators for Millennium Development Goal 8

tors). Topping-up assistance and assistance provided under the original HIPC Initiative were committed in

Goal 8.1 8.2 8.3 8.4 8.5 8.6

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

Table 1.4 1.4 — — — 1.4 1.4, 6.7* 1.4 — 1.4 1.4 6.9*

net present value terms as of the decision point and are converted to end-2010 terms. • MDRI assistance is 100 percent debt relief on eligible debt from IDA, IMF, AfDF, and IDB, delivered in full to countries having reached the HIPC completion point. Data sources Data on ODA are from the OECD. Data on goods admitted free of tariffs and average tariffs are from the World Trade Organization in collaboration with the United Nations Conference on Trade and Development and the International Trade Centre (www. mdg-trade.org). Data on support to agriculture are

— 5.11 5.11 1.3, 5.12

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

from the OECD’s Producer and Consumer Support Estimates, OECD Database 1986–2010. Data on the HIPC Initiative and MDRI are from the World Bank’s Economic Policy and Debt Department.

2012 World Development Indicators

33


1.5

Women in development Female population

% of total 2010

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

34

48.3 49.9 49.5 50.5 51.1 53.5 50.2 51.2 50.5 37.6 49.4 53.5 51.0 50.7 50.1 51.9 49.6 50.8 51.7 50.4 50.9 51.1 50.1 50.4 50.7 50.3 50.6 48.1c 52.6 50.8 50.3 49.9 49.2 49.0 51.9 49.7 49.0 51.0 50.4 49.8 49.9 49.8 52.5 50.7 53.9 50.2 50.9 51.3 49.8 50.6 52.9 51.0 49.1 50.5 51.3 49.5 50.4 50.4

Life expectancy at birth

years Male Female 2010 2010

48 74 71 49 72 71 80 78 68 74 68 65 77 54 64 73 54 70 70 54 49 61 50 79 46 48 76 72c 80 70 47 56 77 54 74 77 77 74 77 70 73 71 67 59 71 57 77 78 61 57 70 78 63 78 67 52 46 61

2012 World Development Indicators

48 80 74 52 79 77 84 83 74 76 69 77 83 57 69 78 52 77 77 56 51 64 52 83 49 51 82 75c 86 77 50 58 82 56 80 81 82 81 81 76 78 75 77 63 81 60 83 85 63 59 77 83 65 83 74 55 49 63

Pregnant women receiving prenatal care

Teenage Women in wage mothers employment in nonagricultural sector

% 2005–10a

% of women ages 15–19 2005–10a

36 97 89 80 99 99 98 .. 77 100 53 99 .. 84 86 99 94 98 .. 85 99 89 82 100 69 53 .. 92 .. 97 86 86 90 85 100 100 99 .. .. 99 .. 74 94 .. .. 28 .. .. .. 98 98 .. 90 .. 93 88 93 85

.. 3 .. 29 .. 5 .. .. 6 .. 33 .. .. 21 18 .. .. .. .. .. .. 8 .. .. .. .. .. .. .. 20 24 27 .. .. 4 .. .. .. .. 21 .. 10 23 .. .. 17 .. .. .. .. 10 .. 13 .. 22 32 .. 14

Unpaid family workers

% of nonagricultural Male Female wage % of male % of female employment employment employment 2009 2010 2010

18 .. 15 .. .. 40 47 48 43 10 .. 56 47 .. 38 36 45 42 51 27 .. .. .. 51 .. .. 37 .. 50 48 .. .. 41 .. 46 43 48 46 50 39 .. 18 48 .. 54 .. 52 49 .. .. 48 48 .. 43 .. 29 .. ..

.. .. .. .. 0.4b 7.6 0.2 1.9 .. 0.5 .. .. 0.3 .. .. 2.6 .. 3.4 0.7 .. .. 25.2 .. 0.1 .. .. 1.1 .. 0.1 3.7 .. .. 0.9 .. 1.1 .. 1.3 0.3 0.1 .. 7.4 .. 7.1 .. 0.2 .. 0.6 0.3 .. .. 19.6 0.3 .. 3.3 .. .. .. ..

.. .. .. .. 1.2b 17.1 0.3 2.4 .. 0.8 .. .. 1.8 .. .. 10.4 .. 6.3 1.5 .. .. 60.8 .. 0.2 .. .. 2.5 .. 1.0 7.1 .. .. 2.6 .. 4.6 .. 2.9 1.0 0.5 .. 19.9 .. 7.2 .. 0.2 .. 0.5 0.9 .. .. 37.9 0.9 .. 9.2 .. .. .. ..

Female Female part-time legislators, employment senior officials, and managers

% of total 2005–10a

% of total 2005–10a

.. .. .. .. 65b 56 70 b 80 .. .. .. .. 80 .. 56 .. 55 68 52 .. .. .. .. 67b .. .. 58 .. .. 60 .. .. 64 .. 60 .. 64 69 63 50 54 .. 55 .. 67 .. 62 78 .. .. .. 80 .. 66 .. .. .. ..

.. .. 5 .. 23b 22 37 29 7 12 10 .. 34 .. 29 .. 30 36 34 31 .. 21 .. 36b .. .. 24 17 29 38 .. .. 30 .. 27 29 13 28 22 31 28b 11 25 .. 37 16b 30 39 .. .. 34 30 .. 30 .. .. .. ..

Women in parliaments

% of total seats 1990 2011

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

28 16 8 39 39 9 25 28 16 3 19 32 39 8 25 17 8 9 21 15 32 21 14 25 13 13 14 21 .. 13 10 7 39 9 24 43 11 22 38 21 32 2 19 22 20 28 43 19 15 8 7 33 8 17 12 .. 10 11


Female population

% of total 2010

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

50.0 52.5 48.3 50.1 49.3 49.8 50.0 50.7 51.1 50.8 51.3 48.6 52.0 50.1 50.9 50.1 .. 40.3 50.7 50.1 54.0 51.2 50.9 49.8 49.3 53.5 49.9 50.2 50.0 49.3 50.0 49.7 50.6 50.7 52.6 50.6 51.0 51.3 50.7 50.3 50.4 50.4 50.9 50.5 49.7 49.4 50.0 41.3 49.2 49.6 49.0 49.5 49.9 49.8 51.8 51.6 51.9 24.3

Life expectancy at birth

years Male Female 2010 2010

71 71 64 67 71 65 78 80 79 70 80 72 64 55 65 77 68 74 65 66 69 70 48 55 72 68 73 65 53 72 50 57 69 74 65 64 70 49 63 62 68 79 79 71 54 51 79 71 64 73 60 70 71 65 72 76 75 78

75 78 67 71 75 72 83 83 84 76 86 75 73 58 72 84 72 76 73 68 78 75 47 57 77 79 77 68 54 76 52 60 77 79 73 72 74 51 66 63 69 83 83 77 55 52 83 76 66 79 65 74 76 72 81 82 83 78

Pregnant women receiving prenatal care

Teenage Women in wage mothers employment in nonagricultural sector

% 2005–10a

% of women ages 15–19 2005–10a

92 .. 75 95 98 84 .. .. .. 99 .. 99 100 92 100 .. .. 100 97 71 .. .. 92 79 93 .. 94 86 92 79 70 75 .. 96 98 100 .. 92 80 95 44 .. .. 90 46 58 .. 99 61 96 79 96 95 91 .. .. .. 100

22 .. 16 9 .. .. .. .. .. 14 .. 5 7 18 .. .. .. .. .. 17 .. .. .. 38 .. .. .. 32 .. .. 36 .. .. .. 6 .. .. .. .. 15 19 .. .. 25 39 23 .. .. 9 .. .. 12 26 10 .. .. .. ..

Unpaid family workers

Female Female part-time legislators, employment senior officials, and managers

% of nonagricultural Male Female wage % of male % of female employment employment employment 2009 2010 2010

42 49 .. 32 .. 12 52 50 44 48 42 16 50 .. .. 42 .. .. 51 .. 55 .. .. .. .. 53 42 .. .. 39 .. .. 37 39 54 51 21 .. .. .. .. 48 51 38 36 .. 50 22 13 42 .. 40 38 42 48 49 42 10

.. 0.2 .. 8.1 4.8 .. 0.7 0.1 1.2 0.4 1.1 0.5 0.6 .. .. 1.2 .. .. .. .. 1.3 .. .. 12.5 .. 1.5 6.3 .. .. 2.6 .. .. 1.0 4.8 1.3 10.3 15.0 .. .. .. .. 0.3 0.7 .. .. .. 0.2 .. 19.7 3.0 .. 10.4 4.5b 9.0 2.5 0.7 0.0 ..

.. 0.4 .. 32.4 29.7 .. 0.7 0.3 2.3 2.1 6.9 0.5 0.9 .. .. 12.5 .. .. .. .. 1.4 .. .. 19.7 .. 1.9 16.6 .. .. 8.1 .. .. 4.9 9.7 3.5 35.1 48.6 .. .. .. .. 1.1 1.3 .. .. .. 0.3 .. 65.0 6.8 .. 11.0 8.7b 17.4 5.5 1.2 0.0 ..

1.5

% of total 2005–10a

% of total 2005–10a

.. 66 .. .. .. .. 76 74 77 .. 70 .. .. .. .. 60 .. .. .. .. 61 .. .. .. .. 61 49 .. .. .. .. .. .. 54 .. .. .. .. .. .. .. 75 72b 51 .. .. 70 .. .. 48 .. 55 61b .. 68 66 .. ..

41 36 .. 22 13 .. 33 32 33 .. 10 .. 38 .. .. 10 .. 14 35 .. 41 8 52 .. .. 41 28 22 .. 24 .. .. 23 31 38 47 13 .. .. 36 14 29 40 b 41 .. .. 34 9 3 48 .. 34 19b 55 36 32 43 7

WORLD VIEW

Women in development

Women in parliaments

% of total seats 1990 2011

2012 World Development Indicators

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

18 9 11 18 3 25 15 19 21 13 11 11 18 10 15 16 .. 8 23 25 20 3 24 13 8 19 31 13 21 10 10 22 19 26 19 4 11 39 4 24 33 39 34 21 13 4 40 0 22 9 1 13 22 22 20 27 .. 0

35


1.5

Women in development Female population

% of total 2010

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

51.5 53.7 50.9 44.6 50.4 50.5 51.2 49.6 51.4 51.1 50.4 50.5 .. 50.6 50.6 49.6 50.8 50.2 50.9 49.4 50.8 50.1 50.9 49.0 50.5 51.5 50.0 50.1 50.8 50.0 54.0 30.5 50.8 50.7 51.7 50.3 49.8 50.6 49.2 49.7 49.9 50.7 49.6 w 50.1 49.3 49.2 49.4 49.4 48.8 52.3 50.6 49.7 48.6 50.0 50.5 51.0

Life expectancy at birth

years Male Female 2010 2010

70 63 54 73 58 71 47 79 72 76 49 51 62 79 72 59 49 80 80 74 64 57 71 61 55 66 73 71 61 53 65 76 79 76 73 65 71 73 71 64 48 51 68 w 58 67 64 71 66 70 66 71 70 64 53 77 78

77 75 56 75 60 77 48 84 79 83 53 53 61 85 78 63 48 84 85 77 71 58 77 63 58 73 77 76 69 54 76 78 82 81 80 71 77 77 74 67 49 49 72 w 60 71 67 75 70 74 75 77 74 67 55 83 84

Pregnant women receiving prenatal care

% 2005–10a

.. .. 98 97 94 98 87 .. .. .. 26 97 .. .. 99 64 97 .. .. 88 80 88 99 84 87 96 96 95 99 94 99 100 .. .. 96 99 .. 91 99 47 94 90d 84 w 69 86 78 94 83 92 .. 97 85 71 74 .. ..

Teenage Women in wage mothers employment in nonagricultural sector % of women ages 15–19 2005–10a

.. .. 6 .. 18 .. 34 .. .. .. .. .. .. .. .. .. 23 .. .. .. .. 23 .. 7 .. .. .. .. .. 25 4 .. .. .. .. .. .. .. .. .. 28 21 .. w .. .. .. .. .. .. .. .. .. 16 .. .. ..

Unpaid family workers

% of nonagricultural Male Female wage % of male % of female employment employment employment 2009 2010 2010

46 53 .. 15 .. 44 .. 45 48 48 .. 45 .. 47 31 .. .. 50 48 15 .. .. 46 .. .. .. .. 24 .. .. 55 20 47 48 46 39 42 .. 18 6 .. .. .. w .. .. .. .. .. .. 48 41 .. .. .. 47 47

6.9 0.1 .. .. .. 4.1 .. 0.3 0.1 3.8 .. 0.4 .. 0.7 4.5b .. .. 0.3 1.6 3.3 .. .. 13.6 .. .. .. .. 5.1 .. .. 0.4 0.0 0.2 0.1 2.1b .. 0.5 .. 5.5 .. .. .. .. w .. .. .. .. .. .. 1.9 3.5 .. .. .. 0.6 0.7

a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2011.

36

2012 World Development Indicators

20.0 0.1 .. .. .. 15.6 .. 1.0 0.2 6.2 .. 1.4 .. 1.0 22.3b .. .. 0.2 2.4 12.4 .. .. 28.5 .. .. .. .. 35.2 .. .. 0.3 0.0 0.4 0.1 0.8b .. 1.2 .. 19.8 .. .. .. .. w .. .. .. .. .. .. 5.3 6.8 .. .. .. 2.2 1.5

Female Female part-time legislators, employment senior officials, and managers

% of total 2005–10a

47 62 .. .. .. .. .. .. 59 59 .. .. .. 78 .. .. .. 63 80 27 .. .. .. .. .. .. .. 58 .. .. .. .. 75 67 64b .. 64 .. .. .. .. .. .. w .. .. .. .. .. .. .. 62 .. .. .. 71 77

% of total 2005–10a

32 37 0 8 .. 36 .. 31 35 35 .. 30 .. 34 24b .. .. 31 33 10 .. 16 24 .. .. 43 .. 10 .. 33 39 10 36 43 40 .. 27 22 10 4 19 ..

Women in parliaments

% of total seats 1990 2011

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

11 14 56 0 23 22 13 22 16 14 7 45 .. 37 6 26 14 45 29 12 19 36 13 29 11 29 28 14 17 35 8 23 22 17 15 22 17 24 .. 0 14 15 19 w 20 18 15 19 18 18 16 23 9 20 20 23 26


1.5

WORLD VIEW

Women in development About the data

Despite much progress in recent decades, gender

terms of earnings, work conditions, or legal and social

personnel for reasons related to pregnancy. • Teen-

inequalities remain pervasive in many dimensions of

protection. The indicator also does not reflect whether

age mothers are women ages 15–19 who already

life—worldwide. But while disparities exist through-

women reap the economic benefits of such employ-

have children or are currently pregnant. • Women

out the world, they are most prevalent in developing

ment. Finally, female employment and the employ-

in wage employment in nonagricultural sector are

countries. Gender inequalities in the allocation of

ment share of the agricultural sector for both men

female wage employees in the nonagricultural sec-

such resources as education, health care, nutrition,

and women tend to be underreported.

tor as a percentage of total nonagricultural wage

and political voice matter because of the strong asso-

Women’s wage work is important for economic

employment. • Unpaid family workers are those

ciation with well-being, productivity, and economic

growth and the well-being of families. But women

who work without pay in a market-oriented estab-

growth. These patterns of inequality begin at an early

often face such obstacles as restricted access to

lishment or activity operated by a related person

age, with boys routinely receiving a larger share of edu-

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

living in the same household. There is no official

cation and health spending than do girls, for example.

cation; time constraints due to traditional family

International Labour Organization definition of full-

Because of biological differences girls are expected

responsibilities; and labor market bias and discrimi-

time work, so the definition of part-time workers dif-

to experience lower infant and child mortality rates

nation. These obstacles force women to limit their

fers across countries and thus comparisons should

and to have a longer life expectancy than boys. This

full participation in paid economic activities, to be

be made with caution. • Female part-time employ-

biological advantage may be overshadowed, however,

less productive, and to receive lower wages. More

ment is the percentage of part-time workers who

by gender inequalities in nutrition and medical inter-

women than men are in unpaid family employment

are female. Part-time workers are employed people

ventions and by inadequate care during pregnancy

and part-time employment. And men and women

whose normal hours of work are less than those of

and delivery, so that female rates of illness and

have different occupational distributions. There is no

comparable full-time workers. The definition of part-

death sometimes exceed male rates. Gender bias

official International Labour Organization definition

time varies across countries. • Female legislators,

can be seen in child mortality rates (table 2.23) and

of full-time work, so the definition of part-time work-

senior officials, and managers are the percentage

life expectancy at birth. Female child mortality rates

ers differs across countries, and thus comparisons

of legilsators, senior officials, and managers (Inter-

that are as high as or higher than male child mortality

should be made with caution.

rates may indicate discrimination against girls.

national Standard Classification of Occupations–88

The female share of high-skilled occupations such

category 1) who are female. • Women in parliaments

Having a child during the teenage years limits girls’

as legislators, senior officials, and managers indi-

are parliamentary seats in a single or lower chamber

opportunities for better education, jobs, and income.

cates gender segregation of employment. Women

held by women.

Pregnancy is more likely to be unintended during the

are vastly underrepresented in decisionmaking

teenage years, and births are more likely to be pre-

positions in government, although there is some

mature and are associated with greater risks of com-

evidence of recent improvement. Gender parity in

Data on female population are from the United

plications during delivery and of death. In many coun-

parliamentary representation is still far from being

Nations Population Division’s World Population Pros-

tries maternal mortality (tables 1.3 and 2.19) is a

realized. Without representation at this level, it is

pects: The 2010 Revision, and data on life expec-

leading cause of death among women of reproductive

difficult for women to influence policy.

tancy for more than half the countries in the table

Data sources

age, although most of those deaths are preventable.

For information on other aspects of gender, see

(most of them developing countries) are from its

Data on women in wage employment in the non-

tables 1.2 (Millennium Development Goals: eradicat-

World Population Prospects: The 2010 Revision, with

agricultural sector show the extent to which women

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

additional data from census reports, other statisti-

have access to paid employment—which affects their

opment Goals: protecting our common environment),

cal publications from national statistical offices,

integration into the monetary economy —and indicate

2.3 (Employment by economic activity), 2.4 (Decent

Eurostat’s Demographic Statistics, the Secretariat

the degree to which labor markets are open to women

work and productive employment), 2.5 (Unemploy-

of the Pacific Community’s Statistics and Demog-

in industry and services—which affects not only equal

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

raphy Programme, and the U.S. Bureau of the Cen-

employment opportunity for women, but also eco-

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

sus International Data Base. Data on pregnant

nomic efficiency through flexibility of the labor market

(Education completion and outcomes), 2.15 (Educa-

women receiving prenatal care are from the United

and the economy’s capacity to adapt to changes over

tion gaps by income and gender), 2.19 (Reproductive

Nations Children’s Fund’s (UNICEF) The State of the

time. In many developing countries nonagricultural

health), 2.22 (Health risk factors and future chal-

World’s Children 2012 based on household surveys,

wage employment accounts for only a small portion

lenges), and 2.23 (Mortality).

including MEASURE DHS Demographic and Health Surveys by ICF International and Multiple Indica-

of total employment. As a result, the contribution of women to the national economy is underestimated

Definitions

tor Cluster Surveys by UNICEF. Data on teenage

and therefore misrepresented. The indicator is dif-

• Female population is the percentage of the popu-

mothers are from MEASURE DHS Demographic and

ficult to interpret without additional information on the

lation that is female. • Life expectancy at birth is

Health Surveys by ICF International. Data on labor

share of women in total employment, which allows

the number of years a newborn infant would live if

force, employment, and occupation are from the

an assessment to be made of whether women are

prevailing patterns of mortality at the time of its birth

International Labour Organization’s Key Indicators

under- or overrepresented in nonagricultural wage

were to stay the same throughout its life. • Pregnant

of the Labour Market, 7th edition. Data on women in

employment. The indicator does not reveal differences

women receiving prenatal care are women attended

parliaments are from the Inter-Parliamentary Union.

in the quality of nonagricultural wage employment in

at least once during pregnancy by skilled health

2012 World Development Indicators

37


1.6

Key indicators for other economies Population Surface Population area density

Gross national income

Atlas method

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

Gross domestic product

Carbon dioxide emissions

Purchasing power parity

thousands 2010

thousand sq. km 2010

people per sq. km 2010

$ millions 2010

Per capita $ 2010

68 85 88 108 343 274 345 65 726 399 496 56 153 735 143 889 68 700 49 860 271 29 57 104 179 755 318 83 100

0.2 0.5 0.4 0.2 13.9 0.4 23.0 0.1 38.4 5.8 4.0 0.3 0.2 1.9 0.4 23.2 0.8 28.1 1.4 18.3 4.0 0.0 410.5 0.3 0.5 215.0 103.0 0.6 0.8

342 181 200 600 34 637 15 1,292 19 76 123 234 807 395 321 38 91 25 35 47 74 2,924 0f 306 331 4 3 145 123

.. 3,447 1,169 .. 6,973 3,454 1,313 .. 1,361 12,461 1,620 .. .. 550 .. 1,105 458 10,182 .. 3,123 .. .. 1,466 724 .. 2,164 10,381 .. 200

..b .. .. 41,750 .. .. 1,795d 20,400 d 13,280 c .. .. ..e 20,610 8,392d 24,800d 12,660 5,183 19,000 6,200 d 3,810 2,139d .. .. ..e 1,870 3,622 4,990 31,800 19,661 50,180 3,270 1,893 3,820 .. .. ..e .. .. ..e 750 802 1,090 .. .. ..e 1,270 2,149 2,460 6,740 812d 11,940 d 14,550 16,635 23,760 .. .. ..e 3,630 3,880 4,510 .. .. ..e .. .. ..e 26,020 .. .. 9,930 d 6,960 1,033d .. .. ..e 3,450 d 2,870 2,606d 32,640 8,991 28,270 .. .. ..e 3,520 d 2,000 352d

About the data

Life Adult expectancy literacy at birth rate

$ millions 2010

Per capita $ 2010

% growth 2009–10

Per capita % growth 2009–10

years 2010

.. 3.6 –5.2 .. 0.9 –5.3 2.9 –8.1 7.4 –1.8 5.4 .. .. 2.1 .. 5.0 0.1 0.9 .. 0.3 .. .. –5.4 –0.8 .. 3.6 –4.0 .. 1.8

.. 2.1 –5.4 .. –0.5 –5.5 –0.6 –8.4 5.6 –3.6 4.5 .. .. –0.6 .. 3.0 –0.1 –1.8 .. –0.6 .. .. –5.4 –0.7 .. 3.3 –3.8 .. –0.2

.. .. .. 75 75 77 76 79 67 78 74 .. 80 61 .. 58 .. 51 80 69 75 .. 68 76 76 70 81 .. ..

% ages 15 thousand and older metric tons 2005–10a 2008

.. .. 99 98 .. .. .. .. 53 95 85 99 .. 74 .. .. .. 93 .. .. .. .. .. .. .. .. .. .. ..

.. 539 447 2,288 2,156 1,353 425 389 733 10,594 308 557 .. 125 .. 524 128 4,815 708 1,254 891 422 576 246 .. 1,525 2,230 .. 29

Definitions

The table shows data for economies with a popula-

• Population is based on the de facto definition of

included in the valuation of output plus net receipts

tion between 30,000 and 1 million and for smaller

population, which counts all residents regardless of

of primary income (compensation of employees and

economies if they are members of the World Bank.

legal status or citizenship—except for refugees not

property income) from abroad. Data are in current

Where data on gross national income (GNI) per capita

permanently settled in the country of asylum, who

U.S. dollars converted using the World Bank Atlas

are not available, the estimated range is given. For

are generally considered part of the population of

method (see Statistical methods). • Purchasing

more information on the calculation of GNI and pur-

their country of origin. The values shown are midyear

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

chasing power parity (PPP) conversion factors, see

estimates. For more information, see About the data

tional dollars using PPP rates. An international dollar

About the data for table 1.1. Additional data for the

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

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

economies in the table are available on the World

area, including areas under inland bodies of water

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

Development Indicators CD-ROM or at http://data.

and some coastal waterways. • Population density

GNI divided by midyear population. • Gross domes-

worldbank.org.

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

38

2012 World Development Indicators


Population Surface Population area density

Gross national income

Atlas method

Liechtenstein Luxembourg Macao SAR, China Maldives Malta Marshall Islands Mayotte Micronesia, Fed. Sts. Monaco Montenegro New Caledonia Northern Mariana Islands Palau Samoa San Marino São Tomé and Príncipe Seychelles Sint Maarten Solomon Islands St. Kitts and Nevis St. Lucia St. Martin St. Vincent & Grenadines Suriname Tonga Turks and Caicos Islands Tuvalu Vanuatu Virgin Islands (U.S.)

Per capita $ 2010

Gross domestic product

1.6

Life Adult expectancy literacy at birth rate

WORLD VIEW

Key indicators for other economies

Carbon dioxide emissions

Purchasing power parity

thousands 2010

thousand sq. km 2010

people per sq. km 2010

$ millions 2010

$ millions 2010

Per capita $ 2010

% growth 2009–10

Per capita % growth 2009–10

years 2010

36 507 544 316 416 54 204 111 35 632 247 61 20 184 32 165 87 38 538 52 174 30 109 525 104 38 10 240 110

0.2 2.6 0.0 0.3 0.3 0.2 0.4 0.7 0.0 13.8 18.6 0.5 0.5 2.8 0.1 1.0 0.5 0.0 28.9 0.3 0.6 0.1 0.4 163.8 0.8 1.0 0.0 12.2 0.4

225 196 19,429 1,053 1,300 300 551 159 17,704 47 14 132 45 65 526 172 189 1,113 19 200 285 556 279 3 144 40 328 20 314

4,903 137,070 .. 39,030 76,980 31,050 18,527 34,880 24,020 1,818 5,750 2,563 7,958 19,130 10,258 197 3,640 .. .. .. ..b 304 2,740 388d 6,479 183,150 .. 4,260 6,740 8,073 .. .. ..e .. .. ..e 134 6,560 225d 549 2,980 782d 1,572 50,400 .. 199 1,200 318 845 9,710 1,835d .. .. ..e 552 1,030 1,192d 615 11,830 831d 1,142 6,560 1,830d .. .. ..e 688 6,320 1,184 d 3,077 5,920 3,991d 342 3,290 477d .. .. ..e .. 47 4,760 g 633 2,640 1,035d .. .. ..e

.. 61,240 45,220 8,110 24,660 .. .. 3,490 d .. 12,770 .. .. 11,000 d 4,250 d .. 1,930 21,090 d .. 2,220 d 15,970 d 10,520 d .. 10,870 d 7,680d 4,580 d .. .. 4,310 d ..

–1.2 2.7 26.4 9.9 3.1 5.2 .. 3.1 –2.6 2.5 .. .. 2.0 1.7 1.9 4.5 6.2 .. 7.0 –5.0 3.1 .. –1.3 3.1 –0.5 .. –1.9 3.0 ..

–1.9 0.8 23.4 8.4 2.6 4.0 .. 2.8 –2.7 2.2 .. .. 1.4 0.8 1.3 2.9 6.6 .. 4.2 –5.5 2.1 .. –1.0 2.1 –0.9 .. –2.1 0.4 ..

.. 80 81 77 81 .. 78 69 .. 74 76 .. .. 72 83 64 73 .. 67 .. 74 .. 72 70 72 .. .. 71 79

% ages 15 thousand and older metric tons 2005–10a 2008

.. .. 93 98 92 .. .. .. .. .. 96 .. .. 99 .. 89 92 .. .. .. .. .. .. 95 99 .. .. 82 ..

.. 10,502 1,335 920 2,560 99 .. 62 .. 1,951 3,150 .. 213 161 .. 128 682 .. 198 249 396 .. 202 2,439 176 158 .. 92 ..

a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,976–$12,275). c. Included in the aggregates for upper middle-income economies based on earlier data. d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Estimated to be high income ($12,276 or more). f. Less than 0.5. g. Included in the aggregates for lower middle-income economies based on earlier data.

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

Data sources

with understanding, read and write a short, simple

The indicators here and throughout the book

statement about their everyday life. • Carbon dioxide

are compiled by World Bank staff from primary

emissions are those stemming from the burning of

and secondary sources. More information about

fossil fuels and the manufacture of cement. They

the indicators and their sources can be found in

include carbon dioxide produced during consumption

the About the data, Definitions, and Data sources

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

entries that accompany each table in subsequent sections.

2012 World Development Indicators

39


PEOPLE


T

he People section documents demographic trends, labor force structure, poverty incidence, income distribution, education inputs and outcomes, and health services and status. Together these indicators provide a multidimensional portrait of human development and social welfare. For 2012 a new table has been added, and the contents of others have changed. Table 2.20 now shows data by sex for three measures of the prevalence of child malnutrition — underweight, stunting, and wasting— and for the proportion of overweight children. Data on antiretroviral therapy coverage and cause of death have been added to table 2.22. And table 2.23 now includes neonatal mortality rates computed with the same method ology used to estimate infant and under-five mortality rates. Also new is table 2.24, which presents selected health indicators by income quintile. These subnational data highlight substantial within-country disparities between the poor and rich in mortality and fertility rates and in child and reproductive health outcomes. Table 2.8 provides more recent data on poverty at international poverty lines for more countries, including global and regional poverty estimates released by the World Bank in February 2012. In 2008, 1.29 billion people, or 22 percent of developing countries’ population, lived below the extreme poverty line of $1.25 a day, down from 1.94 billion people, or 52 percent of developing countries’ population, in 1981. In 1990, the benchmark year for the Millennium Development Goals, the extreme poverty rate was 43.1 percent. Progress has been slower at the $2 a day poverty line, around which many people in lower middle-income economies live. The number of people living below $2 a day fell from 2.59 billion in 1981 to 2.47 billion in 2008 — a decrease of only 120 million — and the number of people living on $1.25–$2 a day almost doubled

to 1.18 billion in 2008. This bunching up just above the extreme poverty line indicates the vulnerabilities faced by a great many people in the world. Looking at the trend from 2005 to 2008, both the absolute number and the proportion of people living in extreme poverty declined in every developing country region. This acrossthe-board reduction over a three-year monitoring cycle is the first since the World Bank began monitoring extreme poverty. Since 2008 food, fuel, and financial crises have had sharp negative impacts on vulnerable populations and slowed poverty reduction in some countries, but global poverty kept falling. In fact, a preliminary survey-based estimate for 2010 — based on a much smaller sample than the global update — indicates that the global poverty rate at $1.25 a day fell to less than half its 1990 value. If these results are confirmed by follow-up studies, the first target of the Millennium Development Goals — cutting the extreme poverty rate to half its 1990 level — may have been achieved on a global level before the 2015 target date. The World Bank’s database of international poverty measures now includes income or consumption data collected by national statistical offices drawn from 850 household surveys and interviews with 1.23 million randomly sampled households in nearly 130 countries. The most recent year for which a reliable global estimate can be calculated is 2008, because for many low-income countries more recent data are either not available or not comparable with previous estimates. The availability, frequency, and quality of poverty monitoring data remain low, especially in small states and in countries and territories with fragile situations. The need to improve household survey programs for poverty monitoring in these countries is urgent. But institutional, political, and financial obstacles continue to hamper data collection, analysis, and public access.

2

2012 World Development Indicators

41


Tables

2.1

Population dynamics Population

Average annual population growth

Population age composition

Dependency ratio

%

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

42

2000

millions 2010

2020

2000–10

2010–20

Ages 0–14 2010

26.0 3.1 30.5 13.9 36.9 3.1 19.2 8.0 8.0 0.6 129.6 10.0 10.3 6.5 8.3 3.7 1.8 174.4 8.2 12.3 6.4 12.4 15.7 30.8 3.7 8.2 15.4 1,262.6 6.7 39.8 49.6 3.1 3.9 16.6 4.4 11.1 0.9 10.3 5.3 8.6 12.3 67.6 5.9 3.7 1.4 65.6 5.2 60.8 1.2 1.3 4.4 a 82.2 19.2 10.9 11.2 8.3 1.2 8.6

34.4 3.2 35.5 19.1 40.4 3.1 22.3 8.4 9.1 1.3 148.7 9.5 10.9 8.9 9.9 3.8 2.0 194.9 7.5 16.5 8.4 14.1 19.6 34.1 4.4 11.2 17.1 1,338.3 7.1 46.3 66.0 4.0 4.7 19.7 4.4 11.3 1.1 10.5 5.5 9.9 14.5 81.1 6.2 5.3 1.3 83.0 5.4 64.9 1.5 1.7 4.5a 81.8 24.4 11.3 14.4 10.0 1.5 10.0

44.8 3.3 40.1 24.8 43.7 3.1 25.2 8.5 10.1 1.5 167.1 9.2 11.1 11.5 11.6 3.6 2.2 209.6 7.0 22.1 10.1 15.9 24.1 37.1 5.3 14.4 18.4 1,381.6 7.8 52.1 85.0 5.0 5.3 24.5 4.3 11.1 1.2 10.7 5.7 11.1 16.3 94.8 6.6 6.8 1.3 100.9 5.5 67.6 1.8 2.2 4.2a 79.8 30.3 11.5 18.3 12.8 1.9 11.3

2.8 0.4 1.5 3.1 0.9 0.1 1.5 0.5 1.2 6.8 1.4 –0.5 0.6 3.1 1.8 0.2 1.3 1.1 –0.8 2.9 2.7 1.3 2.2 1.0 1.7 3.1 1.0 0.6 0.6 1.5 2.8 2.5 1.7 1.7 0.0 0.1 1.6 0.2 0.4 1.4 1.6 1.8 0.4 3.6 –0.2 2.3 0.4 0.7 2.0 2.9 0.1a –0.1 2.4 0.4 2.5 1.8 2.0 1.4

2.6 0.2 1.2 2.6 0.8 0.1 1.2 0.1 1.1 1.9 1.2 –0.4 0.2 2.6 1.5 –0.4 0.9 0.7 –0.7 2.9 1.8 1.2 2.1 0.8 1.9 2.5 0.8 0.3 0.9 1.2 2.5 2.1 1.2 2.2 –0.3 –0.1 1.0 0.2 0.3 1.1 1.2 1.6 0.6 2.6 –0.1 2.0 0.2 0.4 1.9 2.6 –0.7a –0.2 2.2 0.1 2.4 2.4 2.1 1.2

46 23 27 47 25 20 19 15 21 20 31 15 17 44 36 15 33 25 14 45 38 32 41 16 40 45 22 19 12 29 46 41 25 41 15 17 18 14 18 31 30 32 32 42 15 41 17 18 35 44 17 13 39 15 41 43 41 36

2012 World Development Indicators

%

Ages 15–64 2010

Ages 65+ 2010

51 68 68 51 65 69 68 68 73 78 64 71 66 53 59 71 63 68 69 52 59 64 56 69 56 52 69 72 76 66 51 56 69 55 68 70 71 71 66 63 63 63 61 56 67 55 66 65 60 54 69 66 58 67 54 54 55 60

2 10 5 2 11 11 13 18 7 2 5 14 17 3 5 14 4 7 18 2 3 4 4 14 4 3 9 8 13 6 3 4 7 4 17 12 12 15 16 6 6 5 7 2 17 3 17 17 4 2 14 20 4 19 4 3 3 4

% of working-age population Young Old 2010 2010

91 34 40 91 39 29 28 22 29 26 49 21 26 82 61 21 51 38 20 86 64 50 73 24 73 88 32 27 15 44 91 73 36 74 22 25 25 20 27 49 48 50 52 74 23 75 25 28 59 82 24 20 67 22 77 80 75 60

4 14 7 5 16 16 20 26 9 3 7 19 27 6 8 20 6 10 25 4 5 6 6 20 7 6 13 11 17 9 5 7 10 7 25 18 16 21 25 10 10 8 11 4 25 6 26 26 7 4 21 31 7 28 8 6 6 7

Crude death rate

Crude birth rate

per 1,000 people 2010

per 1,000 people 2010

16 6 5 14 8 9 6 9 6 3 6 15 10 12 7 10 13 6 15 12 14 8 14 8 16 16 6 7 6 5 16 11 4 12 12 7 7 10 10 6 5 5 7 8 12 10 10 8 9 9 11 11 8 9 5 13 17 9

44 13 20 42 17 15 13 9 19 20 20 11 12 40 26 9 24 15 10 43 34 22 36 11 35 45 14 12 13 20 43 35 16 34 10 10 12 11 11 22 21 23 20 36 12 31 11 13 27 38 12 8 32 10 32 39 38 27


Population

Average annual population growth

Population age composition

Dependency ratio

%

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

2000

millions 2010

2020

2000–10

2010–20

Ages 0–14 2010

6.2 10.2 1,053.9 213.4 65.3 24.3 3.8 6.3 56.9 2.6 126.9 4.8 14.9 31.3 22.9 47.0 1.7 1.9 4.9 5.3 2.4 3.7 2.0 2.8 5.2 3.5 2.0 15.4 11.2 23.4 11.3 2.6 1.2 100.0 3.6b 2.4 28.8 18.2 45.0 1.9 24.4 15.9 3.9 5.1 10.9 123.7 4.5 2.3 144.5 3.0 5.4 5.3 25.9 77.3 38.5 10.2 3.8 0.6

7.6 10.0 1,224.6 239.9 74.0 32.0 4.5 7.6 60.5 2.7 127.5 6.0 16.3 40.5 24.3 48.9 1.8 2.7 5.4 6.2 2.2 4.2 2.2 4.0 6.4 3.3 2.1 20.7 14.9 28.4 15.4 3.5 1.3 113.4 3.6b 2.8 32.0 23.4 48.0 2.3 30.0 16.6 4.4 5.8 15.5 158.4 4.9 2.8 173.6 3.5 6.9 6.5 29.1 93.3 38.2 10.6 4.0 1.8

9.2 9.8 1,385.2 262.1 80.9 42.3 5.0 8.9 60.8 2.8 123.6 7.2 18.0 52.5 25.3 50.2 1.9 3.4 6.1 7.0 2.1 4.5 2.4 5.2 7.0 3.1 2.1 27.3 20.7 33.0 20.5 4.3 1.3 125.7 3.3b 3.2 35.0 29.1 51.7 2.7 35.1 17.0 4.8 6.6 22.0 203.7 5.2 3.3 205.2 4.0 8.5 7.6 32.3 109.6 38.1 10.5 4.0 2.3

2.0 –0.2 1.5 1.2 1.2 2.8 1.6 1.9 0.6 0.4 0.0 2.3 0.9 2.6 0.6 0.4 0.7 3.4 1.1 1.5 –0.6 1.2 1.0 3.4 1.9 –0.6 0.3 3.0 2.8 1.9 3.1 2.7 0.8 1.3 –0.2b 1.3 1.0 2.5 0.6 1.9 2.1 0.4 1.2 1.3 3.5 2.5 0.8 2.1 1.8 1.7 2.4 1.9 1.2 1.9 –0.1 0.4 0.4 10.9c

1.9 –0.2 1.2 0.9 0.9 2.8 1.0 1.5 0.1 0.3 –0.3 1.8 1.0 2.6 0.4 0.3 0.7 2.2 1.2 1.3 –0.4 0.6 1.0 2.6 1.0 –0.5 0.0 2.8 3.3 1.5 2.9 2.2 0.4 1.0 –0.6b 1.4 0.9 2.2 0.7 1.6 1.6 0.2 1.0 1.3 3.5 2.5 0.7 1.8 1.7 1.4 2.1 1.6 1.1 1.6 0.0 –0.1 –0.1 2.6

37 15 31 27 23 43 21 27 14 29 13 38 24 42 23 16 .. 27 30 35 14 25 37 43 30 15 18 43 46 30 47 40 22 29 17 28 28 44 26 36 36 18 20 34 49 43 19 27 35 29 39 34 30 35 15 15 21 13

%

Ages 15–64 2010

Ages 65+ 2010

59 69 64 67 72 54 67 62 66 63 64 59 69 55 68 72 .. 71 66 62 68 68 58 54 65 69 71 54 51 65 51 57 71 65 72 68 66 53 69 60 60 67 67 61 49 54 67 70 60 64 58 61 64 61 72 67 66 85

4 17 5 6 5 3 12 10 20 8 23 4 7 3 10 11 .. 3 4 4 18 7 4 3 4 16 12 3 3 5 2 3 7 6 11 4 5 3 5 4 4 15 13 5 2 3 15 3 4 7 3 5 6 4 14 18 13 1

% of working-age population Young Old 2010 2010

62 21 47 40 32 81 32 44 21 46 21 64 36 77 34 23 .. 38 46 56 20 36 64 81 47 22 25 80 90 47 93 69 31 45 23 40 42 84 37 61 61 26 31 57 100 80 28 39 59 45 67 55 47 58 21 23 32 16

7 24 8 8 7 6 17 17 31 12 35 7 10 5 14 15 .. 4 7 6 26 11 7 5 7 23 17 6 6 7 4 5 10 10 15 6 8 6 7 6 7 23 20 8 4 6 22 4 7 10 5 8 10 6 19 27 19 1

2.1

PEOPLE

Population dynamics

Crude death rate

Crude birth rate

per 1,000 people 2010

per 1,000 people 2010

5 13 8 7 5 6 6 5 10 7 10 4 9 11 10 5 7 3 7 6 13 7 16 11 4 13 9 6 13 5 15 10 7 5 13 6 6 15 9 8 6 8 7 5 13 14 9 4 7 5 8 5 5 6 10 10 8 2

27 9 22 18 17 35 17 22 9 16 9 25 22 38 14 9 19 18 27 23 9 15 28 39 23 11 11 35 44 20 46 34 12 20 12 23 20 38 17 26 24 11 15 24 49 40 13 18 27 20 30 24 20 25 11 10 13 13

2012 World Development Indicators

43


2.1

Population dynamics Population

Average annual population growth

Population age composition

Dependency ratio

%

2000

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

millions 2010

22.4 21.4 146.3 141.8 8.1 10.6 20.0 27.4 9.5 12.4 7.5 7.3 4.1 5.9 4.0 5.1 5.4 5.4 2.0 2.0 7.4 9.3 44.0 50.0 .. .. 40.3 46.1 18.7 20.9 43.6e 34.2e 1.0 1.1 8.9 9.4 7.2 7.8 16.0 20.4 6.2 6.9 34.0 44.8 63.2 69.1 0.8 1.1 4.8 6.0 1.3 1.3 9.6 10.5 63.6 72.8 4.5 5.0 24.2 33.4 49.2 45.9 3.0 7.5 58.9 62.2 282.2 309.3 3.3 3.4 24.7 28.2 24.3 28.8 77.6 86.9 3.0 4.2 17.7 24.1 10.2 12.9 12.5 12.6 6,117.8 s 6,894.6 s 643.7 796.3 4,424.5 4,970.8 2,146.7 2,518.7 2,277.9 2,452.1 5,068.2 5,767.2 1,813.8 1,961.6 398.5 405.2 514.3 582.6 277.4 331.3 1,398.0 1,633.1 666.3 853.4 1,049.6 1,127.4 315.0 331.8

% 2020

20.9 139.3 14.0 33.1 16.0 7.2 7.2 5.6 5.5 2.1 12.2 52.4 .. 48.4 22.3 54.9e 1.2 9.9 8.0 24.3 7.9 61.0 71.9 1.5 7.3 1.4 11.6 80.7 5.7 45.3 43.3 9.2 65.7 334.9 3.5 31.6 33.1 95.2 5.5 32.2 17.7 15.5 7,635.2 s 979.1 5,478.1 2,901.8 2,576.2 6,457.2 2,068.9 414.4 642.4 386.5 1,860.8 1,084.2 1,178.1 336.7

2000–10

2010–20

–0.5 –0.3 2.7 3.1 2.7 –0.3 3.5 2.3 0.1 0.3 2.3 1.3 .. 1.3 1.1 2.4 e 0.4 0.6 0.9 2.5 1.1 2.8 0.9 3.0 2.3 0.4 1.0 1.3 1.1 3.2 –0.7 9.1 0.6 0.9 0.2 1.4 1.7 1.1 3.2 3.1 2.4 0.0 1.2 w 2.1 1.2 1.6 0.7 1.3 0.8 0.2 1.2 1.8 1.6 2.5 0.7 0.5

–0.3 –0.2 2.8 1.9 2.5 –0.2 2.0 0.9 0.1 0.1 2.7 0.5 .. 0.5 0.7 2.3e 1.2 0.5 0.3 1.7 1.4 3.1 0.4 2.8 2.0 0.2 0.9 1.0 1.2 3.0 –0.6 2.0 0.5 0.8 0.3 1.1 1.4 0.9 2.7 2.9 3.1 2.1 1.0 w 2.1 1.0 1.4 0.5 1.1 0.5 0.2 1.0 1.6 1.3 2.4 0.4 0.1

Ages 0–14 2010

15 15 43 30 44 18d 43 17 15 14 45 30 .. 15 25 40e 38 17 15 37 37 45 21 46 40 21 23 26 29 48 14 17 17 20 23 29 29 24 42 44 46 39 27 w 39 27 32 22 29 22 19 28 31 32 42 17 15

Ages 15–64 2010

70 72 55 67 54 68d 55 74 73 70 52 65 .. 68 67 56e 58 65 68 59 60 52 71 51 57 72 70 68 67 49 70 83 66 67 64 66 65 70 55 53 51 57 66 w 57 67 63 70 65 71 70 65 64 64 54 67 66

Ages 65+ 2010

15 13 3 3 2 14 d 2 9 12 16 3 5 .. 17 8 4e 3 18 17 4 3 3 9 3 3 7 7 6 4 3 15 0 17 13 14 4 6 6 3 3 3 4 8w 4 6 5 8 6 7 11 7 5 5 3 16 18

% of working-age population Young Old 2010 2010

22 21 78 46 81 26d 78 24 21 20 86 46 .. 22 37 71e 66 25 22 62 62 86 29 91 70 28 34 39 44 99 20 21 26 30 35 44 45 34 78 83 92 68 41 w 69 40 51 31 44 31 28 43 48 50 78 26 23

21 18 5 4 4 21d 3 12 17 24 5 7 .. 25 12 6e 6 28 25 7 6 6 13 6 6 10 10 9 6 5 22 1 25 20 22 7 9 9 5 5 6 7 12 w 6 10 8 11 9 10 15 10 7 8 6 23 28

Crude death rate

Crude birth rate

per 1,000 people 2010

per 1,000 people 2010

12 14 12 4 9 14 16 4 10 9 15 15 21 8 7 9e 14 10 8 4 6 10 7 8 11 8 6 5 8 12 15 1 9 8 9 5 5 5 4 6 16 13 8w 11 8 8 7 8 7 11 6 5 8 13 8 9

10 13 41 22 37 9 39 9 11 11 44 21 30 11 18 33e 29 12 10 23 28 41 12 38 32 15 18 18 22 45 11 13 13 14 14 23 21 17 33 38 46 29 20 w 33 19 24 14 21 14 15 19 23 23 37 12 10

a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo. e. Includes South Sudan.

44

2012 World Development Indicators


About the data

2.1

PEOPLE

Population dynamics Definitions

Population estimates are usually based on national

and declining mortality rates are now reflected in the

• Population is based on the de facto definition of

population censuses. Estimates for the years before

larger share of the working-age population.

population, which counts all residents regardless of

and after the census are interpolations or extrapo-

Dependency ratios capture variations in the pro-

lations based on demographic models. Errors and

portions of children, elderly people, and working-age

permanently settled in the country of asylum, who

undercounting occur even in high-income countries;

people in the population that imply the dependency

are generally considered part of the population of

in developing countries errors may be substantial

burden that the working-age population bears in

their country of origin. The values shown are mid-

because of limits in the transport, communications,

relation to children and the elderly. But dependency

year estimates for 2000 and 2010 and projections

and other resources required to conduct and analyze

ratios show only the age composition of a popula-

for 2020. • Average annual population growth is

a full census.

tion, not economic dependency. Some children and

the exponential change for the period indicated. See

elderly people are part of the labor force, and many

Statistical methods for more information. • Popula-

working-age people are not.

tion age composition is the percentage of the total

The quality and reliability of official demographic data are also affected by public trust in the govern-

legal status or citizenship—except for refugees not

ment, government commitment to full and accurate

Vital rates are based on data from birth and death

population that is in specific age groups. • Depen-

enumeration, confidentiality and protection against

registration systems, censuses, and sample surveys

dency ratio is the ratio of dependents—people

misuse of census data, and census agencies’ inde-

by national statistical offices and other organiza-

younger than 15 or older than 64—to the working-

pendence from political influence. Moreover, compara-

tions, or on demographic analysis. Data for 2010

age population —those ages 15–64. • Crude death

bility of population indicators is limited by differences

for some high-income countries are provisional esti-

rate and crude birth rate are the number of deaths

in the concepts, definitions, collection procedures,

mates based on vital registers. The estimates for

and the number of live births occurring during the

and estimation methods used by national statistical

many countries are projections based on extrapola-

year, per 1,000 people, estimated at midyear. Sub-

agencies and other organizations that collect the data.

tions of levels and trends from earlier years or inter-

tracting the crude death rate from the crude birth

Of the 158 economies in the table and the 58

polations of population estimates and projections

rate provides the rate of natural increase, which is

from the United Nations Population Division.

equal to the population growth rate in the absence

economies in table 1.6, 180 (about 86 percent) conducted a census during the 2000 census round

Vital registers are the preferred source for these

(1995–2004). As of January 2012, 141 countries

data, but in many developing countries systems for

have completed a census for the 2010 census round

registering births and deaths are absent or incomplete

(2005–14). The currentness of a census and the avail-

because of deficiencies in the coverage of events or

ability of complementary data from surveys or registra-

geographic areas. Many developing countries carry out

tion systems are objective ways to judge demographic

special household surveys that ask respondents about

data quality. Some European countries’ registration

recent births and deaths. Estimates derived in this

systems offer complete information on population in

way are subject to sampling errors and recall errors.

the absence of a census. See table 2.17 and Primary

The United Nations Statistics Division monitors the

data documentation for the most recent census or

completeness of vital registration systems. Some

survey year and for the completeness of registration.

countries have made progress over the last 60 years,

of migration.

Data sources

Current population estimates for developing coun-

but others still have deficiencies in civil registration

The World Bank’s population estimates are com-

tries that lack recent census data and pre- and

systems. For example, only 57 percent of countries

piled and produced by its Development Data

post-census estimates for countries with census

and areas register at least 90 percent of births, and

Group in consultation with its Human Develop-

data are provided by the United Nations Population

only 53 percent register at least 90 percent of deaths.

ment Network, operational staff, and country

Division and other agencies. The cohort component

Some of the most populous developing countries—

offices. The United Nations Population Division’s

method—a standard method for estimating and

Bangladesh, Brazil, China, India, Indonesia, Nigeria,

World Population Prospects: The 2010 Revision is

projecting population —requires fertility, mortality,

Pakistan —lack complete vital registration systems.

a source of the demographic data for more than

and net migration data, often collected from sample

International migration is the only other factor

half the countries, most of them developing coun-

surveys, which can be small or limited in coverage.

besides birth and death rates that directly deter-

tries, and the source of data on age composition

Population estimates are from demographic model-

mines a country’s population growth. From 1990 to

and dependency ratios for all countries. Other

ing and so are susceptible to biases and errors from

2005 the number of migrants in high-income coun-

important sources are census reports and other

shortcomings in the model and in the data. Because

tries rose 40 million. About 195 million people (3

statistical publications from national statistical

the five-year age group is the cohort unit and five-year

percent of the world population) live outside their

offices; household surveys by national agencies,

period data are used, interpolations to obtain annual

home country. Estimating migration is difficult. At

ICF International (for MEASURE DHS), and the

data or single age structure may not reflect actual

any time many people are located outside their home

U.S. Centers for Disease Control and Prevention;

events or age composition.

country as tourists, workers, or refugees or for other

Eurostat’s Demographic Statistics; Secretariat of

The growth rate of the total population conceals

reasons. Standards for the duration and purpose of

the Pacific Community, Statistics and Demogra-

age-group differences in growth rates. In many devel-

international moves that qualify as migration vary,

phy Programme; and U.S. Bureau of the Census,

oping countries the once rapidly growing under-15

and estimates require information on flows into and

International Data Base.

population is shrinking. Previously high fertility rates

out of countries that is difficult to collect.

2012 World Development Indicators

45


2.2

Labor force structure Labor force participation rate

Labor force

% ages 15 and older Male

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

46

Ages 15 and older average annual % growth

Total millions

Female

Female % of labor force

2000

2010

2000

2010

2000

2010

2000–10

2000

2010

81 73 76 75 74 73 72 69 71 86 86 65 61 81 82 58 80 82 57 91 84 83 77 72 86 80 75 83 73 82 73 71 81 82 63 70 72 70 72 80 84 73 79 90 67 91 67 63 67 83 74 68 77 65 86 78 79 69

80 71 72 77 75 70 73 68 68 87 84 62 61 78 81 59 82 81 60 91 82 87 77 72 85 80 74 80 68 80 72 73 79 81 60 70 71 68 69 79 83 74 79 90 68 90 65 62 65 83 74 67 72 65 88 78 78 71

13 51 12 67 43 58 55 48 57 35 54 53 44 64 60 33 70 55 48 77 86 77 62 59 71 65 35 71 49 49 71 65 37 49 45 38 50 52 60 46 50 20 45 75 52 73 57 48 55 71 55 49 73 40 42 63 63 57

16 50 15 63 47 49 59 54 61 39 57 50 48 67 64 35 72 59 49 78 84 79 64 62 73 65 47 68 51 55 70 68 46 52 46 43 57 49 60 51 54 24 47 80 57 78 56 51 56 72 56 53 67 45 49 65 68 60

6.5 1.3 8.8 5.2 15.4 1.5 9.6 3.9 3.5 0.3 57.3 4.7 4.4 2.6 3.5 1.3 0.8 83.7 3.6 5.5 2.9 5.8 6.2 16.3 1.7 3.2 6.1 724.5 3.3 17.3 18.5 1.3 1.6 6.4 2.0 4.7 0.4 5.2 2.9 3.5 5.4 20.1 2.2 1.7 0.7 29.0 2.6 27.2 0.4 0.5 2.2a 40.3 8.4 4.9 4.0 3.3 0.5 3.2

9.1 1.5 11.2 7.1 18.4 1.4 11.8 4.3 4.6 0.7 72.3 4.5 4.9 3.6 4.6 1.5 1.0 101.6 3.5 7.5 4.3 8.0 8.2 19.0 2.1 4.4 8.0 799.8 3.7 22.1 25.3 1.7 2.2 7.8 2.0 5.3 0.6 5.3 2.9 4.4 6.9 27.1 2.6 2.6 0.7 40.8 2.7 29.9 0.6 0.8 2.4 a 42.2 10.4 5.3 5.7 4.1 0.6 4.2

3.3 1.2 2.4 3.1 1.8 –0.2 2.1 1.1 2.6 8.6 2.3 –0.6 1.0 3.5 2.6 1.1 2.4 1.9 –0.3 3.2 3.9 3.2 2.8 1.6 2.1 3.2 2.8 1.0 1.0 2.5 3.1 3.0 3.2 2.0 0.0 1.1 2.7 0.2 0.3 2.2 2.4 3.0 1.6 4.5 0.6 3.4 0.3 0.9 2.8 3.2 0.8a 0.4 2.1 0.8 3.6 2.2 2.6 2.5

13.3 41.7 13.7 48.4 38.1 48.8 43.8 43.5 47.0 21.4 37.5 48.9 42.9 46.5 43.2 39.1 46.9 41.2 47.1 48.2 53.2 51.2 45.1 45.8 46.4 45.5 33.1 45.0 41.9 38.7 50.1 48.1 30.8 35.0 44.1 35.0 40.8 44.4 46.6 36.6 37.4 21.5 39.6 47.3 48.7 45.2 47.5 45.5 45.5 47.0 46.2 43.7 48.1 39.1 34.6 44.6 45.5 46.5

15.2 41.6 16.9 45.9 40.2 46.5 45.3 45.9 49.1 19.3 39.9 49.0 45.3 47.5 44.8 40.0 46.3 43.7 46.8 47.6 52.1 49.9 45.7 47.1 47.1 45.3 39.6 44.6 46.0 42.5 49.9 48.6 36.2 37.4 45.9 38.0 43.5 43.3 47.2 39.4 39.7 24.2 41.4 48.6 50.4 47.2 47.8 47.1 46.3 47.9 47.0 45.6 47.6 41.5 38.1 45.2 47.3 47.0

2012 World Development Indicators


Labor force participation rate

Labor force

% ages 15 and older Male

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

2.2

PEOPLE

Labor force structure

Ages 15 and older average annual % growth

Total millions

Female

Female % of labor force

2000

2010

2000

2010

2000

2010

2000–10

2000

2010

88 58 83 85 73 69 71 61 61 78 76 68 76 73 88 73 .. 82 74 81 65 71 80 62 73 67 66 90 81 82 66 78 81 83 64 66 79 83 81 65 90 73 73 83 88 67 72 78 84 82 74 87 83 82 64 70 60 92

83 58 81 84 72 69 68 62 60 72 72 65 77 72 84 72 .. 82 78 79 66 71 73 64 77 63 69 89 81 77 70 79 76 81 45 65 75 83 82 70 88 72 74 80 90 63 70 80 83 83 74 86 85 79 64 68 54 95

44 41 34 50 14 13 47 48 35 59 49 13 65 63 74 49 .. 44 56 79 49 19 68 58 27 55 41 84 77 45 37 23 41 39 55 56 29 88 74 49 82 53 57 38 38 45 60 23 16 45 71 51 58 49 49 53 35 39

42 44 29 51 16 14 52 53 38 56 50 15 66 61 72 49 .. 43 55 77 55 23 59 58 30 54 43 84 85 44 37 28 44 44 38 54 26 86 75 58 80 58 62 46 40 48 62 28 22 49 71 57 67 50 48 56 35 52

2.4 4.2 409.4 99.6 18.5 5.5 1.8 2.5 23.3 1.2 67.6 1.2 7.5 11.9 13.6 22.7 .. 1.0 2.1 2.5 1.1 1.1 0.8 1.0 1.8 1.7 0.8 7.3 4.8 9.9 3.1 0.8 0.5 40.3 1.6b 0.9 10.2 8.7 24.2 0.6 12.4 8.2 1.9 1.8 3.5 39.2 2.4 0.8 43.0 1.3 2.3 2.3 12.0 30.9 17.4 5.2 1.4 0.3

3.0 4.3 472.6 118.0 25.3 7.5 2.1 3.2 25.1 1.2 66.7 1.6 8.8 15.5 14.6 24.6 .. 1.4 2.5 3.2 1.2 1.5 0.9 1.4 2.4 1.6 0.9 10.1 6.7 12.0 4.3 1.1 0.6 49.6 1.2b 1.2 11.4 11.1 28.0 0.9 16.0 8.9 2.4 2.4 5.1 50.3 2.6 1.2 59.7 1.6 3.0 3.1 15.5 38.7 18.2 5.6 1.4 1.3

2.4 0.3 1.4 1.7 3.1 3.1 1.9 2.6 0.8 0.3 –0.1 2.5 1.5 2.7 0.7 0.8 .. 3.5 2.0 2.5 0.6 2.4 0.5 3.6 2.8 –0.3 1.3 3.3 3.3 1.9 3.4 3.8 1.2 2.1 –3.0 b 2.2 1.1 2.4 1.5 3.8 2.6 0.8 2.0 2.8 3.7 2.5 1.0 4.4 3.3 2.5 2.7 3.0 2.6 2.2 0.5 0.7 0.3 13.7

34.1 44.7 27.8 37.7 16.0 16.0 40.6 45.7 38.7 44.3 40.7 14.3 49.2 46.8 47.5 40.5 .. 25.1 44.6 49.9 48.1 22.9 49.1 49.0 26.1 49.5 38.8 48.7 49.7 34.7 37.5 23.3 34.5 32.9 49.7 46.8 27.9 55.0 48.3 44.4 48.9 43.1 45.2 32.5 30.9 40.1 46.5 17.1 15.3 35.4 48.5 36.6 41.2 37.5 45.8 45.3 39.5 15.6

34.1 46.0 25.3 38.2 17.9 17.5 43.7 47.1 40.3 45.1 42.3 18.0 49.4 46.5 47.7 41.3 .. 23.9 42.7 49.8 50.1 25.5 46.0 47.7 28.0 50.3 38.6 48.9 51.5 35.8 35.5 26.5 37.7 36.5 49.2 46.4 27.1 53.6 48.9 46.3 49.2 45.6 46.7 37.9 31.2 42.8 46.9 17.9 20.7 37.3 48.3 39.7 44.6 38.8 45.1 47.4 42.3 12.4

2012 World Development Indicators

47


2.2

Labor force structure Labor force participation rate

Labor force

% ages 15 and older Male 2000

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

71 69 85 74 89 .. 63 78 68 64 78 61 .. 66 77 76 72 68 78 80 75 91 81 75 82 77 72 74 74 83 65 92 70 74 76 72 82 83 67 71 85 82 79 w 83 80 81 80 81 83 69 81 74 83 77 71 65

Female 2010

65 71 85 74 88 67 69 77 68 65 77 60 .. 67 76 77 71 68 75 72 75 90 80 74 81 78 70 71 76 80 66 92 69 70 77 74 80 81 66 72 86 90 77 w 83 79 79 78 79 81 70 80 72 81 76 69 65

2000

58 55 86 16 64 .. 67 53 53 51 37 44 .. 41 37 29 43 58 58 20 58 87 65 38 76 47 24 27 48 81 52 34 54 59 52 48 48 74 11 22 75 69 52 w 66 51 39 60 52 68 50 48 18 35 61 51 45

a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Includes Kosovo.

48

2012 World Development Indicators

Ages 15 and older average annual % growth

Total millions 2010

48 56 86 17 66 51 66 57 51 53 38 44 .. 52 35 31 44 59 61 13 57 88 64 38 80 55 25 28 46 76 53 44 56 58 55 48 52 73 15 25 73 83 51 w 68 49 37 59 51 65 50 53 20 32 63 52 50

2000

11.8 73.3 3.8 6.0 3.9 .. 1.6 2.1 2.6 1.0 2.3 15.2 .. 18.2 7.8 10.3 0.3 4.6 4.0 4.8 2.4 16.7 34.8 0.2 2.2 0.6 3.2 21.9 1.7 10.1 23.4 1.7 29.5 147.1 1.6 9.2 10.5 41.3 0.6 4.2 4.5 5.5 2,770.2 t 279.3 1,987.5 828.5 1,159.1 2,266.8 991.2 176.8 224.6 80.3 536.8 257.2 503.4 144.9

2010

10.2 75.5 5.2 9.6 5.4 3.5c 2.3 2.8 2.7 1.0 2.9 18.2 .. 23.2 8.6 14.0 0.4 5.0 4.5 5.5 2.8 22.1 39.4 0.3 2.9 0.7 3.8 26.5 2.2 13.4 23.2 4.9 31.8 157.5 1.7 12.1 13.4 51.1 1.0 6.5 5.5 6.6 3,223.0 t 363.5 2,308.6 994.1 1,314.4 2,672.1 1,118.0 191.8 278.2 104.9 638.8 340.4 550.9 159.6

2000–10

–1.5 0.3 3.2 4.7 3.1 .. 3.7 3.0 0.5 0.8 2.2 1.8 .. 2.4 0.9 3.0 2.2 0.9 1.2 1.2 1.8 2.8 1.2 3.6 3.1 1.8 1.8 1.9 2.2 2.8 –0.1 10.5 0.8 0.7 0.8 2.8 2.4 2.1 4.6 4.3 2.1 1.9 1.5 w 2.6 1.5 1.8 1.3 1.6 1.2 0.8 2.1 2.7 1.7 2.8 0.9 1.0

Female % of labor force 2000

2010

46.6 48.6 52.3 14.7 42.9 .. 53.2 40.6 45.6 46.3 32.8 43.1 .. 39.5 33.1 27.9 40.3 47.1 44.3 20.2 44.1 49.7 46.1 32.6 49.0 39.9 24.9 26.9 40.6 50.0 49.1 12.0 45.2 45.8 43.2 40.6 37.2 49.1 13.4 24.0 47.2 46.4 39.9 w 44.9 38.4 32.1 42.8 39.2 44.3 45.0 38.3 20.0 28.2 44.9 43.2 42.6

44.7 48.9 51.8 14.8 43.9 43.9 50.7 42.3 44.6 46.3 33.6 42.8 .. 44.3 32.2 28.7 39.7 47.0 45.8 15.2 45.2 49.8 45.7 33.3 50.5 43.2 26.9 28.7 39.3 49.3 49.3 14.8 45.9 46.1 44.5 39.8 39.3 48.5 17.8 25.8 46.1 49.3 39.9 w 45.6 38.0 31.5 43.0 39.1 44.1 44.8 41.2 21.5 27.1 45.6 43.9 44.8


About the data

2.2

PEOPLE

Labor force structure Definitions

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

The labor force participation rates in the table are

• Labor force participation rate is the proportion

ducing goods and services in an economy. It includes

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

of the population ages 15 and older that engages

people who are currently employed and people who

7th edition, database. These harmonized estimates

actively in the labor market, by either working or

are unemployed but seeking work as well as first-time

use strict data selection criteria and enhanced meth-

looking for work during a reference period. • Total

job-seekers. Not everyone who works is included,

ods to ensure comparability across countries and

labor force is people ages 15 and older who engage

however. Unpaid workers, family workers, and stu-

over time to avoid the inconsistencies mentioned

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

dents are often omitted, and some countries do not

above. Estimates are based mainly on labor force

ing for work during a reference period. It includes

count members of the armed forces. Labor force size

surveys, with other sources (population censuses

both the employed and the unemployed. • Average

tends to vary during the year as seasonal workers

and nationally reported estimates) used only when

annual percentage growth of the labor force is cal-

enter and leave.

no survey data are available.

culated using the exponential endpoint method (see

Data on the labor force are compiled by the Inter-

The labor force estimates in the table were calcu-

Statistical methods for more information). • Female

national Labour Organization (ILO) from labor force

lated by applying labor force participation rates from

labor force as a percentage of the labor force shows

surveys, censuses, establishment censuses and

the ILO database to World Bank population estimates

the share of women active in the total labor force.

surveys, and administrative records such as employ-

to create a series consistent with these population

ment exchange registers and unemployment insur-

estimates. This procedure sometimes results in

ance schemes. For some countries a combination

labor force estimates that differ slightly from those

of these sources is used. Labor force surveys are

in the ILO’s Yearbook of Labour Statistics and its data-

the most comprehensive source for internationally

base Key Indicators of the Labour Market.

comparable labor force data. They can cover all non-

Estimates of women in the labor force and employ-

institutionalized civilians, all branches and sectors of

ment are generally lower than those of men and are

the economy, and all categories of workers, including

not comparable internationally, reflecting that demo-

people holding multiple jobs. By contrast, labor force

graphic, social, legal, and cultural trends and norms

data from population censuses are often based on a

determine whether women’s activities are regarded

limited number of questions on the economic char-

as economic. In many countries many women work

acteristics of individuals, with little scope to probe.

on farms or in other family enterprises without pay,

The resulting data often differ from labor force survey

and others work in or near their homes, mixing work

data and vary considerably by country, depending on

and family activities during the day.

the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector (ILO, Key Indicators of the Labour Market 2001–2002). The reference period of a census or survey is another important source of differences: in some countries data refer to people’s status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Differing definitions of employment age also affect comparability. For most countries the working age is

Data sources

15 and older, but in some countries children younger

Data on labor force participation rates are from

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

the ILO’s Key Indicators of the Labour Market, 7th

estimates. Similarly, some countries have an upper

edition, database. Labor force numbers were cal-

age limit. As a result, calculations may systemati-

culated by World Bank staff, applying labor force

cally over- or underestimate actual rates. For further

participation rates from the ILO database to popu-

information on source, reference period, or defini-

lation estimates.

tion, consult the original source.

2012 World Development Indicators

49


2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2007–10a

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

50

.. .. .. .. 0b .. 6 6 .. 3 54 .. 3 .. 3b .. .. 31b .. .. .. .. .. 6b .. .. 24 .. 1 2b .. .. 32 .. .. .. 11 .. 7 26 10 b 35 48b .. 23 .. 11 7 .. .. .. 4 66 20 19b .. .. 76

.. .. .. .. 2b 39 4 5 37 .. .. 15 2 .. 34 .. .. 21 8 .. .. 69 .. 3b .. .. 15 .. 0 26 .. .. 17 .. 14 25 5 4 4 21 33 28 33 .. 6 9 6 4 .. .. 51 2 .. 12 .. .. .. ..

2012 World Development Indicators

Industry

Female % of female employment 1990–92a

.. .. .. .. 0b .. 4 8 .. 0 85 .. 3 .. 1b .. .. 25b .. .. .. .. .. 2b .. .. 6 .. 0 1b .. .. 5 .. .. .. 13 .. 3 3 2b 52 15b .. 13 .. 6 5 .. .. .. 4 59 26 3b .. .. 50

2007–10a

.. .. .. .. 0b 49 2 5 40 .. .. 9 1 .. 38 .. .. 12 5 .. .. 75 .. 1b .. .. 6 .. 0 5 .. .. 4 .. 16 9 3 2 1 2 22 46 5 .. 3 10 3 2 .. .. 57 1 .. 13 .. .. .. ..

Male % of male employment 1990–92a

.. .. .. .. 40 b .. 32 47 .. 33 16 .. 41 .. 42b .. .. 27b .. .. .. .. .. 31b .. .. 32 .. 37 35b .. .. 27 .. .. .. 31 .. 37 23 29b 25 23b .. 42 .. 38 39 .. .. .. 50 10 29 36b .. .. 9

2007–10a

.. .. .. .. 33b 25 32 37 19 .. .. 33 34 .. 28 .. .. 29 41 .. .. 8 .. 32b .. .. 31 .. 19 23 .. .. 27 .. 38 22 30 49 29 26 24 27 22 .. 43 25 36 33 .. .. 17 40 .. 28 .. .. .. ..

Services

Female % of female employment 1990–92a

.. .. .. .. 18b .. 12 20 .. 7 9 .. 16 .. 17b .. .. 10 b .. .. .. .. .. 11b .. .. 15 .. 27 25b .. .. 25 .. .. .. 23 .. 16 21 17b 10 23b .. 30 .. 15 17 .. .. .. 24 10 17 27b .. .. 9

2007–10a

.. .. .. .. 10 b 8 9 12 7 .. .. 24 10 .. 9 .. .. 13 25 .. .. 9 .. 10 b .. .. 11 .. 4 16 .. .. 13 .. 15 12 9 23 9 14 11 6 18 .. 18 20 10 10 .. .. 4 14 .. 8 .. .. .. ..

Male % of male employment 1990–92a

.. .. .. .. 59b .. 61 46 .. 64 25 .. 56 .. 55b .. .. 43b .. .. .. .. .. 64b .. .. 45 .. 63 63b .. .. 41 .. .. .. 56 .. 56 52 62b 41 29b .. 36 .. 51 54 .. .. .. 46 23 51 45b .. .. 13

2007–10a

.. .. .. .. 65b 35 64 58 44 .. .. 37 64 .. 38 .. .. 50 51 .. .. 23 .. 65b .. .. 54 .. 80 51 .. .. 51 .. 48 53 65 47 67 48 43 44 45 .. 50 76 58 63 .. .. 33 58 .. 60 .. .. .. ..

Female % of female employment 1990–92a

.. .. .. .. 81b .. 84 72 .. 92 2 .. 82 .. 83b .. .. 65b .. .. .. .. .. 87b .. .. 79 .. 73 74b .. .. 69 .. .. .. 63 .. 81 76 81b 37 63b .. 57 .. 78 78 .. .. .. 73 32 57 70 b .. .. 38

2007–10a

.. .. .. .. 89b 43 88 84 53 .. .. 64 89 .. 53 .. .. 75 70 .. .. 16 .. 89b .. .. 83 .. 96 79 .. .. 82 .. 69 80 88 75 90 83 67 49 77 .. 78 64 87 88 .. .. 40 84 .. 79 .. .. .. ..


Agriculture

Male % of male employment 1990–92a 2007–10a

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

53b 19 .. 54 .. .. 17 5 8 36 6 .. .. .. .. 14 .. .. .. .. .. .. .. .. .. .. .. .. .. 23 .. .. 15 34 .. .. 4b .. .. 45 75 5 13b .. .. .. 7 .. 45 36 .. 3b 1b 53 .. 10 5 ..

48 6 46b 39 19 17 8 3 5 28 4 2 31 .. .. 6 .. .. .. .. 12 .. .. 50 .. 12 20 .. .. 17 .. .. 10 19 34 41b 34 .. .. 23 .. 4 9 42 .. .. 4 .. 37 24 .. 31 1b 42 13 11 2 3

Industry

Female % of female employment 1990–92a

6b 13 .. 57 .. .. 3 2 9 16 7 .. .. .. .. 18 .. .. .. .. .. .. .. .. .. .. .. .. .. 20 .. .. 13 11 .. .. 3b .. .. 52 91 2 8b .. .. .. 3 .. 69 3 .. 0b 0b 32 .. 13 0 ..

2007–10a

10 2 65b 38 31 51 1 1 3 10 4 1 29 .. .. 7 .. .. .. .. 6 .. .. 48 .. 7 20 .. .. 9 .. .. 8 4 28 39b 59 .. .. 8 .. 2 4 8 .. .. 1 .. 75 7 .. 19 1b 24 13 11 1 0

Male % of male employment 1990–92a

18b 43 .. 15 .. .. 35 38 41 25 40 .. .. .. .. 40 .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 36 25 .. .. 33b .. .. 21 4 33 31b .. .. .. 34 .. 20 19 .. 33b 30 b 17 .. 39 27 ..

2007–10a

22 40 24b 22 33 22 29 30 39 24 33 21 26 .. .. 20 .. .. .. .. 34 .. .. 14 .. 33 33 .. .. 32 .. .. 32 30 26 19b 24 .. .. 24 .. 24 31 21 .. .. 31 .. 22 24 .. 25 32b 18 42 38 25 58

PEOPLE

2.3

Employment by economic activity

Services

Female % of female employment 1990–92a

25b 29 .. 13 .. .. 18 15 23 12 27 .. .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. 32 .. .. 48 19 .. .. 46b .. .. 8 1 10 13b .. .. .. 10 .. 15 11 .. 19b 13b 14 .. 24 19 ..

2007–10a

22 20 18b 15 27 4 9 10 14 7 15 9 12 .. .. 13 .. .. .. .. 14 .. .. 5 .. 16 28 .. .. 23 .. .. 22 18 14 11b 15 .. .. 9 .. 6 10 19 .. .. 7 .. 12 10 .. 10 14b 10 16 16 10 5

Male % of male employment 1990–92a

29b 38 .. 31 .. .. 49 57 52 39 54 .. .. .. .. 46 .. .. .. .. .. .. .. .. .. .. .. .. .. 46 .. .. 48 41 .. .. 63b .. .. 34 20 60 56b .. .. .. 58 .. 35 45 .. 64b 69b 29 .. 51 67 ..

2007–10a

30 53 30 b 40 47 61 63 67 57 48 62 77 43 .. .. 73 .. .. .. .. 53 .. .. 37 .. 55 47 .. .. 51 .. .. 58 51 41 40 b 42 .. .. 53 .. 61 61 37 .. .. 65 .. 41 52 .. 44 67b 40 45 51 73 39

Female % of female employment 1990–92a

69b 58 .. 31 .. .. 79 83 68 72 65 .. .. .. .. 54 .. .. .. .. .. .. .. .. .. .. .. .. .. 48 .. .. 39 70 .. .. 51b .. .. 40 8 81 79b .. .. .. 86 .. 16 86 .. 80 b 87b 55 .. 63 80 ..

2012 World Development Indicators

2007–10a

68 78 17b 47 42 46 90 89 83 83 80 90 59 .. .. 81 .. .. .. .. 80 .. .. 47 .. 77 52 .. .. 68 .. .. 70 78 58 50 b 25 .. .. 83 .. 84 86 72 .. .. 92 .. 13 82 .. 71 86b 66 71 73 90 95

51


2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2007–10a

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

29 .. .. .. .. .. .. 1 .. .. .. .. .. 11 .. .. .. 5 5 23 .. 78b 60 .. .. 15 .. 33 .. .. .. .. 3 4 7b .. 17 .. .. 44 47 .. .. w .. .. .. .. .. .. .. 21 .. .. .. 6 7

29 11 .. 5 .. 25 .. 2 4 9 .. 6 .. 6 30 b .. .. 3 4 14 .. .. 44 .. .. 5 .. 18 .. .. .. 5 2 2 16 .. 13 .. 10 .. .. .. .. w .. .. .. .. .. .. 16 19 23 46 .. 4 4

Industry

Female % of female employment 1990–92a

38 .. .. .. .. .. .. 0 .. .. .. .. .. 8 .. .. .. 2 4 54 .. 90 b 62 .. .. 6 .. 72 .. .. .. .. 1 1 1b .. 2 .. .. 83 56 .. .. w .. .. .. .. .. .. .. 15 .. .. .. 5 6

2007–10a

31 7 .. 0 .. 23 .. 1 2 9 .. 4 .. 3 37b .. .. 1 2 24 .. .. 39 .. .. 2 .. 39 .. .. .. 0 1 1 5 .. 2 .. 28 .. .. .. .. w .. .. .. .. .. .. 15 8 43 65 .. 3 3

Male % of male employment 1990–92a

2007–10a

44 .. .. .. .. .. .. 36 .. .. .. .. .. 41 .. .. .. 40 39 28 .. 7b 18 .. .. 34 .. 26 .. .. .. .. 41 34 36b .. 32 .. .. 14 15 .. .. w .. .. .. .. .. .. .. 30 .. .. .. 38 42

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. Limited coverage.

52

2012 World Development Indicators

36 38 .. 23 .. 32 .. 26 50 43 .. 35 .. 34 25b .. .. 31 30 36 .. .. 21 .. .. 44 .. 30 .. .. .. 28 29 25 29 .. 31 .. 29 .. .. .. .. w .. .. .. .. .. .. 35 29 29 24 .. 31 36

Services

Female % of female employment 1990–92a

30 .. .. .. .. .. .. 32 .. .. .. .. .. 17 .. .. .. 12 15 8 .. 1b 13 .. .. 14 .. 11 .. .. .. .. 16 14 21b .. 16 .. .. 2 3 .. .. w .. .. .. .. .. .. .. 14 .. .. .. 19 20

2007–10a

20 19 .. 2 .. 16 .. 17 21 21 .. 13 .. 10 25b .. .. 8 10 9 .. .. 18 .. .. 15 .. 16 .. .. .. 7 7 7 13 .. 11 .. 11 .. .. .. .. w .. .. .. .. .. .. 18 14 14 18 .. 11 12

Male % of male employment 1990–92a

28 .. .. .. .. .. .. 63 .. .. .. .. .. 49 .. .. .. 55 57 49 .. 15b 22 .. .. 51 .. 41 .. .. .. .. 55 62 57b .. 52 .. .. 38 22 .. .. w .. .. .. .. .. .. .. 49 .. .. .. 56 50

2007–10a

35 51 .. 72 .. 43 .. 73 46 48 .. 59 .. 60 27b .. .. 66 61 51 .. .. 35 .. .. 51 .. 52 .. .. .. 66 68 72 56 .. 57 .. 61 .. .. .. .. w .. .. .. .. .. .. 49 52 48 30 .. 64 59

Female % of female employment 1990–92a

33 .. .. .. .. .. .. 68 .. .. .. .. .. 75 .. .. .. 86 81 38 .. 8b 25 .. .. 80 .. 17 .. .. .. .. 82 85 78b .. 82 .. .. 13 18 .. .. w .. .. .. .. .. .. .. 71 .. .. .. 76 73

2007–10a

49 74 .. 98 .. 61 .. 83 77 71 .. 83 .. 88 27b .. .. 91 82 67 .. .. 43 .. .. 82 .. 45 .. .. .. 93 91 92 83 .. 87 .. 61 .. .. .. .. w .. .. .. .. .. .. 66 78 43 17 .. 86 85


About the data

2.3

PEOPLE

Employment by economic activity Definitions

The International Labour Organization (ILO) classifies

services. Such broad classification may obscure fun-

• Agriculture corresponds to division 1 (ISIC revi-

economic activity using the International Standard

damental shifts within countries’ industrial patterns.

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

Industrial Classification (ISIC) of All Economic Activi-

A slight majority of countries report economic activity

sion 3), or tabulation category A (ISIC revision 4) and

ties, revision 2 (1968), revision 3 (1990), and revi-

according to the ISIC revision 2 instead of revision 3

includes hunting, forestry, and fishing. • Industry cor-

sion 4 (2008). Because this classification is based

or revision 4. The use of one classification or the

responds to divisions 2–5 (ISIC revision 2), tabula-

on where work is performed (industry) rather than

other should not have a significant impact on the

tion categories C–F (ISIC revision 3), or tabulation

type of work performed (occupation), all of an enter-

information for the three broad sectors presented

categories B–F (ISIC revision 4) and includes mining

prise’s employees are classified under the same

in the table.

and quarrying (including oil production), manufac-

industry, regardless of their trade or occupation. The

The distribution of economic wealth in the world

turing, construction, and public utilities (electricity,

categories should sum to 100 percent. Where they

remains strongly correlated with employment by

gas, and water). • Services correspond to divisions

do not, the differences are due to workers who are

economic activity. The wealthier economies are

6–9 (ISIC revision 2), tabulation categories G–P

not classified by economic activity.

those with the largest share of total employment in

(ISIC revision 3), or tabulation categories G–U (ISIC

services, whereas the poorer economies are largely

revision 4) and include wholesale and retail trade

agriculture based.

and restaurants and hotels; transport, storage, and

Data on employment are drawn from labor force surveys, household surveys, official estimates, censuses and administrative records of social insurance

The distribution of economic activity by gender

communications; financing, insurance, real estate,

schemes, and establishment surveys when no other

reveals some clear patterns. Men still make up the

and business services; and community, social, and

information is available. The concept of employment

majority of people employed in all three sectors, but

personal services.

generally refers to people above a certain age who

the gender gap is biggest in industry. Employment in

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

agriculture is also male-dominated, although not as

Employment data include both full-time and part-time

much as industry. Segregating one sex in a narrow

workers.

range of occupations significantly reduces economic

There are many differences in how countries define

efficiency by reducing labor market flexibility and thus

and measure employment status, particularly mem-

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

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

regation is particularly harmful for women, who have

unpaid family workers. Where members of the armed

a much narrower range of labor market choices and

forces are included, they are allocated to the service

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

sector, causing that sector to be somewhat over-

mental to men when job losses are concentrated

stated relative to the service sector in economies

in industries dominated by men and job growth is

where they are excluded. Where data are obtained

centered in service occupations, where women have

from establishment surveys, data cover only employ-

better chances, as has been the recent experience

ees; thus self-employed and unpaid family workers

in many countries.

are excluded. In such cases the employment share

There are several explanations for the rising impor-

of the agricultural sector is severely underreported.

tance of service jobs for women. Many service jobs—

Caution should be also used where the data refer

such as nursing and social and clerical work—are

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

considered “feminine” because of a perceived simi-

tural work. Moreover, the age group and area covered

larity to women’s traditional roles. Women often do

could differ by country or change over time within a

not receive the training needed to take advantage of

country. For detailed information, consult the original

changing employment opportunities. And the greater

source.

availability of part-time work in service industries

Countries also take different approaches to the treatment of unemployed people. In most countries

may lure more women, although it is unclear whether this is a cause or an effect.

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

Data sources

revision 2, revision 3, or revision 4. In the table the

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

reported divisions or categories are aggregated

tors of the Labour Market, 7th edition, database.

into three broad groups: agriculture, industry, and

2012 World Development Indicators

53


2.4

Decent work and productive employment Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older

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

54

% ages 15–24

% of relevant age group

Male % of male employment

1991

2010

1991

2010

1991

2010a

1990

46 52 34 66 55 46 57 54 56 61 73 59 46 72 63 39 56 60 47 82 83 80 64 59 72 67 50 75 63 46 66 62 55 63 51 53 57 60 62 52 57 43 57 75 67 76 60 50 52 71 55 56 68 46 62 69 64 58

45 52 39 64 56 41 62 58 60 65 68 50 50 72 69 35 63 65 49 81 77 81 68 61 73 67 55 71 57 59 66 66 60 64 46 56 60 54 60 56 64 44 57 78 51 80 55 51 50 72 54 55 67 48 65 70 68 60

32 40 22 47 48 26 58 61 37 32 64 39 31 66 48 21 36 54 30 76 70 71 42 57 56 49 33 72 54 35 40 40 48 50 27 41 41 49 65 37 45 22 46 69 50 70 49 33 20 58 23 58 43 31 54 51 44 32

31 36 22 46 34 18 61 54 31 32 53 31 25 57 49 17 41 53 24 73 56 70 43 55 54 49 31 57 31 35 39 39 42 48 25 40 34 25 58 37 44 25 42 67 27 71 40 31 15 56 20 47 36 21 58 52 48 29

16 89 60 12 74 .. 132 102 88 100 18 93 101 .. .. .. 49 .. 98 7 5 25 26 101 12 6 97 41 .. 53 21 46 45 .. 83 94 72 91 109 .. 55 69 38 11 100 14 116 100 40 19 95 98 35 94 23 11 5 ..

46 89 95 31 89 92 129 100 .. .. 49 .. 111 .. 80 90 .. 101 88 21 25 46 42 101 13 26 88 81 83 96 38 .. 100 .. 95 89 98 90 117 76 80 .. 65 32 104 36 108 113 .. 54 86 103 58d .. 59 38 .. ..

.. .. .. .. 25c .. 12 .. .. .. .. .. 18 .. 32c .. .. 29c .. .. .. .. .. .. .. .. .. .. .. 30 c .. .. 26 .. .. .. .. .. 8 42 33c .. .. .. 2 .. .. 11 .. .. .. 5 .. 40 .. .. .. ..

2012 World Development Indicators

2007–10b

.. .. .. .. 22c 36 11 9 47 2 .. .. 11 .. 49 .. .. 27 10 .. .. 79 .. .. .. .. 26 .. 10 48 .. .. 19 .. 17 .. 16 17 7 49 37 22 33 .. 6 .. 12 8 .. .. 62 8 .. 29 .. .. .. ..

Female % of female employment 1990

.. .. .. .. 27c .. 9 .. .. .. .. .. 17 .. 50 c .. .. 30 c .. .. .. .. .. .. .. .. .. .. .. 26 c .. .. 21 .. .. .. .. .. 6 30 41c .. .. .. 3 .. .. 11 .. .. .. 6 .. 46 .. .. .. ..

2007–10b

.. .. .. .. 17c 40 7 9 62 1 .. .. 8 .. 67 .. .. 22 8 .. .. 86 .. .. .. .. 25 .. 5 49 .. .. 20 .. 19 .. 12 10 4 30 51 49 46 .. 4 .. 7 6 .. .. 65 6 .. 27 .. .. .. ..

GDP per person employed % growth 1990–92

.. –16.6 –4.6 –5.6 9.0 –24.4 3.0 1.3 –12.6 0.8 2.3 –4.0 1.6 .. 2.6 –14.7 .. –0.3 3.1 1.6 .. 4.0 –6.6 0.8 .. .. 6.6 6.8 5.4 –0.7 –13.0 .. 2.4 –4.1 –7.7 .. –0.9 –5.2 2.5 0.9 –0.1 –1.3 .. .. –8.0 –7.9 1.7 1.4 .. .. –25.4 3.7 0.7 2.1 1.0 .. .. ..

2008–10

.. 6.6 0.8 0.8 0.2 –6.5 0.7 –0.9 5.5 1.9 3.4 3.2 –0.4 .. 1.3 –1.9 .. 1.8 1.5 0.3 .. –1.2 –0.3 0.2 .. .. 0.4 8.8 1.7 0.6 0.4 .. –0.7 0.7 –0.6 .. 0.2 0.2 0.8 2.0 –0.9 2.6 .. .. 1.9 5.8 –1.3 0.1 .. .. 0.3 –0.9 1.9 –1.6 –2.5 .. .. ..


Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older

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

% ages 15–24

% of relevant age group

Male % of male employment

1991

2010

1991

2010

1991

2010a

1990

57 49 58 63 40 32 45 46 45 63 63 32 64 67 79 59 .. 58 60 80 59 39 49 57 44 56 38 83 72 60 47 32 53 57 59 51 46 77 73 45 84 53 57 55 54 53 59 52 47 49 70 68 53 60 54 59 38 79

60 45 54 63 40 34 52 54 44 56 57 36 67 60 74 58 .. 66 61 77 49 42 47 59 49 48 38 84 77 59 48 36 55 58 38 57 45 78 76 40 82 62 63 60 61 51 64 55 51 62 71 69 71 60 51 55 37 86

49 38 46 46 28 20 38 24 33 43 43 17 45 45 73 36 .. 26 41 72 43 26 38 34 25 35 18 71 47 46 35 15 41 50 38 35 39 64 51 24 79 53 54 47 47 29 49 28 38 34 57 64 35 42 32 53 20 51

47 18 34 40 24 17 31 27 20 25 39 20 44 33 57 24 .. 31 40 62 27 23 28 33 29 20 15 71 51 35 36 16 31 43 18 32 30 57 53 11 73 63 50 46 53 32 52 32 41 42 54 57 55 39 27 29 18 66

33 86 46 46 53 40 100 92 79 70 97 82 98 .. .. 91 .. 53 100 21 92 61 24 .. .. 92 76 19 17 57 7 13 55 54 90 82 36 7 23 43 34 120 92 43 7 24 103 45 23 62 12 31 67 70 87 66 .. 84

73 98 60 77 84 .. 117 91 99 93 102 91 97 60 .. 97 .. 101 84 45 95 81 46 .. .. 98 83 31 32 68 38 24 89 87 88 93 .. 25 54 .. .. 120 119 69 13 44 110 100 34 74 .. 67 92 85 97 107 82 94

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

2007–10b

49 8 .. 62 40 .. 18 9 21 41 10 11 30 .. .. 23 .. .. .. .. 8 32 .. 69 .. 10 24 .. .. 23 77 .. 17 27 34 60 47 .. .. .. .. 13 15 45 .. .. 8 .. 59 33 .. 42 34 c 42 20 18 .. 0

Female % of female employment 1990

50 c 7 .. .. .. .. 10 .. 24 37 26 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. 7 15 .. .. .. .. .. .. .. 12 10 .. .. .. .. .. .. 19 .. 31c 46c .. .. 28 .. ..

2.4

PEOPLE

Decent work and productive employment

2007–10b

52 5 .. 67 52 .. 5 5 15 31 11 3 34 .. .. 27 .. .. .. .. 7 16 .. 89 .. 8 22 .. .. 20 89 .. 14 32 28 54 65 .. .. .. .. 10 9 45 .. .. 3 .. 78 28 .. 48 47c 46 17 17 .. 0

GDP per person employed % growth 1990–92

.. 0.3 1.0 6.2 5.7 –32.8 2.6 1.6 0.6 0.7 0.5 –5.0 –15.1 –3.9 .. 5.1 .. –0.2 –13.1 .. –19.6 .. .. .. .. –13.9 –6.9 –5.8 –2.1 6.0 1.3 .. .. 1.0 –23.1 .. –1.6 –3.6 2.0 .. .. 0.4 0.6 .. –5.9 –2.8 3.9 0.6 6.4 .. .. .. –0.8 –3.3 1.1 2.1 .. –0.3

2012 World Development Indicators

2008–10

.. –1.3 5.6 3.2 –0.9 0.3 2.1 0.3 –1.0 –3.1 0.0 0.1 –0.9 0.6 .. 2.7 .. –4.4 –2.0 .. –0.2 .. .. .. .. –0.9 –2.0 –6.1 3.7 0.3 1.9 .. .. –1.2 –2.2 .. 2.7 4.2 .. .. .. –0.3 1.1 .. –2.5 4.6 –0.3 1.4 –0.1 .. .. .. 2.4 1.4 1.9 1.3 .. 11.1

55


2.4

Decent work and productive employment Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older 1991

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

57 59 88 51 68 48e 64 64 59 50 52 37 .. 42 49 47 44 64 67 46 58 79 77 58 70 45 41 53 53 79 58 71 58 61 55 52 55 78 31 39 65 69 62 w 72 63 58 67 64 73 57 57 40 59 63 57 50

2010

52 58 85 47 69 46e 65 63 51 55 53 39 .. 47 52 49 44 58 65 39 58 79 71 54 75 63 41 44 54 75 54 76 57 58 61 54 61 75 31 42 67 83 60 w 71 60 55 65 61 70 54 62 41 55 64 55 51

% ages 15–24 1991

49 41 80 25 59 28e 39 56 46 28 42 15 .. 35 28 32 27 58 69 38 38 70 70 46 56 33 29 48 34 61 36 44 63 54 45 33 38 73 20 24 47 48 52 w 58 52 43 60 53 67 41 47 27 47 46 46 42

2010

24 36 73 12 57 19e 42 34 21 34 39 13 .. 25 30 27 26 38 61 24 38 69 46 41 57 48 23 32 35 55 34 43 48 42 44 35 40 58 16 27 51 73 43 w 55 41 36 48 43 53 34 45 24 37 46 38 34

% of relevant age group 1991

2010a

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

95 89 32 101 37 91 .. .. 89 97 .. 94 .. 119 .. 39 58 100 95 72 87 27 77 56 .. 90 90 78 .. 28 96 .. 102 96 90 105 83 77 86 44 .. .. 68 w 39 69 58 83 64 76 89 90 72 55 36 100 107

Male % of male employment 1990

21 1 .. .. 77 .. .. 10 .. .. .. .. .. 21 .. .. .. .. 8 .. .. 86 c 67 .. .. 22 .. .. .. .. .. .. 13 .. .. .. .. .. .. .. 56 .. .. w .. .. .. .. .. .. .. 30 .. .. .. .. 16

2007–10b

33 6 .. .. .. 30 .. 12 17 15 .. 8 .. 13 38 c .. .. 9 9 34 .. .. 50 .. .. .. .. 28 .. .. .. 1 15 .. 22c .. 30 .. 26 .. .. .. .. w .. .. 70 .. .. .. 18 31 33 78 .. .. 13

a. Provisional data. b. Data are for the most recent year available. c. Limited coverage. d. Data are for 2011. e. Includes Montenegro.

56

2012 World Development Indicators

Female % of female employment 1990

33 1 .. .. 91 .. .. 6 .. .. .. .. .. 25 .. .. .. .. 11 .. .. 96 c 74 .. .. 21 .. .. .. .. .. .. 6 .. .. .. .. .. .. .. 81 .. .. w .. .. .. .. .. .. .. 29 .. .. .. .. 14

2007–10b

33 5 .. .. .. 29 .. 7 7 12 .. 12 .. 9 44 c .. .. 5 9 25 .. .. 55 .. .. .. .. 48 .. .. .. 0 8 .. 24 c .. 32 .. 31 .. .. .. .. w .. .. 77 .. .. .. 19 31 48 86 .. .. 9

GDP per person employed % growth 1990–92

2008–10

–9.3 –7.9 .. 4.8 –1.1 .. .. –2.0 –0.8 –2.3 .. –5.3 .. 1.8 5.5 1.4 .. 1.9 –0.6 6.3 –20.4 –2.3 6.8 .. .. –3.5 2.5 1.0 –13.0 –0.8 –7.9 –3.6 6.1 1.7 5.2 –7.7 4.5 4.6 .. 0.9 –2.7 –4.3 0.9 w –3.4 1.2 0.4 1.5 1.0 6.4 –9.3 1.3 1.0 3.2 –4.8 2.8 2.3

–3.2 –1.4 .. –0.7 0.0 .. .. 5.0 1.6 –1.6 .. –0.4 .. 2.5 4.8 1.7 .. –0.1 –1.1 1.5 5.8 3.3 0.7 .. .. 0.6 1.7 –1.5 4.9 3.1 –4.3 –5.2 –0.9 2.3 3.9 5.4 –3.9 3.4 .. 2.2 3.6 5.4 2.0 w –2.3 4.2 3.7 4.4 4.3 7.9 –0.7 0.1 1.1 4.8 1.5 0.7 –0.1


About the data

2.4

PEOPLE

Decent work and productive employment Definitions

Four targets were added to the UN Millennium Dec-

supplemented by official estimates and censuses

• Employment to population ratio is the proportion of

laration at the 2005 World Summit High-Level Ple-

for a small group of countries. The labor force survey

a country’s population that is employed. People ages

nary Meeting of the 60th Session of the UN General

is the most comprehensive source for internationally

15 and older are generally considered the working-

Assembly. One was full and productive employment

comparable employment, but there are still some

age population. People ages 15–24 are generally

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

limitations for comparing data across countries and

considered the youth population. • Gross enrollment

route for people to escape poverty. The four indi-

over time even within a country. Information from

ratio, secondary, is the ratio of total enrollment in

cators for this target have an economic focus, and

labor force surveys is not always consistent in what

secondary education, regardless of age, to the popu-

three of them are presented in the table.

is included in employment. For example, informa-

lation of the age group that officially corresponds to

The employment to population ratio indicates

tion provided by the Organisation for Economic Co-

secondary education. • Vulnerable employment is

how efficiently an economy provides jobs for people

operation and Development relates only to civilian

unpaid family workers and own-account workers as a

who want to work. A high ratio means that a large

employment, which can result in an underestima-

percentage of total employment. • Labor productiv-

propor tion of the population is employed. But a lower

tion of “employees” and “workers not classified by

ity is the growth rate of gross domestic product

employment to population ratio can be seen as a pos-

status,” espe cially in countries with large armed

(GDP) divided by the number of people engaged in

itive sign, especially for young people, if it is caused

forces. While the categories of unpaid family work-

the production of goods and services.

by an increase in their education. This indicator has

ers and self-employed workers, which include own-

a gender bias because women who do not consider

account workers, would not be affected, their relative

their work employment or who are not perceived as

shares would be. Geographic coverage is another

working tend to be undercounted. This bias has dif-

factor that can limit cross-country comparisons. The

ferent effects across countries and reflects demo-

employment by status data for many Latin Ameri-

graphic, social, legal, and cultural trends and norms.

can countries covers urban areas only. Similarly, in

Comparability of employment ratios across coun-

some countries in Sub-Saharan Africa, where limited

tries is also affected by variations in definitions of

information is available anyway, the members of pro-

employment and population (see About the data for

ducer cooperatives are usually excluded from the

table 2.3). The biggest difference results from the

self-employed category. For detailed information on

age range used to define labor force activity. The

definitions and coverage, consult the original source.

population base for employment ratios can also vary

Labor productivity is used to assess a country’s

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

economic ability to create and sustain decent

noninstitutionalized population of working age living

employment opportunities with fair and equitable

in private households, which excludes members of

remuneration. Productivity increases obtained

the armed forces and individuals residing in men-

through investment, trade, technological progress, or

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

changes in work organization can increase social pro-

countries include members of the armed forces in

tection and reduce poverty, which in turn reduce vul-

the population base of their employment ratio while

nerable employment and working poverty. Productiv-

excluding them from employment data (International

ity increases do not guarantee these improvements,

Labour Organization, Key Indicators of the Labour

but without them—and the economic growth they

Market, 7th edition).

bring—improvements are highly unlikely. For compa-

The proportion of unpaid family workers and own-

rability of individual sectors labor productivity is esti-

account workers in total employment is derived from

mated according to national accounts conventions.

information on status in employment. Each status

However, there are still significant limitations on the

group faces different economic risks, and unpaid

availability of reliable data. Information on consis-

family workers and own-account workers are the

tent series of output in both national currencies and

most vulnerable—and therefore the most likely to

purchasing power parity dollars is not easily avail-

fall into poverty. They are the least likely to have for-

able, especially in developing countries, because the

mal work arrangements, are the least likely to have

definition, coverage, and methodology are not always

social protection and safety nets to guard against

consistent across countries. For example, countries

Data on employment to population ratio, vulner-

economic shocks, and often are incapable of gen-

employ different methodologies for estimating the

able employment, and labor productivity are from

erating sufficient savings to offset these shocks. A

missing values for the nonmarket service sectors

the International Labour Organization’s Key Indi-

high proportion of unpaid family workers in a country

and use different definitions of the informal sector.

cators of the Labour Market, 7th edition, data-

Data sources

indicates weak development, little job growth, and

base. Data on gross enrollment ratios are from

often a large rural economy.

the United Nations Educational, Scientific, and

Data on employment by status are drawn from

Cultural Organization Institute for Statistics.

labor force surveys and household surveys,

2012 World Development Indicators

57


2.5

Unemployment Unemployment

Total % of total labor force 1990–92a 2007–10a

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

58

.. .. 23.0 .. 6.7b .. 10.8 3.6 .. 6.3 1.9 .. 6.7 1.5 5.5b 17.6 13.8 6.4b .. .. 0.5 .. .. 11.2b .. .. 4.4 2.3b 2.0 9.5b .. .. 4.1 6.7 11.1 .. 2.1 2.3 9.0 20.7 8.9b 9.0 7.9b .. 3.7 1.3 11.6 10.2 .. .. .. 6.6 4.7 7.8 .. .. .. 12.7

.. 13.8 11.4 .. 8.6b 28.6 5.2b 4.4 6.0 .. 5.0 .. 8.3 .. 5.2 27.2 .. 8.3 10.2 3.3 .. 1.7 2.9 8.0 b .. .. 8.1 4.3 5.2 11.6 .. .. 7.8 .. 11.8 1.6 6.2 7.3 7.4 14.3 6.5 9.4 7.3 .. 16.9 20.5 8.4 9.3 .. .. 16.5 7.1 .. 12.5 .. .. .. ..

2012 World Development Indicators

Male % of male labor force 1990–92a 2007–10a

.. .. 24.2 .. 6.4b .. 11.4 3.5 .. 5.2 2.0 .. 4.8 2.2 5.5b 15.5 11.7 5.4b .. .. 0.7 .. .. 12.0 b .. .. 3.9 .. 2.0 6.8 b .. .. 3.5 .. 11.1 .. 2.0 2.4 8.3 12.0 6.0 b 6.4 8.4b .. 3.9 1.1 13.3 8.1 .. .. .. 5.3 3.7 4.9 .. .. .. 11.9

.. 12.2 10.0 .. 7.8b 21.9 5.1b 4.6 5.2 .. 4.2 .. 8.1 .. 4.5 25.6 .. 6.1 10.9 .. .. 1.5 2.5 8.7b .. .. 7.2 .. 6.0 9.1 .. .. 6.6 .. 11.4 1.4 6.0 6.4 8.2 9.8 5.2 5.2 9.0 .. 19.5 12.1 9.0 9.0 .. .. 16.8 7.5 .. 9.9 .. .. .. ..

Female % of female labor force 1990–92a 2007–10a

.. .. 20.3 .. 7.0 b .. 10.0 3.8 .. 11.8 1.9 .. 9.5 0.6 5.6b 21.6 17.2 7.9b .. .. 0.3 .. .. 10.2b .. .. 5.3 .. 1.9 13.0 b .. .. 5.4 .. 11.2 .. 2.2 2.1 9.9 35.2 13.2b 17.0 7.2b .. 3.5 1.6 9.6 12.8 .. .. .. 8.4 5.5 12.9 .. .. .. 13.8

.. 15.9 20.0 .. 9.8b 35.0 5.4b 4.2 6.9 .. 7.4 .. 8.5 .. 6.0 30.0 .. 11.0 9.5 .. .. 1.8 3.3 7.2b .. .. 9.6 .. 4.3 15.0 .. .. 9.9 .. 12.2 2.0 6.4 8.5 6.6 21.4 8.4 22.9 4.9 .. 14.3 29.9 7.7 9.7 .. .. 16.1 6.6 .. 16.2 .. .. .. ..

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2007–10a 2007–10a 2007–10a

% of total unemployment Primary Secondary Tertiary 2007–10a 2007–10a 2007–10a

.. .. .. .. .. 58.2 18.5b 25.2 .. .. .. .. 48.8 .. .. .. .. .. 46.4 .. .. .. .. 12.0 b .. .. .. .. .. .. .. .. .. .. 44.4 .. 20.4 43.3 19.1 .. .. .. .. .. 27.4 .. 23.6 40.1 .. .. .. 47.4 .. 45.0 .. .. .. ..

.. .. .. .. .. 52.2 20.3b 27.8 .. .. .. .. 49.6 .. .. .. .. .. 46.3 .. .. .. .. 12.7b .. .. .. .. .. .. .. .. .. .. 41.4 .. 21.0 43.3 20.6 .. .. .. .. .. 26.8 .. 27.0 41.5 .. .. .. 48.1 .. 38.8 .. .. .. ..

.. .. .. .. .. 63.8 16.4b 22.0 .. .. .. .. 47.8 .. .. .. .. .. 46.5 .. .. .. .. 11.0 b .. .. .. .. .. .. .. .. .. .. 47.7 .. 19.8 43.3 16.9 .. .. .. .. .. 28.4 .. 19.3 38.7 .. .. .. 46.3 .. 50.3 .. .. .. ..

.. .. .. .. 48.1 11.1 47.4 37.5 9.9 22.7 .. 10.8 33.9 .. .. 94.9 .. 49.3 42.5 .. .. .. .. 26.3b .. .. 17.8 .. 38.0 21.0 .. .. 66.9 .. 16.0 46.6 26.9 b 29.6 35.5 32.0 .. .. .. .. 25.2 .. 36.6 39.5 .. .. 4.6 32.3 .. 27.7 .. .. .. ..

.. .. .. .. 36.7 68.3 33.6 54.5 75.8 38.8 .. 38.6 40.5 .. .. .. .. 35.7 47.4 .. .. .. .. 41.0 b .. .. 58.5 .. 43.8 53.7 .. .. 23.4 .. 70.4 48.5 42.1b 64.8 37.2 42.2 .. .. .. .. 58.1 .. 45.3 41.7 .. .. 56.1 56.5 .. 48.9 .. .. .. ..

.. .. .. .. 15.3 20.6 19.0 8.0 14.3 31.7 .. 50.6 19.0 .. .. 4.8 .. 4.1 10.1 .. .. .. .. 32.7b .. .. 23.5 .. 17.1 23.0 .. .. 7.9 .. 11.6 3.6 29.7b 5.7 20.8 19.5 .. .. .. .. 18.0 .. 17.4 18.3 .. .. 39.2 11.0 .. 22.4 .. .. .. ..


Unemployment

Total % of total labor force 1990–92a 2007–10a

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

3.2b 9.9 .. 2.8 11.1 .. 15.0 11.2 9.3 15.4 2.2 .. .. .. .. 2.5 .. .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. 3.3 3.1 .. .. 16.0 b .. 6.0 19.0 .. 5.6 10.6b 14.4 .. .. 5.9 .. 5.2 14.7 7.7 5.0 b 9.4b 8.6 13.3 4.1b 16.9 ..

2.9 11.2 .. 7.1 10.5 .. 13.5 6.6 8.4 11.4 5.0 12.9 6.6 .. .. 3.7 45.4 .. 8.6 .. 18.7 9.0 25.3 3.7 .. 17.8 32.0 .. .. 3.7 .. .. 7.7 5.3 6.4 .. 10.0 .. .. 37.6 2.7 4.5 6.5b 5.0 .. .. 3.6 .. 5.0 6.5 .. 5.6 6.3b 7.4 9.6 10.8 13.4 0.5

Male % of male labor force 1990–92a 2007–10a

3.3b 11.0 .. 2.7 9.5 .. 14.9 9.2 6.7 9.4 2.1 .. .. .. .. 2.8 .. .. .. .. .. .. .. .. .. .. .. .. .. 3.4 .. .. 3.2 2.7 .. .. 13.0 b .. 4.7 20.0 .. 4.0 11.4b 11.3 .. .. 6.6 .. 3.8 10.8 9.0 6.0 b 7.5b 7.9 12.2 3.5b 19.1 ..

2.9 11.6 .. 6.1 9.1 .. 16.7 6.8 7.6 8.5 5.4 10.3 5.6 .. .. 4.0 40.7 .. 7.3 .. 21.7 8.6 23.0 3.4 .. 21.2 31.9 .. .. 3.6 .. .. 4.6 5.3 7.8 .. 9.8 .. .. 32.5 3.1 4.4 6.2b 4.9 .. .. 4.1 .. 4.0 5.3 .. 4.4 4.4b 7.6 9.3 9.8 14.9 0.2

Female % of female labor force 1990–92a 2007–10a

3.0 b 8.7 .. 3.0 24.4 .. 15.3 13.9 13.9 22.2 2.2 .. .. .. .. 2.1 .. .. .. .. .. .. .. .. .. .. .. .. .. 4.2 .. .. 3.6 4.0 .. .. 25.3b .. 8.8 19.0 .. 7.8 9.7b 19.5 .. .. 5.1 .. 14.0 22.3 5.9 3.7b 12.5b 9.9 14.7 5.0 b 13.3 ..

2.9 10.7 .. 8.7 16.8 .. 9.5 6.5 9.7 14.8 4.5 24.1 7.5 .. .. 3.3 56.4 .. 9.4 .. 15.7 10.1 28.0 4.1 .. 14.4 32.2 .. .. 3.8 .. .. 12.8 5.3 4.9 .. 10.5 .. .. 43.0 2.4 4.5 6.8b 5.1 .. .. 3.0 .. 8.7 8.5 .. 7.5 8.8b 6.9 10.0 11.9 11.6 2.6

2.5

PEOPLE

Unemployment Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2007–10a 2007–10a 2007–10a

% of total unemployment Primary Secondary Tertiary 2007–10a 2007–10a 2007–10a

.. 50.6 .. .. .. .. 49.0 22.4 48.5 .. 37.6 .. .. .. .. 0.3 81.7 .. .. .. 45.0 .. .. .. .. 41.4 83.1 .. .. .. .. .. .. 2.4 .. .. .. .. .. .. .. 27.6 9.0 b .. .. .. 9.5 .. .. .. .. .. .. .. 25.5 52.3 .. 38.5

.. 51.2 .. .. .. .. 53.9 25.7 47.2 .. 44.8 .. .. .. .. 0.5 82.8 .. .. .. 48.2 .. .. .. .. 42.3 83.5 .. .. .. .. .. .. 2.7 .. .. .. .. .. .. .. 27.7 8.9b .. .. .. 10.6 .. .. .. .. .. .. .. 25.3 51.7 .. 35.3

.. 49.9 .. .. .. .. 38.2 18.5 49.9 .. 25.2 .. .. .. .. 0.3 79.8 .. .. .. 40.6 .. .. .. .. 40.2 82.4 .. .. .. .. .. .. 2.0 .. .. .. .. .. .. .. 27.4 9.0 b .. .. .. 7.7 .. .. .. .. .. .. .. 25.8 52.8 .. 40.3

.. 33.8 .. 43.4 40.4 .. 38.3 22.1 47.2 12.0 66.8 .. 45.1 .. .. 15.3 64.0 .. .. .. 23.3 45.5 57.2 .. .. 15.0 .. .. .. 10.4 .. .. 43.5 51.5 .. 28.2 .. .. .. .. .. 42.0 30.6 .. .. .. 29.9 .. 14.7 35.8 .. 53.5 31.5b 13.1 15.9 67.3 .. 19.0

.. 58.4 .. 40.6 31.0 .. 39.0 48.0 40.3 4.5 .. .. 39.7 .. .. 63.7 46.0 .. .. .. 62.2 19.7 33.5 .. .. 67.7 .. .. .. 60.9 .. .. 29.6 25.2 .. 49.0 .. .. .. .. .. 36.6 39.2 .. .. .. 49.3 .. 10.1 39.8 .. 31.4 30.5b 45.2 71.8 15.9 .. 52.7

2012 World Development Indicators

.. 7.8 .. 10.2 25.5 .. 18.5 28.1 11.2 3.9 33.2 .. 15.2 .. .. 21.1 15.0 .. .. .. 14.5 29.7 0.4 .. .. 17.3 .. .. .. 24.9 .. .. 7.9 21.0 .. 22.1 .. .. .. .. .. 18.7 25.7 .. .. .. 17.9 .. 28.0 23.8 .. 13.4 37.3b 41.2 12.1 13.5 .. 24.0

59


2.5

Unemployment Unemployment

Total % of total labor force 1990–92a 2007–10a

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

.. 5.2 0.3 .. .. .. .. 2.7 .. 7.1 .. .. .. 18.1 14.2b .. .. 5.7 2.8 6.8 .. 3.6b 1.4 .. .. 19.6 .. 8.5 .. 1.0 .. .. 9.7 7.5b 9.0 b .. 7.7 .. .. .. 18.9 .. .. w .. .. .. 3.5 .. 2.5 .. 6.6 12.7 .. .. 7.5 9.1

7.3 7.5 .. 5.4 .. 19.2 .. 5.9 14.4 7.2 .. 23.8 .. 20.1 4.9 .. .. 8.4 4.2 8.4 .. .. 1.2 .. .. 5.3 14.2 11.9 .. 4.2 8.8 4.0 7.8 9.6b 7.3 .. 7.6 2.4 24.5 14.6 .. .. .. w .. .. .. 5.8 .. 4.7 9.5 7.8 10.6 .. .. 8.5 10.0

Male % of male labor force 1990–92a 2007–10a

.. 5.2 0.6 .. .. .. .. 2.7 .. 8.1 .. .. .. 13.9 .. .. .. 6.7 2.3 5.2 .. 2.8b 1.3 .. .. 17.0 .. 8.8 .. 1.3 .. .. 11.5 7.9b 6.8b .. 8.2 .. .. .. 16.3 .. .. w .. .. .. .. .. .. .. 5.4 10.8 .. .. 7.1 7.2

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

60

2012 World Development Indicators

7.9 8.0 .. 3.5 .. 18.4 .. 5.4 14.2 7.4 .. 22.0 .. 19.7 3.5 .. .. 8.5 3.8 5.7 .. .. 1.2 .. .. 3.5 .. 11.4 .. 3.1 6.6 2.0 8.6 10.5b 5.3 .. 7.2 .. 17.7 11.5 .. .. .. w .. .. .. .. .. .. 10.3 6.4 8.8 .. .. 8.7 9.8

Female % of female labor force 1990–92a 2007–10a

.. 5.2 0.2 .. .. .. .. 2.6 .. 6.0 .. .. .. 25.8 .. .. .. 4.6 3.5 14.0 .. 4.3b 1.5 .. .. 23.9 .. 7.8 .. 0.6 .. .. 7.3 7.0 b 11.8b .. 6.8 .. .. .. 22.4 .. .. w .. .. .. .. .. .. .. 8.3 21.5 .. .. 8.0 11.9

6.5 6.9 .. 15.9 .. 20.2 .. 6.5 14.6 7.0 .. 25.9 .. 20.5 7.7 .. .. 8.2 4.8 22.5 .. .. 1.1 .. .. 6.2 .. 13.0 .. 5.1 6.1 12.0 6.7 8.6b 9.7 .. 8.1 .. 38.6 40.9 .. .. .. w .. .. .. .. .. .. 8.8 9.8 18.4 .. .. 8.1 10.2

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2007–10a 2007–10a 2007–10a

% of total unemployment Primary Secondary Tertiary 2007–10a 2007–10a 2007–10a

34.9 35.2 .. .. .. 71.1 .. .. 59.3 43.3 .. 14.4 .. 45.1 .. .. .. 16.6 34.3 .. .. .. .. .. .. .. .. 28.6 .. .. .. .. 32.6 29.0 b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 32.6 44.0

36.9 32.7 .. .. .. 70.1 .. .. 58.3 45.0 .. .. .. 44.6 .. .. .. 18.1 28.3 .. .. .. .. .. .. .. .. 24.7 .. .. .. .. 37.2 29.9b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 34.2 44.2

32.0 38.0 .. .. .. 72.1 .. .. 60.5 41.2 .. .. .. 45.6 .. .. .. 14.8 39.8 .. .. .. .. .. .. .. .. 37.0 .. .. .. .. 26.0 27.7b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 31.3 43.3

28.2 13.1 .. 7.5 .. 20.3 .. 27.2 27.8 23.3 .. 15.4 .. 58.4 45.4b .. .. 32.5 27.9 46.1 66.5 .. 41.5 .. .. 27.9 .. 52.5 .. .. 7.4 19.7 37.1 17.9 59.1 .. .. .. 53.8 .. .. .. .. w .. .. .. .. .. .. 26.1 42.3 .. .. .. 33.9 42.9

62.7 52.8 .. 48.6 .. 68.4 .. 22.7 66.2 58.1 .. 80.7 .. 22.6 22.8b .. .. 44.9 53.7 28.0 28.8 .. 49.3 .. .. 65.9 .. 26.0 .. .. 52.9 42.6 46.5 35.5 27.0 .. .. .. 14.3 .. .. .. .. w .. .. .. .. .. .. 47.8 38.1 .. .. .. 42.2 41.3

6.7 34.1 .. 43.6 .. 11.2 .. 50.1 5.9 16.3 .. 0.8 .. 17.7 31.8b .. .. 16.4 17.7 4.9 4.6 .. 0.2 .. .. 5.2 .. 13.9 .. .. 39.7 33.2 14.3 46.5 13.8 .. .. .. 24.5 .. .. .. .. w .. .. .. .. .. .. 25.6 13.0 .. .. .. 26.8 14.8


About the data

2.5

PEOPLE

Unemployment Definitions

Unemployment and total employment are the broad-

generate statistics that are more comparable inter-

• Unemployment is the share of the labor force with-

est indicators of economic activity as reflected by

nationally. But the age group, geographic coverage,

out work but available for and seeking employment.

the labor market. The International Labour Organiza-

and collection methods could differ by country or

Definitions of labor force and unemployment may

tion (ILO) defines the unemployed as members of the

change over time within a country. For detailed infor-

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

economically active population who are without work

mation, consult the original source.

unemployment is the number of people with continu-

but available for and seeking work, including people

Women tend to be excluded from the unemploy-

ous periods of unemployment extending for a year or

who have lost their jobs or who have voluntarily left

ment count for various reasons. Women suffer more

longer, expressed as a percentage of the total unem-

work. Some unemployment is unavoidable. At any

from discrimination and from structural, social, and

ployed. • Unemployment by educational attainment

time some workers are temporarily unemployed—

cultural barriers that impede them from seeking

is the unemployed by level of educational attainment

between jobs as employers look for the right workers

work. Also, women are often responsible for the

as a percentage of the total unemployed. The levels

and workers search for better jobs. Such unemploy-

care of children and the elderly and for household

of educational attainment accord with the ISCED97

ment, often called frictional unemployment, results

affairs. They may not be available for work during

of the United Nations Educational, Scientific, and

from the normal operation of labor markets.

the short reference period, as they need to make

Cultural Organization.

Changes in unemployment over time may reflect

arrangements before starting work. Furthermore,

changes in the demand for and supply of labor; they

women are considered to be employed when they

may also refl ect changes in reporting practices.

are working part-time or in temporary jobs, despite

Paradoxically, low unemployment rates can disguise

the instability of these jobs or their active search for

substantial poverty in a country, while high unemploy-

more secure employment.

ment rates can occur in countries with a high level of

Long-term unemployment is measured by the

economic development and low rates of poverty. In

length of time that an unemployed person has been

countries without unemployment or welfare benefits

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

people eke out a living in vulnerable employment. In

table are from labor force surveys. The underlying

countries with well developed safety nets workers

assumption is that shorter periods of joblessness

can afford to wait for suitable or desirable jobs. But

are of less concern, especially when the unem-

high and sustained unemployment indicates serious

ployed are covered by unemployment benefi ts or

inefficiencies in resource allocation.

similar forms of support. The length of time that a

The ILO definition of unemployment notwithstand-

person has been unemployed is difficult to measure,

ing, reference periods, the criteria for people consid-

because the ability to recall that time diminishes as

ered to be seeking work, and the treatment of people

the period of joblessness extends. Women’s long-

temporarily laid off or seeking work for the first time

term unemployment is likely to be lower in countries

vary across countries. In many developing countries

where women constitute a large share of the unpaid

it is especially difficult to measure employment and

family workforce.

unemployment in agriculture. The timing of a survey,

Unemployment by level of educational attainment

for example, can maximize the effects of seasonal

provides insights into the relation between the edu-

unemployment in agriculture. And informal sector

cational attainment of workers and unemployment

employment is difficult to quantify where informal

and may be used to draw inferences about changes

activities are not tracked.

in employment demand. Information on educational

Data on unemployment are drawn from labor force

attainment is the best available indicator of skill

sample surveys and general household sample

levels of the labor force. Besides the limitations to

surveys, censuses, and offi cial estimates, which

comparability raised for measuring unemployment,

are generally based on information from different

the different ways of classifying the education level

sources and can be combined in many ways. Admin-

may also cause inconsistency. Education level is

istrative records, such as social insurance statistics

supposed to be classifi ed according to Interna-

and employment office statistics, are not included

tional Standard Classifi cation of Education 1997

in the table because of their limitations in cover-

(ISCED97). For more information on ISCED97, see

age. Labor force surveys generally yield the most

About the data for table 2.11.

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

Data sources

surveys generally use a definition of unemployment

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

that follows the international recommendations more

cators of the Labour Market, 7th edition, database.

closely than that used by other sources and therefore

2012 World Development Indicators

61


2.6

Children at work Survey year

% of children ages 7–14 in employment

% of children ages 7–14

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

62

2005 2001 2004

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

2007 2000 2005 2004 2006

2005 2006 2005 2007

2005

2005 2006 2006 2006 1994 2006 2005

Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

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

Children in employment

Total

Male

Female

Work only

Study and work

.. 25.0 .. 30.1 12.9 .. .. .. 5.2 .. 16.2 11.7 .. 74.4 32.1 10.6 .. 5.2 .. 42.1 11.7 48.9 43.4 .. 67.0 60.4 4.1 .. .. 3.9 39.8 30.1 5.7 45.7 .. .. .. .. .. 5.8 14.3 7.9 7.1 .. .. 56.0 .. .. .. 43.5 31.8 .. 48.9 .. 18.2 48.3 50.5 33.4

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

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

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

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

2012 World Development Indicators

Agriculture Manufacturing

.. .. .. .. .. .. .. .. 91.7 .. .. .. .. .. 73.2 .. .. 54..7 .. 70.9

.. .. .. .. .. .. .. .. 0.7 .. .. .. .. .. 6.1 .. .. 7.6 .. 1.4

82.3 88.5 .. .. .. 24.1 .. .. 41.2 .. .. 40.3 .. .. .. .. .. .. 18.5 69.3 .. 50.1 .. .. 94.6 .. .. .. .. .. .. .. .. 63.7 .. .. ..

4.2 3.1 .. .. .. 6.9 .. .. 10.8 .. .. 9.5 .. .. .. .. .. .. 9.8 6.3 .. 13.3 .. .. 1.5 .. .. .. .. .. .. .. .. 9.7 .. .. ..

Services

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

.. .. .. .. 34.2 .. .. .. 4.1 .. – .. .. .. 0.9 .. .. 5.5 .. 1.9 25.9 6.0 2.5 .. .. .. .. .. .. 22.7 .. .. 15.8 .. .. .. .. .. .. 23.8 3.6 2.2 .. .. 1.7 .. .. .. .. .. .. .. .. 2.0 .. .. ..

.. 1.4 .. 6.2 8.1 .. .. .. 3.8 .. 17.0 9.2 .. .. 9.2 1.6 .. 24.7 .. 2.2 68.6 4.1 9.5 .. 2.0 1.8 .. .. .. 29.1 6.6 4.2 57.7 2.4 .. .. .. .. .. 19.5 15.2 11.4 23.6 .. .. 2.4 .. .. .. 1.1 4.3 .. 6.1 .. 18.8 .. 4.0 1.8

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


Survey year

Children in employment

% of children ages 7–14 in employment

% of children ages 7–14

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

2007 2004–05 2000 2006

2005

2006 2000

2006

2002 2007

2005 2007 2006 2006

2009 2000 2006–07 1998–99 1996 1999 1999

2005 2006

2008 2005 2007 2001 2001

Total

Male

Female

Work only

Study and work

8.7 .. 4.2 8.9 .. 14.7 .. .. .. 9.8 .. .. 3.6 37.7 .. .. .. .. 5.2 .. .. .. 2.6 37.4 .. .. 11.8 26.0 40.3 .. 49.5 .. .. 12.2 33.5 10.1 13.2 1.8 .. 15.4 47.2 .. .. 10.1 47.1 .. .. .. .. 8.9 .. 15.3 42.2 13.3 .. 3.6 .. ..

13.3 .. 4.2 8.8 .. 17.9 .. .. .. 11.3 .. .. 4.4 40.1 .. .. .. .. 5.8 .. .. .. 4.0 37.8 .. .. 14.8 27.7 41.3 .. 55.0 .. .. 16.5 34.1 11.4 13.5 1.9 .. 16.2 42.2 .. .. 16.2 49.2 .. .. .. .. 12.1 .. 22.6 44.8 16.3 .. 4.6 .. ..

4.1 .. 4.2 9.1 .. 11.3 .. .. .. 8.3 .. .. 2.8 35.2 .. .. .. .. 4.6 .. .. .. 1.3 37.1 .. .. 8.6 24.2 39.4 .. 44.1 .. .. 7.6 32.8 8.6 12.8 1.7 .. 14.7 52.4 .. .. 3.9 45.0 .. .. .. .. 5.4 .. 7.7 39.5 10.0 .. 2.6 .. ..

45.1 .. 84.9 24.9 .. 32.4 .. .. .. 2.5 .. .. 1.6 14.1 .. .. .. .. 7.9 .. .. .. 74.4 45.0 .. .. 2.8 40.9 10.5 .. 59.5 .. .. 22.6 3.8 16.4 93.2 100.0 .. 9.5 35.6 .. .. 30.8 66.5 .. .. .. .. 14.6 .. 24.2 4.0 14.8 .. 3.6 .. ..

54.9 .. 15.2 75.1 .. 67.6 .. .. .. 97.5 .. .. 98.4 85.9 .. .. .. .. 92.1 .. .. .. 25.6 55.0 .. .. 97.2 59.1 89.5 .. 40.5 .. .. 77.4 96.2 83.6 6.8 0.0 .. 90.5 64.4 .. .. 69.2 33.5 .. .. .. .. 85.4 .. 75.7 96.0 85.2 .. 96.4 .. ..

2.6

PEOPLE

Children at work Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

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

Agriculture Manufacturing

61.6 .. 69.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 58.0 .. .. .. .. 87.6 .. .. .. .. .. 38.2 .. 91.3 60.6 .. .. 91.5 87.0 .. .. 70.5 .. .. .. .. .. 73.3 .. 60.8 62.6 64.3 .. 48.5 .. ..

10.4 .. 16.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. .. .. 2.9 .. .. .. .. .. 11.7 .. 0.3 8.3 .. .. 0.4 1.4 .. .. 9.7 .. .. .. .. .. 2.9 .. 6.2 5.0 4.1 .. 11.2 .. ..

Services

25.1 .. 12.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 10.4 .. .. .. .. 8.2 .. .. .. .. .. 47.0 .. 6.3 10.1 .. .. 8.0 11.1 .. .. 19.3 .. .. .. .. .. 22.9 .. 32.1 31.1 30.6 .. 33.3 .. ..

3.5 .. 7.1 .. .. .. .. .. .. .. .. .. – .. .. .. .. .. – .. .. .. 3.7 .. .. .. .. 0.1 .. .. .. .. .. 2.7 .. 5.1 2.1 .. .. 0.1 4.2 .. .. 1.2 4.8 .. .. .. .. 12.6 .. 9.3 3.8 4.1 .. .. .. ..

23.0 .. 6.8 17.8 .. 7.0 .. .. .. 16.3 .. .. 4.0 .. .. .. .. .. 3.7 .. .. .. 36.6 1.7 .. .. 3.9 10.0 6.7 .. 1.6 .. .. 34.3 2.9 0.1 10.0 .. .. 4.5 3.3 .. .. 13.8 74.5 .. .. .. .. 11.3 .. 24.8 7.6 22.8 .. .. .. ..

2012 World Development Indicators

73.5 .. 59.3 75.8e .. 85.3 .. .. .. 74.9 .. .. 75.0 .. .. .. .. .. 81.9 .. .. .. 59.7c 79.3 .. .. 89.5 89.9 75.5 .. 80.4 .. .. 63.1 82.0 94.7 81.7 .. .. 95.0 92.4 .. .. 85.0 c .. .. .. .. 76.1c .. 65.8 88.6 73.1 .. .. .. ..

63


2.6

Children at work Survey year

Children in employment

% of children ages 7–14 in employment

% of children ages 7–14

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

2000 2008 2005 2005 2007

2006 1999

1999 2000 2000

2006 2005 2005–06 2005 2006 2000 2006 2005–06 2005

2005 2006 2006 2006 2008 1999

Total

Male

Female

Work only

Study and work

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

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

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

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

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

Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

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

Agriculture Manufacturing

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

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

Services

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

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

92.9e .. 12.8 .. 4.4 5.2 0.9 .. .. .. 1.6 7.1 .. .. 8.3 7.3 10.4 .. .. 21.5 24.2 0.9 13.5 .. 1.6 29.8 .. 34.1 .. 1.5 3.1 .. .. .. .. 3.8 33.1 5.9 .. 6.1 3.9 28.4

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

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

64

2012 World Development Indicators


About the data

2.6

PEOPLE

Children at work Definitions

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

defined activities —is not presented. ISIC revision 2

• Survey year is the year in which the underlying

sense of “economic activity”—that is, children in

and revision 3 are both used, depending on the coun-

data were collected. • Children in employment are

employment, a broader concept than child labor (see

try’s codification for describing economic activity.

children involved in any economic activity for at least

ILO 2009a for details on this distinction).

This does not affect the definition of the groups in

one hour in the reference week of the survey. • Work

the table.

only refers to children who are employed and not

In line with the defi nition of economic activity adopted by the 13th International Conference of

The table also aggregates the distribution of chil-

attending school. • Study and work refer to children

Labour Statisticians, the threshold set by the 1993

dren in employment by three major categories of

attending school in combination with employment.

UN System of National Accounts for classifying a

status in employment, based on the International

• Employment by economic activity is the distribu-

person as employed is to have been engaged at

Classification of Status in Employment (1993): self-

tion of children in employment by the major industrial

least one hour in any activity relating to the pro-

employed workers, wage workers (also known as

categories (ISIC revision 2 or revision 3). • Agricul-

duction of goods and services during the reference

employees), and unpaid family workers. A residual

ture corresponds to division 1 (ISIC revision 2) or

period. Children seeking work are thus excluded.

category—which includes those not classifiable by

categories A and B (ISIC revision 3) and includes

Economic activity covers all market production and

status—is not presented.

agriculture and hunting, forestry and logging, and

certain nonmarket production, including production

In most countries more boys are involved in employ-

fishing. • Manufacturing corresponds to division 3

of goods for own use. It excludes unpaid household

ment, or the gender difference is small. However, girls

(ISIC revision 2) or category D (ISIC revision 3). • Ser-

services (commonly called “household chores”)—

are often more present in hidden or underreported

vices correspond to divisions 6–9 (ISIC revision

that is, the production of domestic and personal

forms of employment such as domestic service, and

2) or categories G–P (ISIC revision 3) and include

services by household members for a household’s

in almost all societies girls bear greater responsibil-

wholesale and retail trade, hotels and restaurants,

own consumption.

ity for household chores in their own homes, work

transport, financial intermediation, real estate, pub-

Data are from household surveys by the Interna-

that lies outside the System of National Accounts

lic administration, education, health and social work,

tional Labor Organization (ILO), the United Nations

production boundary and is thus not considered in

other community services, and private household

Children’s Fund (UNICEF), the World Bank, and

estimates of children’s employment.

activity. • Self-employed workers are people whose

national statistical offices. The surveys yield data

remuneration depends directly on the profits derived

on education, employment, health, expenditure, and

from the goods and services they produce, with or

consumption indicators related to children’s work.

without other employees, and include employers,

Household survey data generally include informa-

own-account workers, and members of produc-

tion on work type—for example, whether a child is

ers cooperatives. • Wage workers (also known as

working for payment in cash or in kind or is involved

employees) are people who hold explicit (written or

in unpaid work, working for someone who is not a

oral) or implicit employment contracts that provide

member of the household, or involved in any type of

basic remuneration that does not depend directly on

family work (on the farm or in a business). Country

the revenue of the unit for which they work. • Unpaid

surveys define the ages for child labor as 5–17. The

family workers are people who work without pay in a

data in the table have been recalculated to present

market-oriented establishment operated by a related

statistics for children ages 7–14.

person living in the same household.

Although efforts are made to harmonize the definition of employment and the questions on employ-

Data sources

ment in survey questionnaires, signifi cant differ-

Data on children at work are estimates produced

ences remain in the survey instruments that collect

by the Understanding Children’s Work project

data on children in employment and in the sampling

based on household survey data sets made avail-

design underlying the surveys. Differences exist

able by the ILO’s International Programme on the

not only across different household surveys in the

Elimination of Child Labour under its Statistical

same country but also across the same type of sur-

Monitoring Programme on Child Labour, UNICEF

vey carried out in different countries, so estimates

under its Multiple Indicator Cluster Survey pro-

of working children are not fully comparable across

gram, the World Bank under its Living Standards

countries.

Measurement Study program, and national sta-

The table aggregates the distribution of children

tistical offices. Information on how the data were

in employment by the industrial categories of the

collected and some indication of their reliability

International Standard Industrial Classifi cation

can be found at www.ilo.org/public/english/

(ISIC): agriculture, manufacturing, and services.

standards/ipec/simpoc/, www.childinfo.org, and

A residual category—which includes mining and

www.worldbank.org/lsms. Detailed country statis-

quarrying; electricity, gas, and water; construction;

tics can be found at www.ucw-project.org.

extraterritorial organization; and other inadequately

2012 World Development Indicators

65


2.7

Poverty rates at national poverty lines Population below national poverty line

Survey year a

Afghanistanb Albaniab Angola Argentina Armeniab Azerbaijanb Bangladesh Belarus Benin Bhutan Bolivia Bosnia and Herzegovinab Botswana Brazil Bulgariab Burkina Faso Burundi Cambodiab Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoireb Croatiab Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Ethiopia Fiji Gabon Gambia, Theb Georgiab Ghana Guatemala Guinea Guinea-Bissau Haiti Honduras India Indonesia Iraq Jamaica Jordan Kazakhstanb Kenya Kosovob Kyrgyz Republicb Lao PDRb Latviab

66

2005 2009d 2009 2001 2005 2008

2006d 2004 1993 2008d 2001 2003 2004

2006d 2004 d 2009d

2009d 2002 2002 2009d 2009d 2005 2008d,e 2000 2003

2008 1998 2000

2009d,e 2005 2010 2006d 2008 2005 2009 2002 2002

Rural %

Urban %

National %

.. 24.2 .. .. 34.9 42.5 43.8 .. .. .. 76.5 22.0 40.4 .. .. 65.5 .. 37.8 .. .. .. .. 12.3 2.8 54.3 .. .. .. 23.0 45.8 .. 47.0 57.5 26.8 49.0 45.4 40.0 .. .. 27.8 49.6 74.5 .. .. .. 64.4 41.8 16.6 .. ..

.. 11.2 .. 13.2 33.7 55.7 28.4 .. .. .. 50.3 11.3 24.7 .. .. 22.1 .. 17.6 .. .. .. .. 13.9 .. 35.8 .. .. .. 20.7 32.3 .. 28.6 25.0 10.1 35.7 36.9 28.0 .. .. 17.5 19.4 27.1 .. .. .. 52.8 25.7 9.9 .. ..

.. 18.5 .. .. 34.1 49.6 40.0 6.1 .. .. 59.9 17.7 32.9 22.6 12.8 51.0 .. 34.7 .. .. .. .. 13.7 .. 40.2 .. .. .. 21.7 40.2 11.2 34.6 36.0 19.6 39.9 44.2 35.0 .. .. 22.7 39.5 56.2 .. .. .. 58.8 37.2 13.3 .. 14.3

.. .. 49.2 .. .. 11.6

.. .. 37.4 .. .. ..

12.1 .. 45.1 31.7 33.5 7.5

2012 World Development Indicators

Poverty gap at national poverty line

Survey year a

Rural %

Urban %

National %

2008 c 2008 2000 c 2010 d 2010 2008 2010 2009 2003c 2007c 2007d 2007 2003 2009d 2007 2009 2006c 2007 2007c 2007c 2008 c 2003c 2009d 2005d 2010 d 2004 c 2006 2005 2010 d 2008 2004 2010 d 2010 d 2008 2009d,e 2005 2009 2005 2010 c 2009 2006 2006 2007c 2002 2001d 2010 d,e 2010 2011 2007 2007d 2006 2009 2005c 2009 2010 2008 2004

37.5 14.6 .. .. 36.0 18.5 35.2 .. 46.0 30.9 77.3 17.8 44.8 .. .. 52.6 68.9 34.5 55.0 44.3 69.4 58.6 12.9 2.5 50.3 48.7 75.7 57.7 .. 54.2 .. .. 53.0 30.0 46.5 39.3 43.3 44.6 73.9 30.7 39.2 70.5 63.0 69.1 88.0 65.4 33.8 15.7 39.3 .. 19.0 .. 49.1 35.3 .. 31.7 12.7

29.0 10.1 62.3 9.9 36.0 14.8 21.3 .. 29.0 1.7 50.9 8.2 19.4 .. .. 27.9 34.0 11.8 12.2 13.2 49.6 24.6 15.5 .. 33.0 34.5 61.5 .. .. 29.4 .. .. 22.5 10.6 33.3 35.1 18.6 29.8 32.7 18.4 10.8 30.0 30.5 51.6 45.0 54.3 20.9 9.2 16.1 .. 12.0 .. 33.7 33.1 .. 17.4 ..

36.0 12.4 .. .. 35.8 15.8 31.5 5.4 39.0 23.2 60.1 14.0 30.6 21.4 10.6 46.7 66.9 30.1 39.9 26.6 62.0 55.0 15.1 .. 37.2 44.8 71.3 50.1 24.2 42.7 11.1 34.4 32.8 22.0 37.8 38.9 31.0 32.7 48.4 24.7 28.5 51.0 53.0 64.7 77.0 60.0 29.8 12.5 22.9 9.9 13.0 8.2 45.9 34.5 33.7 27.6 5.9

Survey year a

Rural %

Urban %

National %

2008 c 2008

8.3 2.6 .. .. .. .. 7.4 .. 14.0 8.1 .. .. 18.4 .. .. 17.4 24.2 8.3 17.5 14.3 35.0 23.3 .. .. .. 17.8 34.9 20.6 .. 20.3 .. .. .. .. .. 8.5 14.8 16.0 .. .. 13.5 .. 22.0 27.8 .. .. 6.8 2.6 9.0 .. .. .. 17.5 .. .. .. ..

6.2 1.9 .. .. .. .. 4.3 .. 8.0 0.4 .. .. 6.5 .. .. 7.8 10.3 2.8 2.8 3.3 29.8 7.4 .. .. .. 12.1 26.2 .. .. 9.5 .. .. .. .. .. 7.7 5.4 8.5 .. .. 3.1 .. 7.7 16.9 .. .. 4.5 1.5 2.7 .. .. .. 11.4 .. .. .. ..

7.9 2.3 .. .. .. .. 6.5 .. 12.0 6.1 .. .. 11.7 .. 3.0 15.1 23.4 7.2 12.3 8.1 33.1 21.6 .. .. .. 16.3 32.2 18.9 .. 15.3 2.6 .. .. .. .. 8.3 10.1 10.0 .. .. 9.6 .. 17.6 25.0 .. .. 6.2 2.1 4.5 .. 2.8 1.3 16.3 9.6 .. .. ..

2010 2003c 2007c

2003 2007 2009 2006c 2007 2007c 2007c 2008 c 2003c

2004 c 2006 2005 2008 2004

2005 2009 2005

2006 2007c 2002

2010 2011 2007 2006 2009 2005c 2009


Population below national poverty line

Lesothob Liberiab Macedonia, FYRb Madagascar Malawi Malaysiab Mali Mauritania Mexico Moldovab Mongolia Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Paraguay Peru Philippines Polandb Romaniab Russian Federation Rwanda São Tomé and Príncipe Senegalb Serbiab Sierra Leone South Africa South Sudan Sri Lanka Sudan Swaziland Tajikistanb Tanzania Thailand Timor-Leste Togo Turkey Uganda Ukraineb Uruguay Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

Poverty gap at national poverty line

Survey year a

Rural %

Urban %

National %

Survey year a

Rural %

Urban %

National %

1994

68.9 .. 21.2 77.3 .. 7.1 57.0 59.0 60.3 36.3 .. 14.8 25.1 55.3 69.0 .. 67.8 .. .. 28.1 62.7 49.8 60.3 .. .. 23.5 22.7 64.2 .. .. 9.6 .. .. .. 15.7 .. .. 55.0 38.6 11.5 .. .. 34.6 34.2 8.1 9.6 .. 20.4 .. 42.5 77.3 ..

36.7 .. 19.8 53.7 .. 2.0 18.5 28.9 40.1 12.6 .. 2.6 7.6 51.5 31.0 .. 30.1 .. .. 14.9 20.0 24.7 21.1 .. .. 8.1 8.1 23.2 .. .. 4.9 .. .. .. 6.7 .. .. 49.4 23.7 3.0 .. .. 9.4 13.7 2.9 21.4 .. 3.9 .. 32.3 29.1 ..

66.6 .. 20.4 72.1 .. 3.6 47.5 46.7 47.7 26.3 .. 6.8 15.3 54.1 58.0 .. 45.8 .. .. 23.9 36.8 35.1 34.8 26.4 14.6 15.1 11.9 58.5 .. .. 6.9 .. 38.0 .. 15.2 .. .. 53.5 35.6 9.0 39.7 .. 17.1 31.1 4.6 20.9 32.6 16.0 31.2 40.1 58.4 ..

2003 2007 2006 2005 2004 2009 2010 2008 2010 d 2010 2008 c 2010 2007 2008 2004 2010 2005 2007c 2004 c 2006 2008 2010 d 2010 2009 2008 2006 2006 2011 2009c 2005c 2010 2003c 2006 2009 2010 2009 2001c 2009 2007 2009 2007 2006 2009 2009 2008 2010 d 2009d 2008 2009 2005 2006 2003c

60.5 67.7 21.3 73.5 55.9 8.4 50.6 59.4 60.8 30.3 46.6 11.3 14.5 56.9 49.0 27.4 67.9 63.9 63.8 27.0 59.8 48.9 54.2 .. .. 22.3 21.2 48.7 .. 61.9 13.6 78.5 .. 55.4 9.4 57.6 75.0 .. 37.4 10.4 .. 74.3 38.7 27.2 4.7 6.2 .. 18.7 .. 40.1 76.8 ..

41.5 55.1 17.7 52.0 25.4 1.7 18.8 20.8 45.5 10.4 26.9 4.0 4.8 49.6 17.0 15.5 29.1 36.7 43.1 13.1 17.7 24.7 19.1 .. .. 6.8 7.4 22.1 .. 35.1 5.7 47.0 .. 24.2 5.3 26.5 49.0 .. 21.8 3.0 .. 36.8 8.9 9.1 2.0 18.7 .. 3.3 .. 20.7 26.7 ..

56.6 63.8 19.0 68.7 52.4 3.8 43.6 42.0 51.3 21.9 35.2 6.6 9.0 54.7 38.0 25.2 46.2 59.5 54.7 22.3 32.7 34.7 31.3 26.5 10.6 13.8 11.1 44.9 66.2 50.8 9.2 66.4 23.0 50.6 8.9 46.5 69.2 46.7 33.4 8.1 49.9 61.7 18.1 24.5 2.9 18.6 28.5 14.5 21.9 34.8 59.3 72.0

2005 2004 2007 2006 2004 2008d 2009 2009 2001 2003 1994 2001

2005 2003 2009d 2009 2006 2007 2005 2005 2006

2009 2000 2007

2007 2000 2008 2001 2008 2005 2007 2009d 2008d 2006 2007 1998 2004

2.7

PEOPLE

Poverty rates at national poverty lines

Survey year a

2007 2006 2005 2004 2009 2010 2008 2010 2008 c 2010 2008 2004 2010 c 2007c 2004 c

2009 2006 2006 2011 2009c 2005c 2003c 2006 2009 2010 2009 2001c 2007

2006 2009 2008

2008 2009 2005 2006

Rural %

Urban %

National %

.. 26.3 7.7 28.9 8.6 1.8 15.6 22.3 .. 6.5 13.4 1.7 .. 22.2 16.0 6.0 .. 21.2 26.6 .. .. .. .. .. .. 5.3 5.5 .. .. 21.5 .. 34.6 .. 26.5 1.8 21.3 37.0 .. 11.0 .. .. 29.3 .. 7.6 0.7 .. .. 4.6 .. 10.6 38.8 ..

.. 20.2 6.9 19.3 2.8 0.3 4.7 4.9 .. 1.8 7.7 0.7 .. 19.1 6.0 3.2 .. 11.3 16.2 .. .. .. .. .. .. 1.4 1.7 .. .. 9.3 .. 16.3 .. 8.8 1.2 7.1 20.0 .. 6.5 .. .. 10.3 .. 1.8 0.3 .. .. 0.5 .. 4.5 9.4 ..

.. 24.4 7.2 26.8 8.0 0.8 13.2 14.5 .. 4.5 10.1 1.1 .. 21.2 13.0 5.4 .. 19.6 22.8 .. .. .. .. 7.2 .. 3.2 2.7 14.8 24.8 16.4 .. 27.5 7.0 23.7 1.7 16.2 32.9 .. 9.9 .. .. 22.9 .. 6.8 0.4 .. .. 3.5 4.9 8.9 28.5 ..

Note: Poverty rates are based on per capita consumption estimated from household survey data, unless otherwise noted. a. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. b. World Bank estimates. c. Estimates based on survey data from earlier years are available but are not comparable with the most recent year reported here; these are available at http://data.worldbank.org and http://povertydata.worldbank.org. d. Based on income per capita estimated from household survey data. e. Measured as share of households.

2012 World Development Indicators

67


2.7

Poverty rates at national poverty lines

About the data

Definitions

Estimates of poverty rates and gaps at national pov-

consumption or income from own production), from

• Survey year is the year in which the underlying

erty lines are useful for comparing poverty across

which it is possible to construct a correctly weighted

household survey data were collected or, when the

time within but not across countries. Table 2.8 shows

distribution of per capita consumption or income.

data collection period bridged two calendar years,

poverty indicators at international poverty lines that

As with any indicator measured from household

the year in which most of the data were collected.

surveys, data quality can affect the precision of

• Population below national poverty line is the per-

For countries with an active poverty monitoring pro-

poverty estimates and their comparability over time.

centage of the rural, urban, or national population liv-

gram, the World Bank—in collaboration with national

These include selective survey nonresponse, sea-

ing below the corresponding rural, urban, or national

institutions, other development agencies, and civil

sonality effects, differences in the number of income

poverty line, based on consumption estimated from

society—periodically prepares poverty assessments

or consumption items in the questionnaire, and the

household survey data, unless otherwise noted.

and other analytical reports to assess the extent

time period over which respondents are asked to

• Poverty gap at national poverty line is the mean

and causes of poverty. These reports review levels

recall their expenditures.

shortfall from the rural, urban, or national poverty

allow for comparisons across countries.

and changes in poverty indicators over time and

line (counting the nonpoor as having zero shortfall)

across regions within countries, assess the impact

National poverty lines

as a percentage of the corresponding rural, urban,

of growth and public policy on poverty and inequal-

National poverty lines are the benchmark for esti-

or national poverty line, based on consumption esti-

ity, review the adequacy of monitoring and evalua-

mating poverty indicators that are consistent with

mated from household survey data, unless otherwise

tion, and contain detailed technical overviews of

the country’s specific economic and social circum-

noted. This measure reflects the depth of poverty as

the underlying household survey data and poverty

stances. National poverty lines reflect local percep-

well as its incidence.

measurement methods used. The reports are a key

tions of the level and composition of consumption or

source of comprehensive information on poverty indi-

income needed to be nonpoor. The perceived bound-

cators at national poverty lines and generally feed

ary between poor and nonpoor typically rises with the

into country-owned processes to reduce poverty,

average income of a country and thus does not pro-

build in-country capacity, and support joint work.

vide a uniform measure for comparing poverty rates

An increasing number of countries have their

across countries. While poverty rates at national

own national programs to monitor and disseminate

poverty lines should not be used for comparing pov-

official poverty estimates at national poverty lines

erty rates across countries, they are appropriate for

along with well documented household survey data

guiding and monitoring the results of country-specific

sources and estimation methodology. Estimates

national poverty reduction strategies.

from national poverty monitoring programs and the

Almost all national poverty lines are anchored to

underlying methods used are periodically reviewed by

the cost of a food bundle—based on the prevailing

the World Bank and included in the table.

national diet of the poor—that provides adequate

The complete online database of poverty estimates

nutrition for good health and normal activity, plus

at national poverty lines (http://data. worldbank.org/

an allowance for nonfood spending. National pov-

topic/poverty) is regularly updated and may contain

erty lines must be adjusted for inflation between

more recent data or revisions not incorporated in

survey years to remain constant in real terms and

the table. In addition, the poverty and equity data

thus allow for meaningful comparisons of poverty

portal (http://povertydata.worldbank.org/poverty/

over time. Because diets and consumption baskets

home/) provides access to both the database and

change over time, countries periodically recalculate

user-friendly dashboards with graphs and interactive

the poverty line based on new survey data. In such

maps that visualize trends in key poverty and inequal-

cases the new poverty lines should be deflated to

ity indicators for different regions and countries. The

obtain comparable poverty estimates from earlier

Data on poverty rates at national poverty lines are

database is maintained by the Global Poverty Work-

years. The table reports indicators based on the two

compiled by the Global Poverty Working Group,

ing Group, a team of poverty experts from the Pov-

most recent years for which survey data are avail-

based on data from World Bank’s country poverty

erty Reduction and Equity Network, the Development

able. Countries for which the most recent indica-

assessments and analytical reports as well as

Research Group, and the Development Data Group.

tors reported are not comparable to those based

country Poverty Reduction Strategies and official

on survey data from an earlier year are footnoted

poverty estimates. Further documentation of

in the table.

the data, measurement methods and tools, and

Data quality

Data sources

Poverty estimates at national poverty lines are com-

research, as well as poverty assessments and

puted from household survey data collected from

analytical reports, are available at http://data.

nationally representative samples of households.

worldbank.org/topic/poverty, www.worldbank.org/

These data must contain sufficiently detailed infor-

poverty, and http://povertydata.worldbank.org/

mation to compute a comprehensive estimate of

poverty/home/.

total household income or consumption (including

68

2012 World Development Indicators


International poverty linea

International poverty line in local currency

Albania Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Czech Republic Côte d’Ivoire Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Fiji Gabon Gambia, The Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Jamaica

$1.25 a day

$2 a day

2005

2005

75.5 48.4 c 88.1 1.7 245.2 2,170.9 31.9 949.5 1.8 c 344.0 23.1 3.2 1.1 4.2 2.0 0.9 303.0 558.8 2,019.1 368.1 97.7 384.3 409.5 484.2 5.1g 1,489.7 368.0 395.3 469.5 348.7c 5.6 19.0 407.3 134.8 25.5c 0.6 2.5 6.0 c 11.0 3.4 1.9 554.7 12.9 1.0 5,594.8 5.7c 1,849.5 355.3 131.5c 24.2c 12.1c 171.9 19.5i 5,241.0i 3,393.5 799.8 54.2c

120.8 77.5c 141.0 2.7 392.4 3,473.5 51.0 1,519.2 2.9c 550.4 36.9 5.1 1.7 6.8 3.1 1.5 484.8 894.1 3,230.6 589.0 156.3 614.9 655.1 774.7 8.2g 2,383.5 588.8 632.5 751.1 557.9c 8.9 30.4 651.6 215.6 40.8 c 1.0 4.0 9.6c 17.7 5.5 3.1 887.5 20.7 1.6 8,951.6 9.1c 2,959.1 568.6 210.3c 38.7c 19.3c 275.0 31.2i 8,385.7i 5,429.6 1,279.7 86.7c

2.8

PEOPLE

Poverty rates at international poverty lines

Survey year b

2005 1988 2009d,e 2007 2001 2005 2007 1998f 2003 2007e 2004 1986 2008f 2003 2003 1998 2007 2001 2003 2006f 2005h 2009 f 2008f 2004 1993e 2002 2009 f 2009 f 2005 2008f 2003 2000 2003 1998 2007 1998 2004f 2003 1993 1993e 2008f 2004 2005h 2009h 1998 2002

Population below $1.25 a day %

<2 7.6 .. 2.0 3.5 6.3 50.5 <2 11.3 .. 26.2 13.1 <2 35.6 6.0 <2 56.5 86.4 32.2 10.8 .. 62.4 .. <2 16.3 9.7 .. .. .. 2.4 <2 <2 23.3 .. 3.0 6.4 2.0 5.4 <2 55.6 29.2 .. 65.6 15.2 39.1 24.4 56.3 52.1 6.9 .. 21.4 <2 41.6 20.4 <2 .. <2

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

<0.5 1.2 .. 1.2 0.7 1.1 14.2 <0.5 4.7 .. 7.0 6.6 <0.5 13.8 3.4 <0.5 20.3 47.3 7.7 2.3 .. 28.3 .. 0.5 4.0 4.7 .. .. .. 1.5 <0.5 <0.5 6.8 .. 0.7 2.9 <0.5 1.9 <0.5 16.2 11.3 .. 33.8 4.1 14.4 13.2 21.3 20.6 1.5 .. 11.8 <0.5 10.5 4.1 <0.5 .. <0.5

7.9 24.6 .. 3.4 20.5 27.1 80.3 <2 26.3 .. 49.5 24.7 <2 54.7 11.3 <2 81.2 95.4 60.1 32.5 .. 81.9 .. 3.2 36.9 18.5 .. .. .. 5.0 <2 <2 46.8 .. 10.0 13.5 18.5 14.0 2.6 86.4 48.7 .. 81.2 34.9 63.3 39.2 80.8 75.7 17.1 .. 32.6 <2 75.6 52.7 8.3 .. 8.5

1.5 6.7 .. 1.7 4.5 6.8 34.3 <0.5 10.0 .. 18.8 10.9 <0.5 25.8 5.3 <0.5 39.3 64.1 22.6 9.5 .. 45.3 .. 1.1 12.5 8.2 .. .. .. 2.3 <0.5 <0.5 17.6 .. 2.7 5.5 3.5 4.8 <0.5 37.9 21.8 .. 49.1 11.8 28.5 20.2 39.7 37.4 5.4 .. 17.5 <0.5 29.5 16.5 1.8 .. 1.5

Survey year b

2008 1995 2000 d 2010d,e 2008 2008 2010 2008 1999 f 2003 2007 2008e 2007 1994 2009 f 2007 2009 2006 2008 2007 2002 2008 2003 2009 f 2008h 2010 f 2004 2006 2005 2009 f 2008 1996e 2008 2002 2010 f 2010 f 2008 2009 f 2004 2005 2009 2005 2003 2008 2006 2006f 2007 2002 1998e 2001e 2009 f 2007 2010h 2010h 2005 2007 2004

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

<2 6.8 54.3 <2 <2 <2 43.3 <2 12.2 47.3 10.2 15.6 <2 31.2 6.1 <2 44.6 81.3 22.8 9.6 21.0 62.8 61.9 <2 13.1 8.2 46.1 87.7 54.1 3.1 <2 <2 23.8 18.8 2.2 4.6 <2 9.0 <2 39.0 5.9 4.8 33.6 15.3 28.6 13.5 43.3 48.9 8.7 61.7 17.9 <2 32.7 18.1 <2 2.8 <2

<0.5 1.4 29.9 0.7 <0.5 <0.5 11.2 <0.5 5.5 15.7 1.8 8.6 <0.5 11.0 3.6 <0.5 14.7 36.4 4.9 1.2 6.1 31.3 25.6 0.7 3.2 3.8 20.8 52.8 22.8 1.8 <0.5 <0.5 7.5 5.3 0.5 2.1 <0.5 4.4 <0.5 9.6 1.1 0.9 11.7 4.6 9.9 4.7 15.0 16.6 2.8 32.3 9.4 <0.5 7.5 3.3 <0.5 <0.5 <0.5

4.3 23.6 70.2 <2 12.4 2.8 76.5 <2 22.0 75.3 29.8 24.9 <2 49.4 10.8 <2 72.6 93.5 53.3 30.4 40.9 80.1 83.3 2.7 29.8 15.8 65.0 95.2 74.4 6.0 <2 <2 46.3 41.2 9.9 10.6 15.4 16.9 <2 77.6 22.9 19.6 55.9 32.2 51.8 26.3 69.6 78.0 18.0 77.5 29.8 <2 68.7 46.1 8.0 21.4 5.4

0.9 6.5 42.4 0.9 2.3 0.6 30.4 <0.5 9.9 33.5 8.5 13.1 <0.5 22.3 5.4 <0.5 31.7 56.1 17.4 8.2 15.2 46.8 43.9 1.2 10.1 6.8 34.2 67.6 38.8 2.7 <0.5 <0.5 17.8 14.6 2.4 4.1 2.8 7.6 0.5 28.9 6.0 5.0 24.4 11.7 21.3 10.5 31.0 34.9 6.7 46.7 14.9 <0.5 24.5 14.3 1.8 4.4 0.8

2012 World Development Indicators

69


2.8

Poverty rates at international poverty lines International poverty linea

International poverty line in local currency

Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Latvia Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mexico Micronesia, Fed. Sts. Moldova Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Romania Russian Federation Rwanda São Tomé and Príncipe Senegal Serbia Seychelles Sierra Leone Slovak Republic Slovenia South Africa Sri Lanka St. Lucia Sudan Suriname Swaziland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago

70

$1.25 a day

$2 a day

2005

2005

0.6 81.2 40.9 16.2 4,677.0 0.4 4.3 0.6 2.1 29.5 945.5 71.2 2.6 362.1 157.1 9.6 0.8 c 6.0 0.6 6.9 14,532.1 6.3 33.1 9.1c 334.2 98.2 25.9 0.8c 2.1c 2,659.7 2.1 30.2 2.7 2.1 16.7 295.9 7,953.9 372.8 42.9 5.6c 1,745.3 23.5 198.2 5.7 50.0 2.4 c 154.4 2.3c 4.7 30.8 1.2 603.1 21.8 0.6c 352.8 5.8 c

1.0 129.9 65.4 26.0 7,483.2 0.7 6.9 1.0 3.3 47.2 1,512.8 113.8 4.2 579.4 251.3 15.3 1.3c 9.7 1.0 11.0 23,251.4 10.1 52.9 14.6c 534.7 157.2 41.4 1.2c 3.4 c 4,255.6 3.3 48.4 4.3 3.4 26.8 473.5 12,726.3 596.5 68.6 9.0 c 2,792.4 37.7 317.2 9.1 80.1 3.8 c 247.0 3.7c 7.5 49.3 1.9 964.9 34.9 1.0 c 564.5 9.2c

2012 World Development Indicators

Survey year b

2008 2008 1997 2008 2002 2007 1994 2004 2008 2005 1998 2007e 2006 2004 2006 2009 2007 2001 2003 1993 2003 2001e 2005 2004 2006 2009 f 2009 f 2009 f 2006 2008 2008 2008 2006 2001 2008 2000 1990 2008e 2003 2006 2002 2001 2007 2000 2008j 2001 1988e

Population below $1.25 a day %

<2 <2 19.6 6.4 44.0 <2 46.2 .. <2 <2 67.8 83.1 <2 51.4 25.4 <2 .. <2 <2 6.3 74.7 49.1 53.1 14.4 50.2 63.1 22.6 5.9 .. 7.6 5.5 22.6 <2 <2 <2 72.1 .. 44.2 <2 <2 62.8 <2 <2 17.4 14.0 .. .. .. 62.9 .. 14.7 84.6 <2 52.9 .. <2

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

<0.5 <0.5 4.6 1.5 12.1 <0.5 25.6 .. <0.5 <0.5 26.5 46.0 <0.5 18.8 7.0 <0.5 .. <0.5 <0.5 0.9 35.4 24.6 18.4 3.7 18.3 28.7 4.1 1.8 .. 3.2 1.6 5.5 <0.5 <0.5 <0.5 34.8 .. 14.3 <0.5 <0.5 44.8 <0.5 <0.5 3.3 2.6 .. .. .. 29.4 .. 4.4 41.6 <0.5 19.1 .. <0.5

2.1 <2 42.7 20.7 76.9 <2 59.7 .. <2 4.3 89.6 93.5 2.9 77.1 52.6 4.9 .. 7.1 <2 24.3 90.0 62.2 77.3 34.4 75.3 83.1 61.0 14.6 .. 14.2 14.0 45.0 <2 2.0 <2 87.4 .. 71.3 <2 <2 75.0 <2 <2 35.7 39.7 .. .. .. 81.0 .. 37.0 95.3 5.0 77.5 .. 8.6

<0.5 <0.5 14.7 5.9 31.1 <0.5 36.1 .. 0.5 0.7 46.9 62.3 <0.5 36.5 19.2 1.0 .. 1.2 <0.5 6.3 53.6 36.5 36.6 11.5 35.6 45.9 18.8 4.9 .. 6.0 4.6 16.4 <0.5 0.6 <0.5 52.2 .. 31.2 <0.5 <0.5 54.0 <0.5 <0.5 12.3 11.9 .. .. .. 45.8 .. 12.2 60.3 0.8 37.1 .. 1.9

Survey year b

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

2010 2009 2005 2009 2008 2008 2003 2007 2008 2009 2010 2004 2009e 2010 2008 2008 2000 2010 2008 2007 2008 2004 e 2010 2005e 2008 2010 2008 2010 f 1996 2010 f 2010 f 2009 2009 2009 2009 2011 2001 2005 2009 2007 2003 2009e 2004 2009 2007 1995e 2009 1999e 2010 2004 2009 2007 2009j 2007 2006 1992e

<2 <2 43.4 6.2 33.9 <2 43.4 83.8 <2 <2 81.3 73.9 <2 50.4 23.4 <2 31.2 <2 <2 2.5 59.6 31.9 24.8 11.9 43.6 68.0 21.0 6.6 35.8 7.2 4.9 18.4 <2 <2 <2 63.2 28.2 33.5 <2 <2 53.4 <2 <2 13.8 7.0 20.9 19.8 15.5 40.6 <2 6.6 67.9 <2 37.4 38.7 4.2

<0.5 <0.5 16.9 1.4 9.0 <0.5 20.8 40.9 <0.5 <0.5 43.3 32.3 <0.5 16.4 6.8 <0.5 16.3 <0.5 <0.5 0.5 25.1 9.5 5.6 2.4 12.4 33.7 3.5 2.1 12.3 3.0 1.3 3.7 <0.5 <0.5 <0.5 26.6 7.9 10.8 <0.5 <0.5 20.3 <0.5 <0.5 2.3 1.0 7.2 5.5 5.9 16.0 <0.5 1.2 28.1 <0.5 8.9 11.4 1.1

<2 <2 67.2 21.7 66.0 <2 62.3 94.9 <2 5.9 92.6 90.5 2.3 78.7 47.7 5.2 44.7 4.4 <2 14.0 81.8 51.1 57.3 31.7 75.2 84.5 60.2 13.8 57.4 13.2 12.7 41.5 <2 <2 <2 82.4 54.2 60.4 <2 <2 76.1 <2 <2 31.3 29.1 40.6 44.1 27.2 60.4 16.9 27.7 87.9 4.6 72.8 69.3 13.5

<0.5 <0.5 31.8 6.0 24.8 <0.5 33.1 59.6 <0.5 0.9 60.1 51.8 <0.5 35.2 17.7 1.3 24.5 0.7 <0.5 3.2 42.9 21.8 19.0 9.6 30.8 50.2 17.9 5.1 25.5 5.7 4.1 13.8 <0.5 0.5 <0.5 44.6 20.6 24.7 <0.5 <0.5 37.5 <0.5 <0.5 10.2 7.4 15.5 15.4 11.7 29.3 3.3 7.0 47.5 0.8 27.0 27.9 3.9


International poverty linea

International poverty line in local currency

Tunisia Turkey Turkmenistan Uganda Ukraine Uruguay Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia

$1.25 a day

$2 a day

2005

2005

0.9 1.3 5,961.1c 930.8 2.1 19.1 1,563.9 7,399.9 2.7c 113.8 3,537.9

1.4 2.0 9,537.7c 1,489.2 3.4 30.6 2,502.2 11,839.8 4.3c 182.1 5,660.7

PEOPLE

2.8

Poverty rates at international poverty lines

Survey year b

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

2000 2007 1993e 2006 2008 2009 f 2005f 2006 2007 1998 2004

2.6 <2 63.5 51.5 <2 <2 13.4 21.4 <2 12.9 64.3

0.5 <0.5 25.8 19.1 <0.5 <0.5 8.2 5.3 <0.5 3.0 32.8

12.8 4.5 85.7 75.6 <2 <2 21.9 48.1 2.5 36.4 81.5

3.0 1.2 44.9 36.4 <0.5 <0.5 11.6 16.3 0.5 11.1 48.3

Survey year b

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

2005 2008 1998 2009 2009 2010 f 2006f 2008 2009 2005 2006

<2 <2 24.8 38.0 <2 <2 6.6 16.9 <2 17.5 68.5

<0.5 <0.5 7.0 12.2 <0.5 <0.5 3.7 3.8 <0.5 4.2 37.0

8.1 4.2 49.7 64.7 <2 <2 12.9 43.4 <2 46.6 82.6

1.8 0.7 18.4 27.4 <0.5 <0.5 5.9 13.5 <0.5 14.8 51.8

a. Based on nominal per capita consumption averages and distributions estimated parametrically from grouped household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Covers urban areas only. e. Based on per capita income averages and distributions estimated parametrically from grouped household survey data. f. Estimated nonparametrically from nominal income per capita distributions based on unit-record household survey 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. j. Estimated nonparametrically from nominal consumption per capita distributions based on unit-record household survey data.

Regional poverty estimates and progress toward

declined from 77 percent in 1981 to 14 percent in

Most of the people who have escaped extreme

the Millennium Development Goals

2008 and the number of people living on less than

poverty remain very poor by the standards of middle-

Global poverty measured at the $1.25 a day pov-

$1.25 a day dropped more than 800 million (fi gure

income countries. The median poverty line for devel-

erty line has been decreasing since the 1980s. The

2.8b). Much of this decline was in China, where the

oping countries in 2005 was $2 a day. The poverty

share of population living on less than $1.25 a day

poverty rate fell from 84 percent to 13 percent,

rate for all developing countries measured at this line

fell almost 10 percentage points, to 43 percent,

leaving about 660 million fewer people poor. Over

fell from nearly 70 percent in 1981 to 43 percent in

in 1990 and then fell about 20 percentage points

the same period the poverty rate in South Asia

2008, but the number of people living on less than

between 1990 and 2008. The number of people liv-

fell from 61 percent to 36 percent (table 2.8c).

$2 a day has remained nearly constant at around

ing in extreme poverty fell from 1.9 billion in 1990

In contrast, the poverty rate fell only slightly in

2.5 billion. The largest decrease, in both number and

to about 1.3 billion in 2008 (figure 2.8a). This sub-

Sub-Saharan Africa—from less than 52 percent in

proportion, occurred in East Asia and Pacific, led by

stantial reduction in extreme poverty over the past

1981 to more than 59 percent in 1993 then down

China. By contrast in Sub-Saharan Africa and South

quarter century, however, disguises large regional

to 47.5 percent in 2008. But the number of people

Asia, particularly India, the number of people living

differences.

living below the poverty line has nearly doubled

on less than $2 a day increased. And globally the

over this period and started declining slightly only

number of people living on $1.25–$2 a day nearly

from 2005 onward.

doubled, to 1.2 billion (see figure 2.8a).

The greatest reduction in poverty occurred in East Asia and Pacifi c, where the poverty rate

While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2 a day has increased 2.8a

80

2.5

People living on $1.25–$2 a day, all developing regions

People living on less than $1.25 a day, other developing regions

40 South Asia

People living on less than $1.25 a day, East Asia & Pacific 20

0.5

Sub-Saharan Africa

60

1.5 1.0

2.8b

Share of population living on less than $1.25 a day, by region (percent)

People living in poverty (billions) 3.0

2.0

Poverty rates are falling in all developing regions

People living on less than $1.25 a day, South Asia

People living on less than $1.25 a day, Sub-Saharan Africa

0 1981

1984

1987

1990

Source: PovcalNet, World Bank.

1993

1996

1999

2002

2005

2008

Europe & Central Asia

Middle East & North Africa

Latin America & Caribbean

East Asia & Pacific

0 1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

Source: PovcalNet, World Bank.

2012 World Development Indicators

71


2.8

Poverty rates at international poverty lines 2.8c

Regional poverty estimates Region or country

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific

1,097

970

848

926

871

640

656

523

332

284

835

720

586

683

633

443

446

363

212

173

8

7

7

9

14

18

18

11

6

2

Latin America & Caribbean

43

53

49

53

53

54

60

63

48

37

Middle East & North Africa

16

15

15

13

12

12

14

12

10

9

568

574

593

617

632

631

619

640

598

571

China Europe & Central Asia

South Asia India Sub-Saharan Africa Total

429

427

443

448

462

463

473

484

466

445

205

239

257

290

330

349

376

390

395

386

1,938

1,858

1,768

1,909

1,910

1,704

1,743

1,639

1,389

1,289

Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific

77.2

65.0

54.1

56.2

50.7

35.9

35.6

27.6

17.1

14.3

84.0

69.4

54.0

60.2

53.7

36.4

35.6

28.4

16.3

13.1

1.9

1.6

1.5

1.9

2.9

3.9

3.8

2.3

1.3

0.5

Latin America & Caribbean

11.9

13.6

12.0

12.2

11.4

11.1

11.9

11.9

8.7

6.5

Middle East & North Africa

9.6

8.0

7.1

5.8

4.8

4.8

5.0

4.2

3.5

2.7

61.1

57.4

55.3

53.8

51.7

48.6

45.1

44.3

39.4

36.0

China Europe & Central Asia

South Asia

59.8

55.7

54.1

51.3

49.7

47.2

45.6

44.5

40.8

37.4

Sub-Saharan Africa

India

51.5

55.2

54.4

56.5

59.4

58.1

57.9

55.7

52.3

47.5

Total

52.2

47.1

42.3

43.1

41.0

34.8

34.1

30.8

25.1

22.4

People living on less than 2005 PPP $2 a day (millions) East Asia & Pacific

1,313

1,316

1,279

1,334

1,301

1,140

1,138

984

758

659

972

963

907

961

926

792

770

655

482

395

36

30

29

32

43

53

57

37

22

10

Latin America & Caribbean

87

104

92

98

100

102

111

118

92

71

Middle East & North Africa

52

51

54

53

53

57

60

57

53

44

China Europe & Central Asia

South Asia India Sub-Saharan Africa Total

811

855

906

959

1,010

1,047

1,069

1,120

1,113

1,125

621

651

689

722

760

788

818

848

857

862

288

324

350

389

434

466

503

533

559

562

2,585

2,680

2,710

2,864

2,941

2,865

2,937

2,848

2,596

2,471

Share of people living on less than 2005 PPP $2 a day (percent) East Asia & Pacific

92.4

88.3

81.6

81.0

75.8

64.0

61.7

51.9

39.0

33.2

97.8

92.9

83.7

84.6

78.6

65.1

61.4

51.2

36.9

29.8

8.3

6.7

6.3

6.9

9.2

11.2

12.1

7.9

4.6

2.2

Latin America & Caribbean

23.8

26.8

22.4

22.4

21.7

21.0

22.0

22.2

16.7

12.4

Middle East & North Africa

30.1

27.1

26.1

23.5

22.1

22.2

22.0

19.7

17.4

13.9

South Asia

87.2

85.6

84.5

83.6

82.7

80.7

77.8

77.4

73.4

70.9

86.6

84.9

84.1

82.6

81.9

80.2

78.9

77.9

75.0

72.4

Sub-Saharan Africa

72.2

74.7

74.3

76.0

78.1

77.5

77.4

76.1

74.1

69.2

Total

69.6

68.0

64.8

64.6

63.1

58.6

57.4

53.5

46.9

43.0

China Europe & Central Asia

India

Source: World Bank PovcalNet.

72

2012 World Development Indicators


2.8

PEOPLE

Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti-

Income is generally more difficult to measure accu-

1993 consumption PPP estimates produced by the

mates for developing countries for World Development

rately, and consumption comes closer to the notion

World Bank. International poverty lines were recently

Report 1990: Poverty (World Bank 1990) using house-

of living standards. And income can vary over time

revised using the new data on PPPs compiled in

hold survey data for 22 countries (Ravallion, Datt, and

even if living standards do not. But consumption data

the 2005 round of the International Comparison

van de Walle 1991). Since then there has been con-

are not always available: the latest estimates reported

Program, along with data from an expanded set of

siderable expansion in the number of countries that

here use consumption data for about two-thirds of

household income and expenditure surveys. The new

field household income and expenditure surveys. The

countries.

extreme poverty line is set at $1.25 a day in 2005

World Bank’s Development Research Group maintains

However, even similar surveys may not be strictly

PPP terms, which represents the mean of the poverty

a database that is updated annually as new survey

comparable because of differences in timing or in the

lines found in the poorest 15 countries ranked by per

data become available (and thus may contain more

quality and training of enumerators. Comparisons

capita consumption. The new poverty line maintains

recent data or revisions that are not incorporated

of countries at different levels of development also

the same standard for extreme poverty—the poverty

into the table) and conducts a major reassessment

pose a potential problem because of differences

line typical of the poorest countries in the world—but

of progress against poverty about every three years.

in the relative importance of the consumption of

updates it using the latest information on the cost of

PovcalNet (http://iresearch.worldbank.org/Povcal-

nonmarket goods. The local market value of all con-

living in developing countries.

Net/) is an interactive computational tool that allows

sumption in kind (including own production, particu-

users to replicate these internationally comparable

larly important in underdeveloped rural economies)

$1.25 and $2 a day global, regional, and country-level

should be included in total consumption expenditure

• International poverty line in local currency is

poverty estimates and to compute poverty measures

but may not be. Most survey data now include valu-

the international poverty lines of $1.25 and $2.00

for custom country groupings and for different pov-

ations for consumption or income from own produc-

a day in 2005 prices, converted to local currency

erty lines. The Poverty and Equity Data portal (http://

tion, but valuation methods vary.

using the PPP conversion factors estimated by the

Definitions

povertydata.worldbank.org/poverty/home/) provides

The statistics reported here are based on con-

International Comparison Program. • Survey year

access to the database and user-friendly dashboards

sumption data or, when unavailable, on income

is the year in which the underlying data were col-

with graphs and interactive maps that visualize trends

surveys. Analysis of some 20 countries for which

lected or, when the data collection period bridged two

in key poverty and inequality indicators for different

income and consumption expenditure data were

calendar years, the year in which most of the data

regions and countries. The country dashboards dis-

both available from the same surveys found income

were collected. • Population below $1.25 a day and

play trends in poverty measures based on the national

to yield a higher mean than consumption but also

population below $2 a day are the percentages of

poverty lines (see table 2.7) alongside the internation-

higher inequality. When poverty measures based on

the population living on less than $1.25 a day and

ally comparable estimates in the table, produced from

consumption and income were compared, the two

$2 a day at 2005 international prices. As a result of

and consistent with PovcalNet.

effects roughly cancelled each other out: there was

revisions in PPP exchange rates, poverty rates for

no significant statistical difference.

individual countries cannot be compared with poverty

Data availability

rates reported in earlier editions. • Poverty gap is

The World Bank’s internationally comparable pov-

International poverty lines

the mean shortfall from the poverty line (counting

erty monitoring database now draws on income or

International comparisons of poverty estimates

the nonpoor as having zero shortfall), expressed as a

detailed consumption data collected from interviews

entail both conceptual and practical problems. Coun-

percentage of the poverty line. This measure reflects

with 1.23 million randomly sampled households

tries have different definitions of poverty, and consis-

the depth of poverty as well as its incidence.

through more than 850 household surveys collected

tent comparisons across countries can be difficult.

by national statistical offices in nearly 130 coun-

Local poverty lines tend to have higher purchasing

tries. Despite progress in the last decade, the chal-

power in rich countries, where more generous stan-

lenges of measuring poverty remain. The timeliness,

dards are used, than in poor countries.

frequency, quality, and comparability of household

Poverty measures based on international poverty

surveys need to increase substantially, particularly in

lines attempt to hold the real value of the poverty line

the poorest countries. The availability and quality of

constant across countries, as is done when making

poverty monitoring data remains low in small states,

comparisons over time. Since World Development

countries with fragile situations, and low-income

Report 1990 the World Bank has aimed to apply a

countries and even some middle-income countries.

common standard in measuring extreme poverty,

The low frequency and lack of comparability of the

anchored to what poverty means in the world’s poor-

data available in some countries create uncertainty

est countries. The welfare of people living in different

over the magnitude of poverty reduction. The need to

countries can be measured on a common scale by

improve household survey programs for monitoring

adjusting for differences in the purchasing power of

poverty is clearly urgent. But institutional, political,

currencies. The commonly used $1 a day standard,

and financial obstacles continue to limit data collec-

measured in 1985 international prices and adjusted

tion, analysis, and public access.

to local currency using purchasing power parities (PPPs), was chosen for World Development Report

Data quality

1990 because it was typical of the poverty lines in

Besides the frequency and timeliness of survey data,

low-income countries at the time.

other data quality issues arise in measuring household

Early editions of World Development Indicators

living standards. The surveys ask detailed questions

used PPPs from the Penn World Tables to convert

on sources of income and how it was spent, which

values in local currency to equivalent purchasing

must be carefully recorded by trained personnel.

power measured in U.S dollars. Later editions used

Data sources The poverty measures are prepared by the World Bank’s Development Research Group. The international poverty lines are based on nationally representative primary household surveys by national statistical offices or by private agencies under the supervision of government or international agencies and obtained from government statistical offi ces and World Bank Group country departments. Detailed information on the methodology adopted by the Socio-Economic Database for Latin America and the Caribbean to process the income data used for countries in this region is available at http://sedlac.econo.unlp.edu.ar/eng/methodology.php. The World Bank Group has prepared an annual review of its poverty work since 1993. For details on data sources and methods used in deriving the World Bank’s latest estimates, see http://iresearch.worldbank.org/povcalnet. For further discussion of the results, see Ravallion, Chen, and Sangraula (2009) and Chen and Ravallion (2011).

2012 World Development Indicators

73


2.9

Distribution of income or consumption Survey year

Afghanistan Albania Algeria Angolac Argentinac Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Hong Kong SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Czech Republic Denmark Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Finland Fiji France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala

74

2008b 2008b 1995b 2000 b 2010 d 2008b 1994 d 2000 d 2008b 2010 b 2008b 2000 d 1999d 2003b 2007b 2007d 2007b 1994b 2009d 2007b 2009b 2006b 2008b 2007b 2000 d 2002b 2008b 2003b 2009d 2005d 1996d 2010 d 2004b 2006b 2005b 2009d 2008b 2008b 1996d 1997d 2002b 2010 d 2010 d 2008b 2009d 2004b 2005b 2000 d 2009b 1995d 2005b 2003b 2008b 2000 d 2006b 2000 d 2006d

2012 World Development Indicators

Gini index

27.8 34.5 35.3 58.6 44.5 30.9 35.2 29.1 33.7 32.1 27.2 33.0 53.1 38.6 38.1 56.3 36.2 61.0 54.7 28.2 39.8 33.3 37.9 38.9 32.6 50.5 56.3 39.8 52.1 42.5 43.4 55.9 64.3 44.4 47.3 50.7 41.5 33.7 25.8 24.7 40.0 47.2 49.3 30.8 48.3 36.0 29.8 26.9 42.8 32.7 41.5 47.3 41.3 28.3 42.8 34.3 55.9

Percentage share of income or consumptiona

Lowest 10%

Lowest 20%

Second 20%

Third 20%

Fourth 20%

Highest 20%

Highest 10%

4.1 3.5 2.9 0.6 1.5 3.7 2.0 3.3 3.4 4.0 3.8 3.4 0.9 3.0 2.8 0.5 2.7 1.3 0.8 3.3 2.9 4.1 3.3 2.9 2.6 1.9 1.2 2.6 1.5 1.8 2.0 0.9 0.9 2.3 2.1 1.2 2.2 3.3 4.3 2.6 2.4 1.8 1.4 4.0 1.0 2.7 4.1 4.0 2.6 2.8 2.6 2.0 2.0 3.2 2.0 2.5 1.1

9.4 8.1 7.0 2.0 4.4 8.8 5.9 8.6 8.0 8.9 9.2 8.5 3.3 7.0 6.6 2.1 6.7 3.1 2.9 8.5 6.7 9.0 7.5 6.7 7.2 4.5 3.4 6.3 4.3 5.0 5.3 3.0 2.6 5.5 5.0 3.9 5.6 8.1 10.2 8.3 6.0 4.7 4.3 9.2 3.7 6.8 9.3 9.6 6.2 7.2 6.2 4.8 5.3 8.5 5.2 6.7 3.1

13.6 12.1 11.6 5.7 9.3 12.8 12.0 13.3 12.1 12.4 13.8 13.0 8.6 10.8 10.8 6.8 11.3 5.8 7.1 13.7 10.6 11.9 11.0 10.5 12.7 7.9 6.9 10.4 7.9 9.9 9.4 6.8 5.4 9.2 8.4 8.0 10.1 12.2 14.3 14.7 10.5 8.6 8.2 13.0 8.8 11.6 13.2 14.1 9.9 12.6 10.1 8.6 10.3 13.7 9.8 11.9 6.9

17.4 15.9 16.2 10.8 14.8 16.7 17.2 17.4 16.2 16.1 17.8 16.3 11.2 15.0 15.4 11.9 16.1 9.6 12.4 17.9 14.8 15.4 14.9 14.9 17.2 12.3 11.1 15.0 11.7 15.0 13.9 11.2 8.9 13.8 13.0 12.4 14.9 16.2 17.5 18.2 15.2 13.2 13.0 16.4 13.7 16.2 16.8 17.5 14.1 17.2 14.5 13.2 15.2 17.8 14.7 16.8 11.4

22.1 20.9 22.6 19.7 22.2 21.9 23.6 22.9 21.7 21.3 22.9 20.8 19.4 21.0 22.0 19.9 22.7 16.4 19.0 23.1 20.9 21.0 20.6 21.7 23.0 19.4 18.0 21.8 18.4 22.2 20.7 18.8 15.1 20.9 20.5 19.9 21.8 21.6 21.7 22.9 21.8 20.8 20.7 21.0 20.7 22.2 21.3 22.1 20.3 22.8 21.0 20.6 22.1 23.1 21.6 23.0 18.5

37.5 43.0 42.6 61.9 49.4 39.8 41.3 37.8 42.1 41.4 36.4 41.4 57.5 46.1 45.2 59.3 43.2 65.0 58.6 36.7 47.0 42.8 45.9 46.2 39.9 55.9 60.6 46.6 57.7 47.9 50.7 60.2 68.0 50.6 53.1 55.9 47.6 42.0 36.2 35.8 46.5 52.8 53.8 40.3 53.1 43.2 39.4 36.7 49.6 40.2 48.2 52.8 47.2 36.9 48.6 41.5 60.3

23.2 29.0 26.9 44.7 32.3 25.4 25.4 23.0 27.4 27.0 21.9 28.1 42.2 31.2 29.4 43.3 27.3 51.2 42.9 22.2 32.2 28.0 31.4 30.4 24.8 40.6 46.1 30.8 42.8 32.0 34.9 44.4 55.2 34.7 37.1 39.5 31.8 27.5 22.7 21.3 30.9 36.4 38.3 26.6 37.0 28.0 25.6 22.6 34.9 25.1 33.0 36.9 31.3 22.1 32.8 26.0 44.9


Survey year

Guinea Guinea-Bissau Guyana Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Rep. Kyrgyz Republic Lao PDR Latvia Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mexico Micronesia, Fed. Sts.c Moldova Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russian Federation

2007b 2002b 1998 d 2001d 2009d 2007b 2005b 2005b 2005b 2007b 2000 d 2001d 2000 d 2004b 1993d 2010 b 2009b 2005b 1998d 2009b 2008b 2008b 2003b 2007b 2008b 2009b 2010 b 2004b 2009d 2010 b 2008b 2010 d 2000 b 2010 b 2008b 2008b 2007b 2008b 2004 d 2010 b 1999d 1997d 2005b 2008b 2010 b 2000 d 2008b 2010d 1996b 2010 d 2010 d 2009b 2009 b 1997d 2007b 2009 b 2009b

Gini index

39.4 35.5 44.5 59.2 57.0 31.2 33.4 34.0 38.3 30.9 34.3 39.2 36.0 45.5 24.9 35.4 29.0 47.7 31.6 36.2 36.7 36.6 52.5 38.2 37.6 43.2 44.1 39.0 46.2 33.0 40.5 47.7 61.1 33.0 36.5 30.0 40.9 45.7 63.9 32.8 30.9 36.2 40.5 34.6 48.8 25.8 30.0 51.9 50.9 52.4 48.1 43.0 34.1 38.5 41.1 30.0 40.1

2.9

PEOPLE

Distribution of income or consumption Percentage share of income or consumptiona

Lowest 10%

Lowest 20%

Second 20%

Third 20%

Fourth 20%

Highest 20%

Highest 10%

2.7 3.1 1.3 0.7 0.4 3.5 3.8 3.7 2.6 3.8 2.9 2.1 2.3 2.3 4.8 3.4 4.0 2.0 2.9 2.8 3.3 2.6 1.0 2.4 2.6 2.0 2.2 3.0 1.8 3.5 2.4 1.4 0.4 3.3 3.0 3.6 2.7 1.9 1.4 3.6 2.5 2.2 2.6 3.6 1.8 3.9 4.4 1.1 1.9 1.0 1.4 2.6 3.3 2.0 1.3 3.4 2.8

6.4 7.3 4.5 2.4 2.0 8.4 8.6 8.3 6.4 8.7 7.4 5.7 6.5 5.4 10.6 7.7 9.1 4.8 7.9 6.8 7.6 6.6 3.0 6.4 6.6 5.1 5.4 7.0 4.5 8.0 6.0 4.4 1.6 7.8 7.1 8.5 6.5 5.2 3.2 8.3 7.6 6.4 6.2 8.1 4.4 9.6 9.6 3.3 4.5 3.3 3.9 6.0 7.7 5.8 3.9 8.3 6.5

10.5 11.6 9.8 6.3 6.1 12.9 12.2 12.0 10.9 12.8 12.3 10.5 12.0 9.0 14.2 11.6 13.2 8.7 13.6 11.4 11.3 11.4 7.2 11.4 11.1 9.5 9.5 10.8 8.7 12.0 10.4 8.9 5.2 12.2 11.2 13.1 10.5 9.5 5.0 12.2 13.2 11.4 10.2 11.8 8.3 14.0 12.9 7.8 7.7 7.8 8.3 9.4 12.0 11.0 .. 13.1 10.4

15.1 16.0 14.7 10.4 11.4 16.9 15.8 15.8 15.5 16.7 16.3 15.9 16.8 13.5 17.6 15.7 17.1 13.2 18.0 16.0 15.3 16.1 12.5 15.7 15.7 14.5 14.1 14.9 13.7 16.3 15.1 13.3 10.2 16.5 15.6 17.2 14.5 13.7 8.2 16.2 17.2 15.8 14.8 15.8 13.0 17.2 16.4 12.5 12.1 12.8 13.6 13.9 16.2 15.5 .. 17.4 14.8

21.9 21.9 21.6 17.6 20.5 22.0 21.0 21.0 22.0 22.0 21.9 23.0 22.8 20.6 22.0 21.5 22.3 20.1 23.1 22.4 20.9 22.3 21.0 21.6 22.1 22.0 20.9 20.8 21.6 22.4 21.5 20.4 19.1 22.3 22.1 22.4 20.6 20.1 15.0 21.9 23.3 22.6 21.5 21.3 20.3 22.0 21.1 20.1 19.3 19.8 21.5 21.0 22.0 21.9 .. 22.9 21.3

46.2 43.2 49.5 63.4 59.9 39.9 42.4 42.8 45.2 39.9 42.0 44.9 42.0 51.6 35.7 43.6 38.4 53.2 37.5 43.4 44.8 43.6 56.4 45.0 44.4 48.9 50.1 46.5 51.5 41.3 47.0 53.0 64.0 41.2 44.0 38.8 47.9 51.5 68.6 41.5 38.7 43.8 47.2 43.1 54.0 37.2 40.0 56.4 56.4 56.4 52.6 49.7 42.1 45.9 52.0 38.3 47.1

30.3 28.1 34.0 47.7 42.4 25.4 28.3 28.5 29.6 25.2 27.2 28.8 26.8 35.9 21.7 28.7 23.8 38.0 22.5 27.8 30.3 28.1 39.4 30.1 29.1 32.4 34.7 31.9 34.7 25.8 31.6 37.2 47.1 26.0 28.4 24.1 33.2 36.7 54.8 26.5 22.9 27.8 31.5 28.5 38.2 23.4 26.1 40.1 40.9 41.1 36.1 33.6 27.1 29.8 35.9 23.5 31.7

2012 World Development Indicators

75


2.9

Distribution of income or consumption Survey year

Rwanda São Tomé and Príncipe Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia South Africa South Sudan Spain Sri Lanka St. Lucia Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2011b 2001b 2005b 2009b 2007b 2003b 1998d 2009d 2004b 2009b 2009b 2000 d 2007b 1995d 2009b 1999d 2010 b 2000 d 2000 d 2004b 2009b 2007b 2009b 2007b 2006b 1992d 2005b 2008b 1998b 2009b 2009 b 1999d 2000d 2010 d 2003b 2006d 2008b 2009b 2005b 2006b 1995b

Gini index

50.8 50.8 39.2 27.8 65.8 42.5 42.5 26.0 31.2 63.1 45.5 34.7 40.3 42.6 35.3 52.9 51.5 25.0 33.7 35.8 30.8 37.6 40.0 31.9 34.4 40.3 41.4 39.0 40.8 44.3 26.4 36.0 40.8 45.3 36.7 44.8 35.6 35.5 37.7 54.6 50.1

Percentage share of income or consumptiona

Lowest 10%

Lowest 20%

Second 20%

Third 20%

Fourth 20%

Highest 20%

Highest 10%

2.1 2.2 2.5 3.7 1.6 2.6 1.9 4.4 3.4 1.2 .. 2.6 3.1 2.0 2.7 1.1 1.7 3.6 2.9 3.4 3.5 2.8 2.8 4.0 3.3 2.1 2.4 2.1 2.6 2.4 4.2 2.1 1.9 1.9 2.9 1.2 3.2 3.2 2.9 1.5 1.8

5.2 5.2 6.2 8.9 3.7 6.1 5.0 10.1 8.2 2.7 .. 7.0 6.9 5.2 6.8 3.2 4.1 9.1 7.6 7.7 8.3 6.8 6.7 9.0 7.6 5.5 5.9 5.7 6.1 5.8 9.7 6.1 5.4 4.9 7.1 4.3 7.4 7.4 7.2 3.6 4.6

8.3 8.5 10.6 13.7 5.7 9.7 9.4 14.1 12.8 4.6 .. 12.1 10.4 9.9 11.7 7.2 7.4 14.0 12.2 11.4 12.8 11.1 10.3 12.5 11.7 10.3 10.1 10.9 10.2 9.6 14.0 11.4 10.7 9.0 11.5 9.5 11.5 11.5 11.3 6.7 8.1

11.9 12.2 15.3 17.8 8.3 14.0 14.6 17.5 17.0 8.2 .. 16.4 14.4 14.8 16.4 12.3 12.0 17.6 16.3 15.5 17.0 15.6 14.5 16.1 16.1 15.5 14.7 15.9 14.7 13.8 17.7 16.0 15.7 13.7 15.7 14.6 15.8 15.8 15.3 11.2 12.2

17.8 17.7 22.0 22.8 12.7 20.9 22.0 22.0 22.6 16.3 .. 22.5 20.5 21.8 22.7 20.4 20.0 22.7 22.6 21.4 22.6 21.7 21.4 21.2 22.2 22.7 21.3 22.4 21.5 20.0 22.4 22.5 22.4 21.5 21.5 22.2 21.8 21.8 21.0 19.2 19.3

56.8 56.4 45.9 36.9 69.6 49.3 49.0 36.2 39.4 68.2 .. 42.0 47.8 48.3 42.4 56.9 56.6 36.6 41.3 43.9 39.4 44.8 47.2 41.3 42.4 45.9 47.9 45.1 47.5 50.7 36.3 44.0 45.8 50.9 44.2 49.4 43.4 43.4 45.3 59.4 55.7

43.2 43.6 30.1 22.2 60.2 33.6 32.8 22.4 24.6 51.7 .. 26.6 32.9 32.5 26.7 40.6 40.1 22.2 25.9 28.9 24.3 29.6 31.5 27.0 27.1 29.9 32.5 29.4 31.7 36.1 22.0 28.5 29.9 34.4 29.5 33.2 28.2 28.2 30.8 43.1 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.

76

2012 World Development Indicators


About the data

Inequality in the distribution of income is reflected

2.9

PEOPLE

Distribution of income or consumption Definitions

• Survey year is the year in which the underlying

in the percentage shares of income or consumption

data were collected. • Gini index measures the

accruing to portions of the population ranked by

extent to which the distribution of income (or con-

income or consumption levels. The portions ranked

sumption expenditure) among individuals or house-

lowest by personal income receive the smallest

holds within an economy deviates from a perfectly

shares of total income. The Gini index provides a con-

equal distribution. A Lorenz curve plots the cumula-

venient summary measure of the degree of inequal-

tive 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

hold survey were available, they have been used to

of absolute equality, expressed as a percentage of

directly calculate the income or consumption shares

the maximum area under the line. Thus a Gini index

by quintile. Otherwise, shares have been estimated

of 0 represents perfect equality, while an index of

from the best available grouped data.

100 implies perfect inequality. • Percentage share

The distribution data have been adjusted for

of income or consumption is the share of total

household size, providing a more consistent measure

income or consumption that accrues to subgroups of

of per capita income or consumption. No adjustment

population indicated by deciles or quintiles.

has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middleincome economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achieving strict comparability is still impossible (see About the data for tables 2.7 and 2.8). Two sources of noncomparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much

Data sources

better welfare indicator, particularly in developing

Data on distribution are compiled by the World

countries. Second, households differ in size (num-

Bank’s Development Research Group using

ber of members) and in the extent of income sharing

PovcalNet

among members. And individuals differ in age and

PovcalNet) based on household survey data

consumption needs. Differences among countries in

obtained from government statistical agencies

these respects may bias comparisons of distribution.

and World Bank country departments. Detailed

World Bank staff have made an effort to ensure

information on the methodology adopted by the

that the data are as comparable as possible. Wher-

Socio-Economic Database for Latin America and

ever possible, consumption has been used rather

the Caribbean to process the income data used

than income. Income distribution and Gini indexes for

for countries in this region is available at http://

high-income economies are calculated directly from

sedlac.econo.unlp.edu.ar/eng/methodology.php.

the Luxembourg Income Study database, using an

Data for high-income economies are computed

estimation method consistent with that applied for

based on data from the Luxembourg Income Study

developing countries.

database.

(http://iresearch.worldbank.org/

2012 World Development Indicators

77


2.10

Assessing vulnerability and security Youth unemployment

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

78

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2007–10a

2007–10a

2007–10a

Year

.. 16 .. 25 34 36 .. .. 25 .. 13 54 .. 23 23 .. .. .. .. .. .. 27 .. .. .. .. .. .. .. 34 21 23 .. .. 24 46 .. .. .. 35 .. 13 .. .. .. 23 .. .. .. .. .. .. 34 .. .. 17 .. 44

2006 2007 2002

.. 26 .. .. 19b 37 12b 9 13 .. .. .. 22 .. .. 45 .. 14 24 .. .. 4 .. 17b .. .. 17 .. 15 18 .. .. 10 .. 30 3 16 18 16 21 12b 17 13 .. 35 .. 22 22 .. .. 32 10 .. 27 .. .. .. ..

2012 World Development Indicators

.. 28 .. .. 25b 55 11b 9 16 .. .. .. 22 .. .. 52 .. 23 22 .. .. 3 .. 12b .. .. 22 .. 10 30 .. .. 13 .. 34 4 17 19 12 45 18b 48 8 .. 30 .. 19 23 .. .. 41 9 .. 41 .. .. .. ..

2008 2008 2005 2005 2007 2007 2004 2008 2005 2005 2008 2009 2006 2008 2008 2004 2006 2006 2007 2004 2005 2008 2007 2008 2008 2008 2008 2004 2004 2010

2007 2007 2008 2004 2009 2008 2004 2005 2005 2006 2004 2005 2004 2005 2008 1993 2004

Public expenditure on pensions

% of labor force

% of workingage population

3.7 37.9 36.7 .. 42.3 32.1 90.7 93.7 35.4 20.2 2.5 93.5 91.4 5.5 12.2 24.5 9.0 55.2 78.7 1.2 3.5 .. 16.2 66.9 1.5 2.7 59.6 26.9 78.9 31.5 14.2 9.7 55.5 12.8 82.9 .. .. 95.4 92.9 25.6 26.4 55.1 22.9 .. 94.5 .. 89.7 87.3 .. 2.7 29.2 86.9 8.1 86.0 20.3 12.1 2.0 ..

2.3 25.7 22.0 .. 33.1 24.6 69.7 68.5 24.7 13.4 1.9 66.8 61.5 4.2 9.4 29.0 7.2 45.7 54.7 1.1 3.3 .. 11.5 53.6 1.3 2.0 39.5 27.7 55.4 20.2 10.5 7.5 40.2 9.1 50.0 .. .. 67.5 86.7 19.1 18.0 27.9 15.7 .. 68.7 .. 67.4 61.4 .. 2.2 22.6 65.6 6.4 58.3 14.7 10.8 1.5 ..

Year

2005 2009 2002 2010 2006 2007 2007 2007 2004 2006 2008 2007 2006 2009 2009 2009 2010 2008 2004 2006 2005 2005 2007 2004 2009 2006 2006 2010

2009 2006 2009

2007 2007 2009 2010 2004 2010 2001 2007 2006 2007 2007 2003 2004 2007 2002 2010 2009 2005

% of GDP

0.5 6.1 3.2 .. 7.4 3.2 3.4 12.3 3.8 0.9 0.3 10.2 8.9 1.5 1.5 9.4 1.3 6.1 8.5 0.7 0.7 0.6 0.4 4.2 0.8 .. 5.0 2.5 1.6 3.5 .. .. 2.8 0.7 10.3 .. 8.5 5.6 0.7 1.8 4.1 1.7 0.3 10.9 0.3 8.3 12.5 .. 0.1 3.0 10.7 1.3 13.5 1.2 .. 2.1 ..

Year

2000 2007

2006

2002

2004

2006

2005

2005

2007

2003

Average pension % of average wage

.. .. .. .. 43.8 20.3 .. .. 24.3 .. .. 41.6 .. .. .. .. .. .. 42.9 .. .. .. .. .. .. .. 53.5 .. .. .. .. .. .. .. 32.4 .. .. 40.7 .. .. .. .. .. .. 35.4 .. .. .. .. .. 13.0 .. .. .. .. .. .. ..


Youth unemployment

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

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2007–10a

2007–10a

2007–10a

Year

26 .. 14 13 .. 11 .. .. .. 41 .. 11 .. 34 .. .. .. .. 25 .. .. .. .. 30 .. .. 8 22 .. .. 12 .. .. .. 34 29 .. .. .. 44 23 .. .. .. 19 19 .. .. 10 .. .. .. 22 17 .. .. .. ..

2008 2008 2006 2008 2001 2009 2005 2008 2005 2004 2005 2006 2004 2006

.. 28 .. 22 20 .. 34 15 27 23 10 23 7 .. .. 11 .. .. .. .. 35 22 29 4 .. 38 35 .. .. 10 .. .. 19 9 16 .. 23 .. .. .. .. 9 17b .. .. .. 11 .. 7 12 .. 9 13b 17 22 21 29 1

.. 25 .. 23 34 .. 21 13 29 33 8 46 8 .. .. 9 .. .. .. .. 34 22 42 8 .. 31 38 .. .. 12 .. .. 29 10 15 .. 19 .. .. .. .. 9 17b .. .. .. 8 .. 11 21 .. 17 16b 20 25 24 22 8

2005

2006 2004 2003 2003 2005 2004 2007 2008 2009 2008 2010 2000 2000 2008 2009 2005 2007 2006 2008 2008 2005 2008 2006 2004 2005 2008 2009 2004 2008 2007 2005 2005

2.10

Public expenditure on pensions

% of labor force

% of workingage population

17.3 92.0 10.3 7.1 34.2 35.6 88.9 .. 90.1 17.2 95.4 38.4 62.5 7.5 .. 49.5 .. .. 40.4 .. 91.7 34.5 4.4 .. 68.5 82.9 52.3 5.3 .. 49.0 7.3 13.1 53.4 27.4 56.7 33.5 23.8 1.9 .. 9.6 3.4 90.7 .. 21.7 1.9 8.1 93.2 .. 3.9 .. 4.4 12.4 21.7 25.0 81.4 92.0 .. ..

11.1 56.7 6.4 8.0 18.9 19.0 64.1 89.1 57.1 12.7 75.0 19.9 48.4 6.4 .. 34.3 .. .. 29.6 6.0 70.6 17.3 3.5 .. 37.5 56.9 33.2 4.9 .. 32.5 4.4 9.4 34.5 19.0 32.1 25.6 13.5 1.7 .. 5.8 2.6 70.7 .. 14.6 1.3 4.8 75.2 .. 2.2 .. 3.3 9.4 15.6 17.0 52.7 71.6 .. ..

Year

2010 2008 2007 2010 2000 2009 2007 2007 2007 2004 2007 2005 2009 2003 2005 2009 2010 2005 2009 2003

2001 2010 2008

2004 2010 2003 2007 2007 2009 2009 2003 2006 2004 2006 2007 2007 2006 2004 2007 2004 2005 2001 2010 2003 2009 2007

% of GDP

0.0 10.5 2.2 1.0 1.1 3.9 3.6 4.8 14.1 0.7 8.8 2.0 3.2 1.1 .. 1.6 2.7c .. 2.7 0.2 8.5 2.1 .. .. 2.1 8.6 9.4 .. .. 0.3 1.6 0.6 2.9 1.4 9.1 4.9 1.9 0.3 .. 1.3 0.2 4.7 4.3 .. 0.7 0.9 4.7 .. 0.5 .. 0.2 1.2 2.5 1.5 10.0 10.8 .. ..

Year

2005

2003

2003 2005

2005 2006

2003

2007

2012 World Development Indicators

Average pension % of average wage

.. 39.8 .. .. .. .. .. .. .. .. .. .. 24.9 .. .. .. .. .. 27.5 .. 33.1 .. .. .. .. 30.9 55.0 .. .. .. .. .. .. .. 20.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 47.1 .. .. ..

79

PEOPLE

Assessing vulnerability and security


2.10

Assessing vulnerability and security Youth unemployment

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

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2007–10a

2007–10a

2007–10a

Year

.. .. 31 .. 20 29 22 .. .. .. .. .. .. .. .. 19 48 .. .. .. .. 24 30 12 .. .. .. .. .. 30 49 .. .. .. .. 18 .. .. .. .. 24 38

2007 2007 2004

22 17 .. 24 .. 31 .. 10 35 15 .. 45 .. 43 17 .. .. 27 7 15 .. .. 4 .. .. 9 .. 21 .. .. .. 8 21 21b 16 .. 12 .. 39 .. .. .. .. w .. .. .. .. .. .. 17 12 19 .. 19 22

22 18 .. 46 .. 41 .. 17 32 14 .. 53 .. 40 28 .. .. 24 8 40 .. .. 5 .. .. 13 .. 23 .. .. .. 22 17 16b 25 .. 16 .. 47 .. .. .. .. w .. .. .. .. .. .. 18 18 36 .. .. 17 22

2003 2003 2004 2008 2003 2008 2007 2005 2006 2005 2009 2005 2005 2008 2006 2008 2003 2008 2004 2007 2004 2010 2005 2005 2007 2005 2008 2008 2009 2006 2006

Public expenditure on pensions

% of labor force

% of workingage population

67.9 66.8 4.6 .. 5.1 45.0 5.5 62.1 78.9 87.4 .. 6.3 .. 69.4 24.1 5.2 15.4 88.8 95.4 26.8 .. 4.3 22.8 .. 7.3 71.1 48.6 58.6 .. 10.3 65.3 .. 93.2 92.2 78.5 .. 33.9 19.3 14.0 10.4 10.9 ..

45.0 49.9 4.1 .. 4.1 40.2 3.8 44.6 55.3 63.8 .. 3.7 .. 48.7 14.9 2.9 10.3 72.2 78.7 14.3 .. 4.0 18.3 .. 5.7 51.2 25.5 30.5 .. 9.2 44.7 .. 71.5 71.4 61.2 86.3 24.2 15.2 6.5 5.0 8.0 ..

a. Data are for the most recent year available. b. Limited coverage. c. Includes expenditure on old-age and survivors benefi ts only.

80

2012 World Development Indicators

Year

2009 2010 2005 2006 2010

2007 2007 2010 2007 2007

2007 2007 2004 2006 2006 2003 2010 2003 2008 2003 2010 2007 2007 2010 2005 2010 2004 2009 2004 2008 2002

% of GDP

8.3 8.3 0.7 .. 1.4 14.0 .. .. 9.3 12.7 .. 2.2 .. 8.0 2.0 .. .. 7.2 6.4 1.3 .. 0.9 0.8 .. 0.8 2.8 4.3 6.2 .. 0.3 17.8 .. 5.4 6.0 8.8 6.5 5.0 2.5 4.0 1.5 1.4 2.3

Year

2005 2003

2005 2005

2006

2000 2003

2007

2007

2006 2005

Average pension % of average wage

41.5 29.2 .. .. .. .. .. .. 44.7 44.3 .. .. .. 58.6 .. .. .. .. 40.0 .. 25.7 .. .. .. .. .. .. 61.3 .. .. 48.3 .. .. 29.2 .. 40.0 .. .. .. .. .. ..


About the data

2.10

Definitions

As traditionally measured, poverty is a static con-

citizenship, residency, or income status. In contri-

• Youth unemployment is the share of the labor

cept, and vulnerability a dynamic one. Vulnerabil-

bution-related schemes, however, eligibility is usually

force ages 15–24 without work but available for and

ity reflects a household’s resilience in the face of

restricted to individuals who have contributed for a

seeking employment. • Female-headed households

shocks and the likelihood that a shock will lead to a

minimum number of years. Definitional issues—relat-

are households with a female head. • Pension con-

decline in well-being. Thus, it depends primarily on

ing to the labor force, for example—may arise in

tributors are members of the labor force or working-

the household’s assets and insurance mechanisms.

comparing coverage by contribution-related schemes

age population (here defined as ages 15 and older)

Because poor people have fewer assets and less

over time and across countries (for country-specific

covered by a pension scheme. • Public expenditure

diversified sources of income than do the better-off,

information, see Hinz and others 2011). The share

on pensions is all government expenditures on cash

fluctuations in income affect them more.

of the labor force covered by a pension scheme may

transfers to the elderly, the disabled, and survivors

be overstated in countries that do not try to count

and the administrative costs of these programs.

informal sector workers as part of the labor force.

• Average pension is the average pension payment

Enhancing security for poor people means reducing their vulnerability to such risks as ill health, providing them the means to manage risk themselves,

Public interventions and institutions can provide

of all pensioners of the main pension schemes

and strengthening market or public institutions for

services directly to poor people, although whether

(including old-age, survivors, disability, military, and

managing risk. Tools include microfinance programs,

these interventions and institutions work well for the

work accident or disease pensions) divided by the

public provision of education and basic health care,

poor is debated. State action is often ineffective,

average wage of all formal sector workers.

and old age assistance (see tables 2.11 and 2.16).

in part because governments can influence only a

Poor households face many risks, and vulnerability

few of the many sources of well-being and in part

is thus multidimensional. The indicators in the table

because of difficulties in delivering goods and ser-

focus on individual risks—youth unemployment,

vices. The effectiveness of public provision is further

female-headed households, income insecurity in

constrained by the fiscal resources at governments’

old age—and the extent to which publicly provided

disposal and the fact that state institutions may not

services may be capable of mitigating some of these

be responsive to the needs of poor people.

risks. Poor people face labor market risks, often hav-

The data on public pension spending cover the

ing to take up precarious, low-quality jobs and to

pension programs of the social insurance schemes

increase their household’s labor market participa-

for which contributions had previously been made.

tion by sending their children to work (see tables

In many cases noncontributory pensions or social

2.4 and 2.6). Income security is a prime concern

assistance targeted to the elderly and disabled are

for the elderly.

also included. A country’s pattern of spending is cor-

Youth unemployment is an important policy issue for many economies. Experiencing unemployment

related with its demographic structure—spending increases as the population ages.

may permanently impair a young person’s productive potential and future employment opportunities. The table presents unemployment among youth ages 15–24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could differ across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limitations on comparability as the general unemployment rate. For further information, see About the data for table 2.5 and the original source.

Data sources

The definition of female-headed household differs

Data on youth unemployment are from the ILO’s

greatly across countries, making cross-country com-

Key Indicators of the Labour Market, 7th edition,

parison difficult. In some cases it is assumed that a

database. Data on female-headed households

woman cannot be the head of any household with an

are from MEASURE DHS Demographic and Health

adult male, because of sex-biased stereotype. Cau-

Surveys by ICF International. Data on pension con-

tion should be used in interpreting the data.

tributors and public expenditure on pensions are

Pension scheme coverage may be broad or

from Hinz and others (2011).

even universal where eligibility is determined by

2012 World Development Indicators

81

PEOPLE

Assessing vulnerability and security


2.11

Education inputs Public expenditure per student

1999

2010a

% of GDP per capita Secondary 1999 2010a

.. .. 12.0 .. 12.9 .. 16.4 24.9 6.9 .. .. .. 18.2 11.8 14.2 .. .. 10.8 15.2 .. 14.4 5.8 .. .. .. .. 14.4 .. 12.4 15.2 .. .. 15.5 14.3 .. 25.0 17.1 11.2 24.6 .. 4.4 .. 8.6 15.0 20.9 .. 17.4 17.9 .. .. .. .. 17.6 11.7 6.7 .. .. ..

.. .. .. .. 16.8 16.5 20.2 24.1 .. .. 8.8 .. 22.4 13.0 .. .. .. 18.5 24.4 .. 19.0 6.8 6.6 .. 4.4 9.6 17.4 .. 15.1 15.7 .. 11.1 14.6 .. 21.8 44.2 28.7 13.6 24.9 7.5 .. .. 8.8 .. 25.9 18.2 18.5 18.0 .. 24.6 14.8 15.6 11.4 .. 10.4 7.0 .. ..

.. .. .. .. 18.2 .. 15.0 30.0 17.0 .. 11.5 .. 23.7 24.0 11.7 .. .. 9.5 18.4 .. 67.8 11.5 .. .. .. .. 14.8 11.5 17.7 16.1 .. .. 21.4 41.3 .. 37.1 28.2 21.7 38.1 .. 9.6 .. 7.5 37.4 27.2 .. 25.9 29.5 .. .. .. .. 40.4 15.6 4.3 .. .. ..

Primary

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

82

2012 World Development Indicators

.. .. .. .. 27.1 17.8 18.8 27.4 .. .. 12.0 .. 36.5 .. .. .. .. 19.5 24.2 .. 64.1 6.8 28.0 .. 14.5 19.1 17.7 .. 18.0 15.3 .. .. 14.4 .. 25.2 51.9 38.3 22.8 31.5 6.7 .. .. 9.4 .. 29.6 9.8 32.2 27.8 .. 16.2 15.5 21.8 27.2 .. 6.2 6.1 .. ..

Public expenditure on education

Tertiary 1999

.. .. .. .. 17.7 .. 26.6 51.8 19.1 .. 46.6 .. 38.2 208.2 44.1 .. .. 57.2 17.6 .. 1,036.1 42.5 .. 44.0 .. .. 19.4 90.0 .. 37.7 .. .. .. 141.0 35.8 77.6 47.9 33.7 65.9 .. .. .. 8.9 430.8 31.8 .. 40.5 30.7 .. .. .. .. .. 26.5 .. .. .. ..

2010a

.. .. .. .. 19.1 7.5 20.7 43.5 22.3 .. 27.7 14.7 36.6 .. .. .. .. 27.6 24.8 .. 477.4 .. 28.0 .. 96.0 279.1 13.7 .. 26.8 29.4 .. 134.2 .. .. 29.2 61.1 57.0 25.7 52.1 .. .. .. 11.7 .. 22.1 31.0 32.5 37.0 .. 94.4 11.4 .. 171.7 .. .. 100.0 .. ..

% of GDP 2010a

.. .. 4.3 .. 6.0 3.2 5.1 5.5 3.2 2.9 2.2 4.5 6.4 4.5 .. .. 7.8 5.4 4.4 .. 9.2 2.6 3.5 4.8 1.2 2.8 4.5 .. 3.6 4.8 .. 6.2 6.3 4.6 4.3 13.4 7.4 4.1 7.7 .. .. 3.8 3.2 .. 5.7 4.7 6.1 5.6 .. 5.0 3.2 4.6 5.5 .. 3.2 2.4 .. ..

Trained Primary teachers school in primary pupil–teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2010a

2010a

2010a

.. .. 20.3 .. 14.0 13.0 12.9 11.2 10.9 11.7 14.1 8.9 12.9 18.2 .. .. 16.2 .. 12.3 .. 25.1 .. 17.9 .. 12.0 10.1 .. .. 20.2 14.9 .. .. 23.1 24.6 .. 18.3 17.4 9.5 15.0 .. .. 11.9 .. .. 14.2 25.4 12.4 10.6 .. 22.8 7.7 10.4 24.4 .. .. 19.2 .. ..

.. .. .. .. .. .. .. .. 100.0 .. 58.4 99.8 .. 42.6 .. .. .. .. .. 85.7b 91.2 99.1 57.1 .. .. 45.3 .. .. 95.6 100.0 91.7 86.8 89.5 100.0 b .. 100.0 .. .. .. 84.9 82.6 .. 92.7 93.8 .. 39.4 .. .. .. .. 94.6 .. 50.6b .. .. 65.2 38.9 ..

44 20 23 46 16 .. .. 11 11 .. 43 15 11 46 .. .. .. 23 17 48b 51 48 46 .. 84 56 23 17 15 28 37 49 18 49b 15 9 14 19 .. 26 17 27 31 38 12 54 14 19 25b 37 8 13 31b .. 28 42 52 ..


Public expenditure per student

1999

2010a

% of GDP per capita Secondary 1999 2010a

.. 17.9 11.9 .. 9.3 .. 11.0 20.5 24.0 13.4 21.1 13.7 .. 21.4 .. 18.4 .. 17.0 .. 2.2 19.5 .. 37.0 .. .. .. .. 5.7 13.4 12.6 15.3 11.6 9.3 11.9 .. .. 17.4 .. .. 22.2 9.1 15.2 20.0 .. .. .. 21.8 10.7 .. 13.7 .. 13.6 7.6 12.0 .. 18.8 .. ..

18.7 21.9 .. 11.4 15.2 .. 18.6 19.5 24.5 19.9 21.5 11.9 .. .. .. 19.4 .. 10.0 .. .. 31.4 .. 24.8 .. .. 18.1 .. 7.8 6.8 14.6 15.0 13.4 9.0 13.7 41.4 14.6 16.9 .. .. 17.8 17.8 17.2 21.9 .. 21.1 .. 18.3 12.8 .. 7.5 .. .. 7.8 9.0 25.3 19.9 .. 10.3

.. 19.0 24.7 .. 10.1 .. 16.8 21.9 27.7 21.0 20.9 15.8 .. 14.4 .. 15.7 .. .. .. 4.4 23.7 .. 82.4 .. .. .. .. .. 9.6 21.9 59.7 36.4 14.2 14.5 .. .. 45.5 .. 6.6 36.3 13.1 22.2 23.7 .. .. .. 30.4 20.9 .. 19.1 .. 18.4 10.8 10.2 10.9 26.6 .. ..

Primary

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

279.7 23.0 .. 12.9 21.1 .. 27.5 20.4 26.7 22.6 22.3 14.3 .. .. .. 23.2 .. 13.7 .. .. 32.3 .. 55.7 .. .. 22.6 .. 11.5 23.3 20.2 37.5 31.2 14.3 13.6 39.4 .. .. .. .. 16.4 11.3 25.0 23.6 .. 41.9 .. 25.6 14.6 .. 9.9 .. .. 8.9 9.1 22.9 31.6 .. 11.0

Public expenditure on education

Tertiary 1999

.. 34.1 95.0 .. 35.5 .. 28.6 30.9 27.6 70.4 15.1 .. .. 207.9 .. 8.4 .. .. 24.3 67.7 27.9 13.8 939.7 .. 23.4 34.2 .. .. 2,503.9 81.6 256.8 80.1 25.4 48.8 .. .. 97.1 1,408.9 27.0 157.3 141.3 47.4 39.5 .. .. .. 45.8 .. .. 33.7 .. 58.9 21.1 14.4 21.1 27.1 .. ..

2010a

.. 24.8 .. 16.8 19.2 .. 32.7 21.3 25.0 50.2 20.9 .. 10.1 .. .. 10.1 .. .. 17.6 .. 14.2 10.0 .. .. .. 17.0 .. 144.8 1,937.6 60.7 135.3 193.9 16.1 38.9 44.8 6.0 83.3 .. .. .. 39.3 41.5 31.4 .. 438.8 .. 46.8 41.6 .. 21.6 .. .. .. .. 18.4 26.7 .. 337.7

% of GDP 2010a

.. 5.1 .. 4.6 4.7 .. 5.7 5.9 4.6 6.1 3.8 .. 3.1 6.7 .. 4.8 4.3 .. 6.0 3.3 5.6 1.8 13.1 2.8 .. 4.9 .. 3.2 5.7b 5.8 4.5 4.3 3.1 4.9 9.1 5.4 5.4 .. .. 8.1 4.7 5.5 7.2 .. 3.8 .. 6.5 4.4 2.4 3.8 .. .. 2.6 2.7 5.1 4.9 .. 2.4

2.11

PEOPLE

Education inputs

Trained Primary teachers school in primary pupil–teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2010a

2010a

2010a

.. 10.4 .. 26.0 19.8 .. 13.4 13.7 9.4 11.5 9.4 .. .. 17.2 .. 15.8 17.4 .. 18.6 13.2 14.7 7.2 23.7 12.1 .. 13.1 .. 13.4 14.7b 18.9 22.0 15.2 11.4 .. 22.3 14.6 25.7 .. .. 22.4 20.2 11.9 16.1 .. 16.9 .. 16.1 .. 9.9 .. .. .. 16.4 16.9 11.8 11.0 .. 8.2

36.4 .. .. .. 98.4 .. .. .. .. .. .. .. .. 96.8 .. .. .. 100.0 68.4 96.9 .. .. 63.4 40.2 .. .. .. 90.4 95.9 .. 50.0 100.0 100.0 95.6 .. 97.6 100.0 b 75.9 99.9 95.6 80.7b .. .. 74.9 96.4b 66.1 .. 100.0 84.2 91.6 .. .. .. .. .. .. 6.6 42.9

33 10 .. 16 20 .. 16 13 .. 21 18 .. 16b 47 .. 22 .. 8 24 30 12 14 34 24 .. 13 16 40 79 13 48b 37 21 28 16 30 26b 58 28 30 30 b .. 14 30 39b 36 .. 12 40 23 .. .. 20 31 10 11 12 12

2012 World Development Indicators

83


2.11

Education inputs Public expenditure per student

1999

2010a

% of GDP per capita Secondary 1999 2010a

.. .. 11.2 .. 13.6 .. .. .. 10.2 26.1 .. 14.2 .. 18.0 .. .. 8.3 22.3 22.7 10.8 .. .. 18.1 .. 7.7 11.5 14.2 9.8 .. .. .. 5.4 13.9 17.7 7.2 .. .. .. .. .. 7.0 12.7 .. m .. .. .. 13.8 .. .. .. 12.6 .. .. .. 17.9 17.9

.. .. 8.2 .. 16.4 61.6 .. 11.5 15.6 .. .. 17.6 .. 20.3 7.4 .. 17.7 26.2 20.5 16.8 .. 21.5 24.4 .. 10.8 14.9 17.3 .. .. 7.2 .. 6.2 23.3 22.5 .. .. .. 19.4 .. .. .. .. 16.4 m .. .. .. 15.9 .. .. .. 12.4 .. 8.8 .. 19.7 19.9

.. .. 42.7 .. .. .. .. .. 18.4 25.6 .. 20.0 .. 24.4 .. .. 23.2 26.1 27.3 21.0 .. .. 16.2 .. 27.7 12.2 24.6 9.6 .. .. .. 7.2 23.8 22.3 9.9 .. .. .. .. .. 19.4 19.3 .. m .. .. .. .. .. .. .. 12.9 .. 13.1 .. 22.3 25.9

Primary

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

a. Provisional data. b. Data are for 2011.

84

2012 World Development Indicators

.. .. 37.1 .. 28.0 14.4 .. 17.5 15.1 .. .. 19.8 .. 25.8 .. .. 37.1 30.9 31.1 14.2 .. 18.8 15.4 .. .. 16.1 24.3 .. .. 20.5 .. 8.6 28.9 24.8 .. .. .. 17.0 .. .. .. .. 22.7 m .. .. .. 15.4 .. .. .. 13.6 .. .. .. 25.1 27.5

Public expenditure on education

Tertiary 1999

32.6 10.9 1,228.1 .. .. .. .. .. 32.8 27.8 .. .. .. 19.6 .. .. 434.9 51.7 53.8 .. .. .. 36.5 .. .. 148.3 81.1 35.3 .. .. 36.5 26.2 25.6 26.8 .. .. .. .. .. .. 160.4 .. .. m .. .. .. .. .. 37.8 .. .. .. 95.0 .. 31.8 30.7

2010a

.. 14.2 186.8 .. 186.9 43.3 .. 28.7 18.3 21.2 .. .. .. 27.3 .. .. .. 41.3 43.8 .. 17.5 873.3 17.6 83.9 .. .. 46.1 .. .. 104.3 .. 19.9 22.2 21.2 .. .. .. 60.6 .. .. .. 75.4 .. m .. .. .. .. .. .. 17.3 .. .. .. .. 27.3 32.5

% of GDP 2010a

.. 4.1 4.7b 5.6 5.6 5.0 4.3 3.3 3.6 5.2 .. 6.0 .. 4.6 2.1 .. 7.4 6.8 5.4 .. 4.0 6.2 3.8 14.0 4.5 .. 6.3 .. .. 3.2 .. 1.0 5.4 5.5 .. .. .. 5.3 .. 5.2 1.3 2.5 4.6 m 3.8 4.4 4.0 4.8 4.1 3.8 4.4 4.4 4.8 2.5 5.0 5.1 5.5

Trained Primary teachers school in primary pupil–teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2010a

2010a

2010a

.. .. 16.9b 19.3 24.0 9.5 18.1 10.3 10.3 11.8 .. 19.2 .. 11.2 8.1 .. 16.0 12.9 16.7 .. 14.7 18.3 22.3 11.7 17.6 .. 22.7 .. .. 15.0 .. 23.4 11.1 13.8 .. .. .. 19.8 .. 16.0 .. 8.3 15.6 m 18.7 .. .. .. .. 17.2 14.0 .. 20.0 12.6 18.9 12.7 11.5

.. .. 91.5 .. 47.9 94.2 48.0 b 94.3 .. .. .. 87.4 .. .. .. 59.7 73.1 .. .. .. 92.9 94.5 .. .. 76.7 88.0 .. .. .. 89.4 99.9 100.0 .. .. .. 100.0 b 88.4 98.3 100.0 .. .. .. .. w 82.9 .. .. .. .. .. .. .. .. .. .. .. ..

16 18 65 11 34 16 31b 17 16 17 .. 31 .. 13 24 38 32 9 .. 18 25 51 16 30 41 18 17 .. .. 49 16 17 18 14 14 16b 12 20 28 31 58 .. 24 w 45 .. .. 19 26 17 17 24 24 .. 46 15 14


About the data

2.11

PEOPLE

Education inputs Definitions

Data on education are collected by the United

class size because of the different practices coun-

• Public expenditure per student is public current

Nations Educational, Scientific, and Cultural Organi-

tries employ, such as part-time teachers, school

and capital spending on education divided by the

zation (UNESCO) Institute for Statistics from official

shifts, and multigrade classes. The comparability

number of students by level as a percentage of gross

responses to its annual education survey. The data

of pupil–teacher ratios across countries is further

domestic product (GDP) per capita. • Public expen-

are used for monitoring, policymaking, and resource

affected by the definition of teachers and by differ-

diture on education is current and capital expendi-

allocation. While international standards ensure

ences in class size by grade and in the number of

tures on education by local (including municipalities),

comparable datasets, data collection methods may

hours taught, as well as the different practices men-

regional, and national governments as a percentage

vary by country and within countries over time.

tioned above. Moreover, the underlying enrollment

of GDP and as a percentage of total government

For most countries the data on education spending

levels are subject to a variety of reporting errors (for

expenditure. • Trained teachers in primary educa-

in the table refer to public spending—government

further discussion of enrollment data, see About the

tion are the percentage of primary school teachers

spending on education at all levels plus subsidies

data for table 2.12).While the pupil–teacher ratio

who have received the minimum organized teacher

provided to households and other private entities—

is often used to compare the quality of schooling

training (pre-service or in-service) required for teach-

and generally exclude foreign aid for education that is

across countries, it is often weakly related to student

ing at the specified level of education in their country.

not included in the government budget. The data may

learning and quality of education.

• Primary school pupil–teacher ratio is the number

also exclude spending by religious schools, which

All education data published by the UNESCO

of pupils enrolled in primary school divided by the

play a significant role in many developing countries.

Institute for Statistics are mapped to the Interna-

number of primary school teachers (regardless of

Data are gathered from ministries of education and

tional Standard Classifi cation of Education 1997

their teaching assignment).

from other ministries or agencies involved in educa-

(ISCED97). This classification system ensures the

tion spending.

comparability of education programs at the interna-

The share of public expenditure devoted to educa-

tional level. UNESCO developed the ISCED to facili-

tion allows an assessment of the priority a govern-

tate comparisons of education statistics and indica-

ment assigns to education relative to other public

tors of different countries on the basis of uniform and

investments, as well as a government’s commitment

internationally agreed definitions. First developed in

to investing in human capital development. However,

the 1970s, the current version was formally adopted

returns on investment to education, especially pri-

in November 1997.

mary and lower secondary education, cannot be

The reference years in the table reflect the school

understood simply by comparing current education

year for which the data are presented. In some coun-

indicators with national income. It takes a long time

tries the school year spans two calendar years (for

before currently enrolled children can productively

example, from September 2009 to June 2010); in

contribute to the national economy (Hanushek 2002).

these cases the reference year refers to the year in

High-quality data on education finance are scarce.

which the school year ended (2010 in the example).

Improving their quality is a priority of the UNESCO Institute for Statistics. Additional resources are being allocated for technical assistance to countries in need, especially 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 education spending is still a challenge for all countries. The share of trained teachers in primary education reveals a country’s commitment to investing in the development of its human capital engaged in teaching, but it does not take into account differences in teachers’ experiences and status, teaching methods, teaching materials, and classroom conditions— all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. The pupil–teacher ratio reflects the average num-

Data sources Data on education inputs are from the UNESCO Institute for Statistics.

ber of pupils per teacher. It differs from the average

2012 World Development Indicators

85


2.12

Participation in education Gross enrollment ratio

Preprimary

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

86

% of relevant age group Primary Secondary

Net enrollment rate

% of relevant age group Primary Secondary

Tertiary

2010a

2010a

2010a

2010a

1991

2010a

1999

.. 56 77 104 74 31 81 96 25 .. 13 99 118 18 45 17 .. 65 79 3c 9 13 28 71 4 2 56 54 .. 49 3 13 71 4c 58 100 81 106 96 38 126 24 64 14 96 5 66 110 42c 30 58 114 69 .. 71 14 7 ..

97 87 110 124 118 103 104 100 94 .. 103 100 105 126 105 88 .. 127 103 79c 156 127 120 99 93 90 106 111 102 115 94 115 110 88 c 93 103 105 106 99 108 114 106 114 45 99 102 99 111 182c 83 109 102 107c .. 116 94 123 ..

46 89 95 31 89 92 129 100 .. .. 49 .. 111 .. 80 90 .. 101 88 23c 25 46 42 101 13 26 88 81 83 96 38 .. 100 .. 95 89 98 90 117 76 80 .. 65 32 104 36 108 113 .. 54 86 103 58 c .. 59 38 .. ..

3 .. 31 4 71 52 76 60 19 .. 11 83 67 .. .. 36 .. 36 53 3 3 8 11 .. 3 2 59 26 60 39 6 6 .. .. 49 95 52 61 74 .. 40 30 23 2 63 5 92 55 .. 4 28 .. 9 .. .. 9 .. ..

28 .. 89 .. .. .. 98 90 89 99 64 .. 96 51 .. .. 89 .. .. 27 50 .. 69 98 53 .. .. 97 .. 71 56 .. 87 46 .. 94 87 .. 98 .. .. .. .. 20 .. 30 99 100 .. 50 .. 84 .. 95 .. 27 .. 21

.. 80 96 86 .. .. 97 .. 84 .. 92 92 99 94 .. 85 .. 94 98 63c .. 96 92 .. 71 .. 94 .. 94 88 .. 91 .. 61 87 99 99 .. 96 90 97 96 94 33 94 81 97 99 .. 66 100 97 84 c .. 97 77 74 ..

.. 68 .. .. 74 86 90 .. 75 86 44 82 .. 18 68 .. 53 66 85 8 .. 14 .. 95 .. 7 .. .. 70 56 .. .. .. 19 81 75 88 81 88 39 46 77 44 18 84 12 95 93 .. .. 76 .. 34 82 24 12 9 ..

2012 World Development Indicators

Adjusted net enrollment rate, primary

% of primary school– age children Male Female

Children out of school

thousand primary school– age children Male Female

2010a

2010a

2010a

2010a

2010a

.. .. .. 12 82 86 85 .. .. .. 46 87 .. .. 68 .. .. 82 83 18 c 16 .. .. .. 11 .. 83 .. 75 74 .. .. .. .. 92 86 96 84 89 62 .. .. 58 29 92 .. 94 98 .. .. 79 .. 49c .. 50 29 .. ..

.. 79 98 93 .. 78 97 .. 85 .. 91 95 99 .. .. 86 .. 96 99 65 98 96 100 .. 81 .. 94 .. 96 92 .. 92 .. 67 93 100 99 .. 95 96 .. 100 94 37 96 85 98 .. .. 68 .. .. 84 c .. 100 83 77 ..

.. 79 96 78 .. 81 98 .. 84 .. 100 97 99 .. .. 88 .. 94 100 58 100 95 88 .. 61 .. 94 .. 98 91 .. 89 .. 56 93 100 99 .. 97 90 .. 96 95 33 96 80 98 .. .. 71 .. .. 85c .. 98 70 73 ..

.. 23 28 119 .. 13 31 .. 40 .. 718 9 4 .. .. 14 .. 290 1 452 10 32 6 .. 63 .. 46 .. 7 187 .. 24 .. 497 7 0b 0b .. 11 28 .. 15 26 203 1 1,023 5 .. .. 44 .. .. 298 c .. 5 131 26 ..

.. 20 53 373 .. 10 23 .. 38 .. 1 5 3 .. .. 11 .. 396 0b 531 1 40 171 .. 135 .. 48 .. 4 188 .. 32 .. 664 6 1 0b .. 6 57 .. 184 21 215 1 1,367 4 .. .. 41 .. .. 270 c .. 27 224 30 ..


Gross enrollment ratio

Preprimary

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

% of relevant age group Primary Secondary

Net enrollment rate

Adjusted net enrollment rate, primary

% of relevant age group Primary Secondary

Tertiary

2010a

2010a

2010a

2010a

1991

2010a

1999

44 85 54 43 43 .. .. 106 97 113 90 36 66c 52 .. 121 .. 82 19 16 84 81 33 .. .. 75 25 9 .. 67 3c .. 96 103 76 77 63c .. 10 .. .. 96 93 55 6c 14 98 45 .. 67 .. 35 78 51 66 82 96 55

116 102 118 118 108 .. 108 113 103 89 103 97 111c 113 .. 104 .. 106 100 121 101 105 103 96 .. 97 89 149 135 .. 82c 102 99 115 94 100 114 c 115 126 107 .. 108 101 118 71c 83 99 105 95 108 60 100 109 107 97 114 93 103

73 98 60 77 84 .. 117 91 99 93 102 91 100 c 60 .. 97 .. 101 84 45 95 81 46 .. .. 98 83 31 32 68 39c 24 89 87 88 93 .. 25 54 .. .. 120 119 69 13 44 110 100 34 74 .. 67 92 82 97 107 82 94

19 62 16 23 43 .. 61 62 66 29 59 42 41c 4 .. 104 .. .. 49 13 60 54 .. .. .. 77 40 4 1 40 6 4 25 27 38 53 13 .. .. 9 .. 63 83 .. 1 .. 74 24 5 45 .. 37 .. 29 71 62 86 10

88 .. .. 95 97 76 90 .. .. 97 100 .. .. .. .. 99 .. 47 .. 59 .. .. 72 .. .. .. .. 72 .. .. .. .. 93 98 .. .. 56 42 .. 82 .. 95 100 70 23 .. 100 69 .. 92 65 94 86 96 .. 98 .. 89

96 92 92 96 .. .. 95 97 98 82 100 90 88 c 83 .. 99 .. 92 87 89 95 92 73 .. .. 93 87 .. 97 .. 63c 74 93 98 88 95 96c 92 .. 85 .. 100 99 92 62c .. 99 94 74 98 .. 85 95 89 96 99 86 92

.. 83 .. 47 .. 30 91 87 85 83 99 77 88 33 .. 96 .. 98 .. 26 .. .. 17 18 .. 90 .. .. 30 66 .. 14 67 56 78 58 30 3 32 39 .. 91 90 35 6 .. 95 61 .. 59 .. 45 63 50 91 80 .. 74

% of primary school– age children Male Female

2.12

PEOPLE

Participation in education

Children out of school

thousand primary school– age children Male Female

2010a

2010a

2010a

2010a

2010a

.. 91 .. 67 .. .. 98 88 93 84 99 84 90 c 50 .. 96 .. 89 79 37 84 75 30 .. .. 91 .. 24 28 68 31c .. .. 70 79 83 .. 16 51 .. .. 87 95 46 10 .. 95 90 34 69 .. 60 78 61 91 .. .. 83

96 98 .. .. .. .. 97 97 100 84 .. 93 99c 84 .. 100 .. 97 95 91 94 94 72 .. .. 97 93 .. .. .. 71 73 92 99 90 98 97c 95 .. 84 .. .. 99 93 64 .. 99 98 81 99 .. 86 97 88 96 99 83 96

98 98 .. .. .. .. 99 97 99 82 .. 95 100 c 85 .. 99 .. 100 95 87 95 93 75 .. .. 97 95 .. .. .. 61 76 94 100 90 98 96c 89 .. 89 .. .. 100 95 52 .. 99 98 67 98 .. 86 97 90 96 100 88 97

23 4 .. .. .. .. 6 13 5 28 .. 30 3c 523 .. 0b .. 3 9 35 3 15 53 .. .. 2 5 .. .. .. 377 72 5 39 8 2 58 c 124 .. 31 .. .. 1 27 478 .. 3 3 1,884 2 .. 62 54 799 47 3 28 2

8 3 .. .. .. .. 3 10 11 32 .. 21 1c 486 .. 14 .. 0b 9 47 3 15 46 .. .. 2 3 .. .. .. 481 61 3 11 7 3 75c 243 .. 21 .. .. 1 21 607 .. 2 2 3,241 4 .. 60 43 662 47 2 18 1

2012 World Development Indicators

87


2.12

Participation in education Gross enrollment ratio

Preprimary

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

% of relevant age group Primary Secondary

2010a

2010a

2010a

77 90 10 11 13 53 7c .. 91 86 .. 65 .. 126 42 27 23 95 102 10 9 33 100 c .. 9 .. .. 22 .. 14 97 .. 81 69 89 26c 73 82 39 1 .. .. 50 w 12 52 50 51 46 48 55 71 .. 54 17 78 107

96 99 143 106 87 96 125c .. 102 98 .. 102 .. 107 99 73 116 100 102 118 102 102 91 117 140 105 109 102 .. 121 99 .. 106 102 113 95c 103 106 91 87 115 .. 107 w 104 109 107 111 108 111 98 117 102 110 100 101 105

95 89 32 101 37 91 .. .. 89 97 .. 94 .. 119 .. 39 58 100 95 72 87 27 79c 56 .. 90 90 78 .. 28 96 .. 102 96 90 106c 83 77 86 44 .. .. 68 w 39 69 58 83 64 76 89 90 72 55 36 100 107

a. Provisional data. b. Less than 0.5. c. Data are for 2011.

88

2012 World Development Indicators

Net enrollment rate

% of relevant age group Primary Secondary

Tertiary 2010a

64 76 5 37 8 49 .. .. 54 87 .. .. .. 73 15 .. .. 71 51 .. 20 2 48 c 17 .. .. 34 46 .. 4 79 .. 59 95 63 9c 78 22 50 .. .. 6 27 w 7 24 16 33 21 25 55 37 27 11 6 70 60

Adjusted net enrollment rate, primary

1991

2010a

73 .. .. .. 45 .. .. .. .. .. .. 90 .. 100 .. .. 74 100 84 91 .. 51 .. .. 65 90 94 89 .. .. .. 97 97 97 91 .. .. .. .. .. .. .. .. w .. .. .. .. .. 96 90 .. .. 68 .. 95 ..

87 93 99 90 75 93 .. .. .. 97 .. 85 .. 100 94 .. 86 99 94 93 97 98 90 85 92 94 98 97 .. 91 91 .. 100 95 99 90 c 93 98 87 78 91 .. 88 w 80 89 85 94 87 .. 92 94 90 86 75 95 98

1999

77 .. .. .. .. .. .. .. .. 90 .. 62 .. 88 .. .. 32 96 84 39 63 5 .. .. 21 69 64 56 .. 13 91 75 94 87 .. .. 48 58 75 31 16 40 51 w 24 50 41 63 47 56 78 59 58 .. 19 88 86

2010a

82 .. .. 81 .. 90 .. .. .. 92 .. .. .. 94 .. .. 33 96 83 67 85 .. 74 c 37 .. .. .. 74 .. .. 86 .. 96 89 70 92 72 .. 84 .. .. .. 60 w 32 60 50 74 56 .. 81 73 64 .. 27 90 92

% of primary school– age children Male Female

Children out of school

thousand primary school– age children Male Female

2010a

2010a

2010a

2010a

92 95 89 90 76 95 .. .. .. 97 .. 89 .. 100 94 .. 86 100 99 100 99 91 90 86 .. 97 .. 98 .. 90 91 .. 100 96 100 94 c 95 .. 90 86 91 .. 91 w 82 92 90 95 90 .. 94 95 94 93 78 95 ..

93 96 92 89 80 94 .. .. .. 97 .. 91 .. 100 94 .. 85 99 99 98 96 93 89 86 .. 94 .. 97 .. 92 91 .. 100 98 99 91c 95 .. 88 70 94 .. 89 w 80 90 86 96 88 .. 93 95 89 89 74 96 ..

35 128 84 154 238 8 .. .. .. 2 .. 372 .. 2 56 .. 15 1 4 2 2 359 304 14 .. 2 .. 59 .. 357 73 .. 3 498 0b 62c 91 .. 23 290 108 ..

32 93 60 164 191 8 .. .. .. 2 .. 326 .. 1 51 .. 15 3 2 16 13 290 307 14 .. 4 .. 103 .. 266 64 .. 6 247 1 86c 80 .. 25 568 76 ..


About the data

2.12

PEOPLE

Participation in education 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 age 3, to a school-type environment and

an education system has the capacity to meet the

countries and crucial for achieving the Millennium

to provide a bridge between the home and school. On

needs of universal primary education.

Development Goal of universal primary education.

completing these programs, children continue their

Enrollment indicators are based on annual school

The reference years in the table reflect the school

education at the primary level. • Primary education

surveys, but do not necessarily reflect actual atten-

year for which the data are presented. In some coun-

(ISCED 1) refers to programs normally designed to

dance or dropout rates during the year. Also, the

tries the school year spans two calendar years (for

give students a sound basic education in reading,

length of primary education differs across coun-

example, from September 2009 to June 2010); in

writing, and mathematics along with an elementary

tries and can influence enrollment rates and ratios,

these cases the reference year refers to the year in

understanding of other subjects such as history,

although the International Standard Classification of

which the school year ended (2010 in the example).

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

Overage or underage 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 education

be inaccurately estimated or misstated, especially

instruction is often organized even more along sub-

in communities where registration of births is not

ject lines, and teachers typically need a higher or

strictly enforced.

more subject-specific qualification. • Tertiary edu-

Population data used to calculate population-

cation 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 (ISCED 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 overage children

research and leading to an advanced research quali-

enrolled in each grade because of repetition or late

fication. • Net enrollment rate is the ratio of total

entry rather than a successful education system.

enrollment of children of official school age to the

The net enrollment rate excludes overage and under-

population of the age group that officially corresponds

age students and more accurately captures the sys-

to the level of education shown. • Adjusted net enroll-

tem’s coverage and internal efficiency. Differences

ment rate, primary, is the ratio of total enrollment of

between the gross enrollment ratio and the net

children of official school age for primary education

enrollment rate show the incidence of overage and

who are enrolled in primary or secondary education to

underage enrollments.

the total primary school–age population. • Children

The adjusted net enrollment rate in primary educa-

out of school are the number of primary school–age

tion captures primary school–age children who have

children not enrolled in primary or secondary school.

progressed to secondary education faster than their peers have and who are not counted in the traditional net enrollment rate. Data on children out of school (primary school– age children not enrolled in primary or secondary

Data sources Data on gross enrollment ratios, net enrollment rates, and out of school children are from the UNESCO Institute for Statistics.

school—dropouts, children never enrolled, and

2012 World Development Indicators

89


2.13

Education efficiency Gross intake ratio in first grade of primary education

Cohort survival rate

Repeaters in primary education

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group Male Female

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

90

Reaching last grade of primary education

Male

Female

Male

Female

2010a

2010a

1991

2009a

1991

2009a

2009a

2009a

124 87 107 182 114 91 .. 103 91 .. 110 96 94 159 113 99 .. .. 100 91b 164 143 144 99 118 134 97 95 114 113 117 109 98 88 b 92 98 107 107 100 113 .. 105 117 44 100 145 99 .. .. 88 100 100 109b .. 131 112 169 ..

91 87 105 148 115 93 .. 101 88 .. 115 96 95 147 111 100 .. .. 99 86b 158 144 123 98 96 104 96 99 119 107 105 108 98 78b 92 98 106 108 100 101 .. 103 110 40 100 129 99 .. .. 88 102 99 111b .. 131 96 164 ..

89 .. 82 .. .. .. 98 .. .. 88 .. .. 87 30 57 .. 73 .. .. 61 66 .. 67 .. 52 43 .. .. .. 53 66 66 70 68 .. .. 96 .. 98 .. .. .. 54 .. .. .. 96 .. 47 59 .. .. 72 .. .. 43 .. 47

.. 95 93 53 96 .. .. .. .. 98 62 .. 96 62 86 73 .. .. .. 73 59 60 76 .. 61 32 .. .. 100 84 62 75 90 66 .. 97 94 99 100 .. .. .. 89 71 99 50 100 .. .. 67 94 .. 80 .. 71 74 .. ..

89 .. 79 .. .. .. 99 .. .. 87 .. .. 90 31 51 .. 81 .. .. 58 61 .. 66 .. 39 22 .. .. .. 59 55 68 73 61 .. .. 97 .. 99 .. .. .. 57 .. .. .. 97 .. 46 53 .. .. 65 .. .. 35 .. 46

.. 95 97 37 95 .. .. .. .. 98 71 .. 97 59 85 73 .. .. .. 78 66 65 77 .. 50 32 .. .. 100 85 58 79 92 66 .. 97 97 100 100 .. .. .. 90 67 99 51 100 .. .. 63 99 .. 77 .. 70 62 .. ..

.. 95 93 37 .. .. .. 96 100 98 67 96 86 .. 85 73 .. .. 94 71 56 52 67 .. 53 24 .. .. .. .. 78 71 88 .. 97 96 .. 99 99 .. .. .. 86 71 98 47 100 .. .. .. 94 98 .. .. 65 74 .. ..

.. 95 97 27 .. .. .. 99 97 98 66 100 88 .. 82 73 .. .. 94 72 64 57 65 .. 39 23 .. .. .. .. 73 71 90 .. 99 95 .. 100 100 .. .. .. 87 67 99 48 99 .. .. .. 99 99 .. .. 64 56 .. ..

2012 World Development Indicators

% of enrollment Male Female

% Male

Female

2010a

2010a

2009a

2009a

.. 1 9 10 6 0 .. 0 0 2 13 0 3 13 1 0 .. .. 2 10 34 10 14 0 21 23 1 0 1 2 14 20 7 19 0 1 0 1 0 9 .. 4 7 16 1 4 1 .. .. 6 0 1 2b .. 12 16 14 ..

.. 1 6 12 4 0 .. 0 0 2 12 0 3 13 1 0 .. .. 1 10 34 8 13 0 21 26 1 0 1 2 14 18 5 19 0 0 0 1 0 5 .. 2 5 13 0 4 0 .. .. 5 0 1 3b .. 10 18 14 ..

.. 97 90 26 96 100 .. 100 98 98 .. 98 99 .. 89 .. .. .. 95 43c 41 80 42 .. 43 77 84 .. 100 97 83 69 93 47 100 98 100 99 99 82 .. .. 95 82 99 91 100 .. .. 80 100 99 91 .. 93 62 .. ..

.. 97 92 45 97 98 .. 100 99 99 .. 100 97 .. 91 .. .. .. 96 62c 31 81 45 .. 45 66 98 .. 100 96 76 67 89 45 99 99 100 99 100 90 .. .. 94 82 98 87 100 .. .. 82 100 99 92 .. 90 51 .. ..


Gross intake ratio in ďŹ rst grade of primary education

Cohort survival rate

Repeaters in primary education

2.13

PEOPLE

Education efficiency

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group Male Female

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

Male

Reaching last grade of primary education Female

Male

Female

2010a

2010a

1991

2009a

1991

2009a

2009a

2009a

125 103 129 121 107 .. 106 98 101 81 103 98 112b .. .. 100 .. 101 106 136 100 108 103 120 .. 93 97 184 150 .. 82b 104 95 117 98 145 110 b 168 152 93 .. 100 .. 146 100 b 93 97 108 129 103 .. 101 103 129 99 103 97 107

120 102 125 118 108 .. 107 101 100 78 103 98 111b .. .. 98 .. 103 104 126 102 106 94 112 .. 94 97 184 159 .. 76b 107 99 117 97 138 109b 159 151 95 .. 99 .. 138 90 b 83 98 103 108 101 .. 98 102 121 99 103 94 107

50 .. .. .. 75 75 .. .. .. 92 100 93 .. .. .. 92 .. .. .. 34 .. .. 53 .. .. .. .. 31 37 86 48 52 .. 81 .. .. 70 42 .. 52 44 .. 96 39 68 .. 99 77 .. .. 55 58 .. .. .. .. .. 98

75 .. .. 83 94 .. 99 100 99 96 100 .. .. .. .. 99 .. 96 .. 66 96 90 76 64 .. .. .. 34 60 98 89c 74 99 95 .. 93 94 56 72 90 60 99 .. 48 74 c 84 100 .. 64 95 .. 81 .. 75 98 .. .. 92

43 .. .. .. 67 70 .. .. .. 94 100 89 .. .. .. 92 .. .. .. 32 .. .. 77 .. .. .. .. 31 33 87 42 47 .. 82 .. .. 64 34 .. 57 32 .. 95 48 65 .. 100 78 .. .. 52 60 .. .. .. .. .. 99

80 .. .. 89 94 .. 100 98 100 96 100 .. .. .. .. 99 .. 96 .. 68 96 91 85 56 .. .. .. 35 62 98 87c 75 97 97 .. 95 94 51 77 93 64 100 .. 55 69c 90 99 .. 59 94 .. 84 .. 82 98 .. .. 99

74 98 .. .. 94 .. .. 100 99 94 100 .. .. .. .. 99 .. 96 98 .. 95 .. .. 49 .. 98 .. .. 63 97 81 71 94 93 95 .. 78 37 .. .. .. .. .. .. 63 77 100 .. 64 94 .. 76 88 72 97 .. .. 91

79 98 .. .. 94 .. .. 98 100 96 100 .. .. .. .. 99 .. 96 97 .. 95 .. .. 43 .. 99 .. .. 57 98 77 70 98 95 96 .. 78 34 .. .. .. .. .. .. 60 83 99 .. 59 94 .. 80 88 80 98 .. .. 97

% of enrollment Male Female

% Male

Female

2010a

2010a

2009a

2009a

1 2 .. 4 2 .. 1 2 0 3 0 1 0b .. .. 0 .. 1 0 18 3 9 23 6 .. 1 0 21 19 .. 13b 3 4 4 0 0 13 8 0 18 12b .. .. 9 4b .. .. 1 5 7 .. 6 7 3 1 .. .. 0

1 2 .. 3 2 .. 1 1 0 2 0 1 0b .. .. 0 .. 1 0 16 2 7 17 7 .. 0 0 19 19 .. 13b 4 3 3 0 0 9 7 0 14 12b .. .. 7 4b .. .. 2 4 4 .. 4 6 2 1 .. .. 0

.. 97 81 91 96 .. .. 70 100 92 .. 99 100 c .. .. 100 .. 99 100 80 94 84 75 64 .. 99 98 65 78 100 74 38 65 95 99 96 84 c 52 77 80 81 .. .. .. 63c .. 100 .. 73 98 .. 89 96 99 99 .. .. 99

.. 98 81 93 97 .. .. 69 100 91 .. 99 100 c .. .. 100 .. 99 99 77 98 91 73 60 .. 99 98 63 76 99 72 31 76 94 98 98 80 c 55 77 83 81 .. .. .. 59c .. 100 .. 74 96 .. 89 93 97 98 .. .. 99

2012 World Development Indicators

91


2.13

Education efficiency Gross intake ratio in ďŹ rst grade of primary education

Cohort survival rate

Repeaters in primary education

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group Male Female

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

2010a

2010a

97 .. 186 104 100 92 133b .. 95 99 .. 94 .. 102 94 83 123 104 92 116 102 96 .. 141 157 104 106 99 .. 153 103 .. .. 101 106 97b 100 .. 91 109 114 .. 116 w 130 114 122 99 117 99 .. .. 105 127 122 101 101

95 .. 182 106 106 92 121b .. 95 98 .. 88 .. 102 95 75 113 103 94 117 98 97 .. 141 150 101 106 97 .. 157 104 .. .. 98 106 94b 98 .. 91 96 117 .. 113 w 123 111 116 101 114 102 .. .. 104 121 114 103 100

Male 1991

a. Provisional data. b. Data are for 2011. c. Data are for 2010.

92

2012 World Development Indicators

.. .. 49 80 78 .. .. .. .. .. .. 61 .. .. 97 .. 58 99 72 87 .. 69 .. .. 55 98 76 93 .. .. .. 78 .. .. 98 .. 69 .. .. .. .. 70 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

Reaching last grade of primary education Female

Male

Female

2009a

1991

2009a

2009a

2009a

.. .. 45 97 73 .. .. 99 .. 100 .. .. .. 99 .. 89 95 99 .. .. .. 87 .. 68 78 90 95 91 .. 56 .. .. .. 98 94 .. 93 .. .. .. 71 .. .. w 62 .. .. .. .. .. .. .. .. .. 67 .. ..

.. .. 51 76 68 .. .. .. .. .. .. 67 .. .. 98 .. 64 99 72 85 .. 71 .. .. 38 99 70 92 .. .. .. 80 .. .. 100 .. 80 .. .. .. .. 72 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. 50 91 75 .. .. 99 .. 99 .. .. .. 100 .. 100 97 99 .. .. .. 93 .. 74 77 94 97 93 .. 58 .. .. .. 89 97 .. 95 .. .. .. 70 .. .. w 63 .. .. .. .. .. .. .. .. .. 68 .. ..

95 .. .. 97 58 98 .. 99 98 100 .. .. .. 99 .. .. 81 99 .. 94 99 76 .. 63 .. 87 .. .. .. .. .. .. .. .. 94 98 c 90 .. .. .. 55 ..

96 .. .. 90 61 99 .. 99 98 99 .. .. .. 100 .. .. 87 99 .. 95 99 87 .. 70 .. 92 .. .. .. .. .. .. .. .. 97 98 c 94 .. .. .. 52 ..

% of enrollment Male Female 2010a

2 .. 14 3 6 1 15b 0 3 1 .. .. .. 3 1 4 17 0 2 9 0 3 .. 19 22 7 8 2 .. 11 0 2 0 0 7 0b 5 .. 0 7 6 .. .. w 11 .. .. 2 .. 1 .. .. 7 .. .. 0 1

2010a

1 .. 14 3 6 0 16b 0 3 0 .. .. .. 2 1 4 13 0 1 6 0 2 .. 15 22 5 5 2 .. 11 0 2 0 0 4 0b 3 .. 0 6 6 .. .. w 11 .. .. 1 .. 1 .. .. 4 .. .. 0 1

% Male

Female

2009a

2009a

98 .. 73 91 71 97 .. 86 97 99 .. .. .. 92 95 96 90 100 .. 94 99 45 .. 84 73 87 81 97 .. 60 100 94 .. .. 75 100 c 96 .. 96 .. 65 .. .. w .. .. 82 .. .. .. .. .. .. 80 .. .. ..

97 .. 72 97 66 99 .. 92 97 98 .. .. .. 95 97 92 92 100 .. 95 98 37 .. 87 67 89 87 97 .. 58 100 99 .. .. 87 98 c 97 .. 98 .. 68 .. .. w .. .. 83 .. .. .. .. .. .. 80 .. .. ..


About the data

2.13

PEOPLE

Education efficiency Definitions

The United Nations Educational, Scientific, and Cul-

low transition rate can signal such problems as an

• Gross intake ratio in first grade of primary educa-

tural Organization (UNESCO) Institute for Statistics

inadequate examination and promotion system or

tion is the number of new entrants in grade 1 regard-

calculates indicators of students’ progress through

insufficient secondary education capacity. The qual-

less of age as a percentage of the population of

school. These indicators measure an education sys-

ity of data on the transition rate is affected when

the official primary school entrance age. • Cohort

tem’s success in reaching students and efficiently

new entrants and repeaters are not correctly distin-

survival rate is the percentage of children enrolled

moving them from one grade to the next.

guished in the first grade of secondary education.

in the first grade of primary education who eventually

The gross intake ratio in the first grade of primary

Students who interrupt their studies after complet-

reach grade 5 or the last grade of primary education.

education indicates the level of access to primary

ing primary education could also affect data quality.

The estimate is based on the reconstructed cohort

education and the education system’s capacity to

The reference years in the table reflect the school

method (see About the data). • Repeaters in primary

provide access to primary education. A low gross

year for which the data are presented. In some coun-

education are the number of students enrolled in the

intake ratio in the first grade of primary education

tries the school year spans two calendar years (for

same grade as in the previous year as a percentage

reflects the fact that many children do not enter pri-

example, from September 2009 to June 2010); in

of all students enrolled in primary education. • Tran-

mary education even though school attendance, at

these cases the reference year refers to the year in

sition rate to secondary education is the number

least through the primary level, is mandatory in most

which the school year ended (2010 in the example).

of new entrants to the first grade of secondary edu-

countries. Because the gross intake ratio includes

cation (general programs only) in a given year as a

all new entrants regardless of age, it can exceed

percentage of the number of students enrolled in the

100 percent in some situations, such as immediately

final grade of primary education in the previous year.

after fees have been abolished or when the number of reenrolled children is large. The indicator is not calculated when new entrants and repeaters are not correctly distinguished in the first grade of primary education. The cohort survival rate to grade 5 and to the last grade of primary education shows the percentage of students entering primary school who are expected to reach the specified grade. It measures an education system’s holding power and internal efficiency. Cohort survival rates are calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years to reflect current patterns of grade transition. Rates approaching 100 percent indicate high retention and low dropout levels. Data on repeaters are often used to indicate an education system’s internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources. Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion—suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills. The transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating

Data sources

in lower secondary education, growing numbers of

Data on education efficiency are from the UNESCO

primary completers will inevitably create pressure

Institute for Statistics.

for more available places at the secondary level. A

2012 World Development Indicators

93


2.14

Education completion and outcomes Primary completion rate

% of relevant age group Total

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

94

Male

1991

2010a

1991

2010a

Female 1991 2010a

28 .. 80 33 100 105 .. .. 95 97 41 94 79 22 71 .. 90 93 90 20 46 45 53 .. 28 18 .. 107 102 73 48 54 79 42 85 99 90 92 98 61 91 .. 65 18 .. 23 97 106 62 45 .. 100 64 99 .. 17 5 27

.. 86 96 47 106 101 .. 99 90 .. 65 103 90 63 99 70 94 .. 95 45 56 87 79 .. 41 33 96 .. 96 114 59 71 96 59 c 95 98 103 101 97 92 106 98 96 40 98 72 98 .. .. 71 116 100 94 c 101 84 64 68 ..

41 .. 86 .. .. .. .. .. 96 96 .. 95 76 30 78 .. 83 .. 88 25 49 .. 57 .. 37 29 .. .. .. 70 61 59 77 53 .. .. 89 91 98 .. 91 .. 64 21 .. 28 98 .. 59 56 .. 99 71 99 .. 24 7 29

.. 86 96 53 104 100 .. 99 90 .. 62 95 89 74 100 69 92 .. 95 48 57 87 85 .. 52 41 102 .. 95 113 67 73 95 65c 95 98 103 101 97 93 105 100 96 43 97 75 98 .. .. 69 116 100 97c 101 87 75 75 ..

14 .. 73 .. .. .. .. .. 94 98 .. 95 82 14 64 .. 98 .. 92 15 43 .. 49 .. 20 7 .. .. .. 76 36 49 81 32 .. .. 90 93 98 .. 92 .. 66 15 .. 18 97 .. 65 34 .. 100 56 98 .. 9 3 26

2012 World Development Indicators

.. 86 96 40 108 103 .. 100 89 .. 69 95 92 53 99 71 96 .. 96 42 55 87 72 .. 30 24 89 .. 96 115 50 69 97 52c 95 99 103 101 98 91 106 97 96 36 98 69 97 .. .. 72 116 100 91c 100 81 53 60 ..

Youth literacy rate

Adult literacy rate

% ages 15–24

% ages 15 and older

%

2005–10b

2009

.. 96 73 70 98 100 .. .. 100 91 56 100 .. 42 91 98 84 90 98 29 67 78 71 .. 55 34 99 94 .. 93 67 .. 96 55 99 100 98 .. .. 88 84 66 84 67 100 30 .. .. 88 46 100 .. 67 97 74 39 52 49

.. 40 .. .. 37 .. 5 8 11 .. .. .. 8 .. .. .. .. 38 24 .. .. .. .. 3 .. .. 22 .. 3 39 .. .. .. .. 12 .. .. 7 5 .. .. .. .. .. 3 .. 2 9 .. .. .. 6 .. 11 .. .. .. ..

Male 1985–94b 2005–10b

.. .. 86 .. 98 100 .. .. .. 97 52 100 .. 55 96 .. 86 .. .. 27 59 .. .. .. 63 26 98 97 .. 89 .. .. .. 60 100 .. 100 .. .. .. 97 71 85 .. 100 39 .. .. 94 .. .. .. .. 99 82 .. .. ..

.. 99 94 81 99 100 .. .. 100 100 74 100 .. 65 99 100 94 97 98 47 77 89 89 .. 72 54 99 99 .. 97 73 87 98 72 100 100 100 .. .. 95 97 88 95 92 100 56 .. .. 99 71 100 .. 81 99 89 68 78 ..

Female 1985–94b 2005–10b

.. .. 62 .. 99 100 .. .. .. 97 38 100 .. 27 92 .. 92 .. .. 14 48 .. .. .. 35 9 99 91 .. 92 .. .. .. 38 100 .. 100 .. .. .. 96 54 85 .. 100 28 .. .. 92 .. .. .. .. 99 71 .. .. ..

.. 99 89 66 99 100 .. .. 100 100 77 100 .. 43 99 100 97 99 97 33 76 86 77 .. 57 39 99 99 .. 98 62 78 99 61 100 100 100 .. .. 97 97 82 95 86 100 33 .. .. 97 60 100 .. 79 99 84 54 64 ..

Students at lowest proficiency on PISA mathematics


Primary completion rate

% of relevant age group Total

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

Male

1991

2010a

1991

2010a

Female 1991 2010a

64 82 64 93 88 58 103 .. 98 94 102 101 103 .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 9 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71

99 98 96 105 104 65 .. 103 103 73 102 101 116c .. .. 101 .. 112 97 79 92 87 70 62 .. 96 92 72 67 .. 55c 75 96 104 93 108 85 61 104 84 .. .. .. 81 46c 74 100 101 67 97 .. 94 102 92 95 .. .. 100

67 89 76 .. 93 63 103 .. 98 90 102 101 103 .. .. 99 .. 58 .. 46 .. .. 42 .. .. .. .. 35 35 91 12 39 115 91 .. .. 57 32 .. 67 70 .. .. 43 21 .. 100 78 .. 86 51 68 .. 85 .. .. .. 71

96 98 96 104 104 74 .. 102 103 74 103 101 116c .. .. 100 .. 110 96 83 94 85 60 67 .. 97 92 72 65 .. 61c 74 96 104 94 107 87 66 101 80 .. .. .. 78 52c 79 101 102 75 97 .. 92 102 89 95 .. .. 99

61 90 52 .. 82 52 103 .. 97 98 102 101 103 .. .. 100 .. 56 .. 36 .. .. 76 .. .. .. .. 37 27 91 7 26 115 92 .. .. 39 21 .. 81 41 .. .. 53 13 .. 100 70 .. 86 42 69 .. 86 .. .. .. 72

102 97 95 105 104 55 .. 105 103 73 102 101 116c .. .. 101 .. 114 97 75 90 89 79 57 .. 94 93 73 68 .. 50 c 76 96 104 91 109 82 55 106 88 .. .. .. 84 40 c 70 100 100 59 97 .. 95 102 94 95 .. .. 100

Youth literacy rate

Adult literacy rate

% ages 15–24

% ages 15 and older

%

2005–10b

2009

84 99 63 92 85 78 .. .. 99 86 .. 92 100 87 100 .. .. 94 99 73 100 90 90 59 89 100 97 64 74 92 26 57 88 93 98 97 56 55 92 89 59 .. .. 78 29 61 .. 87 56 94 60 95 90 95 100 95 90 95

.. 8 .. 44 .. .. 7 21 9 .. 4 35 30 .. .. 2 .. .. 65 .. 6 .. .. .. .. 9 .. .. .. .. .. .. .. 22 .. .. .. .. .. .. .. 3 5 .. .. .. 6 .. .. 51 .. .. 48 .. 6 8 .. 51

Male 1985–94b 2005–10b

.. 99 74 97 92 .. .. .. .. .. .. .. 100 .. .. .. .. 91 .. .. 100 .. .. 66 99 100 99 .. 70 96 .. .. 91 96 100 .. 71 .. .. 86 68 .. .. .. .. 81 .. .. .. 95 .. 96 97 96 100 99 92 89

2.14

93 99 88 100 99 85 .. .. 100 92 .. 99 100 92 100 .. .. 99 100 89 100 98 86 70 100 100 99 66 87 98 47 71 96 99 99 95 87 78 96 91 87 .. .. 85 52 78 .. 98 79 97 65 99 98 97 100 100 87 98

Female 1985–94b 2005–10b

.. 99 49 95 81 .. .. .. .. .. .. .. 100 .. .. .. .. 84 .. .. 100 .. .. 54 96 100 99 .. 49 95 .. .. 92 95 100 .. 46 .. .. 90 33 .. .. .. .. 62 .. .. .. 95 .. 95 94 97 100 99 94 91

95 99 74 99 99 80 .. .. 100 98 .. 99 100 94 100 .. .. 99 100 79 100 99 98 81 100 100 99 64 86 99 31 64 98 98 100 97 72 64 95 95 77 .. .. 89 23 65 .. 98 61 96 70 99 97 98 100 100 88 98

PEOPLE

Education completion and outcomes

Students at lowest proficiency on PISA mathematics

2012 World Development Indicators

95


2.14

Education completion and outcomes Primary completion rate

% of relevant age group Total

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

Male

1991

2010a

1991

2010a

Female 1991 2010a

96 92 50 .. 39 .. .. .. 95 95 .. 76 .. 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 80 81 .. .. .. .. 97 79 w 44 83 68 97 78 101 92 84 .. 62 51 .. 101

91 98 70 93 59 96 74 c .. 98 95 .. .. .. 102 101 58 77 94 95 104 104 90 .. 65 74 91 91 99 .. 57 98 .. .. 104 106 93c 94 .. 95 63 103 .. 88 w 65 92 88 98 87 97 95 102 88 86 67 97 101

96 92 51 .. 48 .. .. .. 95 .. .. 72 .. 104 101 .. 57 96 53 94 .. 56 .. .. 48 99 79 93 .. .. 99 104 .. .. 91 .. 76 .. .. .. .. 99 86 w .. 89 76 101 85 105 93 84 .. 75 57 .. 100

91 .. 65 94 58 96 78 c .. 98 95 .. .. .. 102 101 61 76 93 94 104 106 88 .. 64 84 91 90 100 .. 58 97 .. .. 103 105 94 c 93 .. 97 73 98 .. 90 w 68 93 90 96 89 96 96 101 91 87 71 98 100

96 93 50 .. 31 .. .. .. 96 .. .. 80 .. 103 101 .. 64 96 54 84 .. 55 .. .. 22 105 70 86 .. .. 99 103 .. .. 96 .. 86 .. .. .. .. 96 75 w .. 77 60 94 73 97 92 85 .. 52 47 .. 100

a. Provisional data. b. Data are for the most recent year available. c. Data are for 2011.

96

2012 World Development Indicators

91 .. 74 92 61 97 71c .. 99 95 .. .. .. 102 101 55 78 94 97 103 102 92 .. 67 64 91 92 98 .. 56 98 .. .. 104 106 92c 95 .. 93 53 108 .. 87 w 63 91 86 99 86 98 94 102 85 84 63 97 101

Youth literacy rate

Adult literacy rate

% ages 15–24

% ages 15 and older

%

2005–10b

2009

Male 1985–94b 2005–10b

99 100 75 94 49 .. .. 99 .. 100 .. .. .. 100 .. .. 83 .. .. .. 100 86 .. .. .. 99 .. 97 .. 77 .. 81 .. .. 98 .. 95 94 .. 83 67 97 88 w 66 89 87 94 86 97 99 91 84 71 73 99 ..

97 100 77 99 74 .. 68 100 .. 100 .. 97 .. 100 97 .. 92 .. .. 96 100 78 98 .. 85 100 98 99 100 90 100 94 .. .. 98 100 98 97 99 96 82 98 92 w 75 94 89 99 91 99 99 97 93 85 77 99 ..

Female 1985–94b 2005–10b

99 100 75 81 28 .. .. 99 .. 100 .. .. .. 100 .. .. 84 .. .. .. 100 78 .. .. .. 99 .. 88 .. 63 .. 85 .. .. 99 .. 96 93 .. 35 66 94 79 w 52 79 75 93 75 92 98 92 67 47 58 99 ..

98 100 77 97 56 .. 48 100 .. 100 .. 98 .. 100 99 .. 95 .. .. 93 100 76 98 .. 68 100 96 97 100 85 100 97 .. .. 100 100 99 96 99 72 67 99 87 w 68 88 79 99 85 99 99 97 87 72 67 99 ..

98 100 71 86 50 .. 41 95 .. 100 .. 89 .. 98 91 .. 87 .. .. 84 100 73 94 51 57 99 78 91 100 73 100 90 .. .. 98 99 95 93 95 62 71 92 84 w 61 83 71 93 80 94 98 91 74 61 62 98 ..

Students at lowest proficiency on PISA mathematics

20 10 .. .. .. 18 .. 3 7 7 .. .. .. 9 .. .. .. 8 4 .. .. .. 22 .. .. 30 43 18 .. .. .. .. 6 8 23 .. .. .. .. .. .. ..


About the data

2.14

PEOPLE

Education completion and outcomes Definitions

Many governments publish statistics that indicate how

different lengths of school attendance or levels of

• Primary completion rate, or the gross intake ratio

their education systems are working and developing—

completion. Because definitions and methodolo-

to last grade of primary education, is the number of

statistics on enrollment and such efficiency indicators

gies of data collection differ across countries, data

new entrants in the last grade of primary education,

as repetition rates, pupil–teacher ratios, and cohort

should be used cautiously.

regardless of age, divided by the population at the

progression. The World Bank and the United Nations

The reported literacy data are compiled by the

entrance age for the last grade of primary education.

Educational, Scientific, and Cultural Organization

UNESCO Institute for Statistics based on national

• Youth literacy rate is the percentage of the popula-

(UNESCO) Institute for Statistics jointly developed the

censuses and household surveys during 1985–

tion ages 15–24 that can, with understanding, both

primary completion rate indicator. Increasingly used

2010. For countries without recent literacy data, the

read and write a short simple statement about their

as a core indicator of an education system’s perfor-

UNESCO Institute for Statistics estimates literacy

everyday life. • Adult literacy rate is the percentage

mance, it reflects an education system’s coverage

rates with the Global Age-Specific Literacy Projection

of the population ages 15 and older that can, with

and the educational attainment of students. The indi-

Model. For detailed information on sources, defini-

understanding, both read and write a short simple

cator is a key measure of education outcome at the

tions, and methodology, see www.uis.unesco.org.

statement about their everyday life. • Students at

primary level and of progress toward the Millennium

Literacy statistics for most countries cover the pop-

lowest proficiency on PISA mathematics is the per-

Development Goals and the Education for All initia-

ulation ages 15 and older, but some include younger

centage of students whose mathematics score are

tive. However, a high primary completion rate does

ages or are confined to age ranges that tend to inflate

below 357.77 (level 1) on the PISA.

not necessarily mean high levels of student learning.

literacy rates. The youth literacy rate for ages 15–24

The primary completion rate reflects the primary

reflects recent progress in education. It measures

cycle as defined by the International Standard Clas-

the accumulated outcomes of primary education over

sification of Education (ISCED97), ranging from three

the previous 10 years or so by indicating the pro-

or four years of primary education (in a very small

portion of the population who have passed through

number of countries) to fi ve or six years (in most

the primary education system and acquired basic

countries) and seven (in a small number of countries).

literacy and numeracy skills. Generally, literacy also

The primary completion rate is also called the gross

encompasses numeracy, the ability to make simple

intake ratio to last grade of primary education. It is the

arithmetic calculations.

number of new entrants in the last grade of primary

In many countries national assessments enable

education, regardless of age, divided by the population

ministries of education to monitor progress in learn-

at the entrance age for the last grade of primary educa-

ing outcomes. Of the handful of internationally or

tion. Data limitations preclude adjusting for students

regionally comparable assessments, one of the

who drop out during the final year of primary education.

largest is the Programme for International Student

Thus this rate is a proxy that should be taken as an

Assessment (PISA). Coordinated by the Organisation

upper estimate of the actual primary completion rate.

for Economic Co-operation and Development (OECD),

There are many reasons why the primary comple-

it measures the knowledge and skills of 15-year-olds,

tion rate can exceed 100 percent. The numerator may

the age at which students in most countries are near-

include late entrants and overage children who have

ing the end of their compulsory time in school. The

repeated one or more grades of primary education as

assessment tests reading, mathematical, and sci-

well as children who entered school early, while the

entific literacy in terms of general competencies—

denominator is the number of children at the entrance

that is, how well students can apply the knowledge

age for the last grade of primary education.

and skills they have learned at school to real-life

Basic student outcomes include achievements in reading and mathematics judged against established

challenges. It does not test how well a student has mastered a school’s specific curriculum.

standards. The UNESCO Institute for Statistics has

The table presents the percentage of students

established literacy as an outcome indicator based

at the lowest level of proficiency on the PISA math-

on an internationally agreed definition.

ematics scale. Student achievement is benchmarked

The literacy rate is the percentage of the popu-

in terms of levels of proficiency, ranging from level

lation who can, with understanding, both read and

1 (lowest) to level 6 (highest), as demonstrated

write a short, simple statement about their everyday

through ability to analyze, reason, and communi-

life. In practice, literacy is difficult to measure. To

cate effectively while posing, solving, and interpret-

estimate literacy using such a definition requires

ing mathematical problems that involve quantita-

Data on primary completion rates and literacy

census or survey measurements under controlled

tive, spatial, probabilistic, or other mathematical

rates are from the UNESCO Institute for Statis-

conditions. Many countries estimate the number of

concepts. The average score is 496. Because the

tics. Data on PISA mathematics results are from

literate people from self-reported data. Some use

figures are derived from samples, the data reflect a

the OECD.

educational attainment data as a proxy but apply

small measure of statistical uncertainty.

Data sources

2012 World Development Indicators

97


2.15

Education gaps by income and gender Survey year

Armenia Azerbaijan Bangladesh Belize Benin Bolivia Burundi Cambodia Cameroon Colombia Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Kazakhstan Kenya Kosovo Lesotho Macedonia, FYR Madagascar Malawi Mali Mauritania Moldova Mozambique Namibia Nepal Nicaragua Niger Nigeria Panama Peru Rwanda Serbia Somalia Swaziland Syrian Arab Republic Tanzania Togo Turkey Uganda Vietnam Yemen, Rep. Zambia Zimbabwe

2005 2006 2006 2006 2006 2003 2005 2005 2006 2005 2006 2007 2005 2005 2006 2006 2000 2005 2006 2006 2005 2006 2003 2000 2004 2005 2003– 04 2006 2006 2007 2005 2003 2006 2001 2001 2006 2003 2003 2004 2005 2005 2005 2006 2006 2004 2006 2003 2006 2006 2006 2007 1999

Gross intake rate in first grade of primary education

Gross primary participation rate

Average years of schooling

Primary completion rate

Children out of school

% of relevant age group Poorest Richest quintile quintile

% of relevant age group Poorest Richest quintile quintile

Ages 15–19 Poorest Richest quintile quintile

% of relevant age group Richest quintile Male

% of relevant age group Poorest Richest quintile quintile

93 92 144 80 67 92 201 208 108 161 51 130 107 86 90 107 176 55 135 74 177 118 134 104 169 102 250 234 41 67 96 128 112 184 149 50 78 125 121 274 90 13 147 110 123 115 108 180 99 66 135 106

a. Less than 0.5.

98

2012 World Development Indicators

80 118 147 89 107 95 191 151 75 84 77 112 97 124 104 121 124 119 184 76 188 101 125 119 111 190 153 207 98 96 84 143 104 141 106 90 101 116 90 195 98 44 117 149 123 148 111 144 100 109 123 111

106 100 96 106 61 108 91 113 93 127 57 113 95 47 101 81 81 52 94 105 87 106 92 95 116 89 118 133 46 62 99 75 118 109 85 35 70 108 118 131 98 8 117 102 82 99 97 107 108 50 105 144

102 108 105 113 114 129 144 134 116 99 110 107 99 112 103 117 114 121 166 101 159 103 106 104 124 97 145 169 110 116 95 143 109 139 105 89 108 102 96 151 100 93 114 107 119 128 97 124 100 101 112 144

9 9 8 8 6 6 4 5 6 6 5 7 9 3 15 5 4 5 4 10 4 9 6 9 5 8 3 5 5 5 9 3 7 5 4 4 7 7 7 3 9 8 6 7 5 6 6 5 13 7 5 7

10 11 13 11 8 9 7 8 14 10 8 11 12 6 14 8 8 7 7 10 7 9 9 11 8 10 8 7 8 9 12 6 10 8 9 7 10 11 11 5 10 10 9 8 7 7 7 8 18 10 9 10

Poorest quintile

119 94 65 59 31 76 20 42 43 94 47 69 84 14 102 62 15 32 34 109 31 102 40 82 36 120 42 30 36 17 97 13 81 49 34 31 48 100 106 31 86 2 69 92 32 40 95 27 99 25 50 36

116 109 97 130 95 98 70 121 111 109 127 109 92 90 102 88 80 93 125 118 136 115 76 94 122 119 141 80 79 89 100 100 109 96 124 71 71 94 99 88 96 58 110 93 108 82 85 68 104 103 101 80

113 103 83 107 67 90 44 88 90 100 88 88 92 46 106 93 34 76 80 91 73 102 71 98 69 133 77 49 55 48 96 57 94 69 78 60 70 105 100 48 94 26 85 93 58 67 100 50 96 84 88 51

Female

112 105 86 72 52 81 39 85 74 103 71 106 88 33 104 86 36 48 54 112 82 97 72 83 85 78 77 52 41 52 98 43 90 62 83 30 54 88 97 42 89 20 98 92 60 56 81 42 103 31 73 57

2 20 12 5 57 22 5 37 3 11 4 12 12 74 2 22 7 60 12 2 69 0a 38 1 18 0a 33 0a 67 2 2 46 11 33 40 74 52 1 6 13 1 87 17 0a 44 1 20 25 3 2 22 22

1 11 6 7 12 5 3 13 2 2 3 4 1 30 1 12 3 16 11 1 24 1 11 4 3 0a 3 0a 20 2 1 7 2 6 4 28 6 1 1 8 0a 46 4 0a 15 1 5 7 2 2 3 8


About the data

2.15

PEOPLE

Education gaps by income and gender Definitions

The data in the table describe basic information on

facets of social exclusion. To that extent the index

• Survey year is the year in which the underlying data

school participation and educational attainment

provides only a partial view of the multidimensional

were collected. • Gross intake rate in first grade

by individuals in different socioeconomic groups

concepts of poverty, inequality, and inequity.

of primary education is the number of students in

within countries. The data are from Demographic

Creating one index that includes all asset indica-

grade 1 regardless of age as a percentage of the

and Health Surveys by Macro International with the

tors limits the types of analysis that can be per-

population of the official primary school entrance

support of the U.S. Agency for International Devel-

formed. In particular, the use of a unified index does

age. These data may differ from those in table 2.13.

opment, Multiple Indicator Cluster Surveys by the

not permit a disaggregated analysis to examine

• Gross primary participation rate is the ratio of

United Nations Children’s Fund (UNICEF), and Liv-

which asset indicators have a more or less important

total students attending primary school regardless

ing Standards Measurement Studies by the World

association with education status. In addition, some

of age to the population of the age group that offi -

Bank’s Development Economics Research Group.

asset indicators may reflect household wealth better

cially corresponds to primary education. • Average

These large-scale household sample surveys, con-

in some countries than in others—or reflect differ-

years of schooling are the years of formal school-

ducted periodically in developing countries, collect

ent degrees of wealth in different countries. Taking

ing received, on average, by youths and adults ages

information on a large number of health, nutrition,

such information into account and creating country-

15–19. • Primary completion rate is the number

and population measures as well as on respondents’

specific asset indexes with country-specific choices

of students, regardless of age, in the last grade of

social, demographic, and economic characteristics

of asset indicators might produce a more effective

primary school minus the number of repeaters in that

using detailed questionnaires. The data presented

and accurate index for each country. The asset index

grade, divided by the number of students of official

here draw on responses to individual and household

used in the table does not have this flexibility.

graduation age. These data differ from those in table

The analysis was carried out for about 80 coun-

2.14 because the source is different. • Children out

Typically, the surveys collect basic information on

tries. The table shows the most recent estimates

of school are children of official primary school age

educational attainment and enrollment levels from

for the poorest and richest quintiles by gender only;

who are not attending primary or secondary educa-

every household member ages 5 and older as part of

the full set of estimates for all indicators, other

tion. Children of official primary school age who are

household socioeconomic characteristics. The sur-

subgroups, including by urban and rural location,

attending preprimary education are considered out

veys are not intended for the collection of detailed

and older data are available in the country reports

of school. These data differ from those in table 2.12

education data; thus the education section of the

(see Data sources). The data in the table differ from

because the source is different.

surveys is not as detailed as, for instance, the health

data for similar indicators in preceding tables either

section of the Demographic and Health Survey or

because the indicator refers to a period a few years

the Multiple Indicator Cluster Survey, and the data

preceding the survey date or because the indica-

obtained from them do not replace other data on

tor definition or methodology is different. Findings

education flows. Still, the education data provide

should be used with caution because of measure-

micro-level information on education that cannot be

ment error inherent in the use of survey data.

questionnaires.

obtained from administrative data, such as information on children not attending school. Socioeconomic status as displayed in the table is based on household assets, including ownership of consumer items, features of the household dwelling, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principalcomponent analysis, which was used to create breakpoints defining wealth quintiles, expressed as quintiles of individuals in the population.

Data sources

The selection of the asset index for defining socio-

Data on education gaps by income and gender

economic status was based on pragmatic rather

are from an analysis using the ADePT Educa-

than conceptual considerations: Demographic and

tion software tool (http://go.worldbank.org/

Health Surveys do not collect consumption data but

X385KNDXM0) of MEASURE DHS Demographic

do have detailed information on household owner-

and Health Surveys by ICF International, Mul-

ship of consumer goods and access to a variety of

tiple Indicator Cluster Surveys by UNICEF, and

goods and services. Like income or consumption,

Living Standards Measurement Studies by the

the asset index defines disparities primarily in eco-

World Bank. Country reports and further updates

nomic terms. It therefore excludes other possibilities

are available at www.worldbank.org/education/

of disparities among groups, such as those based

edstats/.

on gender, education, ethnic background, or other

2012 World Development Indicators

99


2.16

Health systems Health expenditure

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

100

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

2010

2010

2010

2010

7.6b 6.5 4.2 2.9c 8.1 4.4 8.7d 11.0 5.9 5.0 3.5 5.6 10.7 4.1 4.8 11.1 8.3 9.0 6.9 6.7 11.6c 5.6 5.1c 11.3 4.0 4.5 8.0 5.1 .. 7.6 7.9 2.5 10.9 5.3 7.8 10.6 6.0 7.9 11.4 6.2 8.1 4.7 6.9 2.7c 6.0 4.9 9.0 11.9 3.5c 5.7 10.1 11.6 5.2 10.2 6.9 4.9 8.5c 6.9

11.7b 39.0 77.9 82.5c 54.6 40.6 68.0 d 77.5 20.3 73.3 33.6 77.7 74.7 49.5 62.8 61.4 72.5 47.0 54.5 51.0 38.2c 37.2 29.6c 70.5 35.4 25.0 48.2 53.6 .. 72.7 42.5 46.7 68.1 21.6 84.9 91.5 41.5 83.7 85.1 43.4 37.2 37.4 61.7 48.2c 78.7 53.5 75.1 77.8 52.9c 50.8 23.6 77.1 59.5 59.4 35.8 11.3 10.0 c 21.4

83.0 b 60.8 20.9 17.5c 29.9 55.1 20.5d 14.6 69.5 14.5 64.1 19.8 20.2 46.8 28.7 38.6 8.1 30.6 44.2 36.2 37.9c 40.4 66.5c 14.7 61.4 72.5 33.3 36.6 .. 19.5 35.9 53.3 27.8 77.5 14.5 8.5 48.8 14.7 13.1 37.2 49.0 61.2 33.9 51.8 c 19.6 37.2 18.8 7.3 47.1c 23.8 68.3 13.0 26.9 38.4 53.9 88.1 66.4 c 40.2

32.0 b 1.8 0.0 2.9c 0.1 14.3 0.0 d 0.0 0.8 0.0 8.0 0.5 0.0 35.9 5.3 1.8 18.3 0.0 0.0 22.9 45.8 c 23.9 13.2c 0.0 13.4 7.9 0.0 0.1 .. 0.0 32.7 4.1 0.6 9.8 0.0 0.0 0.0 0.0 0.0 0.7 0.4 0.6 1.9 38.0 c 60.7 39.4 0.0 0.0 2.4 c 41.2 2.8 0.0 16.9 0.0 1.7 10.8 23.3c 38.3

2012 World Development Indicators

Health workers

per 1,000 people Nurses and Community Physicians midwives health workers

per 1,000 people

2010

2005–10a

2005–10a

2005–10a

44b 577 330 168 c 1,287 239 3,441d 4,388 579 1,083 57 786 4,025 65 233 972 1,145 1,028 947 93 47c 121 122c 4,404 31 62 1,199 379 .. 713 27 104 1,242 98 1,514 431 1,842 2,051 4,537 578 653 289 450 16c 1,226 51 3,281 4,021 522c 80 522 4,332 85 2,853 325 56 100 c 76

0.2 1.2 1.2 .. 3.2 3.8 3.0 4.9 3.8 1.4 0.3 5.2 3.0 0.1 .. 1.6 0.3 1.8 3.7 0.1 .. 0.2 .. 2.0 .. .. 1.0 1.4 .. 0.1 .. 0.1 .. 0.1 2.6 6.7 2.6 3.7 3.4 .. 1.7 2.8 1.6 .. 3.3 0.0 2.9 3.4 .. 0.0 4.8 3.6 0.1 6.2 .. 0.1 0.0 ..

.. .. .. .. .. .. 0.0 .. .. .. 0.3 .. .. .. .. .. 0.5 .. .. 0.1 .. .. .. .. .. .. .. 0.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.3 .. .. .. 0.1 .. .. 0.2 .. .. .. .. ..

0.4 2.8 .. 0.8 4.5 3.7 3.8 7.7 7.5 1.8 0.3 11.1 6.5 0.5 1.1 3.4 1.8 2.4 6.6 0.4 1.9e 0.8 1.3 3.2 1.0e 0.4 2.1 4.2 .. 1.0 0.8 1.6 1.2 0.4 5.4 5.9 3.8 7.1 3.5 1.6 1.5 1.7 1.0 0.7e 5.4 0.2 6.2 6.9 6.3 1.1e 3.1 8.2 0.9e 4.8 0.6 0.3 1.0 1.3

Per capita $ PPP $ 2010

38b 241 178 123c 742 133 4,775d 4,958 332 864 23 320 4,618 31 97 499 615 990 435 40 21c 45 61c 5,222 18 31 947 221 .. 472 16 72 811 60 1,067 607 1,705 1,480 6,422 323 328 123 237 12c 853 16 3,984 4,691 302c 26 272 4,668 67 2,729 196 23 47c 46

Hospital beds

2005–10a

0.5 3.9 1.9 .. .. 4.8 9.6 7.9 8.3 3.7 0.3 13.1 0.5 0.8 .. 5.0 2.8 6.4 4.7 0.7 .. 0.9 .. 10.4 .. .. 0.1 1.4 .. 0.6 .. 0.8 .. 0.5 5.3 9.1 4.3 8.7 16.1 .. 2.0 3.5 0.4 .. 6.6 0.2 24.0 0.3 .. 0.6 3.2 11.1 1.0 0.2 .. 0.0 0.6 ..


Health expenditure

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

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

2010

2010

2010

2010

65.2 69.4 29.2 49.1 40.1 81.2c,f 69.2 60.3 77.6 53.5 82.5 67.7 59.4 44.3 .. 59.0 .. 80.4 56.2 33.3 61.1 39.2 76.2 32.5 68.8 c 73.5 63.8 60.3 60.2 55.5 46.6 53.1c 41.7 48.9 45.8g 55.1 38.0 71.7 12.2 58.4 33.2 79.2 83.2 53.3 50.9 37.9c 83.9 80.1 38.5 75.1 71.5 36.4 54.0 35.3 72.6 68.1 .. 77.5

31.1 24.0 61.2 38.3 57.8 18.8 c,f 15.2 29.2 19.6 33.0 14.3 25.1 40.1 42.7 .. 31.4 .. 17.8 37.8 51.2 37.8 44.7 16.4 35.2 31.2c 25.8 35.9 27.1 11.1 34.2 53.2 44.3c 51.7 47.1 44.9g 41.4 53.6 13.7 81.1 7.4 48.3 5.2 10.5 43.3 41.3 59.2c 15.3 12.3 50.5 19.9 15.9 57.1 39.5 54.0 22.1 24.8 .. 16.0

6.3 0.0 1.2 1.3 0.0 0.8 c,f 0.0 0.0 0.0 2.1 0.0 3.7 0.6 36.1 .. 0.0 .. 0.0 12.8 15.1 0.0 4.7 19.5 55.1 0.6c 1.1 0.8 9.0 63.8 0.0 27.4 10.1c 2.0 0.0 9.6g 3.9 0.4 24.2 8.7 19.0 11.3 0.0 0.0 14.6 29.4 9.2c 0.0 0.0 4.8 0.1 24.0 2.4 1.7 1.3 0.1 0.0 .. 0.0

6.8 7.3 4.1 2.6 5.6 8.4 c,f 9.2 7.6 9.5 4.8 9.5 8.0 4.3 4.8 .. 6.9 .. 2.6 6.2 4.5 6.7 7.0 11.1 11.8 3.9c 7.0 7.1 3.8 6.6 4.4 5.0 4.4 c 6.0 6.3 11.7g 5.4 5.2 5.2 2.0 6.8 5.5 11.9 10.1 9.1 5.2 5.1c 9.5 2.8 2.2 8.1 3.6 5.9 5.1 3.6 7.5 11.0 .. 1.8

2.16

Health workers

Per capita $ PPP $ 2010

137 942 54 77 317 247c,f 4,242 2,183 3,248 247 4,065 357 393 37 .. 1,439 .. 1,223 53 46 718 651 109 29 484 c 781 317 16 26 368 32 43c 449 604 190 g 120 148 21 17 361 30 5,593 3,279 103 18 63c 8,091 574 22 616 49 163 269 77 917 2,367 .. 1,489

2010

263 1,469 132 112 836 340 c,f 3,704 2,186 3,022 372 3,204 448 541 78 .. 2,023 .. 1,133 140 97 1,093 980 170 49 713c 1,299 791 36 65 641 56 79c 803 959 360 g 218 246 49 34 436 66 5,038 3,020 253 37 121c 5,426 598 59 1,123 88 302 481 142 1,476 2,818 .. 1,622

Hospital beds

per 1,000 people Nurses and Community Physicians midwives health workers

per 1,000 people

2005–10a

2005–10a

2005–10a

.. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.7 .. .. .. .. .. .. 0.0 .. .. 0.1 .. .. .. .. .. .. 0.1 .. .. 0.1 .. 0.6 .. .. .. .. .. .. ..

0.8 7.1 0.9 0.6 1.7 1.3 4.9 3.5 3.6 1.9 13.7 1.8 7.6 1.4 .. 10.3 .. 2.0 5.1 0.7 6.4 3.5 1.3 0.8 3.7 6.8 4.5 0.2 1.3e 1.8 0.1 0.4 3.4 e 1.6 6.2 5.8 1.1 0.8 0.6 2.7 5.0 4.7 .. 0.8 0.3 .. 3.3 1.8 0.6 2.2 .. 1.3 1.5 0.5 6.7 3.3 .. 1.2

.. 3.0 0.6 0.3 0.9 0.7 3.2 3.7 3.5 .. 2.1 2.5 4.1 .. .. 2.0 .. 1.8 2.3 0.3 3.0 3.5 .. 0.0 1.9 3.6 2.6 0.2 0.0 0.9 0.0 0.1 .. 2.0 2.7 2.8 0.6 0.0 0.5 0.4 .. 2.9 2.7 .. 0.0 0.4 4.2 1.9 0.8 .. 0.1 .. 0.9 .. 2.2 3.9 .. 2.8

2005–10a

.. 6.4 1.0 2.0 1.4 1.4 15.7 5.2 0.3 .. 4.1 4.0 8.3 .. .. 5.3 .. 4.6 5.7 1.0 4.8 2.2 .. 0.3 6.8 7.2 0.6 .. 0.3 2.7 0.3 0.7 .. .. 6.6 3.5 0.9 0.3 0.8 2.8 .. 0.2 10.9 .. 0.1 1.6 31.9 4.1 0.6 .. 0.5 .. 1.3 .. 5.8 5.3 .. 7.4

2012 World Development Indicators

101

PEOPLE

Health systems


2.16

Health systems Health expenditure

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

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

2010

2010

2010

2010

78.1 62.1 50.1 62.9 55.5 61.9 11.3 36.3 65.9 73.7 .. 44.1 .. 72.8 44.7 29.8 63.7 81.1 59.0 46.0 26.7 67.3 75.0 55.8 44.2 59.9 54.3 75.2 59.4 c 21.7 56.6 74.4 83.9 53.1 67.1 47.5 34.9 37.8 .. 24.2 60.3 .. 62.8 w 38.7 52.0 40.2 54.3 51.8 53.4 65.0 50.2 50.1 30.0 45.1 65.1 76.2

21.5 31.4 22.2 18.6 35.0 36.4 79.4 54.0 30.5 12.6 .. 16.6 .. 20.7 44.9 67.2 15.4 17.0 30.9 54.0 66.5 13.6 13.9 11.3 46.9 32.8 39.8 16.0 40.6c 49.8 40.5 18.8 10.0 11.8 13.0 42.7 59.0 57.6 .. 74.8 26.5 .. 17.7 w 48.1 36.4 52.3 33.4 36.6 36.7 29.0 34.3 47.0 60.5 31.8 13.7 14.3

0.0 0.0 47.0 0.0 18.5 0.8 20.6 0.0 0.0 0.0 .. 2.2 .. 0.0 3.0 3.3 17.2 0.0 0.0 0.7 6.1 48.8 0.3 33.7 15.2 0.1 0.3 0.0 0.3c 25.9 0.4 0.0 0.0 0.0 0.0 0.9 0.0 3.4 .. 4.3 39.2 .. 0.2 w 25.9 0.6 2.8 0.2 1.1 0.4 0.3 0.2 0.7 2.3 10.5 0.0 0.1

5.6 5.1 10.5 4.3 5.7 10.4 13.1 4.0 8.8 9.4 .. 8.9 .. 9.5 2.9 6.3 6.6 9.6 11.5 3.4 6.0 6.0 3.9 9.1 7.7 5.7 6.2 6.7 2.5c 9.0 7.7 3.7 9.6 17.9 8.4 5.8 4.9 6.8 .. 5.2 5.9 .. 10.5 w 5.4 5.7 4.2 6.1 5.7 4.7 5.8 7.8 4.7 3.8 6.5 12.7 10.8

Health workers

Per capita $ PPP $ 2010

428 525 56 680 59 546 43 1,733 1,413 2,154 .. 649 .. 2,883 70 84 203 4,710 7,812 97 49 31 179 57 41 861 238 678 106c 47 234 1,450 3,503 8,362 998 82 663 83 .. 63 73 .. 949 w 27 225 71 382 199 183 439 670 203 47 84 4,879 3,969

2010

811 998 121 968 109 1,169 107 2,273 2,060 2,552 .. 935 .. 3,027 148 141 333 3,757 5,394 174 128 83 330 84 77 1,449 483 1,029 199c 124 519 1,544 3,480 8,362 1,188 184 589 215 .. 122 90 .. 1,023 w 61 369 149 594 329 316 797 854 425 115 143 4,660 3,685

Hospital beds

per 1,000 people Nurses and Community Physicians midwives health workers

per 1,000 people

2005–10a

2005–10a

2.3 4.3 0.0 0.9 0.1 2.1 0.0 1.8 3.0 2.5 0.0 .. .. 4.0 0.5 0.3 .. 3.8 4.1 1.5 2.1 0.0 0.3 .. 0.1 1.2 1.2 1.5 2.4 0.1 3.2 1.9 2.7 2.4 3.7 2.6 .. 1.2 .. 0.3 0.1 .. 1.4 w 0.2 1.2 0.8 1.7 1.1 1.2 3.2 1.8 1.4 0.6 0.2 2.8 3.6

2005–10a

5.9 8.5 0.4 2.1 0.4 4.5 0.2 5.9 0.3 8.4 0.1 .. .. 5.1 1.9 0.8 .. 11.9 16.5 1.9 5.3 0.2 .. .. 0.3 3.6 3.3 0.6 4.4 1.3 8.6 4.1 10.1 9.8 5.5 11.1 .. 1.0 .. .. 0.7 .. 2.8 w 0.5 2.0 1.5 2.6 1.9 1.5 6.7 .. 2.3 0.9 0.8 7.1 4.7

2005–10a

.. .. .. .. .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. w .. .. .. .. .. 0.8 .. .. .. 0.1 .. .. ..

6.6 9.7 1.6 2.2 0.3 5.4 0.4 3.1 6.5 4.6 .. 2.8 .. 3.2 .. 0.7 2.1e 2.8 5.2 1.5 5.2 0.7 2.1 5.9 0.9 2.6 2.1 2.5 4.0 0.5 8.7 1.9 3.3 3.0 1.2 4.6 1.1 3.1 .. 0.7 2.0 3.0 2.9 w .. 2.4 1.4 3.7 2.2 3.9 7.4 1.9 1.6 0.9 .. 5.7 5.8

a. Data are for the most recent year available. b. Nonprofi t institutions (such as nongovernmental organizations) serving households are accounted for in external resources, which are recorded under government expenditure. GDP includes both licit and illicit activity (for example, opium production). Public expenditures include external assistance. c. Derived from incomplete data. d. Excludes expenditure on residential facilities for care of the aged. e. Data are for 2011. f. Excludes northern Iraq. g. Excludes Transnistria.

102

2012 World Development Indicators


About the data

2.16

Definitions

Health systems—the combined arrangements of

expenditure. Thus data for this indicator from 2011

• Total health expenditure is the sum of public and

institutions and actions whose primary purpose

onward should not be compared with data from edi-

private health expenditure. It covers the provision of

is to promote, restore, or maintain health (World

tions before 2011.

health services (preventive and curative), family plan-

Health Organization, World Health Report 2000)—are

External resources for health are disbursements to

ning and nutrition activities, and emergency aid for

increasingly being recognized as key to combating

recipient countries as reported by donors, lagged one

health but excludes provision of water and sanitation.

disease and improving the health status of popu-

year to account for the delay between disbursement

• Public health expenditure is recurrent and capital

lations. The World Bank’s (2007a) Healthy Develop-

and expenditure. Except where a reliable full national

spending from central and local governments, exter-

ment: Strategy for Health, Nutrition, and Population

health account study has been done, most data are

nal borrowing and grants (including donations from

Results emphasizes the need to strengthen health

from the Organisation for Economic Co-operation and

international agencies and nongovernmental organi-

systems, which are weak in many countries, in order

Development Development Assistance Committee’s

zations), and social (or compulsory) health insurance

to increase the effectiveness of programs aimed at

Creditor Reporting System database, which compiles

funds. • Out-of-pocket health expenditure is direct

reducing specific diseases and further reduce mor-

data from government expenditure accounts, govern-

household outlays, including gratuities and in-kind

bidity and mortality. To evaluate health systems,

ment records on external assistance, routine sur-

payments, for health practitioners and pharmaceuti-

the World Health Organization (WHO) has recom-

veys of external financing assistance, and special

cal suppliers, therapeutic appliances, and other goods

mended that key components—such as financing,

services. Because of the variety of sources, caution

and services whose primary intent is to restore or

service delivery, workforce, governance, and infor-

should be used in interpreting the data.

enhance health. • External resources for health are

mation—be monitored using several key indicators

In countries where the fiscal year spans two cal-

funds or services in kind provided by entities that are

(WHO 2008b). The data in the table are a subset of

endar years, expenditure data have been allocated

not part of the country. The resources may come from

the first four indicators. Monitoring health systems

to the later year (for example, 2009 data cover fis-

international organizations, other countries through

allows the effectiveness, efficiency, and equity of

cal year 2008/09). Many low-income countries use

bilateral arrangements, or foreign nongovernmen-

different health system models to be compared.

Demographic and Health Surveys or Multiple Indica-

tal organizations and are part of public and private

Health system data also help identify weaknesses

tor Cluster Surveys funded by donors to obtain health

health expenditure. • Health expenditure per capita

and strengths and areas that need investment, such

system data.

is total health expenditure divided by population in

as additional health facilities, better health informa-

Data on health worker (physicians, nurses and

U.S. dollars and in international dollars converted

midwives, and community health workers) density

using 2005 purchasing power parity (PPP) rates from

Health expenditure data are broken down into

show the availability of medical personnel. The WHO

the World Bank’s International Comparison Project.

public and private expenditures. In general, low-

estimates that at least 2.5 physicians, nurses, and

• Physicians include generalist and specialist medi-

income economies have a higher share of private

midwives per 1,000 people are needed to provide

cal practitioners.• Nurses and midwives include pro-

health expenditure than do middle- and high-income

adequate coverage with primary care interventions

fessional nurses and midwives, auxiliary nurses and

countries, and out-of-pocket expenditure (direct pay-

associated with achieving the Millennium Develop-

midwives, enrolled nurses and midwives, and other

ments by households to providers) makes up the

ment Goals (WHO, World Health Report 2006). The

personnel, such as dental nurses and primary care

largest proportion of private expenditures. High

WHO compiles data from household and labor force

nurses. • Community health workers include tradi-

out-of-pocket expenditures may discourage people

surveys, censuses, and administrative records. Data

tional medicine practitioners, faith healers, assistant

from accessing preventive or curative care and can

comparability is limited by differences in definitions

or community health education workers, community

impoverish households that cannot afford needed

and training of medical personnel varies. In addition,

health officers, family health workers, lady health visi-

care. Health financing data are collected through

human resources tend to be concentrated in urban

tors, health extension package workers, community

national health accounts, which systematically,

areas, so that average densities do not provide a full

midwives, and traditional birth attendants. • Hospital

comprehensively, and consistently monitoring health

picture of health personnel available to the entire

beds are inpatient beds for both acute and chronic

system resource flows. To establish a national health

population.

care available in public, private, general, and special-

tion systems, or better trained human resources.

account, countries must define the boundaries of the

Availability and use of health services, such

health system and classify health expenditure infor-

as hospital beds per 1,000 people, refl ect both

mation along several dimensions, including sources

demand- and supply-side factors. In the absence of

of financing, providers of health services, functional

a consistent definition this is a crude indicator of

Data on health expenditure are from the WHO’s

use of health expenditures, and benefi ciaries of

the extent of physical, financial, and other barriers

National Global Health Expenditure database (see

expenditures. The accounting system can then pro-

to health care.

http://apps.who.int/nha/database for the most

ized hospitals and rehabilitation centers. Data sources

vide an accurate picture of resource envelopes and

recent updates), supplemented by country data.

financial flows and allow analysis of the equity and

Data on physicians, nurses and midwives, and com-

efficiency of financing to inform policy.

munity health workers are from the WHO’s Global

This year’s table, like last year’s, presents out-of-

Atlas of the Health Workforce database (http://

pocket expenditure as a percentage of total health

apps.who.int/globalatlas). Data on hospital beds

expenditure; editions before 2011 presented out-of-

are from the WHO, supplemented by country data.

pocket expenditure as a percentage of private health

2012 World Development Indicators

103

PEOPLE

Health systems


2.17

Health information Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

Completeness

%

1995–2010

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

104

2008 2009 2003 1997 2010 2008 2009 2000 2008 2010 2009 2008 2008 2010 2002 2009 2009 2009 2007 1995 2010

2010 2009 2009 2009 2005 2003 2008 2010 2008 2009 2009 2008 2008 2008 2010 2010 2008 2010 2010 2004 2010 2009 2002 2008

2012 World Development Indicators

1 2 3 0 1 7 14 15 0 1 13 1 7 4 14 7 3 10 9 7 1 0 1 16 0 0 8 15 0 12 7 1 2 2 2 0 6 15 15 8 9 3 16 0 12 4 16 16 0 3 10 15 1 0 14 0 0

2001–11

2010 2008/09 2006 2006/07 2005

2006 2007 2005 2006 2008 2006 2000 1996 2006 2005 2010 2006 2006 2004

2010 2010 2009 1993 2006 2006 1993 2007 2004 2008 2008 2002 2005

2000 2005/06 2005 2008 2002 2005 2010

2001 2008 2010 2001 2006 2011 2009 2010 2011 2009 2011 2002 2001 2011 2010 2011 2006 2008 2008 2005 2011 2003 2009 2002 2010 2006 2006 2007 2011 2011 2002 2001 2011 2011 2010 2010 2006 2007

2007 2010 2006 2003 2003 2002 2011 2010 2011 2002 2009

Birth registration 2005–10a

Infant death reporting 2005–10a

Total death reporting 2005–10a

.. 99 99 .. .. 96 .. .. 94

.. 30 .. .. 100 43 92 100 26 84 .. 62 96 .. .. 48 32 50 98 31 .. .. .. 99 .. .. 100 .. 66 55 .. .. 96 .. 94 100 56 89 86 1 54 56 35 .. 76 .. 84 100 .. .. 49 97 100 69 63 .. ..

.. 85 91 .. 99 100 96 100 79 79 .. 100 99 .. 31 93 43 88 97 90 .. .. .. 98 .. .. 100 97 94 77 .. .. 98 .. 99 100 72 100 97 54 84 100 79 .. 95 100 99 98 .. .. 97 100 .. 91 92 .. ..

10 .. .. 60 .. 100 72 91 .. 64 60 66 70 .. 49 .. 99 .. .. 97 28 81 .. 55 .. 100 .. .. 78 90 99 99 .. .. 7 .. .. .. 55 92 .. 71 .. .. 43 24


Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

2.17

Completeness

%

1995–2010

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

2006 2005 2009 2004 2008 2007 2010 2009 2007 2009 2000 2008 2009 2010 2010 2010

2010 2008 2008 2008 2009 2007 2006 2009 2004 2008 2004 2010 2010 2003 2006 2006 2007 2008 2010 2010 2009 2008 2009 2005 2009 1998 2006 1997 2000 2008 2005 2007 2009 2008

1 3 15 2 8 4 3 16 2 4 1 14 6 2 3 0 16 0 0 6 0 5 11 0 1 0 8 0 2 5 13 6 1 2 16 2 5 3 4 10 11 11 16 15 14 6 8 13 1 1 1 3 14 11 13 15 9

2001–11

2005/06 2005/06 2005/06 2007 2000 2006

2005 2009 2006 2010 2010

1996 2005/06 2006 2000 2009/10 2009 2000 2005 2008/09 2010 2010 2007 1995 2005 2010 2006 2009 2000 2009 2010

2006/07 2006 2008 1995 2010 2003 1996 2004 2008 2008

2003 2001 2001 2011 2010 2006 2011 2009 2012 2011 2010 2004 2009 2009 2009 2010 2010 2009 2005 2011 2006 2008 2006 2011 2010 2008 2010 2009 2000 2011 2010 2004 2010 2004 2007 2001 2001 2011 2006 2005 2001 2006 2001 2010 2010 2002 2007 2010 2011 2011

Birth registration 2005–10a

Infant death reporting 2005–10a

Total death reporting 2005–10a

81 94 .. 41 53 .. 95 .. .. .. 89 .. .. 99 60 100 .. .. .. 94 72 .. .. 45 4 .. .. 94 80 .. .. 81 56 .. .. .. 98 .. 31 72 67 35 .. .. .. 32 30 .. .. 27 .. .. .. 93 .. .. ..

.. 100 89 .. .. .. 100 86 100 99 73 92 .. 83 43 73 82 .. 100 76 .. 65 .. .. .. .. 71 61 .. .. 80 .. .. 90 82 73 60 .. .. 58 .. .. 89 97 66 .. .. 97 100 88 68 .. 34 45 38 89 57

.. 100 98 .. .. 100 100 98 96 98 88 100 82 88 42 93 98 .. 73 91 .. 96 87 .. .. .. 93 100 .. 71 99 .. .. 100 100 91 98 .. .. 100 100 .. 99 96 68 .. 3 99 70 80 90 .. 71 59 84 97 99

2012 World Development Indicators

105

PEOPLE

Health information


2.17

Health information Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

Completeness

%

1995–2010

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

2010 2009 2008 2006 2008 2005 2010 2006 2009 2010 1998 2009 2008 2010 2009 2010 2010 2006 2007 2002 2000 2005 2008 2007 2008 2009 2009 2008 2010 2007 2005 2007 2006 2001

a. Data are for the most recent year available.

106

2012 World Development Indicators

0 2 12 13 5 1 2 8 3 0 13 16 0 3 0 15 14 2 0 9 16 0 4 3 13 0 1 1 5 11 0 6 6 0 13 15 13 1 0 10 1 4 12 3

2001–11

1996 1999 1996 2007/08 2007 2008/09 2005/06 2008 2005

2006 2003

2006/07 2010 2010

2006 2005 2010 2005/06 2009/10 2010 2006 2006 2003 2006 2009/10 2007

2009 2006 2000 2006 2006 2006 2007 2005/06

2010 2010 2011 2010 2002 2010 2002 2011 2004 2010 2011 2011 2001 2008 2001 2001 2008 2010 2010 2004 2010 2002 2010 2010 2010 2011 2004

2002 2001 2010 2011 2010 2004 2001 2009 2007 2004 2010 2002

Birth registration 2005–10a

Infant death reporting 2005–10a

Total death reporting 2005–10a

.. .. .. .. 82 .. 55 99 51 .. .. .. 3 92

100 77 73 76 .. 100 100 37 13 100 100 79 .. 78

100 78 100 100 100 98 100 89 .. 74 100 97 .. 82

.. 97 33 30 .. .. 95 88 16 99 55 78 96 .. 94 96 21 100 .. .. .. .. 100 .. 88 96 22 14 38

84 67 .. .. 100 100 .. 24 .. 53 .. .. 49 .. 60 .. .. 75 96 100 100 77 .. 62 71 28 .. .. ..

94 94 .. 100 98 100 100 72 .. 79 .. .. 94 99 100 .. .. 93 91 96 98 100 .. 87 86 73 16 73 ..


About the data

2.17

Definitions

According to the World Health Organization (WHO),

• Year last national health account completed

health information systems are crucial for moni-

is the latest year for which the health expenditure

toring and evaluating health systems, which are

data are available using the national health account

increasingly recognized as important for combating

approach. • Number of national health accounts

disease and improving health status. Health informa-

completed is the number of national health accounts

tion systems underpin decisionmaking through four

completed during the specified years. • Year of last

data functions: generation, compilation, analysis and

health survey is the latest year the national survey

synthesis, and communication and use. The health

that collects health information was conducted.

information system collects data from the health sec-

• Year of last census is the latest year a census was

tor and other relevant sectors; analyzes the data and

conducted in the last 10 years. • Completeness of

ensures their overall quality, relevance, and timeli-

birth registration is the percentage of children under

ness; and converts data into information for health-

age 5 whose births were registered at the time of the

related decisionmaking (WHO 2008b).

survey. The numerator of completeness of birth regis-

Numerous indicators have been proposed to

tration includes children whose birth certificate was

assess a country’s health information system.

seen by the interviewer or whose mother or caretaker

They can be grouped into two broad types: indica-

says the birth has been registered. • Completeness

tors related to data generation using core sources

of infant death reporting is the number of infant

and methods (health surveys, civil registration, cen-

deaths reported by national statistical authorities

suses, facility reporting, health system resource

to the United Nations Statistics Division’s Demo-

tracking) and indicators related to capacity for data

graphic Yearbook divided by the number of infant

synthesis, analysis, and validation. Indicators related

deaths estimated by the United Nations Population

to data generation reflect a country’s capacity to col-

Division. • Completeness of total death reporting is

lect relevant data at suitable intervals using the most

the number of total deaths from civil registration sys-

appropriate data sources. Benchmarks include peri-

tems reported by national statistical authorities to

odicity, timeliness, contents, and availability. Indi-

the United Nations Statistics Division’s Demographic

cators related to capacity for synthesis, analysis,

Yearbook divided by the number of total deaths esti-

and validation measure the dimensions of the insti-

mated by the United Nations Population Division.

tutional frameworks needed to ensure data quality, including independence, transparency, and access. Benchmarks include the availability of independent

Data sources

coordination mechanisms and micro- and meta-data

Data on year last national health account completed

(WHO 2008a).

and number of national health accounts completed

The indicators in the table are all related to data

were compiled by the World Bank’s Health, Nutri-

generation, including the years the last national

tion, and Population Unit using information on the

health account, last health survey, and latest

health expenditures provided by the WHO National

population census were completed. Frequency of

Health Accounts staff and the OECD. Data on year

data collection, a benchmark of data generation, is

of last health survey are from ICF International and

shown as the number of years for which a national

the United Nations Children’s Fund (UNICEF). Data

health account was completed during the specified

on year of last census are from United Nations Sta-

years. National health account data may be collected

tistics Division’s 2011 World Population and Hous-

using different approaches such as Organisation for

ing Census Program (http://unstats.un.org/unsd/

Economic Co-operation and Development (OECD)

demographic/sources/census/2010_PHC/default.

System of Health Accounts, WHO National Health

htm). Data on completeness of birth registration

Account producers guide approach, local national

are compiled by UNICEF in State of the World’s

health accounting methods, or Pan American

Children 2012 based mostly on household surveys

Health Organization/WHO satellite health accounts

and ministry of health data. Data used to calculate

approach.

completeness of infant death reporting and total

Indicators related to data generation include com-

death reporting are from the United Nations Statis-

pleteness of birth registration, infant death report-

tics Division’s Population and Vital Statistics Report

ing, and total death reporting.

and the United Nations Population Division’s World Population Prospects: The 2010 Revision.

2012 World Development Indicators

107

PEOPLE

Health information


2.18

Disease prevention coverage and quality Access to an improved water source

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

108

Access to improved sanitation facilities

% of population 1990 2010

% of population 1990 2010

.. 97 94 42 94 .. 100 100 70 .. 77 100 100 57 70 97 93 89 100 43 70 31 49 100 58 39 90 67 .. 89 45 .. 93 76 99 82 100 100 100 88 72 93 74 43 98 14 100 100 .. 74 81 100 53 96 81 51 36 59

.. 76 88 29 90 .. 100 100 .. .. 39 93 100 5 18 .. 38 68 99 8 44 9 48 100 11 8 84 24 .. 67 9 .. 93 20 99 80 100 100 100 73 69 72 75 9 95 3 100 100 .. .. 96 100 7 97 62 10 .. 26

50 95 83 51 97 98 100 100 80 .. 81 100 100 75 88 99 96 98 100 79 72 64 77 100 67 51 96 91 .. 92 45 71 97 80 99 94 100 100 100 86 94 99 88 61 98 44 100 100 87 89 98 100 86 100 92 74 64 69

2012 World Development Indicators

37 94 95 58 90 90 100 100 82 .. 56 93 100 13 27 95 62 79 100 17 46 31 49 100 34 13 96 64 .. 77 24 18 95 24 99 91 100 98 100 83 92 95 87 14 95 21 100 100 33 68 95 100 14 98 78 18 20 17

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2010 2010

62 99 95 93 99 97 94 76 67 99 94 99 94 69 79 93 94 99 97 94 92 93 79 93 62 46 93 99 .. 88 68 76 83 70 95 99 87 98 85 79 98 96 92 99 95 81 98 90 55 97 94 96 93 99 93 51 61 59

66 99 95 91 94 94 92 83 72 99 95 98 99 83 80 90 96 98 94 95 96 92 84 80 54 59 92 99 .. 88 63 90 88 85 96 96 99 99 90 88 99 97 92 99 94 86 99 99 45 98 91 93 94 99 94 57 76 59

Children Children with with acute diarrhea who respiratory received oral infection (ARI) rehydration taken to and continuous health feeding provider

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

Tuberculosis

Treatment success rate

Case detection rate

% of children under age 5 with ARI 2005–10c

% of children under age 5 with diarrhea 2005–10c

% of children under age 5 2005–10c

% of children under age 5 with fever 2005–10c

% of new registered cases

% of new estimated cases

2009

2010

.. 70 53 .. .. 57 .. .. 33 .. 37 90 .. 36 51 91 .. 50 .. 39 38 64 35 .. 32 26 .. .. .. 64 40 48 .. 35 .. .. .. .. .. 70 .. 73 67 .. .. 19 .. .. .. 69 74 .. 51 .. .. 42 52 31

.. 63 24 .. .. 59 .. .. 31 .. 68 54 .. 42 29 53 .. .. .. 42 23 50 22 .. 47 23 .. .. .. 52 37 39 .. 45 .. .. .. .. .. 55 .. 19 .. .. .. 15 .. .. .. 38 37 .. 45 .. .. 38 53 43

.. .. .. 17.7 .. .. .. .. .. .. .. .. .. 20.1 .. .. .. .. .. 9.6 45.2 4.2 13.1 .. 15.1 9.8 .. .. .. .. 35.7 6.1 .. 3.0 .. .. .. .. .. .. .. .. .. 48.9 .. 33.1 .. .. 55.1 49.0 .. .. 28.2 .. .. 4.5 35.5 ..

.. .. .. 29.3 .. .. .. .. .. .. .. .. .. 54.0 .. .. .. .. .. 48.0 17.2 0.2 57.8 .. 57.0 35.7 .. .. .. .. 39.1 48.0 .. 36.0 .. .. .. .. .. 0.6 .. .. .. 13.1 .. 9.5 .. .. .. 63.0 .. .. 43.0 .. .. 73.9 51.2 5.1

86 89 91 72 46 73 80 66 62 98 92 64 76 90 86 99 79 72 85 76 90 95 78 75 53 76 72 95 70 77 88 78 54 79 63 90 29 67 53 85 75 88 89 85 59 84 68 .. 55 89 75 77 87 .. 83 79 67 79

47 97 70 77 66 62 84 84 63 84 46 74 87 45 62 71 70 88 79 53 70 65 69 83 47 31 75 87 87 72 53 68 78 83 73 79 68 88 93 59 51 64 96 55 85 72 87 47 42 44 100 89 70 68 37 33 62 62


Access to an improved water source

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

Access to improved sanitation facilities

% of population 1990 2010

% of population 1990 2010

76 96 69 70 90 81 100 100 100 93 100 97 96 44 100 .. .. 99 .. .. 99 100 80 .. 54 .. 100 29 41 88 28 30 99 85 .. 54 73 36 56 64 76 100 100 74 35 47 100 80 85 84 41 52 75 85 .. 96 .. 100

50 100 18 32 79 .. 99 100 .. 80 100 97 96 25 .. 100 .. 100 .. .. .. .. .. .. 97 .. .. 9 39 84 15 16 89 64 .. .. 53 11 .. 24 10 100 .. 43 5 37 100 82 27 58 47 37 54 57 .. 92 .. 100

87 100 92 82 96 79 100 100 100 93 100 97 95 59 98 98 .. 99 90 67 99 100 78 73 .. .. 100 46 83 100 64 50 99 96 96 82 83 47 83 93 89 100 100 85 49 58 100 89 92 93 40 86 85 92 100 99 .. 100

77 100 34 54 100 73 99 100 .. 80 100 98 97 32 80 100 .. 100 93 63 78 .. 26 18 97 .. 88 15 51 96 22 26 89 85 85 51 70 18 76 32 31 100 .. 52 9 31 100 99 48 69 45 71 71 74 90 100 .. 100

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2010 2010

99 99 74 89 99 73 90 98 90 88 94 98 99 86 99 98 .. 98 99 64 93 53 85 64 98 96 98 67 93 96 63 67 99 95 97 97 98 70 88 75 86 96 91 99 71 71 93 97 86 95 55 94 94 88 98 96 .. 99

98 99 72 83 99 65 94 96 96 99 98 98 99 83 93 94 .. 98 96 74 89 74 83 64 98 95 95 74 93 94 76 64 99 95 90 96 99 74 90 83 82 97 93 98 70 69 93 99 88 94 56 90 93 87 99 98 .. 97

Children Children with with acute diarrhea who respiratory received oral infection (ARI) rehydration taken to and continuous health feeding provider

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

2.18 Tuberculosis

Treatment success rate

Case detection rate

% of children under age 5 with ARI 2005–10c

% of children under age 5 with diarrhea 2005–10c

% of children under age 5 2005–10c

% of children under age 5 with fever 2005–10c

% of new registered cases

% of new estimated cases

2009

2010

56 .. 69 66 .. 82 .. .. .. 75 .. 75 71 56 80 .. .. .. 62 32 .. .. 66 62 .. .. 93 42 52 .. 38 45 .. .. 60 63 .. 65 .. 72 43 .. .. .. 47 45 .. .. 69 .. 63 .. 68 50 .. .. .. ..

49 .. 33 54 .. 64 .. .. .. 39 .. 32 48 43 67 .. .. .. 22 49 .. .. 48 47 .. .. 45 49 27 .. 38 32 .. .. 48 47 .. 47 .. 48 37 .. .. .. 34 25 .. .. 37 .. .. .. 60 60 .. .. .. ..

.. .. .. 3.3 .. .. .. .. .. .. .. .. .. 46.7 .. .. .. .. .. 40.5 .. .. .. 26.4 .. .. .. 45.8 56.5 .. 70.2 .. .. .. .. .. .. 22.8 .. 34.0 .. .. .. .. 63.7 29.1 .. .. .. .. .. .. .. .. .. .. .. ..

0.5 .. 8.2 0.8 .. .. .. .. .. .. .. .. .. 23.2 .. .. .. .. .. 8.2 .. .. .. 67.2 .. .. .. 19.7 30.9 .. 31.7 20.7 .. .. .. .. .. 36.7 .. 20.3 0.1 .. .. .. 33.0 49.1 .. .. 3.3 .. .. .. .. 0.0 .. .. .. ..

86 57 88 91 83 90 67 86 .. 70 52 75 62 86 89 83 .. 85 82 93 75 82 70 83 69 73 90 82 88 78 78 63 88 86 54 88 84 85 85 85 90 80 76 85 79 83 82 98 91 80 72 80 81 89 67 84 81 80

74 100 59 66 81 48 88 93 57 72 84 100 82 82 100 90 .. 86 66 72 100 71 85 56 84 76 89 44 65 80 51 21 44 110 63 72 97 34 71 82 72 85 90 100 35 40 93 85 65 89 70 77 100 65 80 79 96 87

2012 World Development Indicators

109

PEOPLE

Disease prevention coverage and quality


2.18

Disease prevention coverage and quality Access to an improved water source

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

Access to improved sanitation facilities

% of population 1990 2010

% of population 1990 2010

75 93 66 89 61 99 38 100 100 100 .. 83 .. 100 67 65 39 100 100 86 .. 55 86 .. 49 88 81 85 .. 43 .. 100 100 99 96 90 90 57 .. 67 49 79 76 w 54 73 70 76 71 68 90 86 86 71 49 99 100

71 74 36 .. 38 .. 11 99 100 100 .. 71 .. 100 70 27 48 100 100 85 .. 7 84 .. 13 93 74 84 98 27 .. 97 100 100 94 84 82 37 .. 24 46 41 47 w 21 39 29 46 37 30 80 68 73 22 26 100 100

.. 97 65 .. 72 99 55 100 100 99 29 91 .. 100 91 58 71 100 100 90 64 53 96 69 61 94 94 100 .. 72 98 100 100 99 100 87 .. 95 85 55 61 80 88 w 65 90 87 93 86 90 96 94 89 90 61 100 100

72 70 55 .. 52 92 13 100 100 100 23 79 .. 100 92 26 57 100 100 95 94 10 96 47 13 92 85 90 98 34 94 98 100 100 100 100 .. 76 92 53 48 40 62 w 37 59 47 73 56 66 84 79 88 38 31 100 100

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2010 2010

95 98 82 98 60 95 82 95 98 95 46 65 .. 95 99 90 94 96 90 82 94 92 98 66 84 92 97 97 99 55 94 94 93 92 95 98 79 98 .. 73 91 84 85 w 78 86 80 96 84 95 96 93 88 77 75 93 93

97 97 80 98 70 91 90 97 99 96 45 63 .. 97 99 90 89 98 96 80 93 91 99 72 92 90 98 96 96 60 90 94 96 95 95 99 78 93 .. 87 82 83 85 w 80 85 79 96 84 94 95 93 89 76 77 95 96

Children Children with with acute diarrhea who respiratory received oral infection (ARI) rehydration taken to and continuous health feeding provider % of children under age 5 with ARI 2005–10c

.. .. 28 .. 47 93 46 .. .. .. 13 .. .. .. 58 90 73 .. .. 77 64 71 84 71 23 74 59 .. 83 73 .. .. .. .. .. 68 .. 83 .. .. .. 43 .. w .. .. 67 .. .. .. .. .. .. 67 .. .. ..

% of children under age 5 with diarrhea 2005–10c

.. .. 24 .. 43 71 57 .. .. .. 7 .. .. .. 67 56 22 .. .. 34 22 50 46 63 24 32 62 22 25 39 .. .. .. .. .. 28 .. 65 .. 48 68 35 .. w 39 .. 37 .. .. .. .. .. .. 37 35 .. ..

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

% of children under age 5 2005–10c

% of children under age 5 with fever 2005–10c

.. .. 69.8 .. 29.2 .. 25.8 .. .. .. 11.4 .. .. .. 2.9 25.3 0.6 .. .. .. 1.3 63.6 .. 41.0 56.9 .. .. .. .. 32.8 .. .. .. .. .. .. .. 5.0 .. .. 56 17.3 .. w .. .. .. .. .. .. .. .. .. .. 34.0 .. ..

.. .. 10.8 .. 9.1 .. 30.1 .. .. .. 7.9 .. .. .. 0.3 35.8 0.6 .. .. .. 1.9 59.1 .. 6.0 33.8 .. .. .. .. 59.6 .. .. .. .. .. .. .. 2.6 .. .. 49.9 23.6 .. w 31.9 .. 13.8 .. .. .. .. .. .. 7.2 37.8 .. ..

Tuberculosis

Treatment success rate

Case detection rate

% of new registered cases

% of new estimated cases

2009

2010

85 55 85 65 85 86 79 82 82 87 85 73 .. .. 86 80 69 85 .. 88 81 88 86 85 81 69 83 91 84 67 60 73 82 60 80 81 84 92 82 88 90 78 86 w 86 87 87 86 87 92 65 77 87 88 79 68 ..

74 78 60 88 31 130 32 87 88 79 38 72 .. 87 69 50 66 87 55 90 44 77 70 87 10 87 91 77 96 61 73 57 91 88 97 48 66 54 16 76 90 56 65 w 58 67 61 81 65 76 73 80 73 58 60 85 ..

a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the most recent year available.

110

2012 World Development Indicators


About the data

2.18

Definitions

People’s health is influenced by the environment

progress, so it is diffi cult to accurately compare

• Access to an improved water source refers to peo-

in which they live. Lack of clean water and basic

use rates across countries. Until the current recom-

ple with access to at least 20 liters of water a person

sanitation is the main reason diseases transmitted

mended method for home management of diarrhea is

a day from an improved source, such as piped water

by feces are so common in developing countries.

adopted and applied in all countries, the data should

into a dwelling, public tap, tubewell, protected dug

Access to drinking water from an improved source

be used with caution. Also, the prevalence of diar-

well, and rainwater collection, within 1 kilometer of

and access to improved sanitation do not ensure

rhea may vary by season. Since country surveys are

the dwelling. • Access to improved sanitation facili-

safety or adequacy, as these characteristics are

administered at different times, data comparability

ties refers to people with at least adequate access

not tested at the time of the surveys. But improved

is further affected.

to excreta disposal facilities that can effectively pre-

drinking water technologies and improved sanitation

Malaria is endemic to the poorest countries in the

vent human, animal, and insect contact with excreta.

facilities are more likely than those characterized

world, mainly in tropical and subtropical regions of

Improved facilities range from protected pit latrines

as unimproved to provide safe drinking water and

Africa, Asia, and the Americas. Insecticide-treated

to flush toilets. • Child immunization rate refers to

to prevent contact with human excreta. The data

nets, properly used and maintained, are one of the

children ages 12–23 months who, before 12 months

are derived by the Joint Monitoring Programme of

most important malaria-preventive strategies to limit

or at any time before the survey, had received one

the World Health Organization (WHO) and United

human-mosquito contact.

dose of measles vaccine and three doses of diphthe-

Nations Children’s Fund (UNICEF) based on national

Prompt and effective treatment of malaria is a criti-

censuses and nationally representative household

cal element of malaria control. It is vital that suffer-

vaccine. • Children with acute respiratory infection

surveys. The coverage rates for water and sanitation

ers, especially children under age 5, start treatment

(ARI) taken to health provider are children under age

are based on information from service users on the

within 24 hours of the onset of symptoms, to pre-

5 with ARI in the two weeks before the survey who

facilities their households actually use rather than

vent progression—often rapid—to severe malaria

were taken to an appropriate health provider. • Chil-

on information from service providers, which may

and death. Data on malaria are from national- level

dren with diarrhea who received oral rehydration and

include nonfunctioning systems. While the estimates

surveys, including Multiple Indicator Cluster Surveys,

continuous feeding are children under age 5 with diar-

are based on use, the Joint Monitoring Programme

Demographic and Health Surveys, and Malaria Indi-

rhea in the two weeks before the survey who received

reports use as access, because access is the term

cator Surveys.

either oral rehydration therapy or increased fluids,

ria, pertussis (whooping cough), and tetanus (DTP3)

Data on the success rate of tuberculosis treatment

with continuous feeding. • Children sleeping under

are provided for countries that have submitted data

treated nets are children under age 5 who slept under

Governments in developing countries usually

to the WHO. The treatment success rate for tuber-

an insecticide-treated net to prevent malaria the night

finance immunization against measles and diphthe-

culosis provides a useful indicator of the quality of

before the survey. • Children with fever receiving

ria, pertussis (whooping cough), and tetanus (DTP)

health services. A low rate suggests that infectious

antimalarial drugs are children under age 5 who were

as part of the basic public health package. In many

patients may not be receiving adequate treatment.

ill with fever in the two weeks before the survey and

developing countries lack of precise information on

An important complement to the tuberculosis treat-

received any appropriate (locally defined) antimalarial

the size of the cohort of one-year-old children makes

ment success rate is the case detection rate, which

drugs. • Tuberculosis treatment success rate is new

immunization coverage diffi cult to estimate from

indicates whether there is adequate coverage by the

registered infectious tuberculosis cases that were

program statistics. The data shown here are based

recommended case detection and treatment strat-

cured or that completed a full course of treatment as

on an assessment of national immunization cover-

egy. Uncertainty bounds for the case detection rate,

a percentage of smear-positive cases registered for

age rates by the WHO and UNICEF. The assessment

not shown in the table, are available at http://data.

treatment outcome evaluation. • Tuberculosis case

considered both administrative data from service

worldbank.org and from the original source.

detection rate is newly identified tuberculosis cases

used in the Millennium Development Goal target for drinking water and sanitation.

providers and household survey data on children’s

The table shows the tuberculosis detection rate for

immunization histories. Based on the data available,

all detection methods. Editions before 2010 included

consideration of potential biases, and contributions

the tuberculosis detection rates by DOTS, the inter-

of local experts, the most likely true level of immuni-

nationally recommended strategy for tuberculosis

zation coverage was determined for each year.

control. Thus data on the case detection rate from

Data on access to water and sanitation are from

2010 onward cannot be compared with data in previ-

the WHO and UNICEF’s Progress on Drinking Water

ous editions.

and Sanitation (2012). Data on immunization are

Acute respiratory infection continues to be a leading cause of death among young children, killing

(including relapses) as a percentage of estimated incident cases (case detection, all forms). Data sources

nearly 1.5 million children under age 5 globally each

For indicators that are from household surveys, the

from WHO and UNICEF estimates (www.who.int/

year. Data are drawn mostly from household health

year in the table refers to the survey year. For more

immunization_monitoring). Data on children with ARI,

surveys in which mothers report on number of epi-

information, consult the original sources.

with diarrhea, sleeping under treated nets, and receiv-

sodes and treatment for acute respiratory infection.

ing antimalarial drugs are from UNICEF’s State of the

Most diarrhea- related deaths are due to dehydra-

World’s Children 2012, Childinfo, and MEASURE DHS

tion, and many of these deaths can be prevented with

Demographic and Health Surveys by ICF International.

the use of oral rehydration salts at home. However,

Data on tuberculosis are from the WHO’s Global Tuber-

recommendations for the use of oral rehydration

culosis Control: A Short Update to the 2011 Report.

therapy have changed over time based on scientific

2012 World Development Indicators

111

PEOPLE

Disease prevention coverage and quality


2.19

Reproductive health Total fertility Adolescent rate fertility rate

births per woman 1990 2010

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

112

8.0 3.2 4.7 7.2 3.0 2.5 1.9 1.5 2.7 3.7 4.5 1.9 1.6 6.7 4.9 1.7 4.7 2.8 1.8 6.8 6.5 5.7 5.9 1.8 5.8 6.7 2.6 2.3 1.3 3.1 7.1 5.4 3.2 6.3 1.6 1.8 2.4 1.9 1.7 3.5 3.7 4.4 4.0 6.2 2.0 7.1 1.8 1.8 5.2 6.1 2.2 1.5 5.6 1.4 5.6 6.7 6.6 5.4

6.3 1.5 2.3 5.4 2.2 1.7 1.9 1.4 2.3 2.5 2.2 1.4 1.8 5.3 3.3 1.1 2.8 1.8 1.5 5.9 4.3 2.6 4.5 1.7 4.6 6.0 1.9 1.6 1.1 2.1 5.8 4.5 1.8 4.4 1.5 1.5 1.5 1.5 1.9 2.6 2.5 2.7 2.3 4.5 1.6 4.2 1.9 2.0 3.3 4.9 1.6 1.4 4.2 1.4 4.0 5.2 5.1 3.3

2012 World Development Indicators

Unmet Contraceptive Pregnant need for prevalence women contraception rate receiving prenatal care

births per 1,000 women ages 15–19 2010

% of married women ages 15–49 2005–10a

Any method % of married women ages 15–49 2005–10a

% 2005–10a

107 16 7 157 55 34 14 11 32 15 73 21 12 103 76 15 47 76 39 120 20 36 120 12 102 149 57 9 4 71 183 115 63 115 13 44 6 10 5 106 81 43 79 59 19 58 9 6 85 71 42 7 66 10 104 143 102 43

.. 13 11 .. .. 13 .. .. 23 .. 17 .. .. 30 20 23 .. .. .. 31 .. 17 3 .. .. .. .. .. .. 7 24 16 .. 29 .. 8 .. .. .. 11 .. 9 .. .. .. 34 .. .. .. .. .. .. 35 .. .. 21 25 38

23 69 61 .. 78 55 .. .. 51 .. 53 73 .. 17 61 36 53 81 .. 17 22 51 29 .. 19 5 58 85 80 79 17 44 80 13 .. 78 .. .. .. 73 .. 60 73 .. .. 15 .. 71 .. .. 53 .. 24 .. 54 9 14 32

36 97 89 80 99 99 98 .. 77 100 53 99 .. 84 86 99 94 98 .. 85 99 89 82 100 69 53 .. 92 .. 97 88 86 90 85 100 100 99 .. .. 99 .. 74 94 .. .. 28 .. .. .. 98 98 .. 90 .. 93 88 93 85

Births attended by skilled health staff

% of total 1990 2005–10a

.. 93 77 .. 96 100 100 .. 97 .. .. 100 .. .. 43 97 78 70 99 .. .. .. 64 .. .. .. .. 94 .. 94 .. .. 98 .. 100 .. .. 100 .. 92 .. 37 90 .. 99 .. .. .. .. 44 97 .. 40 .. .. 31 .. 23

24 99 95 47 98 100 .. .. 88 97 27 100 .. 74 71 100 95 97 100 54 60 71 63 100 44 23 100 99 100 98 79 83 99 57 100 100 .. 100 .. 98 .. 79 96 .. 100 6 .. .. .. 57 100 100 57 .. 51 46 44 26

Maternal mortality ratio

Lifetime risk of maternal mortality

per 100,000 live births National Modeled estimates estimates a 2005–10 1990 2008

.. 21 .. .. 55 27 .. .. 24 .. 190 1 .. 400 310 3 200 75 5 310 620 206 .. .. 540 .. 17 32 .. 76 550 780 21 540 13 43 .. 2 .. 160 61 55 59 .. 7 670 .. .. .. .. 52 .. 450 .. 130 980 410 630

1,700 48 250 1,000 72 51 10 10 64 25 870 37 7 790 510 18 83 120 24 770 1,200 690 680 6 880 1,300 56 110 .. 140 900 460 35 690 8 63 17 15 7 220 230 220 200 930 48 990 7 13 260 750 58 13 630 6 140 1,200 1,200 670

1,400 31 120 610 70 29 8 5 38 19 340 15 5 410 180 9 190 58 13 560 970 290 600 12 850 1,200 26 38 .. 85 670 580 44 470 14 53 10 8 5 100 140 82 110 280 12 470 8 8 260 400 48 7 350 2 110 680 1,000 300

Probability 1 woman in: 2008

11 1,700 340 29 600 1,900 7,400 14,300 1,200 2,200 110 5,100 10,900 43 150 9,300 180 860 5,800 28 25 110 35 5,600 27 14 2,000 1,500 .. 460 24 39 1,100 44 5,200 1,400 6,600 8,500 10,900 320 270 380 350 72 5,300 40 7,600 6,600 110 49 1,300 11,100 66 31,800 210 26 18 93


Total fertility Adolescent rate fertility rate

births per woman 1990 2010

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

5.1 1.8 3.9 3.1 4.8 6.0 2.1 2.8 1.3 2.9 1.5 5.8 2.7 6.0 2.4 1.6 3.9 2.6 3.7 6.2 2.0 3.1 4.9 6.5 4.8 2.0 2.1 6.3 6.8 3.5 7.1 5.9 2.3 3.4 2.4 4.1 4.0 6.2 3.4 5.2 5.2 1.6 2.2 4.8 7.8 6.4 1.9 7.2 6.0 3.0 4.8 4.5 3.8 4.3 2.0 1.4 2.2 4.2

3.1 1.3 2.6 2.1 1.7 4.7 2.1 3.0 1.4 2.3 1.4 3.8 2.6 4.7 2.0 1.2 2.3 2.3 2.9 2.7 1.2 1.8 3.2 5.2 2.6 1.6 1.4 4.7 6.0 2.6 6.3 4.5 1.5 2.3 1.5 2.5 2.3 4.9 2.0 3.2 2.7 1.8 2.2 2.6 7.1 5.5 2.0 2.3 3.4 2.5 4.0 3.0 2.5 3.1 1.4 1.3 1.8 2.3

Unmet Contraceptive Pregnant need for prevalence women contraception rate receiving prenatal care

births per 1,000 women ages 15–19 2010

% of married women ages 15–49 2005–10a

Any method % of married women ages 15–49 2005–10a

% 2005–10a

89 15 79 43 27 91 12 14 5 73 6 25 27 99 1 4 .. 14 33 34 15 16 66 131 3 18 19 127 111 12 176 75 33 68 31 20 13 134 14 62 93 5 24 108 199 114 8 9 30 79 64 69 51 50 13 14 52 16

17 .. 13 15 .. .. .. .. .. .. .. 11 .. 26 .. .. .. .. 1 .. .. .. .. 36 .. .. 34 19 .. .. 31 25 .. .. 7 14 .. .. .. 21 25 .. .. 8 16 20 .. .. 25 .. .. .. 8 22 .. .. .. ..

65 .. 54 56 79 50 65 .. .. 72 54 59 51 46 .. 80 .. .. 48 38 .. .. 47 11 .. .. 14 40 41 .. 8 9 .. 73 68 55 .. 16 41 55 48 69 .. 72 18 15 88 24 27 52 32 79 74 51 .. 67 .. ..

92 .. 75 95 98 84 .. .. .. 99 .. 99 100 92 100 .. .. 100 97 71 .. .. 92 79 93 .. 99 86 92 79 70 75 .. 96 98 100 .. 92 80 95 44 .. .. 90 46 58 .. 99 61 96 79 96 95 91 .. .. .. 100

Births attended by skilled health staff

2.19

Maternal mortality ratio

Lifetime risk of maternal mortality

% of total 1990 2005–10a

per 100,000 live births National Modeled estimates estimates a 2005–10 1990 2008

Probability 1 woman in: 2008

47 99 .. 41 .. 54 .. .. .. 92 100 87 99 50 .. .. .. .. 99 .. 100 .. .. .. .. 100 89 57 55 93 .. 40 91 84 100 .. 31 .. 46 68 7 .. .. .. 15 31 100 .. 19 86 .. 66 53 .. 100 98 .. ..

.. 19 250 230 25 84 .. .. .. .. .. 19 37 488 77 .. .. .. 64 410 32 .. 1,200 990 .. 9 4 500 810 29 460 690 .. 54 45 47 130 500 320 450 280 .. .. 67 650 550 .. 17 250 60 730 130 93 160 2 .. .. ..

240 5,500 140 190 1,500 300 17,800 5,100 15,200 450 12,200 510 950 38 230 4,700 .. 4,500 450 49 3,600 2,000 62 20 540 5,800 7,300 45 36 1,200 22 41 1,600 500 2,000 730 360 37 180 160 80 7,100 3,800 300 16 23 7,600 1,600 93 520 94 310 370 320 13,300 9,800 3,000 4,400

67 100 53 82 97 80 .. .. .. 98 .. 99 100 44 100 .. .. 100 99 37 100 .. 62 46 100 100 100 44 54 99 49 61 99 95 100 100 .. 55 64 81 19 .. .. 74 18 39 .. 99 39 89 53 82 84 62 100 .. .. 100

210 23 570 620 150 93 6 12 10 66 12 110 78 380 270 18 .. 10 77 1,200 57 52 370 1,100 100 34 16 710 910 56 1,200 780 72 93 62 130 270 1,000 420 180 870 10 18 190 1,400 1,100 9 49 490 86 340 130 250 180 17 15 29 15

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

2012 World Development Indicators

113

PEOPLE

Reproductive health


2.19

Reproductive health Total fertility Adolescent rate fertility rate

births per woman 1990 2010

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

1.8 1.9 7.0 5.8 6.6 1.8 5.7 1.9 2.1 1.5 6.6 3.7 .. 1.3 2.5 6.0 5.7 2.1 1.6 5.3 5.2 6.2 2.1 5.3 6.3 2.4 3.6 3.0 4.3 7.1 1.8 4.4 1.8 2.1 2.5 4.1 3.4 3.6 6.5 8.7 6.5 5.2 3.2 w 5.7 3.3 4.2 2.6 3.6 2.6 2.3 3.2 4.9 4.2 6.2 1.8 1.5

1.4 1.5 5.4 2.8 4.8 1.4 5.0 1.2 1.4 1.6 6.3 2.5 3.9 1.4 2.3 4.4 3.4 2.0 1.5 2.9 3.3 5.5 1.6 5.6 4.1 1.6 2.0 2.1 2.4 6.1 1.4 1.7 1.9 2.1 2.0 2.5 2.5 1.8 4.5 5.2 6.3 3.3 2.5 w 4.1 2.3 2.9 1.8 2.6 1.8 1.8 2.2 2.7 2.7 4.9 1.8 1.6

births per 1,000 women ages 15–19 2010

30 26 37 18 96 20 120 6 18 5 69 54 .. 12 23 57 74 6 4 39 27 129 40 58 59 33 5 34 18 136 28 25 30 33 60 13 88 24 50 71 142 58 53 w 94 51 68 29 58 19 27 72 37 73 108 18 8

a. Data are for most recent year available. b. Data are for 2011.

114

2012 World Development Indicators

Unmet Contraceptive Pregnant need for prevalence women contraception rate receiving prenatal care % of married women ages 15–49 2005–10a

.. .. 38 .. 32 29 28 .. .. .. 26 .. .. .. .. 6 24 .. .. 11 24 25 .. 31 41 27 .. 18 .. 41 10 .. .. .. .. 8 .. .. .. 24 27 13 .. w 25 .. 14 .. .. .. .. .. .. 15 25 .. ..

Any method % of married women ages 15–49 2005–10a

.. 80 52 24 12 41 8 .. .. .. 15 .. .. 66 68 8 49 .. .. 54 37 34 80 22 15 43 60 73 48 24 67 .. 84 79 78 65 .. 80 50 28 41 59b 62 w 34 65 50 81 61 78 69 75 62 51 22 .. ..

% 2005–10a

.. .. 98 97 87 98 87 .. .. .. 26 97 40 .. 99 56 97 .. .. 88 80 88 99 84 87 96 96 95 99 94 99 100 .. .. 96 99 .. 91 99 47 94 93b 84 w 69 86 78 94 83 92 .. 97 85 71 74 .. ..

Births attended by skilled health staff

% of total 1990 2005–10a

100 99 26 .. .. .. .. .. 100 100 .. .. .. .. .. 69 .. .. .. .. 90 44 .. .. 31 .. 69 .. .. 38 100 .. .. 99 .. .. .. .. .. 16 51 70 62 w .. 60 38 89 58 82 93 74 47 32 .. .. ..

Maternal mortality ratio

Lifetime risk of maternal mortality

per 100,000 live births National Modeled estimates estimates a 2005–10 1990 2008

99 21 100 17 69 .. 97 14 52 400 99 9 42 860 .. .. 100 10 100 10 33 1,000 .. 400 19 .. .. .. 99 39 49 1,100 82 589 .. .. 100 .. 96 .. 83 86 49 450 99 12 29 560 60 .. 98 .. 95 .. 95 29 100 12 42 440 99 16 100 0 .. .. .. 13 100 34 100 21 .. 57 88 69 99 .. 36 .. 47 590 60 b 730 66 w 44 71 57 98 65 91 98 90 81 48 46 .. ..

170 74 1,100 41 750 13 1,300 6 15 11 1,100 230 .. 7 91 830 260 7 8 120 120 880 50 650 650 86 130 68 91 670 49 28 10 12 39 53 84 170 .. 540 390 390 400 w 860 350 540 110 440 200 69 140 210 610 870 15 11

27 39 540 24 410 8 970 9 6 18 1,200 410 .. 6 39 750 420 5 10 46 64 790 48 370 350 55 60 23 77 430 26 10 12 24 27 30 68 56 .. 210 470 790 260 w 590 210 300 60 290 89 34 86 88 290 650 15 7

Probability 1 woman in: 2008

2,700 1,900 35 1,300 46 7,500 21 10,000 13,300 4,100 14 100 .. 11,400 1,100 32 75 11,400 7,600 610 430 23 1,200 44 67 1,100 860 1,900 500 35 3,000 4,200 4,700 2,100 1,700 1,400 540 850 .. 91 38 42 140 w 39 190 100 880 120 580 1,700 480 380 110 31 3,900 10,100


About the data

2.19

Definitions

Reproductive health is a state of physical and men-

and Health Surveys attempt to measure maternal

• Total fertility rate is the number of children that would

tal well-being in relation to the reproductive system

mortality by asking respondents about survivorship

be born to a woman if she were to live to the end of her

and its functions and processes. Means of achieving

of sisters. The main disadvantage of this method

childbearing years and bear children in accordance with

reproductive health include education and services

is that the estimates of maternal mortality that it

the age-specific fertility rate of the specified year. • Ado-

during pregnancy and childbirth, safe and effec-

produces pertain to 12 years or so before the sur-

lescent fertility rate is the number of births per 1,000

tive contraception, and prevention and treatment

vey, making them unsuitable for monitoring recent

women ages 15–19. • Unmet need for contraception is

of sexually transmitted diseases. Complications of

changes or observing the impact of interventions.

the percentage of fertile, married women of reproductive

pregnancy and childbirth are the leading cause of

In addition, measurement of maternal mortality is

age who do not want to become pregnant and are not

death and disability among women of reproductive

subject to many types of errors. Even in high-income

using contraception. • Contraceptive prevalence rate

age in developing countries.

countries with reliable vital registration systems,

is the percentage of women married or in union ages

misclassification of maternal deaths has been found

15–49 who are practicing, or whose sexual partners

to lead to serious underestimation.

are practicing, any form of contraception. • Pregnant

Total and adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from

The national estimates of maternal mortality

women receiving prenatal care are women attended at

censuses or sample surveys. The estimated rates

ratios in the table are based on national surveys,

least once during pregnancy by skilled health person-

are generally considered reliable measures of fertility

vital registration records, and surveillance data or

nel for pregnancy- related reasons. • Births attended by

in the recent past. Where no empirical information

are derived from community and hospital records.

skilled health staff are live births attended by person-

on age- specific fertility rates is available, a model is

The modeled estimates are based on an exercise by

nel trained to give women the necessary care during

used to estimate the share of births to adolescents.

the World Health Organization (WHO), United Nations

pregnancy, labor, and postpartum; to conduct deliveries

For countries without vital registration systems fertil-

Children’s Fund (UNICEF), United Nations Population

on their own; and to care for newborns. • Maternal mor-

ity rates are generally based on extrapolations from

Fund (UNFPA), and World Bank and include country-

tality ratio is the number of women who die from preg-

trends observed in censuses or surveys from earlier

level time series data. For countries with complete

nancy-related causes while pregnant or within 42 days

years.

vital registration systems with good attribution of

of pregnancy termination per 100,000 live births. • Life-

More couples in developing countries want to limit

cause of death, the data are used directly to esti-

time risk of maternal death is the probability (1 in the

or postpone childbearing but are not using effec-

mate maternal mortality. For countries without com-

number of women likely to die due to a maternal cause)

tive contraception. These couples have an unmet

plete registration data but with other types of data

that a 15-year-old girl will eventually die due to a mater-

need for contraception. Common reasons are lack

and for countries with no data, maternal mortality is

nal cause, if throughout her lifetime she experiences

of knowledge about contraceptive methods and

estimated with a multilevel regression model using

the maternal death risk and overall fertility and mortality

concerns about possible side effects. This indica-

available national maternal mortality data and socio-

rates of the specified year for a given population.

tor excludes women not exposed to the risk of unin-

economic information, including fertility, birth atten-

tended pregnancy because of menopause, infertility,

dants, and GDP. The methodology differs from that

or postpartum anovulation.

used for previous estimates, so data should not be

Contraceptive prevalence reflects all methods—

compared across editions. For further information

ineffective traditional methods as well as highly

on methodology, see the original source. Neither set

effective modern methods. Contraceptive prevalence

of ratios can be assumed to provide an exact esti-

rates are obtained mainly from household surveys,

mate of maternal mortality for any of the countries

including Demographic and Health Surveys, Multiple

in the table.

Indicator Cluster Surveys, and contraceptive preva-

In countries with a high risk of maternal death,

lence surveys (see Primary data documentation for

many girls die before reaching reproductive age. Life-

the most recent survey year). Unmarried women are

time risk of maternal mortality refers to the prob-

often excluded from such surveys, which may bias

ability that a 15-year-old girl will eventually die due

the estimates.

to a maternal cause.

Good prenatal and postnatal care improves mater-

For the indicators that are from household surveys,

nal health and reduces maternal and infant mortality.

the year in the table refers to the survey year. For

However, indicators on use of antenatal care ser-

more information, consult the original sources.

vices provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic

Data sources Data on total fertility are from the United Nations Population Division’s World Population Prospects: The 2010 Revision; census reports and other statistical publications from national statistical offices; household surveys by national agencies, ICF International (for MEASURE DHS), and the U.S. Centers for Disease Control and Prevention; Eurostat’s Demographic Statistics; and the U.S. Bureau of the Census International Data Base. Data on adolescent fertility are from World Population Prospects: The 2010 Revision, with annual data linearly interpolated by the World Bank’s Development Data Group. Data on unmet need for contraception and contraceptive prevalence are from household surveys, including MEASURE DHS Demographic and Health Surveys by ICF International and Multiple Indicator Cluster Surveys by UNICEF. Data on pregnant women receiving prenatal care, births attended by skilled health staff, and national estimates of maternal mortality are from UNICEF’s State of the World’s Children 2012 and Childinfo and MEASURE DHS Demographic and Health Surveys by ICF International. Modeled estimates of maternal mortality and lifetime risk of maternal mortality are from WHO, UNICEF, UNFPA, and the World Bank’s Trends in Maternal Mortality: 1990–2008 (2010).

2012 World Development Indicators

115

PEOPLE

Reproductive health


2.20

Nutrition and growth Prevalence of undernourishment

% of population 1990–92 2006–08

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

116

.. <5 <5 67 <5 45 <5 <5 27 .. 38 <5 <5 20 29 <5 19 11 <5 14 44 38 33 <5 44 60 7 18 c .. 15 .. 42 <5 15 <5 6 <5 <5 <5 28 23 <5 13 67 <5 69 <5 <5 6 14 58 <5 28 <5 15 20 22 63

2012 World Development Indicators

.. <5 <5 41 <5 21 <5 <5 <5 .. 26 <5 <5 12 27 <5 25 6 <5 8 62 25 22 <5 40 39 <5 10 c .. 9 .. 13 <5 14 <5 <5 <5 <5 <5 24 15 <5 9 65 <5 41 <5 <5 <5 19 6 <5 5 <5 22 16 22 57

Prevalence of child malnutrition

Underweight Male Female 2005–10a 2005–10a

.. 6.6 3.7 16.6 2.4 3.4 .. .. 8.7 .. 40.2 1.5 .. 22.7 4.9 2.2 12.1 2.2 .. 27.1 .. 28.8b 18.9 .. .. .. 0.6 3.5 .. 3.5 30.4 12.9 0.6 30.3 .. .. .. .. .. 3.2 .. 8.1 6.5 .. .. 36.5 .. .. .. 16.7 1.3 0.9 15.7 .. 13.9 21.9 16.6 20.4

.. 6.0 3.7 14.6 2.2 5.2 .. .. 8.0 .. 42.4 1.0 .. 17.6 4.0 1.0 10.2 2.1 .. 24.7 .. 29.1b 14.3 .. .. .. 0.5 3.3 .. 3.3 26.1 10.6 1.8 28.4 .. .. .. .. .. 3.7 .. 5.4 6.7 .. .. 32.8 .. .. .. 15.0 1.0 1.3 12.9 .. 12.1 19.7 17.8 17.4

% of children under age 5 Stunting Male Female 2005–10a 2005–10a

.. 22.8 16.7 32.4 8.2 18.8 .. .. 28.5 .. 43.8 4.7 .. 47.9 28.1 12.8 34.0 8.3 .. 37.9 .. 42.3b 39.1 .. .. .. 2.2 9.9 .. 13.7 48.5 33.2 4.8 40.1 .. .. .. .. .. 11.2 .. 33.0 21.3 .. .. 51.8 .. .. .. 28.6 12.3 1.5 30.5 .. 48.7 41.5 29.7 33.2

.. 23.4 15.0 26.1 8.1 17.4 .. .. 24.9 .. 42.6 4.2 .. 41.6 26.2 10.7 28.7 5.8 .. 32.0 .. 39.4b 33.7 .. .. .. 1.8 8.9 .. 11.6 43.3 29.0 6.6 37.8 .. .. .. .. .. 8.9 .. 28.4 19.8 .. .. 49.6 .. .. .. 26.6 10.2 1.2 26.7 .. 47.3 38.5 26.3 26.5

Prevalence of overweight children

Wasting Male Female 2005–10a 2005–10a

.. 11.5 3.9 8.2 1.1 5.8 .. .. 7.8 .. 18.4 2.8 .. 9.0 2.0 3.8 7.5 1.8 .. 11.9 .. 11.2b 8.2 .. .. .. 0.3 2.4 .. 0.9 15.5 8.4 0.6 15.3 .. .. .. .. .. 2.5 .. 8.8 2.4 .. .. 13.7 .. .. .. 8.1 1.8 1.2 9.7 .. 1.1 8.9 5.3 10.2

.. 7.3 4.1 8.1 1.4 5.1 .. .. 5.7 .. 16.5 1.6 .. 7.8 0.8 4.3 6.8 1.4 .. 10.7 .. 10.5b 6.4 .. .. .. 0.2 2.1 .. 0.9 12.5 7.7 1.5 12.4 .. .. .. .. .. 2.1 .. 7.1 0.7 .. .. 10.8 .. .. .. 6.6 1.5 0.8 7.7 .. 1.1 7.8 5.8 10.4

% of children under age 5 Male Female 2005–10a 2005–10a

.. 23.3 13.4 .. 10.2 13.9 .. .. 14.9 .. 1.2 11.3 .. 11.6 9.2 27.4 11.3 6.9 .. 7.9 .. 1.9b 9.8 .. .. .. 9.8 7.5 .. 5.4 6.5 8.4 8.3 5.0 .. .. .. .. .. 9.0 .. 19.8 6.3 .. .. 5.7 .. .. .. 2.9 21.3 3.6 5.8 .. 5.3 5.2 17.6 4.4

.. 23.4 12.4 .. 9.5 9.1 .. .. 12.7 .. 1.0 8.1 .. 11.3 8.1 23.9 11.1 7.7 .. 7.6 .. 1.9b 9.5 .. .. .. 9.1 5.6 .. 4.2 7.2 8.6 7.9 4.9 .. .. .. .. .. 7.5 .. 21.2 5.0 .. .. 4.5 .. .. .. 2.5 18.3 3.3 5.9 .. 4.6 5.0 16.5 3.5


Prevalence of undernourishment

% of population 1990–92 2006–08

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

19 <5 20 16 <5 .. <5 <5 <5 11 <5 <5 <5 33 21 <5 .. 20 17 31 <5 <5 15 30 <5 <5 <5 21 43 <5 27 12 7 <5 <5 28 6 59 .. 32 21 <5 <5 50 37 16 <5 .. 25 18 .. 16 27 24 <5 <5 .. ..

12 <5 19 13 <5 .. <5 <5 <5 5 <5 <5 <5 33 35 <5 .. 5 11 22 <5 <5 14 32 <5 <5 <5 25 27 <5 12 8 5 <5 <5 27 <5 38 .. 18 17 <5 <5 19 16 6 <5 .. 25 15 .. 10 16 13 <5 <5 .. ..

Prevalence of child malnutrition

Underweight Male Female 2005–10a 2005–10a

8.9 .. 43.1 20.7 .. 7.7 .. .. .. 1.9 .. 1.6 5.4 17.3 18.8 .. .. 2.0 2.9 32.5 .. .. 16.0 21.9 6.3 .. 1.7 .. 15.2 13.2 29.7 17.8 .. 4.3 3.0 5.3 .. 20.6 .. 18.5 37.7 .. .. 5.6 42.1 28.6 .. 8.9 .. .. 21.0 .. 4.5 20.9 .. .. .. ..

8.3 .. 43.9 18.6 .. 6.6 .. .. .. 2.6 .. 2.1 4.3 15.5 18.8 .. .. 1.5 2.5 30.6 .. .. 11.1 18.7 4.8 .. 1.9 .. 12.6 12.7 26.0 13.9 .. 2.6 3.4 5.3 .. 16.0 .. 16.5 39.8 .. .. 5.9 37.5 24.8 .. 8.3 .. .. 14.6 .. 4.5 20.6 .. .. .. ..

% of children under age 5 Stunting Male Female 2005–10a 2005–10a

31.5 .. 47.9 41.3 .. 28.7 .. .. .. 3.0 .. 7.9 17.9 37.3 32.4 .. .. 4.2 18.7 48.3 .. .. 43.1 41.9 22.2 .. 13.5 51.6 51.8 17.2 40.7 25.8 .. 16.0 11.0 29.2 .. 46.8 .. 32.0 49.1 .. .. 24.0 56.8 43.1 .. 11.3 .. .. 47.4 .. 30.5 33.5 .. .. .. ..

28.3 .. 48.0 38.8 .. 26.2 .. .. .. 4.4 .. 8.7 16.9 33.1 32.4 .. .. 3.4 17.5 46.8 .. .. 35.0 36.7 19.6 .. 9.2 46.7 44.1 17.2 36.2 20.0 .. 15.0 11.5 25.6 .. 40.7 .. 27.1 49.6 .. .. 21.9 52.6 38.8 .. 8.5 .. .. 39.6 .. 25.9 31.1 .. .. .. ..

2.20 Prevalence of overweight children

Wasting Male Female 2005–10a 2005–10a

1.6 .. 20.7 15.7 .. 6.2 .. .. .. 1.9 .. 1.6 4.5 8.2 5.0 .. .. 1.5 3.5 7.8 .. .. 4.2 7.9 6.8 .. 2.4 .. 4.4 .. 16.2 9.4 .. 2.5 6.0 2.6 .. 4.9 .. 7.3 13.0 .. .. 1.5 13.8 14.8 .. 8.1 .. .. 4.8 .. 0.9 7.4 .. .. .. ..

1.1 .. 19.3 13.8 .. 5.4 .. .. .. 2.2 .. 1.6 2.8 5.8 5.3 .. .. 2.1 3.2 6.8 .. .. 3.5 7.8 6.1 .. 4.5 .. 3.8 .. 14.3 6.8 .. 1.5 5.6 2.8 .. 3.5 .. 7.8 12.4 .. .. 1.4 10.9 14.0 .. 6.0 .. .. 4.0 .. 0.8 6.4 .. .. .. ..

% of children under age 5 Male Female 2005–10a 2005–10a

6.3 .. 2.2 11.3 .. 15.6 .. .. .. .. .. 7.9 15.1 4.7 0.0 .. .. 10.0 12.7 1.5 .. .. 7.7 4.9 23.2 .. 16.6 .. 10.3 .. 4.9 1.3 .. 8.4 8.9 15.6 .. 4.1 .. 4.9 0.6 .. .. 6.7 3.6 10.3 .. 1.5 .. .. 4.2 .. 9.8 3.6 .. .. .. ..

2012 World Development Indicators

5.2 .. 1.7 11.2 .. 14.3 .. .. .. .. .. 5.2 14.5 5.3 0.0 .. .. 8.0 8.6 1.0 .. .. 6.9 3.5 21.6 .. 15.8 .. 8.2 .. 4.6 0.6 .. 6.7 9.3 12.6 .. 3.2 .. 4.4 0.6 .. .. 5.6 3.5 10.7 .. 2.0 .. .. 2.5 .. 9.8 2.9 .. .. .. ..

117

PEOPLE

Nutrition and growth


2.20

Nutrition and growth Prevalence of undernourishment

% of population 1990–92 2006–08

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

<5 <5 44 <5 22 <5d 45 .. <5 <5 .. <5 .. <5 28 39 12 <5 <5 <5 34 29 26 39 43 11 <5 <5 9 19 <5 <5 <5 <5 5 5 10 31 10 30 35 40 16 w 38 17 20 14 19 19 7 12 7 22 32 <5 <5

<5 <5 32 <5 19 <5d 35 .. <5 <5 .. <5 .. <5 20 22 19 <5 <5 <5 26 34 16 31 30 11 <5 <5 7 22 <5 <5 <5 <5 <5 11 7 11 21 30 44 30 13 w 29 12 17 9 14 11 6 9 7 20 22 <5 <5

Prevalence of child malnutrition

Underweight Male Female 2005–10a 2005–10a

.. .. 18.9 6.1 14.4 2.2 24.2 .. .. .. 34.2 .. .. .. 21.6 33.4 6.3 .. .. 11.5 15.9 17.8 6.9 46.8 20.5 .. 3.7 .. .. 18.2 .. .. .. .. .. 4.6 .. 20.5 2.2 .. 16.9 15.1

.. .. 17.2 4.5 14.6 1.3 18.5 .. .. .. 31.3 .. .. .. 21.6 30.0 5.9 .. .. 8.7 14.0 14.6 7.1 43.7 20.5 .. 2.9 .. .. 14.6 .. .. .. .. .. 4.3 .. 19.9 2.3 .. 13.0 12.9

% of children under age 5 Stunting Male Female 2005–10a 2005–10a

.. .. 53.1 10.8 21.3 8.2 39.5 .. .. .. 42.7 .. .. .. 19.8 39.8 33.0 .. .. 28.4 41.1 45.9 16.5 59.8 28.4 .. 9.9 .. .. 41.2 .. .. .. .. .. 19.5 .. 31.9 12.3 .. 48.8 38.6

.. .. 50.3 7.8 18.8 8.0 35.4 .. .. .. 41.3 .. .. .. 18.7 35.8 26.1 .. .. 26.5 37.2 39.2 15.0 55.6 25.2 .. 8.0 .. .. 36.1 .. .. .. .. .. 19.7 .. 29.0 11.2 .. 42.9 33.1

Prevalence of overweight children

Wasting Male Female 2005–10a 2005–10a

.. .. 5.0 12.7 8.8 4.8 10.4 .. .. .. 14.4 .. .. .. 12.1 21.9 3.5 .. .. 12.5 6.8 5.8 4.6 20.4 6.1 .. 3.6 .. .. 7.7 .. .. .. .. .. 5.3 .. 10.2 1.7 .. 6.0 7.6

1.8 .. 4.7 10.8 8.5 4.2 10.6 .. .. .. 11.9 .. .. .. 11.5 20.1 2.4 .. .. 10.5 6.7 4.0 4.8 17.4 5.9 .. 3.3 .. .. 4.9 .. .. .. .. .. 3.7 .. 9.1 1.8 .. 5.2 6.9

% of children under age 5 Male Female 2005–10a 2005–10a

.. .. 7.2 6.3 3.0 20.4 10.3 .. .. .. 4.9 .. .. .. 0.7 5.1 11.8 .. .. 17.8 7.2 6.0 8.8 6.0 4.2 .. 8.5 .. .. 5.0 .. .. .. .. .. 13.1 .. 3.4 13.4 .. 8.6 9.5

a. Data are for the most recent year available. b. Data are for 2011. c. Includes Hong Kong SAR, China; Macao SAR, China; and Taiwan, China. d. Includes Montenegro.

118

2012 World Development Indicators

.. .. 6.3 6.0 1.8 18.2 9.9 .. .. .. 4.5 .. .. .. 1.0 5.5 10.9 .. .. 18.1 6.2 5.0 7.2 5.7 5.1 .. 9.2 .. .. 4.7 .. .. .. .. .. 12.5 .. 2.5 9.4 .. 8.2 8.7


About the data

2.20

Definitions

Good nutrition is the cornerstone for survival, health

childhood obesity and a high prevalence of diabe-

• Prevalence of undernourishment is the percentage

and development. Well nourished children perform

tes, respiratory disease, high blood pressure, and

of the population whose dietary energy consump-

better in school, grow into healthy adults, and in

psychosocial and orthopedic disorders (de Onis and

tion is continuously below a minimum requirement

turn give their children a better start in life. Well

Blössner 2003). Childhood obesity is associated

for maintaining a healthy life and carrying out light

nourished women face fewer risks during pregnancy

with a higher chance of obesity, premature death,

physical activity with an acceptable minimum weight

and childbirth, and their children set off on firmer

and disability in adulthood. In addition to increased

for height. • Prevalence of child malnutrition is the

developmental paths, both physically and mentally

future risks, obese children experience breathing

percent age of children under age 5 whose weight

(United Nations Children’s Fund [UNICEF], www.

difficulties and increased risk of fractures, hyper-

for age (underweight) or height for age (stunting)

childinfo.org).

tension, early markers of cardiovascular disease,

is more than two standard deviations below the

Data on undernourishment are from the Food and

insulin resistance, and psychological effects. Chil-

median for the international reference population

Agriculture Organization (FAO) of the United Nations

dren in low- and middle-income countries are more

ages 0–59 months. Height is measured by recum-

and measure food deprivation based on average

vulnerable to inadequate nutrition before birth and

bent length for children up to two years old and by

food available for human consumption per person,

in infancy and early childhood. Many of these chil-

stature while standing for older children. Data are

the level of inequality in access to food, and the

dren are exposed to high-fat, high-sugar, high-salt,

based on the WHO child growth standards released

minimum calories required for an average person.

calorie-dense, micronutrient-poor foods, which tend

in 2006. • Prevalence of over weight children is the

From a policy and program standpoint, however,

to be lower in cost than more nutritious foods. These

percentage of children under age 5 whose weight for

this measure has its limits. First, food insecurity

dietary patterns, in conjunction with low levels of

height is more than two standard deviations above

exists even where food availability is not a problem

physical activity, result in sharp increases in child-

the median for the international reference population

because of inadequate access of poor households

hood obesity, while undernutrition continues (World

of the corresponding age as established by the WHO

to food. Second, food insecurity is an individual

Health Organization [WHO]).

child growth standards released in 2006.

or household phenomenon, and the average food

New international growth reference standards for

available to each person, even corrected for possible

infants and young children were released in 2006

effects of low income, is not a good predictor of food

by the WHO to monitor children’s nutritional status.

insecurity among the population. And third, nutrition

Differences in growth to age 5 are influenced more

security is determined not only by food security but

by nutrition, feeding practices, environment, and

also by the quality of care of mothers and children

healthcare than by genetics or ethnicity. The previ-

and the quality of the household’s health environ-

ously reported data were based on the U.S. National

ment (Smith and Haddad 2000).

Center for Health Statistics–WHO growth reference.

Undernourished children have lower resistance to

Because of the change in standards, the data in this

infection and are more likely to die from common

edition should not be compared with data in editions

childhood ailments such as diarrheal diseases and

prior to 2008.

respiratory infections. Frequent illness saps the

For indicators from household surveys, the year in

nutritional status of those who survive, locking them

the table refers to the survey year. For more informa-

into a vicious cycle of recurring sickness and falter-

tion, consult the original sources.

ing growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birthweight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Data sources Data on undernourishment are from the FAO’s

Estimates of overweight children are also from

The State of Food Insecurity in the World. Data on

national survey data. Once considered only a high-

malnutrition and overweight children are from the

income economy problem, overweight children have

WHO’s Global Database on Child Growth and Mal-

become a growing concern in developing coun-

nutrition (www.who.int/nutgrowthdb).

tries. Research shows an association between

2012 World Development Indicators

119

PEOPLE

Nutrition and growth


2.21

Nutrition intake and supplements Low-birthweight babies

Exclusive breastfeeding

Consumption of iodized salt

Vitamin A supplementation

Prevalence of anemia

% of births 2005–10a

% of children under 6 months 2005–10a

% of households 2005–10a

% of children 6–59 months 2010

Children under age 5 2005–10a

Pregnant women 2005–10a

.. 7 6 .. 7 7 .. .. 10 .. 22 4 .. 15 6 5 13 8 9 16 11 8 11 .. 13 .. 6 3 .. 6 10 13 7 17 5 5 .. .. .. 11 8 13 .. .. .. 20 .. .. .. 11 5 .. 13 .. 11 12 11 25

83 39 7 .. .. 35 .. .. 12 .. 43 9 .. 43 60 18 20 40 .. 16 69 74 21 .. 23 3 85 28 .. 43 37 19 15 4 98 26 .. .. .. 9 .. 53 31 .. .. 49 .. .. .. 36 11 .. 63 .. 50 48 38 41

96 .. .. 28 .. .. .. .. 89b .. 100 .. .. 100 24 .. 91 .. .. 100 73 95 89 .. 0 68 .. .. .. .. 83 84 .. 100 .. .. .. .. .. .. .. 68b .. 44 .. 84 .. .. 0 100 .. .. 93 .. 36 97 100 21

.. 31 43 .. 17 37 8 11 .. 25 .. 27 9 78 .. 27 .. 55 27 .. 56 55 .. 8 .. 71 24 .. .. 28 71 66 .. 69 23 27 19 18 9 35 38 49 .. 70 23 54 11 8 44 .. 41 8 .. 12 .. 76 75 ..

61 34 43 57 31 .. 12 15 .. 28 .. 26 13 75 .. 35 21 29 30 .. 47 44 .. 12 .. 60 28 .. .. 31 67 55 .. 55 28 39 25 22 12 40 38 34 .. 55 23 31 15 11 46 .. 42 12 .. 19 .. .. 58 50

%

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

120

2012 World Development Indicators

.. 76 61 45 .. 97 .. .. 54 .. 84 94 .. 67 89 62 .. 96 100 34 98 83 49 .. 62 .. .. 97 .. .. 59 82 .. .. 88 88 .. .. .. 19 .. 79 .. .. .. 20 .. .. .. 21 100 .. 32 .. 76 41 12 3


Low-birthweight babies

Exclusive breastfeeding

Consumption of iodized salt

Vitamin A supplementation

% of births 2005–10a

% of children under 6 months 2005–10a

% of households 2005–10a

% of children 6–59 months 2010

10 .. 28 11 7 15 .. .. .. 14 .. 13 6 8 6 .. .. .. 5 11 .. .. .. 14 .. .. 6 16 13 11 19 34 .. 7 6 5 .. 16 9 16 21 .. .. 9 27 12 .. 12 32 .. 10 6 8 21 .. .. .. ..

30 .. 46 15 23 25 .. .. .. 15 .. 22 17 32 .. .. .. .. 32 26 .. .. 54 34 .. .. 16 51 72 .. 38 46 .. .. 46 57 .. 37 24 24 53 .. .. 31 27 13 .. .. 37 .. 56 24 68 34 .. .. .. ..

.. .. 51 62 99 28 .. .. .. .. .. .. 92 98 25 .. .. .. 76 84 .. .. 84 .. .. .. 94 53 50 18 79 23 .. .. 60 83 21 25 93 .. .. .. .. .. 32 .. .. .. .. .. 92 94 .. 81 .. .. .. ..

2.21

Prevalence of anemia

%

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

.. .. 34 80 .. .. .. .. .. .. .. .. .. 62 99 .. .. .. 97 83 .. .. .. 97 .. .. .. 95 96 .. 99 97 .. .. .. 61 .. 100 94 13 91 .. .. 7 98 91 .. .. 87 .. 14 .. .. 91 .. .. .. ..

Children under age 5 2005–10a

Pregnant women 2005–10a

.. 19 74 44 35 56 10 12 11 .. 11 .. .. .. .. .. .. .. .. .. 27 .. 49 .. 34 24 .. .. 73 32 .. 68 .. 24 41 .. .. .. 63 41 48 9 11 20 84 .. 6 .. .. .. 60 30 .. 21 23 13 .. ..

21 21 50 44 .. 38 15 17 15 .. 15 .. 26 .. .. 23 .. 31 34 56 25 32 25 .. 34 24 32 .. 47 .. .. 53 .. 21 36 37 .. 52 50 31 42 13 18 .. 61 .. 9 .. .. .. 55 39 .. 43 25 17 .. 29

2012 World Development Indicators

121

PEOPLE

Nutrition intake and supplements


2.21

Nutrition intake and supplements Low-birthweight babies

Exclusive breastfeeding

Consumption of iodized salt

Vitamin A supplementation

% of births 2005–10a

% of children under 6 months 2005–10a

% of households 2005–10a

% of children 6–59 months 2010

Prevalence of anemia

%

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

.. 6 6 .. 19 6 14 .. .. .. 11 .. .. .. 17 .. 9 .. .. 10 10 10 7 .. 11 19 5 11 4 14 4 6 .. .. 9 5 8 5 7 .. 11 11 15 w 15 15 21 5 15 6 7 8 11 27 13 .. ..

.. .. 85 .. 34 15 11 .. .. .. 9 .. .. .. 76 34 44 .. .. 43 25 50 15 52 63 13 6 42 11 60 18 .. .. .. 57 26 .. 17 27 .. 61 32c 37 w 44 35 37 30 37 26 .. .. 34 47 35 .. ..

.. .. 88 .. 41 32 58 .. .. .. 1 .. .. .. 92 11 52 .. .. 79 62 59 47 60 32 28 .. 69 87 96 18 .. .. .. .. 53 .. 93 86 .. .. 91 70 w 62 71 54 91 70 86 .. .. 69 55 50 .. ..

.. .. 92 .. 97 .. 100 .. .. .. 62 39 .. .. 85 82 38 .. .. 33b 95 99 .. 48 100 .. .. .. .. 64 .. .. .. .. .. 94 .. 95b .. .. 92 49 .. w 88 .. 56 .. .. .. .. .. .. 50 86 .. ..

Children under age 5 2005–10a

40 27 56 33 83 .. 83 19 23 14 .. .. .. 13 .. 85 47 9 6 41 .. 72 .. .. 52 30 .. 33 .. 73 .. 28 .. .. 19 .. 33 .. .. 68 .. 58 .. w .. .. 65 .. .. .. 30 36 48 74 .. .. 10

Pregnant women 2005–10a

30 21 .. 32 58 .. 60 24 25 19 .. 22 .. 18 .. 58 24 13 .. 39 45 58 .. .. 50 30 .. 40 30 64 27 28 15 6 27 .. 40 .. .. 58 .. 47 .. w .. .. 48 .. .. .. 30 .. .. 50 .. 13 14

a. Data are for the most recent year available. b. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Data are for 2011.

122

2012 World Development Indicators


About the data

2.21

Definitions

Low birthweight, which is associated with maternal

newborns in low- and middle-income countries

• Low-birthweight babies are newborns weigh-

malnutrition, raises the risk of infant mortality and

remain unprotected from the lifelong consequences

ing less than 2.5 kilograms within the first hours

stunts growth in infancy and childhood. There is also

of brain damage associated with iodine deficiency

of life, before significant postnatal weight loss has

emerging evidence that low-birthweight babies are

disorders, which affect a child’s ability to learn and

occurred. • Exclusive breastfeeding is the percent-

more prone to noncommunicable diseases such as

to earn a living as an adult, thereby preventing chil-

age of children less than six months old who were

diabetes and cardiovascular diseases. Low birth-

dren, communities, and countries from fulfilling their

fed breast milk alone (no other liquids) in the past 24

weight can arise as a result of a baby being born

potential (UNICEF, www.childinfo.org). Widely used

hours. • Consumption of iodized salt is the percent-

too soon or too small for gestational age. Babies

and inexpensive, iodized salt is the best source of

age of households that use edible salt fortified with

born prematurely who are also small for their ges-

iodine, and a global campaign to iodize edible salt

iodine. • Vitamin A supplementation is the percent-

tational age have the worst prognosis. In low- and

is significantly reducing the risks. The data on con-

age of children ages 6–59 months old who received

middle-income countries low birthweight stems

sumption of iodized salt are derived from household

at least two doses of vitamin A in the previous year.

primarily from poor maternal health and nutrition.

surveys.

• Prevalence of anemia, children under age 5, is the

Three factors have the most impact: poor maternal

Vitamin A is essential for immune system function-

percentage of children under age 5 whose hemoglo-

nutritional status before conception, mother’s short

ing. Vitamin A deficiency, a leading cause of blind-

bin level is less than 110 grams per liter at sea level.

stature (due mostly to undernutrition and infections

ness, also causes a greater risk of dying from a range

• Prevalence of anemia, pregnant women, is the

during childhood), and poor nutrition during preg-

of childhood ailments such as measles, malaria, and

percentage of pregnant women whose hemoglobin

nancy (United Nations Children’s Fund [UNICEF],

diarrhea. In low- and middle-income countries, where

level is less than 110 grams per liter at sea level.

www.childinfo.org). Estimates of low-birthweight

vitamin A is consumed largely in fruits and vegeta-

infants are drawn mostly from hospital records and

bles, daily per capita intake is often insufficient to

household surveys. Many births in developing coun-

meet dietary requirements. Providing young children

tries take place at home and are seldom recorded. A

with two high-dose vitamin A capsules a year is a

hospital birth may indicate higher income and there-

safe, cost-effective, efficient strategy for eliminat-

fore better nutrition, or it could indicate a higher risk

ing vitamin A deficiency and improving child survival.

birth. Caution should therefore be used in interpret-

Giving vitamin A to new breastfeeding mothers helps

ing the data.

protect their children during the first months of life.

For optimal infant and young child feeding, mothers initiate breastfeeding within one hour of birth,

Food fortification with vitamin A is being introduced in many developing countries.

breastfeed exclusively for the first six months, and

Anemia is a condition in which the number of red

continue to breastfeed for two years or more while

blood cells or their oxygen-carrying capacity is insuf-

providing nutritionally adequate, safe, and age-

ficient to meet physiologic needs, which vary by age,

appropriate solid, semisolid, and soft foods (UNICEF,

sex, altitude, smoking status, and pregnancy sta-

www.childinfo.org). Optimal breastfeeding can save

tus. In its severe form it is associated with fatigue,

an estimated 1.4 million children a year. Breast milk

weakness, dizziness, and drowsiness (World Health

alone contains all the nutrients, antibodies, hor-

Organization [WHO], www.who.int/topics/anaemia/).

mones, and antioxidants an infant needs to thrive.

Children under age 5 and pregnant women have the

It protects babies from diarrhea and acute respira-

highest risk for anemia. Data on anemia are com-

tory infections, stimulates their immune systems and

piled by the WHO based mainly on nationally repre-

response to vaccination, and may confer cognitive

sentative surveys between 1993 and 2005, which

benefits. The data on breastfeeding are derived from

measured hemoglobin in the blood. WHO’s hemoglo-

household surveys.

bin thresholds were then used to determine anemia

Iodine defi ciency is the single most important

status based on age, sex, and physiological status.

cause of preventable mental retardation, it con-

Data should be used with caution because surveys

tributes significantly to the risk of stillbirth and mis-

differ in quality, coverage, age group interviewed,

carriage, and it increases infant mortality. A diet low

and treatment of missing values across countries

Data on low-birthweight babies, breastfeeding,

in iodine is the main cause of iodine deficiency. It

and over time.

consumption of iodized salt, and vitamin A supple-

Data sources

usually occurs among populations living in areas

For indicators from household surveys, the year in

mentation are from the United Nations Children’s

where the soil has been depleted of iodine. If soil

the table refers to the survey year. For more informa-

Fund’s The State of the World’s Children 2012 and

is deficient in iodine, so are the plants grown in it,

tion, consult the original sources.

Childinfo. Data on anemia are from the WHO’s

including the grains and vegetables that people and

Worldwide Prevalence of Anemia 1993 –2005

animals consume. There are almost no countries in

(2008c) and Integrated WHO Nutrition Global

the world where iodine deficiency has not been a

Databases.

public health problem. Every year about 40 million

2012 World Development Indicators

123

PEOPLE

Nutrition intake and supplements


2.22

Health risk factors and future challenges Prevalence of smoking

Male 2009

Female 2009

per 100,000 people 2010

.. 60 .. .. 32 51 22 47 41 34 46 49 30 15 42 47 .. 22 48 18 .. 49c 14 24 .. 22 38 51 .. .. 10 10 24 17 36 .. .. 43 30 17 .. 40 .. 10 46 8 28 36 19 31 57 33 11 63 22 25 .. ..

.. 19 .. .. 22 2 19 45 .. 8 2 9 22 1 18 36 .. 13 27 8 .. 5c 2 17 .. 3 33 2 .. .. 2 1 8 4 30 .. .. 31 28 13 .. 1 .. 2 23 1 22 27 3 3 6 25 3 41 4 2 .. ..

189 14 90 304 27 73 6 5 110 23 225 70 9 94 135 50 503 43 40 55 129 437 177 5 319 276 19 78 80 34 327 372 13 139 21 9 4 7 6 67 65 18 28 100 25 261 7 9 553 273 107 5 86 5 62 334 233 230

% of adults

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

124

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

2012 World Development Indicators

% of population ages 20–79 2011

7.8 2.9 7.0 3.0 5.7 8.7 6.8 6.8 2.8 19.9 10.7 8.2 4.9 2.0 6.8 7.7 11.1 10.4 6.9 3.0 2.8 2.9 6.2 8.7 3.2 3.9 9.8 9.0 7.8 10.0 3.2 5.6 9.9 5.0 5.3 9.8 9.5 5.5 5.7 8.3 6.8 16.9 9.7 3.6 7.2 3.4 6.0 5.6 10.6 2.0 2.8 5.5 5.1 5.3 9.5 4.4 3.1 6.8

Female % of total Total % of population population with HIV ages 15–49 1990

.. .. <0.1 0.5 0.3 <0.1 0.1 <0.1 <0.1 .. <0.1 <0.1 <0.1 0.2 0.1 .. 3.5 .. <0.1 3.9 3.9 0.5 0.6 0.1 3.1 1.1 <0.1 .. .. 0.2 .. 5.2 <0.1 2.4 <0.1 <0.1 .. <0.1 <0.1 0.4 0.3 <0.1 0.1 0.3 <0.1 .. <0.1 0.3 0.9 0.1 <0.1 0.1 0.3 0.1 0.1 1.1 0.3 1.3

Antiretroviral therapy coverage

Youth % of population ages 15–24

2009

2009

Male 2009

Female 2009

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

.. .. 30 60 32 <43 31 29 60 .. 30 50 31 58 32 .. 57 .. 29 60 60 63 58 21 61 59 31 .. .. 33 .. 59 29 58 <33 31 .. <42 27 59 31 23 34 60 31 .. <36 32 58 58 43 18 59 31 33 59 60 60

.. .. 0.1 0.6 0.3 <0.1 0.1 0.3 <0.1 .. <0.1 <0.1 <0.1 0.3 0.1 .. 5.2 .. <0.1 0.5 1.0 0.1 1.6 0.1 1.0 1.0 0.2 .. .. 0.2 .. 1.2 0.2 0.7 <0.1 0.1 .. <0.1 0.1 0.3 0.2 <0.1 0.4 0.2 0.3 .. 0.1 0.2 1.4 0.9 <0.1 0.1 0.5 0.1 0.5 0.4 0.8 0.6

.. .. <0.1 1.6 0.2 <0.1 0.1 0.2 0.1 .. <0.1 0.1 <0.1 0.7 0.1 .. 11.8 .. <0.1 0.8 2.1 0.1 3.9 0.1 2.2 2.5 0.1 .. .. 0.1 .. 2.6 0.1 1.5 <0.1 0.1 .. <0.1 0.1 0.7 0.2 <0.1 0.3 0.4 0.2 .. <0.1 0.1 3.5 2.4 <0.1 <0.1 1.3 0.1 0.3 0.9 2.0 1.3

Cause of death % of population

Communicable diseases and maternal, Nonprenatal, % of population with advanced and nutrition communicable diseases Injuries conditions HIV infection 2005–10 b 2005–10b 2005–10b 2005–10b

.. .. 25 24 70 24 .. .. 21 .. 23 29 .. 53 19 .. 83 60 23 46 19 94 28 .. 19 36 63 .. .. 17 .. 23 68 28 80 <95 .. .. .. 47 30 11 53 37 .. .. .. .. 47 18 65 .. 24 .. 44 40 30 43

63 5 30 69 14 6 4 3 11 10 38 2 8 60 35 2 60 14 3 73 67 47 63 5 65 73 9 7 .. 13 72 58 7 58 3 8 4 4 6 22 20 12 17 49 2 57 2 6 52 60 5 5 53 6 35 60 67 54

29 89 63 25 80 90 90 91 85 79 52 87 86 33 57 95 31 74 94 21 26 46 31 89 27 21 83 83 .. 66 21 33 81 33 92 84 90 90 90 68 65 82 67 40 90 34 89 87 41 34 91 92 39 91 47 32 28 41

8 5 8 7 6 4 6 6 4 11 10 11 6 6 8 4 9 12 4 7 7 7 6 6 7 5 8 10 .. 21 7 9 13 9 6 8 6 6 5 10 15 6 16 12 8 9 9 7 7 6 4 4 8 4 18 7 6 5


Prevalence of smoking

Male 2009

Female 2009

per 100,000 people 2010

.. 43 26 61 26 31 .. 29 33 .. 42 47 40 26 .. 49 .. 35 45 51 50 46 .. 14 47 50 .. .. 26 50 28 29 31 24 43 48 33 18 40 30 36 31 27 .. 9 10 31 12 34 17 58 30 .. 47 36 32 .. ..

3 33 4 5 2 4 .. 13 19 .. 12 6 9 1 .. 7 .. 4 2 4 22 31 .. .. 1 22 .. .. 4 2 2 4 2 8 5 6 2 2 8 9 29 26 24 .. 1 3 28 1 6 4 31 14 9 10 25 16 .. ..

51 15 185 189 17 64 8 5 5 7 21 5 151 298 345 97 .. 41 159 90 39 17 633 293 40 69 21 266 219 82 68 337 22 16 182 224 91 544 384 603 163 7 8 42 185 133 6 13 231 48 303 46 106 275 23 29 2 38

% of adults

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

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

% of population ages 20–79 2011

6.8 6.2 9.2 5.2 11.3 9.3 5.4 7.6 5.3 16.0 7.9 12.4 7.9 5.2 8.6 7.7 .. 21.1 6.5 3.3 8.1 20.2 3.5 3.4 14.2 8.0 7.9 4.8 5.7 12.3 2.0 4.4 15.1 15.9 2.8 7.2 7.0 3.1 7.2 8.0 3.7 5.4 8.8 11.2 4.1 4.9 4.8 10.8 8.0 9.8 7.7 6.7 6.1 10.0 9.2 9.8 13.3 20.2

Female % of total Total % of population population with HIV ages 15–49 1990

1.1 0.1 0.1 <0.1 <0.1 .. <0.1 <0.1 0.3 2.1 <0.1 .. <0.1 3.9 .. <0.1 .. .. <0.1 <0.1 <0.1 <0.1 0.8 0.3 .. <0.1 .. 0.2 7.2 0.1 0.4 0.2 <0.1 0.4 <0.1 <0.1 <0.1 1.2 0.2 1.6 0.2 0.1 0.1 <0.1 0.1 1.3 <0.1 <0.1 <0.1 0.2 <0.1 <0.1 0.4 <0.1 <0.1 0.1 .. <0.1

2009

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

Antiretroviral therapy coverage

Youth % of population ages 15–24

2009

Male 2009

Female 2009

32 <33 39 30 29 .. 29 29 33 33 34 .. 60 59 .. 31 .. .. 29 42 30 31 62 61 .. <33 .. 31 59 11 62 31 29 27 42 <29 32 61 35 59 33 30 <37 31 53 59 30 <33 29 31 58 31 25 30 31 31 .. <50

0.3 <0.1 0.1 0.1 <0.1 .. 0.1 0.1 <0.1 1.0 <0.1 .. 0.1 1.8 .. <0.1 .. .. 0.1 0.1 0.2 0.1 5.4 0.3 .. <0.1 .. 0.1 3.1 0.1 0.2 0.4 0.3 0.2 0.1 <0.1 0.1 3.1 0.3 2.3 0.2 0.1 <0.1 0.1 0.2 1.2 <0.1 <0.1 0.1 0.4 0.3 0.2 0.2 <0.1 <0.1 0.3 .. <0.1

0.2 <0.1 0.1 <0.1 <0.1 .. 0.1 <0.1 <0.1 0.7 <0.1 .. 0.2 4.1 .. <0.1 .. .. 0.1 0.2 0.1 <0.1 14.2 0.7 .. <0.1 .. 0.1 6.8 <0.1 0.5 0.3 0.2 0.1 0.1 <0.1 0.1 8.6 0.3 5.8 0.1 <0.1 <0.1 0.1 0.5 2.9 <0.1 <0.1 <0.1 0.3 0.8 0.1 0.1 <0.1 <0.1 0.2 .. <0.1

2.22 Cause of death % of population

Communicable diseases and maternal, Nonprenatal, % of population with advanced and nutrition communicable diseases Injuries conditions HIV infection 2005–10 b 2005–10b 2005–10b 2005–10b

33 27 .. 21 4 .. .. .. .. 46 .. .. 27 48 0 .. .. .. 12 67 12 18 48 14 .. 27 .. 2 46 23 50 25 22 54 17 8 27 30 18 76 11 .. .. 40 22 21 .. <95 4 37 52 37 37 37 22 .. .. ..

23 1 37 28 13 24 7 8 3 21 14 15 8 63 29 6 .. 11 14 41 3 7 63 68 12 3 2 52 63 24 75 60 7 12 5 14 19 64 33 51 43 7 3 20 81 68 7 6 46 19 47 20 30 31 4 9 .. 8

69 93 53 64 72 44 87 87 92 68 80 74 78 28 65 82 .. 76 77 48 90 84 29 28 78 86 95 42 28 67 20 32 87 78 87 72 75 28 40 38 50 89 91 69 16 27 87 83 46 69 44 69 60 61 89 86 .. 69

2012 World Development Indicators

8 6 10 9 14 31 6 5 4 11 6 11 14 9 6 12 .. 13 9 10 8 9 7 4 11 11 3 6 9 9 5 8 6 10 8 13 6 8 27 12 7 4 6 11 3 5 6 11 8 12 9 12 10 8 7 4 .. 23

125

PEOPLE

Health risk factors and future challenges


2.22

Health risk factors and future challenges Prevalence of smoking

Female 2009

per 100,000 people 2010

24 24 .. 1 1 27 8 6 19 22 .. 8 .. 27 1 2 2 .. 21 .. .. 3 3 .. .. 11 5 15 .. 3 13 2 23 25 22 3 .. 2 .. 11 4 4 8w 4 6 4 7 5 3 18 13 3 4 3 21 25

116 106 106 18 288 18 682 35 8 11 286 981 .. 16 66 119 1,287 7 8 20 206 177 137 498 455 19 25 28 66 209 101 3 13 4 21 128 33 199 5 49 462 633 128 w 264 132 174 89 150 123 90 43 42 192 271 14 ..

% of adults Male 2009

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

46 59 .. 24 16 38 39 35 39 30 .. 24 .. 36 27 24 16 .. 31 42 .. 21 45 .. .. 27 58 47 .. 16 50 19 25 33 31 22 .. 48 .. 35 24 30 37 w 28 39 32 46 38 52 50 25 35 29 16 34 35

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

% of population ages 20–79 2011

7.9 10.0 3.2 20.0 3.3 7.9 3.2 9.8 5.9 7.8 4.3 7.1 .. 6.5 7.6 8.7 3.1 4.4 6.0 10.2 6.5 2.8 7.7 7.6 3.3 13.1 9.7 8.1 2.8 2.9 2.9 19.2 5.4 9.6 5.9 6.7 10.5 3.2 9.4 9.9 4.8 9.9 8.3 w 5.9 8.7 8.0 9.4 8.4 8.3 7.7 10.5 11.6 9.0 4.6 7.9 5.8

Female % of total Total % of population population with HIV ages 15–49 1990

2009

2009

<0.1 0.1 30 <0.1 1.0 49 5.2 2.9 61 .. .. .. 0.2 0.9 59 0.1 0.1 24 <0.1 1.6 60 <0.1 0.1 30 <0.1 <0.1 <17 <0.1 <0.1 <29 0.1 0.7 47 0.7 17.8 62 .. .. .. 0.4 0.4 24 <0.1 <0.1 <32 0.1 1.1 58 2.3 25.9 58 0.1 0.1 31 0.2 0.4 32 .. .. .. <0.1 0.2 30 4.8 5.6 59 1.0 1.3 40 .. .. .. 0.6 3.2 59 0.2 1.5 33 <0.1 <0.1 <37 <0.1 <0.1 30 .. .. .. 10.2 6.5 58 0.1 1.1 49 .. .. .. 0.1 0.2 31 0.5 0.6 25 0.1 0.5 32 <0.1 0.1 29 .. .. .. <0.1 0.4 30 .. .. .. .. .. .. 12.7 13.5 57 10.1 14.3 60 0.3 w 0.8 w 37 w 1.8 2.6 46 0.2 0.7 .. 0.3 0.7 38 0.2 0.7 .. 0.3 0.9 39 0.1 0.2 .. 0.1 0.6 42 0.4 0.5 .. 0.1 0.1 28 0.1 0.3 37 2.4 5.5 58 0.2 0.3 28 0.2 0.3 27

Antiretroviral therapy coverage

Youth % of population ages 15–24 Male 2009

0.1 0.2 1.3 .. 0.3 0.1 0.6 <0.1 <0.1 <0.1 0.4 4.5 .. 0.2 <0.1 0.5 6.5 <0.1 0.2 .. <0.1 1.7 .. .. 0.9 1.0 <0.1 <0.1 .. 2.3 0.2 .. 0.2 0.3 0.3 <0.1 .. 0.1 .. .. 4.2 3.3 0.4 w 0.9 .. 0.2 .. .. .. 0.1 .. 0.1 0.1 1.5 0.2 0.1

Female 2009

<0.1 0.3 1.9 .. 0.7 0.1 1.5 <0.1 <0.1 <0.1 0.6 13.6 .. 0.1 <0.1 1.3 15.6 <0.1 0.1 .. <0.1 3.9 .. .. 2.2 0.7 <0.1 <0.1 .. 4.8 0.3 .. 0.1 0.2 0.2 <0.1 .. 0.1 .. .. 8.9 6.9 0.7 w 1.9 .. 0.5 .. .. .. 0.2 .. 0.1 0.1 3.8 0.1 0.1

Cause of death % of population

Communicable diseases and maternal, Nonprenatal, % of population with advanced and nutrition communicable diseases Injuries conditions HIV infection 2005–10 b 2005–10b 2005–10b 2005–10b

81 .. 88 .. 51 38 18 .. 62 .. 6 37 .. .. 20 5 59 .. .. .. 11 30 61 .. 29 .. 53 62 .. 39 10 .. .. .. 49 .. .. 34 .. .. 64 34

4 5 63 13 65 2 77 16 5 4 62 67 .. 5 9 43 62 5 4 13 37 66 17 60 61 12 22 9 19 64 6 13 8 6 8 15 13 16 .. 44 64 75 27 w 58 26 38 11 31 13 6 16 19 39 65 7 5

91 82 29 71 30 95 18 79 90 87 27 29 .. 91 65 44 28 90 90 77 59 27 71 34 34 78 72 85 73 25 86 67 88 87 87 79 66 75 .. 45 27 21 63 w 33 65 53 79 59 76 84 72 69 51 28 87 90

5 12 8 15 5 4 5 5 6 8 11 5 .. 4 26 13 11 5 6 10 4 8 12 5 5 10 7 6 8 10 8 21 4 7 6 6 21 9 .. 11 9 4 9w 9 10 9 10 10 10 9 12 12 10 7 6 5

a. See http://data.worldbank.org or the original source for uncertainty bands. b. Data are for the most recent year available. c. Data are for 2010. d. Includes Hong Kong SAR, China.

126

2012 World Development Indicators


About the data

2.22

Definitions

The limited availability of data on health status is a

Data on HIV are from the Joint United Nations Pro-

• Prevalence of smoking is the percentage of the

major constraint in assessing the health situation in

gramme on HIV/AIDS’s (UNAIDS) Global Report: UNAIDS

population ages 15 and older who smoke any tobacco

developing countries. Surveillance data are lacking

Report on the Global AIDS Epidemic 2010. Changes in

products. It includes daily and nondaily smoking.

for many major public health concerns. Estimates

procedures and assumptions for estimating the data

Estimates are adjusted and age- standardized preva-

of prevalence and incidence are available for some

and better coordination with countries have resulted

lence • Incidence of tuberculosis is the number of

diseases but are often unreliable and incomplete.

in improved estimates of HIV and AIDS. For example,

new and relapse cases of tuberculosis (all types)

National health authorities differ widely in capacity

improved software was used to model the course of

per 100,000 people. • Prevalence of diabetes is

and willingness to collect or report information. To

HIV epidemics and their impacts, making full use of

the percentage of people ages 20–79 who have

compensate for this and improve reliability and inter-

information on HIV prevalence trends from surveillance

type 1 or type 2 diabetes. • Prevalence of HIV is

national comparability, the World Health Organiza-

data as well as survey data. The software explicitly

the percentage of people who are infected with

tion (WHO) prepares estimates in accordance with

includes the effect of antiretroviral therapy when cal-

HIV. Total and youth rates are percentages of the

epidemiological models and statistical standards.

culating HIV incidence and models reduced infectivity

relevant age group. Female rate is as a percentage

Smoking is the most common form of tobacco use

among people receiving antiretroviral therapy, which is

of the total population living with HIV. • Antiretro-

and the prevalence of smoking is therefore a good

having a larger impact on HIV prevalence and allowing

viral therapy coverage is the percentage of adults

measure of the tobacco epidemic (Corrao and others

HIV-positive people to live longer. The software also

and children with advanced HIV infection currently

2000). Tobacco use causes heart and other vascular

allows for changes in urbanization over time—impor-

receiving antiretroviral therapy according to nation-

diseases and cancers of the lung and other organs.

tant because prevalence is higher in urban areas and

ally approved treatment protocols (or WHO/UNAIDS

Given the long delay between starting to smoke and

because many countries have seen rapid urbanization

standards) among the estimated number of people

the onset of disease, the health impact of smoking

over the past two decades. The estimates include plau-

with advanced HIV infection. • Cause of death is the

will increase rapidly only in the next few decades.

sible bounds, not shown in the table, which reflect the

share of all deaths due to the specified underlying

The data presented in the table are age- standardized

certainty associated with each of the estimates. The

cause. • Communicable diseases and maternal,

rates for adults ages 15 and older from the WHO.

bounds are available at http://data.worldbank.org and

perinatal, and nutrition conditions are infectious

from the original source.

and parasitic diseases, respiratory infections, and

Tuberculosis is one of the main causes of adult deaths from a single infectious agent in develop-

Standard antiretroviral therapy consists of the use

nutritional defi ciencies such as underweight and

ing countries. In developed countries tuberculosis

of at least three antiretroviral drugs to maximally sup-

stunting. • Noncommunicable diseases are cancer,

has reemerged largely as a result of cases among

press HIV and stop the progression of HIV disease. Anti-

diabetes mellitus, cardiovascular diseases, digestive

immigrants. Since tuberculosis incidence cannot be

retroviral therapy has led to huge reductions in death

diseases, skin diseases, musculoskeletal diseases,

directly measured, estimates are obtained by elicit-

and suffering of people with advanced HIV infection.

and congenital anomalies. • Injuries include uninten-

ing expert opinion or are derived from measurements

Data are collected through three international moni-

tional and intentional injuries.

of prevalence or mortality. These estimates include

toring and reporting processes: country responses to

uncertainty intervals, which are not shown in the

the WHO; research by the Interagency Task Team on

table but are available at http://data.worldbank.org

Prevention of HIV Infection in Women, Mothers and

and from the original source.

their Children; and country report to UNAIDS through

Diabetes, an important cause of ill health and a risk factor for other diseases in developed countries,

the United Nations General Assembly Special Session Declaration of Commitment on HIV/AIDS.

is spreading rapidly in developing countries. Highest

Data on cause of death are compiled by the WHO,

among the elderly, prevalence rates are rising among

based mainly on data from national vital registry sys-

younger and productive populations in developing

tems, as well as sample registration systems, popu-

countries. Economic development has led to the

lation laboratories, and epidemiological analysis of

Data on smoking are from the WHO’s Report on

spread of Western lifestyles and diet to develop-

specific conditions. Data are classified based on the

the Global Tobacco Epidemic 2011. Data on tuber-

ing countries, resulting in a substantial increase in

International Statistical Classification of Diseases and

culosis are from the WHO’s Global Tuberculosis

diabetes. Without effective prevention and control

Related Health Problems, 10th revision. Data have

Control Report 2011. Data on diabetes are from

programs, diabetes will likely continue to increase.

been carefully analyzed to take into account incomplete

the International Diabetes Federation’s Diabetes

Data are estimated based on sample surveys.

coverage of vital registration and the likely differences

Atlas, 5th edition. Data on HIV are from UNAIDS’s

Adult HIV prevalence rates reflect the rate of HIV

in cause of death patterns that would be expected in

Global Report: UNAIDS Report on the Global AIDS

infection in each country’s population. Low national

undercovered and often poorer subpopulations. Special

Epidemic 2010. Data on antiretroviral therapy

prevalence rates can be misleading, however. They

attention has also been paid to misattribution or mis-

coverage are from the WHO. Data on cause of

often disguise epidemics that are initially concen-

coding of causes of death in cardiovascular diseases,

death are from the WHO’s Health Statistics and

trated in certain localities or population groups and

cancer, injuries, and general ill-defined categories. For

Health Information Systems database (www.

threaten to spill over into the wider population. In

further information, consult the original source.

who.int/healthinfo/global_burden_disease/

many developing countries most new infections

For indicators from household surveys, the year in

occur in young adults, with young women especially

the table refers to the survey year. For more informa-

vulnerable.

tion, consult the original sources.

Data sources

estimates_country).

2012 World Development Indicators

127

PEOPLE

Health risk factors and future challenges


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

128

Mortality Life expectancy at birth

Neonatal mortality rate

Infant mortality rate

Under-five mortality rate

years

per 1,000 live births 1990 2010

per 1,000 live births 1990 2010

per 1,000 live births 1990 2010

1990

2010

42 72 67 41 71 68 77 76 65 72 59 71 76 49 59 67 64 66 72 48 46 55 53 77 49 51 74 69c 77 68 47 56 76 53 72 74 77 71 75 68 69 62 66 48 69 47 75 77 61 53 70 75 57 77 62 44 43 55

48 77 73 51 76 74 82 80 71 75 69 70 80 56 66 75 53 73 74 55 50 63 51 81 48 49 79 73c 83 73 48 57 79 55 76 79 79 77 79 73 75 73 72 61 75 59 80 81 62 58 73 80 64 80 71 54 48 62

2012 World Development Indicators

53 17 29 51 15 26 5 4 31 6 55 7 4 40 39 12 22 28 11 41 49 38 34 4 43 45 9 24 .. 20 48 33 10 46 8 7 5 9 4 29 20 28 18 31 13 48 4 3 31 42 27 4 38 9 28 51 48 38

45 9 18 41 7 11 3 2 19 4 27 3 2 32 23 5 19 12 7 38 42 22 34 4 42 41 5 11 .. 12 46 29 6 41 3 3 2 2 2 15 10 9 6 18 3 35 2 2 26 31 15 2 28 2 15 38 40 27

140 36 55 144 24 46 8 8 74 15 99 14 9 107 84 17 46 50 18 103 110 87 85 7 110 113 16 38 .. 30 117 74 15 105 11 11 10 12 7 48 41 68 48 87 17 111 6 7 68 78 40 7 77 11 56 135 125 104

103 16 31 98 12 18 4 4 39 9 38 4 4 73 42 8 36 17 11 93 88 43 84 5 106 99 8 16 .. 18 112 61 9 86 5 5 3 3 3 22 18 19 14 42 4 68 2 3 54 57 20 3 50 3 25 81 92 70

209 41 68 243 27 55 9 9 93 17 143 17 10 178 121 19 59 59 22 205 183 121 137 8 165 207 19 48 .. 37 181 116 17 151 13 13 11 14 9 62 52 94 62 141 21 184 7 9 93 165 47 9 122 13 78 229 210 151

149 18 36 161 14 20 5 4 46 10 48 6 4 115 54 8 48 19 13 176 142 51 136 6 159 173 9 18 .. 22 170 93 10 123 6 6 4 4 4 27 20 22 16 61 5 106 3 4 74 98 22 4 74 4 32 130 150 165

Child mortality rate

Adult mortality rate

per 1,000 per 1,000 Male Female Male Female 2005–10a,b 2005–10a,b 2006–10a 2006–10a

.. 3 .. .. .. 8 .. .. 9 .. 16 .. .. 64 18 .. .. .. .. .. 65 20 .. .. 74 .. .. .. .. 4 70 49 .. .. 1 .. .. .. .. 6 .. 5 .. .. .. 56 .. .. .. 46 5 .. 38 .. .. 89 110 33

.. 1 .. .. .. 3 .. .. 5 .. 20 .. .. 65 20 .. .. .. .. .. 65 20 .. .. 82 .. .. .. .. 3 64 43 .. .. 1 .. .. .. .. 4 .. 5 .. .. .. 56 .. .. .. 39 4 .. 28 .. .. 86 88 36

409 95 126 386 160 162 82 99 181 95 163 334 107 332 225 134 535 218 205 300 418 262 410 92 469 372 123 138 74 194 407 335 110 377 140 108 77 138 107 200 161 140 281 345 234 304 123 118 288 299 177 101 255 101 226 352 410 264

377 46 102 337 74 79 47 50 74 73 137 112 61 276 167 69 579 114 86 249 381 222 378 55 436 316 57 88 37 89 354 301 58 349 57 68 38 63 65 131 84 85 119 261 77 259 56 55 263 244 67 54 225 46 122 303 358 236


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

Life expectancy at birth

Neonatal mortality rate

Infant mortality rate

Under-five mortality rate

years

per 1,000 live births 1990 2010

per 1,000 live births 1990 2010

per 1,000 live births 1990 2010

1990

2010

66 69 58 62 62 68 75 77 77 71 79 70 68 59 70 71 68 73 68 54 69 69 59 42 68 71 71 51 47 70 44 56 69 71 67 61 64 43 57 61 54 77 75 64 41 46 77 71 61 72 56 68 66 65 71 74 74 74

73 74 65 69 73 68 80 82 82 73 83 73 68 56 69 81 70 75 69 67 73 72 47 56 75 73 75 66 53 74 51 58 73 77 69 68 72 50 65 62 68 81 81 74 54 51 81 73 65 76 62 72 74 68 76 79 79 78

23 12 47 31 28 23 5 6 6 13 3 20 26 31 22 3 .. 9 30 39 12 18 36 53 22 10 17 40 44 9 57 42 16 17 15 27 36 51 44 25 54 5 4 25 48 49 4 22 51 14 30 24 27 23 11 7 .. 10

12 4 32 17 14 20 2 2 2 9 1 13 17 28 18 2 .. 6 19 21 5 12 35 34 10 3 8 22 27 3 48 39 9 7 9 12 19 39 32 17 28 3 3 12 32 40 2 5 41 9 23 14 9 14 4 2 .. 4

45 17 81 56 50 37 8 10 8 31 5 32 48 64 23 6 .. 13 59 100 16 31 72 151 33 14 34 97 131 15 131 80 21 38 30 76 67 146 79 49 97 7 9 52 132 126 7 36 96 26 65 40 55 42 15 11 .. 17

20 5 48 27 22 31 3 4 3 20 2 18 29 55 26 4 .. 10 33 42 8 19 65 74 13 5 10 43 58 5 99 75 13 14 16 26 30 92 50 29 41 4 5 23 73 88 3 8 70 17 47 21 15 23 5 3 .. 7

58 19 115 85 65 46 9 12 10 38 6 38 57 99 45 8 .. 15 72 145 21 38 89 227 45 17 39 159 222 18 255 124 24 49 37 107 86 219 112 73 141 8 11 68 311 213 9 47 124 33 90 50 78 59 17 15 .. 21

24 6 63 35 26 39 4 5 4 24 3 22 33 85 33 5 .. 11 38 54 10 22 85 103 17 7 12 62 92 6 178 111 15 17 19 32 36 135 66 40 50 4 6 27 143 143 3 9 87 20 61 25 19 29 6 4 .. 8

Child mortality rate

2.23 Adult mortality rate

per 1,000 per 1,000 Male Female Male Female 2005–10a,b 2005–10a,b 2006–10a 2006–10a

8 .. 9 13 .. 6 .. .. .. 5 .. 3 5 27 .. .. .. .. 8 .. .. .. .. 62 .. .. 2 30 52 .. 117 53 .. .. 7 11 .. .. .. 24 21 .. .. .. 138 91 .. .. 14 .. .. .. 13 10 .. .. .. ..

9 .. 12 12 .. 7 .. .. .. 6 .. 7 4 25 .. .. .. .. 4 .. .. .. .. 64 .. .. 1 31 54 .. 114 44 .. .. 4 10 .. .. .. 19 18 .. .. .. 135 93 .. .. 22 .. .. .. 4 9 .. .. .. ..

164 229 253 205 163 281 97 79 78 189 85 143 366 380 194 90 .. 102 304 207 247 150 578 349 137 275 126 215 409 147 361 290 206 133 302 294 144 482 235 345 186 75 87 197 313 393 82 138 189 133 315 168 158 262 198 122 133 69

2012 World Development Indicators

115 99 168 169 80 125 57 45 41 117 42 99 147 358 124 41 .. 62 132 167 94 101 613 314 85 95 78 169 411 75 297 220 102 73 146 141 91 444 187 343 160 56 58 111 271 365 50 76 158 70 239 121 97 145 76 53 51 59

129

PEOPLE

Mortality


2.23

Mortality Life expectancy at birth

Neonatal mortality rate

Infant mortality rate

Under-five mortality rate

years

per 1,000 live births 1990 2010

per 1,000 live births 1990 2010

per 1,000 live births 1990 2010

1990

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

70 69 33 69 53 71 39 76 71 73 45 62 .. 77 70 53 59 78 77 71 63 51 72 46 53 69 70 63 63 47 70 72 76 75 72 67 71 65 68 56 47 61 65 w 53 64 59 69 63 68 68 68 64 59 50 75 76

2010

73 69 55 74 59 74 47 82 75 79 51 52 62 82 75 61 48 81 82 76 67 57 74 62 57 70 75 74 65 54 70 77 80 78 76 68 74 75 73 65 48 50 70 w 59 69 65 73 68 72 71 74 72 65 54 80 81

15 12 41 20 40 16 57 4 12 5 52 18 .. 6 18 39 24 3 4 18 37 40 17 48 40 23 23 33 33 36 9 12 5 6 11 30 17 23 .. 43 40 27 32 w 46 33 41 23 35 25 21 23 29 48 43 6 5

8 6 29 10 27 4 45 1 4 2 52 18 .. 3 10 35 21 2 3 9 25 26 8 24 32 18 9 10 23 26 6 4 3 4 6 23 10 12 .. 32 30 27 23 w 33 22 29 11 25 13 11 11 16 33 35 3 2

29 22 99 36 70 25 162 6 15 9 108 47 .. 9 26 78 70 6 7 31 91 95 26 127 87 32 39 66 78 106 18 18 8 9 20 63 28 37 36 90 109 52 62 w 103 61 78 39 68 42 42 43 56 86 105 10 8

11 9 59 15 50 6 114 2 7 2 108 41 .. 4 14 66 55 2 4 14 52 60 11 56 66 24 14 14 47 63 11 6 5 7 9 44 16 19 20 57 69 51 41 w 70 38 50 17 45 20 19 18 27 52 76 5 3

37 27 163 45 139 29 276 8 18 10 180 60 .. 11 32 125 96 7 8 38 116 155 32 169 147 37 49 80 98 175 21 22 9 11 23 77 33 51 45 128 183 78 90 w 165 85 113 49 98 56 51 54 74 120 175 12 10

14 12 91 18 75 7 174 3 8 3 180 57 .. 5 17 103 78 3 5 16 63 92 13 81 103 27 16 18 56 99 13 7 5 8 11 52 18 23 22 77 111 80 58 w 108 51 69 20 63 24 23 23 34 67 121 6 4

Child mortality rate

Adult mortality rate

per 1,000 per 1,000 Male Female Male Female 2005–10a,b 2005–10a,b 2006–10a 2006–10a

.. .. 69 3 43 4 67 .. .. .. 53 .. .. .. .. 38 32 .. .. 5 18 .. .. .. 55 5 .. 6 .. 75 4 .. .. .. .. 11 .. 5 3 10 66 21 .. w 53 .. 21 .. .. .. .. .. .. 11 68 .. ..

.. .. 55 4 39 3 61 .. .. .. 54 .. .. .. .. 30 30 .. .. 3 13 .. .. .. 43 8 .. 6 .. 62 1 .. .. .. .. 7 .. 4 3 11 55 21 .. w 51 .. 22 .. .. .. .. .. .. 15 65 .. ..

185 372 348 126 291 150e 464 77 184 124 368 567 .. 94 186 265 562 69 76 110 224 362 205 259 340 233 123 136 304 400 385 90 95 139 133 243 171 132 142 231 491 543 210 w 297 202 244 161 213 157 273 181 160 239 379 117 107

76 139 315 96 239 82e 444 45 74 54 312 560 .. 43 78 211 580 41 42 72 128 343 101 223 297 136 69 77 159 385 142 68 58 80 60 139 89 89 105 186 493 594 150 w 260 136 175 100 152 105 116 98 95 166 346 62 53

a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Taiwan, China. d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Includes Kosovo.

130

2012 World Development Indicators


About the data

2.23

Definitions

Mortality rates for different age groups (infants, chil-

weighted polynomial regression to obtain a best esti-

• Life expectancy at birth is the number of years

dren, and adults) and overall mortality indicators (life

mate trend line by fitting a set of local regressions

a newborn infant would live if prevailing patterns of

expectancy at birth or survival to a given age) are

of mortality rates against their reference dates. (For

mortality at the time of its birth were to stay the

important indicators of health status in a country.

further discussion of childhood mortality estimates,

same throughout its life. • Neonatal mortality rate

Because data on the incidence and prevalence of

see UN Inter-agency Group for Child Mortality Estima-

is the number of neonatal infants dying before reach-

diseases are frequently unavailable, mortality rates

tion 2011; for a graphic presentation and detailed

ing 28 days of age, per 1,000 live births. • Infant

are often used to identify vulnerable populations.

background data, see www.childmortality.org).

mortality rate is the number of infants dying before

And they are among the indicators most frequently

Neonatal, infant, and child mortality rates are

reaching one year of age, per 1,000 live births.

used to compare socioeconomic development

higher for boys than for girls in countries in which

• Under-five mortality rate is the probability of a

across countries.

parental gender preferences are insignificant. Under-

child born in a specific year dying before reaching age

The main sources of mortality data are vital reg-

five and child mortality rates capture the effect of

5, if subject to the age-specific mortality rate of that

istration systems and direct or indirect estimates

gender discrimination better than neonatal and

year. The probability is derived from life tables and

based on sample surveys or censuses. A “complete”

infant mortality rates do, as malnutrition and medical

is expressed as a rate per 1,000 live births. • Child

vital registration system—covering at least 90 per-

interventions are more important in this age group.

mortality rate is the probability per 1,000 of dying

cent of vital events in the population—is the best

Where female child mortality is higher, as in some

between ages 1 and 5—that is, the probability of a

source of age-specific mortality data. Where reliable

countries in South Asia, girls probably have unequal

1-year-old dying before reaching age 5—if subject to

age-specific mortality data are available, life expec-

access to resources. Child mortality rates in the

current age-specific mortality rates. • Adult mortal-

tancy at birth is directly estimated from the life table

table are not compatible with neonatal, infant, and

ity rate is the probability per 1,000 of dying between

constructed from age-specific mortality data.

under-five mortality rates because of differences in

the ages of 15 and 60—that is, the probability of a

But complete vital registration systems are fairly

methodology and reference year. Child mortality data

15-year-old dying before reaching age 60—if sub-

uncommon in developing countries. Thus estimates

were estimated directly from surveys and cover the

ject to current age-specific mortality rates between

must be obtained from sample surveys or derived

10 years preceding the survey. In addition to esti-

those ages.

by applying indirect estimation techniques to reg-

mates from Demographic Health Surveys, estimates

istration, census, or survey data (see table 2.17

derived from Multiple Indicator Cluster Surveys have

and Primary data documentation). Survey data are

been added to the table; they cover the 5 years pre-

subject to recall error, and surveys estimating infant

ceding the survey. Data sources

deaths require large samples because households

Rates for adult mortality come from life tables.

in which a birth has occurred during a given year

Adult mortality rates increased notably in a dozen

Data on life expectancy at birth are World Bank

cannot ordinarily be preselected for sampling. Indi-

countries in Sub-Saharan Africa in the early 2000s

calculations based on male and female data from

rect estimates rely on model life tables that may be

and in several countries in Europe and Central Asia

World Population Prospects: The 2010 Revision (for

inappropriate for the population concerned. Because

in the first half of the 1990s. In Sub-Saharan Africa

more than half of countries, most of them develop-

life expectancy at birth is estimated using infant mor-

the increase stems from AIDS-related mortality

ing countries), census reports and other statistical

tality data and model life tables for many develop-

and affects both sexes, though women are more

publications from national statistical offices, Euro-

ing countries, similar reliability issues arise for this

affected. In Europe and Central Asia the causes are

stat’s Demographic Statistics, and the U.S. Bureau

indicator. Extrapolations based on outdated surveys

more diverse (high prevalence of smoking, high-fat

of the Census International Data Base. Data on

may not be reliable for monitoring changes in health

diet, excessive alcohol use, stressful conditions

neonatal, infant, and under-five mortality are from

status or for comparative analytical work.

related to the economic transition) and affect men

the UN Inter-agency Group for Child Mortality Esti-

more.

mation’s Levels and Trends in Child Mortality: Report

Estimates of neonatal, infant, and under-five mor-

PEOPLE

Mortality

tality tend to vary by source and method for a given

Annual data series from the United Nations are

2011 and are based mainly on household surveys,

time and place. Years for available estimates also

interpolated based on five-year estimates and thus

censuses, and vital registration data. Data on child

vary by country, making comparison across countries

may not reflect actual events.

mortality are from MEASURE DHS Demographic

and over time difficult. To make neonatal, infant, and

and Health Surveys by ICF International and World

under-fi ve mortality estimates comparable and to

Bank calculations based on infant and under-five

ensure consistency across estimates by different

mortality from Multiple Indicator Cluster Surveys

agencies, the United Nations Inter-agency Group

by UNICEF. Most data on adult mortality are linear

for Child Mortality Estimation, which comprises the

interpolations of five-year data from World Popula-

United Nations Children’s Fund (UNICEF), the United

tion Prospects: The 2010 Revision. Remaining data

Nations Population Division, the World Health Organi-

on adult mortality are from the Human Mortality

zation (WHO), the World Bank, and other universities

Database by the University of California, Berke-

and research institutes, developed and adopted a

ley, and the Max Planck Institute for Demographic

statistical method that uses all available information

Research (www.mortality.org).

to reconcile differences. The method uses a locally

2012 World Development Indicators

131 Text figures, tables, and boxes


2.24

Health gaps by income

Demography Survey year

Infant mortality rate

per 1,000 live births

Algeria Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Burkina Faso Cambodia Cameroon Colombia Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Honduras India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Moldova Namibia Nepal Niger Nigeria Pakistan Philippines Rwanda Senegal Serbia Sierra Leone Somalia Swaziland Syrian Arab Republic Tanzania Thailand Timor-Leste Togo Ukraine Uzbekistan Yemen, Rep. Zambia Zimbabwe

132

2005 2006 2007 2008 2006 2005 2006 2010 2007 2005 2006 2007 2008 2005 2006 2008 2005 2006 2006 2006 2007 2009 2006 2009

2009 2009 2006 2006 2007 2005 2007 2006 2006 2008 2007 2008 2008 2005 2008 2007 2006 2010 2009/10 2006 2007 2006 2006 2007 2006

2012 World Development Indicators

Under-five mortality rate

per 1,000 live births

Total fertility rate

births per woman

Teenage mothers

% of women ages 15–19

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

.. 42 52 66 89 .. 97 101 101 22 113 92 147 43 42 80 106 59 127 .. 78 37 82 56 32 68 66 .. .. 121 61 72 124 89 20 60 71 91 100 94 40 99 77 .. 148 .. 84 18 61 .. 62 92 19 59 94 69 48

.. 14 37 36 26 .. 78 34 51 12 58 56 59 26 17 60 58 47 68 .. 45 19 34 26 29 42 57 .. .. 95 37 62 80 57 16 24 40 67 58 53 15 45 43 .. 93 .. 84 16 63 .. 38 43 9 36 36 74 45

.. 52 63 86 116 .. 196 127 189 29 184 135 229 53 49 130 158 103 217 .. 125 50 118 77 36 82 98 .. .. 176 106 123 233 144 29 92 98 206 219 121 59 161 143 .. 211 .. 118 22 103 .. 87 150 23 72 118 124 72

.. 23 41 43 31 .. 111 43 88 13 97 85 83 28 19 92 72 60 113 .. 55 20 39 32 32 45 69 .. .. 137 48 99 124 87 17 30 47 157 87 60 17 84 56 .. 145 .. 101 20 84 .. 52 62 9 42 118 124 72

.. 1.8 2.3 3.2 6.2 .. 6.6 4.9 6.5 3.2 7.4 6.7 7.4 3.8 3.4 6.6 .. 6.5 6.5 .. 6.6 5.6 3.9 3.0 4.9 3.4 7.0 .. .. 8.0 6.8 7.1 7.6 5.4 2.1 5.1 4.7 7.9 7.1 5.8 5.2 5.8 6.7 .. 6.3 .. 5.5 .. 7.0 .. 7.3 7.3 1.7 .. .. 8.4 5.5

.. 1.5 1.6 2.2 1.9 .. 3.6 2.4 3.2 1.4 4.2 2.9 2.9 1.7 2.7 3.2 .. 2.3 4.2 .. 2.0 2.1 1.8 2.7 2.7 1.2 2.9 .. .. 3.2 2.7 4.1 4.9 3.6 1.4 2.4 1.9 6.4 4.0 3.0 1.9 4.4 3.3 .. 3.2 .. 2.6 .. 3.2 .. 4.2 2.9 1.0 .. .. 3.4 2.3

.. 5 6 42 31 .. 26 11 36 29 29 35 52 37 12 24 .. 18 39 .. 22 31 25 6 5 8 24 .. .. 53 51 43 37 14 8 22 18 40 46 16 19 5 34 .. 49 .. 33 .. 28 .. 9 36 8 .. .. 37 32

.. 0 3 20 8 .. 12 5 14 7 12 13 12 8 5 8 .. 4 20 .. 7 10 5 10 3 5 16 .. .. 20 14 20 23 11 1 5 14 24 5 4 4 4 8 .. 16 .. 15 .. 13 .. 3 7 1 .. .. 14 7


Child health Survey year

Diarrhea

Prevalence % of children under age 5

Algeria Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Burkina Faso Cambodia Cameroon Colombia Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Honduras India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Moldova Namibia Nepal Niger Nigeria Pakistan Philippines Rwanda Senegal Serbia Sierra Leone Somalia Swaziland Syrian Arab Republic Tanzania Thailand Timor-Leste Togo Ukraine Uzbekistan Yemen, Rep. Zambia Zimbabwe

2006 2005 2006 2007 2008 2006 2006 2005 2006 2010 2007 2005 2006 2007 2008 2005 2006 2008 2005 2006 2006 2006 2006 2007 2009 2006 2009 2006 2006 2007 2009 2006 2006 2007 2005 2007 2006 2006 2008 2007 2008 2008 2005 2006 2008 2006 2007 2006 2010 2006 2009/10 2006 2006 2006 2007 2006

Acute respiratory infection (ARI) Treatment % of children under age 5 with diarrhea

Prevalence % of children under age 5

Treatment Children with ARI taken to health provider % of children under age 5 with ARI

Prevalence of child malnutrition (underweight)

Child immunization

Old standards % of children under age 5

All vaccinations % of children ages 12–23 months

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

10 20 13 10 30 4 19 22 30 16 17 15 20 16 10 18 21 25 18 13 25 19 9 18 18 1 20 3 17 19 8 26 13 25 7 13 13 22 14 23 10 16 24 7 13 26 23 9 15 10 13 15 .. .. 35 14 15

8 13 10 8 20 5 16 14 10 7 14 12 13 13 7 14 15 10 17 14 18 11 8 10 14 2 13 4 9 19 10 20 8 19 12 11 12 18 5 20 7 13 23 5 9 14 9 6 16 6 17 9 .. .. 27 16 15

.. 65 66 79 58 .. 57 66 46 67 65 54 55 64 39 24 .. 58 47 .. 48 64 25 60 56 46 80 .. .. 63 52 65 41 29 50 57 27 50 25 52 68 32 51 .. 74 .. 89 .. 56 .. 81 .. .. .. .. 71 65

.. 80 55 88 75 .. 73 40 73 82 55 54 77 63 32 55 .. 69 73 .. 67 68 49 57 64 71 79 .. .. 83 67 76 68 29 70 74 59 66 61 59 82 49 52 .. 86 .. 85 .. 68 .. 76 .. .. .. .. 79 79

7 11 4 6 24 5 6 12 10 7 9 .. 8 9 10 12 6 6 10 4 10 14 6 8 5 1 11 .. 6 8 3 9 10 7 5 7 6 10 4 15 7 16 10 3 7 18 10 4 4 7 2 7 .. .. .. 5 7

5 11 2 3 17 3 2 3 6 4 6 .. 3 4 8 11 5 3 8 7 5 6 4 5 3 2 5 .. 3 9 3 8 11 7 10 1 5 11 1 13 3 14 15 2 4 12 8 6 5 3 2 8 .. .. .. 6 2

38 30 15 45 40 97 45 41 20 54 32 .. 21 63 70 19 68 45 30 32 21 46 61 48 66 37 57 .. 28 59 33 51 9 33 42 65 36 19 32 67 42 16 35 89 39 5 66 72 18 85 53 17 .. .. .. 78 9

68 42 11 73 70 96 73 58 50 67 48 .. 71 65 82 33 68 87 59 82 37 74 80 74 78 53 63 .. 12 89 68 65 50 64 68 94 54 59 66 92 64 43 61 .. 46 28 75 86 62 78 80 28 .. .. .. 56 51

5 5 17 55 11 2 44 43 35 12 33 20 26 7 9 43 26 23 28 21 27 21 61 .. 4 5 31 3 44 26 47 25 37 40 .. 27 54 48 40 .. .. 30 25 4 27 .. 9 13 25 15 .. 37 .. 6 .. 21 18

3 1 4 31 2 4 24 23 6 3 19 5 10 2 7 29 14 11 21 10 7 2 25 .. 0 1 12 3 18 17 28 16 22 13 .. 9 24 30 13 .. .. 10 7 2 15 .. 3 8 12 4 .. 15 .. 3 .. 14 6

81 66 7 80 78 66 64 56 42 64 20 29 58 48 89 14 83 75 29 43 34 77 24 39 82 98 61 .. 18 23 41 66 49 39 82 59 68 20 5 26 64 82 59 50 39 5 82 51 69 92 43 39 .. 90 18 71 43

95 68 32 88 81 71 81 76 72 67 50 73 92 76 94 36 74 84 45 64 56 68 71 75 89 96 70 .. 45 56 82 77 56 25 59 82 94 48 53 64 87 83 65 54 40 22 79 77 85 86 45 63 .. 80 73 78 64

2012 World Development Indicators

133

PEOPLE

2.24

Health gaps by income


2.24

Health gaps by income

Reproductive and women’s health Survey year

Algeria Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Burkina Faso Cambodia Cameroon Colombia Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Honduras India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Moldova Namibia Nepal Niger Nigeria Pakistan Philippines Rwanda Senegal Serbia Sierra Leone Somalia Swaziland Syrian Arab Republic Tanzania Thailand Timor-Leste Togo Ukraine Uzbekistan Yemen, Rep. Zambia Zimbabwe

134

2006 2005 2006 2007 2008 2006 2006 2005 2006 2010 2007 2005 2006 2007 2008 2005 2006 2008 2005 2006 2006 2006 2006 2007 2009 2006 2009 2006 2006 2007 2009 2006 2006 2007 2005 2007 2006 2006 2008 2007 2008 2008 2005 2006 2008 2006 2007 2006 2010 2006 2009/10 2006 2007 2006 2006 2007 2006

Knowledge of contraception

Contraceptive prevalence rate

Any method % of married women ages 15–49

Any method % of married women ages 15–49

Poorest quintile

Richest quintile

.. 97 96 100 90 .. 85 98 72 100 79 97 75 99 100 76 .. 93 87 .. 100 100 99 96 100 99 86 .. .. 72 87 98 70 53 99 97 100 68 41 92 96 99 89 .. 65 .. 100 .. 97 .. 66 94 99 .. .. 98 99

.. 100 99 100 100 .. 99 100 99 100 98 100 99 100 100 96 .. 100 97 .. 100 100 100 100 100 100 99 .. .. 99 100 100 93 93 100 100 100 89 96 99 100 99 99 .. 89 .. 100 .. 100 .. 93 99 100 .. .. 100 100

2012 World Development Indicators

Poorest quintile

56 51 55 55 46 35 9 31 11 76 14 40 9 68 55 4 6 14 5 6 20 53 42 53 54 42 20 50 .. 4 20 38 4 2 67 32 33 11 3 16 41 28 4 33 4 12 37 42 23 74 15 12 62 66 15 41 48

Richest quintile

65 60 57 60 71 35 41 54 44 80 39 46 24 73 65 37 13 31 17 23 38 73 68 64 65 59 55 51 .. 20 57 46 19 19 70 71 61 21 35 43 50 50 25 49 20 19 62 68 51 69 34 19 71 63 44 54 72

Pregnant women receiving prenatal care

Births attended by skilled health staffa

%

Problem accessing health care

%

%

Poorest quintile

Richest quintile

Poorest quintile

Richest quintile

76 86 56 32 79 98 79 58 48 92 78 75 69 87 57 .. 98 93 68 76 72 88 58 83 97 100 84 94 16 68 73 90 20 53 97 90 50 37 24 38 91 95 86 96 84 8 96 68 82 96 75 69 98 98 32 90 92

98 99 98 85 97 100 98 91 98 99 95 98 97 98 93 .. 98 100 98 89 97 99 97 99 100 100 97 99 88 96 97 95 80 94 98 97 92 82 94 92 99 97 99 100 97 51 99 94 95 100 96 100 99 99 79 98 98

88 98 76 7 39 99 56 22 19 86 62 69 29 90 58 .. 28 23 15 19 8 38 21 47 99 100 23 93 3 30 22 43 9 21 99 61 9 5 9 18 98 49 21 98 28 11 51 78 31 93 12 30 99 100 17 26 44

98 100 100 57 99 100 65 92 96 99 98 99 95 98 97 .. 89 96 87 79 71 98 90 96 100 100 82 100 81 82 90 77 76 95 100 98 64 60 86 79 99 76 91 100 72 77 93 99 88 100 71 97 100 100 74 92 97

Poorest quintile

.. 92 94 26 95 .. 88 93 92 .. 95 .. .. 75 87 98 .. 78 93 .. 98 94 67 62 81 .. 39 .. .. 91 75 90 74 .. 85 89 91 87 86 .. 88 89 88 .. 96 .. 70 .. 56 .. 94 .. .. .. .. 77 89

Richest quintile

.. 67 65 17 78 .. 65 72 69 .. 70 .. .. 43 45 82 .. 48 71 .. 75 69 15 25 62 .. 27 .. .. 45 37 53 47 .. 56 33 63 59 49 .. 50 65 54 .. 69 .. 22 .. 16 .. 70 .. .. .. .. 42 46


About the data

2.24

Definitions

Health survey data at the national level do not reveal

• Survey year is the year in which the underlying data

survey who have received at least one antenatal

within-country inequalities associated with socioeco-

were collected. The reference year of the data may be

care during pregnancy before the most recent birth

nomic status. The data in the table describe the health

preceding the survey year. • Infant mortality rate is

from any skilled personnel. • Births attended by

and demographic status as well as use of health ser-

the number of infants dying before reaching one year

skilled health staff are live births in the one, two,

vices by individuals in different socioeconomic groups

of age, per 1,000 live births. • Under-five mortality

or three years preceding the survey attended by any

within countries. The data are from MEASURE DHS

rate is the probability that a child born in a specific

skilled personnel. • Problem accessing health care

Demographic and Health Surveys by ICF International

year will die before reaching age 5, if subject to the

is the percentage of women who report they have

and Multiple Indicator Cluster Surveys by the United

age-specific mortality rate of that year. The probabil-

a big problem accessing health care when they are

Nations Children’s Fund.

ity is derived from life tables and expressed as a

sick due to inadequate knowledge of where to go

Obtaining reliable data on a household’s or individ-

rate per 1,000 live births. • Total fertility rate is the

for treatment, need to get permission or money for

ual’s socioeconomic status is challenging, and meth-

number of children that would be born to a woman if

treatment, distance to health facility, need to take

ods have evolved over time. Earlier measurements

she were to live to the end of her childbearing years

transport, desire not to go alone, or concern that a

relied on indicators such as household income and

and bear children in accordance with age-specific fer-

female provider may not be available.

consumption, which are prone to bias and are time

tility rates of a reference period. • Teenage mothers

and labor intensive when included in survey question-

are women ages 15−19 who are mothers or pregnant

naires. The wealth index, developed by MEASURE DHS

with their first child. • Diarrhea prevalence is the

with partial funding from the World Bank, is calculated

percentage of children under age 5 who had diarrhea

using easy-to-collect data on a household’s ownership

in the two weeks preceding the survey. • Diarrhea

of selected assets, such as televisions and bicycles;

treatment is the percentage of children under age 5

materials used for housing construction; and types of

with diarrhea in the two weeks preceding the survey

water access and sanitation facilities. A single asset

who received oral rehydration salts, recommended

index is developed on the basis of data from the entire

homemade fluids (rehydration salts or recommended

country sample and used. Generated with a statistical

home solution), or increased fluids. • Acute respira-

procedure known as principal components analysis,

tory infection (ARI) prevalence is the percentage

the wealth index places individual households on a

of children under age 5 who were ill with a cough

continuous scale of relative wealth. Demographic and

accompanied by rapid breathing in the two weeks

Health Surveys and Multiple Indicator Cluster Surveys

preceding the survey. • Children with ARI taken to

separate all interviewed households into five wealth

health provider are children under age 5 with ARI in

quintiles to compare the influence of wealth on various

the two weeks preceding the survey who were taken

population, health and nutrition indicators. The wealth

to a health facility. • Prevalence of child malnutri-

index is presented in the final reports of these surveys.

tion (underweight) is the percentage of children

Data disaggregated by wealth quintile provide

under age 5 whose weight for age is more than two

insights into health differentials by socioeconomic

standard deviations below the median for the inter-

status and allow problems particular to the poor,

national reference population. Data are based on the

such as unequal access to health care to be identi-

old standards of the U.S. Centers for Disease Con-

fied. If the poor have a greater disease burden than

trol National Center for Health Statistics and World

the rich, programs should focus on reaching the

Health Organization international reference popula-

poor. But this is rare. Health services too often fail

tion. • Child immunization (all vaccinations) is the

poor people in access, quality, and affordability. In

percentage of children ages 12−23 months who have

low-income countries the poor are particularly disad-

received vaccines for Bacillus Calmette-Guérin and

vantaged in using health care and experience worse

measles; three doses each of diphtheria, pertussis,

health outcomes than the rich. The table shows the

and tetanus; and polio vaccine (excluding polio 0)

estimates for the poorest and richest quintiles only;

by the time of the survey, according to the vaccina-

the full set of estimates for up to 70 indicators is

tion card or the mother’s report. • Knowledge of

available at http://data.worldbank.org and http://

contraception is the percentage of currently married

data.worldbank.org/data-catalog/health-nutrition

women who know at least one contraceptive method.

-population-statistics. The estimates in the table are

• Contraceptive prevalence rate is the percentage

Data on health gaps by income are from MEASURE

based on household survey data, which may refer to

of women ages 15-49 married or in union who are

DHS Demographic and Health Surveys, by ICF

a period preceding the survey date or use a definition

practicing, or whose sexual partners are practicing,

International, downloaded through STATcompiler,

or methodology different from the estimates in the

any form of contraception. • Pregnant women receiv-

and from Multiple Indicator Cluster Surveys by

other tables. Thus the estimates may differ, and cau-

ing prenatal care are women with one or more live

UNICEF through their final reports.

tion should be exercised in using the data.

births in the one, two, or three years preceding the

Data sources

2012 World Development Indicators

135

PEOPLE

Health gaps by income


ENVIRONMENT


T

he indicators in the Environment section measure the use of resources and the way human activities affect the natural and built environment. They include measures of environmental goods (forests, water, cultivatable land) and of degradation (pollution, deforestation, loss of habitat, and loss of biodiversity). Sustainable development and poverty reduction require efficient use of environmental resources. These indicators show that growing populations and expanding economies have placed greater demands on land, water, forests, minerals, and energy resources. But new technologies, increasing productivity, and better policies can ensure that future development is environmentally and socially sustainable. Nowhere are these risks and opportunities more intertwined than in the global effort to mitigate the effects of climate change. At the December 2011 United Nations Conference on Climate Change in Durban, South Africa, all 194 participating countries adopted the Durban Platform for Enhanced Action, which sets the direction of climate negotiations. The platform calls for parties to negotiate a legal agreement on climate change no later than 2015 that would apply to all countries and be effective by 2020. Negotiators also launched a Green Climate Fund that will eventually channel billions of dollars a year to developing countries for adaptation to and mitigation of climate change. As documented in World Development Report 2010: Development and Climate Change (World Bank 2009j), climate change is already eroding development gains and causing disruptions to social and economic systems in some countries. Continued rise in temperature, accompanied by changes in precipitation patterns, is projected for this century, and more frequent, severe, and prolonged climate-related events such as floods and droughts are also projected—posing risks for agriculture, food production, and water supplies. Poor countries and the poorest people in all countries are the most vulnerable to the

impacts. Understanding climate change is thus key for development policy. Because uncertainty increases with climate change, better climate information is critical for wise development decisions. This year’s Environment section includes two new tables on information related to climate change. Table 3.11 presents data on carbon dioxide emissions by economic sector, which shows how differences in industrial structure and production technologies affect the production of carbon dioxide and how these patterns have changed. Table 3.12 presents data on climate variability, exposure to impact, and resilience. Other indicators in this section describe land use, agriculture and food production, forests and biodiversity, water resources, energy use and efficiency, natural resource rents, urbanization, environmental impacts, government commitments, and threatened species. Table 3.8 adds newly available data on the share of the population with access to electricity. Where possible, the indicators come from international sources and are standardized to facilitate comparison across countries. But ecosystems span national boundaries, and access to natural resources may vary within countries. For example, water may be abundant in some parts of a country but scarce in others, and countries often share water resources. Land productivity and optimal land use may be location specific, but widely separated regions can have common characteristics. Greenhouse gas emissions and climate change are measured globally, but their effects are experienced locally, shaping people’s lives and opportunities. Measuring environmental phenomena and their effects at the subnational, national, and supranational levels and incorporating these values in national income accounts and other statistical frameworks remain major challenges for economists, environmentalists, and statisticians.

3

2012 World Development Indicators

137


Tables

3.1

Rural population and land use Rural population

% of total 1990 2010

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

138

82 64 48 63 13 33 15 34 46 12 80 34 4 66 44 61 58 25 34 86 94 87 59 23 63 79 17 73 1 32 72 46 49 60 46 27 33 25 15 45 45 57 51 84 29 87 39 26 31 62 45 27 64 41 59 72 72 72

75 52 34 42 8 36 11 32 48 11 72 26 3 58 34 51 39 14 28 80 89 77 42 19 61 72 11 55 .. 25 65 38 36 50 42 24 30 27 13 30 33 57 39 78 31 82 36 22 14 42 47 26 49 39 51 65 70 50

2012 World Development Indicators

Land area

Land use

average annual % growth 1990–2010

thousand sq. km 2010

Forest area 1990 2010

2.3 –0.9 –0.4 0.7 –1.7 0.5 0.1 –0.4 0.8 7.2 0.5 –1.8 0.0 2.2 0.2 –1.3 –0.7 –2.5 –1.6 2.5 2.2 0.3 0.2 0.6 1.6 2.0 –1.6 –1.1 .. 0.2 1.8 1.5 0.0 0.7 –0.7 –0.1 0.5 0.4 –1.6 –1.2 –0.6 1.7 –0.2 2.4 –0.1 1.8 –0.4 –0.5 –1.5 0.8 0.8 –0.5 0.8 –0.2 1.6 1.5 2.0 –1.4

652.2 27.4 2,381.7 1,246.7 2,736.7 28.5 7,682.3 82.5 82.6 0.8 130.2 202.9 30.3 110.6 1,083.3 51.2 566.7 8,459.4 108.6 273.6 25.7 176.5 472.7 9,093.5 623.0 1,259.2 743.5 9,327.5 1.0 1,109.5 2,267.1 341.5 51.1 318.0 56.0 106.4 9.2 77.3 42.4 48.3 248.4 995.5 20.7 101.0 42.4 1,000.0 303.9 547.7 257.7 10.0 69.5 348.6 227.5 128.9 107.2 245.7 28.1 27.6

2.1 28.8 0.7 48.9 12.7 12.2 20.1 45.8 11.2 0.3 11.5 38.4 22.4 52.1 58.0 43.3 24.2 68.0 30.1 25.0 11.3 73.3 51.4 34.1 37.2 10.4 20.5 16.8 .. 56.3 70.7 66.5 50.2 32.1 33.1 19.2 17.4 34.0 10.5 40.8 49.9 0.0 18.2 16.0 49.3 13.7 71.9 26.5 85.4 44.2 40.0 30.8 32.7 25.6 44.3 29.6 78.8 4.2

% of land area

2.1 28.3 0.6 46.9 10.7 9.2 19.4 47.1 11.3 1.3 11.1 42.5 22.4 41.2 52.8 42.7 20.0 61.4 36.2 20.6 6.7 57.2 42.1 34.1 36.3 9.2 21.8 22.2 .. 54.5 68.0 65.6 51.0 32.7 34.3 27.0 18.7 34.4 12.8 40.8 39.7 0.1 13.9 15.2 52.3 12.3 72.9 29.1 85.4 48.0 39.5 31.8 21.7 30.3 34.1 26.6 71.9 3.7

Permanent cropland 1990 2009

0.2 4.6 0.2 0.4 0.4 2.1 0.0 1.0 3.7 2.9 2.5 0.9 0.5a 0.9 0.1 2.9 0.0 0.8 2.7 0.2 14.0 0.6 2.6 0.7 0.1 0.0 0.3 0.8 .. 1.5 0.5 0.1 4.9 11.0 2.0 4.2 5.5 3.1 0.2 9.3 4.8 0.4 12.5 0.0 0.3 0.5 0.0 2.2 0.6 0.5 4.8 1.3 6.6 8.3 4.5 2.0 4.2 11.6

0.2 3.2 0.4 0.2 0.4 1.9 0.0 0.8 2.7 2.6 7.5 0.6 0.7 2.7 0.2 2.0 0.0 0.9 1.6 0.2 13.6 0.9 3.0 0.8 0.1 0.0 0.6 1.5 .. 1.4 0.3 0.2 5.9 13.5 1.6 3.5 3.6 1.0 0.1 9.7 5.4 0.8 11.1 0.0 0.2 1.0 0.0 1.9 0.6 0.5 1.7 0.6 12.3 8.9 8.8 2.8 8.9 10.9

Arable land 1990 2009

12.1 21.1 3.0 2.3 9.6 14.9 6.2 17.3 20.5 2.9 72.6 30.0 23.3a 14.6 1.9 16.7 0.7 6.0 34.9 12.9 36.2 20.9 12.6 5.0 3.1 2.6 3.8 13.3 .. 3.0 2.9 1.4 5.1 7.6 21.7 31.6 11.5 41.1 60.4 18.6 5.8 2.3 26.5 4.9 26.3 10.0 7.4 32.9 1.1 18.2 11.4 34.3 11.9 22.5 12.1 11.6 8.9 28.3

11.9 22.3 3.1 3.2 11.3 16.0 6.1 16.6 22.7 1.3 58.1 27.3 27.7 22.1 3.4 19.5 0.4 7.2 28.9 21.6 35.0 22.1 12.6 5.0 3.1 3.4 1.7 11.8 .. 1.6 3.0 1.5 3.9 8.8 15.5 34.3 9.4 41.2 57.3 16.6 4.8 2.9 32.7 6.8 14.1 13.9 7.4 33.5 1.3 40.0 6.4 34.3 19.3 19.8 14.0 11.6 10.7 38.1

Arable land hectares per 100 people 1990 2009

41.6 17.6 28.0 28.1 80.9 12.3 280.7 18.6 23.1 0.4 9.0 59.6 0.2 33.8 31.5 21.6 30.4 33.9 44.2 37.8 16.6 38.8 48.8 163.7 65.4 54.5 21.3 10.9 .. 10.0 18.3 20.1 8.5 19.4 27.1 32.1 13.8 32.1 49.8 12.5 15.6 4.0 10.3 0.1 72.7 1.4 45.5 30.9 31.8 18.8 16.3 15.1 18.3 28.5 14.6 49.5 24.6 11.0

23.3 19.2 21.5 21.6 77.4 14.8 214.8 16.4 20.9 0.1 5.2 58.3 7.8 28.5 38.2 26.5 12.6 31.7 41.4 36.9 11.0 27.9 31.1 133.7 45.2 39.3 7.5 8.3 .. 3.9 10.4 12.7 4.4 14.5 19.6 32.4 8.0 30.3 44.0 8.2 8.4 3.6 11.0 13.5 44.5 17.2 42.3 28.4 22.0 23.8 10.2 14.6 18.5 22.6 10.7 29.2 20.2 10.6


Rural population

% of total 1990 2010

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

60 34 75 69 44 30 43 10 33 51 37 28 44 82 42 26 .. 2 62 85 31 17 86 55 24 32 42 76 88 50 77 60 56 29 53 43 52 79 75 72 91 31 15 48 85 65 28 34 69 46 85 51 31 51 39 52 28 8

51 32 70 46 31 34 38 8 32 46 33 22 42 78 37 18 .. 2 63 67 32 13 73 39 22 33 32 70 80 28 67 59 57 22 59 43 43 62 66 62 82 17 13 43 83 50 22 28 63 25 88 39 28 34 39 39 1 4

average annual % growth 1990–2010

1.1 –1.4 0.9 –1.4 –0.6 3.3 –0.2 1.6 0.0 –0.2 –0.6 2.0 1.8 2.2 –0.5 –0.9 .. 2.1 0.6 –0.3 –0.7 –0.2 0.0 2.3 0.7 –0.9 –1.4 2.4 2.5 –1.7 2.2 2.1 0.4 –0.1 0.4 1.2 0.2 1.0 –0.2 0.9 1.2 –2.6 0.3 0.7 3.4 1.1 0.9 2.4 1.1 –1.6 2.3 0.2 0.7 –0.5 0.2 –1.5 –17.9 7.7

Land area

3.1

ENVIRONMENT

Rural population and land use Land use

% of land area thousand sq. km 2010

Forest area 1990 2010

111.9 89.6 2,973.2 1,811.6 1,628.6 437.4 68.9 21.6 294.1 10.8 364.5 88.2 2,699.7 569.1 120.4 96.9 10.9b 17.8 191.8 230.8 62.2 10.2 30.4 96.3 1,759.5 62.7 25.2 581.5 94.1 328.6 1,220.2 1,030.7 2.0 1,944.0 32.9 1,553.6 446.3 786.4 653.5 823.3 143.4 33.8 263.3 120.3 1,266.7 910.8 305.5 309.5 770.9 74.3 452.9 397.3 1,280.0 298.2 304.2 91.5 8.9 11.6

72.7 20.0 21.5 65.4 6.8 1.8 6.7 6.1 25.8 31.9 68.4 1.1 1.3 6.5 68.1 64.5 .. 0.2 4.4 75.0 51.0 12.8 1.3 51.2 0.1 31.0 35.9 23.5 41.3 68.1 11.5 0.4 19.2 36.2 9.7 8.1 11.3 55.2 60.0 10.6 33.7 10.2 29.3 37.5 1.5 18.9 30.0 0.0 3.3 51.0 69.6 53.3 54.8 22.0 29.2 36.4 32.4 0.0

46.4 22.6 23.0 52.1 6.8 1.9 10.7 7.1 31.1 31.1 68.5 1.1 1.2 6.1 47.1 64.2 .. 0.3 5.0 68.2 53.9 13.4 1.4 44.9 0.1 34.5 39.6 21.6 34.4 62.3 10.2 0.2 17.2 33.3 11.7 7.0 11.5 49.6 48.6 8.9 25.4 10.8 31.4 25.9 1.0 9.9 32.9 0.0 2.2 43.7 63.4 44.3 53.1 25.7 30.7 37.8 62.2 0.0

Permanent cropland 1990 2009

3.2 2.6 2.2 6.5 0.8 0.7 0.0 4.1 10.1 9.2 1.3 0.8 0.1 0.8 1.5 1.6 .. 0.1 0.4 0.3 0.4 11.9 0.1 1.6 0.2 0.7 2.2 1.0 1.4 16.0 0.1 0.0 3.0 1.0 14.2 0.0 1.6 0.3 0.8 0.0 0.5 0.9 0.2 1.6 0.0 2.8 0.0 0.1 0.6 2.1 1.2 0.2 0.3 14.8 1.1 8.5 5.6 0.1

3.7 2.1 3.9 10.5 1.1 0.6 0.0 3.6 8.9 9.2 0.9 0.9 0.0 1.1 1.7 2.1 .. 0.2 0.4 0.5 0.1 14.0 0.1 2.2 0.2 0.4 1.4 1.0 1.3 17.6 0.1 0.0 2.0 1.4 9.2 0.0 2.2 0.3 1.7 0.0 0.8 1.0 0.3 1.9 0.0 3.3 0.0 0.1 1.1 2.0 1.5 0.3 0.6 16.9 1.3 8.5 4.5 0.3

Arable land 1990 2009

13.1 56.2 54.8 11.2 9.3 13.3 15.1 15.9 30.6 11.0 13.1 2.0 13.0 8.8 19.0 19.8 .. 0.2 6.9 3.5 27.1 17.9 10.4 3.6 1.0 46.0 23.8 4.7 23.9 5.2 1.7 0.4 49.3 12.5 52.8 0.9 19.5 4.4 14.6 0.8 16.0 26.0 10.0 10.8 8.7 32.4 2.8 0.1 26.6 6.7 0.4 5.3 2.7 18.4 47.3 25.6 7.3 0.9

9.1 50.6 53.1 13.0 10.6 10.4 15.8 14.0 23.4 11.1 11.8 2.3 8.7 9.5 22.0 16.4 27.6 0.6 6.7 5.9 18.8 14.2 11.0 4.2 1.0 32.8 16.7 5.1 38.2 5.5 5.2 0.4 42.9 12.9 55.2 0.6 18.0 6.4 16.9 1.0 16.7 31.2 1.8 15.8 11.8 37.3 2.7 0.3 26.5 7.4 0.6 9.6 2.9 18.1 41.2 12.3 6.8 1.0

Arable land hectares per 100 people 1990 2009

29.9 48.7 18.6 11.0 27.7 31.9 29.6 7.4 15.9 5.0 3.9 5.7 213.2 21.3 11.4 4.6 .. 0.2 29.0 19.1 64.6 6.2 19.3 16.5 41.6 78.0 31.3 24.1 24.0 9.3 23.7 20.0 9.4 28.8 46.8 62.5 35.1 25.5 24.4 46.7 12.0 5.9 76.7 31.6 141.7 30.3 20.3 1.9 18.3 20.7 4.6 49.7 16.1 8.9 37.8 23.5 1.8 2.1

2012 World Development Indicators

13.7 45.8 13.1 9.9 23.5 14.5 24.4 4.1 11.4 4.5 3.4 3.4 145.4 13.7 10.9 3.3 16.8 0.4 24.0 22.3 51.8 3.5 15.6 10.4 27.9 61.5 20.4 14.7 24.9 6.4 42.7 11.6 6.8 22.4 51.0 35.4 25.5 22.1 23.2 35.7 8.2 6.4 10.9 33.3 99.8 22.0 17.3 3.7 12.0 15.8 3.9 59.9 12.7 5.9 32.9 10.6 1.5 0.8

139


3.1

Rural population and land use Rural population

% of total 1990 2010

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

47 27 95 23 61 50 67 – 44 50 70 48 .. 25 83 73 77 17 27 51 68 81 71 79 70 92 42 41 55 89 33 21 11 25 11 60 16 80 32 79 61 71 57 w 79 62 68 56 64 71 37 29 48 75 72 27 29

45 27 81 16 57 48 62 – 43 52 63 38 .. 23 85 55 75 15 26 45 74 74 66 72 57 86 33 30 51 87 32 22 10 18 8 63 6 71 28 68 64 62 49 w 72 51 60 43 54 54 36 21 42 70 63 22 26

Land area

average annual % growth 1990–2010

2012 World Development Indicators

Arable land 1990 2009

Arable land hectares per 100 people 1990 2009

41.2 8.1 35.7 1.6 16.1 .. 6.8 1.5 32.5 9.9 1.6 11.1 .. 30.7 14.4 5.4 10.5 6.9 10.3 26.6 6.1 10.2 34.2 7.4 38.6 7.0 18.7 32.0 2.9 25.0 57.6 0.4 27.4 20.3 7.2 10.5 3.2 16.4 18.1 2.9 3.9 7.5 10.9 w 7.7 11.1 14.8 9.2 10.5 12.1 11.6 6.6 5.9 42.7 6.6 11.7 27.1

40.7 88.8 12.4 21.0 42.7 .. 12.2 0.0 31.0 10.0 15.5 38.2 .. 39.5 5.2 48.3 20.9 33.2 6.1 39.6 15.6 35.3 30.7 14.8 57.3 3.0 35.7 45.5 34.8 28.3 64.0 1.9 11.6 74.4 40.5 20.9 14.3 8.1 5.5 12.8 36.8 27.6 23.7 w 23.8 18.8 18.7 18.9 19.3 12.0 68.5 30.3 22.5 17.8 32.1 42.5 22.9

% of land area thousand sq. km 2010

–0.6 229.9 0.0 16,376.9 2.6 24.7 –5.5 2,000.0 c 2.2 192.5 –0.8 88.4 1.7 71.6 .. 0.7 0.0 48.1 1.2 20.1 1.6 627.3 0.1 1,214.5 .. .. –0.2 499.1 0.9 62.7 0.9 2,376.0 1.1 17.2 0.3 410.3 0.8 40.0 1.3 183.6 1.4 140.0 2.4 885.8 0.1 510.9 1.6 14.9 0.9 54.4 0.0 5.1 –0.2 155.4 –0.2 769.6 0.4 469.9 3.0 197.1 –0.6 579.3 7.7 83.6 –0.1 241.9 –1.0 9,147.4 –1.0 175.0 1.3 425.4 –3.9 882.1 0.4 310.1 2.3 6.0 2.2 528.0 1.4 743.4 0.0 386.9 0.3 w 129,561.0 s 1.5 15,043.5 0.1 80,675.5 0.8 22,789.9 –0.9 57,885.6 0.3 95,718.9 –0.9 15,853.7 0.1 22,748.9 –0.6 20,116.2 1.1 8,643.6 1.0 4,771.2 1.7 23,585.4 –0.5 33,842.1 –0.4 2,552.0

a. Includes Luxembourg. b. Data are from national sources. c. Provisional estimate.

140

Land use

Forest area 1990 2010

27.8 49.4 12.9 0.5 48.6 26.4 43.5 3.0 40.0 59.0 13.2 6.8 .. 27.7 37.5 32.1 27.4 66.5 28.8 2.0 2.9 46.8 38.3 65.0 12.6 47.0 4.1 12.6 8.8 23.8 16.0 2.9 10.8 32.4 5.3 7.2 59.0 28.8 1.5 1.0 71.0 57.3 32.0 w 31.2 33.9 30.7 35.2 33.5 29.0 38.4 51.6 2.4 16.6 31.1 27.9 33.8

28.6 49.4 17.6 0.5 44.0 30.7 38.1 2.9 40.2 62.2 10.8 4.7 .. 36.4 29.7 29.4 32.7 68.7 31.0 2.7 2.9 37.7 37.1 49.9 5.3 44.1 6.5 14.7 8.8 15.2 16.8 3.8 11.9 33.2 10.0 7.7 52.5 44.5 1.5 1.0 66.5 40.4 31.1 w 27.6 32.8 27.9 34.6 31.9 29.6 38.6 47.0 2.4 17.1 28.0 28.8 37.3

Permanent cropland 1990 2009

2.6 0.1 12.4 0.0 0.2 26.2 1.9 1.5 1.0 1.8 0.0 0.7 .. 9.7 15.9 0.0 0.7 0.0 0.6 4.0 0.9 1.1 6.1 3.9 1.7 6.8 12.5 3.9 0.1 9.3 1.9 0.2 0.3 0.2 0.3 0.9 0.9 3.2 19.1 0.2 0.0 0.3 1.1 w 0.7 1.4 1.9 1.1 1.2 2.2 0.4 0.9 0.8 1.8 0.8 0.7 4.8

1.6 0.1 11.3 0.1 0.3 3.4 1.8 0.0 0.5 1.3 0.0 0.8 .. 9.5 15.5 0.1 0.9 0.0 0.6 5.4 1.0 1.7 7.2 4.0 3.3 4.3 14.3 3.8 0.1 11.3 1.6 2.4 0.2 0.3 0.2 0.8 0.7 10.8 19.4 0.5 0.0 0.3 1.2 w 0.9 1.4 2.7 0.9 1.3 3.4 0.4 1.0 1.0 3.1 1.0 0.7 4.2

38.2 7.4 52.7 1.5 20.0 37.7 15.1 0.0 28.7 8.7 1.6 11.8 .. 25.1 19.1 8.5 10.2 6.4 10.2 25.4 5.3 11.3 29.9 11.1 40.4 4.9 17.4 27.7 3.9 33.0 56.1 0.8 25.0 17.8 10.7 10.1 3.1 20.3 16.6 2.2 4.5 10.8 10.7 w 9.6 10.9 16.4 8.7 10.7 11.5 10.5 7.4 5.9 41.4 8.5 10.8 24.4

40.9 85.8 12.6 11.9 31.8 45.1 18.9 0.0 25.5 8.6 11.0 29.1 .. 27.2 5.8 47.5 15.0 28.3 5.3 23.3 10.9 23.0 22.3 15.0 37.3 1.9 25.9 29.7 37.2 20.4 70.5 0.9 9.8 53.1 56.2 15.5 9.7 7.3 2.5 5.0 26.3 33.5 20.4 w 18.5 17.8 15.1 20.6 17.9 9.4 59.2 25.9 15.6 12.3 24.2 33.2 18.8


About the data

3.1

ENVIRONMENT

Rural population and land use Definitions

With more than 3 billion people, including 70 percent

Satellite images show land use that differs from

• Rural population is calculated as the difference

of the world’s poor people, living in rural areas, ade-

that of ground-based measures in area under cultiva-

between the total population and the urban popula-

quate indicators to monitor progress in rural areas

tion and type of land use. Moreover, land use data

tion (see Definitions for tables 2.1 and 3.13). • Land

are essential. However, few indicators are disaggre-

in some countries (India is an example) are based

area is a country’s total area, excluding area under

gated between rural and urban areas (for some that

on reporting systems designed for collecting tax rev-

inland water bodies and national claims to the con-

are, see tables 2.7, 3.5, and 3.13). The table shows

enue. With land taxes no longer a major source of

tinental shelf and to exclusive economic zones. In

indicators of rural population and land use. Rural

government revenue, the quality and coverage of land

most cases the definition of inland water bodies

population is approximated as the midyear nonurban

use data have declined. Data on forest area may be

includes major rivers and lakes. (See table 1.1 for

population. While a practical means of identifying the

particularly unreliable because of irregular surveys

the total surface area of countries.) Variations from

rural population, it is not precise (see box 3.1a for

and differences in definitions (see About the data

year to year may be due to updated or revised data

further discussion).

for table 3.4). Forest area statistics released by the

rather than to change in area. • Land use can be

The data in the table show that land use patterns

FAO between 1948 and 1963 were based mostly on

broken into several categories, three of which are

are changing. They also indicate major differences

data from country questionnaires. Remote sensing,

presented in the table (not shown are land used as

in resource endowments and uses among countries.

statistical monitoring, and expert analysis of country

permanent pasture and land under urban develop-

True comparability of the data is limited, however,

surveys have been applied since 1980 to improve

ment). • Forest area is land under natural or planted

by variations in definitions, statistical methods, and

forest coverage estimates. The FAO’s Global Forest

stands of trees of at least 5 meters in height in situ,

quality of data. Countries use different definitions of

Resources Assessment 2010 covers 230 countries

whether productive or not, and excludes tree stands

rural and urban population and land use. The Food

and is the most comprehensive assessment of for-

in agricultural production systems (for example, in

and Agriculture Organization of the United Nations

ests, forestry, and the benefits of forest resources

fruit plantations and agroforestry systems) and trees

(FAO), the primary compiler of the data, occasion-

in both scope and number of countries and people

in urban parks and gardens. • Permanent cropland

ally adjusts its definitions of land use categories

involved. It examines status and trends for about 90

is land cultivated with crops that occupy the land for

and revises earlier data. Because the data reflect

variables on the extent, condition, uses, and values

long periods and need not be replanted after each

changes in reporting procedures as well as actual

of forests and other wooded land.

harvest, such as cocoa, coffee, and rubber. Land

changes in land use, apparent trends should be inter-

under flowering shrubs, fruit trees, nut trees, and

preted cautiously.

vines is included, but land under trees grown for wood or timber is not. • Arable land is land defined by the

What is rural? Urban?

3.1a

FAO as under temporary crops (double-cropped areas

The rural population identified in table 3.1 is approximated as the difference between total population and

are counted once), temporary meadows for mowing

urban population, calculated using the urban share reported by the United Nations Population Division.

or pasture, land under market or kitchen gardens,

There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy is

and land temporarily fallow. Land abandoned as a

an oversimplification (see About the data for table 3.13). The two distinct images—isolated farm, thriving

result of shifting cultivation is excluded.

metropolis—represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.13 is inadequate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development

Data sources

interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the

Data on urban population shares used to estimate

unit cost of delivering most social services and many types of infrastructure is high. Where large urban

rural population are from the United Nations Popu-

areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and

lation Division’s World Urbanization Prospects: The

it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population

2009 Revision, and data on total population are

density and remoteness together define a set of rural areas that face special development challenges.

World Bank estimates. Data on land area, perma-

Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors’ estimates of

nent cropland, and arable land are from the FAO’s

the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their

electronic files. The FAO gathers these data from

estimates range from 13 percent of the population, based on a population density of less than 20 people

national agencies through annual questionnaires

per square kilometer, to 64 percent, based on a population density of more than 500 people per square

and by analyzing the results of national agricultural

kilometer. Taking remoteness into account, the estimated rural population would be 13–52 percent. The

censuses. Data on forest area are from the FAO’s

estimate for Latin America and the Caribbean in table 3.1 is 21 percent.

Global Forest Resources Assessment 2010.

2012 World Development Indicators

141


3.2

Agricultural inputs Agricultural landa

% of land area 1990–92 2007–09

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

142

58 41 16 46 47 41 61 42 53 13 73 46 44b 21 33 43 46 29 56 35 83 26 19 7 8 38 21 57 .. 41 10 31 42 60 43 63 17 .. 65 54 29 3 70 .. 32 .. 8 55 20 61 46 49 56 71 40 57 51 57

58 44 17 47 51 62 53 38 58 9 70 44 45 30 34 42 46 31 46 44 84 31 20 7 8 39 21 56 .. 38 10 31 35 64 23 63 14 55 62 51 30 4 75 75 22 35 8 53 20 67 36 48 68 64 41 58 58 67

2012 World Development Indicators

Average annual precipitation

% irrigated 2007–09

millimeters 2007–09

4.8 16.8 2.1 .. .. 8.9 0.4 1.4 29.5 .. .. 0.5 0.4 .. .. .. .. .. 1.4 .. .. .. .. .. .. .. 5.6 .. .. .. .. .. 1.5 .. 0.4 .. 20.8 0.3 9.6 .. 12.6 .. 2.1 .. .. 0.5 .. 5.1 .. .. 4.0 .. .. 16.9 .. .. .. ..

327 1,485 89 1,010 591 562 534 1,110 447 83 2,666 618 847 1,039 1,146 1,028 416 1,782 608 748 1,274 1,904 1,604 537 1,343 322 1,522 645 .. 2,612 1,543 1,646 2,926 1,348 1,113 1,335 498 677 703 1,410 2,087 51 1,724 384 626 848 536 867 1,831 836 1,026 700 1,187 652 1,996 1,651 1,577 1,440

Land under cereal production

Fertilizer consumption

Agricultural employment

Agricultural machinery

thousand hectares 1990–92 2008–10

kilograms per hectare % of of arable fertilizer land production 2007–09 2007–09

% of total employment 1990–92 2007–09

Tractors per 100 sq. km of arable land 1990 2009

2,250.0 189.5 3,530.5 1,012.0 8,372.5 162.8 13,319.8 837.7 627.0 .. 10,860.8 2,603.0 386.2b 667.3 680.2 304.1 77.9 20,564.1 2,231.6 2,844.0 220.0 1,733.4 950.3 20,176.4 95.4 1,337.6 694.3 92,582.2 .. 1,440.7 1,921.1 9.9 76.3 1,451.0 592.7 252.3 65.1 .. 1,611.9 153.1 918.4 2,477.1 486.3 .. 453.6 .. 914.5 9,346.0 16.1 78.3 248.5 6,514.4 1,203.5 1,396.1 822.7 819.8 117.2 462.9

3,122.0 145.7 2,988.8 1,670.0 9,358.1 156.7 19,476.4 994.8 954.6 .. 12,349.1 2,380.3 329.5 1,109.3 931.6 286.2 111.3 18,674.3 1,917.5 4,290.2 232.7 3,106.8 1,639.9 13,013.6 163.1 2,467.1 523.1 90,131.6 .. 1,047.7 1,980.4 31.8 76.0 851.0 550.0 401.6 35.7 1,467.2 1,489.4 225.4 875.7 2,967.1 354.3 461.7 274.1 9,340.5 954.2 9,258.8 22.9 322.7 173.2 6,612.9 1,602.1 916.8 923.3 2,028.7 152.5 446.2

146.8 .. 487.9 .. 153.3 .. 151.7 .. .. 5.9 231.5 46.3 .. .. .. .. .. 245.0 115.1 .. .. .. .. 22.2 .. .. 94.7 96.8 .. 227.0 .. .. .. .. 48.7 445.3 .. 112.4 .. .. .. 48.5 .. .. 378.1 .. 75.8 437.6 .. .. 14.3 60.8 .. 333.5 .. .. .. ..

3.2 45.5 7.8 1.1 25.4 29.3 29.0 83.1 13.6 .. 281.7 281.1 .. .. 6.0 24.5 .. 124.9 167.4 9.1 0.9 7.1 7.4 46.8 .. .. 595.8 488.4 .. 496.8 0.5 1.1 826.6 15.9 246.8 14.2 181.9 123.3 103.2 27.2 187.3 502.8 107.4 3.5 69.5 7.9 108.0 148.3 6.1 6.8 43.0 181.4 11.9 83.7 106.8 0.6 .. ..

.. .. .. 5.1 0.4 .. 5.3 7.1 34.7 2.4 66.4 22.3 2.9 .. 2.1 .. .. 28.3 21.2 .. .. .. .. 4.1 .. .. 18.0 58.5 0.7 1.4 .. .. 24.1 .. .. 25.3 12.0 .. 5.1 18.7 6.6 38.4 35.8 .. 18.1 .. 8.9 5.9 .. .. .. 3.7 62.0 21.9 13.6 .. .. 65.6

.. 44.1 .. .. 1.2 44.2 3.3 5.3 38.6 .. .. .. 1.5 .. 36.1 .. .. 17.0 7.1 .. .. 72.2 .. 2.4 .. .. 11.2 39.6 0.2 19.7 .. .. 12.3 .. 13.9 18.6 3.9 3.1 2.5 14.5 28.7 31.6 20.9 .. 4.0 .. 4.6 2.9 .. .. 53.4 1.7 .. 11.9 .. .. .. ..

0.2 212.4 129.1 .. 100.2 345.5 .. 2,373.6 194.8 45.0 2.3 206.9 1,523.3b 1.0 24.8 235.3 140.5 143.8 135.8 2.4 1.8 3.2 0.9 164.8 .. .. 127.6 66.6 .. 96.8 .. .. .. 19.9 35.2 226.2 1,377.4 252.0 634.7 25.9 54.2 249.6 .. 5.0 455.3 .. 916.5 800.0 .. 2.4 295.6 1,309.4 7.1 744.2 .. 12.7 0.8 2.6

0.1 121.9 139.6 .. 86.9 291.6 .. 2,390.3 148.2 75.0 1.2 86.8 1,127.0 .. 20.0 .. 134.8 129.2 172.3 .. .. 5.9 .. 162.5 .. .. 425.9 81.8 .. .. .. .. .. 32.1 49.4 203.2 1,460.1 262.3 486.3 21.5 90.7 390.6 .. 8.3 604.7 .. 784.0 635.3 .. .. 216.9 838.3 4.5 1,004.7 .. 25.1 .. ..


Agricultural landa

% of land area 1990–92 2007–09

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

30 68 61 23 39 23 64 27 54 44 15 13 82 48 21 22 .. 8 53 7 41 59 77 26 9 54 51 63 45 23 26 38 56 54 78 80 69 61 16 47 29 59 61 34 28 80 3 3 34 29 2 44 17 37 62 42 46 6

29 64 61 30 30 20 61 24 47 41 13 12 77 48 24 19 52 8 55 10 29 67 77 27 9 43 40 70 59 24 34 38 48 53 75 75 67 63 19 47 30 57 44 43 35 82 3 6 34 30 3 53 17 40 53 40 21 6

Average annual precipitation

% irrigated 2007–09

millimeters 2007–09

.. 1.8 35.1 .. 19.0 .. .. 28.4 18.8 .. 35.2 9.2 .. 0.0 .. 51.6 .. .. 9.4 .. 0.0 19.9 .. .. .. .. 7.3 2.2 0.5 .. .. .. 21.4 5.5 9.2 .. 4.6 .. 24.8 .. 27.7 10.6 .. .. .. .. .. 4.0 73.9 .. .. .. .. .. 0.4 11.4 8.5 ..

1,976 589 1,083 2,702 228 216 1,118 435 832 2,051 1,668 111 250 630 1,054 1,274 .. 121 533 1,834 641 661 788 2,391 56 656 619 1,513 1,181 2,875 282 92 2,041 752 450 241 346 1,032 2,091 285 1,500 778 1,732 2,391 151 1,150 1,414 125 494 2,692 3,142 1,130 1,738 2,348 600 854 2,054 74

3.2

ENVIRONMENT

Agricultural inputs Land under cereal production

Fertilizer consumption

Agricultural employment

Agricultural machinery

thousand hectares 1990–92 2008–10

kilograms per hectare % of of arable fertilizer land production 2007–09 2007–09

% of total employment 1990–92 2007–09

Tractors per 100 sq. km of arable land 1990 2009

516.8 2,746.0 99,499.5 14,732.7 9,787.4 3,919.5 295.3 104.9 4,225.4 2.9 2,430.9 121.1 22,152.4 1,804.8 1,517.6 1,293.2 .. 0.3 578.0 597.9 696.7 42.1 153.1 120.0 287.7 1,134.0 235.2 1,340.2 1,430.2 693.8 2,451.6 123.6 0.5 9,928.3 675.6 592.6 5,019.6 1,376.9 5,577.5 195.2 2,840.0 180.9 135.3 295.1 7,554.6 16,836.0 359.2 2.8 11,758.2 183.6 2.1 501.0 609.0 6,719.2 8,321.1 751.9 0.4 1.2

538.5 .. 2,585.7 154.0 92,610.0 166.3 17,387.5 110.9 9,440.3 125.4 2,555.5 118.6 276.4 .. 79.1 2.5 3,494.3 186.5 2.0 .. 1,941.8 114.3 44.5 32.3 15,068.4 34.1 2,542.4 .. 1,268.2 .. 970.4 87.8 .. .. 1.1 0.2 579.8 .. 1,090.6 .. 531.2 .. 64.9 3.1 190.5 .. 251.2 .. 329.0 28.2 1,037.6 8.5 161.8 .. 1,729.0 .. 1,831.2 53,107.2 680.1 126.3 3,975.1 .. 291.5 .. 0.1 .. 9,980.0 328.9 884.7 .. 259.1 .. 5,059.7 12.5 2,490.7 .. 8,732.4 185.8 309.9 .. 3,383.2 .. 210.4 16.3 135.5 238.3 464.9 .. 10,629.4 .. 13,808.9 .. 300.6 29.3 3.1 2.5 13,288.7 150.7 154.9 .. 3.4 .. 1,439.3 .. 1,248.7 98,094.6 6,853.3 1,408.8 8,423.6 101.4 324.7 91.8 0.2 .. 2.1 2.8

62.3 80.0 167.8 181.4 69.7 37.2 477.3 189.5 135.5 58.2 235.1 2,444.2 2.4 32.4 .. 388.8 .. 54.5 21.0 .. 64.9 19.9 .. .. 40.3 45.4 56.9 2.6 26.6 769.8 3.2 .. 209.4 51.7 9.4 7.9 20.8 2.9 5.4 1.6 1.4 240.9 1,231.7 22.2 0.1 2.1 191.3 236.4 217.2 46.8 119.8 66.4 104.5 140.5 144.6 159.1 .. 3,191.7

38.2 11.3 .. 54.9 .. .. 11.6 3.5 8.0 27.3 6.4 .. .. .. .. 15.8 .. .. 38.2 .. .. .. .. .. .. .. .. .. .. 21.8 .. .. 14.3 26.8 40.0 .. 3.6 .. 69.1 48.2 81.2 3.7 10.8 38.9 .. .. 5.5 .. 48.3 26.3 .. 1.9 0.8 45.4 25.0 11.5 3.4 ..

34.6 4.6 .. 39.7 21.2 23.4 5.0 1.7 3.7 20.2 3.9 3.0 29.4 .. .. 7.0 .. .. 34.0 .. 8.7 .. .. 47.6 .. 9.2 19.7 .. .. 13.5 .. .. 9.0 13.5 31.1 40.0 40.9 .. .. 16.3 .. 2.5 6.6 29.5 .. .. 2.7 .. 44.7 17.9 .. 26.5 0.8 35.2 13.3 11.2 1.5 2.3

30.9 97.7 60.7 2.2 141.5 65.8 1,623.4 798.8 1,586.5 .. 4,492.9 340.8 62.0 20.0 295.4 211.0 .. 220.0 189.4 8.5 363.7 174.9 57.7 .. 184.3 256.0 730.2 4.9 .. 152.9 10.2 8.4 .. 123.5 310.1 80.3 45.0 .. 13.6 .. 21.9 2,073.1 315.1 20.0 0.2 4.7 1,779.1 41.1 129.7 102.0 59.4 71.6 36.3 65.2 823.6 563.1 438.5 84.0

2012 World Development Indicators

48.7 261.8 128.5 2.0 152.8 92.2 1,476.4 695.3 2,117.1 .. 4,532.1 301.7 24.2 25.2 .. 1,115.4 .. 89.0 188.1 .. 501.4 .. .. .. 218.9 631.8 1,243.8 1.9 .. .. 2.2 9.8 .. 97.7 197.6 40.0 .. .. 10.7 .. 111.7 1,301.5 .. .. .. 6.6 1,539.1 58.1 153.4 147.2 .. 69.1 .. 116.8 1,257.9 1,397.7 525.0 63.1

143


3.2

Agricultural inputs Agricultural landa

% of land area 1990–92 2007–09

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

64 14 74 58 46 .. 39 1 .. 28 70 81 .. 61 37 52 71 8 40 74 32 38 42 22 59 16 60 52 69 60 72 4 75 46 85 65 24 22 61 44 28 35 38 w 35 38 45 35 37 48 29 34 24 55 42 38 49

59 13 81 81 49 58 48 0 40 23 70 82 .. 55 42 58 71 8 38 76 34 40 39 25 62 11 63 51 69 70 71 7 72 44 85 63 24 33 61 44 31 42 38 w 38 38 47 34 38 49 28 36 23 55 45 37 46

Average annual precipitation

% irrigated 2007–09

millimeters 2007–09

2.2 2.0 .. .. .. 0.6 .. .. 1.0 0.9 .. .. .. 11.9 .. 1.0 .. .. .. 8.9 14.8 .. .. .. .. .. 4.1 13.4 .. .. 5.3 .. .. .. 1.2 .. .. .. 4.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

637 460 1,212 59 686 .. 2,526 2,497 824 1,162 282 495 .. 636 1,712 416 788 624 1,537 252 691 1,071 1,622 1,500 1,168 2,200 207 593 161 1,180 565 78 1,220 715 1,265 206 1,875 1,821 402 167 1,020 657 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Land under cereal production

Fertilizer consumption

Agricultural employment

Agricultural machinery

thousand hectares 1990–92 2008–10

kilograms per hectare % of of arable fertilizer land production 2007–09 2007–09

% of total employment 1990–92 2007–09

Tractors per 100 sq. km of arable land 1990 2009

5,773.9 5,016.9 69.0 48.5 32,331.0 13.0 15.6 59,541.3 386.3 .. 1.1 249.5 317.4 8.8 43.8 1,121.9 1,477.5 601.1 4.9 1,087.2 1,873.4 294.0 133.8 .. 649.1 .. .. 432.9 .. .. .. .. 701.3 59.6 95.5 .. 95.7 .. 241.9 112.5 596.3 .. .. 401.6 3,540.2 168.3 49.2 5,352.0 .. .. .. .. 5,984.2 106.1 96.9 7,402.0 1,124.7 2,970.1 257.9 802.6 7,886.4 .. 7.9 8,258.8 56.2 .. .. 61.5 959.1 264.4 69.4 1,119.2 151.8 .. 190.4 201.2 2,620.6 153.2 65.4 3,712.6 443.5 493.4 47.2 273.5 5,018.9 .. 8.7 3,253.6 12,339.5 1,397.2 125.1 10,586.6 106.8 .. .. 77.8 880.8 .. 3.3 573.2 2.2 0.7 85.2 7.1 651.8 10.3 42.3 1,469.9 12,005.1 208.5 96.5 13,731.1 983.6 .. .. 331.3 1,842.5 .. 2.1 1,139.0 14,184.5 44.7 29.7 12,542.3 0.0 30.8 1,033.0 1.4 3,008.4 183.2 239.2 3,488.6 57,488.4 103.6 109.3 65,850.8 785.8 2,741.0 131.1 569.8 1,642.1 77.5 193.3 1,225.3 1,256.3 80.7 200.2 763.1 8,641.7 403.5 402.3 6,953.4 32.5 .. .. 0.0 927.3 .. 12.0 730.0 1,216.2 .. 27.3 797.8 1,901.8 185.2 28.0 1,168.5 94.7 w 122.1 w 699,721.0 s 681,889.9 s 90,727.2 236.5 25.9 57,506.1 101.7 142.2 486,828.8 448,296.9 135.8 118.9 208,267.4 209,263.7 89.6 159.4 278,561.3 239,033.1 102.9 128.6 544,334.9 539,024.0 105.4 .. 142,379.2 150,606.8 92,762.3 35.7 38.8 125,257.0 49,996.0 212.8 92.2 48,465.9 27,682.0 46.8 79.5 31,097.6 168.5 176.0 128,100.5 125,939.2 92,037.8 192.9 10.5 69,034.7 74.3 104.3 155,386.1 142,865.9 30,496.6 98.7 143.4 32,498.0

a. Includes permanent pastures, arable land, and land under permanent crops. b. Includes Luxembourg.

144

2012 World Development Indicators

33.0 15.4 .. .. .. .. .. 0.3 .. .. .. .. .. 9.7 43.6 .. .. 3.3 4.3 28.2 46.8 84.2 60.8 .. .. 11.5 .. 44.7 .. .. 20.8 .. 2.2 2.9 4.5 .. 11.8 .. .. 52.6 49.8 .. .. w .. .. .. 48.5 .. 57.8 23.2 18.9 .. .. .. 6.2 6.7

29.1 140.6 201.2 9.7 97.8 27.1 .. 1.0 0.5 4.1 19.2 .. .. 1.6 2.1 24.0 .. 17.7 .. 4.1 .. 1.1 .. .. 3.6 197.5 154.6 9.1 4,000.0 5,895.2 .. 15.9 12.0 5.1 107.9 43.0 .. .. .. 4.2 483.1 831.2 32.6 163.1 .. .. 7.2 13.8 .. 228.9 87.1 2.2 601.9 592.4 3.1 2,783.1 2,602.9 15.2 128.1 215.0 .. 415.4 310.2 .. 8.2 23.3 41.5 33.0 280.5 .. 8.5 .. .. 0.5 0.6 3.8 1,238.9 1,972.7 .. 82.4 142.6 22.9 279.8 395.3 .. 464.7 .. 65.6 .. .. 15.8 153.3 102.7 4.2 51.4 63.3 1.1 760.6 .. 1.5 238.4 271.3 11.0 260.3 219.5 .. .. .. 8.5 .. .. .. 47.0 262.5 13.4 442.2 737.3 .. 39.0 41.0 .. 21.9 .. .. 60.1 .. .. w 202.4 w .. w .. 8.4 .. .. 84.1 109.4 .. 53.0 .. 31.3 111.9 114.5 .. 75.8 .. 39.6 53.2 .. 16.3 135.9 115.6 14.3 120.9 .. 27.2 114.6 155.8 .. 62.1 120.7 .. 19.8 .. 3.3 472.1 .. 3.6 986.1 841.4


About the data

3.2

ENVIRONMENT

Agricultural inputs Definitions

Agriculture is still a major sector in many economies,

energy price fluctuation. The FAO recently revised

• Agricultural land is permanent pastures, arable

and agricultural activities provide developing coun-

the time series for fertilizer consumption and irriga-

land, and land under permanent crops. Permanent

tries with food and revenue. But agricultural activities

tion for 2002 onward, but recent data are not avail-

pasture is land used for five or more years for for-

also can degrade natural resources. Poor farming

able for all countries. FAO collects fertilizer statistics

age, including natural and cultivated crops. Arable

practices can cause soil erosion and loss of soil

for production, imports, exports, and consumption

land includes land defi ned by the FAO as land

fertility. Efforts to increase productivity by using

through the new FAO fertilizer resources question-

under temporary crops (double-cropped areas are

chemical fertilizers, pesticides, and intensive irriga-

naire. In the previous release, the data were based

counted once), temporary meadows for mowing or

tion have environmental costs and health impacts.

on total consumption of fertilizers, but the data in

for pasture, land under market or kitchen gardens,

Excessive use of chemical fertilizers can alter the

the recent release are based on the nutrients in fer-

and land temporarily fallow. Land abandoned as a

chemistry of soil. Pesticide poisoning is common in

tilizers. Some countries compile fertilizer data on a

result of shifting cultivation is excluded. Land under

developing countries. And salinization of irrigated

calendar year basis, while others do so on a crop

permanent crops is land cultivated with crops that

land diminishes soil fertility. Thus, inappropriate use

year basis (July–June). Previous editions of World

occupy the land for long periods and need not be

of inputs for agricultural production has far-reaching

Development Indicators reported data on a crop year

replanted after each harvest, such as cocoa, cof-

effects.

basis, but this edition uses the calendar year, as

fee, and rubber. Land under flowering shrubs, fruit

adopted by the FAO. Caution should thus be used

trees, nut trees, and vines is included, but land under

The table provides indicators of major inputs to agricultural production: land, fertilizer, labor, and

when comparing data over time.

trees grown for wood or timber is not. • Irrigated

machinery. There is no single correct mix of inputs:

land refers to areas purposely provided with water,

appropriate levels and application rates vary by coun-

including land irrigated by controlled flooding. • Aver-

try and over time and depend on the type of crops,

age annual precipitation is the long-term average in

the climate and soils, and the production process

depth (over space and time) of annual precipitation

used.

in the country. Precipitation is defined as any kind

The agriculture sector is the most water-intensive

of water that falls from clouds as a liquid or a solid.

sector, and water delivery in agriculture is increas-

• Land under cereal production refers to harvested

ingly important. The table shows irrigated agricultural

areas, although some countries report only sown

land as share of total agricultural land area and data

or cultivated area. • Fertilizer consumption is the

on average precipitation to illustrate how countries

quantity of plant nutrients applied to arable land.

obtain water for agricultural use.

Fertilizer products cover nitrogen, potash, and phos-

The data here and in table 3.3 are collected by

phate fertilizers (including ground rock phosphate).

the Food and Agriculture Organization of the United

Traditional nutrients—animal and plant manures—

Nations (FAO) through annual questionnaires. The

are not included. • Fertilizer production is fertilizer

FAO tries to impose standard definitions and report-

consumption, exports, and nonfertilizer use of fertil-

ing methods, but complete consistency across

izer products minus fertilizer imports. • Agricultural

countries and over time is not possible. Thus, data

employment is employment in agriculture, forestry,

on agricultural land in different climates may not be

hunting, and fishing (see table 2.3). • Agricultural

comparable. For example, permanent pastures are

machinery refers to wheel and crawler tractors

quite different in nature and intensity in African coun-

(excluding garden tractors) in use in agriculture at

tries and dry Middle Eastern countries. Data on agri-

the end of the calendar year specified or during the

cultural employment, in particular, should be used

first quarter of the following year.

with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage. Fertilizer consumption measures the quantity of plant nutrients. Consumption is calculated as pro-

Data sources

duction plus imports minus exports. Because some

Data on agricultural inputs are from the FAO’s

chemical compounds used for fertilizers have other

electronic files. Data on agricultural employment

industrial applications, the consumption data may

are from the International Labour Organization’s

overstate the quantity available for crops. Fertil-

Key Indicators of the Labour Market, 7th edition,

izer consumption as a share of production shows

database.

the agriculture sector’s vulnerability to import and

2012 World Development Indicators

145


3.3

Agricultural output and productivity Crop production index

2004–06 = 100 1990 2009

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

146

67.0 85.0 41.0 33.0 53.0 67.0 67.0 89.0 95.0 61.0 66.0 77.0 .. 45.0 53.0 75.0 83.0 59.0 149.0 44.0 98.0 46.0 58.0 82.0 76.0 51.0 60.0 56.0 .. 80.0 125.0 66.0 62.0 68.0 93.0 136.0 136.0 .. 115.0 98.0 66.0 56.0 92.0 .. 129.0 .. 110.0 96.0 78.0 56.0 138.0 93.0 34.0 80.0 57.0 59.0 60.0 107.0

125.0 121.0 121.0 153.0 83.0 125.0 101.0 103.0 110.0 116.0 122.0 111.0 100.0 117.0 106.0 99.0 122.0 117.0 98.0 95.0 61.0 147.0 113.0 109.0 113.0 105.0 101.0 116.0 .. 108.0 98.0 112.0 109.0 103.0 110.0 88.0 73.0 99.0 112.0 107.0 114.0 116.0 112.0 84.0 126.0 126.0 109.0 100.0 118.0 119.0 73.0 106.0 119.0 81.0 125.0 105.0 94.0 109.0

2012 World Development Indicators

Food production index

2004–06 = 100 1990 2009

68.0 69.0 50.0 42.0 63.0 74.0 74.0 93.0 82.0 90.0 63.0 110.0 .. 48.0 61.0 87.0 95.0 51.0 179.0 50.0 98.0 46.0 61.0 73.0 61.0 56.0 58.0 48.0 .. 70.0 120.0 63.0 59.0 66.0 105.0 160.0 98.0 .. 94.0 78.0 56.0 54.0 75.0 .. 157.0 .. 108.0 99.0 91.0 60.0 105.0 107.0 37.0 86.0 57.0 59.0 62.0 96.0

114.0 110.0 117.0 144.0 95.0 131.0 103.0 101.0 115.0 117.0 121.0 120.0 97.0 122.0 113.0 110.0 116.0 116.0 103.0 105.0 65.0 137.0 114.0 107.0 111.0 112.0 101.0 115.0 .. 112.0 99.0 116.0 113.0 108.0 114.0 98.0 87.0 98.0 107.0 116.0 115.0 118.0 109.0 99.0 115.0 122.0 101.0 98.0 115.0 119.0 72.0 105.0 119.0 86.0 122.0 107.0 98.0 106.0

Livestock production index

2004–06 = 100 1990 2009

70.0 58.0 69.0 74.0 84.0 84.0 85.0 95.0 79.0 137.0 56.0 127.0 .. 70.0 68.0 92.0 98.0 44.0 248.0 53.0 103.0 53.0 79.0 68.0 54.0 74.0 55.0 39.0 .. 68.0 99.0 53.0 70.0 80.0 106.0 185.0 69.0 .. 81.0 58.0 48.0 53.0 62.0 .. 175.0 .. 107.0 98.0 86.0 76.0 78.0 114.0 78.0 99.0 70.0 43.0 70.0 59.0

101.0 99.0 109.0 110.0 113.0 134.0 101.0 99.0 118.0 117.0 113.0 129.0 98.0 100.0 119.0 128.0 114.0 113.0 96.0 109.0 136.0 92.0 103.0 95.0 110.0 110.0 101.0 112.0 .. 117.0 102.0 126.0 116.0 111.0 106.0 136.0 95.0 95.0 101.0 126.0 114.0 114.0 107.0 111.0 109.0 113.0 97.0 96.0 102.0 106.0 68.0 103.0 120.0 95.0 103.0 120.0 115.0 102.0

Cereal yield

Agricultural productivity

kilograms per hectare 1990 2010

Agriculture value added per worker 2000 $ 1990 2010

1,201 2,794 688 321 2,232 1,843 1,716 5,577 2,113 .. 2,491 2,741 5,755a 848 1,361 3,553 266 1,755 3,954 600 1,349 1,362 1,259 2,636 810 559 3,620 4,325 .. 2,475 800 624 3,097 887 3,975 2,342 1,886 .. 6,118 3,996 1,724 5,703 1,939 .. 1,304 .. 3,543 6,083 1,750 1,004 1,998 5,411 989 3,036 1,998 1,455 1,531 1,027

1,908 4,762 1,568 617 4,937 2,074 1,721 5,358 2,019 .. 4,144 2,827 9,231 1,402 2,333 3,858 544 4,055 3,665 1,054 1,346 3,108 1,711 3,490 1,465 775 6,822 5,521 .. 3,895 771 785 3,730 1,717 5,486 1,940 1,595 4,691 5,889 4,234 3,117 6,541 2,806 536 2,444 1,674 3,136 7,093 1,782 1,127 1,271 6,716 1,814 4,908 2,299 1,409 1,555 980

.. 764 1,702 206 6,683 1,607 19,447 13,413 1,001 .. 275 2,042 .. 427 683 .. 761 1,625 3,983 107 118 .. 420 28,898 321 170 3,457 258 .. 3,123 213 .. 2,993 658 5,552 4,117 5,808 .. 14,588 1,974 2,109 1,767 1,742 .. 3,288 .. 17,163 21,454 1,216 253 2,359 13,730 .. .. 2,241 166 .. ..

.. .. 2,254 340 12,957 4,723 35,208 25,771 1,241 .. 507 5,700 .. .. 716 15,028 447 4,118 10,923 .. .. 434 .. 44,619 .. .. 6,267 545 .. 2,874 173 .. 5,596 1,056 16,423 3,618 7,927 6,415 53,407 5,083 2,040 3,265 2,752 66 3,156 226 47,514 57,973 1,972 282 1,817 32,866 .. .. 2,803 242 .. ..


Crop production index

2004–06 = 100 1990 2009

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

70.0 100.0 73.0 61.0 55.0 102.0 91.0 95.0 84.0 98.0 123.0 66.0 144.0 72.0 99.0 91.0 .. 39.0 64.0 52.0 106.0 96.0 127.0 62.0 77.0 81.0 102.0 79.0 50.0 59.0 54.0 63.0 107.0 74.0 121.0 230.0 68.0 60.0 42.0 56.0 62.0 91.0 74.0 59.0 49.0 47.0 135.0 63.0 67.0 123.0 72.0 70.0 46.0 72.0 133.0 110.0 138.0 56.0

105.0 89.0 113.0 121.0 99.0 87.0 82.0 97.0 91.0 105.0 94.0 106.0 131.0 105.0 101.0 106.0 .. 106.0 106.0 129.0 114.0 101.0 81.0 97.0 103.0 136.0 110.0 110.0 131.0 109.0 134.0 124.0 95.0 99.0 90.0 216.0 116.0 101.0 122.0 100.0 112.0 106.0 105.0 111.0 138.0 88.0 84.0 112.0 108.0 88.0 117.0 92.0 117.0 111.0 109.0 91.0 100.0 138.0

Food production index

2004–06 = 100 1990 2009

65.0 118.0 71.0 62.0 54.0 118.0 97.0 72.0 89.0 81.0 114.0 57.0 142.0 68.0 92.0 79.0 .. 48.0 76.0 50.0 194.0 83.0 106.0 77.0 75.0 133.0 102.0 80.0 47.0 53.0 61.0 76.0 89.0 65.0 139.0 134.0 68.0 63.0 41.0 82.0 64.0 103.0 64.0 51.0 46.0 49.0 110.0 52.0 60.0 91.0 69.0 64.0 49.0 67.0 110.0 98.0 133.0 66.0

103.0 94.0 114.0 120.0 104.0 92.0 91.0 104.0 97.0 106.0 98.0 110.0 126.0 110.0 100.0 108.0 .. 110.0 101.0 125.0 118.0 104.0 100.0 121.0 104.0 110.0 114.0 112.0 127.0 114.0 146.0 107.0 98.0 101.0 91.0 146.0 117.0 98.0 126.0 90.0 110.0 108.0 98.0 115.0 132.0 92.0 103.0 97.0 115.0 102.0 108.0 110.0 123.0 112.0 104.0 98.0 103.0 168.0

Livestock production index

2004–06 = 100 1990 2009

51.0 161.0 62.0 62.0 51.0 153.0 96.0 56.0 96.0 61.0 107.0 44.0 147.0 67.0 96.0 62.0 .. 54.0 107.0 49.0 253.0 54.0 91.0 79.0 73.0 154.0 97.0 99.0 64.0 58.0 75.0 79.0 45.0 59.0 196.0 132.0 71.0 47.0 25.0 89.0 70.0 106.0 67.0 45.0 42.0 61.0 103.0 40.0 56.0 64.0 66.0 72.0 55.0 49.0 114.0 84.0 132.0 73.0

105.0 96.0 115.0 114.0 115.0 100.0 93.0 109.0 106.0 106.0 101.0 115.0 115.0 114.0 100.0 110.0 .. 112.0 102.0 118.0 114.0 112.0 112.0 124.0 106.0 95.0 114.0 113.0 151.0 109.0 135.0 104.0 109.0 105.0 91.0 142.0 116.0 95.0 138.0 86.0 107.0 109.0 97.0 119.0 122.0 112.0 106.0 77.0 117.0 114.0 91.0 117.0 128.0 114.0 102.0 106.0 104.0 192.0

3.3

ENVIRONMENT

Agricultural output and productivity Cereal yield

Agricultural productivity

kilograms per hectare 1990 2010

Agriculture value added per worker 2000 $ 1990 2010

1,468 4,521 1,891 3,800 1,445 1,061 6,577 3,486 3,945 1,116 5,846 1,220 1,338 1,562 3,916 5,853 .. 3,653 2,772 2,268 1,641 1,878 1,037 1,029 674 1,938 2,652 1,945 992 2,740 726 870 4,191 2,424 2,928 1,098 1,120 477 2,762 457 1,920 6,959 5,034 1,524 310 1,148 4,399 2,160 1,766 1,867 2,603 1,979 2,603 2,065 3,284 1,878 1,080 2,897

1,094 4,759 2,537 4,876 2,359 1,687 7,409 3,015 5,436 1,172 5,852 1,963 804 1,613 3,582 6,196 .. 3,415 2,604 3,751 2,667 2,740 909 1,179 662 2,667 3,329 2,987 2,206 3,800 1,615 946 10,000 3,499 2,696 1,370 1,548 1,006 3,989 373 2,295 8,574 7,387 2,086 479 1,413 3,810 18,987 2,592 2,131 3,839 3,457 3,899 3,232 3,220 3,463 1,878 4,795

1,184 4,177 357 493 1,988 .. .. .. 10,435 2,224 19,563 1,976 1,781 400 .. 5,338 .. .. 685 388 1,896 .. 255 .. .. .. 2,413 213 89 3,826 406 651 3,446 2,250 1,349 1,477 1,807 132 .. 1,267 248 23,743 19,767 .. 239 .. 17,454 1,029 769 2,303 559 1,660 911 854 1,619 4,516 .. ..

2012 World Development Indicators

2,041 8,522 489 730 .. .. 13,931 .. 31,254 2,758 48,794 3,401 1,782 337 .. 19,807 .. .. 996 532 3,837 41,013 202 .. .. 5,996 5,946 187 169 6,680 .. 404 6,401 3,302 1,610 1,524 3,315 234 .. 881 238 47,805 26,556 2,779 .. .. 46,480 .. 947 4,109 683 2,710 1,607 1,119 2,994 7,019 .. ..

147


3.3

Agricultural output and productivity Crop production index

2004–06 = 100 1990 2009

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

82.0 100.0 76.0 83.0 78.0 78.0 b 69.0 90.0 .. 74.0 122.0 83.0 .. 89.0 87.0 50.0 99.0 130.0 118.0 51.0 83.0 61.0 64.0 81.0 67.0 141.0 74.0 78.0 73.0 72.0 98.0 28.0 104.0 79.0 48.0 83.0 76.0 45.0 .. 61.0 61.0 118.0 70.0c w 78.4 73.4 74.8 72.6 73.8 69.9 111.4 75.8 74.7 78.0 71.1 90.7 90.9

85.0 107.0 122.0 95.0 143.0 106.0 101.0 149.0 93.0 87.0 100.0 108.0 .. 105.0 111.0 106.0 97.0 106.0 106.0 93.0 115.0 110.0 111.0 136.0 104.0 113.0 101.0 103.0 92.0 106.0 118.0 100.0 102.0 105.0 134.0 118.0 108.0 110.0 109.0 121.0 129.0 91.0 122.2 w 132.2 128.4 127.2 129.1 128.7 133.1 129.4 128.1 127.3 119.3 128.7 103.9 96.9

Food production index

2004–06 = 100 1990 2009

94.0 117.0 72.0 70.0 76.0 99.0 b 68.0 492.0 .. 76.0 92.0 78.0 .. 83.0 85.0 47.0 90.0 109.0 104.0 51.0 99.0 62.0 69.0 95.0 64.0 84.0 69.0 79.0 51.0 71.0 128.0 29.0 107.0 77.0 60.0 76.0 71.0 47.0 .. 56.0 74.0 94.0 70.0c w 78.3 68.7 72.8 66.5 69.4 62.7 118.2 71.2 72.8 74.5 72.9 90.4 95.6

93.0 106.0 121.0 102.0 136.0 99.0 104.0 98.0 92.0 93.0 100.0 109.0 .. 104.0 112.0 103.0 101.0 101.0 103.0 96.0 118.0 106.0 112.0 129.0 114.0 107.0 101.0 108.0 113.0 105.0 106.0 113.0 101.0 109.0 109.0 123.0 106.0 114.0 101.0 126.0 115.0 92.0 123.0 w 133.2 130.3 128.2 131.4 130.5 135.1 126.3 131.2 131.6 122.7 130.0 106.3 97.7

a. Includes Luxembourg. b. Includes Montenegro. c. Food and Agriculture Organization estimate.

148

2012 World Development Indicators

Livestock production index

2004–06 = 100 1990 2009

116.0 143.0 59.0 56.0 71.0 109.0 b 81.0 535.0 .. 75.0 88.0 79.0 .. 72.0 75.0 47.0 85.0 103.0 102.0 54.0 132.0 61.0 73.0 104.0 68.0 50.0 58.0 83.0 49.0 64.0 166.0 43.0 107.0 78.0 70.0 82.0 71.0 38.0 .. 50.0 77.0 75.0 74.0c w 78.0 62.5 69.4 59.6 63.3 48.8 137.7 69.7 69.3 69.2 80.1 90.7 98.2

99.0 97.0 122.0 111.0 113.0 98.0 116.0 93.0 86.0 93.0 100.0 110.0 .. 98.0 107.0 99.0 110.0 97.0 104.0 99.0 117.0 101.0 112.0 109.0 123.0 104.0 107.0 112.0 126.0 100.0 95.0 143.0 99.0 105.0 100.0 114.0 110.0 133.0 85.0 134.0 96.0 105.0 120.3 w 131.7 131.4 131.8 131.2 131.4 135.2 119.2 132.6 134.7 132.9 125.1 104.0 100.0

Cereal yield

Agricultural productivity

kilograms per hectare 1990 2010

Agriculture value added per worker 2000 $ 1990 2010

3,011 1,743 1,043 4,245 795 2,926b 1,202 .. .. 3,279 793 1,877 .. 2,485 2,965 456 1,284 4,964 5,984 750 994 1,507 2,009 1,608 747 3,326 1,145 2,214 2,210 1,498 2,834 2,216 6,171 4,755 2,183 1,777 2,486 3,073 .. 908 1,352 1,625 2,756c w 1,565 2,556 1,928 3,177 2,429 3,796 2,596 2,089 1,471 1,926 1,033 4,138 4,490

3,331 1,844 1,930 5,621 1,196 4,959 1,554 .. 3,754 5,973 432 4,162 .. 3,231 3,974 452 1,226 4,518 6,084 1,789 2,721 1,332 2,938 2,451 1,187 2,727 1,702 2,727 3,289 1,608 2,727 2,667 6,957 6,988 4,251 4,516 4,038 5,161 1,163 1,092 2,547 752 3,568 w 2,075 3,312 2,718 3,831 3,103 4,925 2,239 3,919 2,379 2,691 1,335 5,319 5,685

2,351 1,915 174 7,863 262 .. .. 21,392 .. 13,217 .. 2,287 .. 8,938 672 512 1,025 23,307 20,556 2,698 370 219 443 .. 376 1,825 2,383 2,254 1,275 189 1,231 .. 19,880 18,703 5,648 1,427 4,458 222 .. 621 213 270 781 w 240 481 472 487 455 305 2,245 2,221 1,796 373 307 14,129 12,590

9,700 2,731 .. 20,233 256 2,057 .. 29,145 9,924 76,633 .. 3,662 .. 22,035 966 929 1,217 51,585 27,066 .. 577 292 706 .. .. 1,024 3,050 3,770 .. 200 2,500 .. 25,681 51,120 8,682 2,782 7,667 367 .. 749 214 160 992 w 288 786 677 871 728 585 3,204 3,663 .. 521 322 24,483 25,752


About the data

3.3

ENVIRONMENT

Agricultural output and productivity Definitions

The agricultural production indexes in the table are

• Crop production index is agricultural production

prepared by the Food and Agriculture Organization of

for the period specified relative to the base period

the United Nations (FAO). The FAO obtains data from

2004–06. It includes all crops except fodder crops.

official and semiofficial reports of crop yields, area

The regional and income group aggregates for the

under production, and livestock numbers. If data are

FAO’s production indexes are calculated from the

unavailable, the FAO makes estimates. The indexes

underlying values in international dollars, normal-

are calculated using the Laspeyres formula: produc-

ized to the base period 2004–06. • Food produc-

tion quantities of each commodity are weighted by

tion index covers food crops that are considered

average international commodity prices in the base

edible and that contain nutrients. Coffee and tea

period and summed for each year. Because the FAO’s

are excluded because, although edible, they have

indexes are based on the concept of agriculture as a

no nutritive value. • Livestock production index

single enterprise, estimates of the amounts retained

includes meat and milk from all sources, dairy prod-

for seed and feed are subtracted from the production

ucts such as cheese, and eggs, honey, raw silk, wool,

data to avoid double counting. The aggregates are

and hides and skins. • Cereal yield, measured in

net production available for any use except as seed

kilograms per hectare of harvested land, includes

and feed. The FAO’s indexes may differ from those

wheat, rice, maize, barley, oats, rye, millet, sorghum,

from other sources because of differences in cov-

buckwheat, and mixed grains. Production data on

erage, weights, concepts, time periods, calculation

cereals refer to crops harvested for dry grain only.

methods, and use of international prices.

Cereal crops harvested for hay or harvested green for

To facilitate cross-country comparisons, the FAO

food, feed, or silage, and those used for grazing, are

uses international commodity prices to value pro-

excluded. The FAO allocates production data to the

duction. These prices, expressed in international

calendar year in which the bulk of the harvest took

dollars (equivalent in purchasing power to the U.S.

place. But most of a crop harvested near the end of

dollar), are derived using a Geary-Khamis formula

a year will be used in the following year. • Agricul-

applied to agricultural outputs (see Inter- Secretariat

tural productivity is the ratio of agricultural value

Working Group on National Accounts 1993, sections

added, measured in 2005 U.S. dollars, to the num-

16.93–96). This method assigns a single price to

ber of workers in agriculture. Agricultural productivity

each commodity so that, for example, one metric ton

is measured by value added per unit of input. (For

of wheat has the same price regardless of where it

further discussion of the calculation of value added

was produced. The use of international prices elimi-

in national accounts, see About the data for tables

nates fluctuations in the value of output due to transi-

4.1 and 4.2.) Agricultural value added includes that

tory movements of nominal exchange rates unrelated

from forestry and fishing. Thus interpretations of land

to the purchasing power of the domestic currency.

productivity should be made with caution.

Data on cereal yield may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.

Data sources Data on agricultural production indexes, cereal yield, and agricultural employment are from the FAO’s electronic files, available on the FAO website (www.fao.org). Data on agricultural value added are from the World Bank’s national accounts files.

2012 World Development Indicators

149


3.4

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km

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

150

1990

2010

14 8 17 610 348 3 1,545 38 9 0c 15 78 7 58 628 22 137 5,748 33 68 3 129 243 3,101 232 131 153 1,571 .. 625 1,604 227 26 102 19 21 2 26 4 20 138 0c 4 16 21 151 219 145 220 4 28 107 74 33 47 73 22 1

14 8 15 585 294 3 1,493 39 9 0c 14 86 7 46 572 22 114 5,195 39 56 2 101 199 3,101 226 115 162 2,069 .. 605 1,541 224 26 104 19 29 2 27 5 20 99 1 3 15 22 123 222 160 220 5 27 111 49 39 37 65 20 1

2012 World Development Indicators

% 1990–2000 2000–10

0.00 0.26 0.54 0.21 0.88 1.31 –0.03 –0.16 0.00 –5.56 0.18 –0.62 0.15 1.29 0.44 0.11 0.90 0.51 –0.14 0.91 3.71 1.14 0.94 0.00 0.13 0.62 –0.37 –1.20 .. 0.16 0.20 0.08 0.76 –0.10 –0.19 –1.70 –0.63 –0.03 –0.89 0.00 1.53 –2.98 1.26 0.28 –0.71 0.97 –0.26 –0.55 0.00 –0.42 0.04 –0.31 1.99 –0.88 1.20 0.51 0.44 0.62

0.00 –0.10 0.57 0.21 0.81 1.48 0.37 –0.13 0.00 –3.55 0.18 –0.43 –0.16 1.04 0.50 0.00 0.99 0.50 –1.53 1.01 1.40 1.34 1.05 0.00 0.13 0.66 –0.25 –1.57 .. 0.17 0.20 0.07 –0.93 –0.15 –0.19 –1.66 –0.09 –0.08 –1.14 0.00 1.81 –1.73 1.45 0.28 0.12 1.08 0.14 –0.39 0.00 –0.41 0.09 0.00 2.08 –0.81 1.40 0.54 0.48 0.76

Threatened species

GEF benefits index for biodiversity

Nationally protected areas

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2011

2011

2011

2011

2008

1990

2010

1990

2010

11 3 14 15 38 9 55 3 7 3 34 4 3 11 20 4 7 81 7 9 11 37 38 12 8 13 20 75 2 52 30 11 9 23 7 14 5 2 2 6 43 17 5 10 1 33 1 9 14 10 10 6 16 10 16 22 12 5

14 5 9 23 49 12 52 7 14 3 30 4 2 6 34 5 10 122 11 7 11 24 20 15 9 9 34 86 20 94 35 2 19 15 10 17 4 5 2 14 73 9 5 12 3 24 4 6 4 8 10 5 13 10 10 13 5 13

5 39 36 39 37 3 103 11 10 8 18 2 11 27 0 31 2 84 19 4 17 42 112 35 3 1 20 113 13 54 83 46 50 45 60 34 19 2 15 21 50 39 14 18 5 14 6 44 61 23 9 23 44 75 25 65 32 20

2 0 12 34 35 1 27 9 0 0 15 0 0 13 72 0 0 389 5 3 2 29 378 1 17 2 34 374 6 215 80 37 112 106 5 155 16 8 1 27 1,714 2 24 4 0 24 1 27 120 4 0 13 117 52 72 22 4 26

3.4 0.2 2.9 8.3 17.7 0.2 87.7 0.3 0.8 0.0 1.4 0.0 0.0 0.2 12.5 0.4 1.4 100.0 0.8 0.3 0.3 3.5 12.5 21.5 1.5 2.2 15.3 66.6 .. 51.5 19.9 3.6 9.7 3.4 0.6 12.5 0.5 0.1 0.2 6.0 29.3 2.9 0.9 0.8 0.1 8.4 0.2 5.3 3.0 0.1 0.6 0.6 1.9 2.8 8.0 2.3 0.6 5.2

0.4 3.4 6.3 12.4 4.6 6.9 7.5 20.1 6.2 1.3 1.7 6.5 3.2 23.8 8.8 0.5 30.3 9.0 2.0 13.7 3.8 0.0 7.0 4.7 17.5 9.4 16.0 13.5 41.1 19.3 10.0 5.4 18.7 22.6 7.8 4.3 7.1 13.6 4.2 22.2 21.6 1.9 0.4 4.9 17.7 17.7 4.2 10.2 4.6 1.5 2.8 31.9 14.6 5.7 25.9 6.8 7.6 0.3

0.4 9.8 6.3 12.4 5.5 8.0 10.6 22.9 7.1 1.3 1.8 7.2 13.8 23.8 18.5 0.6 30.9 26.3 9.2 14.2 4.8 25.8 9.2 7.5 17.7 9.4 16.6 16.6 41.8 20.9 10.0 9.4 20.9 22.6 13.0 6.4 10.5 15.1 4.9 22.2 25.1 5.9 0.8 5.0 20.4 18.4 9.0 16.5 15.1 1.5 3.7 42.4 14.7 16.2 30.6 6.8 16.1 0.3

.. 0.2 0.2 0.1 0.8 .. 10.9 .. .. 0.0 0.4 .. 0.0 0.0 .. 0.7 .. 8.2 0.2 .. .. 0.0 0.4 0.6 .. .. 3.5 0.4 .. 0.9 3.8 0.0 11.9 0.1 1.3 1.3 0.3 .. 3.0 30.4 0.2 4.4 3.1 0.0 25.3 .. 3.5 0.3 0.2 0.1 0.2 35.7 0.0 0.5 0.3 0.0 2.7 0.0

.. 1.6 0.3 0.1 1.1 .. 28.3 .. .. 0.7 0.8 .. 0.0 0.0 .. 0.7 .. 16.5 3.2 .. .. 0.4 0.4 1.2 .. .. 3.7 1.3 .. 15.5 4.4 32.8 12.2 0.1 3.4 4.4 0.6 .. 3.2 30.4 75.4 9.3 3.1 0.0 26.5 .. 5.0 21.3 7.3 0.1 0.4 40.3 0.0 2.6 12.5 0.0 45.8 0.0

Terrestrial % of land area

Marine % of territorial waters


Forest area

Average annual deforestationa

thousand sq. km 1990

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

81 18 639 1,185 111 8 5 1 76 3 250 1 34 37 82 64 .. 0c 8 173 32 1 0c 49 2 19 9 137 39 224 141 4 0c 703 3 125 50 434 392 88 48 3 77 45 19 172 91 0c 25 38 315 212 702 66 89 33 3 0c

2010

52 20 684 944 111 8 7 2 91 3 250 1 33 35 57 62 .. 0c 10 158 34 1 0c 43 2 22 10 126 32 205 125 2 0c 648 4 109 51 390 318 73 36 4 83 31 12 90 101 0c 17 33 287 176 680 77 93 35 6 0c

% 1990–2000 2000–10

2.38 –0.57 –0.22 1.75 0.00 –0.17 –3.16 –1.49 –0.98 0.11 0.03 0.00 0.17 0.35 1.67 0.13 .. –3.46 –0.26 0.46 –0.21 0.00 –0.49 0.63 0.00 –0.38 –0.49 0.42 0.88 0.36 0.58 2.66 0.03 0.52 –0.16 0.67 0.06 0.52 1.17 0.87 2.09 –0.43 –0.69 1.67 3.74 2.68 –0.19 0.00 1.76 1.18 0.45 0.88 0.14 –0.80 –0.20 –0.28 –4.92 0.00

2.06 –0.62 –0.46 0.51 0.00 –0.09 –1.53 –0.07 –0.90 0.11 –0.05 0.00 0.17 0.33 2.00 0.11 .. –2.57 –1.07 0.49 –0.34 –0.45 –0.47 0.67 0.00 –0.68 –0.41 0.45 0.97 0.54 0.61 2.66 1.00 0.30 –1.77 0.73 –0.23 0.54 0.93 0.97 0.70 –0.14 –0.01 2.01 0.98 3.67 –0.80 0.00 2.24 0.36 0.48 0.97 0.18 –0.75 –0.31 –0.11 –1.76 0.00

Threatened species

GEF benefits index for biodiversity

ENVIRONMENT

3.4

Deforestation and biodiversity

Nationally protected areas

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2011

2011

2011

2011

2008

1990

2010

1990

2010

7 2 94 184 16 13 5 15 7 5 28 13 16 28 9 9 .. 6 6 45 1 10 2 18 12 3 5 65 7 70 12 15 6 100 4 11 18 12 45 12 31 4 9 6 12 26 7 9 23 15 39 8 54 38 5 11 3 3

9 8 78 119 20 16 1 13 7 10 39 10 20 31 24 29 .. 8 12 23 4 8 7 11 3 4 9 35 15 45 9 11 11 56 8 20 10 24 43 25 31 2 70 12 7 14 2 11 27 17 37 27 98 74 6 8 8 4

27 9 212 140 29 11 20 36 47 21 64 13 14 68 13 19 .. 11 3 46 6 22 1 53 24 6 13 85 101 64 3 32 13 152 8 1 47 55 39 27 7 13 23 30 4 59 19 26 34 41 42 0 20 71 7 53 19 11

107 8 291 385 1 0 1 0 61 206 6 1 16 126 5 3 .. 0 14 17 0 1 4 47 2 0 0 273 12 674 7 0 88 191 2 0 28 40 37 25 2 0 19 40 2 171 2 6 2 192 142 9 268 210 8 68 51 0

7.2 0.2 39.9 81.0 7.3 1.6 0.6 0.8 3.8 4.4 36.0 0.4 5.1 8.8 0.7 1.7 .. 0.1 1.1 5.0 0.0 0.2 0.3 2.6 1.6 0.0 0.2 29.2 3.5 13.9 1.5 1.3 3.3 68.7 0.0 4.2 3.5 7.2 10.0 5.2 2.1 0.2 20.2 3.3 0.9 6.0 1.3 3.7 4.9 10.9 25.4 2.8 33.4 32.3 0.5 5.5 4.0 0.1

13.6 4.6 4.7 10.0 5.2 0.1 0.6 16.3 5.0 10.2 13.4 0.7 2.4 11.6 4.3 2.2 .. 1.6 6.4 1.5 6.5 0.5 0.5 1.6 0.1 2.0 4.2 2.2 15.0 17.1 2.3 0.5 1.7 2.2 0.9 4.1 1.2 14.8 3.1 14.4 7.7 11.2 25.4 15.4 7.1 11.6 7.0 0.0 10.1 17.2 1.9 2.9 4.7 8.7 15.3 5.8 10.0 1.7

18.2 5.1 5.0 14.1 7.1 0.1 1.8 17.8 15.1 18.9 16.5 1.9 2.5 11.8 5.9 2.4 .. 1.6 6.9 16.6 18.0 0.5 0.5 1.8 0.1 14.5 4.9 3.1 15.0 18.1 2.4 0.5 4.5 11.1 1.4 13.4 1.5 15.8 6.3 14.9 17.0 12.4 26.2 36.7 7.1 12.8 14.6 10.7 10.1 18.7 3.1 5.4 13.6 10.9 22.4 8.3 10.1 2.5

0.0 .. 1.6 0.5 1.0 0.0 0.1 0.4 0.5 0.2 2.0 0.0 .. 5.2 0.1 3.5 .. 0.0 .. .. 4.6 0.0 .. 0.0 0.0 0.8 .. 0.0 .. 1.5 .. 32.1 0.3 1.1 .. .. 0.8 1.8 0.3 0.5 .. 12.8 0.4 0.6 .. 0.2 1.2 0.0 1.8 3.1 0.3 .. 2.8 0.5 3.4 2.1 1.5 0.0

1.9 .. 1.7 2.0 1.7 0.0 0.2 0.4 17.4 4.2 5.5 30.0 .. 10.5 0.1 3.9 .. 0.0 .. .. 6.7 0.1 .. 0.0 0.1 10.7 .. 0.1 .. 2.0 .. 32.1 0.3 16.7 .. .. 1.3 3.3 0.3 8.2 .. 22.1 10.8 37.2 .. 0.2 2.4 1.3 1.8 4.0 0.3 .. 2.8 2.5 4.1 3.1 1.6 0.3

Terrestrial % of land area

Marine % of territorial waters

2012 World Development Indicators

151


3.4

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km

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

1990

2010

64 8,090 3 10 93 23 31 0c 19 12 83 82 .. 138 24 764 5 273 12 4 4 415 195 10 7 2 6 97 41 48 93 2 26 2,963 9 30 520 94 0c 5 528 222 41,582 s 4,721 27,387 7,003 20,384 32,108 4,602 8,735 10,389 207 795 7,379 9,474 859

66 8,091 4 10 85 27 27 0c 19 13 67 57 .. 182 19 699 6 282 12 5 4 334 190 7 3 2 10 113 41 30 97 3 29 3,040 17 33 463 138 0c 5 495 156 40,204 s 4,155 26,420 6,364 20,056 30,575 4,698 8,784 9,460 211 817 6,605 9,629 952

% 1990–2000 2000–10

0.01 0.00 –0.79 0.00 0.49 –0.62 0.65 0.00 0.01 –0.37 0.97 0.00 .. –2.09 1.20 0.80 –0.93 –0.04 –0.37 –1.51 –0.05 1.02 0.28 1.22 3.37 0.30 –2.67 –0.47 0.00 2.03 –0.25 –2.38 –0.68 –0.13 –4.38 –0.54 0.57 –2.28 0.00 0.00 0.32 1.58 0.18 w 0.63 0.22 0.59 0.10 0.28 0.08 –0.02 0.47 –0.10 –0.01 0.55 –0.14 –0.76

Threatened species

2012 World Development Indicators

Nationally protected areas

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2011

2011

2011

2011

2008

1990

0.7 34.1 0.9 3.2 1.0 0.2 1.3 0.1 0.1 0.2 6.1 20.7 .. 6.8 7.9 5.1 0.1 0.3 0.2 0.9 0.7 14.8 8.0 0.6 0.3 2.2 0.5 6.2 1.8 2.8 0.5 0.2 3.5 94.2 1.2 1.1 25.3 12.1 .. 3.2 3.8 1.9 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

2.9 5.0 9.9 7.6 24.1 5.3 4.9 5.0 19.3 7.6 0.6 6.5 .. 7.7 20.3 4.2 3.0 6.0 14.5 0.3 1.9 26.6 14.7 .. 11.3 30.5 1.3 1.7 3.0 7.9 1.8 0.3 22.0 12.4 0.3 2.1 40.1 4.5 0.6 0.0 36.0 18.0 8.4 w 9.6 7.9 7.3 8.2 8.2 10.8 4.3 9.7 3.3 5.3 11.1 8.9 11.3

–0.32 7 11 19 4 0.00 32 49 35 8 –2.38 20 12 9 4 0.00 9 15 23 3 0.49 16 10 45 9 –0.99 6 9 11 2 0.69 17 10 47 48 0.00 11 15 25 57 –0.06 3 6 5 5 –0.16 4 2 29 7 1.07 15 12 27 21 0.00 24 40 87 65 .. .. .. .. .. –0.68 16 9 71 205 1.12 29 15 44 282 0.08 15 15 19 16 –0.84 5 11 4 3 –0.30 1 2 12 4 –0.38 2 1 9 2 –1.29 16 14 34 2 0.00 8 12 5 13 1.13 35 42 174 290 0.02 57 46 97 86 1.40 4 7 5 0 5.13 11 5 24 9 0.32 2 2 24 1 –1.86 13 6 35 6 –1.11 17 14 70 5 0.00 9 16 11 3 2.56 22 21 61 36 –0.21 11 11 21 16 –0.24 7 9 13 0 –0.31 5 2 43 11 –0.13 37 76 183 219 –2.14 11 24 36 0 –0.20 10 15 7 15 0.60 32 27 37 68 –1.65 54 43 68 119 –0.10 3 9 0 0 0.00 9 15 23 158 0.33 9 14 20 9 1.88 9 14 3 14 0.11 w 3,105 s 3,401 s 6,213 s 10,987 s 0.61 .. .. .. .. 0.08 .. .. .. .. 0.30 .. .. .. .. 0.01 .. .. .. .. 0.15 .. .. .. .. –0.44 .. .. .. .. –0.04 .. .. .. .. 0.45 .. .. .. .. –0.15 .. .. .. .. –0.29 .. .. .. .. 0.48 .. .. .. .. –0.04 .. .. .. .. –0.31 .. .. .. ..

a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Less than 0.5.

152

GEF benefits index for biodiversity

Terrestrial % of land area 2010

7.1 9.1 10.0 31.3 24.1 6.0 4.9 5.4 23.2 13.2 0.6 6.9 .. 8.6 21.5 4.2 3.0 10.9 24.9 0.6 4.1 27.5 20.1 6.1 11.3 31.2 1.3 1.9 3.0 10.3 3.5 5.6 26.4 12.4 0.3 2.3 53.8 6.2 0.6 0.5 36.0 28.0 12.3 w 10.7 12.3 9.4 13.4 12.0 15.0 7.5 20.2 3.9 5.9 11.7 12.9 17.0

Marine % of territorial waters 1990

2010

1.6 2.2 .. 0.6 5.8 .. 0.0 0.0 .. 0.0 0.0 0.7 .. 0.6 0.1 0.0 .. 3.9 .. 0.0 .. 3.7 4.0 0.0 0.0 0.2 1.1 2.4 .. .. 4.1 0.3 4.9 21.0 0.2 .. 7.0 0.3 .. 0.0 .. .. 4.6 w .. 2.6 2.7 2.6 2.6 0.5 2.2 5.0 0.8 1.5 3.3 9.3 6.7

33.3 10.8 .. 3.4 12.4 .. 0.0 1.4 .. 0.7 0.0 6.5 .. 3.5 1.1 0.0 .. 5.3 .. 0.6 .. 10.0 4.4 6.7 0.0 2.8 1.2 2.4 .. .. 4.9 2.6 5.7 28.6 0.3 .. 15.3 1.7 .. 1.8 .. .. 10.0 w .. 7.5 4.4 8.5 7.2 1.4 10.4 13.1 2.0 1.7 5.8 16.5 15.1


About the data

3.4

ENVIRONMENT

Deforestation and biodiversity Definitions

As threats to biodiversity mount, the international

nature reserves with limited public access; national

• Forest area is land spanning more than 0.5 hect-

community is increasingly focusing on conserving

parks of national or international significance and not

are with trees higher than 5 meters and a canopy

diversity. Deforestation is a major cause of loss

materially affected by human activity; natural monu-

cover of more than 10 percent or with trees able

of biodiversity, and habitat conservation is vital

ments and natural landscapes with unique aspects;

to reach these thresholds in situ. It excludes land

for stemming this loss. Conservation efforts have

managed nature reserves and wildlife sanctuaries;

that is predominantly under agricultural or urban

focused on protecting areas of high biodiversity.

protected landscapes (which may include cultural

land use. • Average annual deforestation is the

The Food and Agriculture Organization of the United

landscapes); and areas managed mainly for the sus-

permanent conversion of natural forest area to other

Nations (FAO) Global Forest Resources Assessment

tainable use of natural systems to ensure long-term

uses, including agriculture, ranching, settlements,

2010 provides detailed information on forest cover in

protection and maintenance of biological diversity.

and infrastructure. Deforested areas exclude areas

2010 and adjusted estimates of forest cover in 1990

The data in the table cover these six categories

logged but intended for regeneration and areas

and 2000. The current survey uses a uniform defini-

as well as terrestrial protected areas that are not

degraded by fuelwood gathering, acid precipitation,

tion of forest. Because of space limitations, the table

assigned to a category by the IUCN. Designating an

or forest fires. • Threatened species are species

does not break down forest cover between natural

area as protected does not mean that protection

classified by the IUCN as endangered, vulnerable,

forest and plantation, a breakdown the FAO provides

is in force. And for small countries that have only

rare, indeterminate, out of danger, or insufficiently

for developing countries. Thus the deforestation data

protected areas smaller than 1,000 hectares, the

known. Mammals exclude whales and porpoises.

in the table may underestimate the rate at which

size limit in the definition leads to an underestimate

Birds are listed for the country where their breeding

natural forest is disappearing in some countries.

of protected areas.

or wintering ranges are located. Fish are cold-blooded

The number of threatened species is an important

Due to variations in consistency and methods of

aquatic vertebrates of the superclass Pisces. Higher

measure of the immediate need for conservation in

collection, data quality is highly variable across coun-

plants are native vascular plant species. • GEF ben-

an area. Global analyses of the status of threatened

tries. Some countries update their information more

efits index for biodiversity is a composite index of

species have been carried out for few groups of

frequently than others, some have more accurate

relative biodiversity potential based on the species

organisms. Only for mammals, birds, and amphib-

data on extent of coverage, and many underreport

in each country and their threat status and diversity

ians has the status of virtually all known species

the number or extent of protected areas.

of habitat types. • Nationally protected areas are

been assessed. Threatened species are defined

totally or partially protected areas of at least 1,000

using the International Union for Conservation of

hectares that are designated as scientific reserves

Nature’s (IUCN) classification: endangered (in danger

with limited public access, national parks, natural

of extinction and unlikely to survive if causal factors

monuments, nature reserves or wildlife sanctuar-

continue operating) and vulnerable (likely to move

ies, and protected landscapes. Terrestrial protected

into the endangered category in the near future if

areas exclude marine areas, unclassified areas, litto-

causal factors continue operating).

ral (intertidal) areas, and sites protected under local

The Global Environment Facility’s (GEF) benefi ts

or provincial law. Marine protected areas are areas of

index for biodiversity is a comprehensive indicator

intertidal or subtidal terrain—and overlying water and

of national biodiversity status and is used to guide

associated flora and fauna and historical and cultural

its biodiversity priorities. For each country the bio-

features—that have been reserved to protect part of

diversity indicator incorporates the best available

or the entire enclosed environment.

and comparable information in four relevant dimensions: represented species, threatened species, represented ecoregions, and threatened ecoregions. To combine these dimensions into one measure, the indicator uses dimensional weights that reflect the

Data sources

consensus of conservation scientists at the GEF,

Data on forest area are from the FAO’s Global

IUCN, WWF International, and other nongovernmen-

Forest Resources Assessment 2010 and website.

tal organizations.

Data on threatened species are from the elec-

The World Conservation Monitoring Centre (WCMC)

tronic files of the United Nations Environment Pro-

compiles data on protected areas, numbers of cer-

gramme (UNEP) and WCMC and the 2011 IUCN

tain species, and numbers of those species under

Red List of Threatened Species. The GEF benefits

threat from various sources. Because of differences

index for biodiversity is from Pandey and others

in definitions, reporting practices, and reporting peri-

(2006a). Data on nationally protected areas are

ods, cross-country comparability is limited.

from the UNEP and WCMC, based on data from

Nationally protected areas are defined using the six IUCN management categories for areas of at

national authorities, national legislation, and international agreements.

least 1,000 hectares: scientific reserves and strict

2012 World Development Indicators

153


3.5

Freshwater Internal renewable freshwater resourcesa

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

154

Annual freshwater withdrawals

Water productivity

Access to an improved water source

Flows billion cu. m

Per capita cu. m

billion cu. m

% of internal resources

% for agriculture

% for industry

% for domestic

GDP/water use 2000 $ per cu. m

% of urban population

% of rural population

2009

2009

2009b

2009b

2009b

2009b

2009b

2009b

2010

2010

35.6 4.4 52.7 0.4 4.0 36.4 4.6 4.7 35.2 219.8 2.9 7.5 34.0 0.5 0.3 0.9 1.6 0.7 28.7 7.9 2.3 0.5 0.3 1.6 0.0 0.9 1.2 19.5 .. 0.6 0.0 c 0.0 c 2.4 1.7 0.6 19.8 19.3 13.3 10.8 16.6 3.6 119.0 5.5 9.2 14.0 4.6 1.5 15.0 0.1 0.9 2.6 21.0 1.8 12.7 2.6 0.7 0.6 8.6

99 58 64 33 66 66 74 3 76 45 88 19 1 45 57 .. 41 55 16 70 77 94 76 12 1 52 70 65 .. 39 18 9 53 43 2 75 86 2 36 64 92 86 55 95 0c 94 3 12 38 28 65 0c 66 89 55 84 82 78

1 12 14 29 12 4 11 79 19 6 2 54 88 23 15 .. 18 17 68 2 6 2 7 69 16 24 20 23 .. 4 20 22 17 19 14 10 3 57 5 2 3 6 17 0 97 0c 72 69 9 24 13 84 10 2 30 3 5 4

1 30 23 38 22 30 16 18 4 50 10 27 12 32 28 .. 41 28 16 28 17 4 17 20 82 24 9 12 .. 57 63 70 29 38 85 15 10 42 58 34 6 8 28 5 3 6 25 18 53 48 22 16 24 9 15 13 13 19

42 94 79 38 80 97 100 100 71 .. 80 99 100 68 71 98 92 85 100 73 71 58 52 99 51 44 75 85 .. 72 27 32 91 68 97 89 100 100 100 84 89 99 76 57 97 34 100 100 41 85 96 100 80 99 87 65 53 51

78 96 85 60 98 99 100 100 88 100 85 100 100 84 96 100 99 100 100 95 83 87 95 100 92 70 99 98 .. 99 79 95 100 91 100 96 100 100 100 87 96 100 94 74 99 97 100 100 95 92 100 100 91 100 98 90 91 85

55 27 11 148 276 7 492 55 8 0c 105 37 12 10 304 36 2 5,418 21 13 10 121 273 2,850 141 15 884 2,813 .. 2,112 900 222 112 77 38 38 1 13 6 21 432 2 18 3 13 122 107 200 164 3 58 107 30 58 109 226 16 13

1,645 8,425 322 7,976 6,889 2,223 22,413 6,575 907 3 714 3,913 1,111 1,197 31,054 9,422 1,211 28,037 2,769 782 1,231 8,628 14,237 84,495 32,653 1,371 52,136 2,113 .. 46,261 14,018 56,324 24,484 3,971 8,512 3,385 715 1,254 1,086 2,144 30,291 23 2,881 549 9,483 1,503 20,042 3,099 110,997 1,784 13,179 1,306 1,272 5,141 7,781 23,153 10,781 1,319

2012 World Development Indicators

23.1 1.8 6.2 0.6 32.6 2.8 22.6 3.7 12.2 0.4 35.9 4.3 6.2 0.1 2.0 0.3 0.2 58.1 6.1 1.0 0.3 2.2 1.0 46.0 0.1 0.4 11.3 554.1 .. 12.7 0.6 0.0 2.7 1.4 0.6 7.6 0.2 1.7 0.7 3.5 15.3 68.3 1.4 0.6 1.8 5.6 1.6 31.6 0.1 0.1 1.6 32.3 1.0 9.5 2.9 1.6 0.2 1.2

.. 3 12 40 12 1 24 60 2 35 2 6 42 25 6 24 40 15 3 4 3 3 14 18 15 8 9 5 .. 11 10 101 9 8 45 4 66 45 253 11 2 2 11 1 5 3 86 46 46 8 3 62 8 17 9 2 1 3


Internal renewable freshwater resourcesa

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

Annual freshwater withdrawals

Water productivity

3.5

ENVIRONMENT

Freshwater

Access to an improved water source

Flows billion cu. m

Per capita cu. m

billion cu. m

% of internal resources

% for agriculture

% for industry

% for domestic

GDP/water use 2000 $ per cu. m

% of urban population

% of rural population

2009

2009

2009b

2009b

2009b

2009b

2009b

2009b

2010

2010

96 6 1,446 2,019 129 35 49 1 183 9 430 1 75 21 67 65 .. 0c 49 190 17 5 5 200 1 16 5 337 16 580 60 0c 3 409 1 35 29 100 1,003 6 198 11 327 190 4 221 382 1 55 147 801 94 1,616 479 54 38 7 0c

12,877 599 1,197 8,504 1,757 1,132 10,989 100 3,032 3,489 3,371 115 4,686 525 2,764 1,330 .. 0c 9,199 31,151 7,424 1,144 2,433 52,139 96 4,659 2,625 16,746 1,118 20,752 4,024 118 2,158 3,651 280 12,833 917 4,388 21,071 2,747 6,734 665 75,768 33,221 234 1,431 79,110 516 323 42,578 119,492 14,822 56,179 5,223 1,405 3,574 1,790 35

1.2 5.6 761.0 113.3 93.3 66.0 .. 2.0 45.4 0.6 90.0 0.9 33.1 2.7 8.7 25.5 .. 0.9 10.1 4.3 0.4 1.3 0.1 0.2 4.3 2.4 1.0 14.7 1.0 13.2 6.5 1.6 0.7 79.8 1.9 0.5 12.6 0.7 33.2 0.3 9.8 10.6 4.8 1.3 2.4 10.3 2.9 1.3 183.5 0.5 0.4 0.5 19.3 81.6 12.0 8.5 1.0 0.4

1.2 5.4 39.8 5.6 67.7 87.3 .. 101.9 23.7 6.2 20.9 99.4 28.9 8.9 11.2 36.5 .. .. 43.7 1.3 1.2 28.1 1.7 0.1 718.0 9.6 16.1 4.4 5.6 2.3 6.5 14.0 26.4 17.5 16.4 1.4 43.4 0.3 2.8 1.7 4.7 11.7 1.5 0.7 7.0 3.6 0.8 86.6 79.5 0.3 0.0 0.1 1.0 17.0 19.4 12.3 14.0 455.2

58 6 90 82 92 79 .. 58 44 34 63 65 87 79 76 62 .. 54 94 93 12 60 20 34 83 3 12 97 84 34 90 94 68 77 40 44 87 74 89 71 98 1 74 84 88 53 29 88 94 51 0c 71 85 82 10 73 7 59

25 82 2 7 1 15 .. 6 36 22 18 4 12 4 13 12 .. 2 3 4 50 11 40 27 3 90 67 1 4 36 1 2 3 9 52 32 3 3 1 5 0c 87 4 2 1 15 43 1 1 3 43 8 8 10 60 19 2 2

17 12 7 12 7 7 .. 36 20 44 19 31 1 17 10 26 .. 44 3 3 39 29 40 40 14 7 21 2 12 30 9 5 30 14 9 24 10 23 10 24 2 12 21 14 11 31 28 10 5 46 57 20 7 8 31 8 91 39

9 .. 1 2 2 0c .. 83 25 17 54 16 1 7 .. 30 .. 43 0c 1 27 20 20 3 11 7 4 0c 3 10 1 1 9 8 1 4 5 11 .. 19 1 41 13 4 1 8 66 23 1 44 12 19 4 1 20 15 65 122

79 100 90 74 92 56 100 100 100 88 100 92 90 52 97 88 .. 99 85 62 96 100 73 60 .. .. 99 34 80 99 51 48 99 91 93 53 61 29 78 90 88 100 100 68 39 43 100 78 89 83 33 66 65 92 100 100 .. 100

95 100 97 92 97 91 100 100 100 98 100 98 99 82 99 100 .. 99 99 77 100 100 91 88 .. 98 100 74 95 100 87 52 100 97 99 100 98 77 93 99 93 100 100 98 100 74 100 93 96 97 87 99 91 93 100 99 .. 100

2012 World Development Indicators

155


3.5

Freshwater Internal renewable freshwater resourcesa

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

Annual freshwater withdrawals

Water productivity

Access to an improved water source

Flows billion cu. m

Per capita cu. m

billion cu. m

% of internal resources

% for agriculture

% for industry

% for domestic

GDP/water use 2000 $ per cu. m

% of urban population

% of rural population

2009

2009

2009b

2009b

2009b

2009b

2009b

2009b

2010

2010

42 4,313 10 2 26 .. 160 1 13 19 6 45 .. 111 53 30 3 171 40 7 66 84 225 8 12 4 4 227 1 39 53 0c 145 2,818 59 16 722 359 1 2 80 12 42,383 s 4,197 29,152 7,995 21,157 33,348 8,773 5,076 13,425 226 1,990 3,858 9,034 977

1,969 30,405 921 90 2,131 .. 27,878 120 2,325 9,153 658 908 .. 2,422 2,555 706 2,260 18,390 5,217 356 9,774 1,930 3,268 7,469 1,949 2,874 402 3,160 273 1,205 1,153 22 2,346 9,186 17,639 588 25,451 4,178 201 90 6,303 983 6,258 w 5,381 5,944 3,227 8,718 5,867 4,506 12,887 23,323 695 1,236 4,634 8,305 2,952

6.9 66.2 0.2 23.7 2.2 4.1 0.5 .. 0.7 0.9 3.3 12.5 .. 32.5 13.0 37.1 1.0 2.6 2.6 16.8 12.0 5.2 57.3 1.2 0.2 0.2 2.9 40.1 24.9 0.3 38.5 4.0 13.0 478.4 3.7 59.6 9.1 82.0 0.4 3.6 1.7 4.2 3,908.3 s 188.6 2,791.0 1,607.4 1,183.6 2,979.6 952.0 330.4 269.5 276.5 1,026.6 124.6 928.7 185.6

3.2 1.5 1.6 943.3 5.7 .. 0.3 .. 1.4 3.0 22.4 25.0 .. 29.0 24.5 57.6 23.1 1.5 4.9 99.8 74.8 5.4 13.1 14.3 1.2 6.0 61.7 18.8 100.8 0.5 27.6 2,032.0 8.8 15.6 2.6 118.3 0.7 9.3 49.9 168.6 1.7 21.0 7.3 w 4.3 7.0 12.6 5.0 6.7 9.9 5.0 1.6 70.0 33.4 2.2 9.4 16.4

17 20 68 88 93 2 71 .. 3 0c 99 63 .. 61 87 97 97 4 2 88 92 89 90 91 45 9 76 74 97 36 51 83 10 40 87 91 44 95 45 91 76 79 70 w 90 79 88 66 79 73 63 68 86 91 84 41 32

61 60 8 3 3 82 10 .. 50 82 0c 6 .. 22 6 1 1 59 58 4 5 0 5 0c 2 25 4 11 1 18 36 2 33 46 2 3 8 4 7 2 7 7 18 w 2 11 5 20 11 16 26 11 6 2 4 42 52

22 20 24 9 4 17 19 .. 47 18 0c 31 .. 18 6 2 2 37 41 9 4 10 5 8 53 66 13 15 2 46 12 15 57 14 11 6 49 1 48 7 17 14 12 w 8 10 7 14 10 10 11 21 8 7 12 17 17

8 6 22 11 3 2 3 .. 64 27 .. 15 .. 22 2 1 2 110 110 2 0c 4 3 0c 10 61 11 9 0c 36 1 39 132 24 8 0c 18 1 9 4 3 1 10 w 2 3 1 6 3 4 3 10 2 1 4 31 36

.. 92 63 .. 56 98 35 .. 100 99 7 79 .. 100 90 52 65 100 100 86 54 44 95 60 40 93 84 99 72 68 98 100 100 94 100 81 75 93 81 47 46 69 81 w 57 84 83 86 80 84 91 81 81 88 49 98 100

a. Excludes river flows from other countries because of data unreliability. b. Data are for the most recent year available (see Primary data documentation). c. Less than 0.5.

156

2012 World Development Indicators

99 99 76 97 93 99 87 100 100 100 66 99 .. 100 99 67 91 100 100 93 92 79 97 91 89 98 99 100 97 95 98 100 100 100 100 98 94 99 86 72 87 98 96 w 86 96 93 98 95 97 99 98 94 95 83 100 100


About the data

3.5

ENVIRONMENT

Freshwater Definitions

The data on freshwater resources are based on

• Internal renewable freshwater resources are

estimates of runoff into rivers and recharge of

the average annual flows of rivers and groundwater

groundwater. These estimates are based on differ-

from rainfall in the country. Natural incoming flows

ent sources and refer to different years, so cross-

originating outside a country’s borders are excluded.

country comparisons should be made with caution.

Overlapping water resources between surface run-

Because the data are collected intermittently, they

off and groundwater recharge are also deducted.

may hide significant variations in total renewable

• Internal renewable freshwater resources per

water resources from year to year. The data also

capita are calculated using the World Bank’s popu-

fail to distinguish between seasonal and geographic

lation estimates (see table 2.1). • Annual freshwater

variations in water availability within countries. Data

withdrawals are total water withdrawals, not count-

for small countries and countries in arid and semiarid

ing evaporation losses from storage basins. With-

zones are less reliable than those for larger countries

drawals also include water from desalination plants

and countries with greater rainfall.

in countries where they are a significant source. With-

Caution should also be used in comparing data

drawals can exceed 100 percent of total renewable

on annual freshwater withdrawals, which are subject

resources where extraction from nonrenewable aqui-

to variations in collection and estimation methods.

fers or desalination plants is considerable or where

In addition, inflows and outflows are estimated at

water reuse is significant. Withdrawals for agriculture

different times and at different levels of quality and

and industry are total withdrawals for irrigation and

precision, requiring caution in interpreting the data,

livestock production and for direct industrial use

particularly for water-short countries, notably in the

(including for cooling thermoelectric plants). With-

Middle East and North Africa.

drawals for domestic uses include drinking water,

Water productivity is an indication only of the

municipal use or supply, and use for public services,

effi ciency by which each country uses its water

commercial establishments, and homes. • Water

resources. Given the different economic structure

productivity is calculated as GDP in constant prices

of each country, these indicators should be used

divided by annual total water withdrawal. • Access

carefully, taking into account the countries’ sectoral

to an improved water source is the percentage of the

activities and natural resource endowments.

population with reasonable access to an adequate

The data on access to an improved water source

amount of water from an improved source, such as

measure the percentage of the population with ready

piped water into a dwelling, plot, or yard; public tap

access to water for domestic purposes. The data

or standpipe; tubewell or borehole; protected dug

are based on surveys and estimates provided by

well or spring; and rainwater collection. Unimproved

governments to the Joint Monitoring Programme of

sources include unprotected dug wells or springs,

the World Health Organization (WHO) and the United

carts with small tank or drum, bottled water, and

Nations Children’s Fund (UNICEF). The coverage

tanker trucks. Reasonable access is defined as the

rates are based on information from service users

availability of at least 20 liters a person a day from

on actual household use rather than on informa-

a source within 1 kilometer of the dwelling.

tion from service providers, which may include nonfunctioning systems. Access to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have

Data sources

different meaning in different countries despite offi -

Data on freshwater resources and withdrawals

cial WHO definitions (see Definitions). Even in high-

are from the Food and Agriculture Organization’s

income countries treated water may not always be

AQUASTAT database. The GDP estimates used to

safe to drink. Access to an improved water source is

calculate water productivity are from the World

equated with connection to a supply system; it does

Bank national accounts database. Data on access

not take into account variations in the quality and

to water are from WHO and UNICEF’s Progress on

cost (broadly defined) of the service.

Drinking Water and Sanitation (2012).

2012 World Development Indicators

157


3.6

Water pollution Emissions of organic water pollutants

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

158

kilograms per day per worker

thousand kilograms per day 1990 2007a

1990

.. 2.4 .. .. 181.4 .. .. 90.5 41.3 .. 250.8 .. 107.8 .. 11.3 .. 2.5 .. 124.3 .. .. 3.8 .. 300.9 .. .. .. .. .. .. .. .. .. .. 48.5 .. 7.1 177.1 84.5 88.6 28.6 206.5 .. 2.4 21.7 18.5 72.5 326.5 .. 0.8 .. 806.6 .. 50.9 .. .. ..

.. 0.25 .. .. 0.21 .. .. 0.15 0.15 .. 0.15 .. 0.17 .. 0.24 .. 0.30 .. 0.17 .. .. 0.17 .. 0.17 .. .. .. .. .. .. .. .. .. .. 0.17 .. 0.21 0.14 0.18 0.18 0.24 0.19 .. 0.19 0.15 0.23 0.19 0.11 .. 0.27 .. 0.13 .. 0.19 .. .. ..

0.2 3.6 .. .. 155.5 .. .. 84.4 20.0 .. 303.0 .. 95.9 .. 11.5 .. 3.2 .. 102.1 .. .. .. .. 306.6 .. .. 92.5 9,428.9 .. 87.0 .. .. .. .. 42.9 .. 8.0 146.5 61.0 .. 44.7 .. .. 2.5 16.0 32.2 55.3 569.4 .. .. .. 936.2 16.0 60.8 .. .. ..

2012 World Development Indicators

Industry shares of emissions of organic water pollutants

% of total Stone, ceramics, Food and beverages and glass

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

0.21 0.25 .. .. 0.23 .. .. 0.14 0.18 .. 0.14 .. 0.17 .. 0.25 .. 0.23 .. 0.17 .. .. .. .. 0.16 .. .. 0.25 0.13 .. 0.20 .. .. .. .. 0.17 .. 0.23 0.13 0.16 .. 0.28 .. .. 0.20 0.14 0.24 0.14 0.16 .. .. .. 0.14 0.17 0.20 .. .. ..

.. .. .. .. 3.8 .. .. 5.7 8.8 .. 0.7 .. 6.4 .. 0.9 .. .. .. 3.7 .. .. .. .. 4.3 .. .. 7.6 7.2 .. 2.3 .. .. .. .. 3.1 .. 0.3 5.4 1.4 .. 1.8 .. .. 0.2 0.4 1.4 1.0 3.2 .. .. .. 3.8 3.0 3.9 .. .. ..

19.7 .. .. .. 8.4 .. .. 7.1 3.0 .. 2.3 .. 7.9 .. 9.8 .. 2.4 .. 4.3 .. .. .. .. 8.9 .. .. 6.3 3.9 .. 8.9 .. .. .. .. 7.2 .. 8.9 4.8 11.5 .. 7.8 .. .. 4.4 7.3 6.0 15.4 7.4 .. .. .. 7.1 3.8 9.0 .. .. ..

27.9 .. .. .. 15.8 .. .. 9.3 18.5 .. 3.0 .. 18.6 .. 13.1 .. .. .. 8.0 .. .. .. .. 10.9 .. .. 13.7 13.0 .. 17.3 .. .. .. .. 9.5 .. 9.3 10.9 13.1 .. 12.8 .. .. 9.5 7.1 10.9 8.7 15.0 .. .. .. 12.4 15.9 10.1 .. .. ..

14.1 39.8 .. .. 30.5 .. .. 12.2 19.6 .. 7.6 .. 16.4 .. 35.4 .. 43.8 .. 17.7 .. .. .. .. 14.0 .. .. 35.1 7.4 .. 21.3 .. .. .. .. 17.6 .. 36.3 10.9 16.4 .. 46.4 .. .. 27.3 14.6 34.7 9.0 16.6 .. .. .. 11.4 18.6 23.9 .. .. ..

11.7 .. .. .. 3.5 .. .. 5.8 8.4 .. 2.6 .. 3.1 .. 7.7 .. 0.6 .. 4.8 .. .. .. .. 2.8 .. .. 3.6 6.3 .. 5.3 .. .. .. .. 5.9 .. 9.9 6.4 4.8 .. 4.4 .. .. 9.6 5.5 8.3 4.4 3.8 .. .. .. 3.4 4.1 7.0 .. .. ..

23.3 60.2 .. .. 14.3 .. .. 4.3 11.7 .. 79.3 .. 5.5 .. 18.4 .. 3.9 .. 26.8 .. .. .. .. 7.3 .. .. 9.1 20.6 .. 24.1 .. .. .. .. 14.5 .. 5.1 7.4 1.5 .. 12.3 .. .. 29.0 8.0 27.9 2.8 4.8 .. .. .. 2.4 10.2 14.4 .. .. ..

.. .. .. .. 2.1 .. .. 6.0 1.5 .. 0.5 .. 2.2 .. 5.3 .. .. .. 3.0 .. .. .. .. 6.5 .. .. 6.9 1.7 .. 0.9 .. .. .. .. 4.9 .. 8.0 4.4 4.0 .. 2.2 .. .. 0.1 16.4 1.5 7.3 2.4 .. .. .. 1.9 33.3 2.8 .. .. ..

3.1 11.9 .. .. 21.6 .. .. 49.5 28.6 .. 4.2 .. 40.0 .. 9.5 .. 50.0 .. 31.7 .. .. .. .. 45.3 .. .. 17.7 39.9 .. 19.9 .. .. .. .. 37.2 .. 22.2 49.8 47.3 .. 12.3 .. .. 20.3 40.8 9.3 51.4 46.9 .. .. .. 57.6 11.2 28.9 .. .. ..


Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

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

5.2 .. 122.1 .. 721.8 131.6 7.7 36.1 54.6 378.3 .. 1,455.0 15.0 123.5 .. .. 366.9 .. .. 28.9 4.3 39.8 14.7 .. .. .. 54.0 27.0 .. 37.2 .. .. .. 16.8 425.0 29.2 .. .. .. .. .. 26.4 142.3 46.7 .. .. .. 51.8 3.8 .. 10.3 .. 15.3 .. 169.0 446.7 140.6

.. .. 110.6 .. 883.0 160.8 7.7 28.4 52.7 479.2 .. 1,126.9 29.1 97.4 .. .. 319.6 .. .. 12.2 4.3 28.4 14.7 5.3 .. .. 42.2 20.3 92.8 32.7 208.3 .. .. 15.4 .. 18.8 8.8 74.0 .. .. .. 26.8 128.2 61.6 .. .. .. 46.9 7.6 153.7 13.7 .. 10.8 .. 144.6 359.7 87.7

3.6

ENVIRONMENT

Water pollution Industry shares of emissions of organic water pollutants

kilograms per day per worker

Primary metals

Paper and pulp

Chemicals

% of total Stone, ceramics, Food and beverages and glass

Textiles

Wood

Other

1990

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

0.20 .. 0.18 .. 0.18 0.16 0.27 0.19 0.16 0.13 .. 0.14 0.18 0.23 .. .. 0.12 .. .. 0.14 0.14 0.12 0.19 .. .. .. 0.15 0.20 .. 0.40 .. .. .. 0.16 0.18 0.44 .. .. .. .. .. 0.14 0.20 0.24 .. .. .. 0.20 0.15 .. 0.30 .. 0.20 .. 0.17 0.16 0.14

.. .. 0.15 .. 0.19 0.15 0.27 0.16 0.16 0.13 .. 0.15 0.18 0.24 .. .. 0.11 .. .. 0.20 0.14 0.18 0.19 0.13 .. .. 0.17 0.18 0.14 0.39 0.12 .. .. 0.17 .. 0.45 0.21 0.16 .. .. .. 0.16 0.19 0.23 .. .. .. 0.18 0.16 0.17 0.32 .. 0.28 .. 0.15 0.16 0.17

.. .. 2.7 .. 1.4 7.1 13.1 1.3 1.6 3.5 .. 3.3 2.3 33.3 .. .. 4.2 .. .. 9.8 1.8 2.7 0.5 0.9 .. .. 0.9 5.8 0.3 .. 2.8 .. .. 0.4 .. .. 3.7 1.0 .. .. .. 1.6 3.1 2.0 .. .. .. 4.9 4.0 2.2 0.9 .. 3.1 .. 2.6 3.3 0.2

.. .. 6.4 .. 4.1 2.8 25.6 10.2 8.9 5.2 .. 7.0 6.1 2.3 .. .. 5.4 .. .. 6.3 2.2 7.7 7.5 0.5 .. .. 5.7 4.7 1.6 1.4 4.9 .. .. 3.6 .. 3.8 5.1 2.9 .. .. .. 3.9 13.4 12.2 .. .. .. 12.1 4.6 1.9 11.6 .. 9.3 .. 4.2 5.1 8.1

.. .. 10.6 .. 12.0 12.8 29.9 17.6 13.4 10.3 .. 11.2 13.7 8.9 .. .. 12.1 .. .. 8.5 3.8 5.8 6.0 0.3 .. .. 8.3 6.3 12.4 3.7 16.5 .. .. 5.9 .. .. 3.3 7.9 .. .. .. 7.2 14.1 8.6 .. .. .. 7.5 17.8 9.1 6.9 .. 16.7 .. 9.5 11.3 3.4

.. .. 15.2 .. 23.1 16.1 16.9 14.8 16.4 9.3 .. 15.0 20.8 18.7 .. .. 6.3 .. .. 24.2 9.2 21.1 25.5 2.6 .. .. 20.5 15.1 7.6 82.1 9.1 .. .. 14.7 .. 95.2 27.2 16.3 .. .. .. 19.2 18.2 31.1 .. .. .. 19.1 20.4 15.1 55.2 .. 42.6 .. 14.4 18.1 19.8

.. .. 3.7 .. 4.0 13.8 5.4 5.9 2.9 5.4 .. 3.6 11.5 9.3 .. .. 3.0 .. .. 17.5 7.5 4.4 12.9 0.8 .. .. 4.7 3.2 2.8 0.6 3.8 .. .. .. .. .. 9.5 6.5 .. .. .. 29.9 4.0 3.1 .. .. .. 4.3 20.5 4.3 4.0 .. 5.9 .. 2.7 5.5 5.2

.. .. 9.1 .. 29.2 11.2 9.1 0.8 7.9 13.6 .. 5.3 18.6 3.9 .. .. 9.3 .. .. 9.8 49.2 11.8 16.7 93.5 .. .. 17.6 44.7 58.9 7.5 6.6 .. .. 63.9 .. .. 41.6 43.5 .. .. .. 29.4 2.1 5.8 .. .. .. 2.0 2.4 55.6 4.7 .. 11.0 .. 21.6 10.3 16.3

.. .. 3.3 .. 6.3 0.7 .. 3.8 1.2 2.9 .. 2.0 2.3 0.6 .. .. 0.9 .. .. 1.6 21.4 19.1 4.5 .. .. .. 11.4 2.9 6.3 1.1 7.8 .. .. 0.7 .. .. 5.4 2.0 .. .. .. 2.0 2.6 8.0 .. .. .. 6.0 4.0 0.4 1.6 .. 4.5 .. 2.1 4.9 8.5

.. .. 49.0 .. 19.9 35.5 .. 45.5 47.6 49.6 .. 52.5 24.5 23.0 .. .. 58.9 .. .. 22.4 4.9 27.3 26.3 1.4 .. .. 30.8 17.3 10.0 3.6 48.5 .. .. 10.9 .. 0.9 4.1 19.9 .. .. .. 6.8 42.5 29.3 .. .. .. 44.2 26.3 11.2 15.0 .. 6.9 .. 42.9 41.5 38.5

2012 World Development Indicators

159


3.6

Water pollution Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

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

.. .. 411.2 1,521.4 8.1 .. 6.1 .. .. 33.1 72.8 28.1 .. 260.5 .. 348.0 .. .. .. 116.8 .. 59.7 29.1 .. 369.4 .. .. 7.0 .. 175.8 .. 3.3 .. .. 599.9 2,307.0 .. .. .. 141.0 .. 12.6 .. 29.3

.. 6.4 222.1 1,381.7 8.1 106.6 6.6 .. .. 38.3 47.9 28.8 .. 229.6 .. 378.8 266.1 38.6 .. 96.9 .. 80.4 12.8 30.3 581.4 .. .. 7.6 .. 346.4 .. 2.1 498.2 .. 521.7 1,850.8 .. .. .. 544.8 .. 46.5 .. ..

Industry shares of emissions of organic water pollutants

kilograms per day per worker

Primary metals

Paper and pulp

Chemicals

% of total Stone, ceramics, Food and beverages and glass

Textiles

Wood

Other

1990

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

.. .. 0.12 0.16 0.37 .. 0.30 .. .. 0.09 0.13 0.13 .. 0.17 .. 0.16 .. .. .. 0.15 .. 0.16 0.17 .. 0.15 .. .. 0.23 .. 0.18 .. 0.29 .. .. 0.16 0.14 .. .. .. 0.16 .. 0.24 .. 0.20

.. 0.12 0.15 0.17 0.37 0.18 0.29 .. .. 0.09 0.14 0.13 .. 0.17 .. 0.15 0.19 0.29 .. 0.14 .. 0.16 0.24 0.34 0.15 .. .. 0.29 .. 0.15 .. 0.23 0.19 .. 0.17 0.14 .. .. .. 0.14 .. 0.21 .. ..

.. 3.7 4.5 8.4 .. 3.2 4.9 .. .. 0.5 7.9 4.6 .. 9.9 .. 3.1 2.6 0.6 .. 5.3 .. 1.6 28.2 2.6 1.9 .. .. 4.8 .. 3.8 .. .. 13.9 .. 2.7 3.5 .. .. .. 1.4 .. .. .. ..

.. 6.7 3.5 4.9 .. 6.9 6.3 .. .. 5.5 5.4 6.1 .. 6.6 .. 8.0 4.3 1.9 .. 11.9 .. 1.9 2.7 4.8 4.2 .. .. 18.2 .. 3.8 .. 7.8 4.3 .. 12.5 8.1 .. .. .. 3.5 .. 2.1 .. ..

.. 10.5 7.1 11.6 9.0 11.6 23.8 .. .. 11.9 9.1 12.2 .. 10.6 .. 10.8 9.0 7.0 .. 9.9 .. 7.3 2.0 8.6 12.4 .. .. 21.3 .. 8.6 .. 7.3 11.2 .. 13.5 13.1 .. .. .. 6.8 .. 7.4 .. ..

.. 6.5 13.9 17.9 77.1 20.0 44.6 .. .. 5.3 10.7 7.7 .. 15.7 .. 15.3 22.4 57.5 .. 8.6 .. 19.9 18.0 61.2 16.4 .. .. 39.3 .. 12.4 .. 34.8 19.7 .. 14.9 12.0 .. .. .. 12.7 .. 35.9 .. ..

.. 18.1 4.0 8.3 4.3 10.7 3.9 .. .. 1.3 6.0 4.1 .. 5.2 .. 7.9 6.3 14.2 .. 2.6 .. 11.3 8.9 1.9 4.7 .. .. 8.0 .. 6.6 .. 13.3 6.8 .. 3.6 3.9 .. .. .. 6.4 .. 14.6 .. ..

.. 20.7 25.0 6.3 1.9 14.4 10.5 .. .. 2.3 5.0 10.8 .. 10.4 .. 8.4 43.6 8.0 .. 1.2 .. 32.0 38.4 12.7 20.5 .. .. 7.7 .. 32.2 .. 17.2 5.6 .. 4.3 4.3 .. .. .. 40.2 .. 15.5 .. ..

.. 12.5 5.3 4.2 2.9 3.3 0.8 .. .. 0.5 4.2 4.9 .. 4.2 .. 3.8 2.5 1.7 .. 5.6 .. 5.2 0.3 2.9 2.8 .. .. 8.5 .. 1.7 .. 2.3 2.1 .. 2.5 4.1 .. .. .. 3.3 .. 5.1 .. ..

.. 21.3 36.8 38.4 4.8 30.0 5.3 .. .. 72.7 51.7 49.6 .. 37.4 .. 42.7 9.3 9.1 .. 54.9 .. 20.9 1.8 5.3 37.2 .. .. 5.0 .. 30.9 .. 19.6 36.5 .. 46.1 51.1 .. .. .. 25.8 .. 19.4 .. ..

a. Data are derived using the United Nations Industrial Development Organization’s (UNIDO) industry database four-digit International Standard Industrial Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDO’s industry database at the three-digit ISIC.

160

2012 World Development Indicators


About the data

3.6

ENVIRONMENT

Water pollution Definitions

Emissions of organic pollutants from industrial

water pollutants per day for each country and year.

• Emissions of organic water pollutants are mea-

activities are a major cause of degradation of water

The data in the table were derived by updating these

sured as biochemical oxygen demand, or the amount

quality. Water quality and pollution levels are gener-

estimates through 2007.

of oxygen that bacteria in water will consume in

ally measured as concentration or load—the rate of

breaking down waste, a standard water treatment

occurrence of a substance in an aqueous solution.

test for the presence of organic pollutants. Emis-

Polluting substances include organic matter, metals,

sions per worker are total emissions divided by the

minerals, sediment, bacteria, and toxic chemicals.

number of industrial workers. • Industry shares of

The table focuses on organic water pollution result-

emissions of organic water pollutants are emissions

ing from industrial activities. Because water pollu-

from manufacturing activities as defined by two-digit

tion tends to be sensitive to local conditions, the

divisions of the International Standard Industrial

national-level data in the table may not reflect the

Classification revision 3.

quality of water in specific locations. The data in the table come from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank’s Development Research Group. Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water’s dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution. Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same

Data sources

across countries. This finding allowed the authors to

Data on water pollutants are from Hettige, Mani,

estimate BOD loads across countries and over time.

and Wheeler (1998). The data were updated

The estimated BOD intensities per unit of employ-

through 2007 by the World Bank Development

ment were multiplied by sectoral employment num-

Research Group using the same methodology

bers from UNIDO’s industry database for 1980–98.

as the initial study. Data on industrial sectoral

These estimates of sectoral emissions were then

employment are from UNIDO’s industry database.

used to calculate kilograms of emissions of organic

2012 World Development Indicators

161


3.7

Energy production and use Energy production

Total million metric tons of oil equivalent

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

162

Energy use

Total million metric tons of oil equivalent

% of total average annual % growth

1990

2009

1990

2009

1990–2009

.. 2.4 100.1 28.7 48.4 0.1 157.5 8.1 20.6 13.4 10.8 3.3 13.1 1.8 4.9 4.6 0.9 104.2 9.6 .. .. .. 11.0 273.7 .. .. 7.5 886.3 0.0 48.2 12.0 8.7 1.0 3.4 5.1 9.4 0.0 40.9 10.1 1.0 16.5 54.9 1.7 0.7 5.4 14.1 12.1 111.9 14.6 .. 2.1 186.2 4.4 9.2 3.4 .. .. 1.3

.. 1.3 152.3 101.0 80.8 0.8 310.7 11.4 64.6 17.5 24.8 4.0 15.3 2.0 14.2 4.5 0.9 230.3 9.8 .. .. 3.7 8.8 389.8 .. .. 9.3 2,084.9 0.1 99.1 23.3 15.3 2.7 11.9 4.1 5.6 0.1 31.2 23.9 1.9 27.3 88.2 3.2 0.6 4.2 30.4 16.6 129.5 13.6 .. 1.3 127.1 7.0 10.1 6.1 .. .. 1.9

.. 2.7 22.2 5.9 46.1 7.7 86.2 24.8 26.2 4.4 12.7 45.5 48.3 1.7 2.6 7.0 1.3 140.2 28.6 .. .. .. 5.0 208.6 .. .. 13.6 863.0 8.7 24.2 11.8 0.8 2.0 4.3 9.0 19.3 1.4 49.6 17.4 4.1 6.0 31.8 2.5 0.9 9.9 14.9 28.4 223.9 1.2 0.1 12.4 351.4 5.3 21.4 4.4 .. 0.1 1.6

.. 1.7 39.8 11.9 74.2 2.6 131.1 31.7 12.0 9.5 29.6 26.8 57.2 3.5 6.2 6.0 2.0 240.2 17.5 .. .. 5.2 6.9 254.1 .. .. 28.8 2,257.1 14.9 31.8 22.9 1.4 4.9 10.4 8.7 11.5 2.5 42.0 18.6 8.1 11.4 72.0 5.1 0.7 4.7 32.7 33.2 256.2 1.8 0.1 3.2 318.5 9.2 29.4 9.8 .. 0.1 2.6

.. 1.6 3.0 3.8 2.5 –2.3 2.3 1.7 –2.8 4.4 4.6 –1.7 0.9 3.7 3.9 2.9 2.7 3.0 –1.3 .. .. 3.3 1.8 1.4 .. .. 4.3 5.1 2.5 0.8 3.9 3.2 5.0 5.0 1.3 –1.8 2.9 0.1 0.1 3.6 3.9 4.8 3.8 –2.0 –2.0 3.8 1.5 0.9 2.7 .. –6.2 –0.2 3.2 2.2 4.0 .. .. 3.4

2012 World Development Indicators

Alternative and nuclear energy

Per capita kilograms of oil equivalent

Fossil fuel

Combustible renewables and waste

% of total energy use

1990

2009

1990

2009

1990

2009

1990

2009

.. 809 877 569 1,411 2,171 5,053 3,228 3,665 8,826 121 4,470 4,844 348 392 1,629 912 937 3,277 .. .. .. 409 7,505 .. .. 1,028 760 1,518 730 324 334 660 345 1,884 1,824 1,775 4,796 3,377 570 584 560 463 276 6,316 308 5,692 3,848 1,272 64 2,587 4,424 358 2,111 497 .. 73 219

.. 538 1,138 641 1,853 843 5,971 3,784 1,338 8,096 201 2,815 5,300 404 638 1,580 1,034 1,243 2,305 .. .. 371 361 7,534 .. .. 1,698 1,695 2,133 697 357 356 1,067 535 1,965 1,022 2,298 4,004 3,369 826 796 903 828 142 3,543 402 6,213 3,970 1,214 84 723 3,889 388 2,609 701 .. 67 263

.. 76.5 99.9 25.5 88.7 97.2 93.9 79.2 100.0 100.0 45.5 95.6 76.0 4.8 67.2 93.9 66.0 51.2 84.3 .. .. .. 18.7 74.6 .. .. 74.6 75.5 100.0 67.4 11.2 35.1 48.4 23.3 86.5 55.7 99.5 91.7 89.6 74.9 79.1 94.0 31.4 19.4 100.0 5.5 55.5 58.1 32.0 0.0 88.9 86.8 18.2 94.6 28.1 .. 0.0 19.7

.. 54.1 99.8 37.6 89.4 68.4 94.4 70.2 98.2 99.8 69.8 90.3 73.6 40.4 79.1 92.2 64.3 51.3 73.1 .. .. 27.8 30.9 74.9 .. .. 74.5 87.4 95.1 75.2 3.7 44.1 44.7 23.5 83.4 84.1 95.7 79.6 80.4 76.6 86.7 96.3 37.8 22.6 83.4 7.1 48.8 51.0 34.0 0.0 68.0 79.5 24.3 92.4 46.1 .. 0.0 28.1

.. 13.6 0.1 73.5 3.7 0.0 4.6 10.0 0.0 0.0 53.9 0.4 1.6 94.1 28.9 2.3 33.4 34.1 0.6 .. .. .. 76.7 3.9 .. .. 19.7 23.2 0.6 22.8 84.7 59.5 36.6 73.5 3.5 44.3 0.5 1.6 6.6 24.4 13.8 3.3 48.2 80.6 1.9 93.9 16.1 4.9 62.9 .. 3.7 1.4 73.7 4.2 68.5 .. .. 77.7

.. 12.4 0.1 60.1 3.1 0.0 4.4 17.6 0.0 0.0 29.8 6.0 4.9 57.4 17.7 3.1 23.6 31.6 4.3 .. .. 70.7 64.1 4.5 .. .. 17.5 9.0 0.4 14.0 93.7 51.1 15.8 75.2 4.2 15.8 1.8 5.6 16.2 21.8 5.4 2.1 33.8 77.4 14.7 92.0 20.9 5.9 61.8 .. 12.0 7.8 69.8 3.4 52.1 .. .. 71.2

.. 9.2 0.1 1.1 7.5 1.7 1.5 11.0 0.2 0.0 0.6 0.0 23.1 0.0 3.9 3.7 0.0 13.1 13.9 .. .. .. 4.6 21.5 .. .. 5.7 1.3 0.0 9.8 4.1 5.3 14.4 2.6 3.6 0.0 0.0 6.8 0.3 0.7 7.2 2.7 20.3 0.0 0.0 0.6 20.9 38.7 5.1 .. 5.2 11.8 9.3 1.0 3.4 .. .. 2.5

.. 26.4 0.1 2.3 6.8 31.7 1.2 12.0 1.7 0.0 0.5 0.0 21.8 0.0 3.2 9.0 0.0 15.6 24.9 .. .. 0.1 5.0 21.7 .. .. 7.6 3.7 0.0 11.1 2.9 2.0 39.5 1.8 6.8 0.1 2.5 17.5 3.3 1.6 7.0 1.7 28.2 0.0 0.4 1.0 21.8 43.9 4.2 .. 21.4 13.0 6.4 3.0 1.8 .. .. 0.7


Energy production

Total million metric tons of oil equivalent

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

1990

2009

1.7 14.6 291.8 169.1 179.3 104.9 3.5 0.4 25.3 0.5 75.2 0.2 90.5 9.0 28.9 22.6 .. 50.4 2.5 .. 1.1 0.1 .. .. 73.2 4.9 1.3 .. .. 48.8 .. .. .. 194.7 0.1 2.7 0.8 5.6 10.7 0.2 5.5 60.5 11.5 1.5 .. 150.5 119.1 38.3 34.2 0.6 .. 4.6 10.6 17.2 103.9 3.4 .. 26.6

2.2 11.0 502.5 351.8 349.8 119.6 1.5 3.3 27.0 0.5 93.8 0.3 145.8 15.6 20.3 44.3 .. 130.2 1.2 .. 2.1 0.2 .. .. 87.1 4.2 1.6 .. .. 89.7 .. .. .. 220.0 0.1 7.7 0.8 11.9 22.4 0.3 8.8 63.0 15.2 1.7 .. 228.7 213.6 67.2 64.9 0.7 .. 7.4 15.1 23.5 67.5 4.9 .. 139.9

3.7

Energy use

Total million metric tons of oil equivalent 1990

2.4 28.7 316.7 101.3 67.9 18.1 10.0 11.5 146.6 2.8 439.3 3.3 72.7 10.9 33.2 93.1 .. 9.1 7.5 .. 7.9 2.0 .. .. 11.3 16.1 2.5 .. .. 22.0 .. .. 0.5 122.5 9.9 3.4 6.9 5.9 10.7 0.7 5.8 65.7 12.8 2.1 .. 70.6 21.0 4.2 42.8 1.5 .. 3.1 9.7 28.9 103.1 16.7 .. 6.2

2009

4.4 24.9 675.8 202.0 215.9 32.2 14.3 21.5 164.6 3.3 472.0 7.5 65.8 18.7 19.3 229.2 .. 30.2 3.0 .. 4.2 6.6 0.0 .. 20.4 8.4 2.8 .. .. 66.8 .. .. 1.2 174.6 2.4 3.2 15.1 9.8 15.1 1.7 10.0 78.2 17.4 3.1 .. 108.3 28.2 15.1 85.5 3.1 .. 4.8 15.8 38.8 94.0 24.1 .. 23.8

ENVIRONMENT

Energy production and use

Alternative and nuclear energy

% of total average annual % growth 1990–2009

3.6 0.0 3.8 3.5 6.4 3.6 2.6 3.4 1.2 2.1 0.6 4.3 –0.7 2.8 –2.0 4.6 .. 8.1 –3.4 .. –2.1 4.2 .. .. 2.3 –1.9 0.8 .. .. 5.9 .. .. .. 2.0 –5.2 –0.5 4.0 2.9 2.3 5.4 3.0 0.9 1.5 2.6 .. 2.4 1.6 6.4 3.7 3.3 .. 1.7 2.5 1.7 –0.5 2.4 .. 7.5

Per capita kilograms of oil equivalent

Fossil fuel

Combustible renewables and waste

% of total energy use

1990

2009

1990

2009

1990

2009

1990

2009

487 2,762 362 550 1,237 994 2,842 2,463 2,584 1,167 3,556 1,028 4,450 467 1,649 2,171 .. 4,364 1,693 .. 2,949 663 .. .. 2,614 4,357 1,298 .. .. 1,208 .. .. 453 1,453 2,669 1,558 280 437 271 445 303 4,393 3,720 508 .. 724 4,952 2,258 383 603 .. 724 449 469 2,705 1,677 .. 13,098

592 2,480 560 851 2,951 1,035 3,216 2,878 2,735 1,208 3,700 1,260 4,091 474 795 4,701 .. 11,402 566 .. 1,871 1,580 9 .. 3,258 2,512 1,352 .. .. 2,391 .. .. 947 1,559 687 1,194 477 427 316 764 338 4,729 4,032 540 .. 701 5,849 5,554 502 896 .. 749 550 424 2,464 2,266 .. 14,911

30.0 81.5 55.4 54.7 98.9 98.6 84.6 97.2 93.4 82.6 84.5 98.1 96.9 17.5 93.1 83.8 .. 99.9 93.5 .. 81.8 92.5 .. .. 98.9 75.8 98.0 .. .. 88.8 .. .. 0.0 87.2 100.0 96.9 93.8 5.5 14.4 62.0 5.1 95.9 67.0 28.3 .. 19.3 51.9 100.0 52.6 57.5 .. 21.3 63.3 43.4 97.8 80.4 .. 100.0

50.3 74.2 73.0 65.6 99.7 97.6 88.7 96.5 87.5 83.7 81.0 98.0 99.0 16.8 89.0 81.7 .. 100.0 72.5 .. 59.4 95.9 0.0 .. 99.2 55.7 84.2 .. .. 94.7 .. .. 0.0 88.9 91.3 96.4 92.5 7.7 27.7 70.5 11.1 93.1 63.7 44.7 .. 14.7 58.8 100.0 61.8 78.6 .. 28.5 73.5 57.0 92.8 78.0 .. 100.0

63.0 2.3 42.1 42.9 0.3 0.1 1.1 0.0 0.6 17.1 1.1 0.1 0.2 77.9 2.9 0.8 .. 0.1 0.1 .. 8.4 5.3 .. .. 1.1 1.8 0.0 .. .. 9.7 .. .. .. 7.0 0.4 2.5 4.6 93.9 84.7 15.9 93.7 1.5 5.7 53.9 .. 80.2 4.9 0.0 43.8 28.9 .. 72.5 27.5 38.5 2.2 14.8 .. 0.0

44.3 7.1 24.5 26.0 0.2 0.1 2.2 0.1 4.3 15.9 1.4 0.1 0.2 76.0 5.4 1.3 .. 0.0 0.1 .. 30.0 1.8 .. .. 0.8 9.8 7.0 .. .. 4.5 .. .. .. 4.8 3.3 3.2 3.2 81.8 69.9 12.0 85.8 4.4 6.3 45.9 .. 84.9 5.1 0.0 34.5 10.7 .. 53.7 15.4 17.9 7.1 13.6 .. 0.0

8.2 12.8 2.5 2.4 0.8 1.2 0.6 3.1 3.9 0.3 14.4 1.8 0.9 4.5 4.0 15.4 .. 0.0 11.5 .. 4.9 2.2 .. .. 0.0 28.2 1.7 .. .. 1.6 .. .. .. 5.9 0.2 0.0 1.5 0.4 1.0 17.4 1.3 1.4 27.1 17.5 .. 0.5 49.6 0.0 3.6 13.1 .. 76.0 9.2 18.1 0.1 4.8 .. 0.0

5.5 16.8 2.3 8.4 0.3 0.9 2.3 4.8 5.9 0.4 17.6 1.7 0.9 7.2 5.6 17.0 .. 0.0 28.3 .. 7.1 0.8 .. .. 0.0 34.9 4.3 .. .. 0.9 .. .. .. 6.3 0.2 0.0 1.7 14.9 2.4 7.2 2.7 2.0 29.8 9.4 .. 0.4 38.7 0.0 3.7 10.8 .. 99.5 11.1 25.0 0.3 6.6 .. 0.0

2012 World Development Indicators

163


3.7

Energy production and use Energy production

Total million metric tons of oil equivalent 1990

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

2009

Total million metric tons of oil equivalent 1990

40.8 28.3 62.3 1,293.1 1,181.6 879.2 .. .. .. 370.2 528.4 59.8 1.0 1.3 1.7 9.4 19.3a 13.4 a .. .. .. 0.0 0.0 11.5 5.3 5.9 21.3 3.1 3.5 5.7 .. .. .. 114.5 160.6 93.9 .. .. .. 34.6 29.7 90.1 4.2 5.1 5.5 8.8 35.2 10.6 .. .. 0.3 29.7 30.3 47.2 10.3 12.8 24.3 22.3 23.6 10.5 2.0 1.5 5.3 9.1 18.0 9.7 26.6 61.7 41.9 .. .. .. 1.1 2.2 1.3 12.6 44.0 6.0 5.7 7.8 4.9 25.8 30.3 52.8 74.9 40.9 19.6 .. .. .. 135.8 76.9 251.8 110.2 168.8 20.4 208.0 158.9 205.9 1,652.5 1,686.4 1,915.0 1.1 1.5 2.3 38.6 60.7 46.4 148.8 203.5 43.5 24.7 76.6 24.3 .. .. .. 9.4 15.2 2.5 4.9 7.2 5.4 8.5 8.5 9.3 8,815.9 t 12,241.3 t 8,574.0 t 160.7 250.6 186.6 4,810.5 7,351.0 3,886.6 1,247.9 1,944.1 1,085.5 3,564.8 5,410.7 2,803.9 4,969.1 7,597.3 4,059.7 1,224.5 2,758.4 1,138.1 1,770.3 1,673.3 1,585.6 613.1 937.5 457.6 558.2 856.0 183.8 349.4 611.2 386.8 475.6 810.5 313.9 3,878.6 4,694.3 4,538.5 481.9 450.2 1,069.7

a. Includes Kosovo and Montenegro.

164

Energy use

2012 World Development Indicators

2009

34.4 646.9 .. 157.9 2.9 14.4 .. 18.5 16.7 7.0 .. 144.0 .. 126.5 9.3 15.8 0.4 45.4 27.0 22.5 2.3 19.6 103.3 0.1 2.6 20.3 9.2 97.7 19.6 .. 115.5 59.6 196.8 2,162.9 4.1 48.8 66.9 64.0 .. 7.6 7.9 9.5 11,787.1 t 263.3 6,158.2 1,656.0 4,505.9 6,407.1 2,790.9 1,137.6 716.5 454.5 817.1 511.2 5,420.3 1,169.7

Alternative and nuclear energy

% of total average annual % growth 1990–2009

Per capita kilograms of oil equivalent 1990

2009

–1.9 2,683 1,602 –1.0 5,929 4,561 .. .. .. 4.9 3,703 5,888 3.4 233 243 0.4 2,550a 1,974 .. .. .. 0.9 3,760 3,704 –0.2 4,025 3,086 1.8 2,858 3,417 .. .. .. 2.3 2,667 2,921 .. .. .. 2.7 2,319 2,756 3.2 318 449 2.4 401 372 .. 358 373 0.2 5,515 4,883 0.6 3,621 3,480 4.8 849 1,123 –2.9 1,001 342 4.1 382 451 4.9 735 1,504 .. .. 58 4.2 345 445 8.0 4,912 15,158 3.6 607 881 3.6 975 1,359 2.4 5,352 3,933 .. .. .. –3.0 4,852 2,507 5.4 11,258 8,588 0.0 3,597 3,183 1.0 7,672 7,051 2.1 724 1,224 0.5 2,261 1,758 2.0 2,205 2,357 5.3 368 745 .. .. .. 6.0 210 324 2.0 687 617 –0.2 888 763 1.9 w 1,661 w 1,788 w 2.2 386 365 2.6 1,019 1,254 2.4 607 665 2.7 1,375 1,848 2.6 963 1,162 4.8 712 1,436 –1.0 4,067 2,831 2.5 1,048 1,245 5.0 812 1,399 3.8 340 514 2.6 681 689 1.2 4,649 4,856 0.9 3,509 3,536

Fossil fuel 1990

2009

96.1 76.3 93.4 90.2 .. .. 100.0 100.0 43.3 57.8 90.6a 92.4 .. .. 100.0 99.8 81.6 69.5 71.3 69.3 .. .. 86.6 87.8 .. .. 77.4 79.9 24.1 45.3 17.4 30.2 0.0 0.0 37.3 32.0 58.6 53.3 97.7 99.3 71.3 41.2 6.9 11.1 63.8 79.4 .. 0.0 15.1 14.3 99.2 99.9 87.0 85.7 81.8 89.9 100.0 100.0 .. .. 91.8 80.0 100.0 100.0 90.7 87.3 86.4 84.1 58.7 60.3 99.2 98.4 91.5 87.7 20.3 56.2 .. .. 96.9 98.7 15.6 7.6 44.8 25.7 80.9 w 80.7 w 41.5 29.9 78.6 81.5 65.1 68.0 83.7 86.3 77.3 79.8 71.4 84.1 93.0 88.9 70.3 71.8 97.4 98.3 53.5 70.7 41.7 40.1 84.2 81.9 80.0 74.2

Combustible renewables and waste

% of total energy use

1990

2009

1990

2009

1.0 1.4 .. 0.0 56.7 6.0a .. 0.0 0.8 4.7 .. 11.1 .. 4.5 71.0 81.8 .. 11.7 5.9 0.0 0.0 91.7 35.0 .. 82.8 0.8 12.9 13.7 0.0 .. 0.1 0.0 0.3 3.3 24.2 0.0 1.2 77.8 .. 3.1 74.3 50.8 10.2 w 54.7 17.2 30.7 12.0 18.5 26.6 1.5 20.4 1.4 43.9 55.9 2.8 3.1

11.4 1.0 .. 0.0 41.1 2.0 .. 0.2 5.2 7.2 .. 9.8 .. 4.9 51.1 68.0 .. 22.9 8.2 0.0 0.0 87.7 19.9 .. 83.1 0.1 14.1 4.8 0.0 .. 0.8 0.0 2.7 3.9 26.0 0.0 0.8 39.3 .. 1.3 80.9 65.6 10.0 w 65.9 13.2 26.9 8.3 15.0 11.8 1.9 17.0 0.9 26.8 57.3 4.2 6.7

1.6 5.2 .. 0.0 0.0 4.2a .. 0.0 15.5 25.5 .. 2.4 .. 18.1 4.9 0.8 .. 50.9 36.3 2.2 26.7 1.4 1.0 .. 0.6 0.0 0.1 4.6 0.3 .. 8.2 0.0 8.5 10.3 26.8 1.2 7.3 1.9 .. 0.0 12.7 4.0 8.7 w 3.9 4.1 4.4 4.0 4.1 1.9 5.3 9.1 1.1 2.5 2.2 12.8 16.5

12.9 9.0 .. 0.0 0.7 6.4 .. 0.0 24.6 27.3 .. 2.4 .. 15.8 3.6 1.8 .. 42.9 39.2 0.7 58.6 1.2 0.6 .. 0.3 0.0 0.2 5.4 0.0 .. 19.7 0.0 9.8 11.9 11.1 1.6 11.6 4.0 .. 0.0 11.2 3.8 9.2 w 4.1 5.3 5.3 5.3 5.3 4.1 9.3 11.1 0.6 2.4 2.5 13.8 18.6


About the data

3.7

ENVIRONMENT

Energy production and use Definitions

In developing economies growth in energy use is

• Energy production refers to forms of primary

closely related to growth in the modern sectors—

energy—petroleum (crude oil, natural gas liquids,

industry, motorized transport, and urban areas—

and oil from nonconventional sources), natural gas,

but energy use also reflects climatic, geographic,

solid fuels (coal, lignite, and other derived fuels),

and economic factors (such as the relative price

and combustible renewables and waste—and pri-

of energy). Energy use has been growing rapidly in

mary electricity, all converted into oil equivalents

low- and middle-income economies, but high-income

(see About the data). • Energy use refers to the use

economies still use almost five times as much energy

of primary energy before transformation to other

on a per capita basis.

end-use fuels, which is equal to indigenous produc-

Energy data are compiled by the International

tion plus imports and stock changes, minus exports

Energy Agency (IEA). IEA data for economies that

and fuels supplied to ships and aircraft engaged in

are not members of the Organisation for Economic

international transport (see About the data). • Fos-

Co-operation and Development (OECD) are based

sil fuel comprises coal, oil, petroleum, and natural

on national energy data adjusted to conform to

gas products. • Combustible renewables and waste

annual questionnaires completed by OECD member

comprise solid biomass, liquid biomass, biogas,

governments.

industrial waste, and municipal waste. • Alternative

Total energy use refers to the use of primary energy

and nuclear energy production is noncarbohydrate

before transformation to other end-use fuels (such

energy that does not produce carbon dioxide when

as electricity and refined petroleum products). It

generated. It includes hydropower and nuclear, geo-

includes energy from combustible renewables and

thermal, and solar power, among others.

waste—solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see Data sources). All forms of energy—primary energy and primary electricity—are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable. Data sources Data on energy production and use are from IEA electronic files and are published in IEA’s annual publications Energy Statistics of Non-OECD Countries, Energy Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries.

2012 World Development Indicators

165


3.8

Electricity production, sources, and access Electricity production

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

166

Sources of electricitya

Access to electricity

% of total

billion kilowatt hours

Coal

Natural gas

Oil

Hydropower

Renewable sourcesb

Nuclear power

2009

2009

2009

2009

2009

2009

2009

2009

.. 0.0 0.0 0.0 2.3 0.0 77.9 7.7 0.0 0.0 1.7 0.0 6.8 0.0 0.0 59.9 100.0 2.1 49.8 .. .. 0.0 0.0 15.2 .. .. 24.5 78.8 70.8 7.3 0.0 0.0 0.0 0.0 13.1 0.0 0.0 59.6 48.6 12.9 0.0 0.0 0.0 0.0 91.4 0.0 16.0 5.3 0.0 .. 0.0 43.8 0.0 56.0 8.2 .. .. 0.0

.. 0.0 97.2 0.0 51.3 20.3 13.7 18.8 85.1 100.0 89.4 81.7 32.6 0.0 59.7 0.2 0.0 2.9 4.6 .. .. 0.0 7.2 6.2 .. .. 6.5 1.4 28.9 19.3 0.4 36.0 0.0 61.9 17.4 13.4 0.0 1.2 18.5 13.5 6.9 68.7 0.0 0.0 1.2 0.0 13.6 3.9 26.4 .. 12.9 13.5 0.0 18.0 0.0 .. .. 0.0

.. 0.6 2.0 23.9 10.5 0.0 1.0 1.7 2.6 0.0 4.8 17.6 0.3 100.0 1.7 0.1 0.0 3.1 0.8 .. .. 95.6 22.7 1.4 .. .. 20.0 0.4 0.4 0.6 0.1 0.0 4.9 0.1 15.9 82.8 99.2 0.2 3.2 63.6 37.5 21.3 43.7 99.3 0.5 12.4 0.7 1.1 20.0 .. 0.5 1.6 23.2 12.6 34.5 .. .. 71.3

.. 99.4 0.8 76.1 27.8 35.6 4.7 61.4 12.2 0.0 4.1 0.1 0.4 0.0 37.5 39.8 0.0 83.8 8.2 .. .. 3.9 70.0 60.3 .. .. 41.7 16.7 0.0 71.7 99.6 64.0 77.8 35.9 53.0 0.9 0.0 3.0 0.1 9.8 53.5 9.3 26.0 0.0 0.4 87.3 17.6 10.6 53.2 .. 86.6 3.2 76.8 8.8 23.3 .. .. 28.7

.. 0.0 0.0 0.0 1.4 0.1 2.6 9.6 0.0 0.0 0.0 0.2 5.7 0.0 1.0 0.0 0.0 5.2 0.6 .. .. 0.5 0.2 1.9 .. .. 7.2 0.8 0.0 1.2 0.0 0.0 17.4 2.1 0.6 2.9 0.1 2.7 27.6 0.2 2.1 0.8 30.3 0.7 5.8 0.4 12.5 2.4 0.4 .. 0.0 12.8 0.0 4.6 34.0 .. .. 0.0

.. 0.0 0.0 0.0 6.7 44.0 0.0 0.0 0.0 0.0 0.0 0.0 52.6 0.0 0.0 0.0 0.0 2.8 36.0 .. .. 0.0 0.0 15.0 .. .. 0.0 1.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 32.6 76.2 0.0 .. 0.0 23.0 0.0 0.0 0.0 .. .. 0.0

15.6 .. 99.3 26.2 97.2 .. .. .. .. 99.4 41.0 .. .. 24.8 77.5 .. 45.4 98.3 .. 14.6 .. 24.0 48.7 .. .. .. 98.5 99.4 .. 93.6 11.1 37.1 99.3 47.3 .. 97.0 .. .. .. 95.9 92.2 99.6 86.4 32.0 .. 17.0 .. .. 36.7 .. .. .. 60.5 .. 80.5 .. .. 38.5

.. 5.3 42.8 4.2 121.9 5.7 260.9 65.6 18.9 12.1 37.9 30.4 89.8 0.1 6.1 15.7 0.4 466.5 42.4 .. .. 1.2 5.7 603.1 .. .. 60.7 3,695.9 38.7 57.3 7.8 0.5 9.3 5.9 12.7 17.7 5.2 81.7 36.4 15.0 17.2 139.0 5.8 0.3 8.8 4.1 72.1 537.4 1.7 .. 8.6 586.4 9.0 61.1 9.0 .. .. 0.7

2012 World Development Indicators

% of population


Electricity production

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

Sources of electricitya

ENVIRONMENT

3.8

Electricity production, sources, and access

Access to electricity

% of total

billion kilowatt hours

Coal

Natural gas

Oil

Hydropower

Renewable sourcesb

Nuclear power

2009

2009

2009

2009

2009

2009

2009

2009

6.6 35.9 899.4 155.5 203.2 46.1 27.9 55.0 288.3 5.5 1,041.0 14.3 78.7 6.9 21.1 451.7 .. 53.2 11.1 .. 5.6 13.8 .. .. 30.4 14.6 6.8 .. .. 105.1 .. .. .. 261.0 3.6 4.2 21.4 17.0 5.9 1.7 3.1 113.5 43.5 3.5 .. 19.8 132.0 17.8 95.4 6.9 .. 55.0 35.4 61.9 151.1 49.5 .. 24.8

0.0 17.9 68.6 41.8 0.2 0.0 14.4 62.5 15.1 0.0 26.8 0.0 74.9 0.0 38.1 46.2 .. 0.0 2.8 .. 0.0 0.0 .. .. 0.0 0.0 77.7 .. .. 30.9 .. .. .. 11.3 0.0 96.4 52.4 0.0 0.0 17.5 0.0 23.4 7.6 0.0 .. 0.0 0.1 0.0 0.1 0.0 .. 0.0 2.5 26.6 89.1 26.1 .. 0.0

0.0 29.0 12.4 22.1 70.4 0.0 58.4 32.8 51.1 0.0 27.4 88.1 13.1 0.0 0.0 15.6 .. 28.8 8.0 .. 36.0 1.4 .. .. 41.0 14.3 0.0 .. .. 60.7 .. .. .. 53.1 95.0 0.0 13.3 0.1 19.6 0.0 0.0 60.5 20.6 0.0 .. 64.3 3.2 82.0 29.4 0.0 .. 0.0 34.3 32.1 3.2 29.7 .. 100.0

54.9 1.8 2.9 22.8 25.8 93.0 3.3 4.0 9.0 96.4 7.2 11.4 3.2 44.1 2.8 4.4 .. 71.2 0.0 .. 0.1 94.1 .. .. 59.0 5.0 3.7 .. .. 2.0 .. .. .. 17.5 1.3 3.6 20.3 0.0 8.9 0.5 0.4 1.3 0.0 69.1 .. 12.8 0.0 18.0 38.0 43.6 .. 0.0 4.1 8.7 1.8 6.6 .. 0.0

42.5 0.6 11.9 7.3 3.6 7.0 3.2 0.0 17.0 2.0 7.2 0.4 8.7 31.6 59.1 0.6 .. 0.0 89.3 .. 62.1 4.5 .. .. 0.0 2.9 18.6 .. .. 6.3 .. .. .. 10.2 1.5 0.0 12.1 99.9 71.5 82.0 99.6 0.1 55.7 8.6 .. 22.9 95.7 0.0 29.4 56.1 .. 100.0 57.6 15.8 1.6 16.7 .. 0.0

2.6 7.4 2.2 6.0 0.1 0.0 11.2 0.1 7.0 1.6 2.5 0.1 0.0 24.4 0.0 0.4 .. 0.0 0.0 .. 1.8 0.0 .. .. 0.0 1.8 0.0 .. .. 0.0 .. .. .. 3.9 0.0 0.0 1.8 0.0 0.0 0.0 0.0 9.5 15.9 22.3 .. 0.0 0.9 0.0 0.0 0.2 .. 0.0 1.4 16.8 4.2 20.2 .. 0.0

0.0 43.0 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 26.9 0.0 0.0 0.0 0.0 32.7 .. 0.0 0.0 .. 0.0 0.0 .. .. 0.0 74.1 0.0 .. .. 0.0 .. .. .. 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.7 0.0 0.0 .. 0.0 0.0 0.0 3.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0

70.3 .. 66.3 64.5 98.4 86.0 .. 99.7 .. 92.0 .. 99.9 .. 16.1 26.0 .. .. 100.0 .. 55.0 .. 99.9 16.0 .. 99.8 .. .. 19.0 9.0 99.4 .. .. 99.4 .. .. 67.0 97.0 11.7 13.0 34.0 43.6 .. .. 72.1 .. 50.6 .. 98.0 62.4 88.1 .. 96.7 85.7 89.7 .. .. .. 98.7

% of population

2012 World Development Indicators

167


3.8

Electricity production, sources, and access Electricity production

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

Sources of electricitya

Access to electricity

% of total

billion kilowatt hours

Coal

Natural gas

Oil

Hydropower

Renewable sourcesb

Nuclear power

% of population

2009

2009

2009

2009

2009

2009

2009

2009

13.2 47.4 .. 44.8 1.7 0.5 .. 81.0 7.6 3.6 .. 0.0 .. 36.9 0.0 0.0 .. 1.1 1.0 45.7 2.0 36.2 70.7 .. 0.0 99.4 89.7 49.3 100.0 .. 8.1 98.2 44.5 22.8 0.2 75.1 14.7 43.4 .. 0.0 0.0 0.0 21.4 w 20.4 19.2 22.0 18.5 19.2 7.2 39.5 20.7 58.5 16.4 4.6 23.2 23.3

1.8 1.6 .. 55.2 86.4 0.3 .. 18.8 2.4 0.2 .. 0.0 .. 6.5 60.3 52.2 .. 0.5 0.2 50.0 0.0 0.9 0.5 .. 24.6 0.3 9.2 2.5 0.0 .. 0.5 1.8 1.2 1.2 31.1 2.1 12.5 2.5 .. 100.0 0.3 0.3 4.8 w 4.7 5.7 12.1 3.9 5.7 1.5 1.9 12.1 32.9 6.6 4.1 4.1 3.7

26.9 17.6 .. 0.0 8.4 27.4 .. 0.0 16.9 28.7 .. 0.6 .. 9.0 39.5 47.8 .. 48.2 53.6 4.3 98.0 60.2 4.8 .. 73.8 0.0 0.5 18.5 0.0 .. 6.8 0.0 1.4 6.6 59.4 18.7 72.8 36.0 .. 0.0 99.7 53.3 16.1 w 45.7 20.6 16.9 21.6 21.0 16.2 18.0 55.5 4.9 13.6 18.2 11.6 10.2

0.0 0.1 .. 0.0 1.9 0.0 .. 0.1 2.1 1.2 .. 0.1 .. 16.2 0.2 0.0 .. 10.2 2.0 0.0 0.0 0.0 4.0 .. 1.6 0.2 0.6 1.1 0.0 .. 0.0 0.0 5.4 3.7 9.3 0.0 0.0 0.0 .. 0.0 0.0 0.0 3.0 0.9 1.4 2.4 1.2 1.4 1.3 0.2 3.9 0.3 1.9 0.5 4.5 9.1

20.4 16.5 .. 0.0 0.0 0.0 .. 0.0 54.3 35.0 .. 5.2 .. 18.1 0.0 0.0 .. 38.2 41.5 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 48.0 0.0 18.6 19.9 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 13.4 w 0.0 4.6 5.3 4.4 4.5 1.6 16.1 2.4 0.0 2.0 3.1 21.3 30.8

57.7 990.0 .. 217.1 2.9 37.4 .. 41.8 25.9 16.4 .. 246.8 .. 291.0 9.9 6.8 .. 136.6 66.7 43.3 16.1 4.6 148.4 .. 0.1 7.7 15.7 194.8 16.0 .. 173.5 90.6 372.0 4,165.4 8.9 49.9 123.4 83.2 .. 6.7 10.3 7.9 20,079.3 w 185.6 9,255.1 2,007.9 7,245.8 9,461.8 4,307.5 1,785.3 1,296.7 584.2 1,054.5 419.3 10,673.5 2,244.3

37.7 16.5 .. 0.0 0.0 71.9 .. 0.0 16.5 31.3 .. 94.1 .. 12.8 0.0 0.0 .. 0.7 0.0 0.0 0.0 2.7 19.9 .. 0.0 0.0 0.0 28.6 0.0 .. 36.6 0.0 28.5 45.4 0.0 4.1 0.0 18.0 .. 0.0 0.0 46.4 40.4 w 6.9 47.9 39.6 50.2 47.0 71.6 24.0 5.0 2.0 58.5 56.5 34.3 21.6

a. Shares may not sum to 100 percent because some sources of generated electricity are not shown. b. Excludes hydropower.

168

2012 World Development Indicators

.. .. .. 99.0 42.0 .. .. 100.0 .. .. .. 75.0 .. .. 76.6 35.9 .. .. .. 92.7 .. 13.9 99.3 22.0 20.0 99.0 99.5 .. .. 9.0 .. 100.0 .. .. 98.3 .. 99.0 97.6 .. 39.6 18.8 41.5 74.1 w 23.0 81.6 67.3 98.0 73.7 90.8 .. 93.4 92.9 62.1 32.5 .. ..


About the data

Use of energy is important in improving people’s

3.8

ENVIRONMENT

Electricity production, sources, and access Definitions

• Electricity production is measured at the termi-

standard of living. But electricity generation also

nals of all alternator sets in a station. In addition to

can damage the environment. Whether such damage

hydropower, coal, oil, gas, and nuclear power gen-

occurs depends largely on how electricity is gener-

eration, it covers generation by geothermal, solar,

ated. For example, burning coal releases twice as

wind, and tide and wave energy as well as that from

much carbon dioxide—a major contributor to global

combustible renewables and waste. Production

warming—as does burning an equivalent amount of

includes the output of electric plants designed to

natural gas (see About the data for table 3.9). Nuclear

produce electricity only, as well as that of combined

energy does not generate carbon dioxide emissions,

heat and power plants. • Sources of electricity are

but it produces other dangerous waste products. The

the inputs used to generate electricity: coal, gas,

table provides information on electricity production

oil, hydropower, and nuclear power. • Coal is all coal

by source.

and brown coal, both primary (including hard coal

The International Energy Agency (IEA) compiles

and lignite-brown coal) and derived fuels (including

data on energy inputs used to generate electricity.

patent fuel, coke oven coke, gas coke, coke oven

IEA data for countries that are not members of the

gas, and blast furnace gas). Peat is also included in

Organisation for Economic Co-operation and Devel-

this category. • Natural gas is natural gas but not

opment (OECD) are based on national energy data

natural gas liquids. • Oil is crude oil and petroleum

adjusted to conform to annual questionnaires com-

products. • Hydropower is electricity produced by

pleted by OECD member governments. In addition,

hydroelectric power plants. • Renewable sources

estimates are sometimes made to complete major

are geothermal, solar photovoltaic, solar thermal,

aggregates from which key data are missing, and

tide, wind, industrial waste, municipal waste, primary

adjustments are made to compensate for differ-

solid biofuels, biogases, biogasoline, biodiesels,

ences in definitions. The IEA makes these estimates

other liquid biofuels, nonspecified primary biofuels

in consultation with national statistical offices, oil

and waste, and charcoal. • Nuclear power is electric-

companies, electric utilities, and national energy

ity produced by nuclear power plants. • Access to

experts. It occasionally revises its time series to

electricity is the percentage of the population with

reflect political changes. For example, the IEA has

access to electricity.

constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable. Data on access to electricity are collected by the IEA from industry, national surveys, and international sources.

Data sources Data on electricity production and sources are from the IEA’s electronic files and its annual publications Energy Statistics of Non-OECD Countries, Energy Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. Data on access to electricity are from the IEA’s World Energy Outlook (2011).

2012 World Development Indicators

169


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

170

Energy dependency and efficiency and carbon dioxide emissions Net energy importsa

GDP per unit of energy use

% of energy use 1990 2009

2005 PPP $ per kilogram of oil equivalent 1990 2009

.. 8 –351 –387 –5 98 –83 67 21 –209 16 93 73 –7 –89 34 28 26 66 .. .. .. –120 –31 .. .. 45 –3 99 –99 –2 –997 49 22 43 51 100 17 42 75 –175 –72 31 19 45 5 57 50 –1,138 .. 83 47 17 57 24 .. .. 20

.. 27 –283 –749 –9 68 –137 64 –439 –85 16 85 73 43 –128 25 54 4 44 .. .. 29 –28 –53 .. .. 68 8 100 –211 –2 –989 45 –15 53 52 97 26 –29 77 –141 –22 38 23 12 7 50 49 –657 .. 61 60 24 66 39 .. .. 28

2012 World Development Indicators

.. 4.8 7.1 5.8 5.3 1.4 4.7 7.9 1.3 2.0 6.2 1.4 5.2 3.2 7.8 .. 7.6 7.7 2.3 .. .. .. 5.1 3.6 .. .. 6.4 1.4 15.6 8.4 1.9 10.7 9.5 5.5 7.1 .. 7.7 3.4 7.5 6.7 9.3 5.8 7.9 1.8 1.6 1.8 4.1 6.3 11.8 17.6 2.4 5.9 2.5 8.2 6.7 .. 16.5 6.4

.. 13.8 6.5 8.4 7.2 5.7 5.7 9.2 6.4 2.7 7.0 4.1 6.1 3.5 6.7 4.6 11.4 7.6 4.9 .. .. 5.1 5.6 4.6 .. .. 7.7 3.7 18.4 11.8 0.8 10.1 9.5 3.2 8.3 .. 8.2 5.5 9.5 9.5 8.9 5.9 7.2 3.5 4.5 2.2 4.9 7.4 10.7 14.0 6.0 8.3 3.6 9.6 6.1 .. 15.4 4.0

Carbon dioxide emissions

Total million metric tons 1990 2008

2.7 7.5 78.9 4.4 112.6 3.7 287.3 61.0 44.2 11.9 15.5 87.5 108.5 0.7 5.5 3.9 2.2 208.9 77.7 0.6 0.3 0.5 1.7 450.1 0.2 0.1 34.9 2,460.7 27.7 57.3 4.1 1.2 3.0 5.8 16.4 33.3 4.7 139.5 50.4 9.6 16.8 75.9 2.6 .. 23.0 3.0 50.9 399.0 4.8 0.2 15.3 961.0 3.9 72.7 5.1 1.1 0.3 1.0

0.8 4.2 111.3 24.4 192.4 5.5 399.2 67.7 47.1 22.5 46.5 62.8 104.9 4.1 12.8 31.3 4.8 393.2 50.5 1.9 0.2 4.6 5.3 544.1 0.3 0.5 73.1 7,031.9 38.6 67.7 2.8 1.9 8.0 7.0 23.3 31.4 8.6 117.0 46.0 21.6 26.8 210.3 6.1 0.4 18.3 7.1 56.5 377.0 2.5 0.4 5.2 786.7 8.6 97.8 11.9 1.4 0.3 2.4

Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2008

.. 2.8 3.6 0.8 2.4 0.9 3.3 2.5 2.1 2.7 1.2 2.4 2.2 0.4 2.1 0.9 1.7 1.5 2.7 .. .. .. 0.3 2.2 .. .. 2.6 2.9 3.2 2.4 0.3 1.5 1.5 1.3 2.4 1.7 3.4 3.2 2.9 2.3 2.8 2.4 1.1 .. 3.5 0.2 1.8 1.8 4.1 3.1 1.8 2.8 0.7 3.4 1.1 .. 3.4 0.6

.. 2.0 3.0 2.1 2.5 1.9 3.1 2.0 3.5 2.4 1.7 2.2 1.8 1.2 2.1 5.2 2.2 1.6 2.6 .. .. 0.9 0.8 2.0 .. .. 2.5 3.3 2.7 2.2 0.1 1.5 1.6 0.7 2.6 3.0 3.3 2.6 2.4 2.7 2.4 3.0 1.2 0.6 3.4 0.2 1.6 1.4 1.2 3.0 1.7 2.4 0.9 3.2 1.5 .. 3.0 0.9

Per capita metric tons 1990 2008

0.1 2.3 3.1 0.4 3.4 1.1 16.8 7.9 6.0 24.1 0.1 8.6 10.9 0.1 0.8 1.0 1.6 1.4 8.9 0.1 0.1 0.0 0.1 16.2 0.1 0.0 2.6 2.2 4.8 1.7 0.1 0.5 1.0 0.5 3.7 3.2 6.1 13.5 9.8 1.3 1.6 1.3 0.5 .. 15.0 0.1 10.2 6.9 5.2 0.2 3.1 12.0 0.3 7.2 0.6 0.2 0.2 0.1

0.0 1.3 3.2 1.4 4.8 1.8 18.6 8.1 5.4 21.4 0.3 6.5 9.8 0.5 1.3 8.3 2.5 2.1 6.6 0.1 0.0 0.3 0.3 16.3 0.1 0.0 4.4 5.3 5.5 1.5 0.0 0.5 1.8 0.4 5.3 2.8 7.9 11.2 8.4 2.2 1.9 2.7 1.0 0.1 13.6 0.1 10.6 5.9 1.7 0.3 1.2 9.6 0.4 8.7 0.9 0.1 0.2 0.3

kilograms per 2005 PPP $ of GDP 1990 2008

.. 0.6 0.5 0.1 0.5 0.7 0.7 0.3 1.7 1.3 0.2 1.5 0.4 0.1 0.3 .. 0.2 0.2 1.2 0.1 0.1 .. 0.1 0.6 0.1 0.0 0.4 2.0 0.2 0.3 0.2 0.1 0.2 0.2 0.4 .. 0.4 0.9 0.4 0.3 0.3 0.4 0.1 .. 2.0 0.1 0.4 0.3 0.3 0.2 1.2 0.4 0.3 0.4 0.2 0.2 0.2 0.1

0.0 0.2 0.4 0.2 0.4 0.3 0.5 0.2 0.7 0.9 0.2 0.6 0.3 0.3 0.3 1.1 0.2 0.2 0.6 0.1 0.1 0.2 0.1 0.5 0.1 0.0 0.3 0.9 0.1 0.2 0.2 0.1 0.2 0.2 0.3 .. 0.4 0.5 0.2 0.3 0.3 0.5 0.2 0.2 0.7 0.1 0.3 0.2 0.1 0.2 0.3 0.3 0.3 0.3 0.2 0.1 0.2 0.2


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

Net energy importsa

GDP per unit of energy use

% of energy use 1990 2009

2005 PPP $ per kilogram of oil equivalent 1990 2009

29 49 8 –67 –164 –480 65 96 83 83 83 95 –24 18 13 76 .. –453 67 .. 86 93 .. .. –546 69 49 .. .. –122 .. .. .. –59 99 20 89 5 0 67 5 8 11 29 .. –113 –467 –808 20 58 .. –49 –9 40 –1 80 .. –328

50 56 26 –74 –62 –272 89 85 84 84 80 96 –121 17 –5 81 .. –332 61 .. 50 97 .. .. –327 50 42 .. .. –34 .. .. .. –26 96 –138 95 –22 –48 81 11 19 13 45 .. –111 –656 –346 24 79 .. –56 4 40 28 80 .. –487

5.5 4.7 3.3 3.7 5.0 .. 6.4 7.3 9.2 5.1 7.3 3.2 1.6 3.0 .. 5.2 .. 2.5 1.5 .. 3.4 8.7 .. .. .. 2.9 6.6 .. .. 5.5 .. .. 13.5 6.9 1.7 1.6 9.7 0.9 1.3 9.4 2.3 6.0 5.0 3.7 .. 2.0 6.5 6.5 4.2 10.1 .. 5.5 10.0 5.4 3.0 9.6 .. ..

5.9 6.7 5.1 4.3 3.5 3.2 11.3 8.8 9.8 5.7 7.9 4.1 2.5 3.0 .. 5.4 .. 4.6 3.7 .. 6.9 7.5 142.1 .. 4.7 6.0 6.7 .. .. 5.2 .. .. 11.6 7.7 3.8 2.9 8.8 1.9 5.0 7.4 3.1 7.7 6.1 4.6 .. 2.9 8.1 4.4 4.7 13.2 .. 5.5 14.4 7.9 6.8 9.4 .. 4.9

3.9

ENVIRONMENT

Energy dependency and efficiency and carbon dioxide emissions Carbon dioxide emissions

Total million metric tons 1990 2008

2.6 63.0 690.6 149.6 227.2 52.6 30.4 33.5 424.2 8.0 1,094.7 10.4 261.3 5.8 .. 243.8 .. 45.4 10.9 0.2 14.1 9.1 .. 0.5 40.3 22.2 10.8 1.0 0.6 56.6 0.4 2.7 1.5 325.6 21.0 10.0 23.5 1.0 4.3 0.0 0.6 164.1 24.0 2.6 0.8 45.4 31.3 10.4 68.6 3.1 2.1 2.3 21.2 44.5 366.8 43.7 .. 11.8

8.7 54.6 1,742.7 406.0 538.4 102.9 43.6 37.7 445.1 12.2 1,208.2 21.4 237.0 10.4 78.4 509.2 .. 76.7 6.2 1.5 7.6 17.1 .. 0.6 58.3 15.1 11.8 1.9 1.2 208.3 0.6 2.0 4.0 475.8 4.8 10.9 47.9 2.3 12.8 4.0 3.5 173.7 33.1 4.3 0.9 95.8 50.0 45.7 163.2 6.9 2.1 4.1 40.5 83.2 316.1 56.3 .. 68.5

Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2008

1.1 2.2 2.2 1.5 3.3 2.9 3.0 2.9 2.9 2.9 2.5 3.2 3.3 0.5 .. 2.6 .. 5.0 2.2 .. 2.3 4.7 .. .. 3.6 2.0 4.0 .. .. 2.6 .. .. 3.0 2.7 3.1 2.9 3.4 0.2 0.4 0.0 0.1 2.5 1.9 1.3 .. 0.6 1.5 2.5 1.6 2.2 .. 0.7 2.2 1.5 3.6 2.6 .. 1.9

1.9 2.1 2.8 2.1 2.6 3.0 2.9 1.7 2.5 2.9 2.4 3.0 3.4 0.6 3.9 2.2 .. 2.8 2.3 .. 1.7 3.2 .. .. 3.0 1.6 3.9 .. .. 2.9 .. .. 3.3 2.6 1.5 3.5 3.2 0.2 0.8 2.2 0.4 2.2 1.9 1.4 .. 0.9 1.7 2.8 2.0 2.5 .. 0.9 2.7 2.1 3.2 2.3 .. 3.0

Per capita metric tons 1990 2008

0.5 6.1 0.8 0.8 4.1 2.9 8.7 7.2 7.5 3.3 8.9 3.3 15.9 0.2 .. 5.7 .. 21.8 2.4 0.1 5.4 3.1 .. 0.2 9.3 6.0 5.6 0.1 0.1 3.1 0.0 1.3 1.4 3.9 5.7 4.6 1.0 0.1 0.1 0.0 0.0 11.0 7.0 0.6 0.1 0.5 7.4 5.5 0.6 1.3 0.5 0.5 1.0 0.7 9.6 4.4 .. 24.9

1.2 5.4 1.5 1.7 7.4 3.4 9.9 5.2 7.4 4.5 9.5 3.7 15.1 0.3 3.2 10.5 .. 30.1 1.2 0.3 3.3 4.1 .. 0.2 9.5 4.5 5.8 0.1 0.1 7.6 0.0 0.6 3.1 4.3 1.3 4.1 1.5 0.1 0.3 1.8 0.1 10.6 7.8 0.8 0.1 0.6 10.5 17.3 1.0 2.0 0.3 0.7 1.4 0.9 8.3 5.3 .. 49.1

kilograms per 2005 PPP $ of GDP 1990 2008

0.2 0.5 0.7 0.4 0.7 .. 0.5 0.4 0.3 0.6 0.3 1.0 2.7 0.2 .. 0.5 .. 0.6 1.2 0.1 0.9 0.5 .. 0.5 .. 0.6 0.8 0.1 0.1 0.5 0.1 0.8 0.2 0.4 2.1 1.9 0.4 0.2 0.3 0.0 0.0 0.4 0.4 0.3 0.2 0.3 0.2 0.4 0.4 0.2 0.3 0.1 0.2 0.3 1.2 0.3 .. ..

2012 World Development Indicators

0.3 0.3 0.5 0.5 0.7 1.1 0.3 0.2 0.3 0.6 0.3 0.7 1.4 0.2 .. 0.4 .. 0.6 0.6 0.1 0.2 0.4 .. 0.4 0.6 0.3 0.6 0.1 0.1 0.6 0.0 0.3 0.3 0.3 0.5 1.1 0.4 0.1 0.2 0.3 0.1 0.3 0.3 0.3 0.1 0.3 0.2 0.7 0.4 0.2 0.2 0.2 0.2 0.3 0.5 0.2 .. 0.6

171


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

Energy dependency and efficiency and carbon dioxide emissions Net energy importsa

GDP per unit of energy use

% of energy use 1990 2009

2005 PPP $ per kilogram of oil equivalent 1990 2009

34 –47 .. –519 43 31 .. 100 75 46 .. –22 .. 62 24 17 .. 37 58 –113 62 7 37 .. 17 –111 –16 51 –281 .. 46 –441 –1 14 49 17 –242 –2 .. –273 9 8 –3c w 14 –24 –15 –27 –22 –8 –12 –34 –204 10 –52 15 55

18 –83 .. –235 57 35 .. 100 65 49 .. –12 .. 77 45 –123 .. 33 53 –5 35 8 40 .. 17 –117 15 69 –109 .. 33 –183 19 22 63 –24 –204 –20 .. –101 8 10 –4c w 5 –19 –17 –20 –19 1 –47 –31 –88 25 –59 13 62

2.9 2.1 .. 5.2 6.3 4.6 .. 6.7 3.2 5.8 .. 3.0 .. 8.5 6.3 2.6 10.9 4.5 9.2 3.5 3.0 2.2 5.4 .. 2.7 2.2 7.4 8.3 0.7 .. 1.7 6.5 6.2 4.2 10.1 0.9 4.3 2.5 .. 8.6 1.8 .. 4.2 w 2.6 3.0 3.0 3.0 3.0 2.0 2.2 6.9 5.7 3.6 2.8 5.3 6.6

6.7 3.0 .. 3.4 7.1 4.8 .. 12.5 6.3 7.3 .. 3.2 .. 9.8 9.5 5.3 12.6 6.6 10.6 4.2 5.5 2.7 4.8 11.8 2.0 1.5 9.5 8.6 1.7 .. 2.3 5.3 10.1 5.9 9.6 1.5 4.8 3.7 .. 7.0 2.1 .. 5.5 w 3.4 4.5 4.6 4.4 4.4 3.9 3.6 7.7 4.7 5.2 3.2 6.7 8.3

Carbon dioxide emissions

Total million metric tons 1990 2008

158.9 94.7 2,220.7 1,708.7 0.7 0.7 215.1 433.6 3.2 5.0 49.9 45.3b 0.4 1.3 46.9 32.3 45.6 37.6 13.0 17.2 0.0 0.6 333.5 435.9 .. .. 227.6 329.3 3.8 11.8 5.6 14.1 0.4 1.1 51.7 49.0 43.0 40.4 37.5 71.6 7.2 3.1 2.4 6.5 95.8 285.7 .. 0.2 0.8 1.4 17.0 49.8 13.3 25.0 150.8 284.0 28.1 47.8 0.8 3.7 641.7 323.5 52.0 155.1 570.2 522.9 4,879.4 5,461.0 4.0 8.3 114.0 124.9 122.2 169.5 21.4 127.4 .. 2.1 9.6 23.4 2.4 1.9 15.5 9.1 22,310.9d w 32,082.6d w .. 219.1 9,256.8 16,638.0 2,001.1 3,743.9 7,255.3 12,894.1 9,398.8 16,856.9 2,895.4 8,259.3 3,893.0 3,130.1 986.2 1,584.1 579.0 1,230.3 782.0 1,970.2 464.1 684.6 11,572.3 13,284.7 2,718.9 2,633.3

Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2008

2.6 2.8 .. 3.6 1.9 3.0 b .. 4.1 2.5 2.5 .. 3.6 .. 2.5 0.7 0.5 1.4 1.1 1.8 3.6 1.7 0.2 2.3 .. 0.6 2.8 2.7 2.9 2.5 .. 2.9 2.6 2.8 2.5 1.8 2.5 2.8 0.9 .. 3.8 0.5 1.7 2.5d w 0.6 2.5 1.8 2.8 2.4 2.6 3.0 2.2 3.2 2.0 1.7 2.5 2.5

2.4 2.5 .. 2.8 1.7 3.0 .. 1.9 2.1 2.2 .. 2.9 .. 2.4 1.3 1.0 2.5 1.0 1.5 2.8 1.3 0.3 2.7 3.0 0.6 2.6 2.7 2.9 2.1 .. 2.4 2.7 2.5 2.4 2.0 2.5 2.6 2.2 .. 3.2 0.2 1.0 2.5d w 1.0 2.8 2.4 2.9 2.7 3.1 2.6 2.2 2.8 2.6 1.5 2.4 2.1

Per capita metric tons 1990 2008

6.8 14.9 0.1 13.3 0.4 5.9 b 0.1 15.4 8.6 6.5 0.0 9.5 .. 5.9 0.2 0.2 0.5 6.0 6.4 3.0 1.3 0.1 1.7 .. 0.2 14.0 1.6 2.8 7.2 0.0 12.3 28.8 10.0 19.5 1.3 5.3 6.2 0.3 .. 0.8 0.3 1.5 4.2d w .. 2.4 1.1 3.6 2.2 1.8 9.8 2.3 2.6 0.7 0.9 11.8 8.9

4.4 12.0 0.1 16.6 0.4 6.8 0.2 6.7 6.9 8.5 0.1 8.9 .. 7.2 0.6 0.3 1.1 5.3 5.3 3.6 0.5 0.2 4.2 0.2 0.2 37.4 2.4 4.0 9.7 0.1 7.0 25.0 8.5 18.0 2.5 4.6 6.1 1.5 0.5 1.0 0.2 0.7 4.8d w 0.3 3.4 1.5 5.3 3.0 4.3 7.8 2.8 3.8 1.2 0.8 11.9 8.0

kilograms per 2005 PPP $ of GDP 1990 2008

0.9 1.5 0.1 0.7 0.3 0.8b 0.1 0.6 0.9 0.5 .. 1.2 .. 0.3 0.1 0.2 0.1 0.2 0.2 1.0 0.7 0.1 0.4 .. 0.2 1.3 0.4 0.3 2.3 0.1 1.9 0.4 0.4 0.6 0.2 3.1 0.6 0.4 .. 0.4 0.2 .. 0.6d w .. 0.8 0.6 0.9 0.8 1.3 1.3 0.3 0.6 0.6 0.6 0.5 0.4

0.4 0.8 0.1 0.8 0.2 0.7 0.3 0.1 0.3 0.3 .. 0.9 .. 0.3 0.1 0.2 0.2 0.2 0.1 0.8 0.3 0.1 0.6 0.2 0.3 1.6 0.3 0.3 1.5 0.1 1.0 0.5 0.3 0.4 0.2 1.9 0.5 0.6 .. 0.5 0.1 .. 0.5d w 0.3 0.6 0.5 0.7 0.6 0.8 0.7 0.3 0.6 0.5 0.4 0.4 0.3

a. A negative value indicates that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries.

172

2012 World Development Indicators


About the data

3.9

ENVIRONMENT

Energy dependency and efficiency and carbon dioxide emissions Definitions

Because commercial energy is widely traded, its pro-

individual values. Each year the CDIAC recalculates

• Net energy imports are estimated as energy use

duction and use need to be distinguished. Net energy

the entire time series since 1949, incorporating

less production, both measured in oil equivalents.

imports show the extent to which an economy’s use

recent findings and corrections. Estimates exclude

• GDP per unit of energy use is the ratio of gross

exceeds its production. High-income economies are

fuels supplied to ships and aircraft in international

domestic product (GDP) per kilogram of oil equiv-

net energy importers; middle-income economies are

transport because of the difficulty of apportioning

alent of energy use, with GDP converted to 2005

their main suppliers.

the fuels among benefiting countries.

international dollars using purchasing power parity

The ratio of gross domestic product (GDP) to energy

(PPP) rates. An international dollar has the same

use indicates energy efficiency. To produce compa-

purchasing power over GDP that a U.S. dollar has

rable and consistent estimates of real GDP across

in the United States. Energy use refers to the use

economies relative to physical inputs to GDP—that

of primary energy before transformation to other

is, units of energy use—GDP is converted to 2005

end-use fuel, which is equal to indigenous produc-

international dollars using purchasing power parity

tion plus imports and stock changes minus exports

(PPP) rates. Differences in this ratio over time and

and fuel supplied to ships and aircraft engaged in

across economies reflect structural changes in an

international transport (see About the data for table

economy, changes in sectoral energy efficiency, and

3.7). • Carbon dioxide emissions are emissions from

differences in fuel mixes.

the burning of fossil fuels and the manufacture of

Carbon dioxide emissions, largely by-products of

cement and include carbon dioxide produced dur-

energy production and use (see table 3.7), account

ing consumption of solid, liquid, and gas fuels and

for the largest share of greenhouse gases, which are

gas flaring.

associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Carbon intensity is the ratio of carbon dioxide per unit of energy, or the amount of carbon dioxide emitted as a result of using one unit of energy in production. Kilograms per 2005 PPP dollars of GDP shows the share of carbon dioxide per unit of GDP, a measure of how clean production processes are. The U.S. Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division’s World Energy Data Set) and world cement manufacturing (from the U.S. Bureau of Mines Cement Manufacturing Data Set). Carbon dioxide emissions are often calculated and reported as elemental carbon. The values in the table were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to

Data sources

that of carbon dioxide). Although estimates of global

Data on energy use are from the electronic files

carbon dioxide emissions are probably accurate

of the International Energy Agency. Data on car-

within 10 percent (as calculated from global aver-

bon dioxide emissions are from the CDIAC, Envi-

age fuel chemistry and use), country estimates may

ronmental Sciences Division, Oak Ridge National

have larger error bounds. Trends estimated from a

Laboratory, Tennessee, United States.

consistent time series tend to be more accurate than

2012 World Development Indicators

173


3.10

Trends in greenhouse gas emissions Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2008 2008

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

174

–6.9 2.5 1.5 10.4 2.6 3.0 1.7 1.1 0.0 3.0 6.7 –1.3 –0.4 9.3 3.6 16.6 3.7 3.3 –1.9 6.1 –4.5 15.3 4.1 1.4 1.0 11.0 4.3 5.7 1.9 0.0 –3.1 –0.1 5.0 1.4 2.6 –0.8 3.1 –0.7 –1.0 4.5 3.1 5.9 4.4 5.4 –0.6 4.7 1.1 –0.3 –3.6 4.4 –2.6 –1.1 4.7 2.0 5.6 1.5 –0.2 7.6

–69.6 –44.2 41.1 450.2 70.8 50.7 38.9 11.1 6.7 89.1 199.5 –28.2 –3.3 468.7 133.2 694.1 122.2 88.2 –35.0 216.3 –41.0 920.4 205.1 20.9 31.5 237.4 109.5 185.8 39.5 18.1 –30.8 63.0 171.2 21.0 41.7 –5.8 83.8 –16.1 –8.7 125.9 59.3 176.9 133.5 .. –20.6 135.5 10.9 –5.5 –49.0 115.4 –66.1 –18.1 118.6 34.5 134.2 31.9 11.6 145.0

2012 World Development Indicators

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

.. 2,407 54,219 45,409 101,821 2,962 126,488 8,515 36,607 2,766 92,414 11,498 10,063 4,080 30,350 2,741 4,501 492,160 10,867 .. .. 20,215 18,518 89,338 .. .. 18,149 1,333,098 2,820 58,108 56,445 5,584 2,580 10,997 3,864 9,455 616 11,497 7,935 6,081 17,125 46,996 3,131 2,467 2,108 52,243 9,742 77,252 8,218 .. 4,410 67,582 8,990 7,289 8,306 .. .. 4,006

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

.. –5.1 33.1 –8.3 –8.3 2.5 9.7 –15.0 110.7 54.9 6.5 –32.8 –21.8 –15.8 30.9 –53.5 –22.6 56.4 –24.8 .. .. 35.0 37.1 30.8 .. .. 49.8 28.5 84.1 13.5 –41.6 –10.4 –31.2 –2.2 –60.5 –21.0 40.0 –40.3 –0.5 3.8 31.2 68.8 18.0 30.9 –36.8 32.8 –2.8 –0.3 1.4 .. –12.4 –44.8 24.2 2.0 74.7 .. .. 34.8

.. 20.0 83.2 15.6 18.9 50.8 29.7 21.7 82.0 88.7 10.0 7.6 11.6 15.6 25.6 46.7 8.6 7.6 13.0 .. .. 4.9 39.1 32.2 .. .. 24.4 45.8 26.7 19.9 10.2 32.2 9.5 16.9 57.0 11.2 1.8 49.4 16.4 7.8 31.2 50.7 12.4 11.2 42.3 14.3 7.4 44.3 90.4 .. 36.1 32.1 23.3 26.3 12.4 .. .. 12.1

.. 70.8 8.2 27.9 70.6 36.7 55.1 48.6 13.6 0.6 70.5 70.9 56.7 47.8 34.1 42.4 84.1 61.1 18.9 .. .. 76.1 42.4 29.3 .. .. 39.4 38.8 .. 68.0 23.1 31.9 67.2 17.4 33.3 62.4 44.0 33.6 65.2 63.7 57.7 31.7 53.1 73.2 30.5 72.5 20.7 47.7 1.1 .. 50.8 43.8 39.5 50.0 48.8 .. .. 56.2

Total thousand metric tons of carbon dioxide equivalent 2005

.. 1,036 4,898 38,881 49,821 580 62,966 4,448 2,633 82 21,386 11,680 6,571 2,902 15,092 1,196 3,081 235,987 4,227 .. .. 5,794 9,127 40,171 .. .. 8,135 467,213 422 21,288 54,643 3,566 1,334 7,364 2,851 6,356 292 8,878 6,290 2,255 4,571 18,996 1,377 1,189 932 30,510 7,124 49,058 482 .. 2,019 56,560 4,899 5,977 5,376 .. .. 1,438

Other greenhouse gas emissions

% of total From energy % changeb processes Agricultural 1990– 2005 2005 2005

.. –18.7 27.5 –6.7 29.6 –27.6 –0.1 –13.5 0.4 70.8 42.1 –28.3 –27.6 –21.5 3.2 –40.8 –44.1 52.6 –55.2 .. .. 46.9 –13.3 –5.5 .. .. 57.5 48.5 –0.9 5.2 –37.3 –17.2 –26.2 –1.6 –24.5 –31.8 19.2 –10.2 –21.5 11.0 42.2 60.7 7.7 15.7 –50.7 19.4 –4.1 –30.6 58.0 .. –26.8 –23.9 –5.6 –17.1 121.1 .. .. 59.6

.. 7.1 22.6 0.4 3.9 1.2 10.3 31.0 8.3 39.7 7.5 23.1 38.1 4.0 0.7 24.7 1.4 3.4 36.0 .. .. 3.5 2.6 23.7 .. .. 16.6 12.9 38.5 4.4 2.2 1.0 4.5 2.7 36.6 15.1 13.0 53.0 18.0 7.8 3.8 8.3 8.2 3.8 21.5 5.2 42.8 24.2 10.0 .. 35.5 38.2 9.3 22.1 5.5 .. .. 6.2

.. 78.4 58.6 38.4 89.2 81.6 78.2 52.5 77.5 16.0 83.1 72.9 44.3 61.5 36.5 57.8 92.0 67.0 48.1 .. .. 66.1 75.9 58.9 .. .. 73.4 74.3 0.0 86.1 31.3 51.8 85.4 29.3 52.4 78.7 65.5 36.9 73.4 76.8 84.9 80.0 76.2 90.9 60.5 88.8 41.7 66.8 23.3 .. 56.9 52.2 70.5 58.2 56.8 .. .. 84.2

Total thousand metric tons of carbon dioxide equivalent 2005

.. 62 489 20 785 335 6,505 2,329 89 279 0 467 2,106 0 0 571 0 11,816 383 .. .. 0 419 21,943 .. .. 13 141,394 119 83 0 5 62 0 59 129 190 1,121 1,422 0 63 3,181 77 0 40 10 826 15,539 9 .. 12 31,543 15 1,842 481 .. .. 0

% changeb 1990– 2005

.. .. 50.0 .. –65.8 .. 33.5 46.2 –49.4 –89.0 .. .. 583.8 .. .. –7.5 .. 40.5 .. .. .. .. –55.0 69.7 .. .. –31.6 1,073.0 –68.6 97.6 .. .. .. .. –93.4 .. .. .. 457.6 .. .. 54.5 .. .. 1,900.0 .. 726.0 57.1 .. .. .. 8.1 –97.5 –20.8 .. .. .. ..


Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2008 2008

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

7.4 –0.6 4.8 4.5 5.0 3.7 2.4 1.6 0.6 2.4 0.7 4.2 0.4 3.0 1.2 3.7 .. 7.4 –1.7 13.1 –3.3 3.0 .. 5.0 1.8 –1.8 0.4 3.4 3.1 6.3 1.9 –3.9 6.1 1.9 –8.5 –0.2 3.7 5.4 6.6 39.5 7.6 0.0 2.0 4.1 –0.2 5.4 2.7 8.5 4.9 4.0 3.9 2.7 3.7 3.2 –1.1 1.8 .. 6.8

234.5 –13.3 152.4 171.5 137.0 95.9 43.4 12.3 4.9 53.2 10.4 105.5 –9.3 78.5 .. 108.8 .. 69.0 –42.8 553.1 –46.0 87.9 .. 25.8 44.7 –31.7 9.6 93.7 100.6 268.0 40.9 –25.0 170.2 46.1 –77.2 8.5 103.5 131.1 198.8 .. 458.4 5.9 37.8 63.8 2.2 111.0 59.6 341.9 138.0 120.5 –1.5 82.0 91.5 86.7 –13.8 28.8 .. 481.6

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

5,191 7,767 583,978 208,944 114,585 15,937 15,331 3,517 40,790 1,302 42,771 1,796 47,119 22,130 18,195 32,069 .. 14,380 3,591 .. 3,108 1,003 .. .. 14,682 5,516 1,403 .. .. 46,501 .. .. .. 128,209 3,372 6,067 10,573 12,843 77,211 5,057 22,142 21,259 27,635 6,018 .. 130,317 16,870 17,849 137,401 3,219 .. 15,388 17,187 51,889 70,023 12,173 .. 15,706

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

31.5 –22.9 10.5 18.4 32.5 –45.8 14.3 83.8 –13.4 14.4 –36.5 111.5 –27.3 23.3 –15.0 2.4 .. 119.4 –38.2 .. –42.1 46.6 .. .. –34.7 –34.1 –36.5 .. .. 64.7 .. .. .. 26.3 –17.5 –25.9 15.8 18.2 –7.4 47.2 9.7 –30.4 3.6 26.3 .. 10.9 47.2 194.9 50.7 16.5 .. 2.0 22.7 28.6 –36.6 22.4 .. 387.2

7.2 29.1 15.9 25.5 70.6 58.4 11.9 18.4 14.7 11.4 8.1 25.0 66.2 16.9 58.6 19.9 .. 93.4 6.8 .. 53.6 9.7 .. .. 86.3 32.0 32.1 .. .. 69.3 .. .. .. 40.2 45.2 2.5 8.0 22.7 12.6 0.3 5.9 23.4 3.6 6.6 .. 68.9 74.6 94.1 23.7 4.0 .. 3.9 13.5 9.3 62.0 13.8 .. 96.5

78.4 33.6 64.4 46.4 18.2 18.6 76.7 31.2 39.8 50.3 71.2 21.8 25.3 65.5 23.5 38.6 .. 1.1 72.3 .. 27.7 25.5 .. .. 5.7 33.8 46.6 .. .. 12.4 .. .. .. 42.3 29.4 92.1 51.7 44.2 69.0 94.9 82.9 43.4 90.2 74.8 .. 19.8 12.6 3.0 63.5 79.2 .. 84.1 61.3 63.7 21.9 35.4 .. 0.4

Total thousand metric tons of carbon dioxide equivalent 2005

2,865 6,961 212,927 123,275 26,644 3,440 7,486 1,793 28,620 599 29,785 667 17,594 10,542 3,422 13,548 .. 650 1,510 .. 1,253 672 .. .. 1,285 2,451 599 .. .. 15,087 .. .. .. 42,514 849 3,489 5,814 9,501 30,932 3,797 4,516 14,596 12,930 3,340 .. 21,565 4,737 561 26,838 1,204 .. 9,067 7,560 12,950 30,198 5,958 .. 200

Other greenhouse gas emissions

% of total From energy % changeb processes Agricultural 1990– 2005 2005 2005

26.0 –31.2 33.3 43.5 41.1 –9.9 –8.3 41.5 –5.4 29.4 –17.0 39.5 –46.2 14.3 –60.6 34.7 .. 156.9 –57.7 .. –58.7 79.2 .. .. 9.3 –45.7 –33.9 .. .. 13.5 .. .. .. 8.9 –51.0 –30.0 12.2 –12.7 –23.9 47.2 26.0 –10.7 23.5 10.1 .. 12.6 –3.1 82.7 46.0 18.0 .. 0.6 35.4 34.0 4.7 24.3 .. 104.1

3.10

3.8 30.9 12.8 3.7 11.4 9.7 4.4 15.3 39.1 12.1 41.6 8.2 12.8 5.0 13.2 41.3 .. 27.7 11.2 .. 11.6 12.6 .. .. 11.2 5.0 15.9 .. .. 6.7 .. .. .. 10.6 5.5 2.2 3.0 3.4 2.6 1.1 13.0 52.5 3.5 3.3 .. 9.1 46.5 16.0 14.5 4.9 .. 1.7 2.9 9.1 33.5 22.0 .. 33.9

85.9 60.1 73.4 71.5 75.3 63.3 90.5 53.0 43.7 59.0 27.9 55.4 62.5 88.8 62.3 35.9 .. 16.9 72.6 .. 77.4 58.8 .. .. 51.9 86.0 63.9 .. .. 64.9 .. .. .. 75.2 73.5 93.2 82.6 71.4 42.9 94.3 76.8 39.5 94.2 91.7 .. 77.3 39.0 68.0 74.2 83.7 .. 82.6 81.9 73.1 57.7 43.8 .. 25.0

Total thousand metric tons of carbon dioxide equivalent 2005

0 1,552 8,433 1,027 2,569 86 1,151 1,981 13,968 51 53,786 112 339 0 2,794 10,221 .. 931 24 .. 890 0 .. .. 280 656 120 .. .. 994 .. .. .. 4,555 8 0 0 282 0 0 0 3,750 973 0 .. 669 5,202 175 819 0 .. 0 330 365 2,451 783 .. 0

2012 World Development Indicators

% changeb 1990– 2005

.. 121.1 –11.9 –40.5 –2.9 –66.0 3,097.2 88.8 211.2 .. 81.1 .. .. .. .. 66.0 .. 254.0 .. .. .. .. .. .. –0.7 .. .. .. .. 66.2 .. .. .. 53.1 .. .. .. .. .. .. .. –40.9 3.4 .. .. 176.4 –39.4 .. –18.8 .. .. .. .. 125.3 360.7 605.4 .. ..

175

ENVIRONMENT

Trends in greenhouse gas emissions


3.10

Trends in greenhouse gas emissions Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2008 2008

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

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

–2.4 –40.4 24,331 –1.0 –23.1 562,801 0.6 3.2 .. 2.8 101.6 48,152 3.0 56.3 7,129 .. .. 7,782 7.3 243.4 .. –2.1 –31.2 2,237 –0.9 –17.7 3,911 1.6 32.3 3,498 35.9 3,447.0 .. 1.4 30.7 63,785 .. .. .. 2.7 44.7 36,338 7.1 211.8 10,210 7.1 152.8 67,441 9.2 156.9 .. –0.6 –5.1 11,311 –0.3 –6.0 4,748 3.2 91.2 12,458 –2.8 –56.4 3,898 5.3 172.5 32,024 5.6 198.2 83,257 .. .. .. 3.5 83.4 2,889 4.7 193.5 10,070 3.3 88.5 8,160 3.4 88.3 64,251 3.3 70.4 27,984 8.7 358.3 .. –3.1 –49.6 70,360 6.1 198.2 23,283 –0.6 –8.3 65,788 0.7 11.9 548,074 2.3 108.5 19,589 0.6 9.6 39,602 2.6 38.8 61,183 11.3 495.0 82,978 17.3 .. .. 4.7 143.7 6,677 –1.3 –22.8 19,294 –3.4 –41.5 9,539 2.1 w 43.8 w 7,135,850 s 3.0 .. 436,333 3.0 79.7 5,160,313 3.1 87.1 1,704,859 3.0 77.7 3,455,454 3.0 79.4 5,596,645 5.6 185.3 1,928,355 –0.8 –19.6 936,608 2.5 60.6 1,008,557 4.2 112.5 287,084 4.9 151.9 846,145 2.2 47.5 589,897 0.8 14.8 1,539,204 0.1 –3.1 317,704

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

–35.1 –18.3 .. 67.4 35.1 –58.7 .. 136.7 –39.7 0.6 .. 24.6 .. 11.9 –11.2 55.5 .. 1.3 –17.1 –10.8 –9.3 24.0 5.7 .. 5.0 32.0 106.2 46.4 –5.0 .. –42.2 58.0 –44.1 –14.4 24.1 24.0 5.9 40.1 .. 73.5 –28.4 –5.7 6.2 w –3.0 13.6 11.4 14.7 12.1 24.5 –17.3 30.3 20.0 14.6 5.4 –10.8 –18.2

42.7 79.3 .. 83.6 9.9 41.5 .. 60.1 18.2 30.7 .. 45.4 .. 10.4 5.3 7.1 .. 9.9 19.8 53.8 12.8 12.6 16.9 .. 23.5 83.9 55.6 16.0 75.2 .. 62.1 93.1 24.8 41.0 1.5 57.3 47.4 22.7 .. 17.0 6.7 11.4 37.3 w 13.7 38.8 27.2 44.6 36.9 39.2 67.5 17.3 64.7 16.1 30.5 38.9 26.0

36.0 9.1 .. 4.0 68.3 43.7 .. 1.3 39.0 32.1 .. 31.4 .. 56.8 65.2 85.2 .. 28.1 67.6 28.1 68.6 63.2 66.0 .. 39.8 0.7 25.5 33.6 21.6 .. 23.3 2.6 38.2 34.8 94.3 33.7 40.0 63.9 .. 54.9 59.3 73.3 42.6 w 61.2 42.6 52.4 37.8 44.1 43.6 17.5 58.6 20.7 65.4 44.0 37.1 46.8

Total thousand metric tons of carbon dioxide equivalent 2005

Other greenhouse gas emissions

% of total From energy % changeb processes Agricultural 1990– 2005 2005 2005

11,537 –44.0 76,121 –48.7 .. .. 6,501 17.5 4,083 37.2 4,581 –8.8 .. .. 1,068 163.1 3,354 –37.1 1,156 –12.2 .. .. 24,048 12.9 .. .. 26,529 6.5 2,056 18.0 49,472 34.9 .. .. 5,865 –13.1 2,415 –15.5 5,509 33.4 1,378 0.2 21,647 0.8 22,304 15.1 .. .. 1,738 –21.3 230 12.2 2,366 17.9 32,781 12.8 4,276 93.7 .. .. 26,097 –51.3 1,169 78.7 30,565 –44.7 317,153 1.8 7,017 16.1 10,003 9.4 14,935 23.4 23,030 98.3 .. .. 3,250 57.4 25,068 –29.7 6,114 –16.1 2,852,537 s 5.8 w 209,159 –15.0 1,830,347 17.0 686,537 17.2 1,143,810 16.9 2,039,506 12.7 707,496 38.0 214,402 –38.9 442,132 32.9 73,539 36.8 267,722 34.9 334,216 –7.6 813,031 –8.2 219,189 –18.1

32.4 27.8 .. 14.0 2.7 24.2 .. 77.6 52.0 13.2 .. 12.6 .. 18.7 12.1 1.3 .. 26.8 20.8 9.0 1.4 2.5 21.7 .. 5.6 11.5 21.4 22.7 16.4 .. 42.8 18.3 24.8 30.6 1.4 6.2 5.0 6.1 .. 11.2 2.6 3.7 15.4 w 4.1 10.6 8.8 11.7 10.0 10.5 25.3 4.6 10.7 12.6 3.7 29.1 31.7

56.2 44.3 .. 46.1 88.5 63.6 .. 2.8 37.7 70.4 .. 59.8 .. 62.6 65.1 92.6 .. 60.2 59.3 78.1 86.9 78.8 65.5 .. 67.5 60.3 66.4 66.4 78.1 .. 45.6 43.6 60.0 56.4 96.9 84.2 75.2 83.0 .. 72.5 71.7 85.2 66.2 w 62.4 70.8 71.1 70.6 70.0 72.2 56.8 72.4 74.5 74.3 66.1 56.8 55.4

Total thousand metric tons of carbon dioxide equivalent 2005

746 59,673 .. 2,193 0 4,493 .. 2,532 395 473 .. 2,552 .. 9,080 0 0 .. 2,078 2,109 0 383 0 1,104 .. 0 0 0 5,066 73 .. 693 1,075 10,403 239,517 59 608 2,468 0 .. 0 0 0 724,183 s .. 260,798 17,327 243,471 264,291 .. 75,692 20,972 6,717 9,253 .. 459,891 84,230

% changeb 1990– 2005

–62.8 130.6 .. –10.6 .. 353.8 .. 396.5 480.9 –38.5 .. 71.2 .. 47.7 .. .. .. 133.7 97.3 .. –86.4 .. –22.8 .. .. .. .. 96.9 .. .. 209.4 27.5 96.7 158.7 .. .. –24.0 .. .. .. .. .. 122.4 w .. 207.4 3.3 257.7 201.6 .. 114.6 23.5 20.7 –12.5 .. 93.3 37.2

a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emissions since 1990, which is the baseline for Kyoto Protocol requirements.

176

2012 World Development Indicators


About the data

3.10

Definitions

Greenhouse gases—which include carbon dioxide,

• Carbon dioxide emissions are those from the burn-

methane, nitrous oxide, hydrofluorocarbons, per-

ing of fossil fuels and the manufacture of cement and

fluorocarbons, and sulfur hexafluoride—contribute

include carbon dioxide produced during consumption

to climate change.

of solid, liquid, and gas fuels and gas flaring. • Meth-

Carbon dioxide emissions, largely a by-product of

ane emissions are those from human activities such

energy production and use (see table 3.7), account

as agriculture and from industrial methane produc-

for the largest share of greenhouse gases. Anthro-

tion. • Methane emissions from energy processes

pogenic carbon dioxide emissions result primarily

are those from the production, handling, transmis-

from fossil fuel combustion and cement manufactur-

sion, and combustion of fossil fuels and biofuels.

ing. Burning oil releases more carbon dioxide than

• Agricultural methane emissions are those from

burning natural gas, and burning coal releases even

animals, animal waste, rice production, agricultural

more for the same level of energy use. Cement manu-

waste burning (nonenergy, on-site), and savannah

facturing releases about half a metric ton of carbon

burning. • Nitrous oxide emissions are those from

dioxide for each metric ton of cement produced.

agricultural biomass burning, industrial activities,

Methane emissions result largely from agricultural

and livestock management. • Nitrous oxide emis-

activities, industrial production landfills and waste-

sions from energy processes are those produced

water treatment, and other sources such as tropi-

by the combustion of fossil fuels and biofuels.

cal forest and other vegetation fires. The emissions

• Agricultural nitrous oxide emissions are those

are usually expressed in carbon dioxide equivalents

produced through fertilizer use (synthetic and ani-

using the global warming potential, which allows the

mal manure), animal waste management, agricultural

effective contributions of different gases to be com-

waste burning (nonenergy, on-site), and savannah

pared. A kilogram of methane is 21 times as effec-

burning. • Other greenhouse gas emissions are

tive at trapping heat in the earth’s atmosphere as a

those of hydrofluorocarbons (used as a replacement

kilogram of carbon dioxide within 100 years.

for chlorofluorocarbons, mainly in refrigeration and

Nitrous oxide emissions are mainly from fossil fuel

semiconductor manufacturing), perfluorocarbons (a

combustion, fertilizers, rainforest fires, and animal

by-product of aluminum smelting and uranium enrich-

waste. Nitrous oxide is a powerful greenhouse gas,

ment, used as a replacement for chlorofluorocarbons

with an estimated atmospheric lifetime of 114 years,

in semiconductor manufacturing), and sulfur hexaflu-

compared with 12 years for methane. The per kilo-

oride (used to insulate high-voltage electricity power

gram global warming potential of nitrous oxide is

equipment), all of which are to be curbed under the

nearly 310 times that of carbon dioxide within 100

Kyoto Protocol.

years. Other greenhouse gases covered under the Kyoto Protocol are hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Although emissions of these artificial gases are small, they are more powerful greenhouse gases than carbon dioxide, with much higher atmospheric lifetimes and high global warming potential. For a discussion of carbon dioxide sources and the methodology behind emissions calculation, see About the data for table 3.9. Data sources Data on carbon dioxide emissions are from the Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data on methane, nitrous oxide, and other greenhouse gas emissions are compiled by the International Energy Agency.

2012 World Development Indicators

177

ENVIRONMENT

Trends in greenhouse gas emissions


3.11

Carbon dioxide emissions by sector Carbon dioxide emissions

% of total fuel combustion Electricity and heat production 1990 2008

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

178

.. 12.0 42.9 12.5 34.0 29.6 54.1 35.0 44.3 56.2 32.7 47.4 28.9 12.0 29.0 40.6 55.3 14.2 63.4 .. .. .. 1.1 32.9 .. .. 33.0 32.2 72.4 24.3 3.4 0.0 7.3 22.8 37.3 45.2 45.3 42.7 51.7 41.2 15.2 32.5 8.3 .. 73.2 13.6 36.2 18.1 34.4 .. 46.3 42.4 3.0 52.3 7.3 .. .. 25.5

2012 World Development Indicators

.. 5.2 39.2 3.7 34.9 19.6 62.9 34.1 50.3 54.2 43.7 52.9 25.4 3.1 34.8 69.5 25.0 19.0 64.3 .. .. 36.7 34.3 33.5 .. .. 39.0 51.9 68.1 18.7 1.1 3.4 10.2 43.9 33.1 53.3 51.0 56.9 50.1 50.0 20.7 43.0 26.6 42.2 75.8 6.6 47.7 18.9 28.5 .. 19.5 45.2 26.2 53.4 27.6 .. .. 9.8

Manufacturing industries and construction 1990 2008

.. 44.2 14.1 46.1 16.2 22.3 17.7 17.9 24.4 34.3 33.3 28.1 28.4 12.0 11.6 26.4 18.1 29.8 17.9 .. .. .. 8.2 19.8 .. .. 24.9 40.9 9.5 27.4 29.4 8.6 26.8 16.3 28.2 18.4 20.1 30.0 10.9 10.3 16.9 35.8 25.9 .. 12.4 27.1 26.7 22.7 15.6 .. 22.4 18.9 17.7 14.8 22.7 .. .. 21.3

.. 16.1 13.6 24.1 22.2 37.6 12.6 18.1 7.4 31.1 24.5 19.9 24.7 5.2 17.2 6.6 26.8 29.7 15.1 .. .. 3.7 7.0 17.8 .. .. 19.1 33.3 15.7 31.7 35.3 4.1 17.3 9.3 21.2 28.3 15.1 17.7 10.0 6.3 17.5 23.4 23.5 4.4 8.2 24.5 21.5 19.2 38.9 .. 15.5 14.7 15.3 9.8 15.5 .. .. 22.6

Residential buildings and commercial and public services 1990 2008

1990

2008

Other sectors 1990 2008

.. 4.8 20.3 15.7 17.2 30.9 3.3 20.7 7.2 1.0 16.2 10.3 22.6 12.0 10.5 1.2 3.4 8.4 3.9 .. .. .. 25.5 16.8 .. .. 9.2 16.5 4.5 8.1 10.8 10.0 4.2 11.4 10.5 9.1 4.7 19.0 12.5 12.1 14.5 11.9 7.4 .. 4.1 5.4 11.9 24.1 13.3 .. 16.5 20.4 15.9 7.3 14.2 .. .. 8.5

.. 11.2 22.6 25.2 28.1 14.3 23.6 24.2 5.4 8.5 12.2 8.1 18.5 64.0 39.3 9.2 22.2 41.9 8.5 .. .. .. 65.2 28.6 .. .. 31.7 5.3 13.6 37.0 18.9 78.6 60.2 44.9 18.2 16.0 29.9 4.6 20.3 35.5 51.7 19.8 57.9 .. 6.6 41.6 21.3 31.9 35.6 .. 13.1 16.7 59.0 21.5 52.4 .. .. 44.7

.. 59.6 22.4 50.3 24.6 15.8 20.1 31.9 17.9 13.8 14.2 10.2 24.4 61.2 36.0 14.2 43.6 41.0 16.9 .. .. 25.0 50.6 29.4 .. .. 35.1 7.0 10.2 38.5 22.6 87.2 65.5 24.3 29.5 3.0 26.0 15.3 28.3 29.1 49.2 21.9 40.9 24.4 12.9 56.7 22.4 33.9 21.1 .. 37.6 18.5 49.8 23.6 51.3 .. .. 57.3

.. 27.8 0.0 0.7 4.5 2.9 1.3 2.2 18.7 0.0 5.7 6.2 1.5 0.0 9.5 22.6 1.4 5.7 6.2 .. .. .. 0.0 1.9 .. .. 1.2 5.1 0.0 3.2 37.8 0.0 1.5 4.9 5.9 11.2 0.0 3.6 4.6 1.0 1.8 0.0 0.0 .. 3.8 12.2 3.8 3.2 2.2 .. 1.7 1.7 4.4 4.1 3.0 .. .. 0.0

.. 9.8 24.9 21.7 12.9 0.0 3.0 14.7 22.1 0.9 11.9 12.4 23.5 30.9 10.6 0.6 3.1 5.4 2.7 .. .. 26.5 8.4 16.4 .. .. 6.2 6.2 6.0 8.5 10.6 5.4 3.6 12.1 12.6 10.0 4.5 8.8 7.7 13.9 11.4 8.1 8.9 28.9 2.3 12.4 4.8 22.8 9.7 .. 18.5 20.5 6.0 10.5 5.7 .. .. 10.3

Transport

.. 9.1 0.0 0.3 5.3 26.8 1.5 1.2 2.4 0.0 5.7 4.6 1.9 0.0 1.5 9.0 1.3 4.9 1.0 .. .. 7.8 0.0 2.9 .. .. 0.6 1.6 0.0 2.7 30.7 0.0 3.5 10.5 3.7 5.4 3.6 1.3 3.9 0.7 1.2 3.6 0.0 0.0 0.9 0.0 3.5 5.3 1.7 .. 8.9 1.1 2.7 2.6 0.1 .. .. 0.0


3.11

ENVIRONMENT

Carbon dioxide emissions by sector Carbon dioxide emissions

% of total fuel combustion Electricity and heat production 1990 2008

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

0.9 34.8 44.5 37.3 22.7 28.3 36.1 57.6 35.9 27.8 38.4 38.6 47.9 8.0 13.9 27.4 .. 62.7 17.3 .. 52.3 43.3 .. .. 59.7 41.3 64.8 .. .. 35.7 .. .. .. 34.9 44.8 51.3 40.0 10.2 39.9 .. 0.0 38.2 21.6 30.6 .. 28.5 26.9 54.8 27.0 22.8 .. 1.6 20.7 35.5 65.1 41.6 .. 53.3

34.4 37.6 59.9 37.7 29.0 35.6 33.7 66.6 38.3 51.1 44.6 48.0 47.4 32.4 16.2 52.4 .. 68.2 23.1 .. 25.8 49.2 .. .. 62.1 35.3 68.1 .. .. 49.6 .. .. .. 40.2 47.7 62.6 37.1 1.0 20.8 22.6 0.3 38.2 32.8 40.1 .. 36.5 33.4 56.7 32.3 27.0 .. 0.0 26.8 44.0 55.8 39.9 .. 52.1

Manufacturing industries and construction 1990 2008

35.2 23.2 28.6 23.7 26.8 27.3 15.8 12.9 21.1 8.3 27.0 14.2 34.9 25.8 67.4 23.4 .. 21.7 36.9 .. 13.6 5.0 .. .. 13.2 21.6 18.8 .. .. 30.7 .. .. .. 23.9 6.7 22.1 25.7 13.0 28.1 .. 22.7 21.9 28.8 18.0 .. 17.3 24.5 21.8 34.0 21.1 .. 7.9 22.4 21.0 13.7 24.8 .. 35.9

19.9 13.2 19.6 34.0 22.4 24.0 11.5 2.8 15.8 3.3 21.5 14.1 22.3 15.8 62.9 19.1 .. 15.6 28.5 .. 13.8 11.5 .. .. 9.4 21.2 14.2 .. .. 24.2 .. .. .. 14.9 9.8 13.2 17.0 20.7 25.4 6.6 33.9 21.2 18.4 14.7 .. 10.1 21.3 23.1 32.4 18.8 .. 3.0 26.6 18.0 12.6 16.1 .. 31.5

Residential buildings and commercial and public services 1990 2008

1990

2008

Other sectors 1990 2008

15.5 25.2 9.8 13.7 22.3 9.2 29.5 4.2 16.7 4.0 12.9 11.3 0.2 10.9 0.5 26.9 .. 0.7 0.0 .. 13.4 22.7 .. .. 4.8 16.1 4.9 .. .. 4.4 .. .. .. 7.8 2.9 6.4 7.3 7.4 0.3 .. 28.4 18.5 6.0 7.1 .. 14.3 8.7 1.9 14.4 9.3 .. 6.8 18.1 6.7 12.9 5.5 .. 0.6

48.4 12.5 13.8 22.7 21.5 35.1 16.5 19.6 24.0 15.0 19.7 28.9 6.0 48.3 4.1 18.8 .. 15.0 13.3 .. 16.6 28.8 .. .. 22.4 16.5 8.9 .. .. 29.2 .. .. .. 31.5 7.9 12.1 18.7 55.6 31.7 .. 38.6 16.6 39.1 40.4 .. 39.9 35.1 16.8 23.1 46.7 .. 83.8 35.5 34.9 6.0 24.6 .. 10.1

37.7 24.2 9.2 19.7 21.8 31.1 30.6 16.4 27.2 21.6 19.7 24.8 7.0 37.2 1.7 16.8 .. 15.5 24.2 .. 44.9 28.5 .. .. 22.8 35.0 13.3 .. .. 23.3 .. .. .. 37.1 14.7 13.6 25.7 69.9 28.1 48.6 27.9 19.7 41.7 35.3 .. 48.6 37.3 15.3 23.5 47.6 .. 92.1 37.8 31.3 14.8 35.7 .. 16.0

0.0 4.4 3.3 2.7 6.6 0.0 2.2 5.7 2.2 44.9 1.9 7.1 11.0 6.9 14.2 3.4 .. 0.0 32.6 .. 4.1 0.0 .. .. 0.0 4.5 2.6 .. .. 0.0 .. .. .. 1.9 37.6 8.1 8.2 13.9 0.0 .. 11.4 4.8 4.4 3.8 .. 0.0 4.9 4.7 1.6 0.0 .. 0.0 3.4 2.0 2.3 3.5 .. 0.0

3.3 22.7 6.2 6.3 24.2 9.3 22.3 4.4 16.8 3.3 13.3 10.9 0.7 9.6 0.1 10.0 .. 0.7 0.0 .. 11.4 10.8 .. .. 5.6 6.9 4.0 .. .. 2.5 .. .. .. 5.8 23.5 7.3 9.5 7.8 2.6 0.3 31.5 17.9 4.2 6.5 .. 4.8 3.2 1.3 11.4 5.8 .. 4.9 6.7 5.7 13.3 6.8 .. 0.4

Transport

2012 World Development Indicators

4.9 2.2 5.1 2.3 2.5 0.0 1.8 9.9 2.0 20.5 0.9 2.2 22.6 5.1 19.1 1.7 .. 0.0 24.3 .. 4.2 0.0 .. .. 0.0 1.5 0.6 .. .. 0.4 .. .. .. 2.0 4.4 3.2 10.7 1.0 23.1 21.9 6.6 3.0 2.8 3.1 .. 0.0 4.8 3.6 0.3 0.8 .. 0.0 2.2 1.1 3.4 1.6 .. 0.0

179


3.11

Carbon dioxide emissions by sector Carbon dioxide emissions

% of total fuel combustion Electricity and heat production 1990 2008

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

47.3 55.9 .. 50.3 36.8 64.6 .. 78.7 30.3 43.0 .. 56.1 .. 37.4 4.5 9.1 .. 18.4 4.1 25.2 14.0 17.5 36.5 .. 12.3 41.6 33.0 30.2 31.6 .. 51.4 26.4 44.2 43.9 13.9 39.1 40.1 27.7 .. 24.6 6.2 42.8 41.9 w 18.1 42.5 42.6 42.4 41.9 32.0 51.7 29.1 31.0 42.4 48.1 41.8 36.7

50.4 59.5 .. 51.4 28.4 63.3 .. 71.4 36.9 37.4 .. 64.5 .. 37.7 33.1 27.0 .. 22.8 7.0 49.9 17.5 18.5 41.1 .. 1.8 35.9 39.5 42.7 43.4 .. 45.2 50.8 44.5 47.7 39.8 33.3 36.3 29.3 .. 34.3 5.0 56.8 47.6 w 25.5 49.1 48.1 49.4 48.8 50.0 53.4 31.9 36.4 56.8 53.3 46.1 37.8

Note: Shares may not sum to 100 percent because of rounding.

180

2012 World Development Indicators

Manufacturing industries and construction 1990 2008

35.8 13.2 .. 17.8 12.4 16.6 .. 6.7 32.6 20.7 .. 26.9 .. 22.1 13.1 16.9 .. 24.2 14.3 20.6 0.0 22.2 18.8 .. 3.5 45.6 27.6 26.6 0.0 .. 29.0 51.4 15.2 14.4 19.5 4.9 28.0 32.6 .. 3.1 51.5 29.4 22.3 w 50.4 26.5 25.6 26.7 27.0 39.8 19.2 24.4 24.9 29.1 25.8 18.4 21.0

22.3 14.4 .. 22.9 17.4 16.9 .. 11.9 25.7 14.5 .. 13.5 .. 17.4 10.6 10.0 .. 21.0 14.9 20.9 0.0 13.3 29.2 .. 7.3 56.6 17.7 14.6 0.0 .. 29.4 29.8 11.5 11.3 11.4 19.0 28.0 35.1 .. 11.2 49.1 16.1 21.0 w 36.7 26.0 23.0 26.8 26.1 33.1 16.9 22.4 20.6 20.8 13.8 15.1 16.8

Residential buildings and commercial and public services 1990 2008

1990

2008

Other sectors 1990 2008

5.2 13.1 .. 1.6 6.5 3.3 .. 0.6 26.7 15.4 .. 4.4 .. 7.9 2.7 2.5 .. 17.3 44.3 5.0 0.0 19.9 3.1 .. 10.5 1.2 13.4 16.8 0.0 .. 7.8 0.6 17.2 11.1 15.5 0.0 5.0 11.2 .. 10.6 8.5 5.9 12.7 w 2.8 11.7 8.9 12.6 11.5 14.8 10.1 9.2 15.6 10.3 6.0 13.6 18.5

6.9 13.6 .. 30.4 35.8 7.2 .. 14.0 7.1 20.8 .. 11.5 .. 30.6 66.0 71.3 .. 37.5 35.4 30.6 6.6 40.4 34.5 .. 73.7 11.7 20.4 21.9 5.2 .. 7.9 21.6 20.8 29.2 40.0 4.7 26.9 24.3 .. 61.7 29.6 12.9 19.5 w 10.2 13.7 14.8 13.3 13.6 8.1 11.3 33.6 24.1 14.9 18.0 24.3 21.4

16.6 15.3 .. 24.7 46.2 13.1 .. 16.2 19.5 35.5 .. 13.6 .. 34.3 48.2 55.6 .. 50.7 39.5 22.2 9.2 56.8 22.3 .. 78.2 6.1 23.1 17.1 5.9 .. 10.5 17.1 24.4 30.2 34.3 7.8 31.0 24.5 .. 27.1 32.1 12.5 19.6 w 17.5 14.1 16.0 13.6 14.1 8.9 14.1 35.6 23.2 10.9 23.6 25.9 25.8

4.8 4.2 .. 0.0 8.5 8.3 .. 0.0 3.2 0.2 .. 1.2 .. 2.1 13.9 0.2 .. 2.6 2.0 18.6 79.4 0.0 7.1 .. 0.0 0.0 5.5 4.6 63.3 .. 3.8 0.0 2.5 1.3 11.2 51.3 0.0 4.0 .. 0.0 4.2 9.0 3.7 w 18.4 5.6 8.0 4.9 5.9 5.3 7.7 3.7 4.3 3.2 2.0 1.9 2.4

9.1 9.7 .. 1.0 7.3 4.9 .. 0.5 16.9 10.9 .. 7.3 .. 8.1 3.4 5.7 .. 3.7 37.1 2.9 0.0 10.4 2.8 .. 12.7 1.4 14.6 19.3 0.0 .. 13.5 2.3 18.5 9.9 7.1 36.6 4.4 9.5 .. 9.3 2.5 4.0 9.6 w 6.9 8.0 8.6 7.9 8.0 6.0 11.1 6.9 17.0 6.8 7.6 11.5 17.2

Transport

1.6 1.2 .. 0.0 0.8 1.8 .. 0.0 1.0 1.7 .. 1.1 .. 2.5 4.6 1.5 .. 1.7 1.5 4.2 73.3 1.2 4.6 .. 0.0 0.0 5.0 6.3 50.8 .. 1.4 0.0 1.0 0.8 7.5 3.3 0.2 1.6 .. 18.0 11.9 10.6 2.2 w 13.5 2.7 4.4 2.3 2.9 1.9 4.4 3.2 2.9 4.7 1.7 1.4 2.3


About the data

3.11

Definitions

Carbon dioxide emissions account for the largest

Carbon dioxide emissions from transport are emis-

• Carbon dioxide emissions from electricity and

share of greenhouse gases, which are associated

sions from fuel combustion for all transport activity

heat production are those from main activity pro-

with global warming. In 2010 the International Energy

(IPCC source/sink category 1A3), including domestic

ducers of electricity and heat, unallocated auto-

Agency (IEA) released data on carbon dioxide emis-

aviation, domestic navigation, road, rail, and pipeline

producers, and other energy industries. • Carbon

sions by sector for the first time, allowing a more

transport but excluding international marine bunkers

dioxide emissions from manufacturing industries

comprehensive understanding of each sector’s con-

and international aviation. The IEA data do not allow

and construction are those from fuel combustion

tribution to total emissions. The sectoral approach

energy consumption to be categorized by end-use,

in industry (IPCC source/sink category 1A2). • Car-

yields data on carbon dioxide emissions from fuel

and thus emissions from autoproducers are listed

bon dioxide emissions from residential buildings

combustion (Intergovernmental Panel on Climate

separately under unallocated autoproducers.

and commercial and public services are those from

Change [IPCC] source/sink category 1A) as calcu-

Carbon dioxide emissions from other sectors are

fuel combustion in household and all activities of

lated using the IPCC tier 1 sectoral approach. The

emissions from commercial and institutional activi-

ISIC divisions 41, 50–52, 55, 63–67, 70–75, 80,

table does not include all sectors.

ties and from residential, agriculture and forestry,

85, 90–93, and 99. • Carbon dioxide emissions

Carbon dioxide emissions from electricity and heat

fishing, and other processes not specified elsewhere

from transport are those from fuel combustion for all

production are the sum of emissions from main activ-

that are included in IPCC source/sink categories 1A4

transport activities (IPCC source/sink category 1A3).

ity producers of electricity and heat, unallocated auto-

and 1A5. Although in the 1996 IPCC guidelines, this

• Carbon dioxide emissions from other sectors are

producers, and other energy industries. Main activity

category included emissions from autoproducers in

those from commercial and institutional activities

producers (formerly known as public supply under-

the commercial, residential, and agricultural sectors

and from residential, agriculture and forestry, fish-

takings) generate electricity or heat for sale to third

that generate electricity or heat, the IEA data do not

ing, and other emissions not specified elsewhere

parties as their primary activity and may be privately

allow energy consumption to be classified by end-

that are included in IPCC source/sink categories

or publicly owned. Emissions from own onsite use

use, and thus emissions from autoproducers are

1A4 and 1A5.

of fuel in power plants are also included in this cat-

listed separately under unallocated autoproducers.

egory. Unallocated autoproducers are undertakings that generate electricity or heat, wholly or partly for their own use as an activity that supports their primary activity and may be privately or publicly owned. In the 1996 IPCC guidelines these emissions were allocated among industry, transport, and “other” sectors. Emissions from other energy industries are emissions from fuel combusted in petroleum refineries, the manufacture of solid fuels, coal mining, oil and gas extraction, and other energy-producing industries. Carbon dioxide emissions from manufacturing industries and construction are the emissions from fuel combustion in industry (IPCC source/sink Category 1A2). Although in the 1996 IPCC guidelines, this category included emissions from industry autoproducers that generate electricity or heat, the IEA data do not allow energy consumption to be categorized by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers. Emissions from manufacturing industries and construction include those from coke inputs into blast furnaces, which may be reported under the transformation sector, the industry sector, or industrial processes (IPCC source/sink category 2). Carbon emissions from residential buildings and commercial and public services are the sum of emissions from fuel combustion in households (IPCC source/sink category 1A4b) and emissions from all

Data sources

activities of International Standard Industrial Clas-

Data on carbon dioxide emissions by sector are

sification divisions 41, 50–52, 55, 63–67, 70–75,

from IEA electronic files.

80, 85, 90–93, and 99.

2012 World Development Indicators

181

ENVIRONMENT

Carbon dioxide emissions by sector


3.12

Climate variability, exposure to impact, and resilience Climate variability

Exposure to impact

Resilience

% of population Average daily minimum/ maximum temperature degrees Celsius 1961–1990

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

182

5.5 / 19.7 6.3 / 16.5 15.4 / 29.6 14.8 / 28.3 8.2 / 21.4 1.3 / 13.0 14.9 / 28.4 2.3 / 10.4 6.7 / 17.2 20.5 / 33.8 20.4 / 29.6 2.0 / 10.3 5.5 / 13.6 21.7 / 33.4 15.2 / 27.9 5.0 / 14.7 13.6 / 29.4 19.6 / 30.3 5.6 / 15.5 21.5 / 35.0 13.8 / 25.8 22.3 / 31.3 19.1 / 30.1 –10.1 / –0.6 18.3 / 31.5 18.6 / 34.5 3.5 / 13.4 0.9 / 13.0 19.3 / 26.0 19.6 / 29.4 18.4 / 29.6 19.8 / 29.3 19.6 / 30.0 21.1 / 31.6 6.2 / 15.6 20.4 / 30.0 13.1 / 23.8 3.3 / 11.8 4.3 / 10.7 19.9 / 29.2 15.6 / 28.1 14.8 / 29.4 18.4 / 30.5 18.9 / 32.1 1.5 / 8.7 15.5 / 28.9 –2.4 / 5.8 6.5 / 14.9 20.9 / 29.2 20.4 / 34.6 0.4 / 11.2 4.6 / 12.4 22.1 / 32.3 10.5 / 20.3 18.2 / 28.7 19.6 / 31.8 20.9 / 32.6 20.1 / 29.7

2012 World Development Indicators

change in average number of days/nights Projected annual temperature degrees Celsius 2045–2065

2.3 to 3.6 1.9 to 2.9 2.4 to 3.0 2.1 to 2.7 1.4 to 2.1 2.1 to 3.3 1.7 to 2.4 1.9 to 2.9 1.9 to 3.1 2.1 to 3.0 1.7 to 2.4 2.2 to 3.1 1.4 to 2.4 2.0 to 2.5 2.1 to 3.0 1.9 to 3.0 2.5 to 3.3 1.9 to 2.6 1.9 to 3.1 2.2 to 2.8 2.1 to 2.4 1.6 to 2.0 2.1 to 2.4 2.6 to 3.5 2.1 to 2.4 2.3 to 2.6 1.2 to 1.9 2.1 to 3.0 1.4 to 1.9 1.8 to 2.5 2.1 to 2.4 2.0 to 2.3 1.6 to 2.3 1.8 to 2.4 1.9 to 2.9 1.4 to 1.8 1.7 to 2.3 1.9 to 2.8 1.6 to 2.5 1.5 to 1.8 1.8 to 2.3 1.9 to 2.4 1.7 to 2.9 2.1 to 2.7 2.2 to 3.2 2.1 to 2.5 2.4 to 3.7 1.6 to 2.6 1.8 to 2.2 2.1 to 2.7 1.9 to 3.0 1.7 to 2.6 1.8 to 2.4 1.7 to 2.6 1.7 to 2.8 2.0 to 2.6 1.8 to 2.2 1.5 to 1.8

Projected annual Projected annual hot days/ cool days/ warm nights cold nights 2045–2065 2045–2065

–1.4 / –1.5 –1.5 / –1.4 –1.7 / –2.0 –2.2 / –2.6 –1.2 / –1.3 –1.3 / –1.4 –1.8 / –1.9 –1.4 / –1.5 –1.3 / –1.3 –1.5 / –1.9 –1.7 / –2.1 –1.5 / –1.7 –1.4 / –1.5 –2.3 / –2.6 –2.0 / –2.1 –1.5 / –1.5 –1.8 / –2.1 –2.1 / –2.5 –1.4 / –1.3 –2.1 / –2.6 –2.1 / –2.8 –1.8 / –1.7 –2.4 / –2.4 –1.6 / –1.7 –2.3 / –2.4 –1.8 / –2.3 –1.7 / –1.7 –1.5 / –1.6 –1.2 / –1.4 –2.4 / –2.9 –2.1 / –2.7 –2.3 / –2.7 –2.8 / –3.0 –2.4 / –2.5 –1.6 / –1.6 –1.9 / –1.8 –1.9 / –1.8 –1.5 / –1.6 –1.6 / –1.7 –2.8 / –2.9 –2.6 / –2.8 –1.7 / –2.1 –2.2 / –2.3 –2.0 / –2.4 –1.7 / –1.8 –2.0 / –2.7 –1.7 / –1.9 –1.5 / –1.5 –2.6 / –2.9 –2.3 / –2.6 –1.3 / –1.3 –1.5 / –1.5 –2.4 / –2.5 –1.7 / –1.7 –1.7 / –1.7 –2.4 / –2.5 –2.7 / –2.7 –2.8 / –2.8

3.1 / 7.0 3.2 / 6.5 3.9 / 7.8 7.0 / 20.2 2.9 / 6.5 2.8 / 6.3 3.6 / 9.3 2.9 / 5.2 2.7 / 6.2 4.6 / 8.6 3.4 / 11.8 2.1 / 4.5 2.9 / 5.5 5.5 / 19.2 5.1 / 16.9 3.1 / 6.1 4.7 / 12.8 5.6 / 19.6 2.9 / 6.0 5.3 / 16.6 7.8 / 26.2 4.5 / 18.2 6.2 / 22.0 1.9 / 4.4 4.7 / 22.5 5.1 / 13.7 3.8 / 7.3 2.9 / 5.6 7.4 / 10.3 7.9 / 26.6 6.4 / 24.7 9.1 / 26.5 11.8 / 26.8 7.1 / 22.9 3.2 / 6.0 10.5 / 17.6 4.0 / 7.3 2.5 / 4.7 2.6 / 5.2 12.1 / 23.6 9.5 / 24.4 4.0 / 8.1 8.9 / 24.5 6.6 / 14.4 2.1 / 4.8 6.6 / 21.4 1.7 / 4.1 3.6 / 6.3 13.7 / 26.9 6.7 / 16.8 2.8 / 5.9 2.6 / 5.0 6.6 / 22.1 3.5 / 6.5 7.8 / 21.6 6.9 / 20.2 9.9 / 22.2 12.6 / 23.1

Projected annual precipitation millimeters 2045–2065

–58 to 13 –147 to –44 –39 to 4 –89 to 75 –57 to 52 –75 to –7 –67 to 54 –70 to 27 –58 to 3 –19 to 4 –126 to 120 8 to 70 –64 to 29 –207 to 97 –91 to 137 –116 to –7 –106 to 24 –95 to 136 –127 to –21 –229 to 88 –21 to 206 –109 to 95 –71 to 115 17 to 88 –73 to 100 –79 to 41 –118 to 24 –37 to 86 –83 to 73 –80 to 199 –48 to 128 –40 to 134 –239 to 60 –125 to 73 –112 to –2 –108 to 39 –98 to –44 –43 to 55 21 to 87 –128 to 30 22 to 273 –20 to 1 –205 to 14 –55 to 26 33 to 93 –42 to 79 37 to 103 –112 to 2 –51 to 148 –87 to 26 –77 to –3 –38 to 57 –159 to 83 –110 to –40 –186 to 22 –122 to 104 –69 to 130 –125 to 34

Population Land area living in with an areas with elevation of 5 meters an elevation of 5 meters or less or less % of land area 2000 2000

0.0 5.0 0.4 0.2 1.2 0.0 1.1 0.0 20.0 39.0 14.1 0.0 9.2 1.2 0.0 0.1 0.0 1.2 0.4 0.0 0.0 3.8 0.1 2.4 0.0 0.0 3.1 1.4 24.6 0.9 0.0 0.1 2.1 0.2 3.0 12.7 6.4 0.0 17.7 4.1 2.0 4.0 2.5 3.1 3.4 0.7 1.1 2.1 0.5 16.6 1.4 4.9 0.8 6.3 0.6 1.1 9.5 3.9

0.0 8.2 3.5 2.1 4.5 0.0 7.2 0.0 29.8 66.6 14.0 0.0 14.3 14.1 0.0 0.1 0.0 4.9 1.5 0.0 0.0 10.6 0.3 4.0 0.0 0.0 1.6 8.1 26.2 2.0 0.0 1.0 0.8 3.2 3.4 10.0 9.7 0.0 18.5 3.0 7.3 25.6 1.7 1.2 7.2 0.4 4.4 4.0 5.9 33.4 3.3 4.4 2.3 9.9 0.3 3.6 18.8 5.4

Population affected by droughts, floods, and extreme temperature 1990–2009

Disaster risk reduction progress score 1, worst, to 5, best 2011

1.1 5.3 0.0 1.0 0.2 0.5 3.0 0.0 1.1 .. 4.6 0.0 0.0 0.9 1.3 0.5 0.7 0.5 0.0 1.3 2.4 6.6 0.1 0.0 0.2 2.7 0.3 8.0 0.0 0.7 0.0 0.3 0.7 0.0 0.0 0.7 0.0 0.2 0.0 0.1 0.3 0.0 0.4 7.3 0.0 3.3 0.0 0.0 .. 0.2 0.8 0.0 1.0 0.0 1.3 0.2 0.5 0.8

.. .. 3.5 .. 3.3 3.0 4.0 .. .. .. 4.0 .. .. .. 2.3 .. 3.0 4.5 3.5 .. 3.3 .. .. 4.3 .. .. 2.8 .. .. 3.8 .. .. 4.5 2.5 .. 4.5 .. 2.8 .. 3.0 4.8 .. 3.3 .. .. .. 3.5 .. .. .. 2.8 4.3 3.3 .. 3.3 .. 1.0 ..


Climate variability

Exposure to impact

Resilience

% of population

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

change in average number of days/nights

Average daily minimum/ maximum temperature degrees Celsius 1961–1990

Projected annual temperature degrees Celsius 2045–2065

18.9 / 28.1 4.8 / 14.7 17.7 / 29.6 21.1 / 30.6 10.1 / 24.4 14.1 / 28.7 5.9 / 12.7 13.4 / 25.0 9.5 / 17.4 20.6 / 29.3 7.0 / 15.3 11.2 / 25.4 0.6 / 12.2 18.9 / 30.6 0.3 / 11.1 6.5 / 16.5 .. 18.5 / 32.2 –4.6 / 7.7 18.2 / 27.4 1.9 / 9.3 10.7 / 22.1 5.2 / 18.5 20.0 / 30.6 14.4 / 29.2 2.3 / 10.1 4.5 / 15.1 18.0 / 27.3 16.6 / 27.2 21.2 / 29.6 20.9 / 35.6 20.2 / 35.1 18.9 / 25.9 13.5 / 28.5 5.0 / 13.9 –7.7 / 6.3 11.1 / 23.1 18.5 / 29.1 17.7 / 8.4 12.7 / 27.2 1.5 / 14.7 5.5 / 13.0 6.0 / 15.1 20.3 / 29.5 19.4 / 34.9 20.8 / 32.8 –2.0 / 5.0 20.8 / 30.4 13.2 / 27.2 21.3 / 29.5 20.1 / 30.4 17.7 / 29.4 13.5 / 25.7 21.6 / 30.1 3.8 / 11.9 10.2 / 20.1 21.2 / 29.3 21.3 / 33.0

1.6 to 2.6 2.0 to 3.0 1.9 to 2.6 1.5 to 1.8 2.2 to 3.3 2.3 to 3.2 0.9 to 1.4 1.9 to 2.7 1.8 to 2.6 1.3 to 1.8 1.8 to 2.3 2.1 to 2.9 2.2 to 3.1 1.9 to 2.1 2.1 to 2.7 1.8 to 2.3 2.0 to 3.1 2.2 to 3.1 2.3 to 3.2 1.6 to 2.4 2.1 to 3.1 2.0 to 2.9 2.0 to 2.5 1.7 to 2.2 2.0 to 2.4 2.0 to 3.0 1.9 to 3.0 1.7 to 2.1 2.0 to 2.5 1.6 to 1.9 2.5 to 3.1 2.4 to 3.1 1.3 to 1.7 1.7 to 2.8 2.0 to 3.3 2.3 to 3.1 2.1 to 3.0 1.9 to 2.4 1.7 to 2.3 2.2 to 2.9 2.2 to 3.4 1.4 to 2.3 1.1 to 1.7 1.6 to 2.4 2.4 to 2.8 2.0 to 2.5 1.8 to 3.3 2.0 to 2.4 2.4 to 3.4 1.6 to 2.2 1.4 to 1.8 1.9 to 2.6 2.1 to 2.7 1.4 to 1.8 1.9 to 2.8 1.3 to 2.3 1.4 to 1.8 2.3 to 2.9

Projected annual Projected annual hot days/ cool days/ warm nights cold nights 2045–2065 2045–2065

–2.2 / –2.5 –1.5 / –1.7 –2.0 / –2.2 –2.8 / –2.9 –1.4 / –1.4 –1.5 / –1.5 –1.3 / –1.4 –1.6 / –1.8 –1.8 / –1.8 –2.9 / –2.8 –1.9 / –2.2 –1.5 / –1.6 –1.2 / –1.4 –2.0 / –2.9 –1.4 / –1.8 –1.4 / –1.8 –1.5 / –1.3 –1.5 / –1.7 –1.2 / –1.4 –1.1 / –1.3 –1.7 / –1.8 –1.7 / –1.6 –1.6 / –1.9 –2.7 / –2.7 –1.7 / –2.1 –1.6 / –1.8 –1.4 / –1.3 –2.5 / –2.7 –2.1 / –2.5 –2.5 / –2.8 –2.0 / –2.5 –2.0 / –2.4 –2.9 / –2.9 –1.6 / –1.5 –1.5 / –1.6 –1.3 / –1.4 –1.8 / –2.0 –2.2 / –2.5 –1.7 / –1.8 –1.9 / –2.2 –2.1 / –2.1 –1.6 / –1.6 –2.2 / –2.1 –2.6 / –2.8 –1.8 / –2.4 –2.2 / –2.5 –1.8 / –1.8 –1.9 / –2.1 –1.8 / –1.9 –2.9 / –3.0 –2.8 / –2.9 –0.8 / –1.2 –2.5 / –2.7 –2.5 / –2.5 –1.5 / –1.7 –2.0 / –1.9 –3.0 / –2.9 –1.6 / –1.9

10.2 / 24.5 3.0 / 5.3 4.6 / 13.3 16.5 / 26.7 3.3 / 7.1 3.4 / 6.8 3.0 / 5.6 3.7 / 7.7 4.0 / 6.7 14.0 / 23.7 3.5 / 6.0 3.7 / 7.4 2.3 / 5.0 8.1 / 25.0 2.6 / 4.6 3.2 / 5.2 3.1 / 6.3 3.9 / 7.4 3.1 / 6.6 3.1 / 12.7 2.1 / 5.0 3.6 / 7.5 3.7 / 10.1 10.2 / 25.3 3.5 / 7.6 2.0 / 4.8 3.2 / 6.4 7.6 / 15.7 3.6 / 15.9 13.0 / 26.3 5.5 / 12.8 5.1 / 10.6 8.5 / 13.7 5.6 / 12.1 2.8 / 5.2 2.2 / 4.2 3.6 / 7.9 5.0 / 15.0 3.8 / 13.9 5.8 / 13.1 2.5 / 8.0 2.9 / 5.3 4.1 / 7.9 10.4 / 26.0 5.4 / 12.2 5.5 / 19.0 2.2 / 4.8 4.4 / 11.8 3.4 / 8.1 14.0 / 26.9 15.5 / 25.5 3.5 / 11.8 7.7 / 22.9 13.5 / 23.8 2.3 / 4.8 4.5 / 8.7 12.8 / 24.2 4.6 / 8.8

Projected annual precipitation millimeters 2045–2065

–204 to 15 –75 to 23 –91 to 135 –160 to 234 –51 to 6 –38 to –2 –13 to 78 –41 to –11 –108 to –11 –102 to 28 –47 to 80 –28 to –3 –1 to 37 0 to 144 –9 to 129 0 to 181 –148 to –34 –19 to 4 –32 to 35 –95 to 70 31 to 89 –75 to –27 –66 to 89 –115 to 81 –18 to 5 22 to 83 –150 to –43 –104 to 56 –53 to 91 –160 to 117 –142 to 43 –113 to 9 –165 to 67 –178 to 10 –72 to 11 1 to 50 –62 to –5 –69 to 61 –84 to 97 –67 to 23 –116 to 231 –18 to 50 –44 to 53 –220 to 19 –71 to 36 –128 to 89 41 to 131 –16 to 31 –60 to 36 –228 to 107 –163 to 421 –48 to 112 –57 to 181 –136 to 140 –12 to 64 –113 to –7 –133 to 37 –23 to 8

Population Land area living in with an areas with elevation of 5 meters an elevation of 5 meters or less or less % of land area 2000 2000

3.0 0.0 1.4 5.5 1.6 4.0 4.0 7.8 5.2 7.1 5.9 2.0 6.7 0.2 2.4 4.3 .. 8.9 0.0 0.0 3.0 1.7 0.0 0.4 0.8 1.9 0.0 1.3 0.0 3.0 0.0 1.0 7.1 2.9 1.3 0.0 0.7 1.8 4.6 0.3 0.0 58.5 2.7 3.6 0.0 0.5 4.9 1.1 1.4 3.7 1.8 0.0 0.4 6.0 1.7 2.3 7.7 13.4

2.2 0.0 3.8 11.2 5.0 6.5 6.6 8.3 7.5 5.8 16.2 4.2 3.9 1.4 5.3 5.0 .. 22.8 0.0 0.0 23.9 9.1 0.0 3.3 4.7 4.0 0.0 2.0 0.0 9.5 0.0 20.4 5.6 2.7 0.9 0.0 3.8 6.5 14.0 2.9 0.0 61.3 12.6 1.5 0.0 3.0 9.3 5.5 1.3 4.0 2.0 0.0 1.7 10.5 2.5 5.2 11.3 23.1

Population affected by droughts, floods, and extreme temperature 1990–2009

Disaster risk reduction progress score 1, worst, to 5, best 2011

1.3 0.1 4.4 0.2 3.1 0.0 0.0 0.0 0.0 1.1 0.0 0.4 0.2 6.5 2.5 0.1 .. 0.0 2.1 2.7 0.0 0.0 3.4 1.9 0.0 0.0 0.3 0.9 8.8 0.1 0.7 3.1 0.0 0.1 0.3 2.6 0.1 3.7 0.1 3.4 0.7 0.0 0.0 0.8 7.5 0.1 0.0 .. 1.1 0.2 0.7 0.7 2.0 0.8 0.0 0.0 0.0 ..

3.8 .. 3.3 3.3 .. .. .. .. 3.5 3.8 4.5 .. .. 4.0 .. .. .. .. .. 2.3 .. 3.0 2.5 .. .. .. 3.3 3.8 1.8 3.8 .. .. 3.5 4.3 .. 2.8 3.0 4.0 .. .. 2.8 .. 3.8 3.8 .. 4.0 3.8 .. 3.5 3.0 .. 3.8 3.0 .. 3.3 .. .. ..

2012 World Development Indicators

183

ENVIRONMENT

3.12

Climate variability, exposure to impact, and resilience


3.12

Climate variability, exposure to impact, and resilience Climate variability

Exposure to impact

Resilience

% of population Average daily minimum/ maximum temperature degrees Celsius 1961–1990

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

184

3.8 / 13.8 –10.1 / –0.1 11.9 / 23.8 18.2 / 31.1 20.8 / 34.9 .. 21.0 / 31.1 22.6 / 30.3 2.1 / 11.5 4.2 / 13.6 21.2 / 32.9 10.6 / 24.9 .. 7.9 / 18.7 23.3 / 30.6 19.4 / 34.4 15.5 / 27.3 –2.1 / 6.3 2.0 / 9.0 10.8 / 24.7 –3.9 / 7.9 16.7 / 28.0 21.2 / 31.4 .. 21.8 / 32.5 21.5 / 30.0 13.5 / 24.9 5.4 / 16.8 8.7 / 21.5 16.6 / 29.0 4.0 / 12.6 21.0 / 33.0 5.1 / 11.8 2.2 / 14.9 12.2 / 22.9 5.8 / 18.3 20.3 / 30.4 20.6 / 28.3 .. 18.6 / 29.1 14.5 / 28.3 14.2 / 27.8

2012 World Development Indicators

change in average number of days/nights Projected annual temperature degrees Celsius 2045–2065

2.0 to 3.1 2.6 to 3.7 2.1 to 2.4 2.3 to 2.9 2.0 to 2.7 2.0 to 3.1 1.8 to 2.3 1.5 to 1.8 2.0 to 2.9 2.0 to 3.0 1.9 to 2.2 1.9 to 2.7 .. 1.5 to 2.6 1.5 to 1.8 2.1 to 2.6 2.0 to 2.4 2.1 to 3.1 1.7 to 2.8 2.2 to 3.1 2.3 to 3.4 1.9 to 2.2 1.7 to 2.1 1.3 to 1.7 1.9 to 2.5 1.5 to 2.1 1.9 to 2.6 1.9 to 2.9 1.8 to 3.0 2.1 to 2.3 2.1 to 3.0 2.2 to 2.8 1.1 to 1.8 2.0 to 3.0 1.4 to 1.8 2.0 to 3.0 1.8 to 2.9 1.4 to 2.1 2.0 to 2.6 2.0 to 2.6 2.1 to 2.7 2.2 to 2.9

Projected annual Projected annual hot days/ cool days/ warm nights cold nights 2045–2065 2045–2065

–1.5 / –1.6 –1.6 / –1.8 –2.2 / –2.8 –1.5 / –1.7 –2.3 / –2.6 –1.5 / –1.4 –2.7 / –2.6 –2.6 / –2.7 –1.5 / –1.8 –1.4 / –1.5 –2.2 / –2.8 –1.7 / –2.0 .. –1.9 / –1.8 –2.8 / –2.9 –1.8 / –2.2 –1.6 / –2.1 –1.7 / –1.8 –1.4 / –1.5 –1.6 / –1.3 –1.4 / –1.5 –2.1 / –2.8 –1.6 / –1.6 –3.0 / –3.0 –2.4 / –2.5 –2.9 / –3.0 –1.9 / –2.0 –1.5 / –1.4 –1.0 / –1.1 –2.1 / –2.8 –1.4 / –1.6 –1.6 / –2.0 –1.4 / –1.5 –1.4 / –1.5 –0.8 / –1.0 –1.1 / –1.3 –2.5 / –2.9 –1.5 / –1.5 –1.7 / –1.8 –2.0 / –2.2 –2.0 / –2.5 –1.9 / –2.3

3.0 / 5.5 1.7 / 4.0 8.3 / 26.4 4.6 / 9.5 7.1 / 16.9 3.2 / 6.1 9.2 / 24.0 12.4 / 25.9 2.6 / 4.7 3.1 / 5.6 9.5 / 21.2 4.1 / 10.4 .. 4.0 / 8.0 7.9 / 23.9 5.2 / 16.6 3.8 / 10.9 2.0 / 4.4 3.1 / 5.6 3.3 / 6.7 3.2 / 7.0 6.2 / 21.6 3.6 / 17.0 16.1 / 25.9 6.0 / 21.5 14.7 / 26.5 3.4 / 6.8 3.1 / 6.3 2.7 / 5.7 6.4 / 25.6 2.6 / 4.8 4.2 / 9.4 3.0 / 5.4 3.1 / 6.4 2.0 / 5.7 2.6 / 5.7 9.1 / 26.5 5.7 / 14.4 3.8 / 7.9 5.6 / 13.1 4.0 / 17.7 3.8 / 13.2

Projected annual precipitation millimeters 2045–2065

–92 to 2 24 to 79 –12 to 211 –27 to 9 –124 to 37 –125 to –15 –96 to 128 –134 to 49 –44 to 45 –93 to 5 –4 to 111 –78 to 33 .. –112 to –18 11 to 196 –69 to 44 –82 to 34 35 to 107 –89 to 11 –57 to –13 –46 to 27 –9 to 167 –109 to 76 –123 to 206 –175 to 100 –266 to 8 –57 to –16 –103 to –29 –32 to 12 –13 to 224 –43 to 31 –26 to 17 –10 to 66 –26 to 98 0 to 130 –26 to 17 –150 to 85 –107 to 61 –40 to –13 –24 to 51 –54 to 94 –81 to 24

Population Land area living in with an areas with elevation of 5 meters an elevation of 5 meters or less or less % of land area 2000 2000

2.9 1.9 0.0 0.5 4.5 0.2 3.0 8.1 0.0 0.2 0.6 0.1 .. 1.3 3.9 0.1 0.0 1.5 0.0 0.1 0.0 0.2 4.2 2.9 0.6 8.0 2.8 1.0 5.3 0.0 1.5 4.6 7.6 1.7 1.9 0.1 2.3 17.5 14.8 0.6 0.0 0.0 1.8 t 0.7 1.8 1.7 1.8 1.6 2.5 2.5 1.5 1.4 1.5 0.4 2.2 3.6

2.9 2.9 0.0 1.0 14.8 0.1 5.1 12.1 0.0 1.3 2.2 0.5 .. 6.6 5.4 0.2 0.0 6.3 0.0 0.3 0.0 1.3 13.8 4.4 6.1 7.5 9.5 2.4 5.6 0.0 2.1 7.3 8.6 4.1 4.7 0.0 3.7 42.8 11.2 1.8 0.0 0.0 6.5 t 5.1 6.5 6.4 6.5 6.3 10.3 3.0 3.8 9.7 4.4 2.0 7.7 8.5

Population affected by droughts, floods, and extreme temperature 1990–2009

Disaster risk reduction progress score 1, worst, to 5, best 2011

0.1 0.1 1.3 0.0 0.6 0.0 0.2 .. 0.0 0.0 4.6 1.8

3.3 .. .. .. 2.8 .. 3.0 .. .. .. .. ..

0.7 2.2 2.8 9.2 0.0 0.0 0.5 5.4 1.5 3.8 0.0 0.5 0.0 0.1 0.1 0.0 0.9 0.3 .. 0.0 0.2 0.3 0.1 0.2 1.6 .. 0.1 4.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. 3.5 .. .. 3.8 4.8 3.5 .. 3.5 3.8 .. .. .. .. .. .. .. .. .. .. 3.5 .. .. 2.8 .. .. 2.3 3.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..


3.12

About the data

Scientists use the terms climate change and global

wave can be both a prolonged period of excessively

at an elevation of 5 meters or less is the percent-

warming to refer to the gradual increase in the

cold weather and the sudden invasion of very cold

age of the population living in areas with an eleva-

Earth’s surface temperature that has accelerated

air over a large area. Accompanied by frost, it can

tion of 5 meters or less. • Population affected by

since the industrial revolution and especially over

damage agriculture, infrastructure, and property.

droughts, floods, and extreme temperature is the

the past two decades. Most global warming has been

A heat wave is a prolonged period of excessively

average annual percentage of the population that is

caused by human activities that have changed the

hot and sometimes humid weather. Population

affected by natural disasters classified as droughts,

chemical composition of the atmosphere through

affected by these natural disasters is the num-

floods, or extreme temperature events. • Disaster

a buildup of greenhouse gases—primarily carbon

ber of people injured, left homeless, or requiring

risk reduction progress score is the average of self-

dioxide, methane, and nitrous oxide. Rising global

immediate assistance and can include displaced

assessment scores, ranging from 1 to 5, where 1 is

temperatures will cause sea level rise and alter local

or evacuated people.

the worst and 5 is the best, submitted by countries

climate conditions, affecting forests, crop yields,

• Resilience is measured by the disaster risk reduc-

and water supplies, and may affect human health,

tion progress score, an average of self-assess-

animals, and many types of ecosystems. The 2007

ment scores submitted by countries under Prior-

Intergovernmental Panel on Climate Change’s (IPCC)

ity 1 of the Hyogo Framework National Progress

assessment report concluded that global warming

Reports. The Hyogo Framework is a global blueprint

is “unequivocal” and gave the strongest warning yet

for disaster risk reduction efforts that was adopted

about the role of human activities. The report esti-

by 168 countries in 2005. Assessments of Priority

mated that sea levels would rise approximately 49

1 include four indicators that reflect the degree to

centimeters over the next 100 years, with a range of

which countries have prioritized disaster risk reduc-

uncertainty of 20–86 centimeters. That will lead to

tion and the strengthening of relevant institutions.

under Priority 1 of the Hyogo Framework National Progress Reports.

increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding

Definitions

of larger areas and higher elevations. Climate model

• Average daily minimum and maximum tempera-

simulations predict an increase in average surface

ture are the minimum and maximum daily tem-

air temperature of about 2.5°C by 2100 (Kattenberg

peratures in the country averaged over the period

and others 1996) and increase of “killer” heat waves

specified, based on gridded climatologies from the

during the warm season (Karl and others 1997).

Climatic Research Unit of the University of East

Data in the table are categorized into three climate

Anglia. • Projected change in annual temperature

Data sources

change topics.

is the projected change in annual temperature during

Data on average daily minimum and maximum

• Climate variability contains such indicators as aver-

the period specified, relative to the control period

temperature are from Mitchell and others (2003).

age daily minimum and maximum temperature,

1961–2000. • Projected change in annual cool

Data on projected change in annual temperature

projected change in annual temperature, projected

days and cold nights are the projected change in

are from IPCC (2007). Data on projected change

change in annual cool days and cold nights and

the annual number of cool days and cold nights dur-

in annual cool days and cold nights and projected

hot days and warm nights, and projected change

ing the period specified, relative to the control period

change in annual hot days and warm nights are

in annual precipitation. These indicators are use-

1961–2000. Cool days are those that fall below the

from University of Cape Town Climate Systems

ful for understanding critical thresholds related

10th percentile of maximum temperature in the con-

Analysis Group calculations based on data from

to heat stress in such sectors as agriculture and

trol period, and cold nights are those that fall below

Meehl and others (2007). Data on projected

energy.

the 10th percentile of minimum temperature in the

change in annual precipitation are from Meehl and

• Exposure to impact contains indicators that mea-

control period. • Projected change in annual hot

others (2007). Data on land area with an elevation

sure vulnerability to the impacts of climate change,

days and warm nights are the projected change in

of 5 meters or less and population living in areas

such as land area with an elevation of 5 meters

the annual number of hot days and warm nights dur-

with an elevation 5 meters or less are from CIESIN

or less and population living in such areas as well

ing the period specified, relative to the control period

(2007). Data on population affected by droughts,

as population affected by droughts, floods, and

1961–2000. Hot days are those that exceed the

floods, and extreme temperature are from the

extreme temperature. A drought is an extended

90th percentile of maximum temperatures in the con-

United States Agency for International Develop-

period of deficiency in a region’s water supply as

trol period, and warm nights are those that exceed

ment Office of Foreign Disaster Assistance/Center

a result of below average precipitation. A drought

the 90th percentile of minimum temperatures in the

for Research on the Epidemiology of Disasters

can lead to losses in agriculture, affect inland

control period. • Projected change in annual precipi-

International Disaster Database (www.emdat.be),

navigation and hydropower plants, reduce access

tation is the projected change in annual precipitation

Université Catholique de Louvain, and the World

to drinking water, and cause famines. A flood is

during the period specified, relative to the control

Bank. Data on disaster risk reduction progress

a significant rise of water level in a stream, lake,

period 1961–2000. • Land area at an elevation

score are from UNISDR (2009, 2010, 2011) Prog-

reservoir, or coastal region. Extreme temperature

of 5 meters or less is the percentage of land area

ress Reports.

events are either cold waves or heat waves. A cold

with an elevation of 5 meters or less. • Population

2012 World Development Indicators

185

ENVIRONMENT

Climate variability, exposure to impact, and resilience


3.13

Urbanization Urban population

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

186

millions 1990 2010

% of total population 1990 2010

3 1 13 4 28 2 15 5 4 0 21 7 10 2 4 2 1 112 6 1 0 1 5 21 1 1 11 311 6 23 10 1 2 5 3 8 1 8 4 4 6 25 3 0 1 6 3 43 1 0 3 58 5 6 4 2 0 2

18 36 52 37 87 68 85 66 54 88 20 66 96 35 56 39 42 75 66 14 6 13 41 77 37 21 83 27 100 68 28 54 51 40 54 73 67 75 85 55 55 44 49 16 71 13 61 74 69 38 55 73 36 59 41 28 28 29

9 2 24 11 37 2 20 6 5 1 42 7 11 4 7 2 1 169 5 3 1 3 11 28 2 3 15 601 7 35 23 3 3 10 3 9 1 8 5 7 10 35 4 1 1 15 3 50 1 1 2 60 13 7 7 4 0 5

2012 World Development Indicators

25 48 67 59 92 64 89 68 52 89 28 74 97 42 67 49 61 87 72 20 11 23 58 81 39 28 89 45 100 75 35 62 64 50 58 76 70 74 87 71 67 43 61 22 70 18 64 78 86 58 53 74 52 61 50 35 30 50

average annual % growth 1990–2010

4.3 1.7 2.4 4.3 1.1 0.1 1.8 0.6 1.5 7.6 2.8 0.4 0.9 3.8 2.3 1.0 2.5 1.4 –0.3 5.1 5.3 3.9 3.6 1.3 2.3 4.3 1.2 2.5 0.9 1.8 4.5 3.2 2.3 3.3 0.2 0.0 1.5 0.3 0.7 2.4 2.4 1.8 1.0 5.1 0.0 3.9 0.9 0.8 2.4 4.2 1.1 0.0 3.8 0.6 3.4 3.6 2.3 4.1

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2010

% of urban population 1990 2010

% of urban population 1990 2010

% of rural population 1990 2010

7 .. 7 15 39 33 60 20 24 .. 9 16 17 .. 25 .. .. 35 14 6 .. 6 14 40 .. .. 35 9 100 31 13 29 24 17 .. 20 .. 12 20 21 26 21 18 .. .. 4 17 22 .. .. 25 8 13 30 9 16 .. 16

37 21 14 41 37 49 25 30 45 29 32 24 17 31 29 24 21 13 21 42 66 51 19 18 43 38 42 3 100 21 35 54 47 42 27 27 33 16 24 38 28 37 37 72 43 29 28 22 62 62 46 6 22 51 22 55 54 56

.. 94 99 67 93 95 100 100 .. 100 58 91 100 14 28 98 61 80 100 43 41 36 63 100 21 21 91 48 .. 79 23 .. 94 38 99 86 100 100 100 83 86 91 88 58 96 20 100 100 .. .. 97 100 12 100 81 19 .. 44

.. 66 77 6 73 .. 100 100 .. .. 34 96 100 0 6 .. 22 33 98 2 44 5 37 99 5 4 48 15 .. 40 4 .. 91 8 98 64 100 98 100 61 48 57 62 0 94 1 100 100 .. .. 95 100 4 93 48 6 4 19

11 .. 8 25 39 36 58 20 22 .. 14 20 17 .. 33 .. .. 41 16 12 .. 11 20 44 .. .. 35 18 105 38 18 33 31 21 .. 19 .. 11 21 22 31 19 25 .. .. 4 21 22 .. .. 25 8 17 29 8 17 .. 21

44 29 12 43 35 56 22 30 42 16 35 26 18 23 25 22 16 12 22 57 52 48 19 20 42 27 39 3 100 24 38 53 49 42 27 25 31 15 25 31 28 32 41 60 43 20 33 21 49 45 48 6 19 47 16 47 68 43

Access to improved sanitation facilities

60 95 98 85 91 95 100 100 86 100 57 91 100 25 35 99 75 85 100 50 49 73 58 100 43 30 98 74 .. 82 24 20 95 36 99 94 100 99 100 87 96 97 89 52 96 29 100 100 33 70 96 100 19 99 87 32 44 24

30 93 88 19 77 80 100 100 78 .. 55 97 100 5 10 92 41 44 100 6 46 20 36 99 28 6 83 56 .. 63 24 15 96 11 98 81 100 97 100 75 84 93 83 4 94 19 100 100 30 65 93 100 8 97 70 11 9 10


Urban population

millions 1990 2010

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

2 7 223 56 31 13 2 4 38 1 78 2 9 4 12 32 .. 2 2 1 2 2 0 1 3 2 1 3 1 9 2 1 0 60 2 1 12 3 10 0 2 10 3 2 1 34 3 1 34 1 1 2 15 30 23 5 3 0

4 7 369 129 51 21 3 7 41 1 85 5 10 9 15 40 .. 3 2 2 2 4 1 2 5 2 1 6 3 21 5 1 1 88 1 2 18 9 16 1 5 14 4 3 3 79 4 2 64 3 1 4 21 62 23 6 4 2

% of total population 1990 2010

40 66 26 31 56 70 57 90 67 49 63 72 56 18 58 74 .. 98 38 15 69 83 14 45 76 68 58 24 12 50 23 40 44 71 47 57 48 21 25 28 9 69 85 52 15 35 72 66 31 54 15 49 69 49 61 48 72 92

49 68 30 54 70 66 62 92 68 54 67 79 59 22 63 82 .. 98 37 33 68 87 27 62 78 67 68 30 20 72 33 41 43 78 41 58 57 38 34 38 18 83 87 57 17 50 78 72 37 75 13 62 72 66 61 61 99 96

average annual % growth 1990–2010

2.9 0.4 2.3 3.1 1.9 2.8 0.8 1.8 0.7 0.6 0.2 2.3 1.9 4.0 1.0 0.5 .. 3.4 1.6 5.0 –0.6 0.9 3.7 5.2 1.7 –1.4 0.9 4.0 5.7 2.9 4.7 2.9 0.6 1.6 –0.8 1.9 1.6 4.4 2.7 3.4 4.4 1.2 1.3 1.8 4.0 4.0 1.3 2.6 2.9 2.6 2.1 2.7 1.2 2.8 0.0 1.1 0.5 9.7

3.13

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2010

% of urban population 1990 2010

% of urban population 1990 2010

% of rural population 1990 2010

29 29 4 14 21 32 46 48 9 49 42 37 12 32 21 33 .. 68 38 70 49 53 49 108 26 23 40 36 24 12 37 53 30 25 38 46 22 27 30 36 23 9 30 34 36 14 22 27 21 65 31 53 39 27 7 53 60 54

71 100 51 56 83 .. 100 100 .. 78 100 98 96 27 .. 100 .. 100 94 .. .. 100 .. .. 97 95 92 15 48 88 33 29 91 76 .. .. 81 36 .. 62 37 100 .. 59 19 39 100 96 72 73 78 61 71 69 96 97 .. 100

36 100 7 21 74 .. 98 100 .. 81 100 95 97 25 .. 100 .. 100 .. .. .. .. .. .. 96 .. .. 7 38 81 10 8 88 34 .. .. 27 4 .. 9 7 100 88 26 2 36 100 55 7 40 42 15 17 45 .. 87 .. 100

12 19 10 10 24 27 26 56 19 .. 46 27 7 6 13 51 .. 67 .. .. .. 44 .. .. 20 .. .. 8 .. 8 9 .. .. 33 .. .. 18 6 9 .. .. 13 25 18 6 12 .. .. 15 35 .. 26 27 14 4 37 44 ..

14 17 12 9 24 23 25 57 17 .. 49 18 8 9 12 48 .. 84 .. .. .. 46 .. .. 17 .. .. 9 .. 9 11 .. .. 35 .. .. 19 7 11 .. .. 12 32 23 7 15 .. .. 18 39 .. 31 31 14 4 39 69 ..

28 25 6 7 14 28 40 47 8 40 43 23 14 39 18 24 .. 86 43 40 46 53 39 34 22 24 35 30 29 7 33 51 28 22 44 61 18 18 27 41 19 8 37 28 40 13 23 33 20 52 37 51 43 19 7 44 70 28

Access to improved sanitation facilities

85 100 58 73 100 76 100 100 .. 78 100 98 97 32 86 100 .. 100 94 89 82 100 32 29 97 95 92 21 49 96 35 51 91 87 89 64 83 38 83 57 48 100 .. 63 34 35 100 100 72 75 71 90 81 79 96 100 .. 100

2012 World Development Indicators

69 100 23 39 100 67 98 100 .. 82 100 98 98 32 71 100 .. 100 93 50 71 .. 24 7 96 .. 82 12 51 95 14 9 88 79 82 29 52 5 73 17 27 100 .. 37 4 27 100 95 34 51 41 40 37 69 80 100 .. 100

187

ENVIRONMENT

Urbanization


3.13

Urbanization Urban population

millions 1990 2010

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

188

12 109 0 12 3 4 1 3 3 1 2 18 .. 29 3 7 0 7 5 6 2 5 17 0 1 0 5 32 2 2 35 1 51 188 3 8 17 13 1 2 3 3 2,262 s 108 1,456 560 896 1,564 463 246 309 117 286 144 698 215

12 103 2 23 5 4 2 5 3 1 3 31 .. 36 3 20 0 8 6 11 2 12 24 0 3 0 7 51 2 4 31 6 56 255 3 10 27 25 3 8 5 5 3,482 s 225 2,401 992 1,409 2,626 901 259 462 192 492 319 856 244

2012 World Development Indicators

% of total population 1990 2010

53 73 5 77 39 50 33 100 57 50 30 52 .. 75 17 27 23 83 73 49 32 19 29 21 30 9 58 59 45 11 67 79 89 75 89 40 84 20 68 21 39 29 43 w 21 38 32 44 36 29 63 71 52 25 28 73 71

55 73 19 84 43 52 38 100 57 48 37 62 .. 77 15 45 26 85 74 55 27 26 34 28 43 14 67 70 50 13 68 78 90 82 93 37 94 29 72 32 36 38 51 w 28 48 39 57 46 46 64 79 58 30 37 78 74

average annual % growth 1990–2010

0.1 –0.1 4.5 4.0 3.3 –0.1 3.0 1.8 0.4 –0.2 3.5 2.1 .. 0.5 0.9 4.5 2.6 0.9 1.1 2.6 1.5 4.7 1.6 3.6 3.7 2.9 1.6 1.9 2.1 4.4 –0.3 8.0 0.8 1.2 0.5 1.8 1.9 2.7 2.8 4.9 2.0 2.0 2.0 w 3.6 2.2 2.7 1.9 2.3 2.7 0.5 1.6 2.2 2.5 3.9 1.0 0.6

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2010

% of urban population 1990 2010

9 17 .. 34 19 15 .. 99 .. .. 16 28 .. 22 .. 9 .. 12 15 31 .. 5 10 .. 17 .. .. 24 .. 4 12 26 26 42 50 10 34 9 .. 5 10 10 17 w 8 14 12 16 13 .. 16 32 20 10 11 .. 18

9 18 .. 39 23 15 .. 95 .. .. 16 34 .. 24 .. 12 .. 14 15 34 .. 7 10 .. 28 .. .. 29 .. 5 14 21 26 45 49 8 32 13 .. 10 11 13 20 w 11 18 14 22 17 .. 18 35 20 13 14 .. 18

17 8 57 19 50 30 40 99 .. 27 53 10 .. 15 21 33 22 15 20 26 35 27 35 79 56 45 14 20 25 38 7 33 15 9 56 26 17 25 .. 26 24 35 17 w 34 15 16 14 16 9 15 24 26 9 27 20 16

17 10 57 21 54 29 40 95 .. 26 43 12 .. 16 22 26 25 16 20 27 39 28 30 55 64 32 11 21 26 36 9 27 15 8 53 21 11 25 .. 31 31 34 16 w 33 13 14 12 15 7 16 22 22 11 26 19 15

Access to improved sanitation facilities

% of urban population 1990 2010

% of rural population 1990 2010

88 80 69 100 62 96 22 99 100 100 .. 82 .. 100 85 51 62 100 100 95 93 10 94 .. 26 93 95 96 99 32 97 98 100 100 95 95 89 63 91 70 61 54 76 w 38 68 61 71 66 54 88 80 89 54 42 100 100

52 58 34 .. 22 .. 5 .. 100 100 .. 60 .. 100 67 18 44 100 100 75 .. 6 80 .. 8 93 44 66 97 26 .. 95 100 99 83 76 45 30 .. 12 37 35 27 w 17 22 17 27 21 20 68 38 57 11 19 99 99

88 74 52 100 70 96 23 100 100 100 52 86 .. 100 88 44 64 100 100 96 95 20 95 73 26 92 96 97 99 34 96 98 100 100 100 100 .. 94 92 93 57 52 79 w 47 75 66 82 72 76 87 84 94 60 42 100 100

54 59 56 .. 39 88 6 .. 99 100 6 67 .. 100 93 14 55 100 100 93 94 7 96 37 3 92 64 75 97 34 89 95 100 99 99 100 .. 68 92 34 43 32 47 w 32 45 34 62 43 57 80 59 80 28 23 99 100


About the data

3.13

Definitions

There is no consistent and universally accepted

• Urban population is the midyear population of

standard for distinguishing urban from rural areas,

areas defined as urban in each country and reported

in part because of the wide variety of situations

to the United Nations (see About the data). • Popula-

across countries (see About the data for table 3.1).

tion in urban agglomerations of more than 1 million

Most countries use an urban classification related

is the percentage of a country’s population living in

to the size or characteristics of settlements. Some

metropolitan areas that in 2005 had a population of

define urban areas based on the presence of cer-

more than 1 million. • Population in largest city is

tain infrastructure and services. And other countries

the percentage of a country’s urban population living

designate urban areas based on administrative

in that country’s largest metropolitan area. • Access

arrangements.

to improved sanitation facilities is the percentage

The population of a city or metropolitan area

of the urban or rural population with access to at

depends on the boundaries chosen. For example, in

least adequate excreta disposal facilities (private or

1990 Beijing, China, contained 2.3 million people in

shared but not public) that can effectively prevent

87 square kilometers of “inner city” and 5.4 million

human, animal, and insect contact with excreta.

in 158 square kilometers of “core city.” The popula-

Improved facilities range from simple but protected

tion of “inner city and inner suburban districts” was

pit latrines to flush toilets with a sewerage connec-

6.3 million and that of “inner city, inner and outer

tion. To be effective, facilities must be correctly con-

suburban districts, and inner and outer counties”

structed and properly maintained.

was 10.8 million. (Most countries use the last definition.) For further discussion of urban-rural issues see box 3.1a in About the data for table 3.1. Estimates of the world’s urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China’s State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China’s population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years. Because the estimates in the table are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank’s estimates of total population (see table 2.1). The table shows access to improved sanitation facilities for both urban and rural populations to

Data sources

allow comparison of access. Definitions of access

Data on urban population and the population in

and urban areas vary, however, so comparisons

urban agglomerations and in the largest city are

between countries can be misleading.

from the United Nations Population Division’s World Urbanization Prospects: The 2010 Revision. Data on total population are World Bank estimates. Data on access to sanitation are from the World Health Organization and United Nations Children’s Fund’s Progress on Drinking Water and Sanitation (2012).

2012 World Development Indicators

189

ENVIRONMENT

Urbanization


3.14

Urban housing conditions Census year

Household size

number of people National Urban

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

190

2001 1998 2001 2001 2001 2001 1999 2001 1999 2001 1992 2001 2001 2000 2001 1996 1990 2005 1987 2001 2003 1993 2002 2000 1993 1984 1984 2000 1998 2001 2002 2001 2001 2002 2001 1996 1992 2000 1994 2000 1999 2003 1993 2002 2001 2000 2001 2002 1996 1982

.. 4.2 4.9 .. 3.6 4.1 3.8 2.4 4.7 .. 4.8 .. 2.6 5.9 4.2 .. 4.2 3.8 2.7 6.2 4.7 5.0 5.2 2.6 5.2 5.1 3.4 3.4 .. 4.8 5.4 10.5 4.0 5.4 3.0 3.1 .. 2.4 2.2 3.9 3.5 4.7 .. .. 2.4 4.8 2.2 2.5 5.2 8.9 3.5 2.3 5.1 3.0 4.4 6.7 .. 4.2

2012 World Development Indicators

.. 3.9 .. .. .. 4.0 .. .. 4.4 .. 4.8 .. .. .. 4.3 .. 3.9 3.7 2.7 5.8 .. 4.9 5.1 .. 5.8 5.1 3.5 3.2 .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. .. 2.3 4.7 .. .. .. .. 3.5 .. 5.1 .. 4.7 .. .. ..

Overcrowding

Households living in overcrowded dwellingsa % of total National Urban

.. .. .. .. 19 4 1 2 .. .. .. .. 0b .. 40 .. 27 .. .. 30 .. 35 67 .. 32 .. .. .. .. 27b 55 .. 22 .. .. 5 .. .. .. .. 30 .. 63 .. 3 .. .. .. .. .. .. .. .. 1 .. 63 .. 26

.. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. .. 47 .. .. 53 .. 32 77 .. 36b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. .. .. .. 97 93 .. .. .. .. 21b .. .. 26 43 .. 88 .. 79 .. .. 79 77 .. 78 .. 91 82 .. 83b .. .. 88 .. .. .. .. .. .. 97 81 .. 67 .. .. .. .. .. .. 18 .. .. 45 .. 67 .. .. ..

.. .. .. .. .. 93 .. .. .. .. 42b .. .. .. 58 .. 90 b .. 89 .. .. 88 .. .. 92 .. 92 .. .. .. .. .. .. .. .. .. .. .. .. .. 88 .. 83 .. .. 23 .. .. .. .. .. .. .. .. 80 .. .. ..

.. 65b 67 .. .. 95 .. 48 74 .. 88b .. 67 59 70 .. 61 74 98 .. .. 58 73 64 85 .. 66 88 .. 68b .. 76 72 .. .. .. .. 52 .. .. 68 b .. 70 .. .. .. 64 55 .. 68 .. 43 57 .. 81 76 .. 92

.. 30 b .. .. .. 90 .. .. 62 .. 61b .. .. .. 59 .. 47 75 98 .. .. 57 48 .. 74 .. 65 74 .. .. .. .. .. .. .. .. .. .. .. .. 58b .. 68 .. .. 54 .. .. .. .. .. .. .. .. 74 .. .. 68

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. 4 1 .. .. 4 .. .. .. 32b .. 3b .. 1 .. .. .. .. 27 27 32 .. .. 13 .. .. 13 .. .. 2 .. .. .. .. 49 .. 8 9 75 3 .. 72 .. 44 .. .. .. .. .. 53 .. 2 .. .. ..

.. .. .. .. .. 1 .. .. 5 .. .. .. .. .. 5b .. .. .. .. .. .. 32 42 .. .. .. 15 .. .. .. .. .. 3 .. .. .. .. .. .. .. 14 .. 6 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. ..

.. 12 19 .. 16b .. .. .. .. .. .. .. .. .. 6 .. .. .. 23 .. .. .. .. 8 .. .. 11 1 .. 10 b .. .. 9 .. 12 .. .. 12 .. 11 12 .. 11 .. 13 .. .. 7 .. .. .. 7 5 .. 13 .. .. 9

.. 13 .. .. .. .. .. .. .. .. .. .. .. .. 4 .. .. .. 17 .. .. .. .. .. .. .. 10 .. .. .. .. .. 6 .. .. .. .. .. .. .. 7 .. 11 .. .. .. .. .. .. .. .. .. .. .. 11 .. .. 19


Census year

Household size

number of people National Urban

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

2001 2001 2001 2000 1996 1997 2002 1995 2001 2001 2000 2004 1999 2000 1993 1995 1999 1995 2000 2001 1974 2001 2002 1993 1998 2000 1998 1988 2000 2005 2003 2000 1982 1997 2001 2001 2001 1995 2001 1991 1980 2003 1998 2000 1990 2002 2007 2000 1988 2001 2005

4.4 2.6 5.3 4.0 4.8 7.7 3.0 3.5 2.8 3.5 2.7 5.3 .. 4.6 3.8 4.4 .. 6.4 4.4 6.1 3.0 .. 5.0 4.8 6.4 2.6 3.6 4.9 4.4 4.5 5.6 .. 3.9 4.0 .. 4.4 5.9 4.4 .. 5.3 5.4 .. 2.8 5.3 6.4 5.0 2.7 7.1 6.8 4.1 4.5b 4.6 3.9 4.9 3.2 2.8 2.8 ..

.. .. 5.3 .. 4.6 7.2 .. .. .. .. .. 5.1 .. 3.4 .. .. .. .. 3.6 6.1 2.6 .. .. .. .. .. 3.6b 4.8 4.4 4.4 .. .. 3.8 3.9 .. 4.5 5.3 4.9 .. .. 4.9 .. .. .. 6.0 4.7 .. .. 6.8 .. 6.5 4.5 3.9 .. .. .. . ..

Overcrowding

Households living in overcrowded dwellingsa % of total National Urban

.. 2 77 .. 33b .. .. .. .. .. .. 35 .. .. 23 .. .. .. .. .. 4 .. 10 b 31 .. 7 8b 64 30 .. .. .. 6 24 .. .. .. 37 .. .. .. .. 1b .. .. .. 1 .. .. 28b .. 38b 35 .. .. .. 1 ..

.. .. 71 .. 26b .. .. .. .. .. .. 34 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 57 .. .. .. .. 7 20 .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. .. .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

69 .. 83 .. 72 88 .. .. .. 98b .. .. .. 35 .. .. .. .. .. 49 88 .. .. 20 .. .. 95b .. 48 .. .. .. 91 .. .. .. .. 7 .. .. .. .. .. 79 .. .. .. .. 58 88 .. 95b .. .. .. .. .. ..

85 .. 81 .. 76 96 .. .. .. .. .. .. .. 72 .. .. .. .. .. 77 .. .. .. .. .. .. 95b .. 84 .. .. .. 94 .. .. .. .. 20 .. .. .. .. .. 87 .. .. .. .. 86 98b .. 98b .. .. .. .. .. ..

.. .. 87 .. 73 70 .. .. .. 58b 61 64 .. 72 50 .. .. .. .. 96 58 .. 84 1 .. .. 48b 81 86 .. .. .. 87 .. .. .. .. 92 .. .. 88 .. 65 84 77 .. 67 .. 81 80 .. 79 .. 71 .. 76 75 ..

.. .. 67 .. 67 66 .. .. .. .. .. 60 .. 25 .. .. .. .. .. 86 .. .. .. .. .. .. .. 59 47 .. .. .. 81 .. .. .. .. 83 .. .. .. .. .. 86 40 .. .. .. .. 66b 44 75 .. .. .. .. .. ..

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. .. 4 8b .. .. 2b 37 72 .. .. 15 .. .. 9b .. .. 74 .. 0 .. .. .. .. .. .. 10 b .. .. .. .. .. 48 .. 1 .. .. .. .. 17 0 .. .. 38 .. .. 10 b .. 1b .. 12 .. 86 .. ..

.. .. .. .. .. 5 .. .. .. .. .. 80 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 16b .. .. .. .. .. 56 .. 1 .. .. .. .. .. 0 .. .. .. .. .. 10 b 8 2b ..

14 4 6 .. .. 13 .. .. 21 .. .. .. .. 39 .. .. .. 11 .. .. 0 .. .. .. 7 .. 7b .. .. .. .. .. 7 3 .. .. .. 0 .. .. 0 .. 10 8 .. .. .. .. .. 14 .. 6b ..

.. .. 9 .. .. 15 .. .. .. .. .. .. .. 17 .. .. .. .. .. .. .. .. .. .. .. .. 3b .. .. .. .. .. 6 2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b ..

.. .. .. ..

1 .. .. ..

.. .. .. ..

2012 World Development Indicators

191

ENVIRONMENT

3.14

Urban housing conditions


3.14

Urban housing conditions Census year

Household size

number of people National Urban

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

2002 2002 2002 2004 2002 2001 1985 2000 2002 1975 2007 2001 2001 1993 1997 1990 2000 1981 2000 2002 2000 2000 1994 1990 2002 2003 2001 2005 1996 2001 1999 1997 1994 2000 1992

2.9 2.8 4.4 5.5 9.2 2.9 6.8 4.4 .. 2.8 .. 3.0 .. 2.9 3.8 5.8 5.4 2.0 2.2 6.3 .. 4.9 3.8 .. .. 3.7 8.0 5.0 .. 4.7 .. .. .. 2.5 3.3 .. 4.4 4.6 7.1 6.7 5.3 4.8

Overcrowding

Households living in overcrowded dwellingsa % of total National Urban

2.8 2.7 3.7 .. 8.0 2.2 .. .. .. 2.7 .. 2.8 .. .. .. 6.0 3.7 .. .. 6.0 .. 4.5b .. .. .. .. .. .. .. 3.9 .. .. 2.4 .. 3.4b .. .. 4.5 .. 6.8 5.9 4.2

a. More than two people per room. b. Data are from a previous census.

192

2012 World Development Indicators

20 7 43 .. 72 .. .. .. .. 14 .. 16 .. 1 .. .. .. .. 1 .. .. 33b .. .. .. 9b .. .. .. .. .. .. .. 0 22b .. .. .. .. 54b .. ..

20 5 36 .. 68 .. .. .. .. 17 .. 15 .. .. .. .. .. .. .. .. .. 7b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. .. 13 92b .. .. 34 .. .. .. .. .. .. .. 93b .. .. .. .. .. .. .. 93 .. .. 98b 99 .. .. 19 .. .. .. .. .. .. .. 77 .. .. .. ..

.. .. 31 .. .. .. .. .. .. .. .. .. .. .. 92b .. .. .. .. .. .. .. 93 .. .. .. .. .. .. 61 .. .. .. .. .. .. .. 89 .. .. .. ..

84 .. 79 43 74 .. 68 .. .. 91 .. 43 .. 82 70 b 86b .. .. 34 .. .. 82b 81 .. .. 74b 71 70 .. 76 .. .. .. 74 57b .. 78 95 78 88b 94 94

72 .. 41 .. 54 .. .. .. .. 87 .. 40 .. .. 58b 58b .. .. .. .. .. 43b 62 .. .. .. 89b .. .. 28 .. .. 69 .. 57b .. .. 86 .. 68b 30 30

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. 73 36 .. .. .. .. .. .. 33 .. .. .. .. 1 0b .. 54 77 .. .. .. 3 .. .. 17b 6 .. .. 37 .. .. .. 26 .. .. 14 .. 45 3b .. 6

.. 86 60 .. .. .. .. .. .. 56 .. .. .. .. 14b 1b .. .. .. .. .. .. .. .. .. .. 10 b .. .. 71 .. .. 19 .. .. .. .. .. .. 11b .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. 13 .. .. 1 .. .. .. .. 3 .. .. .. 15 .. .. .. .. .. .. .. 13b .. 16 .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. 1b .. .. .. .. .. .. .. .. .. .. .. 12b .. .. .. .. .. .. .. 13b .. .. .. .. .. .. ..


About the data

3.14

Definitions

Urbanization can yield important social benefi ts,

• Census year is the year in which the underlying

improving access to public services and the job mar-

data were collected. • Household size is the aver-

ket. It also leads to significant demands for services.

age number of people within a household, calcu-

Inadequate living quarters and demand for housing

lated by dividing total population by the number

and shelter are major concerns for policymakers.

of households in the country and in urban areas.

The unmet demand for affordable housing, along

• Overcrowding refers to the number of households

with urban poverty, has led to the emergence of

living in dwellings with two or more people per room

slums in many poor countries. Improving the shel-

as a percentage of total households in the country

ter situation requires a better understanding of the

and in urban areas. • Durable dwelling units are

mechanisms governing housing markets and the pro-

the number of housing units in structures made of

cesses governing housing availability. That requires

durable building materials (concrete, stone, cement,

good data and adequate policy-oriented analysis so

brick, asbestos, zinc, and stucco) expected to main-

that housing policy can be formulated in a global

tain their stability for 20 years or longer under local

comparative perspective and drawn from lessons

conditions with normal maintenance and repair, tak-

learned in other countries. Housing policies and

ing into account location and environmental hazards

outcomes affect such broad socioeconomic condi-

such as floods, mudslides, and earthquakes, as a

tions as the infant mortality rate, performance in

percentage of total dwellings. • Home ownership

school, household saving, productivity levels, capital

refers to the number of privately owned dwellings as

formation, and government budget deficits. A good

a percentage of total dwellings. When the number

understanding of housing conditions thus requires

of private dwellings is not available from the census

an extensive set of indicators within a reasonable

data, the share of households that own their housing

framework.

unit is used. Privately owned and owner-occupied

There is a strong demand for quantitative indi-

units are included, depending on the definition used

cators that can measure housing conditions on a

in the census data. State- and community-owned

regular basis to monitor progress. However, data

units and rented, squatted, and rent-free units are

deficiencies and lack of rigorous quantitative analy-

excluded. • Multiunit dwellings are the number

sis hamper informed decisionmaking on desirable

of multiunit dwellings, such as apartments, flats,

policies to improve housing conditions. The data

condominiums, barracks, boardinghouses, orphan-

in the table are from housing and population cen-

ages, retirement houses, hostels, hotels, and col-

suses, collected using similar definitions. The table

lective dwellings, as a percentage of total dwellings.

will incorporate household survey data in future edi-

• Vacancy rate is the percentage of completed dwell-

tions. The table focuses attention on urban areas,

ing units that are currently unoccupied. It includes

where housing conditions are typically most severe.

all vacant units, whether on the market or not (such

Not all the compiled indicators are presented in the

as second homes).

table because of space limitations.

Data sources Data on urban housing conditions are from national population and housing censuses.

2012 World Development Indicators

193

ENVIRONMENT

Urban housing conditions


3.15

Traffic and congestion Motor vehicles

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

194

Passenger Road cars density

km. of road per 100 sq. km. of % of total land area consumption

per 1,000 people

per kilometer of road

per 1,000 people

2009

2009

2009

2009

29 120 112 38 314 103 688 569 104 537 3 282 552 22 68 138 133 209 375 11 6 21 14 607 0 6 174 47 74 71 5 27 166 20 384 38 675 482 478 128 57 45 54 11 471 3 532 598 .. 8 151 564 30 573 98 .. 33 ..

11 .. 35 .. .. 42 18 45 13 104 .. .. 39 .. 9 20 7 18 72 1 .. 7 .. 15 .. 1 38 16 254 25 .. .. 20 5 58 .. 48 39 36 .. 18 37 .. .. 11 5 36 39 .. 3 28 72 6 55 .. .. .. ..

21 89 74 8 .. 94 550 521 86 451 2 240 483 18 18 121 69 178 330 7 2 18 10 420 0 2 118 34 56 53 .. 16 130 16 346 21 529 423 380 87 36 33 49 6 407 1 459 496 .. 5 126 510 18 455 37 .. 27 ..

6 63 5 4 8 26 11 127 61 575 166 46 504 17 7 43 4 21 36 34 44 21 6 14 4 3 10 40 188 15 7 5 76 25 52 55 134 166 170 26 17 10 48 3 129 4 23 173 3 33 29 180 46 89 13 18 12 15

2012 World Development Indicators

Road sector energy consumption

Fuel price

Particulate matter concentration

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Diesel

Urban-populationweighted PM10 micrograms per cubic meter

2009

2009

2009

2009

2010

2010

1990

2009

.. 23 23 13 17 22 18 23 12 12 7 7 15 27 32 15 31 24 15 .. .. 7 12 17 .. .. 21 5 13 21 1 27 30 4 22 4 30 14 22 17 38 18 17 5 14 4 12 16 6 .. 22 16 16 24 21 .. .. 9

.. 123 265 85 307 182 1,103 878 155 955 14 192 814 110 206 234 318 298 336 .. .. 26 42 1,313 .. .. 355 92 285 144 4 98 319 21 435 40 682 557 727 144 305 159 140 7 493 17 728 642 78 .. 156 612 62 632 145 .. .. 25

.. 120 177 45 140 38 433 594 45 369 10 107 657 41 98 148 131 149 192 .. .. 13 23 405 .. .. 208 56 224 77 0 68 156 17 268 16 294 338 456 59 174 86 62 6 292 14 409 471 53 .. 69 323 37 269 70 .. .. 19

.. 15 67 36 105 60 610 208 109 535 3 50 125 63 68 81 174 73 81 .. .. 12 18 886 .. .. 147 45 50 54 3 26 147 6 153 22 351 186 292 78 148 61 71 1 218 2 299 123 22 .. 79 236 27 358 68 .. .. 23

1.15 1.46 0.32 0.65 0.96 1.08 1.27 1.63 0.75 0.21 1.09 1.08 1.87 1.04 0.70 1.42 0.93 1.58 1.51 1.44 1.43 1.15 1.20 1.21 1.71 1.32 1.38 1.11 1.92 1.41 1.28 1.27 1.14 1.68 1.59 1.72 1.47 1.75 2.00 1.23 0.53 0.48 0.92 2.54 1.54 0.91 1.94 1.98 .. .. 1.13 1.90 0.82 2.05 0.95 0.95 .. ..

1.00 1.40 0.19 0.43 1.05 0.99 1.23 1.55 0.56 0.13 0.63 0.86 1.62 1.21 0.54 1.42 0.97 1.14 1.58 1.28 1.42 0.98 1.10 1.08 1.69 1.31 1.02 1.04 1.32 0.95 1.27 0.84 0.97 1.30 1.49 1.24 1.47 1.69 1.79 1.03 0.28 0.32 0.89 1.07 1.57 0.78 1.60 1.72 .. .. 1.13 1.68 0.83 1.78 0.85 0.95 .. ..

69 92 112 112 104 367 22 39 133 75 228 24 31 77 105 36 92 39 108 143 60 107 122 25 60 209 89 115 .. 38 71 130 43 87 45 31 60 41 28 43 36 214 44 196 44 108 22 18 9 136 224 27 38 64 69 103 114 68

32 37 75 56 60 61 14 27 29 45 121 7 21 48 60 19 66 19 45 63 28 37 58 16 33 82 53 60 .. 20 38 60 29 29 25 15 28 17 16 16 20 88 28 64 9 51 15 12 5 61 54 16 21 31 68 54 45 33

kilograms of oil equivalent per capita

$ per liter


Motor vehicles

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

Passenger Road cars density

Road sector energy consumption

km. of road per 100 sq. km. of % of total land area consumption

per 1,000 people

per kilometer of road

per 1,000 people

2009

2009

2009

2009

95 347 18 79 128 77 513 316 672 120 589 154 199 23 .. 355 .. 495 59 20 459 .. .. 3 290 555 155 26 8 350 14 .. 166 276 146 72 70 12 7 103 5 522 718 58 8 31 578 215 13 141 9 91 68 33 508 509 596 532

.. 18 5 38 51 55 24 128 80 15 63 117 33 14 .. 165 .. 212 9 3 15 .. .. .. .. 23 23 .. .. 69 8 .. 102 81 41 2 38 9 10 5 8 63 33 15 6 .. 30 12 8 35 .. 16 16 .. 50 67 92 ..

29 301 12 45 113 27 434 265 596 84 454 113 167 13 .. 267 .. 412 44 2 401 .. .. 2 225 508 138 7 4 313 8 .. 129 191 107 48 53 9 5 46 3 459 603 18 6 31 465 166 10 101 6 39 41 8 432 495 582 335

12 212 125 25 11 9 137 83 162 201 320 9 4 11 21 105 .. 37 17 17 107 67 20 10 5 125 54 8 13 30 2 1 101 19 38 3 13 4 4 5 14 329 35 17 1 21 29 18 32 19 4 8 10 67 123 90 301 67

Fuel price

Particulate matter concentration

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Diesel

Urban-populationweighted PM10 micrograms per cubic meter

2009

2009

2009

2009

2010

2010

1990

2009

23 17 7 14 18 32 28 27 22 16 14 23 6 7 2 12 .. 13 28 .. 20 26 .. .. 20 16 15 .. .. 21 .. .. .. 28 11 11 25 5 6 37 6 14 24 17 .. 8 12 13 13 18 .. 27 29 19 16 25 .. 9

136 433 37 119 531 334 900 766 602 189 530 290 235 31 13 569 .. 1,492 160 .. 370 418 .. .. 640 405 203 .. .. 492 .. .. .. 439 75 135 119 20 19 281 19 673 949 90 .. 53 704 721 63 162 .. 200 162 79 400 572 .. 1,331

65 259 26 44 223 185 519 347 376 65 173 108 39 21 7 286 .. 491 103 .. 245 5 .. .. 405 242 116 .. .. 178 .. .. .. 123 52 42 98 17 8 89 13 380 394 49 .. 4 549 59 38 124 .. 150 113 47 225 406 .. 675

64 149 11 75 237 134 347 380 172 177 332 173 205 11 5 158 .. 920 51 .. 137 386 .. .. 206 103 59 .. .. 295 .. .. .. 295 23 119 16 4 9 174 4 240 531 38 .. 45 258 617 11 151 .. 37 34 29 105 137 .. 591

1.04 1.67 1.15 0.79 0.10 0.78 1.78 1.85 1.87 0.98 1.60 1.04 0.71 1.33 .. 1.52 1.63 0.23 0.85 1.26 1.48 1.13 0.97 0.98 0.17 1.59 1.52 1.52 1.71 0.59 1.42 1.16 1.55 0.81 1.21 1.11 1.23 1.11 0.80 1.06 1.18 2.13 1.47 1.09 1.07 0.44 2.12 0.31 0.86 0.85 .. 1.28 1.41 1.05 1.57 1.85 .. 0.19

0.92 1.61 0.82 0.51 0.02 0.56 1.69 1.87 1.69 0.98 1.37 0.73 0.51 1.27 .. 1.35 1.60 0.21 0.79 0.97 1.49 0.77 1.07 0.96 0.13 1.42 1.27 1.26 1.54 0.56 1.25 0.99 1.23 0.72 1.08 1.04 0.88 0.86 0.80 1.09 0.91 1.71 0.97 0.99 1.16 0.77 2.01 0.38 0.92 0.77 .. 1.01 1.10 0.84 1.50 1.58 .. 0.19

43 34 110 132 92 168 23 66 41 56 42 107 43 64 169 51 .. 113 79 92 38 39 108 68 101 52 45 80 82 35 258 145 21 66 118 181 39 112 107 50 67 44 14 46 199 195 22 133 216 55 35 107 96 56 58 49 23 54

34 15 57 68 55 110 12 23 21 29 25 29 17 30 56 33 .. 95 35 45 12 28 43 31 81 15 18 30 33 19 106 68 14 33 33 101 23 24 41 45 30 30 12 24 92 42 15 82 101 33 16 65 43 17 34 20 14 31

kilograms of oil equivalent per capita

$ per liter

2012 World Development Indicators

195

ENVIRONMENT

3.15

Traffic and congestion


3.15

Traffic and congestion Motor vehicles

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

196

Passenger Road cars density

km. of road per 100 sq. km. of % of total land area consumption

per 1,000 people

per kilometer of road

per 1,000 people

2009

2009

2009

2009

230 271 5 192 22 252 6 156 348 566 .. 162 .. 596 47 27 89 519 563 63 38 7 134 .. 2 353 114 142 106 8 167 313 523 802 200 .. 147 13 30 35 21 114 170 w 10 66 33 99 61 54 219 178 87 17 28 618 593

20 39 .. 20 16 42 2 232 43 30 .. .. .. 41 .. .. 25 8 61 20 .. .. 50 .. 1 .. 61 29 .. .. 45 271 77 38 .. .. .. 7 22 .. .. .. 30 w .. 18 10 25 15 18 36 23 40 5 .. 37 47

198 233 2 139 16 224 5 121 293 522 .. 110 .. 478 19 19 45 462 519 30 29 4 57 .. 2 .. 76 95 80 3 142 293 460 439 179 .. 107 13 25 16 13 98 125 w 7 54 21 84 50 38 183 143 67 11 23 447 418

83 6 53 11 8 50 .. 473 89 192 3 30 .. 132 148 1 21 129 173 37 19 11 35 .. 21 162 12 46 5 29 28 5 172 67 44 18 11 48 93 14 12 25 30 w .. 23 48 17 21 37 9 18 9 102 .. 46 140

2012 World Development Indicators

Road sector energy consumption

2009

14 7 .. 21 21 13 .. 15 11 24 .. 11 .. 24 18 16 .. 16 21 22 4 6 18 .. 11 5 16 14 4 .. 6 14 19 23 23 4 27 14 .. 24 2 4 15 w 6 11 11 11 10 7 8 24 20 7 9 19 18

Fuel price

Particulate matter concentration

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Diesel

Urban-populationweighted PM10 micrograms per cubic meter

2009

2009

2009

2010

2010

1990

2009

1.46 0.84 1.63 0.16 1.57 1.50 0.94 1.42 1.70 1.67 .. 1.19 .. 1.56 1.19 0.62 1.07 1.87 1.66 0.96 1.02 1.22 1.41 1.40 1.18 .. 0.94 2.52 0.22 1.42 1.01 0.47 1.92 0.76 1.49 0.92 0.02 0.88 1.71 0.35 1.66 1.29 1.21 w 1.18 1.08 1.08 1.13 1.12 1.08 1.21 1.04 0.94 1.12 1.22 1.63 1.78

1.46 0.72 1.62 0.07 1.34 1.48 0.94 1.04 1.53 1.62 .. 1.14 .. 1.47 0.66 0.43 1.10 1.82 1.77 0.45 0.91 1.19 0.95 0.90 1.17 .. 0.82 2.03 0.20 1.11 0.92 0.71 1.98 0.84 1.44 0.83 0.01 0.77 1.54 0.23 1.52 1.15 1.07 w 1.15 0.97 0.91 1.02 0.98 0.93 1.13 0.98 0.56 0.83 1.15 1.55 1.62

42 41 53 160 94 .. 87 108 46 39 82 39 .. 41 93 285 30 15 34 135 84 56 77 .. 56 136 71 77 196 29 71 273 24 30 229 110 21 123 .. 133 125 55 79 w 127 96 124 78 98 112 57 57 125 130 119 37 33

14 16 23 103 80 .. 37 23 12 26 28 26 .. 25 71 137 38 10 22 71 30 20 53 .. 28 98 23 37 41 11 17 62 13 18 142 37 9 50 .. 43 30 37 43 w 56 49 58 43 50 56 23 30 66 68 46 23 19

kilograms of oil equivalent per capita

223 330 .. 1,241 50 265 .. 545 339 836 .. 320 .. 660 79 58 .. 773 737 248 14 26 275 .. 51 704 145 185 172 .. 153 1,229 616 1,640 281 67 626 108 .. 77 10 27 259 w 20 132 70 195 120 105 230 299 283 38 61 939 646

147 114 .. 543 41 162 .. 347 194 514 .. 134 .. 528 54 39 .. 353 286 154 0 19 163 .. 17 313 96 120 0 .. 46 606 351 384 165 11 102 59 .. 2 4 17 110 w 13 65 37 93 60 58 100 123 142 26 26 357 418

67 221 .. 635 9 70 .. 174 105 288 .. 176 .. 126 22 17 .. 357 424 83 12 6 74 .. 31 355 41 34 164 .. 102 563 255 1,134 99 53 459 46 .. 66 10 10 134 w 7 61 30 92 55 51 125 137 115 10 33 519 186

$ per liter


About the data

3.15

Definitions

Traffic congestion in urban areas constrains eco-

Considerable uncertainty surrounds estimates

• Motor vehicles include cars, buses, and freight

nomic productivity, damages people’s health, and

of particulate matter concentrations, and caution

vehicles but not two-wheelers. Population figures are

degrades the quality of life. In recent years owner-

should be used in interpreting them. They allow for

midyear population in the year for which data are

ship of passenger cars has increased, and the expan-

cross-country comparisons of the relative risk of

available. Roads refer to motorways (a road designed

sion of economic activity has led to more goods and

particulate matter pollution facing urban residents.

and built for motor traffic that separates the traf-

services being transported by road over greater dis-

Major sources of urban outdoor particulate matter

fic flowing in opposite directions), highways, main

tances (see table 5.10). These developments have

pollution are traffic and industrial emissions, but

or national roads, and secondary or regional roads.

increased demand for roads and vehicles, adding to

nonanthropogenic sources such as dust storms may

• Passenger cars are road motor vehicles, other than

urban congestion, air pollution, health hazards, and

also be a substantial contributor for some cities.

two-wheelers, intended for the carriage of passen-

traffic accidents and injuries.

Country technology and pollution controls are impor-

gers and designed to seat no more than nine people

The data in the table on motor vehicles, passenger

tant determinants of particulate matter. Data on par-

(including the driver). • Road density is the ratio of

cars, and road density are compiled by the Interna-

ticulate matter for selected cities are in table 3.16.

the length of the country’s total road network to the

tional Road Federation (IRF) through questionnaires

country’s land area. The road network includes all

sent to national organizations. The IRF uses a hier-

roads in the country— motorways, highways, main

archy of sources to gather as much information as

or national roads, secondary or regional roads, and

possible. Primary sources are national road asso-

other urban and rural roads. • Road sector energy

ciations. If they lack data or do not respond, other

consumption is the total energy used in the road

agencies are contacted, including road directorates,

sector, including energy from petroleum products,

ministries of transport or public works, and central

natural gas, combustible and renewable waste,

statistical offices. As a result, data quality is uneven.

and electricity (see table 3.7). • Total energy con-

Coverage of each indicator may differ across coun-

sumption is the country’s total energy consumption

tries because of different definitions. Comparability

from all sources (see table 3.7). • Diesel is heavy

is also limited when time series data are reported.

oils used in internal combustion in diesel engines.

The IRF is taking steps to improve the quality of the

• Gasoline fuel is light hydrocarbon oil used in inter-

data in its World Road Statistics. Because this effort

nal combustion engines such as motor vehicles,

covers only 2003–09, time series data may not be

excluding aircraft. • Fuel price is the pump price of

comparable. Another reason is coverage. Road den-

super grade gasoline and of diesel fuel, converted

sity is a rough indicator of accessibility and does not

from the local currency to U.S. dollars (see About

capture road width, type, or condition. Thus com-

the data). • Particulate matter concentration is fine

parisons over time and across countries should be

suspended particulates of less than 10 microns in

made with caution.

diameter (PM10) that are capable of penetrating

Road sector energy consumption includes energy

deep into the respiratory tract and causing severe

from petroleum products, natural gas, renewable and

health damage. Data are urban-population-weighted

combustible waste, and electricity. Biodiesel and bio-

PM10 levels in residential areas of cities with more

gasoline, forms of renewable energy, are biodegrad-

than 100,000 residents. The estimates represent

able and emit less sulfur and carbon monoxide than

the average annual exposure level of the average

petroleum-derived ones. They can be produced from

urban resident to outdoor particulate matter.

vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. Data on fuel prices are compiled by the German Agency for International Cooperation (GIZ), from its global network and other sources, including the

Data sources

Allgemeiner Deutscher Automobile Club (for Europe)

Data on vehicles and road density are from the

and the Latin American Energy Organization (for Latin

IRF’s electronic files and its annual World Road

America). Local prices are converted to U.S. dollars

Statistics, except where noted. Data on road sec-

using the exchange rate in the Financial Times inter-

tor energy consumption are from the IRF and the

national monetary table on the survey date. When

International Energy Agency. Data on fuel prices

multiple exchange rates exist, the market, parallel,

are from the GIZ’s electronic files. Data on par-

or black market rate is used. Prices were compiled

ticulate matter concentrations are from Pandey

in mid-November 2010, based on the crude oil price

and others (2006b).

of $81 a barrel Brent.

2012 World Development Indicators

197

ENVIRONMENT

Traffic and congestion


3.16

Air pollution City

City population

Particulate matter concentration

Sulfur dioxide

Nitrogen dioxide

About the data

Indoor and outdoor air pollution place a major burden

Argentina Australia Austria Belgium Brazil Bulgaria Canada Chile China Colombia Croatia Cuba Czech Republic Denmark Ecuador Egypt, Arab Rep. Finland France Germany Ghana Greece Hungary Iceland India

198

Buenos Aires Córdoba Melbourne Perth Sydney Vienna Brussels Rio de Janeiro São Paulo Sofia Montréal Toronto Vancouver Santiago Anshan Beijing Changchun Chengdu Chongqing Dalian Foshan Guangzhou Guiyang Harbin Jinan Kunming Lanzhou Liupanshui Nanchang Shanghai Shenyang Shenzhen Tianjin Wuhan Xi’an Zhengzhou Zibo Bogotá Zagreb Havana Prague Copenhagen Guayaquil Quito Cairo Helsinki Paris Berlin Frankfurt Munich Accra Athens Budapest Reykjavik Ahmadabad Bengaluru

2012 World Development Indicators

Urbanpopulation-weighted PM10 micrograms micrograms per per micrograms per cubic meter cubic meter cubic meter thousands 2010 1990 2009 2001 a 2001 a

on world health. More than half the world’s people

13,074 1,493 3,853 1,599 4,429 1,706 1,904 11,950 20,262 1,196 3,783 5,449 2,220 5,952 1,663 12,385 3,597 4,961 9,401 3,306 4,969 8,884 2,154 4,251 3,237 3,116 2,285 1,221b 2,701 16,575 5,166 9,005 7,884 7,681 4,747 2,966 2,456 8,500 779 b 2,130 1,162 1,186 2,690 1,846 11,001 1,117 10,485 3,450 680 b 1,349 2,342 3,257 1,706 319 b 5,717 7,218

pollution, which is responsible for 1.6 million deaths a

159 78 17 16 27 45 33 50 57 118 24 29 17 100 132 141 117 136 194 79 107 99 111 121 148 111 145 94 124 115 160 89 198 125 221 154 117 51 48 35 42 30 33 44 274 24 14 30 27 27 37 69 35 23 126 68

92 45 11 11 17 32 23 25 28 49 15 18 11 60 68 73 61 71 101 41 56 52 58 63 77 58 75 49 65 60 83 46 103 65 115 80 61 27 26 17 17 17 18 24 112 17 10 18 16 16 21 33 16 16 66 35

.. .. .. 5 28 14 20 129 43 39 10 17 14 29 115 90 21 77 340 61 .. 57 424 23 132 19 102 102 69 53 99 .. 82 40 .. 63 198 .. 31 1 14 7 15 22 69 4 14 18 11 8 .. 34 39 5 30 ..

.. 97 30 19 81 42 48 .. 83 122 42 43 37 81 88 122 64 74 70 100 .. 136 53 30 45 33 104 .. 29 73 73 .. 50 43 .. 95 43 .. .. 5 33 54 .. .. .. 35 57 26 45 53 .. 64 51 42 21 ..

rely on dung, wood, crop waste, or coal to meet basic energy needs. Cooking and heating with these fuels on open fires or stoves without chimneys lead to indoor air year—one every 20 seconds. In many urban areas air pollution exposure is the main environmental threat to health. Long-term exposure to high levels of soot and small particles contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease. Particulate pollution, alone or with sulfur dioxide, creates an enormous burden of ill health. Sulfur dioxide and nitrogen dioxide emissions lead to deposition of acid rain and other acidic compounds over long distances, which can lead to the leaching of trace minerals and nutrients critical to trees and plants. Sulfur dioxide emissions can damage human health, particularly that of the young and old. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogen fertilizers, fuel and biomass combustion, and aerobic decomposition of organic matter in soils and oceans. Where coal is the primary fuel for power plants without effective dust controls, steel mills, industrial boilers, and domestic heating, high levels of urban air pollution are common— especially particulates and sulfur dioxide. Elsewhere the worst emissions are from petroleum product combustion. Sulfur dioxide and nitrogen dioxide concentration data are based on average observed concentrations at urban monitoring sites, which not all cities have. The data on particulate matter are estimated average annual concentrations in residential areas away from air pollution “hotspots,” such as industrial districts and transport corridors. The data are from the World Bank’s Development Research Group and Environment Department estimates of annual ambient concentrations of particulate matter in cities with populations exceeding 100,000 (Pandey and others 2006b). A country’s technology and pollution controls are important determinants of particulate matter concentrations. Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Thus these data should be considered only a general indication of air quality, and comparisons should be made with caution. Current World Health Organization (WHO) air quality guidelines are annual mean concentrations of 20 micrograms per cubic meter for particulate matter less than 10 microns in diameter and 40 micrograms for nitrogen dioxide and daily mean concentrations of 20 micrograms per cubic meter for sulfur dioxide.


City

City population

Particulate matter concentration

Sulfur dioxide

Nitrogen dioxide

3.16

Definitions

• City population is the number of residents of

India (continued) Indonesia Iran, Islamic Rep. Ireland Italy Japan Kenya Korea, Rep Malaysia Mexico Netherlands New Zealand Norway Philippines Poland Portugal Romania Russian Federation Singapore Slovak Republic South Africa Spain Sweden Switzerland Thailand Turkey Ukraine United Kingdom United States Venezuela, RB

Chennai Delhi Hyderabad Kanpur Kolkata Lucknow Mumbai Nagpur Pune Jakarta Tehran Dublin Milan Rome Turin Osaka-Kobe Tokyo Yokohama Nairobi Busan Seoul Daegu Kuala Lumpur Mexico City Amsterdam Auckland Oslo Manila Katowice Lódz Warsaw Lisbon Bucharest Moscow Omsk Singapore Bratislava Cape Town Durban Johannesburg Barcelona Madrid Stockholm Zurich Bangkok Ankara Istanbul Kiev Birmingham London Manchester Chicago Los Angeles New York Caracas

Urbanpopulation-weighted PM10 micrograms micrograms per per micrograms per cubic meter cubic meter cubic meter thousands 2010 1990 2009 2001 a 2001 a

the city or metropolitan area as defined by national

7,547 22,157 6,751 3,364 15,552 2,873 20,041 2,607 5,002 9,210 7,241 1,099 2,967 3,362 1,665 11,337 36,669 3,654b 3,523 3,425 9,773 2,458 1,519 19,460 1,049 1,404 888 11,628 309b 742b 1,712 2,824 1,934 10,550 1,124 4,837 500 b 3,405 2,879 3,670 5,083 5,851 1,285 1,150 6,976 3,906 10,525 2,805 2,302 8,631 2,253 9,204 12,762 19,425 3,090

diameter (PM10) that are capable of penetrating

56 227 62 165 193 165 95 84 71 137 92 24 46 44 66 48 54 42 67 52 55 59 36 88 45 13 27 78 60 60 65 44 47 42 44 108 44 24 47 49 43 37 14 32 88 75 88 91 33 27 24 33 46 28 31

29 118 32 86 101 86 50 44 37 70 55 13 24 23 35 28 32 25 31 33 35 38 19 43 30 11 19 24 35 34 38 18 16 16 17 23 12 16 32 33 27 23 9 21 60 35 42 21 20 17 12 20 28 17 13

15 24 12 15 49 26 33 6 .. .. 209 20 31 .. .. 19 18 100 .. 60 44 81 24 74 10 3 8 33 83 21 16 8 10 109 20 20 21 21 31 19 11 24 3 11 11 55 120 14 9 25 26 14 9 26 33

17 41 17 14 34 25 39 13 .. .. .. .. 248 .. .. 63 68 13 .. 51 60 62 .. 130 58 20 43 .. 79 43 32 52 71 .. 34 30 27 72 .. 31 43 66 20 39 23 46 .. 51 45 77 49 57 74 79 57

authorities and reported to the United Nations. • Particulate matter concentration is fi ne suspended particulates of less than 10 microns in deep into the respiratory tract and causing severe health damage. Data are urban- population-weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. • Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. • Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities.

Data sources Data on city population are from the United Nations Population Division’s World Urbanization Prospects: The 2010 Revision. Data on particulate matter concentrations are from Pandey and others (2006b). Data on sulfur dioxide and nitrogen dioxide concentrations are from the WHO’s Healthy Cities Air Management Information System and the World Resources Institute.

a. Data are for the most recent year available. b. Data are from national sources.

2012 World Development Indicators

199

ENVIRONMENT

Air pollution


3.17

Government commitment Biodiversity Environmental assessments, strategies strategies, or or action action plans plans

Participation in treatiesa

Climate changeb

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

200

1993 2001 1992 1992 1998 1991 1993 1994 1990 1993 1994 1999 1990 1990 1994

1994 1990 1988 1991 1988 1994 1989 1989 1994 1993 1994

1998 1990 1994 2001

1988 1990 1990 1992 1991 2000

1994 1994 1993 1992 1994 1995 1998 1994 1995 1990 1992 1998 1992 1994 1994 1993 1999

1995 1995 1988 1988 1991 1990 1989 1988 1988 1988 1991

2012 World Development Indicators

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocol 1997

1992

1985

1987

1982

1992

2002 1994 d 1993 2000 1994 1993e 1992 1994 1995 1994 1994 2000 f 1996 1994 1994 2000 d 1994 1994 1995 1993 1997 1995d 1994 1992 1995 1994 1995 1993

2004 d 1999d 1992d 2000 d 1990 1999d 1987d 1987 1996d 1990d 1990 d 1986e 1988 1993d 1994 d 1993g 1991d 1990 d 1990d 1989 1997d 2001d 1989d 1986 1993d 1989d 1990 1989d

2004 d 1999d 1992d 2000 d 1990 1999d 1989 1989 1996d 1990 d 1990 d 1988f 1988 1993d 1994 d 1992g 1991d 1990 d 1990 d 1989 1997d 2001d 1989d 1988 1993d 1994 1990 1991d

2003 1996 1990 1995 2002 1994 1995 1985 2001 2006 1998 1997 1995 1994 1990 1988 1996 2005 1985 2003 2009 1997 1996

2002 1994 d 1995 1998 1994 1993e 1993 1994 2000 f 1996 1994 1993 1996 1994 1994 2002e 1995 1994 1996 1993 1997 1995d 1994 1992 1995 1994 1994 1993

1995 1995 1996 1994 1994 1996e 1994 1997 1993f 1995 1998 1993 1994 1995 1995d 1994 1994 1994 e 1994 1998 1994 1994 d 1993 1995 1994 1995 1993 1995 1996

1990 d 1994 d 1994 d 1991d 1993d 1992g 1992d 1992d 1993g 1988 1993d 1990d 1988 1992 2005d 1996d 1994 d 1986 1987f 1994 d 1990d 1996d 1988 1989d 1988 1987d 1992d 2002d 2000 d

1993d 1994 d 1994 1991d 1993d 1992g 1992d 1992d 1993g 1988 1993d 1990 d 1988 1992d 2005d 1996d 1994 d 1988 1988f 1994 d 1990 d 1996d 1988 1992 1988 1989d 1992d 2002d 2000 d

1989 2008 1992 1984 1995 1984 1988 1996 2004 2009 1983 2005 1996 1996 1998 1984 1996 1994 1983 1995 1997 1985 1986 1996

1994 1994 1996 1994 1994 1996 1994 1996 1993f 1993 1996 1993 1994 1994 1996d 1994 1994 e 1994 1997 1994 1994 d 1993 1994 1994 1995 1993 1995 1996

CITES

CCD

Stockholm Convention

1973

1994

2001

2005d 2005d 2007 2001 2003d 2007 2002 2000 d 2006d 2001d 2005d 2002 2002d 1999 2007d 2003d 2002 2002 2005d 2001d 2002d 2002d 2002 2008d 2009d 2002 2002f

1985d 2003d 1983d 1981 2008d 1976 1982d 1998d 1981 1995d 1983 1984 d 1979 2009d 1977d 1975 1991d 1989d 1988d 1997 1981d 1975 1980 d 1989d 1975 1981d

1995d 2000 d 1996 1997 1997 1997 2000 1997d 1998d 1997d 1996 2001d 1997d 1996 1996 2002d 1996 1997 2001d 1996 1997 1997 1997 1995 1996 1996 1997 1997

2004 2006 2006d 2005 2003 2004 2002 2004 d 2006 2007 2004 d 2006 2004 2003 2010 2002d 2004 2004 2004 2005 2006 2009 2001 2008 2004 2005 2004

2001d 2005d 2007d 2002 2007d 2007 2002 1992d 2001f 2002 2002d 2000 2005 1998 2005d 2002 2005d 2002 2002f 2006d 2001d 1999d 2002 2003d 2002 1999 2000 d 2005d 2005d

1981 1976d 1983d 1975 1994 d 2000 d 1990 d 1974 1993 1977 1986d 1975 1978 1987d 1994 d 1992d 1989d 1976d 1978f 1989d 1977d 1996d 1976 1975 1992d 1979 1981d 1990 d

1999 1997 1999 1998 1997 2000 d 1997 2000 d 2000 d 1995 1997d 1995 1995 1997d 1996 1997 1995d 1997 1996d 1996 1999 1996 1996 1997 1998d 1997 1995 1996

2008 2005d 2007 2007 2004 2007 2007 2005d 2002 2003 2007 2004 2003 2008 2005d 2008d 2003 2002d 2004f 2007 2006 2006 2002 2003 2006 2008 2007 2008


Biodiversity Environmental assessments, strategies strategies, or or action action plans plans

Participation in treatiesa

Climate changeb

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

3.17

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocol

CITES

CCD

Stockholm Convention

1992

1985

1987

1982

1992

1997

1973

1994

2001

1993 1995 1993 1993 1994 1991 1994

1994 1993 1992

1995 1994 1993 1994 1996 2009d 1994 1996 1994 1995 1993e 1993 1995 1994 1994f 1993

1993d 1988d 1991d 1992d 1990 d 2008d 1988d 1992d 1988 1993d 1988d 1989d 1998d 1988d 1995d 1992d

1993d 1989d 1992d 1992 1990 d 2008d 1988 1992 1988 1993d 1988e 1989d 1998d 1988 1995d 1992

1993 2002 1995 1986 1985 1996 1995 1983 1996 1995 1989 1996

1995 1994 1994 1994 1996 2009d 1996 1995 1994 1995 1993e 1993 1994 1994 1994f 1994

2000 2002d 2002d 2004 2005d 2009d 2002 2004 2002 1999d 2002d 2003d 2009 2005d 2005d 2002

1985d 1985d 1976 1978d 1976 2002 1979 1979 1997d 1980e 1978d 2000 d 1978 1993d

1997 1999d 1996 1998 1997 2010 d 1997 1996 1997 1997d 1998e 1996 1997 1997 2003d 1999

2005 2008 2006 2009 2006

1995 1995 1989 1988 1994 1991 1988 1990 2002 1995 1994 1992 1993 1994 1994 1994 1990 1994 1990 1992 1989 1993 1995

1991 1988 1989 1988 1988 1989 1991 1992 1994 1991 1993 1988 1989 1991

1994 d 2000 1995d 1995 1994 1995 2002 1999 1995 1998d 1999 1994 1994 1995 1994 1992 1993 1995 1993 1995 1995 1994 1995 1994 1993e 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1994 1994 1993

1992d 2000 d 1998d 1995d 1993d 1994 d 1996d 1990 d 1995d 1994g 1996d 1991d 1989d 1994d 1994 d 1992d 1987 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988e 1987 1993d 1992d 1988d 1986 1999d 1992d 1989d 1992d 1992d 1989 1991d 1990 d 1988d

1992d 2000 d 1998d 1995d 1993d 1994 d 1996d 1990 d 1995d 1994g 1996d 1991d 1989d 1994 d 1994 d 1992d 1988e 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988e 1988 1993d 1992d 1988d 1988 1999d 1992d 1989 1992d 1992d 1993d 1991 1990 d 1988

1986 1998 2004 1995 2007 2008 2003 1994 2001 2010 1996 1985 1996 1994 1983 2007 1996 2007 1997 1996 1983 1998 1996 1996 2000 1986 1996 1989 1997 1996 1997 1986 1984 1998 1997

2002 1996d 1996d 1995 1994 1995 2000 2001 1996 1997d 1996 1994 1995 1996 1992 1993 1995 1993 1995 1995 1994 1997 1993 1994 e 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1993 1996 1993

2005d 2003d 2003d 2002 2006d 2000d 2002d 2006d 2003 2004 d 2003d 2001d 2002 2002 2005d 2001d 2000 2008d 1999d 2002d 2005d 2003d 2003d 2005d 2002e 2002 1999 2004 2004 d 2002 2005d 2005d 1999 2002 1999 2002 2003 2002 2002f

2002 2007d 2004 d 1997d 2003 1981d 2003d 2001d 2000 d 1975 1982d 1977d 1994 d 1998d 1975 1991d 2001d 1996d 1975 1981d 1997d 1990 d 1975d 1984 1989d 1977d 1975 1974 1976 2008d 1976d 1978 1975d 1976 1975 1981 1989 1980

1997 1997d 1996d 2002d 1996 1995 1998d 1996 2003d 2002d 1997 1996 1997 1995 1996 1996 1995 1999d 1996 1996 1997 1997d 1997 1996 1995e 2000 d 1998 1996 1997 1996 1996d 1997 1996 2000 d 1997 1995 2000 2001d 1996

2006 2006 2006 2004 2003 2002 2002d 2005d 2006 2004 2005 2009 2003 2005 2004 2003 2004 2004 2004 2005 2004 d 2005d 2007 2002e 2004 2005 2006 2004 2002 2005 2008 2003 2003 2004 2005 2004 2008 2004 e

1996d

1996d

1996d

2002

1996

2005a

2001d

1999d

2004 d

2012 World Development Indicators

2010

2007 2002d 2004 2007 2004 2002d 2007

201

ENVIRONMENT

Government commitment


3.17

Government commitment Biodiversity Environmental assessments, strategies strategies, or or action action plans plans

Participation in treatiesa

Climate changeb

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

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocol

CITES

CCD

Stockholm Convention

1992

1985

1987

1982

1992

1997

1973

1994

2001

1995 1999 1991 1984 1994 1993 1994 1993

1994 1991 1995

1994 1994 1998 1994 d 1994 2001a 1995 1997 1994f 1995 2009d 1997

1993d 1986e 2001d 1993d 1993d 2001g 2001d 1989d 1993g 1992g 2001d 1990d

1993d 1988e 2001d 1993d 1993 2001g 2001d 1989d 1993g 1992g 2001d 1990 d

1996 1997 1996 1984 2001 1994 1994 1996 1995 1989 1997

1994 1995 1996 2001d 1994 2002 1994 d 1995 1994f 1996 2009d 1995

2001 2008 2004 d 2005d 2001d 2007d 2006d 2006d 2002 2002 2010 2002d

1994 d 1992h 1980 d 1996d 1977d 2006h 1994 d 1986d 1993g 2000 d 1985d 1975

1998d 2003d 1998 1997d 1995 2007a 1997 1999d 2002d 2001d 2002d 1997

2004 2011 2002d

1994 1999 1994 1991 1994 1998 1994 1999 1995 1995

1991 1988 1988 1988 1994 1995 1993

1993 1993 1993 1996 1993 1993 1996d 1998d 1996 1994 1995e 1994 1993 2004 d 1995d 1993 1997 1995d 1993 1992 1994 1993d 1994 1994

1988d 1989d 1993d 1992d 1986 1987 1989d 1996d 1993d 1989d 1991d 1989d 1989d 1991d 1993d 1988d 1986e 1989d 1987 1986 1989d 1993d 1988d 1994 d

1988 1989d 1993d 1992d 1988 1988 1989d 1998d 1993d 1989 1991 1989d 1989d 1991d 1993d 1988 1988e 1989d 1988 1988 1991d 1993d 1989 1994 d

1997 1994 1985 2009 1996 1985 2011 1985 1986 1985 1990 1999 1997 1992 1994

1993 1994 1995 1994 1993 1994 1996 1997d 1996 2003 1995e 1996 1993 1997 1996d 1993 1995 2000 1994 1993 1995d 1994 1994

2002 2002d 2004 d 2006d 2002 2003 2006d 2008d 2002d 2002 2004 d 1999 2003d 2006d 1999d 2002d 2004 2005d 2002 2001 1999 2005d 2002

1986d 1979d 1982 1997d 1974 1974 2003d 1979 1983 1978 1984 d 1974 1996d 1991d 1999d 1990 d 1976 1974 1975 1997d 1977 1994 d

1996 1998d 1995 1996 1995 1996 1997 1997d 1997 2001d 1995e 2000 d 1995 1998 1996 1997 2002d 1998d 1996 2000 1999d 1995 1998d 1998d

1996 1994 1987

1992

1996 1993 1992

1996d 1990 d 1992d

1996d 1990 d 1992d

1987 1983 1993

1996 1993 1994

2004 d 2006 2009d

1997d 1980 d 1981d

1997d 1996 1997

2003 2009 2003d 2005 2002 2004 2010 d 2002 2004 2005 2006 2006d 2002 2003 2005 2007 2004 2005 2004 2002d 2004 2009 2004 d 2007 2002 2005 2004 2005 2002 2004 2006

a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention of December 10, 1982. d. Accession. e. Acceptance. f. Approval. g. Succession. h. Continuation.

202

2012 World Development Indicators


About the data

3.17

Definitions

National environmental strategies and participation

Environment and Development (the Earth Summit) in

• Environmental strategies or action plans pro-

in international treaties on environmental issues pro-

Rio de Janeiro, which produced Agenda 21—an array

vide a comprehensive analysis of conservation and

vide some evidence of government commitment to

of actions to address environmental challenges:

resource management issues that integrate envi-

sound environmental management. But the signing

• The Framework Convention on Climate Change

ronmental concerns with development. They include

of these treaties does not always imply ratification,

aims to stabilize atmospheric concentrations of

national conservation strategies, environmental

nor does it guarantee that governments will comply

greenhouse gases at levels that will prevent human

action plans, environmental management strategies,

with treaty obligations.

activities from interfering dangerously with the

and sustainable development strategies. The date

global climate.

is the year a country adopted a strategy or action

In many countries efforts to halt environmental degradation have failed, primarily because govern-

• The Vienna Convention for the Protection of the

plan. • Biodiversity assessments, strategies, or

ments have neglected to make this issue a priority, a

Ozone Layer aims to protect human health and the

action plans include biodiversity profiles (see About

reflection of competing claims on scarce resources.

environment by promoting research on the effects

the data). • Participation in treaties covers nine

To address this problem, many countries are prepar-

of changes in the ozone layer and on alternative

international treaties (see About the data). • Cli-

ing national environmental strategies—some focus-

substances (such as substitutes for chlorofluoro-

mate change refers to the Framework Convention

ing narrowly on environmental issues, and others

carbon) and technologies, monitoring the ozone

on Climate Change (signed in 1992). • Ozone layer

integrating environmental, economic, and social

layer, and taking measures to control the activities

refers to the Vienna Convention for the Protection

concerns. Among such initiatives are conservation

that produce adverse effects.

of the Ozone Layer (signed in 1985). • CFC control

strategies and environmental action plans. Some

• The Montreal Protocol for Chlorofl uorocarbon

countries have also prepared country environmen-

Control requires that countries help protect the

the Ozone Layer (the Montreal Protocol for Chloro-

tal profiles and biodiversity strategies and profiles.

earth from excessive ultraviolet radiation by cut-

fluorocarbon Control) (signed in 1987). • Law of

refers to the Protocol on Substances That Deplete

National conservation strategies—promoted by

ting chlorofluorocarbon consumption by 20 percent

the Sea refers to the United Nations Convention on

the World Conservation Union (IUCN)—provide a

over their 1986 level by 1994 and by 50 percent

the Law of the Sea (signed in 1982). • Biological

comprehensive, cross-sectoral analysis of conser-

over their 1986 level by 1999, with allowances for

diversity refers to the Convention on Biological Diver-

vation and resource management issues to help inte-

increases in consumption by developing countries.

sity (signed at the Earth Summit in 1992). • Kyoto

grate environmental concerns with the development

• The United Nations Convention on the Law of the

Protocol refers to the protocol on climate change

process. Such strategies discuss current and future

Sea, which became effective in November 1994,

adopted at the third conference of the parties to the

needs, institutional capabilities, prevailing technical

establishes a comprehensive legal regime for seas

United Nations Framework Convention on Climate

conditions, and the status of natural resources in

and oceans, establishes rules for environmental

Change in December 1997. • CITES is the Conven-

a country.

standards and enforcement provisions, and devel-

tion on International Trade in Endangered Species of

ops international rules and national legislation to

Wild Fauna and Flora, an agreement among govern-

prevent and control marine pollution.

ments to ensure that the survival of wild animals

National environmental action plans, supported by the World Bank and other development agencies, describe a country’s main environmental concerns,

• The Convention on Biological Diversity promotes

and plants is not threatened by uncontrolled exploita-

identify the principal causes of environmental prob-

conservation of biodiversity through scientifi c

tion. Adopted in 1973, it entered into force in 1975.

lems, and formulate policies and actions to deal with

and technological cooperation among countries,

• CCD is the United Nations Convention to Combat

them. These plans are a continuing process in which

access to financial and genetic resources, and

Desertification, an international convention address-

governments develop comprehensive environmental

transfer of ecologically sound technologies.

ing the problems of land degradation in the world’s

policies, recommend specific actions, and outline

But 10 years after the Earth Summit in Rio de

drylands. Adopted in 1994, it entered into force in

the investment strategies, legislation, and institu-

Janeiro the World Summit on Sustainable Develop-

1996. • Stockholm Convention is an international

tional arrangements required to implement them.

ment in Johannesburg recognized that many of the

legally binding instrument to protect human health

Biodiversity profiles—prepared by the World Con-

proposed actions had yet to materialize. To help

and the environment from persistent organic pollut-

servation Monitoring Centre and the IUCN—provide

developing countries comply with their obligations

ants. Adopted in 2001, it entered into force in 2004.

basic background on species diversity, protected

under these agreements, the Global Environment

areas, major ecosystems and habitat types, and

Facility (GEF) was created to focus on global improve-

legislative and administrative support. In an effort

ment in biodiversity, climate change, international

to establish a scientific baseline for measuring prog-

waters, and ozone layer depletion. The UNEP, United

Data on environmental strategies and participa-

ress in biodiversity conservation, the United Nations

Nations Development Programme, and World Bank

tion in international environmental treaties are

Environment Programme (UNEP) coordinates global

manage the GEF according to the policies of its gov-

from the Secretariat of the United Nations Frame-

biodiversity assessments.

erning body of country representatives. The World

work Convention on Climate Change, the Ozone

Bank is responsible for the GEF Trust Fund and chairs

Secretariat of the UNEP, the World Resources

the GEF.

Institute, the UNEP, the Center for International

To address global issues, many governments have also signed international treaties and agreements

Data sources

launched in the wake of the 1972 United Nations

Earth Science Information Network, and the

Conference on the Human Environment in Stock-

United Nations Treaty Series.

holm and the 1992 United Nations Conference on

2012 World Development Indicators

203

ENVIRONMENT

Government commitment


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

204

Contribution of natural resources to gross domestic product Total natural resources rents

Oil rents

Natural gas rents

Coal rents

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2010

2010

2010

2010

2010

2010

2.3 3.6 25.7 46.3 6.1 1.7 8.3 0.3 46.5 23.2 3.3 1.9 0.0 1.7 18.1 2.8 4.7 5.3 2.3 7.1 13.3 1.2 9.3 3.8 5.9 43.3 18.9 4.0 0.0 7.9 29.8 64.1 0.4 7.1 1.2 5.1 0.0 0.3 2.1 0.3 20.6 10.1 0.5 0.6 1.1 4.7 0.8 0.1 49.8 2.5 0.8 0.1 10.5 0.2 2.4 21.2 4.6 0.6

.. 3.0 17.6 46.1 4.0 0.0 0.9 0.1 42.6 16.4 0.0 1.3 0.0 0.0 4.6 0.0 0.0 2.2 0.0 .. .. 0.0 7.1 2.3 .. 41.2 0.0 1.5 0.0 6.5 3.9 61.6 0.0 4.3 0.5 3.0 0.0 0.0 1.8 0.0 20.2 5.9 0.0 0.0 0.0 0.0 0.0 0.0 46.4 .. 0.2 0.0 0.0 0.0 0.8 .. .. 0.0

.. 0.0 7.8 0.1 1.4 0.0 0.7 0.1 3.9 6.9 2.8 0.0 0.0 0.0 9.0 0.0 0.0 0.1 0.0 .. .. 0.0 0.2 0.3 .. .. 0.1 0.1 0.0 0.5 0.0 0.1 0.0 1.0 0.5 0.6 0.0 0.0 0.3 0.0 0.1 3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.2 .. 0.0 0.0 0.0 0.0 0.0 .. .. 0.0

0.0 0.0 0.0 .. 0.0 .. 1.8 0.0 .. .. 0.1 .. 0.0 .. .. 2.4 0.4 0.0 1.1 .. .. .. 0.0 0.1 .. .. 0.0 3.8 .. 1.4 0.1 .. .. .. 0.0 .. .. 0.7 0.0 .. .. 0.0 .. .. 1.9 .. .. 0.0 .. .. 0.1 0.1 .. 0.3 .. .. .. ..

0.0 0.5 0.3 0.0 0.6 1.7 6.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.2 2.3 4.6 2.7 2.0 4.1 1.3 0.0 0.2 0.8 0.0 0.0 18.3 2.2 0.0 0.8 16.4 0.0 0.1 0.7 0.0 1.5 0.0 0.0 0.0 0.2 0.0 0.3 0.0 0.0 0.0 0.3 0.2 0.0 0.1 0.0 0.6 0.0 8.8 0.1 0.8 17.0 0.0 0.0

2.3 0.1 0.1 0.2 0.1 0.0 0.1 0.1 0.0 0.0 0.5 0.5 0.0 1.7 0.4 0.6 0.1 0.3 0.3 2.9 12.0 1.2 1.8 0.5 5.8 2.1 0.5 0.2 0.0 0.1 9.5 2.5 0.3 1.1 0.2 0.1 0.0 0.3 0.0 0.0 0.3 0.1 0.5 0.6 1.1 4.4 0.7 0.1 3.1 2.5 0.1 0.0 1.6 0.0 0.8 4.2 4.6 0.6

2012 World Development Indicators


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

3.18

Total natural resources rents

Oil rents

Natural gas rents

Coal rents

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2010

2010

2010

2010

2010

2010

1.8 0.6 4.0 6.0 31.6 69.3 0.2 0.3 0.2 1.1 0.0 1.6 27.6 1.3 .. 0.0 1.5 43.0 9.2 14.1 1.5 0.0 1.3 14.6 46.1 1.8 7.4 3.1 4.1 10.9 15.0 54.7 0.0 7.3 0.2 23.4 2.6 8.5 .. 1.0 3.4 1.2 2.4 3.0 3.3 32.6 13.1 39.2 3.9 0.1 36.3 1.3 11.3 2.6 1.0 0.3 0.0 27.9

0.0 0.3 1.0 2.4 23.7 69.1 0.0 0.0 0.1 0.0 0.0 0.0 22.4 0.0 .. 0.0 .. 41.2 0.7 .. 0.0 0.0 .. .. 42.3 0.1 0.0 .. .. 6.3 .. .. .. 6.0 0.1 2.0 0.0 0.0 .. 0.0 0.0 0.1 0.7 0.0 .. 29.5 10.1 31.6 0.8 0.0 18.4 0.0 1.1 0.1 0.1 0.0 .. 13.4

0.0 0.2 0.4 0.9 6.8 0.2 0.0 0.2 0.0 0.0 0.0 0.1 2.7 0.0 .. 0.0 .. 1.8 0.0 .. 0.0 0.0 .. .. 3.8 0.0 0.0 .. .. 3.9 .. .. .. 0.5 0.0 0.0 0.0 5.0 .. 0.0 0.0 1.1 0.5 0.0 .. 2.2 2.9 7.6 2.3 0.0 .. 0.0 0.7 0.3 0.1 0.0 .. 14.5

.. 0.1 2.6 3.4 0.0 .. 0.0 0.0 0.0 .. 0.0 .. 5.5 .. .. 0.0 .. .. 0.4 0.0 .. .. .. .. .. .. 1.5 .. .. 0.1 .. .. .. 0.1 .. 22.9 0.0 0.0 .. .. 0.0 0.0 0.1 .. 0.0 0.0 0.0 .. 0.1 .. .. .. 0.0 0.2 1.3 0.0 .. ..

0.7 0.0 2.1 1.9 1.0 0.0 0.1 0.1 0.0 1.0 0.0 1.5 2.5 0.1 .. 0.0 1.5 0.0 8.5 12.6 0.0 0.0 0.0 2.1 0.0 0.0 7.3 1.3 0.0 0.1 14.0 54.2 0.0 0.6 0.0 21.2 2.5 0.1 .. 1.0 0.0 0.0 0.4 1.3 1.3 0.0 0.0 0.0 0.1 0.0 32.2 0.0 9.3 2.0 0.6 0.2 0.0 0.0

1.1 0.1 0.6 0.7 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 1.2 .. 0.0 .. 0.0 0.0 1.5 1.5 0.0 1.3 12.5 0.0 1.6 0.1 1.8 4.1 0.6 1.1 0.5 0.0 0.1 0.1 0.2 0.1 3.3 .. 0.1 3.4 0.0 0.8 1.7 2.0 0.9 0.1 0.0 0.7 0.1 4.1 1.3 0.1 0.2 0.2 0.1 .. ..

2012 World Development Indicators

205

ENVIRONMENT

Contribution of natural resources to gross domestic product


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

Contribution of natural resources to gross domestic product Total natural resources rents

Oil rents

Natural gas rents

Coal rents

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2010

2010

2010

2010

2010

2010

2.2 19.9 3.4 53.7 2.4 0.9 4.5 0.0 0.4 0.2 .. 4.6 .. 0.1 0.5 19.3 1.7 1.2 0.0 16.5 0.9 6.8 2.7 0.3 3.9 37.3 6.7 0.5 43.9 4.9 2.9 20.5 1.5 1.0 1.0 29.4 19.6 10.4 .. 21.9 28.1 3.4 4.0 w 6.5 9.8 11.4 9.3 9.8 7.9 14.2 8.0 18.7 6.2 16.7 1.4 0.3

1.2 14.2 .. 50.5 0.0 0.8 .. 0.0 0.0 0.0 .. 0.0 .. 0.0 0.0 18.5 .. 0.0 0.0 14.4 0.2 0.0 1.1 .. 0.0 11.5 4.4 0.2 19.7 .. 0.9 18.0 1.2 0.7 0.0 3.3 18.0 8.0 .. 19.0 0.0 0.0 1.8 w 1.2 4.6 5.7 4.3 4.5 1.7 8.9 4.7 14.9 0.9 12.0 0.6 0.0

0.7 3.6 .. 3.2 0.0 0.1 .. 0.0 0.0 0.0 .. 0.0 .. 0.0 0.0 0.0 .. 0.0 0.0 1.9 0.1 0.5 1.2 .. 0.0 25.8 1.0 0.0 24.2 .. 1.7 2.4 0.3 0.1 0.0 18.1 0.9 1.3 .. 3.0 0.0 0.0 0.4 w 1.3 0.9 1.2 0.8 0.9 0.4 2.5 0.4 3.2 0.6 0.5 0.2 0.1

Note: Components may not sum to 100 percent because of rounding.

206

2012 World Development Indicators

0.3 1.4 .. .. .. 2.0 .. .. 0.0 0.2 .. 5.1 .. 0.0 .. .. 0.0 0.0 .. .. 0.3 0.0 0.1 .. 0.0 .. .. 0.2 .. .. 3.1 .. 0.0 0.4 .. 0.2 0.1 3.1 .. .. 0.0 2.9 0.8 w .. 2.0 2.2 2.0 2.0 3.4 1.3 0.1 0.0 2.3 .. 0.2 0.0

0.0 1.7 0.1 0.0 1.2 0.0 0.9 0.0 0.0 0.0 .. 3.8 .. 0.0 0.0 0.1 0.0 0.7 0.0 0.2 0.6 4.2 0.1 0.0 1.9 0.0 1.1 0.2 0.0 0.0 0.1 0.0 0.0 0.1 0.2 8.1 0.7 0.3 .. 0.0 26.7 1.2 0.8 w 1.8 2.0 1.8 2.1 2.0 2.1 1.2 2.6 0.6 1.7 2.8 0.3 0.0

0.2 0.3 3.3 0.0 1.1 .. 3.6 0.0 0.3 0.2 .. 0.8 .. 0.0 0.5 0.7 1.7 0.5 0.0 0.0 0.0 2.1 0.3 0.3 2.0 0.0 0.1 0.1 .. 4.9 0.3 .. 0.0 0.1 0.9 0.0 0.0 0.7 .. 0.0 1.5 2.2 0.2 w 2.3 0.3 0.6 0.2 0.3 0.2 0.3 0.2 0.1 0.7 1.3 0.1 0.1


About the data

3.18

Definitions

Accounting for the contribution of natural resources

• Total natural resources rents are the sum of oil

to economic output is important in building an ana-

rents, natural gas rents, coal rents (hard and soft),

lytical framework for sustainable development. In

mineral rents, and forest rents. • Oil rents are the

some countries earnings from natural resources,

difference between the value of crude oil produc-

especially from fossil fuels and minerals, account

tion at world prices and total costs of production.

for a sizable share of GDP, and much of these earn-

• Natural gas rents are the difference between the

ings come in the form of economic rents—revenues

value of natural gas production at world prices and

above the cost of extracting the resources. Natural

total costs of production. • Coal rents are the dif-

resources give rise to economic rents because they

ference between the value of both hard and soft

are not produced. For produced goods and services

coal production at world prices and their total costs

competitive forces expand supply until economic

of production. • Mineral rents are the difference

profits are driven to zero, but natural resources in

between the value of production for a stock of miner-

fixed supply often command returns well in excess

als at world prices and their total costs of production.

of their cost of production. Rents from nonrenew-

Minerals included in the calculation are tin, gold,

able resources—fossil fuels and minerals—as well

lead, zinc, iron, copper, nickel, silver, bauxite, and

as rents from overharvesting of forests indicate the

phosphate. • Forest rents are roundwood harvest

liquidation of a country’s capital stock. When coun-

times the product of average prices and a region-

tries use such rents to support current consumption

specific rental rate (based on a number of reviews;

rather than to invest in new capital to replace what is

World Bank 2011c).

being used up, they are, in effect, borrowing against their future. The estimates of natural resources rents shown in the table are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the world price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs (including a normal return on capital). These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP). This definition of economic rent differs from that used in the System of National Accounts, where rents are a form of property income, consisting of payments to landowners by a tenant for the use of the land or payments to the owners of subsoil assets by institutional units permitting them to extract subsoil deposits. The Environment section of previous editions of World Development Indicators included a table “Toward a broader measure of savings,” which showed the derivation of adjusted net savings, taking into account consumption of fixed and natural capital, pollution damage, and additions to human capital. Adjusted net savings measures the net additions or subtractions from a country’s stock of tangible and intangible capital. This table is included

Data sources

in the Economy section as table 4.11, along with

Data on contributions of natural resources to GDP

the closely related table 4.10, “Toward a broader

are estimates based on sources and methods

measure of income.”

described in World Bank (2011c).

2012 World Development Indicators

207

ENVIRONMENT

Contribution of natural resources to gross domestic product


ECONOMY


T

he data in the Economy section provide a picture of the global economy and the economic activity of more than 200 countries and territories that produce, trade, and consume the world’s output. The indicators measure changes in the size and structure of the global economy and the effects of these changes on national economies. They include measures of macroeconomic performance (gross domestic product [GDP], consumption, investment, and international trade), stability (central government budgets, prices, the money supply, and the balance of payments), and broader measures of income and savings adjusted for pollution, depreciation, and depletion of resources. In 2010 the world economy grew 4.2 percent, a quick rebound from 2.3 percent in 2009 and well above the annual average of 2.9 percent since 2000. Total output measured in GDP at current prices increased more than $10 trillion. Upper middle-income economies, including China, were affected by slowing investment and widespread uncertainty in financial markets but still grew 7.8 percent. Lower middle-income economies grew 6.9 percent, and low-income economies grew 5.9 percent. High-income economies, accounting for 68 percent of the world’s GDP, grew 3.1 percent in 2010. Developing economies grew faster over the last decade than in the previous two and faster than high-income economies. World output in 2010 reached $63 trillion, measured in GDP at current prices—a nominal increase of 96 percent increase over 2000. Developing economies’ share of global output increased from 18 percent to 31 percent. The developing economies in East Asia and Pacific grew the most, quadrupling their output and more than doubling their share of global output from 5 percent to 12 percent.

The accuracy of GDP estimates and their comparability across countries depend on timely revisions to data on GDP and its components. The frequency of revisions to GDP data varies: some countries revise numbers monthly, others quarterly or annually, and others less frequently. Such revisions are usually small and based on additional information received during the year. However, in some cases larger revisions are required because of new methodologies, changes to the base year, or changes in coverage. Comprehensive revisions of GDP data usually result in upward adjustments as improved data sources increase the coverage of the economy and as new weights for growing industries more accurately reflect their contributions to the economy. Revisions to data can lead to changes in income or lending classification, but such changes have been rare. Revisions to GDP data may cause breaks in series unless they are applied consistently to historical data. For constant price series a break caused by rebasing can be eliminated by linking the old series to the new using historical growth rates. But for nominal GDP data a break in the time series cannot be avoided unless the statistics office revises historical series. Other data series are affected by revisions to GDP. Because rebasing real GDP and its components leaves the pre−base year current price series unchanged, the GDP deflator calculated from these two series is skewed for pre−base years. Other series affected by the break in GDP are fiscal indicators expressed as a percentage of GDP. When nominal GDP is revised upward, the ratio of revenue and expenditure to GDP look smaller than previously reported. Information on significant revisions and breaks in series will be included in the next release of the World Development Indicators database.

4

2012 World Development Indicators

209


4.a Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Belize Benin Bolivia Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Hong Kong SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Czech Republic

210

Recent economic performance Gross domestic product

Exports of goods and services

Imports of goods and services

GDP deflator

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

3.5 3.3 2.3 –5.2 9.2 2.1 2.3 2.3 5.0 6.1 7.6 2.3 2.9 3.0 4.1 7.2 7.5 0.2 9.2 3.9 6.0 2.6 3.2 5.4 3.3 4.3 5.2 10.4 7.0 4.3 2.1 7.2 8.8 4.2 3.0 –1.2 2.3

4.2 .. .. .. 14.6 21.7 5.3 8.3 24.2 0.9 7.1 9.9 .. .. 9.9 1.2 11.5 16.2 .. .. 20.6 –0.3 6.4 .. .. .. –0.3 28.4 16.8 1.2 .. 9.4 .. 5.4 –0.5 6.0 18.0

–8.5 .. .. .. 34.0 13.8 5.1 8.0 1.3 0.7 11.9 8.7 .. .. 11.0 5.0 36.2 4.5 .. .. 16.8 4.6 13.1 .. .. .. 26.3 20.1 17.3 18.1 .. 17.0 .. 15.2 7.6 –1.3 18.0

3.5 16.2 26.6 2.0 15.4 9.2 0.1 1.8 11.2 6.5 10.2 1.8 0.9 1.8 8.8 14.7 7.3 2.9 4.0 7.8 3.1 3.2 2.9 3.3 3.2 11.6 14.4 6.6 0.5 3.1 3.8 22.4 19.8 7.8 0.7 1.0 –1.2

3.0 3.0 7.0 3.0 7.5 4.2 1.7 2.8 0.2 6.5 3.8 2.1 2.1 3.4 4.5 6.8 2.9 2.0 5.8 4.4 6.1 3.8 2.3 5.8 4.0 6.0 6.2 9.1 4.7 4.9 2.3 6.7 5.1 3.8 –5.8 1.2 2.1

2012 World Development Indicators

5.0 .. .. .. 5.5 6.0 5.2 6.6 1.0 12.3 7.2 4.5 .. .. 5.6 7.2 4.4 5.0 .. .. 5.2 7.4 4.2 .. .. .. 5.7 11.3 4.9 7.0 .. 16.0 .. 6.1 –2.0 5.8 5.0

3.2 .. .. .. 9.9 7.5 9.2 5.9 2.5 10.5 2.0 5.0 .. .. 5.4 5.1 9.7 5.0 .. .. 3.4 7.3 6.0 .. .. .. 10.5 14.4 5.0 6.8 .. 13.6 .. 7.6 5.1 5.5 6.8

1.9 9.6 12.4 –3.9 23.4 6.6 4.1 2.0 17.7 9.9 35.2 1.9 5.2 1.3 11.9 12.0 6.9 –0.3 9.5 4.6 4.6 4.5 4.1 –4.4 2.6 19.9 4.6 4.9 12.1 3.8 5.0 15.2 14.7 4.9 –6.5 2.0 2.4

Current account balance

Gross international reserves

% of GDP 2010 2011 a

$ millions 2011 a

months of import coverage 2011 a

2,421 183,122 28,348 148 43,321 1,959 42,921 11,482 10,274 8,533 6,076 18,311 237 976 9,984 8,337 350,414 15,321 843 282 3,471 3,199 65,658 278 155 951 41,932 3,204,610 285,306 30,504 161 1,273 5,641 4,756 4,192 14,484 39,692

4.1 36.2 6.3 2.5 6.1 4.8 1.7 0.6 9.3 2.9 1.7 0.5 3.1 4.3 17.4 16.7 14.0 5.6 3.1 5.3 4.6 5.2 1.4 2.4 5.6 4.1 6.0 19.7 6.2 6.7 11.6 2.3 6.2 3.2 4.7 7.0 2.9

–11.9 .. 8.8 –13.7 0.8 –14.7 –2.8 3.0 29.1 2.1 –15.2 1.4 –3.3 .. 4.4 0.3 –2.3 –1.5 .. –18.7 –7.8 –3.8 –3.1 –11.2 .. .. 1.8 5.2 5.7 –3.1 .. .. .. –4.0 .. –1.6 –3.1

–11.7 .. 7.0 –13.9 0.0 –12.8 –2.2 2.5 26.6 0.7 –15.7 –0.5 –3.3 .. 4.7 –3.3 –2.5 1.3 .. –13.4 –12.1 –2.9 –2.7 –16.7 .. .. –0.4 3.7 –0.5 –2.5 .. .. .. –5.2 .. –2.8 –3.7


Denmark Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya

Gross domestic product

Exports of goods and services

Imports of goods and services

GDP deflator

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

1.3 0.1 7.8 3.6 5.1 1.4 0.9 2.2 3.1 10.1 0.3 3.7 1.5 5.7 5.0 6.4 3.7 6.6 –3.5 2.8 1.9 3.5 3.6 –5.1 2.8 1.3 –4.0 8.8 6.1 –0.4 4.7 1.5 –0.6 4.0 3.1 7.3 5.3

3.2 .. 11.6 2.3 –3.0 12.3 .. .. 10.7 14.4 .. 8.6 9.7 3.0 4.1 .. 13.7 53.7 4.2 4.4 1.5 .. .. –7.3 6.0 14.3 0.4 17.9 14.9 6.3 13.4 12.2 .. 23.9 7.6 1.9 6.1

3.5 .. 14.4 16.3 –3.2 11.1 .. .. 8.8 15.9 .. 7.4 8.8 9.3 7.0 .. 11.7 69.4 –7.2 12.4 0.1 .. .. 19.7 10.2 12.8 4.0 9.2 17.3 2.7 12.5 12.7 .. 9.8 7.1 –4.0 3.0

3.9 0.0 5.1 7.6 10.1 1.2 19.1 11.6 1.5 3.8 8.1 0.4 0.8 18.0 8.4 8.7 0.6 14.0 1.7 5.0 19.7 1.7 5.9 5.4 5.7 3.1 6.9 10.5 8.0 –2.4 1.1 0.4 10.6 –2.2 6.3 19.5 3.9

1.3 0.9 4.9 5.1 0.5 1.5 2.8 8.2 7.6 7.7 1.3 3.1 1.6 6.0 5.3 5.4 3.0 13.6 –5.4 2.8 4.3 4.8 4.6 6.7 3.4 3.4 2.8 7.0 6.5 2.2 4.5 0.7 1.3 –0.2 2.5 6.6 4.3

6.6 .. 6.8 4.6 –4.8 7.5 .. .. 6.1 9.0 .. 0.0 5.7 3.7 4.7 .. 5.9 41.0 –1.0 4.5 1.8 .. .. 14.5 2.7 8.7 6.2 23.0 15.9 7.8 4.3 6.2 .. 0.5 –3.7 11.0 8.9

5.4 .. 5.1 7.0 –11.7 7.0 .. .. 6.5 11.0 .. 3.5 5.0 9.6 4.3 .. 6.8 35.9 –10.0 5.5 5.1 .. .. 0.2 2.9 7.9 6.7 14.0 13.6 6.8 12.5 4.8 .. 3.5 –0.5 11.0 8.6

3.2 4.9 8.4 7.2 15.1 3.4 6.9 14.7 5.4 10.8 0.4 2.2 1.5 11.4 5.5 –1.7 1.9 22.7 2.3 8.7 10.1 –9.2 3.9 3.2 7.3 5.3 1.7 9.1 7.1 2.0 2.6 2.0 7.3 –1.2 1.4 12.9 10.1

4.a

ECONOMY

Recent economic performance Current account balance

Gross international reserves

% of GDP 2010 2011 a

$ millions 2011 a

months of import coverage 2011 a

81,794 81 3,755 1,710 15,046 2,165 3,054 113 195 .. 832 7,942 52,819 2,157 223 2,818 72,796 .. 1,442 5,847 .. 225 798 1,389 2,751 48,686 8,454 272,249 106,665 1,410 74,874 53,421 2,273 1,259,494 11,489 25,316 4,264

6.1 3.5 2.4 0.9 3.3 2.4 7.8 1.8 0.1 .. 4.6 0.9 0.8 5.0 8.2 4.6 0.5 .. 0.2 3.8 .. 9.4 5.4 3.5 2.9 5.4 14.9 5.7 6.4 0.1 9.5 0.9 3.8 16.0 6.8 5.7 3.4

5.5 –15.3 –8.6 –3.1 –2.1 –2.3 .. .. 3.5 –1.4 –12.9 1.9 –1.7 .. 6.5 –11.5 5.7 –8.6 –10.3 –1.5 –7.2 .. –7.2 –2.5 –6.2 1.1 –8.0 –3.0 0.8 0.5 2.9 –3.5 –6.6 3.6 –4.8 2.0 –8.0

5.8 –21.9 –8.2 –2.7 1.8 –3.8 .. .. 0.9 –10.6 –7.2 1.0 –2.0 .. 1.9 –10.4 5.3 –7.0 –8.6 –2.2 –14.2 .. –10.6 –13.4 –6.4 2.8 –8.1 –3.4 0.4 0.6 0.6 –3.9 –11.2 2.2 –8.5 6.3 –9.2

2012 World Development Indicators

211


4.a Korea, Rep. Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Morocco Mozambique Namibia Nepal Netherlands Nicaragua Niger Nigeria Norway Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Romania Russian Federation

212

Recent economic performance Gross domestic product

Exports of goods and services

Imports of goods and services

GDP deflator

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

6.2 –1.4 9.4 –0.3 7.0 3.3 1.3 2.7 1.8 1.6 7.1 7.2 4.5 5.0 4.0 5.4 6.9 3.7 7.2 4.8 4.6 1.7 7.6 8.8 7.9 0.7 4.1 4.8 8.0 15.0 8.8 7.6 3.9 1.4 0.9 4.0

3.8 5.5 7.9 3.8 3.0 3.1 5.8 1.9 3.8 2.6 5.6 4.8 5.4 5.1 4.1 4.0 6.0 4.3 7.4 3.9 4.0 1.4 4.1 6.0 7.0 1.6 3.2 8.1 9.0 4.9 6.3 3.7 4.0 –1.5 2.4 4.1

2012 World Development Indicators

14.5 –4.2 29.3 10.3 0.4 2.2 16.3 2.8 23.4 .. .. 9.9 .. .. –4.2 25.6 12.8 16.3 2.2 –42.3 –13.7 10.8 13.2 .. .. 1.8 15.8 6.2 .. 34.3 2.5 21.0 12.1 8.8 10.5 7.1

9.1 6.5 6.5 7.0 –9.1 5.9 5.8 6.0 7.2 .. .. 4.1 .. .. 1.9 8.6 20.0 7.3 7.3 10.9 22.1 6.2 6.1 .. .. 6.3 11.7 7.1 .. 4.0 7.2 –7.7 6.1 6.0 14.7 5.0

16.9 1.6 4.1 8.6 –7.8 9.6 17.6 4.6 10.9 .. .. 15.1 .. .. –0.6 23.5 13.7 3.3 1.7 –60.0 26.7 10.6 10.8 .. .. 9.9 4.4 21.2 .. 29.3 23.8 22.5 13.9 5.4 10.5 25.6

6.3 5.5 5.0 5.0 –4.1 5.4 9.0 6.2 5.8 .. .. 5.2 .. .. 13.5 7.2 10.0 8.8 6.0 1.9 13.5 5.7 7.9 .. .. 6.7 8.8 7.7 .. 5.1 9.1 1.6 7.1 –1.5 11.9 10.0

3.7 6.9 9.4 –2.3 4.4 3.7 2.0 4.9 2.2 8.1 7.7 5.1 3.6 19.3 1.6 4.4 11.2 0.6 12.7 9.3 13.4 1.3 2.9 1.7 7.5 6.4 12.0 3.0 9.3 6.7 6.9 4.2 1.4 1.0 3.6 11.4

1.5 17.8 7.7 6.4 2.4 –0.7 10.0 2.4 0.7 9.2 9.2 4.1 2.1 5.6 1.6 2.7 3.7 –0.7 9.5 3.9 11.6 1.8 7.9 3.7 23.5 6.3 11.2 5.3 9.8 10.6 4.2 5.0 2.9 2.3 5.4 11.7

Current account balance

Gross international reserves

% of GDP 2010 2011 a

months of import coverage 2011 a

2.8 –8.3 0.4 3.0 –22.8 –19.8 1.5 7.7 –2.2 .. .. 11.5 .. .. –8.2 –0.6 –8.3 –4.3 –11.6 0.3 .. 6.6 –14.7 .. 1.3 12.3 –0.8 –10.7 –6.7 –3.5 –1.5 4.5 –4.7 –10.0 –4.0 4.7

2.5 –6.4 –14.0 –0.7 –20.6 –24.5 –2.3 6.1 –5.1 .. .. 9.7 .. .. –11.1 –0.8 –9.4 –6.7 –13.6 –0.5 .. 7.3 –16.3 .. 14.3 16.9 0.5 –12.3 –24.0 –3.1 –2.7 2.1 –5.1 –7.6 –4.5 5.1

$ millions 2011 a

304,349 1,707 .. 6,011 34,236 .. 7,925 904 2,343 1,279 212 131,867 1,418 485 2,589 144,174 1,965 19,572 2,473 1,796 3,567 21,322 1,892 659 35,249 49,273 14,636 1,792 4,172 4,951 47,266 67,565 92,824 2,635 43,118 455,474

5.8 4.2 .. 4.5 12.7 .. 3.4 0.2 4.0 4.2 1.1 7.0 5.2 3.3 4.3 4.5 4.1 4.7 6.5 3.3 6.1 0.4 3.1 3.3 4.8 4.3 3.7 1.0 6.2 4.8 12.9 9.7 4.6 0.3 6.0 13.9


Rwanda Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovak Republic Slovenia South Africa Spain Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam Yemen, Rep. Zambia Zimbabwe

Gross domestic product

Exports of goods and services

Imports of goods and services

GDP deflator

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

average annual % growth 2010 2011 a

.. .. 3.5 .. .. 16.6 16.3 7.2 5.5 5.4 13.0 .. .. .. 0.9 12.7 7.3 8.3 10.9 21.5 .. .. 3.8 20.7 7.8 11.1 8.6 12.5 16.2 .. .. –2.9 14.1 –6.8 .. 6.6

2.1 12.4 1.4 –0.8 14.4 –0.5 0.5 –1.1 8.1 1.0 7.3 5.1 2.1 17.6 6.1 1.2 0.1 6.3 7.7 3.7 1.4 4.4 4.0 6.3 9.1 15.0 2.9 0.8 5.1 18.5 2.8 46.7 11.