28969
Low income Afghanistan Angola Azerbaijan Bangladesh Benin Bhutan Burkina Faso Burundi Cambodia Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d'Ivoire Equatorial Guinea Eritrea Ethiopia Gambia, The Georgia Ghana Guinea Guinea-Bissau Haiti India Indonesia Kenya Korea, Dem. Rep. Kyrgyz Republic Lao PDR Lesotho Liberia Madagascar Malawi Mali Mauritania Moldova Mongolia Mozambique Myanmar Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda São Tomé and Principe Senegal Sierra Leone Solomon Islands Somalia Sudan Tajikistan Tanzania Timor-Leste Togo Uganda Uzbekistan
Vietnam Yemen, Rep. Zambia Zimbabwe Lower middle income Albania Algeria Armenia Belarus Bolivia Bosnia and Herzegovina Brazil Bulgaria Cape Verde China Colombia Cuba Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Fiji Guatemala Guyana Honduras Iran, Islamic Rep. Iraq Jamaica Jordan Kazakhstan Kiribati Macedonia, FYR Maldives Marshall Islands Micronesia, Fed. Sts. Morocco Namibia Paraguay Peru Philippines Romania Russian Federation Samoa Serbia and Montenegro South Africa Sri Lanka St. Vincent and the Grenadines Suriname Swaziland Syrian Arab Republic Thailand Tonga Tunisia Turkey Turkmenistan Ukraine Vanuatu West Bank and Gaza
Upper middle income American Samoa Argentina Belize Botswana Chile Costa Rica Croatia Czech Republic Dominica Estonia Gabon Grenada Hungary Latvia Lebanon Libya Lithuania Malaysia Mauritius Mayotte Mexico Northern Mariana Islands Oman Palau Panama Poland Saudi Arabia Seychelles Slovak Republic St. Kitts and Nevis St. Lucia Trinidad and Tobago Uruguay Venezuela, RB High income Andorra Antigua and Barbuda Aruba Australia Austria Bahamas, The Bahrain Barbados Belgium Bermuda Brunei Canada Cayman Islands Channel Islands Cyprus Denmark Faeroe Islands Finland France French Polynesia Germany Greece Greenland Guam
Hong Kong, China Iceland Ireland Isle of Man Israel Italy Japan Korea, Rep. Kuwait Liechtenstein Luxembourg Macao, China Malta Monaco Netherlands Netherlands Antilles New Caledonia New Zealand Norway Portugal Puerto Rico Qatar San Marino Singapore Slovenia Spain Sweden Switzerland United Arab Emirates United Kingdom United States Virgin Islands (U.S.)
INCOME MAP
The world by income
The world by income Low ($735 or less) Lower middle ($736–2,935) Upper middle ($2,936–9,075) High ($9,076 or more) No data
Designed, edited, and produced by Communications Development Incorporated, Washington, DC, with Grundy & Northedge, London
Classified according to World Bank estimates of 2002 GNI per capita
2004
WORLD DEVELOPMENT INDICATORS
Copyright 2004 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, DC 20433, USA
All rights reserved Manufactured in the United States of America First printing March 2004
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, Massachusetts 01923, USA.
Photo credits: Front cover, from top to bottom and left to right, Mark Hakansson/Panos Pictures, Photodisc, Photodisc, Photodisc, Alex Baluyut/World Bank; Back cover, Curt Carnemark/World Bank; Page 251, Curt Carnemark/World Bank.
If you have questions or comments about this product, please contact:
Development Data Center The World Bank 1818 H Street NW, Room MC2-812, Washington, DC 20433, USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email:
[email protected] Web site: www.worldbank.org or www.worldbank.org/data
ISBN 0-8213-5729-8
2004
WORLD DEVELOPMENT INDICATORS
The World Bank
“When we read statistics, we must see real people. When we confront problems, we must cast them as oppor tunities. When we doubt our energy or question our faith in development, we must take fresh resolve from the reality that on our work depends the fate of millions.”
Barber Conable, 1922–2003 President, World Bank, 1986–91
FOREWORD Development is about people. But to measure development and see its effect on people, we need good statistics. Statistics that tell us that life expectancy in the last 40 years has gone up 20 years in developing countries, more than in all the time before that. That literacy has improved. That infant mortality and maternal mortality have decreased. And that fewer people are living in extreme poverty.
However, the statistics also tell us that malnutrition and disease still claim the lives of millions of young children. That millions more never receive a primary education. And that in countries at the center of the HIV/AIDS epidemic, life expectancy has been falling. Of the 6 billion people on the planet today, 5 billion live in developing countries. But in the next 30 years the world’s population will grow by 2 billion—from 6 billion to 8 billion—and all but 50 million of them will live in today’s developing countries. What will their lives be like? We hope, much better than today.
The Millennium Development Goals set specific targets for improving people’s lives. They were proposed and adopted by the General Assembly of the United Nations—not as vague and lofty statements of our good intentions, but as a practical guide to what can and should be accomplished by the international community in the opening quarter of the 21st century. That is why they were presented so clearly, with precise, quantified targets, based on widely accepted statistical indicators. Setting goals and measuring progress toward them in a transparent process is a proven management technique for holding our focus, avoiding wasteful diversions of effort, and encouraging robust public discussion of both means and ends.
Since the adoption of the Millennium Development Goals another important step in deepening the international consensus on development was the Monterrey Conference on Financing for Development. At Monterrey developing countries recognized the need to put reducing poverty and achieving the human and environmental goals of the Millennium Declaration at the center of their development programs. Developed countries accepted an obligation to uphold their share of a partnership for development by providing resources, opening trade, and relieving the burden of debt on the poorest countries. That consensus requires monitoring not only the outcomes in developing countries—but also the policies and actions of rich countries and development agencies to meet their commitments.
A comprehensive development strategy calls for a comprehensive set of statistics. Any user of World Development Indicators recognizes the many gaps in sound, available information. At the World Bank we are committed to working with our partners to improve the quality and availability of development statistics. This effort starts with strengthening national statistical systems in developing countries. But it must be matched by a commitment of the international community to provide the necessary technical and financial support.
The World Bank’s mission statement asks us to “fight poverty with passion and professionalism for lasting results.” Ultimately, it is the results that count. If we act now with realism and foresight based on good information, if we think globally and allocate our resources accordingly, we can make a lasting difference in people’s lives.
James D. Wolfensohn President The World Bank Group
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ACKNOWLEDGMENTS This book and its companion volumes, The World Bank Atlas and The Little Data Book, are prepared by a team coordinated by David Cieslikowski. Team members are Mehdi Akhlaghi, Mahyar Eshragh-Tabary, Richard Fix, Amy Heyman, Masako Hiraga, M. H. Saeed Ordoubadi, Sulekha Patel, Eric Swanson, K. M. Vijayalakshmi, Vivienne Wang, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erobelly, Reza Farivari, and William Prince. The work was carried out under the management of Shaida Badiee.
The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank’s four thematic networks—Environmentally and Socially Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Private Sector Development and Infrastructure—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 the Credits section. For a listing of our key partners, see the Partners section.
Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont and Bruce Ross-Larson. The editing and production team consisted of Joseph Costello, Elizabeth McCrocklin, Christopher Trott, and Elaine Wilson. Communications Development’s London partner, Grundy & Northedge, provided art direction and design. Staff from External Affairs oversaw publication and dissemination of the book.
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PREFACE Four years have passed since the Millennium Development Goals sharpened the focus on measuring the results of development—not the number of projects undertaken or the dollars spent, but the improvements in people’s lives. The emphasis on quantitative targets and the requirement for monitoring progress on country poverty reduction strategies have increased the demand for statistics. And that showed us how deficient the statistical systems are in many parts of the developing world. Good statistics are not just a technical issue— they are a development issue, requiring concerted action by the entire global community. As Trevor Manuel, South Africa’s minister of finance, has put it, “If you can’t measure it, you can’t manage it.” That is why data, statistics, and indicators are at the heart of the results agenda. Governments need them. Politicians need them. Managers of development programs need them. And citizens need them—to hold governments accountable for their actions and their results. The global effort to improve the quality of development statistics has three pillars: •
Strengthening the capacity of developing countries to produce, analyze, and use reliable statistics.
•
Providing financial support to countries expanding their statistical capacity.
•
Improving the quality and availability of international statistics for monitoring global progress.
Much is already happening. Around the world, 37 developing countries have prepared strategic plans to guide their statistical development. The African Development Bank is systematically carrying out statistical assessments in 47 countries in that region, a key step in identifying shortcomings and constraints and in better targeting support. The Trust Fund for Statistical Capacity Building, managed by the World Bank, has provided grants to support statistical projects in more than 60 countries. Interagency cooperation is much stronger than it was even two years ago. Joint efforts have improved the measurement of such indicators as child mortality and immunizations. And the International Comparison Program is proceeding with an ambitious plan to measure purchasing power parities in more than 100 countries. Much has been achieved, but much remains to be done. The Second Roundtable on Development Results—held at Marrakech, Morocco, and sponsored by the multilateral development banks and the Development Assistance Committee of the Organisation for Economic Co-operation and Development—identified six broad sets of actions to improve national and international statistics: •
Mainstream the strategic planning of statistical systems and help all low-income countries prepare national statistical development strategies by 2006.
•
Strengthen preparations for the 2010 censuses. A core source of development statistics, censuses underpin the ability to monitor progress toward the Millennium Development Goals.
•
Increase financial support for statistical capacity building. Countries that adopt good policies for their statistical systems should receive the financial support they need for their statistics.
•
Set up an international household survey network to coordinate and improve the effectiveness of international survey programs.
•
Undertake urgent improvements needed to monitor the Millennium Development Goals for 2005.
•
Increase the accountability of the international statistical system.
Good quality information is not produced overnight. We plan today for better information tomorrow. In doing so, we must be careful not to overburden fragile national systems. We must also recognize that the cost of making mistakes and allocating resources inefficiently can dwarf the cost of producing good statistics. World Development Indicators reflects the strengths and weaknesses of the international statistical system. As development statistics improve, the results will appear here—as we continue striving to meet the needs of policymakers, researchers, commentators, and interested citizens. You can find out more about our products at http://www.worldbank.org/data. And you can send queries and comments to
[email protected].
Shaida Badiee, Director Development Data Group
2004 World Development Indicators
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TABLE OF CONTENTS FRONT
Foreword Acknowledgments Preface Par tners Users guide
v vi vii xiii xxvi
1. WORLD VIEW
Introduction Millennium Development Goals, targets, and indicators
1 12
Size of the economy Millennium Development Goals: eradicating poverty and improving lives Millennium Development Goals: protecting our common environment Millennium Development Goals: overcoming obstacles Women in development Key indicators for other economies
14
Tables
1.1 1.2 1.3 1.4 1.5 1.6
Text figures and boxes 1a Pover ty rates have been falling in all regions except Sub-Saharan Africa 1b But more than 1.1 billion people remain in extreme pover ty 1c Most regions are on a path to cut extreme pover ty by half by 2015 1d With continuing growth the number of people living in extreme pover ty will fall 1e And the propor tion of people in extreme pover ty will reach an all-time low 1f But more than 2 billion people will live on less than $2 a day 1g And more than half the population of South Asia and Sub-Saharan Africa will be ver y poor 1h The undernourished are ever ywhere 1i Malnourished children are among the most vulnerable
1j 1k 1l 1m 1n 1o 1p 1q 1r 1s 1t 1u 1v 1w 1x 1.2a 1.3a 1.4a 1.5a viii
2004 World Development Indicators
Many girls still do not have equal access to education Literacy rates have been rising as more children remain in school, but girls lag behind boys Few countries are on track to meet the child mor tality target To reduce early childhood deaths, immunization programs must be extended and sustained Extreme risks of dying from pregnancy or childbir th in some regions The presence of skilled health staff lowers the risk of maternal death HIV strikes at youth—and women are par ticularly vulnerable Treated bednets are a proven way to combat malaria, but they are still not widely used Greenhouse gas emissions rise with income Access to water and sanitation ser vices will require large investments Slums are growing in newly urbanized areas Aid has increased, but not by as much as domestic subsidies to agriculture New commitments by donors, the first major increase in more than a decade, will still meet only a fraction of the need Location of indicators for Millennium Development Goals 1–5 Location of indicators for Millennium Development Goals 6–7 Location of indicators for Millennium Development Goal 8 Income and gender affect children’s access to basic health care
18 22 26 28 32
1 1 2 3 3 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 10 11 11 21 25 27 31
2. PEOPLE Introduction Tables
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19
3. ENVIRONMENT
35
Introduction
113
Rural environment and land use Agricultural inputs Agricultural output and productivity Deforestation and biodiversity Freshwater Water pollution Energy production and use Energy efficiency, dependency, and emissions Sources of electricity Urbanization Urban environment Traffic and congestion Air pollution Government commitment Toward a broader measure of savings
116 120 124 128 132 136 140 144 148 152 156 160 164 166 170
Tables Population dynamics Labor force structure Employment by economic activity Unemployment Pover ty Social indicators of pover ty Distribution of income or consumption Assessing vulnerability Enhancing security Education inputs Par ticipation in education Education efficiency Education outcomes Health expenditure, ser vices, and use Disease prevention: coverage and quality Reproductive health Nutrition Health risk factors and future challenges Mor tality
Text figures and boxes 2a Pover ty and illiteracy are related 2b Defining income pover ty 2c Why public ser vices fail poor people 2d Poor women are much less likely to receive exper t care in childbir th 2.3a Women tend to suffer dispropor tionately from underemployment 2.6a Education lowers bir th rates dramatically for rich women, but not for poor ones 2.10a Education suffers in primar y schools with high teacher absence rates 2.11a Girls from rural areas and poor households have the lowest attendance rates in Guinea 2.13a There is a strong positive relationship between primar y school enrollment ratios and literacy among youth 2.14a High health personnel absence rates lower the quality of health care 2.15a Children in rural households are less likely to use bednets 2.16a Does household wealth affect antenatal care? 2.18a HIV prevalence rates var y by method of data collection 2.18b In some countries men know more about preventing HIV than women do 2.19a Under-five mor tality rates are higher in poor households than in rich ones
38 42 46 50 54 58 60 64 68 72 76 80 84 88 92 96 100 104 108
35 36 37 37 49 59 75 79 87 91 95 99 107 107 111
3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15
Text figures and boxes 3a High-income countries use more than half the world’s energy 3b Emissions of carbon dioxide var y widely, even among the five largest producers of emissions 3c Emissions of some greenhouse and ozone-depleting gases have begun to fall or slow since Rio 3.1a All regions are becoming less rural 3.2a The 10 countries with the most arable land per person in 1999–2001—and the 10 with the least 3.3a The 15 countries with the highest cereal yield in 2001–03—and the 15 with the lowest 3.5a The distribution of freshwater resources is uneven 3.5b Latin America and the Caribbean has more than 20 times the freshwater resources per capita as the Middle East and Nor th Africa 3.6a High- and middle-income countries account for most water pollution from organic waste 3.7a Energy use varies by countr y, even among the five largest energy users 3.7b People in high-income countries use more than five times as much energy as do people in low-income countries 3.8a Per capita emissions of carbon dioxide var y, even among the five largest producers of emissions 3.9a Sources of electricity generation have shifted differently in different income groups 3.10a More people now live in urban areas in low-income countries than in high-income countries . . . 3.10b Latin America was as urban as the average high-income countr y in 2002 3.11a The use of public transpor tation for work trips varied widely across cities in 1998 3.12a The 10 countries with the most vehicles per 1,000 people in 2001—and the 10 with the fewest 3.14a The Kyoto Protocol on climate change 3.14b Global atmospheric concentrations of chlorofluorocarbons have leveled off 3.14c Global focus on biodiversity and climate change
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114 115 115 119 123 127 135
135 139 143 143 147 151 155 155 159 163 166 167 168
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TABLE OF CONTENTS 4. ECONOMY Introduction
175
Growth of output Structure of output Structure of manufacturing Growth of merchandise trade Structure of merchandise expor ts Structure of merchandise impor ts Structure of ser vice expor ts Structure of ser vice impor ts Structure of demand Growth of consumption and investment Central government finances Central government expenditures Central government revenues Monetar y indicators and prices Balance of payments current account External debt External debt management
182 186 190 194 198 202 206 210 214 218 222 226 230 234 238 242 246
Tables
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
Introduction
251
Private sector investment Investment climate Business environment Stock markets Financial depth and efficiency Tax policies Relative prices and exchange rates Defense expenditures and arms transfers Transpor t infrastructure Power and communications The information age Science and technology
254 258 262 266 270 274 278 282 286 290 294 298
Tables
Text figures and boxes 4a Economic growth varies by region 4b With two decades of rapid growth, East Asia and Pacific has caught up with Latin America and the Caribbean 4.a Recent economic per formance 4.b Key macroeconomic indicators 4.3a Manufacturing continues to show strong growth in East Asia 4.5a Some developing countr y regions are increasing their share of merchandise expor ts 4.6a Top 10 developing countr y expor ters in 2002 4.7a Top 10 developing countr y expor ters of commercial ser vices in 2002 4.8a Developing economies are consuming less transport services 4.10a Per capita consumption has risen in Asia, fallen in Africa 4.11a Some developing economies spend a large par t of their current revenue on interest payments 4.12a Interest payments are a large par t of government expenditure for some developing economies 4.13a Poor countries rely more on indirect taxes 4.15a Worker remittances are an impor tant source of income for many developing economies 4.16a Since 2000, GDP has been larger than external debt for the heavily indebted poor countries 4.17a When the present value of a countr y’s external debt exceeds 220 percent of expor ts or 80 percent of GNI the World Bank classifies it as severely indebted
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5. STATES AND MARKETS
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175 176 178 179 193 201 205 209 213 221 225 229 233 241 245
249
5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12
Text figures and boxes 5a Higher income economies often have less regulated labor markets than lower income economies 5.1a Foreign direct investment has expanded rapidly in many developing countries, contributing to increased productivity 5.10a Mobile phone subscribers are approaching (or surpassing) 500 per 1,000 people in some developing and transition economies
253 257
293
6. GLOBAL LINKS Introduction
303
Integration with the global economy Direction and growth of merchandise trade OECD trade with low- and middle-income economies Primar y commodity prices Regional trade blocs Tariff barriers Global private financial flows Net financial flows from Development Assistance Committee members Aid flows from Development Assistance Committee members Aid dependency Distribution of net aid by Development Assistance Committee members Net financial flows from multilateral institutions Foreign labor and population in selected OECD countries Travel and tourism
306 310 313 316 318 322 326
Tables
6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14
Text figures and boxes 6a More than half of world output is globally traded 6b Aid after Monterrey 6c Immigrant labor plays an impor tant role in some high-income economies 6.2a Rich markets for developing countr y expor ts 6.3a Manufactured goods from developing countries dominated impor ts by OECD countries in 2002 6.8a Who were the largest donors in 2002? 6.9a Official development assistance from selected non-DAC donors, 1998–2002 6.10a Where did aid go in 2002? 6.11a Top aid recipients from top DAC donors reflect historical alliances and geopolitical events 6.13a Migration to OECD countries is growing 6.14a Tourism is highest in high-income countries
BACK
Primar y data documentation Acronyms and abbreviations Statistical methods Credits Bibliography Index of indicators
353 361 362 364 366 374
330 332 334 338 342 346 348
303 304 305 312 315 331 333 337 341 347 351
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PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, this section includes Web addresses for organizations that maintain Web sites. The addresses shown were active on 1 March 2004. Information about the World Bank is also provided.
International and government agencies Bureau of Verification and Compliance, U.S. Department of State The Bureau of Verification and Compliance, U.S. Department of State, is responsible for international agreements on conventional, chemical, and biological weapons and on strategic forces; treaty verification and compliance; and support to ongoing negotiations, policymaking, and interagency implementation efforts. For information, contact the Public Affairs Officer, Bureau of Verification and Compliance, U.S. Department of State, 2201 C Street NW, Washington, DC 20520, USA; telephone: 202 647 6946; Web site: www.state.gov/t/vc. 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 information, contact the CDIAC, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 378316335, USA; telephone: 865 574 0390; fax: 865 574 2232; email:
[email protected]; Web site: http://cdiac.esd.ornl.gov.
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Deutsche Gesellschaft für Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government–owned corporation for international cooperation with worldwide operations. GTZ’s aim is to positively shape political, economic, ecological, and social development in partner countries, thereby improving people’s living conditions and prospects. The organization has more than 10,000 employees in some 130 countries of Africa, Asia, Latin America, and Eastern Europe. For publications, contact Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH Corporate Communications, Dag-Hammarskjöld-Weg 1-5, 65760 Eschborn, Germany; telephone: 49 0 6196 79 1174; fax: 49 0 6196 79 6196; email:
[email protected]; Web site: www.gtz.de. Food and Agriculture Organization The Food and Agriculture Organization (FAO), 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. Statistical publications of the FAO include the Production Yearbook, Trade Yearbook, and Fertilizer Yearbook. The FAO makes much of its data available online through its FAOSTAT and AQUASTAT systems. FAO publications can be ordered from national sales agents or directly from the FAO Sales and Marketing Group, Viale delle Terme di Caracalla, 00100 Rome, Italy; telephone: 39 06 5705 5727; fax: 39 06 5705 3360; email:
[email protected]; Web site: www.fao.org. International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, was founded on December 7, 1944. It is responsible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. The ICAO works to achieve the highest practicable degree of uniformity worldwide in civil aviation issues whenever this will facilitate and improve air safety, efficiency, and regularity. To obtain ICAO publications, contact the ICAO, Document Sales Unit, 999 University Street, Montreal, Quebec H3C 5H7, Canada; telephone: 514 954 8022; fax: 514 954 6769; email:
[email protected]; Web site: www.icao.int. International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. Founded in 1919, it is the only surviving major creation of the Treaty of Versailles, which brought the League of Nations into being. It became the first specialized agency of the United Nations in 1946. Unique within the United Nations system, the ILO’s tripartite structure has workers and employers participating as equal partners with governments in the work of its governing organs. As part of its mandate, the ILO maintains an extensive statistical publication program. The Yearbook of Labour Statistics is its most comprehensive collection of labor force data. Publications can be ordered from sales agents and major booksellers throughout the world and ILO offices in many countries or from ILO Publications, 4 route des Morillons, CH-1211 Geneva 22, Switzerland; telephone: 41 22 799 6111; fax: 41 22 798 8685; email:
[email protected]; Web site: www.ilo.org.
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International Monetary Fund The International Monetary Fund (IMF) was established at a conference in Bretton Woods, New Hampshire, United States, on July 1–22, 1944. (The conference also established the World Bank.) The IMF came into official existence on December 27, 1945, and commenced financial operations on March 1, 1947. It currently has 184 member countries. The statutory purposes of the IMF are to promote international monetary cooperation, facilitate the expansion and balanced growth of international trade, promote exchange rate stability, help to establish a multilateral payments system, make the general resources of the IMF temporarily available to its members under adequate safeguards, and shorten the duration and lessen the degree of disequilibrium in the international balance of payments of members. The IMF maintains an extensive program for developing and compiling international statistics and is responsible for collecting and reporting statistics on international financial transactions and the balance of payments. In April 1996 it undertook an important initiative to improve the quality of international statistics, establishing the Special Data Dissemination Standard (SDDS) to guide members that have, or seek, access to international capital markets in providing economic and financial data to the public. In 1997 the IMF established the General Data Dissemination System (GDDS) to guide countries in providing the public with comprehensive, timely, accessible, and reliable economic, financial, and sociodemographic data. Building on this work, the IMF established the Data Quality Assessment Framework (DQAF) to assess data quality in subject areas such as debt and poverty. The DQAF comprises dimensions of data quality such as methodological soundness, accuracy, serviceability, and accessibility. In 1999 work began on Reports on the Observance of Standards and Codes (ROSC), which summarize the extent to which countries observe certain internationally recognized standards and codes in areas including data, monetary and financial policy transparency, fiscal transparency, banking supervision, securities, insurance, payments systems, corporate governance, accounting, auditing, and insolvency and creditor rights. The IMF’s major statistical publications include International Financial Statistics, Balance of Payments Statistics Yearbook, Government Finance Statistics Yearbook, and Direction of Trade Statistics Yearbook. For more information on IMF statistical publications, contact the International Monetary Fund, Publications Services, Catalog Orders, 700 19th Street NW, Washington, DC 20431, USA; telephone: 202 623 7430; fax: 202 623 7201; telex: RCA 248331 IMF UR; email:
[email protected]; Web site: www.imf.org; SDDS and GDDS bulletin board: http://dsbb.imf.org. International Telecommunication Union Founded in Paris in 1865 as the International Telegraph Union, the International Telecommunication Union (ITU) took its current name in 1934 and became a specialized agency of the United Nations in 1947. The ITU is unique among international organizations in that it was founded on the principle of cooperation between governments and the private sector. With a membership encompassing telecommunication policymakers and regulators, network operators, equipment manufacturers, hardware and software developers, regional standards-making organizations, and financing institutions, ITU’s activities, policies, and strategic direction are determined and shaped by the industry it serves. The ITU’s standardization activities, which have already helped foster the growth of new technologies such as mobile telephony and the Internet, are now being put to use in defining the building blocks of the emerging global information infrastructure and in designing advanced multimedia systems that deftly handle a mix of voice, data, audio, and video signals. ITU’s continuing role in managing the radio-frequency spectrum ensures that radio-based systems such as cellular phones and pagers, aircraft and maritime navigation systems, scientific research stations, satellite communication systems, and radio and television broadcasting continue to 2004 World Development Indicators
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function smoothly and provide reliable wireless services to the world’s inhabitants. And ITU’s increasingly important role as a catalyst for forging development partnerships between government and private industry is helping bring about rapid improvements in telecommunication infrastructure in the world’s developing economies. The ITU’s main statistical publications are the ITU Yearbook of Statistics and the World Telecommunication Development Report. Publications can be ordered from ITU Sales and Marketing Service, Web site: www.itu.int/ITU-D/ict/ publications/index.htm; telephone: 41 22 730 6141 (English), 41 22 730 6142 (French), and 41 22 730 6143 (Spanish); fax: 41 22 730 5194; email:
[email protected]; telex: 421 000 uit ch; telegram: ITU GENEVE; Web site: www.itu.int. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. It is responsible for promoting science and engineering through almost 20,000 research and education projects. In addition, the NSF fosters the exchange of scientific information among scientists and engineers in the United States and other countries, supports programs to strengthen scientific and engineering research potential, and evaluates the impact of research on industrial development and general welfare. As part of its mandate, the NSF biennially publishes Science and Engineering Indicators, which tracks national and international trends in science and engineering research and education. Electronic copies of NSF documents can be obtained from the NSF’s online document system (www.nsf.gov/ pubsys/ods/index.html) or requested by email from its automated mailser ver (
[email protected]). Documents can also be requested from the NSF Publications Clearinghouse by mail, at PO Box 218, Jessup, MD 20794-0218, USA, or by telephone, at 301 947 2722. For more information, contact the National Science Foundation, 4201 Wilson Boulevard, Arlington, VA 22230, USA; telephone: 703 292 5111; Web site: www.nsf.gov. Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) was set up in 1948 as the Organisation for European Economic Co-operation (OEEC) to administer Marshall Plan funding in Europe. In 1960, when the Marshall Plan had completed its task, the OEEC’s member countries agreed to bring in Canada and the United States to form an organization to coordinate policy among industrial countries. The OECD is the international organization of the industrialized, market economy countries. Representatives of member countries meet at the OECD to exchange information and harmonize policy with a view to maximizing economic growth in member countries and helping nonmember countries develop more rapidly. The OECD has set up a number of specialized committees to further its aims. One of these is the Development Assistance Committee (DAC), whose members have agreed to coordinate their policies on assistance to developing and transition economies. Also associated with the OECD are several agencies or bodies that have their own governing statutes, including the International Energy Agency and the Centre for Co-operation with Economies in Transition. The OECD’s main statistical publications include Geographical Distribution of Financial Flows to Aid Recipients, National Accounts of OECD Countries, Labour Force Statistics, Revenue Statistics of OECD Member Countries, International Direct Investment Statistics Yearbook, Basic Science and Technology Statistics, Industrial Structure Statistics, Trends in International Migration, and Services: Statistics on International Transactions.
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For information on OECD publications, contact the OECD, 2, rue André Pascal, F-75775 Paris Cedex 16, France; telephone: 33 1 45 24 81 67; fax: 33 1 45 24 19 50; email:
[email protected]; Web sites: www.oecd.org and www.oecd.org/bookshop. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) was established by the Swedish Parliament as an independent foundation in July 1966. 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 research work is disseminated through books and reports as well as through symposia and seminars. SIPRI’s main publication, SIPRI Yearbook, serves as a single authoritative and independent source on armaments and arms control, armed conflicts and conflict resolution, security arrangements, and disarmament. SIPRI Yearbook provides an overview of developments in international security, weapons and technology, military expenditure, the arms trade and arms production, and armed conflicts, along with efforts to control conventional, nuclear, chemical, and biological armaments. For more information on SIPRI publications contact SIPRI at Signalistgatan 9, SE-169 70 Solna, Sweden; telephone: 46 8 655 97 00; fax:46 8 655 97 33; email:
[email protected]; for book orders: http://home.sipri.se/publications.html; Web site: www.sipri.org. United Nations The United Nations and its specialized agencies maintain a number of programs for the collection of international statistics, some of which are described elsewhere in this book. At United Nations headquarters the Statistics Division provides a wide range of statistical outputs and services for producers and users of statistics worldwide. The Statistics Division publishes statistics on international trade, national accounts, demography and population, gender, industry, energy, environment, human settlements, and disability. Its major statistical publications include the International Trade Statistics Yearbook, Yearbook of National Accounts, and Monthly Bulletin of Statistics, along with general statistics compendiums such as the Statistical Yearbook and World Statistics Pocketbook. For publications, contact United Nations Publications, Room DC2-853, Department I004, 2 UN Plaza, New York, NY 10017, USA; telephone: 212 963 8302 or 800 253 9646 (toll free); fax: 212 963 3489; email:
[email protected]; Web site: www.un.org. United Nations Centre for Human Settlements (Habitat), Global Urban Observatory The Urban Indicators Programme of the United Nations Centre for Human Settlements (Habitat) 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. In 1997 the Urban Indicators Programme was integrated into the Global Urban Observatory, the principal United Nations program for monitoring urban conditions and trends and for tracking progress in implementing the goals of the Habitat Agenda. 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.
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Contact the Co-ordinator, Global Urban Observatory and Statistics, Urban Secretariat, UN-HABITAT, PO Box 30030, Nairobi, Kenya; telephone: 254 2 623119; fax: 254 2 623080; email: habitat.publications@ unhabitat.org or
[email protected]; Web site: www.unhabitat.org. United Nations Children’s Fund The United Nations Children’s Fund (UNICEF), the only organization of the United Nations dedicated exclusively to children, works with other United Nations bodies and with governments and nongovernmental organizations to improve children’s lives in more than 140 developing countries through community-based services in primary health care, basic education, and safe water and sanitation. UNICEF’s major publications include The State of the World’s Children and The Progress of Nations. For information on UNICEF publications contact the Chief, EPS, Division of Communication, UNICEF, 3 United Nations Plaza, New York, NY 10017, USA; telephone: 212 326 7000; fax: 212 303 7985; email:
[email protected]; Web site: www.unicef.org and www.un.org/Publications. 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. It was established as a permanent intergovernmental body in 1964 in Geneva with a view to accelerating 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. UNCTAD produces a number of publications containing trade and economic statistics, including the Handbook of International Trade and Development Statistics. For information, contact UNCTAD, Palais des Nations, 8-14, Avenue de la Paix, 1211 Geneva 10, Switzerland; telephone: 41 22 907 1234; fax: 41 22 907 0043; email:
[email protected]; Web site: www.unctad.org. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations established in 1945 to promote “collaboration among nations through education, science, and culture in order to further universal respect for justice, for the rule of law, and for the human rights and fundamental freedoms . . . for the peoples of the world, without distinction of race, sex, language, or religion.” The UNESCO Institute for Statistics’ principal statistical publications are the Global Education Digest (GED) and regional statistical reports, as well as the on-line database. For publications, contact the UNESCO Institute for Statistics, C.P. 6128, Succursale Centre-ville, Montreal, Quebec, H3C 3J7, Canada; telephone: 514 343 6880; fax: 514 343 6882; email:
[email protected]; Web site: www.unesco.org; and for the Institute for Statistics: www.uis.unesco.org. United Nations Environment Programme The mandate of the United Nations Environment Programme (UNEP) 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. UNEP publications include Global Environment Outlook and Our Planet (a bimonthly magazine). For information, contact the UNEP, PO Box 30552, Nairobi, Kenya; telephone: 254 2 621234; fax: 254 2 624489/90; email:
[email protected]; Web site: www.unep.org.
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2004 World Development Indicators
United Nations Industrial Development Organization The United Nations Industrial Development Organization (UNIDO) was established in 1966 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. In 1985 UNIDO became the 16th specialized agency of the United Nations, with a mandate to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. UNIDO’s databases and information services include the Industrial Statistics Database (INDSTAT), Commodity Balance Statistics Database (COMBAL), Industrial Development Abstracts (IDA), and the International Referral System on Sources of Information. Among its publications is the International Yearbook of Industrial Statistics. For information, contact UNIDO Public Information Section, Vienna International Centre, PO Box 300, A-1400 Vienna, Austria; telephone: 43 1 26026 5031; fax: 43 1 21346 5031 or 26026 6843; email:
[email protected]; Web site: www.unido.org. World Bank Group The World Bank Group is made up of five organizations: the International Bank for Reconstruction and Development (IBRD), the International Development Association (IDA), the International Finance Corporation (IFC), the Multilateral Investment Guarantee Agency (MIGA), and the International Centre for Settlement of Investment Disputes (ICSID). Established in 1944 at a conference of world leaders in Bretton Woods, New Hampshire, United States, the World Bank is the world’s largest source of development assistance. In 2003 the World Bank provided $18.5 billion in development assistance and worked in more than 100 developing countries, bringing finance and technical expertise to help them reduce poverty. The World Bank Group’s mission is to fight poverty and improve the living standards of people in the developing world. It is a development bank, providing loans, policy advice, technical assistance, and knowledge sharing services to low- and middle-income countries to reduce poverty. The Bank promotes growth to create jobs and to empower poor people to take advantage of these opportunities. It uses its financial resources, trained staff, and extensive knowledge base to help each developing country onto a path of stable, sustainable, and equitable growth in the fight against poverty. The World Bank Group has 184 member countries. For information about the World Bank, visit its Web site at www.worldbank.org. For more information about development data, contact the Development Data Group, World Bank, 1818 H Street NW, Washington, DC 20433, USA; telephone: 800 590 1906 or 202 473 7824; fax: 202 522 1498; email:
[email protected]; Web site: www.worldbank.org/data. World Health Organization The constitution of the World Health Organization (WHO) was adopted on July 22, 1946, by the International Health Conference, convened in New York by the Economic and Social Council of the United Nations. The objective of the WHO, a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. The WHO carries out a wide range of functions, including coordinating international health work; helping governments strengthen health services; providing technical assistance and emergency aid; working for the prevention and control of disease; promoting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceutical, and similar products; and standardizing diagnostic procedures. 2004 World Development Indicators
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The WHO publishes the World Health Statistics Annual and many other technical and statistical publications. For publications, contact the World Health Organization, Marketing and Dissemination, CH-1211 Geneva 27, Switzerland; telephone: 41 22 791 2476; fax: 41 22 791 4857; email:
[email protected]; Web site: www.who.int. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is an international organization dedicated to helping to ensure that the rights of creators and owners of intellectual property are protected worldwide and that inventors and authors are thus recognized and rewarded for their ingenuity. This international protection acts as a spur to human creativity, pushing forward the boundaries of science and technology and enriching the world of literature and the arts. By providing a stable environment for the marketing of intellectual property products, WIPO also oils the wheels of international trade. WIPO’s main tasks include harmonizing national intellectual property legislation and procedures, providing services for international applications for industrial property rights, exchanging intellectual property information, providing legal and technical assistance to developing and other countries facilitating the resolution of private intellectual property disputes, and marshalling information technology as a tool for storing, accessing, and using valuable intellectual property information. A substantial part of its activities and resources is devoted to development cooperation with developing countries. For information, contact the World Intellectual Property Organization, 34, chemin des Colombettes, CH-1211 Geneva 20, Switzerland; telephone: 41 22 338 9734; fax: 41 22 740 1812; email:
[email protected]; Web site: 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 knowhow. The organization began as the International Union of Official Tourist Publicity Organizations, set up in 1925 in The Hague. Renamed the World Tourism Organization, it held its first general assembly in Madrid in May 1975. Its membership includes 141 countries, seven territories, and some 350 Affiliate Members representing the private sector, educational institutions, tourism associations, and local tourism authorities. The World Tourism Organization publishes the Yearbook of Tourism Statistics, Compendium of Tourism Statistics, and Travel and Tourism Barometer (triannual). For information, contact the World Tourism Organization, Calle Capitán Haya, 42, 28020 Madrid, Spain; telephone: 34 91 567 8100; fax: 34 91 571 3733; email:
[email protected]; Web site: www.world-tourism.org. World Trade Organization The World Trade Organization (WTO), established on January 1, 1995, is the successor to the General Agreement on Tariffs and Trade (GATT). The WTO has 144 member countries and 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
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2004 World Development Indicators
trading system—are WTO’s agreements, negotiated and signed by a large majority of the world’s trading nations and ratified by their parliaments. The WTO’s International Trade Statistics is its main statistical publication, providing comprehensive, comparable, and up-to-date statistics on trade. For publications, contact the World Trade Organization, Publications Services, Centre William Rappard, rue de Lausanne 154, CH-1211, Geneva 21, Switzerland; telephone: 41 22 739 5208 or 5308; fax: 41 22 739 5792; email:
[email protected]; Web site: www.wto.org.
Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. It has more than 850 pages of data, including detailed information on more than 560 container ports in more than 150 countries and a review section that features two-year rankings for 350 ports. The information can be accessed on the Web at www.ci-online.co.uk, which also provides a comprehensive online daily business news and information service for the container industry. For more information, contact Informa UK at 69-77 Paul Street, London, EC2A 4LQ, UK; telephone: 44 1206 772061; fax: 44 1206 772563; email:
[email protected]. Euromoney Publications PLC Euromoney Publications PLC provides a wide range of financial, legal, and general business information. The monthly magazine Euromoney is an authoritative source of detailed yet concise information on the trends and developments in international banking and capital markets and carries a semiannual rating of country creditworthiness. For information, contact Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, UK; telephone: 44 870 90 62 600; email:
[email protected]; Web site: www.euromoney.com. Institutional Investor, Inc. Institutional Investor, Inc., develops country credit ratings every six months based on information provided by leading international banks. It publishes the monthly magazine Institutional Investor, and InstitutionalInvestor.com strives to be the gateway to all Institutional Investor publications online, offering selected articles from its 40 publications. For information, contact Institutional Investor, Inc., 225 Park Avenue South, New York, NY 10003, USA; telephone: 212 224 3800; email:
[email protected]; Web site: www.institutionalinvestor.com. International Data Corporation International Data Corporation (IDC) is a premier global market intelligence and advisory firm in the information technology and telecommunications industries. IDC analyzes and predicts technology trends to enable clients to make strategic, fact-based decisions on information technology purchases and business strategy. More than 700 IDC analysts in 50 countries have provided local expertise and insights on technology markets for 40 years. For further information on IDC’s products and services, contact IDC, Corporate Headquarters, 5 Speen Street, Framingham, MA 01701 USA; telephone: 508 872 8200; Web site: www.idc.com. 2004 World Development Indicators
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International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization with public and private sector members in some 70 countries. The IRF’s mission is to encourage and promote development and maintenance of better and safer roads and road networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF believes that rationally planned, efficiently managed and well-maintained road networks offer high levels of user safety and have a significant impact on sustainable economic growth, prosperity, social well-being, and human development. The IRF has a major role to play in all aspects of road policy and development worldwide. For governments and financial institutions, the IRF provides a wide base of expertise for planning road development strategy and policy. For its members, the IRF is a business network, a link to external institutions and agencies and a business card of introduction to government officials and decisionmakers. For the community of road professionals, the IRF is a source of support and information for national road associations, advocacy groups, companies, and institutions dedicated to the development of road infrastructure. The IRF publishes World Road Statistics. Contact the Geneva office at chemin de Blandonnet 2, CH-1214 Vernier, Geneva, Switzerland; telephone: 41 22 306 0260; fax: 41 22 306 0270; or the Washington, DC, office at 1010 Massachusetts Avenue NW, Suite 410, Washington, DC 20001, USA; telephone: 202 371 5544; fax: 202 371 5565; email:
[email protected]; Web site: www.irfnet.org. Moody’s Investors Service Moody’s Investors Service is a global credit analysis and financial opinion firm. It provides the international investment community with globally consistent credit ratings on debt and other securities issued by North American state and regional government entities, by corporations worldwide, and by some sovereign issuers. It also publishes extensive financial data in both print and electronic form. Its clients include investment banks, brokerage firms, insurance companies, public utilities, research libraries, manufacturers, and government agencies and departments. Moody’s publishes Sovereign, Subnational and Sovereign-Guaranteed Issuers. For information, contact Moody’s Investors Service, 99 Church Street, New York, NY 10007, USA; telephone: 212 553 0377; fax: 212 553 0882; Web site: www.moodys.com. Netcraft Netcraft is an Internet services company based in Bath, United Kingdom. Netcraft’s work includes the provision of network security services and research data and analysis of the Internet. It is an authority on the market share of Web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For information, visit www.netcraft.com. PricewaterhouseCoopers Drawing on the talents of 120,000 people in 139 countries, PricewaterhouseCoopers provides industryfocused assurance, tax, and advisory services for public and private clients in corporate accountability, risk management, structuring and mergers and acquisitions, and performance and process improvement. PricewaterhouseCoopers publishes Corporate Taxes: Worldwide Summaries and Individual Taxes: Worldwide Summaries.
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2004 World Development Indicators
For information, contact PricewaterhouseCoopers, 1177 Avenue of the Americas, New York, NY 10036, USA; telephone: 646 471 4000; fax: 646 471 3188; Web site: www.pwcglobal.com. The PRS Group, Inc. The PRS Group, Inc., is a global leader in political and economic risk forecasting and market analysis and has served international companies large and small for more than 20 years. The data it contributed to this year’s World Development Indicators come from the International Country Risk Guide, a monthly publication that monitors and rates political, financial, and economic risk in 140 countries. The guide’s data series and commitment to independent and unbiased analysis make it the standard for any organization practicing effective risk management. For information, contact The PRS Group, Inc., 6320 Fly Road, Suite 102, East Syracuse, NY 13057-9358, USA; telephone: 315 431 0511; fax: 315 431 0200; email: custser
[email protected]; Web site: www.prsgroup.com or www.ICRGOnline.com. Standard & Poor’s Equity Indexes and Rating Services For more than 140 years Standard & Poor’s, a division of the McGraw-Hill Corporation, has been a preeminent global provider of independent highly valued investment data, valuation, analysis, and opinions and is still delivering on that original mission. The S&P 500 index, one of its most popular products, is calculated and maintained by Standard & Poor’s Index Services, a leading provider of equity indexes. Standard & Poor’s indexes are used by investors around the world for measuring investment performance and as the basis for a wide range of financial instruments. Standard & Poor’s Sovereign Ratings provides issuer and local and foreign currency debt ratings for sovereign governments and for sovereign-supported and supranational issuers worldwide. Standard & Poor’s Rating Services monitors the credit quality of $1.5 trillion worth of bonds and other financial instruments and offers investors global coverage of debt issuers. Standard & Poor’s also has ratings on commercial paper, mutual funds, and the financial condition of insurance companies worldwide. For information on equity indexes, contact Standard & Poor’s Index Services, 55 Water Street, New York, NY 10041, USA; telephone: 212 438 1000; email:
[email protected]; Web site: www.spglobal.com. For information on ratings contact the McGraw-Hill Companies, Inc., Executive Offices, 1221 Avenue of the Americas, New York, NY 10020, USA; telephone: 212 512 4105 or 800 352 3566 (toll free); fax: 212 512 4105; email:
[email protected]; Web site: www.ratingsdirect.com. Standard & Poor’s Emerging Markets Data Base Standard & Poor’s Emerging Markets Data Base (EMDB) is the world’s leading source for information and indices on stock markets in developing countries. The EMDB was the first database to track emerging stock markets. It currently covers 53 markets and more than 2,200 stocks. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indices to serve as benchmarks that are consistent across national boundaries. Standard & Poor’s calculates one index, the S&P/IFCG (Global) index, that reflects the perspective of local investors and those interested in broad trends in emerging markets and another, the S&P/IFCI (Investable) index, that provides a broad, neutral, and historically consistent benchmark for the growing emerging market investment community. For information on subscription rates, contact S&P Emerging Markets Data Base, 55 Water Street, 42nd Floor, New York, NY, 10041-0003; Telephone: 212 438 2046; Fax: 212 438 3429; Email: indexser
[email protected]; Web site: www.standardandpoors.com. 2004 World Development Indicators
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World Conservation Monitoring Centre The World Conservation Monitoring Centre (WCMC) 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. Committed to the principle of data exchange with other centers and noncommercial users, the WCMC, whenever possible, places the data it manages in the public domain. For information, contact the World Conservation Monitoring Centre, 219 Huntington Road, Cambridge CB3 0DL, UK; telephone: 44 12 2327 7314; fax: 44 12 2327 7136; email:
[email protected]; Web site: www.unep-wcmc.org. World Information Technology and Services Alliance The World Information Technology and Services Alliance (WITSA) is a consortium of 53 information technology industry associations from around the world. WITSA members represent more than 90 percent of the world information technology market. As the global voice of the information technology industry, WITSA is dedicated to advocating policies that advance the industry’s growth and development; facilitating international trade and investment in information technology products and services; strengthening WITSA’s national industry associations by sharing knowledge, experience, and information; providing members with a network of contacts in nearly every region; and hosting the World Congress on Information Technology. WITSA’s publication, Digital Planet 2002: The Global Information Economy, uses data provided by the International Data Corporation. For information, contact WITSA, 1401 Wilson Boulevard, Suite 1100, Arlington, VA 22209, USA; telephone: 703 284 5333; fax: 617 687 6590; email:
[email protected]; Web site: www.witsa.org. World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides—and helps other institutions provide— objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute’s current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For information, contact the World Resources Institute, Suite 800, 10 G Street NE, Washington, DC 20002, USA; telephone: 202 729 7600; fax: 202 729 7610; email:
[email protected]; Web site: www.wri.org.
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USERS GUIDE Tables
result in discrepancies between subgroup aggre-
affecting the collection and reporting of data. For
The tables are numbered by section and display the
gates and overall totals. For further discussion of
these reasons, although data are drawn from the
identifying icon of the section. Countries and
aggregation methods, see Statistical methods.
sources thought to be most authoritative, they
economies are listed alphabetically (except for Hong
should be construed only as indicating trends and
Kong, China, which appears after China). Data are
Aggregate measures for regions
characterizing major differences among economies
shown for 152 economies with populations of more
The aggregate measures for regions include only low-
rather than offering precise quantitative measures
than 1 million, as well as for Taiwan, China, in select-
and middle-income economies (note that these
of those differences. Discrepancies in data present-
ed tables. Table 1.6 presents selected indicators for
measures include developing economies with popu-
ed in different editions of the World Development
56 other economies—small economies with popula-
lations of less than 1 million, including those listed
Indicators reflect updates by countries as well as
tions between 30,000 and 1 million and smaller
in table 1.6).
revisions to historical series and changes in
economies if they are members of the International
The country composition of regions is based on
methodology. Thus readers are advised not to com-
Bank for Reconstruction and Development (IBRD) or,
the World Bank's analytical regions and may differ
pare data series between editions of the World
as it is commonly known, the World Bank. The term
from common geographic usage. For regional classi-
Development Indicators or between different World
country, used interchangeably with economy, does
fications, see the map on the inside back cover and
Bank publications. Consistent time-series data for
not imply political independence, but refers to any
the list on the back cover flap. For further discussion
1960–2002 are available on the World Development
territory for which authorities report separate social
of aggregation methods, see Statistical methods.
Indicators CD-ROM and in WDI Online.
measures for income and regional groups appear at
Statistics
are in real terms. (See Statistical methods for infor-
the end of each table.
or economic statistics. When available, aggregate
Except where otherwise noted, growth rates
Data are shown for economies as they were consti-
mation on the methods used to calculate growth
Indicators are shown for the most recent year or
tuted in 2002, and historical data are revised to
rates.) Data for some economic indicators for some
period for which data are available and, in most
reflect current political arrangements. Exceptions are
economies are presented in fiscal years rather than
tables, for an earlier year or period (usually 1990 in
noted throughout the tables.
calendar years; see Primary data documentation. All
this edition). Time-series data are available on the
Additional information about the data is provided
dollar figures are current U.S. dollars unless other-
World Development Indicators CD-ROM and in WDI
in Primary data documentation. That section sum-
wise stated. The methods used for converting nation-
Online.
marizes national and international efforts to improve
al currencies are described in Statistical methods.
Known deviations from standard definitions or
basic data collection and gives information on pri-
breaks in comparability over time or across countries
mary sources, census years, fiscal years, and other
are either footnoted in the tables or noted in About
background. Statistical methods provides technical
the data. When available data are deemed to be too
information on some of the general calculations and
China. On July 1, 1997, China resumed its exercise
weak to provide reliable measures of levels and
formulas used throughout the book.
of sovereignty over Hong Kong, and on December 20,
trends or do not adequately adhere to international
Country notes
1999, it resumed its exercise of sovereignty over Data consistency and reliability
Macao. Unless otherwise noted, data for China do
Considerable effort has been made to standardize
not include data for Hong Kong, China; Taiwan,
Aggregate measures for income groups
the data, but full comparability cannot be assured,
China; or Macao, China.
The aggregate measures for income groups include
and care must be taken in interpreting the indica-
208 economies (the economies listed in the main
tors. Many factors affect data availability, compara-
Democratic Republic of Congo. Data for the
tables plus those in table 1.6) wherever data are
bility, and reliability: statistical systems in many
Democratic Republic of Congo (Congo, Dem. Rep., in
available. To maintain consistency in the aggregate
developing economies are still weak; statistical
the table listings) refer to the former Zaire. (The
measures over time and between tables, missing
methods, coverage, practices, and definitions differ
Republic of Congo is referred to as Congo, Rep., in
data are imputed where possible. The aggregates
widely; and cross-country and intertemporal com-
the table listings.)
are totals (designated by a t if the aggregates include
parisons involve complex technical and conceptual
standards, the data are not shown.
gap-filled estimates for missing data and by an s, for
problems that cannot be unequivocally resolved.
Czech Republic and Slovak Republic. Data are
simple totals, where they do not), median values (m),
Data coverage may not be complete because of spe-
shown whenever possible for the individual countries
weighted averages (w), or simple averages (u). Gap
cial circumstances or for economies experiencing
formed from the former Czechoslovakia—the Czech
filling of amounts not allocated to countries may
problems (such as those stemming from conflicts)
Republic and the Slovak Republic.
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2004 World Development Indicators
Eritrea. Data are shown for Eritrea whenever possi-
previous years refer to aggregated data for the for-
economies are those with a GNI per capita of more
ble, but in most cases before 1992 Eritrea is includ-
mer People's Democratic Republic of Yemen and the
than $735 but less than $9,076. Lower-middle-
ed in the data for Ethiopia.
former Yemen Arab Republic unless otherwise noted.
income and upper-middle-income economies are
Germany. Data for Germany refer to the unified
Changes in the System of National Accounts
income economies are those with a GNI per capita
Germany unless otherwise noted.
World Development Indicators uses terminology in
of $9,076 or more. The 12 participating member
line with the 1993 United Nations System of
countries of the European Monetary Union (EMU)
Jordan. Data for Jordan refer to the East Bank only
National Accounts (SNA). For example, in the 1993
are presented as a subgroup under high-income
unless otherwise noted.
SNA gross national income (GNI) replaces gross
economies.
separated at a GNI per capita of $2,935. High-
national product (GNP). See About the data for Serbia and Montenegro. On February 4, 2003, the Federal Republic of Yugoslavia changed its name to
tables 1.1 and 4.9. Most economies continue to compile their
Symbols ..
national accounts according to the 1968 SNA, but
means that data are not available or that aggregates
more and more are adopting the 1993 SNA.
cannot be calculated because of missing data in the
Timor-Leste. On May 20, 2002, Timor-Leste became
Economies that use the 1993 SNA are identified in
years shown.
an independent country. Data for Indonesia include
Primar y data documentation. A few low-income
Timor-Leste through 1999 unless otherwise noted.
economies still use concepts from older SNA guide-
0 or 0.0
lines, including valuations such as factor cost, in
means zero or less than half the unit shown.
Serbia and Montenegro.
Union of Soviet Socialist Republics. In 1991 the
describing major economic aggregates. /
Union of Soviet Socialist Republics came to an end. Available data are shown for the individual countries
Classification of economies
in dates, as in 1990/91, means that the period of
now existing on its former territor y (Armenia,
For operational and analytical purposes the World
time, usually 12 months, straddles two calendar
Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan,
Bank's main criterion for classifying economies is
years and refers to a crop year, a survey year, an aca-
Kyrgyz Republic, Latvia, Lithuania, Moldova, Russian
GNI per capita. Every economy is classified as low
demic year, or a fiscal year.
Federation, Tajikistan, Turkmenistan, Ukraine, and
income, middle income (subdivided into lower middle
Uzbekistan). External debt data presented for the
and upper middle), or high income. For income
$
Russian Federation prior to 1992 are for the former
classifications see the map on the inside front cover
means current U.S. dollars unless otherwise noted.
Soviet Union. The debt of the former Soviet Union is
and the list on the front cover flap. Low- and middle-
included in the Russian Federation data after 1992
income economies are sometimes referred to as
>
on the assumption that 100 percent of all outstand-
developing economies. The use of the term is con-
means more than.
ing external debt as of December 1991 has become
venient; it is not intended to imply that all economies
a liability of the Russian Federation. Beginning in
in the group are experiencing similar development or
<
1993 the data for the Russian Federation have been
that other economies have reached a preferred or
means less than.
revised to include obligations to members of the for-
final stage of development. Note that classification
mer Council for Mutual Economic Assistance and
by income does not necessarily reflect development
Data presentation conventions
other countries in the form of trade-related credits
status. Because GNI per capita changes over time,
•
amounting to $15.4 billion as of the end of 1996.
the country composition of income groups may
A blank means not applicable or, for an aggregate, not analytically meaningful.
change from one edition of World Development
•
A billion is 1,000 million.
República Bolivariana de Venezuela. In December
Indicators to the next. Once the classification is fixed
•
A trillion is 1,000 billion.
1999 the official name of Venezuela was changed to
for an edition, based on GNI per capita in the most
•
Figures in italics refer to years or periods other
República Bolivariana de Venezuela (Venezuela, RB,
recent year for which data are available (2002 in this
in the table listings).
edition), all historical data presented are based on the same country grouping.
Republic of Yemen. Data for the Republic of Yemen
Low-income economies are those with a GNI per
refer to that country from 1990 onward; data for
capita of $735 or less in 2002. Middle-income
than those specified. •
Data for years that are more than three years from the range shown are footnoted.
The cutoff date for data is February 1, 2004.
2004 World Development Indicators
xxvii
1 WORLD VIEW
T
he Millennium Development Goals put the world community on a time table. When 189 member states of the United Nations adopted the Millennium Declaration in September 2000, they looked backwards to 1990 and ahead to 2015 and gave themselves 25 years to produce substantial improvements in the lives of people. At the time, it was clear that in many places development progress had slowed and would have to be accelerated if the ambitious targets of the Millennium Development Goals were to be achieved.
As in the past four editions, this section of World Development Indicators reviews progress toward the major development goals. Until recently we have been gauging progress toward the Millennium Development Goals based on the record of the 1990s. Now, we are closer to 2015 than to 1990, and we are getting our first look at the record of the 21st century. There are hopeful signs. Global poverty rates continue to fall. Fewer people are living in extreme poverty, after an increase in the late 1990s. In countries that have laid a good foundation for growth, indicators of social development are also improving. But progress is uneven. Slow growth, low educational achievement, poor health, and civil disturbances remain obstacles for many. It is still too early to conclude that the world as a whole is on track to achieve the Millennium Development Goals—or that it is not. What is clear is that the goals remain a great challenge and that hard work lies ahead.
1a
1b
Poverty rates have been falling in all regions except Sub-Saharan Africa
But more than 1.1 billion people remain in extreme poverty
Share of people living on less than $1 a day (%)
Number of people living on less than $1 a day (millions) 1,500
70
60 1,200 50
Sub-Saharan Africa
900
40 South Asia
30
600 East Asia and Pacific China
20 Latin America and Caribbean
10 Middle East and North Africa
300 Europe and Central Asia
0
0 1981
1984
1987
Source: World Bank staff estimates.
1990
1993
1996
1999
2001
1981
1984
1987
1990
1993
1996
1999
2001
South Asia
Sub-Saharan Africa
East Asia and Pacific
Latin America & Caribbean
Middle East & North Africa
Europe & Central Asia
Source: World Bank staff estimates.
2004 World Development Indicators
1
1 Eradicate extreme poverty . . . The first Millennium Development Goal calls for cutting in half the proportion of people living in extreme poverty—and those suffering from hunger—between 1990 and 2015. A poverty line of $1 a day ($1.08 in 1993 purchasing power parity terms) has been accepted as the working definition of extreme poverty in low-income countries. In middle-income countries a poverty line of $2 a day ($2.15 in 1993 purchasing power parity terms) is closer to a practical minimum, and national poverty lines may be set even higher. In 1990, 1,219 million people, 28 percent of the population of low- and middle-income countries, lived on less than $1 a day. Over the next 11 years gross domestic product (GDP) in low- and middle-income countries grew 31 percent, and by 2001 the poverty rate had fallen to 21 percent. During the same period population in those countries grew by 15 percent to 5 billion, leaving about 1,100 million people in extreme poverty. New estimates of poverty rates, based on reexamination of household survey data back to 1981, show that global trends in poverty reduction have been dominated by rapid growth in China and the East Asia and Pacific region. GDP per capita more than tripled while the proportion of people in extreme poverty fell from 56 percent to 16 percent. Poverty also fell in South Asia over the past 20 years, and while the decline was not as rapid, almost 50 million fewer people were living in extreme poverty by 2001. But in Sub-Saharan Africa, where GDP per capita shrank 14 percent, poverty rose from 41 percent in 1981 to 46 percent in 2001, and an additional 140 million people were living in
extreme poverty. Other regions have seen little or no change. In the early 1990s the transition economies of Europe and Central Asia experienced a sharp drop in income. Poverty rates rose to 6 percent at the end of the decade before beginning to recede. Continued progress in poverty reduction depends on economic growth and the distribution of income. Growth without poverty reduction is at least a theoretical possibility, and in regions such as Latin America, where the distribution of income is less equitable, the poverty reducing effects of growth are weaker. In looking ahead, income distribution is assumed to remain unchanged on average. If projected growth remains on track through 2015, global poverty rates measured at $1 a day will fall to 12.7 percent—less than half the 1990 level of 28 percent—and 363 million fewer people will live in extreme poverty than at the beginning of the 21st century. Poverty rates will fall fastest in East Asia and Pacific outside of China, but the huge reduction in the number of people below the $1 a day poverty line in China will dominate global totals. In Europe and Central Asia and in the Middle East and North Africa, where poverty rates measured at $1 a day are low, a continuation of current trends will cut poverty rates to half their current levels. South Asia, led by continuing growth in India, is likely to reach or exceed the target. But growth and poverty reduction are proceeding more slowly in Latin America and the Caribbean, which will not reach the target unless growth picks up. The most difficult case is Sub-Saharan Africa, where poverty has increased since 1990 and will, on present trends, fall very slowly in the next 11 years, unless there is a major change in prospects.
1c Most regions are on a path to cut extreme poverty by half by 2015 Share of people living on less than $1 (or $2) a day (%)
East Asia & Pacific
Europe & Central Asia
Latin America & the Caribbean
50 40 29.6
28.4
30
24.5 19.7
20
14.8
10.3
15.6
10 0
20.5
12.3
1990
1995
2000
2005
2010
3.7
2.3
0.5
2015
1990
Middle East & North Africa
1995
2000
11.3
9.5
7.6
1.3 0.3
2005
2010
2015
South Asia
5.6
1990
1995
2000
2005
2010
2015
Sub-Saharan Africa
50
44.6 41.3
42.3
46.5
40 30
31.1
23.2
21.4
22.3
20.6
20 10.2
10 2.4
2.3
0
1990
1995
2000
16.4
1.2 1.2
2005
2010
2015
1990
1990 Source: World Bank staff estimates.
2
2004 World Development Indicators
1995
2001
2000 Goal 2015
2005
2010
2015
Poverty rate at $1 a day
1990 Actual Projected Path to goal
1995
2000
2005
Poverty rate at $2 a day
2010
2015
Actual Projected
New poverty estimates trace the decline of global poverty levels over the last two decades
1d With continuing growth the number of people living in extreme poverty will fall People living on less than $1 a day (millions) Region East Asia & Pacific China Europe & Central Asia Latin America & Caribbean Middle East & North Africa South Asia India Sub-Saharan Africa Total
1981
1984
1987
1990
1993
1996
1999
2001
796
562
426
472
415
287
282
284
634
425
308
375
334
212
223
212
1
1
2
2
17
20
30
18
36
46
45
49
52
52
54
50
9
8
7
6
4
6
8
7
475
460
473
462
476
461
453
428
382
374
370
357
380
400
352
359
164
198
219
227
241
270
292
314
1,480
1,276
1,171
1,219
1,206
1,095
1,117
1,101
1e And the proportion of people in extreme poverty will reach an all-time low in 2015 Share of people living on less than $1 a day (%) Region
1981
1984
1987
1990
1993
1996
1999
2001
East Asia & Pacific
57.7
38.9
28
29.6
24.9
16.6
15.7
15.6
63.8
41.0
28.5
33.0
28.4
17.4
17.8
16.6
Europe & Central Asia
0.3
0.3
0.4
0.5
3.7
4.2
6.2
3.7
Latin America & Caribbean
9.7
11.8
10.9
11.3
11.3
10.7
10.5
9.5
5.1
3.8
3.2
2.3
1.6
2.0
2.6
2.4
51.5
46.8
45
41.3
40.1
36.6
34.0
31.1
China
Middle East & North Africa South Asia
54.4
49.8
46.3
42.1
42.3
42.2
35.3
34.7
Sub-Saharan Africa
India
41.6
46.3
46.8
44.6
43.7
45.3
45.4
46.5
Total
40.3
32.8
28.4
27.9
26.2
22.7
22.2
21.3
1f But more than 2 billion people will live on less than $2 a day People living on less than $2 a day (millions) Region East Asia & Pacific China Europe & Central Asia Latin America & Caribbean Middle East & North Africa South Asia India Sub-Saharan Africa Total
1981
1984
1987
1990
1993
1996
1999
2001
1,170
1,109
1,028
1,116
1,079
922
900
868
876
814
731
825
803
650
628
594
8
9
8
58
78
97
112
94
99
119
115
125
136
117
127
128
52
50
53
51
52
61
70
70
821
859
911
958
1,005
1,029
1,034
1,059
630
661
697
731
770
806
804
826
288
326
355
382
409
445
487
514
2,438
2,471
2,470
2,689
2,759
2,672
2,730
2,733
1g And more than half the population of South Asia and Sub-Saharan Africa will be very poor Share of people living on less than $2 a day (%) Region
1981
1984
1987
1990
1993
1996
1999
2001
East Asia & Pacific
84.8
76.6
67.7
69.9
64.8
53.3
50.3
47.6
88.1
78.5
67.4
72.6
68.1
53.4
50.1
46.7
1.9
2.0
1.7
12.3
16.6
20.6
23.5
19.7
Latin America & Caribbean
26.9
30.4
27.8
28.4
29.5
24.1
25.1
24.5
Middle East & North Africa
28.9
25.2
24.2
21.4
20.2
22.3
24.3
23.2
South Asia
89.1
87.2
86.7
85.5
84.5
81.7
77.7
76.9
India
89.6
88.2
87.3
86.1
85.7
85.2
80.6
79.9
Sub-Saharan Africa
73.3
76.1
76.1
75.0
74.3
74.8
75.7
76.3
Total
66.4
63.5
59.9
61.6
60.0
55.5
54.2
52.8
China Europe & Central Asia
Source: World Bank staff estimates. 2004 World Development Indicators
3
1 . . . and reduce hunger and malnutrition The world produces enough food to feed everyone, but hunger remains a persistent problem. Although famines and droughts cause terrible short-term crises and grab most of the headlines, the root cause of hunger is poverty. The Food and Agriculture Organization (FAO) estimates that worldwide there are more than 840 million people who are chronically undernourished, most of them living in low-income countries. But there are hungry people everywhere, including 10 million undernourished people living in industrial countries. Undernourishment means consuming too little food to maintain normal levels of activity. The FAO sets the average requirement at 1,900 calories a day. Among the less severely affected the average daily shortfall is less than 200 calories a person. In the FAO’s estimation extreme hunger occurs with a shortfall of more than 300 calories, but the needs of individuals vary with age, sex, and height. Adding to the problems of undernourishment are diets that lack essential nutrients and illnesses that deplete nutrients. The Millennium Development Goals call for cutting the prevalence of hunger to half of its 1990 levels by 2015. Prevalence rates have been falling in most regions, but too slowly to achieve the 2015 target, and in many regions the number of hungry people continues to grow. By 2001 only the East Asia and Pacific and Latin America and the Caribbean regions had
fewer undernourished people than 10 years earlier. Countries that have succeeded in reducing hunger had higher economic growth, especially in their agricultural sector and rural regions. They have also had lower population growth and lower rates of HIV infection. Malnutrition in children often begins at birth, when poorly nourished mothers give birth to underweight babies. Improper feeding and child care practices contribute to the harm done by an inadequate diet, putting poor children at a permanent disadvantage. Malnourished children develop more slowly, enter school later, and perform less well. And malnutrition is an underlying factor in more than half the deaths of children under age five. Progress in reducing child malnutrition has been fastest in East Asia and Pacific. where child malnutrition rates declined by 33 percent, and South Asia, where rates declined 25 percent. But many countries, especially in Sub-Saharan Africa, lag far behind. In many others data are inadequate for tracking progress. In the 74 countries with two or more observations since 1988, only 29 are currently on track to achieve the target by 2015. But faster progress is possible. Programs to encourage breastfeeding and to improve the diets of pregnant and lactating mothers along with micronutrient supplementation help to prevent malnutrition. Appropriate care and feeding of sick children, oral rehydration therapy, control and treatment of parasitic diseases, and programs to treat vitamin A deficiency have all been shown to reduce malnutrition rates.
1h
1i
The undernourished are everywhere
Malnourished children are among the most vulnerable
Prevalence of undernourishment (%)
Prevalence of underweight children (%)
40
60 1990–92
Around 1990
1995–97 1999–2001
Around 1992
50
Millennium Development Goal target
30 40
20
30
20 10
1993–95
10
0 Sub-Saharan Africa
South Asia
East Asia & Pacific
Latin America Europe & Middle East & Caribbean Central Asia & North Africa
Source: FAO 2003, The State of Food Insecurity in the World.
4
2004 World Development Indicators
0 South Asia
Sub-Saharan Africa
East Asia & Pacific
Source: WHO and World Bank staff estimates.
Latin America Middle East & Low- and & Caribbean North Africa middle-income economies
2 Achieve universal primary education Education is the foundation of democratic societies and globally competitive economies. It is the basis for reducing poverty and inequality, increasing productivity, enabling the use of new technologies, and creating and spreading knowledge. In an increasingly complex, knowledge-dependent world, primary education, as the gateway to higher levels of education, must be the first priority. The Millennium Development Goals call on the world to ensure that by 2015 all children are able to complete a course of primary education. This target can be achieved—and it must be, if all developing countries are to compete in the global economy. Progress toward this target is commonly measured by the net enrollment ratio—the ratio of enrolled children of official school age to the number of children of that age in the population. Ratios at or near 100 percent imply that all children will receive a full primary education. But lower ratios are ambiguous. Schools may fail to enroll all students in the first grade, or many students may drop out in later grades. Chad, for example, reports a net enrollment rate of almost 60 percent, but barely 20 percent complete the final year of primary education. Primary completion rates—the proportion of each age group finishing primary school—directly measure progress toward the Millennium Development Goal. To achieve 100 percent completion rates, school systems must enroll all children in first grade and keep them in school throughout the primary cycle. To reach the target of universal primary education by 2015, school systems with low completion rates will need to start now to train teachers, build classrooms, and improve the quality of education. Three regions—East Asia and Pacific, Europe and Central Asia, and Latin America and the Caribbean—are on track to achieve the
goal. Many countries in these regions have already reached the target. China, Mexico, and Russia are at or near full enrollment. Others, such as Brazil, Bulgaria, and Laos made rapid progress in the 1990s and are likely to reach the target by 2015. But three regions, with 150 million primary school-age children, are in danger of falling short. Sub-Saharan Africa lags farthest behind, with little progress since 1990. South Asia has chronically low enrollment and completion rates. And completion rates in the Middle East and North Africa stagnated in the 1990s. But even in these regions some countries have made large gains. Removing impediments and reducing costs can boost enrollments. Malawi and Uganda lowered school fees but could not provide spaces for all the new students. Many countries face the challenge of improving school quality while attracting and keeping more children in school. If current trends persist, children in more than half of developing countries will not complete a full course of primary education in 2015. But faster progress is possible, and successful countries have set an example by: • Committing a higher share of their budgets to public education. • Managing to efficiently control costs. • Providing an adequate level of complementary inputs. • Keeping pupil-teacher ratios around 40 and repetition rates below 10 percent. Many poor countries cannot afford the cost of expanding their education systems to reach the goal. They will need help from donors that are prepared to make long-term commitments to supporting education. The World Bank estimates the financing gap in low-income countries at $2.4–3.7 billion a year (Bruns, Mingat, and Rakotomalala 2003, p. 13).
1j
1k
To reach the goal, all children need to complete primary school
Schools need to do more to lower costs and attract students
Average primary school completion rate, 2000–02 (%)
Reasons for leaving primary school Upper-middle-income average
100
Mali 1995/96
Lower-middle-income average
80
Low-income average
60
Other 15% Pregnancy or marriage 11%
Did not like school 42%
Zambia 1996
Failed exams 12% Family needed labor or money 20%
40
20
Other 16% Pregnancy or marriage 12% Did not like school 16%
0 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Caribbean & North Africa
Source: World Bank staff estimates.
South Asia
Sub-Saharan Africa
School fees 37%
Failed exams 19%
Source: Demographic and Health Survey EdData Education Profiles (www.dhseddata.com).
2004 World Development Indicators
5
3 Promote gender equality and empower women Gender disparities exist everywhere in the world. Women are underrepresented in local and national decisionmaking bodies. They earn less than men and are less likely to participate in wage employment. And in many low-income countries girls are less likely to attend school. Evidence from around the world shows that eliminating gender disparities in education is one of the most effective development actions a country can take. When a country educates both its boys and its girls, economic productivity tends to rise, maternal and infant mortality rates usually fall, fertility rates decline, and the health and educational prospects of the next generation improve. With this in mind, the Millennium Development Goals call for eliminating gender disparities in primary and secondary school by 2005 and at all levels by 2015. But all regions except Latin America are still short of the first target. The differences between boys' and girls' schooling are greatest in regions with the lowest primary school completion rates and the lowest average incomes. In South Asia girls’ enrollment in primary schools is 12 points lower than boys’, and only 61 percent of girls complete primary school compared with 86 percent of boys. One consequence is that illiteracy rates among young women ages 15–24 are almost 40 percent in South Asia and 26 percent in Sub-Saharan Africa, and in both regions they are more than half again as high as those of young men. The disparities are even greater in the Middle East and North Africa, a region of higher average incomes but a long history of neglecting female education. The failure to educate women has consequences for development. A recent study (Klasen 1999) estimates that if countries in
South Asia, Sub-Saharan Africa, and the Middle East and North Africa had closed the gender gap in schooling between 1960 and 1992 as quickly as East Asia did, their income would have grown by an additional 0.5 to 0.9 percentage point per year. In Africa this would have meant close to doubling per capita income growth. What does improving girls’ enrollments require? Mainly overcoming the social and economic obstacles that stop parents from sending their daughters to school. For many poor families the economic value of girls’ work at home exceeds the perceived returns to schooling. Improving the quality and affordability of schools is a first step. The World Bank's Girls' Education Initiative outlines many gender-sensitive strategies and interventions, including construction of toilet blocks and water sources in schools, provision of nursery and preschool centers where girls can leave younger siblings, abolition of school fees and uniforms, and provision of free or subsidized textbooks. Overcoming women’s disadvantages in the labor force and increasing their representation in public life will also help encourage girls to attend and stay in school. Progress is possible. Over the past decade gender differences at the primary level have been eliminated or greatly reduced in Algeria, Angola, Bangladesh, China, the Arab Republic of Egypt, and The Gambia. Because the Millennium Development Goals are mutually reinforcing, progress toward one goal affects progress toward all the others. Success in many of the goals will have positive impacts on gender equality, just as progress toward gender equality will further other goals. Increasing opportunities for women will also contribute toward the goal of reducing poverty, educating children, improving health, and better managing environmental resources.
1l
1m
Many girls still do not have equal access to education
Literacy rates have been rising as more children remain in school, but girls lag behind boys
Ratio of girls to boys in primary and secondary education (%)
Youth literacy rate, ages 15–24 (%)
120
100
Female 1990 Male 1990
1990
Female 2002
2000
Male 2002
100
80
80 60 60 40 40
20
1998
1998
1997
20
0
0 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Caribbean & North Africa
South Asia
Sub-Saharan Africa
Source: United Nations Economic, Scientific and Cultural Organization and World Bank staff estimates.
6
2004 World Development Indicators
East Asia & Pacific
Europe & Latin America Middle East Central Asia & Caribbean & North Africa
South Asia
Sub-Saharan Africa
Source: United Nations Economic, Scientific and Cultural Organization and World Bank staff estimates.
4 Reduce child mortality Every year more than 10 million children in developing countries die before the age of five. Rapid improvements before 1990 gave hope that mortality rates for infants and children under five could be cut by two-thirds in the following 25 years. But progress slowed almost everywhere in the 1990s. And no region, except possibly Latin America and the Caribbean, is on track to achieve the target. Progress has been particularly slow in Sub-Saharan Africa, where civil disturbances and the HIV/AIDS epidemic have driven up rates of infant and child mortality in several countries. For the region the under-five mortality rate stands at 171 deaths per 1,000 live births. Child mortality is closely linked to poverty. In 2002 the average under-five mortality rate was 122 deaths per 1,000 live births in low-income countries, 42 in lower-middle-income countries, and 21 in upper-middle-income countries. In high-income countries the rate was less than 7. For 70 percent of the deaths the cause is a disease or a combination of diseases and malnutrition that would be preventable in a high-income country: acute respiratory infections, diarrhea, measles, and malaria. Improvements in infant and child mortality have come slowly in low-income countries, where mortality rates have fallen by only 12 percent since 1990. Upper-middle-income countries have made the greatest improvement, reducing average mortality rates by 36 percent. But even this rate of improvement
falls short of that needed to reach the target. There is evidence that improvements in child mortality have been greatest among the better-off. In 20 developing countries with disaggregated data, child mortality rates fell only half as fast for the poorest 20 percent of the population as for the whole population. In Bolivia, which is nearly on track to achieve the target, under-five mortality rates fell 34 percent among the wealthiest 20 percent but only 8 percent among the poorest. In Vietnam mortality rates also fell among the better-off but scarcely changed for the poor. But in Egypt in the late 1990s under-five mortality fell faster among the poor than among the general population. In the effort to reach the Millennium Development Goals, the poor do not need to be left behind. Just as child deaths are the result of many causes, reducing child mortality will require multiple, complementary interventions. Raising incomes will help. So will increasing public spending on health services. But a greater effort is needed to ensure that health care and other public services reach the poor. Access to safe water, better sanitation facilities, and improvements in education, especially for girls and mothers, are closely linked to reduced mortality. Also needed are roads to improve access to health facilities and modern forms of energy to reduce dependence on traditional fuels, which cause damaging indoor air pollution. The Millennium Development Goals remind us of the need to look at health and health care from the broadest possible perspective.
1n
1o
Few countries are on track to meet the child mortality target
To reduce early childhood deaths, immunization programs must be extended and sustained
Under-five mortality rate, 2001 (deaths per 1,000 live births)
Measles immunization, 2001 (% of children under 12 months) 100
350
Middle-income economies average
300 80 Countries above the line are progressing too slowly to meet the target
250
Low-income economies average
60
200
150
40
Iraq
100
Bhutan
20 Countries below the line are on track to achieve a two-thirds reduction in mortality rates
50 Egypt
0
0 0
50
100
150
200
Under-five mortality rate, 1990 Source: World Bank staff estimates.
250
300
350
East Asia & Pacific
Europe & Latin America Middle East Central Asia & Caribbean & North Africa
South Asia
Sub-Saharan Africa
Source: WHO, UNICEF, and World Bank staff estimates.
2004 World Development Indicators
7
5 Improve the health of mothers In rich countries 13 women die in childbirth for every 100,000 live births. In some poor countries 100 times more women die. Overall, more than 500,000 women die each year in childbirth, most of them in developing countries. What makes maternal mortality such a compelling problem is that it strikes young women undergoing what should be a normal function. They die because they are poor. Malnourished. Weakened by disease. Exposed to multiple pregnancies. And they die because they lack access to trained health care workers and modern medical facilities. The Millennium Development Goals call for reducing the maternal mortality ratio by three-quarters between 1990 and 2015, or an average of 5.4 percent a year. Maternal mortality is difficult to measure accurately. Deaths from pregnancy or childbirth are relatively rare and may not be captured in general-purpose surveys or surveys with small sample sizes. Maternal deaths may be underreported in countries that lack good administrative statistics or where many women give birth outside the formal health system. For these reasons, efforts to monitor maternal mortality often rely on proxy indicators or statistical models. The share of births attended by skilled health staff is frequently used to identify where the need for intervention is greatest. Only 56 percent of women in developing countries are attended in childbirth by a trained midwife or doctor. In Latin America, where the share of births attended by skilled health personnel is high, maternal mortality is relatively low. But in Africa, where skilled attendants and health facilities are not readily available, it is very high. The maternal mortality ratio measures the risk of a woman dying once she becomes pregnant. Women who have more preg-
nancies are exposed more often to the risk of maternal death and thus face a higher lifetime risk of death due to pregnancy or childbirth. The greatest number of maternal deaths each year occur in populous India, which has a maternal mortality ratio of 540 per 100,000 and a lifetime risk of maternal death of 1 in 48. But in little Togo, with a similar maternal mortality ratio but higher fertility rate, women are exposed to almost twice the risk of death (AbouZhar and Wardlaw 2003). New estimates of trends in maternal mortality suggest that all regions, except possibly the Middle East and North Africa, will fall short of the 2015 target (World Bank 2003). Across the developing world 17 percent of countries, with almost a third of the population of developing countries, are on track to achieve the maternal mortality target. In Sub-Saharan Africa, where maternal mortality ratios are on average the highest, the rate of improvement is expected to be less than in any region except Europe and Central Asia. Significant progress in reducing maternal mortality will require a comprehensive approach to providing health services: deaths in childbirth often involve complications, such as hemorrhaging, that require fully equipped medical facilities, accessible roads, and emergency transportation. Causes of complications during pregnancy and childbirth include inadequate nutrition, unsafe sex, and poor health care. Gender inequality in controlling household resources and making decisions also contributes to poor maternal health. Early childbearing and closely spaced pregnancies increase the risks for mothers and children. Access to family planning services helps women plan whether and when to have children. Fewer pregnancies means a lower lifetime exposure to the risk of maternal mortality.
1p
1q
Extreme risks of dying from pregnancy or childbirth in some regions
The presence of skilled health staff lowers the risk of maternal death
Lifetime risk of maternal death, 2000
Births attended by skilled health staff, 1995–2000 (% of total) 100
1 in 16 Middle-income economies average
80
60 Low-income economies average
1 in 46
40 1 in 120
1 in 140
1 in 160
1 in 210 1 in 840 1 in 2,800
Sub-Saharan SouthAfrica Central Asia
Western Asia
SouthLatin Eastern America & Asia Caribbean
North Africa
20
Eastern Developed Asia countries
0 The lifetime risk of maternal death is the risk of an individual woman dying from pregnancy or childbirth during her lifetime. A 1 in 3,000 lifetime risk represents a low risk of dying from pregnancy or childbirth, while a 1 in 100 lifetime risk is a high risk of dying. a. Excludes Australia, Japan, and New Zealand. Source: AbouZhar and Wardlaw 2003.
8
2004 World Development Indicators
East Asia & Pacific
Europe & Latin America Middle East Central Asia & Caribbean & North Africa
Source: World Bank staff estimates.
South Asia
Sub-Saharan Africa
6 Combat HIV/AIDS, malaria, and other diseases Epidemic diseases exact a huge toll in human suffering and lost opportunities for development. Poverty, civil disturbances, and natural disasters all contribute to, and are made worse by, the spread of disease. In Africa the spread of HIV/AIDS has reversed decades of improvements in life expectancy and left millions of children orphaned. It is draining the supply of teachers and eroding the quality of education. HIV has infected more than 60 million people worldwide. Each day 14,000 people are newly infected, more than half of them below age 25. The Millennium Development Goals have set the target of reducing prevalence among 15–24 year olds by 25 percent by 2005 in the most severely affected countries and by 2010 globally. At the end of 2002, 42 million adults and 5 million children were living with HIV/AIDS—more than 95 percent of them in developing countries and 70 percent in Sub-Saharan Africa. There were almost a million new cases in South and East Asia, where more than 7 million people now live with HIV/AIDS. Projections suggest that by 2010, 45 million more people in low- and middleincome countries will become infected unless the world mounts an effective campaign to halt the disease’s spread. But there are success stories: Brazil, Thailand, and Uganda are controlling the spread of HIV/AIDS. Thailand has reduced the number of new infections from 140,000 a decade ago to 30,000 in 2001. The World Health Organization (WHO 2002) estimates that 300–500 million cases of malaria occur each year, leading to 1.1 million deaths. Almost 90 percent of cases occur in SubSaharan Africa, and most deaths are among children younger than five. Malaria is a disease of poverty: almost 60 percent of
deaths occur among the poorest 20 percent of the population. The disease is estimated to have slowed economic growth in African countries by 1.3 percent a year (World Bank 2001). Because children bear the greatest burden of the disease, the Millennium Development Goals call for monitoring efforts focusing on children under five. An effective means of preventing new infections is the use of insecticide-treated bednets. Vietnam, where 16 percent of children sleep under treated bednets, has made significant strides in controlling malaria. But in Africa only 7 of 27 countries with survey data reported rates of bednet use of 5 percent or more. The emergence of drugresistant strains of malaria has increased the urgency of finding new means of treatment and prevention. Tuberculosis kills some 2 million people a year, most of them 15–45 years old. The emergence of drug-resistant strains of tuberculosis; the spread of HIV/AIDS, which reduces resistance to tuberculosis; and the growing number of refugees and displaced persons have allowed the disease to spread more rapidly. Each year there are 8 million new cases—2 million in SubSaharan Africa, 3 million in Southeast Asia, and more than a quarter million in Eastern Europe and the former Soviet Union. Poorly managed tuberculosis programs allow drug-resistant strains to spread. WHO has developed a treatment strategy—directly observed treatment, short course (DOTS)—that emphasizes positive diagnosis followed by a course of treatment and follow-up care. DOTS produces cure rates of up to 95 percent, even in poor countries. While some countries have made rapid progress in DOTS detection rates, those with high tuberculosis burdens are not increasing detection rates toward the 70 percent target.
1r
1s
HIV strikes at youth—and women are particularly vulnerable
Treated bednets are a proven way to combat malaria, but they are still not widely used
Youth ages 15–24 living with HIV/AIDS, end 2001 (%)
Children under age five sleeping under insecticide-treated bednets, 2000 (%)
15
25 Women Men
12
20
9
15
6
10
3
5
0
0 SubEastern & Southern Saharan Africa Africa
West & Central Africa
Latin Europe & Central America & Caribbean Asia
Source: Joint United Nations Programme on HIV/AIDS.
South Asia
Middle East East & Asia & Pacific North Africa
Swaziland Sudan
Niger
Burundi Tanzania Rwanda
The Vietnam São Tomé Gambia & Principe
Source: World Health Organization.
2004 World Development Indicators
9
7 Ensure environmental sustainability Sustainable development can be ensured only by protecting the environment and using its resources wisely. Because poor people are often dependent on environmental resources for their livelihood, they are most affected by environmental degradation and by natural disasters, such as fires, storms, and earthquakes, whose effects are worsened by environmental mismanagement. The Millennium Development Goals draw attention to some of the environmental conditions that need to be closely monitored—changes in forest coverage and biological diversity, energy use and the emission of greenhouse gases, the availability of adequate water and sanitation services, and the plight of slum dwellers in rapidly growing cities. As a result of economic and demographic growth most developing regions have increased their carbon dioxide emissions, primarily due to the burning of fossil fuels such as coal, oil, and natural gas, and land-use practices. In the last decade carbon dioxide emissions have increased by 25 percent in low-income countries, though from a significantly lower level than in other income groups. Globally, the increase in carbon dioxide emissions has slowed in the last decade, and annual emissions per capita have declined from 4.1 metric tons to 3.8 a year. Still, greenhouse gases accumulate and increase the risk of climate changes, which will affect all of us for generations to come. Lack of clean water and basic sanitation is the main reason that diseases transmitted by feces are so common in developing countries. In 1990 diarrhea resulted in 3 million deaths, 85 percent of them among children. In 2000, 1.2 billion people still lacked access to a reliable source of water that was reasonably
1u
1v
Greenhouse gas emissions rise with income
Access to water and sanitation services will require large investments
Slums are growing in newly urbanized areas
Per capita emissions of carbon dioxide (metric tons)
Share of population with access to an improved source, 2000 (%)
Number of urban residents (millions)
100
Water
2000
1,000
Slum dwellers
12
80
800
9
60
600
6
40
400
3
20
200
Source: Carbon Dioxide Information Analysis Centre and World Bank staff estimates.
10
2004 World Development Indicators
South SubAsia Saharan Africa
Source: World Health Organization, UNICEF, and World Bank staff estimates.
Note: United Nations–defined regions. Source: UN Habitat 2003.
Western Asia
0 East Europe & Latin Middle Asia & Central America East & Pacific Asia & North Caribbean Africa
Sub-Saharan Africa
High income
Latin American & Caribbean
Upper middle income
South-Central Asia
0 Lower middle income
Developed economies
0 Low income
Non-slum population
Sanitation
South-Eastern Asia
1990
Eastern Asia
15
North Africa
1t
protected from contamination, 40 percent of them in East Asia and Pacific and 25 percent in Sub-Saharan Africa. Improved sanitation services and good hygiene practices are also needed to reduce the risk of disease. A basic sanitation system provides disposal facilities that can effectively prevent human, animal, and insect contact with excreta. Such systems do not, however, ensure that effluents are treated to remove harmful substances before they are released into the environment. Meeting the Millennium Development Goals will require providing about 1.5 billion people with access to safe water and 2 billion with access to basic sanitation facilities between 2000 and 2015. The world is rapidly urbanizing. While the movement of people to cities may reduce immediate pressure on the rural environment, it increases people’s exposure to other environmental hazards. The United Nations Human Settlements Programme (UN Habitat 2003) estimates that in 2001, 924 million people lived in slums, where they lack basic services, live in overcrowded and substandard housing, and are exposed to unhealthy living conditions and hazardous locations. The Millennium Development Goals call for improving the lives of at least 100 million slum dwellers by 2020. Polluted air is one of many hazards faced by urban dwellers. Poor people, who live in crowded neighborhoods close to traffic corridors and industrial plants, are likely to suffer the most. Every year an estimated 0.5–1.0 million people die prematurely from respiratory and other illnesses associated with urban air pollution (World Bank 2002i). Much can be done to improve the lives of slum dwellers by improving basic infrastructure, mitigating environmental hazards, increasing access to education and health services, and empowering them to control and manage their own lives.
8 Develop a global partnership for development The eighth and final goal complements the first seven. It commits wealthy countries to work with developing countries to create an environment in which rapid, sustainable development is possible. It calls for an open, rule-based trading and financial system, more generous aid to countries committed to poverty reduction, and relief for the debt problems of developing countries. It draws attention to the problems of the least developed countries and of landlocked countries and small island developing states, which have greater difficulty competing in the global economy. And it calls for cooperation with the private sector to address youth unemployment, ensure access to affordable, essential drugs, and make available the benefits of new technologies. Important steps toward implementing the global partnership envisioned in the Millennium Declaration were taken at international meetings held in 2001 in Doha, which launched a new “development round” of trade negotiations, and in 2002 at the International Conference on Financing for Development held in Monterrey, Mexico, where developed and developing countries reached a new consensus stressing mutual responsibilities for reaching the Millennium Development Goals. The Monterrey Consensus calls for developing countries to improve their policies and governance aimed at increasing economic growth and reducing poverty and for developed countries to increase their support, especially by providing more and better aid and greater access to their markets. What is at stake? Greater access to markets in rich countries for the exports of developing country goods and services could generate substantial gains in real incomes and reduce the
number of people living in poverty in 2015 by 140 million more than in current projections. But progress on trade issues has slowed since the Doha meetings, and the subsequent World Trade Organization meetings at Cancun failed to reach agreement on outstanding issues, particularly the agricultural policies of highincome economies. Subsidies to agriculture by Organisation for Economic Co-operation and Development members were greater than $300 billion in 2002. By distorting world prices and restricting access to markets, subsidies hurt growth in the agricultural sector, where many of the poorest people work. Trade in manufactured goods faces fewer barriers. But tariff peaks are used selectively to keep out exports of developing countries. The force of the Monterrey Consensus is that more aid should go to countries with good track records and to support reform programs that produce results. After falling throughout most of the last decade, aid levels rose in 2002, and commitments made during or following the Monterrey Conference would increase the real level of aid by $18.6 billion dollars more in 2006. This is a substantial increase, but it will fall short of the $30–50 billion extra needed to meet the identified needs of the poorest countries to set them on the path to achieving the Millennium Development Goals. The quality of aid is important as well. Aid is most effective in reducing poverty when it goes to poor countries with good economic policies and sound governance and advances country-owned poverty reduction programs. But about a third of official development assistance goes to middle-income economies. And when aid flows are affected by geopolitical considerations, donors may overlook weaknesses in the recipient country’s policies and institutions.
1w
1x
Aid has increased, but not by as much as domestic subsidies to agriculture
New commitments by donors, the first major increase in more than a decade, will still meet only a fraction of the need
$ billions
Net official development assistance ($ billions)
120
Total agricultural support 1986–88
120
Total agricultural support 2000–02 Net official development assistance 1986–88
100
Net official development assistance 2000–02
With an additional $50 billion, aid would be equal to 0.35 percent of donors’ GNI, about what it was in the early 1990s
100
80
60
80
40
Monterrey commitments of $18.6 billion
60 20
0
40 European Union
United States
Japan
Source: Organisation for Economic Co-operation and Development, Development Assistance Committee, and World Bank staff estimates.
1990
1995
2000
2006
Source: Organisation for Economic Co-operation and Development, Development Assistance Committee, and World Bank staff estimates.
2004 World Development Indicators
11
Goals, targets, and indicators Goals and targets from the Millennium Declaration Goal 1 Target 1
Eradicate extreme poverty and hunger Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day
Indicators for monitoring progress 1 1a 2 3
Target 2
Halve, between 1990 and 2015, the proportion of people who suffer from hunger
4 5
Goal 2 Target 3
Goal 3 Target 4
Achieve universal primary education Ensure that, by 2015, children everywhere, boys and girls alike, will be able to complete a full course of primary schooling Promote gender equality and empower women Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015
Net enrollment ratio in primary education Proportion of pupils starting grade 1 who reach grade 5 b Literacy rate of 15- to 24-year-olds
9
Ratios of girls to boys in primary, secondary, and tertiary education Ratio of literate women to men ages 15–24 Share of women in wage employment in the nonagricultural sector Proportion of seats held by women in national parliaments
10 11 12
Reduce child mortality Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate
Goal 5 Target 6
Improve maternal health Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio
Goal 6 Target 7
Combat HIV/AIDS, malaria, and other diseases Have halted by 2015 and begun to reverse the spread 18 of HIV/AIDS 19 19a 19b
Target 8
Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases
13 14 15
Under-five mortality rate Infant mortality rate Proportion of one-year-old children immunized against measles
16 17
Maternal mortality ratio Proportion of births attended by skilled health personnel
HIV prevalence among pregnant women ages 15–24 Condom use rate of the contraceptive prevalence rate c Condom use at last high-risk sex Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS d 19c Contraceptive prevalence rate 20 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14
21 22 23 24
Ensure environmental sustainability Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources
25 26 27 28 29
Target 10 Halve, by 2015, the proportion of people without sustainable access to safe drinking water and basic sanitation
12
Prevalence of underweight children under five years of age Proportion of population below minimum level of dietary energy consumption
6 7 8
Goal 4 Target 5
Goal 7 Target 9
Proportion of population below $1 (PPP) a day a Poverty headcount ratio (percentage of population below the national poverty line) Poverty gap ratio [incidence x depth of poverty] Share of poorest quintile in national consumption
2004 World Development Indicators
30 31
Prevalence and death rates associated with malaria Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures e Prevalence and death rates associated with tuberculosis Proportion of tuberculosis cases detected and cured under directly observed treatment, short course (DOTS) Proportion of land area covered by forest Ratio of area protected to maintain biological diversity to surface area Energy use (kilograms of oil equivalent) per $1 GDP (PPP) Carbon dioxide emissions per capita and consumption of ozone-depleting chlorofluorocarbons (ODP tons) Proportion of population using solid fuels Proportion of population with sustainable access to an improved water source, urban and rural Proportion of population with access to improved sanitation, urban and rural
Goals and targets from the Millennium Declaration
Indicators for monitoring progress
Target 11 By 2020, to have achieved a significant improvement in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 12 Develop further an open, rule-based, predictable, nondiscriminatory trading and financial system
32
Includes a commitment to good governance, development and poverty reduction—both nationally and internationally
Target 13 Address the special needs of the least developed countries Includes tariff and quota free access for the least developed countries’ exports; enhanced programme of debt relief for heavily indebted poor countries (HIPC) and cancellation of official bilateral debt; and more generous ODA for countries committed to poverty reduction
Target 14 Address the special needs of landlocked countries and small island developing states (through the Programme of Action for the Sustainable Development of Small Island Developing States and the outcome of the 22nd special session of the General Assembly)
Proportion of households with access to secure tenure
Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked countries and small island developing states. Official development assistance (ODA) 33 Net ODA, total and to the least developed countries, as a percentage of OECD/DAC donors’ gross national income 34 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic education, primary health care, nutrition, safe water and sanitation) 35 Proportion of bilateral official development assistance of OECD/DAC donors that is untied 36 ODA received in landlocked countries as a proportion of their gross national incomes 37 ODA received in small island developing states as proportion of their gross national incomes Market access 38 Proportion of total developed country imports (by value and excluding arms) from developing countries and from the least developed countries, admitted free of duty 39 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from developing countries 40 Agricultural support estimate for OECD countries as a percentage of their gross domestic product 41 Proportion of ODA provided to help build trade capacity
Target 15 Deal comprehensively with the debt problems of developing countries through national and international measures in order to make debt sustainable in the long term
Debt sustainability 42 Total number of countries that have reached their HIPC decision points and number that have reached their HIPC completion points (cumulative) 43 Debt relief committed under HIPC Debt Initiative 44 Debt service as a percentage of exports of goods and services
Target 16 In cooperation with developing countries, develop and implement strategies for decent and productive work for youth
45
Unemployment rate of 15- to 24-year-olds, male and female and total f
Target 17 In cooperation with pharmaceutical companies, provide access to affordable essential drugs in developing countries
46
Proportion of population with access to affordable essential drugs on a sustainable basis
Target 18 In cooperation with the private sector, make available the benefits of new technologies, especially information and communications
47 Telephone lines and cellular subscribers per 100 people 48a Personal computers in use per 100 people 48b Internet users per 100 people
Note: Goals, targets, and indicators effective September 8, 2003. a. For monitoring country poverty trends, indicators based on national poverty lines should be used, where available. b. An alternative indicator under development is “primary completion rate.” c. Among contraceptive methods, only condoms are effective in preventing HIV transmission. Since the condom use rate is only measured among women in union, it is supplemented by an indicator on condom use in high-risk situations (indicator 19a) and an indicator on HIV/AIDS knowledge (indicator 19b). Indicator 19c (contraceptive prevalence rate) is also useful in tracking progress in other health, gender, and poverty goals. d. This indicator is defined as the percentage of 15- to 24-year-olds who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can transmit HIV. However, since there are currently not a sufficient number of surveys to be able to calculate the indicator as defined above, UNICEF, in collaboration with UNAIDS and WHO, produced two proxy indicators that represent two components of the actual indicator. They are the percentage of women and men ages 15–24 who know that a person can protect herself from HIV infection by “consistent use of condom,” and the percentage of women and men ages 15–24 who know a healthy-looking person can transmit HIV. e. Prevention to be measured by the percentage of children under age five sleeping under insecticide-treated bednets; treatment to be measured by percentage of children under age five who are appropriately treated. f. An improved measure of the target for future years is under development by the International Labour Organization.
2004 World Development Indicators
13
1.1
Size of the economy Population
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
14
Surface area
Population density
thousand
people
millions
sq. km
per sq. km
$ billions
rank
$
rank
$ billions
$
rank
% growth
% growth
2002
2002
2002
2002 b
2002
2002 b
2002
2002
2002
2002
2001–02
2001–02
43 115 13 11 13 109 3 97 94 1,042 48 315 59 8 81 3 21 72 43 275 71 34 3 6 7 21 137 .. 42 23 11 77 52 80 103 132 127 178 46 67 310 43 32 67 17 108 5 139 74 236 89 82 111 32 51 301
.. 4.6 53.8 9.3 154.0 2.4 384.1 192.1 5.8 51.1 13.5 237.1 2.5 7.9 5.4 5.1 494.5 14.1 2.9 0.7 3.8 8.7 702.0 1.0 1.8 66.3 1,234.2 167.6 79.6 5.0 2.2 16.1 10.2 20.3 .. 56.0 162.6 .. 19.1 97.6 13.6 0.8 5.7 6.5 124.2 1,362.1 i 4.0 0.4 3.4 1,876.3 5.5 123.9 21.0 3.2 0.2 3.6
.. 120 48 89 27 145 14 20 108 51 80 18 144 96 112 114 12 78 139 179 126 94 8 171 151 43 6 25 42 115 147 75 87 66 .. 46 26 .. 70 37 79 173 109 102 29 5 123 193 135 3 111 30 64 137 203 129
.. d 1,450 1,720 710 4,220 790 19,530 23,860 710 380 1,360 22,940 380 900 1,310 3,010 2,830 1,770 250 100 300 550 22,390 250 210 4,250 960 24,690 1,820 100 610 4,070 620 4,540 .. h 5,480 30,260 .. h 1,490 1,470 2,110 190 4,190 100 23,890 22,240 i 3,060 270 650 22,740 270 11,660 1,760 410 130 440
.. 120 114 146 74 144 29 18 146 171 124 21 171 140 125 88 91 111 187 206 178 156 23 187 194 73 136 16 109 206 153 77 152 71 .. 68 9 .. 118 119 101 196 75 206 17 24 87 184 151 22 184 48 112 169 205 165
.. 16 173 e 24 e 387 10 539 233 25 241 55 291 7 21 .. 13 1,300 56 13 e 4e 25 e 30 907 4e 8 147 5,792 g 187 269 e 32 e 3 34 e 24 45 .. 152 164 54 e 43 253 31 e 4e 16 52 e 136 1,609 7 2e 12 e 2,226 42 e 200 48 e 16 1e 13 e
.. 4,960 5,530 e 1,840 e 10,190 3,230 27,440 28,910 3,010 1,770 5,500 28,130 1,060 2,390 .. 7,740 7,450 7,030 1,090 e 630 e 1,970 e 1,910 28,930 1,170 e 1,010 9,420 4,520 g 27,490 6,150 e 630 e 710 8,560 e 1,450 10,000 .. 14,920 30,600 6,270 e 3,340 3,810 4,790 e 1,040 e 11,630 780 e 26,160 27,040 5,530 1,660 e 2,270 e 26,980 2,080 e 18,770 4,030 e 2,060 680 e 1,610 e
.. 112 103 163 72 139 19 12 142 165 105 16 185 149 .. 84 86 87 184 204 159 162 11 183 187 76 125 18 98 204 202 81 177 74 .. 55 8 97 138 132 120 186 63 200 25 21 103 169 152 22 156 43 129 157 203 172
.. 4.7 4.1 15.3 –10.9 12.9 2.7 1.0 10.6 4.4 4.7 0.7 6.0 2.8 3.9 3.1 1.5 4.8 4.6 3.6 5.5 4.4 3.3 –0.8 9.9 2.1 8.0 2.3 1.6 3.0 3.5 3.0 –1.8 5.2 .. 2.0 2.1 4.1 3.4 3.0 2.1 1.8 6.0 2.7 1.6 1.2 3.0 –3.1 5.6 0.2 4.5 4.0 2.2 4.2 –7.2 –0.9
.. 4.1 2.5 12.0 –12.0 13.6 1.4 0.8 9.8 2.6 5.2 0.2 3.3 0.5 2.5 2.1 0.3 5.5 2.1 1.7 3.6 2.3 2.3 –2.2 6.7 0.9 7.3 1.3 0.0 0.0 0.6 1.2 –3.8 5.2 .. 2.1 1.8 2.5 1.8 1.1 0.4 –0.5 6.5 0.5 1.4 0.7 0.8 –5.7 6.6 0.0 2.7 3.6 –0.4 2.0 –9.8 –2.7
28 c 3 31 13 36 3 20 8 8 136 10 10 7 9 4 2 174 8 12 7 12 16 31 4 8 16 1,280 7 44 52 4 4 17 4 11 10 5 9 13 66 6 4 1 67 5 59 1 1 5 82 20 11 12 8 1 8
652 29 2,382 1,247 2,780 30 7,741 84 87 144 208 31 113 1,099 51 582 8,547 111 274 28 181 475 9,971 623 1,284 757 9,598 f .. 1,139 2,345 342 51 322 57 111 79 43 49 284 1,001 21 118 45 1,104 338 552 268 11 70 357 239 132 109 246 36 28
2004 World Development Indicators
Gross national income
Gross national income per capita
PPP gross national income a
Gross domestic product
Per
Per
capita
capita
Population
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Surface area
Population density
thousand
people
millions
sq. km
per sq. km
$ billions
rank
$
rank
$ billions
$
rank
% growth
% growth
2002
2002
2002
2002 b
2002
2002 b
2002
2002
2002
2002
2001–02
2001–02
112 93 3,287 1,905 1,648 438 70 21 301 11 378 89 2,725 580 121 99 18 200 237 65 10 30 111 1,760 65 26 587 118 330 1,240 1,026 2 1,958 34 1,567 447 802 677 824 147 42 271 130 1,267 924 324 310 796 76 463 407 1,285 300 313 92 9
61 110 353 117 40 55 57 318 196 242 349 58 6 55 187 483 131 26 24 38 434 59 34 3 54 80 28 114 74 9 3 597 53 129 2 66 24 74 2 169 477 15 44 9 146 15 8 188 40 12 14 21 268 127 111 436
105 49 11 28 33 .. 38 35 7 100 2 92 62 85 .. 13 55 158 153 95 72 170 190 .. 81 132 124 154 40 142 175 118 9 155 167 58 128 .. 131 110 15 50 125 149 54 23 67 45 83 140 103 47 41 22 34 ..
930 5,290 470 710 1,720 .. h 23,030 16,020 19,080 2,690 34,010 1,760 1,520 360 .. d 9,930 16,340 290 310 3,480 3,990 550 140 .. j 3,670 1,710 230 160 3,540 240 280 3,860 5,920 460 430 1,170 200 .. d 1,790 230 23,390 13,260 710 180 300 38,730 7,830 420 4,020 530 1,170 2,020 1,030 4,570 10,720 .. k
138 69 161 146 114 .. 20 37 30 93 7 112 117 174 .. 53 36 181 176 86 79 156 201 .. 83 116 191 200 84 189 183 81 66 164 166 128 195 .. 110 191 19 44 146 197 178 3 59 168 78 158 128 103 134 70 50 ..
17 e 133 2,778 e 650 438 .. 116 125 1,510 10 3,481 22 84 32 .. 808 41 e 8 9 21 20 5e .. .. 35 13 12 6 207 10 5e 13 887 7 4 111 18 e .. 14 e 33 458 81 13 e 9e 106 166 33 284 18 e 12 e 25 e 130 356 404 181 ..
2,540 e 13,070 2,650 e 3,070 6,690 .. 29,570 19,000 26,170 3,680 27,380 4,180 5,630 1,010 .. 16,960 17,780 e 1,560 1,660 9,190 4,600 2,970 e .. .. 10,190 6,420 730 570 8,500 860 1,790 e 10,820 8,800 1,600 1,710 3,730 990 e .. 6,880 e 1,370 28,350 20,550 2,350 e 800 e 800 36,690 13,000 1,960 6,060 e 2,180 e 4,590 e 4,880 4,450 10,450 17,820 ..
147 58 146 141 91 .. 9 41 24 134 20 127 101 187 .. 51 47 175 169 77 123 143 .. .. 72 95 201 207 82 192 164 67 80 173 167 133 189 .. 89 179 15 39 150 195 195 3 59 160 99 153 124 117 126 70 46 ..
2.5 3.3 4.6 3.7 6.7 .. 6.9 –0.8 0.4 1.1 0.3 4.9 9.8 1.0 .. 6.3 –1.0 –0.5 5.0 6.1 1.0 3.8 3.3 .. 6.7 0.7 –12.7 1.8 4.1 4.4 3.3 4.4 0.9 7.2 4.0 3.2 7.7 .. 2.7 –0.5 0.2 4.3 1.0 3.0 –0.9 1.0 0.0 2.8 0.8 –0.5 –2.3 4.9 4.4 1.4 0.4 ..
0.0 3.6 3.0 2.3 5.1 .. 5.4 –2.7 0.4 0.3 0.2 2.0 10.1 –0.9 .. 5.7 –3.3 –1.5 2.6 7.0 –0.3 2.8 0.8 .. 7.1 0.6 –15.2 –0.2 1.9 1.9 0.8 3.4 –0.5 7.6 2.8 1.6 5.6 .. 0.6 –2.7 –0.4 2.8 –1.6 –0.1 –3.1 0.4 –2.3 0.4 –0.7 –2.8 –4.4 3.3 2.3 1.4 0.2 ..
7 10 1,049 212 66 24 4 7 58 3 127 5 15 31 22 48 2 5 6 2 4 2 3 5 3 2 16 11 24 11 3 1 101 4 2 30 18 49 2 24 16 4 5 11 133 5 3 145 3 5 6 27 80 39 10 4
Gross national income
6.3 53.7 494.8 149.9 112.9 .. 90.3 105.2 1,100.7 7.0 4,323.9 9.1 22.6 11.2 .. 473.0 38.0 1.4 1.7 8.1 17.7 1.0 0.5 .. 12.7 3.5 3.8 1.7 86.1 2.7 0.8 4.7 597.0 1.7 1.1 34.7 3.6 .. 3.5 5.5 377.6 52.2 3.8 2.0 39.5 175.8 19.9 60.9 11.8 2.8 6.4 54.0 82.4 176.6 109.1 ..
Gross national income per capita
PPP gross national income a
1.1
WORLD VIEW
Size of the economy
Gross domestic product
Per
Per
capita
capita
2004 World Development Indicators
15
1.1
Size of the economy Population
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Surface area
Population density
thousand
people
millions
sq. km
per sq. km
$ billions
rank
$
rank
$ billions
$
rank
% growth
% growth
2002
2002
2002
2002 b
2002
2002 b
2002
2002
2002
2002
2001–02
2001–02
93 83 182 60 176 .. 208 31 61 45 .. 75 36 135 166 122 26 7 136 191 206 88 177 79 94 96 121 180 119 30 23 4 85 171 110 151 .. 195 195 153
4.3 4.3 9.4 1.0 1.1 4.0 6.3 2.2 4.4 2.9 .. 3.0 2.0 4.0 5.5 3.6 1.9 0.1 2.7 9.1 6.3 5.4 4.6 2.7 1.7 7.8 14.9 6.7 4.8 1.8 1.8 2.4 –10.8 4.2 –8.9 7.0 –19.1 3.6 3.3 –5.6 1.9 w 4.0 3.1 4.9 –1.2 3.3 6.7 4.6 –0.8 3.0 4.3 2.8 1.6 0.8
4.8 4.8 6.3 –1.8 –1.2 35.7 4.2 1.4 4.4 3.6 .. 1.8 1.6 2.7 3.3 1.7 1.5 –0.7 0.3 8.5 4.1 4.7 2.4 2.1 0.6 6.1 13.1 3.8 5.6 –5.0 1.5 1.4 –11.3 2.9 –10.5 5.8 –22.5 0.5 1.6 –6.7 0.7 w 2.1 2.3 4.1 –2.4 2.0 5.8 5.1 –2.2 1.0 2.6 0.5 1.0 0.5
22 238 144 17,075 8 26 22 2,150 10 197 8l 102 5 72 4 1 5 49 2 20 9 638 45 1,221 41 506 19 66 33 2,506 1 17 9 450 7 41 17 185 6 143 35 945 62 513 5 57 1 5 10 164 70 775 5 488 25 241 49 604 3 84 59 243 288 9,629 3 176 25 447 25 912 80 332 3 .. 19 528 10 753 13 391 6,199 s 133,895 s 2,495 33,612 2,738 67,886 2,408 54,969 329 12,917 5,232 101,498 1,838 16,301 473 24,206 525 20,450 306 11,135 1,401 5,140 689 24,267 966 32,397 305 2,474
Gross national income
97 41.7 9 306.6 331 1.8 10 186.8 52 4.6 .. 11.6 l 73 0.7 6,826 86.1 .. 21.3 98 20.4 15 .. 37 113.4 82 596.5 293 16.1 14 12.2 63 1.4 22 231.8 184 263.7 92 19.1 45 1.1 40 9.7 m 121 123.3 88 1.3 254 8.8 63 19.5 90 173.3 10 .. 125 5.9 84 37.9 38 .. 246 1,510.8 31 10,207.0 19 14.6 61 7.8 28 102.3 247 34.8 .. 3.6 35 9.1 14 3.5 34 .. 48 w 31,720 t 77 1,070 41 5,056 45 3,372 26 1,682 53 6,123 116 1,768 20 1,023 26 1,721 28 685 293 638 29 311 31 25,596 125 6,207
53 16 150 21 119 84 177 39 63 65 .. 32 10 74 82 159 19 17 69 164 88 31 161 93 68 24 .. 107 56 .. 4 1 77 98 36 57 130 91 133 ..
Gross national income per capita
1,870 2,130 230 8,530 470 1,400 l 140 20,690 3,970 10,370 .. d 2,500 14,580 850 370 1,240 25,970 36,170 1,130 180 290 m 2,000 270 6,750 1,990 2,490 .. h 240 780 .. k 25,510 35,400 4,340 310 4,080 430 1,110 490 340 .. d 5,120 w 430 1,850 1,400 5,110 1,170 960 2,160 3,280 2,240 460 450 26,490 20,320
108 99 191 57 161 123 201 27 80 52 .. 94 40 142 173 127 12 4 130 197 181 104 184 63 105 95 .. 189 145 .. 13 6 72 176 76 166 131 160 175 ..
PPP gross national income a
145 1,165 10 e 277 e 15 e .. 3 99 68 36 .. 445 e 868 67 57 e 5 230 232 59 6 20 425 7e 12 63 438 23 33 e 234 77 e 1,574 10,414 26 41 131 185 .. 15 8 28 48,462 t 5,269 15,884 12,749 3,145 21,105 7,874 3,263 3,650 1,733 3,453 1,174 27,516 7,850
Gross domestic product
Per
Per
capita
capita
6,490 8,080 1,260 e 12,660 e 1,540 e .. 500 23,730 12,590 18,480 .. 9,810 e 21,210 3,510 1,740 e 4,730 25,820 31,840 3,470 930 580 6,890 1,450 e 9,000 6,440 6,300 4,780 1,360 e 4,800 24,030 e 26,580 36,110 7,710 1,640 5,220 2,300 .. 800 800 2,180 7,820 w 2,110 5,800 5,290 9,550 4,030 4,280 6,900 6,950 5,670 2,460 1,700 28,480 25,700
a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimate does not account for recent refugee flows. d. Estimated to be low income ($735 or less). e. The estimate is based on regression; others are extrapolated from the latest International Comparison Programme benchmark estimates. f. Includes Taiwan, China; Macao, China; and Hong Kong, China. g. Estimate based on bilateral comparison between China and the United States (Ruoen and Kai, 1995). h. Estimated to be lower middle income ($736–$2,935). i. GNI and GNI per capita estimates include the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. j. Estimated to be upper middle income ($2,936–$9,075). k. Estimated to be high income ($9,076 or more). l. Excludes data for Kosovo. m. Data refer to mainland Tanzania only.
16
2004 World Development Indicators
About the data
1.1
WORLD VIEW
Size of the economy Definitions
Population, land area, income, and output are basic
shows GNI and GNI per capita estimates converted
• Population is based on the de facto definition of
measures of the size of an economy. They also pro-
into international dollars using purchasing power pari-
population, which counts all residents regardless of
vide a broad indication of actual and potential
ty (PPP) rates. PPP rates provide a standard measure
legal status or citizenship—except for refugees not
resources. Population, land area, income—as meas-
allowing comparison of real price levels between coun-
permanently settled in the country of asylum, who are
ured by gross national income (GNI)—and output—
tries, just as conventional price indexes allow compar-
generally considered part of the population of their
as measured by gross domestic product (GDP)—are
ison of real values over time. The PPP conversion
country of origin. The values shown are midyear esti-
therefore used throughout World Development
factors used here are derived from price surveys cov-
mates for 2002. See also table 2.1. • Surface area
Indicators to normalize other indicators.
ering 118 countries conducted by the International
is a country’s total area, including areas under inland
Population estimates are generally based on
Comparison Program. For Organisation for Economic
bodies of water and some coastal water ways.
extrapolations from the most recent national census.
Co-operation and Development (OECD) countries data
• Population density is midyear population divided by
For further discussion of the measurement of popu-
come from the most recent round of surveys, com-
land area in square kilometers. • Gross
lation and population growth, see About the data for
pleted in 1999; the rest are either from the 1996 sur-
income (GNI) is the sum of value added by all resi-
table 2.1 and Statistical methods.
national
vey, or data from the 1993 or earlier round and
dent producers plus any product taxes (less subsi-
The surface area of a country or economy includes
extrapolated to the 1996 benchmark. Estimates for
dies) not included in the valuation of output plus net
inland bodies of water and some coastal waterways.
countries not included in the surveys are derived from
receipts of primary income (compensation of employ-
Surface area thus differs from land area, which
statistical models using available data.
ees and property income) from abroad. Data are in
excludes bodies of water, and from gross area, which
All economies shown in World Development
current U.S. dollars converted using the World Bank
may include offshore territorial waters. Land area is
Indicators are ranked by size, including those that
Atlas method (see Statistical methods). • GNI per
particularly important for understanding the agricul-
appear in table 1.6. The ranks are shown only in table
capita is gross national income divided by midyear
tural capacity of an economy and the effects of
1.1. (World Bank Atlas includes a table comparing the
population. GNI per capita in U.S. dollars is convert-
human activity on the environment. (For measures of
GNI per capita rankings based on the Atlas method
ed using the World Bank Atlas method. • PPP GNI is
land area and data on rural population density, land
with those based on the PPP method for all economies
gross national income converted to international dol-
use, and agricultural productivity, see tables
with available data.) No rank is shown for economies
lars using purchasing power parity rates. An interna-
3.1–3.3.) Recent innovations in satellite mapping
for which numerical estimates of GNI per capita are
tional dollar has the same purchasing power over GNI
techniques and computer databases have resulted in
not published. Economies with missing data are
as a U.S. dollar has in the United States. • Gross
more precise measurements of land and water areas.
included in the ranking process at their approximate
domestic product (GDP) is the sum of value added
GNI (or gross national product in the terminology of
level, so that the relative order of other economies
by all resident producers plus any product taxes (less
the 1968 United Nations System of National
remains consistent. Where available, rankings for
subsidies) not included in the valuation of output.
Accounts) measures the total domestic and foreign
small economies are shown in World Bank Atlas.
Growth is calculated from constant price GDP data in
value added claimed by residents. GNI comprises
Growth in GDP and growth in GDP per capita are
GDP plus net receipts of primary income (compensa-
based on GDP measured in constant prices. Growth
tion of employees and property income) from non-
in GDP is considered a broad measure of the growth
resident sources.
of an economy, as GDP in constant prices can be
local currency. • GDP per capita is gross domestic product divided by midyear population.
The World Bank uses GNI per capita in U.S. dollars
estimated by measuring the total quantity of goods
to classify countries for analytical purposes and to
and services produced in a period, valuing them at
determine borrowing eligibility. See the Users guide
an agreed set of base year prices, and subtracting
for definitions of the income groups used in World
the cost of intermediate inputs, also in constant
Data sources
Development Indicators. For further discussion of
prices. For further discussion of the measurement of
Population estimates are prepared by World Bank
the usefulness of national income as a measure of
economic growth, see About the data for table 4.1.
staff from a variety of sources (see Data sources
productivity or welfare, see About the data for tables
for table 2.1). The data on surface and land area
4.1 and 4.2.
are from the Food and Agriculture Organization
When calculating GNI in U.S. dollars from GNI
(see Data sources for table 3.1). GNI, GNI per
reported in national currencies, the World Bank fol-
capita, GDP growth, and GDP per capita growth
lows its Atlas conversion method. This involves using
are estimated by World Bank staff based on
a three-year average of exchange rates to smooth the
national accounts data collected by Bank staff
effects of transitory exchange rate fluctuations. (For
during economic missions or reported by national
fur ther discussion of the Atlas method, see
statistical offices to other international organiza-
Statistical methods.) Note that growth rates are cal-
tions such as the OECD. Purchasing power parity
culated from data in constant prices and national
conversion factors are estimates by World Bank
currency units, not from the Atlas estimates.
staff based on data collected by the International
Because exchange rates do not always reflect inter-
Comparison Program.
national differences in relative prices, this table also
2004 World Development Indicators
17
1.2
Millennium Development Goals: eradicating pover ty and improving lives Eradicate extreme poverty and hunger Share of poorest quintile
Prevalence of
Ratio of female to
Reduce child mortality
Improve maternal health Maternal mortality ratio
in national
child malnutrition
Primary
male enrollments
Under-five
per 100,000
Weight for age
completion
in primary and
mortality rate
live births
by skilled
or income
% of children
rate
secondary school a
per 1,000
Modeled
health staff
live births
estimates
%
18
Promote gender equality
consumption
1990–2002 b, c
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
Achieve universal primary education
.. 9.1 7.0 .. 3.1 e 6.7 5.9 8.1 7.4 9.0 8.4 8.3 .. 4.0 9.5 2.2 2.0 6.7 4.5 5.1 6.9 5.6 7.0 2.0 .. 3.3 4.7 5.3 2.7 .. .. 4.2 5.5 8.3 .. 10.3 8.3 5.1 3.3 8.6 2.9 .. 6.1 9.1 9.6 7.2 .. 4.0 6.4 8.5 5.6 7.1 2.6 6.4 5.2 ..
under age 5 1990
2002
.. .. 9 20 .. .. .. .. .. 66 .. .. .. 11 .. .. 7 .. .. .. .. 15 .. .. .. .. 17 .. 10 .. .. 3 .. .. .. 1 .. 10 .. 10 15 .. .. 48 .. .. .. .. .. .. 30 .. .. .. .. 27
.. 14 6 31 .. 3 .. .. 17 48 .. .. 23 .. 4 13 .. .. .. 45 45 .. .. .. 28 1 10 .. 7 31 .. .. .. .. 4 .. .. 5 .. 4 .. 40 .. 47 .. .. 12 17 .. .. .. .. .. 33 25 17
2004 World Development Indicators
%
%
Births attended
% of total
2000/01–2002/03 b, d
1990/91
2001/02 d
1990
2002
2000
1990
.. 100 96 .. 100 74 .. .. 100 77 131 .. 45 89 77 91 82 94 29 27 71 57 .. .. 22 96 102 .. 90 .. 58 90 48 90 100 .. .. 95 99 91 86 33 103 18 .. .. 92 69 92 .. 59 .. 59 .. .. ..
50 90 80 .. .. .. 96 90 94 72 .. 97 .. 89 .. 107 .. 94 61 82 .. 82 94 61 .. 98 81 .. 104 69 88 96 .. 97 101 94 96 .. 97 78 100 82 99 68 105 98 .. 64 94 94 .. 93 .. 43 .. ..
.. 102 99 .. 103 104 99 97 98 105 102 106 65 98 .. 102 103 98 70 78 84 85 100 .. 55 100 .. .. 103 .. 87 101 69 101 97 101 102 109 100 93 97 75 99 69 106 100 96 86 105 99 89 101 93 .. 65 ..
260 42 69 260 28 60 10 9 106 144 21 9 185 122 22 58 60 16 210 184 115 139 9 180 203 19 49 .. 36 205 110 17 157 13 13 11 9 65 57 104 60 147 17 204 7 9 96 154 29 9 125 11 82 240 253 150
257 24 49 260 19 35 6 5 96 73 20 6 151 71 18 110 37 16 207 208 138 166 7 180 200 12 38 .. 23 205 108 11 191 8 9 5 4 38 29 39 39 80 12 171 5 6 85 126 29 5 97 5 49 165 211 123
1,900 55 140 1,700 82 55 8 4 94 380 35 10 850 420 31 100 260 32 1,000 1,000 450 730 6 1,100 1,100 31 56 .. 130 990 510 43 690 8 33 9 5 150 130 84 150 630 63 850 6 17 420 540 32 8 540 9 240 740 1,100 680
.. .. 77 .. 96 .. 100 .. .. .. .. .. .. 38 97 .. 76 .. .. .. .. 58 .. .. .. .. 50 .. 76 .. .. 98 45 .. .. .. .. 92 66 37 52 .. .. .. .. .. .. 44 .. .. 40 .. .. 31 .. 23
1995–2000 b
12 99 92 45 98 97 100 100 f 84 12 100 100 f 66 69 100 94 88 .. 31 25 32 60 98 44 16 100 76 .. 86 61 .. 98 63 100 100 99 100 f 98 69 61 90 21 .. 6 100 f 99 f 86 55 96 100 f 44 .. 41 35 35 24
Eradicate extreme poverty and hunger Share of poorest quintile
Prevalence of
Promote gender equality Ratio of female to
Reduce child mortality
Improve maternal health Maternal mortality ratio
in national
child malnutrition
Primary
male enrollments
Under-five
per 100,000
consumption
Weight for age
completion
in primary and
mortality rate
live births
by skilled
or income
% of children
rate
secondary school a
per 1,000
Modeled
health staff
live births
estimates
%
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Achieve universal primary education
WORLD VIEW
1.2
Millennium Development Goals: eradicating pover ty and improving lives
under age 5
1990–2002 b, c
1990
2002
2.7 7.7 8.9 8.4 5.1 .. 7.1 6.9 6.5 6.7 10.6 7.6 8.2 5.6 .. 7.9 .. 9.1 7.6 7.6 .. 1.5 .. .. 7.9 8.4 4.9 4.9 4.4 4.6 6.2 .. 3.1 7.1 5.6 6.5 6.5 .. 1.4 7.6 7.3 6.4 3.6 2.6 4.4 9.6 .. 8.8 2.4 4.5 2.2 2.9 5.4 7.3 5.8 ..
18 2 64 .. .. 12 .. .. .. 5 .. 6 .. .. .. .. .. .. .. .. .. 16 .. .. .. .. 41 28 25 .. 48 .. 17 .. 12 10 .. 32 26 .. .. .. .. 43 35 .. 24 40 6 .. 4 11 34 .. .. ..
17 .. .. 25 .. 16 .. .. .. .. .. .. .. .. 28 .. .. 6 40 .. .. 18 27 .. .. .. 33 25 .. 33 32 .. .. .. .. .. .. .. .. 48 .. .. 10 40 .. .. .. .. .. .. .. 7 .. .. .. ..
%
%
Births attended
% of total
2000/01–2002/03 b, d
1990/91
2001/02 d
1990
2002
2000
1990
70 .. 77 107 123 .. .. .. .. 90 .. 98 99 56 .. .. .. 94 73 90 68 65 .. .. 106 95 41 55 .. 39 46 108 96 80 107 68 22 71 95 73 .. .. 75 21 .. .. 72 .. 86 59 89 98 90 95 .. ..
103 96 68 91 80 75 99 99 95 97 96 93 .. .. .. 93 97 100 75 96 .. 124 .. .. 93 94 .. 79 98 57 67 98 96 103 107 67 73 95 111 53 93 96 .. 54 76 97 86 47 96 77 95 93 .. 96 99 ..
.. 100 79 98 96 76 .. 100 98 101 100 101 98 97 .. .. 104 99 83 101 102 105 .. 103 99 98 .. .. 104 .. 92 98 101 .. 112 85 77 98 104 83 97 104 105 67 .. 101 98 .. 101 97 98 .. 102 98 102 ..
61 16 123 91 72 50 9 12 10 20 6 43 52 97 55 9 16 83 163 20 37 148 235 42 13 41 168 241 21 250 183 25 46 37 107 85 240 130 84 143 8 11 66 320 235 9 30 138 34 101 37 80 63 19 15 ..
42 9 90 43 41 125 6 6 6 20 5 33 99 122 55 5 10 61 100 21 32 132 235 19 9 26 135 182 8 222 183 19 29 32 71 43 205 108 67 83 5 6 41 264 201 4 13 101 25 94 30 39 37 9 6 ..
110 16 540 230 76 250 5 17 5 87 10 41 210 1,000 67 20 5 110 650 42 150 550 760 97 13 23 550 1,800 41 1,200 1,000 24 83 36 110 220 1,000 360 300 740 16 7 230 1,600 800 16 87 500 160 300 170 410 200 13 5 25
45 .. .. 32 .. 54 .. .. .. 79 100 87 .. 50 .. 98 .. .. .. .. .. .. .. .. .. .. 57 55 .. .. 40 .. .. .. .. 31 .. .. 68 7 .. .. .. 15 31 100 .. 19 .. .. 53 46 .. .. 98 ..
2004 World Development Indicators
1995–2000 b
56 .. 43 64 90 72 100 99 f .. 95 100 97 99 44 97 100 98 98 19 100 89 60 51 94 .. 97 46 56 97 41 57 99 86 99 97 40 44 56 78 11 100 100 67 16 42 100 f 95 20 90 53 71 59 58 99 f 100 ..
19
1.2
Millennium Development Goals: eradicating pover ty and improving lives Eradicate extreme poverty and hunger Share of poorest quintile
Prevalence of
Promote gender equality Ratio of female to
Reduce child mortality
Improve maternal health Maternal mortality ratio
in national
child malnutrition
Primary
male enrollments
Under-five
per 100,000
consumption
Weight for age
completion
in primary and
mortality rate
live births
by skilled
or income
% of children
rate
secondary school a
per 1,000
Modeled
health staff
live births
estimates
% 1990–2002 b, c
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Achieve universal primary education
8.2 4.9 .. .. 6.4 .. .. 5.0 8.8 9.1 .. 2.0 7.5 8.0 .. 2.7 9.1 6.9 .. 8.0 6.8 6.1 .. 5.5 6.0 6.1 6.1 5.9 8.8 .. 6.1 5.4 4.8 e 9.2 3.0 8.0 .. 7.4 3.3 4.6
under age 5 1990
6 .. 29 .. 22 .. 29 .. .. .. .. .. .. .. .. .. .. .. .. .. 29 .. 25 .. 10 .. .. 23 .. .. .. .. 6 .. 8 45 .. 30 25 12 .. w .. .. .. .. .. 19 .. .. .. 64 .. .. ..
2002
3 6 24 .. 23 2 27 .. .. .. 26 .. .. 33 11 10 .. .. 7 .. .. .. .. 6 4 .. 12 23 3 .. .. .. .. .. 4 34 .. .. 28 .. .. w .. .. 9 .. .. 15 .. .. .. .. .. .. ..
% 2000/01–2002/03 b, d
94 99 25 66 49 .. .. .. .. 96 .. 90 .. 108 .. 74 .. .. 89 101 58 91 84 108 98 95 .. 67 98 .. .. .. 95 98 58 104 66 68 59 .. .. w 74 98 97 89 86 100 97 87 91 78 48 g .. ..
% 1990/91
2001/02 d
95 .. 98 82 69 96 67 89 98 97 .. 103 99 99 75 .. 97 92 82 .. 97 94 59 98 82 77 .. .. .. 96 97 95 .. .. 101 .. .. .. .. 96 84 w 74 84 82 96 80 83 .. .. 79 68 79 96 97
100 100 94 94 85 101 .. .. 101 101 .. 101 102 .. 86 93 115 96 92 88 100 95 69 101 100 85 .. .. 100 100 110 100 105 98 104 93 .. 56 .. 95 .. w 84 .. .. 101 .. .. 97 102 91 81 .. 101 100
1990
2002
32 21 173 44 148 30 302 8 15 9 225 60 9 26 120 110 6 8 44 127 163 40 152 24 52 78 98 160 22 14 10 10 24 65 27 53 .. 142 180 80 95 w 144 51 54 34 103 59 44 53 77 130 187 9 9
21 21 203 28 138 19 284 4 9 5 225 65 6 19 94 149 3 6 28 116 165 28 140 20 26 41 86 141 20 9 7 8 15 65 22 26 .. 114 182 123 81 w 121 37 40 22 88 42 37 34 54 95 174 7 6
2000
49 67 1,400 23 690 11 2,000 30 3 17 1,100 230 4 92 590 370 2 7 160 100 1,500 44 570 160 120 70 31 880 35 54 13 17 27 24 96 130 .. 570 750 1,100 403 w 657 106 112 67 440 115 58 193 165 566 917 13 10
Births attended
% of total 1990
.. .. 26 .. .. .. .. .. .. 100 .. .. .. .. .. .. .. .. .. .. 44 .. 31 .. 69 .. .. 38 .. .. .. 99 .. .. .. .. .. 16 51 70 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
1995–2000 b
98 99 31 91 58 99 42 100 .. 100 f 34 84 .. 97 86 f 70 100 f .. 76 f 71 36 99 49 96 90 81 97 39 100 96 99 99 100 96 94 70 .. 22 43 73 60 w 41 80 78 92 56 72 93 82 70 35 44 99 ..
a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education 1976 (ISCED76) to ISCED97. For information on ISCED, see About the data for table 2.10. b. Data are for the most recent year available. c. See table 2.7 for survey year and whether share is based on income or consumption expenditure. d. Data are preliminary. e. Urban data. f. Data refer to period other than specified, differ from the standard definition, or refer to only part of a country. g. Represent only 60% of the population.
20
2004 World Development Indicators
About the data
1.2
WORLD VIEW
Millennium Development Goals: eradicating pover ty and improving lives Definitions
This table and the following two present indicators
Indicators, progress was measured by net enroll-
• Share of poorest quintile in national consumption
for 17 of the 18 targets specified by the Millennium
ment ratios. But official enrollments sometimes dif-
or income is the share of consumption or, in some
Development Goals. Each of the eight goals com-
fer significantly from actual attendance, and even
cases, income that accrues to the poorest 20 per-
prises one or more targets, and each target has
school systems with high average enrollment ratios
cent of the population. • Prevalence of child mal-
associated with it several indicators for monitoring
may have poor completion rates. Estimates of pri-
nutrition is the percentage of children under age five
progress toward the target. Most of the targets are
mary school completion rates have been calculated
whose weight for age is more than two standard
set as a value of a specific indicator to be attained
by World Bank staff using data provided by the
deviations below the median for the international
by a certain date. In some cases the target value is
United Nations Educational, Scientific, and Cultural
reference population ages 0–59 months. The refer-
set relative to a level in 1990. In others it is set at
Organization (UNESCO) and national sources.
ence population, adopted by the World Health
Eliminating gender disparities in education would
Organization in 1983, is based on children from the
help to increase the status and capabilities of
United States, who are assumed to be well nour-
The indicators in this table relate to goals 1–5. Goal
women. The ratio of girls’ to boys’ enrollments in pri-
ished. • Primary completion rate is the number of
1 has two targets between 1990 and 2015: to reduce
mary and secondary school provides an imperfect
students successfully completing (or graduating
by half the proportion of people whose income is less
measure of the relative accessibility of schooling for
from) the last year of primary school in a given year,
than $1 a day and to reduce by half the proportion of
girls. With a target date of 2005, this is the first of
divided by the number of children of official gradua-
people who suffer from hunger. Estimates of poverty
the goals to fall due. The targets for reducing under-
tion age in the population. • Ratio of female to male
rates can be found in table 2.5. The indicator shown
five and maternal mortality are among the most chal-
enrollments in primary and secondary school is the
here, the share of the poorest quintile in national con-
lenging. Although estimates of under-five mortality
ratio of female students enrolled in primary and sec-
sumption, is a distributional measure. Countries with
rates are available at regular intervals for most coun-
ondary school to male students. • Under-five mor-
less equal distributions of consumption (or income)
tries, maternal mortality is difficult to measure, in
tality rate is the probability that a newborn baby will
will have a higher rate of poverty for a given average
part because it is relatively rare.
die before reaching age five, if subject to current
an absolute level. Some of the targets for goals 7 and 8 have not yet been quantified.
income. No single indicator captures the concept of
Most of the 48 indicators relating to the Millennium
age-specific mor tality rates. The probability is
suffering from hunger. Child malnutrition is a symptom
Development Goals can be found in the World
expressed as a rate per 1,000. • Maternal mortal-
of inadequate food supply, lack of essential nutrients,
Development Indicators. Table 1.2a shows where to
ity ratio is the number of women who die from preg-
illnesses that deplete these nutrients, and undernour-
find the indicators for the first five goals. For more
nancy-related
ished mothers who give birth to underweight children.
information about data collection methods and limi-
childbirth, per 100,000 live births. The data shown
causes
during
pregnancy
and
Progress toward achieving universal primary edu-
tations, see About the data for the tables listed there.
here have been collected in various years and
cation is measured by primary school completion
For information about the indicators for goals 6, 7,
adjusted to a common 1995 base year. The values
rates. Before last year’s World Development
and 8, see About the data for tables 1.3 and 1.4.
are modeled estimates (see About the data for table 2.17). • Births attended by skilled health staff are
1.2a
the percentage of deliveries attended by personnel
Location of indicators for Millennium Development Goals 1–5
trained to give the necessary supervision, care, and
Goal 1. Eradicate extreme poverty and hunger
advice to women during pregnancy, labor, and the
1. Proportion of population below $1 a day (table 2.5)
postpartum period; to conduct deliveries on their
2. Poverty gap ratio (table 2.5)
own; and to care for newborns.
3. Share of poorest quintile in national consumption (tables 1.2 and 2.7) 4. Prevalence of underweight in children under five (tables 1.2 and 2.17) 5. Proportion of population below minimum level of dietary energy consumption (table 2.17) Goal 2. Achieve universal primary education 6. Net enrollment ratio (table 2.11) 7. Proportion of pupils starting grade 1 who reach grade 5 (table 2.12) 8. Literacy rate of 15- to 24-year-olds (table 2.13) Goal 3. Promote gender equality and empower women 9. Ratio of girls to boys in primary, secondary, and tertiary education (see ratio of girls to boys in primary and secondary education in table 1.2) 10. Ratio of literate females to males among 15- to 24-year-olds (tables 1.5 and 2.12) 11. Share of women in wage employment in the nonagricultural sector (table 1.5) 12. Proportion of seats held by women in national parliament (table 1.5) Goal 4. Reduce child mortality 13. Under-five mortality rate (tables 1.2 and 2.19) 14. Infant mortality rate (table 2.19) 15. Proportion of one-year-old children immunized against measles (table 2.15) Goal 5. Improve maternal health 16. Maternal mortality ratio (tables 1.2 and 2.16) 17. Proportion of births attended by skilled health personnel (tables 1.2 and 2.16)
Data sources 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 these data series with those published by the United Nations Millennium Development Goals Web site (www.un.org/millenniumgoals), but some differences in timing, sources, and definitions remain.
2004 World Development Indicators
21
1.3
Millennium Development Goals: protecting our common environment Combat HIV/AIDS and other diseases
HIV prevalence % ages 15–24 a Male Female 2001 2001
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
22
.. .. .. 2.2 0.9 0.2 0.1 0.2 0.1 0.0 c 0.6 0.1 1.2 0.1 .. 16.1 0.6 .. 4.0 5.0 1.0 5.4 0.3 5.8 2.4 0.4 0.2 0.0 0.9 2.9 3.3 0.6 2.9 0.0 0.1 0.0 0.1 2.1 0.3 .. 0.8 2.8 2.5 4.4 0.0 c 0.3 2.3 0.5 0.1 0.1 1.4 0.1 0.9 0.6 1.1 4.1
2004 World Development Indicators
.. .. .. 5.7 0.3 0.1 0.0 c 0.1 0.0 c 0.0 c 0.2 0.1 3.7 0.1 .. 37.5 0.5 .. 9.7 11.0 2.5 12.7 0.2 13.5 4.3 0.1 0.1 0.0 0.2 5.9 7.8 0.3 8.3 0.0 0.0 c 0.0 0.1 2.8 0.2 .. 0.4 4.3 0.6 7.8 0.0 c 0.2 4.7 1.4 0.0 c 0.0 c 3.0 0.1 0.8 1.4 3.0 5.0
Ensure environmental sustainability
Incidence of tuberculosis per 100,000 people 2002
333 27 52 335 46 77 6 15 82 221 83 14 86 234 60 657 62 48 157 359 549 188 6 338 222 18 113 93 45 383 395 15 412 47 12 13 13 95 137 29 60 268 55 370 10 14 248 230 85 10 211 20 77 215 196 319
Carbon dioxide emissions per capita metric tons 1990 2000
Access to an improved water source % of population 1990 2000
0.1 2.2 3.2 0.5 3.4 1.1 15.6 7.4 6.4 0.1 9.3 10.1 0.1 0.8 1.1 1.7 1.4 8.6 0.1 0.0 0.0 0.1 15.4 0.1 0.0 2.7 2.1 4.6 1.6 0.1 0.8 1.0 1.0 3.5 3.0 13.4 9.9 1.3 1.6 1.4 0.5 .. 16.2 0.1 10.6 6.3 7.0 0.2 2.8 11.1 0.2 7.1 0.6 0.2 0.8 0.2
.. .. .. .. 94 .. 100 100 .. 94 .. .. .. 71 .. 93 83 .. .. 69 .. 51 100 48 .. 90 71 .. 94 .. .. .. 80 .. .. .. .. 83 71 94 66 .. .. 25 100 .. .. .. .. .. 53 .. 76 45 .. 53
0.0 0.9 2.9 0.5 3.9 1.1 18.0 7.6 3.6 0.2 5.9 10.0 0.3 1.3 4.8 2.3 1.8 5.2 0.1 0.0 0.0 0.4 14.2 0.1 0.0 3.9 2.2 5.0 1.4 0.1 0.5 1.4 0.7 4.4 2.8 11.6 8.4 3.0 2.0 2.2 1.1 0.1 11.7 0.1 10.3 6.2 2.8 0.2 1.2 9.6 0.3 8.5 0.9 0.2 0.2 0.2
13 97 89 38 .. .. 100 100 78 97 100 .. 63 83 .. 95 87 100 42 78 30 58 100 70 27 93 75 .. 91 45 51 95 81 .. 91 .. 100 86 85 97 77 46 .. 24 100 .. 86 62 79 .. 73 .. 92 48 56 46
Develop a global partnership for development Access to improved sanitation facilities % of population 1990 2000
.. .. .. .. 82 .. 100 100 .. 41 .. .. 20 52 .. 60 71 .. .. 87 .. 77 100 24 18 97 29 .. 83 .. .. .. 46 .. .. .. .. 66 70 87 73 .. .. 8 100 .. .. .. .. .. 61 .. 70 55 44 23
12 91 92 44 .. .. 100 100 81 48 .. .. 23 70 .. 66 76 100 29 88 17 79 100 25 29 96 40 .. 86 21 .. 93 52 .. 98 .. .. 67 86 98 82 13 .. 12 100 .. 53 37 100 .. 72 .. 81 58 56 28
Unemployment % ages 15–24 2002
.. .. .. .. 32 .. 12 5 .. 11 .. 16 .. 9 .. .. 18 38 .. .. .. .. 14 .. .. 19 3 11 36 .. .. 13 .. 37 .. 16 7 23 15 .. 11 .. 22 .. 21 20 .. .. 20 10 .. 26 .. .. .. ..
Fixed line and mobile phone subscribers per 1,000 people b 2002
2 348 74 15 396 162 1,178 1,275 220 13 346 1,280 41 172 433 328 424 701 13 11 30 50 1,013 5 6 659 328 1,507 286 11 74 362 83 952 52 1,211 1,522 317 231 177 241 9 1,001 6 1,391 1,216 240 101 234 1,378 33 1,337 202 15 9 33
Combat HIV/AIDS and other diseases
HIV prevalence % ages 15–24 a Male Female 2001 2001
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
1.2 0.1 0.3 0.1 0.0 c .. 0.1 0.1 0.3 0.8 0.0 c .. 0.1 6.0 .. 0.0 c .. 0.0 0.0 c 0.9 .. 17.4 .. .. 0.2 0.0 0.1 6.3 0.7 1.4 0.4 0.0 c 0.4 0.5 .. .. 6.1 1.0 11.1 0.3 0.2 0.1 0.2 0.9 3.0 0.1 .. 0.1 1.9 0.3 0.1 0.4 0.0 c 0.1 0.4 ..
1.5 0.0 c 0.7 0.1 0.0 c .. 0.1 0.1 0.3 0.9 0.0 c .. 0.0 c 15.6 .. 0.0 c .. 0.0 0.0 c 0.2 .. 38.1 .. .. 0.0 c 0.0 0.2 14.9 0.1 2.1 0.6 0.0 c 0.1 0.1 .. .. 14.7 1.7 24.3 0.3 0.1 0.0 c 0.1 1.5 5.8 0.0 c .. 0.1 1.3 0.4 0.0 c 0.2 0.0 c 0.0 c 0.2 ..
Ensure environmental sustainability
Incidence of tuberculosis per 100,000 people 2002
86 32 168 256 29 167 13 10 8 8 33 5 146 540 160 91 26 142 170 78 14 726 247 21 66 41 234 431 95 334 188 64 33 154 209 114 436 154 751 190 8 11 64 193 304 6 11 181 47 254 70 202 320 32 47 7
Carbon dioxide emissions per capita metric tons 1990 2000
Access to an improved water source % of population 1990 2000
0.5 5.6 0.8 0.9 3.9 2.7 8.5 7.4 7.0 3.3 8.7 3.2 15.3 0.2 12.3 5.6 19.9 2.4 0.1 4.8 2.5 .. 0.2 8.8 5.8 5.5 0.1 0.1 3.0 0.0 1.3 1.1 3.7 4.8 4.7 1.0 0.1 0.1 0.0 0.0 10.0 6.8 0.7 0.1 0.9 7.5 7.1 0.6 1.3 0.6 0.5 1.0 0.7 9.1 4.3 3.3
83 99 68 71 .. .. .. .. .. 93 .. 97 .. 45 .. .. .. .. .. .. .. .. .. 71 .. .. 44 49 .. 55 37 100 80 .. .. 75 .. .. 72 67 100 .. 70 53 53 100 37 83 .. 40 63 74 87 .. .. ..
0.7 5.4 1.1 1.3 4.9 3.3 11.1 10.0 7.4 4.2 9.3 3.2 8.1 0.3 8.5 9.1 21.9 0.9 0.1 2.5 3.5 .. 0.1 10.9 3.4 5.5 0.1 0.1 6.2 0.1 1.2 2.4 4.3 1.5 3.1 1.3 0.1 0.2 1.0 0.1 8.7 8.3 0.7 0.1 0.3 11.1 8.2 0.8 2.2 0.5 0.7 1.1 1.0 7.8 5.9 2.3
88 99 84 78 92 85 .. .. .. 92 .. 96 91 57 100 92 .. 77 37 .. 100 78 .. 72 67 .. 47 57 .. 65 37 100 88 92 60 80 57 72 77 88 100 .. 77 59 62 100 39 90 90 42 78 80 86 .. .. ..
1.3
WORLD VIEW
Millennium Development Goals: protecting our common environment
Develop a global partnership for development Access to improved sanitation facilities % of population 1990 2000
61 99 16 47 .. .. .. .. .. 99 .. 98 .. 80 .. .. .. .. .. .. .. .. .. 97 .. .. 36 73 .. 70 30 100 70 .. .. 58 .. .. 33 20 100 .. 76 15 53 .. 84 36 .. 82 93 60 74 .. .. ..
75 99 28 55 83 79 .. .. .. 99 .. 99 99 87 99 63 .. 100 30 .. 99 49 .. 97 67 .. 42 76 .. 69 33 99 74 99 30 68 43 64 41 28 100 .. 85 20 54 .. 92 62 92 82 94 71 83 .. .. ..
Unemployment % ages 15–24 2002
Fixed line and mobile phone subscribers per 1,000 people b 2002
6 13 .. .. .. .. 8 19 26 .. 10 .. .. .. .. 8 .. .. .. 21 .. .. .. .. 29 .. .. .. .. .. .. .. 5 .. .. .. .. .. 11 .. 6 11 20 .. .. 11 .. 13 29 .. 14 15 19 44 12 21
2004 World Development Indicators
97 1,037 .. 92 220 29 1,266 1,422 1,419 704 1,195 355 195 52 21 1,168 723 88 21 695 426 56 3 127 746 448 14 15 567 10 104 559 401 238 142 247 19 8 145 15 1,362 1,070 70 3 19 1,578 255 34 311 14 336 152 233 554 1,247 662
23
1.3
Millennium Development Goals: protecting our common environment Combat HIV/AIDS and other diseases
HIV prevalence % ages 15–24 a Male Female 2001 2001
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
0.0 c 1.9 4.9 .. 0.2 .. 2.5 0.1 0.0 0.0 .. 10.7 0.5 0.0 c 1.1 15.2 0.1 0.5 .. 0.0 3.5 1.1 2.0 2.4 .. .. 0.0 2.0 2.0 .. 0.1 0.5 0.5 0.0 c 0.7 0.3 .. .. 8.1 12.4 0.8 w 1.1 0.6 0.6 0.6 0.9 0.2 1.1 0.7 .. 0.3 4.1 0.3 0.2
0.0 c 0.7 11.2 .. 0.5 .. 7.5 0.2 0.0 0.0 .. 25.6 0.2 0.0 c 3.1 39.5 0.0 c 0.4 .. 0.0 8.1 1.7 5.9 3.2 .. .. 0.0 4.6 0.9 .. 0.1 0.2 0.2 0.0 0.1 0.2 .. .. 21.0 33.0 1.3 w 2.4 0.8 0.8 0.4 1.6 0.2 0.4 0.5 .. 0.6 9.3 0.1 0.1
Incidence of tuberculosis per 100,000 people 2002
148 126 389 42 242 38 405 43 24 21 405 558 30 54 217 1,067 5 8 44 109 363 128 361 13 23 32 94 377 95 18 12 5 29 101 42 192 27 92 668 683 142 w 226 108 116 43 164 147 88 67 57 176 358 18 15
Ensure environmental sustainability
Carbon dioxide emissions per capita metric tons 1990 2000
Access to an improved water source % of population 1990 2000
6.7 13.3 0.1 11.3 0.4 12.4 0.1 13.8 8.4 6.2 0.0 8.3 5.5 0.2 0.1 0.6 5.7 6.4 3.0 3.7 0.1 1.7 0.2 13.9 1.6 2.6 7.2 0.0 11.5 33.0 9.9 19.3 1.3 5.3 5.8 0.3 .. 0.7 0.3 1.6 4.1 w 0.8 3.8 3.6 5.7 2.5 1.9 10.3 2.2 3.3 0.7 0.9 11.8 8.4
.. .. .. .. 72 .. .. 100 .. 100 .. 86 .. 68 67 .. 100 100 .. .. 38 80 51 91 75 79 .. 45 .. .. 100 100 .. .. .. 55 .. .. 52 78 74 w 66 76 75 .. 71 71 .. 82 .. 72 53 .. ..
3.8 9.9 0.1 18.1 0.4 3.7 0.1 14.7 6.6 7.3 .. 7.4 7.0 0.6 0.2 0.4 5.3 5.4 3.3 0.6 0.1 3.3 0.4 20.5 1.9 3.3 7.5 0.1 6.9 21.0 9.6 19.8 1.6 4.8 6.5 0.7 .. 0.5 0.2 1.2 3.8 w 0.9 3.4 3.0 6.2 2.2 2.1 6.7 2.7 4.2 0.9 0.7 12.4 8.0
58 99 41 95 78 98 57 100 100 100 .. 86 .. 77 75 .. 100 100 80 60 68 84 54 90 80 82 .. 52 98 .. 100 100 98 85 83 77 .. 69 64 83 81 w 76 82 81 .. 79 76 91 86 88 84 58 .. ..
Develop a global partnership for development Access to improved sanitation facilities % of population 1990 2000
.. .. .. .. 57 .. .. 100 .. .. .. 86 .. 85 58 .. 100 100 .. .. 84 79 37 99 76 87 .. .. .. .. 100 100 .. .. .. 29 .. 32 63 56 45 w 30 47 45 .. 39 35 .. 72 .. 22 54 .. ..
53 .. 8 100 70 100 66 100 100 .. .. 87 .. 94 62 .. 100 100 90 90 90 96 34 99 84 90 .. 79 99 .. 100 100 94 89 68 47 .. 38 78 62 56 w 43 61 59 .. 52 47 .. 77 85 34 54 .. ..
Unemployment % ages 15–24 2002
18 .. .. .. .. .. .. 5 37 16 .. 56 22 24 .. .. 13 6 .. .. .. 6 .. .. .. 20 .. .. 24 .. 11 12 34 .. 23 .. .. .. .. ..
Fixed line and mobile phone subscribers per 1,000 people b 2002
430 362 16 361 77 489 18 1,258 812 1,341 13 410 1,330 96 27 95 1,625 1,534 147 39 24 365 45 528 169 629 79 18 300 1,010 1,431 1,134 472 74 369 72 180 49 21 55 286 w 40 316 263 431 162 155 424 294 159 42 31 1,283 1,360
a. Data are an average of high and low estimates. b. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report 2003. Please cite the ITU for third-party use of these data. c. Less than 0.05.
24
2004 World Development Indicators
1.3
WORLD VIEW
Millennium Development Goals: protecting our common environment About the data The Millennium Development Goals address issues
Antenatal care clinics are a key site for monitoring
economies. Fixed telephone lines and mobile phones
of common concern to people of all nations.
sexually transmitted diseases such as HIV and
are among the telecommunications technologies
Diseases and environmental degradation do not
syphilis. The prevalence of HIV in young people pro-
that are changing the way the global economy works.
respect national boundaries. Epidemic diseases,
vides an indicator of the spread of the epidemic.
For more information on goal 8, see table 1.4.
wherever they persist, pose a threat to people every-
Prevalence rates in the older population can be
where. And damage done to the environment in one
affected by life-prolonging treatment. The table shows
location may affect the well-being of plants, animals,
the estimated prevalence among men and women
and human beings in distant locations.
ages 15–24. The incidence of tuberculosis is based
The indicators in the table relate to goals 6 and 7 and the targets of goal 8 that address youth employment and access to new technologies. For the other targets of goal 8, see table 1.4. Measuring the prevalence or incidence of a dis-
Definitions
• Prevalence of HIV is the percentage of people
on data on case notifications and estimates of the
ages 15–24 who are infected with HIV. • Incidence
proportion of cases detected in the population.
of tuberculosis is the estimated number of new
Carbon dioxide emissions are the primary source
tuberculosis cases (pulmonar y, smear positive,
of greenhouse gases, which are believed to con-
extrapulmonary). • Carbon dioxide emissions are
tribute to global warming.
those stemming from the burning of fossil fuels and
ease can be difficult. Much of the developing world
Access to reliable supplies of safe drinking water and
the manufacture of cement. They include carbon
lacks reporting systems needed for monitoring the
sanitary disposal of excreta are two of the most impor-
dioxide produced during consumption of solid, liquid,
course of a disease. Estimates are often derived
tant means of improving human health and protecting
and gas fuels and gas flaring. • Access to an
from surveys and reports from sentinel sites that
the environment. There is no widespread program for
improved water source refers to the percentage of
must be extrapolated to the general population.
testing the quality of water. The indicator shown here
the population with reasonable access to an ade-
Tracking diseases such as HIV/AIDS, which has a
measures the proportion of households with access to
quate amount of water from an improved source,
long latency between contraction of the virus and the
an improved source, such as piped water or protected
such as a household connection, public standpipe,
appearance of outward symptoms, or malaria, which
wells. Improved sanitation facilities prevent human, ani-
borehole, protected well or spring, or rainwater col-
has periods of dormancy, can be particularly difficult.
mal, and insect contact with excreta but do not include
lection. Unimproved sources include vendors, tanker
For some of the most serious illnesses international
treatment to render sewage outflows innocuous.
trucks,
organizations have formed coalitions such as the
and
unprotected
wells
and
springs.
The eighth goal—to develop a global partnership
Reasonable access is defined as the availability of at
Joint United Nations Programme on HIV/AIDS
for development—takes note of the need for decent
least 20 liters a person a day from a source within 1
(UNAIDS) and the Roll Back Malaria campaign to
and productive work for youth. Labor market infor-
kilometer of the dwelling. • Access to improved san-
gather information and coordinate global efforts to
mation, such as unemployment rates, is still gener-
itation facilities refers to the percentage of the pop-
treat victims and prevent the spread of disease.
ally unavailable for most low- and middle-income
ulation with access to at least adequate excreta disposal facilities (private or shared but not public)
1.3a
that can effectively prevent human, animal, and
Location of indicators for Millennium Development Goals 6–7 Goal 6. Combat HIV/AIDS, malaria, and other diseases 18. HIV prevalence among 15- to 24-year-old pregnant women (tables 1.3 and 2.18) 19. Knowledge and use of methods to prevent HIV transmission* 20. School attendance of orphans and nonorphans* 21. Prevalence and death rates associated with malaria* 22. Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures* (see children sleeping under treated bednets in table 2.15) 23. Tuberculosis prevalence and death rates (see incidence of tuberculosis in tables 1.3 and 2.18) 24. Proportion of tuberculosis cases detected and cured under directly observed treatment, shortcourse (table 2.15) Goal 7. Ensure environmental sustainability 25. Proportion of land area covered by forest (table 3.4)
insect contact with excreta. 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. • Unemployment refers to the share of the labor force without work but available for and seeking employment. Definitions of labor force and unemployment differ by country. • Fixed line and mobile phone subscribers are telephone mainlines connecting a customer’s equipment to the public switched telephone network, and users of portable telephones subscribing to an automatic public mobile telephone ser vice using cellular technology that provides access to the public switched telephone network.
26. Ratio of area protected to maintain biological diversity to surface area (table 3.4) 27. Energy use (kilograms of oil equivalent) per $1 of GDP (PPP) (see GDP per unit of energy use in table 3.8) 28. Carbon dioxide emissions per capita (table 3.8) and consumption of ozone-depleting chlorofluorocarbons* 29. Proportion of population using solid fuels (see combustible renewables and waste as a percentage of total energy use in table 3.7) 30. Proportion of population with sustainable access to an improved water source (tables 2.15 and 3.5)
Data sources The indicators here throughout this book have been compiled by World Bank staff from primary and secondary sources. Efforts have been made to harmonize these data series with those published
on
the
United
Nations
Millennium
31. Proportion of urban population with access to improved sanitation (table 2.15)
Development Goals Web site (http://www.un.org/
32. Proportion of population with access to secure tenure (table 3.11)
millenniumgoals), but some differences in timing,
* No data available in the World Development Indicators database.
sources, and definitions remain.
2004 World Development Indicators
25
1.4
Millennium Development Goals: overcoming obstacles
Development Assistance Committee members Official development assistance (ODA) by donor
Market access to high-income countries
Support to agriculture
ODA for basic social
Australia Canada European Union Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom Japan New Zealand Norway Switzerland United States
services a
Goods
% of total
(excluding arms)
Agricultural
Average tariff on exports of least developed countries
Net ODA
sector-allocatable
admitted free of tariffs
products
Textiles
% of donor GNI
ODA
%
%
%
2002
2000–02
1996
2002
1996
0.26 0.28
17.7 22.4
0.26 0.43 0.96 0.35 0.38 0.27 0.21 0.40 0.20 0.77 0.81 0.27 0.26 0.83 0.31 0.23 0.22 0.89 0.32 0.13
14.7 20.4 7.8 14.3 .. 10.3 3.9 30.8 10.7 19.8 26.7 3.1 11.5 11.8 29.9 4.8 8.3 15.1 19.8 27.0
98.3 78.3 94.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 57.0 .. .. 50.8 22.6
96.1 64.5 99.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 85.7 .. .. 93.3 51.2
0.5 3.5 3.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 10.1 .. .. 8.5 5.3
2002
0.2 2.9 0.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 12.0 .. .. 5.8 3.1
Clothing %
% of GDP
1996
2002
1996
2002
2002
10.0 10.9 0.0
6.2 7.4 0.2
1.7
0.7
0.0 7.2
0.0 6.3
31.2 22.4 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. 0.0 15.5
19.6 17.9 0.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. 0.0 14.6
0.4 0.8 1.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.4 0.3 1.5 2.0 0.9
Heavily indebted poor countries (HIPCs) HIPC decision point b
HIPC completion point c
Estimated total nominal debt service relief
HIPC decision point b
HIPC completion point c
$ millions
Benin Bolivia Burkina Faso Cameroon Chad Congo, Dem. Republic Côte d’Ivoire Ethiopia Gambia Ghana Guinea Guinea-Bissau Guyana Honduras Madagascar
Jul. 2000 Feb. 2000 Jul. 2000 Oct. 2000 May 2001 Jul. 2003 Mar. 1998 Nov. 2001 Dec. 2000 Feb. 2002 Dec. 2000 Dec. 2000 Nov. 2000 Jul. 2000 Dec. 2000
Mar. 2003 Jun. 2001 Apr. 2002 Floating Floating Floating .. Floating Floating Floating Floating Floating Dec. 2003 Floating Floating
460 2,060 930 2,000 260 10,389 800 1,930 90 3,700 800 790 877 900 1,500
Estimated total nominal debt service relief $ millions
Madagascar Malawi Mali Mauritania Mozambique Nicaragua Niger Rwanda São Tomé & Principe Senegal Sierra Leone Tanzania Uganda Zambia
Dec. 2000 Dec. 2000 Sep. 2000 Feb. 2000 Apr. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Jun. 2000 Mar. 2002 Apr. 2000 Feb. 2000 Dec. 2000
Floating Floating Mar. 2003 Jun. 2002 Sep. 2001 Jan. 2004 Floating Floating Floating Floating Floating Nov. 2001 May. 2000 Floating
1,500 1,000 895 1,100 4,300 4,500 900 800 200 850 950 3,000 1,950 3,850
a. Includes basic health, education, nutrition, and water and sanitation services. b. Except for Côte d’Ivoire the date refers to the HIPC enhanced framework. The following countries also reached decision points under the original framework: Bolivia in September 1997, Burkina Faso in September 1997, Côte d’Ivoire in March 1998, Guyana in December 1997, Mali in September 1998, Mozambique in April 1998, and Uganda in April 1997. c. Except for Côte d’Ivoire the date refers to the HIPC enhanced framework. The following countries also reached completion points under the original framework: Bolivia in September 1998, Burkina Faso in July 2000, Guyana in May 1999, Mali in September 2000, Mozambique in July 1999, and Uganda in April 1998.
26
2004 World Development Indicators
1.4
WORLD VIEW
Millennium Development Goals: overcoming obstacles About the data Achieving the Millennium Development Goals will
launched a special program of concessions to
initiative yielded significant early progress, multilateral
require an open, rule-based global economy in which
exports from Sub-Saharan Africa.
organizations, bilateral creditors, HIPC governments,
all countries, rich and poor, participate. Many poor
The average tariffs in the table were calculated by
and civil society have engaged in an intensive dialogue
countries, lacking the resources to finance their devel-
the World Trade Organization (WTO). They reflect the
about its strengths and weaknesses. A major review in
opment, burdened by unsustainable levels of debt,
tariff schedules applied by high-income OECD mem-
1999 led to an enhancement of the original framework.
and unable to compete in the global marketplace,
bers to exports of countries designated “least devel-
need assistance from rich countries. For goal 8—
oped countries” (LDCs) by the United Nations.
develop a global partnership for development—many
Agricultural commodities and textiles and clothing
of the indicators therefore monitor the actions of mem-
are three of the most important categories of goods
• Net official development assistance (ODA) com-
bers of the Development Assistance Committee (DAC)
exported by developing economies. Although average
prises grants and loans (net of repayments of princi-
of the Organisation for Economic Co-operation and
tariffs have been falling, averages may disguise high
pal) that meet the DAC definition of ODA and are made
Development (OECD).
tariffs targeted at specific goods (see table 6.6 for
to countries and territories in part I of the DAC list of
Official development assistance (ODA) has declined
estimates of the share of tariff lines with “interna-
recipient countries. • ODA for basic social services is
in recent years as a share of donor countries’ gross
tional peaks” in each country’s tariff schedule). The
aid reported by DAC donors for basic health, educa-
national income (GNI). The poorest countries will
averages in the table include ad valorem duties and
tion, nutrition, and water and sanitation services.
need additional assistance to achieve the Millennium
ad valorem equivalents of non-ad valorem duties.
• Goods admitted free of tariffs are the value of
Definitions
Development Goals. Recent estimates suggest that
Subsidies to agricultural producers and exporters
exports of goods (excluding arms) from least devel-
$30–60 billion more a year would allow most of them
in OECD countries are another form of barrier to
oped countries admitted without tariff, as a share of
to achieve the goals, if the aid goes to countries with
developing economies’ exports. The table shows
total exports from LDCs. • Average tariff is the simple
good policies. At the United Nations International
the value of total support to agriculture as a share
mean tariff, the unweighted average of the effectively
Conference on Financing for Development in 2002
of the economy’s gross domestic product (GDP).
applied rates for all products subject to tariffs.
many donor countries made new commitments that,
Agricultural subsidies in OECD economies are esti-
• Agricultural products comprise plant and animal
if fulfilled, would add $18.6 billion to ODA.
mated at $318 billion in 2002.
products, including tree crops but excluding timber
One of the most important actions that high-income
The Debt Initiative for Heavily Indebted Poor
and fish products. • Textiles and clothing include nat-
economies can take to help is to reduce barriers to
Countries (HIPCs) is the first comprehensive approach
ural and synthetic fibers and fabrics and articles of
the exports of low- and middle-income economies.
to reducing the external debt of the world’s poorest,
clothing made from them. • Support to agriculture is
The European Union has announced a program to
most heavily indebted countries. It represents an
the value of subsidies to the agricultural sector.
eliminate tariffs on developing country exports of
important step forward in placing debt relief within an
• HIPC decision point is the date at which a heavily
“everything but arms,” and the United States has
overall framework of poverty reduction. While the
indebted poor country with an established track record of good performance under adjustment programs sup-
1.4a
ported by the International Monetary Fund and the
Location of indicators for Millennium Development Goal 8
World Bank commits to undertake additional reforms
Goal 8. Develop a global partnership for development
and to develop and implement a poverty reduction
33.
Net ODA as a percentage of DAC donors’ gross national income (table 6.9)
strategy. • HIPC completion point is the date at which
34.
Proportion of ODA for basic social services (table 1.4)
the country successfully completes the key structural
35.
Proportion of ODA that is untied (table 6.9)
reforms agreed on at the decision point, including
36.
Proportion of ODA received in landlocked countries as a percentage of GNI*
developing and implementing its poverty reduction
37.
Proportion of ODA received in small island developing states as a percentage of GNI*
strategy. The country then receives the bulk of debt
38.
Proportion of total developed country imports (by value, excluding arms) from developing
relief under the HIPC Debt Initiative without further pol-
countries admitted free of duty (table 1.4)
icy conditions. • Estimated total nominal debt service
Average tariffs imposed by developed countries on agricultural products and textiles and clothing
relief is the amount of debt service relief, calculated
from developing countries (see related indicators in table 6.6)
at the decision point, that will allow the country to
40.
Agricultural support estimate for OECD countries as a percentage of GDP (table 1.4)
achieve debt sustainability at the completion point.
41.
Proportion of ODA provided to help build trade capacity*
42.
Number of countries reaching HIPC decision and completion points (table 1.4)
Data sources
43.
Debt relief committed under new HIPC initiative (table 1.4)
The indicators here, and where they appear through-
44.
Debt service as a percentage of exports of goods and services (table 4.17)
out the rest of the book, have been compiled by
45.
Unemployment rate of 15- to 24-year-olds (see tables 2.4 and 2.8 for related indicators)
World Bank staff from primary and secondary
46.
Proportion of population with access to affordable, essential drugs on a sustainable basis*
sources. The WTO, in collaboration with the UN
47.
Telephone lines and cellular subscribers per 100 people (tables 1.3 and 5.10)
Conference on Trade and Development and the
39.
48a. Personal computers in use per 100 people (table 5.10)
International Trade Centre, provided the estimates
48b. Internet users per 100 people (table 5.10)
of goods admitted free of tariffs and average tariffs.
* No data available in the World Development Indicators database.
Subsidies to agriculture are compiled by the OECD.
2004 World Development Indicators
27
1.5 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
28
Women in development Female population
Life expectancy at birth
% of total 2002
years Male Female 2002 2002
49.0 48.9 49.4 50.5 50.9 51.4 50.1 51.3 50.9 49.7 53.1 50.9 50.7 50.2 50.5 50.2 50.7 51.4 50.4 51.0 51.2 50.0 50.5 51.2 50.5 50.5 48.4 50.9 50.5 50.4 51.0 50.1 49.2 51.7 50.0 51.2 50.5 49.3 49.8 49.1 50.9 50.4 53.5 49.8 51.2 51.4 50.4 50.5 52.5 50.9 50.2 50.8 49.6 49.7 50.6 50.9
43 72 69 45 71 71 76 76 62 62 63 75 51 62 71 38 65 69 42 42 53 48 76 42 47 73 69 78 69 45 50 75 45 70 75 72 75 64 69 67 67 50 65 41 75 76 52 52 69 75 54 75 63 46 44 50
2004 World Development Indicators
44 76 72 48 78 79 82 82 69 63 74 82 55 65 77 38 73 75 44 42 56 49 82 43 50 79 72 83 75 46 54 80 46 78 79 79 79 70 72 71 73 52 77 43 82 83 54 55 78 81 56 81 69 47 47 54
Pregnant women receiving prenatal care
Teenage mothers
Literacy gender parity index
Labor force gender parity index
Women in nonagricultural sector
% 1995–2002 a
% of women ages 15–19 1995–2002 a
ages 15–24 2002
1990
2002
% of total 2000–02 a
37 95 79 66 95 b 92 100 b 100 b 66 40 100 .. 81 83 99 91 86 .. 61 78 38 75 .. 62 42 95 b 90 .. 91 68 .. 70 88 .. 100 99 b .. 98 69 53 76 49 .. 27 100 b 99 b 94 91 95 .. 88 .. 60 71 62 79
.. .. .. .. .. 6 .. .. .. 35 .. .. 22 14 .. .. 18 .. 25 .. 8 31 .. 36 39 .. .. .. 19 .. .. .. 31 .. .. .. .. 21 .. 9 .. 23 .. 16 .. .. 33 .. .. .. 14 .. 22 37 .. 18
.. 1.0 0.9 .. 1.0 1.0 .. .. .. 0.7 1.0 .. 0.5 1.0 1.0 1.1 1.0 1.0 0.5 1.0 0.9 .. .. 0.7 0.8 1.0 1.0 .. 1.0 .. 1.0 1.0 0.7 1.0 1.0 .. .. 1.0 1.0 0.8 1.0 .. 1.0 0.8 .. .. .. .. .. .. 1.0 1.0 0.9 .. .. 1.0
0.5 0.7 0.3 0.9 0.4 0.9 0.7 0.7 0.8 0.7 1.0 0.7 0.9 0.6 0.6 0.9 0.5 0.9 0.9 1.0 1.2 0.6 0.8 .. 0.8 0.4 0.8 0.6 0.6 0.8 0.8 0.4 0.5 0.7 0.6 0.9 0.9 0.4 0.3 0.4 0.5 0.9 1.0 0.7 0.9 0.8 0.8 0.8 0.9 0.7 1.0 0.5 0.3 0.9 0.7 0.8
0.6 0.7 0.4 0.9 0.5 0.9 0.8 0.7 0.8 0.7 1.0 0.7 0.9 0.6 0.6 0.8 0.6 0.9 0.9 0.9 1.1 0.6 0.9 .. 0.8 0.5 0.8 0.6 0.6 0.8 0.8 0.5 0.5 0.8 0.7 0.9 0.9 0.5 0.4 0.4 0.6 0.9 1.0 0.7 0.9 0.8 0.8 0.8 0.9 0.7 1.0 0.6 0.4 0.9 0.7 0.7
.. 41.1 12.2 .. 42.9 .. 48.1 43.5 45.4 22.9 56.0 44.8 .. 36.4 .. 44.8 45.7 50.2 .. .. 51.7 .. 48.8 .. .. 36.6 39.2 45.5 49.1 .. .. 40.1 .. 45.9 37.8 46.6 48.9 34.3 41.4 19.6 31.2 .. 51.7 .. 50.2 46.3 .. .. 48.6 45.5 .. 40.5 39.2 .. .. ..
Unpaid family workers
Women in parliaments
Male Female % of male % of female employment employment 2000–02 a 2000–02 a
.. .. .. .. 0.7 1.1 0.4 1.4 .. 10.1 .. .. .. 5.2 .. 16.9 .. .. .. .. 31.6 .. 0.1 .. .. .. .. .. 5.1 .. .. 2.5 .. 2.4 .. 0.2 .. .. 4.4 8.2 .. .. 0.8 .. 0.6 .. .. .. 23.2 0.5 .. 4.2 .. .. .. ..
.. .. .. .. 1.8 0.8 0.7 3.7 .. 73.2 .. .. .. 11.1 .. 17.4 .. .. .. .. 53.3 .. 0.3 .. .. .. .. .. 7.1 .. .. 3.6 .. 7.8 .. 1.1 .. .. 10.2 26.0 .. .. 0.9 .. 0.4 .. .. .. 40.2 2.1 .. 14.7 .. .. .. ..
% of total seats 2003
.. 6 6 16 31 5 25 34 11 2 10 35 6 19 17 17 9 26 12 18 7 9 21 7 6 13 22 .. 12 .. 9 35 9 21 36 17 38 17 16 2 11 22 19 8 38 12 9 13 7 32 9 9 9 19 8 4
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Female population
Life expectancy at birth
% of total 2002
years Male Female 2002 2002
49.7 52.3 48.4 50.1 49.8 49.2 50.5 50.3 51.5 50.8 51.1 48.3 51.6 49.8 49.7 49.7 46.7 51.1 50.0 54.1 50.8 50.3 49.7 48.3 52.9 50.0 50.1 50.8 49.4 50.9 50.4 50.5 51.4 52.4 50.3 50.0 51.4 50.3 50.5 48.7 50.5 51.1 50.2 50.6 50.6 50.4 47.4 48.3 49.6 48.5 49.6 49.7 49.6 51.4 52.0 51.9
63 68 63 65 68 61 74 77 75 74 78 70 57 45 61 71 75 61 53 65 69 37 46 70 68 71 54 37 70 40 49 69 71 63 64 66 40 55 42 60 76 76 67 46 45 76 73 63 73 56 69 68 68 70 73 72
69 77 64 69 70 64 80 81 82 78 85 74 67 46 64 78 79 70 56 76 73 39 48 75 78 76 57 38 75 42 53 76 77 71 67 70 42 60 41 60 81 81 71 47 46 82 76 65 77 58 73 72 72 78 79 81
Pregnant women receiving prenatal care
Teenage mothers
Literacy gender parity index
Labor force gender parity index
Women in nonagricultural sector
% 1995–2002 a
% of women ages 15–19 1995–2002 a
ages 15–24 2002
1990
2002
% of total 2000–02 a
83 .. 60 89 77 77 .. .. .. 99 .. 96 91 76 .. .. 95 97 27 .. 87 85 85 81 .. 100 71 91 .. 57 64 .. 86 99 97 42 76 76 91 28 .. 95 b 86 41 64 .. 100 43 72 78 89 84 86 .. .. ..
.. .. 21 12 .. .. .. .. .. .. .. 6 7 21 .. .. .. 9 .. .. .. .. .. .. .. .. 36 33 .. 40 16 .. .. .. .. .. 40 .. .. 21 .. .. 27 43 22 .. .. .. .. .. .. 13 7 .. .. ..
1.0 1.0 .. 1.0 .. .. .. 1.0 1.0 1.1 .. 1.0 1.0 1.0 .. .. 1.0 .. 0.8 1.0 .. 0.6 0.6 0.9 1.0 .. .. 0.8 1.0 0.5 0.7 1.0 1.0 1.0 1.0 0.8 0.6 1.0 1.0 0.6 .. .. 1.1 0.4 1.0 .. 1.0 0.6 1.0 .. 1.0 1.0 1.0 .. 1.0 1.0
0.4 0.8 0.5 0.6 0.3 0.2 0.5 0.6 0.6 0.9 0.7 0.2 0.9 0.8 0.8 0.6 0.3 0.9 .. 1.0 0.4 0.6 0.6 0.2 0.9 0.7 0.8 1.0 0.6 0.9 0.8 0.4 0.4 0.9 0.9 0.5 0.9 0.8 0.7 0.7 0.6 0.8 0.5 0.8 0.5 0.8 0.1 0.3 0.5 0.7 0.4 0.4 0.6 0.8 0.7 0.5
0.5 0.8 0.5 0.7 0.4 0.3 0.5 0.7 0.6 0.9 0.7 0.3 0.9 0.9 0.8 0.7 0.5 0.9 .. 1.0 0.4 .. 0.7 0.3 0.9 0.7 0.8 0.9 0.6 0.9 0.8 0.5 0.5 0.9 0.9 0.5 0.9 0.8 0.7 0.7 0.7 0.8 0.6 0.8 0.6 0.9 0.2 0.4 0.6 0.7 0.4 0.5 0.6 0.9 0.8 0.6
51.7 46.1 17.1 29.7 .. .. 46.5 48.5 40.6 45.8 40.4 20.8 .. 37.8 .. 41.5 .. 44.8 .. 52.7 .. .. .. .. 51.3 41.9 .. 12.2 36.5 .. .. 39.0 37.2 52.7 .. 26.6 .. .. 48.8 .. 44.3 50.9 .. .. .. 48.3 25.3 7.9 41.7 .. 38.4 34.6 42.2 46.9 46.3 39.0
1.5
Unpaid family workers
Women in parliaments
Male Female % of male % of female employment employment 2000–02 a 2000–02 a
.. 0.4 .. .. .. .. 0.8 0.2 3.0 .. 1.6 .. .. .. .. 1.8 .. .. .. 4.2 .. .. .. .. 2.8 .. .. .. .. .. .. .. 6.8 4.7 .. .. .. .. .. .. 0.2 0.6 .. .. .. 0.2 .. 16.7 .. .. .. 4.7 .. 4.0 1.1 0.2
WORLD VIEW
Women in development
.. 1.0 .. .. .. .. 1.5 0.7 6.0 .. 10.1 .. .. .. .. 19.5 .. .. .. 4.9 .. 12 .. .. 3.5 .. .. .. .. .. .. .. 12.5 10.7 .. .. .. .. .. .. 1.1 1.2 .. .. .. 0.5 .. 50.1 .. .. .. 11.5 .. 6.8 3.2 1.0
2004 World Development Indicators
% of total seats 2003
6 10 9 8 4 8 13 15 12 12 7 6 10 7 20 6 0 10 23 21 2 8 .. 11 .. 4 9 10 10 4 6 23 13 11 11 30 .. 26 6 37 28 21 1 5 36 .. 22 10 1 9 18 18 20 19 ..
29
1.5 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Women in development Female population
Life expectancy at birth
% of total 2002
years Male Female 2002 2002
51.1 53.3 50.4 45.9 50.2 50.2 50.9 48.7 51.4 51.3 50.4 51.7 51.1 50.6 49.7 51.7 50.3 50.4 49.5 50.1 50.4 50.8 50.3 50.1 49.5 49.5 50.5 50.0 53.5 34.4 50.8 51.1 51.5 50.3 49.7 50.6 49.3 49.0 50.2 49.9 49.6 w 49.2 49.6 49.5 50.8 49.5 48.9 52.0 50.7 49.2 48.5 50.2 50.6 51.0
66 60 39 71 51 70 36 76 69 72 46 46 75 72 57 44 78 77 68 64 43 67 49 70 71 68 61 43 63 74 75 75 71 64 71 67 71 57 37 39 65 w 58 68 67 70 63 68 64 68 67 62 45 75 75
74 72 40 75 54 75 39 80 77 80 49 48 82 76 60 44 82 83 73 70 44 72 51 75 75 73 68 44 74 77 80 80 79 70 77 72 75 58 37 39 69 w 60 72 72 77 66 71 73 74 70 64 47 81 82
Pregnant women receiving prenatal care
% 1995–2002 a
.. .. 92 90 79 .. 68 .. 98 b 98 b 32 94 .. 98 60 87 .. .. 71 71 49 92 73 92 92 68 98 92 .. 97 .. 99 b 94 97 94 68 .. 34 93 93
Teenage mothers
Literacy gender parity index
% of women ages 15–19 1995–2002 a
ages 15–24 2002
.. .. 7 .. 22 .. .. .. .. .. .. 16 .. .. .. .. .. .. .. .. 25 .. 19 .. .. 10 4 31 .. .. .. .. .. 10 .. 6 .. 16 32 21 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1.0 1.0 1.0 1.0 0.7 .. .. 1.0 1.0 1.0 .. 1.0 1.0 1.0 0.9 1.0 .. .. 1.0 1.0 1.0 1.0 0.8 1.0 0.9 1.0 1.0 0.9 1.0 1.1 .. .. 1.0 1.0 1.0 1.0 .. 0.6 0.9 1.0 0.9 w 0.9 1.0 1.0 1.0 0.9 1.0 1.0 1.0 0.9 0.8 0.9 .. ..
Labor force gender parity index
Women in nonagricultural sector
1990
2002
% of total 2000–02 a
0.8 0.9 1.0 0.1 0.7 0.7 0.6 0.6 0.9 0.9 0.8 0.6 0.5 0.5 0.4 0.6 0.9 0.6 0.3 0.7 1.0 0.9 0.7 0.5 0.4 0.5 0.8 0.9 1.0 0.1 0.7 0.8 0.6 0.8 0.5 1.0 .. 0.4 0.8 0.8 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
0.8 1.0 1.0 0.2 0.7 0.8 0.6 0.6 0.9 0.9 0.8 0.6 0.6 0.6 0.4 0.6 0.9 0.7 0.4 0.8 1.0 0.9 0.7 0.5 0.5 0.6 0.8 0.9 1.0 0.2 0.8 0.9 0.7 0.9 0.5 1.0 .. 0.4 0.8 0.8 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
45.7 49.7 .. 14.2 .. .. .. 46.9 51.9 47.7 .. .. 39.3 46.6 .. 29.6 50.7 47.2 17.4 51.6 .. 46.8 .. 39.9 .. 18.9 .. .. 53.0 13.8 49.7 48.4 46.5 37.9 39.6 .. .. .. .. 20.2 .. .. .. .. .. .. .. .. .. .. .. .. .. 42.8
Unpaid family workers
Male Female % of male % of female employment employment 2000–02 a 2000–02 a
10.4 .. .. .. .. .. .. 0.3 0.1 3.8 .. 0.7 1.0 .. .. .. 0.3 .. .. .. .. 16.4 .. 1.0 .. 10.2 .. .. 0.8 .. 0.2 0.1 .. .. .. .. 6.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
a. Data are for the most recent year available. b. Data refer to a period other than specified, differ from the standard definition, or refer to only part of a country.
30
2004 World Development Indicators
Women in parliaments
29.1 .. .. .. .. .. .. 1.7 0.2 7.0 .. 1.4 3.3 .. .. .. 0.4 .. .. .. .. 39.8 .. 0.6 .. 51.3 .. .. 1.7 .. 0.5 0.1 .. .. .. .. 27.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
% of total seats 2003
11 8 49 0 19 8 15 16 19 12 .. 30 28 4 10 3 45 27 12 13 22 9 7 19 12 4 26 25 5 0 18 14 12 7 10 27 .. 0 12 10
1.5
WORLD VIEW
Women in development About the data Despite much progress in recent decades, gender
complications that arise during pregnancy. In high-
For information on other aspects of gender, see tables
inequalities remain pervasive in many dimensions of
income countries most women have access to health
1.2 (Millennium Development Goals: eradicating poverty
life—worldwide. But while disparities exist throughout
care during pregnancy, but in developing countries an
and improving lives), 2.3 (employment by economic
the world, they are most prevalent in poor developing
estimated 35 percent of pregnant women—some 45 mil-
activity), 2.4 (unemployment), 2.12 (education efficien-
countries. Gender inequalities in the allocation of such
lion each year—receive no care at all (United Nations
cy), 2.13 (education outcomes), 2.16 (reproductive
resources as education, health care, nutrition, and polit-
2000b). This is reflected in the differences in maternal
health), 2.18 (health risk factors and future challenges),
ical voice matter because of the strong association with
mortality ratios between high- and low-income countries.
and 2.19 (mortality).
well-being, productivity, and economic growth. This pat-
Women’s wage work is important for economic growth
tern of inequality begins at an early age, with boys rou-
and the well-being of families. But restricted access to
tinely receiving a larger share of education and health
education and vocational training, heavy workloads at
spending than do girls, for example.
Definitions
home and in nonpaid domestic activities, and labor market
• Female population is the percentage of the population
Because of biological differences girls are expected to
discrimination often limit women’s participation in paid
that is female. • Life expectancy at birth is the number
experience lower infant and child mortality rates and to
economic activities, lower their productivity, and reduce
of years a newborn infant would live if prevailing patterns
have a longer life expectancy than boys. This biological
their wages. A gender labor force parity index of less than
of mortality at the time of its birth were to stay the same
advantage, however, may be overshadowed by gender
1.0 shows that women have lower activity rates than men.
throughout its life. •Teenage mothers are the percentage
inequalities in nutrition and medical interventions, and
However, a gender labor force parity index of 1.0 or more
of women ages 15–19 who already have children or are
by inadequate care during pregnancy and delivery, so
does not necessarily imply equality in employment oppor-
currently pregnant. • Pregnant women receiving prenatal
that female rates of illness and death sometimes
tunities. Women’s unemployment rates tend to be higher
care are the percentage of women attended at least once
exceed male rates, particularly during early childhood
than men’s (table 2.4), and in many countries a large pro-
during pregnancy by skilled health personnel for reasons
and the reproductive years. In high-income countries
portion of women who are reported as employed are
related to pregnancy. • Literacy gender parity index is
women tend to outlive men by four to eight years on
unpaid family workers. Women’s wage employment also
the ratio of the female literacy rate to the male rate for
average, while in low-income countries the difference is
tends to be concentrated in the agricultural sector.
ages 15–24. • Labor force gender parity index is the
narrower—about two to three years. The difference in
Nonsalaried men tend to be self-employed, while non-
ratio of the percentage of women who are economically
child mortality rates (table 2.19) is another good indica-
salaried women tend to be unpaid family workers. There
active to the percentage of men who are. According to the
tor of female social disadvantage because nutrition and
are several reasons for this. Most women have less
International Labour Organization’s (ILO) definition, the
medical interventions are particularly important for the
access to credit markets, capital, land, training, and
economically active population is all those who supply
1–5 age group. Female child mortality rates that are as
education, which may be required to start up a busi-
labor for the production of goods and services during a
high as or higher than male child mortality rates might
ness. Cultural norms may prevent women from working
specified period. It includes both the employed and the
be indicative of discrimination against girls.
on their own or from supervising other workers. Also,
unemployed. While national practices vary in the treat-
Having a child during the teenage years limits girls’
women may face time constraints due to their tradition-
ment of such groups as the armed forces and seasonal
opportunities for better education, jobs, and income and
al family responsibilities. Because of biases and mis-
or part-time workers, in general the labor force includes
increases the likelihood of divorce and separation.
classification substantial numbers of employed women
the armed forces, the unemployed, and first-time job
Pregnancy is more likely to be unintended during the
may be underestimated or reported as unpaid family
seekers, but excludes homemakers and other unpaid
teenage years, and births are more likely to be prema-
workers even when they work in association or equally
caregivers and workers in the informal sector. • Women
ture and are associated with greater risks of complica-
with their husbands in the family enterprise.
in nonagricultural sector refer to women wage employees
tions during delivery and of death.
Women are vastly underrepresented in decisionmaking
in the nonagricultural sector as a percentage of total
In many countries maternal mortality (tables 1.2 and
positions in government, although there is some evidence
nonagricultural employment. • Unpaid family workers are
2.16) is a leading cause of death among women of
of recent improvement. Gender parity in parliamentary rep-
those who work without pay in a market-oriented estab-
reproductive age. Most maternal deaths result from pre-
resentation is still far from being realized. In 2003 women
lishment or activity operated by a related person living in
ventable causes—hemorrhage, infection, and complica-
represented 15 percent of parliamentarians worldwide,
the same household. • Women in parliaments are the
tions from unsafe abortions. Prenatal care is essential
compared with 9 percent in 1987. Without representation
percentage of parliamentary seats in a single or lower
for recognizing, diagnosing, and promptly treating
at this level, it is difficult for women to influence policy.
chamber occupied by women.
1.5a Income and gender affect children’s access to basic health care
Data sources
Medical treatment of fever, by income quintile and gender (%)
The data on female population and life expectancy are from the World Bank’s population database. The
100
Male
Female
data on pregnant women receiving prenatal care are from United Nations Children’s Fund’s (UNICEF) State
80
of the World’s Children 2004. The data on teenage 60
mothers are from Demographic and Health Surveys by Macro International. The data on the literacy gen-
40
der parity index are from the UNESCO Institute for Statistics. The data on the labor force gender parity
20
index are from the ILO database Estimates and Projections of the Economically Active Population,
0 Poorest Richest Indonesia, 1997
Poorest Richest Haiti, 2000
Poorest Richest Ethiopia, 2000
1950–2010. The data on unpaid family workers are from the ILO database Key Indicators of the Labour
Boys are more likely to receive treatment for fever than girls. But poverty has a larger impact than gender on access to basic health care.
Market, third edition. The data on women in parlia-
Source: Demographic and Health Survey data.
Trends and Statistics 2000.
ments are from the United Nations’ World’s Women:
2004 World Development Indicators
31
1.6
Key indicators for other economies Population
Surface area
Population density
Gross national income
Gross domestic product
Life Adult expectancy literacy at rate birth
Carbon dioxide emissions
PPP a
thousands 2002
American Samoa Andorra Antigua and Barbuda Aruba Bahamas, The Bahrain Barbados Belize Bermuda Bhutan Brunei Cape Verde Cayman Islands Channel Islands Comoros Cyprus Djibouti Dominica Equatorial Guinea Faeroe Islands Fiji French Polynesia Greenland Grenada Guam Guyana Iceland Isle of Man
69 68 69 97 314 698 269 253 63 851 351 458 39 149 586 765 693 72 482 46 823 240 57 102 159 766 284 75
thousand sq. km 2002
people per sq. km 2002
$ millions 2002 b
0.2 0.5 0.4 0.2 13.9 0.7 0.4 23.0 0.1 47.0 5.8 4.0 0.3 0.2 2.2 9.3 23.2 0.8 28.1 1.4 18.3 4.0 410.5 0.3 0.6 215.0 103.0 0.6
344 136 157 511 31 983 626 11 1,260 18 67 114 150 745 263 83 30 96 17 33 45 66 0 300 289 4 3 125
.. .. 671 .. .. 7,326 2,365 750 .. 512 .. 572 .. .. 228 9,372 590 216 437 .. 1,750 .. .. 361 .. 656 7,940 ..
About the data
Per capita $ 2002 b
.. c .. d 9,720 .. d .. d 10,500 8,790 e 2,970 .. d 600 .. d 1,250 .. d .. d 390 12,320 850 3,000 930 h .. d 2,130 .. d .. d 3,530 .. d 860 27,960 .. d
$ millions 2002
.. .. 717 .. .. 11,298 3,943 1,390 .. .. .. 2,252 g .. .. 990 14,201 g 1,412 357 4,390 .. 4,385 .. .. 673 .. 3,020 8,305 ..
Per capita $ 2002
.. .. 10,390 .. .. 16,190 14,660 5,490 .. .. .. 4,920 g .. .. 1,690 g 18,560 g 2,040 g 4,960 9,110 g .. 5,330 g .. .. 6,600 .. 3,940 g 29,240 ..
% growth 2001–02
Per capita % growth 2001–02
years 2002
% ages 15 and older 2002
.. .. 2.9 .. .. 3.5 –2.1 3.7 .. 7.7 .. 4.6 .. .. 3.0 2.0 1.6 –5.2 16.2 .. 4.1 .. .. 1.2 .. –1.1 –0.5 ..
.. .. 1.5 .. .. 1.4 –2.4 1.3 .. 4.8 .. 1.9 .. .. 0.5 1.5 –0.3 –5.2 13.3 .. 3.3 .. .. –0.8 .. –1.6 –1.2 ..
.. .. 75 .. 70 73 75 74 .. 63 77 69 .. 79 61 78 44 77 52 .. 70 74 69 73 78 62 80 ..
.. .. .. .. .. 89 100 77 f .. .. 94 f 76 .. .. 56 97 f .. .. .. .. 93 f .. .. .. .. .. .. ..
thousand metric tons 2000
286 .. 352 1,924 1,795 19,500 1,176 780 462 396 4,668 139 286 .. 81 6,423 385 103 205 649 725 542 557 213 4,071 1,598 2,158 ..
Definitions
The table shows data for 56 economies with popula-
• Population is based on the de facto definition of
from abroad. Data are in current U.S. dollars con-
tions from 30,000 to 1 million and smaller
population, which counts all residents regardless of
verted using the World Bank Atlas method (see
economies if they are members of the World Bank.
legal status or citizenship—except for refugees not
Statistical methods). • GNI per capita is gross
Where data on gross national income (GNI) per capi-
permanently settled in the country of asylum, who
national income divided by midyear population. GNI
ta are not available, an estimated range is given. For
are generally considered part of the population of
per capita in U.S. dollars is converted using the
more information on the calculation of GNI (or gross
their countr y of origin. The values shown are
World Bank Atlas method. • PPP GNI is gross
national product in the 1968 System of National
midyear estimates for 2002. See also table 2.1.
national income converted to international dollars
Accounts) and purchasing power parity (PPP) conver-
• Surface area is a country’s total area, including
using purchasing power parity rates. An internation-
sion factors, see About the data for table 1.1. Since
areas under inland bodies of water and some
al dollar has the same purchasing power over GNI
2000 this table has excluded France’s overseas
coastal waterways. • Population density is midyear
as a U.S. dollar has in the United States. • Gross
depar tments—French
Guadeloupe,
population divided by land area in square kilome-
domestic product (GDP) is the sum of value added
Guiana,
Martinique, and Réunion—for which GNI and other
ters. • Gross national income (GNI) is the sum of
by all resident producers plus any product taxes
economic measures are now included in the French
value added by all resident producers plus any prod-
(less subsidies) not included in the valuation of out-
national accounts.
uct taxes (less subsidies) not included in the valua-
put. Growth is calculated from constant price GDP
tion of output plus net receipts of primary income
data in local currency. • Life expectancy at birth is
(compensation of employees and property income)
the number of years a newborn infant would live if
32
2004 World Development Indicators
Population
Surface area
Population density
Gross national income
Gross domestic product
1.6
Life Adult expectancy literacy at rate birth
WORLD VIEW
Key indicators for other economies
Carbon dioxide emissions
PPP a
thousands 2002
Kiribati Liechtenstein Luxembourg Macao, China Maldives Malta Marshall Islands Mayotte Micronesia, Fed. Sts. Monaco Netherlands Antilles New Caledonia Northern Mariana Islands Palau Qatar Samoa São Tomé and Principe Seychelles Solomon Islands San Marino St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Timor-Leste Tonga Vanuatu Virgin Islands (U.S.)
95 33 444 439 287 397 53 160 122 32 219 220 76 20 610 176 154 84 443 28 46 160 117 433 780 101 206 110
thousand sq. km 2002
0.7 0.2 2.6 .. 0.3 0.3 0.2 0.4 0.7 0.0 0.8 18.6 0.5 0.5 11.0 2.8 1.0 0.5 28.9 0.1 0.4 0.6 0.4 163.3 14.9 0.8 12.2 0.3
people per sq. km 2002
130 205 171 .. 957 1,241 265 400 174 16,842 274 12 159 43 55 62 160 187 16 277 128 262 300 3 52 140 17 324
$ millions 2002 b
91 .. 17,523 6,335 i 622 3,678 126 .. 240 .. .. .. .. 136 .. 251 46 569 256 .. 301 600 330 841 402 146 221 ..
Per capita $ 2002 b
960 .. d 39,470 14,600 i 2,170 9,260 2,380 .. c 1,970 .. d .. d .. d .. c 6,820 .. d 1,430 300 6,780 580 .. d 6,540 3,750 2,820 1,940 520 1,440 1,070 .. d
$ millions 2002
.. .. 23,659 9,618 .. 7,030 .. .. .. .. .. .. .. .. .. 981 .. .. 705 g .. 494 793 607 .. .. 689 587 ..
Per capita $ 2002
.. .. 53,290 21,910 g .. 17,710 .. .. .. .. .. .. .. .. .. 5,570 g .. .. 1,590 g .. 10,750 4,950 5,190 .. .. 6,820 g 2,850 g ..
% growth 2001–02
Per capita % growth 2001–02
years 2002
2.8 .. 1.1 10.1 5.6 1.5 4.0 .. 0.8 .. .. .. .. 3.0 .. 1.9 4.1 0.3 –2.7 .. 2.1 0.0 1.1 3.0 .. 1.6 –0.3 ..
0.6 .. 0.2 8.9 3.0 1.0 4.0 .. –0.8 .. .. .. .. 3.0 .. 0.7 2.1 –2.1 –5.3 .. –0.1 –1.2 0.2 2.1 .. 1.6 –2.7 ..
63 .. 78 79 69 78 .. 60 69 .. 76 74 .. .. 75 69 66 73 69 .. 71 74 73 70 .. 71 69 78
% ages 15 and older 2002
.. .. .. 91 f 97 93 .. .. .. .. 97 97 f .. .. 84 f 99 .. 92 f .. .. .. .. .. .. .. 99 f .. ..
thousand metric tons 2000
26 .. 8,482 1,634 498 2,814 .. .. .. .. 9,929 1,667 .. 242 40,685 139 88 227 165 .. 103 322 161 2,118 .. 121 81 13,106
a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($2,936–$9,075). d. Estimated to be high income ($9,076 or more). e. Included in the aggregates for high-income economies on the basis of earlier data. f. Census data. g. The estimate is based on regression; others are extrapolated from the latest International Comparison Programme benchmark estimates. h. Included in the aggregates for low-income economies on the basis of earlier data. i. Refers to GDP and GDP per capita.
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, with understanding, read and write a short, simple statement about their everyday life. • Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide
Data sources
produced during consumption of solid, liquid, and
The indicators here and throughout the book were
gas fuels and gas flaring.
compiled by World Bank Group staff from primary and secondary sources. More information about the indicators and their sources can be found in the About the data, Definitions, and Data sources entries that accompany each table in subsequent sections.
2004 World Development Indicators
33
2 PEOPLE
T
he ultimate aim of development is to improve human welfare in a substantial way. But
development has often bypassed the poor, and so attacking poverty directly through its many dimensions has become an urgent global priority.
To accelerate progress in human development, economic growth is, of course, necessary. But it is not enough. Because the most significant asset of people poor is their labor, the most effective way to improve their welfare is to increase their employment opportunities and the productivity of their labor through investments in human capital—the product of education and improvements in health and nutrition. Thus freedom from illiteracy (figure 2a) and freedom from illness are two of the most important ways that poor people can escape poverty. But although developing countries have made large investments in human capital, assisted by the private sector and official development agencies, good health and basic education remain elusive to many. To reinforce this paramount task of development, the Millennium Development Goals set specific targets for poverty reduction, education, status of women, and health, among others, in order to measure improvements in people’s lives (see section 1 for a fuller discussion of the Millennium Development Goals).
2a Poverty and illiteracy are related Population under $1 a day, latest available data (%) 100
The challenge for governments is 80
formidable. They need to provide not only services that are linked
60
to human development, but also effective mechanisms that reduce
40
vulnerability to economic shocks, ill health, and disability. This section
tracks
the
20
progress
countries have made in developing their human capital and in
0 0
20
40
60
80
100
Illiteracy rate (%)
reducing the vulnerability of their people.
Source: World Bank data files.
2004 World Development Indicators
35
Population in sustainable development In the second half of the 20th century the world population underwent unprecedented growth—from 2.5 billion in 1950 to 6 billion in 1999—even as the population growth rate was declining. The decline was triggered largely by a drop in fer tility rates. Between 1950–55 and 2002 fer tility rates halved, from 5.1 to 2.6 births per woman. Thus while the world population grew at 1.5 percent a year during 1980–2002, it is expected to grow more slowly, at 1 percent a year, during 2002–15, benefiting from continuing fertility declines (table 2.1). But most developing countries will not benefit from this decline. Between 2002 and 2015 roughly 1 billion people will be added to the world, and most (95 percent) will be born in low- and middle-income countries. Despite the increase in mortality rates brought on by AIDS, the fastest growing region will be Sub-Saharan Africa, and the largest number of people will be added in Asia. And the populations of some high-income and Eastern European countries will continue their decline. Research shows that changes in population growth, age structure, and spatial distribution interact closely with development. Fertility decline in high-fertility countries, by slowing population growth, can have important economic benefits by reducing the number of children relative to the working age population. This can create a unique opportunity to increase investments in health, education, and infrastructure. Unfortunately, in many of the poorest countries that most need such a break, high levels of unwanted fertility and the pervasive HIV/AIDS pandemic are prematurely curtailing such opportunity. Together, the continuing dependency of youthful populations and the premature deaths of young adults prevent countries from benefiting from their demographic transition. Enabling poverty reduction In many developing countries agriculture is still the main economic activity for both men and women (table 2.3). But as economies grow, more people work for wages. The enlarged proportion of working-age populations in countries undergoing fertility decline provides for increased labor force participation (table 2.2). This contributes to economic growth, especially when it occurs in the formal sector. In developing countries gross domestic product (GDP) grew 4.3 percent a year in the 1990s, and the share of people living on less than $1 a day fell from 28.3 percent to 21.6 percent. By 2000, 137 million fewer people were living in extreme poverty (table 2.5). And if projected growth remains on track, global poverty rates will fall to 12.5 percent by 2015, meeting the global Millennium Development Goal target of halving the 1990 poverty rate. Progress in reducing poverty has been uneven. But because many poor people continue to be excluded from all but the lowest level of economic activity, there are large gaps
36
2004 World Development Indicators
2b Defining income poverty The most familiar definition of poverty uses a composite measure of total household consumption per member (with adjustments for household size and composition), derived from household surveys. Poor people are then defined as living in households below a particular threshold of this measure of consumption. But many surveys do not include consumption data, which are difficult to collect. Another approach, used by the World Bank, is to aggregate indicators of a household’s asset ownership and housing characteristics into an index and then rank households into quintiles according to this index. These are typically referred to as asset or wealth quintiles. Source: World Bank data files.
in social indicators between the rich and poor, confirming the persistence of deprivation (box 2b; table 2.6). Globally, much of the decline in income poverty took place in East Asia, where sustained growth in China has lifted more than 150 million people out of poverty since 1990. And faster growth in India has led to a modest decline in the number of poor people in South Asia. But in other regions the number of poor people has increased even as their share in the population has declined—and in Europe and Central Asia both the number and the share of poor people have risen. Unemployment is high in many of the formerly centrally planned economies, with long-term unemployment hovering around 40–50 percent of total unemployment in Croatia, the Czech Republic, and Hungary in 2002 (table 2.4). Enhancing security for poor people Poor people face many risks. They face labor market risks, often having to take precarious jobs in the informal sector and to put their children to work to increase household income. In Sub-Saharan Africa one in four children ages 10–14 was in the labor force in 2002 (table 2.8). Poor people also face health risks, with illness and injury having both direct and opportunity costs. In South Asia nearly 80 percent of all spending on health comes from private sources, much of it out of pocket, exposing many poor households to the impoverishing effects of needed health care (table 2.8). Enhancing security for poor people means reducing their vulnerability to ill health and economic shocks. Marketbased insurance and pension schemes can reduce risk significantly, but they play only a minor role in many developing countries. In 16 developing countries public spending on pensions amounted to less than 0.5 percent of GDP in the 1990s (table 2.9). To increase the security of poor people, national poverty reduction strategies must support their immediate consumption needs and protect their assets by ensuring access to basic services. Education, health, and
2c Why public services fail poor people Public services are failing the poor in four ways: • Public spending on health and education is typically enjoyed by people who are not poor. In Nepal the richest 20 percent of the population benefits from 46 percent of education spending, while the poorest 20 percent gets just 11 percent. • Even when public spending is reallocated to the poor, very little of it reaches frontline public service providers. In Uganda in the early 1990s primary schools received an average of just 13 percent of nonsalary spending allocations intended for them, and poorer schools received much less. • There is a high degree of absenteeism among teachers, doctors, and nurses in public sector facilities. A survey of primary health care in Bangladesh found a 74 percent absentee rate among doctors. • The poor quality of service, opportunity costs of travel time to schools or health facilities, and cultural factors create lack of demand or weak
immunization, sanitation, access to safe drinking water, and safe motherhood initiatives (tables 2.15 and 2.16). But the Millennium Development Goals remain unattainable for many countries. Some 20 countries have rates of child malnutrition greater than 30 percent (table 2.17). An estimated 40 million people are living with HIV/AIDS, an unprecedented public health challenge (table 2.18), and more than half a million women in developing countries die each year during childbirth, often because births are not attended by trained personnel (figure 2d). And the reemergence of old diseases such as tuberculosis in Europe and Central Asia and parts of South and East Asia has put severe strains on health budgets. A high prevalence of disease puts a brake on poverty reduction. Beyond its direct impact on a household’s living standards through out-of-pocket spending, illhealth has an indirect impact on labor productivity and the number of hours people can work.
demand for services.
*
Source: World Bank, World Development Report 2004.
nutrition services are often the most needed and most valued by poor people. Yet many governments fail the poor in the provision of these services (box 2c). Remaining and emerging challenges in building human capital Investments in education widen horizons, making it easier for people to take advantage of new opportunities and helping them to participate in social and economic life. But despite increased spending on education, particularly primary education (table 2.10), enrollment rates remain low in many countries (table 2.11), and primary completion rates are even lower (table 2.12), hampering achievement of the Millennium Development Goal target of universal primary education by 2015. Most children who do not attend primary school, or who drop out early, live in poor households and in poor countries (table 2.12). But in many poor countries there is also a gender dimension to school attendance, reflecting traditional biases against girls’ education and reliance on girls’ contributions to the household. One consequence of this imbalance: higher rates of illiteracy among women. In 2002, 33 developing countries had female literacy rates of 60 percent or lower (table 2.13). And in predominantly illiterate societies there is likely to be less pressure for those who cannot read or write to achieve literacy. The Millennium Development Goals for health cover health status, nutritional status, illness, mortality rates, and reproductive health. The public sector is the main provider of health care in developing countries—training medical personnel, investing in hospitals, and directly providing medical care (table 2.14). To reduce inequities, many countries have emphasized primary health care, including
*
*
There are many ways to measure poverty and its effects on people’s lives. The indicators reported here suffer from many shortcomings, noted in About the data for each table. But taken together, the indicators provide a broad picture of how well different economies are doing in reducing poverty, enhancing human security, and building human capital—and how large a task still lies ahead.
2d Poor women are much less likely to receive expert care in childbirth Births attended by medically trained personnel, by income group (%) 100
80
60
40
20
0 Brazil
Egypt
Ghana Richest group
India
Indonesia
Jordan
Poorest group
Source: World Bank data files.
2004 World Development Indicators
37
2.1
Population dynamics Total population
1980
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
38
16.0 2.7 18.7 7.0 28.1 3.1 14.7 7.6 6.2 85.4 9.6 9.8 3.5 5.4 4.1 0.9 121.6 8.9 7.0 4.1 6.8 8.8 24.6 2.3 4.5 11.1 981.2 5.0 28.4 27.9 1.8 2.3 8.2 4.6 9.7 10.2 5.1 5.7 8.0 40.9 4.6 2.4 1.5 37.7 4.8 53.9 0.7 0.6 5.1 78.3 11.0 9.6 6.8 4.5 0.8 5.4
millions 2002
28.0 a 3.2 31.3 13.1 36.5 3.1 19.7 8.0 8.2 135.7 9.9 10.3 6.6 8.8 4.1 1.7 174.5 8.0 11.8 7.1 12.5 15.8 31.4 3.8 8.3 15.6 1,280.4 6.8 43.7 51.6 3.7 3.9 16.5 4.5 11.3 10.2 5.4 8.6 12.8 66.4 6.4 4.3 1.4 67.2 5.2 59.5 1.3 1.4 5.2 82.5 20.3 10.6 12 7.7 1.4 8.3
2004 World Development Indicators
Average annual population growth rate
1980–2002
2002–15
Ages 0–14 2002
2.6 0.7 2.4 2.8 1.2 0.0 1.3 0.3 1.3 2.1 0.1 0.2 2.9 2.3 0.0 2.9 1.6 –0.5 2.4 2.4 2.8 2.7 1.1 2.3 2.8 1.5 1.2 1.4 2.0 2.8 3.2 2.5 3.2 –0.1 0.7 0.0 0.2 1.9 2.2 2.2 1.5 2.7 –0.4 2.6 0.4 0.4 2.9 3.5 0.1 0.2 2.8 0.4 2.6 2.5 2.7 2.0
2.5 0.8 1.5 2.8 1.2 –0.1 0.8 –0.1 0.7 1.5 –0.5 0.1 2.4 1.7 0.2 0.4 1.1 –0.7 2.1 1.7 1.5 1.7 0.5 1.5 2.8 1.0 0.6 0.2 1.2 2.9 2.8 1.4 1.6 –0.3 0.3 –0.2 0.1 1.2 1.4 1.5 1.6 2.0 –0.6 2.0 0.1 0.3 2.2 1.9 –0.8 –0.2 1.7 0.3 2.3 1.8 2.6 1.7
43.8 28.0 34.6 47.6 27.3 21.6 20.2 16.2 27.7 36.2 17.4 17.1 45.4 38.7 17.8 41.8 27.9 14.8 47.0 45.7 42.0 41.3 18.4 42.1 48.8 27.4 24.2 16.2 31.9 47.8 46.7 30.5 41.8 16.4 20.7 15.8 18.5 32.5 33.2 34.1 35.0 44.7 16.5 45.7 17.8 18.7 40.4 40.4 19.2 15.1 42.5 14.8 42.9 44.0 44.2 39.6
% 2015
38.8 3.5 38.3 18.9 42.9 3.0 21.7 8.0 9.0 166 9.3 10.4 9.0 10.9 4.2 1.8 201 7.3 15.6 8.8 15.1 19.7 33.5 4.6 12.1 17.8 1,389.5 7.0 51.4 75.2 5.2 4.7 20.2 4.3 11.7 9.9 5.4 10.1 15.4 80.9 7.9 5.6 1.3 87.3 5.3 61.8 1.7 1.8 4.7 80.3 25.2 11 16.3 9.8 2.0 10.3
Population age composition
Dependency ratio
% Ages 15–64 2002
Ages 65+ 2002
dependents as proportion of working-age population Young Old 2002 2002
53.4 64.9 61.4 49.5 63.0 68.7 67.4 67.9 65.0 60.5 68.8 66.2 51.9 56.9 71.7 56.0 66.8 68.9 50.3 51.8 55.1 55.0 68.8 54.4 48.3 65.3 68.6 72.3 63.3 49.6 50.2 63.8 55.6 68.1 69.0 70.4 66.6 63.0 62.0 61.6 60.1 52.7 68.4 51.5 67.0 65.2 54.1 56.3 67.1 68.1 53.0 66.8 53.7 53.4 52.3 56.9
2.8 7.1 4.0 2.9 9.8 9.7 12.4 15.9 7.3 3.3 13.8 16.7 2.7 4.4 10.6 2.2 5.3 16.3 2.7 2.6 2.8 3.7 12.8 3.5 2.9 7.3 7.2 11.4 4.8 2.6 3.2 5.8 2.6 15.5 10.3 13.8 14.9 4.5 4.8 4.2 5.0 2.6 15.1 2.8 15.2 16.1 5.6 3.3 13.8 16.9 4.5 18.4 3.5 2.6 3.5 3.5
0.8 0.4 0.6 1.0 0.4 0.3 0.3 0.2 0.4 0.6 0.3 0.3 0.9 0.7 0.2 0.7 0.4 0.2 0.9 0.9 0.8 0.8 0.3 0.8 1.0 0.4 0.4 0.2 0.5 1.0 0.9 0.5 0.8 0.2 0.3 0.2 0.3 0.5 0.5 0.6 0.6 0.8 0.2 0.9 0.3 0.3 0.7 0.7 0.3 0.2 0.8 0.2 0.8 0.8 0.8 0.7
0.1 0.1 0.1 0.1 0.2 0.1 0.2 0.2 0.1 0.1 0.2 0.3 0.1 0.1 0.1 0.0 0.1 0.2 0.1 0.0 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.0 0.2 0.1 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.2 0.2 0.1 0.1 0.2 0.2 0.1 0.3 0.1 0.0 0.1 0.1
Crude death rate
Crude birth rate
per 1,000 people 2002
per 1,000 people 2002
21 6 5 19 8 8 7 10 7 8 14 10 13 8 8 23 7 14 19 20 12 16 7 20 16 5 8 5 6 18 14 4 17 12 8 11 11 7 6 6 6 13 14 20 10 10 15 14 10 10 13 11 7 17 20 14
49 17 22 50 19 9 13 9 16 28 9 11 38 29 12 30 19 9 43 39 27 36 11 36 45 17 15 7 21 45 44 20 37 10 12 9 12 23 23 24 26 38 9 40 11 13 35 37 8 9 29 9 33 38 49 32
Total population
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
1980
millions 2002
3.6 10.7 687.3 148.3 39.1 13.0 3.4 3.9 56.4 2.1 116.8 2.2 14.9 16.6 17.2 38.1 1.4 3.6 3.2 2.5 3.0 1.3 1.9 3.0 3.4 1.9 8.9 6.2 13.8 6.6 1.6 1.0 67.6 4.0 1.7 19.4 12.1 33.7 1.0 14.6 14.2 3.1 2.9 5.6 71.1 4.1 1.1 82.7 2.0 3.1 3.1 17.3 48.0 35.6 9.8 3.2
6.8 10.2 1,048.6 211.7 65.5 24.2 3.9 6.6 57.7 2.6 127.2 5.2 14.9 31.3 22.5 47.6 2.3 5.0 5.5 2.3 4.4 1.8 3.3 5.4 3.5 2.0 16.4 10.7 24.3 11.4 2.8 1.2 100.8 4.3 2.4 29.6 18.4 48.8 2.0 24.1 16.1 3.9 5.3 11.4 132.8 4.5 2.5 144.9 2.9 5.4 5.5 26.7 79.9 38.6 10.2 3.9
Average annual population growth rate
1980–2002
2002–15
Ages 0–14 2002
2.9 –0.2 1.9 1.6 2.3 2.8 0.6 2.4 0.1 0.9 0.4 3.9 0.0 2.9 1.2 1.0 2.4 1.5 2.5 –0.4 1.8 1.5 2.6 2.6 0.1 0.3 2.8 2.5 2.6 2.5 2.5 1.0 1.8 0.3 1.8 1.9 1.9 1.7 3.0 2.3 0.6 1.1 2.7 3.3 2.8 0.5 3.8 2.5 1.9 2.5 2.6 2.0 2.3 0.4 0.2 0.9
2.1 –0.4 1.2 1.1 1.3 1.9 0.8 1.4 –0.3 1.0 –0.2 2.2 0.3 1.4 0.5 0.4 1.9 1.1 2.1 –0.7 1.2 0.9 2.2 1.8 –0.4 0.5 2.4 1.8 1.5 2.4 2.0 0.9 1.4 –0.2 1.3 1.4 1.6 1.0 1.1 2.0 0.3 0.8 2.0 2.7 1.9 0.3 2.2 2.2 1.2 1.9 2.0 1.3 1.6 0.0 0.0 0.7
41.1 16.5 32.8 29.8 30.8 40.1 21.4 27.5 14.1 30.1 14.3 37.8 25.3 42.6 26.0 21.0 25.1 32.5 42.1 15.8 30.9 41.7 44.3 33.0 18.2 21.9 44.4 44.7 33.3 47.2 43.1 25.2 32.9 21.1 32.5 33.5 42.5 32.3 41.8 40.4 18.4 22.1 41.5 48.9 43.7 19.8 42.3 40.6 30.4 41.1 38.8 32.4 36.5 18.2 17.2 23.6
% 2015
8.9 9.6 1,231.6 245.5 77.5 31.1 4.3 7.9 55.1 3.0 124.6 6.8 15.5 37.5 24.0 50.0 3.0 5.8 7.3 2.1 5.2 2.0 4.4 6.9 3.3 2.2 22.5 13.6 29.6 15.6 3.6 1.4 120.6 4.1 2.9 35.4 22.7 55.7 2.3 31.1 16.7 4.4 7.0 16.3 169.4 4.7 3.4 192.8 3.5 6.9 7.2 31.5 98.2 38.4 10.2 4.2
Population age composition
Dependency ratio
% Ages 15–64 2002
Ages 65+ 2002
dependents as proportion of working-age population Young Old 2002 2002
55.5 68.8 62.2 65.4 64.4 56.9 67.4 62.8 67.2 62.9 67.6 59.1 67.0 54.8 67.7 71.8 73.1 61.4 54.4 69.1 63.2 53.1 53.0 63.4 67.8 67.7 52.6 51.9 62.4 50.0 53.7 68.5 62.0 67.9 63.5 62.2 53.8 63.1 54.4 55.8 67.8 66.2 55.4 48.8 53.7 65.2 55.1 56.0 63.9 56.5 57.7 62.7 59.6 69.4 67.6 66.2
3.4 14.6 5.0 4.8 4.7 3.0 11.2 9.7 18.7 6.9 18.1 3.1 7.7 2.7 6.4 7.2 1.7 6.1 3.5 15.2 5.9 5.2 2.7 3.6 13.9 10.4 3.0 3.5 4.3 2.9 3.1 6.3 5.1 11.1 4.0 4.3 3.7 4.5 3.8 3.8 13.8 11.7 3.1 2.3 2.6 15 2.7 3.3 5.7 2.4 3.5 4.9 3.9 12.4 15.2 10.2
0.7 0.2 0.5 0.5 0.5 0.7 0.3 0.4 0.2 0.5 0.2 0.6 0.4 0.8 0.4 0.3 0.3 0.5 0.8 0.2 0.5 0.8 0.8 0.5 0.3 0.3 0.8 0.9 0.5 0.9 0.8 0.4 0.5 0.3 0.5 0.5 0.8 0.5 0.8 0.7 0.3 0.3 0.7 1.0 0.8 0.3 0.8 0.7 0.5 0.7 0.7 0.5 0.6 0.3 0.3 0.4
0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.3 0.1 0.3 0.1 0.1 0.0 0.1 0.1 0.0 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.0 0.0 0.2 0.0 0.1 0.1 0.0 0.1 0.1 0.1 0.2 0.2 0.2
2.1
PEOPLE
Population dynamics
Crude death rate
Crude birth rate
per 1,000 people 2002
per 1,000 people 2002
6 13 9 7 6 8 8 6 11 6 8 4 12 16 11 7 3 7 12 14 6 23 20 4 12 9 12 25 5 22 15 7 4 13 6 6 21 12 21 10 9 7 5 20 17 10 3 8 5 10 5 6 6 9 11 8
2004 World Development Indicators
30 10 24 20 18 29 15 20 9 20 9 28 15 35 17 12 20 20 36 8 19 33 43 27 9 14 39 45 22 48 35 17 20 11 23 21 40 23 35 32 12 14 29 49 39 12 26 33 20 33 30 22 26 9 12 15
39
2.1
Population dynamics Total population
1980
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
22.2 139.0 5.2 9.4 5.5 9.8 b 3.2 2.4 5.0 1.9 6.5 27.6 37.4 14.6 19.4 0.6 8.3 6.3 8.7 4.0 18.6 46.7 2.5 1.1 6.4 44.5 2.9 12.8 50.0 1.0 56.3 227.2 2.9 16.0 15.1 53.7 .. 8.5 5.7 7.1 4,430.1 s 1,561.8 2,038.1 1,801.0 237.0 3,599.8 1,359.4 425.8 356.4 173.7 901.3 383.2 830.2 285.5
millions 2002
Average annual population growth rate
% 2015
22.3 21.4 144.1 134.5 8.2 10.0 21.9 30.8 10.0 12.8 8.2 10.7 5.2 6.7 4.2 4.8 5.4 5.4 2.0 1.9 9.3 14.0 45.3 47.0 40.9 41.5 19.0 21.9 32.8 42.6 1.1 1.3 8.9 9.0 7.3 7.5 17.0 22.0 6.3 7.2 35.2 43.9 61.6 66.3 4.8 6.2 1.3 1.4 9.8 11.5 69.6 81.3 4.8 5.7 24.6 33.6 48.7 44.7 3.2 3.7 59.2 59.6 288.4 319.9 3.4 3.6 25.3 30.0 25.1 30.3 80.4 92.4 3.2 4.9 18.6 27.3 10.2 11.9 13.0 14.1 6,198.5 s 7,090.7 s 2,494.6 3,044.0 2,737.8 3,039.0 2,408.5 2,658.4 329.3 380.6 5,232.4 6,083.0 1,838.3 2,036.9 472.9 478.2 524.9 619.4 305.8 382.7 1,401.5 1,683.7 688.9 882.1 966.2 1,007.7 305.5 305.2
1980–2002
0.0 0.2 2.1 3.9 2.7 0.4 c 2.2 2.5 0.3 0.1 1.6 2.3 0.4 1.2 2.4 3.0 0.3 0.6 3.0 2.1 2.9 1.3 2.9 0.8 1.9 2.0 2.3 3.0 –0.1 5.1 0.2 1.1 0.6 2.1 2.3 1.8 .. 3.5 2.6 2.7 1.5 w 2.1 1.3 1.3 1.5 1.7 1.4 0.5 1.8 2.6 2.0 2.7 0.7 0.3
2002–15
–0.3 –0.5 1.6 2.6 1.9 2.1 1.9 1.1 0.0 –0.2 3.1 0.3 0.1 1.1 2.0 1.2 0.1 0.2 2.0 1.0 1.7 0.6 2.0 0.8 1.3 1.2 1.3 2.4 –0.7 1.1 0.0 0.8 0.6 1.3 1.4 1.1 3.2 2.9 1.2 0.6 1.0 w 1.5 0.8 0.8 1.1 1.2 0.8 0.1 1.3 1.7 1.4 1.9 0.3 0.0
Population age composition
Dependency ratio
Ages 0–14 2002
% Ages 15–64 2002
Ages 65+ 2002
dependents as proportion of working-age population Young Old 2002 2002
17.2 16.9 46.6 40.4 44.0 19.8 44.1 21.1 18.8 15.2 47.9 32.1 15.0 25.6 39.7 42.2 17.7 16.7 39.0 37.6 45.0 23.2 43.6 24.3 28.2 28.4 34.7 49.0 16.5 25.5 18.4 21.1 24.5 35.4 33.0 31.4 45.8 45.7 44.9 44.0 29.2 w 36.5 26.4 26.1 28.9 31.2 26.3 20.9 30.9 35.3 34.2 43.8 18.3 16.0
69.1 70.2 50.3 56.6 53.3 66.3 53.3 71.4 69.8 70.4 49.7 63.4 68.0 67.8 56.8 55.0 64.8 67.8 57.8 57.9 52.6 70.3 53.3 69.3 65.8 65.8 60.9 49.1 68.8 71.6 65.6 66.4 62.9 60.0 62.5 63.3 50.9 51.6 52.9 52.8 63.7 w 59.3 66.5 66.9 63.7 63.1 67.2 67.9 63.6 60.7 61.2 53.3 67.3 67.2
13.7 12.9 3.1 2.9 2.7 13.9 2.6 7.5 11.4 14.4 2.4 4.5 17.0 6.5 3.5 2.9 17.5 15.5 3.1 4.6 2.4 6.4 3.2 6.4 6.0 5.9 4.4 1.9 14.7 2.9 16.1 12.5 12.6 4.6 4.5 5.3 3.2 2.7 2.2 3.1 7.1 w 4.2 7.1 7.0 7.4 5.7 6.5 11.2 5.5 4.0 4.6 3.0 14.4 16.8
a. Estimate does not account for recent refugee flows. b. Includes population for Kosovo until 2001. c. Data are for 1980–2001.
40
2004 World Development Indicators
0.2 0.2 0.9 0.7 0.8 0.3 0.8 0.3 0.3 0.2 1.0 0.5 0.2 0.4 0.7 0.8 0.3 0.2 0.7 0.6 0.9 0.3 0.8 0.4 0.4 0.4 0.6 1.0 0.2 0.4 0.3 0.3 0.4 0.6 0.5 0.5 0.9 0.9 0.8 0.8 0.5 w 0.6 0.4 0.4 0.5 0.5 0.4 0.3 0.5 0.6 0.6 0.8 0.3 0.2
0.2 0.2 0.1 0.1 0.1 0.2 0.0 0.1 0.2 0.2 0.0 0.1 0.2 0.1 0.1 0.1 0.3 0.2 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.2 0.0 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 w 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.2
Crude death rate
Crude birth rate
per 1,000 people 2002
per 1,000 people 2002
13 15 22 4 13 12 25 5 10 10 18 20 9 6 10 18 11 9 4 7 18 8 15 7 6 7 8 18 15 4 10 9 10 6 5 6 4 10 23 21 9w 11 8 8 6 9 8 12 6 6 9 18 9 10
10 10 44 31 35 12 44 11 11 9 50 25 10 18 33 35 11 10 29 23 38 15 36 16 18 22 22 44 9 17 11 14 16 20 23 19 35 41 39 29 21 w 29 17 17 19 22 16 13 21 24 26 39 12 10
About the data
2.1
PEOPLE
Population dynamics Definitions
Population estimates are usually based on national
rates and declining mortality rates are now reflected
population censuses, but the frequency and quality
in the larger share of the working-age population.
• Total population of an economy includes all residents regardless of legal status or citizenship—
of these vary by country. Most countries conduct a
Dependency ratios take into account the variations
except for refugees not permanently settled in the
complete enumeration no more than once a decade.
in the proportions of children, elderly people, and
country of asylum, who are generally considered part
Pre- and post-census estimates are interpolations or
working-age people in the population. Separate cal-
of the population of their country of origin. The val-
extrapolations based on demographic models. Errors
culations of young-age and old-age dependency sug-
ues shown are midyear estimates for 1980 and
and undercounting occur even in high-income coun-
gest the burden of dependency that the working-age
2002 and projections for 2015. • Average annual
tries; in developing countries such errors may be
population must bear in relation to children and the
population growth rate is the exponential change for
substantial because of limits in the transport, com-
elderly. But dependency ratios show the age compo-
the period indicated. See Statistical methods for
munications, and other resources required to con-
sition of a population, not economic dependency.
more information. • Population age composition
duct a full census.
Some children and elderly people are part of the
refers to the percentage of the total population that
labor force, and many working-age people are not.
is in specific age groups. • Dependency ratio is the
The quality and reliability of official demographic data are also affected by the public trust in the gov-
The vital rates shown in the table are based on
ernment, the government’s commitment to full and
data derived from birth and death registration sys-
older than 64—to the working-age population—those
accurate enumeration, the confidentiality and protec-
tems, censuses, and sample surveys conducted by
ages 15–64. • Crude death rate and crude birth
tion against misuse accorded to census data, and
national statistical offices, United Nations agencies,
rate are the number of deaths and the number of live
the independence of census agencies from undue
and other organizations. The estimates for 2002 for
births occurring during the year, per 1,000 popula-
political influence. Moreover, the international com-
many countries are based on extrapolations of levels
tion estimated at midyear. Subtracting the crude
parability of population indicators is limited by differ-
and trends measured in earlier years.
death rate from the crude birth rate provides the rate
ences in the concepts, definitions, data collection
Vital registers are the preferred source of these
procedures, and estimation methods used by nation-
data, but in many developing countries systems for
al statistical agencies and other organizations that
registering births and deaths do not exist or are
collect population data.
incomplete because of deficiencies in the coverage of
Of the 152 economies listed in the table, 125
events or of geographic areas. Many developing coun-
(about 82 percent) conducted a census between
tries carry out special household surveys that esti-
1995 and 2003. The currentness of a census, along
mate vital rates by asking respondents about births
with the availability of complementary data from sur-
and deaths in the recent past. Estimates derived in
veys or registration systems, is one of many objec-
this way are subject to sampling errors as well as
tive ways to judge the quality of demographic data. In
errors due to inaccurate recall by the respondents.
ratio of dependents—people younger than 15 or
of natural increase, which is equal to the population growth rate in the absence of migration.
some European countries registration systems offer
The United Nations Statistics Division monitors the
complete information on population in the absence
completeness of vital registration systems. The
of a census. See Primary data documentation for the
share of countries with at least 90 percent complete
most recent census or survey year and for the com-
vital registration increased from 45 percent in 1988
Data sources
pleteness of registration.
to 55 percent in 2002. Still, some of the most pop-
The World Bank’s population estimates are pro-
Current population estimates for developing coun-
ulous developing countries—China, India, Indonesia,
duced by its Human Development Network and
tries that lack recent census-based data, and pre- and
Brazil, Pakistan, Bangladesh, Nigeria—do not have
Development Data Group in consultation with its
post-census estimates for countries with census data,
complete vital registration systems. Fewer than 30
operational staff and country offices. Important
are provided by national statistical offices, the United
percent of births and 40 percent of deaths worldwide
inputs to the World Bank’s demographic work
Nations Population Division, and other agencies. The
are thought to be registered and reported.
come from the following sources: census reports
standard estimation method requires fertility, mortali-
International migration is the only other factor besides
and other statistical publications from national
ty, and net migration data, which are often collected
birth and death rates that directly determines a coun-
statistical offices; Demographic and Health
from sample surveys, some of which may be small or
try’s population growth. From 1990 to 2000 the number
Surveys conducted by national agencies, Macro
limited in coverage. The population estimates are the
of migrants in high-income countries increased by 23 mil-
International, and the U.S. Centers for Disease
product of demographic modeling and so are suscep-
lion. About 175 million people currently live outside their
Control and Prevention; United Nations Statistics
tible to biases and errors because of shortcomings in
home country, accounting for about 3 percent of the
Division, Population and Vital Statistics Report
the model as well as in the data. Population projec-
world’s population. Estimating international migration is
(quarterly); United Nations Population Division,
tions are made using the cohort component method.
difficult. At any time many people are located outside
World Population Prospects: The 2002 Revision;
The growth rate of the total population conceals
their home country as tourists, workers, or refugees or
Eurostat, Demographic Statistics (various years);
the fact that different age groups may grow at very
for other reasons. Standards relating to the duration and
Centro Latinoamericano de Demografía, Boletín
different rates. In many developing countries the
purpose of international moves that qualify as migration
Demográfico (various years); and U.S. Bureau of
population under 15 was earlier growing rapidly but
vary, and accurate estimates require information on
the Census, International Database.
is now starting to shrink. Previously high fertility
flows into and out of countries that is difficult to collect.
2004 World Development Indicators
41
2.2
Labor force structure Labor force participation rate
Labor force
Average annual % ages 15–64 Male
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
42
Female
1980
2002
1980
2002
89.3 86.1 80.4 91.7 86.4 77.4 86.6 84.9 77.8 90.9 83.4 79.8 86.9 85.9 77.8 84.9 89.4 82.7 94.2 93.9 85.9 89.8 86.0 .. 91.7 81.4 91.4 86.1 83.1 87.6 86.3 88.8 91.5 80.5 83.4 84.8 88.3 86.3 86.9 83.5 89.6 88.4 85.4 86.9 79.3 81.6 87.7 93.0 81.1 86.6 83.0 83.5 91.7 91.7 92.4 85.5
87.6 a 85.5 79.8 90.1 84.3 78.2 82.8 78.6 78.0 87.8 80.9 72.3 82.5 83.5 78.0 83.9 87.2 77.3 89.9 93.9 86.2 86.0 82.8 .. 89.1 81.9 89.2 85.0 83.5 84.7 83.4 84.3 87.3 75.2 85.1 82.6 84.4 86.7 85.6 82.3 87.2 86.6 81.7 85.6 75.6 75.2 85.3 90.0 79.2 81.0 82.4 78.3 88.3 87.0 90.6 82.4
49.8 60.6 19.1 77.8 32.6 68.1 52.0 54.4 67.4 70.2 74.3 41.3 77.8 39.6 37.0 72.2 35.7 70.4 82.8 86.9 85.2 49.6 57.3 .. 68.3 28.7 75.5 50.5 26.6 65.8 57.2 24.3 45.5 53.2 39.7 75.1 71.3 30.5 22.5 29.3 32.2 78.1 79.3 60.2 68.3 55.2 67.6 71.2 71.0 56.2 82.8 31.8 27.6 83.2 59.7 64.2
50.3 a 65.8 33.8 74.8 44.1 71.1 67.1 56.6 61.3 67.9 73.4 51.8 75.4 49.4 49.1 66.5 47.0 70.8 77.7 85.4 84.8 51.7 72.0 .. 70.6 43.8 79.5 56.3 52.2 62.5 58.7 40.9 45.5 59.7 57.4 74.5 76.7 44.0 35.7 38.3 50.7 76.6 74.1 58.6 71.7 62.3 66.5 70.7 66.6 62.8 81.0 48.7 39.9 79.6 59.5 58.1
2004 World Development Indicators
1980
6.8 1.2 4.8 3.5 10.7 1.4 6.7 3.4 2.7 40.3 5.1 3.9 1.7 2.0 1.6 0.4 47.7 4.6 3.8 2.3 3.7 3.7 12.2 1.2 2.2 3.8 538.7 2.5 9.4 12.4 0.8 0.8 3.3 2.2 3.7 5.3 2.7 2.1 2.5 14.3 1.6 1.2 0.8 16.9 2.4 23.8 0.4 0.3 2.6 37.5 5.2 3.8 2.3 2.3 0.4 2.5
Total
growth rate
Female
millions
%
% of labor force
2002
1980–2002
1980
2002
11.7 a 1.6 11.0 6.1 15.7 1.6 10.0 3.8 3.7 72.4 5.3 4.3 3.0 3.6 1.9 0.8 81.7 4.1 5.8 3.9 6.6 6.5 16.8 1.8 4.0 6.5 769.3 3.6 19.4 21.4 1.5 1.6 6.7 2.1 5.6 5.7 2.9 3.9 5.1 25.9 2.8 2.2 0.8 28.9 2.6 27.0 0.6 0.7 2.6 41.1 9.7 4.6 4.5 3.7 0.7 3.6
2.4 1.3 3.7 2.5 1.8 0.4 1.8 0.5 1.4 2.7 0.2 0.4 2.7 2.7 0.8 3.0 2.5 –0.6 1.9 2.4 2.7 2.6 1.5 1.9 2.7 2.4 1.6 1.7 3.3 2.5 3.1 3.2 3.2 –0.1 1.9 0.3 0.3 2.8 3.2 2.7 2.7 2.6 –0.3 2.4 0.3 0.6 2.2 3.4 0.0 0.4 2.8 0.9 3.0 2.1 2.4 1.6
34.8 38.8 21.4 47.0 27.6 47.9 36.8 40.5 47.5 42.3 49.9 33.9 47.0 33.3 32.8 50.1 28.4 45.3 47.6 50.2 55.4 36.8 39.5 .. 43.4 26.3 43.2 34.3 26.2 44.5 42.4 20.8 32.2 40.2 31.4 47.1 44.0 24.7 20.1 26.5 26.5 47.4 50.6 42.3 46.5 40.1 45.0 44.8 49.3 40.1 51.0 27.9 22.4 47.1 39.9 44.6
35.8 a 41.5 29.0 46.2 34.4 48.6 44.0 40.4 44.7 42.5 48.9 41.1 48.3 38.0 38.2 45.1 35.5 48.0 46.5 48.6 51.5 38.2 46.0 .. 44.8 34.5 45.2 37.2 39.1 43.3 43.5 31.6 33.6 44.4 39.9 47.2 46.5 31.4 28.7 31.0 37.3 47.4 48.9 41.0 48.2 45.3 44.8 45.2 46.8 42.4 50.4 38.2 30.1 47.2 40.5 42.8
Labor force participation rate
2.2
PEOPLE
Labor force structure Labor force
Average annual % ages 15–64 Male
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Female
Total
growth rate
Female
millions
%
% of labor force
1980
2002
1980
2002
1980
2002
90.4 84.8 88.6 85.8 83.9 80.1 85.0 81.9 81.8 85.7 86.1 78.7 82.3 91.7 82.5 77.6 86.3 79.9 .. 84.8 77.7 87.8 87.3 85.6 83.2 80.2 91.3 89.6 84.6 92.3 91.4 85.2 85.8 82.8 90.4 84.6 92.6 90.4 87.7 90.6 81.0 85.8 88.6 95.2 89.1 83.5 88.3 88.2 82.4 90.2 91.9 82.0 84.5 84.2 88.5 72.5
87.1 78.2 86.8 84.7 79.8 76.3 79.4 79.2 78.7 83.8 84.9 79.3 79.9 89.2 84.5 79.8 79.8 77.7 .. 82.4 81.1 85.0 83.6 77.5 81.1 76.2 88.9 86.4 81.2 89.7 87.5 83.7 85.6 79.5 86.2 82.6 90.3 89.4 82.9 86.2 78.2 82.2 86.2 92.6 86.2 81.0 77.9 86.4 82.8 87.7 87.6 81.6 83.1 77.8 82.4 74.1
31.9 62.0 47.8 45.6 20.6 16.3 34.7 42.0 39.2 72.6 52.1 14.6 70.5 77.7 65.7 50.2 21.0 68.8 .. 77.9 21.4 50.0 55.7 23.3 74.7 46.8 72.5 81.4 42.8 75.9 71.5 28.5 31.1 74.6 75.7 38.1 86.8 69.7 55.2 58.6 38.2 46.0 34.8 73.6 50.0 59.8 7.4 27.7 37.3 71.2 34.1 25.8 46.0 67.7 53.4 31.3
43.6 61.1 45.0 59.1 32.8 20.4 44.7 57.5 50.3 74.8 62.4 29.4 68.9 76.8 66.8 59.1 43.9 68.1 .. 74.6 33.3 49.9 56.1 27.1 70.9 57.3 70.5 78.4 51.3 73.6 65.0 42.0 42.7 69.9 77.3 44.3 83.3 68.5 56.9 58.4 56.3 68.1 51.2 71.1 49.7 73.9 22.1 38.7 47.5 69.1 39.5 37.7 51.8 66.2 63.3 42.2
1.2 5.1 299.5 58.6 11.7 3.5 1.3 1.5 22.6 1.0 57.2 0.5 7.0 7.8 7.5 15.5 0.5 1.5 1.7 1.4 0.8 0.5 0.8 0.9 1.8 0.8 4.3 3.1 5.3 3.4 0.8 0.3 22.0 2.1 0.8 7.0 6.7 17.1 0.4 7.1 5.6 1.3 1.0 2.8 29.5 1.9 0.3 29.3 0.7 1.5 1.1 5.4 18.7 18.5 4.6 1.0
2.6 4.9 470.2 104.2 21.1 6.8 1.7 2.9 25.7 1.4 68.0 1.6 7.4 16.3 11.8 24.6 1.0 2.2 2.6 1.3 1.6 0.7 1.3 1.6 1.8 1.0 7.8 5.2 10.3 5.6 1.3 0.5 42.3 2.2 1.2 12.1 9.6 26.1 0.8 11.3 7.5 2.0 2.2 5.4 52.9 2.4 0.7 55.3 1.3 2.7 2.1 10.4 34.2 19.9 5.2 1.5
1980–2002
1980
2002
3.6 –0.2 2.0 2.6 2.7 3.0 1.3 3.1 0.6 1.7 0.8 5.0 0.2 3.3 2.1 2.1 3.1 1.6 2.1 –0.5 2.9 1.4 2.3 2.3 0.0 0.8 2.7 2.3 3.0 2.2 2.3 1.9 3.0 0.1 2.2 2.5 1.6 1.9 2.8 2.1 1.3 1.8 3.6 3.0 2.7 0.9 3.3 2.9 2.8 2.5 2.8 3.0 2.7 0.3 0.5 1.7
25.2 43.3 33.7 35.2 20.4 17.3 28.1 33.7 32.9 46.3 37.9 14.7 47.6 46.0 44.8 38.7 13.1 47.5 .. 50.8 22.6 37.9 38.4 18.6 49.7 36.1 45.2 50.6 33.7 46.7 45.0 25.7 26.9 50.3 45.7 33.5 49.0 43.7 40.1 38.8 31.5 34.3 27.6 44.6 36.2 40.5 6.2 22.7 29.9 41.7 26.7 23.9 35.0 45.3 38.7 31.8
32.6 44.8 32.5 41.2 28.4 20.4 35.0 41.7 38.7 46.2 41.7 25.6 47.1 46.1 43.3 41.8 32.1 47.2 .. 50.5 30.1 37.0 39.6 24.0 48.0 42.0 44.7 48.4 38.3 46.1 43.5 33.0 33.8 48.4 47.1 34.9 48.4 43.4 41.0 40.5 40.9 45.2 36.6 44.3 36.7 46.5 18.9 29.5 35.7 42.4 30.4 31.9 38.0 46.5 44.1 37.8
2004 World Development Indicators
43
2.2
Labor force structure Labor force participation rate
Labor force
Average annual % ages 15–64 Male
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Female
1980
2002
1980
2002
83.6 84.3 95.1 86.3 88.9 81.4 86.8 84.7 83.5 81.9 89.6 85.1 84.5 83.7 88.6 86.4 85.4 89.9 82.1 79.6 89.9 89.3 89.9 85.6 84.8 87.5 81.4 93.4 82.7 94.9 89.2 83.8 85.3 78.6 83.9 89.9 .. 83.0 89.5 87.4 87.5 w 88.4 88.2 88.7 84.8 88.3 90.3 83.6 86.7 83.2 88.7 89.2 84.4 83.7
77.0 79.7 94.4 81.1 86.8 76.4 84.6 82.6 82.4 75.4 87.2 82.0 80.0 81.8 85.9 83.1 83.9 90.2 80.5 77.3 88.3 89.9 87.2 81.1 83.2 84.9 80.4 91.0 78.4 87.9 83.3 81.0 82.5 77.9 83.0 83.8 .. 84.1 87.2 86.3 85.3 w 86.4 85.9 86.3 82.5 86.1 88.1 79.8 85.4 80.8 86.7 86.6 81.1 78.7
69.1 74.7 87.4 9.6 63.2 50.5 44.6 47.4 69.4 67.0 66.2 46.5 32.5 32.3 30.8 41.5 69.3 51.9 23.6 68.3 86.0 79.7 54.7 39.7 34.5 47.8 69.9 83.3 73.7 16.0 57.0 58.2 37.3 70.4 32.3 74.9 .. 28.5 69.2 67.4 57.3 w 53.8 61.9 64.2 44.3 58.5 70.8 69.0 33.2 24.8 47.9 63.0 52.6 47.2
61.3 72.2 85.4 24.5 63.5 58.6 46.9 54.7 74.3 65.1 64.6 50.2 48.5 47.0 36.5 44.5 81.2 65.4 31.1 63.6 82.7 77.9 55.0 49.7 40.6 53.6 67.4 81.2 69.7 34.2 67.1 70.1 59.4 68.1 47.3 77.5 .. 31.9 66.8 67.0 60.8 w 54.4 65.1 67.2 49.0 60.3 75.1 66.7 46.0 33.8 47.1 62.2 63.5 56.9
1980
10.9 76.0 2.6 2.8 2.5 4.5 b 1.2 1.1 2.5 1.0 3.0 10.3 14.0 5.4 7.1 0.2 4.2 3.1 2.5 1.5 9.5 24.4 1.1 0.4 2.2 18.7 1.2 6.6 26.4 0.6 26.9 110.1 1.2 6.5 5.2 25.6 .. 2.5 2.4 3.2 2,036.6 s 683.4 978.8 885.1 93.7 1,662.3 704.1 214.1 128.8 53.9 388.7 172.7 374.3 123.0
Total
growth rate
Female
millions
%
% of labor force
2002
10.7 77.6 4.4 7.2 4.4 3.9 2.0 2.0 3.0 1.0 4.0 18.1 18.1 8.4 13.2 0.4 4.8 3.9 5.6 2.5 18.1 37.5 2.0 0.6 4.0 33.7 2.1 12.1 24.9 1.6 29.6 148.3 1.6 11.0 10.5 41.8 .. 5.9 4.4 6.1 3,028.6 s 1,138.6 1,419.4 1,276.7 142.7 2,558.0 1,049.3 239.3 229.6 105.0 629.8 305.1 470.6 141.6
a. Estimate does not account for recent refugee flows. b. Includes labor force for Kosovo until 2001. c. Data are for 1980–2001.
44
2004 World Development Indicators
1980–2002
–0.1 0.1 2.4 4.4 2.5 0.6 c 2.1 2.8 0.8 0.2 1.3 2.5 1.2 2.0 2.8 3.2 0.6 1.1 3.7 2.3 2.9 2.0 2.7 1.5 2.7 2.7 2.7 2.7 –0.3 4.7 0.4 1.4 1.3 2.4 3.2 2.2 .. 3.9 2.8 2.9 1.8 w 2.3 1.7 1.7 1.9 2.0 1.8 0.5 2.6 3.0 2.2 2.6 1.0 0.6
1980
2002
45.8 49.4 49.1 7.6 42.2 38.7 b 35.5 34.6 45.3 45.8 43.4 35.1 28.3 26.9 26.9 33.5 43.8 36.7 23.5 46.9 49.8 47.4 39.3 31.4 28.9 35.5 47.0 47.9 50.2 5.1 38.9 41.0 30.8 48.0 26.7 48.1 .. 32.5 45.4 44.4 39.1 w 37.4 40.5 41.2 34.2 39.2 42.6 46.7 27.8 23.8 33.8 42.0 38.4 36.4
44.5 49.2 48.7 17.7 42.6 43.1 37.1 39.2 47.7 46.6 43.4 37.9 37.5 36.9 30.0 37.8 48.1 40.8 27.6 45.2 49.0 46.2 40.0 34.9 32.1 38.1 45.9 47.6 48.8 15.9 44.3 46.2 42.2 46.9 35.4 48.7 .. 28.3 44.6 44.5 40.7 w 37.7 42.2 42.8 37.0 40.2 44.5 46.3 35.2 28.6 33.6 42.0 43.4 41.4
About the data
2.2
PEOPLE
Labor force structure Definitions
The labor force is the supply of labor available for the
But in many developing countries children under 15
• Labor force participation rate is the proportion of
production of goods and services in an economy. It
work full or part time. And in some high-income coun-
the population ages 15–64 that is economically
includes people who are currently employed and peo-
tries many workers postpone retirement past age 65.
active: all people who supply labor for the production
ple who are unemployed but seeking work as well as
As a result, labor force participation rates calculated
of goods and services during a specified period.
first-time job-seekers. Not everyone who works is
in this way may systematically over- or under-esti-
• Total labor force comprises people who meet the
included, however. Unpaid workers, family workers,
mate actual rates. High participation rates are found
ILO definition of the economically active population.
and students are among those usually omitted, and
in Sub-Saharan Africa, where men and women can-
It includes both the employed and the unemployed.
in some countries members of the military are not
not afford to forgo work, because of a lack of social
While national practices vary in the treatment of
counted. The size of the labor force tends to vary dur-
protection. The largest gap between men and women
such groups as the armed forces and seasonal or
ing the year as seasonal workers enter and leave it.
in labor force participation is observed in the Middle
part-time workers, the labor force generally includes
Data on the labor force are compiled by the
East and North Africa, where low participation of
the armed forces, the unemployed, and first-time job-
International Labour Organization (ILO) from labor
women in the work force also brings down the over-
seekers, but excludes homemakers and other unpaid
force surveys, censuses, establishment censuses
all labor force participation rate.
caregivers and workers in the informal sector.
and surveys, and various types of administrative
In general, estimates of women in the labor force
• Average annual growth rate of the labor force is
records such as employment exchange registers and
are lower than those of men and are not comparable
calculated using the exponential endpoint method
unemployment insurance schemes. For some coun-
internationally, reflecting the fact that for women,
(see Statistical methods for more information).
tries a combination of sources is used. While the
demographic, social, legal, and cultural trends and
• Females as a percentage of the labor force show
resulting statistics may provide rough estimates of
norms determine whether their activities are regard-
the extent to which women are active in the labor
the labor force, they are not comparable across
ed as economic. In many countries large numbers of
force.
countries because of the noncomparability of the
women work on farms or in other family enterprises
original data and the different ways the original
without pay, while others work in or near their homes,
sources may be combined.
mixing work and family activities during the day.
For international comparisons the most compre-
Countries differ in the criteria used to determine the
hensive source is labor force surveys. Despite the
extent to which such workers are to be counted as
ILO’s efforts to encourage the use of international
part of the labor force. In most economies the gap
standards, labor force data are not fully comparable
between male and female labor force participation
because of differences among countries, and some-
rates has been narrowing since 1980.
times within countries, in their scope and coverage. In some countries data on the labor force refer to people above a specific age, while in others there is no specific age provision. The reference period of the 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 the 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 irregular intervals, the estimated labor force may be significantly smaller than the numbers actually working (ILO, Yearbook of Labour Statistics 1997). The labor force estimates in the table were calculated by World Bank staff by applying labor force participation rates from the ILO database to World Bank
Data sources
population estimates to create a series consistent
The labor force participation rates are from the
with these population estimates. This procedure
ILO database Estimates and Projections of the
sometimes results in estimates of labor force size
Economically Active Population, 1950–2010. The
that differ slightly from those in the ILO’s Yearbook
ILO publishes estimates of the economically
of Labour Statistics. The labor force participation
active population in its Yearbook of Labour
rate of the population ages 15–64 provides an
Statistics.
indication of the relative size of the supply of labor.
2004 World Development Indicators
45
2.3
Employment by economic activity Agriculture a
Male
Female
Male
Female
Male
Female
% of female
% of male
% of female
% of male
% of female
employment
46
Services a
% of male 1980
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
Industry a
66 .. 27 67 .. .. 8 .. .. .. .. 4 66 .. 26 .. 34 .. 92 .. .. 65 7 79 82 22 .. 2 2 62 42 34 60 .. 30 13 .. .. .. 45 51 79 .. .. 15 10 59 78 .. .. .. 26 .. 86 81 ..
2000–02 b
.. .. .. .. 1 .. 6 5 37 53 .. .. .. 6 .. 22 24 .. .. .. 71 .. 4 .. .. 18 .. 0c 33 .. .. 22 .. 16 .. 6 5 21 10 27 34 .. 10 .. 7 2 .. .. 53 3 .. 15 50 .. .. ..
2004 World Development Indicators
employment 1980
86 .. 69 87 .. .. 4 .. .. .. .. 2 69 .. 38 .. 20 .. 93 .. .. 87 3 90 95 3 .. 1 1 84 81 6 75 .. 10 11 .. .. .. 10 10 88 .. .. 12 7 74 93 .. .. .. 42 .. 97 98 ..
2000–02 b
.. .. .. .. 0c .. 3 6 43 77 .. .. .. 3 .. 17 16 .. .. .. 70 .. 2 .. .. 5 .. 0c 7 .. .. 4 .. 15 .. 3 2 2 4 39 4 .. 4 .. 4 1 .. .. 53 2 .. 18 18 .. .. ..
employment 1980
2000–02 b
9 .. 33 13 .. .. 39 .. .. .. .. 44 10 .. 45 .. 30 .. 3 .. .. 11 37 5 6 27 .. 47 39 18 20 25 10 .. 32 57 .. .. .. 21 21 7 .. .. 45 45 18 10 .. .. .. 34 .. 2 3 ..
.. .. .. .. 30 .. 30 43 14 11 .. .. .. 39 .. 26 27 .. .. .. 9 .. 33 .. .. 29 .. 27 19 .. .. 27 .. 37 .. 50 36 26 30 25 25 .. 42 .. 40 34 .. .. 12 44 .. 30 18 .. .. ..
employment 1980
12 .. 6 1 .. .. 16 .. .. .. .. 18 4 .. 24 .. 13 .. 2 .. .. 2 16 1 0c 16 .. 56 26 4 2 20 5 .. 22 39 .. .. .. 13 21 2 .. .. 23 22 6 3 .. .. .. 18 .. 1 0 ..
employment
employment
2000–02 b
1980
2000–02 b
1980
2000–02 b
.. .. .. .. 12 .. 10 14 7 9 .. .. .. 14 .. 14 10 .. .. .. 12 .. 11 .. .. 13 .. 10 17 .. .. 15 .. 21 .. 28 14 17 16 7 22 .. 23 .. 14 13 .. .. 6 18 .. 12 23 .. .. ..
26 .. 40 20 .. .. 53 .. .. .. .. 51 24 .. 30 .. 36 .. 5 .. .. 24 56 15 12 51 .. 52 59 20 38 40 30 .. 39 30 .. .. .. 33 28 14 .. .. 39 46 24 13 .. .. .. 40 .. 12 17 ..
.. .. .. .. 69 .. 64 52 49 30 .. .. .. 55 .. 51 49 .. .. .. 20 .. 64 .. .. 53 .. 73 48 .. .. 51 .. 47 .. 44 59 53 60 48 42 .. 48 .. 53 64 .. .. 35 52 .. 56 27 .. .. ..
2 .. 25 11 .. .. 80 .. .. .. .. 79 27 .. 39 .. 67 .. 5 .. .. 11 81 9 4 81 .. 43 74 12 17 74 20 .. 68 50 .. .. .. 69 69 11 .. .. 65 71 21 5 .. .. .. 40 .. 3 3 ..
.. .. .. .. 87 .. 87 80 50 12 .. .. .. 82 .. 67 74 .. .. .. 18 .. 87 .. .. 83 .. 90 76 .. .. 80 .. 63 .. 68 85 81 79 54 74 .. 73 .. 82 86 .. .. 41 80 .. 70 56 .. .. ..
Agriculture a
Services a
Male
Female
Male
Female
Male
Female
% of male
% of female
% of male
% of female
% of male
% of female
employment
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Industry a
PEOPLE
2.3
Employment by economic activity
1980
2000–02 b
.. .. .. 57 .. 21 .. 8 13 .. 9 .. .. 23 39 .. 2 .. 77 .. 13 26 69 16 .. .. 73 .. 34 86 65 29 .. .. .. .. 72 .. 52 .. .. 13 .. .. .. 10 52 .. .. 76 2 .. 60 .. .. 8
.. 9 .. .. .. .. 11 3 6 .. 5 .. .. 20 .. 9 .. .. .. 18 .. .. .. .. 20 23 .. .. 21 .. .. .. 24 52 .. .. .. .. 33 .. 4 12 .. .. .. 6 .. 44 29 .. 39 11 45 19 12 3
employment 1980
.. .. .. 54 .. 62 .. 4 16 .. 13 .. .. 25 52 .. 0 .. 82 .. 20 64 89 63 .. .. 93 .. 44 92 79 30 .. .. .. .. 97 .. 42 .. .. 7 .. .. .. 6 24 .. .. 92 0c .. 37 .. .. 0c
2000–02 b
.. 4 .. .. .. .. 2 1 5 .. 5 .. .. 16 .. 12 .. .. .. 12 .. .. .. .. 12 25 .. .. 14 .. .. .. 6 50 .. .. .. .. 29 .. 2 6 .. .. .. 2 .. 73 6 .. 20 6 25 19 14 0c
employment
employment
employment
employment
1980
2000–02 b
1980
2000–02 b
1980
2000–02 b
1980
2000–02 b
.. .. .. 13 .. 24 .. 39 43 .. 40 .. .. 24 37 .. 36 .. 7 .. 29 52 9 29 .. .. 9 .. 26 2 11 19 .. .. .. .. 14 .. 22 .. .. 38 .. .. .. 41 21 .. .. 8 35 .. 16 .. .. 27
.. 42 .. .. .. .. 39 34 39 .. 37 .. .. 24 .. 34 .. .. .. 35 .. .. .. .. 34 36 .. .. 34 .. .. .. 28 18 .. .. .. .. 17 .. 31 32 .. .. .. 33 .. 20 20 .. 21 24 18 40 44 27
.. .. .. 13 .. 11 .. 16 28 .. 28 .. .. 9 20 .. 3 .. 4 .. 21 5 1 3 .. .. 2 .. 20 1 2 40 .. .. .. .. 1 .. 10 .. .. 19 .. .. .. 13 33 .. .. 2 13 .. 15 .. .. 24
.. 26 .. .. .. .. 14 12 20 .. 21 .. .. 10 .. 19 .. .. .. 16 .. .. .. .. 21 30 .. .. 29 .. .. .. 22 10 .. .. .. .. 7 .. 9 12 .. .. .. 9 .. 9 10 .. 10 10 12 18 23 14
.. .. .. 29 .. 55 .. 52 44 .. 51 .. .. 53 24 .. 62 .. 16 .. 58 22 22 55 .. .. 19 .. 40 12 25 47 .. .. .. .. 14 .. 27 .. .. 48 .. .. .. 49 27 .. .. 16 63 .. 25 .. .. 65
.. 49 .. .. .. .. 50 62 55 .. 57 .. .. 57 .. 57 .. .. .. 47 .. .. .. .. 45 41 .. .. 45 .. .. .. 48 31 .. .. .. .. 49 .. 64 56 .. .. .. 58 .. 36 51 .. 40 65 37 40 44 69
.. .. .. 33 .. 28 .. 79 56 .. 58 .. .. 65 28 .. 97 .. 13 .. 59 31 10 34 .. .. 5 .. 36 7 19 31 .. .. .. .. 2 .. 47 .. .. 73 .. .. .. 81 43 .. .. 6 86 .. 48 .. .. 75
.. 71 .. .. .. .. 83 86 75 .. 73 .. .. 75 .. 70 .. .. .. 72 .. .. .. .. 67 46 .. .. 57 .. .. .. 72 40 .. .. .. .. 63 .. 86 82 .. .. .. 88 .. 18 85 .. 69 84 63 63 63 86
2004 World Development Indicators
47
2.3
Employment by economic activity Agriculture a
Services a
Male
Female
Male
Female
Male
Female
% of male
% of female
% of male
% of female
% of male
% of female
employment 1980
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Industry a
22 .. 88 45 74 .. 63 2 .. .. 69 .. 20 44 66 .. 8 8 .. .. .. 68 70 .. 33 4 .. .. .. 5 4 5 .. .. 20 .. 22 60 69 .. .. w .. .. .. .. .. .. .. .. .. .. .. 8 ..
2000–02 b
40 .. .. .. .. .. .. 0c 8 10 .. .. 8 .. .. .. 3 5 .. .. .. 50 .. .. .. 24 .. .. 22 9 2 3 6 .. 15 .. 9 .. .. .. .. w .. .. .. 16 .. .. .. 21 .. .. .. 4 5
employment 1980
39 .. 98 25 90 .. 82 1 .. .. 90 .. 18 51 88 .. 3 5 .. .. .. 74 67 .. 53 9 .. .. .. 0c 1 2 .. .. 2 .. 25 98 85 .. .. w .. .. .. .. .. .. .. .. .. .. .. 6 ..
2000–02 b
45 .. .. .. .. .. .. 0c 4 10 .. .. 5 .. .. .. 1 3 .. .. .. 48 .. .. .. 56 .. .. 17 0c 1 1 2 .. 2 .. 26 .. .. .. .. w .. .. .. 8 .. .. .. 9 .. .. .. 3 4
employment 1980
52 .. 5 17 9 .. 20 33 .. .. 12 .. 42 19 9 .. 45 47 .. .. .. 13 12 .. 30 36 .. .. .. 40 48 39 .. .. 31 .. 43 19 13 .. .. w .. .. .. .. .. .. .. .. .. .. .. 41 ..
2000–02 b
30 .. .. .. .. .. .. 31 48 46 .. .. 42 .. .. .. 36 36 .. .. .. 20 .. .. .. 28 .. .. 39 36 36 32 32 .. 28 .. 32 .. .. .. .. w .. .. .. 32 .. .. .. 27 .. .. .. 35 40
employment 1980
34 .. 1 5 2 .. 4 40 .. .. 2 .. 21 18 4 .. 16 23 .. .. .. 8 7 .. 32 31 .. .. .. 7 23 19 .. .. 18 .. 25 1 3 .. .. w .. .. .. .. .. .. .. .. .. .. .. 22 ..
2000–02 b
22 .. .. .. .. .. .. 18 26 29 .. .. 15 .. .. .. 11 13 .. .. .. 17 .. .. .. 15 .. .. 22 14 11 12 14 .. 12 .. 11 .. .. .. .. w .. .. .. 19 .. .. .. 14 .. .. .. 15 16
a. Data may not add up to 100 because of workers not classified by sector. b. Data are for the most recent year available. c. Less than 0.5.
48
2004 World Development Indicators
employment 1980
26 .. 7 39 17 .. 17 65 .. .. 19 .. 38 30 24 .. 47 46 .. .. .. 20 19 .. 37 60 .. .. .. 55 49 56 .. .. 49 .. 36 21 19 .. .. w .. .. .. .. .. .. .. .. .. .. .. 51 ..
2000–02 b
30 .. .. .. .. .. .. 69 44 43 .. .. 51 .. .. .. 61 59 .. .. .. 30 .. .. .. 48 .. .. 33 55 62 65 62 .. 57 .. 58 .. .. .. .. w .. .. .. 51 .. .. .. 52 .. .. .. 60 55
employment 1980
27 .. 1 70 8 .. 14 59 .. .. 8 .. 61 28 8 .. 81 72 .. .. .. 18 26 .. 16 60 .. .. .. 93 76 80 .. .. 79 .. 50 1 13 .. .. w .. .. .. .. .. .. .. .. .. .. .. 72 ..
2000–02 b
33 .. .. .. .. .. .. 81 71 61 .. .. 81 .. .. .. 88 84 .. .. .. 35 .. .. .. 29 .. .. 55 86 88 87 85 .. 86 .. 62 .. .. .. .. w .. .. .. 73 .. .. .. 76 .. .. .. 82 80
2.3
PEOPLE
Employment by economic activity About the data
The International Labour Organization (ILO) classifies
are classified according to their last job. But in some
growth is centered in service occupations, where
economic activity on the basis of the International
countries the unemployed and people seeking their
women often dominate, as has been the recent experi-
Standard Industrial Classification (ISIC) of All
first job are not classifiable by economic activity.
ence in many countries.
Economic Activities. Because this classification is
Because of these differences, the size and distribu-
There are several explanations for the rising impor-
based on where work is performed (industry) rather
tion of employment by economic activity may not be
tance of service jobs for women. Many service
than on what type of work is performed (occupation),
fully comparable across countries (ILO, Yearbook of
jobs—such as nursing and social and clerical work—
all of an enterprise’s employees are classified under
Labour Statistics 1996, p. 64).
are considered “feminine” because of a perceived
the same industry, regardless of their trade or occu-
The ILO’s Yearbook of Labour Statistics and its data-
similarity to women’s traditional roles. Women often
pation. The categories should add up to 100 percent.
base Key Indicators of the Labour Market report data
do not receive the training needed to take advantage
Where they do not, the differences arise because of
by major divisions of the ISIC revision 2 or ISIC revision
of changing employment opportunities. And the
workers who cannot be classified by economic activity.
3. In this table the reported divisions or categories are
greater availability of part-time work in service indus-
Data on employment are drawn from labor force sur-
aggregated into three broad groups: agriculture, indus-
tries may lure more women, although it is not clear
veys, household surveys, establishment censuses
try, and services. Classification into such broad groups
whether this is a cause or an effect.
and surveys, administrative records of social insur-
may obscure fundamental shifts within countries’
ance schemes, and official national estimates. The
industrial patterns. Most economies report economic
concept of employment generally refers to people
activity according to the ISIC revision 2, although a
above a certain age who worked, or who held a job,
group of economies moved to ISIC revision 3. The use
• Agriculture corresponds to division 1 (ISIC revision
during a reference period. Employment data include
of one classification or another should not have a sig-
2) or tabulation categories A and B (ISIC revision 3)
both full-time and part-time workers. There are, how-
nificant impact on the information for the three broad
and includes hunting, forestry, and fishing. • Industry
ever, many differences in how countries define and
sectors presented in this table.
corresponds to divisions 2–5 (ISIC revision 2) or tab-
Definitions
measure employment status, particularly for students,
The distribution of economic activity by gender reveals
ulation categories C–F (ISIC revision 3) and includes
part-time workers, members of the armed forces, and
some interesting patterns. Industry accounts for a larg-
mining and quarrying (including oil production), man-
household or contributing family workers. Where the
er share of male employment than female employment
ufacturing, construction, and public utilities (electrici-
armed forces are included, they are allocated to the
worldwide, whereas a higher proportion of women work
ty, gas, and water). • Services correspond to divi-
service sector, causing that sector to be somewhat
in the services sector. Employment in agriculture is also
sions 6–9 (ISIC revision 2) or tabulation categories
overstated relative to the service sector in economies
male-dominated, although not as much as industry.
G–P (ISIC revision 3) and include wholesale and retail
where they are excluded. Where data are obtained
Segregating one sex in a narrow range of occupations
trade and restaurants and hotels; transport, storage,
from establishment surveys, they cover only employ-
significantly reduces economic efficiency by reducing
and communications; financing, insurance, real
ees; thus self-employed and contributing family work-
labor market flexibility and thus the economy’s ability to
estate, and business services; and community,
ers are excluded. In such cases the employment share
adapt to change. This segregation is particularly harm-
social, and personal services.
of the agricultural sector is severely underreported.
ful for women, who have a much narrower range of
Countries also take very different approaches to
labor market choices and lower levels of pay than men.
the treatment of unemployed people. In most coun-
But it is also detrimental to men when job losses are
tries unemployed people with previous job experience
concentrated in industries dominated by men and job
2.3a Women tend to suffer disproportionately from underemployment Underemployment as share of total employment (%) 15
Male Female
12 9 6 3 0 Paraguay
Namibia
Pakistan
Thailand
Time-related underemployment includes people who work less than the normal duration of work, as defined by national authorities, but who desire and seek to work additional hours. More women tend to be underemployed than men, as discrimination and women’s household responsibilities may make it more difficult for them to have stable and high-paid work.
Data sources The employment data are from the ILO database Key Indicators of the Labour Market, third edition.
Source: International Labour Organization, Key Indicators of the Labour Market, third edition.
2004 World Development Indicators
49
2.4
Unemployment Unemployment
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
50
Long-term unemployment
Male
Female
Total
% of male
% of female
% of total
labor force
labor force
labor force
Unemployment by level of educational attainment
% of total unemployment % of total unemployment
Primary
Secondary
Tertiary
Male
Female
Total
1999–
1999–
1999–
1980
2000–02 a
1980
2000–02 a
1980
2000–02 a
2000–02 a
2000–02 a
2000–02 a
2001 a
2001 a
2001 a
.. .. .. .. .. .. 5.0 1.6 .. .. .. 5.5 .. .. .. .. 2.8 .. .. .. .. .. 7.0 .. .. 10.6 .. 3.9 7.5 .. .. 5.3 .. 3.4 .. .. 6.5 .. .. 3.9 .. .. .. .. 4.6 4.1 .. .. .. .. .. 3.3 .. .. .. ..
.. 18.8 33.9 .. 14.1 .. 6.2 3.5 1.1 3.2 1.9 6.2 .. 4.5 .. 14.7 7.5 20.2 .. .. 1.5 .. 8.1 .. .. 7.5 .. 8.4 11.6 .. .. 5.6 .. 13.4 .. 5.9 4.2 9.4 7.1 5.1 8.0 .. 12.9 .. 9.0 7.9 .. .. 11.6 8.7 .. 6.2 2.5 .. .. ..
.. .. .. .. .. .. 7.4 2.3 .. .. .. 15.0 .. .. .. .. 2.8 .. .. .. .. .. 8.2 .. .. 10.0 .. 3.4 11.5 .. .. 7.8 .. 8.2 .. .. 7.6 .. .. 19.2 .. .. .. .. 4.7 9.1 .. .. .. .. .. 5.7 .. .. .. ..
.. 28.4 29.7 .. 16.4 .. 5.8 3.8 1.5 3.3 2.6 7.8 .. 6.2 .. 17.2 11.9 18.4 .. .. 2.2 .. 7.1 .. .. 8.5 .. 6.0 19.1 .. .. 7.9 .. 18.5 .. 9.0 4.3 26.0 16.2 22.7 3.5 .. 12.2 .. 9.1 10.1 .. .. 10.7 8.3 .. 14.6 4.3 .. .. ..
.. 5.6 .. .. 2.3 .. 5.9 1.9 .. .. .. 9.1 .. .. .. .. 2.8 .. .. .. .. .. 7.5 .. .. 10.4 4.9 3.8 9.1 .. .. 5.9 .. 5.3 .. .. 7.0 .. .. 5.2 12.9 .. .. .. 4.7 6.1 .. .. .. .. .. 2.4 1.7 .. .. ..
.. 22.7 29.8 .. 17.8 .. 6.0 3.6 1.3 3.3 2.3 6.9 .. 5.2 .. 15.8 9.4 19.4 .. .. 1.8 .. 7.7 .. .. 7.8 3.1 7.3 17.9 .. .. 6.4 .. 15.2 3.3 7.3 4.3 15.6 11.0 9.0 6.2 .. 12.6 .. 9.0 8.9 .. .. 11.0 8.6 .. 9.6 3.1 .. .. ..
.. .. .. .. .. 72.2 25.9 25.8 .. .. .. 45.8 .. .. .. .. .. .. .. .. .. .. 9.9 .. .. .. .. .. .. .. .. 8.9 .. .. .. 50.2 17.1 2.2 .. .. .. .. .. .. 30.0 30.2 .. .. .. 44.9 .. 47.1 .. .. .. ..
.. .. .. .. .. 70.8 17.1 24.2 .. .. .. 53.3 .. .. .. .. .. .. .. .. .. .. 8.4 .. .. .. .. .. .. .. .. 13.3 .. .. .. 51.0 22.1 1.3 .. .. .. .. .. .. 22.6 33.1 .. .. .. 48.7 .. 55.7 .. .. .. ..
.. .. .. .. .. 71.6 22.1 25.1 .. .. .. 49.4 .. .. .. .. .. .. .. .. .. .. 9.3 .. .. .. .. .. .. .. .. 10.9 .. 56.4 .. 50.6 19.5 1.6 .. .. .. .. .. .. 26.2 31.7 .. .. .. 46.6 .. 52.4 .. .. .. ..
.. .. .. .. .. .. 54.3 36.0 4.5 54.3 7.9 50.0 .. 60.2 .. .. 26.1 36.7 46.8 .. .. .. 30.7 .. .. 22.7 .. .. 22.8 .. .. 71.6 .. 19.1 .. 27.3 35.1 .. 26.8 .. .. .. 19.3 26.9 38.2 .. .. .. 5.5 26.8 .. 35.1 .. .. .. ..
.. .. .. .. .. .. 31.5 57.6 35.4 22.7 15.3 34.9 .. 32.5 .. .. 20.2 53.0 19.3 .. .. .. 30.3 .. .. 54.9 .. .. 57.2 .. .. 15.2 .. 71.3 .. 69.1 44.9 .. 50.8 .. .. .. 62.7 61.3 45.8 .. .. .. 33.1 60.4 .. 49.4 .. .. .. ..
.. .. .. .. .. .. 14.0 6.4 60.1 8.4 76.9 15.1 .. 4.4 .. .. 2.5 10.3 5.6 .. .. .. 39.0 .. .. 21.6 .. .. 17.2 .. .. 10.0 .. 9.1 .. 3.6 20.0 .. 20.2 .. .. .. 18.1 8.1 16.0 .. .. .. 61.4 12.8 .. 14.5 .. .. .. ..
2004 World Development Indicators
Unemployment
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Long-term unemployment
Male
Female
Total
% of male
% of female
% of total
labor force
labor force
labor force
PEOPLE
2.4
Unemployment
Unemployment by level of educational attainment
% of total unemployment % of total unemployment
Primary
Secondary
Tertiary
Male
Female
Total
1999–
1999–
1999–
1980
2000–02 a
1980
2000–02 a
1980
2000–02 a
2000–02 a
2000–02 a
2000–02 a
2001 a
2001 a
2001 a
8.6 .. .. .. .. .. 11.4 4.1 4.8 16.3 2.0 .. .. .. .. 6.2 .. .. .. .. .. .. .. .. .. 15.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 4.3 .. .. .. .. 1.2 .. 3.0 6.3 .. 3.8 .. 3.2 .. 3.3 19.5
3.4 6.1 .. .. .. .. 4.6 10.1 6.9 .. 5.6 11.8 .. .. .. 3.5 0.8 .. .. 14.1 .. .. .. .. 19.7 31.7 .. .. .. .. .. 5.6 2.4 8.7 .. .. .. .. 28.3 .. 2.8 5.0 12.8 .. .. 4.1 .. 6.1 10.5 .. .. 7.5 9.4 19.1 4.2 13.0
6.0 .. .. .. .. .. 8.2 6.0 13.2 39.6 2.0 .. .. .. .. 3.5 .. .. .. .. .. .. .. .. .. 32.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 5.2 .. .. .. .. 2.1 .. 7.5 13.3 .. 4.8 .. 7.5 .. 12.1 12.3
4.7 5.4 .. .. .. .. 3.7 10.6 12.2 .. 5.1 20.7 .. .. .. 2.5 0.6 .. .. 11.5 .. .. .. .. 14.2 32.3 .. .. .. .. .. 12.6 2.4 5.9 .. .. .. .. 39.0 .. 3.6 5.3 9.4 .. .. 3.7 .. 17.3 18.2 .. .. 10.0 10.3 20.9 6.1 9.1
7.3 .. .. .. .. .. 10.5 4.8 7.6 27.3 2.0 .. .. .. .. 5.2 .. .. .. .. .. .. .. .. .. 22.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 4.6 .. .. .. .. 1.6 .. 3.6 8.4 .. 4.1 .. 4.8 .. 6.7 17.1
3.8 5.8 .. 6.1 .. .. 4.2 10.3 9.0 .. 5.4 13.2 .. .. .. 3.1 0.8 8.6 .. 12.8 .. .. .. .. 13.8 31.9 .. .. 3.9 .. .. 8.0 2.4 7.3 .. .. .. .. 33.8 .. 3.1 5.2 11.2 .. .. 3.9 .. 7.8 13.2 .. .. 8.7 9.8 19.9 5.1 11.4
.. 47.1 .. .. .. .. 35.9 .. 58.0 24.4 34.8 .. .. .. .. 3.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.0 .. .. .. .. .. .. .. 21.5 14.9 .. .. .. 8.1 .. .. 24.0 .. .. .. .. 45.1 31.9 ..
.. 41.7 .. .. .. .. 18.2 .. 61.6 36.2 21.6 .. .. .. .. 1.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.3 .. .. .. .. .. .. .. 20.7 10.0 .. .. .. 3.7 .. .. 35.7 .. .. .. .. 52.0 31.4 ..
.. 44.8 .. .. .. .. 29.4 .. 59.9 31.7 29.7 .. .. .. .. 2.5 .. .. .. .. .. .. .. .. 57.8 .. .. .. .. .. .. .. 0.7 .. .. .. .. .. .. .. 21.1 12.6 .. .. .. 6.2 .. .. 29.3 .. .. .. .. 48.4 31.6 ..
.. 35.4 29.0 46.0 .. .. 60.8 20.7 49.1 .. 21.5 .. 7.2 .. .. 26.1 .. 33.4 .. 24.6 .. .. .. .. 15.4 34.0 .. .. .. .. .. 35.5 51.5 .. .. .. .. .. .. .. 49.5 0.5 56.3 .. .. 25.0 .. .. 47.0 .. .. 15.8 .. 19.1 73.3 ..
.. 60.5 40.3 36.6 .. .. 20.8 44.2 41.9 .. 53.4 .. 52.5 .. .. 51.0 11.9 55.7 .. 67.0 .. .. .. .. 55.8 52.1 .. .. .. .. .. 63.9 23.9 .. .. .. .. .. .. .. 35.9 44.5 23.4 .. .. 50.0 .. .. 35.5 .. .. 54.9 .. 76.8 13.6 ..
.. 4.1 30.7 6.7 .. .. 16.1 34.1 7.2 .. 24.8 .. 40.3 .. .. 22.9 2.7 10.9 .. 8.2 .. .. .. .. 28.8 7.8 .. .. .. .. .. .. 22.2 .. .. .. .. .. .. .. 13.2 19.2 14.7 .. .. 22.6 .. .. 11.3 .. .. 28.3 .. 4.2 8.1 ..
2004 World Development Indicators
51
2.4
Unemployment Unemployment
Male
Female
Total
% of male
% of female
% of total
labor force
labor force
labor force
1980
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
.. .. .. .. .. .. .. 2.9 .. .. .. .. 10.4 .. .. .. 1.9 0.2 3.8 .. .. 1.0 .. 8.0 .. 9.0 .. .. .. .. 8.3 6.9 .. .. .. .. .. .. 32.7 .. .. w .. .. .. .. .. .. .. .. .. .. .. 5.5 5.5
2000–02 a
7.1 9.3 .. .. .. 22.6 .. 3.5 18.6 5.6 .. 26.1 8.0 6.8 .. .. 5.6 2.8 8.0 .. .. 1.8 .. .. .. 10.9 .. .. 11.2 2.2 5.6 5.9 11.5 .. 11.6 .. 27.3 .. .. .. .. w .. .. .. .. .. .. 11.3 .. .. .. .. 5.4 7.9
a. Data are for the most recent year available.
52
Long-term unemployment
2004 World Development Indicators
1980
.. .. .. .. .. .. .. 3.4 .. .. .. .. 13.1 .. .. .. 2.6 0.3 3.8 .. .. 0.7 .. 14.0 .. 23.0 .. .. .. .. 4.8 7.4 .. .. .. .. .. .. 59.0 .. .. w .. .. .. .. .. .. .. .. .. .. .. 7.0 10.8
2000–02 a
5.9 8.5 .. .. .. 22.1 .. 3.4 18.7 6.3 .. 33.3 16.4 11.2 .. .. 4.7 3.1 23.9 .. .. 1.7 .. .. .. 9.9 .. .. 11.0 2.6 4.4 5.6 19.7 .. 14.6 .. 14.1 .. .. .. .. w .. .. .. .. .. .. 11.1 .. .. .. .. 6.7 11.6
1980
.. .. .. .. .. .. .. 3.0 .. .. .. .. 11.1 .. .. .. 2.2 0.2 3.9 .. .. 0.8 .. 10.0 .. 10.9 .. .. .. .. 6.8 7.1 .. .. 5.9 .. .. .. 42.2 .. .. w .. 4.8 4.9 .. .. 4.7 .. .. .. .. .. 6.0 7.1
Unemployment by level of educational attainment
% of total unemployment % of total unemployment
Primary
Secondary
Tertiary
Male
Female
Total
1999–
1999–
1999–
2000–02 a
2000–02 a
2000–02 a
2000–02 a
2001 a
2001 a
2001 a
6.6 8.9 .. .. .. 22.3 .. 3.4 18.6 5.9 .. 29.5 11.4 8.2 .. .. 5.2 2.9 11.2 .. .. 1.8 .. .. .. 10.6 .. .. 11.1 2.3 5.1 5.8 17.2 .. 12.8 .. 25.5 .. .. .. .. w .. 4.9 4.3 9.0 .. 3.7 11.1 9.2 .. .. .. 6.2 9.8
.. .. .. .. .. .. .. .. .. 58.6 .. .. 31.6 .. .. .. 23.0 19.0 .. .. .. .. .. 20.3 .. 26.4 .. .. .. .. 26.4 8.9 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 24.7 40.7
.. .. .. .. .. .. .. .. .. 61.4 .. .. 41.8 .. .. .. 18.1 23.9 .. .. .. .. .. 34.7 .. 34.5 .. .. .. .. 17.0 8.1 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 22.8 44.6
.. .. .. .. .. .. .. .. .. 59.9 .. .. 37.5 .. .. .. 20.9 21.3 .. .. .. .. .. 27.6 .. 28.5 .. .. .. .. 22.8 8.5 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 24.1 42.8
20.6 16.8 60.7 .. .. .. .. 25.5 19.8 33.3 .. .. 57.1 41.0 .. .. 28.6 43.0 .. .. .. 70.6 .. 38.2 .. 60.1 .. .. 8.6 .. 33.7 20.3 50.7 .. 57.9 .. .. .. .. 16.4 30.0 w 30.0 .. .. 34.8 28.8 .. 21.3 31.3 .. 29.3 .. 31.1 42.1
72.7 41.6 24.1 .. .. .. .. 26.9 77.1 63.2 .. .. 19.7 .. .. .. 56.6 43.0 .. .. .. 7.2 .. 60.7 .. 29.0 .. .. 27.3 .. 44.4 35.3 21.2 .. 24.0 .. .. .. .. 81.8 40.2 w 41.4 .. .. 52.5 40.0 .. 45.8 28.3 .. 40.3 .. 41.8 43.7
5.5 41.6 5.9 .. .. .. .. 32.0 3.0 5.3 .. .. 22.2 56.1 .. .. 13.1 14.0 .. .. .. 19.2 .. 0.8 .. 8.4 .. .. 64.1 .. 12.7 44.4 27.8 .. 14.4 .. .. .. .. 0.8 25.2 w 27.9 .. .. 11.3 25.8 .. 32.6 9.6 .. 31.0 .. 25.9 13.3
About the data
2.4
PEOPLE
Unemployment Definitions
Unemployment and total employment in an economy
closely with social insurance schemes and registration
• Unemployment refers to the share of the labor
are the broadest indicators of economic activity as
with such offices is a prerequisite for receipt of unem-
force without work but available for and seeking
reflected by the labor market. The International
ployment benefits, the two sets of unemployment esti-
employment. Definitions of labor force and unem-
Labour Organization (ILO) defines the unemployed as
mates tend to be comparable. Where registration is
ployment differ by country (see About the data).
members of the economically active population who
voluntary and where employment offices function only
•Long-term unemployment refers to the number of
are without work but available for and seeking work,
in more populous areas, employment office statistics
people with continuous periods of unemployment
including people who have lost their jobs and those
do not give a reliable indication of unemployment.
extending for a year or longer, expressed as a per-
who have voluntarily left work. Some unemployment
Most commonly excluded from both these sources are
centage of the total unemployed. • Unemployment
is unavoidable in all economies. At any time some
discouraged workers who have given up their job
by level of educational attainment shows the unem-
workers are temporarily unemployed—between jobs
search because they believe that no employment
ployed by level of educational attainment, as a per-
as employers look for the right workers and workers
opportunities exist or do not register as unemployed
centage of the total unemployed. The levels of edu-
search for better jobs. Such unemployment, often
after their benefits have been exhausted. Thus meas-
cational attainment accord with the International
called frictional unemployment, results from the nor-
ured unemployment may be higher in countries that
Standard Classification of Education 1997 of the
mal operation of labor markets.
offer more or longer unemployment benefits.
United Nations Educational, Cultural, and Scientific
Changes in unemployment over time may reflect
Women tend to be excluded from the unemploy-
changes in the demand for and supply of labor, but
ment count for various reasons. Women suffer more
they may also reflect changes in reporting practices.
from discrimination and from structural, social, and
Ironically, low unemployment rates can often disguise
cultural barriers that impede them from actively
substantial poverty in a country, while high unem-
seeking work. Also, women are often responsible for
ployment rates can occur in countries with a high level
the care of children and the elderly or for other
of economic development and low incidence of pover-
household affairs. They may not be available for
ty. In countries without unemployment or welfare ben-
work during the short reference period, as they need
efits, people eke out a living in the informal sector. In
to make arrangement before star ting work.
countries with well-developed safety nets, workers
Furthermore, women are considered to be employed
can afford to wait for suitable or desirable jobs. But
when they are working part-time or in temporary jobs
high and sustained unemployment indicates serious
in the informal sector, despite the instability of
inefficiencies in the allocation of resources.
these jobs and that they may be actively looking for
The ILO definition of unemployment notwithstand-
Organization (UNESCO).
more secure employment.
ing, reference periods, the criteria for those consid-
Long-term unemployment is measured in terms of
ered to be seeking work, and the treatment of people
duration, that is, the length of time that an unem-
temporarily laid off and those seeking work for the
ployed person has been without work and looking for
first time vary across countries. In many developing
a job. The underlying assumption is that shorter peri-
countries it is especially difficult to measure employ-
ods of joblessness are of less concern, especially
ment and unemployment in agriculture. The timing of
when the unemployed are covered by unemployment
a survey, for example, can maximize the effects of
benefits or similar forms of welfare support. The
seasonal unemployment in agriculture. And informal
length of time a person has been unemployed is diffi-
sector employment is difficult to quantify where infor-
cult to measure, because the ability to recall the
mal activities are not registered and tracked.
length of that time diminishes as the period of job-
Data on unemployment are drawn from labor force
lessness extends. Women’s long-term unemployment
sample surveys and general household sample sur-
is likely to be lower in countries where women consti-
veys, censuses, and other administrative records such
tute a large share of the unpaid family workforce.
as social insurance statistics, employment office sta-
Women in such countries have more access than men
tistics, and official estimates, which are usually based
to nonmarket work and are more likely to drop out of
on information drawn from one or more of the above
the labor force and not be counted as unemployed.
sources. Labor force surveys generally yield the most
Unemployment by level of educational attainment
comprehensive data because they include groups not
provide insights into the relationship between the
covered in other unemployment statistics, particularly
educational attainment of workers and unemploy-
people seeking work for the first time. These surveys
ment. Besides the limitations to comparability raised
generally use a definition of unemployment that follows
for measuring unemployment, the different ways of
the international recommendations more closely than
classifying the level of education across countries
that used by other sources and therefore generate sta-
may also cause inconsistency. The level of education
tistics that are more comparable internationally.
is
supposed
to
be
classified
according
to
In contrast, the quality and completeness of data
International Standard Classification of Education
from employment offices and social insurance pro-
1997 (ISCED97). For more information on ISCED97,
grams vary widely. Where employment offices work
see About the data for table 2.10.
Data source The unemployment data are from the ILO database Key Indicators of the Labour Market, third edition.
2004 World Development Indicators
53
2.5
Pover ty National poverty line
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau
54
International poverty line
Population below the
Population below the
Population
Poverty
Population
poverty line
poverty line
below
gap at
below
Poverty gap at
Survey
Rural
Urban
National
Survey
Rural
Urban
National
Survey
$1 a day
$1 a day
$2 a day
$2 a day
year
%
%
%
year
%
%
%
year
%
%
%
%
.. .. 16.6 ..
.. .. 12.2 ..
2002 a 1995 a
44.8 .. .. .. 53.0 .. .. .. 81.7 .. .. .. .. 51.0 .. 40.1 49.9 .. .. .. .. 4.6 .. 79.0 .. .. ..
.. .. 7.3 .. 29.9 60.4 .. .. .. 36.6 .. .. .. .. .. .. .. .. 16.5 .. 21.1 22.1 .. .. .. .. <2 .. 55.0 .. .. ..
53.7 .. .. 49.6 49.8 41.9 .. .. 62.7 .. .. .. 12.8 45.3 .. 36.1 40.2 .. .. .. 17.0 4.6 .. 64.0 .. .. ..
.. .. .. .. .. 42.1 .. .. .. .. .. 45.0 .. .. .. 61.0 .. .. 49.9 .. 74.5 .. ..
.. .. .. .. .. 20.5 .. .. .. .. .. 37.0 .. .. .. 48.0 .. .. 18.6 .. 27.1 .. ..
.. .. .. 1996 b .. .. 28.6 1998 b .. 1998 b 16.7 2000 a .. 2000 b .. .. 1998 a 44.2 1999–2000 a .. .. .. .. 1998 a .. 2001 a .. 39.5 1999 a .. 56.2 2000 b .. ..
.. <2 <2 .. 3.3 12.8 .. .. 3.7 36.0 <2 .. .. 14.4 .. 23.5 8.2 4.7 44.9 58.4 34.1 17.1 .. 66.6 .. <2 16.6 .. 8.2 .. .. 2.0 15.5 <2 .. <2 .. .. <2 17.7 3.1 31.1 .. <2 26.3 .. .. .. 59.3 2.7 .. 44.8 .. 16.0 .. ..
.. <0.5 <0.5 .. 0.5 3.3 .. .. <1 8.1 <0.5 .. .. 5.4 .. 7.7 2.1 1.4 14.4 24.9 9.7 4.1 .. 38.1 .. <0.5 3.9 .. 2.2 .. .. 0.7 3.8 <0.5 .. <0.5 .. .. <0.5 7.1 <0.5 14.1 .. <0.5 5.7 .. .. .. 28.8 0.9 .. 17.3 .. 4.6 .. ..
.. 11.8 15.1 .. 14.3 49.0 .. .. 9.1 82.8 <2 .. .. 34.3 .. 50.1 22.4 16.2 81.0 89.2 77.7 50.6 .. 84.0 .. 9.6 46.7 .. 22.6 .. .. 9.5 50.4 <2 .. <2 .. .. <2 40.8 43.9 58.0 .. 5.2 80.7 .. .. .. 82.9 15.7 .. 78.5 .. 37.4 .. ..
.. 2.0 3.8 .. 4.7 17.3 .. .. 3.5 36.3 0.1 .. .. 14.9 .. 22.8 8.8 5.7 40.6 51.3 34.5 19.3 .. 58.4 .. 2.5 18.4 .. 8.8 .. .. 3.0 18.9 <0.5 .. <0.5 .. .. <0.5 17.7 11.3 29.7 .. 0.8 31.8 .. .. .. 51.1 4.6 .. 40.8 .. 16.0 .. ..
2002 1995 1995 1996
1995 1995–96 1998 1995 1997 2001–02 1990 1997 1994 1990 1993–94 1996
1995–96 1996 1996 1995
1992
1996 1992 1994 1995–96 1992 1993–94 1995 1995–96
1992 1997
.. 29.6 30.3 .. .. 48.0 .. .. .. 55.2 .. .. .. 77.3 19.9 .. 32.6 .. 51.0 36.0 43.1 59.6 .. .. 67.0 .. 7.9 .. 79.0 .. .. 25.5 .. .. .. .. .. 86.5 49.0 47.0 23.3 55.7 .. 14.7 47.0 .. .. .. .. 9.9 ..
..
13.8 .. 13.1 .. 10.4 43.0 24.8 41.4 .. .. 63.0 .. <2 .. 48.0 .. .. 19.2 .. .. .. .. .. .. 19.3 25.0 22.5 43.1 .. 6.8 33.3 .. .. .. .. 12.1 ..
.. 71.9 .. ..
.. 33.7 .. ..
14.7 .. 28.4 58.8 .. .. .. 29.4 .. .. ..
1992 1989 1994 1991
2004 World Development Indicators
.. 25.4 22.6 .. 54.7 .. .. 68.1 51.0 33.0 .. 33.0 63.2 19.5 .. 17.4 36.0 44.5
1998 1998 1998–99
2001 2000 2000
1999
2001 1998
39.0 1997 53.3 2001 .. .. 64.0 19.9 1998 6.0 1998 .. 60.0 1999 .. .. 22.0 .. .. .. .. .. 45.1 33.9 1998 35.0 22.9 1999–2000 48.3 53.0 8.9 45.5 1999–2000 .. .. .. 64.0 1998 11.1 .. 50.0 1998 .. 57.9 2000 40.0 48.7
2001 b 1998 a
2001 a 2000 a 2000 a
1999 a 1993 a 2001 b 2001 a 1998 a 1998 a 1997 a 2001 a 1993 a 2000 b 2001 a 1999 b
2000 b 1998 a 2000 a
National poverty line
Guyana Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland
PEOPLE
2.5
Pover ty
International poverty line
Population below the
Population below the
Population
Poverty
Population
poverty line
poverty line
below
gap at
below
Poverty gap at
Survey
Rural
Urban
National
Survey
Rural
Urban
National
Survey
$1 a day
$1 a day
$2 a day
$2 a day
year
%
%
%
year
%
%
%
year
%
%
%
%
.. .. 46.0 .. 37.3
.. .. 56.0 .. 32.4
.. 66.0 51.0 .. 30.2
.. .. 57.0 .. 24.7
.. .. .. .. .. 37.0 .. .. 39.0 47.0 .. .. .. 64.5 48.7 .. .. .. .. .. .. .. 76.0 .. .. 75.9 65.5 .. .. 26.7 33.1 18.0 71.3 .. .. 44.0 .. .. 76.1 66.0 49.5 .. .. 33.4 64.9 41.3 28.5 67.0 53.1 ..
.. .. .. .. .. .. .. .. 30.0 29.0 .. .. .. 28.5 33.1 .. .. .. .. .. .. .. 63.2 .. .. 30.1 30.1 .. .. .. 38.5 7.6 62.0 .. .. 23.0 .. .. 31.9 52.0 31.7 .. .. 17.2 15.3 16.1 19.7 46.1 28.0 ..
.. .. .. .. .. 25.1 .. .. .. 53.0 .. .. .. 69.7 41.0 .. .. .. .. .. .. .. 76.7 66.5 .. .. 61.2 .. .. .. .. 27.2 .. .. .. .. .. .. 68.5 .. 36.4 .. .. 35.9 .. .. .. 64.7 50.7 ..
.. .. .. .. .. .. .. .. .. 49.0 .. .. .. 49.0 26.9 .. .. .. .. .. .. .. 52.1 54.9 .. .. 25.4 .. .. .. .. 12.0 .. .. .. .. .. .. 30.5 .. 30.4 .. .. 24.2 .. .. .. 40.4 21.5 ..
<2 .. 23.8 <2 34.7 7.5 <2 .. .. .. .. <2 .. <2 <2 23.0 .. <2 .. <2 26.3 <2 .. 36.4 .. .. <2 <2 49.1 41.7 <2 72.8 25.9 .. 9.9 22.0 13.9 <2 37.9 .. 34.9 37.7 .. .. 45.1 61.4 70.2 .. .. 13.4 7.2 .. 14.9 18.1 14.6 <2
<0.5 .. 11.6 <0.5 8.2 0.9 <0.5 .. .. .. .. <0.5 .. <0.5 <0.5 6.0 .. <0.5 .. <0.5 6.3 <0.5 .. 19.0 .. .. <0.5 <0.5 18.3 14.8 <0.5 37.4 7.6 .. 3.7 5.8 3.1 <0.5 12.0 .. 14.0 9.7 .. .. 16.7 33.9 34.9 .. .. 2.4 2.3 .. 6.8 9.1 2.7 <0.5
6.1 .. 44.4 7.3 79.9 52.4 7.3 .. .. .. .. 13.3 .. 7.4 8.5 58.6 .. <2 .. 27.2 73.2 8.3 .. 56.1 .. .. 13.7 4.0 83.3 76.1 9.3 90.6 63.1 .. 26.3 63.7 50.0 14.3 78.4 .. 55.8 82.5 .. .. 79.9 85.3 90.8 .. .. 65.6 17.6 .. 30.3 37.7 46.4 <2
1.7 .. 23.1 1.7 35.3 15.7 1.5 .. .. .. .. 2.7 .. 1.4 1.4 24.1 .. <0.5 .. 5.9 29.6 2.0 .. 33.1 .. .. 4.2 0.6 44.0 38.3 2.0 60.5 26.8 .. 10.9 25.1 17.5 3.1 36.8 .. 30.4 37.5 .. .. 41.2 54.8 59.0 .. .. 22.0 7.4 .. 14.7 18.5 17.2 <0.5
1993 1987 1992 1993 1993–94 1996
1995 1991 1996 1994
1997 1993
1997 1990–91 1989 1998 1996 1988 1997 1995 1990–91 1996–97
1995–96
1993 1989–93 1985
1993 1997 1996 1991 1994 1994 1993
43.2 1998 65.0 1995 50.0 1993 14.5 1997 36.0 1999–2000 15.7 1999 .. .. .. .. .. 27.5 2000 .. 15.0 1997 34.6 40.0 1997 .. .. .. 51.0 1999 45.0 1997–98 .. .. .. .. .. .. .. 73.3 1999 54.0 1997–98 15.5 63.8 50.0 2000 .. 10.1 23.3 36.3 13.1 1998–99 69.4 .. .. 42.0 .. .. 50.3 1998 63.0 43.0 1992–93 .. .. 28.6 1998–99 37.3 37.5 21.8 53.5 1997 40.6 1997 23.8
35.0 1998 b .. 53.0 1998 b 17.3 1998 b 28.6 1999–2000 a 27.1 2002 a .. 1998 a .. .. .. .. 18.7 2000 a .. 11.7 1997 a .. 2001 a 52.0 1997 a .. .. 1998 b .. 64.1 2001 a 38.6 1997–98 a .. 1998 a .. .. 1995 a .. .. .. 2000 a .. 1998 a 71.3 1999 a 65.3 1997–98 a .. 1997 b .. 1994 a 46.3 2000 a .. .. 2000 b .. 2001 a .. 1995 a 19.0 1999 a .. 1996 a .. .. 1993 b .. 1995 a .. .. 47.9 2001 a .. 1995 a 34.1 1997 a .. .. 32.6 1998 a .. 2000 b .. .. 1999 b 49.0 2000 b 36.8 2000 a .. 1999 b
2004 World Development Indicators
55
2.5
Pover ty National poverty line
Portugal Puerto Rico Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Population below the
Population below the
Population
Poverty
Population
poverty line
poverty line
below
gap at
below
Poverty gap at
Survey
Rural
Urban
National
Survey
Rural
Urban
National
Survey
$1 a day
$1 a day
$2 a day
$2 a day
year
%
%
%
year
%
%
%
year
%
%
%
%
.. .. 27.9 .. .. .. 40.4 .. 76.0 .. .. .. .. .. .. 22.0 .. .. .. .. .. .. 40.8 .. .. 20.0 13.1 .. .. .. .. .. .. .. .. 30.5 .. 57.2 .. 45.0 82.8 35.8
.. .. 20.4 .. .. .. .. .. 53.0 .. .. .. .. .. .. 15.0 .. .. .. .. .. ..
.. .. 21.5 30.9 51.2 .. 33.4 .. 68.0 .. .. .. .. .. .. 20.0 .. 40.0 .. .. .. .. 38.6 18.0 32.3 21.0 7.4 .. .. 55.0 31.7 .. .. .. .. 27.5 31.3 50.9 .. 41.8 69.2 25.8
.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 27.0 .. .. .. .. .. .. 38.7 15.5 .. .. 13.9 .. .. .. .. .. .. .. .. .. .. .. .. .. 83.1 48.0
.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 15.0 .. .. .. .. .. ..
.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25.0 .. .. .. .. .. .. 35.7 13.1 .. .. 7.6 .. .. 44.0 .. .. .. .. .. .. .. .. .. .. 72.9 34.9
1994 b
<2 .. 2.1 6.1 35.7 .. 26.3 .. 57.0 .. <2 <2 .. 7.1 .. 6.6 .. .. .. .. .. 10.3 19.9 <2 .. 12.4 <2 <2 12.1 .. 2.9 .. .. .. <2 21.8 15.0 17.7 .. 15.7 63.7 36.0
<0.5 .. 0.6 1.2 7.7 .. 7.0 .. 39.5 .. <0.5 <0.5 .. 1.1 .. 1.0 .. .. .. .. .. 2.6 4.8 <0.5 .. 3.5 <0.5 <0.5 2.6 .. 0.6 .. .. .. <0.5 5.4 6.9 3.3 .. 4.5 32.7 9.6
<0.5 .. 20.5 23.8 84.6 .. 67.8 .. 74.5 .. 2.4 <2 .. 23.8 .. 45.4 .. .. .. .. .. 50.8 59.7 32.5 .. 39.0 6.6 10.3 44.0 .. 45.7 .. .. .. 3.9 77.5 32.0 63.7 .. 45.2 87.4 64.2
<0.5 .. 5.2 8.0 36.7 .. 28.2 .. 51.8 .. 0.7 <0.5 .. 8.6 .. 13.5 .. .. .. .. .. 16.3 23.0 9.0 .. 14.6 1.3 2.5 15.4 .. 16.3 .. .. .. 0.8 28.9 15.2 22.9 .. 15.0 55.4 29.4
1994 1994 1993 1992 1989
1990–91 1995
1991 1990 1987–89 1992 1990
1993 1995
2000 1989 1993 1998 1996 1990–91
a. Based on expenditure. b. Based on income.
56
International poverty line
2004 World Development Indicators
.. .. 24.0 3.5 .. .. .. .. .. .. .. .. 22.5 .. 25.9 .. 30.8 46.0 3.4
1995–96
2000–01 1992
1995
1997
1998 1995–96
10.2 .. .. 3.6 .. .. .. .. .. .. .. .. .. .. .. .. .. 56.0 7.9
2000 a 2000 a 1983–85 a 1995 a 1989 a 1996 b 1998 a 1995 a 1995–96 a
1998 a 1993 a 2000 a 1992 b 2000 a 2000 a 1998 a 1999 b
2000 b 2000 a 1998 b 1998 a 1998 a 1998 a 1990–91 a
2.5
PEOPLE
Pover ty About the data
International comparisons of poverty data entail both
living. As with international comparisons, when the
difference. So income data are used to estimate
conceptual and practical problems. Different coun-
real value of the poverty line varies, it is not clear
poverty directly, with no adjustment of the income
tries have different definitions of poverty, and con-
how meaningful such urban-rural comparisons are.
mean.
sistent comparisons between countries can be diffi-
The problems of making poverty comparisons do
In all cases the measures of poverty have been
cult. Local pover ty lines tend to have higher
not end there. More issues arise in measuring
calculated from primary data sources (tabulations or
purchasing power in rich countries, where more gen-
household living standards. The choice between
household data) rather than existing estimates.
erous standards are used than in poor countries. Is
income and consumption as a welfare indicator is
Estimation from tabulations requires an interpolation
it reasonable to treat two people with the same stan-
one issue. Income is generally more difficult to
method; the method chosen was Lorenz curves with
dard of living—in terms of their command over com-
measure accurately, and consumption accords better
flexible functional forms, which have proved reliable
modities—differently because one happens to live in
with the idea of the standard of living than does
in past work. Empirical Lorenz curves were weighted
a better-off country? Can we hold the real value of
income, which can vary over time even if the stan-
by household size, so they are based on percentiles
the poverty line constant across countries, just as
dard of living does not. But consumption data are not
of population, not households.
we do when making comparisons over time?
always available, and when they are not there is litDefinitions
Poverty measures based on an international pover-
tle choice but to use income. There are still other
ty line attempt to do this. The commonly used $1 a
problems. Household survey questionnaires can dif-
day standard, measured in 1985 international prices
fer widely, for example, in the number of distinct cat-
• Survey year is the year in which the underlying data
and adjusted to local currency using purchasing
egories of consumer goods they identify. Survey qual-
were collected. • Rural poverty rate is the percent-
power parities (PPPs), was chosen for the World
ity varies, and even similar surveys may not be
age of the rural population living below the national
Bank’s World Development Report 1990: Poverty
strictly comparable.
rural poverty line. • Urban poverty rate is the per-
because it is typical of the poverty lines in low-income
Comparisons across countries at different levels of
centage of the urban population living below the
countries. PPP exchange rates, such as those from
development also pose a potential problem, because
national urban poverty line. • National poverty rate
the Penn World Tables or the World Bank, are used
of differences in the relative importance of con-
is the percentage of the population living below the
because they take into account the local prices of
sumption of nonmarket goods. The local market
national poverty line. National estimates are based
goods and services not traded internationally. But
value of all consumption in kind (including consump-
on population-weighted subgroup estimates from
PPP rates were designed not for making international
tion from own production, particularly important in
household surveys. • Population below $1 a day
poverty comparisons but for comparing aggregates
underdeveloped rural economies) should be included
and population below $2 a day are the percentages
from national accounts. Thus there is no certainty
in the measure of total consumption expenditure.
of the population living on less than $1.08 a day and
that an international poverty line measures the same
Similarly, the imputed profit from production of non-
$2.15 a day at 1993 international prices. As a result
degree of need or deprivation across countries.
market goods should be included in income. This is
of revisions in PPP exchange rates, poverty rates
This year’s edition of the World Development
not always done, though such omissions were a far
cannot be compared with poverty rates reported in
Indicators (like those of the past four years) uses
bigger problem in surveys before the 1980s. Most
previous editions for individual countries. • Poverty
1993 consumption PPP estimates produced by the
survey data now include valuations for consumption
gap is the mean shortfall from the poverty line
World Bank. The international poverty line, set at $1
or income from own production. Nonetheless, valua-
(counting the nonpoor as having zero shortfall),
a day in 1985 PPP terms, has been recalculated in
tion methods vary. For example, some surveys use
expressed as a percentage of the poverty line. This
1993 PPP terms at about $1.08 a day. Any revisions
the price in the nearest market, while others use the
measure reflects the depth of poverty as well as its
in the PPP of a country to incorporate better price
average farm gate selling price.
incidence.
indexes can produce dramatically different poverty
Wherever possible, consumption has been used as the welfare indicator for deciding who is poor. Where
Data sources
Problems also exist in comparing poverty meas-
consumption data are unavailable, income data are
The poverty measures are prepared by the World
ures within countries. For example, the cost of living
used. Beginning with last year’s World Development
Bank’s Development Research Group. The national
is typically higher in urban than in rural areas. So the
Indicators, there has been a change in how income
poverty lines are based on the World Bank’s coun-
urban monetary poverty line should be higher than
surveys are used. Before that, average income was
try poverty assessments. The international poverty
the rural poverty line. But it is not always clear that
adjusted to accord with consumption and income
lines are based on nationally representative primary
the difference between urban and rural poverty lines
data from national accounts. This approach was test-
household surveys conducted by national statistical
found in practice properly reflects the difference in
ed using data for more than 20 countries for which
offices or by private agencies under the supervision
the cost of living. In some countries the urban pover-
the surveys provided both income and consumption
of government or international agencies and
ty line in common use has a higher real value than
expenditure data. Income gave a higher mean than
obtained from government statistical offices and
does the rural poverty line. Sometimes the differ-
consumption but also greater income inequality.
World Bank country departments. The World Bank
ence has been so large as to imply that the inci-
These two effects roughly canceled each other out
has prepared an annual review of its poverty work
dence of poverty is greater in urban than in rural
when poverty measures based on consumption were
since 1993. Partnerships in Development: Progress
areas, even though the reverse is found when adjust-
compared with those based on income from the
in the Fight against Poverty is forthcoming.
ments are made only for differences in the cost of
same survey; statistically, there was no significant
lines in local currency.
2004 World Development Indicators
57
2.6
Social indicators of pover ty Survey year
Prevalence of child malnutrition
Under-five mortality rate
2000 2000 1996 1998 1996 1998–99 2000 1998 1994–95 1996–97 2000 1996 1994 2000 1995 2000 2000 1998 1998 1999 2000 1999 1997 1997 1999 1998 1997 1997 2000 2001 2000–01 1992 1997 1992 2001 2001 1998 1990 1990 1990 2000 1998 2000 1997 1998 1999 1998 1998 2000–01 1996 1997 1997 2001 1999
Contraceptive prevalence
Weight for age
Measles
% of children
% of children
% of women
ages 12–23 months b
ages 15–49
under age 5
Armenia Bangladesh Benin Bolivia Brazil Burkina Faso Cambodia Cameroon Central African Republic Chad Colombia Comoros Côte d’Ivoire Egypt, Arab Rep. Eritrea Ethiopia Gabon Ghana Guatemala Guinea Haiti India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Madagascar Malawi Mali Mauritania Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Paraguay Peru Philippines Rwanda Senegal South Africa Tanzania Togo Turkey Uganda Uzbekistan Vietnam Yemen, Rep. Zambia Zimbabwe
Child immunization rate
per 1,000
Births attended by skilled health staff a
% of total
Poorest
Richest
Poorest
Richest
Poorest
Richest
Poorest
Richest
Poorest
Richest
quintile
quintile
quintile
quintile
quintile
quintile
quintile
quintile
quintile
quintile
3 60 37 14 12 38 52 33 37 50 9 36 31 7 51 49 19 33 34 29 24 61 .. 9 5 32 13 45 33 39 37 17 37 36 57 16 52 40 54 6 15 .. 27 .. .. 32 32 17 27 25 .. 56 33 18
2 29 19 3 3 26 34 9 20 29 3 18 13 2 25 37 8 12 10 17 8 26 .. 3 6 10 8 32 13 17 18 2 14 13 31 3 37 22 26 1 1 .. 14 .. .. 22 12 3 12 13 .. 30 20 6
61 140 208 147 99 239 155 199 193 171 39 129 190 98 152 159 93 139 78 230 164 141 109 42 82 136 96 195 231 248 98 112 278 110 130 64 282 240 125 57 93 80 246 181 87 160 168 85 192 70 63 163 192 100
30 72 110 32 33 155 64 87 98 172 20 87 c 97 34 104 147 55 52 39 133 109 46 29 25 45 61 49 101 149 148 78 39 145 76 68 19 184 120 74 20 18 29 154 70 22 135 97 33 106 50 23 73 92 62
68 59 49 58 78 33 44 37 31 12 74 51 31 95 37 18 34 61 80 33 43 28 59 90 74 64 82 32 80 40 42 62 33 69 61 76 23 35 28 48 81 68 84 .. 74 63 35 64 49 96 64 16 81 80
74 c 86 80 85 90 69 82 78 80 39 85 86 79 99 92 52 71 87 91 73 63 81 85 93 76 c 89 81 79 90 77 86 95 94 79 83 94 66 70 75 69 92 92 89 .. 85 89 63 89 65 93 88 73 88 86
16 37 1 7 56 2 13 1 1 0d 54 7 1 43 0d 3 6 8 5 1 17 29 46 28 49 13 44 2 20 4 0d 18 1 5 24 50 1 1 1 21 37 20 2 1 34 6 3 24 11 46 47 1 11 41
29 50 9 46 77 16 25 17 9 5 66 19 13 61 19 23 18 18 60 9 24 55 57 47 55 50 54 24 40 18 17 48 17 57 55 71 18 12 23 46 58 29 15 24 70 32 13 48 41 52 56 24 53 67
93 4 34 20 72 18 15 28 14 3 64 26 17 31 5 1 67 18 9 12 4 16 21 91 99 23 96 30 43 21 15 5 18 51 4 78 4 12 5 41 21 21 17 20 68 29 25 53 20 92 49 7 20 57
100 42 98 98 99 75 81 89 82 47 99 85 84 94 74 25 97 86 92 82 70 84 89 99 99 80 100 89 83 86 93 78 82 91 45 99 63 70 55 98 99 92 60 86 98 83 91 98 77 100 99 50 91 94
a. Based on births in the five years before the survey. b. Refers to children who were immunized before 12 months or, in some cases, at any time before the survey (between 12–23 months). c. The data contain large sampling errors because of the small number of cases. d. Less than 0.5.
58
2004 World Development Indicators
About the data
2.6
PEOPLE
Social indicators of pover ty Definitions
The data in the table describe the health status and
but do have detailed information on households’ own-
use of health services by individuals in different socioe-
ership of consumer goods and access to a variety of
data were collected. • Prevalence of child malnutri-
conomic groups within countries. The data are from
goods and services. Like income or consumption, the
tion is the percentage of children whose weight for
Demographic and Health Surveys conducted by Macro
asset index defines disparities in primarily economic
age is more than two standard deviations below the
International with the support of the U.S. Agency for
terms. It therefore excludes other possibilities of dis-
median reference standard for their age as estab-
International Development. These large-scale house-
parities among groups, such as those based on gen-
lished by the World Health Organization, the U.S.
hold sample surveys, conducted periodically in devel-
der, education, ethnic background, or other facets of
Centers for Disease Control and Prevention, and the
oping countries, collect information on a large number
social exclusion. To that extent the index provides
U.S. National Center for Health Statistics. The fig-
of health, nutrition, and population measures as well
only a partial view of the multidimensional concepts
ures in the table are based on children under age
as on respondents’ social, demographic, and econom-
of poverty, inequality, and inequity.
three, four, or five years of age, depending on the
• Survey year is the year in which the underlying
ic characteristics using a standard set of question-
Creating one index that includes all asset indica-
country. • Under-five mortality rate is the probabili-
naires. The data presented here draw on responses to
tors limits the types of analysis that can be per-
ty that a newborn baby will die before reaching age
individual and household questionnaires.
formed. In particular, the use of a unified index does
five, if subject to current age-specific mortality rates.
The table defines socioeconomic status in terms of
not permit a disaggregated analysis to examine
The probability is expressed as a rate per 1,000.
a household’s assets, including ownership of con-
which asset indicators have a more or less important
Data in the table are based on births in the 10 years
sumer items, features of the household’s dwelling,
association with health status or use of health serv-
preceding the survey and may therefore differ from
and other characteristics related to wealth. Each
ices. In addition, some asset indicators may reflect
the estimates in table 2.19. • Child immunization
household asset on which information was collected
household wealth better in some countries than in
rate is the percentage of children ages 12–23
was assigned a weight generated through principal
others—or reflect different degrees of wealth in dif-
months at the time of the survey who received a
component analysis. The resulting scores were stan-
ferent countries. Taking such information into
dose of measles vaccine by 12 months, or at any
dardized in relation to a standard normal distribution
account and creating country-specific asset indexes
time before the interview date. These data may dif-
with a mean of zero and a standard deviation of one.
with country-specific choices of asset indicators
fer from those in table 2.15. • Contraceptive preva-
The standardized scores were then used to create
might produce a more effective and accurate index
lence is the percentage of women who are practic-
break points defining wealth quintiles, expressed as
for each country. The asset index used in the table
ing, or whose sexual partners are practicing, any
quintiles of individuals in the population rather than
does not have this flexibility.
modern method of contraception. It is usually meas-
quintiles of individuals at risk with respect to any one health indicator.
The analysis has been carried out for 54 countries,
ured for married women ages 15–49. • Births
with the results issued in country reports. The table
attended by skilled health staff are the percentage
The choice of the asset index for defining socioe-
shows the estimates for the poorest and richest
of deliveries attended by personnel trained to give
conomic status was based on pragmatic rather than
quintiles only; the full set of estimates for more than
the necessary supervision, care, and advice to
conceptual considerations: Demographic and Health
70 indicators is available in the country reports (see
women during pregnancy, labor, and the postpartum
Surveys do not provide income or consumption data
Data sources).
period; to conduct deliveries on their own; and to care for newborns. Skilled health staff include
2.6a
doctors, nurses, or trained midwives, but exclude
Education lowers birth rates dramatically for rich women, but not for poor ones
trained or untrained traditional birth attendants. Data in the tables are based on births in the five
Highest wealth quintile
Lowest wealth quintile
Total fertility rate, 1995–2000
years preceding the survey and may therefore differ
10
10
from the estimates in table 2.16.
8
8
6
6
4
4
2
2
Total fertility rate, 1995–2000
Data sources 0
0
20
40
60
80
Women completing grade 5 (%)
100
0
0
20
40
60
80
100
Women completing grade 5 (%)
It is well known that women’s education strongly affects the number of children they bear. But the effect varies with the wealth of the household. Education greatly reduces fertility rates among wealthy women, but the effect is very weak among poor women.
The data are from an analysis of Demographic and Health Surveys by the World Bank and Macro International. Country reports are available at http://www.worldbank.org/poverty/health/data/ index.htm.
Source: Demographic and Health Survey data.
2004 World Development Indicators
59
2.7
Distribution of income or consumption Survey year
Afghanistan Albania Algeria Angola Argentina c Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Guyana Haiti
60
2002 1995
a,b
2001 1998 1994 1997 2001 2000 2000 1996
d,e
1999 2001 1993 1998 2001 1998 1998 1997 2001 1998 1993
a,b
2000 2001 1996 1999
d,e
2000 1998 2001
d,e
1996 1997 1998 1998 1999 2000
d,e
2000 2000 2000 1995
d,e
1998 2001 2000 1999 1998 2000 1994 1993 1999
a,b
2004 World Development Indicators
a,b
a,b d,e d,e a,b a,b a,b d,e
a,b a,b d,e d,e a,b a,b a,b a,b d,e a,b
a,b d,e d,e
a,b a,b
d,e d,e a,b a,b d,e
a,b d,e d,e
a,b d,e a,b d,e d,e a,b a,b a,b
Gini Index
.. 28.2 35.3 .. 52.2 37.9 35.2 30.0 36.5 31.8 30.4 25.0 .. 44.7 26.2 63.0 59.1 31.9 48.2 33.3 40.4 44.6 33.1 61.3 .. 57.1 44.7 43.4 57.6 .. .. 46.5 45.2 29.0 .. 25.4 24.7 47.4 43.7 34.4 53.2 .. 37.2 30.0 26.9 32.7 .. 38.0 36.9 28.3 30.0 35.4 48.3 40.3 47.0 43.2 ..
Percentage share of income or consumption
Lowest
Lowest
Second
Third
Fourth
Highest
Highest
10%
20%
20%
20%
20%
20%
10%
.. 3.8 2.8 .. 1.0 2.6 2.0 3.1 3.1 3.9 3.5 2.9 .. 1.3 3.9 0.7 0.5 2.4 1.8 1.7 2.9 2.3 2.5 0.7 .. 1.2 1.8 2.0 0.8 .. .. 1.4 2.2 3.4 .. 4.3 2.6 2.1 0.9 3.7 0.9 .. 1.9 3.9 4.0 2.8 .. 1.5 2.3 3.2 2.1 2.9 0.9 2.6 2.1 1.3 ..
.. 9.1 7.0 .. 3.1 6.7 5.9 8.1 7.4 9.0 8.4 8.3 .. 4.0 9.5 2.2 2.0 6.7 4.5 5.1 6.9 5.6 7.0 2.0 .. 3.3 4.7 5.3 2.7 .. .. 4.2 5.5 8.3 .. 10.3 8.3 5.1 3.3 8.6 2.9 .. 6.1 9.1 9.6 7.2 .. 4.0 6.4 8.5 5.6 7.1 2.6 6.4 5.2 4.5 ..
.. 13.5 11.6 .. 7.2 11.3 12.0 13.2 11.5 12.5 13.0 14.1 .. 9.2 14.2 4.9 5.7 13.1 7.4 10.3 10.7 9.3 12.7 4.9 .. 6.6 9.0 9.4 6.6 .. .. 8.9 9.6 12.8 .. 14.5 14.7 8.6 7.5 12.1 7.4 .. 12.1 13.2 14.1 12.6 .. 7.6 11.4 13.7 10.1 11.4 5.9 10.4 8.8 9.9 ..
.. 17.3 16.1 .. 12.3 15.4 17.2 17.3 15.3 15.9 17.0 17.7 .. 14.8 17.9 8.2 10.0 17.9 10.6 15.1 14.7 13.7 17.0 9.6 .. 10.5 14.2 13.9 10.8 .. .. 13.7 13.6 16.8 .. 17.7 18.2 13.0 11.7 15.4 12.4 .. 15.9 16.8 17.5 17.2 .. 12.3 16.1 17.8 14.9 15.8 9.8 14.8 13.1 14.5 ..
.. 22.8 22.7 .. 21.0 21.6 23.6 22.9 21.2 21.2 22.5 22.7 .. 22.9 22.6 14.4 18.0 23.4 16.7 21.5 20.1 20.4 22.9 18.5 .. 17.4 22.1 20.7 18.0 .. .. 21.7 20.1 22.6 .. 21.7 22.9 20.0 19.4 20.4 20.2 .. 22.0 21.5 22.1 22.8 .. 20.8 22.6 23.1 22.8 22.0 17.6 21.2 19.4 21.4 ..
.. 37.4 42.6 .. 56.4 45.1 41.3 38.5 44.5 41.3 39.1 37.3 .. 49.1 35.8 70.3 64.4 38.9 60.7 48.0 47.6 50.9 40.4 65.0 .. 62.2 50.0 50.7 61.9 .. .. 51.5 51.1 39.6 .. 35.9 35.8 53.3 58.0 43.6 57.1 .. 44.0 39.4 36.7 40.2 .. 55.2 43.6 36.9 46.6 43.6 64.1 47.2 53.4 49.7 ..
.. 22.4 26.8 .. 38.9 29.7 25.4 23.5 29.5 26.7 24.1 22.6 .. 32.0 21.4 56.6 46.7 23.7 46.3 32.8 33.8 35.4 25.0 47.7 .. 47.0 33.1 34.9 46.5 .. .. 34.8 35.9 24.5 .. 22.4 21.3 37.9 41.6 29.5 40.6 .. 28.5 25.5 22.6 25.1 .. 38.0 27.9 22.1 30.0 28.5 48.3 32.0 39.3 33.8 ..
Survey year
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg 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
1999 1999 1999–2000 2002 1998
d,e
1996 1997 2000 2000 1993 1997 2001 1997
d,e
1998
d,e
2001 1997 1998
a,b
1995
a,b
2000 2000 1998 2001 1997 1997 1994 2000
a,b
2000 2001 1998 1998–99 1996–97
d,e
1993 1995–96 1994 1997 2001 1995 1996–97 2000
d,e
1998–99 2000 1996 1999 2000 2000 1999 1997
a,b
a,b a,b a,b a,b
d,e d,e a,b d,e a,b a,b a,b
a,b d,e
d,e a,b a,b a,b d,e a,b a,b
a,b a,b a,b a,b
a,b d,e d,e d,e a,b a,b d,e
d,e a,b d,e d,e a,b a,b d,e
Gini Index
55.0 24.4 32.5 34.3 43.0 .. 35.9 35.5 36.0 37.9 24.9 36.4 31.3 44.5 .. 31.6 .. 29.0 37.0 32.4 .. 63.2 .. .. 31.9 30.8 28.2 47.5 50.3 49.2 50.5 39.0 .. 54.6 36.2 44.0 39.5 39.6 .. 70.7 36.7 32.6 36.2 55.1 50.5 50.6 25.8 .. 33.0 56.4 50.9 56.8 49.8 46.1 31.6 38.5 ..
2.7
PEOPLE
Distribution of income or consumption Percentage share of income or consumption
Lowest
Lowest
Second
Third
Fourth
Highest
Highest
10%
20%
20%
20%
20%
20%
10%
0.9 2.6 3.9 3.6 2.0 .. 2.8 2.4 2.3 2.7 4.8 3.3 3.4 2.3 .. 2.9 .. 3.9 3.2 2.9 .. 0.5 .. .. 3.2 3.5 3.3 1.9 1.9 1.7 1.8 2.5 .. 1.0 2.8 2.1 2.6 2.5 .. 0.5 3.2 2.8 2.2 1.2 0.8 1.6 3.9 .. 3.7 0.7 1.7 0.6 0.7 2.2 2.9 2.0 ..
2.7 7.7 8.9 8.4 5.1 .. 7.1 6.9 6.5 6.7 10.6 7.6 8.2 5.6 .. 7.9 .. 9.1 7.6 7.6 .. 1.5 .. .. 7.9 8.4 8.4 4.9 4.9 4.4 4.6 6.2 .. 3.1 7.1 5.6 6.5 6.5 .. 1.4 7.6 7.3 6.4 3.6 2.6 4.4 9.6 .. 8.8 2.4 4.5 2.2 2.9 5.4 7.3 5.8 ..
6.7 13.4 12.3 11.9 9.4 .. 11.8 11.4 12.0 10.7 14.2 11.4 12.5 9.3 .. 13.6 .. 13.2 11.4 12.9 .. 4.3 .. .. 12.7 12.9 14.0 8.5 8.5 8.1 8.0 10.6 .. 7.2 11.5 10.0 10.6 10.8 .. 3.0 11.5 12.7 11.4 7.2 7.1 8.2 14.0 .. 12.5 6.5 7.9 6.5 8.3 8.8 11.8 11.0 ..
11.8 18.0 16.0 15.4 14.1 .. 15.8 16.3 16.8 15.0 17.6 15.5 16.8 13.6 .. 18.0 .. 16.9 15.3 17.1 .. 8.9 .. .. 16.9 17.1 17.7 12.7 12.3 12.9 11.9 15.2 .. 11.7 15.8 13.8 14.8 15.1 .. 5.4 15.1 17.2 15.8 11.3 13.9 12.5 17.2 .. 15.9 11.2 11.9 11.5 14.1 13.1 16.2 15.5 ..
19.9 23.4 21.2 21.0 21.5 .. 22.0 22.9 22.8 21.7 22.0 21.1 22.9 20.2 .. 23.1 .. 22.5 20.8 22.1 .. 18.8 .. .. 22.6 22.7 23.1 20.4 18.3 20.3 19.3 22.3 .. 19.0 22.0 19.4 21.3 21.1 .. 11.5 21.0 22.8 22.6 18.3 23.1 19.3 22.0 .. 20.6 19.6 19.2 19.5 21.5 20.5 22.2 21.9 ..
58.9 37.5 41.6 43.3 49.9 .. 43.3 44.3 42.0 46.0 35.7 44.4 39.6 51.2 .. 37.5 .. 38.3 45.0 40.3 .. 66.5 .. .. 40.0 38.9 36.7 53.5 56.1 54.3 56.2 45.7 .. 59.1 43.7 51.2 46.6 46.5 .. 78.7 44.8 40.1 43.8 59.7 53.3 55.7 37.2 .. 42.3 60.3 56.5 60.2 53.2 52.3 42.5 45.9 ..
42.2 22.8 27.4 28.5 33.7 .. 27.6 28.2 26.8 30.3 21.7 29.8 24.2 36.1 .. 22.5 .. 23.3 30.6 25.9 .. 48.3 .. .. 24.9 23.8 22.1 36.6 42.2 38.4 40.4 29.5 .. 43.1 28.4 37.0 30.9 31.7 .. 64.5 29.8 25.1 27.8 45.0 35.4 40.8 23.4 .. 28.3 43.3 40.5 43.6 37.2 36.3 27.4 29.8 ..
2004 World Development Indicators
61
2.7
Distribution of income or consumption Survey year
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka St. Lucia 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 c Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2000 2000 1983–85
a,b
1995
a,b
1989 1998 1996 1998–99
a,b
1995 1990 1995 1995
a,b
1994 2000 1992
d,e
1998 1993 2000
a,b
1992 2000 2000 1998 1999 1999
d,e
1999 2000 2000 2000 1998 1998
d,e
1998 1998 1995
a,b
a,b a,b
d,e d,e d,e
d,e a,b d,e
d,e d,e
a,b a,b
a,b a,b a,b a,b a,b
d,e d,e a,b d,e a,b
a,b a,b
Gini Index
30.3 45.6 28.9 .. 41.3 .. 62.9 42.5 25.8 28.4 .. 59.3 32.5 34.4 42.6 .. 60.9 25.0 33.1 .. 34.7 38.2 43.2 .. 40.3 39.8 40.0 40.8 43.0 29.0 .. 36.0 40.8 44.6 26.8 49.1 36.1 .. 33.4 52.6 56.8
Percentage share of income or consumption
Lowest
Lowest
Second
Third
Fourth
Highest
Highest
10%
20%
20%
20%
20%
20%
10%
3.3 1.8 4.2 .. 2.6 .. 0.5 1.9 3.1 3.6 .. 0.7 2.8 3.5 2.0 .. 1.0 3.6 2.6 .. 3.2 2.8 2.5 .. 2.1 2.3 2.3 2.6 2.3 3.7 .. 2.1 1.9 1.8 3.6 0.6 3.6 .. 3.0 1.1 1.8
8.2 4.9 9.7 .. 6.4 .. 1.1 5.0 8.8 9.1 .. 2.0 7.5 8.0 5.2 .. 2.7 9.1 6.9 .. 8.0 6.8 6.1 .. 5.5 6.0 6.1 6.1 5.9 8.8 .. 6.1 5.4 4.8 9.2 3.0 8.0 .. 7.4 3.3 4.6
13.1 9.5 13.2 .. 10.3 .. 2.0 9.4 14.9 14.2 .. 4.3 12.6 11.8 9.9 .. 5.8 14.0 12.7 .. 12.9 11.0 9.5 .. 10.3 10.3 10.6 10.2 10.0 13.3 .. 11.4 10.7 9.3 14.1 8.4 11.4 .. 12.2 7.6 8.1
17.4 14.1 16.5 .. 14.5 .. 9.8 14.6 18.7 18.1 .. 8.3 17.0 15.8 14.8 .. 10.0 17.6 17.3 .. 17.0 15.1 13.5 .. 15.5 14.8 14.9 14.7 14.0 17.4 .. 16.0 15.7 14.2 17.9 13.7 15.2 .. 16.7 12.5 12.2
22.9 20.3 21.6 .. 20.6 .. 23.7 22.0 22.8 22.9 .. 18.9 22.6 21.5 21.8 .. 17.1 22.7 22.9 .. 22.1 21.6 20.9 .. 22.7 21.7 21.8 21.5 20.3 22.7 .. 22.5 22.4 21.6 22.6 21.6 20.9 .. 22.5 20.0 19.3
38.4 51.3 39.1 .. 48.2 .. 63.4 49.0 34.8 35.7 .. 66.5 40.3 42.8 48.3 .. 64.4 36.6 40.3 .. 40.0 45.5 50.0 .. 45.9 47.3 46.7 47.5 49.7 37.8 .. 44.0 45.8 50.1 36.3 53.4 44.5 .. 41.2 56.6 55.7
23.6 36.0 24.2 .. 33.5 .. 43.6 32.8 20.9 21.4 .. 46.9 25.2 28.0 32.5 .. 50.2 22.2 25.2 .. 25.2 30.1 33.8 .. 29.9 31.5 30.7 31.7 34.9 23.2 .. 28.5 29.9 33.5 22.0 36.3 29.9 .. 25.9 41.0 40.3
a. Data refer to consumption shares by percentiles of population. b. Ranked by per capita consumption. c. Urban data. d. Data refer to income shares by percentiles of population. e. Ranked by per capita income.
62
2004 World Development Indicators
About the data
2.7
PEOPLE
Distribution of income or consumption Definitions
Inequality in the distribution of income is reflected in
Wherever possible, consumption has been used
• Survey year is the year in which the underlying
the percentage shares of income or consumption
rather than income. Income distribution and Gini
data were collected. • Gini index measures the
accruing to segments of the population ranked by
indexes for high-income countries are calculated
extent to which the distribution of income (or, in
income or consumption levels. The segments ranked
directly from the Luxembourg Income Study data-
some cases, consumption expenditure) among indi-
lowest by personal income receive the smallest
base, using an estimation method consistent with
viduals or households within an economy deviates
shares of total income. The Gini index provides a con-
that applied for developing countries.
from a perfectly equal distribution. A Lorenz curve
venient summary measure of the degree of inequality.
plots the cumulative percentages of total income
Data on personal or household income or con-
received against the cumulative number of recipi-
sumption come from nationally representative
ents, starting with the poorest individual or house-
household surveys. The data in the table refer to dif-
hold. The Gini index measures the area between the
ferent years between 1989 and 2002. Footnotes to
Lorenz curve and a hypothetical line of absolute
the survey year indicate whether the rankings are
equality, expressed as a percentage of the maximum
based on per capita income or consumption. Each
area under the line. Thus a Gini index of 0 represents
distribution is based on percentiles of population—
perfect equality, while an index of 100 implies per-
rather than of households—with households ranked
fect inequality. • Percentage share of income or
by income or expenditure per person.
consumption is the share that accrues to subgroups
Where the original data from the household survey
of population indicated by deciles or quintiles.
were available, they have been used to directly cal-
Percentage shares by quintile may not sum to 100
culate the income (or consumption) shares by quin-
because of rounding.
tile. Otherwise shares have been estimated from the best available grouped data. The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and 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 table 2.5). 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 usually differ among surveys. Consumption is usually a much better welfare
Data sources
indicator, par ticularly in developing countries.
The data on distribution are compiled by the World
Second, households differ in size (number of mem-
Bank’s Development Research Group using pri-
bers) and in the extent of income sharing among
mary household survey data obtained from gov-
members. And individuals differ in age and consump-
ernment statistical agencies and World Bank
tion needs. Differences among countries in these
country departments. The data for high-income
respects may bias comparisons of distribution.
economies are from the Luxembourg Income
World Bank staff have made an effort to ensure
Study database.
that the data are as comparable as possible.
2004 World Development Indicators
63
2.8
Assessing vulnerability Urban informal sector employment
% of urban employment
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
64
Youth unemployment Male
Female
% of male
% of female
labor force
labor force
Male
Female
ages 15–24
ages 15–24
1995–2001 a
1995–2001 a
1995–2002 a
1995–2002 a
.. .. .. .. .. .. .. .. .. .. .. .. 50 .. .. .. 27 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 39 .. .. .. .. 21 .. .. .. .. .. .. ..
.. .. .. .. .. .. .. .. .. .. .. .. 41 .. .. .. 27 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 65 .. .. .. .. 7 .. .. .. .. .. .. ..
.. .. .. .. 31 .. 13 5 .. 11 .. 16 .. 7 .. 38 15 42 .. .. .. .. 15 .. .. 17 .. 14 32 .. .. 12 .. 35 .. 15 9 16 11 14 14 .. 19 .. 21 18 .. .. 20 11 .. 19 .. .. .. ..
.. .. .. .. 33 .. 12 6 .. 10 .. 15 .. 10 .. 47 22 35 .. .. .. .. 12 .. .. 22 .. 9 41 .. .. 16 .. 40 .. 17 5 34 20 37 10 .. 26 .. 20 23 .. .. 20 8 .. 34 .. .. .. ..
2004 World Development Indicators
Children in the labor force
Female-headed households
% ages 10–14 1980
28 4 7 30 8 0 0 0 0 35 0 0 30 19 1 26 19 0 71 50 27 34 0 .. 42 0 30 6 12 33 27 10 28 0 0 0 0 25 9 18 17 44 0 46 0 0 29 44 0 0 16 5 19 41 43 33
2002
Pension contributors
% of Year
24 0 0 26 2 0 2000 0 0 0 27 1999–2000 0 0 26 2001 10 1998 0 14 14 1996 0 40 1998–99 48 23 2000 22 1998 0 .. 1994–95 36 1996–97 0 6 0 6 2000 28 25 4 18 1998–99 0 0 0 0 12 1999 4 8 2000 13 38 1995 0 41 2000 0 0 12 2000 33 0 0 11 0 13 1998–99 30 1999 36 22 2000
total
.. .. .. .. .. 28 .. .. .. 8 .. .. 20 19 .. .. 20 .. 6 .. 25 22 .. 21 21 .. .. .. 27 .. .. .. 14 .. .. .. .. 32 .. 11 .. 30 .. 23 .. .. 25 .. .. .. .. .. 19 12 .. 42
Year
1995 1997 1995 2002 1993 1996 1993 1992 1995 1996 1999
1996 1994 1993 1993 1993 1992 1990 2001 1994 1999 1992 1998 1997 2001 1995 1993 2001 2002 1994 1996 1995 1993 1993 1995 2000 1995 1993 1996 1999 1993
Private health expenditure
% of
% of
% of
working-age
total
labor force
population
2001
.. 32.0 31.0 .. 53.0 64.4 .. 95.8 52.0 3.5 97.0 86.2 4.8 14.8 .. .. 36.0 64.0 3.1 3.3 .. 13.7 91.9 .. 1.1 54.8 17.6 .. 35.0 .. 5.8 50.6 9.3 67.0 .. 85.0 89.6 26.8 23.2 50.0 26.2 .. 76.0 .. 90.3 88.4 15.0 .. 41.7 94.2 7.2 88.0 22.8 1.5 .. ..
.. 31.0 23.0 .. 39.0 48.3 .. 76.6 46.0 2.6 94.0 65.9 .. 13.3 .. .. 31.0 63.0 3.0 3.0 .. 11.5 80.2 .. 1.0 34.9 17.4 .. 29.3 .. 5.6 38.5 9.1 57.0 .. 67.2 88.0 17.7 14.9 34.2 25.0 .. 67.0 .. 83.6 74.6 14.0 .. 40.2 82.3 9.0 73.0 19.3 1.8 .. ..
47.4 35.4 25.0 36.9 46.6 58.8 32.1 30.7 24.9 55.8 13.3 28.3 53.1 33.7 63.2 33.8 58.4 17.9 .. 41.0 85.1 62.9 29.2 48.8 24.0 56.0 62.8 .. 34.3 55.6 36.2 31.5 84.0 18.2 13.8 8.6 17.6 63.9 49.7 51.1 53.3 34.9 22.2 59.5 24.4 24.0 52.1 50.6 62.2 25.1 40.4 44.0 51.7 45.9 46.2 46.6
Urban informal sector employment
% of urban employment
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Youth unemployment Male
Female
% of male
% of female
labor force
labor force
Male
Female
ages 15–24
ages 15–24
1995–2001 a
1995–2001 a
1995–2002 a
1995–2002 a
.. .. 54 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 33 .. .. .. .. .. .. 50 .. .. .. .. .. .. .. 18 .. .. .. .. .. .. 60 .. .. .. .. .. .. .. 64 .. .. .. .. 16 .. .. ..
.. .. 41 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. .. .. .. .. 27 .. .. .. .. .. .. .. 22 .. .. .. .. .. .. 76 .. .. .. .. .. .. .. 61 .. .. .. .. 19 .. .. ..
7 13 .. 12 .. .. 9 19 23 24 11 .. .. .. .. 10 .. .. .. 20 .. 38 .. .. 31 .. .. .. .. .. .. .. 5 .. .. 16 .. .. 33 .. 6 12 20 .. .. 12 .. 11 25 .. 12 13 17 44 10 23
8 12 .. 15 .. .. 7 18 31 46 9 .. .. .. .. 7 .. .. .. 21 .. 59 .. .. 26 .. .. .. .. .. .. .. 6 .. .. 15 .. .. 41 .. 6 11 20 .. .. 11 .. 29 37 .. 17 14 23 44 14 16
Children in the labor force
Female-headed households
% ages 10–14 1980
14 0 21 13 14 11 1 0 2 0 0 4 0 45 3 0 0 0 31 0 5 28 26 9 0 1 40 45 8 61 30 5 9 3 4 21 39 28 34 56 0 0 19 48 29 0 6 23 6 28 15 4 14 0 8 0
2002
7 0 11 7 2 2 0 0 0 0 0 0 0 38 0 0 0 0 25 0 0 20 14 0 0 0 33 30 2 50 21 1 4 0 1 0 32 22 16 41 0 0 11 43 23 0 0 14 2 16 5 2 4 0 1 0
Pension contributors
% of Year
1998–99 1997
1997 1999 1998
1997
1997 2000 2001 2000–01
1992 1997 1992 2001
1997–98 1998 1999
1991
1990 2000 1998
2.8
PEOPLE
Assessing vulnerability
Private health expenditure
% of
% of
% of
working-age
total
total
Year
labor force
population
2001
.. .. 10 12 .. .. .. .. .. .. .. 9 33 31 .. .. .. 26 .. .. .. .. .. .. .. .. 21 26 .. 11 29 .. .. .. .. 16 26 .. 30 16 .. .. 30 13 16 .. .. 7 .. .. 16 19 14 .. .. ..
1999 1996 1992 1995 2000
20.6 77.0 10.6 8.0 30.0 .. 79.3 82.0 87.0 44.4 97.5 40.0 38.0 18.0 .. 58.0 .. 44.0 .. 60.5 .. .. .. .. 77.0 49.0 5.4 .. 48.7 2.5 5.0 60.0 30.0 .. 61.4 17.3 2.0 .. .. .. 91.7 .. 14.3 1.3 1.3 94.0 .. 3.5 51.6 .. 18.0 31.0 28.3 68.0 84.3 ..
17.7 65.0 7.9 7.0 15.9 .. 64.7 63.0 68.0 45.8 92.3 25.0 28.3 24.0 .. 43.0 .. 42.0 .. 52.3 .. .. .. .. 60.0 47.0 4.8 .. 37.8 2.0 4.0 57.0 31.0 .. 49.1 11.3 2.1 .. .. .. 75.4 .. 13.3 1.5 1.3 85.8 .. 2.1 40.7 .. 12.0 19.0 13.6 64.0 80.0 ..
46.9 25.0 82.1 74.9 58.1 68.2 24.0 30.8 24.7 57.9 22.1 53.0 39.6 78.6 26.6 55.6 19.0 51.3 44.5 47.5 .. 21.1 24.1 44.0 29.5 15.1 34.1 65.0 46.3 61.4 27.6 40.5 55.7 44.2 27.7 60.7 32.6 82.2 32.2 70.3 36.7 23.2 51.5 60.9 76.8 14.5 19.3 75.6 31.0 11.0 61.7 45.0 54.8 28.1 31.0 ..
1992 1992 1997 1999 1994 1995 2001 1995 1996 1997 1995
2002 1995 1993 1993 1990 1995 1995 1997 2002 2000 1995
1993 1999 1992 1993 1993 1993 1998 2001 2001 1996 1996 1996
2004 World Development Indicators
65
2.8
Assessing vulnerability Urban informal sector employment
% of urban employment
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Male
Female
% of male
% of female
labor force
labor force
Male
Female
ages 15–24
ages 15–24
1995–2001 a
1995–2001 a
1995–2002 a
1995–2002 a
.. 10 .. .. .. .. .. .. .. .. .. 16 .. .. .. .. .. .. .. .. 60 .. .. .. .. 10 .. .. 5 .. .. .. .. .. .. .. .. .. .. ..
.. 9 .. .. .. .. .. .. .. .. .. 28 .. .. .. .. .. .. .. .. 85 .. .. .. .. 6 .. .. 5 .. .. .. .. .. .. .. .. .. .. ..
a. Data are for the most recent year available.
66
Youth unemployment
2004 World Development Indicators
18 24 .. .. .. .. .. 4 39 15 .. 58 18 20 .. 42 14 7 .. .. .. 7 .. 22 .. 21 .. .. 23 6 13 13 29 .. 20 .. .. .. .. 17 .. w .. .. .. 21 .. .. .. 17 .. .. .. 12 14
17 26 .. .. .. .. .. 6 36 18 .. 53 27 31 .. 48 12 4 .. .. .. 6 .. 31 .. 18 .. .. 25 6 9 11 42 .. 28 .. .. .. .. 11 .. w .. .. .. 27 .. .. .. 24 .. .. .. 13 17
Children in the labor force
Female-headed households
% ages 10–14 1980
0 0 43 5 43 0 19 2 0 0 38 1 0 4 33 17 0 0 14 0 43 25 36 1 6 21 0 49 0 0 0 0 4 0 4 22 .. 26 19 37 20 w 25 21 23 6 23 27 3 13 14 23 35 0 1
2002
Pension contributors
% of Year
0 0 41 2000 0 26 1997 0 13 0 0 0 31 0 1998 0 1 27 12 0 0 2 0 36 1999 11 26 1998 0 0 7 1998 0 2000 43 2000–01 0 0 0 0 1 0 1996 0 4 1997 .. 18 1997 15 2001–02 26 1999 11 w 18 5 5 2 12 6 1 8 4 14 28 0 0
% of
% of
% of
working-age
total 2001
total
Year
labor force
population
.. .. 36 .. 18 .. .. .. .. .. .. 41 .. .. .. .. .. .. .. .. 23 .. 24 .. .. 10 26 27 .. .. .. .. .. 22 .. 24 .. 9 22 33
1994
55.0 .. 9.3 .. 4.3 .. .. 73.0 73.0 86.0 .. .. 85.3 28.8 12.1 .. 91.1 98.1 .. .. 2.0 18.0 15.9 .. 40.0 37.1 .. 8.2 69.8 .. 89.7 94.0 82.0 .. 23.6 8.4 .. .. 10.2 12.0
48.0 .. 13.3 .. 4.7 .. .. 56.0 72.0 68.7 .. .. 61.4 20.8 12.0 .. 88.9 96.8 .. .. 2.0 17.0 15.0 .. 23.0 27.4 .. .. 66.1 .. 84.5 91.9 78.0 .. 18.2 10.0 .. .. 7.9 10.0
1993 1998
1995 1996 1995
1994 1992 1995 1994 1994
1996 1999 1997 2000 1997 1994 1995 1994 1993 1995 1999 1998
1994 1995
Private health expenditure
20.8 31.8 44.5 25.4 41.2 20.8 39.0 66.5 10.7 25.1 55.4 58.6 28.6 51.1 81.3 31.5 14.8 42.9 56.1 71.1 53.3 42.9 51.4 56.7 24.3 36.8 26.7 42.5 32.2 24.2 17.8 55.6 53.7 25.5 37.9 71.5 .. 65.9 46.9 54.7 40.8 w 73.7 48.9 52.8 42.3 53.0 61.2 27.6 52.0 40.7 78.4 58.7 37.9 26.5
About the data
2.8
PEOPLE
Assessing vulnerability Definitions
As traditionally defined and measured, poverty is a
Reliable estimates of child labor are difficult to
• Urban informal sector employment is broadly char-
static concept, and vulnerability a dynamic one.
obtain. In many countries child labor is officially pre-
acterized as employment in urban areas in units that
Vulnerability reflects a household’s resilience in the
sumed not to exist and so is not included in surveys
produce goods or services on a small scale with the
face of shocks and the likelihood that a shock will
or in official data. Underreporting also occurs
primary objective of generating employment and
lead to a decline in well-being. Thus it depends pri-
because data exclude children engaged in agricul-
income for those concerned. These units typically
marily on the household’s asset endowment and
tural or household activities with their families.
operate at a low level of organization, with little or no
insurance mechanisms. Because poor people have
Available statistics suggest that more boys than
division between labor and capital as factors of pro-
fewer assets and less diversified sources of income
girls work. But the number of girls working is often
duction. Labor relations are based on casual employ-
than the better-off, fluctuations in income affect
underestimated because surveys exclude girls work-
ment, kinship, or social relationships rather than con-
them more.
ing as unregistered domestic help or doing full-time
tractual arrangements. • Youth unemployment refers
Poor households face many risks, and vulnerability is thus multidimensional. The indicators in the table
household work to enable their parents to work out-
to the share of the labor force ages 15–24 without
side the home.
work but available for and seeking employment.
focus on individual risks—informal sector employ-
The data on female-headed household are from
Definitions of labor force and unemployment may dif-
ment, youth unemployment, child labor, female-
recent Demographic and Health Surveys. The defini-
fer by country (see About the data). • Children in the
headed household, income insecurity in old age, pri-
tion and concept of the female-headed household dif-
labor force refer to the share of children ages 10–14
vate health expenditure—and the extent to which
fer greatly across economies, making cross-country
active in the labor force. • Female-headed house-
publicly provided services may be capable of miti-
comparison difficult. In some cases it is assumed
holds refer to the percentage of households with a
gating some of these risks. Poor people face labor
that a woman cannot be the head of any household
female head. • Pension contributors refer to the
market risks, often having to take up precarious, low-
in which an adult male is present, because of sex-
share of the labor force or working-age population
quality jobs in the informal sector and to increase
biased stereotype. Users need to be cautious when
(here defined as ages 15–64) covered by a pension
their household’s labor market participation through
interpreting the data.
scheme. • Private health expenditure includes direct
their children. Income security is a prime concern for
The data on pension contributors come from
(out-of-pocket) spending by households, private insur-
the elderly. And affordable access to health care is a
national
Labour
ance, spending by nonprofit institutions serving
primary concern for all poor people, for whom illness
Organization, and International Monetary Fund coun-
households (other than social insurance), and direct
and injury have both direct and opportunity costs.
try reports. Coverage by pension schemes may be
service payments by private corporations.
sources,
the
International
For informal sector employment, the data are from
broad or even universal where eligibility is deter-
labor force and special informal sector surveys, vari-
mined by citizenship, residency, or income status. In
Data sources
ous household surveys, surveys of household indus-
contribution-related schemes, however, eligibility is
The data on urban informal sector employment and
tries or economic activities, surveys of small and
usually restricted to individuals who have made con-
youth unemployment are from the International
micro enterprises, and official estimates. The inter-
tributions for a minimum number of years.
Labour Organization (ILO) database Key Indicators
national comparability of the data is affected by dif-
Definitional issues—relating to the labor force, for
of the Labour Market, third edition. The child labor
ferences among countries in definitions and coverage
example—may arise in comparing coverage by
force participation rates are from the ILO database
and in the treatment of domestic workers and those
contribution-related schemes over time and across
Estimates and Projections of the Economically
who have a secondary job in the informal sector. The
countries (for countr y-specific information, see
Active Population, 1950–2010. The data on
data in the table are based on national definitions of
Palacios and Pallares-Miralles 2000). Coverage of
female-headed household are from Demographic
urban areas established by countries. For details on
the share of the labor force covered by a pension
and Health Surveys by Macro International. The
these definitions, see the notes in Data sources.
scheme may be overstated in countries that do not
data on pension contributors are drawn from
attempt to count informal sector workers as part of
Robert Palacios and Montserrat Pallares-Miralles’s
Youth unemployment is an important policy issue for many economies. Experiencing unemployment
the labor force.
“International Patterns of Pension Provision”
may permanently impair a young person’s productive
The expenditure on health in a countr y can be
(2000), and updates. For further updates, notes,
potential and future employment opportunities. In
divided into two main categories by source of fund-
and sources, go to “Knowledge and information”
this table unemployment among youth ages 15–24 is
ing: public and private. Public health expenditure
on the World Bank’s Web site on pensions
presented, but the lower age limit for young people
consists of spending by central and local govern-
(http://www.worldbank.org/pensions). The data on
could be determined by the minimum age for leaving
ments, including social health insurance funds.
private health expenditure for developing countries
school, so age groups could differ across countries.
Private health expenditure includes private insur-
are largely from the World Health Organization’s
Also since this age group is likely to include school
ance, direct out-of-pocket payments by households,
World Health Report 2003 and updates, from
leavers, the level of youth unemployment varies sig-
spending by nonprofit institutions ser ving house-
household surveys, and from World Bank poverty
nificantly over the year as a result of different school
holds, and direct payments by private corporations.
assessments and sector studies. The data on pri-
opening and closing dates. The youth unemployment
In countries where the share of out-of-pocket
vate health expenditure for member countries of
rate shares similar limitations on comparability to
spending is large, poor households may be par ticu-
the Organisation for Economic Co-operation and
the general unemployment rate. For further informa-
larly vulnerable to the impoverishing effects of
Development (OECD) are from the OECD.
tion, see About the data for table 2.4.
health care needs.
2004 World Development Indicators
67
2.9
Enhancing security Public expenditure on pensions
Public expenditure on health
Public expenditure on education
Average % of Year
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
68
1995 1997 1994 2002 1997 1995 1996 1992 1997 1997 1993 2000
1997 1996 1992 1991 1993 1997 1990 1997 2001 1996 1994 1992 1997 1997 2001 1992 1999 1997 2000 2002 1994 1997 2001 2002 1993 1997 1997
2000 1997 1996 1993 1995
2004 World Development Indicators
GDP
Year
.. 5.1 2.1 .. 6.2 2.5 5.9 14.9 2.5 0.0 7.7 12.9 0.4 4.5 .. .. 9.8 7.3 0.3 0.2 .. 0.4 5.4 0.3 0.1 2.9 2.7 .. 1.1 .. 0.9 4.2 0.3 13.2 12.6 9.8 8.8 0.8 1.4 2.5 1.3 0.3 6.7 0.9 12.1 13.4 .. .. 2.7 12.1 1.1 11.9 0.7 .. .. ..
.. 1995 1991 .. .. 1996 1989 1993 1996 .. 1995 .. 1993 .. .. .. .. 1995 1992 1991 .. .. 1994 .. .. 1993 .. .. 1989 .. .. 1993 .. .. .. 1996 1994 2000 2002 1994 .. .. 1995 .. 1994 .. .. .. 1996 1995 .. 1990 1995 .. .. ..
Per student
pension
% of
% of
% of GDP
% of per
GDP
GDP
per capita
capita income
2001
2001/02 a
2001/02 a
.. 36.4 75.0 .. .. 18.7 37.3 69.3 51.4 .. 31.2 .. 189.7 .. .. .. .. 39.3 207.3 57.4 .. .. 54.3 .. .. 56.1 .. .. 72.2 .. .. 76.1 .. .. .. 37.0 46.7 42.0 55.3 45.0 .. .. 56.7 .. 57.4 .. .. .. 12.6 62.8 .. 85.6 27.6 .. .. ..
2.7 2.4 3.1 2.8 5.1 3.2 6.2 5.5 0.6 1.5 4.8 6.4 2.1 3.5 2.8 4.4 3.2 3.9 2.0 2.1 1.7 1.2 6.8 2.3 2.0 3.1 2.0 .. 3.6 1.5 1.4 4.9 1.3 7.3 6.2 6.7 7.0 2.2 2.3 1.9 3.7 3.7 4.3 1.4 5.3 7.3 1.7 3.2 1.4 8.1 2.8 5.2 2.3 1.9 3.2 2.7
.. .. .. 2.8 4.6 3.2 4.6 5.8 3.5 2.3 6.0 5.9 3.3 5.5 .. 8.6 4.0 3.2 .. 3.6 2.0 3.2 5.2 1.9 2.5 d 3.9 2.2 .. 4.4 .. 0.1 4.7 4.6 4.2 8.5 4.4 8.3 2.4 .. .. 2.5 2.7 7.4 4.8 5.9 5.8 3.9 2.7 2.5 4.5 4.1 3.8 1.7 1.9 2.1 ..
.. .. .. .. 14.5 14.3 16.5 .. 12.4 11.2 .. .. 14.2 15.0 .. 7.1 12.7 15.6 .. 25.6 7.6 12.2 9.1 .. 14.2 15.1 11.5 .. 19.5 .. .. 19.1 22.0 5.8 42.6 20.6 37.7 .. .. .. 8.6 .. 27.5 .. .. .. 8.7 .. .. .. .. .. .. .. .. ..
Public expenditure on pensions
Public expenditure on health
Public expenditure on education
Average % of Year
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
1994 1996
1994 1997 1996 1997 1996 1997 1995 2001 1993 1997 1990 1997 1995
2002 1998 1990 1999 1991 1992 1999 2000 1996 2002 1994 1996
1997 1997 1996 1992 1991 1997 1993 1996 2000 2000 1993 1997 1997
GDP
0.6 9.7 .. .. 1.5 .. 4.6 5.9 17.6 .. 6.9 4.2 3.8 0.5 .. 1.3 3.5 6.4 .. 10.2 .. .. .. .. 7.1 8.7 0.2 .. 6.5 0.4 0.2 4.4 0.3 b 7.5 5.8 1.8 0.0 .. .. .. 11.1 6.5 2.5 0.1 0.1 8.2 .. 0.9 4.3 .. 0.8 c 2.6 1.0 15.5 10.0 ..
Year
.. 1996 .. .. .. .. 1993 1992 .. 1989 1989 1995 2001 .. .. .. .. 2001 .. 1994 .. .. .. .. 1995 1996 .. .. .. .. .. .. .. .. .. 1994 .. .. .. .. 1989 .. .. .. 1991 1994 .. .. .. .. .. .. .. 1995 1989 ..
2.9 Per student
pension
% of
% of
% of GDP
% of per
GDP
GDP
per capita
capita income
2001
2001/02 a
2001/02 a
3.2 5.1 0.9 0.6 2.7 1.0 4.9 6.0 6.3 2.9 6.2 4.5 1.9 1.7 1.9 2.6 3.5 1.9 1.7 3.4 2.2 4.3 3.3 1.6 4.2 5.8 1.3 2.7 2.0 2.2 2.6 2.0 2.7 2.8 4.6 2.0 4.0 0.4 4.7 1.5 5.7 6.4 3.8 1.4 0.8 6.8 2.4 1.0 4.8 3.9 3.0 2.6 1.5 4.6 6.3 ..
4.0 4.9 4.1 1.3 5.0 .. 4.3 7.3 4.6 6.3 3.6 4.6 4.4 6.3 .. 3.6 6.1 3.1 3.2 5.9 2.9 10.0 .. 2.7 .. 3.7 d 2.5 4.1 7.9 2.8 3.6 3.3 4.4 4.0 6.2 5.0 2.4 1.3 8.1 3.4 4.8 6.6 5.0 2.3 .. 6.8 3.9 1.8 4.3 2.3 4.7 3.3 3.2 5.0 5.8 ..
.. 20.5 20.8 6.0 15.7 .. .. 23.0 .. 23.1 20.4 15.5 .. 4.7 .. 15.0 .. 12.8 11.0 23.7 .. .. .. .. .. 19.7 d .. .. 23.2 26.7 22.5 13.0 15.0 .. .. .. .. 7.8 28.2 13.8 .. 21.6 .. 26.3 .. 25.9 17.5 .. 19.1 13.3 15.8 .. 11.4 18.8 .. ..
.. 33.6 .. .. .. .. 77.9 48.1 .. 25.9 33.9 144.0 23.0 .. .. .. .. 45.0 .. 47.6 .. .. .. .. 21.3 91.6 .. .. .. .. .. .. .. .. .. 118.0 .. .. .. .. 48.5 .. .. .. 40.5 49.9 .. .. .. .. .. .. .. 61.2 44.6 ..
PEOPLE
Enhancing security
2004 World Development Indicators
69
2.9
Enhancing security Public expenditure on pensions
Public expenditure on health
Public expenditure on education
Average % of
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Year
GDP
Year
1996 1996
5.1 5.7 .. .. 1.5 .. .. 1.4 9.1 13.6 .. .. 10.9 2.4 .. .. 11.1 13.4 0.5 3.0 .. .. 0.6 0.6 4.2 4.5 2.3 0.8 8.6 .. 10.3 7.5 15.0 5.3 2.7 1.6 .. 0.1 0.1 ..
1994 1995 .. .. 1997 .. .. .. 1994 1996 .. .. 1995 .. .. .. 1994 1993 .. .. .. .. 1993 .. 1991 1993 .. .. 1995 .. .. 1989 1996 1995 .. .. .. .. .. ..
1998
1996 1994 1996
1997 1996
1997 1997 1991 1996
1997 1996 2000 1997 1996 1997 1996 1997 1997 1996 1995 2001 1998 1994 1993
Per student
pension
% of
% of
% of GDP
% of per
GDP
GDP
per capita
capita income
2001
2001/02 a
2001/02 a
3.5 3.1 2.8 8.3 6.5 d .. 1.0 .. 4.1 .. .. 5.7 4.5 1.3 .. 5.5 7.7 5.5 4.1 2.4 2.2 5.0 4.8 4.0 6.8 3.7 .. 2.5 4.2 1.9 4.4 4.9 2.5 .. .. 2.8 .. 10.0 2.3 10.4 4.1 m 3.1 4.5 4.0 4.4 3.8 3.2 4.3 4.5 4.3 2.3 3.4 5.2 5.2
.. .. 12.8 .. 21.0 .. .. .. 16.8 .. .. 17.7 .. 6.1 .. 19.2 30.7 28.9 .. .. .. 17.7 16.0 18.4 23.9 16.4 .. .. 17.0 10.0 15.8 20.8 9.9 .. .. .. .. .. .. 18.0 .. m .. .. .. 15.0 .. 10.2 .. 15.0 .. 11.2 .. .. ..
34.1 18.3 .. .. 85.0 c .. .. .. 44.5 49.3 .. .. 54.1 .. .. .. 78.0 44.4 .. .. .. .. 178.8 .. 89.5 56.0 .. .. 30.9 .. .. 33.0 64.1 45.8 .. .. .. .. .. ..
5.2 3.7 3.1 3.4 2.8 6.5 2.6 1.3 5.1 6.3 1.2 3.6 5.4 1.8 0.6 2.3 7.4 6.4 2.4 1.0 2.0 2.1 1.5 1.7 4.9 4.4 3.0 3.4 2.9 2.6 6.3 6.2 5.1 2.7 3.7 1.5 .. 1.5 3.0 2.8 5.6 w 1.1 3.1 2.7 3.7 2.7 1.9 4.3 3.4 2.8 1.0 2.5 6.3 6.8
a. Data are preliminary. b. Refers only to the scheme for civil servants. c. Refers to system covering private sector workers. d. Data are for 2002/03.
70
2004 World Development Indicators
About the data
2.9
PEOPLE
Enhancing security Definitions
Enhancing security for poor people means reducing
can be interpreted as reflecting a country’s effort in
• Public expenditure on pensions includes all gov-
their vulnerability to such risks as ill health, providing
education. It often bears a weak relationship to the
ernment expenditures on cash transfers to the eld-
them the means to manage risk themselves, and
output of the education system as reflected in edu-
erly, the disabled, and survivors and the administra-
strengthening market or public institutions for man-
cational attainment. The pattern in this relationship
tive costs of these programs. • Average pension is
aging risk. The tools include microfinance programs,
suggests wide variations across countries in the effi-
estimated by dividing total pension expenditure by
old age assistance and pensions, and public provi-
ciency with which the government’s resources are
the number of pensioners. • Public expenditure on
sion of basic health care and education.
translated into education outcomes. Data for educa-
health consists of recurrent and capital spending
tion expenditure are reported for school years.
from government (central and local) budgets, exter-
Public interventions and institutions can provide services directly to poor people, although whether
nal borrowings and grants (including donations from
these work well for the poor is debated. State action
international agencies and nongovernmental organi-
is often ineffective, in part because governments
zations), and social (or compulsory) health insurance
can influence only a few of the many sources of well-
funds. • Public expenditure on education consists
being and in part because of difficulties in delivering
of public spending on public education plus subsi-
goods and services. The effectiveness of public pro-
dies to private education at the primary, secondary,
vision is further constrained by the fiscal resources
and tertiary levels.
at governments’ disposal and the fact that state institutions may not be responsive to the needs of poor people. The data on public pension spending are from national sources and cover all government expenditures, including the administrative costs of pension programs. They cover noncontributory pensions or social assistance targeted to the elderly and disabled and spending by social insurance schemes for which contributions had previously been made. The pattern of spending in a country is correlated with its demographic structure—spending increases as the population ages. The lack of consistent national health accounting systems in most developing countries makes crosscountry comparisons of health spending difficult. Compiling estimates of public health expenditures is complicated in countries where state or provincial and local governments are involved in financing and delivering health care because the data on public
Data sources
spending often are not aggregated. The data in the
The data on pension spending are drawn from
table are the product of an effort to collect all avail-
Rober t
able information on health expenditures from nation-
Miralles’s “International Patterns of Pension
al and local government budgets, national accounts,
Provision” (2000) and updates. For fur ther
household surveys, insurance publications, interna-
updates, notes, and sources, go to “Knowledge
tional donors, and existing tabulations.
and information” on the World Bank’s Web site on
Palacios
and
Montserrat
Pallares-
The data on education spending in the table refer
pensions (http://www.worldbank.org/pensions).
solely to public spending—government spending on
The estimates of health expenditure come from
public education plus subsidies for private education.
the World Health Organization’s World Health
The data generally exclude foreign aid for education.
Report 2003 and updates, from the Organisation
They may also exclude spending by religious schools,
for Economic Co-operation and Development for
which play a significant role in many developing coun-
its member countries, and from countries’ nation-
tries. Data for some countries and for some years
al health accounts, supplemented by World Bank
refer to spending by the ministry of education only
country and sector studies. The data on educa-
(excluding education expenditures by other ministries
tion expenditure are from the UNESCO Institute
and departments and local authorities). The share of
for Statistics.
gross domestic product (GDP) devoted to education
2004 World Development Indicators
71
2.10
Education inputs Public expenditure per student a
Public expenditure on education
% of GDP per capita
Trained teachers in primary education
Primary pupilteacher ratio
government
% of
pupils per
expenditure
total
teacher
2001/02 b
2001/02 b
2001/02 b
% of total Primary
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
72
Secondary
1990/91
2001/02 b
.. .. .. .. .. .. 15.9 18.0 .. .. 25.7 .. .. .. .. 7.4 .. 22.1 .. .. .. .. .. .. 7.0 8.4 5.4 8.0 .. .. .. 7.8 .. .. .. .. 21.9 2.5 .. .. .. .. .. 31.1 21.8 11.9 .. 13.1 .. .. .. 8.1 2.7 10.8 .. 5.7
.. .. .. .. 12.4 .. 16.0 .. .. 8.3 .. .. 10.1 12.0 .. 6.0 10.7 .. .. 11.6 7.4 .. .. .. 9.5 14.3 .. .. 16.4 .. 0.4 14.6 14.9 .. 32.7 13.0 23.4 6.6 .. .. .. .. 23.6 .. .. .. 4.7 .. .. .. .. .. 7.7 9.2 .. ..
2004 World Development Indicators
1990/91
.. .. .. .. .. .. 34.6 23.6 .. 15.2 6.9 27.1 .. .. .. 44.2 .. .. .. 117.8 .. .. .. 17.7 .. 7.7 12.5 .. 10.4 .. .. 15.8 .. .. .. .. 31.2 .. 9.5 .. .. .. 38.7 46.1 25.8 20.7 .. 28.2 .. .. .. 12.4 4.4 34.9 .. ..
2001/02 b
.. .. .. .. 15.8 14.8 14.3 .. 20.1 13.4 .. .. 18.5 10.2 .. 5.5 10.0 .. .. 61.7 6.2 .. .. .. 28.4 14.7 .. .. 18.5 .. .. 20.2 49.4 .. 43.3 22.3 37.7 5.0 .. .. .. .. 29.7 .. .. .. 18.9 .. .. .. .. .. 4.8 .. .. ..
Tertiary 1990/91
.. .. .. .. .. .. 50.7 35.1 .. 26.0 17.8 29.0 .. .. .. 161.5 .. 30.9 .. .. .. 302.0 27.5 347.1 .. 27.1 102.4 51.3 33.0 .. .. 45.8 328.5 .. .. 45.9 40.4 .. 23.9 50.4 .. .. 55.9 506.6 40.3 22.9 .. .. .. .. .. 16.0 34.7 572.0 .. ..
2001/02 b
.. .. .. .. 17.8 38.9 23.5 .. 14.0 42.5 .. .. .. 45.0 .. 88.6 48.5 .. .. 691.5 42.0 .. 47.2 .. 422.7 19.2 .. .. 38.5 .. 8.8 45.8 .. 36.4 96.5 32.8 69.0 .. .. .. 9.3 17.4 31.8 .. .. .. .. .. .. .. .. .. .. .. .. ..
.. .. .. .. 13.7 .. 13.8 15.1 23.1 15.8 .. 11.6 .. 18.4 .. .. 10.4 .. .. 21.8 10.1 12.5 .. .. .. 17.5 .. .. 18.0 .. 12.6 21.1 21.5 .. 16.8 9.7 15.3 13.2 8.0 .. 19.4 .. .. 13.8 12.2 11.5 .. 14.2 13.1 9.9 .. 7.0 11.4 25.6 4.8 ..
.. .. 97.1 .. 67.0 .. .. .. 100.0 65.6 97.9 .. 65.0 74.1 .. 89.5 91.9 .. 80.4 .. 96.0 .. .. .. .. 94.9 96.8 .. .. .. 64.6 89.5 99.1 100.0 100.0 .. .. .. 68.6 99.9 .. 72.6 .. 69.3 .. .. 95.3 73.1 87.6 .. 64.9 .. 100.0 .. 35.1 ..
43 22 28 .. 20 19 .. 13 16 55 17 12 53 25 .. 27 23 17 47 49 56 61 17 .. 71 32 20 .. 26 .. 56 24 44 18 14 18 10 33 24 22 26 44 14 57 16 19 63 38 14 15 32 13 30 47 44 ..
Public expenditure per student a
Public expenditure on education
% of GDP per capita
2.10
Trained teachers in primary education
Primary pupilteacher ratio
government
% of
pupils per
expenditure
total
teacher
2001/02 b
2001/02 b
2001/02 b
2001/02 b
.. 31.4 85.8 21.0 81.5 .. .. 29.9 .. 70.5 17.2 .. .. 256.7 .. 7.4 .. .. 94.5 22.0 9.8 .. .. 24.9 .. 20.8 c 191.6 .. 83.5 .. .. 48.7 45.2 .. .. .. .. 28.5 .. 82.3 .. 25.1 .. 304.5 .. 40.9 50.2 .. 41.2 .. 47.1 .. 13.9 16.1 .. ..
.. 14.1 12.7 9.6 21.7 .. 10.7 .. 9.5 12.3 10.5 20.6 .. 22.5 .. 17.4 .. 18.6 10.6 .. 11.1 18.4 .. .. .. .. .. .. 25.2 .. .. 13.3 22.6 15.0 .. .. .. 18.1 21.0 13.9 10.4 .. 13.0 .. .. 16.2 .. 7.8 7.3 .. 9.7 21.1 .. 12.2 12.8 ..
% of total Primary 1990/91
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
.. 20.3 .. .. 6.2 .. 11.1 12.7 15.1 10.8 18.6 .. .. 13.0 .. 12.0 35.4 .. 5.0 .. .. 16.7 .. .. .. .. .. 6.5 12.4 .. 16.9 10.1 3.5 .. .. .. 11.0 .. .. .. 12.1 17.1 10.0 .. .. .. .. .. .. .. 3.1 .. .. .. 15.4 ..
Secondary
2001/02 b
.. 19.2 13.7 3.7 11.6 .. .. 21.0 .. 15.7 21.4 16.0 .. 0.9 .. 18.4 .. .. 9.1 23.1 8.3 21.4 .. .. .. 16.6 c 10.7 .. 17.0 14.4 14.0 9.0 11.8 .. .. 17.9 .. 5.8 .. 12.5 .. 19.6 .. 16.8 .. 26.8 12.6 .. 10.5 12.4 12.9 .. 11.8 28.8 .. ..
1990/91
16.5 25.4 13.6 .. 14.1 .. 18.5 27.6 21.4 14.0 18.4 .. .. .. .. 9.9 13.6 28.4 25.3 19.4 .. 61.8 .. .. 26.9 30.9 .. 44.0 16.9 .. 85.2 17.1 8.3 .. .. 47.1 27.4 .. 50.1 8.1 21.7 15.0 9.7 105.4 .. 17.2 17.2 14.7 12.9 .. 6.7 .. .. .. 18.0 ..
2001/02 b
.. 18.8 23.0 7.3 13.6 .. .. 22.4 .. 24.5 21.0 19.0 .. 2.2 .. 16.8 .. .. 10.2 24.7 .. 52.9 .. .. .. 17.1 c .. .. 27.5 .. 44.0 13.9 13.8 .. .. 47.5 .. 7.0 .. 11.8 .. 21.9 .. 56.7 .. 17.1 20.8 .. 13.8 19.2 15.4 .. 9.4 11.8 .. ..
Tertiary 1990/91
76.6 81.3 92.0 .. 79.7 .. 36.1 32.7 .. 132.3 .. 78.9 .. .. .. 5.8 353.8 53.5 52.2 18.6 .. 609.1 .. .. 54.1 62.5 167.9 851.2 116.6 .. 396.3 177.1 23.6 .. 119.4 73.1 .. .. 259.5 90.8 54.1 67.8 .. .. .. 27.7 56.3 155.1 43.5 .. 38.0 .. .. .. 32.5 ..
.. .. .. 93.5 97.9 .. .. .. .. 79.5 .. .. .. .. .. .. .. 49.3 76.1 .. 14.9 74.8 .. .. .. .. .. 51.2 .. .. .. 100.0 .. .. 92.9 .. 59.9 85.4 36.0 51.8 .. .. 72.9 72.7 .. .. 99.8 .. 75.7 100.0 .. 78.2 .. .. .. ..
2004 World Development Indicators
34 11 40 21 24 21 22 12 11 34 20 20 19 32 .. 32 14 24 30 15 17 47 38 .. 16 18 c 54 c .. 20 56 39 25 27 20 32 28 66 33 32 40 10 15 37 41 40 .. 23 44 24 36 .. 29 35 11 13 ..
73
PEOPLE
Education inputs
2.10
Education inputs Public expenditure per student a
Public expenditure on education
% of GDP per capita
Trained teachers in primary education
Primary pupilteacher ratio
government
% of
pupils per
expenditure
total
teacher
2001/02 b
2001/02 b
2001/02 b
2001/02 b
.. 9.6 575.0 .. .. .. .. .. 27.9 .. .. 56.8 .. .. .. 253.2 52.0 53.2 .. .. .. 31.1 297.7 68.5 68.0 48.5 .. .. 35.3 .. 25.7 23.0 24.6 .. .. .. .. .. .. .. .. m .. .. .. 30.6 .. .. .. 44.9 .. 60.4 .. 66.5
.. 10.6 .. .. .. .. .. .. 13.8 .. .. 18.1 11.3 .. .. .. 13.6 15.2 .. .. .. 28.3 23.2 13.4 17.4 .. .. .. 15.0 .. 11.4 15.5 10.0 .. .. .. .. 32.8 .. .. .. m .. .. .. 13.7 .. .. .. 13.2 .. 13.0 .. 11.5
.. .. 81.2 93.3 100.0 100.0 60.7 .. .. .. .. 67.6 .. .. .. 90.4 .. .. .. 81.6 .. .. 80.5 78.1 94.1 .. .. .. 99.7 .. .. .. .. .. .. 87.0 .. .. 100.0 95.3 86.2 m 76.1 90.4 93.0 77.7 84.9 93.5 .. 76.7 96.8 66.9 80.4 .. ..
20 17 51 12 49 20 31 .. 19 13 .. 37 14 .. .. 32 11 14 24 22 47 19 35 19 22 .. .. 59 19 15 18 15 21 .. .. 26 .. .. 45 38 28 m 40 22 22 21 30 22 17 26 24 42 45 17 14
% of total Primary 1990/91
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
23.2 .. .. .. 17.3 .. .. .. 22.8 17.5 .. .. 11.8 .. .. 7.0 46.5 34.9 .. .. .. 13.3 8.3 4.5 .. .. .. .. 20.9 .. 15.1 20.2 .. .. 2.4 .. .. .. 5.6 20.1 .. m .. .. .. .. .. .. .. .. .. .. .. 40.2
Secondary
2001/02 b
.. .. 6.9 .. 13.8 .. .. .. 11.4 .. .. 14.3 .. 10.0 .. 10.4 24.3 22.8 12.8 .. .. 15.9 11.0 14.2 15.8 11.6 .. .. .. .. 13.6 18.0 7.2 .. .. .. .. .. .. 16.2 .. m .. .. .. 12.4 .. 5.7 .. 13.1 .. 8.7 .. 26.2
1990/91
5.0 .. .. .. .. .. .. 13.6 7.9 15.4 .. .. 13.6 .. .. .. 18.8 13.5 15.0 .. .. 15.9 36.4 15.5 27.6 8.4 .. .. 9.5 .. 26.5 22.1 8.6 .. 7.8 .. .. .. .. 34.0 .. m .. .. .. 16.9 .. .. .. 9.9 .. 14.7 .. 31.0
2001/02 b
.. .. 22.0 .. .. .. .. .. 16.8 .. .. 18.3 .. .. .. 29.7 27.8 27.8 23.1 .. .. 13.0 26.0 20.1 25.7 13.8 .. .. 16.9 .. 14.5 22.5 8.3 .. .. .. .. .. .. 24.2 .. m .. .. .. .. .. 10.4 .. .. .. 10.4 .. ..
Tertiary 1990/91
32.3 .. .. 133.2 .. .. .. 43.4 63.7 38.7 .. 90.9 18.0 78.6 .. 305.1 38.6 43.7 46.6 .. .. .. 572.8 67.4 115.5 .. .. .. 19.5 .. 40.9 20.2 24.0 .. 36.3 .. .. .. .. 195.9 .. m .. .. .. 61.8 .. .. 49.9 .. 76.0 90.8 .. 47.1
a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education 1976 (ISCED76) to ISCED97. For information on ISCED, see About the data. b. Data are preliminary. c. Data are for 2002/03.
74
2004 World Development Indicators
2.10
About the data
Data on education are compiled by the UNESCO
practices vary with respect to whether parents or
statistics. In 1998 UNESCO introduced ISCED97 and
Institute for Statistics from official responses to sur-
schools pay for books, uniforms, and other supplies.
adjusted its data collection program and country
veys and from reports provided by education author-
For greater detail, see the country- and indicator-
reporting of education statistics to this new classifi-
ities in each country. Such data are used for moni-
specific notes in the source.
cation. The adjustments were made to ease the
toring, policymaking, and resource allocation. For a
The share of public expenditure devoted to educa-
international compilation and comparison of educa-
variety of reasons, however, education statistics gen-
tion allows an assessment of the priority a govern-
tion statistics and to take into account new types of
erally fail to provide a complete and accurate picture
ment assigns to education relative to other public
learning opportunities and activities for both children
of a country’s education system. Statistics often lag
investments. It also reflects a government’s commit-
and adults. Thus the time-series data for the years
by two to three years, though an effort is being made
ment to investing in human capital development.
through 1997 are not consistent with those for 1998
to shorten the delay. Moreover, coverage and data
The share of trained teachers in primary schools
collection methods vary across countries and over
measures the quality of the teaching staff. It does not
time within countries, so the results of comparisons
take account of competencies acquired by teachers
should be interpreted with caution.
through their professional experience or self-
and later. Any time-series analysis should therefore be undertaken with extreme caution.
Definitions
The data on education spending in the table refer
instruction, or of such factors as work experience,
solely to public spending—government spending on
teaching methods and materials, or classroom condi-
• Public expenditure per student is public current
public education plus subsidies for private education.
tions, all of which may affect the quality of teaching.
spending on education divided by the number of stu-
The data generally exclude foreign aid for education.
Since the training teachers receive varies greatly, care
dents by level, as a percentage of gross domestic
They may also exclude spending by religious schools,
should be taken in comparing across countries.
product (GDP) per capita. • Public expenditure on
which play a significant role in many developing coun-
The comparability of pupil-teacher ratios across
education is current and capital public expenditure
tries. Data for some countries and for some years
countries is affected by the definition of teachers
on education expressed as a percentage of total gov-
refer to spending by the ministry of education only
and by differences in class size by grade and in the
ernment expenditure. • Trained teachers in primary
(excluding education expenditures by other ministries
number of hours taught. Moreover, the underlying
education are the percentage of primary school
and departments and local authorities).
enrollment levels are subject to a variety of reporting
teachers who have received the minimum organized
Many developing countries have sought to supple-
errors (for further discussion of enrollment data, see
teacher training (preservice or in service) required
ment public funds for education. Some countries
About the data for table 2.11). While the pupil-
for teaching. • Primary pupil-teacher ratio is the
have adopted tuition fees to recover part of the cost
teacher ratio is often used to compare the quality of
number of pupils enrolled in primary school divided
of providing education services or to encourage devel-
schooling across countries, it is often weakly related
by the number of primary school teachers (regard-
opment of private schools. Charging fees raises diffi-
to the value added of schooling systems (Behrman
less of their teaching assignment).
cult questions relating to equity, efficiency, access,
and Rosenzweig 1994).
and taxation, however, and some governments have
Data for education are reported for school years.
used scholarships, vouchers, and other methods of
For two decades the International Standard
public finance to counter criticism. Data for a few
Classification of Education, 1976 (ISCED76), was
countries include private spending, although national
used to assemble, compile, and present education
2.10a Education suffers in primary schools with high teacher absence rates Teacher absence rate, 2000–03 (%) 30 25 20 15 10 5 0 Uganda
India
Indonesia
Zambia
Bangladesh
Ecuador
Papua New Guinea
Peru
Data sources The data are from the UNESCO Institute for
The primary school teacher absence rate is the percentage of full-time teachers who were absent from a random sample of primary schools during a surprise visit, regardless of the reasons for their absence. Many teachers were absent for valid reasons, but even authorized absences reduce the quantity and quality of primary education.
Statistics, which compiles international data on education in cooperation with national commissions and national statistical services.
Source: Chaudhury and others 2004; NRI and World Bank 2003; Habyarimana and others 2003.
2004 World Development Indicators
75
PEOPLE
Education inputs
2.11
Par ticipation in education Gross enrollment ratio a
Net enrollment ratio a
% of relevant age group Preprimary
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
76
Primary
% of relevant age group
Secondary
Tertiary
2001/02 b
1990/91
2001/02 b
1990/91
2001/02 b
1990/91
.. 44 4 .. 61 30 104 82 23 19 99 112 6 47 .. .. 67 70 1 1 7 14 65 .. .. 77 27 .. 37 1 4 115 3 38 111 92 90 35 73 12 46 5 102 2c 54 114 13 20 41 101 41 70 55 .. 3 ..
27 100 100 92 106 104 108 102 114 72 95 101 58 95 70 113 106 98 33 73 121 101 103 65 54 100 125 102 102 70 133 101 67 85 98 96 98 97 116 94 81 46 111 33 99 108 .. 64 97 101 75 98 78 37 56 74
23 107 108 .. 120 96 102 103 93 98 110 105 104 114 .. 103 148 99 48 c 71 123 107 100 66 73 103 114 .. 110 .. 86 108 80 96 100 104 102 126 117 97 112 61 103 62 c 102 105 134 79 92 103 81 97 103 77 70 ..
9 78 61 12 71 93 82 104 90 19 93 103 12 37 65 43 38 75 7 6 32 28 101 12 8 73 49 80 50 22 53 42 22 76 89 91 109 40 55 76 26 15 102 14 116 99 .. 19 95 98 36 93 23 10 9 21
12 78 72 19 100 87 154 99 80 47 84 154 26 84 .. 73 108 94 10 11 22 33 106 .. 12 85 68 .. 65 .. 32 67 23 88 89 95 128 67 59 85 56 28 110 17 c 126 108 51 34 79 99 38 96 33 .. 18 ..
2 7 11 1 39 20 35 35 24 4 48 40 3 21 15 3 11 31 1 1 1 3 95 2 1 21 3 19 13 2 5 27 3 24 21 16 36 20 20 16 16 .. 26 1 49 40 .. .. 37 34 1 36 8 1 1 1
2004 World Development Indicators
Primary
2001/02 b
.. 15 .. 1 57 26 65 57 23 6 62 58 4 39 .. 5 18 40 .. 2 3 5 59 2 1 37 13 .. 24 .. 4 21 .. 36 27 30 59 .. .. .. 17 2 59 2 85 54 .. .. 36 .. 3 61 .. .. 0d ..
Secondary
1990/91
2001/02 b
1990/91
2001/02 b
.. .. 93 .. .. .. 99 90 .. 64 .. 97 49 91 .. 93 86 86 27 52 .. .. 97 53 .. 88 97 .. 69 54 .. 86 47 79 92 .. 98 .. .. .. 75 24 94 .. 99 100 .. 51 .. 84 .. 94 .. .. .. 22
.. 97 95 .. 100 85 96 91 80 87 94 100 71 94 .. 81 97 90 35 53 86 .. 100 .. 58 89 93 .. 87 .. .. 91 63 88 96 90 99 97 99 90 89 43 98 46 100 100 78 73 91 86 60 95 85 61 45 ..
.. .. 54 .. .. .. 79 91 .. 18 .. 88 .. 29 .. 34 15 63 7 5 .. .. 89 .. .. 55 .. .. 34 15 .. 36 .. 63 69 .. 87 .. .. .. .. .. 82 .. 93 86 .. 18 .. 89 .. 83 .. .. .. ..
.. 74 62 .. 81 85 88 88 76 44 78 .. 20 67 .. 55 72 87 8 8 21 .. 98 .. 8 75 .. .. 54 .. .. 51 .. 86 83 88 89 41 50 78 46 21 92 15 95 92 .. 28 .. 88 30 85 28 .. .. ..
Gross enrollment ratio a
Net enrollment ratio a
% of relevant age group Preprimary
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Primary
2.11
PEOPLE
Par ticipation in education
% of relevant age group
Secondary
2001/02 b
1990/91
2001/02 b
1990/91
2001/02 b
21 79 26 20 23 5 3 112 96 87 84 31 13 44 .. 79 73 14 8 57 74 21 56 8 53 29 3 .. 89 2 .. 87 75 39 32 60 .. 2 23 13 96 87 26 1 .. 79 5 55 51 39 30 60 33 49 70 ..
109 95 97 115 112 111 103 95 103 101 100 71 87 95 .. 105 60 111 105 94 120 112 29 105 91 99 103 68 94 26 49 109 114 93 97 67 67 106 129 108 102 106 94 29 91 100 86 61 106 72 105 118 111 98 123 121
106 102 99 111 92 99 119 114 101 101 101 99 99 96 .. 100 94 102 115 99 103 124 105 114 104 99 104 .. 95 57 86 106 110 85 99 107 99 90 106 122 108 99 105 40 96 101 83 73 110 77 112 121 112 100 121 ..
33 79 44 44 55 47 101 85 83 65 97 45 98 24 .. 90 43 100 25 93 73 25 14 86 92 56 18 8 56 7 14 53 53 80 82 35 8 23 44 33 120 89 40 7 25 103 46 23 63 12 31 67 73 81 67 61
.. 98 48 58 81 38 .. 93 96 84 102 86 89 32 .. 94 85 85 41 93 77 34 .. 105 98 85 .. .. 70 .. 22 80 73 72 76 41 13 39 61 44 124 113 57 6 .. 115 79 .. 69 23 64 .. 82 101 114 ..
Tertiary 1990/91
9 14 6 9 10 13 29 34 32 7 30 16 40 2 .. 39 12 14 1 25 29 1 3 15 34 17 3 1 7 1 3 4 15 36 14 11 0d 4 3 5 40 40 8 1 4 42 4 3 21 3 8 30 28 22 23 45
Primary
Secondary
2001/02 b
1990/91
2001/02 b
14 40 11 15 19 14 47 53 50 17 48 31 39 4 .. 82 .. 44 4 64 45 2 .. 58 59 24 2 .. 26 2 3 11 20 29 35 10 1 11 7 5 55 72 .. 1 .. 70 7 .. 34 .. 18 .. 30 55 50 ..
89 91 .. 97 97 79 91 .. .. 96 100 66 .. .. .. 100 45 .. 61 83 .. 73 .. 96 .. 94 .. 50 .. 21 .. 95 100 .. .. 58 47 .. 89 .. 95 100 72 25 .. 100 70 .. 91 .. 93 .. 97 97 100 ..
87 90 83 92 87 91 90 100 100 95 100 91 90 70 .. 99 85 82 83 91 90 84 70 .. 97 93 69 .. 95 .. 67 93 99 78 87 88 60 82 78 70 99 98 82 34 .. 100 75 67 99 77 92 100 93 98 .. ..
1990/91
21 75 .. 38 .. 37 80 .. .. 64 97 33 .. .. .. 86 45 .. 15 .. .. 15 .. .. .. .. .. .. .. 5 .. .. 45 .. .. .. 7 .. 31 .. 84 85 .. 6 .. 88 49 .. 51 .. 26 .. 57 76 70 ..
2004 World Development Indicators
2001/02 b
.. 87 .. 47 .. 33 .. 88 88 75 100 80 84 24 .. 91 77 .. 31 89 .. 22 .. .. 92 82 .. .. 69 .. 15 62 58 68 71 31 11 35 38 .. 90 92 37 5 .. 95 68 .. 62 23 50 .. 56 91 85 ..
77
2.11
Par ticipation in education Gross enrollment ratio a
Net enrollment ratio a
% of relevant age group Preprimary
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Primary
% of relevant age group
Secondary
Tertiary
Primary
Secondary
2001/02 b
1990/91
2001/02 b
1990/91
2001/02 b
1990/91
2001/02 b
1990/91
2001/02 b
1990/91
2001/02 b
73 92 3 5 3c 44 4 .. 81 75 .. 35 102 .. 20 .. 74 95 10 10 .. 86 3 63 20 7 .. 4 72 71 82 58 63 21 52 43 .. 0d .. 39 40 w 24 40 36 63 33 29 58 60 21 28 .. 90 98
91 109 70 73 59 72 50 104 98 108 11 122 109 106 53 111 100 90 108 91 70 99 109 97 113 99 91 71 89 104 104 102 109 81 96 103 .. 58 99 116 102 w 88 113 115 102 102 121 98 106 96 90 74 103 105
99 114 117 67 75 99 76 .. 103 100 .. 105 107 110 59 100 110 107 112 107 70 98 124 105 112 94 .. 136 90 92 101 100 108 103 106 103 .. 81 79 99 103 w 94 111 112 104 103 111 103 129 96 95 87 102 104
92 93 8 44 16 63 17 68 87 91 6 74 104 74 24 44 90 99 52 102 5 30 24 80 45 47 107 13 93 67 85 93 81 99 35 32 .. 58 24 50 55 w 35 56 55 64 47 47 85 49 57 39 23 94 97
82 92 14 69 19 89 .. .. 87 106 .. 86 114 81 32 45 149 100 45 82 6 83 36 70 79 76 .. .. 97 79 158 94 101 99 69 70 .. 46 .. 43 70 w 46 75 75 81 63 66 89 89 70 48 .. 106 106
10 52 1 12 3 18 1 19 19 24 3 13 37 5 3 4 32 26 18 22 0d 17 3 7 9 13 22 1 47 9 30 75 30 30 29 2 .. 4 2 5 16 w 5 13 12 20 10 5 34 17 12 5 3 47 35
27 68 2 22 .. 36 2 .. 30 61 .. 15 57 .. .. 5 70 42 .. 15 1 37 4 7 23 25 .. 3 57 .. 59 71 38 9 18 10 .. .. 2 4 24 w 10 22 20 33 17 14 48 23 .. 10 .. 61 54
77 .. 66 59 48 69 .. .. .. .. .. 99 100 .. .. 88 100 84 95 .. 51 .. 75 91 94 89 .. .. .. 94 97 96 91 .. 88 .. .. .. .. .. .. w .. 95 95 92 .. 97 .. 89 .. .. .. 98 93
93 .. 96 59 58 75 .. .. 89 93 .. 90 100 .. 46 77 100 99 98 98 54 86 92 94 97 88 .. .. 82 81 100 94 90 .. 92 94 .. 67 66 83 88 w 80 92 91 93 86 92 .. 94 83 82 .. 97 99
.. .. 7 31 .. 62 .. .. .. .. .. 51 .. .. .. 33 85 80 46 .. .. .. 18 65 43 41 .. .. .. 59 79 86 .. .. 19 .. .. .. .. .. .. w .. .. .. 50 .. .. .. 29 .. .. .. 87 87
80 .. .. 53 .. .. .. .. 75 96 .. 62 93 .. .. 32 96 88 39 79 5 .. 27 65 68 .. .. .. 91 72 95 87 72 .. 57 65 .. 35 .. 40 .. w .. .. .. 69 .. .. .. 65 54 .. .. 91 90
a. Break in series between 1997 and 1998 due to change from ISCED76 to ISCED97. For information on ISCED, see About the data for table 2.10. b. Data are preliminary. c. Data are for 2002/03. d. Less than 0.5.
78
2004 World Development Indicators
2.11
PEOPLE
Par ticipation in education About the data
School enrollment data are reported to the UNESCO
some education systems ages for children repeating
which, other things equal, leads to underreporting of
Institute for Statistics by national education authori-
a grade may be deliberately or inadvertently underre-
repeaters and overestimation of dropouts.
ties. Enrollment ratios help to monitor two important
ported. As an international indicator, the gross pri-
Thus gross enrollment ratios indicate the capacity
issues for universal primary education: whether the
mary enrollment ratio has been used to indicate
of each level of the education system, but a high
Millennium Development Goal that implies achieving a
broad levels of participation as well as school capac-
ratio does not necessarily mean a successful educa-
net primary enrollment ratio of 100 percent is on track,
ity. It has an inherent weakness: the length of pri-
tion system. The net enrollment ratio excludes over-
and whether an education system has sufficient capac-
mary education differs significantly across countries.
age students in an attempt to capture more accu-
ity to meet the needs of universal primary education,
A short duration tends to increase the ratio, and a
rately the system’s coverage and internal efficiency.
as indicated in part by its gross enrollment ratios. The
long duration to decrease it (in part because there
It does not solve the problem completely, however,
gross enrollment ratio shows the share of children in
are more dropouts among older children).
because some children fall outside the official
the population who are enrolled in school regardless of
Other problems affecting cross-country compar-
school age because of late or early entry rather than
their age. Net enrollment ratios show the share of chil-
isons of enrollment data stem from errors in esti-
because of grade repetition. The difference between
dren of primary school age who are enrolled in school
mates of school-age populations. Age-gender struc-
gross and net enrollment ratios shows the incidence
and thus also the share who are not.
tures from censuses or vital registration systems, the
of overage and underage enrollments.
Enrollment ratios, while a useful measure of partici-
primary sources of data on school-age populations,
pation in education, also have significant limitations.
are commonly subject to underenumeration (espe-
They are based on data collected during annual school
cially of young children) aimed at circumventing laws
surveys, which are typically conducted at the begin-
or regulations; errors are also introduced when par-
• Gross enrollment ratio is the ratio of total enroll-
ning of the school year. They do not reflect actual rates
ents round up children’s ages. While census data are
ment, regardless of age, to the population of the age
of attendance or dropouts during the school year. And
often adjusted for age bias, adjustments are rarely
group that officially corresponds to the level of edu-
school administrators may report exaggerated enroll-
made for inadequate vital registration systems.
cation shown. • Net enrollment ratio is the ratio of
ments, especially if there is a financial incentive to do
Compounding these problems, pre- and post-census
children of official school age (as defined by the
so. Often the number of teachers paid by the govern-
estimates of school-age children are interpolations or
national education system) who are enrolled in
ment is related to the number of pupils enrolled.
projections based on models that may miss impor-
school to the population of the corresponding official
tant demographic events (see the discussion of
school age. Based on the International Standard
demographic data in About the data for table 2.1).
Classification of Education 1997 (ISCED97).
Overage or underage enrollments frequently occur, particularly when parents prefer, for cultural or
Definitions
economic reasons, to have children start school at
In using enrollment data, it is also important to con-
• Preprimary education refers to the initial stage of
other than the official age. Children’s age at enroll-
sider repetition rates. These rates are quite high in
organized instruction, designed primarily to introduce
ment may be inaccurately estimated or misstated,
some developing countries, leading to a substantial
very young children to a school-type environment.
especially in communities where registration of
number of overage children enrolled in each grade and
• Primary education provides children with basic
births is not strictly enforced. Parents who want to
raising the gross enrollment ratio. A common error
reading, writing, and mathematics skills along with
enroll their underage children in primary school may
that may also distort enrollment ratios is the lack of
an elementary understanding of such subjects as
do so by overstating the age of the children. And in
distinction between new entrants and repeaters,
history, geography, natural science, social science, art, and music. • Secondary education completes
2.11a
the provision of basic education that began at the pri-
Girls from rural areas and poor households have the lowest attendance rates in Guinea
mary level and aims at laying the foundations for life-
Net attendance ratio, 1999
long learning and human development by offering Male
100
100
80
80
60
60
40
40
20
20
Female
more subject- or skill-oriented instruction using more specialized teachers. • Tertiary education, whether or not leading to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.
0
0 Urban
Rural
Richest quintile
Poorest quintile
Household surveys can provide data on attendance at school that cannot usually be derived from administrative data. In Guinea more children attend school in urban areas than in rural areas, and more than four times as many rich children attend school as poor children. Regardless of location and wealth, more boys than girls attend school.
Data source The data are from the UNESCO Institute for Statistics.
Source: Global Education Report 2003, UNESCO Institute for Statistics 2003.
2004 World Development Indicators
79
2.12
Education efficiency Apparent intake rate in grade 1
Share of cohort reaching grade 5
Primary completion rate
% of relevant
% of relevant age group
age group
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
80
% of grade 1 students
Male
Female
2001/02 a
2001/02 a
1990/91
2000/01 a
1990/91
.. 103 102 .. 112 97 .. 108 91 106 .. .. 127 119 .. 114 130 98 53 92 174 115 .. 76 94 97 .. .. 130 .. 67 101 82 97 95 102 100 148 139 95 135 70 98 96 98 .. 97 88 93 100 86 .. 126 77 106 ..
.. 101 100 .. 112 95 .. 105 88 108 .. .. 96 121 .. 110 119 98 39 73 161 99 .. 53 70 96 .. .. 125 .. 61 101 62 98 96 101 100 137 138 92 128 59 94 74 98 .. 97 88 92 99 84 .. 123 67 79 ..
.. .. 95 .. .. .. .. .. .. .. .. .. 55 .. .. 94 .. 91 71 65 .. .. .. 25 58 .. .. .. 71 58 58 81 75 .. .. .. 94 .. 40 .. 56 85 92 61 100 .. .. 85 .. .. 81 99 .. 64 .. ..
.. .. 95 .. 91 .. .. .. .. 63 .. .. 89 79 .. 87 .. .. 68 68 71 63 .. .. 58 101 .. .. 59 .. .. 93 73 .. 95 98 .. 71 77 99 67 89 100 63 99 98 102 75 .. .. 67 .. 57 90 41 ..
.. .. 93 .. .. .. .. .. .. .. .. .. 56 .. .. 98 .. 90 68 58 .. .. .. 22 43 .. .. .. 50 50 67 84 70 .. .. .. 94 .. 41 .. 60 80 94 54 100 .. .. 89 .. .. 79 100 .. 48 .. ..
2004 World Development Indicators
Repeaters in primary school
Male
Total
Female
2000/01–
Male
Female
2000/01 – 2000/01 –
% of enrollment Total
2000/01 a 2002/03 a, b 2002/03 a, b 2002/03 a, b 2001/02 a
.. .. 97 .. 95 .. .. .. .. 68 .. .. 78 77 .. 92 .. .. 71 59 70 60 .. .. 48 101 .. .. 63 .. .. 95 65 .. 96 99 .. 79 79 99 73 74 99 59 101 97 102 63 .. .. 65 .. 54 77 34 ..
.. 100 96 .. 100 74 .. .. 100 77 131 .. 45 89 77 91 82 94 29 27 71 57 .. .. 22 96 102 .. 90 .. 58 90 48 90 100 .. .. 95 99 91 86 33 103 18 .. .. 92 69 92 .. 59 .. 59 .. .. ..
.. 101 96 .. 98 74 .. .. 101 76 .. .. 58 91 .. 87 .. 95 34 30 75 58 .. .. 31 95 .. .. 87 .. 59 89 57 90 101 .. .. 91 99 92 86 38 108 .. .. .. 92 77 92 .. 61 .. 63 .. .. ..
.. 99 95 .. 102 74 .. .. 99 78 .. .. 32 87 .. 95 .. 92 24 24 66 56 .. .. 13 97 .. .. 92 .. 56 92 38 89 99 .. .. 100 99 89 86 29 98 .. .. .. 92 60 91 .. 57 .. 55 .. .. ..
.. 4.1 11.7 29.0 6.2 0.1 .. .. 0.3 6.3 0.3 .. 20.1 2.7 .. 3.2 21.5 2.4 14.0 c 26.3 9.6 25.2 .. .. 25.5 2.0 0.6 .. 6.6 .. 24.8 8.2 23.3 0.4 1.2 1.1 .. 5.9 2.0 5.2 6.5 17.5 2.3 9.9 c 0.5 4.2 34.4 10.6 0.3 1.8 5.2 .. 14.2 20.8 24.0 ..
Male
Female
2001/02 a 2001/02 a
.. 4.6 14.2 29.0 7.3 0.1 .. .. 0.3 6.7 0.6 .. 20.1 2.9 .. 4.0 25.0 2.8 17.5 25.6 10.2 25.9 .. .. 25.3 2.4 .. .. 7.3 .. 25.1 9.5 23.1 0.5 1.7 1.3 .. 7.1 2.3 6.4 7.3 17.1 3.2 8.6 c 0.7 4.2 35.1 10.7 0.5 2.0 5.3 .. 14.8 19.7 23.6 ..
.. 3.4 9.0 29.0 5.0 0.1 .. .. 0.3 6.0 0.6 .. 20.1 2.5 .. 2.5 25.0 2.0 17.7 27.2 8.9 24.4 .. .. 25.9 1.6 .. .. 5.9 .. 24.4 6.9 23.6 0.3 0.6 0.9 .. 4.6 1.8 3.9 5.6 17.9 1.3 11.7 c 0.3 4.2 33.7 10.5 0.2 1.6 5.0 .. 13.5 22.4 24.5 ..
Apparent intake rate in grade 1
Share of cohort reaching grade 5
Primary completion rate
% of relevant
Repeaters in primary school
% of relevant age group
age group
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
2.12
% of grade 1 students
Male
Female
Male
2001/02 a
2001/02 a
1990/91
2000/01 a
1990/91
138 99 136 119 86 118 101 .. 96 99 .. 103 107 105 .. 102 96 111 133 94 98 158 204 .. 102 98 119 .. 93 65 114 90 110 95 100 119 126 116 96 128 99 99 142 67 .. .. 74 107 120 102 114 115 137 98 .. ..
138 97 114 113 86 104 100 .. 95 99 .. 103 106 101 .. 100 95 108 117 93 96 139 174 .. 100 98 116 .. 93 54 110 93 110 92 103 115 112 117 98 117 98 98 134 48 .. .. 74 80 117 90 112 116 127 97 .. ..
.. .. .. .. 91 .. 100 .. 100 .. 100 100 .. .. .. 99 .. .. 56 .. .. 58 .. .. .. .. 22 71 98 73 75 98 81 .. .. 75 37 .. 61 52 .. 90 51 61 .. 100 95 .. .. 60 69 .. .. .. .. ..
.. .. 59 87 94 .. 98 .. 95 88 .. 98 .. .. .. 100 .. .. 62 .. 92 60 44 .. .. .. 33 .. 96 88 54 99 88 .. .. 84 56 59 94 57 .. .. 51 73 .. .. 96 .. 88 61 76 88 76 99 .. ..
.. .. .. .. 89 .. 100 .. 100 .. 100 100 .. .. .. 100 .. .. 50 .. .. 83 .. .. .. .. 21 57 98 70 75 98 82 .. .. 76 28 .. 65 52 .. 91 57 65 .. 100 96 .. .. 58 72 .. .. .. .. ..
Total
Female
2000/01–
Male
Female
2000/01 – 2000/01 –
% of enrollment Total
2000/01 a 2002/03 a, b 2002/03 a, b 2002/03 a, b 2001/02 a
.. .. 59 92 94 .. 99 .. 98 93 .. 97 .. .. .. 100 .. .. 63 .. 96 74 21 .. .. .. 34 .. 96 79 56 99 89 .. .. 83 47 61 94 69 .. .. 58 68 .. .. 96 .. 89 58 78 87 83 99 .. ..
70 .. 77 107 123 .. .. .. .. 90 .. 98 99 56 .. .. .. 94 73 90 68 65 .. .. 106 95 41 55 .. 39 46 108 96 80 107 68 22 71 95 73 .. .. 75 21 .. .. 72 .. 86 59 89 98 90 95 .. ..
69 .. 85 106 125 .. .. .. .. 88 .. 97 99 54 .. .. .. 96 78 .. 65 55 .. .. 106 96 40 62 .. 48 48 108 96 80 106 72 27 71 91 78 .. .. 71 25 .. .. 75 .. 85 63 88 98 87 94 .. ..
70 .. 69 108 120 .. .. .. .. 92 .. 99 99 58 .. .. .. 92 69 .. 71 75 .. .. 106 95 41 48 .. 31 43 107 97 80 109 64 17 71 100 67 .. .. 79 17 .. .. 69 .. 87 55 90 99 94 95 .. ..
.. 2.5 3.7 5.3 4.3 12.3 1.6 .. 0.3 3.5 .. 0.5 0.2 .. .. .. 2.8 0.2 20.0 2.0 8.7 19.7 2.7 .. 0.7 0.1 29.0 c .. .. 19.3 14.1 4.3 5.5 .. 0.6 12.6 22.9 0.7 13.0 21.6 .. .. 6.7 8.6 .. .. 4.3 .. 5.6 .. 8.0 10.7 2.3 0.6 .. ..
Male
Female
2001/02 a 2001/02 a
.. 3.0 3.7 5.5 5.2 14.1 1.7 .. 0.4 4.3 .. 0.5 0.2 .. .. .. 2.9 0.2 21.2 2.7 10.1 22.1 2.4 .. 0.9 0.1 31.5 .. .. 19.0 13.8 4.9 6.5 .. 0.7 14.1 22.5 0.7 14.7 21.8 .. .. 7.7 8.5 .. .. 5.2 .. 6.6 .. 9.2 10.9 2.9 1.0 .. ..
2004 World Development Indicators
.. 2.0 3.7 5.1 3.3 10.0 1.4 .. 0.2 2.6 .. 0.5 0.1 .. .. .. 2.7 0.1 18.5 1.2 7.2 17.3 3.0 .. 0.5 0.1 29.4 .. .. 19.7 14.4 3.7 4.4 .. 0.6 10.8 23.4 0.7 11.3 21.4 .. .. 5.7 8.7 .. .. 3.3 .. 4.6 .. 6.7 10.4 1.6 0.2 .. ..
81
PEOPLE
Education efficiency
2.12
Education efficiency Apparent intake rate in grade 1
Share of cohort reaching grade 5
Primary completion rate
% of relevant
% of relevant age group
age group
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
% of grade 1 students
Male
Female
Male
2001/02 a
2001/02 a
1990/91
2000/01 a
1990/91
102 .. 132 68 87 98 .. .. 100 106 .. 117 .. .. 58 100 .. 92 124 117 107 99 117 100 98 .. .. .. 119 100 .. .. 104 104 107 103 .. 104 86 121 116 w 121 98 98 101 117 96 93 125 96 130 92 .. 99
102 .. 133 67 86 99 .. .. 100 106 .. 116 .. .. 48 96 .. 96 121 112 100 92 104 96 99 .. .. .. 118 98 .. .. 104 104 104 97 .. 79 87 118 104 w 105 98 97 101 105 97 92 118 95 110 82 .. 98
.. .. 61 82 .. .. .. .. .. .. .. 72 100 94 90 74 100 76 94 .. 77 .. 55 96 92 98 .. .. .. 80 .. .. 93 .. 83 .. .. .. .. 96 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
.. .. 39 94 70 .. .. .. .. .. .. .. .. .. .. 69 .. 101 93 .. 79 92 88 98 95 .. .. .. .. 97 .. .. 87 .. 82 90 .. 80 79 .. .. w 66 .. .. 90 .. .. .. .. 93 59 .. .. ..
.. .. 59 84 .. .. .. .. .. .. .. 79 100 95 95 78 100 75 94 .. 81 .. 44 96 78 97 .. .. .. 80 .. .. 96 .. 90 .. .. .. .. 89 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
Total
Female
2000/01–
Male
2004 World Development Indicators
Female
2000/01 – 2000/01 –
% of enrollment Total
2000/01 a 2002/03 a, b 2002/03 a, b 2002/03 a, b 2001/02 a
.. .. 41 94 65 .. .. .. .. .. .. .. .. .. .. 79 .. 101 92 .. 83 96 80 101 96 .. .. .. .. 98 .. .. 90 .. 88 88 .. 98 75 .. .. w 68 .. .. 92 .. .. .. .. 95 61 .. .. ..
94 99 25 66 49 .. .. .. .. 96 .. 90 .. 108 .. 74 .. .. 89 101 58 91 84 108 98 95 .. 67 98 .. .. .. 95 98 58 104 66 68 59 .. .. w 74 98 97 89 86 100 97 87 91 78 48 d .. ..
95 .. 25 66 53 .. .. .. .. 99 .. 89 .. 113 .. 77 .. .. 93 104 57 92 100 107 99 105 .. 73 98 .. .. .. 93 98 51 106 61 90 64 .. .. w 78 98 98 88 88 101 99 83 94 84 51 d .. ..
a. Data are preliminary. b. Data are for the most recent year available. c. Data are for 2002/03. d. Represent only 60% of the population.
82
Repeaters in primary school
94 .. 24 66 44 .. .. .. .. 93 .. 91 .. 103 .. 72 .. .. 85 98 59 90 67 110 98 85 .. 62 97 .. .. .. 97 98 65 101 72 45 54 .. .. w 68 97 96 90 82 98 95 92 87 71 44 d .. ..
3.2 0.9 36.1 5.2 13.6 1.0 .. .. 2.4 0.8 .. 8.8 .. 0.8 11.3 16.7 .. 1.7 6.8 0.4 2.5 3.9 22.5 8.0 9.8 .. .. .. 0.2 2.8 .. .. 9.0 .. 7.7 2.4 .. 9.0 6.2 .. 5.6 w 6.7 4.8 4.7 5.2 5.7 2.0 .. 13.0 7.8 4.6 .. .. 2.2
Male
Female
2001/02 a 2001/02 a
3.8 .. 36.0 6.3 13.7 1.0 .. .. 2.6 0.9 .. 10.2 .. .. 10.9 18.9 .. 1.8 7.7 0.3 2.5 4.0 21.9 8.4 11.5 .. .. .. 0.2 3.2 .. .. 10.5 .. 9.3 2.8 .. 11.1 6.5 .. .. w 6.8 .. .. 6.2 .. .. .. 12.5 9.3 4.6 .. .. 2.3
2.5 .. 36.2 3.9 13.6 1.0 .. .. 2.1 0.6 .. 7.3 .. .. 11.8 14.3 .. 1.5 5.7 0.4 2.5 3.7 23.2 7.5 8.0 .. .. .. 0.2 2.4 .. .. 7.4 .. 5.9 1.9 .. 5.5 5.9 .. .. w 6.7 .. .. 4.2 .. .. .. 11.3 6.1 4.6 .. .. 2.1
About the data
2.12
Definitions
Indicators of students’ progress through school are
requires setting curriculum standards and measuring
• Apparent intake rate in grade 1 is the number of
estimated by the UNESCO Institute for Statistics and
students’ learning progress against those standards
new entrants in the first grade of primary education
the World Bank. These indicators measure an edu-
through standardized assessments or tests.
regardless of age, expressed as a percentage of the
cation system’s success in extending coverage to all
The World Bank and the UNESCO Institute for
students, maintaining the flow of students from one
Statistics are working jointly on development of the
age. • Share of cohort reaching grade 5 is the per-
grade to the next, and, ultimately, imparting a par-
primary completion rate indicator. The primary com-
centage of children enrolled in the first grade of pri-
ticular level of education.
population of the official primary school entrance
pletion rate is increasingly used as a core indicator
mary school who eventually reach grade 5. The esti-
Apparent intake rate indicates the general level of
of an education system’s performance. It reflects
mate is based on the reconstructed cohort method
access to primary education. It also indicates the
both the coverage of the education system and the
(see About the data). • Primary completion rate is
capacity of the education system to provide access to
educational attainment of students. It is vital as a
the percentage of students successfully completing
primary education. Low apparent intake rates in grade
key measure of educational outcome at the primary
the last year of primary school. It is calculated by tak-
1 reflect the fact that many children do not enter pri-
level and of progress on the Millennium Development
ing the total number of students in the last grade of
mary school even though school attendance, at least
Goals and the Education for All initiative. However,
primary school, minus the number of repeaters in
through the primary level, is mandatory in all coun-
because curricula and standards for school comple-
that grade, divided by the total number of children
tries. Because the apparent intake rate includes all
tion vary across countries, a high rate of primary
of official graduation age. • Repeaters in primary
new entrants regardless of age, it can be more than
completion does not necessarily mean high levels of
school refer to the total number of pupils who are
100 percent. Once enrolled, students drop out for a
student learning.
enrolled in the same grade as in a previous year,
variety of reasons, including low quality of schooling,
The primary completion rate reflects the primary
expressed as a percentage of the total enrollment. It
discouragement over poor performance, and the direct
cycle as nationally defined, ranging from three or four
is calculated by taking the total number of students
and indirect costs of schooling. Students’ progress to
years of primary education (in a very small number of
in the last grade of primary school, minus the num-
higher grades may also be limited by the availability of
countries) to five or six years (in most countries) and
ber of repeaters in that grade, divided by the total
teachers, classrooms, and educational materials.
seven or eight years (in a small number of countries).
number of children of official graduation age.
The cohort survival rate is estimated as the pro-
The data shown in the table are for the proxy pri-
portion of an entering cohort of grade 1 students
mary completion rate, calculated by subtracting the
that eventually reaches grade 5. It measures the
number of students who repeat the final primary
holding power and internal efficiency of an education
grade from the number of students in that grade and
system. Cohort survival rates approaching 100 per-
dividing the result by the number of children of offi-
cent indicate a high level of retention and a low level
cial graduation age in the population. Data limita-
of dropout.
tions preclude adjusting this number for students
Cohort survival rates are typically estimated from
who drop out during the final year of primary school.
data on enrollment and repetition by grade for two
Thus proxy rates should be taken as an upper-bound
consecutive years, in a procedure called the recon-
estimate of the actual primary completion rate.
structed cohort method. This method makes three
The numerator may include overage children who
simplifying assumptions: dropouts never return to
have repeated one or more grades of primary school
school; promotion, repetition, and dropout rates
but are now graduating successfully as well as chil-
remain constant over the entire period in which the
dren who entered school early. The denominator is
cohort is enrolled in school; and the same rates
the number of children of official graduation age,
apply to all pupils enrolled in a given grade, regard-
which could cause the primary completion rate to
less of whether they previously repeated a grade
exceed 100 percent. There are other data limitations
(Fredricksen 1993). Given these assumptions, cross-
that contribute to completion rates exceeding 100
country comparisons should be made with caution,
percent, such as the use of estimates for the popu-
because other flows—caused by new entrants, reen-
lation, the conduct of the school and population sur-
trants, grade skipping, migration, or school transfers
veys at different times of year, and other discrepan-
Data sources
during the school year—are not considered.
cies in the numbers used in the calculation.
The data on the apparent intake rate, the cohort
The UNESCO Institute for Statistics measures
Repeaters not only increase the cost of education
reaching grade 5, and repeaters are from the
cohort survival to grade 5 because research sug-
for the family and for the school system, but also use
UNESCO Institute for Statistics. The data on the
gests that five to six years of schooling is a critical
up limited school resources. Countries have different
primary completion rate are compiled by staff in
threshold for the achievement of sustainable basic
policies on repetition and promotion of students; in
the Development Data Group of the World Bank,
literacy and numeracy skills. But the cohort survival
some cases the number of repeaters is controlled
in collaboration with the Education Anchor of the
rate only indirectly reflects the quality of schooling,
because of limited capacity of the school system.
Human Development Network of the World Bank
and a high rate does not guarantee these learning
Care should be taken in cross-country comparisons
and the UNESCO Institute for Statistics.
outcomes. Measuring actual learning outcomes
of this indicator.
2004 World Development Indicators
83
PEOPLE
Education efficiency
2.13
Education outcomes Adult literacy rate
Youth literacy rate
% ages 15 and older Male
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
84
Expected years of schooling
% ages 15–24
Female
Male
Female
Male
Female
1990
2002 a
1990
2002 a
1990
2002 a
1990
2002 a
1990/91
2000/01
1990/91
2000/01
.. 87 64 .. 96 99 .. .. .. 44 100 .. 38 87 .. 66 83 98 25 48 78 69 .. 47 37 94 87 .. 89 .. 77 94 51 99 95 .. .. 80 90 60 76 .. 100 37 .. .. .. .. .. .. 70 98 69 .. .. 43
.. 99 b 78 .. 97 100 b .. .. .. 50 100 .. 55 93 b 98 76 86 b 99 19 b 58 81 77 c .. 65 c 55 96 b 95 b .. 92 .. 89 96 .. 99 b 97 .. .. 84 92 b 67 b 82 .. 100 b 49 .. .. .. .. .. .. 82 99 77 .. .. 54
.. 67 41 .. 96 96 .. .. .. 24 99 .. 15 70 .. 70 81 96 8 27 49 48 .. 21 19 94 69 .. 88 .. 58 94 26 95 95 .. .. 79 85 34 69 .. 100 20 .. .. .. .. .. .. 47 92 53 .. .. 37
.. 98 b 60 .. 97 99 b .. .. .. 31 100 .. 26 81 b 91 82 87 b 98 8b 44 59 60 c .. 33 c 38 96 b 87 b .. 92 .. 77 96 .. 97 b 97 .. .. 84 90 b 44 b 77 .. 100 b 34 .. .. .. .. .. .. 66 96 62 .. .. 50
.. 97 86 .. 98 100 .. .. .. 51 100 .. 57 96 .. 79 91 100 36 58 81 86 .. 66 58 98 97 .. 94 .. 95 97 65 100 99 .. .. 87 96 71 85 .. 100 52 .. .. .. .. .. .. 88 99 80 .. .. 56
.. 99 b 94 .. 98 100 b .. .. .. 58 100 .. 73 99 b 100 85 93 b 100 26 b 67 85 .. .. 70 c 76 99 b 99 b .. 97 .. 98 98 70 c 100 b 100 .. .. 91 96 b 79 b 90 .. 100 b 63 .. .. .. .. .. .. 94 100 86 .. .. 66
.. 92 68 .. 98 99 .. .. .. 33 100 .. 25 89 .. 87 93 99 14 45 66 76 .. 39 38 98 93 .. 96 .. 90 98 40 100 99 .. .. 88 95 51 83 .. 100 34 .. .. .. .. .. .. 75 100 66 .. .. 54
.. 99 b 86 .. 99 100 b .. .. .. 41 100 .. 38 96 b 100 93 96 b 100 14 b 65 76 .. .. 47 c 64 99 b 99 b .. 98 .. 97 99 52 c 100 b 100 .. .. 92 96 b 67 b 88 .. 100 b 52 .. .. .. .. .. .. 90 100 74 .. .. 67
.. .. 11 .. .. .. 13 15 .. 6 .. 14 .. .. .. 10 .. 12 3 6 .. .. 17 .. .. .. .. .. .. .. .. .. .. .. 12 .. 14 .. .. .. .. .. 12 .. 15 14 .. .. .. 15 .. 13 .. .. .. ..
.. 11 .. .. 14 8 17 15 11 8 12 16 9 .. .. 12 13 13 .. .. 8 .. 14 .. 7 14 .. .. 11 .. .. 10 .. 12 12 14 15 .. .. 10 11 6 14 6 16 15 .. .. 6 15 8 15 .. .. .. ..
.. .. 9 .. .. .. 13 14 .. 4 .. 14 .. .. .. 11 .. 12 2 4 .. .. 17 .. .. .. .. .. .. .. .. .. .. .. 13 .. 14 .. .. .. .. .. 12 .. 16 15 .. .. .. 14 .. 13 .. .. .. ..
.. 11 .. .. 15 9 17 15 10 8 13 16 5 .. .. 12 14 13 .. .. 7 .. 15 .. 4 13 .. .. 11 .. .. 10 .. 12 12 14 16 .. .. 10 11 4 15 4 17 16 .. .. 6 15 7 15 .. .. .. ..
2004 World Development Indicators
Adult literacy rate
Youth literacy rate
% 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. 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
Expected years of schooling
% ages 15–24
Female
Male
Female
Male
Female
1990
2002 a
1990
2002 a
1990
2002 a
1990
2002 a
1990/91
2000/01
1990/91
2000/01
69 99 62 87 72 .. .. 95 98 78 .. 90 99 81 .. .. 79 .. 70 100 .. 65 55 83 100 .. .. 69 87 28 46 85 91 99 98 53 49 87 77 47 .. .. 63 18 59 .. 67 49 90 .. 92 92 92 .. 91 92
80 b 99 .. 92 84 c .. .. 97 99 84 .. 96 100 90 .. .. 85 .. 77 100 b .. 74 c 72 92 100 b .. .. 76 92 b 27 b 51 88 b 93 b 100 98 b 63 62 89 84 62 .. .. 77 c 25 74 .. 82 53 b 93 .. 93 c 91 c 93 b .. 95 94
67 99 36 73 54 .. .. 88 97 86 .. 72 98 61 .. .. 73 .. 43 100 .. 89 23 51 99 .. .. 36 74 10 24 75 84 96 97 25 18 74 72 14 .. .. 63 5 38 .. 38 20 88 .. 88 79 91 .. 84 91
80 b 99 .. 83 70 c .. .. 93 98 91 .. 86 99 79 .. .. 81 .. 55 100 b .. 90 c 39 71 100 b .. .. 49 85 b 12 b 31 81 b 89 b 99 98 b 38 31 81 83 26 .. .. 77 c 9 59 .. 65 29 b 92 .. 90 c 80 c 93 b .. 91 94
78 100 73 97 92 .. .. 99 100 87 .. 98 100 93 .. .. 88 .. 79 100 .. 77 75 99 100 .. .. 76 95 38 56 91 96 100 99 68 66 90 86 67 .. .. 68 25 81 .. 95 63 96 .. 96 97 97 .. 99 95
87 b 100 .. 99 .. .. .. 100 100 91 .. 99 100 96 .. .. 92 .. 86 100 b .. .. 86 100 100 b .. .. 82 97 b 32 b 57 94 b 97 b 100 97 b 77 77 92 91 78 .. .. 84 c 34 91 .. 100 65 b 97 .. 96 c 98 c 94 b .. 100 97
81 100 54 93 81 .. .. 98 100 95 .. 95 100 87 .. .. 87 .. 61 100 .. 97 39 83 100 .. .. 51 94 17 36 91 94 100 99 42 32 86 89 27 .. .. 69 9 66 .. 75 31 95 .. 95 92 97 .. 100 97
91 b 100 .. 98 .. .. .. 99 100 98 .. 100 100 95 .. .. 94 .. 73 100 b .. .. 55 94 100 b .. .. 63 97 b 17 b 42 95 b 96 b 100 98 b 61 49 91 94 46 .. .. 89 c 15 87 .. 97 42 b 97 .. 96 c 96 c 96 b .. 100 98
.. 11 .. 10 .. .. 12 .. .. 11 .. 9 .. .. .. 14 7 .. 9 .. .. 9 .. .. .. .. .. .. .. 3 .. .. .. .. .. .. 4 .. .. .. 15 14 .. .. .. 14 10 .. .. .. 9 .. .. 12 13 ..
.. 13 .. .. .. 10 14 14 15 11 14 12 12 8 .. 16 8 .. 9 12 13 10 11 .. 14 12 6 .. 12 .. 7 12 12 9 9 9 7 7 12 .. 16 16 .. 3 .. 16 9 .. 12 6 10 13 11 14 15 ..
.. 11 .. 9 .. .. 13 .. .. 11 .. 9 .. .. .. 13 7 .. 6 .. .. 11 .. .. .. .. .. .. .. 1 .. .. .. .. .. .. 3 .. .. .. 15 15 .. .. .. 14 9 .. .. .. 8 .. .. 12 14 ..
.. 14 .. .. .. 8 15 15 15 11 14 13 12 8 .. 14 9 .. 7 14 13 10 8 .. 15 12 6 .. 12 .. 6 12 11 10 11 7 5 7 12 .. 16 17 .. 2 .. 18 9 .. 13 6 10 11 12 15 16 ..
2004 World Development Indicators
85
PEOPLE
2.13
Education outcomes
2.13
Education outcomes Adult literacy rate
Youth literacy rate
% ages 15 and older Male 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
99 100 63 76 38 .. .. 94 .. 100 .. 82 98 93 60 74 .. .. 82 99 76 95 60 98 72 89 .. 69 100 71 .. .. 96 99 90 94 .. 55 79 87 79 w 64 88 87 92 78 88 98 87 66 59 60 .. ..
% ages 15–24
Female 2002 a
98 b 100 75 84 49 .. .. 97 b 100 b 100 .. 87 99 95 71 82 .. .. 91 100 b 85 95 b 74 99 83 93 b 99 b 79 100 76 .. .. 97 100 94 94 b .. 69 86 94 84 w 72 92 92 95 83 93 99 90 76 67 71 .. ..
1990
96 99 44 50 19 .. .. 83 .. 100 .. 80 95 85 32 70 .. .. 48 97 51 89 29 96 47 66 .. 43 99 71 .. .. 97 98 88 87 .. 13 59 75 63 w 42 75 74 88 62 71 95 83 40 34 40 .. ..
Male
2002 a
96 b 99 63 69 30 .. .. 89 b 100 b 100 .. 85 97 90 49 80 .. .. 74 99 b 69 91 b 45 98 63 75 b 98 b 59 100 81 .. .. 98 99 93 87 b .. 29 74 86 71 w 53 83 82 92 70 82 96 89 55 44 56 .. ..
1990
99 100 78 91 50 .. .. 99 .. 100 .. 89 100 96 76 85 .. .. 92 100 89 99 79 100 93 97 .. 80 100 82 .. .. 98 100 95 94 .. 74 86 97 87 w 75 95 95 97 86 97 99 93 81 70 75 .. ..
Female 2002 a
98 b 100 86 95 61 .. .. 99 b 100 b 100 .. 92 100 97 84 90 .. .. 97 100 b 94 98 b 88 100 98 98 b 100 b 86 100 88 .. .. 99 100 98 .. .. 84 91 99 89 w 82 97 96 98 89 98 100 95 87 77 83 .. ..
a. Data are preliminary. b. National estimates based on census data. c. National estimates based on survey data.
86
2004 World Development Indicators
Expected years of schooling
1990
99 100 67 79 30 .. .. 99 .. 100 .. 88 100 94 54 85 .. .. 67 100 77 98 48 100 75 88 .. 60 100 89 .. .. 99 100 97 94 .. 25 76 91 78 w 59 91 91 95 77 93 98 93 61 50 60 .. ..
Male
2002 a
98 b 100 84 92 44 .. .. 100 b 100 b 100 .. 92 100 97 74 92 .. .. 93 100 b 89 98 b 67 100 91 93 b 100 b 74 100 95 .. .. 99 100 99 .. .. 51 87 96 83 w 70 94 94 98 82 97 99 96 75 61 74 .. ..
Female
1990/91
2000/01
1990/91
2000/01
11 .. .. 9 .. .. .. .. .. .. .. 13 .. .. .. 11 13 14 11 .. .. .. 11 11 11 .. .. .. .. 10 14 15 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 15 15
12 .. .. .. .. 10 7 .. 13 14 .. 13 15 .. .. 13 15 16 .. 11 5 11 12 11 14 .. .. .. 11 .. 16 15 13 .. 10 .. .. 11 7 10 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
11 .. .. 7 .. .. .. .. .. .. .. 13 .. .. .. 10 13 13 9 .. .. .. 6 11 10 .. .. .. .. 11 14 16 .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 15 15
12 .. .. 9 .. 11 5 .. 13 15 .. 13 16 .. .. 12 17 15 .. 9 5 11 8 12 14 .. .. .. 12 .. 17 16 14 .. 11 .. .. 5 7 9 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
2.13
About the data
Many governments collect and publish statistics that
national estimates are received from countries and
Because the calculation of this indicator assumes
indicate how their education systems are working
are based on national censuses or household sur-
that the probability of a child’s being enrolled in
and developing—statistics on enrollment and on
veys during 1995–2004. The UNESCO Institute for
school at any future age is equal to the current
such efficiency indicators as repetition rates, pupil-
Statistics estimates were assessed in July 2002.
enrollment ratio for that age, it does not account for
teacher ratios, and cohort progression through
The estimation methodology can be reviewed at
changes and trends in future enrollment ratios. The
school. But until recently, despite an obvious interest
www.uis.unesco.org.
expected number of years and the expected number
in what education achieves, few systems in high-
Literacy statistics for most countries cover the pop-
of grades completed are not necessarily consistent,
income or developing countries had systematically
ulation ages 15 and older, by five-year age groups, but
because the first includes years spent in repetition.
collected information on outcomes of education.
some include younger ages or are confined to age
Comparability across countries and over time may be
Basic student outcomes include achievements in
ranges that tend to inflate literacy rates. As an alter-
affected by differences in the length of the school
reading and mathematics judged against established
native, the UNESCO Institute for Statistics has pro-
year or changes in policies on automatic promotions
standards. In many countries national learning
posed the narrower age range of 15–24, which better
and grade repetition.
assessments are enabling ministries of education to
captures the ability of participants in the formal edu-
monitor progress in these outcomes. Internationally,
cation system. The youth illiteracy rate reported in the
the United Nations Educational, Scientific, and
table measures the accumulated outcomes of primary
Cultural Organization (UNESCO) Institute for Statistics
education over the previous 10 years or so by indicat-
• Adult literacy rate is the percentage of people ages
has established literacy as an outcome indicator
ing the proportion of people who have passed through
15 and older who can, with understanding, both read
based on an internationally agreed definition.
the primary education system without acquiring basic
and write a short, simple statement about their every-
The literacy rate is defined as the percentage of
literacy and numeracy skills (or never entered the sys-
day life. • Youth literacy rate is the literacy rate
people who can, with understanding, both read and
tem). Reasons for this may include difficulties in
among people ages 15–24. • Expected years of
write a short, simple statement about their everyday
attending school or dropping out before reaching
schooling are the average number of years of formal
life. In practice, literacy is difficult to measure. To esti-
grade 5 (see About the data for table 2.12) and there-
schooling that children are expected to receive, includ-
mate literacy using such a definition requires census
by failing to achieve basic learning competencies.
ing university education and years spent in repetition.
or survey measurements under controlled conditions.
Expected years of schooling is an estimate of the
Many countries estimate the number of literate peo-
total years of schooling that a typical child at the age
ple from self-reported data. Some use educational
of school entry will receive, including years spent on
attainment data as a proxy but apply different lengths
repetition, given the current patterns of enrollment
of school attendance or level of completion. Because
across cycles of education. It may also be interpreted
definition and methodologies of data collection differ
as an indicator of the total education resources,
across countries, data need to be used with caution.
measured in school years, that a child will acquire over
The reported literacy data are national estimates
his or her “lifetime” in school—or as an indicator of an
or UNESCO Institute for Statistics estimates. The
Definitions
They reflect the underlying age-specific enrollment ratios for primary, secondary, and tertiary education.
education system’s overall level of development.
2.13a There is a strong positive relationship between primary school enrollment ratios and literacy among youth Literacy rate, 2002 (%) 100 80 60 40 20
Data sources The data on literacy are national estimates col-
0 0
50
100
150
Gross primary enrollment ratio, 1990 Children learn basic reading and writing along with other subjects in primary school. The primary school enrollment ratio and the literacy rate among young people (15–24) have a strong positive relationship, suggesting the push to achieve universal primary education will increase the number of literate young people.
lected by the UNESCO Institute for Statistics and estimates and projections by the UNESCO Institute for Statistics, assessed in July 2002. The data on expected years of schooling are from the UNESCO Institute for Statistics.
Source: UNESCO Institute for Statistics.
2004 World Development Indicators
87
PEOPLE
Education outcomes
2.14
Health expenditure, ser vices, and use Health expenditure
Total % of GDP 2001
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
88
5.2 3.7 4.1 4.4 9.5 7.8 9.2 8.0 0.9 3.5 5.6 8.9 4.4 5.3 7.5 6.6 7.6 4.8 .. 3.6 11.8 3.3 9.5 4.5 2.6 7.0 5.5 .. 5.5 3.5 2.1 7.2 6.2 9.0 7.2 7.4 8.4 6.1 4.5 3.9 8.0 5.7 5.5 3.6 7.0 9.6 3.6 6.4 3.6 10.8 4.7 9.4 4.8 3.5 5.9 5.0
Public % of GDP 2001
Public % of total 2001
2.7 2.4 3.1 2.8 5.1 3.2 6.2 5.5 0.6 1.5 4.8 6.4 2.1 3.5 2.8 4.4 3.2 3.9 2.0 2.1 1.7 1.2 6.8 2.3 2.0 3.1 2.0 .. 3.6 1.5 1.4 4.9 1.0 7.3 6.2 6.7 7.0 2.2 2.3 1.9 3.7 3.7 4.3 1.4 5.3 7.3 1.7 3.2 1.4 8.1 2.8 5.2 2.3 1.9 3.2 2.7
52.6 64.6 75.0 63.1 53.4 41.2 67.9 69.3 75.1 44.2 86.7 71.7 46.9 66.3 36.8 66.2 41.6 82.1 .. 59.0 14.9 37.1 70.8 51.2 76.0 44.0 37.2 .. 65.7 44.4 63.8 68.5 16.0 81.8 86.2 91.4 82.4 36.1 50.3 48.9 46.7 65.1 77.8 40.5 75.6 76.0 47.9 49.4 37.8 74.9 59.6 56.0 48.3 54.1 53.8 53.4
2004 World Development Indicators
Health expenditure per capita
$ 2001
8 48 73 31 679 28 1,741 1,866 8 12 68 1,983 16 49 85 190 222 81 .. 4 30 20 2,163 12 5 296 49 .. 105 5 18 293 41 394 185 407 2,545 153 76 46 174 10 226 3 1,631 2,109 127 19 22 2,412 12 1,001 86 13 8 22
Physicians
Hospital beds
Inpatient admission rate
Average length of stay
Outpatient visits per capita
per 1,000 people 1995– 1980 2002 a
per 1,000 people 1995– 1980 2002 a
% of population 1995– 2002 a
days 1995– 2002 a
1995– 2002 a
.. 1.4 .. .. .. 3.2 .. 1.6 3.4 0.1 3.0 2.3 0.1 .. 1.0 0.1 .. 2.5 0.0 c .. .. .. 1.8 0.0 c .. .. 1.2 0.8 .. .. .. .. .. 1.7 .. 2.3 2.2 .. .. 1.1 0.3 .. 2.9 0.0 c 1.7 2.0 .. .. 4.1 2.3 .. 2.4 .. .. 0.1 ..
0.1 1.4 1.0 0.1 2.7 2.9 2.5 3.2 3.6 0.2 4.5 3.9 0.1 1.3 1.4 .. 1.3 3.4 0.0 c .. 0.3 0.1 2.1 0.0 c .. 1.1 1.4 1.3 1.2 0.1 0.3 0.9 0.1 2.4 5.3 3.4 3.4 2.2 1.7 1.6 1.1 0.0 c 3.1 .. 3.1 3.3 .. 0.0 c 3.9 3.3 0.1 4.4 0.9 0.1 0.2 0.2
.. 4.3 .. .. .. 8.4 12.3 11.2 9.7 0.2 12.5 9.4 1.5 .. 4.8 2.4 .. 8.9 .. .. .. .. 6.8 1.6 .. 3.4 2.2 4.0 1.6 .. .. 3.3 .. 7.2 .. 11.3 8.1 .. 1.9 2.0 .. .. 12.2 0.3 15.6 11.1 .. .. 10.2 11.5 .. 6.2 .. .. 1.9 0.7
.. 3.3 2.1 .. 3.3 4.3 7.9 8.6 8.5 .. 12.6 7.3 .. 1.7 3.2 .. 3.1 7.2 1.4 .. .. .. 3.9 .. .. 2.7 2.5 .. 1.5 .. .. 1.7 .. 6.0 5.1 8.8 4.5 1.5 1.6 2.1 1.6 .. 6.7 .. 7.5 8.2 .. .. 4.3 9.1 .. 4.9 1.0 .. .. 0.7
.. .. .. .. .. 8 16 30 6 .. 26 20 .. .. .. .. 0b .. 2 .. .. .. 10 .. .. .. 4 .. .. .. .. 9 .. .. .. 21 20 .. .. 3 .. .. 18 .. 27 23 .. .. 5 24 .. 15 .. .. .. ..
.. .. .. .. .. 15 16 9 .. .. 18 12 .. .. 15 .. .. 12 3 .. .. .. 9 .. .. .. 12 .. .. .. .. 6 .. .. .. 11 6 .. .. 6 .. .. 9 .. 11 13 .. .. 11 12 .. 8 .. .. .. ..
.. .. .. .. .. 2 6 7 1 .. 11 7 .. .. .. .. 2 .. 0b .. .. .. 6 .. .. .. .. .. .. .. .. 1 .. .. .. 13 6 .. .. 4 .. .. 5 .. 4 7 .. .. 1 7 .. 3 .. .. .. ..
Health expenditure
Total % of GDP 2001
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
6.1 6.8 5.1 2.4 6.6 3.2 6.5 8.7 8.4 6.8 8.0 9.5 3.1 7.8 2.5 6.0 4.3 4.0 3.1 6.4 12.4 5.5 4.3 2.9 6.0 6.8 2.0 7.8 3.8 4.3 3.6 3.4 6.1 5.1 6.4 5.1 5.9 2.1 7.0 5.2 8.9 8.3 7.8 3.7 3.4 8.0 3.0 3.9 7.0 4.4 8.0 4.7 3.3 6.1 9.2 ..
Public % of GDP 2001
Public % of total 2001
3.2 5.1 0.9 0.6 2.7 1.0 4.9 6.0 6.3 2.9 6.2 4.5 1.9 1.7 1.9 2.6 3.5 1.9 1.7 3.4 2.2 4.3 3.3 1.6 4.2 5.8 1.3 2.7 2.0 1.7 2.6 2.0 2.7 2.8 4.6 2.0 4.0 0.4 4.7 1.5 5.7 6.4 3.8 1.4 0.8 6.8 2.4 1.0 4.8 3.9 3.0 2.6 1.5 4.6 6.3 ..
53.1 75.0 17.9 25.1 41.9 31.8 76.0 69.2 75.3 42.1 77.9 47.0 60.4 21.4 73.4 44.4 81.0 48.7 55.5 52.5 18.0 78.9 75.9 56.0 70.5 84.9 65.9 35.0 53.7 38.6 72.4 59.5 44.3 55.8 72.3 39.3 67.4 17.8 67.8 29.7 63.3 76.8 48.5 39.1 23.2 85.5 80.7 24.4 69.0 89.0 38.3 55.0 45.2 71.9 69.0 ..
Health expenditure per capita
$ 2001
59 345 24 16 363 225 1,711 1,641 1,584 191 2,627 163 44 29 22 532 630 12 10 210 .. 23 1 143 206 115 6 13 143 11 12 128 370 18 25 59 11 197 110 12 2,138 1,073 60 6 15 2,981 225 16 258 24 97 97 30 289 982 ..
2.14
Physicians
Hospital beds
Inpatient admission rate
Average length of stay
Outpatient visits per capita
per 1,000 people 1995– 1980 2002 a
per 1,000 people 1995– 1980 2002 a
% of population 1995– 2002 a
days 1995– 2002 a
1995– 2002 a
.. 24 .. .. .. .. 15 .. 18 .. 10 11 15 .. .. 6 .. 21 .. 21 17 .. .. .. 24 9 1 .. .. 1 .. .. 6 19 .. 3 .. .. .. .. 10 13 .. 28 .. 17 9 .. .. .. .. 1 .. 16 12 ..
.. 9 .. .. .. .. 8 .. 8 .. 40 4 16 .. .. 13 .. 13 .. 14 4 .. .. .. 11 12 5 .. .. 7 .. .. 4 18 .. 7 .. .. .. .. 33 8 .. 5 .. 9 4 .. .. .. .. 6 .. 8 9 ..
.. 2.3 0.4 .. .. 0.6 .. 3.1 2.6 .. 1.3 0.8 3.0 .. .. .. 1.7 2.6 .. 3.6 .. .. .. 1.3 .. 1.3 .. .. 0.3 0.0 c .. 0.5 .. 2.8 .. .. 0.0 c .. .. 0.0 c 1.9 1.6 0.4 .. 0.1 2.0 0.5 0.3 .. 0.1 .. 0.7 0.1 1.8 2.0 ..
0.8 2.9 .. .. 0.9 0.6 2.4 3.7 4.3 1.4 1.9 1.7 3.6 0.1 3.0 1.4 1.9 2.6 0.2 2.9 2.1 0.1 0.0 1.3 4.0 2.2 0.1 .. 0.7 0.1 0.1 0.9 1.5 2.7 2.4 0.5 .. 0.3 0.3 0.0 c 3.3 2.2 0.9 0.0 c .. 3.0 1.3 0.6 1.7 0.1 1.1 0.9 1.2 2.2 3.2 1.8
1.3 9.1 0.8 .. 1.5 1.9 13.0 6.8 9.6 .. 13.7 1.3 13.1 .. .. 1.7 4.1 12.0 .. 13.9 .. .. c .. 12.1 5.2 .. .. .. .. .. 3.1 0.7 12.1 11.2 .. 1.1 0.9 .. 0.2 12.3 10.2 .. .. 0.9 16.5 1.6 0.6 .. 5.5 .. .. 1.7 5.6 5.2 ..
1.1 8.2 .. .. 1.6 1.5 9.7 6.2 4.9 2.1 16.5 1.8 7.0 .. .. 6.1 2.8 5.5 .. 8.2 2.7 .. .. 4.3 9.2 4.8 0.4 1.3 2.0 0.2 .. .. 1.1 5.9 .. 1.0 .. .. .. 0.2 10.8 6.2 1.5 0.1 .. 14.6 2.2 .. 2.2 .. 1.3 1.5 .. 4.9 4.0 3.3
2004 World Development Indicators
.. 12 .. .. .. .. .. .. 6 .. 14 .. 0b .. .. 9 .. 1 .. 4 .. .. .. .. .. 7 3 1 2 .. 0b .. .. 3 8 .. .. .. .. .. .. 6 4 .. 0b .. .. 4 .. .. .. .. .. .. 6 3 ..
89
PEOPLE
Health expenditure, ser vices, and use
2.14
Health expenditure, ser vices, and use Health expenditure
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Total % of GDP 2001
Public % of GDP 2001
6.5 5.4 5.5 4.6 4.8 8.2 4.3 3.9 5.7 8.4 2.6 8.6 7.5 3.6 3.5 3.3 8.7 11.1 5.4 3.4 4.4 3.7 2.8 4.0 6.4 6.9 4.1 5.9 4.3 3.5 7.6 13.9 10.9 3.6 6.0 5.1 .. 4.5 5.7 6.2 9.8 w 4.4 6.0 5.8 6.4 5.8 4.9 5.8 7.0 4.9 4.8 6.0 10.8 9.3
5.2 3.7 3.1 3.4 2.8 6.5 2.6 1.3 5.1 6.3 1.2 3.6 5.4 1.8 0.6 2.3 7.4 6.4 2.4 1.0 2.0 2.1 1.5 1.7 4.9 4.4 3.0 3.4 2.9 2.6 6.3 6.2 5.1 2.7 3.7 1.5 .. 1.5 3.0 2.8 5.6 w 1.1 3.1 2.7 3.7 2.7 1.9 4.3 3.4 2.8 1.0 2.5 6.3 6.8
Public % of total 2001
79.2 68.2 55.5 74.6 58.8 79.2 61.0 33.5 89.3 74.9 44.6 41.4 71.4 48.9 18.7 68.5 85.2 57.1 43.9 28.9 46.7 57.1 48.6 43.3 75.7 63.0 73.3 57.5 67.8 75.8 82.2 44.4 46.3 74.5 62.1 28.5 .. 34.1 53.1 45.3 59.2 w 26.3 51.1 47.2 57.7 47.0 38.8 72.4 48.0 59.3 21.6 41.3 62.1 73.5
Health expenditure per capita
$ 2001
117 115 11 375 22 103 7 816 216 821 6 222 1,088 30 14 41 2,150 3,779 65 6 12 69 8 279 134 .. 57 14 33 849 1,835 4,887 603 17 307 21 .. 20 19 45 500 w 23 118 85 357 72 48 123 255 166 22 29 2,841 1,856
a. Data are for the most recent year available. b. Less than 0.5. c. Less than 0.05.
90
2004 World Development Indicators
Physicians
Hospital beds
Inpatient admission rate
Average length of stay
Outpatient visits per capita
per 1,000 people 1995– 1980 2002 a
per 1,000 people 1995– 1980 2002 a
% of population 1995– 2002 a
days 1995– 2002 a
1995– 2002 a
1.5 .. 0.0 c .. .. .. 0.1 0.9 .. 1.8 0.0 c .. .. 0.1 0.1 .. 2.2 2.4 0.4 .. .. 0.1 0.1 0.7 0.3 0.6 2.8 .. 3.5 1.1 1.3 2.0 .. 2.7 0.8 0.2 .. .. 0.1 0.2 1.1 w 0.4 1.2 1.2 .. 0.8 1.0 .. .. .. 0.3 .. 1.9 2.2
1.9 4.2 .. 1.7 0.1 2.1 0.1 1.6 3.6 2.2 0.0 c 0.6 3.3 0.4 0.1 0.2 3.0 3.5 1.3 2.1 0.0 c 0.4 0.1 0.8 0.7 1.3 3.0 .. 3.0 1.8 2.0 2.7 3.7 2.9 2.4 0.5 0.5 0.2 0.1 0.1 .. w .. 1.9 2.0 1.8 .. 1.4 3.1 1.4 .. .. .. 2.8 3.5
8.8 .. 1.5 .. .. .. 1.2 4.0 .. 7.0 .. .. 5.4 2.9 0.9 .. 15.1 .. 1.1 .. 1.4 1.5 .. .. 2.1 2.2 10.5 .. 12.1 2.8 8.1 6.0 .. 9.2 0.3 3.5 .. .. .. 3.0 3.7 w 1.2 3.0 2.9 3.8 2.2 2.2 .. .. .. 0.7 .. 8.6 9.9
7.5 10.8 .. 2.3 0.4 5.3 .. .. 7.8 5.2 .. .. 4.1 .. .. .. 3.6 17.9 1.4 6.4 .. 2.0 .. 5.1 1.7 2.6 7.1 .. 8.7 2.6 4.1 3.6 4.4 5.3 1.5 1.7 1.2 0.6 .. .. .. w .. 3.7 3.7 3.4 .. 2.5 8.9 2.2 .. .. .. 7.4 8.0
18 22 .. 11 .. .. .. .. 19 .. .. .. 12 .. .. .. 18 15 .. .. .. .. .. .. .. 8 .. .. 20 .. 15 12 .. .. .. 8 9 .. .. .. 9w .. 7 6 11 .. 4 18 2 .. .. .. 14 20
10 17 .. 4 10 12 .. .. 10 .. .. .. 9 .. .. .. 6 13 .. .. .. .. .. .. .. 6 .. .. .. .. 10 7 .. .. .. 7 3 .. .. .. .. w .. 11 12 6 .. 12 13 .. .. .. .. 14 12
4 8 .. 1 1 2 .. .. .. .. .. .. 9 .. .. .. 3 .. .. .. .. 1 .. .. .. 3 .. .. 10 .. 5 9 .. .. .. .. 4 .. .. .. .. w .. .. .. 5 .. .. 6 2 .. .. .. 8 7
2.14
About the data
National health accounts track financial flows in the
Indicators on health services (physicians and hospi-
surgeries are counted as hospital admissions. And in
health sector, including public and private expendi-
tal beds per 1,000 people) and health care utilization
many countries outpatient visits, especially emergency
tures, by source of funding. In contrast with high-
(inpatient admission rates, average length of stay, and
visits, may result in double counting if a patient receives
income countries, few developing countries have
outpatient visits) come from a variety of sources (see
treatment in more than one department.
health accounts that are methodologically consistent
Data sources). Data are lacking for many countries,
with national accounting approaches. The difficulties
and for others comparability is limited by differences
in creating national health accounts go beyond data
in definitions. In estimates of health personnel, for
collection. To establish a national health accounting
example, some countries incorrectly include retired
• Total health expenditure is the sum of public and
system, a country needs to define the boundaries of
physicians (because deletions to physician rosters are
private health expenditure. It covers the provision of
the health care system and to define a taxonomy of
made only periodically) or those working outside the
health services (preventive and curative), family plan-
health care delivery institutions. The accounting sys-
health sector. There is no universally accepted defini-
ning activities, nutrition activities, and emergency aid
tem should be comprehensive and standardized, pro-
tion of hospital beds. Moreover, figures on physicians
designated for health but does not include provision
viding not only accurate measures of financial flows
and hospital beds are indicators of availability, not of
of water and sanitation. • Public health expenditure
but also information on the equity and efficiency of
quality or use. They do not show how well trained the
consists of recurrent and capital spending from
health financing to inform health policy.
physicians are or how well equipped the hospitals or
government (central and local) budgets, external bor-
The absence of consistent national health
medical centers are. And physicians and hospital beds
rowings and grants (including donations from inter-
accounting systems in most developing countries
tend to be concentrated in urban areas, so these indi-
national agencies and nongovernmental organiza-
makes cross-countr y comparisons of health spend-
cators give only a partial view of health services avail-
tions), and social (or compulsory) health insurance
ing difficult. Records of private out-of-pocket spend-
able to the entire population.
funds. • Physicians are graduates of any faculty or
Definitions
ing are often lacking. And compiling estimates of
The average length of stay in hospitals is an indicator
school of medicine who are working in the country in
public health expenditures is complicated in coun-
of the efficiency of resource use. Longer stays may
any medical field (practice, teaching, research).
tries where state or provincial and local govern-
reflect a waste of resources if patients are kept in hos-
• Hospital beds include inpatient beds available in
ments are involved in financing and delivering
pitals beyond the time medically required, inflating
public, private, general, and specialized hospitals
health care, because the data on public spending
demand for hospital beds and increasing hospital costs.
and rehabilitation centers. In most cases beds for
often are not aggregated. The data in the table are
Aside from differences in cases and financing methods,
both acute and chronic care are included.
the product of an effor t by the World Health
cross-country variations in average length of stay may
• Inpatient admission rate is the percentage of the
Organization (WHO), the Organisation for Economic
result from differences in the role of hospitals. Many
population admitted to hospitals during a year.
Co-operation and Development (OECD), and the
developing countries do not have separate extended
• Average length of stay is the average duration of
World Bank to collect all available information on
care facilities, so hospitals become the source of both
inpatient hospital admissions. • Outpatient visits
health expenditures from national and local govern-
long-term and acute care. Other factors may also
per capita are the number of visits to health care
ment budgets, national accounts, household sur-
explain the variations. Data for some countries may not
facilities per capita, including repeat visits.
veys, insurance publications, international donors,
include all public and private hospitals. Admission rates
and existing tabulations.
may be overstated in some countries if outpatient
2.14a High health personnel absence rates lower the quality of health care
Data sources
Health personnel absence rate, 2000–03 (%)
The estimates of health expenditure come mostly
40
from the WHO’s World Health Report 2003 and
35
updates and from the OECD for its member coun-
30
tries, supplemented by World Bank pover ty
25
assessments and country and sector studies.
20
Data are also drawn from World Bank public expen-
15
diture reviews, the International Monetary Fund’s
10
Government Finance Statistics database, and
5
other studies. The data on private expenditure in developing countries are drawn largely from house-
0 India
Indonesia
Uganda
Bangladesh
Peru
Papua New Guinea
hold surveys conducted by governments or by statistical or international organizations. The data on
Health personnel absence rate is the percentage of full-time medical personnel who were absent from a random sample of primary health centers during surprise visits. Some personnel were absent for valid reasons, but even authorized absences reduce the quantity and quality of primary health care. Absence rates tend to be higher in remote areas, affecting the quality of health care available in these areas.
physicians, hospital beds, and utilization of health services are from the WHO, OECD, and TransMONEE, supplemented by country data.
Source: Chaudhury and others 2004; NRI and World Bank 2003; Habyarimana and others 2003.
2004 World Development Indicators
91
PEOPLE
Health expenditure, ser vices, and use
2.15
Disease prevention: coverage and quality Access to an improved water source
Access to improved sanitation facilities
Tetanus vaccinations
% of % of
% of
pregnant
population
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
92
Tuberculosis treatment success rate
% of children
Children sleeping under treated bednets b
DOTS detection rate
% of
% of
ages 12–23 months a
% of children
registered
estimated
women
Measles
DPT
under age 5
cases
cases
1990
2000
1990
2000
2002
2002
2002
1999–2001 c
2001
2002
.. .. .. .. 94 .. 100 100 .. 94 .. .. .. 71 .. 93 83 .. .. 69 .. 51 100 48 .. 90 71 .. 94 .. .. .. 80 .. .. .. .. 83 71 94 66 .. .. 25 100 .. .. .. .. .. 53 .. 76 45 .. 53
13 97 89 38 .. .. 100 100 78 97 100 .. 63 83 .. 95 87 100 42 78 30 58 100 70 27 93 75 .. 91 45 51 95 81 .. 91 .. 100 86 85 97 77 46 .. 24 100 .. 86 62 79 .. 73 .. 92 48 56 46
.. .. .. .. 82 .. 100 100 .. 41 .. .. 20 52 .. 60 71 .. .. 87 .. 77 100 24 18 97 29 .. 83 .. .. .. 46 .. .. .. .. 66 70 87 73 .. .. 8 100 .. .. .. .. .. 61 .. 70 55 44 23
12 91 92 44 .. .. 100 100 81 48 .. .. 23 70 .. 66 76 100 29 88 17 79 100 25 29 96 40 .. 86 21 .. 93 52 .. 98 .. .. 67 86 98 82 13 .. 12 100 .. 53 37 100 .. 72 .. 81 58 56 28
34 .. .. 62 .. .. .. .. .. 89 .. .. 66 .. .. .. .. .. 44 42 36 65 .. 63 39 .. .. .. .. 44 41 .. 80 .. .. .. .. .. .. 70 .. 50 .. 24 .. .. 50 .. .. .. 73 .. .. 43 41 52
44 96 81 74 97 91 94 78 97 77 99 75 78 79 89 90 93 90 46 75 52 62 96 35 55 95 65 .. 89 45 37 94 56 95 98 97 99 92 80 97 93 84 95 52 96 85 55 90 73 89 81 88 92 54 47 53
47 98 86 47 88 94 93 83 97 85 99 90 79 81 80 97 96 94 41 74 54 48 97 40 40 94 79 .. 85 43 41 94 54 95 99 98 98 72 89 97 81 83 97 56 98 98 38 90 84 97 80 88 84 47 50 43
.. .. .. 2.3 .. .. .. .. 1.4 .. .. .. 7.4 .. .. .. .. .. .. 1.3 .. 1.3 .. 1.5 0.6 .. .. .. 0.7 0.7 .. .. 1.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 14.7 .. .. .. .. 1.2 .. 7.4 ..
84 98 84 66 64 90 66 64 66 84 .. 64 79 82 98 78 67 87 65 80 92 62 67 61 .. 83 96 78 85 77 66 72 73 .. 93 73 .. 85 82 82 88 80 64 76 .. .. 49 71 67 67 42 .. 85 74 51 75
19 24 114 91 51 28 25 41 43 32 .. 64 98 75 47 73 10 43 18 28 52 60 52 49 42 112 27 51 9 52 69 79 25 .. 91 57 .. 43 31 53 57 14 61 33 .. .. 73 73 50 52 41 .. 45 54 43 41
2004 World Development Indicators
population
Child immunization rate
Access to an improved water source
Access to improved sanitation facilities
Tetanus vaccinations
% of % of
% of
pregnant
population
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
population
Child immunization rate
2.15 Tuberculosis treatment success rate
% of children
Children sleeping under treated bednets b
DOTS detection rate
% of
% of
ages 12–23 months a
% of children
registered
estimated
women
Measles
DPT
under age 5
cases
cases
1990
2000
1990
2000
2002
2002
2002
1999–2001 c
2001
2002
83 99 68 71 .. .. .. .. .. 93 .. 97 .. 45 .. .. .. .. .. .. .. .. .. 71 .. .. 44 49 .. 55 37 100 80 .. .. 75 .. .. 72 67 100 .. 70 53 53 100 37 83 .. 40 63 74 87 .. .. ..
88 99 84 78 92 85 .. .. .. 92 .. 96 91 57 100 92 .. 77 37 .. 100 78 .. 72 .. .. 47 57 .. 65 37 100 88 92 60 80 57 72 77 88 100 .. 77 59 62 100 39 90 90 42 78 80 86 .. .. ..
61 99 16 47 .. .. .. .. .. 99 .. 98 .. 80 .. .. .. .. .. .. .. .. .. 97 .. .. 36 73 .. 70 30 100 70 .. .. 58 .. .. 33 20 100 .. 76 15 53 .. 84 36 .. 82 93 60 74 .. .. ..
75 99 28 55 83 79 .. .. .. 99 .. 99 99 87 99 63 .. 100 30 .. 99 49 .. 97 .. .. 42 76 .. 69 33 99 74 99 30 68 43 64 41 28 100 .. 85 20 54 .. 92 62 92 82 94 71 83 .. .. ..
.. .. 78 81 .. 70 .. .. .. .. .. .. .. 60 .. .. .. .. 35 .. .. .. 41 .. .. .. 35 82 .. 32 40 .. .. .. .. .. 67 71 85 69 .. .. .. 36 44 .. .. 56 .. 34 .. .. 87 .. .. ..
97 99 67 76 99 90 73 95 70 86 98 95 95 78 .. 97 99 98 55 98 96 70 57 91 98 98 61 69 92 33 81 84 96 94 98 96 58 75 68 71 96 85 98 48 40 88 99 57 79 71 82 95 73 98 87 ..
95 99 70 75 99 81 84 97 95 87 95 95 95 84 .. 97 98 98 55 97 92 79 51 93 95 96 62 64 96 57 83 88 91 97 98 94 60 77 77 72 98 90 84 23 26 91 99 63 89 57 77 89 70 99 96 ..
.. .. .. 0.1 .. .. .. .. .. .. .. .. .. 2.9 .. .. .. .. .. .. .. .. .. .. .. .. 0.2 2.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.0 .. .. .. .. .. .. .. .. .. .. .. ..
86 46 85 86 84 89 .. 79 40 78 75 86 78 80 91 .. .. 81 77 73 91 71 .. .. 75 88 69 70 79 50 .. 93 83 66 87 87 77 81 68 88 76 9 83 .. 79 87 90 77 65 67 86 90 88 77 78 80
114 39 31 30 60 21 .. 58 63 68 33 72 93 49 88 .. .. 45 43 78 68 61 .. 106 62 37 62 36 78 15 .. 25 73 19 69 83 45 73 76 64 54 48 85 22 12 26 106 13 88 15 8 84 58 55 94 65
2004 World Development Indicators
93
PEOPLE
Disease prevention: coverage and quality
2.15
Disease prevention: coverage and quality Access to an improved water source
Access to improved sanitation facilities
Tetanus vaccinations
% of % of
% of
pregnant
population
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
population
1990
2000
1990
2000
.. .. .. .. 72 .. .. 100 .. 100 .. 86 .. 68 67 .. 100 100 .. .. 38 80 51 91 75 79 .. 45 .. .. 100 100 .. .. .. 55 .. .. 52 78 74 w 66 76 75 .. 71 71 .. 82 .. 72 53 .. ..
58 99 41 95 78 98 57 100 100 100 .. 86 .. 77 75 .. 100 100 80 60 68 84 54 90 80 82 .. 52 98 .. 100 100 98 85 83 77 .. 69 64 83 81 w 76 82 81 .. 79 76 91 86 88 84 58 .. ..
.. .. .. .. 57 .. .. 100 .. .. .. 86 .. 85 58 .. 100 100 .. .. 84 79 37 99 76 87 .. .. .. .. 100 100 .. .. .. 29 .. 32 63 56 45 w 30 47 45 .. 39 35 .. 72 .. 22 54 .. ..
53 .. 8 100 70 100 66 100 100 .. .. 87 .. 94 62 .. 100 100 90 90 90 96 34 99 84 90 .. 79 99 .. 100 100 94 89 68 47 .. 38 78 62 55 w 43 60 58 .. 51 46 .. 77 85 34 53 .. ..
Child immunization rate
Tuberculosis treatment success rate
% of children
Children sleeping under treated bednets b
DOTS detection rate
% of
% of
ages 12–23 months a
% of children
registered
estimated
women
Measles
DPT
under age 5
cases
cases
2002
2002
2002
1999–2001 c
2001
2002
.. .. 5.0 .. 1.7 .. 1.5 .. .. .. 0.3 .. .. .. 0.4 0.1 .. .. .. 1.9 2.1 .. 2.0 .. .. .. .. 0.2 .. .. .. .. .. .. .. 15.8 .. .. 1.1 ..
78 67 61 77 53 88 80 88 87 82 86 65 .. 80 80 36 62 .. 81 .. 81 75 55 .. 90 .. 75 56 .. 62 .. 70 85 76 80 93 .. 80 75 71
41 6 29 37 54 22 36 39 35 68 28 96 .. 79 33 31 59 .. 42 3 43 73 6 .. 92 .. 36 47 .. 25 .. 87 70 24 65 82 .. 49 40 46
.. .. 83 .. 75 .. 60 .. .. .. 60 52 .. .. 35 .. .. .. .. .. 86 .. 38 .. .. 37 .. 50 .. .. .. .. .. .. .. 89 .. 39 60 77
98 98 69 97 54 92 60 91 99 94 45 78 97 99 49 72 94 79 98 84 89 94 58 88 94 82 88 77 99 94 83 .. 92 97 78 96 .. 65 85 58 72 w 65 80 78 94 71 70 93 91 92 66 58 90 85
99 96 88 95 60 95 50 92 99 92 40 82 96 98 40 77 99 95 99 84 89 96 64 89 96 78 98 72 99 94 91 .. 93 98 63 75 .. 69 78 58 75 w 65 85 84 90 73 78 92 88 92 70 54 95 96
a. Refers to children who were immunized before 12 months or, in some cases, at any time before the survey (between 12–23 months). b. For malaria prevention only. c. Data are for the most recent year available.
94
2004 World Development Indicators
About the data
2.15
Definitions
The indicators in the table are based on data provided
and one booster shot during each subsequent preg-
• Access to an improved water source refers to the
to the World Health Organization (WHO) by member
nancy, with five doses considered adequate for life-
percentage of the population with reasonable access
states as part of their efforts to monitor and evaluate
time protection. Information on tetanus shots during
to an adequate amount of water from an improved
progress in implementing national health strategies.
pregnancy is collected through surveys in which preg-
source, such as a household connection, public
Because reliable, observation-based statistical data
nant respondents are asked to show antenatal cards
standpipe, borehole, protected well or spring, or rain-
for these indicators do not exist in some developing
on which tetanus shots have been recorded. Because
water collection. Unimproved sources include ven-
countries, some of the data are estimated.
not all women have antenatal cards, respondents are
dors, tanker trucks, and unprotected wells and
People’s health is influenced by the environment in
also asked about their receipt of these injections. Poor
springs. Reasonable access is defined as the avail-
which they live. Lack of clean water and basic sanita-
recall may result in a downward bias in estimates of
ability of at least 20 liters a person a day from a
tion is the main reason diseases transmitted by feces
the share of births protected. But in settings where
source within 1 kilometer of the dwelling. • Access
are so common in developing countries. The data on
receiving injections is common, respondents may erro-
to improved sanitation facilities refers to the per-
access to an improved water source measure the
neously report having received tetanus shots.
centage of the population with at least adequate
share of the population with ready access to water for
Governments in developing countries usually
access to excreta disposal facilities (private or
domestic purposes. The data are based on surveys
finance immunization against measles and diphthe-
shared but not public) that can effectively prevent
and estimates provided by governments to the Joint
ria, pertussis (whooping cough), and tetanus (DPT)
human, animal, and insect contact with excreta.
Monitoring Programme of the WHO and United Nations
as part of the basic public health package. In many
Improved facilities range from simple but protected
Children’s Fund (UNICEF). The coverage rates for water
developing countries, however, lack of precise infor-
pit latrines to flush toilets with a sewerage connec-
and sanitation are based on information from service
mation on the size of the cohort of children under
tion. To be effective, facilities must be correctly con-
users on the facilities their households actually use
one year of age makes immunization coverage diffi-
structed and properly maintained. • Tetanus vacci-
rather than on information from service providers, who
cult to estimate. The data shown here are based on
nations refer to the percentage of pregnant women
may include nonfunctioning systems. Access to drink-
an assessment of national immunization coverage
who receive two tetanus toxoid injections during their
ing water from an improved source does not ensure
rates by the WHO and UNICEF. The assessment con-
first pregnancy and one booster shot during each
that the water is safe or adequate, as these charac-
sidered both administrative data from ser vice
subsequent pregnancy, with five doses considered
teristics are not tested at the time of the surveys.
providers and household survey data on children’s
adequate for a lifetime. • Child immunization rate is
Neonatal tetanus is an important cause of infant
immunization histories. Based on the data available,
the percentage of children ages 12–23 months who
mortality in some developing countries. It can be pre-
consideration of potential biases, and contributions
received vaccinations before 12 months or at any
vented through immunization of the mother during
of local experts, the most likely true level of immu-
time before the survey for four diseases—measles
pregnancy. Recommended doses for full protection are
nization coverage was determined for each year.
and diphtheria, pertussis (whooping cough), and
generally two tetanus shots during the first pregnancy
Sleeping under treated bednets, if properly used
tetanus (DPT). A child is considered adequately
and maintained, is one of the most important malar-
immunized against measles after receiving one dose
ia preventive strategies to limit human-mosquito con-
of vaccine and against DPT after receiving three
Children in rural households are less likely to use bednets
tact. Studies have emphasized that mortality rates
doses. • Children sleeping under treated bednets
could be reduced by about 25–30 percent if every
refer to the percentage of children under age five
Children under five sleeping under bednets, 2000 (%)
child under five in malaria-risk areas such as Africa
who slept under an insecticide-impregnated bednet
slept under a treated bednet every night.
to prevent malaria. • Tuberculosis treatment suc-
2.15a 40
30
20
10
Data on the success rate of tuberculosis treatment
cess rate is the percentage of new, registered
are provided for countries that have implemented the
smear-positive (infectious) cases that were cured or
recommended control strategy: directly observed
in which a full course of treatment was completed.
treatment, short course (DOTS). Countries that have
• DOTS detection rate is the percentage of estimat-
not adopted DOTS or have only recently done so are
ed new infectious tuberculosis cases detected under
omitted because of lack of data or poor comparability
the directly observed treatment, short course case
or reliability of reported results. The treatment suc-
detection and treatment strategy.
cess rate for tuberculosis provides a useful indicator
0 Urban Untreated bednets
Rural Insecticide-treated bednets
Even though malaria is often more prevalent in rural areas, fewer children under age five sleep under a bednet in rural areas than in urban ones. The ratio of urban-rural difference is even greater for insecticide-treated bednets because they are more expensive than untreated bednets, and retreatment of insecticide-treated nets is still uncommon, especially in rural areas. Source: WHO and UNICEF 2003.
of the quality of health services. A low rate or no suc-
Data sources
cess suggests that infectious patients may not be
Data are drawn from a variety of sources, includ-
receiving adequate treatment. An essential comple-
ing WHO and UNICEF estimates of National
ment to the tuberculosis treatment success rate is
Immunization Coverage, the WHO’s Global
the DOTS detection rate, which indicates whether
Tuberculosis Control Report 2003; UNICEF’s
there is adequate coverage by the recommended
State of the World’s Children 2004; and the WHO
case detection and treatment strategy. A country with
and UNICEF’s Global Water Supply and Sanitation
a high treatment success rate may still face big chal-
Assessment 2000 Report.
lenges if its DOTS detection rate remains low.
2004 World Development Indicators
95
PEOPLE
Disease prevention: coverage and quality
2.16
Reproductive health Total fertility rate
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
96
Adolescent Women at risk Contraceptive fertility of unintended prevalence rate pregnancy rate
Births attended by skilled health staff
births
% of
per 1,000
married
% of
births
women
women
women
per woman
ages 15–19
ages 15–49
ages 15–49
2002
1990–2002 a
1990–2002 a
1985
1995–2002 a
.. .. .. .. .. 12 .. .. .. 15 .. .. 27 26 .. .. 7 .. 26 .. 30 20 .. 16 10 .. .. .. 6 .. .. .. 28 .. .. .. .. 12 .. 11 .. 28 .. 36 .. .. 28 .. .. .. 23 .. 23 24 .. 40
.. .. 51 .. .. 61 .. .. 55 54 .. .. 19 49 .. .. 77 .. 12 .. 24 19 .. 15 4 .. 83 .. 77 .. .. .. 15 .. .. 69 .. 70 66 56 60 8 .. 8 .. 71 33 .. 41 .. 22 .. 38 6 .. 28
.. .. .. .. .. .. .. .. .. .. 100 100 .. .. .. .. 81 .. .. 19 .. .. 100 .. .. .. .. .. .. .. .. 97 .. .. .. .. 100 .. .. .. .. .. .. .. .. .. .. .. .. 100 .. .. 35 .. .. ..
12 99 92 45 98 97 100 100 b 84 12 100 100 b 66 69 100 94 88 .. 31 25 32 60 98 44 16 100 76 .. 86 61 .. 98 63 100 100 99 100 b 98 69 61 90 21 .. 6 100 b 99 b 86 55 96 100 b 44 .. 41 35 35 24
1980
2002
7.0 3.6 6.7 6.9 3.3 2.3 1.9 1.6 3.2 6.1 2.0 1.7 7.0 5.5 2.1 6.1 3.9 2.0 7.5 6.8 5.7 6.4 1.7 5.8 6.9 2.8 2.5 2.0 3.9 6.6 6.3 3.6 7.4 1.9 2.0 2.1 1.5 4.2 5.0 5.1 4.9 7.5 2.0 6.6 1.6 1.9 4.5 6.5 2.3 1.4 6.5 2.2 6.3 6.1 7.1 5.9
6.8 2.2 2.8 7.0 2.4 1.1 1.8 1.3 2.1 3.0 1.3 1.6 5.3 3.8 1.3 3.8 2.1 1.3 6.3 5.8 3.8 4.6 1.5 4.6 6.2 2.2 1.9 1.0 2.5 6.7 6.3 2.3 4.6 1.5 1.6 1.2 1.7 2.6 2.8 3.0 2.9 4.8 1.3 5.6 1.7 1.9 4.1 4.8 1.1 1.4 4.1 1.3 4.3 5.0 6.6 4.2
2004 World Development Indicators
151 11 17 225 60 35 18 20 44 129 21 11 103 75 23 68 68 49 133 50 57 127 20 124 182 43 15 6 75 226 146 69 118 18 67 23 8 89 64 46 87 101 26 135 10 10 156 139 27 14 81 17 100 153 215 72
Maternal mortality ratio
per 100,000 live births % of total
National
Modeled
estimates
estimates
1985–2002 a
2000
.. 20 140 .. 41 22 .. .. 25 380 14 .. 500 390 10 330 160 15 480 .. 440 430 .. 1,100 830 23 53 .. 78 950 .. 29 600 2 30 3 10 230 b 160 84 120 1,000 46 870 6 10 520 .. 67 8 210 b 1 190 530 910 520
1,900 55 140 1,700 82 55 8 4 94 380 35 10 850 420 31 100 260 32 1,000 1,000 450 730 6 1,100 1,100 31 56 .. 130 990 510 43 690 8 33 9 5 150 130 84 150 630 63 850 6 17 420 540 32 8 540 9 240 740 1,100 680
Total fertility rate
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Adolescent Women at risk Contraceptive fertility of unintended prevalence rate pregnancy rate
Births attended by skilled health staff
births
% of
per 1,000
married
% of
births
women
women
women
per woman
ages 15–19
ages 15–49
ages 15–49
2002
1990–2002 a
1990–2002 a
1985
1995–2002 a
.. .. 16 9 .. .. .. .. .. .. .. 14 9 24 .. .. .. 12 .. .. .. .. .. .. .. .. 26 30 .. 29 32 .. .. .. .. 20 23 .. 22 28 .. .. 15 17 17 .. .. 32 .. .. 15 10 19 .. .. ..
62 73 52 57 73 .. 60 .. .. 65 .. 56 66 39 .. .. .. 60 25 .. 61 23 .. 45 .. .. 17 31 .. 8 8 75 65 74 60 59 6 .. 29 39 75 .. 60 8 15 .. 24 28 .. 26 57 69 47 .. .. 78
41 .. .. 36 .. .. .. 99 .. .. .. .. .. .. .. .. 96 .. .. .. .. .. .. .. .. .. .. .. .. 32 .. .. .. .. .. 26 .. .. .. .. .. .. .. .. .. .. 87 .. .. .. .. .. .. 99 .. ..
56 .. 43 64 90 72 100 99 b .. 95 100 97 99 44 97 100 98 98 19 100 89 60 51 94 .. 97 46 56 97 41 57 99 86 99 97 40 44 56 78 11 100 100 67 16 42 100 b 95 20 90 53 71 59 58 99 b 100 ..
1980
2002
6.5 1.9 5.0 4.3 6.7 6.4 3.2 3.2 1.6 3.7 1.8 6.8 2.9 7.8 2.8 2.6 5.3 4.1 6.7 1.9 4.0 5.5 6.8 7.3 2.0 2.5 6.6 7.6 4.2 7.1 6.4 2.7 4.7 2.4 5.3 5.4 6.5 4.9 5.9 6.1 1.6 2.0 6.3 8.0 6.9 1.7 9.9 7.0 3.7 5.8 5.2 4.5 4.8 2.3 2.2 2.6
4.0 1.3 2.9 2.3 2.0 4.1 1.9 2.7 1.3 2.3 1.3 3.5 1.8 4.2 2.1 1.5 2.5 2.4 4.8 1.2 2.2 4.3 5.8 3.3 1.3 1.8 5.2 6.1 2.8 6.4 4.6 2.0 2.4 1.4 2.4 2.8 5.0 2.8 4.8 4.2 1.7 1.9 3.4 7.1 5.1 1.8 4.0 4.5 2.4 4.3 3.8 2.6 3.2 1.3 1.5 1.9
110 21 98 52 25 35 15 19 8 84 3 30 35 100 2 4 30 29 91 32 23 77 196 32 33 31 157 137 23 176 113 39 62 44 45 44 153 29 103 112 5 30 122 205 111 10 54 62 75 68 75 61 33 15 23 64
2.16 Maternal mortality ratio
per 100,000 live births % of total
National
Modeled
estimates
estimates
1985–2002 a
2000
110 5 540 380 37 290 6 5 7 97 8 41 50 590 110 20 5 44 530 25 100 b .. 580 77 13 15 490 1,100 30 580 750 21 79 44 160 230 1,100 230 270 540 7 15 120 590 .. 6 23 530 70 370 b 190 190 170 4 8 ..
2004 World Development Indicators
110 16 540 230 76 250 5 17 5 87 10 41 210 1,000 67 20 5 110 650 42 150 550 760 97 13 23 550 1,800 41 1,200 1,000 24 83 36 110 220 1,000 360 300 740 16 7 230 1,600 800 16 87 500 160 300 170 410 200 13 5 25
97
PEOPLE
Reproductive health
2.16
Reproductive health Total fertility rate
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Adolescent Women at risk Contraceptive fertility of unintended prevalence rate pregnancy rate births
% of
per 1,000
married
% of
births
women
women
women
per woman
ages 15–19
ages 15–49
ages 15–49
2002
1990–2002 a
1990–2002 a
41 46 52 91 89 32 182 8 25 9 204 43 9 20 56 103 9 5 38 24 115 72 82 42 10 55 16 182 31 64 27 47 64 37 90 28 81 97 129 86 63 w 98 36 33 54 68 25 38 70 41 98 126 24 11
.. .. 36 .. 35 .. .. .. .. .. .. 15 .. .. .. .. .. .. .. .. 22 .. 32 .. .. 10 10 35 .. .. .. .. .. 14 .. 7 .. 39 27 13
1980
2002
2.4 1.9 8.3 7.3 6.8 2.3 6.5 1.7 2.3 2.1 7.3 4.6 2.2 3.5 6.1 6.2 1.7 1.5 7.4 5.6 6.7 3.5 6.8 3.3 5.2 4.3 4.9 7.2 2.0 5.4 1.9 1.8 2.7 4.8 4.2 5.0 .. 7.9 7.0 6.4 3.7 w 5.5 3.2 3.1 3.6 4.1 3.1 2.5 4.1 6.2 5.3 6.6 1.9 1.8
1.3 1.3 5.7 5.3 4.9 1.7 5.6 1.4 1.3 1.1 6.9 2.8 1.3 2.1 4.4 4.2 1.6 1.5 3.4 2.9 5.0 1.8 4.9 1.8 2.1 2.2 2.7 6.0 1.2 3.0 1.7 2.1 2.2 2.3 2.7 1.9 4.9 6.0 5.1 3.7 2.6 w 3.5 2.1 2.1 2.4 2.8 2.1 1.6 2.5 3.1 3.2 5.1 1.7 1.5
64 34 13 21 11 .. .. .. .. .. .. 62 .. .. 10 .. .. .. 45 .. 25 72 24 .. 60 64 62 23 72 .. .. 64 .. 56 .. 79 42 21 26 54 .. w .. .. 86 .. .. 83 .. .. 53 50 .. .. ..
Births attended by skilled health staff
per 100,000 live births % of total 1985
.. .. .. .. 41 .. .. .. .. .. .. .. .. .. .. .. 100 .. .. .. .. .. .. 98 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
1995–2002 a
98 99 31 91 58 99 42 100 .. 100 b 34 84 .. 97 86 b 70 100 b .. 76 b 71 36 99 49 96 90 81 97 39 100 96 99 99 100 96 94 70 .. 22 43 73 60 w 41 80 78 92 56 72 93 82 70 35 44 99 ..
a. Data are for most recent year available. b. Data refer to period other than specified, differ from the standard definition, or refer to only part of a country.
98
2004 World Development Indicators
Maternal mortality ratio
National
Modeled
estimates
estimates
1985–2002 a
2000
34 37 1,100 .. 560 7 1,800 6 16 17 .. 150 0 92 550 230 5 5 110 b 45 530 36 480 70 69 130 b 9 510 18 3 7 8 26 34 60 95 .. 350 650 700
49 67 1,400 23 690 11 2,000 30 3 17 1,100 230 4 92 590 370 2 7 160 100 1,500 44 570 160 120 70 31 880 35 54 13 17 27 24 96 130 .. 570 750 1,100 403 w 657 106 112 67 440 115 58 193 165 506 917 13 10
2.16
About the data
Reproductive health is a state of physical and mental
because of menopause, infertility, or postpartum anovu-
The maternal mortality ratios shown in the table as
well-being in relation to the reproductive system and
lation. Common reasons for not using contraception are
national estimates are based on national surveys,
its functions and processes. Means of achieving repro-
lack of knowledge about contraceptive methods and
vital registration, or surveillance or are derived from
ductive health include education and services during
concerns about their possible health side-effects.
community and hospital records. Those shown as
pregnancy and childbirth, provision of safe and effec-
Contraceptive prevalence reflects all methods—
modeled estimates are based on an exercise carried
tive contraception, and prevention and treatment of
ineffective traditional methods as well as highly effec-
out by the World Health Organization (WHO), United
sexually transmitted diseases. The complications of
tive modern methods. Contraceptive prevalence rates
Nations Children’s Fund (UNICEF), and United Nations
pregnancy and childbirth are the leading cause of
are obtained mainly from Demographic and Health
Population Fund (UNFPA). In this exercise maternal
death and disability among women of reproductive age
Surveys and contraceptive prevalence surveys (see
mortality was estimated with a regression model using
in developing countries. Reproductive health services
Primary data documentation for the most recent sur-
information on fertility, birth attendants, and HIV
will need to expand rapidly over the next two decades,
vey year). Unmarried women are often excluded from
prevalence. Neither set of ratios can be assumed to
when the number of women and men of reproductive
such surveys, which may bias the estimates.
provide an accurate estimate of maternal mortality in any of the countries in the table.
age is projected to increase by more than 600 million.
The share of births attended by skilled health staff is
Total and adolescent fertility rates are based on
an indicator of a health system’s ability to provide ade-
data on registered live births from vital registration
quate care for pregnant women. Good antenatal and
systems or, in the absence of such systems, from cen-
postnatal care improve maternal health and reduce
suses or sample surveys. As long as the surveys are
maternal and infant mortality. But data may not reflect
• Total fertility rate is the number of children that
fairly recent, the estimated rates are generally consid-
such improvements because health information systems
would be born to a woman if she were to live to the
ered reliable measures of fertility in the recent past.
are often weak, maternal deaths are underreported, and
end of her childbearing years and bear children in
Where no empirical information on age-specific fertili-
rates of maternal mortality are difficult to measure.
accordance with current age-specific fertility rates.
Definitions
• Adolescent fertility rate is the number of births per
ty rates is available, a model is used to estimate the
Maternal mortality ratios are generally of unknown
share of births to adolescents. For countries without
reliability, as are many other cause-specific mortality
1,000 women ages 15–19. • Women at risk of unin-
vital registration systems, fertility rates are generally
indicators. Household surveys such as the Demo-
tended pregnancy are fertile, married women of repro-
based on extrapolations from trends observed in cen-
graphic and Health Surveys attempt to measure mater-
ductive age who do not want to become pregnant and
suses or surveys from earlier years.
nal mortality by asking respondents about survivorship
are not using contraception. • Contraceptive preva-
An increasing number of couples in the developing
of sisters. The main disadvantage of this method is that
lence rate is the percentage of women who are prac-
world want to limit or postpone childbearing but are not
the estimates of maternal mortality that it produces per-
ticing, or whose sexual partners are practicing, any
using effective contraceptive methods. These couples
tain to 12 years or so before the survey, making them
form of contraception. It is usually measured for mar-
face the risk of unintended pregnancy, shown in the
unsuitable for monitoring recent changes or observing
ried women ages 15–49 only. • Births attended by
table as the percentage of married women of reproduc-
the impact of interventions. In addition, measurement
skilled health staff are the percentage of deliveries
tive age who do not want to become pregnant but are
of maternal mortality is subject to many types of errors.
attended by personnel trained to give the necessary
not using contraception (Bulatao 1998). Information on
Even in high-income countries with vital registration sys-
supervision, care, and advice to women during preg-
this indicator is collected through surveys and excludes
tems, misclassification of maternal deaths has been
nancy, labor, and the postpartum period; to conduct
women not exposed to the risk of unintended pregnancy
found to lead to serious underestimation.
deliveries on their own; and to care for newborns. • Maternal mortality ratio is the number of women
2.16a
who die from pregnancy-related causes during pregnancy and childbirth, per 100,000 live births.
Does household wealth affect antenatal care? Pregnant women attending antenatal clinics, by wealth quintile (%) 100 80 60
Data sources 40
The data on reproductive health come from Demographic and Health Surveys, the WHO’s
20
Coverage of Maternity Care (1997) and other WHO sources, UNICEF, and national statistical offices.
0 Poorest
Second
Third
Fourth
Richest
Across 22 countries in Sub-Saharan Africa rich women were about 1.5 times more likely to attend antenatal clinics than were poor women. The lack of care can contribute to women’s death during pregnancy or childbirth and can also compromise the health and survival of their infants.
Modeled estimates for maternal mortality ratios are from Carla AbouZahr and Tessa Wardlaw’s “Maternal Mortality in 2000: Estimates Developed by WHO, UNICEF, and UNFPA” (2003).
Source: WHO and UNICEF 2003.
2004 World Development Indicators
99
PEOPLE
Reproductive health
2.17 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
100
Nutrition Prevalence of undernourishment
Prevalence of child malnutrition
% of population 1990–92 1999–2001
% of children under age 5 Weight for age Height for age 1996–2002 a 1996–2002 a
58 5c 5 61 <3 55 c .. .. 37 c 35 <3 c .. 20 26 13 c 18 12 8c 22 49 43 33 .. 50 58 8 17 d <3 17 31 37 7 18 18 c 8 <3 c .. 27 8 5 12 .. 10 c .. .. .. 11 22 45 c .. 35 .. 16 40 .. 65
70 b 4 6 49 <3 51 .. .. 21 32 3 .. 16 22 8 24 9 16 17 70 38 27 .. 44 34 4 11 d <3 13 75 30 6 15 12 11 <3 .. 25 4 3 14 61 4 42 .. .. 7 27 26 .. 12 .. 25 28 .. 49
2004 World Development Indicators
49 14 6 31 5 3 0 .. 17 48 .. .. 23 8 4 13 6 .. 34 45 45 22 .. .. 28 1 10 .. 7 31 .. 5 21 1 4 .. .. 5 14 4 12 40 .. 47 .. .. 12 17 3 .. 25 .. 24 33 25 17
48 32 18 45 12 13 0 .. 20 45 .. .. 31 27 10 23 11 .. 37 57 45 29 .. .. 29 2 14 .. 14 38 .. 6 25 1 5 .. .. 6 26 19 23 38 .. 52 .. .. 21 19 12 .. 26 .. 46 41 30 23
Prevalence of overweight
Year
1997 2000 2000 1996 1995–96 2000–01 1995–96 2000 1999–2000
2001 1998 2000 2000 1996 1998–99 1987 2000 1998 1995 2000 2002 2000 2000 2001 1987 1996 1998–99 1995–96 1991 1996 1995–96 1998 1995–96 2000
2000–01 1999 1998–99 1998–99 1999 2000
Lowbirthweight babies
% of children under age 5
% of births 1998–2002 a
4.0 22.5 10.1 0.5 9.2 10.4 5.2 .. 3.8 0.4 .. .. 1.8 6.5 13.2 6.9 4.9 .. 1.0 1.1 2.0 5.0 .. 0.8 1.5 8.0 2.6 .. 3.7 3.9 0.7 6.2 2.5 5.9 .. 4.1 .. 4.9 .. 8.6 2.6 0.9 .. 1.2 .. .. 3.7 .. 12.7 .. 1.7 .. 4.4 2.7 .. 2.0
.. 3 7 12 7 7 7 7 11 30 5 8e 16 9 4 10 10 e 10 19 16 11 11 6 14 17 e 5 6 .. 9 12 .. 7 17 6 6 7 5 14 16 12 13 21 e 4 15 4 7 14 17 6 7 11 8 13 12 22 21
Exclusive breastfeeding
Consumption of iodized salt
Vitamin A supplementation
% of children under 6 months 1995–2002 a
% of households 1997–2002 a
% of children 6–59 months 2001
.. 6 13 11 .. 30 .. .. 7 46 .. .. 38 39 6 34 42 f .. 6 62 12 12 .. 17 10 73 f 67 f .. 32 24 4f 35 e, f 10 23 41 .. .. 11 29 f 57 16 52 .. 55 .. .. 6 26 18 f .. 31 .. 39 11 37 24
2 62 69 35 90 e 84 .. .. 26 70 37 .. 72 65 77 66 95 e .. 23 e 96 14 84 .. 86 58 100 93 .. 92 72 .. 97 e 31 90 73 .. .. 18 99 28 91 e 97 .. 28 .. .. 15 8 8 .. 28 .. 49 12 2 11
84 .. .. 75 .. .. .. .. .. 90 .. .. 95 31 .. 85 .. .. 97 95 57 100 .. 90 91 .. .. .. .. 98 100 .. 97 .. .. .. .. 35 50 .. .. 61 .. 16 .. .. 89 91 .. .. 100 .. .. 93 100 ..
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Prevalence of undernourishment
Prevalence of child malnutrition
% of population 1990–92 1999–2001
% of children under age 5 Weight for age Height for age 1996–2002 a 1996–2002 a
23 <3 c 25 9 5 7 .. .. .. 14 .. 4 <3 c 44 18 <3 22 28 c 29 3c 3 27 33 <3 4c 15 c 35 49 3 25 14 6 5 5c 34 6 69 10 20 18 .. .. 30 42 13 .. .. 26 20 25 18 40 26 <3 c .. ..
20 <3 21 6 5 27 b .. .. .. 9 .. 6 22 37 34 <3 4 7 22 6 3 25 42 <3 <3 10 36 33 <3 21 10 5 5 12 38 7 53 7 7 17 .. .. 29 34 8 .. .. 19 26 27 13 11 22 <3 .. ..
17 .. 47 25 11 16 .. .. .. 4 .. 5 4 22 28 .. 2 6 40 .. 3 18 27 .. .. 6 33 25 .. 33 32 .. 8 .. 13 9 26 28 .. 48 .. .. 10 40 31 .. 18 .. 8 .. .. 7 32 .. .. ..
29 .. 45 .. 15 22 .. .. .. 4 .. 8 10 33 45 .. 3 25 41 .. 12 45 40 .. .. 7 49 49 .. 38 35 .. 18 .. 25 23 36 42 .. 51 .. .. 20 40 34 .. 10 .. 18 .. .. 25 32 .. .. ..
Prevalence of overweight
Year
2001 1980–88 1998–99 1995 1998
1975–77 1999 1978–81 1997 1999 1993
1996–97 1997
1999–2000
1999 1997 2000 2001 1995 1998–99 1999 1992 1997 1997 1992 2001 1980 1998 2000 1993 1998 1990–94 1997 1982–83 1990 2000 1998
% of children under age 5
2.2 2.0 2.2 4.0 4.3 .. .. .. 4.4 3.8 1.6 2.8 3.0 3.5 .. .. 5.7 6.3 .. .. .. .. 2.3 .. .. 4.9 2.0 4.3 .. 1.5 .. 4.0 5.3 .. 4.8 6.8 3.4 7.7 3.3 0.2 1.6 .. 2.6 0.8 3.3 .. 1.0 1.3 4.2 1.6 3.9 7.6 1.0 .. .. ..
Lowbirthweight babies
% of births 1998–2002 a
14 9 30 10 e 7e 15 6 8 6 9 8 10 e 8 11 7 4 7 7e 14 5 6 14 .. 7e 4 5 14 16 10 23 42 13 9 5 8 11 e 14 e 15 16 e 21 .. 6 13 17 12 5 8 19 e 10 e 11 e 9e 11 e 20 6 8 ..
2.17
Exclusive breastfeeding
Consumption of iodized salt
Vitamin A supplementation
% of children under 6 months 1995–2002 a
% of households 1997–2002 a
% of children 6–59 months 2001
35 .. 37 f 42 44 12 .. .. .. .. .. 34 36 5 97 f .. 12 f 24 23 .. 27 f 15 35 .. .. 37 41 44 29 f 38 20 16 e, f 38 e,f .. 51 66 f 30 11 26 f 69 .. .. 31 1 17 .. .. 16 f 25 59 7f 71 37 .. .. ..
80 .. 50 65 94 40 .. .. .. 100 .. 88 20 91 .. .. .. 27 75 .. 87 69 .. 90 e .. 100 52 49 .. 74 2 0e 90 33 45 41 62 e 48 63 63 .. 83 96 15 98 .. 61 17 95 .. 83 93 24 .. .. ..
2004 World Development Indicators
62 .. 25 61 .. .. .. .. .. .. .. .. .. 90 99 .. .. .. 70 .. .. .. 100 .. .. .. 73 63 .. 74 98 .. .. .. 93 .. 71 97 84 98 .. .. .. 89 77 .. .. 100 .. .. .. 6 84 .. .. ..
101
PEOPLE
Nutrition
2.17 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Nutrition Prevalence of undernourishment
Prevalence of child malnutrition
% of population 1990–92 1999–2001
% of children under age 5 Weight for age Height for age 1996–2002 a 1996–2002 a
<3 c 4c 43 4 23 5c 46 .. 4c 3c 68 .. .. 29 31 10 .. .. 5 22 c 35 28 33 13 <3 <3 15 c 23 <3 c 4 .. .. 6 10 c 11 27 .. 35 45 43 21 w 26 15 16 .. 21 17 .. 14 7 27 31 .. ..
<3 4 41 3 24 9 50 .. 5 <3 71 b .. .. 25 25 12 .. .. 4 71 43 19 25 12 <3 3 7 19 4 <3 .. .. 3 26 18 19 .. 33 50 39 17 w 24 10 11 .. 17 12 9 11 8 23 32 .. ..
3 6 24 .. 23 2 27 .. .. .. 26 .. .. 33 11 10 .. .. 7 .. 29 .. 25 6 4 8 12 23 3 7 .. .. .. 19 4 34 4 46 28 13 .. w 42 .. 9 .. .. 15 .. 9 .. 48 .. .. ..
10 11 43 .. 25 5 34 .. .. .. 23 .. .. .. .. 30 .. .. 19 31 44 .. 22 4 12 16 22 39 16 .. .. .. .. 31 13 37 7 52 47 27 .. w .. 25 17 .. .. 14 .. 19 .. 47 .. .. ..
Prevalence of overweight
Year
2002 2000 2000 1996 1970–77
1995 1987
1999 1995 1998 1987 1996–97 1998 1995 2000
1988–94 1992–93 1996 2000 2000 1996 1996 2001–02 1999
% of children under age 5
5.5 .. 4.0 .. 2.2 12.9 .. 0.5 .. .. .. 6.7 .. 0.1 .. .. .. .. .. .. 1.7 2.8 1.5 3.0 4.5 2.2 .. 2.8 20.1 .. .. 4.5 6.2 14.4 3.2 2.7 2.3 4.3 3.0 7.0
Lowbirthweight babies
% of births 1998–2002 a
9 6 9 11 e 18 4 .. 8 7 6 .. 15 6e 22 31 9 4 6 6 15 13 9 15 23 7 16 6 12 5 15 e 8 8 8 7 7 9 .. 32 e 10 11 15 w 21 9 9 8 16 8 9 10 12 28 14 7 7
Exclusive breastfeeding
Consumption of iodized salt
Vitamin A supplementation
% of children under 6 months 1995–2002 a
% of households 1997–2002 a
% of children 6–59 months 2001
.. .. 84 31 f 24 f 11 f 4 .. .. .. 9 7 .. 54 f 16 24 .. .. 81 f 14 32 4f 18 2 46 7 13 65 22 34 f .. .. .. 16 7f 31 .. 18 40 33
.. 30 e 90 .. 16 73 23 .. .. .. .. 62 .. 88 1 59 .. .. 40 20 67 74 67 1 97 64 75 95 5 .. .. .. .. 19 90 40 .. 39 68 93 66 w 52 79 78 89 66 82 36 88 58 48 62 .. ..
.. .. 94 .. 85 .. 91 .. .. .. 62 .. .. .. 92 .. .. .. .. .. 93 .. 77 .. .. .. .. 37 .. .. .. .. .. .. .. 59 .. 100 83 .. .. w 55 .. .. .. 51 .. .. .. .. 42 76 .. ..
a. Data are for the most recent year available. b. Data are for 1998–2000. c. Data are for 1993–95. d. Includes Taiwan, China. e. Data refer to period other than specified, differ from the standard definition, or refer to only part of a country. f. Refers to exclusive breastfeeding for less than four months.
102
2004 World Development Indicators
About the data
2.17
Definitions
Data on undernourishment are produced by the Food
from hospital records and household surveys. Many
• Prevalence of undernourishment is the percent-
and Agriculture Organization (FAO) based on the calo-
births in developing countries take place at home,
age of the population that is undernourished.
ries available from local food production, trade, and
and these births are seldom recorded. A hospital
• Prevalence of child malnutrition is the percentage
stocks; the number of calories needed by different
birth may indicate higher income and therefore better
of children under five whose weight for age or height
age and gender groups; the proportion of the popula-
nutrition, or it could indicate a higher-risk birth, pos-
for age is more than two standard deviations below
tion represented by each age group; and a coefficient
sibly skewing the data on birthweights downward. The
the median for the international reference population
of distribution to take account of inequality in access
data should therefore be treated with caution.
ages 0–59 months. For children up to two years of
to food (FAO 2000). From a policy and program stand-
It is estimated that breastfeeding can save some
age height is measured by recumbent length. For
point, however, this measure has its limits. First, food
1.5 million children a year. Breast milk alone con-
older children height is measured by stature while
insecurity exists even where food availability is not a
tains all the nutrients, antibodies, hormones, and
standing. The reference population, adopted by the
problem because of inadequate access of poor
antioxidants an infant needs to thrive. It protects
WHO in 1983, is based on children from the United
households to food. Second, food insecurity is an
babies from diarrhea and acute respiratory infec-
States, who are assumed to be well nourished.
individual or household phenomenon, and the aver-
tions, stimulates their immune systems and
• Prevalence of overweight is the percentage of
age food available to each person, even corrected for
response to vaccination, and according to some
children under five whose weight for height is more
possible effects of low income, is not a good predic-
studies, confers cognitive benefits as well. The data
than two standard deviations above the median for
tor of food insecurity among the population. And third,
on breastfeeding are derived from national surveys.
the international reference population of the corre-
nutrition security is determined not only by food secu-
Iodine deficiency is the single most important
sponding age, established by the U.S. National
rity but also by the quality of care of mothers and chil-
cause of preventable mental retardation, and it con-
Center for Health Statistics and the WHO. • Low-
dren and the quality of the household’s health envi-
tributes significantly to the risk of stillbirth and mis-
birthweight babies are newborns weighing less than
ronment (Smith and Haddad 2000).
carriage. Iodized salt is the best source of iodine,
2,500 grams, with the measurement taken within
Estimates of child malnutrition, based on weight
and a global campaign to iodize edible salt is signifi-
the first hours of life, before significant postnatal
for age (underweight) and height for age (stunting),
cantly reducing the risks (UNICEF, The State of the
weight loss has occurred. • Exclusive breastfeeding
are from national survey data. The proportion of chil-
World’s Children 1999).
refers to the percentage of children less than 6
dren who are underweight is the most common indi-
Vitamin A is essential for the functioning of the
cator of malnutrition. Being underweight, even mild-
immune system. A child deficient in vitamin A faces
liquids). • Consumption of iodized salt refers to the
ly, increases the risk of death and inhibits cognitive
a 23 percent greater risk of dying from a range of
percentage of households that use edible salt forti-
development in children. Moreover, it perpetuates
childhood ailments such as measles, malaria, and
fied with iodine. • Vitamin A supplementation refers
the problem from one generation to the next, as mal-
diarrhea. Improving the vitamin A status of pregnant
to the percentage of children ages 6–59 months who
nourished women are more likely to have low-birth-
women helps reduce anemia, improves their resist-
received at least one high-dose vitamin A capsule in
weight babies. Height for age reflects linear growth
ance to infection, and may reduce their risk of dying
the previous six months.
achieved pre- and postnatally, and a deficit indicates
during pregnancy and childbirth. Giving vitamin A to
long-term, cumulative effects of inadequacies of
new mothers who are breastfeeding helps to protect
health, diet, or care. It is often argued that stunting
their children during the first months of life.
months old who are fed breast milk alone (no other
is a proxy for multifaceted deprivation and is a better indicator of long term changes in malnutrition. Estimates of children who are overweight are also from national survey data. Overweight in children has become a growing concern in developing countries. Researchers show an association between obesity in childhood and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blossner 2000). The survey data were analyzed in a
Data sources
standardized way by the World Health Organization
Data are drawn from a variety of sources, includ-
(WHO) to allow comparisons across countries.
ing the FAO’s State of Food Insecurity in the World
Low birthweight, which is associated with maternal malnutrition, raises the risk of infant mortality and
2003;
the
United
Nations
Administrative
Committee on Coordination, Subcommittee on
stunts growth in infancy and childhood. There is also
Nutrition’s Update on the Nutrition Situation; the
emerging evidence that low-birthweight babies are
WHO’s World Health Report 2003; and the United
more prone to noncommunicable diseases such
Nations Children’s Fund’s (UNICEF) State of the
as diabetes and cardiovascular heart diseases.
World’s Children 2004.
Estimates of low-birthweight infants are drawn mostly
2004 World Development Indicators
103
PEOPLE
Nutrition
2.18
Health risk factors and future challenges Prevalence of smoking
Incidence of tuberculosis
% of adults
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
104
Prevalence of HIV
% ages 15–24 a
per 100,000
% of
Male
Female
people
adults
Male
Female
2000
2000
2002
2001
2001
2001
.. 60 44 .. 47 64 21 30 30 54 55 30 .. 43 .. .. 38 49 .. .. 66 .. 27 .. 24 26 67 .. 24 .. .. 29 42 34 48 36 32 24 46 35 38 .. 44 .. 27 39 .. 34 61 39 28 47 38 60 .. 11
.. 18 7 .. 34 1 18 19 1 24 5 26 .. 18 .. .. 29 24 .. .. 8 .. 23 .. .. 18 4 .. 21 6 .. 7 2 32 26 22 29 17 17 2 12 .. 20 .. 20 30 .. 2 15 31 4 29 18 44 .. 9
333 27 52 335 46 77 6 15 82 221 83 14 86 234 60 657 62 48 157 359 549 188 6 338 222 18 113 93 45 383 395 15 412 47 12 13 13 95 137 29 60 268 55 370 10 14 248 230 85 10 211 20 77 215 196 319
<0.01 <0.01 0.10 5.50 0.70 0.20 0.10 0.20 <0.10 <0.10 0.30 0.20 3.60 <0.10 0.10 38.80 0.70 <0.10 6.50 8.30 2.70 11.80 0.30 12.90 3.60 0.30 0.10 0.10 0.40 4.90 7.20 0.60 9.70 <0.10 <0.10 <0.10 0.20 2.50 0.30 <0.10 0.60 2.80 1.00 6.40 <0.10 0.30 4.16 1.60 <0.10 0.10 3.00 0.20 1.00 1.54 2.80 6.10
.. .. .. 2.23 0.86 0.22 0.12 0.22 0.06 0.01 0.58 0.12 1.17 0.11 .. 16.08 0.64 .. 3.97 4.95 0.96 5.44 0.28 5.82 2.38 0.35 0.16 0.00 0.85 2.92 3.28 0.58 2.91 0.00 0.09 0.00 0.14 2.10 0.31 .. 0.77 2.78 2.48 4.39 0.04 0.26 2.32 0.52 0.08 0.10 1.36 0.14 0.90 0.57 1.06 4.06
.. .. .. 5.74 0.34 0.06 0.01 0.12 0.01 0.01 0.19 0.12 3.71 0.05 .. 37.49 0.48 .. 9.73 11.05 2.48 12.67 0.17 13.54 4.28 0.13 0.09 0.00 0.19 5.91 7.80 0.27 8.31 0.00 0.05 0.00 0.06 2.76 0.15 .. 0.35 4.30 0.62 7.82 0.03 0.17 4.72 1.35 0.02 0.05 2.97 0.06 0.85 1.43 2.98 4.95
2004 World Development Indicators
Prevalence of smoking
Incidence of tuberculosis
% of adults
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Prevalence of HIV
% ages 15–24 a
per 100,000
% of
Male
Female
people
adults
Male
Female
2000
2000
2002
2001
2001
2001
36 44 29 59 27 40 32 33 32 .. 53 48 60 67 .. 65 30 60 41 49 46 39 .. .. 51 40 .. 20 49 .. .. 45 51 46 68 35 .. 44 65 48 37 25 .. .. 15 31 16 36 56 46 24 42 54 44 30 ..
11 27 3 4 3 5 31 24 17 .. 13 10 7 32 .. 5 2 16 15 13 35 1 .. .. 16 32 .. 9 4 .. .. 3 18 18 26 2 .. 22 35 29 29 25 .. .. 2 32 2 9 20 28 6 16 11 25 7 ..
86 32 168 256 29 167 13 10 8 8 33 5 146 540 160 91 26 142 170 78 14 726 247 21 66 41 234 431 95 334 188 64 33 154 209 114 436 154 751 190 8 11 64 193 304 6 11 181 47 254 70 202 320 32 47 7
1.60 0.10 0.80 0.10 <0.10 <0.10 0.10 0.10 0.40 1.20 <0.10 <0.10 0.10 6.70 b <0.01 <0.10 0.12 <0.10 <0.10 0.40 0.09 31.00 2.80 0.20 0.10 <0.10 0.30 15.00 0.40 1.70 c 0.52 0.10 0.30 0.20 <0.10 0.10 13.00 1.99 22.50 0.50 0.20 0.10 0.20 1.35 5.80 0.10 0.10 0.10 1.50 0.70 0.11 0.40 <0.10 0.10 0.50 ..
1.20 0.09 0.34 0.06 0.05 .. 0.06 0.06 0.28 0.82 0.01 .. 0.13 6.01 .. 0.03 .. 0.00 0.05 0.94 .. 17.40 .. .. 0.16 0.00 0.06 6.35 0.70 1.37 0.38 0.04 0.37 0.46 .. .. 6.13 1.04 11.10 0.26 0.20 0.05 0.23 0.95 2.99 0.08 .. 0.06 1.88 0.33 0.13 0.41 0.01 0.09 0.41 ..
1.50 0.02 0.71 0.06 0.01 .. 0.05 0.06 0.26 0.86 0.04 .. 0.03 15.56 .. 0.01 .. 0.00 0.03 0.24 .. 38.08 .. .. 0.05 0.00 0.23 14.89 0.12 2.08 0.59 0.04 0.09 0.14 .. .. 14.67 1.72 24.29 0.28 0.09 0.01 0.08 1.50 5.82 0.04 .. 0.05 1.25 0.39 0.04 0.18 0.01 0.05 0.19 ..
2004 World Development Indicators
105
PEOPLE
2.18
Health risk factors and future challenges
2.18
Health risk factors and future challenges Prevalence of smoking
Incidence of tuberculosis
% of adults
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Prevalence of HIV
% ages 15–24 a
per 100,000
% of
Male
Female
people
adults
Male
Female
2000
2000
2002
2001
2001
2001
148 126 389 42 242 38 405 43 24 21 405 558 30 54 217 1,067 5 8 44 109 363 128 361 13 23 32 94 377 95 18 12 5 29 101 42 192 27 92 668 683 142 w 226 108 116 43 164 147 88 67 57 176 358 18 15
<0.10 0.90 8.90 0.01 0.50 0.20 7.00 0.20 <0.10 <0.10 1.00 15.60 d 0.50 <0.10 2.60 33.40 0.10 0.50 0.01 <0.10 7.80 1.80 6.00 2.50 0.04 <0.10 <0.10 5.00 1.00 0.18 0.10 0.60 0.30 <0.10 0.50 0.30 .. 0.10 15.60 e 33.70 1.27 w 2.31 0.69 0.70 0.57 1.45 0.19 0.45 0.67 0.10 0.64 8.38 0.33 0.28
0.02 1.87 4.91 .. 0.19 .. 2.48 0.14 0.00 0.00 .. 10.66 0.51 0.03 1.08 15.23 0.06 0.46 .. 0.00 3.55 1.11 2.05 2.41 .. .. 0.00 1.99 1.96 .. 0.10 0.47 0.52 0.01 0.65 0.31 .. .. 8.06 12.38 0.83 w 1.11 0.68 0.69 0.56 0.91 0.19 1.03 0.68 .. 0.27 4.14 0.26 0.25
0.02 0.67 11.20 .. 0.54 .. 7.53 0.16 0.00 0.00 .. 25.64 0.24 0.04 3.13 39.49 0.05 0.40 .. 0.00 8.06 1.66 5.93 3.23 .. .. 0.00 4.63 0.88 .. 0.05 0.22 0.20 0.00 0.15 0.17 .. .. 20.98 33.01 1.57 w 2.51 0.91 0.98 0.44 1.77 0.17 0.39 0.47 .. 0.54 9.44 0.14 0.15
62 63 7 22 .. 52 .. 27 55 30 .. 42 42 26 24 25 19 39 51 .. 50 44 .. 42 62 65 27 52 51 18 27 26 32 49 42 51 .. 60 35 34 46 w 37 56 58 44 48 63 56 40 37 33 .. 36 37
25 10 4 1 .. 42 .. 3 30 20 .. 11 25 2 1 2 19 28 10 .. 12 3 .. 8 8 24 1 17 19 1 26 22 14 9 39 4 .. 29 10 1 11 w 7 10 9 21 9 5 17 24 6 6 .. 21 26
a. Average of high and low estimates. b. Demographic and Health Survey 2003. c. Demographic and Health Survey 2001. d. Demographic and Health Survey 2002. e. Demographic and Health Survey 2001/02.
106
2004 World Development Indicators
2.18
About the data The limited availability of data on health status is a major
onset of disease, the health impact of smoking in devel-
occur in young adults, with young women especially vul-
constraint in assessing the health situation in developing
oping countries will increase rapidly in the next few
nerable. The estimates of HIV prevalence are based on
countries. Surveillance data are lacking for many major
decades. Because the data present a one-time esti-
extrapolations from data collected through surveys and
public health concerns. Estimates of prevalence and inci-
mate, with no information on the intensity or duration of
surveillance of small, nonrepresentative groups.
dence are available for some diseases but are often
smoking, they should be interpreted with caution.
Estimates from recent Demographic and Health
unreliable and incomplete. National health authorities
Tuberculosis is one of the main causes of death from
Surveys (DHS) that have collected data on HIV/AIDS dif-
differ widely in their capacity and willingness to collect or
a single infectious agent among adults in developing
fer from those of the Joint United Nations Programme on
report information. To compensate for the paucity of
countries. In high-income countries tuberculosis has
HIV/AIDS (UNAIDS) and WHO, which are based on sur-
data and ensure reasonable reliability and international
reemerged largely as a result of cases among immi-
veillance systems that focus on pregnant women who
comparability, the World Health Organization (WHO) pre-
grants. The estimates of tuberculosis incidence in the
attend sentinel antenatal clinics. There are reasons to
pares estimates in accordance with epidemiological
table are based on a new approach in which reported
be cautious about comparing the two sets of estimates.
models and statistical standards.
cases are adjusted using the ratio of case notifications
DHS is a household survey that uses a representative
to the estimated share of cases detected by panels of
sample from the whole population, whereas surveillance
80 epidemiologists convened by the WHO.
data from antenatal clinics is limited to pregnant women.
Smoking is the most common form of tobacco use in many countries, and the prevalence of smoking is therefore a good measure of the extent of the tobacco epi-
Adult HIV prevalence rates reflect the rate of HIV infec-
Representative household surveys also frequently pro-
demic (Corrao and others 2000). While the prevalence
tion in each country’s population. Low national preva-
vide better coverage of rural populations. However, the
of smoking has been declining in some high-income
lence rates can be very misleading, however. They often
fact that some respondents refuse to participate or are
countries, it has been increasing in many developing
disguise serious epidemics that are initially concentrated
absent from the household adds considerable uncer-
countries. Tobacco use causes heart and other vascular
in certain localities or among specific population groups
tainty to survey-based HIV estimates, because the pos-
diseases and cancers of the lung and other organs.
and threaten to spill over into the wider population. In
sible association of absence or refusal with higher HIV
Given the long delay between starting to smoke and the
many parts of the developing world most new infections
prevalence is unknown. UNAIDS and WHO estimates are generally based on surveillance systems that focus on
2.18a
pregnant women who attend sentinel antenatal clinics.
HIV prevalence rates vary by method of data collection
UNAIDS and WHO use a methodology to estimate HIV
Prevalence rate (%)
prevalence for the adult population (ages 15–49) that
UNAIDS and WHO surveillance data
Country
Demographic and Health Survey data
assumes that prevalence among pregnant women is a good approximation of prevalence among men and
Zambia
21.5
15.6
South Africa
20.1
15.6
Kenya
15.0
6.7
countries or over time. There are also other potential
1.7
1.7
biases associated with the use of antenatal clinic data,
Mali
women. However, this assumption might not apply to all
Recent household survey data from Demographic and Health Surveys show significantly lower HIV prevalence rates than those from UNAIDS and WHO, which are based on surveillance. This indicates that different data collection methodologies, and their quality and coverage, record different prevalence rates. Source: UNAIDS and WHO 2002; Demographic and Health Survey data.
such as differences among women who attend antenatal clinics and those who do not.
Definitions
2.18b
• Prevalence of smoking is the percentage of men
In some countries men know more about preventing HIV than women do
and women who smoke cigarettes. The age range
Population with correct knowledge of HIV prevention (%)
varies among countries but in most is 18 and older or 15 and older. • Incidence of tuberculosis is the esti-
40
Men
Women
mated number of new tuberculosis cases (pulmonary, smear positive, extrapulmonary). • Prevalence of HIV
30
is the percentage of people who are infected with HIV. 20
Data sources The data are drawn from a variety of sources,
10
including the WHO’s World Health Report 2003, Tobacco Atlas 2002, and Global Tuberculosis
0 Uganda
Malawi
Tanzania
Rwanda
Haiti
Dominican Republic
More men correctly identified the ways of preventing HIV transmission and had fewer misconceptions about it than did women. Countries where populations are more informed about HIV transmission do not necessarily have a low HIV prevalence rate, because it takes time to change people’s behavior. However, there is no doubt that knowledge is an important prerequisite for behavior change.
Control Repor t 2003; the National Tobacco Information Online System (NATIONS) database (http://apps.nccd.cdc.gov/nations/); and the Joint United Nations Programme on HIV/AIDS (UNAIDS) and WHO’s AIDS Epidemic Update 2002.
Source: Demographic and Health Surveys, 1996–2000.
2004 World Development Indicators
107
PEOPLE
Health risk factors and future challenges
2.19
Mor tality Life expectancy at birth
Infant mortality rate
Under-five mortality rate
Child mortality rate
Adult mortality rate
Survival to age 65
per 1,000 per 1,000 years
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
108
live births
per 1,000
Male
Female
1997–
1997–
Male
per 1,000 Female
Male
% of cohort Female
1980
2002
1980
2002
1980
2002
2002 a
2002 a
2000–02 a
2000–02 a
2002
2002
40 69 59 41 70 73 74 73 68 49 71 73 48 52 70 58 63 71 44 47 39 50 75 46 42 69 67 74 66 49 50 73 49 70 74 70 74 63 63 56 57 44 69 42 73 74 48 40 71 73 53 74 57 40 39 51
43 74 71 47 74 75 79 79 65 62 68 79 53 64 74 38 69 72 43 42 54 48 79 42 48 76 71 80 72 45 52 78 45 74 77 75 77 67 70 69 70 51 71 42 78 79 53 53 73 78 55 78 65 46 45 52
183 56 94 158 33 22 11 14 91 129 21 12 126 112 31 62 67 20 140 114 110 105 11 121 124 31 49 .. 40 130 88 24 114 20 22 17 9 71 64 118 84 141 21 143 8 10 75 144 34 13 96 20 97 175 173 132
165 22 39 154 16 30 6 5 76 48 17 5 93 56 15 80 33 14 107 123 96 95 5 115 117 10 30 .. 19 129 81 9 116 7 7 4 4 32 25 33 33 59 10 114 4 4 63 91 24 4 60 5 36 106 130 79
280 66 134 265 38 80 13 17 117 205 26 15 213 170 39 84 86 24 247 190 190 173 13 189 225 39 64 .. 56 210 125 26 172 23 22 19 10 92 98 173 118 210 24 220 9 13 105 231 43 16 157 23 139 300 290 195
257 24 49 260 19 35 6 5 96 73 20 6 151 71 18 110 37 16 207 208 138 166 7 180 200 12 38 .. 23 205 108 11 191 8 9 5 4 38 29 39 39 80 12 171 5 6 85 126 29 5 97 5 49 165 211 123
.. .. .. .. .. 5 .. .. .. 28 .. .. 72 26 .. .. .. .. 131 .. 34 69 .. .. 106 .. .. .. 4 .. .. .. 83 .. .. .. .. 13 .. 15 .. 55 .. 83 .. .. 32 .. .. .. 53 .. 15 101 .. 52
.. .. .. .. .. 3 .. .. .. 38 .. .. 79 29 .. .. .. .. 128 .. 30 75 .. .. 99 .. .. .. 3 .. .. .. 58 .. .. .. .. 8 .. 16 .. 50 .. 86 .. .. 33 .. .. .. 51 .. 18 98 .. 54
437 209 155 492 184 223 100 122 261 262 381 126 384 264 200 703 259 239 559 648 386 488 101 620 449 151 161 97 238 571 475 131 553 150 143 160 128 234 199 210 250 493 316 594 144 130 380 373 250 125 379 114 286 432 495 524
376 95 119 386 92 106 52 58 150 252 133 65 328 219 93 669 136 103 507 603 334 440 57 573 361 67 110 50 115 493 406 78 494 110 94 75 80 146 120 147 148 441 114 535 61 57 330 320 133 59 326 47 182 366 427 373
32 77 73 34 75 70 84 83 58 59 54 82 43 59 75 13 62 69 28 26 42 35 83 24 38 79 72 85 71 31 35 82 31 71 81 75 80 63 72 69 68 37 60 26 80 82 45 40 71 82 47 83 58 32 34 38
33 85 79 39 87 83 92 91 72 61 81 91 50 67 86 18 79 83 32 29 48 41 92 29 43 89 79 92 83 35 44 90 34 87 88 88 88 75 77 75 81 42 85 30 91 92 51 47 87 91 51 91 72 33 39 47
2004 World Development Indicators
Life expectancy at birth
Infant mortality rate
Under-five mortality rate
Child mortality rate
2.19
Adult mortality rate
Survival to age 65
per 1,000 per 1,000 years
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
live births
per 1,000
Male
Female
1997–
1997–
Male
per 1,000 Female
Male
% of cohort Female
1980
2002
1980
2002
1980
2002
2002 a
2002 a
2000–02 a
2000–02 a
2002
2002
60 70 54 55 58 62 73 73 74 71 76 64 67 55 67 67 71 65 45 69 65 53 51 60 71 .. 51 44 67 42 47 66 67 66 58 58 44 51 53 48 76 73 59 40 46 76 60 55 70 51 67 60 61 70 71 74
66 72 63 67 69 63 77 79 78 76 82 72 62 46 62 74 77 65 55 70 71 38 47 72 73 73 55 38 73 41 51 73 74 67 65 68 41 57 42 60 78 78 69 46 45 79 74 64 75 57 71 70 70 74 76 77
75 24 113 79 92 63 12 16 15 28 8 52 45 73 32 16 29 90 135 21 38 115 157 55 21 53 106 157 31 176 118 33 56 41 97 99 140 94 84 124 9 13 85 191 108 9 73 105 34 79 46 89 55 21 25 ..
32 8 65 32 34 102 6 6 4 17 3 27 76 78 42 5 9 52 87 17 28 91 157 16 8 22 84 113 8 122 120 17 24 27 58 39 128 77 55 62 5 6 32 155 100 4 11 76 19 70 26 30 28 8 5 ..
103 26 173 125 130 83 14 19 17 34 11 67 58 115 43 18 35 115 200 26 44 168 235 70 22 69 175 265 42 300 175 40 74 53 140 144 233 134 114 183 11 16 120 320 216 11 95 156 46 108 61 126 81 24 31 ..
42 9 90 43 41 125 6 6 6 20 5 33 99 122 55 5 10 61 100 21 32 132 235 19 9 26 135 182 8 222 183 19 29 32 71 43 205 108 67 83 5 6 41 264 201 4 13 101 25 94 30 39 37 9 6 ..
.. .. 25 19 .. .. .. .. .. .. .. 5 11 36 .. .. .. 10 .. .. .. .. .. .. .. .. 75 101 .. 132 38 .. .. .. .. .. 85 .. .. 28 .. .. 12 184 66 .. .. .. .. .. .. 19 21 .. .. ..
.. .. 37 20 .. .. .. .. .. .. .. 5 6 38 .. .. .. 11 .. .. .. .. .. .. .. .. 68 102 .. 125 38 .. .. .. .. .. 82 .. .. 40 .. .. 11 202 69 .. .. .. .. .. .. 17 19 .. .. ..
221 295 250 227 170 258 108 99 110 169 98 199 366 578 238 186 100 335 355 328 192 667 448 210 286 160 385 701 202 518 357 228 180 325 280 174 674 343 695 314 95 108 225 473 443 105 187 221 145 359 173 190 249 226 164 148
157 123 191 175 139 208 62 56 53 127 44 144 201 529 192 71 68 299 299 122 136 630 385 157 106 89 322 653 113 446 302 109 101 165 199 113 612 245 661 314 64 69 161 308 393 59 135 198 93 329 129 139 142 88 66 55
59 67 62 64 71 63 80 84 81 80 86 74 47 28 55 72 82 56 45 60 71 15 33 73 66 75 49 20 72 25 43 70 75 58 65 68 25 46 22 58 83 83 67 30 33 84 78 64 78 49 70 69 70 71 77 76
72 85 66 72 75 67 89 90 91 87 94 81 71 33 62 86 88 75 50 84 79 20 37 83 87 84 55 23 83 29 49 85 85 75 71 76 30 58 25 56 90 90 77 37 36 91 84 70 86 53 80 78 77 87 89 91
2004 World Development Indicators
109
PEOPLE
Mor tality
2.19
Mor tality Life expectancy at birth
Infant mortality rate
Under-five mortality rate
Child mortality rate
Adult mortality rate
Survival to age 65
per 1,000 per 1,000 years 1980
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
69 67 46 61 45 70 35 71 70 70 43 57 76 68 48 52 76 76 62 66 50 64 49 68 62 61 64 48 69 68 74 74 70 67 68 60 .. 49 50 55 63 w 53 66 65 68 60 64 68 65 58 54 48 74 74
live births 2002
70 66 40 73 52 73 37 78 73 76 47 46 78 74 58 44 80 80 70 67 43 69 50 72 73 70 65 43 68 75 77 77 75 67 74 70 73 57 37 39 67 w 59 70 69 73 65 69 69 71 69 63 46 78 78
a. Data are for the most recent year available.
110
2004 World Development Indicators
per 1,000
Male
Female
1997–
1997–
Male
Female
Male
Female
2002 a
2000–02 a
2000–02 a
2002
2002
260 420 667 181 355 180 587 114 204 170 516 621 122 244 341 642 87 99 170 293 569 245 460 209 169 218 280 617 365 143 108 135 185 282 178 203 154 278 725 650 234 w 310 211 212 197 255 184 317 222 193 252 519 128 125
117 149 599 116 303 100 531 61 82 76 452 583 49 124 291 602 55 58 132 204 520 150 406 133 99 120 156 567 135 93 65 80 89 176 99 139 97 226 687 612 166 w 259 128 131 103 186 129 137 125 143 202 461 66 58
1980
2002
1980
2002
2002 a
29 28 130 65 128 36 192 11 20 16 133 64 13 34 86 99 7 9 54 .. 106 45 106 35 72 103 86 107 22 23 12 13 37 51 34 44 .. 135 90 69 79 w 110 57 59 42 86 56 45 61 94 115 116 12 13
19 18 118 23 79 16 165 3 8 4 133 52 5 16 64 106 3 5 23 90 104 24 87 17 21 35 70 83 16 8 5 7 14 55 19 20 .. 83 102 76 55 w 79 30 32 19 60 32 31 28 44 68 103 5 4
36 35 219 85 218 44 336 13 23 18 225 91 16 46 142 143 9 11 73 .. 175 58 176 40 100 133 109 185 27 27 14 15 42 60 42 66 .. 205 155 108 119 w 174 76 79 54 131 79 57 82 134 176 197 15 16
21 21 203 28 138 19 284 4 9 5 225 65 6 19 94 149 3 6 28 116 165 28 140 20 26 41 86 141 20 9 7 8 15 65 22 26 .. 114 182 123 81 w 121 37 40 22 88 42 37 34 54 95 174 7 6
.. .. 105 .. 76 .. .. .. .. .. .. 18 .. .. .. .. .. .. .. .. 61 .. 73 .. .. 10 19 78 .. .. .. .. .. .. .. 10 .. 33 89 35 .. w .. .. .. .. .. .. .. .. .. 25 .. .. ..
.. .. 97 .. 74 .. .. .. .. .. .. 13 .. .. .. .. .. .. .. .. 58 .. 65 .. .. 13 17 70 .. .. .. .. .. .. .. 13 .. 36 74 31 .. w .. .. .. .. .. .. .. .. .. 37 .. .. ..
per 1,000
% of cohort
64 48 23 76 38 73 24 83 70 76 38 27 83 76 53 26 85 85 69 62 27 67 37 74 75 69 57 25 56 80 81 81 74 63 75 68 74 50 16 18 69 w 64 63 61 68 64 69 59 67 68 62 40 81 ..
81 77 25 83 47 83 29 89 86 89 44 33 92 84 58 30 92 93 79 75 31 77 42 82 83 79 72 28 80 85 89 91 88 77 85 78 83 53 21 20 78 w 69 80 78 82 73 76 80 81 73 65 46 90 ..
2.19
About the data
Mortality rates for different age groups—infants,
(see Primary data documentation). Extrapolations
high prevalence of smoking, a high-fat diet, exces-
children, or adults—and overall indicators of
based on outdated surveys may not be reliable for
sive alcohol use, and stressful conditions related to
mortality—life expectancy at birth or survival to a
monitoring changes in health status or for compara-
the economic transition.
given age—are important indicators of health status
tive analytical work.
The percentage of a cohort surviving to age 65
in a country. Because data on the incidence and
To produce harmonized estimates of infant and
reflects both child and adult mortality rates. Like life
prevalence of diseases (morbidity data) are fre-
under-five mortality rates that make use of all avail-
expectancy, it is a synthetic measure based on cur-
quently unavailable, mortality rates are often used to
able information in a transparent way, the United
rent age-specific mortality rates and used in the con-
identify vulnerable populations. And they are among
Nations Children’s Fund (UNICEF) and the World
struction of life tables. It shows that even in coun-
the indicators most frequently used to compare lev-
Bank developed and adopted a methodology that fits
tries where mortality is high, a certain share of the
els of socioeconomic development across countries.
a regression line to the relationship between mortal-
current birth cohort will live well beyond the life
The main sources of mortality data are vital regis-
ity rates and their reference dates using weighted
expectancy at birth, while in low-mortality countries
tration systems and direct or indirect estimates based
least squares. (For further discussion of methodolo-
close to 90 percent will reach at least age 65.
on sample surveys or censuses. A “complete” vital
gy for childhood mortality estimates, see Hill and othDefinitions
registration system—one covering at least 90 percent
ers 1999.) Some of the estimates shown in the table
of vital events in the population—is the best source of
this year are World Bank estimates. Estimates may
age-specific mortality data. But such systems are fair-
change after the harmonization process with UNICEF
• Life expectancy at birth is the number of years a
ly uncommon in developing countries. Thus estimates
and the World Health Organization is completed.
newborn infant would live if prevailing patterns of
must be obtained from sample surveys or derived by
Infant and child mortality rates are higher for boys
applying indirect estimation techniques to registration,
than for girls in countries in which parental gender
same throughout its life. • Infant mortality rate is
census, or survey data. Survey data are subject to
preferences are insignificant. Child mortality cap-
the number of infants dying before reaching one year
recall error, and surveys estimating infant deaths
tures the effect of gender discrimination better than
of age, per 1,000 live births in a given year. • Under-
require large samples because households in which a
does infant mortality, as malnutrition and medical
five mortality rate is the probability that a newborn
birth or an infant death has occurred during a given
interventions are more important in this age group.
baby will die before reaching age five, if subject to
year cannot ordinarily be preselected for sampling.
Where female child mortality is higher, as in some
current age-specific mortality rates. The probability is
Indirect estimates rely on estimated actuarial (“life”)
countries in South Asia, girls probably have unequal
expressed as a rate per 1,000. • Child mortality
tables that may be inappropriate for the population
access to resources.
rate is the probability of dying between the ages of
mortality at the time of its birth were to stay the
concerned. Because life expectancy at birth is con-
Adult mortality rates have increased in many coun-
one and five, if subject to current age-specific mor-
structed using infant mortality data and model life
tries in Sub-Saharan Africa and Europe and Central
tality rates. The probability is expressed as a rate per
tables, similar reliability issues arise for this indicator.
Asia. In Sub-Saharan Africa the increase stems from
1,000. • Adult mortality rate is the probability of
Life expectancy at birth and age-specific mortality
AIDS-related mortality and affects both men and
dying between the ages of 15 and 60—that is, the
rates are generally estimates based on vital regis-
women. In Europe and Central Asia the causes are
probability of a 15-year-old dying before reaching age
tration or the most recent census or survey available
more diverse and affect men more. They include a
60—if subject to current age-specific mortality rates between ages 15 and 60. • Survival to age 65
2.19a
refers to the percentage of a cohort of newborn
Under-five mortality rates are higher in poor households than in rich ones
infants that would survive to age 65, if subject to cur-
Under-five mortality per 1,000 people, by income quintile
rent age-specific mortality rates. Bangladesh, 2000
Uganda, 2000–01 200
200
Male Female
150
150
100
100
50
50
Data sources The data are from the United Nations Statistics
0
0 Poorest
Second
Third
Fourth
Richest
Division’s Population and Vital Statistics Report, Poorest
Second
Third
Fourth
Richest
publications and other releases from national staHigher under-five mortality rates for children from poor households than for those from wealthier households indicate the deprivation among the poor. Under-five mortality is usually higher for boys than for girls, except in cases of parental discrimination against girls.
tistical offices, Demographic and Health Surveys from national sources and Macro International, and UNICEF’s State of the World’s Children 2004.
Source: Demographic and Health Survey data.
2004 World Development Indicators
111
PEOPLE
Mor tality
3 ENVIRONMENT
E
conomic development has led to dramatic improvements in the quality of life in
developing countries, producing gains unparalleled in human history. But the picture is far from entirely positive. Gains have been unevenly distributed, and a large part of the world’s population remains desperately poor. At the same time, natural resources—land, water, and forests—are being degraded at alarming rates in many countries, and environmental
factors such as indoor and outdoor air pollution, waterborne diseases, and exposure to toxic chemicals threaten the health of millions of people. Addressing these concerns, successive international conferences, including the latest World Summit on Sustainable Development,
have
reaffirmed
the
commitment
to
eliminate
poverty
through
environmentally sound and socially responsible economic development.
If the vision of a world without poverty is to be realized, sustainable development is the key. A healthy environment is central to the international development agenda and an integral part of meeting the Millennium Development Goals (see section 1, World View). The Millennium Development Goals call for integrating principles of environmental sustainability into country policies and programs and reversing environmental losses. This requires measuring and monitoring the state of the environment and its changes as well as the links between the economy and the environment.
Given such close links, there is a strong argument for developing indicators that integrate economic activity and environmental change. One approach that appears to hold much promise is environmental accounting. Aimed at deriving “greener” measures of national income, savings, and wealth, environmental accounting adds natural resources and pollutants to the assets and liabilities measured in the standard national accounts. But preparing full-fledged integrated environmental and economic accounts is costly, and not all countries are doing so. In the absence of such integrated accounts, physical indicators and descriptive statistics can provide useful information for monitoring the state of the environment.
Many such indicators are presented here, but despite greater awareness of the importance of environmental issues and efforts to improve environmental data, information on many aspects of the environment remains sparse. The available data are often of uneven quality,
2004 World Development Indicators
113
relate to different periods, and are sometimes out of date. The lack of adequate data hampers efforts to measure the state of the environment and design sound policies. Another problem is that many environmental indicators are not meaningful at the national level. Climate change has impacts that go beyond national boundaries. Other environmental factors such as air and water pollution may have relevance only to the locality where they are measured. So global, regional, or city (tables 3.11 and 3.13) indicators are often more meaningful than national aggregates. Fragile land and increasing demand for food Almost three of every five people in developing countries—some 3 billion—live in rural areas (table 3.1). In many of these countries agriculture is still the main source of employment. But most of the land available to meet current and future food requirements is already in production; any further expansion must necessarily involve fragile and marginal lands. This is particularly so in developing countries where population growth is high, poverty is endemic, and existing institutional capacities for land management are weak. Because land resources are finite, fragile, and nonrenewable, countries must meet their increased need for food and other agricultural products mainly by raising and sustaining crop and livestock yields and by using land more intensively. Low-income countries are increasing the land under cereal production, but their use of agricultural machinery lags far behind that in other countries (table 3.2). These countries, where current cereal yields are a third those in high-income countries, will have to expand their arable land—a strategy that cannot be sustained for long (table 3.3). Shrinking forests and threatened biodiversity A substantial number of the world’s 1.2 billion extremely poor people—those living on less than $1 a day—depend for their livelihoods on forests and forest products . But the forests are shrinking, as is the diversity of the plants and animals they support. With growth and development, forests are being converted to agricultural land and urban areas. At the beginning of the 20th century the earth had about 5 billion hectares of forested area. Now it has less than 4 billion hectares. The loss has been concentrated in developing countries, driven by the growing demand for timber and agricultural land, exacerbated by weak monitoring institutions. Low-income countries lost some 73 million hectares—about 8 percent of their forest—in the 1990s. By contrast, high-income countries reforested about 8 million hectares of forest in the same period (table 3.4). Closely linked to changes in land use is biodiversity, another important dimension of environmental sustainability. Many countries have designated a share of their land as protected areas (table 3.4). But even where protected areas are increased and environmental protections are effectively respected, losses of biologically diverse areas cannot be reversed. About 12 percent of the world’s nearly 10,000 bird species are vulnerable or in immediate danger of extinction, as are 24 percent of the world’s 4,800 mammal species and an estimated 30 percent of all fish species. A thirsty planet—and getting thirstier Water is crucial to economic growth and development—and to the survival of both terrestrial and aquatic systems. But more than 1 billion
114
2004 World Development Indicators
people lack access to safe water, and more than 430 million live in countries facing chronic and widespread water shortages—with water stress (less than 1,700 cubic meters of freshwater available per person a year) or water scarcity (less than 1,000 cubic meters; table 3.5). Global per capita water supplies are declining, further growth in population and economic activity will add to the demand for water, and by 2050 the share of the world’s population facing water stress could increase more than fivefold. These trends pose a significant challenge for meeting the Millennium Development Goal of halving the proportion of people without sustainable access to safe drinking water by 2015. Energy use improves welfare, but has its consequences The use of energy, especially electricity, is important in raising people’s standard of living. High-income countries use more than five times as much energy as developing countries on a per capita basis, and with only 15 percent of the world’s population they use more than half its energy (table 3.7 and figure 3a). At the same time, energy use and electricity generation also have environmental consequences. Generating energy produces emissions of carbon dioxide, the main greenhouse gas contributing to global warming. Anthropogenic (human caused) carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing, with high-income countries contributing half (table 3.8), Among countries in all income groups, per capita emissions vary widely (figure 3b). The extent of environmental damage depends largely on how energy is generated. For example, burning coal releases twice as much carbon dioxide as does burning an equivalent amount of natural gas (see About the data for table 3.8). More urban—and more polluted The world is become increasingly urban. Now urban areas are home to 48 percent of the world’s population—two of five people in low- and middle-income countries and almost four of five in highincome countries. Most of Latin America is as urbanized as Europe, with 76 percent of the population living in urban areas. Asia is urbanizing rapidly. Even such traditionally rural countries as
3a High-income countries use more than half the world’s energy Global energy use, 2001
Rest of the world 21%
United States 23%
India 5% Russian Federation 6%
Source: Table 3.7.
Japan 5% China 11%
Other high-income countries 29%
3b Emissions of carbon dioxide vary widely, even among the five largest producers of emissions Carbon dioxide emissions (billions of metric tons) 6 1980 5
2000
4 3 2 1
policy instruments more than their effectiveness. Still, making a formal commitment is an essential first step toward taking action. Beyond national environmental problems, governments are increasingly concerned about global environmental issues. To address these issues, countries have reached agreements and signed treaties on areas relating to the quality of life on earth (for example, figure 3c shows the decline in chlorofluorocarbons as a result of such agreements). Many of these agreements resulted from the 1992 United Nations Conference on Environment and Development in Rio de Janeiro, which produced Agenda 21—an array of actions to address environmental challenges. But 10 years after Rio the World Summit on Sustainable Development recognized that many of the proposed actions have yet to materialize.
0 United States
China
Russian Federation
Japan
India
Japan
India
Carbon dioxide emissions per capita (metric tons) 20
15
10
5
0 United States
China
Russian Federation
Note: No data for 1980 are available for the Russian Federation. Source: Table 3.8 and World Bank staff estimates.
China, India, and Indonesia now have hundreds of millions of people living in urban areas, with both the number of people and the share of the population in cities growing rapidly (table 3.10). Urbanization can yield important social benefits, improving access to public services such as education, health care, and cultural facilities (table 3.11). Urbanization can also lead to adverse environmental effects that require policy responses. Greater urbanization usually means greater pollution, which can overwhelm the natural capacities of air and water to absorb pollutants. The costs of controlling pollution can be enormous. And pollution exposes people to severe health hazards. Several major urban air pollutants—lead, sulfur dioxide, suspended particulate matter—are known to harm human health (table 3.13). A big source of urban air pollution is motor vehicles, whose numbers are strongly linked to rising income. The number of passenger cars in developing countries has increased from 16 cars per 1,000 people in 1990 to 28 in 2001. At the same time, the number of passenger cars in high-income countries has increased from 400 per 1,000 people to 440 (table 3.12). Commitment to change—necessary, but not sufficient The strength of environmental policies in any country reflects the priority its government gives to problems of environmental degradation— and that priority reflects the benefits expected from using scarce resources that have competing uses. But measuring governments’ commitment to these goals is difficult. The indicators of government commitment in table 3.14 are imperfect, measuring the existence of
Adjusted net savings—moving toward a measure of sustainability The question of an economy’s sustainability can be reduced to the question of whether welfare is expected to decline along the future development path as a result of decisions made today. Because flows of income and well-being are ultimately derived from the stocks of produced, natural, and human assets, a drop in the aggregate value of these stocks must eventually lead to a decline in welfare. One measure of change in total assets is provided by net adjusted savings—a measure of savings that accounts not only for a country’s economic surplus but also for its depletion of natural resources, accumulation of pollutants and their damages, and investments in human capital. The data limitations and the approximations used in calculating net adjusted savings mean that these estimates still must be used with caution (for more details on the assumptions made, see About the data for table 3.15). Many developing countries have low or negative adjusted net savings. Broadly speaking, the lowest adjusted savings rates are recorded for countries that depend heavily on resource rents, particularly those endowed with minerals and fossil fuels. These rents account for a sizable share of GDP in many countries, suggesting that managing natural resources and resource revenues should receive even more attention as these countries strive to ensure the sustainability of their economies and the welfare of future generations.
3c Emissions of some greenhouse and ozone-depleting gases have begun to fall or slow since Rio Methane and gaseous chlorine emissions, parts per trillion 3,000 Gaseous chlorine
2,000 Methane
1,000
0 1980
1985
1990
1995
2000
Source: World Research Institute 2002.
2004 World Development Indicators
115
3.1
Rural environment and land use Rural population
% of total
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China b Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
116
1980
2002
84 66 56 79 17 34 14 33 47 85 43 5 73 55 64 82 33 39 92 96 88 69 24 65 81 19 80 9 37 .. 58 53 65 50 32 25 16 49 53 56 56 86 30 90 40 27 50 80 48 17 69 42 63 81 83 76
77 56 42 65 12 33 9 32 48 74 30 3 56 37 56 50 18 32 83 90 82 50 21 58 75 14 62 0 24 .. 33 40 56 41 24 25 15 33 36 57 38 80 31 84 41 24 17 68 43 12 63 39 60 72 67 63
2004 World Development Indicators
Rural population density
Land area
Land use
average
people
annual %
per sq. km
thousand
growth
of arable land
sq. km
1980–2002
2001
2001
1980
2001
1980
2001
1980
2001
652 27 2,382 1,247 2,737 28 7,682 83 87 130 207 33 a 111 1,084 51 567 8,457 111 274 26 177 465 9,221 623 1,259 749 9,327 .. 1,039 2,267 342 51 318 56 110 77 42 48 277 995 21 101 42 1,000 305 550 258 10 69 349 228 129 108 246 28 28
12.1 21.4 2.9 2.3 10.6 .. 5.7 18.6 .. 68.3 .. 23.2 a 13.6 1.8 .. 0.7 5.3 34.6 10.0 36.2 11.3 12.7 4.9 3.0 2.5 5.1 10.4 7.0 3.6 2.9 0.4 5.5 6.1 .. 23.9 .. 62.3 22.1 5.6 2.3 26.9 .. .. .. 7.8 31.8 1.1 15.5 .. 34.5 8.4 22.5 11.7 2.9 9.1 28.3
12.1 21.1 3.2 2.4 12.3 17.6 6.5 16.9 19.6 62.1 29.5 25.7 a 18.1 2.7 13.6 0.7 7.0 40.0 14.4 35.0 21.0 12.8 5.0 3.1 2.9 2.6 15.4 .. 2.4 3.0 0.5 4.4 9.7 26.1 33.1 39.8 54.0 22.7 5.9 2.9 31.9 5.0 16.0 10.7 7.2 33.5 1.3 25.0 11.4 33.9 16.3 21.1 12.5 3.6 10.7 28.3
0.2 4.3 0.3 0.4 0.4 .. 0.0 1.2 .. 2.0 .. 0.4 a 0.8 0.1 .. 0.0 0.9 3.2 0.1 12.5 0.4 2.2 0.0 0.1 0.0 0.3 0.4 1.0 1.4 0.4 0.1 4.4 7.2 .. 6.4 .. 0.3 7.2 3.3 0.2 11.7 .. .. .. 0.0 2.5 0.6 0.4 .. 1.4 7.5 7.9 4.4 1.8 1.7 11.6
0.2 4.4 0.2 0.2 0.5 2.3 0.0 0.9 2.7 3.1 0.6 0.7 a 2.4 0.2 3.0 0.0 0.9 1.9 0.2 14.0 0.6 2.6 0.0 0.1 0.0 0.4 1.2 .. 1.7 0.5 0.1 5.9 13.8 2.3 7.6 3.1 0.2 10.3 4.9 0.5 12.1 0.0 0.4 0.8 0.0 2.1 0.7 0.5 3.9 0.6 9.7 8.8 5.0 2.6 8.8 11.6
87.7 74.4 96.8 97.3 89.0 .. 94.2 80.2 .. 29.6 .. 76.4 a 85.7 98.1 .. 99.3 93.7 62.2 89.8 51.3 88.3 85.1 95.0 96.9 97.5 94.6 89.3 92.0 95.0 96.6 99.5 90.1 86.6 .. 69.7 .. 37.4 70.6 91.1 97.5 61.4 .. .. .. 92.2 65.7 98.2 84.1 .. 64.1 84.2 69.6 83.9 95.4 89.2 60.1
87.6 74.5 96.5 97.4 87.2 80.1 93.4 82.2 77.7 34.8 69.9 73.6 a 79.5 97.1 83.4 99.3 92.1 58.1 85.4 50.9 78.4 84.6 95.0 96.8 97.1 96.9 83.4 .. 95.9 96.5 99.4 89.7 76.4 71.6 59.3 57.1 45.8 67.0 89.2 96.6 56.1 95.0 83.5 88.5 92.8 64.4 98.1 74.5 84.7 65.6 74.1 70.1 82.4 93.8 80.5 60.1
2.2 0.0 1.0 1.9 –0.4 –0.3 –1.0 0.2 1.4 1.5 –1.5 –2.4 1.7 0.4 –0.6 0.7 –1.2 –1.3 2.0 2.2 2.5 1.2 0.4 1.8 2.5 0.1 0.1 .. –0.1 .. 0.7 1.2 2.5 –1.0 –0.6 –0.0 –0.2 0.1 0.4 2.3 –0.3 2.4 –0.3 2.3 0.5 0.0 –2.0 2.8 –0.4 –1.4 2.4 0.1 2.3 2.0 1.8 1.1
268 309 170 277 13 204 3 187 230 1,228 49 32 182 110 333 232 54 59 243 699 274 130 14 114 171 108 561 .. 420 .. 690 697 292 128 76 85 35 263 285 1,306 370 679 62 517 97 78 71 372 286 86 343 154 516 614 317 664
% of land area Arable land
Permanent cropland
Other land
Rural population
% of total
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Rural population density
Land area
3.1
ENVIRONMENT
Rural environment and land use Land use
average
people
annual %
per sq. km
thousand
growth
of arable land
sq. km
2001
2001
1980
2001
1980
2001
1980
2001
288 78 460 591 160 134 150 157 232 648 603 449 31 439 353 491 684 232 495 51 257 380 461 36 37 146 378 406 554 165 228 701 102 137 87 146 302 346 164 668 184 36 117 195 251 128 1,533 438 230 2,060 77 191 564 104 176 2,681
112 92 2,973 1,812 1,636 437 69 21 294 11 365 89 2,700 569 120 99 18 192 231 62 10 30 96 1,760 65 25 582 94 329 1,220 1,025 2 1,909 33 1,567 446 784 658 823 143 34 268 121 1,267 911 307 310 771 74 453 397 1,280 298 304 92 9
13.3 54.4 54.8 9.9 7.9 12.0 16.1 15.8 32.2 12.5 13.3 3.4 .. 6.7 19.0 20.9 0.1 .. 3.4 .. 20.5 9.6 3.9 1.0 .. .. 4.4 16.1 3.0 1.6 0.2 49.3 12.1 .. 0.8 16.9 3.7 14.6 0.8 16.0 23.3 9.8 8.8 2.8 30.6 2.7 0.1 25.9 5.8 0.4 4.1 2.5 17.5 48.0 26.5 8.3
9.5 50.1 54.4 11.3 8.7 13.1 15.2 16.4 27.8 16.1 12.2 2.7 8.0 8.1 20.8 17.2 0.7 7.3 3.8 29.7 16.6 10.9 3.9 1.0 45.2 22.3 5.1 23.4 5.5 3.8 0.5 49.3 13.0 55.3 0.8 19.6 5.1 15.2 1.0 21.7 26.7 5.6 15.9 3.5 31.3 2.9 0.1 27.9 7.4 0.5 7.6 2.9 18.9 45.9 21.7 3.9
2.4 3.3 1.8 4.4 0.4 0.4 0.0 4.3 10.0 9.7 1.6 0.4 .. 0.8 2.4 1.4 0.0 .. 0.1 .. 8.9 0.1 2.1 0.2 .. .. 0.9 0.9 11.6 0.0 0.0 3.4 0.8 .. 0.0 1.1 0.3 0.7 0.0 0.2 0.9 3.4 1.4 0.0 2.8 .. 0.1 0.4 1.6 1.1 0.3 0.3 14.8 1.1 7.8 7.3
3.2 2.1 2.7 7.2 1.4 0.8 0.0 4.2 9.5 10.2 1.0 1.8 0.1 1.0 2.5 2.0 0.1 0.3 0.4 0.5 14.0 0.1 2.3 0.2 0.9 1.8 1.0 1.5 17.6 0.0 0.0 3.0 1.3 10.8 0.0 2.2 0.3 1.0 0.0 0.6 1.0 7.0 1.9 0.0 3.0 .. 0.1 0.9 2.0 1.4 0.2 0.4 16.8 1.1 7.8 5.5
84.3 42.2 43.4 85.6 91.6 87.6 83.9 80.0 57.7 77.8 85.1 96.2 .. 92.5 78.6 77.8 99.9 .. 96.5 .. 70.6 90.2 94.0 98.8 .. .. 94.8 83.0 85.4 98.3 99.8 47.3 87.1 .. 99.2 82.0 96.0 84.8 99.2 83.8 75.7 86.8 89.7 97.2 66.6 .. 99.8 73.7 92.5 98.5 95.6 97.2 67.7 50.9 65.7 84.3
87.2 47.8 42.9 81.5 89.9 86.1 84.8 79.4 62.7 73.8 86.8 95.5 92.0 90.9 76.7 80.9 99.2 92.4 95.8 69.9 69.4 89.0 93.8 98.8 53.9 75.9 93.9 75.1 76.9 96.1 99.5 47.8 85.7 33.9 99.2 78.2 94.6 83.8 99.0 77.7 72.3 87.4 82.1 96.4 65.7 .. 99.7 71.3 90.7 98.1 92.2 96.7 64.3 53.0 70.4 90.5
1980
2002
1980–2002
65 43 77 78 50 34 45 11 33 53 24 40 46 84 43 43 9 62 88 32 26 87 65 31 39 47 81 91 58 82 72 58 34 60 48 59 87 76 77 93 12 17 50 87 73 29 68 72 50 87 58 35 63 42 71 33
45 35 72 57 35 32 40 8 33 43 21 21 44 65 39 17 4 66 80 40 10 71 54 12 31 40 69 85 41 68 40 58 25 58 43 43 66 71 68 87 10 14 43 78 54 25 23 66 43 82 43 27 40 37 33 24
1.3 –1.2 1.6 0.2 0.6 2.5 0.2 0.8 0.0 –0.0 –0.2 1.0 –0.2 1.7 0.8 –3.2 –1.6 1.7 2.1 0.6 –2.8 0.6 1.7 –1.7 –0.9 –0.3 2.1 2.2 1.0 1.7 –0.2 1.1 0.5 0.1 1.3 0.5 0.6 1.4 2.5 2.0 0.1 0.3 2.1 2.8 1.5 –0.3 –1.2 2.2 1.2 2.3 1.2 0.7 0.3 –0.2 –3.2 –0.6
% of land area Arable land
Permanent cropland
Other land
2004 World Development Indicators
117
3.1
Rural environment and land use Rural population
% of total 1980
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
51 30 95 34 64 54 76 0 48 52 78 52 27 78 80 82 17 43 53 66 85 83 77 37 48 56 53 91 38 29 11 26 15 59 21 81 .. 81 60 78 61 w 78 61 65 34 68 79 41 35 52 78 79 27 27
2002
45 27 94 13 51 48 62 0 42 51 72 42 22 77 62 73 17 33 48 72 66 80 66 25 33 33 55 85 32 12 10 22 8 63 13 75 .. 75 60 63 52 w 69 47 51 25 58 62 36 24 42 72 67 22 22
a. Includes Luxembourg. b. Includes Taiwan, China.
118
2004 World Development Indicators
Rural population density
Land area
average
people
annual %
per sq. km
thousand
growth
of arable land
sq. km
1980–2002
2001
2001
–0.6 –0.3 2.0 –0.6 1.6 –1.3 1.3 .. –0.3 0.0 1.3 1.3 –0.6 1.1 1.2 2.4 0.3 –0.6 2.6 2.5 1.7 1.1 2.2 –0.9 0.2 –0.3 2.5 2.7 –1.0 1.3 –0.1 0.3 –2.3 2.4 0.1 1.5 .. 3.2 2.6 1.8 0.8 w 1.6 0.2 0.2 0.1 0.9 0.3 –0.1 0.0 1.5 1.7 1.8 –0.3 –0.5
107 32 743 79 203 .. 644 0 158 581 622 129 69 1,607 125 440 55 571 173 485 575 326 123 441 118 97 148 401 48 773 109 37 20 352 122 923 .. 923 115 255 476 w 510 473 492 190 494 568 124 203 601 553 350 205 139
230 16,889 25 2,150 193 .. 72 1 48 20 627 1,221 499 65 2,376 17 412 40 184 141 884 511 54 5 155 770 470 197 579 84 241 9,159 175 414 882 325 .. 528 743 387 130,145 s 32,424 66,725 54,034 12,691 99,149 15,885 23,722 20,053 11,105 4,781 23,603 30,996 2,436
Land use
% of land area Arable land
Permanent cropland
Other land
1980
2001
1980
2001
1980
2001
42.7 .. 30.8 0.9 12.2 28.0 6.3 3.3 .. .. 1.6 10.2 31.1 13.2 5.2 10.8 7.2 9.9 28.5 .. 3.5 32.3 35.9 13.6 20.5 32.9 .. 20.7 .. 0.2 28.7 20.6 8.0 .. 3.4 18.2 .. 2.6 6.9 6.5 10.3 w 11.7 8.2 8.6 7.0 9.6 10.1 37.1 6.4 4.4 42.5 5.5 12.1 27.3
40.8 7.3 40.5 1.7 12.8 .. 7.0 1.6 30.4 8.6 1.7 12.1 26.1 13.9 6.8 10.3 6.5 10.4 25.2 6.6 4.5 29.4 46.1 14.6 17.9 30.9 3.7 25.9 56.2 0.6 23.5 19.1 7.4 10.8 2.9 20.0 .. 2.8 7.1 8.3 10.8 w 12.5 9.6 9.9 8.0 10.5 13.3 11.2 7.4 4.9 42.5 6.7 11.6 25.7
2.9 .. 10.3 0.0 0.0 2.9 0.7 9.8 .. .. 0.0 0.7 9.9 15.9 0.0 0.2 0.0 0.5 2.5 .. 1.0 3.5 1.6 9.0 9.7 4.1 .. 8.1 .. 0.1 0.3 0.2 0.3 .. 0.8 1.9 .. 0.2 0.0 0.3 0.8 w 1.0 0.9 1.0 0.7 1.0 1.5 3.1 0.9 0.4 1.5 0.7 0.5 4.8
2.3 0.1 12.2 0.1 0.2 .. 0.9 0.0 2.8 1.5 0.0 0.8 9.9 15.7 0.2 0.7 0.0 0.6 4.4 0.9 1.1 6.5 2.2 9.2 13.7 3.3 0.1 10.7 1.6 2.2 0.2 0.2 0.2 0.8 0.9 6.0 .. 0.2 0.0 0.3 1.0 w 1.5 1.0 0.9 1.0 1.1 2.7 0.4 1.0 0.8 2.2 0.9 0.5 4.6
54.4 .. 58.9 99.1 87.8 69.1 93.0 86.9 .. .. 98.4 89.1 59.0 70.9 94.8 89.0 92.8 89.6 69.1 .. 95.5 64.2 62.6 77.4 69.7 63.0 .. 71.2 .. 99.7 71.0 79.2 91.7 .. 95.8 79.8 .. 97.2 93.1 93.3 88.9 w 87.3 90.9 90.3 92.3 89.4 88.5 59.8 92.8 95.2 56.1 93.8 87.4 67.9
56.9 92.6 47.3 98.2 87.0 .. 92.1 98.4 66.8 89.9 98.3 87.1 64.1 70.4 93.0 89.0 93.4 89.0 70.3 92.5 94.4 64.2 51.6 76.2 68.4 65.8 96.1 63.5 42.2 97.2 76.3 80.6 92.3 88.3 96.1 74.1 .. 97.0 92.9 91.3 88.2 w 86.0 89.5 89.2 90.9 88.3 84.0 88.4 91.6 94.4 55.3 92.4 87.9 69.7
About the data
3.1
ENVIRONMENT
Rural environment and land use Definitions
Indicators of rural development are sparse, as few
in the category other, may be particularly unreliable
• Rural population is calculated as the difference
indicators are disaggregated between rural and
because of differences in definitions and irregular
between the total population and the urban popula-
urban areas (for some that are, see tables 2.5, 3.5,
surveys (see About the data for table 3.4).
tion (see Definitions for tables 2.1 and 3.10).
and 3.10). This table shows indicators of rural popu-
• Rural population density is the rural population
lation and land use. Rural population is approximat-
divided by the arable land area. • Land area is a
ed as the midyear nonurban population.
country’s total area, excluding area under inland
The data in the table show that land use patterns
water bodies, national claims to the continental
are changing. They also indicate major differences in
shelf, and exclusive economic zones. In most cases
resource endowments and uses among countries.
the definition of inland water bodies includes major
True comparability of the data is limited, however,
rivers and lakes. (See table 1.1 for the total surface
by variations in definitions, statistical methods, and
area of countries.) • Land use is broken into three
the quality of data collection. Countries use different
categories. • Arable land includes land defined by
definitions of rural population and land use, for exam-
the FAO as land under temporary crops (double-
ple. The Food and Agriculture Organization (FAO), the
cropped areas are counted once), temporary mead-
primary compiler of these data, occasionally adjusts
ows for mowing or for pasture, land under market or
its definitions of land use categories and sometimes
kitchen gardens, and land temporarily fallow. Land
revises earlier data. (In 1985, for example, the FAO
abandoned as a result of shifting cultivation is
began to exclude from cropland the land used for
excluded. • Permanent cropland is land cultivated
shifting cultivation but currently lying fallow.) And fol-
with crops that occupy the land for long periods and
lowing FAO practice, this year’s edition of World
need not be replanted after each harvest, such as
Development Indicators, like the previous five, breaks
cocoa, coffee, and rubber. This category includes
down the category cropland, used in the earliest edi-
land under flowering shrubs, fruit trees, nut trees,
tions, into arable land and permanent cropland.
and vines, but excludes land under trees grown for
Because the data reflect changes in data reporting
wood or timber. • Other land includes forest and
procedures as well as actual changes in land use,
woodland as well as logged-over areas to be forest-
apparent trends should be interpreted with caution.
ed in the near future. Also included are uncultivated
Satellite images show land use that differs from
land, grassland not used for pasture, wetlands,
that given by ground-based measures in both area
wastelands, and built-up areas—residential, recre-
under cultivation and type of land use. Moreover, land
ational, and industrial lands and areas covered by
use data in countries such as India are based on
roads and other fabricated infrastructure.
reporting systems that were designed for the collection of tax revenue. Because taxes on land are no longer a major source of government revenue, the quality and coverage of land use data (except for cropland) have declined. Data on forest area, aggregated
3.1a All regions are becoming less rural
Data sources
Rural population as a share of total population, by region (%)
The data on urban population shares used to esti1980
80
2002
mate rural population come from the United
70
Nations Population Division’s World Urbanization
60
Prospects: The 2001 Revision. The total popula-
50
tion figures are World Bank estimates. The data
40
on land area and land use are from the FAO’s
30
electronic files, which may contain more recent
20
information than those published in its Production
10
Yearbook. The FAO gathers these data from national agencies through annual questionnaires
0 East Asia & Pacific
Europe & Central Asia
Latin America Middle East & & Caribbean North Africa
South Asia
Sub-Saharan Africa
World
and by analyzing the results of national agricultural censuses.
Source: Table 3.1.
2004 World Development Indicators
119
3.2
Agricultural inputs Arable land
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium a Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
120
Irrigated land
Land under cereal production
Fertilizer consumption
Agricultural machinery
tractors
tractors
hundreds of grams
per 1,000
per 100
hectares
% of
thousands
per hectare
agricultural
sq. km of
per capita
cropland
of hectares
of arable land
workers
arable land
1979–81
1999–2001
1979–81
0.50 0.22 0.37 0.41 1.03 .. 2.97 0.20 .. 0.10 .. 0.08 0.43 0.36 .. 0.44 0.37 0.43 0.39 0.23 0.29 0.67 1.86 0.81 0.70 0.34 0.10 0.00 0.13 0.24 0.08 0.12 0.24 .. 0.27 .. 0.52 0.19 0.20 0.06 0.12 .. .. .. 0.49 0.32 0.42 0.26 .. 0.15 0.17 0.30 0.19 0.16 0.32 0.15
0.30 0.19 0.25 0.24 0.91 0.16 2.58 0.17 0.21 0.06 0.61 0.08 0.31 0.35 0.17 0.22 0.34 0.54 0.34 0.13 0.31 0.39 1.48 0.52 0.45 0.13 0.11 .. 0.06 0.14 0.05 0.06 0.19 0.33 0.32 0.30 0.43 0.13 0.13 0.04 0.10 0.12 0.71 0.16 0.42 0.31 0.26 0.18 0.15 0.14 0.19 0.26 0.12 0.12 0.22 0.10
31.1 53.0 3.4 2.2 5.2 .. 3.5 0.2 .. 17.1 .. 1.7 0.3 6.6 .. 0.5 3.0 28.3 0.4 4.2 5.8 0.2 1.3 .. 0.4 31.1 45.1 37.5 7.7 0.1 0.6 12.1 1.0 .. 22.9 .. 14.5 11.7 24.8 100.0 4.6 .. .. .. 2.5 7.2 2.4 0.6 .. 3.7 0.2 24.2 5.0 7.9 5.6 6.4
2004 World Development Indicators
1999–2001
29.6 48.6 6.8 2.3 4.5 51.3 4.7 0.3 74.8 49.6 2.1 4.7 0.5 4.2 0.4 0.3 4.4 17.4 0.6 5.9 7.1 0.5 1.6 .. 0.6 82.7 36.3 .. 20.2 0.1 0.5 20.6 1.0 0.2 19.5 0.7 19.5 17.2 29.0 100.0 5.0 4.2 0.4 1.7 2.9 13.4 3.0 0.8 44.2 4.0 0.2 37.3 6.8 6.3 3.1 6.8
1979–81
2000–02
1979–81
1999–2001
1979–81
1999–2001
3,037 367 2,968 705 11,154 .. 15,986 1,062 .. 10,823 .. 426 525 559 .. 153 20,612 2,110 2,026 203 1,264 1,021 19,561 194 907 820 94,647 0 1,361 1,115 19 136 1,008 .. 224 .. 1,818 149 419 2,007 422 .. .. .. 1,190 9,804 6 54 .. 7,692 902 1,600 716 708 142 416
2,302 183 1,770 928 10,714 191 17,097 821 735 11,712 2,491 336 921 754 381 177 17,799 1,988 3,061 205 2,014 747 17,106 180 1,900 626 83,012 .. 1,147 2,043 10 68 1,381 710 202 1,615 1,550 168 867 2,700 363 293 290 7,440 1,170 9,106 17 137 338 7,001 1,425 1,287 660 742 154 448
62 1,556 277 49 39 .. 269 2,615 .. 459 .. 5,323 11 22 .. 32 777 2,334 26 11 45 56 416 5 6 338 1,494 .. 812 12 27 2,650 261 .. 2,024 .. 2,453 572 471 2,864 1,376 .. .. .. 2,024 3,260 20 136 .. 4,249 104 1,927 726 16 24 43
12 277 126 5 253 122 478 1,591 58 1,662 1,288 3,549 211 25 581 128 1,103 328 96 41 0 83 550 3 49 2,421 2,562 .. 2,397 2 286 7,096 217 1,445 447 1,075 1,516 869 1,177 4,401 1,189 212 393 150 1,384 2,367 9 40 521 2,367 34 1,635 1,449 36 60 158
0 15 27 4 132 .. 751 945 .. 0 .. 917 0 4 .. 9 31 66 0 0 0 0 827 0 0 43 2 0 8 0 2 22 1 .. 78 .. 973 3 6 4 5 .. .. .. 721 737 5 0 .. 624 1 120 3 0 0 0
0 11 37 2 205 74 705 1,737 31 0 102 1,299 0 4 304 20 61 86 0 0 0 0 1,870 0 0 55 2 .. 6 0 1 21 1 13 100 192 1,132 3 11 10 4 0 602 0 1,355 1,411 7 0 33 1,018 1 323 2 0 0 0
1979–81 1999–2001
1 173 68 35 63 .. 75 2,084 .. 5 .. 1,416 1 21 .. 54 118 161 0 1 6 1 144 0 1 90 76 10 77 3 49 210 16 .. 259 .. 708 20 40 158 61 .. .. .. 893 836 43 3 .. 1,340 19 485 32 2 1 2
1 140 122 34 89 369 64 2,371 178 7 118 1,266 1 20 433 166 139 57 5 2 5 1 160 0 0 273 70 .. 80 4 40 311 12 16 215 293 547 17 90 307 53 10 575 3 889 687 46 2 221 873 10 912 32 6 1 2
Arable land
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Irrigated land
Land under cereal production
Fertilizer consumption
3.2
ENVIRONMENT
Agricultural inputs
Agricultural machinery
tractors
tractors
hundreds of grams
per 1,000
per 100
hectares
% of
thousands
per hectare
agricultural
sq. km of
per capita
cropland
of hectares
of arable land
workers
arable land
1979–81
1999–2001
1979–81
0.42 0.47 0.24 0.12 0.36 0.40 0.33 0.08 0.17 0.06 0.04 0.14 .. 0.23 0.13 0.05 0.00 .. 0.24 .. 0.07 0.23 0.20 0.58 .. .. 0.29 0.25 0.07 0.31 0.13 0.10 0.34 .. 0.71 0.39 0.24 0.28 0.64 0.16 0.06 0.84 0.38 0.62 0.39 0.20 0.02 0.24 0.22 0.05 0.52 0.19 0.11 0.41 0.25 0.02
0.19 0.46 0.16 0.10 0.24 0.24 0.28 0.05 0.14 0.07 0.04 0.05 1.44 0.15 0.11 0.04 0.00 0.28 0.17 0.78 0.04 0.19 0.12 0.35 0.84 0.28 0.19 0.21 0.08 0.43 0.18 0.08 0.25 0.42 0.50 0.31 0.22 0.21 0.43 0.13 0.06 0.39 0.37 0.42 0.22 0.20 0.02 0.15 0.19 0.04 0.55 0.14 0.07 0.36 0.20 0.01
4.1 3.6 22.8 16.2 35.5 32.1 .. 49.3 19.3 10.1 56.0 11.0 .. 0.9 44.0 59.6 83.3 .. 13.3 .. 28.3 0.3 0.3 10.7 .. .. 21.2 1.1 6.7 4.5 22.8 15.0 20.3 .. 3.0 15.0 2.1 10.4 0.6 22.5 58.5 5.2 6.2 0.7 0.7 .. 74.5 72.7 5.0 .. 3.4 32.3 12.8 0.7 20.1 27.2
1999–2001
5.2 4.6 32.2 14.4 44.2 60.8 .. 46.0 24.2 8.8 54.7 19.3 10.8 1.7 52.1 60.4 85.8 74.2 18.2 1.1 32.0 0.3 0.5 21.9 0.2 9.0 31.0 1.3 4.8 3.0 9.8 19.5 23.1 14.1 7.1 13.5 2.6 18.3 0.9 36.2 59.9 8.6 4.5 1.5 0.8 .. 78.2 81.6 5.1 .. 2.2 28.4 14.6 0.7 24.0 47.6
1979–81
421 2,878 104,350 11,825 8,062 2,159 425 129 5,082 4 2,724 158 .. 1,692 1,625 1,689 0 .. 751 .. 34 203 203 538 .. .. 1,309 1,155 729 1,346 125 0 9,356 .. 559 4,414 1,077 5,133 195 2,251 225 193 266 3,872 6,048 311 2 10,693 166 2 307 732 6,790 7,875 1,099 1
2000–02
1979–81
1999–2001
1979–81
1999–2001
399 2,936 97,956 15,004 7,740 2,526 287 84 4,187 2 2,017 52 13,082 2,017 1,278 1,177 2 618 765 440 53 236 141 342 938 207 1,412 1,602 705 2,769 185 0 10,322 989 196 5,181 1,894 6,880 292 3,308 223 141 464 7,693 19,783 326 2 12,300 123 3 638 1,217 6,514 8,643 543 1
171 2,906 345 645 430 172 5,373 2,384 2,295 1,231 4,131 404 .. 160 3,346 3,920 4,500 .. 35 .. 1,663 150 123 357 .. .. 30 203 4,273 61 57 2,547 570 .. 83 268 107 111 0 98 8,620 1,879 415 10 59 3,146 475 525 692 452 44 381 636 2,393 1,113 ..
1,408 835 1,074 1,243 905 668 5,871 2,696 2,078 1,095 3,162 913 19 322 1,061 4,539 1,002 157 107 305 3,105 247 0 363 533 665 27 195 6,695 95 30 3,667 736 25 26 415 40 180 4 260 4,755 5,317 159 10 68 2,196 1,690 1,362 584 530 227 715 1,337 1,110 1,146 ..
5 59 2 0 17 23 607 304 370 9 209 47 .. 1 12 1 3 .. 0 .. 28 6 0 101 .. .. 1 0 4 0 1 4 16 .. 32 7 1 1 11 0 560 619 6 0 1 824 1 5 27 1 14 5 1 112 72 ..
7 209 6 1 37 91 1,031 350 1,219 12 745 32 36 1 20 80 10 46 1 350 177 6 0 319 429 449 1 0 25 1 1 6 37 84 16 10 1 1 11 0 603 448 7 0 2 1,263 1 13 20 1 23 4 1 302 260 ..
1979–81 1999–2001
22 111 24 5 57 44 1,289 809 1,117 208 2,723 153 .. 17 196 14 220 .. 7 .. 141 47 8 134 .. .. 10 8 77 5 13 33 54 .. 82 34 20 9 39 10 2,238 352 20 0 3 1,603 43 50 122 82 45 37 20 425 351 ..
2004 World Development Indicators
44 228 94 35 158 107 1,586 728 1,973 177 4,601 234 24 27 280 1,112 94 186 12 302 453 61 9 187 347 948 12 7 239 6 8 37 131 230 42 49 15 11 39 15 1,644 501 15 0 11 1,511 40 150 93 56 57 36 20 933 848 ..
121
3.2
Agricultural inputs Arable land
Fertilizer consumption
Agricultural machinery
tractors
tractors
hundreds of grams
per 1,000
per 100
hectares
% of
thousands
per hectare
agricultural
sq. km of
cropland
of hectares
of arable land
workers
arable land
0.44 .. 0.15 0.20 0.42 0.73 0.14 0.00 .. .. 0.15 0.45 0.42 0.06 0.64 0.30 0.36 0.06 0.60 .. 0.16 0.35 0.77 0.06 0.51 0.57 .. 0.32 .. 0.01 0.12 0.83 0.48 .. 0.19 0.11 .. 0.16 0.89 0.35 0.25 w 0.23 0.18 0.16 0.36 0.20 0.12 .. 0.36 0.29 0.23 0.32 0.44 0.23
1999–2001
0.42 0.85 0.12 0.17 0.25 .. 0.10 0.00 0.27 0.09 0.12 0.34 0.33 0.05 0.52 0.17 0.31 0.06 0.29 0.15 0.12 0.25 0.55 0.06 0.30 0.36 0.37 0.22 0.66 0.02 0.10 0.62 0.39 0.18 0.11 0.08 .. 0.09 0.53 0.25 0.23 w 0.17 0.24 0.22 0.32 0.20 0.11 0.56 0.29 0.19 0.15 0.24 0.37 0.21
a. Includes Luxembourg.
122
Land under cereal production
per capita 1979–81
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Irrigated land
2004 World Development Indicators
1979–81
1999–2001
1979–81
2000–02
1979–81
1999–2001
21.9 31.2 6,340 5,696 1,448 309 .. 3.6 .. 41,919 .. 117 0.4 0.4 239 298 3 3 28.9 42.8 388 615 228 1,036 2.6 2.9 1,216 1,174 104 162 1.9 .. 4,310 .. 1,261 .. 4.1 5.4 434 213 58 4 .. .. .. .. 22,333 30,423 .. 11.2 .. .. .. 610 .. 1.3 .. 102 .. 4,384 13.3 18.7 638 671 9 5 8.4 9.2 6,760 4,633 874 510 14.8 20.1 7,391 6,658 1,012 1,674 28.3 33.6 864 809 1,800 2,768 14.4 11.7 4,447 7,468 51 32 34.0 36.8 70 56 1,050 343 2.4 4.2 1,505 1,163 1,654 1,055 6.2 5.7 172 177 4,623 2,277 9.6 22.5 2,642 3,028 250 731 .. 68.3 .. 376 .. 114 3.1 3.3 2,834 2,980 110 56 16.4 27.1 10,625 11,257 177 1,120 0.3 0.4 416 703 14 74 2.9 3.3 4 3 1,064 1,163 4.8 7.7 1,416 782 212 389 9.6 16.9 13,499 13,946 529 825 .. 100.1 .. 819 .. 603 0.1 0.1 752 1,395 1 11 .. 7.2 .. 13,436 .. 136 237.7 32.6 0 0 2,250 7,090 2.0 1.8 3,930 3,203 3,191 3,251 10.8 12.6 72,639 55,818 1,092 1,097 5.4 13.5 614 522 564 846 .. 88.6 .. 1,581 .. 1,637 10.1 16.9 814 928 696 1,012 25.6 37.6 5,962 8,301 302 3,407 .. .. .. .. .. .. 19.9 30.2 865 654 93 102 0.4 0.9 595 641 145 65 3.1 3.5 1,633 1,685 610 520 17.5 w 19.6 w 588,621 s 666,427 s 860 w 988 w 19.8 26.4 199,719 244,864 289 717 22.6 19.4 232,191 289,922 941 1,020 25.8 20.5 196,509 249,113 962 1,063 11.5 13.4 35,682 40,809 871 804 21.2 22.1 431,910 534,786 625 903 36.3 35.5 139,927 135,938 1,113 2,145 .. 10.9 .. 114,548 .. 335 10.8 12.5 49,845 48,623 536 815 25.8 37.9 25,653 25,446 421 808 28.7 39.9 132,128 128,481 360 1,081 4.0 4.2 46,978 81,750 158 128 10.2 12.1 156,711 131,641 1,327 1,238 14.1 19.4 35,996 31,617 2,703 2,170
1979–81
39 .. 0 2 0 140 0 3 .. .. 1 92 200 4 2 29 715 494 29 .. 1 1 0 50 30 38 .. 0 .. 6 726 1,230 171 .. 50 1 .. 3 2 7 19 w 2 8 6 50 5 2 .. 25 12 2 3 430 427
1999–2001
1979–81 1999–2001
100 96 0 16 0 .. 0 22 .. .. 1 43 686 2 2 34 1,108 700 68 37 1 10 0 54 37 65 73 1 90 5 931 1,586 174 57 61 6 .. 2 2 7 20 w 4 12 9 117 8 2 102 40 25 5 1 895 911
150 174 .. 63 1 1 10 27 2 3 616 .. 6 2 220 650 .. 164 .. .. 17 17 140 50 335 668 141 90 8 7 173 219 623 616 2,428 2,710 54 212 .. 325 35 19 11 147 0 0 337 360 79 123 169 391 .. 289 6 9 .. 101 106 68 744 860 253 272 236 255 .. 380 131 189 38 254 .. .. 33 41 9 11 66 75 173 w 189 w 20 66 110 127 104 103 133 253 66 103 55 76 .. 171 87 119 61 131 25 92 23 15 385 439 879 984
About the data
3.2
ENVIRONMENT
Agricultural inputs Definitions
Agricultural activities provide developing countries
To smooth annual fluctuations in agricultural activ-
• Arable land includes land defined by the FAO as
with food and revenue, but they also can degrade
ity, the indicators in the table have been averaged
land under temporary crops (double-cropped areas
natural resources. Poor farming practices can
over three years.
are counted once), temporary meadows for mowing
cause soil erosion and loss of soil fer tility. Effor ts
or for pasture, land under market or kitchen gardens,
to increase productivity through the use of chemical
and land temporarily fallow. Land abandoned as a
fer tilizers, pesticides, and intensive irrigation have
result of shifting cultivation is excluded. • Irrigated
environmental costs and health impacts. Excessive
land refers to areas purposely provided with water,
use of chemical fer tilizers can alter the chemistr y
including land irrigated by controlled flooding.
of soil. Pesticide poisoning is common in develop-
• Cropland refers to arable land and permanent
ing countries. And salinization of irrigated land
cropland (see table 3.1). • Land under cereal pro-
diminishes soil fer tility. Thus inappropriate use of
duction refers to harvested areas, although some
inputs for agricultural production has far-reaching
countries repor t only sown or cultivated area.
effects.
• Fertilizer consumption is the quantity of plant
This table provides indicators of major inputs to
nutrients used per unit of arable land. Fertilizer prod-
agricultural production: land, fertilizer, and agricultur-
ucts cover nitrogenous, potash, and phosphate fer-
al machinery. There is no single correct mix of
tilizers (including ground rock phosphate). Traditional
inputs: appropriate levels and application rates vary
nutrients—animal and plant manures—are not
by country and over time, depending on the type of
included. The time reference for fertilizer consump-
crops, the climate and soils, and the production
tion
process used.
• Agricultural machinery refers to wheel and crawler
is
the
crop
year
(July
through
June).
The data shown here and in table 3.3 are collect-
tractors (excluding garden tractors) in use in agricul-
ed by the Food and Agriculture Organization (FAO)
ture at the end of the calendar year specified or dur-
through annual questionnaires. The FAO tries to
ing the first quar ter of the following year.
impose standard definitions and reporting methods,
• Agricultural workers refer to all economically
but exact consistency across countries and over time
active people engaged principally in agriculture,
is not possible. Data on agricultural employment, in
forestry, hunting, or fishing.
particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. Fertilizer consumption measures the quantity of plant nutrients. Consumption is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops.
3.2a The 10 countries with the most arable land per person in 1999–2001—and the 10 with the least Ares per capita Country
Arable land
Australia
258.1
Singapore
Country
Arable land
Canada
148.2
Kuwait
0.5
Kazakhstan
143.6
Puerto Rico
0.9
0.0
Argentina
90.6
Oman
1.6
Russian Federation
85.5
United Arab Emirates
1.8
Lithuania
83.7
Japan
3.5
Latvia
77.8
Korea, Rep.
3.6
Estonia
71.0
Papua New Guinea
4.0
Ukraine
65.9
Lebanon
4.2
makes available to the World Bank and that may
United States
62.5
Egypt, Arab Rep.
4.4
contain more recent information than those pub-
Note: An are equals 100 square meters or 0.01 hectare. Source: Table 3.2.
Data sources The data are from electronic files that the FAO
lished in the FAO’s Production Yearbook.
2004 World Development Indicators
123
3.3
Agricultural output and productivity Crop production index
Food production index
Livestock production index
Cereal yield
Agricultural productivity Agriculture value added
kilograms 1989–91 = 100
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium a Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
124
1989–91 = 100
1989–91 = 100
1979–81
2000–02
1979–81
2000–02
1979–81
2000–02
1979–81
2000–02
.. .. 77.4 101.9 83.6 .. 79.9 92.8 .. 80.2 .. 84.9 53.8 71.9 .. 86.4 75.4 107.7 59.3 79.9 55.0 87.3 77.6 102.9 66.8 70.7 67.1 133.6 84.1 73.0 86.4 66.2 73.7 .. 84.1 .. 65.2 96.5 78.2 75.5 120.4 .. .. .. 76.3 87.4 76.2 79.2 .. 90.0 67.0 86.8 85.8 89.7 64.9 103.4
.. .. 128.0 195.7 165.2 99.8 152.2 103.6 63.4 135.6 90.3 143.9 195.6 177.2 .. 89.8 135.8 66.9 166.8 92.7 147.2 141.6 106.7 136.6 160.8 132.6 155.6 .. 106.4 83.2 127.9 147.0 133.8 90.6 66.3 88.6 89.9 89.6 143.2 154.9 98.9 121.9 76.8 160.6 99.7 107.0 121.4 132.8 43.7 118.2 190.0 110.6 131.8 158.7 147.2 87.2
.. .. 68.8 89.9 91.7 .. 91.3 92.2 .. 79.3 .. 88.5 66.8 71.5 .. 87.3 69.5 105.5 62.7 79.9 48.9 80.6 79.7 79.7 79.8 71.5 60.8 99.8 75.5 72.8 83.8 69.5 70.6 .. 90.1 .. 83.3 85.2 77.4 68.5 88.9 .. .. .. 93.8 93.6 79.0 82.4 .. 91.4 68.5 91.2 68.0 93.1 68.3 101.2
.. .. 136.2 172.5 142.5 79.3 138.8 104.7 83.7 138.3 62.1 113.5 173.9 151.6 .. 89.8 153.2 68.2 157.9 93.2 152.0 138.3 123.5 146.5 151.2 140.2 185.9 .. 120.3 86.3 130.3 150.0 136.5 68.5 70.9 78.0 106.0 107.8 153.8 158.2 111.7 116.3 39.8 152.6 93.7 104.3 116.7 127.2 74.9 97.1 181.2 101.3 136.2 161.8 142.2 101.7
.. .. 54.6 83.8 100.9 .. 85.6 94.5 .. 81.3 .. 88.8 93.2 75.5 .. 87.6 67.9 96.3 59.9 82.3 27.3 61.3 88.3 48.9 89.2 75.8 45.4 194.3 72.6 83.5 81.6 77.1 73.9 .. 96.0 .. 95.0 68.8 73.0 67.0 86.5 .. .. .. 107.5 97.8 86.6 93.7 .. 98.7 78.7 99.9 76.9 91.7 78.0 100.2
.. .. 128.9 137.2 108.8 67.9 116.1 103.2 81.8 142.1 58.4 109.8 116.7 129.7 .. 89.7 169.8 62.9 147.8 76.1 166.9 121.8 142.2 147.4 122.2 151.4 226.7 .. 122.4 98.3 135.5 136.6 139.3 55.2 71.6 70.8 118.6 138.2 170.1 165.9 116.3 112.0 33.8 129.8 91.8 105.5 118.9 102.7 93.8 87.7 127.3 94.0 130.3 188.8 127.2 156.2
1,337 2,500 656 526 2,184 .. 1,321 4,131 .. 1,938 .. 4,861 698 1,183 .. 203 1,496 3,853 575 1,081 1,006 849 2,173 529 587 2,124 3,027 1,712 2,452 807 838 2,498 867 .. 2,458 .. 4,040 3,024 1,633 4,053 1,702 .. .. .. 2,511 4,700 1,718 1,284 .. 4,166 807 3,090 1,578 958 711 1,009
1,533 3,154 1,343 606 3,374 2,049 1,758 5,589 2,583 3,312 2,369 8,002 1,077 1,786 3,186 156 3,081 2,961 968 1,325 1,978 1,696 2,521 1,069 697 5,235 4,845 .. 3,411 774 779 3,968 1,213 4,748 2,519 4,297 5,912 4,525 2,122 7,244 2,264 351 2,028 1,293 3,219 6,796 1,652 1,231 2,004 6,355 1,191 3,555 1,758 1,403 972 840
2004 World Development Indicators
per worker
per hectare
1995 $ 1979–81
2000–02
.. 1,184 1,357 .. 7,148 .. 20,872 11,082 .. 232 .. 21,861 311 693 .. 657 2,049 2,754 133 177 .. 826 16,002 380 160 3,488 161 .. 3,034 241 385 3,139 945 .. .. .. 19,350 2,129 3,839 721 1,925 .. .. .. 17,885 19,318 1,814 325 .. 9,119 671 8,600 2,143 .. 237 ..
.. 1,868 1,919 137 10,317 2,827 36,327 33,828 1,029 318 3,038 57,462 621 754 7,634 575 4,899 8,282 185 151 422 1,213 43,064 502 211 6,226 338 .. 3,619 212 469 5,270 1,046 9,741 .. 6,382 63,131 3,458 3,310 1,316 1,678 68 3,650 154 42,306 59,243 2,102 307 .. 33,686 571 13,860 2,115 286 324 ..
Crop production index
Food production index
Livestock production index
Cereal yield
3.3
ENVIRONMENT
Agricultural output and productivity
Agricultural productivity Agriculture value added
kilograms 1989–91 = 100
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
1989–91 = 100
1989–91 = 100
per worker
per hectare
1979–81
2000–02
1979–81
2000–02
1979–81
2000–02
1979–81
2000–02
90.4 93.3 70.9 65.9 57.5 74.7 93.6 99.8 106.1 101.4 108.3 54.6 .. 70.2 .. 87.8 37.1 .. 73.5 .. 49.9 98.2 .. 76.3 .. .. 83.1 85.7 75.3 54.5 62.1 93.3 86.5 .. 44.6 54.8 109.9 89.0 80.1 61.9 79.8 74.4 124.1 89.2 51.7 94.8 60.1 65.6 96.9 86.5 58.7 82.1 88.3 84.6 85.0 131.3
114.1 79.7 124.2 122.9 151.5 76.7 111.3 97.4 101.9 127.5 87.1 132.6 89.5 123.1 .. 114.3 198.1 153.2 177.5 78.7 100.4 147.9 .. 129.4 76.5 94.9 108.5 156.0 119.4 143.9 126.2 98.1 123.6 61.9 29.7 91.8 141.1 178.5 126.9 137.9 111.7 142.9 141.3 147.5 156.0 77.6 160.3 122.8 83.3 120.7 115.5 180.3 123.1 84.0 91.7 67.9
88.3 90.7 68.2 63.1 61.2 77.3 83.5 85.0 101.4 93.6 94.1 57.4 .. 65.6 .. 77.5 81.0 .. 70.3 .. 60.6 96.6 .. 78.7 .. .. 83.8 93.1 55.6 77.2 86.5 89.6 85.3 .. 88.1 55.8 100.7 88.2 107.6 65.4 86.5 90.7 117.8 97.5 57.2 93.9 62.1 66.3 85.5 86.1 60.8 77.3 86.1 87.9 72.2 99.8
121.1 79.5 131.8 123.6 154.8 77.5 106.7 115.3 102.3 125.9 91.6 147.4 73.5 122.2 .. 132.3 229.0 132.5 186.4 42.4 108.9 111.6 .. 134.1 64.7 89.5 115.8 174.0 142.1 128.6 108.3 109.0 135.7 51.1 91.9 103.6 127.5 176.5 96.8 135.8 98.4 135.2 154.3 140.1 155.8 91.0 163.1 152.7 105.8 124.3 141.0 175.0 137.1 86.0 102.2 84.0
81.0 94.1 62.6 51.0 68.0 81.2 83.5 78.4 93.0 85.5 85.1 51.5 .. 60.5 .. 52.4 94.5 .. 56.0 .. 95.0 96.6 .. 68.4 .. .. 87.7 78.4 41.0 95.6 89.4 64.0 86.2 .. 93.2 59.8 85.8 89.1 116.0 77.3 88.3 95.5 139.7 110.0 83.3 96.2 61.5 59.5 71.3 84.9 62.1 78.0 73.8 98.0 71.8 90.3
153.8 73.3 149.8 124.7 158.3 67.9 107.7 127.6 105.1 126.2 93.2 167.7 46.7 118.0 .. 159.9 211.2 80.7 188.8 31.1 157.0 87.2 .. 134.9 52.6 89.9 114.2 125.4 142.1 123.2 107.0 145.3 150.1 32.9 97.4 124.6 103.9 169.4 93.3 129.3 96.5 123.9 148.1 128.9 145.3 97.4 144.5 171.9 138.6 146.0 136.9 159.1 177.8 83.3 122.3 89.4
1,170 4,519 1,324 2,837 1,108 832 4,733 1,840 3,548 1,667 5,252 521 .. 1,364 3,694 4,986 3,124 .. 1,402 .. 1,307 977 1,251 430 .. .. 1,664 1,161 2,828 804 384 2,536 2,164 .. 573 811 603 2,521 377 1,615 5,696 4,089 1,475 440 1,265 3,634 982 1,608 1,524 2,087 1,535 1,946 1,611 2,345 1,102 7,970
1,382 4,026 2,390 4,141 2,163 945 7,053 2,853 4,815 1,002 5,879 1,301 1,149 1,516 3,189 6,118 2,206 2,742 3,140 2,189 2,575 926 983 631 2,807 2,642 2,007 1,134 3,132 943 860 7,577 2,870 2,345 751 1,129 848 3,453 400 2,178 7,531 6,230 1,761 417 1,105 3,760 2,319 2,266 2,753 3,919 2,034 3,302 2,692 3,072 2,702 1,731
1995 $ 1979–81
2000–02
696 3,390 269 604 2,165 .. .. .. 11,090 1,123 17,378 1,141 .. 265 .. 3,765 .. .. .. .. .. 611 .. .. .. .. 158 96 3,939 242 289 2,891 1,482 .. 994 1,146 .. .. 1,064 156 24,360 16,637 1,549 229 417 17,138 .. 416 2,122 692 2,641 1,299 1,381 .. 3,796 ..
1,037 5,625 401 748 3,737 .. .. .. 27,064 1,487 33,077 1,145 1,753 213 .. 14,251 .. 1,861 621 2,773 29,874 575 .. .. 3,431 4,243 155 124 6,912 274 447 5,494 1,813 971 1,444 1,513 136 .. 1,545 203 59,476 28,740 1,618 197 729 37,073 .. 716 2,967 823 3,318 1,863 1,458 1,637 7,567 ..
2004 World Development Indicators
125
3.3
Agricultural output and productivity Crop production index
Food production index
Livestock production index
Cereal yield
Agricultural productivity Agriculture value added
kilograms 1989–91 = 100 1979–81
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
2000–02
114.1 .. 84.9 27.2 77.2 96.3 80.3 595.0 .. .. .. 94.9 83.0 99.3 127.1 72.5 93.1 95.5 100.7 .. 80.5 79.1 70.6 121.5 68.1 76.6 .. 67.5 .. 38.9 80.1 98.6 86.8 .. 76.3 65.8 .. 82.3 64.6 77.8 79.1 w 71.7 74.3 72.5 79.4 73.3 68.5 .. 80.3 66.0 71.9 75.4 93.4 90.7
a. Includes Luxembourg.
126
2004 World Development Indicators
91.0 86.1 115.4 84.2 111.0 .. 75.4 48.2 .. 81.9 .. 110.1 115.8 114.8 165.9 85.2 89.3 89.6 177.2 62.2 107.7 124.3 138.0 87.9 98.4 118.8 77.7 138.9 71.3 659.7 97.2 118.3 135.3 89.0 119.3 180.3 .. 133.6 96.2 113.9 131.5 w 134.0 147.0 154.2 116.2 142.7 166.1 .. 138.6 136.4 131.6 132.3 112.5 105.3
1989–91 = 100 1979–81
113.0 .. 85.8 26.7 74.1 94.3 84.5 154.3 .. .. .. 90.5 81.9 98.1 105.2 81.1 100.6 95.8 93.6 .. 75.4 79.7 78.3 111.1 66.3 75.8 .. 69.7 .. 42.7 92.2 94.5 87.1 .. 80.2 62.5 .. 74.8 73.0 83.3 78.8 w 70.7 71.8 68.8 78.8 71.5 63.4 .. 78.3 64.8 69.6 78.3 91.9 91.4
2000–02
87.1 66.6 117.3 98.5 122.4 .. 84.0 31.9 .. 100.9 .. 111.1 120.1 117.2 167.5 99.9 96.0 95.6 163.6 60.5 112.8 123.5 131.4 127.8 115.0 114.6 131.6 136.7 52.4 549.9 92.4 122.5 124.8 122.3 135.0 171.4 .. 142.6 107.2 108.6 133.1 w 135.1 150.3 158.0 118.6 145.2 170.6 .. 141.9 137.1 133.3 133.5 113.2 105.2
1989–91 = 100 1979–81
110.0 .. 80.3 32.7 65.7 94.2 84.1 173.7 .. .. .. 86.0 83.9 92.0 89.3 99.9 103.8 98.8 72.1 .. 69.2 64.5 56.2 96.9 60.3 80.4 .. 81.9 .. 42.2 98.1 89.0 85.9 .. 84.9 50.1 .. 68.9 86.2 89.7 79.6 w 68.4 69.6 60.8 82.8 69.3 47.9 .. 79.8 64.1 64.0 84.1 90.6 93.9
2000–02
80.7 52.6 112.3 152.6 147.0 .. 126.6 31.8 .. 108.7 .. 104.3 134.2 147.7 161.1 126.4 100.2 94.9 136.1 41.6 126.9 135.3 115.2 157.3 164.4 103.9 138.0 130.6 45.7 200.6 93.1 123.6 110.4 114.8 138.8 193.8 .. 160.4 130.2 121.5 136.4 w 146.8 164.4 181.9 114.0 159.9 214.6 .. 144.8 145.3 154.3 124.4 112.5 101.9
per hectare 1979–81
2,854 .. 1,134 820 690 3,601 1,249 .. .. .. 474 2,105 1,986 2,462 645 1,345 3,595 4,883 1,156 .. 1,063 1,911 729 3,167 828 1,869 .. 1,555 .. 2,224 4,792 4,151 1,644 .. 1,904 2,049 .. 1,038 1,676 1,359 1,605 w 1,090 1,759 1,682 1,842 1,397 2,034 2,854 1,786 925 1,510 895 3,274 4,035
2000–02
2,562 1,846 1,011 3,818 755 .. 1,234 .. .. 5,452 547 2,633 3,091 3,520 600 1,512 4,878 6,466 2,114 1,561 1,438 2,654 1,008 2,807 2,218 2,176 2,621 1,651 2,399 414 6,841 5,830 3,243 3,644 3,278 4,375 .. 966 1,481 872 2,233 w 1,321 2,497 2,181 2,926 1,966 3,147 2,640 2,804 1,726 2,222 1,064 3,746 5,517
per worker 1995 $ 1979–81
2000–02
1,397 .. 271 2,152 345 .. 674 16,664 .. .. .. 2,857 7,556 642 .. 1,752 20,865 .. 2,206 .. .. 616 365 3,536 1,743 1,872 .. .. .. .. 20,326 20,672 6,563 .. 3,935 .. .. .. 186 310 .. w .. .. .. .. .. .. .. 2,239 .. 285 419 .. ..
3,588 3,822 254 15,796 354 .. 359 42,920 .. 37,671 .. 4,072 22,412 725 .. 1,936 40,368 .. 2,636 728 187 863 503 3,034 3,115 1,848 690 346 1,576 .. 32,918 53,907 8,177 1,449 5,399 256 .. 412 194 355 .. w 415 820 713 3,937 626 .. 2,353 3,570 2,340 412 360 .. 30,154
About the data
3.3
ENVIRONMENT
Agricultural output and productivity Definitions
The agricultural production indexes in the table are
nominal exchange rates unrelated to the purchasing
• Crop production index shows agricultural produc-
prepared by the Food and Agriculture Organization
power of the domestic currency.
tion for each period relative to the base period
(FAO). The FAO obtains data from official and semi-
Data on cereal yield may be affected by a variety of
1989–91. It includes all crops except fodder crops.
official reports of crop yields, area under production,
reporting and timing differences. The FAO allocates
The regional and income group aggregates for the
and livestock numbers. If data are not available, the
production data to the calendar year in which the
FAO’s production indexes are calculated from the
FAO makes estimates. The indexes are calculated
bulk of the harvest took place. But most of a crop
underlying values in international dollars, normalized
using the Laspeyres formula: production quantities
harvested near the end of a year will be used in the
to the base period 1989–91. The data in this table
of each commodity are weighted by average interna-
following year. Cereal crops harvested for hay or har-
are three-year averages. • Food production index
tional commodity prices in the base period and
vested green for food, feed, or silage, and those
covers food crops that are considered edible and
summed for each year. Because the FAO’s indexes
used for grazing, are generally excluded. But millet
that contain nutrients. Coffee and tea are excluded
are based on the concept of agriculture as a single
and sorghum, which are grown as feed for livestock
because, although edible, they have no nutritive
enterprise, estimates of the amounts retained for
and poultry in Europe and North America, are used
value. • Livestock production index includes meat
seed and feed are subtracted from the production
as food in Africa, Asia, and countries of the former
and milk from all sources, dairy products such as
data to avoid double counting. The resulting aggre-
Soviet Union. So some cereal crops are excluded
cheese, and eggs, honey, raw silk, wool, and hides
gate represents production available for any use
from the data for some countries and included else-
and skins. • Cereal yield, measured in kilograms per
except as seed and feed. The FAO’s indexes may dif-
where, depending on their use.
hectare of harvested land, includes wheat, rice,
fer from other sources because of differences in cov-
Agricultural productivity is measured by value
maize, barley, oats, rye, millet, sorghum, buckwheat,
erage, weights, concepts, time periods, calculation
added per unit of input. (For further discussion of the
and mixed grains. Production data on cereals refer to
methods, and use of international prices.
calculation of value added in national accounts, see
crops harvested for dry grain only. Cereal crops har-
To ease cross-country comparisons, the FAO uses
About the data for tables 4.1 and 4.2.) Agricultural
vested for hay or harvested green for food, feed, or
international commodity prices to value production.
value added includes that from forestry and fishing.
silage, and those used for grazing, are excluded.
These prices, expressed in international dollars (equiv-
Thus interpretations of land productivity should be
• Agricultural productivity refers to the ratio of agri-
alent in purchasing power to the U.S. dollar), are
made with caution. To smooth annual fluctuations in
cultural value added, measured in constant 1995
derived using a Geary-Khamis formula applied to agri-
agricultural activity, the indicators in the table have
U.S. dollars, to the number of workers in agriculture.
cultural outputs (see Inter-Secretariat Working Group
been averaged over three years.
on National Accounts 1993, sections 16.93–96). This method assigns a single price to each commodity so that, for example, one metric ton of wheat has the same price regardless of where it was produced. The use of international prices eliminates fluctuations in the value of output due to transitory movements of
3.3a The 15 countries with the highest cereal yield in 2001–03—and the 15 with the lowest Kilograms per hectare of arable land Country
Cereal yield
Country
Cereal yield
Belgium a
8,002
Botswana
156
Mauritius
7,577
Eritrea
351
Netherlands
7,531
Namibia
400
Egypt, Arab Rep.
7,244
United Arab Emirates
414
Ireland
7,053
Niger
417
United Kingdom
6,841
Somalia
547
Data sources
France
6,796
Sudan
600
Switzerland
6,466
Angola
606
Germany
6,355
Libya
631
by the FAO and published annually in its
New Zealand
6,230
Chad
697
Production Yearbook. The FAO makes these data
Korea, Rep.
6,118
Mongolia
751
and the data on cereal yield and agricultural
Denmark
5,912
Senegal
755
employment available to the World Bank in elec-
Japan
5,879
Congo, Dem. Rep.
774
tronic files that may contain more recent informa-
United States
5,830
Congo, Rep.
779
tion than the published versions. For sources of
Austria
5,589
Haiti
840
data on agricultural value added, see Data
a. Includes Luxembourg. Source: Table 3.3.
The agricultural production indexes are prepared
sources for table 4.2.
2004 World Development Indicators
127
3.4
Deforestation and biodiversity Forest area
Average annual deforestation
Mammals
Higher plants a
Birds
Nationally protected areas
% of
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
128
% of
thousand
total
sq. km
land area
2000
2000
14 10 21 698 346 4 1,581 39 11 13 94 7 27 531 23 124 5,325 37 71 1 93 239 2,446 229 127 155 1,589 .. 496 1,352 221 20 71 18 23 26 5 14 106 1 1 16 21 46 219 153 218 5 30 107 63 36 29 69 22 1
c
2.1 36.2 0.9 56.0 12.7 12.4 20.6 47.0 12.6 10.2 45.3 22.2 24.0 48.9 44.8 21.9 63.0 33.4 25.9 3.7 52.9 51.3 26.5 36.8 10.1 20.7 17.0 .. 47.8 59.6 64.6 38.5 22.4 31.9 21.4 34.1 10.7 28.4 38.1 0.1 5.8 15.7 48.7 4.6 72.0 27.9 84.7 48.1 43.0 30.8 27.8 27.9 26.3 28.2 77.8 3.2
2004 World Development Indicators
Threatened
c
sq. km
%
Threatened
Threatened
thousand
total
Species
species
Species
species
Species
species
sq. km
land area
1990–2000 1990–2000
2002
2002
2002
2002
2002
2002
2003 b
2003 b
.. 78 –266 1,242 2,851 –42 0 –77 –130 –165 –2,562 –10 699 1,611 0 1,184 22,264 –204 152 147 561 2,218 0 300 817 203 –13,483 .. 1,905 5,324 175 158 2,649 –20 –277 –5 –10 0 1,372 –20 72 54 –125 403 –80 –616 101 –45 0 0 1,200 –300 537 347 216 70
119 68 92 276 320 84 252 83 99 125 74 58 188 316 72 164 394 81 147 107 123 409 193 209 134 91 394 .. 359 200 450 205 230 76 31 81 43 20 302 98 135 112 65 277 60 93 190 117 107 76 222 95 250 190 108 20
13 3 13 19 34 11 63 7 13 23 7 11 8 24 10 6 81 14 7 6 24 40 14 14 17 21 79 1 41 15 40 14 19 9 11 8 5 5 33 13 2 12 4 35 5 18 15 3 13 11 14 13 6 12 3 4
181 193 183 265 362 236 497 230 229 166 194 191 112 504 205 184 686 248 138 145 183 165 310 168 141 157 618 .. 708 130 345 279 252 224 86 205 196 79 640 123 141 138 204 262 243 283 156 154 208 247 206 255 221 109 235 62
11 3 6 15 39 4 37 3 8 23 3 2 2 28 3 7 114 10 2 7 19 15 8 3 5 22 74 11 78 3 28 13 12 4 18 2 1 15 62 7 0 7 3 16 3 5 5 2 3 5 8 7 6 10 0 14
4,000 3,031 3,164 5,185 9,372 3,553 15,638 3,100 4,300 5,000 2,100 1,550 2,500 17,367 .. 2,151 56,215 3,572 1,100 2,500 .. 8,260 3,270 3,602 1,600 5,284 32,200 .. 51,220 6,000 11,007 12,119 3,660 4,288 6,522 1,900 1,450 5,657 19,362 2,076 2,911 .. 1,630 6,603 1,102 4,630 6,651 974 4,350 2,682 3,725 4,992 8,681 3,000 1,000 5,242
1 0 2 19 42 1 38 3 0 12 0 0 11 70 1 0 .. 0 2 2 29 155 1 10 2 40 168 .. 213 33 55 109 101 0 160 4 3 29 197 2 23 3 0 22 1 2 71 3 .. 12 115 2 77 21 4 27
2.0 1.0 119.1 82.3 180.6 2.1 1,029.4 27.3 5.3 1.0 13.1 0.9 12.6 145.3 0.3 104.8 566.6 5.0 31.5 1.5 32.7 20.9 1,023.5 54.2 114.6 141.5 727.5 0.5 105.9 113.4 22.2 11.7 19.1 4.2 75.9 12.4 14.4 25.1 50.7 96.6 0.1 4.3 5.0 169.0 28.3 73.2 1.8 0.2 1.6 113.8 12.7 4.6 21.7 1.7 .. 0.1
0.3 3.8 5.0 6.6 6.6 7.6 13.4 33.0 6.1 0.8 6.3 2.6 11.4 13.4 0.5 18.5 6.7 4.5 11.5 5.7 18.5 4.5 11.1 8.7 9.1 18.9 7.8 .. 10.2 5.0 6.5 23.0 6.0 7.5 69.1 16.1 34.0 51.9 18.3 9.7 0.4 4.3 11.8 16.9 9.3 13.3 0.7 2.3 2.3 31.9 5.6 3.6 20.0 0.7 .. 0.4
c
.. 0.8 –1.3 0.2 0.8 –1.3 0.0 –0.2 –1.3 –1.3 –3.2 –0.2 2.3 0.3 0.0 0.9 0.4 –0.6 0.2 9.0 0.6 0.9 0.0 0.1 0.6 0.1 –0.9 .. 0.4 0.4 0.1 0.8 3.1 –0.1 –1.3 –0.0 –0.2 0.0 1.2 –3.4 4.6 0.3 –0.6 0.8 –0.0 –0.4 0.0 –1.0 0.0 0.0 1.7 –0.9 1.7 0.5 0.9 5.7
c
Forest area
Average annual deforestation
Mammals
3.4
Higher plants a
Birds
Nationally protected areas
% of
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
thousand
total
sq. km
land area
2000
2000
54 18 641 1,050 73 8 7 1 100 3 241 1 121 171 82 63 0 10 126 29 0 0 35 4 20 9 117 26 193 132 3 0 552 3 106 30 306 344 80 39 4 79 33 13 135 89 0 25 29 306 234 652 58 93 37 2
48.1 19.9 21.6 58.0 4.5 1.8 9.6 6.4 34.0 30.0 66.1 1.0 4.5 30.0 68.2 63.3 0.3 5.2 54.4 47.1 3.5 0.5 36.1 0.2 30.8 35.6 20.2 27.6 58.7 10.8 0.3 7.9 28.9 9.9 6.8 6.8 39.0 52.3 9.8 27.3 11.1 29.7 27.0 1.0 14.8 28.9 0.0 3.2 38.6 67.6 58.8 50.9 19.4 30.6 40.1 25.8
ENVIRONMENT
Deforestation and biodiversity
% of Threatened sq. km
%
1990–2000 1990–2000
590 –72 –381 13,124 0 0 –170 –50 –295 54 –34 0 –2,390 931 0 49 –2 –228 527 –127 1 0 760 –47 –48 0 1,174 707 2,377 993 98 1 6,306 –7 600 12 637 5,169 734 783 –10 –390 1,172 617 3,984 –310 0 304 519 1,129 1,230 2,688 887 –110 –570 5
1.0 –0.4 –0.1 1.2 0.0 0.0 –3.0 –4.9 –0.3 1.5 –0.0 0.0 –2.2 0.5 0.0 0.1 –5.2 –2.6 0.4 –0.4 0.3 0.0 2.0 –1.4 –0.2 0.0 0.9 2.4 1.2 0.7 2.7 0.6 1.1 –0.2 0.5 0.0 0.2 1.4 0.9 1.8 –0.3 –0.5 3.0 3.7 2.6 –0.4 0.0 1.1 1.6 0.4 0.5 0.4 1.4 –0.1 –1.7 0.2
Threatened
Threatened
thousand
total
Species
species
Species
species
Species
species
sq. km
land area
2002
2002
2002
2002
2002
2002
2003 b
2003 b
173 83 390 515 140 81 25 116 90 24 188 71 178 359 .. 49 21 83 172 83 57 33 193 76 68 78 141 195 300 137 61 .. 491 68 133 105 179 300 250 181 55 .. 200 131 274 54 56 188 218 214 305 460 153 84 63 ..
10 9 88 147 22 11 5 14 14 5 37 10 16 51 13 13 1 7 31 4 5 3 17 8 5 11 50 8 50 13 10 3 70 6 14 16 14 39 15 31 10 8 6 11 27 10 9 19 20 58 10 49 50 15 17 2
232 208 458 929 293 140 143 162 250 75 210 117 379 344 150 138 35 168 212 216 116 123 146 76 201 199 172 219 254 191 172 .. 440 175 274 206 144 310 201 274 192 .. 215 125 286 241 109 237 302 414 233 695 404 233 235 ..
5 8 72 114 13 11 1 12 5 12 34 8 15 24 19 25 7 4 20 3 7 7 11 1 4 3 27 11 37 4 2 9 39 5 16 9 16 35 11 25 4 63 5 3 9 2 10 17 16 32 26 76 67 4 7 8
5,680 2,214 18,664 29,375 8,000 .. 950 2,317 5,599 3,308 5,565 2,100 6,000 6,506 2,898 2,898 234 4,500 8,286 1,153 3,000 1,591 2,200 1,825 1,796 3,500 9,505 3,765 15,500 1,741 1,100 .. 26,071 1,752 2,823 3,675 5,692 7,000 3,174 6,973 1,221 .. 7,590 1,460 4,715 1,715 1,204 4,950 9,915 11,544 7,851 17,144 8,931 2,450 5,050 ..
108 1 244 384 1 0 1 0 3 206 11 0 1 98 3 0 0 1 18 0 0 0 46 1 0 0 162 14 681 6 0 .. .. 0 0 2 36 37 5 6 0 .. 39 2 119 2 6 2 193 142 10 269 193 4 15 ..
7.2 6.5 154.6 373.2 78.5 0.0 1.2 3.3 23.2 .. 24.8 3.0 72.9 45.5 3.1 6.8 0.3 28.9 6.9 8.3 0.1 0.1 1.6 1.8 6.7 1.8 25.0 10.5 18.7 45.1 17.4 0.2 194.7 0.5 180.1 3.1 65.9 2.0 112.0 12.7 4.8 79.3 21.6 97.5 30.1 20.9 43.3 37.8 16.2 10.4 13.9 78.1 17.0 37.7 6.0 0.3
2004 World Development Indicators
6.4 7.0 5.2 20.6 4.8 0.0 1.7 15.8 7.9 .. 6.8 3.4 2.7 8.0 2.6 6.9 1.5 12.5 3.6 13.4 0.5 0.2 1.7 0.1 10.3 7.1 4.3 11.2 5.7 3.7 1.7 7.8 10.2 1.4 11.5 0.7 8.4 0.3 13.6 8.9 14.2 29.6 17.8 7.7 3.3 6.8 14.0 4.9 21.7 2.3 3.5 6.1 5.7 12.4 6.6 3.5
129
3.4
Deforestation and biodiversity Forest area
Average annual deforestation
Mammals
Higher plants a
Birds
Nationally protected areas
% of
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
thousand
total
sq. km
land area
2000
2000
64 8,514 3 15 62 29 11 0 20 11 75 89 144 19 616 5 271 12 5 4 388 148 5 3 5 102 38 42 96 3 26 2,260 13 20 495 98 .. 4 312 190 38,480 s 9,031 21,493 19,065 2,428 30,525 4,238 9,464 9,438 168 782 6,436 7,955 846
28.0 50.4 12.4 0.7 32.2 .. 14.7 3.3 42.5 55.0 12.0 7.3 28.8 30.0 25.9 30.3 65.9 30.3 2.5 2.8 43.9 28.9 9.4 50.5 3.3 13.3 8.0 21.3 16.5 3.8 10.7 24.7 7.4 4.8 56.1 30.2 .. 0.9 42.0 49.2 29.7 w 27.1 32.7 31.8 34.5 30.9 27.2 39.7 47.1 1.5 16.3 27.3 26.1 37.0
% of Threatened sq. km
%
Threatened
Threatened
thousand
total
Species
species
Species
species
Species
species
sq. km
land area
1990–2000 1990–2000
2002
2002
2002
2002
2002
2002
2003 b
2003 b
–147 –1,353 150 0 450 14 361 0 –69 –22 769 80 –860 348 9,589 –58 –6 –43 0 –20 913 1,124 209 22 –11 –220 0 913 –310 –78 –200 –3,880 –501 –46 2,175 –516 .. 92 8,509 3,199 95,009 s 73,087 29,869 14,730 15,139 102,956 11,613 –8,143 45,873 –239 889 52,963 –7,947 –2,978
84 269 151 77 192 96 147 85 85 75 171 247 82 88 267 .. 60 75 63 84 316 265 196 100 78 116 103 345 108 25 50 428 81 97 323 213 .. 66 233 270
17 45 9 8 12 12 12 3 9 9 19 42 24 22 23 4 7 5 4 9 42 37 9 1 11 17 13 20 16 3 12 37 6 9 26 40 1 5 12 12
257 528 200 125 175 238 172 142 199 201 179 304 281 126 280 .. 259 199 145 210 229 285 117 131 165 278 204 243 245 34 229 508 115 203 547 262 .. 93 252 229
8 38 9 15 4 5 10 7 4 1 10 28 7 14 6 5 2 2 8 7 33 37 0 1 5 11 6 13 8 8 2 55 11 9 24 37 1 12 11 10
3,400 11,400 2,288 2,028 2,086 4,082 2,090 2,282 3,124 3,200 3,028 23,420 5,050 3,314 3,137 .. 1,750 3,030 3,000 5,000 10,008 11,625 3,085 2,259 2,196 8,650 .. 4,900 5,100 .. 1,623 19,473 2,278 4,800 21,073 10,500 .. 1,650 4,747 4,440
1 7 3 3 7 1 43 54 2 0 17 45 14 280 17 .. 3 2 0 2 236 78 9 1 0 3 0 33 1 0 13 .. 1 1 67 126 .. 52 8 14
–0.2 –0.0 3.9 0.0 0.7 0.0 2.9 0.0 –0.3 –0.2 1.0 0.1 –0.6 1.6 1.4 –1.2 –0.0 –0.4 0.0 –0.5 0.2 0.7 3.4 0.8 –0.2 –0.2 0.0 2.0 –0.3 –2.8 –0.8 –0.2 –5.0 –0.2 0.4 –0.5 .. 1.8 2.4 1.5 0.2 w 0.8 0.1 –0.1 0.5 0.3 0.2 –0.1 0.5 –0.1 0.1 0.8 –0.1 –0.3
10.8 1,317.3 1.5 823.3 22.3 .. 1.5 0.0 11.0 1.2 5.0 67.2 42.5 8.7 123.6 0.6 37.5 11.9 .. 5.9 263.3 71.0 4.3 0.3 0.5 12.3 19.7 48.5 22.6 0.0 50.3 2,372.2 0.5 8.3 562.7 12.0 .. .. 237.1 46.8 13,750.0 s 2,665.5 6,073.9 3,891.0 2,183.0 8,739.5 1,454.8 1,610.2 2,237.8 1,169.3 228.6 2,038.8 5,010.5 324.9
a. Flowering plants only. b. Data may refer to earlier years. They are the most recent reported by the World Conservation Monitoring Center in 2003. c. Includes Luxembourg.
130
2004 World Development Indicators
4.7 7.8 6.2 38.3 11.6 3.3 2.1 4.9 22.8 6.0 0.8 5.5 8.5 13.5 5.2 3.5 9.1 30.0 .. 4.2 29.8 13.9 7.9 6.0 0.3 1.6 4.2 24.6 3.9 0.0 20.9 25.9 0.3 2.0 63.8 3.7 .. .. 31.9 12.1 10.7 w 8.4 9.1 7.2 17.3 8.9 9.2 6.8 11.2 11.3 4.8 8.7 19.5 13.5
About the data
3.4
ENVIRONMENT
Deforestation and biodiversity Definitions
The estimates of forest area are from the Food and
• National parks of national or international signifi-
• Forest area is land under natural or planted stands
Agriculture Organization’s (FAO) State of the World’s
cance (not materially affected by human activity).
of trees, whether productive or not. • Average annu-
Forests 2003, which provides information on forest
• Natural monuments and natural landscapes with
al deforestation refers to the permanent conversion
cover in 2000 and an estimate of forest cover in
unique aspects.
of natural forest area to other uses, including shifting
1990. The current survey is the latest global forest
• Managed nature reserves and wildlife sanctuaries.
cultivation, permanent agriculture, ranching, settle-
assessment and the first to use a uniform global def-
• Protected landscapes and seascapes (which
ments, and infrastructure development. Deforested
inition of forest. According to this assessment, the
may include cultural landscapes).
areas do not include areas logged but intended for
global rate of net deforestation has slowed to 9.5 mil-
Designating land as a protected area does not nec-
regeneration or areas degraded by fuelwood gather-
lion hectares a year, a rate 20 percent lower than that
essarily mean that protection is in force. For small
ing, acid precipitation, or forest fires. Negative num-
previously reported. No breakdown of forest cover
countries that may only have protected areas small-
bers indicate an increase in forest area. • Mammals
between natural forest and plantation is shown in the
er than 1,000 hectares, this size limit in the defini-
exclude whales and porpoises. • Birds refer to breed-
table because of space limitations. (This breakdown
tion will result in an underestimate of the extent and
ing species and are listed for countries included with-
is provided by the FAO only for developing countries.)
number of protected areas.
For this reason the deforestation data in the table
in their breeding ranges. • Higher plants refer to
Threatened species are defined according to the
native vascular plant species. • Threatened species
may underestimate the rate at which natural forest is
IUCN’s classification categories: endangered (in dan-
are the number of species classified by the IUCN as
disappearing in some countries.
ger of extinction and unlikely to survive if causal fac-
endangered, vulnerable, rare, indeterminate, out of
Deforestation is a major cause of loss of biodiver-
tors continue operating), vulnerable (likely to move
danger, or insufficiently known. • Nationally protect-
sity, and habitat conservation is vital for stemming
into the endangered category in the near future if
ed areas are totally or partially protected areas of at
this loss. Conservation efforts traditionally have
causal factors continue operating), rare (not endan-
least 1,000 hectares that are designated as scientif-
focused on protected areas, which have grown sub-
gered or vulnerable but at risk), indeterminate
ic reserves with limited public access, national parks,
stantially in recent decades. Measures of species
(known to be endangered, vulnerable, or rare but not
natural monuments, nature reserves or wildlife sanc-
richness are among the most straightforward ways to
enough information is available to say which), out of
tuaries, and protected landscapes and seascapes.
indicate the importance of an area for biodiversity.
danger (formerly included in one of the above cate-
The data do not include sites protected under local or
The number of small plants and animals is usually
gories but now considered relatively secure because
provincial law. Total land area is used to calculate the
estimated by sampling plots. It is also important to
appropriate conservation measures are in effect),
percentage of total area protected (see table 3.1).
know which aspects are under the most immediate
and insufficiently known (suspected but not definite-
threat. This, however, requires a large amount of data
ly known to belong to one of the above categories).
and time-consuming analysis. For this reason global
Figures on species are not necessarily comparable
analyses of the status of threatened species have
across countries because taxonomic concepts and
been carried out for few groups of organisms. Only for
coverage vary. And while the number of birds and
birds has the status of all species been assessed. An
mammals is fairly well known, it is difficult to make
estimated 45 percent of mammal species remain to
an accurate count of plants. Although the data in the
be assessed. For plants the World Conservation
table should be interpreted with caution, especially
Union’s (IUCN) 1997 IUCN Red List of Threatened
for numbers of threatened species (where knowledge
Plants provides the first-ever comprehensive listing of
is very incomplete), they do identify countries that
threatened species on a global scale, the result of
are major sources of global biodiversity and show
more than 20 years’ work by botanists from around
national commitments to habitat protection.
the world. Nearly 34,000 plant species, 12.5 percent of the total, are threatened with extinction.
The dataset on protected areas is tentative and is being revised. Due to variations in consistency and
The table shows information on protected areas,
methodology of collection, the quality of the data are
numbers of certain species, and numbers of those
highly variable across countries. Some countries
species under threat. The World Conser vation
update their information more frequently than others,
Monitoring Centre (WCMC) compiles these data from
some may have more accurate data on extent of cov-
a variety of sources. Because of differences in defi-
erage, and many underreport the number or extent of
nitions and reporting practices, cross-country com-
protected areas.
Data sources The forestry data are from the FAO’s State of the
parability is limited. Compounding these problems,
World’s Forests 2003. The data on species are
available data cover different periods.
from the WCMC’s electronic files and the IUCN’s
Nationally protected areas are areas of at least
2002 IUCN Red List of Threatened Animals and
1,000 hectares that fall into one of five management
1997 IUCN Red List of Threatened Plants. The
categories defined by the WCMC:
data on protected areas are from the United
• Scientific reserves and strict nature reserves
Nations Environment Programme and WCMC.
with limited public access.
2004 World Development Indicators
131
3.5
Freshwater Renewable freshwater resources
Annual freshwater withdrawals
Access to improved water source
Net flows
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
132
Internal
from other
Total
flows
countries
resources
billion
% of total
billion
billion
per capita
cu. m
resources
% for
% for
% for
% of urban
% of rural
cu. m
cu. m
cu. m a
1980–
1980–
agriculture
industry
domestic
population
population
2000
2000
2000
2000 b
2000 a,b
1987
1987
1987
1990
2000
1990
2000
10.0 15.7 0.4 .. 623.0 1.5 0.0 29.0 21.0 1,105.6 20.8 4.0 15.5 7.2 2.0 11.8 1,900.0 0.2 2.0 .. 355.6 0.0 52.0 .. 28.0 0.0 17.2 .. 0.0 313.0 610.0 .. .. 33.7 0.0 1.0 .. .. 0.0 66.7 .. 6.0 0.1 0.0 3.0 11.0 0.0 5.0 8.4 71.0 22.9 15.0 0.0 0.0 11.0 ..
2,322 13,524 457 14,023 23,693 3,455 25,022 10,437 3,561 8,922 5,844 1,548 3,938 35,271 9,120 8,586 41,941 2,662 1,226 509 38,136 17,312 92,532 36,911 5,155 56,707 2,210 .. 48,293 23,517 227,509 28,513 4,645 15,991 3,383 1,391 1,116 2,438 33,703 1,032 2,774 2,048 9,426 1,636 21,158 3,186 124,715 5,760 12,845 2,158 2,624 6,867 9,106 29,184 18,659 1,569
26.1 1.4 5.0 0.5 28.6 2.9 14.6 2.4 16.5 14.6 2.7 .. 0.1 1.2 1.0 0.1 54.9 13.9 0.4 0.1 0.5 0.4 45.1 0.1 0.2 20.3 525.5 .. 8.9 0.4 0.0 5.8 0.7 0.8 5.2 2.7 1.2 8.3 17.0 66.0 0.7 .. 0.2 2.2 2.2 32.3 0.1 0.0 3.5 46.3 0.3 8.7 1.2 0.7 0.0 1.0
40.2 3.3 35.0 0.3 3.2 27.4 3.0 2.9 56.7 1.2 4.7 .. 0.4 0.4 2.7 0.7 0.8 65.6 2.8 2.8 0.1 0.1 1.6 0.1 0.5 2.3 18.6 .. 0.4 0.0 0.0 5.2 0.9 1.1 13.6 19.0 20.0 39.5 3.9 96.4 3.9 .. 1.6 2.0 2.0 17.0 0.1 0.0 5.3 26.0 0.6 11.9 1.1 0.3 0.0 7.7
99 c 71 52 c 76 c 75 66 33 9 70 86 35 .. 67 c 87 60 48 c 61 22 81 c 64 c 94 35 c 12 74 c 82 c 84 78 .. 37 23 c 11 c 80 67 c 0 51 2 43 89 82 82 c 46 .. 5 86 c 3 10 6c 91 c 59 20 52 c 87 74 87 c 36 c 94
0c 0 14 c 10 c 9 4 2 58 25 2 43 .. 10 c 3 10 20 c 18 75 0c 0c 1 19 c 70 5c 2c 11 18 .. 4 16 c 27 c 7 11 c 50 0 57 27 0 6 11 c 20 .. 39 3c 85 72 22 c 2c 20 69 13 c 3 17 3c 4c 1
1c 29 34 c 14 c 16 30 65 33 5 12 22 .. 23 c 10 30 32 c 21 3 19 c 36 c 5 46 c 18 21 c 16 c 5 5 .. 59 61 c 62 c 13 22 c 50 49 41 30 11 12 7c 34 .. 56 11 c 12 18 72 c 7c 21 11 35 c 10 9 10 c 60 c 5
.. .. .. .. .. .. 100 100 .. 99 .. .. .. 91 .. 100 93 .. .. 96 .. 78 100 71 .. 98 99 .. 98 .. .. .. 97 .. .. .. .. 92 82 97 88 .. .. 80 100 .. .. .. .. .. 85 .. 88 72 .. 59
19 99 94 34 97 87 100 100 93 99 100 .. 74 95 .. 100 95 100 66 91 54 78 100 89 31 99 94 .. 99 89 71 99 92 .. 95 .. 100 90 90 99 91 63 .. 81 100 .. 95 80 90 .. 91 .. 98 72 79 49
.. .. .. .. .. .. 100 100 .. 93 .. .. .. 47 .. 88 54 .. .. 67 .. 32 99 35 .. 49 60 .. 84 .. .. .. 69 .. .. .. .. 71 58 92 48 .. .. 17 100 .. .. .. .. .. 36 .. 69 36 .. 50
11 95 82 40 73 45 100 100 58 97 100 .. 55 64 .. 90 53 100 37 77 26 39 99 57 26 58 66 .. 70 26 17 92 72 .. 77 .. 100 78 75 96 64 42 .. 12 100 .. 47 53 61 .. 62 .. 88 36 49 45
55 27 14 184 276 9 492 55 8 105 37 12 10 304 36 3 5,418 21 13 4 121 273 2,850 141 15 884 2,812 .. 2,112 900 222 112 77 38 38 13 6 21 432 2 18 3 13 110 107 179 164 3 58 107 30 58 109 226 16 13
2004 World Development Indicators
Renewable freshwater resources
Annual freshwater withdrawals
3.5
ENVIRONMENT
Freshwater
Access to improved water source
Net flows
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Internal
from other
Total
flows
countries
resources
billion
% of total
billion
billion
per capita
cu. m
resources
% for
% for
% for
% of urban
% of rural
cu. m
cu. m
cu. m a
1980–
1980–
agriculture
industry
domestic
population
population
2000
2000
2000
2000 b
2000 a,b
1987
1987
1987
1990
2000
1990
2000
0.0 114.0 647.2 .. .. 75.9 3.0 0.9 6.8 .. 0.0 .. 34.2 10.0 10.1 4.9 0.0 0.0 143.1 18.7 0.0 0.0 32.0 .. 9.3 1.0 0.0 1.1 .. 40.0 11.0 0.0 49.0 10.7 .. 0.0 111.0 165.0 39.3 12.0 80.0 0.0 0.0 29.0 59.0 11.0 .. 170.3 .. .. .. 144.0 0.0 8.0 35.0 ..
14,109 11,812 1,819 13,405 1,961 4,596 13,265 259 3,281 3,592 3,382 135 7,368 963 3,428 1,465 .. 9,293 60,307 15,141 1,081 2,926 70,410 110 7,178 3,140 20,503 1,601 23,863 8,792 4,093 1,815 4,543 2,750 14,210 978 11,390 21,432 22,922 8,713 5,637 83,016 35,511 2,845 2,109 86,602 394 1,534 50,136 148,940 17,060 65,797 5,992 1,595 7,173 ..
1.5 6.8 500.0 74.3 70.0 42.8 0.8 1.6 42.0 0.9 91.4 1.0 33.7 2.0 14.2 23.7 0.5 10.1 1.0 0.3 1.3 0.1 0.1 4.5 0.3 1.9 16.3 0.9 12.7 1.4 1.6 .. 77.8 3.0 0.4 11.5 0.6 4.0 0.2 29.0 7.8 2.0 1.3 0.5 3.6 2.0 1.2 155.6 1.6 0.1 0.4 19.0 55.4 12.3 7.3 ..
1.6 5.7 26.2 2.6 54.5 38.5 1.5 94.1 22.2 9.6 21.3 .. 30.7 6.6 18.4 34.0 .. 21.7 0.3 0.8 27.1 1.9 0.0 .. 1.2 29.7 4.8 5.2 2.2 1.4 14.0 .. 17.0 25.6 1.1 39.7 0.3 0.4 0.4 13.8 8.6 0.6 0.7 1.5 1.3 0.5 .. 70.0 1.1 0.0 0.4 1.1 11.6 20.0 10.0 ..
91 36 92 93 92 92 10 54 c 48 77 64 75 81 76 c 73 63 60 94 82 13 68 56 c 60 c 84 c 3 74 99 c 86 c 77 97 c 92 77 c 78 26 53 89 c 89 c 90 68 c 99 34 44 84 82 c 54 c 8 94 97 70 49 78 86 88 11 48 ..
5 55 3 1 2 5 74 7c 34 7 17 3 17 4c 16 11 2 3 10 32 6 22 13 c 3c 16 15 .. c 3c 13 1c 2 7c 5 65 27 2c 2c 3 3c 0 61 10 2 2c 15 c 72 2 2 2 22 7 7 4 76 37 ..
4 9 5 6 6 3 16 39 c 19 15 19 22 2 20 c 11 26 37 3 8 55 27 22 c 27 c 13 c 81 12 1c 10 c 11 2c 6 16 c 17 9 20 10 c 9c 7 29 c 1 5 46 14 16 c 31 c 20 5 2 28 29 15 7 8 13 15 ..
89 100 88 92 .. .. .. .. .. 98 .. 99 .. 91 .. .. .. .. .. .. .. .. .. 72 .. .. 85 90 .. 65 34 100 90 .. .. 94 .. .. 98 93 100 100 93 65 83 100 41 96 .. 88 80 88 93 .. .. ..
95 100 95 90 98 96 .. .. .. 98 .. 100 98 88 100 97 .. 98 61 .. 100 88 .. 72 .. .. 85 95 .. 74 34 100 95 97 77 98 81 89 100 94 100 100 91 70 78 100 41 95 99 88 93 87 91 .. .. ..
78 98 61 62 .. .. .. .. .. 87 .. 92 .. 31 .. .. .. .. .. .. .. .. .. 68 .. .. 31 43 .. 52 40 100 52 .. .. 58 .. .. 63 64 100 .. 44 51 37 100 30 77 .. 32 46 42 82 .. .. ..
81 98 79 69 83 48 .. .. .. 85 .. 84 82 42 100 71 .. 66 29 .. 100 74 .. 68 .. .. 31 44 94 61 40 100 69 88 30 56 41 66 67 87 100 .. 59 56 49 100 30 87 79 32 59 62 79 .. .. ..
96 6 1,261 2,838 129 35 49 1 183 9 430 1 75 20 67 65 0 47 190 17 5 5 200 1 16 5 337 16 580 60 0 2 409 1 35 29 99 881 6 198 11 327 190 4 221 382 1 52 147 801 94 1,616 479 54 38 ..
2004 World Development Indicators
133
3.5
Freshwater Renewable freshwater resources
Annual freshwater withdrawals
Access to improved water source
Net flows
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Internal
from other
Total
flows
countries
resources
billion
% of total
billion
billion
per capita
cu. m
resources
% for
% for
% for
% of urban
% of rural
cu. m
cu. m
cu. m a
1980–
1980–
agriculture
industry
domestic
population
population
2000
2000
2000
2000 b
2000 a,b
1987
1987
1987
9,520 31,222 637 110 4,009 23,039 30,564 .. 15,356 9,521 1,685 1,103 2,725 2,636 4,544 4,136 20,529 7,325 2,632 12,706 2,587 6,653 2,521 2,914 470 3,369 12,706 2,683 2,866 62 2,482 9,772 39,572 4,527 28,796 11,081 .. 220 11,324 1,085 8,513 w 6,416 9,938 9,401 13,848 8,258 6,020 13,511 30,925 1,377 2,684 7,951 .. 3,826
26.0 77.1 0.8 17.0 1.4 13.0 0.4 .. 1.8 1.3 0.8 13.3 35.2 9.8 17.8 .. 2.9 1.2 12.0 11.9 1.2 33.1 0.1 0.3 2.8 35.5 23.8 0.2 26.0 2.1 11.8 467.3 0.7 58.1 4.1 54.3 .. 2.9 1.7 1.2 3,325 s 1,041 1,430 1,229 201 2,471 776 387 263 238 735 73 854 185
42 170.0 4,313 185.5 5 .. 2 .. 26 13.0 44 144.0 160 0.0 .. .. 13 70.0 19 0.0 6 9.7 45 5.2 111 0.3 50 0.0 30 119.0 3 1.9 171 12.2 40 13.0 7 37.7 66 13.3 82 9.0 210 199.9 12 0.5 4 .. 4 0.4 227 7.6 1 59.5 39 27.0 53 86.5 0 .. 145 2.0 2,800 18.0 59 74.0 16 98.1 723 .. 367 524.7 .. .. 4 .. 80 35.8 14 .. 42,900 s 9,463.8 s 11,185 4,815.6 22,898 4,275.4 19,341 3,260.3 3,556 1,015.2 34,082 9,091.0 9,454 1,415.6 5,255 1,134.8 13,429 2,833.8 234 183.1 1,816 1,945.1 3,895 1,578.7 8,818 372.8 910 258.8
12.2 1.7 15.4 .. 3.6 6.9 0.3 .. 2.2 7.0 5.1 26.6 31.6 19.6 11.9 .. 1.6 2.2 26.8 14.9 1.3 8.1 0.8 7.9 60.9 15.1 39.1 0.3 18.6 .. 8.0 16.6 0.5 50.8 0.6 6.1 .. 70.7 1.5 8.5 6w 7 5 5 4 6 7 6 2 57 20 1 9 16
59 20 94 c 90 92 c 8c 89 4 .. 1c 97 c 72 68 96 c 94 c 96 9 4 90 92 c 89 91 c 25 6c 86 c 73 98 60 30 67 3c 42 91 94 46 87 .. 92 77 c 79 c 71 w 92 73 73 71 81 81 57 74 88 94 85 42 38
33 62 2c 1 3c 86 c 4 51 .. 80 c 0c 11 19 2c 1c 2 55 73 2 4c 2 4c 13 26 c 1c 12 1 8 52 9 77 c 45 3 2 10 10 .. 1 7c 7c 20 w 4 18 19 14 12 14 33 9 5 3 6 42 47
8 19 5c 9 5c 6c 7 45 .. 20 c 3c 17 13 2c 4c 2 36 23 8 3c 9 5c 62 68 c 13 c 16 1 32 18 24 20 c 13 6 4 44 4 .. 7 16 c 14 c 10 w 5 9 9 15 7 5 10 18 7 4 10 16 15
1990
.. .. .. .. 90 .. .. 100 .. 100 .. 99 .. 91 86 .. 100 100 .. .. 76 87 82 .. 91 83 .. 81 .. .. 100 100 .. .. .. 86 .. .. 88 99 94 w 88 95 96 .. 93 97 .. 92 .. 90 86 .. ..
2000
91 100 60 100 92 99 75 100 100 100 .. 99 .. 98 86 .. 100 100 94 93 90 95 85 .. 92 81 .. 80 100 .. 100 100 98 94 85 95 .. 74 88 100 94 w 90 95 95 .. 93 93 96 94 96 94 83 .. ..
1990
.. .. .. .. 60 .. .. 100 .. 100 .. 73 .. 62 60 .. 100 100 .. .. 28 78 38 .. 54 72 .. 40 .. .. 100 100 .. .. .. 48 .. .. 28 69 62 59 63 63 .. 61 61 .. 58 .. 66 40 .. ..
2000
16 96 40 64 65 97 46 .. 100 100 .. 73 .. 70 69 .. 100 100 64 47 57 81 38 .. 58 86 .. 47 94 .. 100 100 93 79 70 72 .. 68 48 73 71 w 70 70 70 77 70 67 83 65 78 80 46 .. ..
a. River flows from other countries and river outflows are included when available. b. Data are for the most recent year available. c. Data refer to a year other than 1987 (see Primary data documentation).
134
2004 World Development Indicators
About the data
3.5
ENVIRONMENT
Freshwater Definitions
The data on freshwater resources are based on esti-
different times and with different levels of quality
• Renewable freshwater resources refer to total
mates of runoff into rivers and recharge of groundwater.
and precision, these data must be used with caution,
renewable resources, broken down between internal
These estimates are based on different sources and
particularly in case of water-short countries, notably
flows (internal river flows and groundwater from rain-
refer to different years, so cross-country comparisons
in the Middle East.
fall) in the country and net river flows from other
should be made with caution. Because the data are col-
The data on access to an improved water source
countries. • Net flows from other countries refer to
lected intermittently, they may hide significant variations
measure the share of the population with reasonable
river flows arising outside countries minus river out-
in total renewable water resources from one year to the
and ready access to an adequate amount of safe
flows, when these data are available. • Freshwater
next. The data also fail to distinguish between season-
water for domestic purposes. An improved source
resources per capita are calculated using the World
al and geographic variations in water availability within
can be any form of collection or piping used to make
Bank’s population estimates (see table 2.1).
countries. Data for small countries and countries in arid
water regularly available. While information on
• Annual freshwater withdrawals refer to total water
and semiarid zones are less reliable than those for larg-
access to an improved water source is widely used,
withdrawals, not counting evaporation losses from
er countries and countries with greater rainfall. Finally,
it is extremely subjective, and such terms as safe,
storage basins. Withdrawals also include water from
caution is also needed in comparing data on annual
improved, adequate, and reasonable may have very
desalination plants in countries where they are a sig-
freshwater withdrawals, which are subject to variations
different meanings in different countries despite offi-
nificant source. Withdrawals can exceed 100 percent
in collection and estimation methods.
cial World Health Organization definitions (see
of total renewable resources where extraction from
freshwater
Definitions). Even in high-income countries treated
nonrenewable aquifers or desalination plants is con-
resources and river flows arising outside countries.
water may not always be safe to drink. While access
siderable or where there is significant water reuse.
River outflows are also taken into account. However,
to an improved water source is equated with con-
Withdrawals for agriculture and industry are total
because inflows and outflows may be estimated at
nection to a public supply system, this does not take
withdrawals for irrigation and livestock production
into account variations in the quality and cost (broad-
and for direct industrial use (including withdrawals
ly defined) of the service once connected. Thus
for cooling thermoelectric plants). Withdrawals for
The distribution of freshwater resources is uneven
cross-country comparisons must be made cautious-
domestic uses include drinking water, municipal use
ly. Changes over time within countries may result
or supply, and use for public services, commercial
Internal freshwater flows, 2000
from changes in definitions or measurements. The
establishments, and homes. • Access to an
definition in this table and in table 2.15 differs from
improved water source refers to the percentage of
that used for the city-level data shown in table 3.11,
the population with reasonable access to an ade-
which is more stringent.
quate amount of water from an improved source,
The
table
shows
both
internal
3.5a
South Asia 4% Sub-Saharan Africa 9% Europe & Central Asia 12%
Middle East & North Africa 1%
High income 21%
such as a household connection, public standpipe, borehole, protected well or spring, or rainwater collection. Unimproved sources include vendors, tanker trucks,
East Asia & Pacific 22%
Latin America & Caribbean 31%
and
unprotected
wells
and
springs.
Reasonable access is defined as the availability of at least 20 liters a person a day from a source within 1 kilometer of the dwelling.
Source: Table 3.5.
3.5b Latin America and the Caribbean has more than 20 times the freshwater resources per capita as the Middle East and North Africa Total freshwater resources per capita, 2000 (thousands of cubic meters)
Data sources The data on freshwater resources and with-
35
drawals are compiled by the World Resources
30
Institute from various sources and published in
25
World Resources 2000–01 and World Resources
20
2002–03 (produced in collaboration with the
15
United Nations Environment Programme, United
10
Nations Development Programme, and World
5
Bank). These are supplemented by the Food and Agriculture Organization’s AQUASTAT data. The
0 Latin America & Caribbean
Europe & Central Asia
Sub-Saharan Africa
East Asia & Pacific
South Asia
Middle East & North Africa
data on access to an improved water source come from the World Health Organization.
Source: Table 3.5.
2004 World Development Indicators
135
3.6
Water pollution Emissions of organic water pollutants
Industry shares of emissions of organic water pollutants
% of total kilograms kilograms per day 1980
2000 a
Afghanistan 6,680 .. Albania .. 6,512 Algeria 60,290 45,645 Angola .. 1,472 Argentina 244,711 177,882 Armenia .. 10,014 Australia 204,333 95,369 Austria 108,416 80,789 Azerbaijan .. 45,025 Bangladesh 66,713 273,082 Belarus .. .. Belgium 136,452 102,460 Benin 1,646 .. Bolivia 9,343 12,759 Bosnia and Herzegovina .. 8,903 Botswana 1,307 4,635 Brazil 866,790 629,406 Bulgaria 152,125 107,945 Burkina Faso 2,385 2,598 Burundi 769 1,644 Cambodia .. 12,078 Cameroon 14,569 10,714 Canada 330,241 307,325 Central African Republic 861 670 Chad .. .. Chile 44,371 72,850 China 3,377,105 6,204,237 Hong Kong, China 102,002 35,649 Colombia 96,055 93,879 Congo, Dem. Rep. .. .. Congo, Rep. 1,039 .. Costa Rica .. 32,914 Côte d’Ivoire 15,414 12,401 Croatia .. 48,447 Cuba 120,703 .. Czech Republic 316,429 258,413 Denmark 65,465 83,591 Dominican Republic 54,935 .. Ecuador 25,297 32,266 Egypt, Arab Rep. 169,146 203,633 El Salvador 9,390 22,760 Eritrea .. .. Estonia .. .. Ethiopia 16,754 21,533 Finland 92,275 62,610 France 729,776 278,878 Gabon 2,661 1,886 Gambia, The 549 832 Georgia .. .. Germany .. 792,194 Ghana 15,868 14,449 Greece 65,304 57,178 Guatemala 20,856 19,253 Guinea .. .. Guinea-Bissau .. .. Haiti 4,734 ..
136
2004 World Development Indicators
per day per worker
Stone, Primary
Paper
Food and
ceramics,
metals
and pulp
Chemicals
beverages
and glass
Textiles
Wood
Other
1980
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
0.17 .. 0.19 .. 0.18 .. 0.18 0.16 .. 0.16 .. 0.16 0.28 0.22 .. 0.24 0.16 0.13 0.29 0.22 .. 0.29 0.18 0.26 .. 0.21 0.14 0.11 0.19 .. 0.21 .. 0.23 .. 0.24 0.13 0.17 0.38 0.23 0.19 0.24 .. .. 0.22 0.17 0.14 0.15 0.30 .. .. 0.20 0.17 0.25 .. .. 0.19
.. 0.29 0.24 0.20 0.21 .. 0.21 0.13 0.17 0.14 .. 0.17 .. 0.25 0.18 0.20 0.20 0.17 0.22 0.24 0.16 0.20 0.15 0.17 .. 0.24 0.14 0.18 0.21 .. .. 0.21 0.24 0.17 .. 0.13 0.17 .. 0.27 0.20 0.18 .. .. 0.23 0.19 0.10 0.26 0.34 .. 0.13 0.17 0.20 0.28 .. .. ..
.. 14.3 23.4 7.6 6.5 .. .. 14.9 11.6 1.8 .. 13.7 .. 0.9 20.5 1.7 17.7 11.7 3.5 0.0 0.0 3.1 10.8 0.0 .. 6.9 20.6 0.9 3.1 .. .. 1.8 .. 7.2 .. 23.2 4.4 .. 2.3 11.8 2.1 .. .. 1.9 9.8 14.9 0.0 .. .. 11.2 9.8 6.3 4.9 .. .. ..
.. 0.9 2.0 3.0 12.5 3.9 .. 18.2 2.5 6.8 .. 18.0 .. 20.5 13.1 15.8 12.9 7.9 1.1 8.3 3.4 6.3 23.9 .. .. 11.3 10.8 43.6 16.2 .. .. 10.0 5.5 14.4 .. 9.5 29.1 .. 10.8 7.9 10.2 .. .. 10.8 43.3 30.9 6.0 .. .. 22.3 16.9 11.8 7.2 .. .. ..
.. 5.5 3.3 9.1 8.0 .. 6.5 11.1 12.0 6.6 .. 10.5 1.2 6.6 6.6 0.8 7.6 8.2 5.8 5.1 3.3 3.6 9.8 4.0 .. 8.9 15.3 4.2 10.3 .. .. 7.1 5.0 8.6 .. 7.9 7.0 1.9 7.1 8.3 7.1 .. .. .. 2.2 10.3 5.0 1.7 .. 9.8 10.5 9.1 8.1 .. .. ..
.. 73.5 59.5 65.9 59.4 72.5 81.7 32.8 49.0 23.2 .. 40.4 .. 61.4 33.3 56.4 44.4 48.1 73.8 67.8 59.2 52.7 34.8 62.0 .. 62.7 28.4 27.4 53.2 .. .. 62.2 71.9 45.2 .. 31.5 44.2 .. 71.8 49.8 43.5 .. .. 61.5 30.2 37.7 79.7 .. .. 34.4 39.5 54.0 72.8 .. .. ..
.. 0.3 0.7 0.3 0.1 .. 0.1 0.4 0.2 0.1 .. 0.2 .. 0.3 0.2 0.2 0.1 0.1 0.1 0.1 0.6 0.0 0.1 0.0 .. 0.1 0.5 0.1 0.2 .. .. 0.1 0.0 0.2 .. 0.4 0.2 .. 0.1 0.3 0.1 .. .. 0.2 0.2 0.3 0.1 .. .. 0.2 0.2 0.2 0.1 .. .. ..
.. 4.6 7.6 5.5 7.4 .. 2.8 5.1 18.1 64.1 .. 6.0 .. 7.1 17.6 17.2 9.8 17.0 4.1 16.7 24.7 3.6 5.4 13.8 .. 5.0 14.8 17.9 14.2 .. .. 13.9 8.6 14.6 .. 12.2 2.2 .. 6.0 18.9 34.1 .. .. 18.7 2.8 9.7 1.2 .. .. 3.2 9.1 13.2 6.9 .. .. ..
.. 0.0 0.8 4.4 1.5 .. 3.1 5.3 1.0 0.5 .. 2.0 .. 2.4 5.8 1.4 1.4 2.0 10.1 1.6 5.8 5.6 5.1 19.6 .. 2.6 0.8 0.1 1.0 .. .. 1.7 5.9 3.8 .. 2.0 3.5 .. 1.5 0.4 0.5 .. .. 1.6 4.4 2.7 6.9 .. .. 2.3 12.4 1.5 0.8 .. .. ..
.. 0.8 –0.0 4.1 4.5 .. .. 12.1 5.6 .. .. 7.5 .. 0.9 2.8 1.8 4.5 6.6 1.9 0.8 3.1 0.4 10.0 .. .. 2.5 8.9 6.0 2.4 .. .. 2.9 .. 6.0 .. 12.8 8.6 .. 1.3 2.9 1.4 .. .. 0.8 7.0 .. 1.2 .. .. 16.5 1.7 3.8 .. .. .. ..
Emissions of organic water pollutants
3.6
ENVIRONMENT
Water pollution
Industry shares of emissions of organic water pollutants
% of total kilograms kilograms per day 1980
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
2000 a
13,067 34,036 201,888 152,531 1,422,564 1,582,285 214,010 752,834 72,334 101,900 32,986 19,617 43,544 49,144 39,113 54,149 442,712 495,411 11,123 17,507 1,456,016 1,332,302 4,146 16,142 .. .. 26,834 53,029 .. .. 281,900 303,091 6,921 11,412 .. 20,700 .. .. .. 25,106 14,586 14,899 993 3,123 .. .. 3,532 .. .. 35,689 .. 23,490 9,131 .. 12,224 11,805 77,215 158,761 .. .. .. .. 9,224 17,700 130,993 296,093 .. 34,234 9,254 7,939 26,598 88,779 .. 10,230 .. 3,356 .. 7,350 18,692 26,550 165,416 124,182 59,012 46,099 9,647 .. 372 .. 72,082 82,477 67,897 55,439 .. 5,789 75,125 100,821 8,121 11,462 4,365 .. .. 3,250 50,367 52,644 182,052 201,952 580,869 388,153 105,441 121,013 24,034 15,367
per day per worker
Stone, Primary
Paper
Food and
ceramics,
metals
and pulp
Chemicals
beverages
and glass
Textiles
Wood
Other
1980
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
0.23 0.15 0.21 0.22 0.15 0.19 0.19 0.15 0.13 0.25 0.14 0.17 .. 0.19 .. 0.14 0.16 .. .. .. 0.20 0.24 .. 0.21 .. .. 0.23 0.32 0.15 .. .. 0.21 0.22 .. 0.19 0.15 .. .. .. 0.25 0.18 0.21 0.28 0.19 0.17 0.19 .. 0.17 0.26 0.22 .. 0.18 0.19 0.14 0.15 0.16
0.20 0.17 0.20 0.18 0.17 0.16 0.15 0.16 0.13 0.29 0.15 0.18 .. 0.25 .. 0.12 0.17 0.16 .. 0.19 0.19 0.16 .. .. 0.18 0.18 .. 0.29 0.12 .. .. 0.15 0.20 0.29 0.18 0.18 0.31 0.13 0.35 0.14 0.18 0.22 .. .. 0.17 0.20 0.16 0.18 0.31 .. 0.28 0.21 0.18 0.16 0.14 0.14
1.1 8.0 13.9 2.8 20.6 8.8 1.3 3.7 9.5 6.9 7.4 3.9 .. 4.1 .. 12.2 2.5 13.7 .. 2.8 0.9 1.2 .. .. 1.2 11.7 .. 0.0 6.5 .. .. 0.9 7.8 0.2 1.8 0.7 1.1 14.0 0.0 1.5 7.3 3.2 .. .. 1.4 8.7 6.1 11.6 1.6 .. 2.3 8.1 5.2 13.8 4.0 1.9
7.8 12.1 6.6 8.6 8.0 14.1 14.2 19.7 16.9 7.2 21.8 16.2 .. 11.9 .. 17.0 16.4 0.2 .. 8.7 15.6 4.0 .. .. 11.2 9.6 .. 16.0 14.5 .. .. 6.6 12.5 4.0 4.3 7.0 7.1 9.0 5.0 8.1 26.7 21.7 .. .. 15.4 31.7 13.1 7.0 13.7 .. 9.9 13.5 9.8 6.2 17.4 14.9
3.9 7.9 9.6 8.6 8.0 15.1 11.4 9.4 10.8 3.8 8.9 14.5 .. 5.8 .. 12.4 10.9 0.9 .. 0.8 3.3 0.7 .. 11.0 5.0 6.2 2.5 3.7 16.5 .. .. 2.6 10.4 1.4 2.9 9.7 4.1 40.5 1.6 3.9 11.3 5.2 5.1 .. 11.3 4.9 6.9 8.1 3.8 0.9 6.0 10.5 7.3 6.8 4.5 19.5
55.5 48.0 52.2 50.1 39.7 39.4 56.4 43.9 30.3 70.8 41.7 51.4 .. 70.0 .. 26.0 49.4 54.8 .. 64.5 60.7 39.7 .. .. 55.6 45.0 .. 70.0 34.1 .. .. 32.8 55.6 81.7 64.2 54.4 81.2 27.0 90.4 43.3 43.0 57.3 .. .. 40.2 42.9 50.4 39.9 74.6 .. 73.6 52.8 54.5 48.8 33.6 34.4
0.1 0.2 0.2 0.1 0.5 0.7 0.2 0.2 0.3 0.1 0.2 0.5 .. 0.1 .. 0.2 0.4 0.4 .. 0.1 0.5 0.1 .. .. 0.2 0.1 .. 0.0 0.2 .. .. 0.1 0.2 0.2 0.3 0.4 0.1 0.5 0.1 1.2 0.2 0.1 .. .. 0.1 0.1 0.8 0.2 0.2 .. 0.3 0.2 0.2 0.4 0.4 0.2
26.8 14.1 13.1 22.0 17.3 16.7 3.1 12.1 16.0 9.8 5.4 7.2 .. 8.5 .. 15.7 12.1 21.0 .. 11.5 10.2 51.3 .. .. 17.6 20.9 .. 7.8 7.5 .. .. 55.4 7.5 10.8 24.6 27.2 5.8 4.9 1.2 39.3 2.3 4.6 .. .. 23.5 1.4 14.1 30.3 4.2 .. 6.7 11.7 16.4 13.6 27.4 15.5
4.0 2.4 0.3 5.3 0.7 0.3 1.6 1.8 3.7 1.3 1.7 3.3 .. 1.8 .. 1.3 2.9 1.0 .. 9.7 4.6 0.6 .. 6.0 0.4 .. 1.7 7.0 3.2 .. .. 0.9 1.3 4.9 0.9 1.4 2.9 0.9 1.7 1.2 2.1 .. .. 4.7 3.0 3.8 0.3 0.5 .. 0.3 2.0 2.0 2.6 5.4 1.4 ..
0.8 7.3 4.2 4.5 5.4 4.8 11.8 9.3 12.5 0.0 12.7 3.0 .. .. .. 15.3 5.8 8.0 .. .. 3.4 2.3 .. .. .. .. .. .. 19.7 .. .. 0.8 3.7 .. .. .. .. .. 0.4 1.6 7.4 .. .. .. 5.1 6.5 5.9 2.1 .. .. .. 2.8 4.3 5.1 11.1 ..
2004 World Development Indicators
137
3.6
Water pollution Emissions of organic water pollutants
Industry shares of emissions of organic water pollutants
% of total kilograms kilograms per day 1980
2000 a
Romania 343,145 333,168 Russian Federation .. 1,485,833 Rwanda .. .. Saudi Arabia 18,181 24,436 Senegal 9,865 6,643 Serbia and Montenegro .. 101,535 Sierra Leone 1,612 4,170 Singapore 28,558 32,119 Slovak Republic .. 57,970 Slovenia .. 38,601 Somalia .. .. South Africa 237,599 234,012 Spain 376,253 374,589 Sri Lanka 30,086 83,058 Sudan .. .. Swaziland 2,826 2,009 Sweden 130,439 103,913 Switzerland .. 123,752 Syrian Arab Republic 36,262 15,115 Tajikistan .. .. Tanzania 21,084 35,155 Thailand 213,271 355,819 Togo 963 .. Trinidad and Tobago 7,835 11,787 Tunisia 20,294 46,052 Turkey 160,173 170,685 Turkmenistan .. .. Uganda .. .. Ukraine .. 499,886 United Arab Emirates 4,524 .. United Kingdom 964,510 569,736 United States 2,742,993 1,968,196 Uruguay 34,270 17,972 Uzbekistan .. .. Venezuela, RB 84,797 94,175 Vietnam .. .. West Bank and Gaza .. .. Yemen, Rep. .. 7,823 Zambia 13,605 11,433 Zimbabwe 32,681 26,810
per day per worker
Stone, Primary
Paper
2004 World Development Indicators
ceramics,
metals
and pulp
Chemicals
beverages
and glass
Textiles
Wood
Other
1980
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
2000 a
0.12 .. .. 0.12 0.31 .. 0.24 0.10 .. .. .. 0.17 0.16 0.18 .. 0.26 0.15 .. 0.19 .. 0.21 0.22 0.27 0.18 0.16 0.20 .. .. .. 0.15 0.15 0.14 0.21 .. 0.20 .. .. .. 0.23 0.20
0.14 0.16 .. 0.14 0.36 0.16 0.32 0.09 0.15 0.17 .. 0.17 0.15 0.18 .. 0.23 0.14 0.17 0.20 .. 0.25 0.16 .. 0.28 0.16 0.17 .. .. 0.18 .. 0.15 0.12 0.28 .. 0.21 .. .. 0.25 0.22 0.19
17.1 17.7 .. 4.4 0.0 9.5 .. 1.4 17.2 32.3 .. 13.7 6.7 0.6 .. .. 11.3 24.9 4.1 .. 1.5 6.1 .. 4.4 5.8 11.0 .. .. 22.8 .. 7.2 10.5 1.2 .. 13.7 .. .. 0.0 3.4 5.2
6.7 7.4 .. 15.9 6.6 12.0 9.6 26.2 12.7 15.6 .. 16.3 19.8 7.4 .. 79.8 35.0 23.6 1.5 .. 9.4 5.3 .. 10.9 8.0 7.1 .. .. 3.4 .. 30.4 11.0 11.1 .. 10.4 .. .. 9.1 10.8 10.2
8.3 9.3 .. 5.8 4.0 8.0 5.5 16.0 7.9 8.5 .. 9.1 8.9 9.8 .. 0.3 7.8 10.4 8.3 .. 4.9 5.3 2.3 18.3 6.5 7.6 .. .. 6.6 .. 10.0 13.8 6.7 .. 9.8 .. .. 12.9 6.9 4.6
34.3 46.8 .. 45.1 87.0 46.9 82.3 21.6 37.5 24.4 .. 40.3 42.5 52.6 .. .. 26.6 25.0 69.8 .. 69.3 42.2 .. 72.6 41.1 44.5 .. .. 51.6 .. 32.1 38.4 71.2 .. 53.1 .. .. 71.1 63.6 54.2
0.3 0.3 .. 1.0 0.1 0.3 0.1 0.1 0.3 0.2 .. 0.2 0.3 0.2 .. 0.2 0.1 0.2 0.9 .. 0.1 0.2 .. 0.1 0.4 0.3 .. .. 0.3 .. 0.2 0.2 0.1 .. 0.3 .. .. 0.3 0.2 0.3
18.5 6.9 .. 3.8 1.8 13.3 2.0 4.0 11.9 11.1 .. 10.2 9.3 29.8 .. 16.5 1.3 3.2 19.4 .. 14.0 35.4 .. 2.9 33.5 23.6 .. .. 5.8 .. 5.6 7.1 8.5 .. 7.5 .. .. 4.9 9.3 16.3
4.8 2.1 .. 2.0 0.2 2.2 2.2 1.4 2.7 2.1 .. 3.4 4.0 1.1 .. 2.0 3.0 4.2 0.2 .. 1.5 1.5 .. 1.3 1.5 1.1 .. .. 1.6 .. 2.5 4.1 0.7 .. 1.5 .. .. 1.0 2.9 2.8
9.4 9.5 .. 6.8 0.7 7.7 .. 28.6 9.9 5.9 .. 6.8 8.6 .. .. .. 14.9 8.7 .. .. 1.4 3.9 .. .. 3.3 5.0 .. .. 7.9 .. 12.0 14.9 1.8 .. 3.3 .. .. 0.9 2.4 3.1
Note: Industry shares may not sum to 100 percent because data may be from different years. a. Data refer to any year from 1993 to 2000.
138
Food and
About the data
3.6
ENVIRONMENT
Water pollution Definitions
Emissions of organic pollutants from industrial activ-
water pollution are more widely understood and much
• Emissions of organic water pollutants are meas-
ities are a major cause of degradation of water qual-
less expensive than those for air pollution.
ured in terms of biochemical oxygen demand, which
ity. Water quality and pollution levels are generally
Hettige, Mani, and Wheeler (1998) used plant- and
refers to the amount of oxygen that bacteria in water
measured in terms of concentration or load—the
sector-level information on emissions and employ-
will consume in breaking down waste. This is a stan-
rate of occurrence of a substance in an aqueous
ment from 13 national environmental protection agen-
dard water treatment test for the presence of organic
solution. Polluting substances include organic mat-
cies and sector-level information on output and
pollutants. Emissions per worker are total emissions
ter, metals, minerals, sediment, bacteria, and toxic
employment from the United Nations Industrial
divided by the number of industrial workers.
chemicals. This table focuses on organic water pol-
Development Organization (UNIDO). Their econometric
• Industry shares of emissions of organic water pol-
lution resulting from industrial activities. Because
analysis found that the ratio of BOD to employment in
lutants refer to emissions from manufacturing activi-
water pollution tends to be sensitive to local condi-
each industrial sector is about the same across
ties as defined by two-digit divisions of the
tions, the national-level data in the table may not
countries. This finding allowed the authors to estimate
International Standard Industrial Classification (ISIC)
reflect the quality of water in specific locations.
BOD loads across countries and over time. The esti-
revision 2: primary metals (ISIC division 37), paper
The data in the table come from an international
mated BOD intensities per unit of employment were
and pulp (34), chemicals (35), food and beverages
study of industrial emissions that may be the first to
multiplied by sectoral employment numbers from
(31), stone, ceramics, and glass (36), textiles (32),
include data from developing countries (Hettige,
UNIDO’s industry database for 1980–98. The esti-
wood (33), and other (38 and 39).
Mani, and Wheeler 1998). These data were updated
mates of sectoral emissions were then totaled to get
through 2000 by the World Bank’s Development
daily emissions of organic water pollutants in kilo-
Research Group. Unlike estimates from earlier stud-
grams per day for each country and year. The data in
ies based on engineering or economic models, these
the table were derived by updating these estimates
estimates are based on actual measurements of
through 2000.
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 the most reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste in terms of the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water’s dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Data on water pollution are more readily available than other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring
3.6a High- and middle-income countries account for most water pollution from organic waste Emissions of organic water pollutants, 1998 Low income, excluding India 6%
Data sources The data come from a 1998 study by Hemamala
India 7%
Hettige, Muthukumara Mani, and David Wheeler, High income 36%
Middle income, excluding China 20%
“Industrial Pollution in Economic Development: Kuznets Revisited” (available at http://www. worldbank.org/nipr). These data were updated
China 31%
through 2000 by the World Bank’s Development Research Group using the same methodology as the initial study. Sectoral employment numbers are from UNIDO’s industry database.
Source: World Bank staff estimates.
2004 World Development Indicators
139
3.7
Energy production and use Total energy production
140
Energy use per capita
Total
Combustible
thousands of
thousands of
renewables
average
metric tons of
metric tons of
and waste
annual
kg of oil
annual
oil equivalent
oil equivalent
% of total
% growth
equivalent
% growth
1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
Energy use
.. 2,449 104,559 28,652 47,384 263 157,712 8,080 18,150 10,747 4,103 12,490 1,774 4,923 3,642 .. 97,069 9,613 .. .. .. 12,090 273,680 .. .. 7,641 902,689 43 48,445 12,027 9,005 1,032 3,395 4,346 6,271 38,474 9,835 1,031 16,400 54,869 1,722 .. 4,118 14,158 12,081 111,278 14,630 .. 1,470 186,157 4,392 9,200 3,390 .. .. 1,253
2004 World Development Indicators
2001
.. 673 144,330 43,559 82,862 602 250,436 9,717 19,581 16,200 3,533 12,967 1,483 6,938 3,277 .. 145,933 10,297 .. .. .. 12,485 379,207 .. .. 8,673 1,138,617 48 73,920 15,707 13,668 1,733 6,177 3,720 6,656 30,489 27,171 1,485 22,872 59,301 2,329 .. 2,989 18,000 15,156 132,709 14,788 .. 1,265 133,745 5,995 9,965 5,230 .. .. 1,542
1990
.. 2,662 23,926 6,280 45,039 4,298 87,536 25,042 16,675 12,937 39,703 48,685 1,678 2,774 4,474 .. 132,985 28,820 .. .. .. 5,031 209,090 .. .. 13,630 870,441 10,662 25,014 11,911 1,056 2,025 4,420 6,714 16,524 47,401 17,609 4,139 6,054 32,024 2,535 .. 6,271 15,151 29,171 227,114 1,287 .. 8,762 356,218 5,337 22,181 4,477 .. .. 1,585
2001
.. 1,715 29,438 8,454 57,601 2,297 115,627 30,721 11,582 20,410 24,415 59,001 2,028 4,271 4,359 .. 185,083 19,476 .. .. .. 6,445 248,184 .. .. 23,801 1,139,369 16,278 29,245 15,039 931 3,481 6,497 7,904 13,651 41,396 19,783 7,810 8,727 48,012 4,269 .. 4,697 19,161 33,815 265,570 1,702 .. 2,413 351,092 8,180 28,704 7,313 .. .. 2,088
1990
2001
.. 13.6 0.1 68.8 3.8 0.0 4.5 9.8 0.0 53.0 1.5 1.4 93.2 27.2 3.6 .. 31.0 0.6 .. .. .. 75.9 3.9 .. .. 19.6 23.0 0.5 23.3 83.9 69.4 36.6 71.9 3.8 33.7 1.2 6.6 24.2 13.6 3.3 48.2 .. 2.9 92.8 15.6 4.8 57.7 .. 7.7 1.3 73.1 4.0 67.9 .. .. 76.5
.. 7.5 0.3 68.7 5.2 0.0 4.5 10.4 0.0 37.9 4.3 1.6 71.2 16.8 4.1 .. 23.4 2.8 .. .. .. 79.0 4.2 .. .. 17.7 19.0 0.3 17.9 93.0 64.9 11.0 66.6 3.7 24.4 1.7 9.2 18.4 8.4 2.8 32.9 .. 11.5 93.1 19.7 4.5 55.7 .. .. 2.3 66.3 3.5 53.3 .. .. 72.7
1990–2001
.. –1.6 1.6 2.7 2.9 –2.3 2.7 1.6 –4.0 4.3 –3.9 1.8 1.5 5.7 5.1 .. 3.6 –2.6 .. .. .. 2.4 1.8 .. .. 6.1 2.8 3.9 1.5 2.2 –2.8 4.8 4.1 2.1 –0.5 –1.0 0.7 6.5 3.4 3.9 4.5 .. –2.2 2.4 1.7 1.2 2.4 .. –11.7 –0.0 4.1 2.4 4.8 .. .. 3.5
average
1990
2001
.. 812 956 672 1,395 1,231 5,130 3,241 2,259 118 3,886 4,885 356 422 1,086 .. 899 3,306 .. .. .. 431 7,524 .. .. 1,041 767 1,869 715 319 423 664 375 1,405 1,555 4,574 3,426 586 590 611 496 .. 4,091 296 5,851 4,003 1,350 .. 1,612 4,485 349 2,183 512 .. .. 245
.. 548 955 663 1,593 744 5,956 3,825 1,428 153 2,449 5,735 318 496 1,074 .. 1,074 2,428 .. .. .. 417 7,985 .. .. 1,545 896 2,421 680 300 262 899 402 1,771 1,216 4,049 3,692 921 692 737 677 .. 3,444 291 6,518 4,487 1,322 .. 462 4,264 410 2,710 626 .. .. 257
1990–2001
.. –1.0 –0.3 –0.1 1.6 –0.9 1.5 1.3 –5.0 2.5 –3.6 1.6 –1.2 3.1 4.7 .. 2.1 –2.0 .. .. .. –0.2 0.8 .. .. 4.6 1.8 2.1 –0.4 –0.5 –5.8 2.6 1.1 3.1 –1.0 –0.9 0.3 4.7 1.4 1.9 2.4 .. –1.0 0.1 1.3 0.8 –0.4 .. –11.4 –0.3 1.6 2.1 2.1 .. .. 1.3
Total energy production
Energy use per capita
Total
Combustible
thousands of
thousands of
renewables
average
metric tons of
metric tons of
and waste
annual
kg of oil
annual
oil equivalent
oil equivalent
% of total
% growth
equivalent
% growth
1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Energy use
ENVIRONMENT
3.7
Energy production and use
1,694 14,239 333,978 161,518 179,738 106,715 3,467 433 25,547 485 73,209 162 89,007 10,272 28,725 21,908 48,519 1,818 .. 794 143 .. .. 73,173 4,189 .. .. .. 48,727 .. .. .. 194,454 58 .. 773 6,846 10,651 218 5,501 60,316 12,256 1,495 .. 150,453 120,304 38,312 34,360 612 .. 4,578 10,596 15,901 99,228 2,805 ..
2001
1,535 10,824 438,099 234,314 246,644 123,296 1,729 685 26,264 487 104,006 280 83,752 12,644 19,251 34,207 108,851 1,353 .. 1,717 161 .. .. 74,363 4,144 .. .. .. 77,623 .. .. .. 230,236 62 .. 583 7,560 15,275 294 7,338 60,437 14,932 1,540 .. 207,024 226,570 64,534 48,606 678 .. 6,077 9,363 20,006 79,861 3,396 ..
1990
2,416 28,467 363,153 92,815 68,775 20,841 10,575 12,112 152,552 2,943 436,523 3,499 79,661 12,479 32,874 92,578 8,413 5,066 .. 5,979 2,309 .. .. 11,541 11,077 .. .. .. 22,455 .. .. .. 124,028 6,884 .. 6,725 7,203 10,683 652 5,806 66,491 14,016 2,118 .. 70,905 21,492 4,562 43,424 1,490 .. 3,089 9,952 28,292 99,847 17,158 ..
2001
3,236 25,340 531,453 152,304 120,000 28,476 14,981 21,193 171,998 4,009 520,729 5,116 40,324 15,377 20,440 194,780 16,368 2,235 .. 4,297 5,434 .. .. 15,992 8,023 .. .. .. 51,608 .. .. .. 152,273 3,140 .. 11,006 7,687 12,159 1,159 8,416 77,214 18,294 2,792 .. 95,444 26,607 9,984 64,506 3,180 .. 3,756 12,113 42,151 90,570 24,732 ..
average
1990
2001
1990–2001
1990
2001
62.0 1.3 48.4 43.9 1.0 0.1 1.0 0.0 0.6 16.2 1.0 0.1 0.1 78.4 2.9 0.3 0.1 0.1 .. 8.1 4.5 .. .. 1.1 1.5 .. .. .. 9.5 .. .. .. 5.9 0.5 .. 4.7 94.4 84.4 16.0 93.4 1.1 4.9 53.3 .. 79.8 4.8 .. 43.2 28.3 .. 72.1 26.9 34.8 2.2 11.0 ..
41.1 1.6 38.5 31.5 0.7 0.1 1.2 0.0 1.4 11.9 1.0 0.1 0.2 78.2 4.9 1.2 .. 0.2 .. .. 2.3 .. .. 0.9 8.2 .. .. .. 4.6 .. .. .. 5.4 1.9 .. 4.0 88.3 77.4 15.2 85.2 1.6 6.4 48.2 .. 77.5 5.6 .. 37.2 14.6 .. 58.0 18.7 23.1 4.8 8.3 ..
2.6 –0.7 3.6 4.4 5.2 4.2 3.6 5.3 1.2 3.0 1.8 3.7 –7.7 2.2 –5.1 7.1 9.9 –6.4 .. –3.4 8.0 .. .. 2.1 –2.5 .. .. .. 7.2 .. .. .. 1.8 –8.3 .. 4.4 0.0 1.7 5.3 3.4 1.1 2.8 2.6 .. 2.5 1.9 5.4 3.8 6.1 .. 2.8 2.6 4.2 –0.7 3.7 ..
496 2,746 427 521 1,264 1,153 3,016 2,599 2,690 1,231 3,534 1,104 4,823 534 1,647 2,160 3,959 1,114 .. 2,272 635 .. .. 2,680 2,994 .. .. .. 1,234 .. .. .. 1,490 1,582 .. 280 509 264 445 320 4,447 4,065 554 .. 737 5,066 2,804 402 621 .. 744 461 463 2,619 1,734 ..
488 2,487 515 729 1,860 1,202 3,876 3,291 2,981 1,545 4,099 1,017 2,705 500 914 4,114 7,195 451 .. 1,822 1,239 .. .. 2,994 2,304 .. .. .. 2,168 .. .. .. 1,532 735 .. 377 425 252 596 357 4,814 4,714 536 .. 735 5,896 4,029 456 1,098 .. 697 460 538 2,344 2,435 ..
2004 World Development Indicators
1990–2001
–0.2 –0.4 1.8 2.9 3.6 1.6 2.7 2.4 1.0 2.2 1.5 –0.1 –6.6 –0.4 –6.1 6.1 6.3 –7.5 .. –2.2 6.1 .. .. 0.1 –1.9 .. .. .. 4.6 .. .. .. 0.2 –8.1 .. 2.6 –2.2 0.1 2.5 0.9 0.5 1.6 –0.2 .. –0.3 1.3 1.8 1.3 4.3 .. 0.4 0.8 1.9 –0.8 3.4 ..
141
3.7
Energy production and use Total energy production
142
Energy use per capita
Total
Combustible
thousands of
thousands of
renewables
average
metric tons of
metric tons of
and waste
annual
kg of oil
annual
oil equivalent
oil equivalent
% of total
% growth
equivalent
% growth
1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Energy use
2001
40,834 28,222 1,118,707 996,161 .. .. 368,753 476,831 1,362 1,765 11,835 10,774 .. .. .. 64 5,273 6,550 2,765 3,161 .. .. 114,534 145,287 34,648 33,022 4,191 4,462 8,775 21,551 .. .. 29,754 34,377 9,831 12,367 22,570 34,377 1,553 1,267 9,063 13,001 25,908 40,059 778 1,056 12,612 18,385 6,127 6,886 25,857 26,154 48,822 50,443 .. .. 110,170 83,428 108,472 144,566 207,007 261,939 1,650,408 1,711,814 1,149 1,211 40,461 55,630 148,854 216,020 24,988 50,346 .. .. 9,792 22,687 4,923 6,052 8,250 8,531 8,711,744 t 10,140,706 t 980,692 1,330,614 4,481,928 4,952,955 3,356,996 3,544,563 1,124,932 1,408,392 5,462,620 6,283,569 1,219,107 1,595,491 1,860,581 1,516,768 613,090 845,705 965,686 1,254,273 388,777 514,705 415,379 556,627 3,249,124 3,857,137 466,100 439,167
2004 World Development Indicators
1990
2001
62,403 36,841 774,823 621,349 .. .. 60,834 110,586 2,238 3,179 15,002 16,061 .. .. 13,357 29,158 21,426 18,717 5,008 6,838 .. .. 91,229 107,738 91,209 127,381 5,516 7,923 10,627 13,525 .. .. 46,667 51,054 25,106 28,019 11,928 13,955 9,087 3,036 9,808 13,917 43,215 75,542 1,001 1,422 5,795 8,693 5,536 8,243 53,005 72,458 11,307 15,309 .. .. 218,376 141,577 17,611 32,624 212,176 235,158 1,927,572 2,281,414 2,251 2,703 44,994 50,650 43,918 54,856 24,690 39,356 .. .. 2,626 3,560 5,469 6,423 9,084 9,882 8,572,434 t 10,009,627 t 923,451 1,233,424 3,400,356 3,641,578 2,825,424 2,929,430 572,246 709,943 4,311,187 4,850,856 1,138,460 1,550,628 1,716,074 1,273,037 455,450 595,827 257,289 413,276 437,361 642,291 321,164 405,176 4,287,850 5,187,992 1,049,967 1,189,043
1990
2001
1.0 1.6 .. 0.0 60.6 5.0 .. .. 0.8 5.3 .. 11.4 4.5 71.0 81.8 .. 11.8 4.1 0.0 .. 91.0 33.9 77.7 0.8 18.7 13.6 .. .. 0.1 0.2 0.3 3.2 24.3 .. 1.2 76.5 .. 2.9 73.5 52.0 10.9 w 52.0 10.8 12.0 4.1 18.9 26.4 1.9 18.2 1.4 48.9 56.7 2.9 3.1
6.4 1.1 .. 0.0 55.5 5.0 .. .. 1.9 6.6 .. 11.7 3.2 52.9 80.3 .. 16.0 6.0 0.0 .. 91.5 17.1 74.3 0.3 15.2 8.7 .. .. 0.2 0.1 1.0 3.1 15.6 .. 1.0 58.3 .. 2.2 81.5 57.4 10.6 w 46.2 10.4 11.9 4.2 18.9 21.0 2.3 14.5 1.1 39.2 57.6 3.0 3.5
1990–2001
–3.8 –2.4 .. 4.3 3.4 1.8 .. 5.8 –0.9 3.3 .. 1.9 3.1 3.8 4.2 .. 0.7 0.8 2.6 –8.9 3.2 5.2 4.5 3.8 3.9 3.8 2.4 .. –4.5 5.3 0.8 1.7 2.4 1.7 2.2 4.5 .. 2.2 1.3 0.7 1.5 w 2.7 0.7 0.4 1.9 1.1 3.1 –3.0 2.8 4.1 3.6 2.3 1.8 1.1
average
1990
2001
1990–2001
2,689 1,644 –3.5 5,211 4,293 –2.1 .. .. .. 3,850 5,195 1.6 305 325 0.8 1,435 1,508 1.6 .. .. .. 4,384 7,058 2.8 4,056 3,480 –1.1 2,508 3,459 3.3 .. .. .. 2,592 2,404 –0.3 2,349 3,127 2.7 339 423 2.5 426 421 1.7 .. .. .. 5,452 5,740 0.4 3,740 3,875 0.2 984 841 –0.3 1,631 487 –10.1 385 404 0.4 777 1,235 4.4 290 305 1.6 4,770 6,708 3.2 679 852 2.3 944 1,057 2.0 2,912 3,244 0.3 .. .. .. 4,187 2,884 –3.8 9,550 10,860 1.2 3,686 3,982 0.6 7,728 7,996 0.4 725 809 1.7 2,098 2,029 –0.0 2,252 2,227 0.1 373 495 2.8 .. .. .. 221 197 –1.1 703 638 –1.0 887 769 –1.3 1,677 w 1,686 w 0.1 w 480 518 0.7 1,417 1,339 –0.4 1,337 1,226 –0.7 2,027 2,176 0.6 1,012 966 –0.4 715 854 1.9 3,681 2,684 –3.1 1,051 1,151 1.2 1,087 1,383 2.0 391 469 1.7 693 661 –0.3 4,847 5,423 1.1 3,580 3,904 0.8
About the data
In developing countries growth in energy use is closely related to growth in the modern sectors—industry, motorized transport, and urban areas—but energy
Definitions
3.7a
• Total energy production refers to forms of primary
Energy use varies by country, even among the five largest energy users
use also reflects climatic, geographic, and economic
Total energy use (millions of metric tons of oil equivalent)
factors (such as the relative price of energy). Energy
2,500
use has been growing rapidly in low- and middle-
2001
2,000
bers of the Organisation for Economic Co-operation
electricity, all converted into oil equivalents (see
consumption, which is equal to indigenous production plus imports and stock changes, minus exports
1,000
and fuels supplied to ships and aircraft engaged in international transpor t (see About the data).
500
and Development (OECD) are based on national ener-
• Combustible renewables and waste comprise 0
gy data adjusted to conform to annual questionnaires
United States
completed by OECD member governments. Total energy use refers to the use of domestic pri-
solid fuels (coal, lignite, and other derived fuels), and
About the data). • Energy use refers to apparent 1,500
Energy data are compiled by the International Energy Agency (IEA). IEA data for countries that are not mem-
energy—petroleum (crude oil, natural gas liquids, and oil from nonconventional sources), natural gas,
combustible renewables and waste—and primary 1990
income countries, but high-income countries still use more than five times as much on a per capita basis.
3.7
ENVIRONMENT
Energy production and use
China
Russian India Federation
Japan
waste, and municipal waste, measured as a per-
Energy use per capita (thousands of kg of oil equivalent)
mary energy before transformation to other end-use
solid biomass, liquid biomass, biogas, industrial
centage of total energy use.
8
fuels (such as electricity and refined petroleum prod-
7
ucts). It includes energy from combustible renew-
6
ables and waste—solid biomass and animal
5
products, gas and liquid from biomass, and industri-
4
al and municipal waste. Biomass is defined as any
3
plant matter used directly as fuel or converted into
2
fuel, heat, or electricity. (The data series published in
1
World Development Indicators 1998 and earlier edi-
0 United States
tions did not include energy from combustible renew-
China
Russian India Federation
Japan
ables and waste.) Data for combustible renewables and waste are often based on small surveys or other incomplete information. Thus the data give only a broad impression of developments and are not strictly comparable between countries. The IEA reports (see Data sources) include country notes that
Source: Table 3.7.
3.7b People in high-income countries use more than five times as much energy as do people in low-income countries
explain some of these differences. All forms of energy—primary energy and primary electricity—are
Energy use per capita (thousands of kg of oil equivalent)
converted into oil equivalents. To convert nuclear
6
electricity into oil equivalents, a notional thermal effi-
5
1990
2001
ciency of 33 percent is assumed; for hydroelectric power 100 percent efficiency is assumed.
4 3 2 1 0 Low income
Source: Table 3.7.
Lower middle income
Upper middle income
High income
Data sources The data on energy production and use come from IEA electronic files. The IEA’s data are published in its annual publications, Energy Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries.
2004 World Development Indicators
143
3.8
Energy efficiency, dependency, and emissions GDP per unit of energy use
Net energy imports a
Carbon dioxide emissions
1995 PPP $
kg per
per kg
% of
oil equivalent
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
144
Total
energy use
Per capita
million metric tons
1995 PPP $
metric tons
of GDP
1990
2001
1990
2001
1990
2000
1990
2000
1990
2000
.. 3.5 5.1 2.7 5.8 1.2 3.7 6.6 1.4 9.1 1.1 4.1 2.1 4.5 .. .. 6.6 1.9 .. .. .. 4.4 2.9 .. .. 5.0 2.0 9.7 7.0 4.2 2.4 8.5 4.1 4.3 .. 2.7 6.4 5.9 2.8 4.3 6.6 .. 1.7 1.8 3.4 5.0 4.3 .. 1.3 4.7 4.1 5.7 6.1 .. .. 8.3
.. 6.4 5.0 2.2 6.8 3.3 4.2 6.8 1.7 9.7 1.9 4.3 2.9 4.3 4.8 .. 6.2 2.5 .. .. .. 4.2 3.2 .. .. 5.6 4.2 9.9 7.9 1.9 3.3 8.3 3.7 4.7 .. 3.2 7.3 5.7 4.4 4.5 6.2 .. 2.8 2.2 3.6 5.3 4.2 .. 4.2 5.6 4.3 5.8 5.7 .. .. 5.8
.. 8 –337 –356 –5 94 –80 68 –9 17 90 74 –6 –77 19 .. 27 67 .. .. .. –140 –31 .. .. 44 –4 100 –94 –1 –753 49 23 35 62 19 44 75 –171 –71 32 .. 34 7 59 51 –1,037 .. 83 48 18 59 24 .. .. 21
.. 61 –390 –415 –44 74 –117 68 –69 21 86 78 27 –62 25 .. 21 47 .. .. .. –94 –53 .. .. 64 0 100 –153 –4 –1,368 50 5 53 51 26 –37 81 –162 –24 45 .. 36 6 55 50 –769 .. 48 62 27 65 28 .. .. 26
2.6 7.3 80.4 4.6 109.7 3.7 266.0 57.5 47.1 15.4 94.6 100.5 0.6 5.5 4.7 2.2 202.6 75.3 1.0 0.2 0.5 1.5 428.8 0.2 0.1 35.3 2,401.7 26.2 55.9 4.1 2.0 2.9 11.9 16.8 32.0 137.9 50.7 9.4 16.6 75.4 2.6 .. 24.9 3.0 52.9 357.5 6.7 0.2 15.1 890.2 3.5 72.2 5.1 1.0 0.8 1.0
0.9 2.9 89.4 6.4 138.2 3.5 344.8 60.8 29.0 29.3 59.2 102.2 1.6 11.1 19.3 3.9 307.5 42.3 1.0 0.2 0.5 6.5 435.9 0.3 0.1 59.5 2,790.5 33.1 58.5 2.7 1.8 5.4 10.5 19.6 30.9 118.8 44.6 25.1 25.5 142.2 6.7 0.6 16.0 5.6 53.4 362.4 3.5 0.3 6.2 785.5 5.9 89.6 9.9 1.3 0.3 1.4
0.1 2.2 3.2 0.5 3.4 1.1 15.6 7.4 6.4 0.1 9.3 10.1 0.1 0.8 1.1 1.7 1.4 8.6 0.1 0.0 0.0 0.1 15.4 0.1 0.0 2.7 2.1 4.6 1.6 0.1 0.8 1.0 1.0 3.5 3.0 13.4 9.9 1.3 1.6 1.4 0.5 .. 16.2 0.1 10.6 6.3 7.0 0.2 2.8 11.1 0.2 7.1 0.6 0.2 0.8 0.2
0.0 0.9 2.9 0.5 3.9 1.1 18.0 7.6 3.6 0.2 5.9 10.0 0.3 1.3 4.8 2.3 1.8 5.2 0.1 0.0 0.0 0.4 14.2 0.1 0.0 3.9 2.2 5.0 1.4 0.1 0.5 1.4 0.7 4.4 2.8 11.6 8.4 3.0 2.0 2.2 1.1 0.1 11.7 0.1 10.3 6.2 2.8 0.2 1.2 9.6 0.3 8.5 0.9 0.2 0.2 0.2
.. 0.8 0.7 0.3 0.4 0.7 0.8 0.4 2.0 0.1 2.1 0.5 0.2 0.4 .. 0.3 0.2 1.3 0.2 0.0 0.0 0.1 0.7 0.1 0.0 0.5 1.4 0.3 0.3 0.1 0.8 0.2 0.7 0.6 .. 1.2 0.4 0.4 1.0 0.6 0.2 .. 2.4 0.1 0.5 0.3 1.2 0.1 1.4 0.5 0.2 0.6 0.2 0.1 1.0 0.1
.. 0.3 0.6 0.4 0.3 0.5 0.7 0.3 1.6 0.2 1.3 0.4 0.3 0.6 1.0 0.3 0.3 0.9 0.1 0.1 0.0 0.3 0.5 0.1 0.0 0.5 0.6 0.2 0.3 0.1 0.6 0.2 0.4 0.5 .. 0.9 0.3 0.6 0.7 0.7 0.3 0.2 1.3 0.1 0.4 0.3 0.5 0.1 0.6 0.4 0.2 0.6 0.2 0.1 0.3 0.1
2004 World Development Indicators
GDP per unit of energy use
Net energy imports a
3.8
Carbon dioxide emissions
1995 PPP $
kg per
per kg
% of
oil equivalent
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
ENVIRONMENT
Energy efficiency, dependency, and emissions
Total
energy use
Per capita
million metric tons
1995 PPP $
metric tons
of GDP
1990
2001
1990
2001
1990
2000
1990
2000
1990
2000
4.3 3.7 3.6 3.9 3.3 .. 4.6 5.7 7.4 2.8 6.0 3.2 0.9 1.9 .. 3.9 2.5 1.6 .. 2.5 3.7 .. .. .. 2.6 .. .. .. 4.1 .. .. .. 4.7 1.3 .. 11.0 .. .. 10.4 3.0 4.5 3.9 3.4 .. 1.1 4.8 3.8 3.7 6.6 .. 5.9 7.7 7.4 2.5 7.0 ..
4.6 4.7 4.4 3.7 3.0 .. 7.0 5.6 7.8 2.1 5.8 3.7 1.7 1.8 .. 3.5 2.2 3.2 .. 4.1 3.2 .. .. .. 3.7 .. .. .. 3.6 .. .. .. 5.3 1.7 .. 9.0 .. .. 9.3 3.5 5.2 4.0 .. .. 1.1 5.5 3.0 3.8 5.1 .. 6.1 9.4 6.8 3.9 6.4 ..
30 50 8 –74 –161 –412 67 96 83 84 83 95 –12 18 13 76 –477 64 .. 87 94 .. .. –534 62 .. .. .. –117 .. .. .. –57 99 .. 89 5 0 67 5 9 13 29 .. –112 –460 –740 21 59 .. –48 –6 44 1 84 ..
53 57 18 –54 –106 –333 88 97 85 88 80 95 –108 18 6 82 –565 39 .. 60 97 .. .. –365 48 .. .. .. –50 .. .. .. –51 98 .. 95 2 –26 75 13 22 18 45 .. –117 –752 –546 25 79 .. –62 23 53 12 86 ..
2.6 58.5 675.3 165.2 212.4 49.3 29.8 34.6 398.9 8.0 1,070.7 10.2 252.7 5.8 244.6 241.2 42.2 11.0 0.2 12.7 9.1 .. 0.5 37.8 21.4 10.6 0.9 0.6 55.3 0.4 2.6 1.2 305.4 20.9 10.0 23.5 1.0 4.1 0.0 0.6 150.0 23.6 2.6 1.1 88.7 31.7 11.5 67.9 3.1 2.4 2.3 21.7 44.3 347.6 42.3 11.8
4.8 54.2 1,070.9 269.6 310.3 76.3 42.2 63.1 428.2 10.8 1,184.5 15.6 121.3 9.4 188.9 427.0 47.9 4.6 0.4 6.0 15.2 .. 0.4 57.1 11.9 11.2 2.3 0.8 144.4 0.6 3.1 2.9 424.0 6.6 7.5 36.5 1.2 9.1 1.8 3.4 138.9 32.1 3.7 1.2 36.1 49.9 19.8 104.8 6.3 2.4 3.7 29.5 77.5 301.3 59.8 8.7
0.5 5.6 0.8 0.9 3.9 2.7 8.5 7.4 7.0 3.3 8.7 3.2 15.3 0.2 12.3 5.6 19.9 2.4 0.1 4.8 2.5 .. 0.2 8.8 5.8 5.5 0.1 0.1 3.0 0.0 1.3 1.1 3.7 4.8 4.7 1.0 0.1 0.1 0.0 0.0 10.0 6.8 0.7 0.1 0.9 7.5 7.1 0.6 1.3 0.6 0.5 1.0 0.7 9.1 4.3 3.3
0.7 5.4 1.1 1.3 4.9 3.3 11.1 10.0 7.4 4.2 9.3 3.2 8.1 0.3 8.5 9.1 21.9 0.9 0.1 2.5 3.5 .. 0.1 10.9 3.4 5.5 0.1 0.1 6.2 0.1 1.2 2.4 4.3 1.5 3.1 1.3 0.1 0.2 1.0 0.1 8.7 8.3 0.7 0.1 0.3 11.1 8.2 0.8 2.2 0.5 0.7 1.1 1.0 7.8 5.9 2.3
0.2 0.6 0.5 0.5 0.9 .. 0.6 0.5 0.4 1.0 0.4 0.9 3.5 0.2 .. 0.7 0.9 1.4 0.1 0.8 1.1 .. .. .. 0.8 0.9 0.1 0.1 0.6 0.1 1.0 0.2 0.5 2.4 2.9 0.3 .. .. 0.0 0.0 0.5 0.4 0.4 0.2 1.2 0.3 0.7 0.4 0.3 0.4 0.1 0.3 0.2 1.4 0.4 0.3
0.3 0.5 0.5 0.5 0.9 .. 0.4 0.6 0.3 1.3 0.4 0.8 2.0 0.3 .. 0.7 1.3 0.7 0.1 0.4 0.9 .. .. .. 0.4 0.9 0.2 0.1 0.8 0.1 0.8 0.3 0.5 1.3 2.2 0.4 .. .. 0.2 0.1 0.3 0.5 0.4 0.2 0.4 0.3 0.7 0.4 0.4 0.2 0.2 0.3 0.3 0.9 0.4 0.2
2004 World Development Indicators
145
3.8
Energy efficiency, dependency, and emissions GDP per unit of energy use
Net energy imports a
Carbon dioxide emissions
1995 PPP $
kg per
per kg
% of
oil equivalent 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
2.3 1.5 .. 2.9 4.1 .. .. 3.1 2.5 4.3 .. 3.4 6.2 6.4 2.3 .. 3.7 7.1 2.4 0.9 1.2 5.3 5.2 1.4 6.3 5.1 1.7 .. 1.6 .. 5.0 3.4 8.9 0.7 2.4 2.8 .. 3.0 1.2 2.8 3.5 w 3.1 2.7 2.5 3.7 2.8 .. 1.9 5.4 3.8 3.8 2.5 4.3 5.3
2001
3.4 1.6 .. 2.0 4.3 .. .. 2.9 3.1 4.5 .. 3.5 6.0 7.3 3.3 .. 4.0 7.0 3.5 1.7 1.2 4.8 4.2 1.3 7.0 4.9 1.3 .. 1.4 .. 5.8 4.0 9.7 0.7 2.4 4.0 .. 3.8 1.2 2.8 4.2 w 3.6 3.7 3.7 4.0 3.7 .. 2.2 5.7 3.4 4.6 2.5 4.7 5.8
a. A negative value indicates that a country is a net exporter.
146
2004 World Development Indicators
Total
energy use 1990
35 –44 .. –506 39 21 .. .. 75 45 .. –26 62 24 17 .. 36 61 –89 83 8 40 22 –118 –11 51 –332 .. 50 –516 2 14 49 10 –239 –1 .. –273 10 9 0w –6 –32 –19 –97 –27 –7 –8 –35 –275 11 –29 24 56
Per capita
million metric tons 2001
23 –60 .. –331 44 33 .. 100 65 54 .. –35 74 44 –59 .. 33 56 –146 58 7 47 26 –111 16 64 –229 .. 41 –343 –11 25 55 –10 –294 –28 .. –537 6 14 0w –8 –36 –21 –98 –30 –3 –19 –42 –203 20 –37 26 63
1990
155.1 1,984.0 0.5 177.9 2.9 130.5 0.3 41.9 44.7 12.3 0.0 291.1 211.8 3.9 3.5 0.4 48.5 42.7 35.8 20.6 2.3 95.7 0.7 16.9 13.3 143.8 28.0 0.8 600.0 60.9 569.3 4,815.9 3.9 113.3 113.8 22.5 .. 9.4 2.4 16.6 21,297.5 t 1,653.2 9,169.8 7,561.2 1,608.5 10,823.2 3,051.3 4,818.2 962.7 751.1 765.9 471.8 10,480.8 2,463.9
2000
86.3 1,435.1 0.6 374.3 4.2 39.5 0.6 59.0 35.4 14.6 .. 327.3 282.9 10.2 5.2 0.4 46.9 39.1 54.2 4.0 4.3 198.6 1.8 26.4 18.4 221.6 34.6 1.5 342.8 58.9 567.8 5,601.5 5.4 118.6 157.7 57.5 .. 8.4 1.8 14.8 22,994.5 t 2,066.7 9,129.1 7,116.3 2,012.0 11,196.2 3,752.3 3,162.6 1,357.4 1,227.2 1,220.3 478.8 11,804.3 2,414.6
1995 PPP $
metric tons 1990
2000
6.7 13.3 0.1 11.3 0.4 12.4 0.1 13.8 8.4 6.2 0.0 8.3 5.5 0.2 0.1 0.6 5.7 6.4 3.0 3.7 0.1 1.7 0.2 13.9 1.6 2.6 7.2 0.0 11.5 33.0 9.9 19.3 1.3 5.3 5.8 0.3 .. 0.7 0.3 1.6 4.1 w 0.8 3.8 3.6 5.7 2.5 1.9 10.3 2.2 3.3 0.7 0.9 11.8 8.4
3.8 9.9 0.1 18.1 0.4 3.7 0.1 14.7 6.6 7.3 .. 7.4 7.0 0.6 0.2 0.4 5.3 5.4 3.3 0.6 0.1 3.3 0.4 20.5 1.9 3.3 7.5 0.1 6.9 21.0 9.6 19.8 1.6 4.8 6.5 0.7 .. 0.5 0.2 1.2 3.8 w 0.9 3.4 3.0 6.2 2.2 2.1 6.7 2.7 4.2 0.9 0.7 12.4 8.0
of GDP 1990
1.1 1.7 0.1 1.0 0.3 .. 0.1 1.0 1.0 0.6 .. 0.9 0.4 0.1 0.1 0.1 0.3 0.2 1.2 2.6 0.2 0.4 0.1 2.1 0.4 0.5 1.4 0.1 1.7 .. 0.5 0.7 0.2 3.7 1.1 0.3 .. 1.2 0.4 0.6 0.7 w 0.5 1.0 1.1 0.8 0.9 1.0 1.4 0.4 0.8 0.5 0.6 0.6 0.4
2000
0.7 1.5 0.1 1.7 0.3 .. 0.3 0.7 0.6 0.5 .. 0.9 0.4 0.2 0.1 0.1 0.2 0.2 1.1 0.9 0.3 0.6 0.3 2.4 0.3 0.6 2.1 0.1 1.9 .. 0.4 0.6 0.2 3.5 1.2 0.4 .. 0.6 0.3 0.5 0.6 w 0.5 0.7 0.7 0.7 0.6 0.6 1.2 0.4 0.9 0.4 0.5 0.5 0.4
3.8
ENVIRONMENT
Energy efficiency, dependency, and emissions About the data
The ratio of GDP to energy use provides a measure
facturing releases about half a metric ton of carbon
because of the difficulty of apportioning these fuels
of energy efficiency. To produce comparable and con-
dioxide for each metric ton of cement produced.
among the countries benefiting from that transport.
sistent estimates of real GDP across countries rela-
The Carbon Dioxide Information Analysis Center
tive to physical inputs to GDP—that is, units of
(CDIAC), sponsored by the U.S. Department of
energy use—GDP is converted to 1995 constant
Energy, calculates annual anthropogenic emissions of
international dollars using purchasing power parity
carbon dioxide. These calculations are based on data
• GDP per unit of energy use is the PPP GDP per
(PPP) rates. Differences in this ratio over time and
on fossil fuel consumption (from the World Energy
kilogram of oil equivalent of energy use. PPP GDP is
across countries reflect in part structural changes in
Data Set maintained by the United Nations Statistics
gross domestic product converted to 1995 constant
the economy, changes in the energy efficiency of par-
Division) and data on world cement manufacturing
international dollars using purchasing power parity
ticular sectors, and differences in fuel mixes.
(from the Cement Manufacturing Data Set maintained
rates. An international dollar has the same purchas-
Because commercial energy is widely traded, it is
by the U.S. Bureau of Mines). Emissions of carbon
ing power over GDP as a U.S. dollar has in the United
necessary to distinguish between its production and
dioxide are often calculated and reported in terms of
States. • Net energy imports are estimated as ener-
its use. Net energy imports show the extent to which
their content of elemental carbon. For this table these
gy use less production, both measured in oil equiva-
an economy’s use exceeds its domestic production.
values were converted to the actual mass of carbon
lents. A negative value indicates that the country is
High-income countries are net energy importers; mid-
dioxide by multiplying the carbon mass by 3.664 (the
a net exporter. • Carbon dioxide emissions are
dle-income countries have been their main suppliers.
ratio of the mass of carbon to that of carbon dioxide).
those stemming from the burning of fossil fuels and
Definitions
Carbon dioxide emissions, largely a by-product of
Although the estimates of global carbon dioxide
the manufacture of cement. They include carbon
energy production and use (see table 3.7), account
emissions are probably within 10 percent of actual
dioxide produced during consumption of solid, liquid,
for the largest share of greenhouse gases, which are
emissions (as calculated from global average fuel
and gas fuels and gas flaring.
associated with global warming. Anthropogenic car-
chemistry and use), country estimates may have larg-
bon dioxide emissions result primarily from fossil
er error bounds. Trends estimated from a consistent
fuel combustion and cement manufacturing. In com-
time series tend to be more accurate than individual
bustion, different fossil fuels release different
values. Each year the CDIAC recalculates the entire
amounts of carbon dioxide for the same level of ener-
time series from 1950 to the present, incorporating its
gy use. Burning oil releases about 50 percent more
most recent findings and the latest corrections to its
carbon dioxide than burning natural gas, and burning
database. Estimates do not include fuels supplied to
coal releases about twice as much. Cement manu-
ships and aircraft engaged in international transport
3.8a Per capita emissions of carbon dioxide vary, even among the five largest producers of emissions Carbon dioxide emissions (billions of metric tons) 6 1990
2000
5 4 3 2 1 0 United States
China
Russian Federation
Japan
India
Per capita carbon dioxide emissions (metric tons) 20
15
Data sources 10
The underlying data on energy production and use are from electronic files of the International
5
Energy Agency. The data on carbon dioxide emissions are from the CDIAC, Environmental
0 United States
China
Russian Federation
Japan
India
Sciences Division, Oak Ridge National Laboratory, in the U.S. state of Tennessee.
Source: Table 3.8.
2004 World Development Indicators
147
3.9
Sources of electricity Electricity production
Access to electricity
billion kwh
population
Sources of electricity a
% of
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
148
% of total Hydropower
Coal
Oil
Gas
Nuclear power
1990
2001
2000
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
.. 3.2 16.1 0.8 51.0 9.0 154.3 49.3 19.7 7.7 37.6 70.3 0.0 2.1 6.5 .. 222.8 42.1 .. .. .. 2.7 481.9 .. .. 18.4 621.2 28.9 36.2 5.6 0.5 3.5 2.0 8.9 15.0 62.6 26.0 3.7 6.3 42.3 2.2 .. 11.8 1.2 54.4 416.8 1.0 .. 11.2 547.6 5.7 34.8 2.3 .. .. 0.6
.. 3.7 26.6 1.6 90.2 5.7 216.9 62.4 19.0 16.3 25.0 78.6 0.1 4.0 10.4 .. 327.9 43.5 .. .. .. 3.5 587.9 .. .. 43.9 1471.7 32.4 43.5 5.7 0.3 6.9 4.9 11.8 15.3 74.2 37.7 10.3 11.1 82.7 3.9 .. 8.5 1.8 74.5 546.0 1.4 .. 6.9 579.8 7.9 53.1 5.9 .. .. 0.5
2.0 .. 98.0 12.0 94.6 .. .. .. .. 20.4 .. .. 22.0 60.4 .. 22.0 94.9 .. 13.0 .. 15.8 20.0 .. .. .. 99.0 98.6 .. 81.0 6.7 20.9 95.7 50.0 .. 97.0 .. .. 66.8 80.0 93.8 70.8 17.0 .. 4.7 .. .. 31.0 .. .. .. 45.0 .. 66.7 .. .. 34.0
.. 89.1 0.8 86.2 35.6 33.8 9.2 63.9 8.9 11.4 0.0 0.4 .. 55.3 52.2 .. 92.8 4.5 .. .. .. 98.5 61.6 .. .. 55.3 20.4 .. 76.0 99.6 99.4 97.5 72.4 48.8 0.6 2.3 0.1 9.4 78.5 23.5 73.5 .. 0.0 88.4 20.0 12.8 72.1 .. 58.3 3.2 100.0 5.1 76.0 .. .. 76.5
.. 96.3 0.3 63.2 41.1 16.8 7.6 67.0 6.9 6.0 0.1 0.6 2.3 54.6 48.8 .. 81.7 4.0 .. .. .. 98.1 56.7 .. .. 49.4 18.9 .. 73.2 99.7 99.7 81.5 36.7 52.7 0.5 2.8 0.1 5.4 64.0 17.1 29.8 .. 0.1 98.7 17.7 13.6 63.1 .. 79.9 3.5 84.1 4.0 32.9 .. .. 51.7
.. .. .. .. 1.3 .. 77.1 14.2 .. .. .. 28.2 .. .. 47.8 .. 2.1 35.4 .. .. .. .. 17.1 .. .. 34.3 71.2 98.3 9.8 .. .. .. .. .. .. 71.8 90.3 1.2 .. .. .. .. 90.0 .. 33.0 8.5 .. .. .. 58.8 .. 72.4 .. .. .. ..
.. .. .. .. 1.7 .. 78.3 12.7 .. .. .. 16.2 .. .. 50.7 .. 3.1 45.4 .. .. .. .. 20.1 .. .. 16.5 76.2 61.5 7.3 .. .. .. .. 13.9 .. 71.7 47.3 5.3 .. .. .. .. 90.2 .. 23.5 4.5 .. .. .. 51.9 .. 66.8 8.5 .. .. ..
.. 10.9 5.4 13.8 9.7 43.3 2.7 3.8 91.1 4.3 52.1 1.9 100.0 5.3 .. .. 2.5 4.7 .. .. .. 1.5 3.4 .. .. 7.6 7.9 1.7 1.1 0.4 0.6 2.5 27.6 35.8 91.5 4.8 3.7 88.6 21.5 36.9 6.8 .. 4.5 11.6 3.1 2.1 11.2 .. 5.0 1.9 .. 22.3 9.0 .. .. 20.6
.. 3.7 2.9 36.8 2.0 .. 1.3 3.2 28.4 9.4 7.7 2.1 97.7 17.4 0.5 .. 5.4 1.3 .. .. .. 1.9 2.9 .. .. 1.6 3.2 0.4 0.2 0.3 0.3 1.4 0.3 18.0 93.9 0.5 11.1 88.9 36.0 14.7 45.0 .. 0.5 1.0 0.9 1.0 20.6 .. 0.4 1.1 15.9 16.0 44.1 .. .. 48.3
.. .. 93.7 .. 39.0 22.9 10.6 15.7 .. 84.3 47.9 7.7 .. 37.6 .. .. 0.0 20.6 .. .. .. .. 2.0 .. .. 1.3 0.5 .. 12.4 .. .. .. .. 15.4 0.2 1.0 2.7 .. .. 39.6 .. .. 5.5 .. 8.6 0.7 16.4 .. 36.6 7.4 .. 0.3 .. .. .. ..
.. .. 96.8 .. 46.7 48.6 12.1 13.6 64.8 84.6 92.2 20.1 .. 26.1 .. .. 2.6 4.4 .. .. .. .. 6.1 .. .. 28.7 0.4 38.1 18.0 .. .. .. 63.0 15.4 0.0 4.2 24.6 .. .. 68.2 .. .. 9.1 .. 15.5 3.1 15.8 .. 19.7 9.9 .. 11.6 .. .. .. ..
.. .. .. .. 14.3 .. .. .. .. .. .. 60.8 .. .. .. .. 1.0 34.8 .. .. .. .. 15.1 .. .. .. .. .. .. .. .. .. .. .. .. 20.1 .. .. .. .. .. .. .. .. 35.3 75.4 .. .. .. 27.8 .. .. .. .. .. ..
.. .. .. .. 7.8 34.6 .. .. .. .. .. 59.0 .. .. .. .. 4.4 44.9 .. .. .. .. 13.0 .. .. .. 1.2 .. .. .. .. .. .. .. .. 19.9 .. .. .. .. .. .. .. .. 30.6 77.1 .. .. .. 29.5 .. .. .. .. .. ..
2004 World Development Indicators
Electricity production
Access to electricity
billion kwh
population
Sources of electricity a
% of
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
ENVIRONMENT
3.9
Sources of electricity
% of total Hydropower
Coal
Oil
Gas
Nuclear power
1990
2001
2000
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
2.3 28.4 289.4 37.0 59.1 24.0 14.2 20.9 213.2 2.5 850.8 3.6 82.7 3.0 27.7 105.4 18.5 11.9 .. 3.8 1.5 .. .. 16.8 16.5 .. .. .. 23.0 .. .. .. 122.7 11.2 .. 9.6 0.5 2.5 1.4 0.9 71.9 32.3 1.4 .. 12.6 121.6 4.5 37.7 2.7 .. 27.2 13.8 25.2 134.4 28.4 ..
4.0 36.4 576.5 101.7 130.1 34.9 24.6 43.8 271.9 6.7 1,033.2 7.5 55.4 4.4 20.2 281.5 33.5 13.7 .. 4.3 8.2 .. .. 21.5 14.4 .. .. .. 71.4 .. .. .. 209.6 3.6 .. 16.1 8.8 5.7 1.4 1.9 93.7 39.9 2.5 .. 18.1 121.3 9.7 72.4 5.1 .. 45.4 20.8 46.2 143.7 46.2 ..
54.5 .. 43.0 53.4 97.9 95.0 .. 100.0 .. 90.0 .. 95.0 .. 7.9 20.0 .. 100.0 .. .. .. 95.0 5.0 .. 99.8 .. .. 8.0 5.0 96.9 .. .. 100.0 .. .. 90.0 71.1 7.2 5.0 34.0 15.4 .. .. 48.0 .. 40.0 .. 94.0 52.9 76.1 .. 74.7 73.0 87.4 .. .. ..
98.3 0.6 24.8 17.6 10.3 10.8 4.9 0.0 14.8 3.6 10.5 0.3 8.3 81.6 56.3 6.0 .. 77.4 .. 65.8 33.3 .. .. .. 1.9 .. .. .. 17.3 .. .. .. 19.1 2.3 .. 12.7 62.6 48.1 95.2 99.9 0.1 72.3 28.8 .. 34.9 99.6 .. 44.9 83.2 .. 99.9 75.8 24.0 1.1 32.3 ..
59.5 0.5 12.8 10.5 3.9 1.8 2.4 0.0 17.2 1.7 8.1 0.6 14.6 54.7 52.5 1.5 .. 90.9 .. 66.2 4.1 .. .. .. 2.3 .. .. .. 9.9 .. .. .. 13.6 2.0 .. 5.4 99.5 32.1 96.7 99.0 0.1 53.8 8.0 .. 38.2 99.3 .. 26.2 48.8 .. 99.9 84.7 15.4 1.6 30.4 ..
.. 30.5 67.5 28.8 .. .. 57.4 50.1 16.8 .. 14.5 .. 72.3 .. 40.1 16.8 .. 9.1 .. .. .. .. .. .. .. .. .. .. 4.7 .. .. .. 6.3 34.4 .. 23.0 13.9 1.6 1.5 .. 38.3 1.5 .. .. 0.2 0.2 .. 0.1 .. .. .. .. 7.7 97.5 32.1 ..
.. 24.5 78.3 28.9 .. .. 37.6 75.1 13.5 .. 23.1 .. 69.9 .. 42.5 39.2 .. 4.5 .. 1.0 .. .. .. .. .. .. .. .. 3.4 .. .. .. 11.1 3.3 .. 72.2 .. .. 0.4 .. 28.5 3.7 .. .. .. 0.2 .. 0.4 .. .. .. 0.9 40.6 95.2 29.5 ..
1.7 4.8 2.7 46.8 37.3 89.2 10.0 49.9 48.2 92.4 29.7 87.8 8.8 7.6 3.6 17.9 17.1 .. .. 7.9 66.7 .. .. 100.0 7.4 .. .. .. 55.9 .. .. .. 57.3 26.4 .. 64.4 23.6 10.9 3.3 0.1 4.3 0.0 39.8 .. 36.5 0.0 18.4 20.6 14.7 .. 0.0 21.5 46.7 1.2 33.1 ..
38.6 11.5 1.2 23.6 21.2 98.2 21.1 24.8 27.6 96.7 11.3 89.2 4.9 34.4 5.0 8.5 76.6 .. .. 2.2 95.9 .. .. 100.0 5.0 .. .. .. 8.6 .. .. .. 44.2 0.9 .. 21.1 0.5 10.9 2.9 1.0 3.3 .. 82.0 .. 8.2 0.0 17.7 36.0 50.8 .. 0.0 9.7 21.3 1.7 20.2 ..
.. 15.7 2.9 3.8 52.5 .. 27.7 .. 18.6 .. 19.4 11.9 10.6 .. .. 9.1 82.9 13.6 .. 26.3 .. .. .. .. 2.2 .. .. .. 22.0 .. .. .. 10.6 36.9 .. .. 0.2 39.3 .. .. 50.9 17.6 .. .. 28.5 .. 81.6 33.6 .. .. .. 1.7 .. 0.1 .. ..
.. 24.3 3.6 34.2 74.9 .. 37.1 0.0 38.3 .. 24.9 10.2 10.6 .. .. 10.8 23.4 4.5 .. 30.5 .. .. .. .. 13.0 .. .. .. 78.1 .. .. .. 24.0 93.7 .. .. 0.0 57.0 .. .. 58.9 31.2 .. .. 53.6 0.2 82.3 34.3 .. .. .. 3.8 0.1 0.9 15.6 ..
.. 48.3 2.1 .. .. .. .. .. .. .. 23.8 .. .. .. .. 50.2 .. .. .. .. .. .. .. .. 88.5 .. .. .. .. .. .. .. 2.4 .. .. .. .. .. .. .. 4.9 .. .. .. .. .. .. 0.8 .. .. .. .. .. .. .. ..
.. 38.8 3.4 .. .. .. .. .. .. .. 31.0 .. .. .. .. 39.8 .. .. .. .. .. .. .. .. 79.1 .. .. .. .. .. .. .. 4.2 .. .. .. .. .. .. .. 4.2 .. .. .. .. .. .. 3.2 .. .. .. .. .. .. .. ..
2004 World Development Indicators
149
3.9
Sources of electricity Electricity production
Access to electricity
billion kwh
population
Sources of electricity a
% of 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
2001
2000
64.3 53.9 .. 1008.5 889.3 .. .. .. .. 64.9 137.4 97.7 0.9 1.7 30.1 36.5 31.8 .. .. .. .. 15.7 33.1 100.0 23.4 31.9 .. 12.1 14.5 .. .. .. .. 165.4 211.5 66.1 151.2 234.7 .. 3.2 6.6 62.0 1.5 2.6 30.0 .. .. .. 146.0 161.7 .. 54.6 70.5 .. 11.6 25.5 85.9 16.8 14.4 .. 1.6 2.8 10.5 44.2 102.4 82.1 0.1 0.0 9.0 3.6 5.6 99.0 5.8 11.2 94.6 57.5 122.7 .. 13.2 10.8 .. .. .. 3.7 252.5 172.8 .. 17.1 40.2 96.0 317.8 383.5 .. 3,181.5 3,863.8 .. 7.4 9.3 98.0 50.9 47.9 .. 59.3 90.0 94.0 8.7 30.6 75.8 .. .. .. 1.7 3.1 50.0 8.0 8.2 12.0 9.4 7.9 39.7 11,696.7 s 15,442.7 s .. w 606.4 1,017.2 37.4 3,756.1 5,133.7 94.0 3,091.3 4,098.3 93.9 664.8 1,035.4 .. 4,362.5 6,150.9 65.1 789.5 1,849.8 87.3 2,143.0 1,855.8 .. 607.0 962.2 86.6 261.5 514.6 90.4 338.9 673.7 40.8 222.5 294.8 24.7 7,334.2 9,291.8 .. 1,652.7 2,066.1 ..
% of total Hydropower
2004 World Development Indicators
Oil 1990
Gas 2001
1990
Nuclear power
1990
2001
1990
2001
17.7 17.0 .. .. .. 31.1 .. .. 8.0 28.2 .. 0.6 16.8 99.8 63.2 .. 49.7 54.6 48.6 94.7 95.1 11.3 4.6 .. 0.8 40.2 0.0 .. 3.2 .. 1.6 8.6 94.2 12.3 62.3 61.7 .. .. 99.2 40.5 18.1 w 32.8 21.2 22.0 17.2 22.8 21.6 12.9 63.7 10.0 27.6 18.4 15.4 11.0
27.7 19.6 .. .. .. 36.5 .. .. 15.5 26.2 .. 1.0 17.5 47.0 48.3 .. 49.0 58.6 39.0 97.7 91.7 6.2 6.3 .. 0.5 19.6 0.0 .. 7.0 .. 1.1 5.2 99.4 12.5 67.2 59.5 .. .. 99.4 37.8 16.6 w 22.7 22.5 23.5 18.6 22.5 18.3 16.9 56.5 6.1 14.7 19.7 12.7 12.4
28.8 15.3 .. .. .. 65.4 .. .. 32.2 36.2 .. 94.3 40.1 .. .. .. 1.2 0.1 .. .. .. 25.0 .. .. .. 35.1 .. .. 26.2 .. 65.0 53.4 .. 4.9 .. 23.0 .. .. 0.5 59.5 38.0 w 38.9 34.7 34.9 33.6 35.3 60.8 31.6 3.8 0.8 57.7 72.6 39.7 34.4
37.2 18.4 10.0 35.1 15.0 19.0 9.9 3.4 45.7 42.4 .. .. .. .. .. .. 61.5 63.5 38.5 36.5 .. 98.0 100.0 2.0 0.1 60.9 1.9 1.0 1.6 1.6 .. .. .. .. .. .. 100.0 52.7 .. 45.1 19.5 3.4 2.2 4.9 8.5 34.0 2.5 0.9 0.2 2.0 .. .. .. .. .. 94.0 .. .. .. .. 30.6 5.7 10.5 1.0 10.0 .. 0.2 53.0 .. .. .. 36.8 51.7 .. .. .. .. .. .. .. 2.1 0.8 1.7 0.3 0.2 .. 0.5 0.1 0.6 1.2 .. 32.4 19.9 18.9 41.1 .. .. .. 5.3 2.3 3.2 4.9 5.1 .. .. 19.2 23.5 2.9 40.2 70.5 .. 95.4 93.8 .. .. .. 0.1 0.1 99.0 99.4 .. 35.5 9.8 63.7 89.4 31.3 6.9 8.5 17.7 40.4 .. .. .. 100.0 100.0 .. .. .. .. .. 27.5 10.1 4.0 31.2 17.4 .. 3.7 7.9 96.3 92.1 34.8 10.9 1.9 1.6 37.2 51.3 4.1 3.5 12.0 16.7 .. 5.1 0.2 .. .. 4.2 6.9 11.4 75.9 71.8 .. 11.5 10.4 26.2 22.4 10.5 15.2 15.5 0.1 14.5 .. .. .. .. .. .. 100.0 100.0 .. .. 0.2 0.3 0.4 .. .. 61.8 .. 0.4 .. .. 38.8 w 11.3 w 7.4 w 13.9 w 18.3 w 49.2 13.0 8.8 13.1 16.4 38.8 15.2 10.1 20.4 20.9 42.5 13.1 6.7 21.8 19.7 24.0 24.9 23.4 14.2 25.8 40.5 14.9 9.8 19.4 20.2 65.1 13.2 5.2 3.6 9.5 31.0 12.7 4.3 29.5 31.4 4.8 19.0 17.7 9.5 15.5 2.3 51.7 41.3 37.4 50.3 67.1 4.7 5.6 8.1 8.8 69.1 3.4 2.9 1.7 4.4 37.6 9.2 5.9 10.6 17.1 27.0 9.5 6.9 8.7 15.3
a. Shares may not sum to 100 percent because some sources of generated electricity are not shown.
150
Coal
2001
1990
2001
.. 11.9 .. .. .. .. .. .. 51.4 32.9 .. 5.1 35.9 .. .. .. 46.7 43.3 .. .. .. .. .. .. .. .. .. .. 29.2 .. 20.7 19.2 .. .. .. .. .. .. .. .. 17.2 w 1.1 7.6 7.2 9.5 6.7 .. 12.3 2.1 .. 1.9 3.8 23.4 35.5
10.1 15.4 .. .. .. .. .. .. 53.7 36.3 .. 5.1 27.1 .. .. .. 44.6 38.0 .. .. .. .. .. .. .. .. .. .. 44.1 .. 23.5 20.9 .. .. .. .. .. .. .. .. 17.2 w 2.1 6.9 6.9 7.1 6.1 0.9 16.0 3.1 .. 3.2 3.6 24.5 35.3
3.9
ENVIRONMENT
Sources of electricity About the data
Use of energy in general, and access to electricity in
some sources of generated electricity (such as wind,
particular, are important in improving people’s stan-
solar, and geothermal) are not shown.
The IEA makes these estimates in consultation with national statistical offices, oil companies, elec-
dard of living. But electricity generation also can dam-
The International Energy Agency (IEA) compiles
tricity utilities, and national energy experts. The IEA
age the environment. Whether such damage occurs
data on energy inputs used to generate electricity.
occasionally revises its time series to reflect political
depends largely on how electricity is generated. For
IEA data for countries that are not members of the
changes. Since 1990, for example, it has construct-
example, burning coal releases twice as much carbon
Organisation
and
ed energy statistics for countries of the former Soviet
dioxide—a major contributor to global warming—as
Development (OECD) are based on national energy
Union. In addition, energy statistics for other coun-
does burning an equivalent amount of natural gas
data adjusted to conform to annual questionnaires
tries have undergone continuous changes in cover-
(see About the data for table 3.8). Nuclear energy
completed by OECD member governments. In addi-
age or methodology as more detailed energy
does not generate carbon dioxide emissions, but it
tion, estimates are sometimes made to complete
accounts have become available in recent years.
produces other dangerous waste products. The table
major aggregates from which key data are missing,
Breaks in series are therefore unavoidable.
provides information on electricity production by
and adjustments are made to compensate for differ-
source. Shares may not sum to 100 percent because
ences in definitions.
for
Economic
Co-operation
There is no single internationally accepted definition for access to electricity. The definition used here covers access at the household level—that is, the number
3.9a
of people who have electricity in their home. It includes
Sources of electricity generation have shifted differently in different income groups
commercially sold electricity, both on-grid and off-grid.
Sources of electricity generation, by income group (% of total production)
For countries where access to electricity has been assessed through surveys by government agencies,
Low-income countries 1990
50
2001
the definition also includes self-generated electricity. The data do not capture unauthorized connections.
40 30
Definitions
20 10
• Electricity production is measured at the termi-
0 Hydropower
Coal
Oil
Gas
Nuclear power
nals of all alternator sets in a station. In addition to hydropower, coal, oil, gas, and nuclear power gener-
Lower-middle-income countries
ation, it covers generation by geothermal, solar,
50
wind, and tide and wave energy as well as that from
40
combustible renewables and waste. Production
30
includes the output of electricity plants designed to
20
produce electricity only, as well as that of combined
10
heat and power plants. • Access to electricity refers to the number of people with access to electricity
0 Hydropower
Coal
Oil
Gas
Nuclear power
(both on-grid and off-grid) as a percentage of the total population (see table 2.1). • Sources of elec-
Upper-middle-income countries
50
tricity refer to the inputs used to generate electrici-
40
ty: hydropower, coal, oil, gas, and nuclear power.
30
• Hydropower refers to electricity produced by hydro-
20
electric power plants. • Oil refers to crude oil and petroleum products. • Gas refers to natural gas but
10
not natural gas liquids. • Nuclear power refers to
0 Hydropower
Coal
Oil
Gas
Nuclear power
electricity produced by nuclear power plants.
High-income countries
Data sources
50
The data on electricity production are from the
40
IEA’s electronic files and its annual publications
30
Energy Statistics and Balances of Non-OECD 20
Countries, Energy Statistics of OECD Countries,
10
and Energy Balances of OECD Countries. Data on
0 Hydropower
Coal
Oil
Gas
Nuclear power
access to electricity are from the IEA’s World Energy Outlook 2002: Energy and Poverty.
Source: Table 3.9.
2004 World Development Indicators
151
3.10
Urbanization Urban population
millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
152
Population in urban agglomerations of more than 1 million
Population in largest city
% of total
% of total
% of urban
% of urban
% of rural
population
population
population
population
population
1980
2002
1980
2002
1980
2.5 0.9 8.1 1.5 23.3 2.0 12.6 5.1 3.3 12.7 5.4 9.4 0.9 2.4 1.5 0.2 81.2 5.4 0.6 0.2 0.8 2.8 18.6 0.8 0.8 9.1 192.8 4.6 17.8 .. 0.8 1.1 2.8 2.3 6.6 7.6 4.3 2.9 3.7 17.9 2.0 0.3 1.0 4.0 2.9 39.5 0.3 0.1 2.6 64.7 3.4 5.6 2.6 0.9 0.1 1.3
6.4 1.4 18.2 4.7 33.6 2.1 18.0 5.4 4.2 35.5 6.9 10.1 2.9 5.6 1.8 0.9 143.5 5.4 2.0 0.7 2.2 7.9 24.8 1.6 2.0 13.5 481.8 6.8 33.2 .. 2.4 2.4 7.3 2.6 8.5 7.6 4.6 5.7 8.2 28.4 4.0 0.8 0.9 10.9 3.1 45.0 1.1 0.4 2.9 72.5 7.4 6.4 4.8 2.2 0.5 3.1
16 34 44 21 83 66 86 67 53 15 57 95 27 45 36 18 67 61 8 4 12 31 76 35 19 81 20 91 63 .. 42 47 35 50 68 75 84 51 47 44 44 14 70 10 60 73 50 20 52 83 31 58 37 19 17 24
23 44 58 35 88 67 91 68 52 26 70 97 44 63 44 50 82 68 17 10 18 50 79 42 25 86 38 100 76 .. 67 60 44 59 76 75 85 67 64 43 62 20 69 16 59 76 83 32 57 88 37 61 40 28 33 37
6 .. 8 13 42 34 61 27 26 6 14 12 .. 14 .. .. 32 12 .. .. .. 11 32 .. .. 33 13 91 26 8 27 .. 15 .. 20 12 27 34 23 23 16 .. .. 3 13 21 .. .. 22 39 9 31 11 12 .. 13
2004 World Development Indicators
Access to improved sanitation facilities
2000
2015
1980
2001
1990
2000
1990
2000
10 .. 6 20 41 34 56 26 24 13 18 11 .. 18 .. .. 34 15 .. .. .. 21 37 .. .. 36 14 100 32 10 41 .. 21 .. 20 12 26 61 32 23 22 .. .. 4 23 21 .. .. 24 41 10 30 28 25 .. 22
14 .. 7 25 40 35 55 26 26 18 20 11 .. 20 .. .. 34 16 .. .. .. 27 38 .. .. 37 17 100 35 12 44 .. 25 .. 20 12 26 67 37 24 25 .. .. 6 25 20 .. .. 29 43 14 29 32 32 .. 28
39 .. 20 62 43 51 26 40 48 26 24 13 .. 33 .. .. 16 20 45 .. 44 19 16 .. .. 41 6 100 21 .. 263 56 44 28 29 15 32 50 29 38 35 .. .. 30 24 23 .. .. 42 10 30 54 29 75 .. 55
45 22 16 60 37 55 22 38 47 38 24 11 8 28 31 27 13 22 45 54 53 23 20 42 38 42 3 100 21 .. 158 43 54 42 27 16 29 47 27 35 35 63 42 27 31 22 55 100 .. 9 27 49 72 60 74 62
.. .. .. .. .. .. 100 100 .. 81 .. .. 46 73 .. 87 82 .. .. 65 .. 97 100 38 70 98 57 .. 96 .. .. .. 70 .. .. .. .. 70 88 96 87 .. .. 24 100 .. .. .. .. .. 56 .. 82 94 87 33
25 99 99 70 87 .. 100 100 90 71 .. .. 46 86 .. 88 84 100 39 68 56 92 100 38 81 96 69 .. 96 54 .. 89 71 .. 99 .. .. 70 92 100 89 66 93 33 100 .. 55 41 100 .. 74 .. 83 94 95 50
.. .. .. .. .. .. 100 100 .. 31 .. .. 6 26 .. 41 38 .. .. 89 .. 64 99 16 4 92 18 .. 55 .. .. .. 29 .. .. .. .. 60 49 79 62 .. .. 6 100 .. .. .. .. .. 64 .. 62 41 33 19
8 85 81 30 47 .. 100 100 70 41 .. .. 6 42 .. 43 43 100 27 90 10 66 99 16 13 97 27 .. 56 6 .. 97 35 .. 95 .. .. 60 74 96 76 1 .. 7 100 .. 43 35 99 .. 70 .. 79 41 44 16
Urban population
millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Population in urban agglomerations of more than 1 million
Population in largest city
% of total
% of total
% of urban
% of urban
% of rural
population
population
population
population
population
1980
2002
1980
2002
1.2 6.1 158.5 32.9 19.4 8.5 1.9 3.4 37.6 1.0 89.0 1.3 8.0 2.7 9.8 21.7 1.2 1.4 0.4 1.7 2.2 0.2 0.7 2.1 2.1 1.0 1.6 0.6 5.8 1.2 0.4 0.4 44.8 1.6 0.9 8.0 1.6 8.1 0.2 1.0 12.5 2.6 1.5 0.7 19.1 2.9 0.3 23.2 1.0 0.4 1.3 11.2 18.0 20.6 2.9 2.1
3.7 6.6 294.5 91.0 42.9 16.3 2.3 6.0 38.8 1.5 100.5 4.1 8.3 11.0 13.7 39.5 2.2 1.7 1.1 1.4 4.0 0.5 1.5 4.8 2.4 1.2 5.1 1.7 14.3 3.6 1.7 0.5 75.4 1.8 1.4 16.8 6.3 14.0 0.6 3.0 14.5 3.4 3.0 2.5 60.7 3.4 2.0 48.9 1.7 1.0 3.2 19.7 48.1 24.2 6.8 2.9
35 57 23 22 50 66 55 89 67 47 76 60 54 16 57 57 91 38 12 68 74 13 35 69 61 53 19 9 42 18 28 42 66 40 52 41 13 24 23 7 88 83 50 13 27 71 32 28 50 13 42 65 37 58 29 67
55 65 28 43 65 68 60 92 67 57 79 79 56 35 61 83 96 34 20 60 90 29 46 88 69 60 31 15 59 32 60 42 75 42 57 57 34 29 32 13 90 86 57 22 46 75 77 34 57 18 57 73 60 63 67 76
1980
2000
2015
.. 19 8 8 21 29 .. 37 24 .. 34 29 6 5 11 40 60 .. .. .. 40 .. .. 26 .. .. 6 .. 7 .. .. .. 28 .. .. 15 6 7 .. .. 14 .. .. .. 8 .. .. 15 .. .. 22 25 14 18 19 34
.. 18 10 10 23 31 .. 35 19 .. 38 29 8 8 14 47 60 .. .. .. 47 .. .. 34 .. .. 10 .. 6 .. .. .. 28 .. .. 18 17 9 .. .. 14 .. .. .. 12 .. .. 21 .. .. 23 29 16 18 57 36
.. 19 12 13 24 34 .. 33 20 .. 39 32 9 10 16 45 55 .. .. .. 48 .. .. 34 .. .. 13 .. 6 .. .. .. 25 .. .. 20 21 11 .. .. 14 .. .. .. 15 .. .. 25 .. .. 26 30 17 19 68 37
Access to improved sanitation facilities
1980
2001
1990
2000
1990
2000
33 34 5 18 26 39 48 41 14 .. 25 49 12 32 19 38 67 .. .. 49 55 .. .. 38 .. .. 33 .. 16 40 .. .. 29 .. 49 26 35 27 .. .. 8 30 36 37 13 22 .. 22 62 .. 52 39 33 16 46 51
28 28 6 13 17 31 44 35 11 46 26 30 13 22 24 26 46 43 62 53 53 46 34 37 24 36 35 33 10 34 39 35 25 37 56 21 19 33 38 26 8 34 35 35 15 23 28 22 73 28 41 39 22 14 60 48
88 100 44 66 .. .. .. .. .. 99 .. 100 .. 91 .. .. .. .. .. .. .. .. .. 97 .. .. 70 96 .. 95 44 100 87 .. .. 88 .. .. 84 69 100 .. 97 71 69 100 98 77 .. 92 96 77 85 .. .. ..
93 100 61 69 86 93 .. .. .. 99 .. 100 100 96 99 76 .. 100 67 .. 100 72 .. 97 .. .. 70 96 .. 93 44 100 88 100 46 86 68 84 96 73 100 .. 95 79 66 .. 98 95 99 92 94 79 93 .. .. ..
41 98 6 38 .. .. .. .. .. 99 .. 95 .. 77 .. .. .. .. .. .. .. .. .. 96 .. .. 25 70 .. 62 19 100 26 .. .. 31 .. .. 14 15 100 .. 53 4 44 .. 61 17 .. 80 91 21 63 .. .. ..
55 98 15 46 79 31 .. .. .. 99 .. 98 98 82 100 4 .. 100 19 .. 87 40 .. 96 .. .. 30 70 98 58 19 99 34 98 2 44 26 57 17 22 100 .. 72 5 45 .. 61 43 83 80 93 49 69 .. .. ..
2004 World Development Indicators
153
ENVIRONMENT
3.10
Urbanization
3.10
Urbanization Urban population
millions 1980
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
154
2002
10.9 12.4 97.0 105.0 0.2 0.5 6.2 19.1 2.0 4.8 4.5 4.2 0.8 2.0 2.4 4.2 2.6 3.1 0.9 1.0 1.4 2.6 13.3 26.5 27.2 31.9 3.1 4.4 3.9 12.4 0.1 0.3 6.9 7.4 3.6 4.9 4.1 8.9 1.4 1.7 2.7 12.0 8.0 12.5 0.6 1.6 0.7 1.0 3.3 6.5 19.5 46.4 1.3 2.2 1.1 3.7 30.9 33.2 0.7 2.8 50.0 53.1 167.6 224.0 2.5 3.1 6.5 9.3 12.0 21.9 10.3 20.1 .. .. 1.6 4.7 2.3 4.1 1.6 4.8 1,741.3 s 2,953.1 s 348.3 763.1 785.9 1,438.9 629.7 1,190.5 156.2 248.4 1,134.2 2,202.0 288.6 701.8 249.2 301.0 231.8 401.1 83.4 177.2 201.1 392.9 80.2 227.8 607.1 751.1 209.5 237.3
2004 World Development Indicators
Population in urban agglomerations of more than 1 million
Population in largest city
% of total
% of total
% of urban
% of urban
% of rural
population
population
population
population
population
1980
2002
1980
49 70 5 66 36 46 24 100 52 48 22 48 73 22 20 18 83 57 47 34 15 17 23 63 52 44 47 9 62 71 89 74 85 41 79 19 .. 19 40 22 39 w 22 39 35 66 32 21 59 65 48 22 21 73 73
55 73 6 87 49 52 38 100 58 49 28 58 78 23 38 27 83 67 52 28 34 20 34 75 67 67 45 15 68 88 90 78 92 37 87 25 .. 25 40 37 48 w 31 53 49 75 42 38 64 76 58 28 33 78 78
9 18 .. 19 17 11 .. 100 .. .. .. 27 20 .. 6 .. 17 .. 28 .. 5 10 .. .. 18 19 .. .. 14 .. 25 38 42 11 28 14 .. .. 9 9 .. w .. .. 16 .. .. .. 16 29 21 8 .. .. 26
2000
9 19 .. 25 22 14 .. 89 .. .. .. 32 17 .. 9 .. 18 .. 28 .. 12 12 .. .. 20 27 .. .. 15 .. 23 38 37 9 29 13 .. .. 16 14 .. w .. .. 18 .. .. .. 18 32 22 12 .. .. 27
2015
10 21 .. 24 27 15 .. 83 .. .. .. 36 17 .. 11 .. 18 .. 31 .. 18 15 .. .. 21 30 .. .. 17 .. 23 37 35 8 30 14 .. .. 22 19 .. w .. .. 21 .. .. .. 20 32 24 14 .. .. 27
Access to improved sanitation facilities
1980
2001
1990
18 8 .. 17 48 25 47 100 .. .. 27 13 16 .. 30 .. 20 20 26 .. 30 59 .. .. 35 23 .. 42 7 34 15 9 49 28 21 33 .. 15 23 39 18 w 17 18 16 29 18 13 15 27 30 9 27 18 17
16 8 76 25 46 30 43 100 15 26 48 12 13 16 24 28 22 19 27 30 19 61 46 6 30 21 23 39 7 35 15 8 43 24 15 24 .. 31 41 40 16 w 18 15 13 26 16 9 15 24 26 11 26 18 16
.. .. .. .. 86 .. .. .. .. 100 .. 93 .. 94 87 .. 100 100 .. .. 84 95 71 .. 96 97 .. .. .. .. 100 100 .. .. .. 52 .. 69 86 70 75 w 58 75 72 .. 68 61 .. 85 .. 52 75 .. ..
2000
86 .. 12 100 94 100 88 100 100 .. .. 93 .. 97 87 .. 100 100 98 97 99 96 69 .. 96 97 .. 93 100 .. 100 100 95 97 71 82 .. 89 99 71 81 w 71 82 81 .. 78 72 .. 86 94 66 76 .. ..
1990
2000
.. .. .. .. 38 .. .. .. .. .. .. 80 .. 82 48 .. 100 100 .. .. 84 75 24 .. 48 70 .. .. .. .. 100 100 .. .. .. 23 .. 21 48 50 27 w 20 29 28 .. 24 24 .. 41 .. 11 45 .. ..
10 .. 8 100 48 99 53 .. 100 .. .. 80 .. 93 48 .. 100 100 81 88 86 96 17 .. 62 70 .. 77 98 .. 100 100 85 85 48 38 .. 21 64 57 38 w 31 43 42 64 36 36 .. 52 72 21 46 .. ..
3.10
About the data
The population of a city or metropolitan area
Estimates of the world’s urban population would
dreds of towns reclassified as cities in recent years.
depends on the boundaries chosen. For example, in
change significantly if China, India, and a few other
Because the estimates in the table are based on
1990 Beijing, China, contained 2.3 million people in
populous nations were to change their definition of
national definitions of what constitutes a city or met-
87 square kilometers of “inner city” and 5.4 million
urban centers. According to China’s State Statistical
ropolitan area, cross-country comparisons should be
in 158 square kilometers of “core city.” The popula-
Bureau, by the end of 1996 urban residents account-
made with caution. To estimate urban populations,
tion of “inner city and inner suburban districts” was
ed for about 43 percent of China’s population, while
the United Nations’ ratios of urban to total population
6.3 million, and that of “inner city, inner and outer
in 1994 only 20 percent of the population was con-
were applied to the World Bank’s estimates of total
suburban districts, and inner and outer counties”
sidered urban. In addition to the continuous migration
population (see table 2.1).
was 10.8 million. (For most countries the last defini-
of people from rural to urban areas, one of the main
The urban population with access to improved san-
tion is used.)
reasons for this shift was the rapid growth in the hun-
itation facilities is defined as people with access to at least adequate excreta disposal facilities that can
3.10a
effectively prevent human, animal, and insect con-
More people now live in urban areas in low-income countries than in high-income countries . . .
tact with excreta. The rural population with access is
Urban population, by income group (millions)
included to allow comparison of rural and urban
1,200 1980
2002
access. This definition and the definition of urban areas vary, however, so comparisons between coun-
1,000
tries can be misleading.
800 600
Definitions
400 200
• Urban population is the midyear population of areas defined as urban in each country and reported
0 Low income
Lower middle income
Upper middle income
High income
to the United Nations (see About the data).
. . . and the urban population is growing fastest in low- and lower-middle-income countries
• Population in urban agglomerations of more than 1 million is the percentage of a country’s population
Urban population as share of total population, by income group (%) 80
living in metropolitan areas that in 1990 had a popu-
70
lation of more than 1 million. • Population in largest
60
city is the percentage of a country’s urban population
50
living in that country’s largest metropolitan area.
40
• Access to improved sanitation facilities refers to
30
the percentage of the urban or rural population with
20
access to at least adequate excreta disposal facilities
10
(private or shared but not public) that can effectively
0 Low income
Lower middle income
Upper middle income
prevent human, animal, and insect contact with exc-
High income
reta. Improved facilities range from simple but proSource: Table 3.10.
tected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be cor-
3.10b
rectly constructed and properly maintained.
Latin America was as urban as the average high-income country in 2002 Urban population as share of total population, by region (%) High-income countries, 2002
80 70
1980
60
2002
Data sources
50
The data on urban population and the population
40
in urban agglomerations and in the largest city
30
come from the United Nations Population
20
Division’s World Urbanization Prospects: The
10
2001 Revision. The total population figures are World Bank estimates. The data on access to san-
0 East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
itation in urban and rural areas are from the World Health Organization.
Source: Table 3.10.
2004 World Development Indicators
155
ENVIRONMENT
Urbanization
3.11
Urban environment City
Algeria Argentina
Armenia Bangladesh
Barbados Belize Bolivia Bosnia and Herzegovina Brazil
Bulgaria
Burkina Faso
Burundi Cambodia Cameroon Canada Central African Republic Chad Chile
Colombia
Congo Côte d’Ivoire Croatia Cuba
Czech Republic Dem. Rep. of Congo Dominican Republic Ecuador
156
Urban population
Secure tenure
thousands 2000
% of population 1998 a
Algiers 2,562 b Buenos Aires 2,996 b Córdoba 1,322 b Rosario 1,248 b Yerevan 1,250 b Chittagong 2,301 b Dhaka 10,000 b Sylhet 242 b Tangail 152 b Bridgetown .. Belize City 55 b Santa Cruz de la Sierra 1,065 c Sarajevo 522 c Belém 1,638 c Icapui .. Maranguape .. Porto Alegre 3b Recife 3,088 b Rio de Janeiro 10,192 b Santo Andre 1,658 b Bourgas .. Sofia 1,200 b Troyan 24 b Veliko Tarnovo .. Bobo-Dioulasso .. Koudougou .. Ouagadougou 1,130 c Bujumbura 373 b Phnom Penh 1,000 b Douala 1,148 b Yaoundé 968 b Hull 254 b Bangui .. N’Djamena 998 c Gran Concepción .. Santiago de Chile 5,737 b Tome .. Valparaiso 851 b Viña del mar 851 b Armenia .. Marinilla 170 b Medellin 2,901 b Brazzaville 989 b Abidjan 3,201 b Zagreb 2,497 b Baracoa .. Camaguey .. Cienfuegos .. Ciudad Habana .. Pinar Del Rio .. Santa Clara .. Brno .. Prague 1,193 b Kinshasa 5,398 b Santiago de los Caballeros 691 b Ambato 286 b
2004 World Development Indicators
93.2 92.1 85.0 .. 100.0 .. .. .. 85.7 99.7 .. 87.0 .. .. 91.7 .. .. .. .. 80.3 .. 100.0 100.0 100.0 100.0 .. 100.0 97.0 .. .. .. 100.0 94.0 .. .. .. .. 91.8 92.7 94.1 94.5 .. 87.9 .. 96.5 96.2 84.7 96.3 .. 96.4 98.8 .. 99.3 94.9 .. ..
House price to annual income ratio
Work trips by public transportation
1998 a
% 1998 a
.. 5.10 6.80 5.7 4.0 8.1 16.7 6.0 13.9 4.4 .. 29.3 .. .. 4.5 .. .. 12.5 .. 23.4 5.1 13.2 3.7 5.4 .. .. .. .. 8.9 13.4 .. .. .. .. .. .. .. .. .. 5.0 8.5 .. .. 14.5 7.8 .. .. 4.0 8.5 .. .. .. .. .. .. ..
.. 59 44 .. 84 27 9 10 .. .. .. .. 100 .. .. 30 .. 46 .. 43 61 79 44 46 .. .. 2 48 0 .. 42 16 66 35 57 60 .. 55 .. 42 18 38 55 .. 56 .. 2 .. 58 .. 7 50 55 72 .. ..
Travel time to work
minutes 1998 a
75 42 32 22 30 45 45 50 30 .. .. 29 12 .. 30 20 .. 35 .. 40 32 32 22 30 .. .. .. 25 45 40 45 .. 60 .. 35 38 .. .. .. 60 15 35 20 45 31 .. 60 80 83 80 48 25 22 57 30 ..
Households with access to services
Potable water % 1998 a
.. 100 99 98 98 44 60 29 12 98 .. 53 95 .. 88 73 99 89 88 98 100 95 99 98 24 30 30 26 45 34 34 100 31 42 100 100 92 98 97 90 98 100 56 26 98 83 72 100 100 97 95 100 99 72 75 90
Sewerage connection % 1998 a
.. 98 40 67 98 .. 22 0 0 5 .. 33 90 .. .. .. 87 41 80 95 93 91 82 98 .. .. .. 62 75 1 1 100 .. 0 91 99 52 92 97 50 93 99 0 15 97 3 47 73 85 48 42 96 100 0 80 81
Electricity % 1998 a
.. 100 99 93 100 95 90 93 90 99 .. 98 100 .. 90 .. 100 100 10 100 100 100 100 100 29 26 47 57 76 95 95 100 18 13 95 99 98 97 98 99 100 100 52 41 100 93 97 100 100 100 100 100 100 66 .. 91
Wastewater treated
Telephone % 1998 a
.. 70 80 76 88 .. 7 40 12 78 .. 59 .. .. 33 .. .. 29 .. 79 .. 89 45 96 6 7 11 19 40 9 9 100 11 6 69 73 58 63 65 97 65 87 18 5 94 32 .. 9 14 .. 43 69 100 1 71 87
% 1998 a
80 .. 49 1 36 .. .. .. .. 7 .. 53 .. .. .. .. .. 33 .. .. 93 94 .. 50 .. .. 19 21 .. 5 24 100 0 21 6 3 57 100 93 .. .. .. .. 45 .. .. .. 2 .. .. .. 100 .. .. 80 ..
City
Ecuador
El Salvador Estonia Gabon Gambia Georgia Ghana Guatemala Guinea Indonesia
Iraq Italy Jamaica Jordan Kenya
Korea, Rep
Kuwait Kyrgyz Republic Lao Latvia Lebanon Liberia Libya Lithuania Madagascar Malawi Malaysia Mauritania Mexico Moldova Mongolia Morocco Myanmar Nicaragua Niger Nigeria Oman Panama Paraguay Peru
Cuenca Guayaquil Manta Puyo Quito Tena San Salvador Riik Tallin Libreville Banjul Tbilisi Accra Kumasi Quetzaltenango Conakry Jakarta Semarang Surabaya Baghdad Aversa Kingston Montego Bay Amman Kisumu Mombasa Nairobi Hanam Pusan Seoul Kuwait City Bishkek Vientiane Riga Sin El Fil Monrovia Tripoli Vilnius Antananarivo Lilongwe Penang Nouakchott Ciudad Juárez Chisinau Ulaanbaatar Casablanca Rabat Yangon Leon Niamey Ibadan Lagos Muscat Colón Asunción Cajamarca
Urban population
Secure tenure
thousands 2000
% of population 1998 a
.. 2,317 b 126 b 40 b 1,531 b .. 1,863 b .. 397 c 523 c 50 b 1,310 c 1,500 b 780 b 333 b 1,824 c 9,489 b 1,076 b 2,373 b 4,797 c .. 655 c .. 1,621 b 134 b .. 2,310 c 124 b 3,843 b 10,389 b 1,165 c 60 b 562 b 775 c .. b 651 b 1,773 b 578 b 1,507 c 765 c .. 881 c 1,018 b .. 627 b 3,292 b 646 b 3,692 b .. 731 c 1,731 c 13,427 c 887 b 132 b 1,262 c ..
91.0 45.8 .. .. 93.8 .. 90.5 99.5 98.8 .. 91.8 100.0 .. 77.7 .. .. 95.5 80.2 97.6 .. .. .. .. 97.3 97.3 .. .. .. 100.0 98.6 .. 94.8 92.2 97.4 .. 57.6 .. 100.0 .. .. .. 89.9 .. .. 51.6 .. .. .. 98.8 87.4 85.8 93.0 .. .. 90.2 90.0
House price to annual income ratio
1998 a
4.6 3.4 .. 2.1 2.4 6.3 3.5 .. 6.4 .. 11.4 9.4 14.0 13.7 4.3 .. 14.6 .. 3.4 .. .. .. .. 6.1 8.5 .. .. 3.7 4.0 5.7 6.5 .. 23.2 15.6 8.3 28.0 0.8 20.0 .. .. 7.2 5.4 .. .. 7.8 .. .. 8.3 .. .. .. .. .. 14.2 10.7 3.9
Work trips by public transportation
% 1998 a
.. 89 .. .. .. .. .. .. .. 80 55 .. 54 51 .. 26 .. .. 18 .. .. .. .. 21 43 47 71 .. 39 71 21 95 2 .. 50 80 18 52 .. 27 55 45 24 80 80 .. 40 69 .. .. 46 48 .. .. .. ..
Travel time to work
minutes 1998 a
25 45 30 15 33 5 .. .. 35 30 22 .. 21 21 15 45 .. .. 35 .. .. .. .. 25 24 20 57 .. 42 60 10 35 27 .. 10 60 20 37 .. 5 40 50 23 23 30 30 20 45 15 30 45 60 20 15 25 20
3.11
Households with access to services
Potable water % 1998 a
Sewerage connection % 1998 a
Electricity % 1998 a
97 70 70 80 85 80 82 92 98 55 23 .. .. 65 60 30 50 34 41 .. .. 97 78 98 38 .. 89 81 98 100 100 30 87 95 80 .. 97 89 .. 65 99 .. 89 100 60 83 93 78 78 33 26 .. 80 .. 46 86
92 42 52 30 70 60 80 90 98 0 12 98 .. .. 55 32 65 .. 56 .. .. .. .. 81 31 .. .. 68 69 99 98 23 .. 93 30 .. 90 89 .. 12 .. .. 77 95 60 93 97 81 .. 0 12 .. 90 .. 8 69
97 .. 98 90 96 .. 98 98 100 95 24 100 .. 95 80 54 99 85 89 .. .. 88 86 99 49 .. .. 100 100 100 100 100 100 100 98 .. 99 100 .. 50 100 .. 96 100 100 91 52 85 84 51 41 41 89 .. 86 81
Wastewater treated
Telephone % 1998 a
48 44 40 60 55 .. 70 55 86 45 .. 58 .. 51 40 6 .. .. 71 .. .. .. .. 62 .. .. .. 100 100 .. 98 20 87 70 80 .. 6 77 .. 10 98 .. 45 83 90 .. .. 17 21 4 .. .. 53 .. 17 38
2004 World Development Indicators
% 1998 a
82 9 .. .. .. .. .. .. 10 44 .. .. .. .. .. .. 16 .. .. .. 90 20 15 54 65 50 52 81 69 99 .. 15 20 .. .. .. 40 54 .. .. 20 .. .. 71 96 .. .. .. .. .. .. .. .. .. .. 62
157
ENVIRONMENT
Urban environment
3.11
Urban environment City
Peru
Philippines Poland
Qatar Russian Federation
Rwanda Samoa Serbia and Montenegro Singapore Slovenia Spain Sweden
Switzerland Syria Thailand Togo Trinidad and Tobago Tunisia Turkey Uganda Uruguay West Bank and Gaza Yemen Zimbabwe
Huanuco Huaras Iquitos Lima Tacna Tumbes Cebu Bydgoszcz Gdansk Katowice Poznan Doha Astrakhan Belgorod Kostroma Moscow Nizhny Novgorod Novomoscowsk Omsk Pushkin Surgut Veliky Novgorod Kigali Apia Belgrad Singapore Ljubljana Madrid Pamplona Amal Stockholm Umea Basel Damascus Bangkok Chiang Mai Lomé Port of Spain Tunis Ankara Entebbe Jinja Montevideo Gaza Aden Sana’a Bulawayo Chegutu Gweru Harare Mutare
Urban population
Secure tenure
thousands 2000
% of population 1998 a
747 b 54 b 347 b 7,431 b .. .. 2,189 b .. 893 c 3,487 c .. 391 c .. .. .. 9,321 c 1,458 c .. 1,216 c .. .. .. 358 b 34 b 1,182 b 3,164 b 273 b 4,577 b .. 13 b 736 b 104 b 170 b 2,335 b 5,647 b 499 b 663 b .. 2,023 b 2,837 b 65 b 92 b 1,670 b 367 b 1,200 b 1,200 b 900 b .. .. 1,634 b 149 b
.. .. 97.3 80.6 .. .. 95.0 60.5 .. 27.8 65.5 .. 100.0 100.0 100.0 100.0 100.0 100.0 99.7 100.0 100.0 100.0 .. .. 96.5 100.0 98.9 .. .. .. .. .. .. .. 77.2 96.5 64.0 78.6 .. 91.3 74.0 82.0 88.0 87.3 .. .. 99.4 51.5 94.0 99.9 ..
House price to annual income ratio
1998 a
30.0 6.7 5.6 10.4 4.0 .. 13.3 4.3 4.4 1.7 5.8 .. 5.0 4.0 6.9 5.1 6.9 4.2 3.9 9.6 4.5 3.4 11.4 10.0 13.5 3.1 7.8 .. .. 2.9 6.0 5.3 12.3 10.3 8.8 6.8 .. .. 5.0 4.5 10.4 15.4 5.6 5.4 .. .. .. 3.4 .. .. ..
Work trips by public transportation
% 1998 a
.. .. 25 82 .. .. .. 35 56 29 51 .. 66 .. 68 85 79 61 86 60 81 75 32 .. 72 53 20 16 .. .. 48 .. .. 33 28 5 40 44 .. .. 65 49 60 .. 78 78 75 20 .. 32 70
Travel time to work
minutes 1998 a
20 15 10 .. 25 20 35 18 20 36 25 .. 35 25 20 62 35 25 43 15 57 30 45 .. 40 30 30 32 .. .. 28 16 .. 40 60 30 30 .. .. 32 20 12 45 .. 20 20 15 22 15 45 20
Households with access to services
Potable water % 1998 a
57 .. 73 75 65 60 41 95 99 99 95 .. 81 90 88 100 98 99 87 99 98 97 36 60 95 100 100 .. 100 100 100 100 100 98 99 95 .. .. 75 97 48 65 98 85 .. 30 100 100 100 100 88
Sewerage connection % 1998 a
28 .. 60 71 58 35 92 87 94 94 96 .. 79 89 84 100 98 93 87 99 98 97 20 0 86 100 100 .. .. 100 100 100 100 71 100 60 70 .. 47 98 13 43 79 38 .. 9 100 68 100 100 88
Electricity % 1998 a
80 71 82 99 74 80 80 100 100 100 100 .. 100 100 100 100 100 100 100 100 100 100 57 98 100 100 100 .. 100 100 100 100 100 95 100 100 51 .. 95 100 42 55 100 99 96 96 98 9 90 88 74
Wastewater treated
Telephone % 1998 a
32 .. 62 .. 16 25 25 85 56 75 86 .. 51 51 46 102 64 62 41 89 50 51 6 96 86 100 97 .. .. .. .. .. 99 10 60 75 18 .. 27 .. 0 5 75 38 .. .. .. 3 61 42 4
% 1998 a
.. .. .. 4 64 .. .. 28 100 67 78 .. 92 96 96 98 98 97 89 100 93 95 20 .. 20 100 98 100 79 100 100 100 100 3 .. 70 .. .. 83 80 30 30 34 .. 30 30 80 69 95 .. 100
a. Data are preliminary. b. Data are for 1998 and are from United Nations Centre for Human Settlements. c. Data are for 2000 and are from the United Nations Population Division’s World Urbanization Prospects: The 2001 Revision.
158
2004 World Development Indicators
About the data
3.11
Definitions
Despite the importance of cities and urban agglom-
Programme. As a result, the database excludes a
• Urban population refers to the population of the
erations as home to almost half the world’s people,
large number of major cities. The table reflects this
urban agglomeration, a contiguous inhabited territory
data on many aspects of urban life are sparse. The
bias as well as the criterion of data availability for the
without
available data have been scattered among interna-
indicators shown.
• Secure tenure refers to the percentage of the pop-
regard
to
administrative
boundaries.
tional agencies with different mandates, and compil-
The data should be used with care. Because dif-
ulation protected from involuntary removal from land
ing comparable data has been difficult. Even within
ferent data collection methods and definitions may
or residence—including subtenancy, residence in
cities it is difficult to assemble an integrated data
have been used, comparisons can be misleading. In
social housing, and residences owned, purchased, or
set. Urban areas are often spread across many juris-
addition, the definitions used here for access to
privately rented—except through due legal process.
dictions with no single agency responsible for col-
potable water and urban population are more strin-
• House price to annual income ratio is the average
lecting and reporting data for the entire area. Adding
gent than those used for tables 3.5 and 3.10 (see
house price divided by the average household
to the difficulties of data collection are gaps and
Definitions).
income. • Work trips by public transportation are the
overlaps in the data collection and reporting respon-
percentage of trips to work made by bus or minibus,
sibilities of different administrative units. Creating a
tram, or train. Buses or minibuses are road vehicles
comprehensive, comparable international data set is
other than cars taking passengers on a farepaying
further complicated by differences in the definition of
basis. Other means of transport commonly used in
an urban area and by uneven data quality.
developing countries, such as taxi, ferry, rickshaw, or
The United Nations Global Plan of Action calls for
animal, are not included. • Travel time to work is the
monitoring the changing role of the world’s cities and
average time in minutes, for all modes, for a one-way
human settlements. The international agency with
trip to work. Train and bus times include average walk-
the mandate to assemble information on urban
ing and waiting times, and car times include parking
areas is the United Nations Centre for Human
and walking to the workplace. • Households with
Settlements (UNCHS, or Habitat). Its Urban
access to services are the percentage of households
Indicators Programme is intended to provide data for
in formal settlements with access to potable water
monitoring and evaluating the performance of urban
and connections to sewerage, electricity, and tele-
areas and for developing government policies and
phone service. Households with access to potable
strategies. These data are collected through ques-
water are those with access to safe or potable drink-
tionnaires completed by city officials in more than a
ing water within 200 meters of the dwelling.
hundred countries.
• Potable water is water that is free from contami-
The table shows selected indicators for more than
nation and safe to drink without further treatment.
160 cities from the UNCHS data set. A few more
• Wastewater treated is the percentage of all waste-
indicators are included on the World Development
water undergoing some form of treatment.
Indicators CD-ROM. The selection of cities in the UNCHS database does not reflect population weights or the economic importance of cities and is therefore biased toward smaller cities. Moreover, it is based on demand for participation in the Urban Indicators
3.11a The use of public transportation for work trips varied widely across cities in 1998
Country
City
Share of total work trips (%) 2
Country
City
Share of total work trips (%)
Lao PDR
Vientiane
Kyrgyz Republic
Bishkek
95
Spain
Madrid
16
Russian Federation
Moscow
85
Canada
Hull
16
Armenia
Yerevan
84
Libya
Tripoli
18
Peru
Lima
82
Slovenia
Ljubljana
20
Gabon
Libreville
80
Kuwait
Kuwait City
21
Liberia
Monrovia
80
Jordan
Amman
21
Mongolia
Ulaanbaatar
80
Mexico
Ciudad Juarez
24
Moldova
Chisinau
80
Guinea
Conakry
26
Bulgaria
Sofia
79
Malawi
Lilongwe
27
Yemen, Rep.
Aden
78
Data sources The data are from the Global Urban Indicators database of the UNCHS and the United Nations Population Division’s World Urbanization Prospects: The 2001 Revision.
Source: Table 3.11.
2004 World Development Indicators
159
ENVIRONMENT
Urban environment
3.12
Traffic and congestion Motor vehicles
1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
160
.. 11 .. 19 181 5 530 421 52 1 61 423 3 41 114 18 88 163 4 .. 1 10 605 1 2 81 5 66 .. .. 18 87 24 .. 37 246 368 75 35 29 33 1 211 1 441 494 32 13 107 405 .. 248 .. 4 7 ..
Passenger cars
Two-wheelers
Road traffic
per 1,000
per kilometer
per 1,000
per 1,000
million vehicle
people
of road
people
people
kilometers
1999–2001
.. 66 .. .. 181 .. .. 536 52 1 112 515 .. 53 .. 69 .. 273 .. .. 6 .. 580 1 .. 133 12 77 51 .. .. .. .. 274 32 364 420 .. 48 .. 61 .. 404 2 461 575 .. .. 70 .. .. 328 52 .. .. ..
2004 World Development Indicators
1990
.. 3 .. .. 27 2 11 30 7 0 13 30 2 6 24 3 8 39 3 .. 0 3 20 0 0 13 4 253 .. .. 3 7 6 .. 16 46 27 48 8 33 14 1 22 2 29 32 4 5 27 53 .. 22 .. 1 2 ..
1999–2001
.. 11 .. .. 37 .. .. 22 17 .. .. 35 .. 8 .. 11 .. 60 .. .. 49 .. 20 0 .. 25 11 279 19 .. .. 13 .. 44 6 67 31 0 14 .. 36 .. 11 3 31 38 .. .. 15 .. .. .. 119 .. .. ..
1990
.. 2 .. 14 134 1 450 387 36 0 59 385 2 25 101 10 .. 146 2 .. 0 6 468 1 1 52 1 42 .. .. 12 55 15 .. 18 228 320 21 31 21 17 1 154 1 386 405 19 6 89 386 .. 171 .. 2 4 ..
1999–2001
.. 43 .. .. 140 .. .. 495 42 0 145 462 .. .. .. 30 .. 234 .. .. .. .. 458 0 .. 87 7 57 43 .. .. .. .. 247 16 335 359 .. 43 .. 30 .. 339 1 403 477 .. .. 55 516 .. 254 1 .. .. ..
1990
.. 3 .. .. 1 .. 18 71 5 1 .. 14 34 9 .. .. .. 55 9 .. 9 .. 12 0 0 2 3 4 8 .. .. 14 .. .. 19 113 9 .. 2 6 0 .. 66 0 12 55 .. .. 5 18 .. 120 .. .. .. ..
1999–2001
.. 1 .. .. .. .. 18 77 1 1 52 29 .. 3 .. 1 28 64 .. .. 134 .. 11 .. .. 2 26 5 12 .. .. 22 .. 14 16 73 13 .. 2 .. 5 .. 5 0 35 40 .. .. 1 56 .. 220 12 .. .. ..
1990
.. .. .. .. 43,119 .. 138,501 .. .. .. 10,026 .. .. 1,139 .. .. .. .. .. .. 314 .. .. 1,494 .. .. .. 8,192 50,945 .. .. .. .. .. .. .. 36,304 .. 10,306 .. 2,002 .. .. .. 39,750 422,000 .. .. 4,620 446,000 .. .. .. .. .. ..
Fuel prices
$ per liter Super
Diesel
1999–2001
2002
2002
.. 29 .. .. 27,458 .. .. .. .. .. 4,964 156,633 .. .. .. .. .. 213 .. .. 7,210 .. 73,500 .. .. .. 840,960 10,781 41,587 .. .. 551,139 .. 15,168 .. 7,753 45,165 .. 17,528 .. 4,244 .. 6,539 26,450 46,010 519,400 .. .. .. 589,500 15,320 79,377 4,547 .. .. ..
0.34 0.80 0.22 0.19 0.63 0.42 0.50 0.84 0.37 0.52 0.50 1.04 0.54 0.69 0.74 0.41 0.55 0.68 0.83 0.58 0.63 0.68 0.51 1.00 0.79 0.58 0.42 1.47 0.44 0.70 0.69 0.64 0.85 0.89 0.90 0.81 1.09 0.49 0.55 0.19 0.46 0.36 0.58 0.52 1.12 1.05 0.69 0.46 0.48 1.03 0.28 0.78 0.48 0.66 .. 0.54
0.27 0.51 0.10 0.13 0.46 0.29 0.48 0.73 0.16 0.29 0.36 0.80 0.41 0.42 0.74 0.38 0.31 0.59 0.62 0.54 0.44 0.57 0.43 0.87 0.77 0.39 0.37 0.77 0.24 0.69 0.48 0.44 0.60 0.74 0.45 0.71 0.94 0.27 0.27 0.80 0.33 0.25 0.56 0.32 0.80 0.80 0.53 0.40 0.41 0.82 0.23 0.68 0.32 0.56 .. 0.30
Motor vehicles
1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
22 212 4 16 34 14 270 210 529 .. 469 60 76 12 .. 79 .. 44 9 135 321 11 14 .. 160 132 6 4 124 3 10 59 119 53 21 37 4 .. 71 .. 405 524 19 6 30 458 130 6 75 .. .. .. 10 168 222 ..
Passenger cars
Two-wheelers
Road traffic
per 1,000
per kilometer
per 1,000
per 1,000
million vehicle
people
of road
people
people
kilometers
1999–2001
60 271 10 25 .. .. 408 275 606 .. 572 .. 86 11 .. 255 .. .. .. 281 .. .. .. .. 345 170 .. .. .. .. .. 106 159 82 31 51 .. .. 82 .. 428 696 30 .. .. 511 .. 9 .. .. .. 43 32 307 347 ..
1990
10 21 2 10 14 6 10 74 99 .. 52 26 8 5 .. 60 .. 10 3 6 183 4 4 .. 12 30 2 4 26 2 3 35 41 17 1 15 2 .. 1 .. 58 19 5 4 21 22 9 4 18 .. .. .. 4 18 34 ..
1999–2001
28 16 .. .. .. .. .. 108 74 .. 62 .. 16 4 .. 120 .. .. .. 11 .. .. .. .. 17 27 .. 0 .. .. .. 64 44 24 2 26 .. .. 2 .. 58 .. 8 .. .. 25 .. 5 .. .. .. 13 12 32 .. ..
1990
.. 188 2 7 25 1 227 174 476 .. 283 .. 50 10 .. 48 .. 44 6 106 300 3 7 .. 133 121 4 2 101 2 7 44 82 48 6 28 3 .. 39 .. 368 436 10 5 12 380 83 4 60 .. .. .. 7 138 162 ..
1999–2001
51 237 6 .. .. .. 349 233 542 .. 413 .. 67 8 .. 171 .. 38 .. 235 .. .. .. .. 317 .. .. .. .. .. .. 78 107 64 18 41 .. .. 38 .. 384 578 12 .. .. 411 .. 5 .. .. .. 27 10 259 321 ..
1990
.. 16 15 34 36 .. 6 8 45 .. 146 0 .. 1 .. 32 .. .. 18 76 13 .. .. .. 52 1 .. .. 167 .. .. 54 3 45 22 1 .. .. 1 .. 44 24 3 .. 5 48 3 8 2 .. .. .. 6 36 5 ..
1999–2001
14 14 29 59 .. .. 8 12 125 .. 110 .. 8 1 .. 59 .. 4 .. 9 .. .. .. .. 5 .. .. .. 233 .. .. 101 .. .. 10 1 .. .. 2 .. 25 20 5 .. .. 55 .. 15 3 .. .. .. 16 21 77 ..
1990
3,288 22,898 .. .. .. .. 24,205 18,212 344,726 .. 628,581 1,098 18,248 5,170 .. 30,464 .. 5,220 .. 3,932 .. .. .. .. .. 3,102 41,500 .. .. .. .. .. 55,095 .. 340 .. 1,889 .. 1,896 .. 90,150 .. 108 178 .. .. .. .. .. .. .. .. 6,189 59,608 28,623 ..
3.12 Fuel prices
$ per liter Super
Diesel
1999–2001
2002
2002
.. 23,670 .. .. .. .. .. 37,010 67,916 .. 775,723 490,248 .. .. .. 67,266 4,450 1,933 .. .. .. .. .. .. 872 .. .. .. .. .. .. 78 .. 557 2,093 16,834 .. .. 2,317 .. 109,955 35,200 440 .. .. 32,589 .. 234,515 .. .. .. .. 9,548 138,100 47,943 ..
0.63 0.94 0.66 0.27 0.07 0.02 0.90 0.90 1.05 0.52 0.91 0.52 0.35 0.70 0.55 1.09 0.20 0.39 0.36 0.70 0.65 0.50 .. 0.10 0.69 0.85 1.08 0.66 0.35 0.69 0.63 .. 0.62 0.45 0.38 0.87 0.46 0.36 0.45 0.66 1.12 0.55 0.54 0.77 0.20 1.23 0.31 0.52 0.51 0.53 0.56 0.74 0.35 0.83 0.97 ..
0.46 0.85 0.41 0.19 0.02 0.01 0.80 0.62 0.86 0.44 0.66 0.17 0.29 0.56 0.41 0.51 0.18 0.25 0.30 0.65 0.25 0.47 .. 0.08 0.59 0.63 0.65 0.62 0.19 0.55 0.39 .. 0.47 0.31 0.37 0.55 0.43 0.28 0.43 0.34 0.81 0.33 0.41 0.55 0.19 1.18 0.26 0.35 0.36 0.34 0.34 0.48 0.27 0.68 0.71 ..
2004 World Development Indicators
161
ENVIRONMENT
Traffic and congestion
3.12
Traffic and congestion Motor vehicles
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
162
Passenger cars
Two-wheelers
Road traffic
per 1,000
per kilometer
per 1,000
per 1,000
million vehicle
people
of road
people
people
kilometers
1990
1999–2001
72 87 2 165 11 137 10 130 194 306 2 139 360 21 9 66 464 491 26 3 5 46 24 .. 48 50 .. 2 63 121 400 758 138 .. .. .. .. 34 14 .. 118 w 7 40 25 149 26 9 97 100 48 4 21 505 429
160 176 .. .. 14 163 0 168 266 465 .. .. 467 37 .. 71 494 534 29 .. .. .. .. .. 79 85 .. .. .. .. 391 779 .. .. .. .. .. .. .. .. .. w 11 52 34 213 37 17 199 108 .. 10 .. 668 553
2004 World Development Indicators
1990
11 14 1 19 6 31 4 142 57 42 1 26 43 4 22 18 29 46 10 1 2 36 11 .. 19 8 .. .. 20 52 64 30 45 .. .. .. .. 8 3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1999–2001
1990
1999–2001
18 48 .. .. 2 39 .. .. 34 46 .. .. 54 7 .. 21 21 54 .. .. .. .. .. .. .. 14 .. .. .. .. 62 34 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
56 65 1 98 8 133 7 89 163 289 1 97 309 7 8 35 426 449 10 0 1 14 16 .. 23 34 .. 1 63 97 341 573 122 .. .. .. .. 14 8 .. 91 w 4 26 13 114 16 4 82 .. 31 2 14 396 379
139 132 .. .. 11 150 0 122 236 426 .. .. 408 12 .. 35 450 493 9 .. .. .. .. .. 53 63 .. .. 104 .. 384 481 .. .. .. .. .. .. .. .. .. w 8 40 26 166 28 10 152 72 .. 6 .. 436 494
1990
13 .. .. 0 0 3 2 40 61 8 .. 8 79 24 .. 3 11 114 .. .. .. 86 8 .. .. 10 .. 0 .. .. 14 17 74 .. .. 45 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1999–2001
14 43 .. .. 0 3 0 32 8 6 .. 4 92 42 .. 3 31 102 .. .. .. .. .. .. 1 15 .. 3 46 .. 3 15 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1990
1999–2001
23,907 39,184 .. 59,522 .. .. .. .. .. 4,013 .. 1,428 996 .. .. .. .. 543 5,620 9,449 .. .. .. .. 100,981 201,896 3,468 15,630 .. .. .. .. 61,040 128,200 48,660 54,707 .. .. .. 1,730 .. .. 45,769 .. .. .. .. .. .. 14,635 27,041 52,631 .. .. .. .. 59,500 .. .. .. 399,000 462,400 2,527,441 2,653,043 .. .. .. .. .. .. .. .. .. .. 8,681 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
Fuel prices
$ per liter Super
Diesel
2002
2002
0.64 0.35 0.84 0.24 0.75 0.74 0.51 0.85 0.74 0.76 0.35 0.43 0.83 0.54 0.30 0.47 1.06 0.89 0.53 0.36 0.67 0.36 0.56 0.40 0.29 1.02 0.02 0.83 0.47 0.29 1.18 0.40 0.46 0.38 0.05 0.34 0.99 0.21 0.72 0.85 0.58 m 0.54 0.54 0.52 0.60 0.54 0.36 0.64 0.54 0.29 0.54 0.64 0.87 1.00
0.57 0.25 0.84 0.10 0.53 0.66 0.50 0.38 0.70 0.67 0.29 0.40 0.72 0.31 0.24 0.44 0.96 0.93 0.18 0.24 0.61 0.32 0.46 0.21 0.19 0.78 0.01 0.70 0.34 0.30 1.20 0.39 0.20 0.26 0.05 0.27 0.52 0.10 0.60 0.72 0.44 m 0.41 0.39 0.36 0.43 0.40 0.31 0.56 0.36 0.18 0.34 0.51 0.66 0.80
About the data
3.12
Definitions
Traffic congestion in urban areas constrains eco-
tions. Comparability also is limited when time-series
• Motor vehicles include cars, buses, and freight
nomic productivity, damages people’s health, and
data are reported. Moreover, the data do not capture
vehicles but not two-wheelers. Population figures
degrades the quality of their lives. The particulate air
the quality or age of vehicles or the condition or width
refer to the midyear population in the year for which
pollution emitted by motor vehicles—the dust and
of roads. Thus comparisons over time and between
data are available. Roads refer to motor ways,
soot in exhaust—is proving to be far more damaging
countries should be made with caution.
highways, main or national roads, and secondary or
to human health than was once believed. (For infor-
The data on fuel prices are compiled by the
regional roads. A motorway is a road specially
mation on particulate matter and other air pollutants,
German Agency for Technical Cooperation (GTZ) from
designed and built for motor traffic that separates the
see table 3.13.)
its global network of regional offices and represen-
traffic flowing in opposite directions. • Passenger
In recent years ownership of passenger cars has
tatives as well as other sources, including the
cars refer to road motor vehicles, other than two-
increased, and the expansion of economic activity
Allgemeiner Deutscher Automobil Club (for Europe)
wheelers, intended for the carriage of passengers
has led to the transport by road of more goods and
and a project of the Latin American Energy
and designed to seat no more than nine people
services over greater distances (see table 5.9).
Organization (OLADE, for Latin America). Local prices
(including the driver). • Two-wheelers refer to mope-
These developments have increased demand for
have been converted to U.S. dollars using the
ds and motorcycles. • Road traffic is the number of
roads and vehicles, adding to urban congestion, air
exchange rate on the survey date as listed in the
vehicles multiplied by the average distances they
pollution, health hazards, traffic accidents, and
international monetary table of the Financial Times.
travel. • Fuel prices refer to the pump prices of the
injuries.
For countries with multiple exchange rates, the mar-
most widely sold grade of gasoline and of diesel fuel.
ket, parallel, or black market rate was used rather
Prices have been converted from the local currency to
than the official exchange rate.
U.S. dollars (see About the data).
Congestion, the most visible cost of expanding vehicle ownership, is reflected in the indicators in the table. Other relevant indicators—such as average vehicle speed in major cities or the cost of traffic congestion, which takes a heavy toll on economic productivity—are not included because data are incomplete or difficult to compare. The data in the table—except for those on fuel prices—are compiled by the International Road Federation (IRF) through questionnaires sent to national organizations. The IRF uses a hierarchy of sources to gather as much information as possible. The primary sources are national road associations. Where such an association lacks data or does not respond, other agencies are contacted, including road directorates, ministries of transport or public works, and central statistical offices. As a result, the compiled data are of uneven quality. The coverage of each indicator may differ across countries because of differences in defini-
3.12a The 10 countries with the most vehicles per 1,000 people in 2001—and the 10 with the fewest Vehicles per 1,000 people Country
Motor vehicles
Country
Motor vehicles
United States
779
Mongolia
31
New Zealand
696
Nicaragua
30
Italy
606
Syrian Arab Republic
29
Canada
580
Indonesia
25
France
575
India
10
Japan
572
Pakistan
9
Austria
536
Cambodia
6
Switzerland
534
Uganda
5
Belgium
515
Ethiopia
2
Norway
511
Bangladesh
1
Data sources The data on vehicles and traffic are from the IRF’s electronic files and its annual World Road Statistics. The data on fuel prices are from the
Note: Data are for the most recent year available between 1999 and 2001. Source: Table 3.12.
GTZ’s electronic files.
2004 World Development Indicators
163
ENVIRONMENT
Traffic and congestion
3.13
Air pollution City
City population
Particulate matter
Sulfur dioxide
Nitrogen dioxide
About the data
In many towns and cities exposure to air pollution is the main environmental threat to human health. micrograms per
micrograms per
microgram per
thousands
cubic meter
cubic meter
cubic meter
2000
1999
1995–2001 a
1995–2001 a
52 15 15 22 39 31 40 46 83 22 26 15 73 99 106 88 103 147 60 74 84 91 112 84 109 70 94 80 .. 87 120 105 149 61 94 116 88 33 39 28 27 24 26 34 178 22 15 25 22 22 31 50 26 21 104 56
.. .. 5 28 14 20 129 43 39 10 17 14 29 115 90 21 77 340 61 57 424 23 132 19 102 102 69 75 190 53 99 211 82 60 40 63 198 .. 31 1 14 7 15 22 69 4 14 18 11 8 .. 34 39 5 30 ..
Long-term exposure to high levels of soot and small particles in the air contributes to a wide range of health effects, including respiratory diseases, lung
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
164
Córdoba Melbourne Perth Sydney Vienna Brussels Rio de Janeiro São Paulo Sofia Montreal Toronto Vancouver Santiago Anshan Beijing Changchun Chengdu Chongquing Dalian Guangzhu Guiyang Harbin Jinan Kunming Lanzhou Liupanshui Nanchang Pinxiang Quingdao Shanghai Shenyang Taiyuan Tianjin Urumqi Wuhan Zhengzhou Zibo Bogota Zagreb Havana Prague Copenhagen Guayaquil Quito Cairo Helsinki Paris Berlin Frankfurt Munich Accra Athens Budapest Reykjavik Ahmedabad Bangalore
2004 World Development Indicators
1,370 3,293 1,245 3,855 1,904 983 5,902 9,984 1,177 3,519 4,535 1,880 4,522 3,132 9,302 3,766 4,401 3,945 4,389 495 2,103 4,545 3,037 2,037 2,044 2,330 1,594 1,754 2,316 10,367 5,881 2,811 7,333 1,467 4,842 2,214 3,139 5,442 908 2,270 1,211 1,371 2,120 1,598 7,941 1,095 9,851 3,555 668 1,275 1,938 3,229 1,958 164 4,154 5,180
97 30 19 81 42 48 .. 83 122 42 43 37 81 88 122 64 74 70 100 136 53 30 45 33 104 .. 29 .. 64 73 73 55 50 70 43 95 43 .. .. 5 33 54 .. .. .. 35 57 26 45 53 .. 64 51 42 21 ..
cancer, and heart disease. Particulate pollution, on its own or in combination with sulfur dioxide, leads to an enormous burden of ill health. Emissions of sulfur dioxide and nitrogen oxides lead to the deposition of acid rain and other acidic compounds over long distances. Acid deposition changes the chemical balance of soils and can lead to the leaching of trace minerals and nutrients critical to trees and plants. Where coal is the primary fuel for power plants, steel mills, industrial boilers, and domestic heating, the result is usually high levels of urban air pollution— especially par ticulates and sometimes sulfur dioxide—and, if the sulfur content of the coal is high, widespread acid deposition. Where coal is not an important primary fuel or is used in plants with effective dust control, the worst emissions of air pollutants stem from the combustion of petroleum products. The data on sulfur dioxide and nitrogen dioxide concentrations are based on reports from urban monitoring sites. Annual means (measured in micrograms per cubic meter) are average concentrations observed at these sites. Coverage is not comprehensive because not all cities have monitoring systems. The data on particulate matter concentrations are estimates, for selected cities, of average annual concentrations in residential areas away from air pollution “hotspots,” such as industrial districts and transport corridors. The data have been extracted from a complete set of estimates developed by the World Bank’s Development Research Group and Environment Department in a study of annual ambient concentrations of particulate matter in world cities with populations exceeding 100,000 (Pandey and others 2003). Pollutant concentrations are sensitive to local conditions, and even in the same city different monitoring sites may register different concentrations. Thus these data should be considered only a general indication of air quality in each city, and cross-country comparisons should be made with caution. The current World Health Organization (WHO) air quality guidelines for annual mean concentrations are 50 micrograms per cubic meter for sulfur dioxide and 40 micrograms for nitrogen dioxide. The WHO has set no guidelines for particulate matter concentrations below which there are no appreciable health effects.
City
City population
Particulate matter
Sulfur dioxide
Nitrogen dioxide
3.13
Definitions
• City population is the number of residents of the city or metropolitan area as defined by national micrograms per
micrograms per
microgram per
thousands
cubic meter
cubic meter
cubic meter
2000
1999
1995–2001 a
1995–2001 a
153 .. 187 51 136 136 79 69 58 103 71 23 36 35 53 39 43 32 49 43 45 49 24 69 37 15 23 60 45 49 30 25 27 28 41 22 15 29 30 43 37 15 24 82 53 62 45 17 23 19 27 38 23 18
49 15 24 12 15 26 33 6
34 17 41 17 14 25 39 13
.. 209 20 31 .. .. 19 18 100 .. 60 44 81 24 74 10 3 8 33 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
.. .. .. 248 .. .. 63 68 13 .. 51 60 62 .. 130 58 20 43 .. 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 refers to fine suspended particulates less than 10 microns in diameter that are
India
Indonesia Iran, Islamic Rep. Ireland Italy
Japan
Kenya Korea, Rep
Malaysia Mexico Netherlands New Zealand Norway Philippines Poland Portugal Romania Russian Federation Singapore Slovak Republic South Africa
Spain Sweden Switzerland Thailand Turkey Ukraine United Kingdom
United States
Venezuela, RB
Calcutta Chennai Delhi Hyderabad Kanpur Lucknow Mumbai Nagpur Pune Jakarta Tehran Dublin Milan Rome Torino Osaka Tokyo Yokohama Nairobi Pusan Seoul Taegu Kuala Lumpur Mexico City Amsterdam Auckland Oslo Manila Lodz Warsaw Lisbon Bucharest Moscow Omsk Singapore Bratislava Capetown Durban Johannesburg Barcelona Madrid Stockholm Zurich Bangkok Ankara Istanbul Kiev Birmingham London Manchester Chicago Los Angeles New York Caracas
a. Data are for the most recent year available.
13,822 6,002 10,558 5,448 2,546 2,093 15,797 2,087 3,128 10,845 7,689 991 1,381 2,713 969 2,626 12,483 3,366 2,383 4,075 11,548 2,417 1,530 18,017 1,131 989 805 10,432 873 1,716 3,318 2,070 8,811 1,206 3,163 456 2,942 1,364 2,344 1,645 3,068 916 980 7,296 3,702 9,286 2,622 2,344 7,812 2,325 9,024 16,195 20,951 3,488
capable of penetrating deep into the respiratory tract and causing significant health damage. The state of a country’s technology and pollution controls is an important determinant of particulate matter concentrations. • Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. It contributes to acid rain and can damage human health, particularly that of the young and the elderly. • Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogenous fertilizers, combustion of fuels and biomass, and aerobic decomposition of organic matter in soils and oceans.
Data sources City population data are from the United Nations Population Division. The data on sulfur dioxide and nitrogen dioxide concentrations are from the WHO’s Healthy Cities Air Management Information System and the World Resources Institute, which relies on various national sources as well as, among others, the Organisation for Economic Co-operation and Development’s (OECD) OECD Environmental Data Compendium 1999, the U.S. Environmental Protection Agency’s National Air Quality and Emissions Trends Report 1995, the Aerometric Information Retrieval System (AIRS) Executive International database, and the United Nations Centre for Human Settlements’ (UNCHS) Urban Indicators database. The data on particulate matter concentrations are from a recent World Bank study by Kiran D. Pandey, Katharine Bolt, Uwe Deichman, Kirk Hamilton, Bart Ostro, and David Wheeler, “The Human Cost of Air Pollution: New Estimates for Developing Countries” (2003).
2004 World Development Indicators
165
ENVIRONMENT
Air pollution
3.14
Government commitment Environmental strategies or action plans
3.14a
Participation in treaties a
Biodiversity assessments, strategies or action plans
The Kyoto Protocol on climate change The Kyoto Protocol was adopted at the third conference of the parties to the United Nations Law Climate change b
2002 1995 1994 2000 1994 1994 1994 1994 1995 1994 2000 1996 1994 1995 2000 1994 1994 1995 1994 1997 1996 1995 1994 1995 1994 1995 1994 .. 1995 1995 1997 1994 1995 1996 1994 1994 1994 1999 1994 1995 1996 1995 1994 1994 1994 1994 1998 1994 1994 1994 1995 1994 1996 1994 1996 1996
Ozone
Framework Convention on Climate Change, held in
CFC
of the
Biological
Kyoto
layer
control
Sea c
diversity b
Protocol
.. 1999 1992 2000 1990 1999 1987 1987 1996 1990 1986 1988 1993 1994 1992 1991 1990 1990 1989 1997 2001 1989 1986 1993 1989 1990 1989 .. 1990 1994 1994 1991 1993 1991 1992 1993 1988 1993 1990 1988 1992 .. 1996 1994 1986 1987 1994 1990 1996 1988 1989 1988 1987 1992 2002 2000
.. 1999 1992 2000 1990 1999 1989 1989 1996 1990 1988 1988 1993 1994 1992 1991 1990 1990 1989 1997 2001 1989 1988 1993 1994 1990 1991 .. 1993 1994 1994 1991 1993 1991 1992 1993 1988 1993 1990 1988 1992 .. 1996 1994 1988 1988 1994 1990 1996 1988 1989 1988 1989 1992 2002 2000
.. 2003 f 1996 1994 1996 2002 f 1995 1995 .. 2001 .. 1998 1997 1995 1994 g 1994 1994 1996 .. .. .. 1994 2003 .. .. 1997 1996 .. .. 1994 .. 1994 1994 1994 g 1994 1996 .. .. .. 1994 .. .. .. .. 1996 1996 1998 1998 1996 f 1994 f 1994 1995 1997 1994 1994 1996
Kyoto, Japan, in December 1997 and was open for signature from March 1998 onward.
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
166
.. 1993 2001 .. 1992 .. 1992 .. 1998 1991 .. .. 1993 1994 .. 1990 .. .. 1993 1994 1999 .. 1990 .. 1990 .. 1994 .. 1998 .. .. 1990 1994 2001 .. 1994 1994 .. 1993 1992 1994 1995 1998 1994 1995 1990 .. 1992 1998 .. 1992 .. 1994 1994 1993 1999
.. .. .. .. .. .. 1994 .. .. 1990 .. .. .. 1988 .. 1991 1988 1994 .. 1989 .. 1989 1994 .. .. 1993 1994 .. 1988 1990 1990 1992 1991 2000 .. .. .. 1995 1995 1988 1988 .. .. 1991 .. .. 1990 1989 .. .. 1988 .. 1988 1988 1991 ..
2004 World Development Indicators
2002 .. 1994 f .. 1995 .. 1998 .. 1995 2001 1993 d 2003 f 1993 .. 1994 2002 2000 e 2000 f 1994 2001 f 1993 .. 1997 2002 1994 2002 f 1995 1999 2002 f .. 1996 2003 f 1994 2002 1996 2002 1993 .. 1997 2001 f 1995 f 2002 f 1995 2002 f 1993 2002 1995 .. 1994 .. 1994 2002 1993 2002 e .. .. 1995 2001 f 1995 .. 1996 .. 1994 2002 1995 .. 1997 .. 1994 .. 1994 e 2001 e 1994 2002 1996 2002 f 1993 2000 1994 .. 1994 1998 1996 f .. 1994 2002 1994 .. 1994 d 2002 1994 2002 e 2000 .. 1994 2001 f 1994 f 1999 f 1994 2002 1994 2003 f 1994 2002 1995 1999 1993 2000 f 1996 .. 1996 ..
At the heart of the protocol are its legally binding greenhouse gas emissions targets for industrial and transition economies (known as “Annex I Parties”), which accounted for at least 55 percent of carbon dioxide emissions in 1990. The emissions targets amount to an aggregate reduction of greenhouse gas emissions by all Annex I Parties of at least 5 percent from 1990 levels during the commitment period, 2008–12. All Annex I Parties have individual emissions targets, which were decided in Kyoto after intensive negotiation and are listed in the protocol’s Annex B. The protocol’s rules focus on: • Commitments, including legally binding emissions targets and general commitments. • Implementation, including domestic steps and three novel implementing mechanisms. • Minimization of impacts on developing countries, including use of an Adaptation Fund. • Accounting, reporting, and review, including indepth review of national reporting. • Compliance, including a Compliance Committee to assess and deal with problem cases. In addition to emissions targets for Annex I Parties, the Kyoto Protocol also contains a set of general commitments that apply to all parties, such as: • Improving the quality of emissions data. • Mounting national mitigation and adaptation programs. • Promoting environmentally friendly technology transfer. • Cooperating in scientific research and international climate observation networks. • Supporting education, training, public awareness, and capacity building initiatives. The Protocol is subject to ratification, acceptance, approval, or accession by Parties to the Convention, which bind the parties to the protocol’s commitments, once the protocol comes into force. The table contains the latest information on dates of signature and ratification from the Secretary-General of the United Nations, the depository of the Kyoto Protocol. The dates are those of the receipt of the instrument of ratification, acceptance, approval, or accession. As of November 2003, 84 parties had signed the Kyoto Protocol and 120 parties had ratified or accepted it.
Environmental strategies or action plans
3.14b
Participation in treaties a
Biodiversity assessments, strategies or action plans
3.14
Global atmospheric concentrations of chlorofluorocarbons have leveled off Parts per trillion Law
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
1993 1995 1993 1993 .. .. .. .. .. 1994 .. 1991 .. 1994 .. .. .. 1995 1995 .. .. 1989 .. .. .. .. 1988 1994 1991 .. 1988 1990 .. 2002 1995 .. 1994 .. 1992 1993 1994 1994 1994 .. 1990 .. .. 1994 1990 1992 .. .. 1989 1993 1995 ..
.. .. 1994 1993 .. .. .. .. .. .. .. .. .. 1992 .. .. .. .. .. .. .. .. .. .. .. .. 1991 .. 1988 1989 .. .. 1988 .. .. 1988 .. 1989 .. .. .. .. .. 1991 1992 1994 .. 1991 .. 1993 .. 1988 1989 1991 .. ..
Climate
Ozone
CFC
of the
Biological
Kyoto
change b
layer
control
Sea c
diversity b
Protocol
1996 1994 1994 1994 1996 .. 1994 1996 1994 1995 1994 1994 1995 1994 1995 1994 1995 2000 1995 1995 1995 1995 2003 1999 1995 1998 1999 1994 1994 1995 1994 1994 1994 1995 1994 1996 1995 1995 1995 1994 1994 1994 1996 1995 1994 1994 1995 1994 1995 1994 1994 1994 1994 1994 1994 ..
1993 1988 1991 1992 1990 .. 1988 1992 1988 1993 1988 1989 1998 1988 1995 1992 1992 2000 1998 1995 1993 1994 1996 1990 1995 1994 1996 1991 1989 1994 1994 1992 1987 1996 1996 1995 1994 1993 1993 1994 1988 1987 1993 1992 1988 1986 1999 1992 1989 1992 1992 1989 1991 1990 1988 ..
1993 1989 1992 1992 1990 .. 1988 1992 1988 1993 1988 1989 1998 1988 1995 1992 1992 2000 1998 1995 1993 1994 1996 1990 1995 1994 1996 1991 1989 1994 1994 1992 1988 1996 1996 1995 1994 1993 1993 1994 1988 1988 1993 1992 1988 1988 1999 1992 1989 1992 1992 1993 1991 1990 1988 ..
1994 2002 1995 1994 .. 1994 1996 .. 1995 1994 1996 1995 f .. 1994 .. 1996 1994 .. 1998 .. 1995 .. .. .. 2003 f 1994 g 2001 .. 1997 1994 1996 1994 1994 .. 1997 .. 1997 1996 1994 1998 1996 1996 2000 .. 1994 1996 1994 1997 1996 1997 1994 .. 1994 1998 1997 ..
1995 1994 1994 1994 1996 .. 1996 1995 1994 1995 1993 d 1994 1994 1994 1995 e 1995 2002 1996 e 1996 e 1996 1995 1995 2000 2001 1996 1997 f 1996 1994 1994 1995 1996 1993 1993 1996 1993 1995 1995 1995 1997 1994 1994 d 1993 1996 1995 1994 1993 1995 1994 1995 1993 1994 1993 1994 1996 1994 ..
2000 2002 f 2002 f .. .. .. 2002 .. 2002 1999 f 2002 d 2003 f .. .. .. 2002 .. 2003 f 2003 f 2002 .. 2000 f 2002 f .. 2003 .. 2003 f 2001 f 2002 2002 .. 2001 f 2000 2003 f 1999 f 2002 f .. 2003 f 2003 f .. 2002 f 2002 1999 .. .. 2002 .. .. 1999 2002 1999 2002 2003 2002 2002 e ..
600 Chlorofluorocarbon-12 500
400
300
Chlorofluorocarbon-11
200
Chlorofluorocarbon-113
100
0 1980
1985
1990
1995
2001
Note: Chlorofluorocarbon-11, chlorofluorocarbon-12, and chlorofluorocarbon-113 are potent depletors of stratospheric ozone. Source: World Resources Institute and others 2002.
2004 World Development Indicators
167
ENVIRONMENT
Government commitment
3.14
Government commitment Environmental strategies or action plans
3.14c
Participation in treaties a
Biodiversity assessments, strategies or action plans
Global focus on biodiversity and climate change
Law
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
1995 1999 1991 .. 1984 .. 1994 1993 .. 1994 .. 1993 .. 1994 .. .. .. .. 1999 .. 1994 .. 1991 .. 1994 1998 .. 1994 1999 .. 1995 1995 .. .. .. .. .. 1996 1994 1987
.. 1994 .. .. 1991 .. .. 1995 .. .. .. .. .. 1991 .. .. .. .. .. .. 1988 .. .. .. 1988 .. .. 1988 .. .. 1994 1995 .. .. .. 1993 .. 1992 .. ..
Climate
Ozone
CFC
of the
Biological
Kyoto
change b
layer
control
Sea c
diversity b
Protocol
1994 1995 1998 1995 1995 2001 1995 1997 1994 1996 .. 1997 1994 1994 1994 1997 1994 1994 1996 1998 1996 1995 1995 1994 1994 .. 1995 1994 1997 1996 1994 1994 1994 1994 1995 1995 .. 1996 1994 1994
1993 1986 2001 1993 1993 1992 2001 1989 1993 1992 2001 1990 1988 1989 1993 1992 1986 1987 1989 1996 1993 1989 1991 1989 1989 1991 1993 1988 1986 1989 1987 1986 1989 1993 1988 1994 .. 1996 1990 1992
1993 1988 2001 1993 1993 1992 2001 1989 1993 1992 2001 1990 1988 1989 1993 1992 1988 1988 1989 1998 1993 1989 1991 1989 1989 1991 1993 1988 1988 1989 1988 1988 1991 1993 1989 1994 .. 1996 1990 1992
1997 1997 .. 1996 1994 2001 g 1995 1994 1996 1994 g 1994 1997 1997 1994 1994 .. 1996 .. .. .. 1994 .. 1994 1994 f 1994 .. .. 1994 1999 .. 1997 f .. 1994 .. .. 1994 .. 1994 1994 1994
1994 1995 1996 2001 e 1995 2002 1995 e 1996 1994 e 1996 .. 2000 1994 1994 1996 1995 1994 1995 1996 1997 e 1996 .. 1996 d 1996 1993 1997 1996 e 1993 1995 2000 1994 .. 1994 1995 e 1994 1995 .. 1996 1993 1995
2001 .. .. .. 2001 f .. .. .. 2002 2002 .. 2002 f 2002 2002 f .. .. 2002 2003 .. .. 2002 f 2002 .. 1999 2003 f .. 1999 2002 f .. .. 2002 .. 2001 1999 .. 2002 .. .. .. ..
a. Ratification of the treaty. b. The years shown refer to the year the treaty entered into force in that country. c. Convention became effective November 16, 1994. d. Acceptance. e. Approval. f. Accession. g. Succession.
168
2004 World Development Indicators
Allocation of funds for Global Environment Facility programs, February 1991–January 2004 Total allocation: $19,944 million
By region Regional projects 1%
Global projects 12% Europe & Central Asia 15%
Asia 38% Africa 19%
Latin America & Caribbean 20%
By focal area Multiple areas 6% International waters 11%
Biodiversity 26%
Ozone depletion 2% Persistent organic pollutants 1%
Climate change 54%
Source: Global Environment Facility data.
About the data
3.14
ENVIRONMENT
Government commitment Definitions
National environmental strategies and participation
and Development (the Earth Summit) in Rio de
• Environmental strategies and action plans provide
in international treaties on environmental issues pro-
Janeiro, which produced Agenda 21—an array of
a comprehensive, cross-sectoral analysis of conserva-
vide some evidence of government commitment to
actions to address environmental challenges:
tion and resource management issues to help inte-
sound environmental management. But the signing
• The Framework Convention on Climate Change
grate environmental concerns with the development
of these treaties does not always imply ratification,
aims to stabilize atmospheric concentrations of
process. They include national conservation strate-
nor does it guarantee that governments will comply
greenhouse gases at levels that will prevent
gies, national environmental action plans, national
with treaty obligations.
human activities from interfering dangerously
environmental management strategies, and national
with the global climate.
sustainable development strategies. The year shown
In many countries efforts to halt environmental degradation have failed, primarily because govern-
• The Vienna Convention for the Protection of the
ments have neglected to make this issue a priority, a
Ozone Layer aims to protect human health and
action plan was adopted. • Biodiversity assessments,
reflection of competing claims on scarce resources.
the environment by promoting research on the
strategies, and action plans include biodiversity pro-
To address this problem, many countries are prepar-
effects of changes in the ozone layer and on
files (see About the data). • Participation in treaties
ing national environmental strategies—some focus-
alternative substances (such as substitutes for
covers five international treaties (see About the data).
ing narrowly on environmental issues, and others
chlorofluorocarbons) and technologies, monitor-
• Climate change refers to the Framework Convention
integrating environmental, economic, and social con-
ing the ozone layer, and taking measures to con-
on Climate Change (signed in New York in 1992).
cerns. Among such initiatives are conservation
trol the activities that produce adverse effects.
• Ozone layer refers to the Vienna Convention for the
strategies and environmental action plans. Some
• The Montreal Protocol for CFC Control requires
Protection of the Ozone Layer (signed in 1985). • CFC
countries have also prepared country environmental
that countries help protect the earth from exces-
control
profiles and biodiversity strategies and profiles.
sive ultraviolet radiation by cutting chlorofluoro-
Chlorofluorocarbon Control (formally, the Protocol on
for a country refers to the year in which a strategy or
refers
to
the
Montreal
Protocol
for
National conservation strategies—promoted by the
carbon consumption by 20 percent over their
Substances That Deplete the Ozone Layer, signed in
World Conservation Union (IUCN)—provide a compre-
1986 level by 1994 and by 50 percent over their
1987). • Law of the Sea refers to the United Nations
hensive, cross-sectoral analysis of conservation and
1986 level by 1999, with allowances for increas-
Convention on the Law of the Sea (signed in Montego
resource management issues to help integrate envi-
es in consumption by developing countries.
Bay, Jamaica, in 1982). • Biological diversity refers to
ronmental concerns with the development process.
• The United Nations Convention on the Law of the
the Convention on Biological Diversity (signed at the
Such strategies discuss current and future needs,
Sea, which became effective in November 1994,
Earth Summit in Rio de Janeiro in 1992). The year
institutional capabilities, prevailing technical condi-
establishes a comprehensive legal regime for
shown for a country refers to the year in which a treaty
tions, and the status of natural resources in a country.
seas and oceans, establishes rules for environ-
entered into force in that country. • Kyoto Protocol
National environmental action plans, supported by
mental standards and enforcement provisions,
refers to the protocol on climate change adopted at
the World Bank and other development agencies,
and develops international rules and national leg-
the third conference of the parties to the United
describe a country’s main environmental concerns,
islation to prevent and control marine pollution.
Nations Framework Convention on Climate Change,
identify the principal causes of environmental prob-
• The Convention on Biological Diversity promotes
held in Kyoto, Japan, in December 1997 (for more
lems, and formulate policies and actions to deal with
conser vation of biodiversity among nations
them (table 3.14a). These plans are a continuing
through scientific and technological cooperation,
process in which governments develop comprehen-
access to financial and genetic resources, and
sive environmental policies, recommend specific
details see box 3.14a).
transfer of ecologically sound technologies.
actions, and outline the investment strategies, legis-
But 10 years after Rio the World Summit on
lation, and institutional arrangements required to
Sustainable Development recognized that many of
implement them.
the proposed actions have yet to materialize. To help
Biodiversity profiles—prepared by the World
developing countries comply with their obligations
Conservation Monitoring Centre and the IUCN—pro-
under these agreements, the Global Environment
vide basic background on species diversity, protect-
Facility (GEF) was created to focus on global improve-
ed areas, major ecosystems and habitat types, and
ment in biodiversity, climate change, international
legislative and administrative support. In an effort to
waters, and ozone layer depletion. The UNEP, United
Data sources
establish a scientific baseline for measuring
Nations Development Programme (UNDP), and the
The data are from the Secretariat of the United
progress in biodiversity conservation, the United
World Bank manage the GEF according to the policies
Nations Framework Convention on Climate
Nations Environment Programme (UNEP) coordinates
of its governing body of country representatives. The
Change, the Ozone Secretariat of the UNEP, the
global biodiversity assessments.
World Bank is responsible for the GEF Trust Fund and
World Resources Institute, the UNEP, the U.S.
is chair of the GEF.
National Aeronautics and Space Administration’s
To address global issues, many governments have also signed international treaties and agreements
Socioeconomic Data and Applications Center, and
launched in the wake of the 1972 United Nations
Center for International Earth Science Information
Conference on Human Environment in Stockholm and
Network.
the 1992 United Nations Conference on Environment
2004 World Development Indicators
169
3.15 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
170
Toward a broader measure of savings Gross national savings a
Consumption of fixed capital
Net national savings
Education expenditure
Energy depletion
Mineral depletion
Net forest depletion
Carbon dioxide damage
Particulate emissions damage
Adjusted net savings
% of
% of
% of
% of
% of
% of
% of
% of
% of
% of
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
.. 13.8 .. 23.3 22.3 13.9 19.7 21.4 21.5 28.5 18.6 23.4 9.2 12.2 7.5 .. 19.7 15.4 8.0 11.0 18.5 .. 23.2 .. .. 24.5 43.7 32.1 13.7 .. 34.6 15.1 20.5 21.2 .. 23.0 23.4 20.4 .. 15.1 14.2 21.7 20.2 15.4 27.0 21.1 40.7 .. 15.0 20.4 20.4 19.7 14.8 17.1 .. ..
.. 9.1 11.1 10.4 11.1 8.5 16.2 14.4 15.0 5.8 9.3 14.5 8.1 9.1 8.5 11.8 10.8 10.3 6.7 6.4 7.3 8.9 13.0 7.8 7.2 10.0 9.0 12.9 10.5 6.8 12.5 5.9 9.2 11.5 .. 12.2 15.4 5.3 10.6 9.5 10.4 5.2 14.2 6.1 16.1 12.4 12.9 8.4 15.7 14.8 7.1 8.7 10.1 8.1 7.7 1.9
.. 4.7 .. 12.9 11.2 5.3 3.5 7.0 6.5 22.7 9.3 8.9 1.1 3.1 –1.0 .. 8.9 5.1 1.3 4.6 11.2 .. 10.2 .. .. 14.4 34.7 19.2 3.2 .. 22.2 9.2 11.3 9.7 .. 10.7 8.0 15.1 .. 5.6 3.8 16.6 6.0 9.3 11.0 8.7 27.8 .. –0.7 5.5 13.3 11.0 4.7 9.0 .. ..
.. 2.8 4.5 4.4 3.2 1.8 5.2 5.0 3.0 1.3 5.4 3.0 2.7 4.8 .. 5.6 4.7 3.0 2.4 3.9 1.8 2.3 6.9 1.6 1.4 3.4 2.0 2.8 3.1 0.9 5.9 5.2 4.5 .. 6.1 4.4 7.7 1.7 3.2 4.4 2.2 1.4 6.3 4.0 7.0 5.6 2.2 3.4 4.3 4.4 2.8 3.1 1.6 2.0 .. 1.5
.. 1.0 33.4 36.3 5.4 0.0 1.2 0.1 38.7 0.8 2.2 0.0 0.1 5.9 0.1 0.0 2.9 0.2 0.0 0.0 0.0 6.2 4.0 0.0 0.0 0.2 2.7 0.0 6.5 1.8 47.4 0.0 0.0 0.6 .. 0.1 0.3 0.0 13.8 4.6 0.0 0.0 0.5 0.0 0.0 0.0 27.8 0.0 0.5 0.1 0.0 0.1 0.7 0.0 0.0 0.0
.. 0.0 0.1 0.0 0.2 0.1 1.4 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.2 1.1 0.4 0.0 0.1 0.0 0.0 0.1 0.0 0.0 4.7 0.2 0.0 0.2 0.0 0.2 0.0 0.0 0.0 .. 0.0 0.0 0.3 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 1.7 0.0 0.0
.. 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0 1.3 0.0 0.0 0.0 0.0 0.0 1.2 10.4 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.6 0.0 .. 0.0 0.0 0.0 0.0 0.2 0.6 0.0 0.0 12.8 0.0 0.0 0.0 0.6 0.0 0.0 2.7 0.0 0.9 1.9 0.0 0.9
.. 0.3 1.3 0.5 0.8 1.1 0.6 0.2 5.2 0.4 3.4 0.3 0.4 1.1 2.4 0.6 0.5 2.2 0.3 0.2 0.1 0.6 0.5 0.2 0.1 0.7 2.2 0.2 0.5 0.3 0.8 0.2 0.5 0.6 .. 1.2 0.2 0.9 1.2 1.0 0.3 0.5 2.2 0.6 0.3 0.2 0.6 0.5 1.0 0.3 0.8 0.5 0.3 0.3 0.7 0.3
.. 0.1 0.7 .. 1.6 2.0 0.1 0.2 1.0 0.3 0.0 0.2 0.3 0.7 0.4 .. 0.2 2.1 0.5 0.1 0.1 0.7 0.2 0.4 .. 1.0 1.0 0.0 0.1 0.0 .. 0.3 0.6 0.3 .. 0.1 0.1 0.2 0.1 1.4 0.2 0.5 0.2 0.3 0.1 0.0 0.1 0.7 2.5 0.1 0.2 0.7 0.2 0.6 0.9 0.2
.. 6.1 .. –19.6 b 6.5 4.0 5.4 11.5 –35.3 21.7 9.2 11.4 1.8 –0.5 .. .. 9.0 3.3 1.8 –2.3 11.9 .. 12.3 .. .. 11.2 30.7 21.8 –1.0 .. .. 13.5 14.1 .. .. 13.8 15.0 15.4 .. 2.7 4.9 16.9 9.4 –0.5 17.5 14.0 1.5 .. –0.5 9.5 11.3 12.8 4.2 6.5 .. ..
2004 World Development Indicators
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
3.15
Gross national savings a
Consumption of fixed capital
Net national savings
Education expenditure
Energy depletion
Mineral depletion
Net forest depletion
Carbon dioxide damage
Particulate emissions damage
Adjusted net savings
% of
% of
% of
% of
% of
% of
% of
% of
% of
% of
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
23.3 23.5 22.3 18.2 38.9 .. 28.0 13.4 19.8 20.7 27.2 26.2 25.5 13.7 .. 27.3 19.4 17.4 .. 19.6 2.1 22.0 .. .. 17.4 12.9 8.5 0.8 34.5 3.2 .. 27.7 18.3 14.4 26.7 26.1 27.7 12.4 39.6 22.1 22.2 19.4 11.2 .. 13.1 32.0 .. 25.6 24.2 .. 14.2 17.2 24.5 16.6 19.3 ..
5.6 11.8 9.7 5.4 9.7 .. 12.5 13.2 13.6 11.6 15.8 10.6 10.0 7.9 .. 12.0 6.7 8.2 8.0 10.8 10.3 6.5 8.2 .. 10.1 9.8 7.9 7.0 11.7 8.4 8.1 10.8 10.5 7.3 12.1 10.0 8.3 .. 11.5 2.4 15.0 10.8 .. 7.1 8.3 15.9 11.5 7.7 7.6 8.6 9.0 10.4 7.9 11.2 15.3 7.4
17.7 11.7 12.6 12.7 29.2 .. 15.5 0.2 6.1 9.1 11.4 15.6 15.5 5.7 .. 15.3 12.7 9.2 .. 8.9 –8.2 15.4 .. .. 7.2 3.1 0.6 –6.3 22.8 –5.2 .. 16.9 7.8 7.2 14.6 16.1 19.4 .. 28.1 19.8 7.2 8.6 .. .. 4.8 16.1 .. 17.9 16.6 .. 5.2 6.7 16.5 5.4 4.0 ..
3.5 4.3 3.2 0.6 3.5 .. 5.7 6.9 4.7 5.8 3.6 5.6 4.4 6.1 .. 3.7 5.0 5.1 1.8 6.1 2.5 6.4 .. .. 5.2 4.9 1.9 4.4 4.1 2.1 3.7 3.3 4.6 10.3 5.7 4.9 3.8 0.9 8.5 3.2 4.9 6.9 3.7 2.3 0.5 6.9 4.1 2.3 4.8 .. 3.9 2.6 2.9 7.5 5.3 ..
0.0 0.3 2.3 8.6 29.7 .. 0.0 0.0 0.1 0.0 0.0 0.0 33.4 0.0 .. 0.0 42.2 1.0 0.0 0.0 0.0 0.0 0.0 .. 0.3 0.0 0.0 0.0 8.9 0.0 0.0 0.0 4.9 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.1 0.9 0.0 0.0 38.7 4.2 40.3 3.6 0.0 10.0 0.0 0.9 0.0 0.3 0.0 0.0
0.2 0.0 0.3 1.2 0.2 .. 0.1 0.1 0.0 1.2 0.0 1.1 0.0 0.0 .. 0.0 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 20.5 0.0 0.1 0.0 2.3 0.2 0.0 .. 0.4 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 4.2 0.0 1.4 0.1 0.1 0.0 0.0
0.0 0.0 1.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 .. 0.0 0.0 0.0 0.0 0.0 0.0 2.5 3.3 .. 0.0 0.0 0.0 1.4 0.0 0.0 0.9 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 4.2 0.0 0.0 0.9 3.6 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
0.5 0.7 1.7 0.9 2.1 .. 0.4 0.4 0.2 0.9 0.2 1.2 4.4 0.4 .. 0.7 0.8 2.6 0.2 0.7 0.6 0.0 1.1 .. 0.9 2.0 0.3 0.3 1.0 0.2 2.4 0.4 0.5 3.3 4.8 0.7 0.3 .. 0.4 0.4 0.3 0.4 0.6 0.4 0.5 0.3 0.6 1.2 0.6 0.5 0.5 0.3 0.7 1.3 0.3 0.2
0.2 0.4 0.7 0.5 0.7 .. 0.1 0.0 0.2 0.3 0.4 0.7 0.4 0.2 .. 0.8 2.0 0.2 0.2 0.3 0.6 0.4 0.0 .. 0.7 0.3 0.2 0.2 0.1 0.5 .. .. 0.5 0.5 0.5 0.2 0.4 .. 0.2 0.1 0.4 0.0 0.0 0.4 0.8 0.1 .. 1.0 0.3 0.0 0.4 0.6 0.4 0.7 0.4 ..
20.4 14.6 9.8 2.1 0.1 .. 20.6 6.7 10.3 12.4 14.4 18.2 –18.3 11.0 .. 17.5 –27.3 10.5 .. 13.9 –6.8 18.9 .. .. 10.5 5.8 2.0 –3.7 16.8 –3.8 .. .. 6.5 13.6 12.7 19.9 22.4 .. 35.6 18.2 11.3 14.2 .. .. –34.7 18.4 .. 13.4 20.4 .. 8.3 6.0 18.0 10.5 8.6 ..
2004 World Development Indicators
171
ENVIRONMENT
Toward a broader measure of savings
3.15 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Toward a broader measure of savings Gross national savings a
Consumption of fixed capital
Net national savings
Education expenditure
Energy depletion
Mineral depletion
Net forest depletion
Carbon dioxide damage
Particulate emissions damage
Adjusted net savings
% of
% of
% of
% of
% of
% of
% of
% of
% of
% of
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
GNI
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
19.9 30.6 12.2 28.9 11.5 .. .. 42.7 23.2 25.2 .. 16.5 24.0 19.9 13.1 7.2 21.4 26.8 24.3 5.0 14.5 30.4 8.0 28.9 22.7 16.7 36.3 15.8 27.1 .. 14.4 14.4 13.5 17.2 26.6 33.6 .. 24.1 .. .. 19.5 w 21.5 27.7 30.8 21.4 26.6 38.8 22.7 19.3 23.4 23.1 15.9 17.4 21.1
9.8 10.4 7.3 10.0 8.4 9.6 7.1 14.4 11.2 11.7 .. 13.3 12.8 5.1 8.3 8.9 13.4 14.9 10.4 7.4 7.5 14.9 7.8 11.9 10.1 7.0 9.7 7.6 19.0 .. 11.3 11.8 11.1 9.9 7.4 8.1 7.3 9.5 8.2 9.0 12.5 w 8.4 10.1 9.9 10.6 9.8 9.2 10.5 10.3 10.0 9.0 10.2 13.1 13.8
10.0 20.1 4.9 18.9 3.2 .. .. 28.3 12.0 13.5 .. 3.2 11.1 14.8 4.8 –1.7 8.0 12.0 13.9 –2.5 7.0 15.5 0.2 17.0 12.6 9.8 26.6 8.2 8.1 .. 3.1 2.6 2.4 7.3 19.2 25.4 .. 14.6 .. .. 7.0 w 13.1 17.6 20.9 10.8 16.8 29.6 12.2 9.0 13.4 14.0 5.8 4.3 7.4
3.6 3.6 3.5 7.2 3.7 .. 0.9 2.3 4.6 5.3 .. 7.6 4.6 2.9 0.9 5.1 8.3 4.9 2.6 2.0 2.4 3.6 4.2 3.3 6.6 2.2 .. 1.9 6.4 .. 5.3 5.4 3.0 9.4 4.3 2.8 .. .. 2.0 6.9 4.7 w 2.6 3.8 3.2 5.0 3.6 2.2 4.8 4.2 5.2 2.9 5.1 5.0 4.8
2.3 25.5 0.0 42.2 0.0 0.9 0.0 0.0 0.0 0.0 .. 1.6 0.0 0.0 0.0 0.0 0.1 0.0 27.5 0.4 0.0 0.8 0.0 21.9 3.6 0.3 53.6 0.0 7.6 .. 0.6 0.9 0.0 51.7 27.0 6.7 0.0 36.0 0.0 0.3 1.9 w 5.9 7.7 6.6 9.7 7.4 3.4 9.7 5.2 26.3 2.2 8.1 0.7 0.0
0.0 0.3 0.0 0.0 0.2 0.1 0.0 0.0 0.0 0.0 .. 1.2 0.0 0.0 0.1 0.0 0.1 0.0 0.1 0.0 0.4 0.0 0.6 0.0 0.5 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 1.1 0.3 0.1 w 0.4 0.3 0.3 0.2 0.3 0.3 0.1 0.6 0.1 0.3 0.5 0.0 0.0
0.0 0.0 3.9 0.0 0.3 0.0 5.2 0.0 0.0 0.0 .. 0.3 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 3.9 0.0 0.1 0.0 0.0 5.6 0.0 .. 0.0 0.0 0.3 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 w 0.8 0.0 0.1 0.0 0.2 0.1 .. 0.0 0.0 1.0 0.7 .. ..
1.4 3.1 0.3 0.8 0.6 1.7 0.4 0.6 1.2 0.5 .. 2.3 0.3 0.3 0.2 0.2 0.1 0.1 1.7 3.8 0.3 1.1 1.0 1.9 0.7 0.7 4.8 0.2 6.3 .. 0.2 0.4 0.3 10.9 1.0 1.0 0.0 1.1 0.4 1.1 0.5 w 1.3 1.4 1.7 0.7 1.4 1.8 2.1 0.5 1.3 1.5 1.1 0.3 0.3
a. The cutoff date for these data is February 2004; later revisions are not captured in this table. b. Adjusted net savings do not include particulate emission damage.
172
2004 World Development Indicators
0.2 0.6 0.0 1.0 .. 0.2 0.4 0.4 0.1 0.2 .. 0.2 0.4 0.3 0.6 0.1 0.0 0.2 0.8 0.2 0.2 0.4 0.3 0.0 0.3 1.2 0.3 0.0 1.0 .. 0.1 0.3 1.9 0.6 0.0 0.4 .. 0.5 .. 0.5 0.3 w 0.6 0.7 0.7 0.6 0.6 0.8 0.6 0.5 0.9 0.7 0.4 0.3 0.2
9.7 –5.7 4.2 –17.9 .. .. .. 29.6 15.2 18.2 .. 5.2 15.0 16.3 4.8 3.1 16.0 16.6 –13.6 –4.8 8.5 16.5 –1.4 –3.4 13.9 9.8 .. 4.3 –0.5 .. 7.5 6.4 3.0 –46.6 –4.8 19.3 .. .. .. .. 8.8 w 6.7 11.3 14.6 4.5 10.5 25.5 .. 6.3 –10.0 11.3 0.0 .. ..
About the data
3.15
Definitions
Adjusted net savings measure the change in value of
return on capital). Unit rents are then multiplied by
• Gross national savings are calculated as the dif-
a specified set of assets, excluding capital gains. If
the physical quantity extracted or harvested in order
ference between gross national income and public
a country’s net savings are positive and the account-
to arrive at a depletion figure. This figure is one of a
and private consumption, plus net current transfers.
ing includes a sufficiently broad range of assets,
range of depletion estimates that are possible,
• Consumption of fixed capital represents the
economic theory suggests that the present value of
depending on the assumptions made about future
replacement value of capital used up in the process
social welfare is increasing. Conversely, persistently
quantities, prices, and costs, and there is reason to
of production. • Net national savings are equal to
negative adjusted net savings indicate that an econ-
believe that it is at the high end of the range. Some
gross national savings less the value of consumption
omy is on an unsustainable path.
of the largest depletion estimates in the table should
of fixed capital. • Education expenditure refers to
therefore be viewed with caution.
public current operating expenditures in education,
Adjusted net savings are derived from standard national accounting measures of gross national sav-
A positive net depletion figure for forest resources
ings by making four adjustments. First, estimates of
implies that the harvest rate exceeds the rate of nat-
investments in buildings and equipment. • Energy
capital consumption of produced assets are deduct-
ural growth; this is not the same as deforestation,
depletion is equal to the product of unit resource
ed to obtain net national savings. Second, current
which represents a change in land use (see
rents and the physical quantities of energy extracted.
expenditures on education are added to net national
Definitions for table 3.4). In principle, there should
It covers coal, crude oil, and natural gas. • Mineral
savings (in standard national accounting these
be an addition to savings in countries where growth
depletion is equal to the product of unit resource
expenditures are treated as consumption). Third,
exceeds harvest, but empirical estimates suggest
rents and the physical quantities of minerals extract-
estimates of the depletion of a variety of natural
that most of this net growth is in forested areas that
ed. It refers to tin, gold, lead, zinc, iron, copper, nick-
resources are deducted to reflect the decline in
cannot be exploited economically at present.
el, silver, bauxite, and phosphate. • Net forest
asset values associated with their extraction and
Because the depletion estimates reflect only timber
depletion is calculated as the product of unit
harvest. And fourth, deductions are made for dam-
values, they ignore all the external and nontimber
resource rents and the excess of roundwood harvest
age from carbon dioxide and particulate emissions.
benefits associated with standing forests.
over natural growth. • Carbon dioxide emissions
including wages and salaries and excluding capital
The exercise treats education expenditures as an
Pollution damage from emissions of carbon dioxide
damage is estimated to be $20 per ton of carbon (the
addition to savings effort. But because of the wide
is calculated as the marginal social cost per unit mul-
unit damage in 1995 U.S. dollars) times the number
variability in the effectiveness of government educa-
tiplied by the increase in the stock of carbon dioxide.
of tons of carbon emitted. • Particulate emissions
tion expenditures, these figures cannot be con-
The unit damage figure represents the present value
damage is calculated as the willingness to pay to
strued as the value of investments in human
of global damage to economic assets and to human
avoid mortality attributable to particulate emissions.
capital. The accounting for human capital is also
welfare over the time the unit of pollution remains in
• Adjusted net savings are equal to net national sav-
incomplete because depreciation of human capital
the atmosphere.
ings plus education expenditure and minus energy
is not estimated.
Pollution damage from particulate emissions is
depletion, mineral depletion, net forest depletion, and
There are also gaps in the accounting of natural
estimated by valuing the human health effects from
resource depletion and pollution costs. Key esti-
exposure to particulate matter less than 10 microns
mates missing on the resource side include the
in diameter. The estimates are calculated as willing-
Data sources
value of fossil water extracted from aquifers, net
ness to pay to avoid mortality attributable to partic-
Gross national savings are derived from the World
depletion of fish stocks, and depletion and degrada-
ulate emissions (in particular, mortality relating to
Bank’s national accounts data files, described in
tion of soils. Important pollutants affecting human
cardiopulmonary disease in adults, lung cancer in
the Economy section. Consumption of fixed capi-
health and economic assets are excluded because
adults, and acute respiratory infections in children).
tal is from the United Nations Statistics Division’s
carbon dioxide and particulate emissions damage.
no internationally comparable data are widely avail-
National Accounts Statistics: Main Aggregates
able on damage from ground-level ozone or from sul-
and Detailed Tables, 1997, extrapolated to 2002.
fur oxides.
The education expenditure data are from the
Estimates of resource depletion are based on the
United Nations Statistics Division’s Statistical
calculation of unit resource rents. An economic rent
Yearbook 1997, extrapolated to 2002. The wide
represents an excess return to a given factor of
range of data sources and estimation methods
production—in this case the returns from resource
used to arrive at resource depletion estimates
extraction or harvest are higher than the normal rate
are described in a World Bank working paper,
of return on capital. Natural resources give rise to
“Estimating National Wealth” (Kunte and others
rents because they are not produced; in contrast, for
1998). The unit damage figure for carbon dioxide
produced goods and services competitive forces will
emissions is from Fankhauser (1995). The esti-
expand supply until economic profits are driven to
mates of damage from particulate emissions are
zero. For each type of resource and each country,
from Pandey and others (2003). The conceptual
unit resource rents are derived by taking the differ-
underpinnings of the savings measure appear in
ence between world prices and the average unit
Hamilton and Clemens (1999).
extraction or harvest costs (including a “normal”
2004 World Development Indicators
173
ENVIRONMENT
Toward a broader measure of savings
4 ECONOMY
I
n 2002 the world economy grew by 1.9 percent, a slight increase from 1.3 percent in
2001, but below the 2.7 percent annual average in the 1990s. The world’s recorded
output—and income—grew by more than $1.1 trillion. Lower-middle-income economies saw the fastest growth, followed by low-income economies. Upper-middle-income economies, affected by slowing investment and widespread uncertainty in financial markets, experienced negative growth. High-income economies, accounting for 81 percent of the world’s gross domestic product (GDP), almost doubled their growth over 2001, from 0.9 percent to 1.6 percent (figure 4a).
Over the past decade economic growth was fastest in East Asia and Pacific (averaging 7.3 percent a year) and South Asia (5.4 percent). Leading this growth were China and India, each accounting for more than 70 percent of its region’s output. These two regions even did comparatively well in 2002, with East Asia registering 6.7 percent growth—demonstrating its continuing recovery from the financial crisis in 1998, when annual growth fell to 0.7 percent—and South Asia recording 4.3 percent growth, a slight decline over 2001.
4a Output declined in the transition
Economic growth varies by region
economies of Europe and Central
Average annual growth (%) 8
Asia in the 1990s, but recovered
1980–90
in the early 2000s, averaging 3.5
1990–2000 6
percent
growth
for
2001
2001–02.
2002
Several countries of the former Soviet Union, such as Armenia,
4
Azerbaijan, Kazakhstan, Moldova, Tajikistan, have
and
been
Turkmenistan,
registering
2
growth
rates of more than 7 percent,
0
buoyed by increased exports of natural
gas
and
petroleum
products. But in Russia growth
–2 East Asia & Pacific
Europe & Central Asia
South Asia
SubMiddle East Saharan & North Africa Africa
Latin America & Caribbean
High income
declined from 5 percent in 2001 to 4.3 percent in 2002.
Note: No data are available for Europe and Central Asia for 1980–90. Source: World Bank data files.
2004 World Development Indicators
175
In Latin America and the Caribbean and the Middle East and North Africa growth was faster in the 1990s than in the 1980s. But in Latin America growth decelerated sharply in 2001 and turned negative in 2002, with Argentina, Uruguay, and Venezuela experiencing large declines in growth and with Mexico growing only 0.9 percent and Brazil only 1.5 percent. However, the heavily indebted poor countries, many in SubSaharan Africa, registered 4.1 percent growth in 2002, following 4.7 percent growth in 2001. As a result, Sub-Saharan Africa did better in 2001 and 2002 than in the 1990s, when growth declined sharply. With two decades of high growth, the East Asia and Pacific region has nearly reached the GDP level of the Latin America and Caribbean region (figure 4b). By contrast GDP in the Europe and Central Asia region, almost equal to that of Latin America and the Caribbean in 1990, is now only about half its size after half a decade of negative growth. Steady growth has also moved South Asia ahead of the Middle East and North Africa, but GDP per capita lags far behind in this populous region. Patterns of change Most developing economies are following familiar patterns of growth, with agriculture giving way first to manufacturing and later to services as the main source of income. But some, such as Jordan and Panama, have moved directly from agriculture to service-based economies. For most economies services have been the fastest growing sector. In 1990–2002 the service sector grew by 3.6 percent a year in developing and transition economies and by 3 percent in high-income economies. Among developing regions South Asia had the fastest growth in services in the 1990s, at 7 percent a year, and Europe and Central Asia the slowest, at 0.8 percent (table 4.1).
4b With two decades of rapid growth, East Asia and Pacific has caught up with Latin America and the Caribbean Gross domestic product (1995 $ billions) 2,000
Latin America & Caribbean East Asia & Pacific
1,500 Europe & Central Asia 1,000 South Asia Middle East & North Africa 500 Sub-Saharan Africa
0 1980
1985
1990
Source: World Bank data files.
176
2004 World Development Indicators
1995
2002
In developing economies ser vices generated more than half of GDP in 2002, compared with 71 percent in highincome economies (table 4.2). But in East Asia and Pacific ser vices produced only 38 percent of GDP in 2002, and from 1990 to 2002 growth in manufacturing, at 9.8 percent a year, outpaced growth in ser vices, at 6.4 percent. This trend reflects the rapid growth of manufacturing in China (11.9 percent a year), which also had rapid expansion in ser vices (8.8 percent a year). The contribution of trade After expanding by 6.7 percent a year in 1990–2001, global trade (exports plus imports) grew by only 3.7 percent in 2002. High-income economies, which account for more than 75 percent of global trade, grew by only 2.3 percent in 2002, recovering from a slowdown in 2001. But trade by low-income economies grew by 5.6 percent. Trade in ser vices has grown rapidly, but trade in merchandise—primary commodities and manufactured goods— continues to dominate. In 2002 merchandise accounted for 81 percent of all exports of goods and commercial services, and manufactured goods for 78 percent of merchandise exports (tables 4.5 and 4.7). Exporters of primary nonfuel commodities saw their trade volumes increase, but a continuing decline in their terms of trade left them with less income (table 4.4). The economies of Sub-Saharan Africa were hit particularly hard. The structure of trade in services is also changing. Transport services are being replaced in importance by travel services. In the 1990s high-income countries were the main exporters of financial services. Now, many developing countries are emerging as exporters of these new services along with computer, information, and business services. The share of low- and middle-income economies in these new service exports is increasing slowly, rising by 1.1 percentage points between 1990 and 2002 (table 4.7). Increased globalization has enabled greater labor mobility, and worker remittances have been steadily growing in countries like India, resulting in favorable current account balances and increased reserves. India has the ninth largest reserves, ahead of many high-income countries. Japan has the largest reserves, followed by China. The increase in the price of gold from $277 in 2001 to $343 in 2002 resulted in a considerable increase in the reserves of many countries (table 4.15). Steady trends in consumption, investment, and saving Most of the world’s output goes to final consumption by households (including individuals) and governments. The share of final consumption in world output has remained fairly constant over time, averaging about 80 percent in 1990–2002 (table 4.9). Growth of per capita household consumption expenditure provides an important indicator of the potential for reducing poverty. In 1990–2002 per capita consumption grew by 5.5 percent a year in East Asia and Pacific but rose by only 0.1 percent in Sub-Saharan Africa. It rose by 1.7 in Europe and Central Asia and by 2.7 percent in South Asia (table 4.10).
Output that is not consumed goes to exports (less imports) and gross capital formation (investment). Investment is financed out of domestic and foreign savings. In 2002 the global savings rate averaged 20 percent of total output. But global averages disguise large differences between countries. Savings rates are consistently lower in Sub-Saharan Africa. And they tend to be volatile in countries dependent on commodity exports. Gross domestic savings in the Middle East and North Africa rose from 23 percent of GDP in 1999 to 30 percent in 2000 and 29 percent in 2002, buoyed by higher oil prices. The highest savings rate was in East Asia and Pacific, where gross domestic savings averaged above 35 percent during most of the past decade and was 37 percent in 2002 (table 4.9). In 1990–2002 the rate of gross capital formation increased by about 6.9 percent a year in East Asia and Pacific and 6.5 percent in South Asia, but declined by 6.6 percent in Europe and Central Asia. East Asia and Pacific continued to have the highest investment rate in the world, at 32 percent of GDP in 2002. By contrast, investment averaged only 18 percent of GDP in Sub-Saharan Africa (tables 4.9 and 4.10). Fiscal affairs Developing countries have had larger overall central government deficits than high-income countries. But with the exception of East Asia and Pacific and Latin America and the Caribbean, deficits have been falling. The South Asia region has the largest deficit among the developing regions. Central governments of developing economies had expenditures averaging 21 percent of GDP in 1999 and revenues (mainly from taxes on goods and services) averaging 17 percent of GDP, leaving a fiscal deficit of about 3 percent of GDP after taking grants into account (table 4.11). Government expenditures go mostly to the purchase of goods and services (including the wages and salaries of public employees) and to subsidies and current transfers to private and public enterprises and local governments. The rest go to interest payments and capital expenditures. In 2000 subsidies and current transfers accounted for 59 percent of government spending in high-income economies and 51 percent in Europe and Central Asia, but only 14 percent in the Middle East and North Africa (table 4.12). The sources of government revenue have been changing. Taxes on income, profits, and capital gains generated 23 percent of current revenues in 1990, but their share fell to 18 percent in 2000, whereas taxes on goods and services rose from 27 percent to 34 percent. High-income economies depended more on income taxes (26 percent) than did low- and middleincome economies, which derived 35 percent of their revenue from taxes on goods and services and 9 percent from taxes on trade (table 4.13). Governments, because of their size, have a large effect on economic performance. High taxes and subsidies can distort economic behavior, and large fiscal deficits make it harder to manage the growth of the money supply and thus increase the likelihood of inflation. As governments have adopted policies
leading to greater fiscal stability, inflation rates and interest rates have tended to decline (table 4.14). External debt increases In 2002 the external debt of low- and middle-income economies increased by $74 billion, or about 3 percent of their total debt stock, reversing the decline in 2001. Middle-income economies accounted for 75 percent of the increase. The increase was $47 billion in Europe and Central Asia, $12 billion in South Asia, $11 billion in the Middle East and North Africa, and $8 billion in Sub-Saharan Africa. By contrast, debt stocks fell by $2 billion in East Asia, and $1 billion in Latin America and the Caribbean (table 4.16). Debt management indicators are shown in table 4.17. Data on the economy The indicators in this section measure changes in the size and structure of the global economy and the varying effects of these changes on national economies. They include measures of macroeconomic performance (gross domestic product, consumption, investment, and international trade) and of stability (central government budgets, prices, the money supply, the balance of payments, and external debt). Other important economic indicators appear throughout the book, especially in the States and markets section (credit, investment, financial markets, tax policies, exchange rates) and the Global links section (trade and tariffs, foreign investment, and aid flows). Most of the indicators in this section remain the same as last year, with a few exceptions. Tables 4.7 and 4.8 now break out insurance and financial services and computer, information, and communications services. Balance of payments data (table 4.15) are presented in calendar years for all countries except Bhutan and Myanmar, which are still in fiscal years. Thus for countries whose data were previously reported in fiscal years, such as Egypt, India, and Pakistan, this year’s data will not be comparable with previous data. The switch from fiscal year to calendar year was made so that data will be consistent among countries and with the calendar year data in tables 4.5 and 4.6. In table 4.17 the gross national income (GNI) and export values used as denominators for calculating the ratio of the present value of debt are three-year averages instead of single year values. The switch, made to even out fluctuations in GNI and exports, is consistent with the methodology followed in other World Bank publications. Workers’ remittances are not included as part of exports. And because the level of public and publicly guaranteed debt is the primary concern of the Heavily Indebted Poor Countries (HIPC) Debt Initiative and of the Millennium Development Goals, public and publicly guaranteed debt service replaces total debt service as a ratio of GNI and as a ratio of exports, and multilateral debt service as a ratio of public and publicly guaranteed debt replaces public and publicly guaranteed debt service as a ratio of central government current revenue. The indicators dropped are still available on the World Development Indicators CD-ROM. 2004 World Development Indicators
177
4.a Recent economic performance Gross domestic product
Exports of goods and services
Imports of goods and services
annual
annual
annual
GDP deflator
Current account balance
Total reserves a months
% growth
Algeria Argentina Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Cameroon Chile China Colombia Costa Rica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ghana Guatemala Honduras India Indonesia Iran, Islamic Rep. Jamaica Jordan Kazakhstan Kenya Latvia Lesotho Lithuania
% growth
of import
% growth
% growth
% of GDP
2002
2003
2002
2003
2002
2003
2002
2003
2002
2003
4.1 –10.9 12.9 10.6 4.4 2.8 3.9 3.1 1.5 4.8 4.4 2.1 8.0 1.6 3.0 4.1 3.4 3.0 2.1 6.0 4.5 2.2 2.5 4.3 3.7 6.7 1.1 4.9 9.8 1.0 6.1 4.5 6.7
6.8 7.0 12.0 9.3 5.3 2.4 3.5 3.7 –0.2 4.7 4.2 4.0 8.2 2.5 5.0 –1.3 2.5 3.1 2.2 4.5 4.7 2.4 1.5 6.8 4.1 6.2 3.0 3.0 9.0 1.3 7.0 3.9 6.3
4.7 3.1 29.0 16.6 –2.3 12.4 5.3 –4.8 7.8 6.2 1.6 5.6 29.4 –4.4 5.1 13.0 0.9 –10.4 5.7 6.0 –1.7 –3.2 2.1 9.9 –1.2 2.5 –5.3 11.6 22.6 –18.5 6.3 43.0 19.4
6.8 3.7 40.2 8.1 –0.4 14.6 11.5 6.9 14.2 14.3 3.8 5.7 22.7 5.4 8.5 8.0 1.8 14.0 3.4 3.8 2.7 5.2 –2.5 6.9 4.0 2.5 .. 4.1 26.7 5.7 8.2 0.0 23.1
17.8 –50.1 14.2 49.8 –11.2 7.7 –1.9 3.8 –12.8 4.7 3.4 0.4 27.5 0.6 7.0 13.9 17.2 –10.8 0.5 10.2 –4.4 4.2 2.1 17.9 –8.3 16.1 3.6 0.7 4.3 –16.7 4.5 15.0 16.1
4.6 39.1 33.8 39.2 0.8 –7.1 10.6 6.9 –1.9 21.2 –2.6 7.1 31.0 3.3 4.5 –7.0 1.7 0.2 5.2 7.9 7.7 2.9 1.3 14.2 2.0 13.1 .. 6.6 42.4 8.8 5.1 0.7 21.9
1.0 30.6 2.3 0.7 3.2 2.7 2.1 5.5 8.5 3.9 0.7 2.6 –0.3 6.1 9.1 6.4 11.8 4.0 1.3 4.1 22.8 8.0 6.2 3.0 7.2 21.5 8.0 0.5 5.8 8.7 1.8 9.0 0.0
7.3 10.7 4.0 5.9 4.4 3.7 1.1 5.6 10.1 2.5 1.1 3.0 1.1 7.4 9.3 28.0 8.8 3.9 2.8 4.4 27.6 5.5 9.8 4.3 6.6 23.4 10.0 0.8 6.6 7.2 3.0 9.8 0.0
.. 9.4 –6.3 –12.6 1.6 –4.4 –38.2 .. –1.7 –4.4 .. –0.9 2.8 –2.0 –5.6 –4.0 –5.0 0.7 –2.7 –12.3 –0.5 –5.1 –4.1 0.9 4.3 .. –14.2 5.0 –2.8 .. –7.7 –15.1 –5.2
10.9 6.1 –7.1 –26.9 0.6 –0.9 –17.7 9.0 0.4 –7.0 –3.1 –1.4 1.1 –2.5 –5.9 4.5 –3.3 1.9 .. –12.9 –0.5 –4.3 –7.6 –0.3 3.9 –1.5 –11.6 4.4 –1.7 –1.4 –8.3 –12.2 –5.9
$ millions
coverage
2003
2003
.. 1,313 550 732 2,454 843 1,418 5,853 35,869 5,774 114 18,707 410,049 10,586 1,300 665 1,072 .. 1,607 1,386 811 2,667 1,492 78,222 36,246 23,706 1,037 3,940 4,852 1,564 .. 417 ..
.. 0.6 4.3 1.8 2.8 4.4 4.1 29.0 5.1 5.4 0.4 9.1 10.7 6.1 1.7 0.9 1.4 .. .. 2.4 2.4 4.3 4.5 8.8 7.0 7.6 2.2 6.5 4.0 4.1 .. 4.5 ..
continues on page 180
178
2004 World Development Indicators
ECONOMY
4.b Key macroeconomic indicators Nominal exchange rate
Real effective exchange rate
Money and quasi money
Gross domestic credit
annual
annual
1995 = 100
% growth
% growth
Real interest rate
Shortterm debt a
%
exports
local currency units per $ 2003
Algeria Argentina Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Cameroon Chile China Colombia Costa Rica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ghana Guatemala Honduras India Indonesia Iran, Islamic Rep. Jamaica Jordan Kazakhstan Kenya Latvia Lesotho Lithuania
72.6 2.9 566.0 4,923.0 58.8 7.8 1.5 4.4 2.9 1.5 519.4 599.4 8.3 2,780.8 418.5 37.3 1.0 6.2 8.8 12.4 8,753.9 8.0 17.7 45.6 8,465.0 8,272.1 60.5 0.7 144.2 76.1 0.5 6.6 2.8
% change
% of
2002
2003
2002
2003
2002
2003
2002
2003
2002
2003
2002
2.4 232.2 4.1 2.5 1.6 9.8 –16.0 –21.7 52.2 –15.1 –16.0 8.6 0.0 24.5 10.8 23.6 0.0 0.2 0.0 –15.6 15.3 –2.4 6.3 –0.3 –14.0 354.2 7.4 0.0 2.9 –1.9 –6.9 –28.8 –17.2
–8.9 –12.5 –3.2 0.6 1.5 4.5 –17.0 –18.7 –18.2 –17.8 –17.0 –15.9 0.0 –2.9 10.5 75.8 0.0 36.7 0.0 –16.9 5.8 3.0 4.9 –5.0 –5.3 4.0 19.2 0.0 –6.7 –1.2 –8.9 –23.1 –16.6
101.7 .. 95.9 .. .. 115.4 .. .. .. 135.6 102.1 90.7 121.4 90.4 109.4 112.1 113.8 .. .. .. 81.0 .. .. .. .. 198.1 .. .. .. .. .. 60.8 ..
91.1 .. 85.8 .. .. 97.7 .. .. .. 143.2 106.5 85.3 111.8 77.2 98.1 73.0 111.7 .. .. .. 81.6 .. .. .. .. 187.3 .. .. .. .. .. 68.6 ..
.. 19.7 34.0 14.6 13.3 –6.9 9.4 –1.1 23.0 12.2 15.9 –0.3 19.4 13.6 20.9 10.3 .. 12.6 –3.1 11.2 48.9 11.8 13.7 16.8 4.5 27.5 12.0 8.6 30.1 11.7 19.9 8.8 16.9
.. 26.8 13.7 30.8 14.7 6.4 8.0 .. 2.3 19.6 3.7 9.8 20.4 10.2 17.3 72.6 .. .. 2.0 10.9 .. 21.3 17.3 11.9 8.4 23.0 12.6 16.2 43.0 12.2 20.6 1.5 18.3
.. 97.1 –8.1 84.2 12.2 4.9 27.7 –55.8 21.5 27.4 4.4 6.5 29.3 14.2 26.5 22.6 .. 13.1 –1.4 27.6 22.8 16.1 7.0 16.0 5.4 29.4 30.0 6.2 30.2 9.2 38.3 120.5 22.3
.. –4.0 –15.0 27.1 6.7 3.4 21.3 .. 7.3 37.3 2.1 2.5 19.5 11.5 19.2 57.3 .. .. 10.5 28.7 .. 7.2 30.3 9.8 3.3 42.5 68.3 4.4 35.8 9.3 38.4 –67.9 44.1
7.4 16.2 18.5 16.5 12.4 17.5 10.4 9.9 50.1 5.3 17.2 5.0 5.6 9.7 15.8 18.4 2.9 9.4 .. 2.5 .. 8.2 15.5 8.2 11.0 .. 9.7 9.7 .. 9.0 6.1 6.7 6.9
8.2 7.6 13.9 16.7 5.7 9.7 7.1 5.9 44.9 1.9 14.9 0.4 1.7 5.9 4.1 3.9 4.9 –0.8 .. –1.6 .. 1.8 5.4 1.7 3.7 .. 4.3 4.7 .. .. –0.5 0.9 0.8
.. 46.6 1.6 2.9 5.3 21.5 3.2 0.5 31.2 9.6 .. 16.0 12.8 22.1 20.0 19.3 30.3 17.3 16.9 27.9 22.6 16.9 16.3 5.2 34.1 6.5 16.9 8.0 9.9 22.7 101.1 0.7 27.5
continues on page 181
2004 World Development Indicators
179
4.a Recent economic performance Gross domestic product
Exports of goods and services
Imports of goods and services
annual
annual
annual
GDP deflator
Current account balance
Total reserves a months
% growth
Macedonia, FYR Malawi Malaysia Mauritius Mexico Moldova Morocco Nicaragua Pakistan Panama Paraguay Peru Philippines Poland Romania Russian Federation Senegal Serbia and Montenegro Slovak Republic South Africa Sri Lanka Swaziland Syrian Arab Republic Thailand Trinidad and Tobago Tunisia Turkey Ukraine Uruguay Uzbekistan Venezuela, RB Zambia Zimbabwe
% growth
of import
% growth
% growth
2003
2002
2003
2002
2003
2002
2003
2002
2003
0.7 1.8 4.1 4.4 0.9 7.2 3.2 1.0 2.8 0.8 –2.3 4.9 4.4 1.0 4.3 4.3 1.1 4.0 4.4 3.0 4.0 1.6 2.7 5.3 2.7 1.7 7.8 4.8 –10.8 4.2 –8.9 3.3 –5.6
3.0 5.9 4.6 4.5 1.5 6.0 5.5 2.3 5.8 2.5 1.5 4.0 4.2 3.5 4.8 6.5 6.3 3.0 3.9 3.0 5.5 2.2 0.9 6.4 4.0 6.0 4.8 7.5 –1.0 1.0 –12.0 4.2 –13.6
–4.4 –3.8 3.6 9.4 1.4 14.6 6.3 –3.3 10.3 –4.2 –9.3 6.8 3.6 3.1 16.9 10.2 1.7 12.3 5.9 –1.4 5.6 1.6 2.1 10.9 –9.6 –2.1 4.8 9.1 –10.9 –8.8 –7.8 11.4 –0.8
9.3 –0.6 5.3 2.6 –0.3 11.8 0.6 –5.1 18.8 2.3 5.1 9.7 3.5 13.6 8.2 3.7 1.5 27.7 8.5 –0.9 5.5 –6.0 –17.8 6.8 10.2 4.0 4.2 5.0 10.0 2.8 –10.9 11.1 –10.0
10.7 17.6 6.2 5.2 1.6 13.9 5.6 –0.5 4.5 5.3 –15.0 2.4 4.7 –5.3 12.1 19.1 1.9 26.3 5.3 3.1 11.2 1.6 –2.4 11.3 2.5 –2.4 20.0 3.7 –28.3 –12.6 –26.7 3.5 –4.8
3.5 –16.6 10.6 4.1 –0.9 13.3 7.4 –7.1 20.2 8.1 6.3 8.8 6.2 8.3 6.4 2.7 –0.1 22.9 7.1 –0.7 7.9 –4.0 5.4 6.9 6.9 3.0 11.6 10.4 –3.0 –1.7 –37.5 4.1 –5.0
3.6 17.5 3.6 5.1 4.6 8.1 0.6 5.3 3.1 1.2 14.6 0.6 4.9 1.7 24.2 15.2 2.7 25.5 3.9 8.5 8.3 13.5 4.4 0.8 0.8 2.3 43.8 3.2 18.8 45.5 31.6 19.9 107.5
0.4 6.9 3.0 5.3 3.5 11.9 1.5 6.1 4.0 1.2 8.8 3.1 3.3 1.0 16.0 14.0 0.8 16.5 5.0 4.1 5.1 9.0 1.5 1.4 2.7 2.3 25.2 5.1 22.9 30.0 30.0 20.1 ..
–8.6 –10.6 7.6 5.7 –2.2 –6.4 4.1 –22.2 6.6 –1.3 5.3 –2.1 5.4 –2.6 –3.3 8.6 –9.5 –8.8 .. 0.3 –1.6 –3.8 .. 6.0 .. –3.5 –0.8 7.7 2.2 3.0 8.0 .. ..
–5.5 –12.5 8.3 3.3 –1.8 –8.2 0.7 –17.6 5.9 –3.8 0.3 –2.1 2.6 –0.1 –4.9 9.9 –6.6 –8.3 –5.6 0.5 –2.2 –6.3 0.1 9.6 5.9 –3.5 –3.2 6.5 2.7 6.7 8.6 –14.8 0.6
Note: Data for 2003 are the latest preliminary estimates and may differ from those in earlier World Bank publications. a. International reserves including gold valued at London gold price. Source: World Bank staff estimates.
180
% of GDP
2002
2004 World Development Indicators
$ millions
coverage
2003
2003
861 .. .. 1,151 52,705 258 .. 447 9,630 1,269 859 11,026 16,115 31,747 7,794 74,098 555 3,325 12,126 7,495 2,200 272 4,450 42,100 3,401 .. 36,832 6,874 1,486 1,743 15,844 245 ..
4.2 .. .. 5.1 3.1 1.9 .. 2.5 7.0 2.9 3.1 10.1 3.9 6.4 4.3 8.9 2.9 4.5 7.0 2.4 3.2 1.8 6.8 6.8 8.2 .. 5.8 3.4 5.8 6.9 12.2 1.5 ..
ECONOMY
4.b Key macroeconomic indicators Nominal exchange rate
Real effective exchange rate
Money and quasi money
Gross domestic credit
annual
annual
1995 = 100
% growth
% growth
Real interest rate
Shortterm debt a
%
exports
local currency units per $ 2003
Macedonia, FYR 52.2 Malawi 108.4 Malaysia 3.8 Mauritius 26.1 Mexico 11.2 Moldova 13.2 Morocco 8.7 Nicaragua 15.6 Pakistan 57.2 Panama 1.0 Paraguay 6,115.0 Peru 3.5 Philippines 55.6 Poland 3.7 Romania 32,595.0 Russian Federation 29.5 Senegal 519.4 Serbia and Montenegro .. Slovak Republic 33.0 South Africa 6.6 Sri Lanka 96.7 Swaziland 6.6 Syrian Arab Republic 11.2 Thailand 39.6 Trinidad and Tobago 6.3 Tunisia 1.2 Turkey 1,396,638.0 Ukraine 5.3 Uruguay 29.3 Uzbekistan .. Venezuela, RB 1,596.0 Zambia 4,770.7 Zimbabwe 826.4
% change
% of
2002
2003
2002
2003
2002
2003
2002
2003
2002
2003
2002
–15.3 29.5 0.0 –3.9 12.8 5.6 –12.1 6.0 –3.8 0.0 51.7 2.0 3.3 –3.7 6.0 5.5 –16.0 .. –17.4 –28.8 3.8 –28.8 0.0 –2.4 0.2 –9.1 13.3 0.6 84.2 .. 83.7 13.2 0.0
–16.0 34.6 0.0 –10.6 9.0 –4.4 –13.9 6.0 –2.3 0.0 –13.9 –1.5 4.7 –2.6 –2.7 –7.3 –17.0 .. –17.6 –23.1 0.0 –23.1 0.0 –8.3 –0.4 –9.4 –15.0 0.0 7.7 .. 13.9 10.1 1,401.7
72.6 115.0 91.3 .. .. 100.2 103.4 111.6 90.0 .. 75.6 .. 85.6 133.4 110.2 109.0 .. .. 105.8 62.6 .. .. .. .. 126.6 96.2 .. 112.4 87.9 .. 132.9 115.8 ..
71.6 85.4 81.7 .. .. 97.5 103.4 95.3 83.9 .. 68.2 .. 74.4 115.9 133.6 117.7 .. .. 106.2 84.8 .. . . .. 118.8 91.9 .. 96.5 66.3 .. 114.0 114.3 ..
15.7 20.7 3.1 12.5 4.6 38.6 6.4 13.3 16.8 –0.3 3.1 5.1 10.4 –2.8 38.2 33.9 8.2 .. 4.1 14.5 13.4 13.1 18.5 1.4 5.7 4.4 29.1 42.3 28.2 .. 15.8 31.1 191.7
11.1 30.8 8.2 11.2 5.1 30.4 8.5 .. 18.4 .. 7.5 –3.7 5.1 4.0 27.2 39.0 .. .. 10.0 7.7 .. 7.8 .. 6.6 .. 7.6 11.6 47.4 –1.6 .. 54.2 16.8 472.6
–14.7 75.8 7.5 7.0 18.1 25.2 4.3 4.7 1.6 –4.5 13.8 –3.1 5.5 2.6 39.9 26.5 –5.2 .. –7.0 7.8 8.1 –206.4 0.1 7.8 11.0 4.6 28.3 28.9 71.6 .. 19.6 12.1 128.7
12.5 44.9 9.1 9.4 2.1 23.6 3.2 .. 11.0 .. –25.9 –8.8 7.6 7.9 48.5 28.8 .. .. –9.9 29.3 .. 164.7 .. 2.0 .. 5.9 15.4 39.3 4.0 .. –3.2 –6.2 485.0
14.3 28.1 2.7 15.1 3.4 14.3 12.5 17.0 .. 9.7 21.0 14.1 4.1 10.7 .. 0.4 .. .. 6.1 6.6 4.5 4.0 4.4 6.1 11.6 .. .. 21.4 91.4 .. 3.8 21.1 –34.2
8.0 35.8 0.0 10.9 0.8 0.2 .. .. .. 7.8 25.8 8.7 1.3 .. .. –9.9 .. .. –2.2 5.1 .. –0.9 .. 2.6 .. .. .. 3.4 .. .. –31.9 5.0 49.7
5.0 27.1 7.6 29.4 5.3 6.2 10.9 42.8 9.7 4.4 15.6 22.8 12.2 14.8 2.9 12.9 16.8 61.7 24.2 19.8 6.8 5.6 66.7 13.9 18.5 5.6 25.7 2.4 50.0 11.0 12.7 9.5 ..
Note: Data for 2003 are preliminary and may not cover the entire year. a. More recent data on short-term debt are available on a Web site maintained by the Bank for International Settlements, the International Monetary Fund, the Organisation for Economic Co-operation and Development, and the World Bank: www.oecd.org/dac/debt. Source: International Monetary Fund, International Financial Statistics; World Bank, Debtor Reporting System.
2004 World Development Indicators
181
4.1
Growth of output Gross domestic product
Afghanistan Albania a Algeria a Angola Argentina a Armenia a Australia a Austria a Azerbaijan a Bangladesh a Belarus a Belgium a Benin Bolivia a Bosnia and Herzegovina Botswana Brazil a Bulgaria a Burkina Faso Burundi a Cambodia a Cameroon a Canada a Central African Republic a Chad a Chile a China Hong Kong, China a Colombia a Congo, Dem. Rep. Congo, Rep. Costa Rica a Côte d’Ivoire Croatia a Cuba Czech Republic a Denmark a Dominican Republic Ecuador Egypt, Arab Rep. a El Salvador Eritrea a Estonia a Ethiopia a Finland a France a Gabon Gambia, The a Georgia a Germany a Ghana Greece a Guatemala Guinea a Guinea-Bissau a Haiti a
182
Agriculture
Industry
Services
Total
Manufacturing
average annual
average annual
average annual
average annual
% growth
% growth
% growth
% growth
1980–90
1990–2002
1980–90
1990–2002
1980–90
1990–2002
.. 1.5 2.7 3.6 –0.7 .. 3.4 2.3 .. 3.7 .. 2.1 2.5 –0.2 .. 11.0 2.7 3.4 3.6 4.4 .. 3.4 3.2 1.4 6.1 4.2 10.3 6.8 3.7 1.6 3.3 3.0 0.7 .. .. .. 2.0 3.1 2.1 5.4 0.2 .. 2.2 2.3 3.3 2.4 0.9 3.6 0.4 2.3 3.0 0.9 0.8 .. 4.0 –0.2
.. 5.4 2.2 2.7 2.7 0.4 3.8 2.2 1.2 4.9 –0.1 2.1 4.9 3.6 .. 5.1 2.7 –0.7 4.0 –1.8 6.6 2.4 3.2 2.1 2.5 5.9 9.7 3.8 2.3 –4.4 1.6 4.9 2.8 1.3 3.9 1.3 2.5 6.0 1.9 4.5 4.3 4.3 1.0 4.6 2.9 1.9 2.5 3.3 –4.3 1.6 4.3 2.6 4.0 4.3 0.7 –1.0
.. 1.9 4.1 0.5 0.7 .. 3.2 1.4 .. 2.1 .. 2.2 5.1 1.5 .. 2.5 2.8 –2.1 3.1 3.1 .. 2.2 2.3 1.6 2.3 5.9 5.9 .. 2.9 2.5 3.4 3.1 0.3 .. .. .. 2.6 –1.0 4.5 2.7 –1.1 .. .. 0.6 –0.4 1.3 1.2 0.9 .. 1.6 1.0 –0.1 1.2 .. 4.7 –0.1
.. 3.7 3.6 1.4 2.9 1.4 3.8 4.0 0.8 3.1 –3.5 2.6 5.7 2.7 .. –1.2 3.4 3.0 3.7 –0.7 3.2 5.6 0.8 4.0 3.8 2.1 3.9 .. –1.5 0.3 1.3 3.6 3.4 –1.3 3.5 3.7 2.8 3.9 –0.4 3.2 0.9 –1.4 –2.5 2.2 1.5 1.9 –0.5 4.3 –0.9 1.6 3.5 0.3 2.7 4.6 3.1 –4.4
.. 2.1 2.6 6.3 –1.3 .. 3.1 1.8 .. 6.0 .. 2.4 3.4 –2.3 .. 11.4 2.0 5.2 3.8 4.5 .. 5.9 2.9 1.4 8.1 3.5 11.1 .. 5.0 0.9 5.2 2.8 4.4 .. .. .. 2.0 3.0 1.3 3.3 0.2 .. .. 3.1 3.2 1.4 1.5 4.7 .. 1.4 3.3 1.3 –0.2 .. 2.2 –1.7
.. 2.5 2.0 5.2 1.8 –4.2 2.8 2.7 1.8 7.1 –0.7 2.0 4.5 3.8 .. 4.3 2.2 –3.3 2.8 –2.6 14.8 0.7 3.0 1.4 4.6 5.4 12.6 .. 1.4 –6.8 2.9 5.5 4.2 –1.0 5.0 –0.3 2.4 6.7 2.0 4.6 4.9 11.5 –0.7 4.0 4.4 1.5 2.4 2.9 7.8 –0.1 3.2 1.9 3.9 4.8 –2.5 –2.6
2004 World Development Indicators
1980–90
.. .. 4.1 –11.1 –0.8 .. 1.9 2.5 .. 5.2 .. .. 5.1 –1.1 .. 11.4 1.6 .. 2.0 5.7 .. 5.0 3.8 5.0 .. 3.4 10.8 .. 3.5 1.6 6.8 3.0 3.0 .. .. .. 1.3 2.3 0.1 .. –0.1 .. .. 2.7 .. 1.3 1.8 7.8 .. .. 3.9 .. 0.0 .. .. –1.7
1990–2002
.. 8.7 –1.9 1.6 0.9 –2.0 2.2 2.8 –14.1 6.9 0.4 2.8 6.0 3.5 .. 4.0 1.6 .. 1.6 –8.0 17.8 2.7 4.0 0.7 .. 3.8 11.9 .. –1.5 .. –0.7 5.8 3.4 –1.5 4.7 .. 2.2 4.6 1.1 6.5 5.0 8.1 7.1 4.0 6.9 2.4 0.6 1.7 .. 0.2 –1.4 1.9 2.6 4.4 –1.8 –8.1
average annual % growth 1980–90
.. –0.4 3.0 1.4 0.0 .. 3.8 2.8 .. 3.8 .. 1.8 0.7 –0.2 .. 14.3 3.3 4.7 3.8 5.6 .. 2.1 3.2 1.0 6.7 2.9 13.5 .. 3.1 1.3 2.2 3.3 –0.1 .. .. .. 1.9 4.2 1.8 7.8 0.7 .. .. 4.9 3.6 3.0 0.1 2.7 .. 3.0 5.7 0.9 0.9 .. 3.5 0.9
1990–2002
.. 9.2 2.3 –1.4 3.1 –2.5 4.3 1.9 1.8 4.6 0.5 1.9 4.4 3.8 .. 7.2 2.8 –3.3 4.6 –1.0 5.8 0.6 3.2 –1.1 1.7 4.5 8.8 .. 3.7 –10.5 0.5 4.6 2.0 2.5 3.2 2.1 2.5 6.0 2.5 4.6 4.8 4.6 2.7 6.9 2.5 2.1 3.2 4.1 15.5 2.6 5.4 3.0 4.5 3.3 –0.0 0.8
Gross domestic product
Honduras a Hungary a India a Indonesia Iran, Islamic Rep. a Iraq Ireland a Israel Italy a Jamaica Japan a Jordan a Kazakhstan a Kenya a Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic a Lao PDR a Latvia a Lebanon a Lesotho a Liberia a Libya a Lithuania a Macedonia, FYR a Madagascar a Malawi a Malaysia Mali a Mauritania a Mauritius a Mexico a Moldova a Mongolia Morocco Mozambique a Myanmar Namibia a Nepal a Netherlands a New Zealand a Nicaragua Niger Nigeria a Norway a Oman Pakistan a Panama a Papua New Guinea Paraguay Peru a Philippines Poland a Portugal a Puerto Rico
Agriculture
4.1
Industry
Services
Total
Manufacturing
average annual
average annual
average annual
average annual
% growth
% growth
% growth
% growth
1980–90
1990–2002
1980–90
1990–2002
1980–90
2.7 1.3 5.7 6.1 1.7 –6.8 3.2 3.5 2.5 2.0 4.1 2.5 .. 4.2 .. 8.9 1.3 .. 3.7 3.5 .. 4.5 –7.0 –7.0 .. .. 1.1 2.5 5.3 0.8 1.8 6.0 1.1 2.8 5.4 4.2 –0.1 0.6 1.3 4.6 2.4 1.9 –1.9 –0.1 1.6 3.0 8.4 6.3 0.5 1.9 2.5 –0.1 1.0 .. 3.2 4.0
3.1 2.2 5.8 3.6 3.8 .. 7.8 4.6 1.7 0.7 1.3 4.7 –1.6 1.9 .. 5.6 2.9 –2.2 6.3 –1.0 4.9 3.5 7.4 .. –0.9 –0.1 2.1 3.1 6.2 4.2 4.4 5.2 3.0 –7.1 1.5 2.6 6.9 7.4 3.7 4.7 2.9 3.2 4.3 2.6 2.4 3.6 4.3 3.6 4.2 3.1 1.8 4.1 3.5 4.3 2.8 4.3
2.7 1.7 3.1 3.6 4.5 .. .. .. –0.5 0.9 1.3 6.8 .. 3.3 .. 3.0 14.7 .. 3.5 2.3 .. 2.8 .. .. .. .. 2.5 2.0 3.4 3.3 1.7 2.6 0.8 .. 1.4 6.7 6.6 0.5 1.9 4.0 3.6 4.1 –2.2 1.7 3.3 0.1 7.9 4.0 2.5 1.8 3.6 3.0 1.0 .. 1.5 1.8
2.1 –0.8 2.7 1.9 4.2 .. .. .. 1.2 –0.1 –2.9 –2.4 –5.4 1.2 .. 1.8 .. 2.5 4.9 –4.0 1.7 2.0 6.5 .. –1.0 –0.2 1.9 6.8 0.3 2.5 3.7 0.4 1.6 –8.1 3.2 0.1 5.1 5.7 2.8 2.7 1.7 3.0 3.1 3.2 3.5 1.8 .. 3.8 2.7 3.2 2.1 5.3 2.0 1.1 –0.2 ..
3.3 0.2 6.9 7.3 3.3 .. .. .. 1.8 2.4 4.2 1.7 .. 3.9 .. 11.4 1.0 .. 6.1 4.3 .. 5.3 .. .. .. .. 0.9 2.9 6.8 4.3 4.9 9.2 1.1 .. 6.6 3.0 –4.5 0.5 0.0 8.8 1.6 1.0 –2.3 –1.7 –1.1 4.0 10.3 7.7 –1.3 1.9 0.3 0.1 –0.9 .. 3.4 3.6
1990–2002
3.5 3.9 6.0 4.5 –2.0 .. .. .. 1.2 –0.8 –0.0 4.9 –5.4 1.5 .. 6.2 .. –7.6 10.9 –5.1 –0.8 4.8 –11.2 .. 4.2 –2.1 2.1 0.7 7.5 8.2 2.4 5.4 3.5 –9.5 0.1 3.3 14.1 10.5 2.7 6.4 1.7 2.2 3.0 2.2 0.9 3.2 .. 3.9 4.3 3.7 3.0 4.4 3.5 5.8 2.9 ..
ECONOMY
Growth of output
1980–90
3.7 .. 7.4 12.8 4.5 .. .. .. 2.1 2.7 .. 0.5 .. 4.9 .. 12.1 2.3 .. 8.9 4.4 .. 9.8 .. .. .. .. 2.1 3.6 9.3 6.8 –2.1 10.4 1.5 .. .. 4.1 .. –0.2 3.7 9.3 .. .. –3.2 –2.7 0.7 0.2 20.6 8.1 0.4 0.1 4.0 –0.2 0.2 .. .. 3.6
1990–2002
4.1 7.6 6.6 5.9 5.5 .. .. .. 1.4 –2.0 0.7 5.6 5.3 1.8 .. 7.6 .. –13.4 12.6 –4.6 –2.4 6.0 .. .. 6.7 –4.0 2.2 –1.0 8.8 –2.2 –1.0 5.3 4.0 –1.1 .. 2.8 18.9 7.9 3.3 7.5 2.4 2.0 1.7 2.8 1.2 2.3 .. 4.0 2.9 3.8 0.8 3.3 3.1 8.3 2.3 ..
average annual % growth 1980–90
1990–2002
2.5 2.1 6.9 6.5 –1.0 .. .. .. 3.0 1.6 4.2 2.3 .. 4.9 .. 8.4 2.1 .. 3.3 3.2 .. 4.0 .. .. .. .. 0.3 3.3 4.9 1.9 0.4 5.1 1.4 .. 8.4 4.2 9.1 0.8 3.6 3.9 2.6 2.0 –1.5 –0.7 3.7 2.8 5.9 6.8 0.7 2.0 3.1 –0.4 2.8 .. 2.5 4.6
2004 World Development Indicators
3.9 2.1 7.9 3.4 8.1 .. .. .. 1.9 1.7 2.2 4.8 –1.4 2.9 .. 5.6 .. –3.0 6.5 3.6 3.0 3.8 –12.5 .. 5.5 1.3 2.4 2.1 6.4 3.5 5.9 6.3 3.0 0.4 0.8 2.9 3.4 7.2 4.2 5.8 3.2 3.6 5.5 2.3 2.8 3.8 .. 4.3 4.4 2.6 1.0 3.7 4.2 4.4 2.3 ..
183
4.1
Growth of output Gross domestic product
Services
Total
Manufacturing
average annual
average annual
average annual
% growth
% growth
% growth
% growth
1.3 .. 2.2 –1.3 3.1 .. 0.5 6.7 2.0 .. 2.1 1.0 3.1 4.0 2.3 6.7 2.5 2.0 1.5 2.0 .. 7.6 1.7 –0.8 3.3 5.3 .. 2.9 .. –2.1 3.2 3.5 0.5 .. 1.1 4.6 .. .. 1.0 3.6 3.3 w 4.7 2.9 4.0 0.8 3.2 7.5 .. 1.7 1.4 5.5 1.6 3.3 2.4
1990–2002
–0.2 –2.7 1.7 2.1 3.9 0.1 –3.8 6.7 2.3 4.1 .. 2.2 2.8 4.8 5.5 3.2 2.3 1.0 4.7 –6.8 3.5 3.7 2.0 3.5 4.6 3.1 –1.0 6.9 –6.6 4.2 2.6 3.3 2.0 0.8 1.1 7.6 –0.8 5.9 1.1 1.1 2.7 w 4.3 3.2 3.2 3.0 3.4 7.3 –0.5 2.9 3.2 5.4 2.6 2.5 2.0
1980–90
1.9 .. 0.5 12.5 2.8 .. 3.1 –5.3 1.6 .. 3.3 2.9 3.1 2.2 1.8 2.3 1.4 .. –0.6 –2.8 .. 3.9 5.6 –5.9 2.8 1.2 .. 2.1 .. 9.6 2.4 3.2 0.1 .. 3.1 2.8 .. .. 3.6 3.1 2.6 w 3.0 3.5 3.7 2.8 3.4 4.6 .. 2.3 5.0 3.1 2.3 1.9 1.3
a. Components are at basic prices. b. Data cover mainland Tanzania only.
184
Industry
average annual 1980–90
Romania a Russian Federation a Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone a Singapore Slovak Republic a Slovenia a Somalia a South Africa a Spain a Sri Lanka a Sudan Swaziland a Sweden a Switzerland a Syrian Arab Republic Tajikistan a Tanzania b Thailand Togo Trinidad and Tobago Tunisia Turkey a Turkmenistan a Uganda a Ukraine a United Arab Emirates a United Kingdom a United States a Uruguay a Uzbekistan Venezuela, RB Vietnam West Bank and Gaza a Yemen, Rep. Zambia a Zimbabwe a World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Agriculture
2004 World Development Indicators
1990–2002
–1.4 –1.9 4.3 1.7 1.8 .. –3.6 –3.1 2.5 –0.1 .. 1.2 0.7 1.6 9.0 1.9 –0.7 .. 4.5 –4.6 3.4 1.5 3.3 3.3 1.8 1.1 –3.2 3.9 –4.0 .. –1.1 3.8 1.6 1.3 1.3 4.2 –4.2 5.6 3.5 2.9 1.8 w 2.7 2.1 2.2 1.4 2.3 3.1 –0.8 2.3 2.9 2.9 2.8 1.2 1.4
1980–90
–1.0 .. 2.5 –3.8 4.3 .. 1.7 5.2 2.0 .. 1.0 0.7 2.7 4.6 1.6 12.0 2.8 .. 6.6 5.5 .. 9.8 1.1 –5.5 3.1 7.7 .. 5.0 .. –4.2 3.3 3.0 –0.2 .. 1.7 4.4 .. .. 1.0 3.2 3.1 w 5.6 2.7 4.0 –0.1 3.1 8.5 .. 1.4 –0.4 6.9 1.3 3.1 1.7
1990–2002
1980–90
1990–2002
–0.3 –4.5 –1.0 1.8 5.3 .. –4.0 7.3 –3.4 4.7 .. 1.3 2.5 6.1 6.1 3.3 4.5 .. 8.7 –11.7 4.1 4.9 2.8 3.8 4.7 3.1 –1.6 11.2 –7.9 .. 1.2 3.4 –0.0 –2.1 1.8 11.4 –6.7 6.5 –2.8 –1.1 2.1 w 4.7 3.4 3.6 2.9 3.6 9.7 –2.2 2.6 1.8 5.9 1.9 1.8 1.1
.. .. 2.6 6.2 4.6 .. .. 6.6 .. .. –1.7 1.1 .. 6.3 4.8 15.7 .. .. .. 5.6 .. 9.5 1.7 –10.1 3.7 7.9 .. 3.9 .. 3.1 .. .. 0.4 .. 4.4 1.9 .. .. 4.1 2.8 .. w 7.9 3.7 4.4 1.9 4.2 9.5 .. 1.4 4.9 7.3 1.7 .. ..
.. .. –3.7 5.4 4.6 .. 5.0 6.9 4.3 4.7 .. 1.6 3.9 7.0 2.0 2.6 8.6 .. 9.6 –10.8 3.3 6.1 4.2 5.5 5.5 3.8 .. 13.0 –7.2 .. .. 3.9 –1.3 .. 1.3 11.2 –0.5 3.0 1.5 –2.0 2.9 w 5.7 5.3 5.7 4.0 5.3 9.8 .. 2.0 4.6 6.3 1.9 2.3 1.5
average annual % growth 1980–90
.. .. 3.6 0.6 2.8 .. –0.9 7.6 0.6 .. 0.9 2.4 3.3 4.7 4.5 4.8 2.4 .. 1.6 3.4 .. 7.3 –0.3 6.7 3.5 4.5 .. 2.8 .. 3.6 3.1 3.3 1.0 .. 0.5 7.1 .. .. –0.2 3.0 3.5 w 5.4 3.1 4.4 1.1 3.4 8.6 .. 1.9 1.9 6.4 2.4 3.5 2.9
1990–2002
1.0 –0.6 0.5 2.4 4.1 .. –2.9 6.8 7.7 4.0 .. 2.8 3.0 5.3 3.1 3.5 1.8 .. 3.3 –0.3 3.3 3.1 0.4 3.3 5.3 3.4 –3.2 7.9 –8.0 .. 3.4 3.7 3.1 1.9 0.5 7.1 2.4 5.8 3.0 2.0 3.1 w 5.4 3.3 3.3 3.3 3.6 6.4 0.8 3.0 4.2 7.0 2.8 3.0 2.4
About the data
4.1
ECONOMY
Growth of output Definitions
An economy’s growth is measured by the change in
estimating household outputs produced for home use,
• Gross domestic product (GDP) at purchaser
the volume of its output or in the real incomes of per-
sales in informal markets, barter exchanges, and illic-
prices is the sum of gross value added by all resi-
sons resident in the economy. The 1993 United
it or deliberately unreported activities. The consisten-
dent producers in the economy plus any product
Nations System of National Accounts (1993 SNA)
cy and completeness of such estimates depend on
taxes (less subsidies) not included in the valuation
offers three plausible indicators from which to calcu-
the skill and methods of the compiling statisticians
of output. It is calculated without making deductions
late growth: the volume of gross domestic product
and the resources available to them.
for depreciation of fabricated capital assets or for
(GDP), real gross domestic income, and real gross
depletion and degradation of natural resources.
national income. The volume of GDP is the sum of
Rebasing national accounts
Value added is the net output of an industry after
value added, measured at constant prices, by house-
When countries rebase their national accounts, they
adding up all outputs and subtracting intermediate
holds, government, and the industries operating in the
update the weights assigned to various components
inputs. The industrial origin of value added is deter-
economy. This year’s edition of World Development
to better reflect the current pattern of production or
mined by the International Standard Industrial
Indicators continues to follow the practice of past edi-
uses of output. The new base year should represent
Classification (ISIC) revision 3. • Agriculture corre-
tions, measuring the growth of the economy by the
normal operation of the economy—that is, it should
sponds to ISIC divisions 1–5 and includes forestry
change in GDP measured at constant prices.
be a year without major shocks or distortions—but
and fishing. • Industry covers mining, manufactur-
Each industry’s contribution to the growth in the
the choice of base year is often constrained by lack
ing (also reported as a separate subgroup), con-
economy’s output is measured by the growth in
of data. Some developing countries have not
struction, electricity, water, and gas (ISIC divisions
value added by the industry. In principle, value
rebased their national accounts for many years.
10–45). • Manufacturing corresponds to industries
added in constant prices can be estimated by meas-
Using an old base year can be misleading because
belonging to ISIC divisions 15–37. • Services cor-
uring the quantity of goods and services produced in
implicit price and volume weights become progres-
respond to ISIC divisions 50–99. This sector is
a period, valuing them at an agreed set of base year
sively less relevant and useful.
derived as a residual (from GDP less agriculture and
prices, and subtracting the cost of intermediate
To obtain comparable series of constant price
industry) and may not properly reflect the sum of
inputs, also in constant prices. This double-deflation
data, the World Bank rescales GDP and value added
service output, including banking and financial serv-
method, recommended by the 1993 SNA and its
by industrial origin to a common reference year, cur-
ices. For some countries it includes product taxes
predecessors, requires detailed information on the
rently 1995. This process gives rise to a discrepan-
(minus subsidies) and may also include statistical
structure of prices of inputs and outputs.
cy between the rescaled GDP and the sum of the
discrepancies.
In many industries, however, value added is extrapo-
rescaled components. Because allocating the dis-
lated from the base year using single volume indexes
crepancy would give rise to distortions in the growth
of outputs or, more rarely, inputs. Particularly in the
rates, the discrepancy is left unallocated. As a
service industries, including most of government, value
result, the weighted average of the growth rates of
added in constant prices is often imputed from labor
the components generally will not equal the GDP
inputs, such as real wages or the number of employ-
growth rate.
ees. In the absence of well-defined measures of out-
Growth rates of GDP and its components are calcu-
put, measuring the growth of services remains difficult.
lated using constant price data in the local currency.
Data sources
Moreover, technical progress can lead to improve-
Regional and income group growth rates are calculat-
The national accounts data for most developing
ments in production processes and in the quality of
ed after converting local currencies to constant price
countries are collected from national statistical
goods and services that, if not properly accounted
U.S. dollars using an exchange rate in the common
organizations and central banks by visiting and
for, can distort measures of value added and thus of
reference year. The growth rates in the table are aver-
resident World Bank missions. The data for high-
growth. When inputs are used to estimate output, as
age annual compound growth rates. Methods of com-
income economies come from data files of the
is the case for nonmarket services, unmeasured
puting growth rates and the alternative conversion
Organisation for Economic Co-operation and
technical progress leads to underestimates of the
factor are described in Statistical methods.
Development (for information on the OECD’s national accounts series, see its monthly Main
volume of output. Similarly, unmeasured changes in the quality of goods and services produced lead to
Changes in the System of National Accounts
Economic Indicators). The World Bank rescales
underestimates of the value of output and value
World Development Indicators adopted the terminol-
constant price data to a common reference year.
added. The result can be underestimates of growth
ogy of the 1993 SNA in 2001. Although most coun-
The complete national accounts time series is
and productivity improvement, and overestimates of
tries continue to compile their national accounts
available on the World Development Indicators
inflation. These issues are highly complex, and only
according to the SNA version 3 (referred to as the
2004 CD-ROM. The United Nations Statistics
a few high-income countries have attempted to intro-
1968 SNA), more and more are adopting the 1993
Division publishes detailed national accounts for
duce any GDP adjustments for these factors.
SNA. Some low-income countries still use concepts
United Nations member countries in National
Informal economic activities pose a particular meas-
from the even older 1953 SNA guidelines, including
Accounts Statistics: Main Aggregates and
urement problem, especially in developing countries,
valuations such as factor cost, in describing major
Detailed Tables and publishes updates in the
where much economic activity may go unrecorded.
economic aggregates. Countries that use the 1993
Monthly Bulletin of Statistics.
Obtaining a complete picture of the economy requires
SNA are identified in Primary data documentation.
2004 World Development Indicators
185
4.2
Structure of output Gross domestic product
Agriculture
$ millions
% of GDP
Industry
Total 1990
Afghanistan Albania a Algeria a Angola Argentina a Armenia a Australia a Austria a Azerbaijan a Bangladesh a Belarus a Belgium a Benin Bolivia a Bosnia and Herzegovina Botswana Brazil a Bulgaria a Burkina Faso Burundi a Cambodia a Cameroon a Canada a Central African Republic a Chad a Chile a China Hong Kong, China a Colombia a Congo, Dem. Rep. Congo, Rep. Costa Rica a Côte d’Ivoire Croatia a Cuba Czech Republic a Denmark a Dominican Republic Ecuador Egypt, Arab Rep. a El Salvador Eritrea a Estonia a Ethiopia a Finland a France a Gabon Gambia, The a Georgia a Germany a Ghana Greece a Guatemala Guinea a Guinea-Bissau a Haiti a
186
.. 2,102 62,045 10,260 141,352 2,257 310,588 161,692 4,991 30,129 17,370 197,174 1,845 4,868 .. 3,791 461,952 20,726 3,120 1,132 1,115 11,152 574,204 1,488 1,739 30,323 354,644 75,433 40,274 9,348 2,799 5,713 10,796 18,156 .. 34,880 133,361 7,074 10,356 43,130 4,807 477 4,649 8,609 137,224 1,215,893 5,952 317 7,738 1,671,312 5,886 84,075 7,650 2,818 244 2,864
2002
.. 4,835 55,914 11,248 102,042 2,367 409,420 204,066 6,090 47,563 14,304 245,395 2,695 7,801 5,599 5,273 452,387 15,486 3,127 719 4,005 9,060 714,327 1,046 2,002 64,153 1,266,052 161,531 80,925 5,707 3,017 16,837 11,682 22,436 .. 69,514 172,928 21,651 24,311 89,854 14,284 642 6,507 6,059 131,508 1,431,278 4,971 357 3,396 1,984,095 6,160 132,824 23,277 3,213 203 3,435
2004 World Development Indicators
Services
Manufacturing
% of GDP
% of GDP
% of GDP
1990
2002
1990
2002
1990
2002
1990
2002
.. 36 11 18 8 17 4 4 30 30 24 2 36 17 .. 5 8 17 28 56 .. 25 3 48 29 9 27 0 17 30 13 18 32 10 .. 6 4 13 13 19 17 31 17 49 6 4 7 29 32 2 45 11 26 24 61 ..
52 25 10 8 11 26 4 2 16 23 11 1 36 15 18 2 6 13 32 49 36 43 .. 57 38 9 15 0 14 56 6 8 26 8 7 4 3 12 9 17 9 12 5 40 3 3 8 26 21 1 34 7 22 24 62 ..
.. 48 48 41 36 52 29 34 33 21 47 33 13 39 .. 57 39 49 20 19 .. 29 32 20 18 41 42 25 38 28 41 29 23 34 .. 49 27 31 38 29 27 12 50 13 35 30 43 13 33 39 17 28 20 33 19 ..
24 19 53 68 32 37 26 32 52 26 37 27 14 33 37 48 21 28 18 19 28 20 .. 22 17 34 51 13 30 19 63 29 20 30 46 40 27 33 28 33 30 25 30 12 33 25 46 14 23 30 24 22 19 37 13 ..
.. .. 11 5 27 33 14 23 19 13 39 .. 8 18 .. 5 25 .. 15 13 .. 15 17 11 14 20 33 17 21 11 8 22 21 28 .. .. 18 18 19 18 22 8 42 8 .. 21 6 7 24 28 10 .. 15 5 8 ..
18 10 8 4 21 23 12 22 20 16 31 19 9 15 23 4 13 17 13 .. 20 11 .. 9 15 16 35 5 16 4 5 22 13 21 37 .. 17 16 11 19 23 12 19 .. 26 18 5 5 .. 23 9 12 13 4 10 ..
.. 16 40 41 56 31 67 62 37 48 29 65 51 44 .. 39 53 34 52 25 .. 46 65 33 53 50 31 74 45 42 46 53 44 56 .. 45 69 55 49 52 56 57 34 38 59 66 50 58 35 59 38 61 54 43 21 ..
24 56 37 24 57 37 71 66 32 51 52 72 50 52 45 50 73 59 50 31 36 38 .. 21 45 57 34 87 56 25 30 62 53 62 47 57 71 55 63 50 61 63 65 48 64 72 46 60 56 69 42 70 58 39 25 ..
Gross domestic product
Agriculture
$ millions
% of GDP
Industry
Total 1990
Honduras a Hungary a India a Indonesia Iran, Islamic Rep. a Iraq Ireland a Israel Italy a Jamaica Japan a Jordan a Kazakhstan a Kenya a Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic a Lao PDR a Latvia a Lebanon a Lesotho a Liberia a Libya a Lithuania a Macedonia, FYR a Madagascar a Malawi a Malaysia Mali a Mauritania a Mauritius a Mexico a Moldova a Mongolia Morocco Mozambique a Myanmar Namibia a Nepal a Netherlands a New Zealand a Nicaragua Niger Nigeria a Norway a Oman Pakistan a Panama a Papua New Guinea Paraguay Peru a Philippines Poland a Portugal a Puerto Rico
3,049 33,056 316,937 114,426 120,404 48,657 47,301 52,490 1,102,437 4,592 3,053,143 4,020 26,931 8,551 .. 252,622 18,428 2,659 866 7,279 2,838 615 384 28,905 10,259 4,472 3,081 1,881 44,024 2,421 1,020 2,383 262,710 3,549 .. 25,821 2,463 .. 2,350 3,628 294,290 43,618 1,009 2,481 28,472 116,107 10,535 40,010 5,313 3,221 5,265 26,294 44,331 58,976 71,466 30,604
2002
6,564 65,843 510,177 172,911 108,243 .. 121,449 103,689 1,184,273 7,871 3,993,433 9,301 24,637 12,330 .. 476,690 35,369 1,603 1,680 8,406 17,294 714 562 19,131 13,796 3,791 4,400 1,901 94,900 3,364 969 4,533 637,203 1,624 1,119 36,093 3,599 .. 2,904 5,549 417,910 58,581 4,003 2,171 43,540 190,477 20,309 59,071 12,296 2,814 5,508 56,517 77,954 189,021 121,595 67,897
4.2
ECONOMY
Structure of output
Services
Manufacturing
% of GDP
% of GDP
% of GDP
1990
2002
1990
2002
1990
2002
1990
2002
22 15 31 19 24 .. 9 .. 4 7 2 8 27 29 .. 9 1 34 61 22 .. 24 .. .. 27 9 29 45 15 46 30 13 8 43 17 18 37 57 12 52 4 7 31 35 33 4 3 26 9 29 28 9 22 8 9 1
13 4 23 17 12 .. 3 .. 3 6 1 2 9 16 .. 4 .. 39 51 5 12 16 .. .. 7 12 32 37 9 34 21 7 4 24 30 16 23 57 11 41 3 .. 18 40 37 2 .. 23 6 27 22 8 15 3 4 1
26 39 28 39 29 .. 35 .. 34 40 39 28 45 19 .. 43 52 36 15 46 .. 33 .. .. 31 46 13 29 42 16 29 33 28 33 30 32 18 11 38 16 30 28 21 16 41 36 58 25 15 30 25 27 34 50 32 42
31 31 27 44 39 .. 42 .. 29 31 31 26 39 19 .. 41 .. 26 23 25 21 43 .. .. 31 30 13 15 47 30 29 31 27 25 16 30 34 10 31 22 26 .. 25 17 29 38 .. 23 14 42 29 28 33 30 30 43
16 23 17 21 12 .. 28 .. 25 19 27 15 9 12 .. 29 12 28 10 34 .. 14 .. .. 21 36 11 19 24 9 10 25 21 .. .. 18 10 8 14 6 19 19 17 7 6 13 4 17 9 9 17 18 25 .. 22 40
20 23 16 25 14 .. 33 .. 21 14 21 16 16 13 .. 29 .. 11 18 15 10 20 .. .. 20 19 11 10 31 3 9 23 19 17 5 17 13 7 11 8 16 .. 14 7 4 .. .. 16 6 9 15 16 23 18 .. 40
51 46 41 41 48 .. 56 .. 63 52 58 64 29 52 .. 48 47 30 24 32 .. 43 .. .. 42 46 59 26 43 39 42 54 64 24 52 50 44 32 50 32 65 65 48 49 26 61 39 49 76 41 47 64 44 42 60 57
56 65 51 38 49 .. 54 .. 69 63 68 72 53 65 .. 55 .. 35 26 71 67 41 .. .. 62 57 55 49 44 36 50 62 69 51 54 54 43 33 58 38 71 .. 57 43 34 60 .. 53 80 32 49 64 53 66 66 56
2004 World Development Indicators
187
4.2
Structure of output Gross domestic product
Agriculture
$ millions
% of GDP
Industry
Total 1990
Romania a Russian Federation a Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone a Singapore Slovak Republic a Slovenia a Somalia a South Africa a Spain a Sri Lanka a Sudan Swaziland a Sweden a Switzerland a Syrian Arab Republic Tajikistan a Tanzania b Thailand Togo Trinidad and Tobago Tunisia Turkey a Turkmenistan a Uganda a Ukraine a United Arab Emirates a United Kingdom a United States a Uruguay a Uzbekistan Venezuela, RB Vietnam West Bank and Gaza a Yemen, Rep. Zambia a Zimbabwe a World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
38,299 516,814 2,584 116,778 5,699 .. 650 36,902 15,485 12,673 917 112,014 509,968 8,032 13,167 882 245,941 228,415 12,309 2,629 4,259 85,345 1,628 5,068 12,291 150,642 3,232 4,304 81,456 34,132 989,564 5,750,800 9,286 13,361 48,592 6,472 .. 4,828 3,288 8,784 21,676,054 t 765,007 3,229,351 2,326,049 905,385 3,991,257 674,196 1,099,616 1,098,727 424,126 404,808 298,443 17,683,764 5,503,913
2002
45,749 346,520 1,732 188,479 5,037 15,681 783 86,969 23,682 21,960 .. 104,242 653,075 16,567 13,516 1,186 240,313 267,445 20,783 1,212 9,382 126,905 1,384 9,628 21,024 183,665 7,672 5,803 41,477 70,960 1,566,283 10,383,100 12,129 7,932 94,340 35,086 3,396 9,984 3,697 8,304 32,312,146 t 1,123,865 5,139,306 3,426,319 1,708,823 6,259,154 1,833,073 1,132,845 1,668,800 670,722 649,079 319,288 26,052,812 6,648,492
1990
24 17 33 6 20 .. 32 .. 7 6 65 5 6 26 .. 13 4 .. 28 33 46 12 34 3 16 18 32 57 26 2 2 2 9 33 5 39 .. 24 21 16 5w 29 14 16 9 16 24 17 9 15 31 18 3 3
a. Components are at basic prices. b. Data cover mainland Tanzania only.
188
2004 World Development Indicators
Services
Manufacturing
% of GDP 2002
13 6 41 5 15 15 53 0 4 3 .. 4 3 20 39 16 2 1 23 24 44 9 40 2 10 13 29 32 15 .. 1 2 9 35 3 23 6 15 22 17 4w 24 9 10 6 11 15 9 7 11 23 18 2 2
1990
50 48 25 49 19 .. 13 .. 59 46 .. 40 35 26 .. 42 32 .. 24 38 18 37 23 46 30 30 30 11 45 64 35 28 35 33 50 23 .. 27 51 33 34 w 30 39 39 39 38 40 44 36 38 27 34 33 34
% of GDP 2002
38 34 21 51 22 32 32 36 29 36 .. 32 30 26 18 50 28 27 28 24 16 43 22 42 29 27 51 22 38 .. 26 23 27 22 43 39 13 40 26 24 29 w 30 34 34 34 33 47 32 26 41 26 29 27 28
1990
34 .. 18 9 13 .. 5 .. .. 35 5 24 .. 15 .. 35 .. .. 20 25 9 27 10 9 17 20 .. 6 44 8 23 19 28 .. 20 12 .. 9 36 23 22 w 17 24 26 21 23 29 .. 23 13 17 17 22 24
% of GDP 2002
17 .. 11 10 14 .. 5 28 21 27 .. 19 18 16 9 38 23 .. 25 21 8 34 9 7 19 17 .. 10 23 .. 17 15 17 9 6 21 11 5 12 13 19 w 17 21 22 18 20 32 .. 15 13 16 15 19 21
1990
26 35 43 45 61 .. 55 .. 33 49 .. 55 59 48 .. 45 64 .. 48 29 36 50 44 51 54 52 38 32 30 35 63 70 56 34 44 39 .. 49 28 50 60 w 41 47 44 53 46 37 39 55 47 43 48 64 62
2002
49 60 37 44 63 53 16 64 67 61 .. 64 66 54 43 35 70 72 49 52 39 48 38 56 60 60 20 46 47 .. 73 75 64 44 54 38 80 44 52 59 68 w 46 57 56 60 55 38 59 67 48 51 54 71 70
4.2
ECONOMY
Structure of output About the data
An economy’s gross domestic product (GDP) repre-
agricultural inputs that cannot easily be allocated to
Monetary Fund for the year shown. An alternative
sents the sum of value added by all producers in that
specific outputs are frequently “netted out” using
conversion factor is applied if the official exchange
economy. Value added is the value of the gross out-
equally crude and ad hoc approximations. For further
rate is judged to diverge by an exceptionally large
put of producers less the value of intermediate
discussion of the measurement of agricultural pro-
margin from the rate effectively applied to transac-
goods and services consumed in production, before
duction, see About the data for table 3.3.
tions in foreign currencies and traded products.
taking account of the consumption of fixed capital in
Ideally, industrial output should be measured
the production process. Since 1968 the United
through regular censuses and surveys of firms. But
Nations System of National Accounts has called for
in most developing countries such surveys are infre-
estimates of value added to be valued at either basic
quent, so earlier survey results must be extrapolated
• Gross domestic product (GDP) at purchaser
prices (excluding net taxes on products) or producer
using an appropriate indicator. The choice of sam-
prices is the sum of gross value added by all resi-
prices (including net taxes on products paid by pro-
pling unit, which may be the enterprise (where
dent producers in the economy plus any product
ducers but excluding sales or value added taxes).
responses may be based on financial records) or the
taxes (less subsidies) not included in the valuation
Both valuations exclude transport charges that are
establishment (where production units may be
of output. It is calculated without making deductions
invoiced separately by producers. Some countries,
recorded separately), also affects the quality of the
for depreciation of fabricated assets or for depletion
however, report such data at purchaser prices—the
data. Moreover, much industrial production is organ-
and degradation of natural resources. Value added
prices at which final sales are made (including trans-
ized in unincorporated or owner-operated ventures
is the net output of an industry after adding up all
port charges)—which may affect estimates of the
that are not captured by surveys aimed at the formal
outputs and subtracting intermediate inputs. The
distribution of output. Total GDP as shown in the
sector. Even in large industries, where regular sur-
industrial origin of value added is determined by the
table and elsewhere in this book is measured at pur-
veys are more likely, evasion of excise and other
International Standard Industrial Classification
chaser prices. Value added by industry is normally
taxes and nondisclosure of income lower the esti-
(ISIC) revision 3. • Agriculture corresponds to ISIC
measured at basic prices. When value added is
mates of value added. Such problems become more
divisions 1–5 and includes forestry and fishing.
measured at producer prices, this is noted in Primary
acute as countries move from state control of indus-
• Industry covers mining, manufacturing (also
data documentation.
try to private enterprise, because new firms enter
reported as a separate subgroup), construction,
While GDP estimates based on the production
business and growing numbers of established firms
electricity, water, and gas (ISIC divisions 10–45).
approach are generally more reliable than estimates
fail to report. In accordance with the System of
• Manufacturing corresponds to industries belong-
compiled from the income or expenditure side, dif-
National Accounts, output should include all such
ing to ISIC divisions 15–37. • Services correspond
ferent countries use different definitions, methods,
unreported activity as well as the value of illegal
to ISIC divisions 50–99. This sector is derived as a
and reporting standards. World Bank staff review the
activities and other unrecorded, informal, or small-
residual (from GDP less agriculture and industry) and
quality of national accounts data and sometimes
scale operations. Data on these activities need to be
may not properly reflect the sum of service output,
make adjustments to increase consistency with inter-
collected using techniques other than conventional
including banking and financial services. For some
national guidelines. Nevertheless, significant dis-
surveys of firms.
countries it includes product taxes (minus subsidies)
Definitions
crepancies remain between international standards
In industries dominated by large organizations and
and actual practice. Many statistical offices, espe-
enterprises, such as public utilities, data on output,
cially those in developing countries, face severe lim-
employment, and wages are usually readily available
itations in the resources, time, training, and budgets
and reasonably reliable. But in the service industry
Data sources
required to produce reliable and comprehensive
the many self-employed workers and one-person
The national accounts data for most developing
series of national accounts statistics.
businesses are sometimes difficult to locate, and
countries are collected from national statistical
they have little incentive to respond to surveys, let
organizations and central banks by visiting and
Data problems in measuring output
alone report their full earnings. Compounding these
resident World Bank missions. The data for high-
Among the difficulties faced by compilers of national
problems are the many forms of economic activity
income economies come from data files of the
accounts is the extent of unreported economic activ-
that go unrecorded, including the work that women
Organisation for Economic Co-operation and
ity in the informal or secondary economy. In devel-
and children do for little or no pay. For further dis-
Development (for information on the OECD’s
oping countries a large share of agricultural output is
cussion of the problems of using national accounts
national accounts series, see its monthly Main
either not exchanged (because it is consumed within
data, see Srinivasan (1994) and Heston (1994).
Economic Indicators). The complete national
and may also include statistical discrepancies.
accounts time series is available on the World
the household) or not exchanged for money. Dollar conversion
Development Indicators 2004 CD-ROM. The
indirectly, using a combination of methods involving
To produce national accounts aggregates that are
United Nations Statistics Division publishes
estimates of inputs, yields, and area under cultiva-
measured in the same standard monetary units, the
detailed national accounts for United Nations
tion. This approach sometimes leads to crude
value of output must be converted to a single com-
member countries in National Accounts Statistics:
approximations that can differ from the true values
mon currency. The World Bank conventionally uses
Main Aggregates and Detailed Tables and publish-
over time and across crops for reasons other than
the U.S. dollar and applies the average official
es updates in the Monthly Bulletin of Statistics.
climatic conditions or farming techniques. Similarly,
exchange rate repor ted by the International
Agricultural production often must be estimated
2004 World Development Indicators
189
4.3
Structure of manufacturing Manufacturing value added
$ millions 1990
Afghanistan .. Albania .. Algeria 6,452 Angola 513 Argentina 37,868 Armenia 681 Australia 38,868 Austria 33,386 Azerbaijan 1,092 Bangladesh 3,839 Belarus 6,630 Belgium .. Benin 145 Bolivia 826 Bosnia and Herzegovina .. Botswana 181 Brazil 89,966 Bulgaria .. Burkina Faso 460 Burundi 134 Cambodia 58 Cameroon 1,581 Canada 91,674 Central African Republic 154 Chad 239 Chile 5,613 China 116,573 Hong Kong, China 12,639 Colombia 8,034 Congo, Dem. Rep. 1,029 Congo, Rep. 234 Costa Rica 1,107 Côte d’Ivoire 2,257 Croatia 4,770 Cuba .. Czech Republic .. Denmark 20,757 Dominican Republic 1,270 Ecuador 1,988 Egypt, Arab Rep. 7,296 El Salvador 1,044 Eritrea 35 Estonia 1,632 Ethiopia 624 Finland .. France 228,263 Gabon 332 Gambia, The 18 Georgia 1,773 Germany 456,400 Ghana 575 Greece .. Guatemala 1,151 Guinea 126 Guinea-Bissau 19 Haiti ..
190
2000
.. 376 3,897 264 46,877 418 42,528 37,189 675 6,933 3,444 39,986 198 1,121 480 253 80,280 1,985 281 60 583 940 117,240 81 152 10,663 375,455 9,197 12,207 205 112 3,677 1,591 3,219 .. .. 23,156 3,325 2,171 17,969 3,031 67 830 .. 27,771 215,860 205 18 .. 385,839 449 11,441 2,542 121 21 ..
2004 World Development Indicators
Food, beverages, and tobacco
% of total
Textiles and clothing
% of total
Machinery and transport equipment
% of total
Chemicals
% of total
Other manufacturing a
% of total
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
.. 24 13 .. 20 .. 18 15 .. 24 .. 17 .. 28 12 51 14 22 .. 83 .. 61 15 57 .. 25 15 8 31 .. .. 47 .. 22 .. .. 22 .. 22 19 36 .. .. 62 13 13 45 .. .. .. .. 22 .. .. .. 51
.. .. 33 .. 30 .. .. 12 .. 22 .. 19 .. 31 .. .. .. 20 .. .. .. 47 13 .. .. 32 14 7 33 .. .. 46 42 .. .. .. .. .. 38 18 29 .. .. 54 7 .. .. .. .. .. .. 28 .. .. .. 46
.. 33 17 .. 10 .. 6 7 .. 38 .. 7 .. 5 15 12 12 9 .. 9 .. –13 6 6 .. 7 15 36 15 .. .. 8 .. 15 .. .. 4 .. 10 15 14 .. .. 21 4 6 2 .. .. .. .. 20 .. .. .. 9
.. .. 8 .. 7 .. .. 3 .. 33 .. 6 .. 4 .. .. .. 10 .. .. .. 15 3 .. .. 4 11 20 9 .. .. 6 10 .. .. .. .. .. 6 12 28 .. .. 12 2 .. .. .. .. .. .. 11 .. .. .. 19
.. .. .. .. 13 .. 20 28 .. 7 .. .. .. 1 18 .. 27 19 .. .. .. 1 26 2 .. 5 24 21 9 .. .. 7 .. 20 .. .. 24 .. 5 9 4 .. .. 1 24 31 1 .. .. .. .. 12 .. .. .. ..
.. .. .. .. 15 .. .. 41 .. 16 .. .. .. 1 .. .. .. 5 .. .. .. 1 36 .. .. 5 30 33 5 .. .. 6 3 .. .. .. .. .. 3 12 3 .. .. 7 24 .. .. .. .. .. .. 11 .. .. .. ..
.. .. .. .. 12 .. 7 7 .. 17 .. 13 .. 3 7 .. .. 5 .. 2 .. 5 10 6 .. 10 13 2 14 .. .. 9 .. 8 .. .. 12 .. 8 14 24 .. .. 2 8 9 7 .. .. .. .. 10 .. .. .. ..
.. .. .. .. 12 .. .. 6 .. 10 .. 16 .. 3 .. .. .. .. .. .. .. 4 8 .. .. 14 12 4 17 .. .. 12 12 .. .. .. .. .. 4 16 16 .. .. 5 2 .. .. .. .. .. .. 11 .. .. .. ..
.. 44 70 .. 46 .. 48 43 .. 15 .. 62 .. 63 49 36 48 45 .. 7 .. 46 44 28 .. 52 34 33 31 .. .. 30 .. 36 .. .. 39 .. 56 43 23 .. .. 14 52 41 45 .. .. .. .. 36 .. .. .. 40
.. .. 59 .. 36 .. .. 38 .. 19 .. 59 .. 60 .. .. .. 65 .. .. .. 32 39 .. .. 45 33 37 35 .. .. 29 33 .. .. .. .. .. 50 42 24 .. .. 22 64 .. .. .. .. .. .. 38 .. .. .. 34
Manufacturing value added
$ millions 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
2000
443 1,025 6,613 9,534 48,808 66,024 23,643 37,393 14,503 15,456 .. .. 11,982 28,130 .. .. 247,930 203,247 853 1,018 810,232 1,040,351 520 1,125 1,941 3,139 864 1,163 .. .. 72,837 144,376 2,142 .. 703 105 85 292 2,524 926 .. 1,560 71 131 .. .. .. .. 2,113 1,995 1,411 621 314 430 313 197 10,665 29,447 200 86 94 78 491 918 49,992 107,195 .. 183 .. 58 4,753 5,858 230 442 .. .. 292 342 209 486 51,978 55,742 7,574 8,479 170 553 163 122 1,562 1,635 13,450 17,076 396 .. 6,184 8,637 502 713 289 286 883 1,033 3,926 7,707 11,008 16,878 .. 28,625 13,631 18,926 12,126 23,375
Food, beverages, and tobacco
% of total
Textiles and clothing
% of total
Machinery and transport equipment
% of total
Chemicals
ECONOMY
4.3
Structure of manufacturing
Other manufacturing a
% of total
% of total
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
45 14 12 27 12 20 27 14 8 41 9 28 .. 38 .. 11 4 .. .. .. .. .. .. .. .. 20 .. 38 13 .. .. 30 22 .. 33 22 .. .. .. 37 21 28 .. 37 15 18 .. 24 51 .. 55 23 39 21 15 16
42 19 13 19 .. .. 16 12 10 48 11 28 .. 48 .. 8 7 .. .. 39 .. .. .. .. .. 32 .. 44 10 .. .. 31 25 .. .. 36 .. .. .. 35 23 31 .. 20 30 16 12 23 51 .. .. 26 33 26 .. 8
10 9 15 15 20 16 4 9 13 5 5 7 .. 10 .. 12 3 .. .. .. .. .. .. .. .. 26 .. 10 6 .. .. 46 5 .. 37 17 .. .. .. 31 3 8 .. 29 46 2 .. 27 8 .. 16 11 11 9 21 5
22 8 12 17 .. .. 1 9 12 7 3 6 .. 8 .. 8 4 .. .. 12 .. .. .. .. .. 18 .. 8 4 .. .. 48 4 .. .. 16 .. .. .. 34 2 .. .. 9 11 2 5 26 5 .. .. 10 9 6 .. 3
3 26 25 12 20 4 29 32 34 .. 40 4 .. 10 .. 32 2 .. .. .. .. .. .. .. .. 14 .. 1 31 .. .. 2 24 .. 1 8 .. .. .. 1 25 13 .. .. 13 25 .. 9 2 .. .. 8 13 26 13 18
2 26 20 25 .. .. 31 32 26 .. 39 5 .. 9 .. 45 4 .. .. 15 .. .. .. .. .. 15 .. 5 46 .. .. 2 28 .. .. 8 .. .. .. 3 25 14 .. .. 8 29 4 13 .. .. .. 6 15 23 .. 15
5 12 14 9 8 11 16 9 7 .. 10 15 .. 9 .. 9 3 .. .. .. .. .. .. .. .. 9 .. 18 11 .. .. 4 18 .. 1 12 .. .. .. 5 16 7 .. .. 4 9 .. 15 8 .. .. 9 12 7 6 44
5 7 22 11 .. .. 36 5 8 .. 10 17 .. 8 .. 9 2 .. .. 6 .. .. .. .. .. 11 .. 16 11 .. .. 5 15 .. .. 13 .. .. .. 6 14 13 .. .. 26 8 5 16 6 .. .. 10 13 6 .. 58
36 39 34 37 40 49 24 37 37 54 37 47 .. 33 .. 36 88 .. .. .. .. .. .. .. .. 31 .. 33 39 .. .. 17 32 .. 27 41 .. .. .. 25 35 44 .. 34 22 46 .. 25 31 .. 29 49 26 37 45 17
29 40 33 28 .. .. 16 42 44 46 36 45 .. 28 .. 30 83 .. .. 29 .. .. .. .. .. 24 .. 28 30 .. .. 15 28 .. .. 27 .. .. .. 23 35 43 .. 71 25 46 74 22 37 .. .. 49 29 38 .. 15
2004 World Development Indicators
191
4.3
Structure of manufacturing Manufacturing value added
$ millions 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania b Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
2000
9,152 4,768 .. .. 473 205 10,049 18,235 747 566 .. .. 31 28 .. 24,407 .. 4,075 4,008 4,468 41 .. 24,043 21,643 .. 95,110 1,077 2,459 .. 1,059 250 348 .. 47,689 .. .. 2,508 4,579 653 237 361 624 23,217 41,212 162 118 438 599 2,075 3,537 26,882 26,994 .. 643 230 527 32,977 5,099 2,643 .. 206,727 232,507 1,040,600 1,520,300 2,597 3,392 .. 645 9,809 15,621 793 5,786 .. 591 449 493 1,048 329 1,799 1,003 4,475,773 t 5,826,313 t 112,968 149,818 698,289 1,114,738 490,903 806,107 170,285 302,007 755,166 1,263,141 188,907 514,058 .. .. 243,987 307,798 47,699 81,370 61,101 85,928 42,805 37,493 3,708,270 4,573,059 1,221,575 1,119,610
Food, beverages, and tobacco
% of total
2004 World Development Indicators
% of total
Machinery and transport equipment
% of total
Chemicals
% of total
Other manufacturing a
% of total
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
19 .. .. .. 60 .. .. 4 .. 12 .. 14 18 51 .. 69 10 10 35 .. 51 24 .. 30 19 16 .. .. .. .. 13 12 31 .. 17 .. .. .. 44 28
.. 16 .. .. 44 28 .. 2 .. 11 .. 14 14 42 .. .. 7 9 27 .. 45 .. .. .. 18 13 .. .. .. .. .. .. 37 .. 22 .. .. .. .. 30
18 .. .. .. 3 .. .. 3 .. 15 .. 8 8 24 .. 8 2 4 29 .. 3 30 .. 3 20 15 .. .. .. .. 5 5 18 .. 5 .. .. .. 11 19
.. 2 .. .. 5 8 .. 1 .. 9 .. 7 7 26 .. .. 1 3 24 .. 0 .. .. .. 33 18 .. .. .. .. .. .. 12 .. 2 .. .. .. .. 7
14 .. .. .. 5 .. .. 53 .. 16 .. 18 25 4 .. 1 32 34 .. .. 6 19 .. 3 5 16 .. .. .. .. 32 31 9 .. 5 .. .. .. 7 9
.. 19 .. .. 3 13 .. 62 .. 16 .. 20 23 8 .. .. 39 27 .. .. 5 .. .. .. 9 17 .. .. .. .. .. .. 3 .. .. .. .. .. .. 29
4 .. .. .. 9 .. .. 10 .. 9 .. 9 10 4 .. 0 9 .. .. .. 11 2 .. 19 4 10 .. .. .. .. 11 12 10 .. 9 .. .. .. 9 6
.. 9 .. .. 26 11 .. 14 .. 11 .. 9 10 4 .. .. 11 .. .. .. 7 .. .. .. 9 11 .. .. .. .. .. .. 8 .. .. .. .. .. .. 6
45 .. .. .. 23 .. .. 29 .. 48 .. 50 39 17 .. 22 47 53 36 .. 28 26 .. 44 52 43 .. .. .. .. 38 40 32 .. 64 .. .. .. 29 38
.. 54 .. .. 21 40 .. 20 .. 52 .. 50 47 19 .. .. 42 60 49 .. 43 .. .. .. 31 41 .. .. .. .. .. .. 39 .. 76 .. .. .. .. 28
a. Includes unallocated data. b. Data cover mainland Tanzania only.
192
Textiles and clothing
4.3
ECONOMY
Structure of manufacturing About the data
The data on the distribution of manufacturing value
United Nations International Standard Industrial
as advertising, accounting, and many other service activ-
added by industry are provided by the United Nations
Classification (ISIC) revision 2. First published in 1948,
ities. In some cases the processes may be carried out
Industrial Development Organization (UNIDO). UNIDO
the ISIC has its roots in the work of the League of
by different technical units within the larger enterprise,
obtains data on manufacturing value added from a vari-
Nations Committee of Statistical Experts. The commit-
but collecting data at such a detailed level is not practi-
ety of national and international sources, including the
tee’s efforts, interrupted by the Second World War, were
cal. Nor would it be useful to record production data at
United Nations Statistics Division, the World Bank, the
taken up by the United Nations Statistical Commission,
the very highest level of a large, multiplant, multiproduct
Organisation for Economic Co-operation and Develop-
which at its first session appointed a committee on
firm. The ISIC has therefore adopted as the definition of
ment, and the International Monetary Fund. To improve
industrial classification. The latest revision, ISIC revision
an establishment “an enterprise or part of an enterprise
comparability over time and across countries, UNIDO
3, was completed in 1989, and many countries have
which independently engages in one, or predominantly
supplements these data with information from industri-
now switched to it. But revision 2 is still widely used for
one, kind of economic activity at or from one
al censuses, statistics supplied by national and inter-
compiling cross-country data. Concordances matching
location…for which data are available…” (United
national organizations, unpublished data that it collects
ISIC categories to national systems of classification and
Nations 1990, p. 25). By design, this definition matches
in the field, and estimates by the UNIDO Secretariat.
to related systems such as the Standard International
the reporting unit required for the production accounts of
Nevertheless, coverage may be less than complete, par-
Trade Classification (SITC) are readily available.
the United Nations System of National Accounts.
ticularly for the informal sector. To the extent that direct
In establishing a classification system, compilers
information on inputs and outputs is not available, esti-
must define both the types of activities to be described
mates may be used that may result in errors in industry
and the organizational units whose activities are to be
totals. Moreover, countries use different reference peri-
reported. There are many possibilities, and the choices
• Manufacturing value added is the sum of gross out-
ods (calendar or fiscal year) and valuation methods
made affect how the resulting statistics can be inter-
put less the value of intermediate inputs used in pro-
(basic, producer, or purchaser prices) to estimate value
preted and how useful they are in analyzing economic
duction for industries classified in ISIC major division
added. (See also About the data for table 4.2.)
behavior. The ISIC emphasizes commonalities in the
3. • Food, beverages, and tobacco correspond to
The data on manufacturing value added in U.S. dollars
production process and is explicitly not intended to
ISIC division 31. • Textiles and clothing correspond
are from the World Bank’s national accounts files. These
measure outputs (for which there is a newly developed
to ISIC division 32. • Machinery and transport equip-
figures may differ from those used by UNIDO to calculate
Central Product Classification). Nevertheless, the ISIC
ment comprise ISIC groups 382–84. • Chemicals cor-
the shares of value added by industry, in part because
views an activity as defined by “a process resulting in
respond to ISIC groups 351 and 352. • Other
of differences in exchange rates. Thus estimates of
a homogeneous set of products” (United Nations 1990
manufacturing covers wood and related products (ISIC
value added in a particular industry calculated by apply-
[ISIC, series M, no. 4, rev. 3], p. 9).
division 33), paper and related products (ISIC division
Definitions
34), petroleum and related products (ISIC groups
ing the shares to total manufacturing value added will
Firms typically use a multitude of processes to pro-
not match those from UNIDO sources. The classification
duce a final product. For example, an automobile manu-
353–56), basic metals and mineral products (ISIC divi-
of manufacturing industries in the table accords with the
facturer engages in forging, welding, and painting as well
sions 36 and 37), fabricated metal products and professional goods (ISIC groups 381 and 385), and other
4.3a
industries (ISIC group 390). When data for textiles and
Manufacturing continues to show strong growth in East Asia
clothing, machinery and transport equipment, or
Value added in manufacturing (1990 = 100)
chemicals are shown in the table as not available, they
350
are included in “other manufacturing.”
East Asia & Pacific 300
Data sources The data on value added in manufacturing in U.S.
250
dollars are from the World Bank’s national South Asia
200
accounts files. The data used to calculate shares of value added by industry are provided to the
Middle East & North Africa
World Bank in electronic files by UNIDO. The most 150
Latin America & Caribbean
recent published source is UNIDO’s International Yearbook of Industrial Statistics 2003. The ISIC
100
Sub-Saharan Africa
system is described in the United Nations’ International Standard Industrial Classification of
50
1990
All Economic Activities, Third Revision (1990). The 1992
1994
1996
1998
2000
2002
Manufacturing continues to be the dominant sector in East Asia and Pacific. Growing by an average 10 percent a year in 1990–2002, value added in manufacturing has more than tripled.
discussion of the ISIC draws on Jacob Ryten’s paper “Fifty Years of ISIC: Historical Origins and Future Perspectives” (1998).
Source: World Bank data files.
2004 World Development Indicators
193
4.4
Growth of merchandise trade Export volume
Import volume
Export value
Import value
average annual
average annual
average annual
average annual
% growth
% growth
% growth
1980–90
Afghanistan Albania a Algeria Angola Argentina Armenia a Australia a Austria a Azerbaijan a Bangladesh Belarus a Belgium a, b Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria a Burkina Faso Burundi Cambodia Cameroon Canada a Central African Republic Chad Chile China† Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia a Cuba Czech Republic a Denmark a Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia a Ethiopia Finland a France a Gabon Gambia, The Georgia Germany a, c Ghana Greece a Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
194
–9.7 .. 3.3 10.0 4.9 .. 6.3 6.6 .. 8.4 .. 4.5 11.9 3.1 .. 14.9 6.2 .. –0.3 3.4 .. 8.4 6.4 –0.0 8.7 9.1 .. 15.3 7.9 14.8 7.3 3.7 2.6 .. –1.1 .. 4.1 –0.9 7.1 2.1 –4.6 .. .. –0.4 2.3 3.6 2.6 –4.2 .. 4.5 –17.2 5.0 –1.1 .. –2.1 –0.4 ..
1990–2001
–3.9 .. 2.3 5.6 8.8 .. 7.1 .. .. 28.2 .. 6.2 2.3 3.2 .. 9.3 5.4 .. 11.4 10.3 .. 2.3 8.7 18.8 1.5 10.7 13.4 7.7 4.3 –10.0 5.7 12.6 3.0 .. –1.0 .. 5.2 3.3 5.6 2.8 3.0 26.0 .. 6.8 9.3 6.4 3.4 –13.2 .. 5.9 10.0 8.9 8.2 4.7 17.1 12.2 5.3
2004 World Development Indicators
1980–90
–1.8 .. –8.0 –1.8 –6.8 .. 6.0 .. .. 3.0 .. 4.0 –10.0 –1.3 .. 9.3 0.8 .. 3.8 0.9 .. 4.8 7.4 4.2 10.7 –2.9 .. 13.8 –2.1 37.8 –2.5 5.2 –2.1 .. –0.5 .. 3.1 0.8 –1.9 8.0 4.5 .. .. 3.6 4.4 3.7 –3.5 –6.0 .. 4.9 –20.1 6.4 0.1 .. –0.3 –4.6 ..
1990–2001
–0.2 .. 2.0 7.3 13.6 .. 8.7 .. .. 23.3 .. 5.4 6.6 8.1 .. 5.3 14.7 .. 1.6 6.0 .. 5.5 8.7 3.2 3.6 9.3 8.1 8.2 7.6 –6.1 1.8 13.8 3.5 .. 3.1 .. 6.0 12.8 6.5 1.1 7.5 7.1 .. 3.1 4.3 5.6 2.8 0.1 .. 4.3 9.7 8.9 10.2 –1.9 –5.5 12.7 6.8
1980–90
–10.5 .. –4.4 16.5 2.2 .. 6.6 10.2 .. 8.0 .. 7.8 18.7 –1.9 .. 18.7 5.2 –12.3 7.8 2.5 .. 2.4 6.8 3.5 9.4 8.1 .. 16.7 7.7 7.7 2.1 4.7 1.8 .. –0.9 .. 8.4 –2.1 –0.5 –3.3 –4.7 .. .. –1.0 7.4 7.5 –3.9 –0.0 .. 9.2 –2.6 5.8 –2.3 .. 4.2 –1.2 ..
1990–2001
–4.7 13.3 3.0 6.6 9.2 –6.8 4.7 5.3 –6.4 14.2 13.9 6.3 2.4 4.4 .. 8.5 5.6 2.2 10.4 –5.6 .. –0.3 7.8 3.0 1.8 8.6 12.8 7.3 6.7 –6.7 7.5 14.8 5.0 1.6 –1.5 9.9 3.6 3.9 6.0 3.7 8.9 24.4 17.4 9.2 6.9 3.8 0.7 –13.7 .. 3.7 10.6 2.1 8.9 0.8 13.3 11.8 6.6
Net barter terms of trade
% growth 1980–90
–0.1 .. –2.7 3.7 –6.6 .. 6.4 8.7 .. 3.5 .. 6.4 –4.8 –0.4 .. 9.1 –1.8 –14.0 4.4 2.2 .. 0.1 7.9 7.8 12.5 2.8 .. 14.7 –0.2 26.7 –0.7 4.4 –1.5 .. 1.5 .. 6.3 3.3 –1.3 12.6 2.4 .. .. 3.9 6.9 6.5 1.1 2.4 .. 7.1 –0.4 6.6 0.5 .. 5.3 –2.9 ..
1995 = 100
1990–2001
1990
2001
–1.1 17.2 1.2 8.0 13.4 0.5 5.4 3.8 4.0 9.6 14.1 4.5 6.9 8.4 .. 1.7 10.9 5.6 1.5 –6.3 .. 2.1 7.0 –0.7 6.2 8.9 10.3 7.7 8.5 –0.5 1.4 12.8 3.6 7.7 2.3 10.0 3.8 12.9 7.9 3.8 10.6 5.8 18.9 7.7 4.4 3.2 2.4 –0.4 .. 3.2 9.0 4.2 11.0 –2.3 –3.2 13.7 7.2
99 .. 128 118 97 .. 117 .. .. 100 .. 100 100 115 .. 109 60 .. 91 79 .. 90 100 124 116 84 .. 101 95 108 122 72 82 .. 96 .. 100 97 141 86 69 91 .. 90 100 97 126 100 .. 102 94 108 98 135 143 116 102
100 .. 174 109 108 .. 105 .. .. 92 .. .. 82 111 .. 130 91 .. 83 44 .. 95 103 54 110 69 81 102 109 125 156 95 103 .. 92 .. 102 104 114 85 80 97 .. 79 88 96 113 100 .. 95 101 107 83 100 70 89 117
Honduras Hungary a India Indonesia Iran, Islamic Rep. Iraq Ireland a Israel a Italy a Jamaica Japan a Jordan Kazakhstan a Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic a Lao PDR a Latvia a Lebanon Lesotho Liberia Libya Lithuania a Macedonia, FYR a Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova a Mongolia Morocco Mozambique Myanmar Namibia a Nepal a Netherlands a New Zealand a Nicaragua Niger Nigeria Norway a Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland a Portugal a Puerto Rico
Export volume
Import volume
Export value
Import value
average annual
average annual
average annual
average annual
% growth
% growth
% growth
1980–90
1990–2001
4.1 3.4 4.2 8.1 17.1 2.3 9.3 6.9 4.3 1.6 5.1 7.8 .. 1.7 .. 12.3 .. .. .. .. –5.6 6.3 –3.5 0.1 .. .. –2.2 2.3 14.6 4.4 4.1 11.5 15.4 .. 3.1 5.6 –9.5 –5.9 .. .. 4.5 3.5 –4.8 –5.2 –4.4 4.2 11.0 7.9 –0.6 4.6 12.6 2.7 17.3 4.8 11.9 ..
2.5 10.9 11.2 8.6 –0.9 29.5 15.1 9.6 5.4 4.6 2.3 5.3 .. 4.1 .. 15.4 20.0 .. .. 7.2 2.4 13.9 7.4 –4.1 .. .. 4.7 3.0 10.7 11.5 2.1 2.4 14.8 .. .. 6.7 18.8 16.7 2.1 .. 6.8 4.4 10.1 3.7 2.6 6.3 11.0 3.0 6.1 2.5 1.2 9.6 21.9 9.8 .. ..
1980–90
1.6 1.3 4.7 .. –2.4 –4.5 4.8 5.8 5.3 3.0 6.6 1.1 .. 2.5 .. 11.7 .. .. .. .. –7.5 3.5 –7.6 –6.6 .. .. –4.4 –0.1 .. 3.0 –2.9 11.8 0.8 .. .. 3.1 –2.7 –10.0 .. .. 4.5 4.3 –3.5 –5.2 –21.4 3.5 .. 2.6 –6.6 .. 10.1 –2.0 18.3 1.5 .. ..
Net barter terms of trade
% growth
1990–2001
1980–90
1990–2001
12.3 12.0 12.6 .. –7.5 9.8 11.2 8.2 4.7 7.3 5.1 3.9 .. 6.6 .. 9.3 .. .. .. .. 8.6 1.6 9.7 0.3 .. .. 6.2 –2.3 .. 5.3 4.3 3.1 12.3 .. .. 7.2 3.0 13.7 4.8 .. 6.7 5.5 8.8 –2.2 2.8 7.4 .. 1.9 6.4 .. 2.3 9.3 16.5 17.2 .. ..
1.6 1.4 7.3 –0.8 7.2 –4.0 12.8 8.3 8.7 1.1 8.9 6.0 .. –1.1 .. 15.0 .. .. 11.0 .. –5.6 3.8 –3.1 –7.3 .. .. –1.0 2.0 8.6 6.1 8.2 14.3 5.8 .. 5.0 6.1 –9.6 –7.2 .. 8.1 4.6 6.2 –5.8 –5.4 –8.4 5.3 –2.2 8.0 –0.6 4.8 11.5 –1.5 3.9 1.4 15.1 ..
6.3 12.8 9.1 7.1 1.2 29.4 13.4 10.5 4.2 2.2 3.3 6.7 12.4 5.6 .. 9.1 16.3 4.9 13.2 10.7 4.1 12.2 4.6 –2.2 9.5 1.8 9.7 1.1 14.0 7.5 –2.4 3.1 15.4 –0.2 –1.7 6.6 12.9 16.0 0.3 10.8 5.2 3.5 9.5 0.2 3.7 5.5 14.8 4.0 9.1 5.9 2.7 8.3 17.1 9.9 4.7 ..
1980–90
0.5 0.1 4.2 .. 0.2 –2.2 7.0 5.9 6.9 2.8 5.1 –1.9 .. 1.7 .. 12.0 .. .. 6.6 .. –5.5 3.4 –7.2 –4.4 .. .. –2.4 3.2 .. 2.7 –1.8 12.8 6.3 .. 5.0 3.6 0.1 –5.1 .. 6.9 4.4 5.4 –3.1 –3.5 –15.6 6.2 .. 2.9 –3.6 .. 4.2 1.3 2.9 –3.2 10.3 ..
4.4
ECONOMY
Growth of merchandise trade
1995 = 100
1990–2001
1990
2001
13.2 13.2 9.5 .. –6.5 10.3 10.4 7.3 3.2 6.8 4.3 5.2 5.1 5.6 .. 6.5 .. 4.7 10.5 17.4 8.9 –0.4 8.8 1.8 13.2 4.4 7.4 –0.9 .. 3.8 0.2 3.1 13.0 2.6 0.8 5.2 1.6 21.3 3.6 8.2 5.0 4.8 11.0 0.9 3.5 3.5 .. 2.5 7.3 .. 3.6 9.2 10.3 16.7 4.7 ..
81 106 79 .. 170 132 107 97 98 105 91 80 .. 68 .. 96 .. .. .. .. 105 97 112 145 .. .. 102 141 .. 122 96 104 109 .. .. 95 115 117 115 .. 98 103 119 136 162 111 .. 91 69 .. 87 93 109 86 .. ..
102 96 91 .. 225 162 99 106 102 87 88 85 .. 88 .. 69 .. .. .. .. 112 100 89 200 .. .. 148 96 .. 95 96 113 107 .. .. 115 79 65 98 .. 99 106 71 77 157 157 .. 83 100 .. 84 78 118 95 .. ..
2004 World Development Indicators
195
4.4
Growth of merchandise trade Export volume
Import volume
Export value
Import value
average annual
average annual
average annual
average annual
% growth
% growth
% growth
1980–90
Romania a Russian Federation a Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic a Slovenia a Somalia South Africa a, d Spain a Sri Lanka Sudan Swaziland Sweden a Switzerland a Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine a United Arab Emirates United Kingdom a United States a Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. a Zambia Zimbabwe
.. .. 3.3 –6.3 1.2 .. –2.7 12.2 .. .. –1.5 1.6 2.7 4.6 –3.0 7.7 4.4 3.7 19.4 .. .. 13.7 –1.2 –10.9 4.9 19.4 .. –13.5 .. 8.9 4.5 3.6 4.4 .. 3.4 .. .. .. –0.5 4.1
1990–2001
.. .. –4.2 1.6 6.8 .. –36.3 10.8 .. .. –0.5 5.0 10.9 6.9 17.1 1.2 8.4 .. 1.2 .. 6.5 9.1 9.3 3.6 5.8 10.8 .. 15.6 .. 2.1 6.2 6.2 5.3 .. 5.1 .. .. .. 6.2 8.7
1980–90
.. .. 2.3 .. 0.4 .. –3.0 8.5 .. .. –11.1 –0.9 10.5 1.7 –7.7 2.3 5.0 .. –1.0 .. .. 11.3 0.6 –20.4 1.6 15.4 .. –6.7 .. –1.3 6.7 7.2 1.2 .. –4.0 .. .. .. 2.1 3.4
1990–2001
.. .. 1.2 .. 5.5 .. –8.0 7.1 .. .. 2.3 7.7 9.2 10.2 11.2 3.8 6.2 .. 8.2 .. –0.8 2.2 5.7 10.2 4.8 9.6 .. 24.1 .. 9.2 6.8 8.8 8.8 .. 5.3 .. .. .. 4.3 9.6
1980–90
–4.0 .. –0.3 –13.3 3.6 .. –2.4 9.9 .. .. –1.1 0.7 10.8 5.4 –2.5 4.6 8.0 9.5 2.4 .. .. 13.8 1.1 –9.4 3.4 14.1 .. –8.3 .. –0.8 5.9 5.7 4.5 .. –4.4 .. .. .. 0.9 2.9
1990–2001
8.7 9.1 –1.4 3.5 3.7 .. –24.8 8.6 9.8 7.5 –2.4 2.3 7.9 10.1 14.0 2.2 4.9 2.5 0.8 .. 7.1 9.6 7.1 7.3 5.9 8.8 .. 13.0 7.0 4.0 4.7 6.6 4.0 .. 5.7 .. .. .. –1.4 2.4
Net barter terms of trade
% growth 1980–90
–3.8 .. 3.3 .. 1.4 .. –8.7 8.1 .. .. –9.2 –1.4 10.6 2.2 –6.4 –0.5 6.7 8.8 –8.5 .. .. 12.6 2.0 –12.3 2.7 9.3 .. 3.4 .. 0.7 8.5 8.2 –1.3 .. –3.3 .. .. .. –0.0 –0.4
1995 = 100
1990–2001
1990
2001
7.4 2.5 –0.8 .. 3.3 .. –2.7 6.6 10.7 8.3 1.7 5.0 6.0 12.3 9.9 4.8 3.7 1.9 10.2 .. 0.7 4.7 5.0 11.3 5.1 8.8 .. 19.0 6.3 11.2 4.9 9.1 8.3 .. 5.6 .. .. .. 1.9 3.1
.. .. 36 .. 109 .. 73 111 .. .. 99 99 96 83 123 100 99 .. 131 .. 110 102 134 117 103 104 .. 74 .. 174 101 98 100 .. 142 .. .. .. 109 100
.. .. 72 .. 91 .. .. 92 .. .. 82 100 99 .. 141 100 89 .. .. .. 95 78 107 172 99 93 .. 78 .. 213 104 99 87 .. 132 .. .. .. 56 104
a. Data are from the International Monetary Fund’s International Financial Statistics database. b. Includes Luxembourg. c. Data prior to 1990 refer to the Federal Republic of Germany before unification. d. Data refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland).
196
2004 World Development Indicators
About the data
4.4
ECONOMY
Growth of merchandise trade Definitions
Data on international trade in goods are available
which maintains the Commodity Trade (COMTRADE)
• Export and import volumes are average annual
from each country’s balance of payments and cus-
database. The United Nations Conference on Trade
growth rates calculated for low- and middle-income
toms records. While the balance of payments focus-
and Development (UNCTAD) compiles a variety of
economies from UNCTAD’s quantum index series
es on the financial transactions that accompany
international trade statistics, including price and vol-
and for high-income economies from export and
trade, customs data record the direction of trade and
ume indexes, based on the COMTRADE data. The
import data deflated by the IMF’s trade price defla-
the physical quantities and value of goods entering
IMF and the World Trade Organization also compile
tors. • Export and import values are average annu-
or leaving the customs area. Customs data may dif-
data on trade prices and volumes. The growth rates
al growth rates calculated from UNCTAD’s value
fer from data recorded in the balance of payments
and terms of trade for low- and middle-income
indexes or from current values of merchandise
because of differences in valuation and the time of
economies shown in the table were calculated from
exports and imports. • Net barter terms of trade
recording. The 1993 System of National Accounts
index numbers compiled by UNCTAD. Volume meas-
are calculated as the ratio of the export price index
and the fifth edition of the International Monetary
ures for high-income economies were derived by
to the corresponding import price index measured
Fund’s (IMF) Balance of Payments Manual (1993)
deflating the value of trade using deflators from the
relative to the base year 1995.
attempted to reconcile the definitions and reporting
IMF’s International Financial Statistics. In some
standards for international trade statistics, but dif-
cases price and volume indexes from different
ferences in sources, timing, and national practices
sources may vary significantly as a result of differ-
limit comparability. Real growth rates derived from
ences in estimation procedures. All indexes are
trade volume indexes and terms of trade based on
rescaled to a 1995 base year. Terms of trade were
unit price indexes may therefore differ from those
computed from the same indicators.
derived from national accounts aggregates.
The terms of trade measures the relative prices of
Trade in goods, or merchandise trade, includes all
a country’s exports and imports. There are a number
goods that add to or subtract from an economy’s
of ways to calculate terms of trade. The most com-
material resources. Thus the total supply of goods in
mon is the net barter (or commodity) terms of trade,
an economy is made up of gross output plus imports
constructed as the ratio of the export price index to
less exports (currency in circulation, titles of owner-
the import price index. When a country’s net barter
ship, and securities are excluded, but nonmonetary
terms of trade increase, its exports are becoming
gold is included). Trade data are collected on the
more valuable or its imports cheaper.
basis of a country’s customs area, which in most cases is the same as its geographic area. Goods provided as part of foreign aid are included, but goods destined for extraterritorial agencies (such as embassies) are not. Collecting and tabulating trade statistics are difficult. Some developing countries lack the capacity to report timely data; this is a problem especially for countries that are landlocked and those whose territorial boundaries are porous. As a result, it is necessary to estimate their trade from the data reported by their partners. (For further discussion of the use of partner country reports, see About the data for table 6.2.) Countries that belong to common customs unions may need to collect data through direct inquiry of companies. In some cases economic or political concerns may lead national authorities to suppress or misrepresent data on certain trade
Data sources
flows, such as oil, military equipment, or the exports
The main source of trade data for developing
of a dominant producer. In other cases reported
countries is UNCTAD’s annual Handbook of
trade data may be distorted by deliberate under- or
International Trade and Development Statistics.
over-invoicing to effect capital transfers or avoid
The IMF’s International Financial Statistics
taxes. And in some regions smuggling and black
includes data on the export and import values
market trading result in unreported trade flows.
and deflators for high-income and selected devel-
By international agreement customs data are
oping economies.
reported to the United Nations Statistics Division,
2004 World Development Indicators
197
4.5
Structure of merchandise expor ts Merchandise exports
Food
$ millions 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium a Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong, China b Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
198
235 230 12,930 3,910 12,353 .. 39,752 41,265 .. 1,671 .. 117,703 288 926 276 1,784 31,414 5,030 152 75 86 2,002 127,629 120 188 8,372 62,091 82,390 6,766 2,326 981 1,448 3,072 4,597 5,100 12,170 36,870 2,170 2,714 3,477 582 15 .. 298 26,571 216,588 2,204 31 .. 421,100 897 8,105 1,163 671 19 160 67,245
Agricultural raw materials
% of total 2002
101 330 19,130 7,600 25,352 508 65,034 78,694 2,168 6,093 8,100 224,185 365 1,310 950 2,510 60,362 5,745 166 30 1,500 1,700 252,394 160 180 18,340 325,565 201,150 12,001 1,210 2,215 5,258 4,390 4,899 1,500 38,403 57,045 5,183 5,030 4,381 2,992 14 4,336 415 44,836 331,780 2,560 15 326 613,093 1,840 10,353 2,232 750 51 280 135,065
2004 World Development Indicators
Fuels
% of total
Ores and metals
% of total
Manufactures
% of total
% of total
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
.. .. 0 0 56 .. 22 3 .. 14 .. .. 15 19 .. .. 28 .. .. .. .. 20 9 .. .. 24 13 3 33 .. .. 58 .. 13 .. .. 27 21 44 10 57 .. .. .. 2 16 .. .. .. 5 51 30 67 .. .. 14 4
.. 4 0 .. 46 16 22 6 3 7 8 9 23 34 .. 3 28 10 22 88 .. 21 7 .. .. 26 5 2 19 .. .. 31 59 11 59 3 19 41 43 9 33 .. 12 69 2 11 1 81 26 4 45 24 53 2 .. .. 1
.. .. 0 0 4 .. 10 4 .. 7 .. .. 56 8 .. .. 3 .. .. .. .. 14 9 .. .. 9 3 0 4 .. .. 5 .. 6 .. .. 3 0 1 10 1 .. .. .. 10 2 .. .. .. 1 15 3 6 .. .. 1 2
.. 7 0 .. 2 2 5 2 1 1 4 1 71 3 .. 0 4 3 56 1 .. 20 5 .. .. 10 1 0 6 .. .. 3 14 4 0 2 3 .. 7 0 1 .. 8 15 6 1 12 1 2 1 10 3 4 0 .. .. 1
.. .. 96 93 8 .. 21 1 .. 1 .. .. 15 25 .. .. 2 .. .. .. .. 50 10 .. .. 1 8 0 37 .. .. 1 .. 9 .. .. 3 0 52 29 2 .. .. .. 1 2 .. .. .. 1 9 7 2 .. .. 0 1
.. 1 97 .. 17 4 22 2 89 0 20 4 0 27 .. 0 4 9 3 .. .. 47 13 .. .. 1 3 1 36 .. .. 1 11 9 1 4 6 16 40 34 5 .. 5 0 3 2 83 0 9 1 11 11 7 1 .. .. 1
.. .. 0 6 2 .. 20 3 .. .. .. .. 0 44 .. .. 14 .. .. .. .. 7 9 .. .. 55 2 1 0 .. .. 1 .. 5 .. .. 1 0 0 9 3 .. .. .. 4 3 .. .. .. 3 17 7 0 .. .. 0 1
.. 3 0 .. 4 18 16 3 0 0 1 3 0 20 .. 5 8 11 0 10 .. 4 4 .. .. 41 2 2 1 .. .. 1 0 3 29 1 1 2 0 5 3 .. 2 1 3 2 2 0 27 2 17 8 1 68 .. .. 1
.. .. 3 0 29 .. 24 88 .. 77 .. .. 13 5 .. .. 52 .. .. .. .. 9 59 .. .. 11 72 95 25 .. .. 27 .. 68 .. .. 60 78 2 42 38 .. .. .. 83 77 .. .. .. 89 8 54 24 .. .. 85 93
.. 86 2 .. 31 61 29 82 6 92 64 79 6 17 .. 91 54 61 19 1 .. 7 63 .. .. 18 90 95 38 .. .. 63 21 73 10 89 66 34 10 35 58 .. 72 14 85 81 2 17 35 86 16 52 35 28 .. .. 94
Merchandise exports
Food
$ millions 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
831 10,000 17,969 25,675 19,305 12,380 23,743 12,080 170,304 1,158 287,581 1,064 .. 1,031 1,857 65,016 7,042 .. 79 .. 494 62 868 13,225 .. 1,199 319 417 29,452 359 469 1,194 40,711 .. 661 4,265 126 325 1,085 204 131,775 9,394 330 282 13,596 34,047 5,508 5,615 340 1,177 959 3,230 8,117 14,320 16,417 ..
Agricultural raw materials
% of total 2002
1,270 34,337 49,251 57,130 24,440 13,520 88,224 29,513 250,975 1,105 416,726 2,743 9,709 2,094 724 162,470 15,426 486 298 2,284 1,046 395 220 10,970 5,560 1,112 785 478 93,265 947 315 1,755 160,682 667 501 7,930 682 3,015 1,096 568 244,304 14,363 596 303 15,107 60,971 11,172 9,913 846 1,550 1,030 7,688 36,265 41,010 25,621 ..
Fuels
% of total
Ores and metals
% of total
ECONOMY
4.5
Structure of merchandise expor ts
Manufactures
% of total
% of total
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
82 23 16 11 .. .. 22 8 6 19 1 11 .. 49 .. 3 1 .. .. .. .. .. .. 0 .. .. 73 93 12 36 .. 32 12 .. .. 26 .. 51 .. 13 20 47 77 .. 1 7 1 9 75 22 52 21 19 13 7 ..
64 7 12 12 4 .. 7 4 7 23 1 15 5 32 .. 2 .. 18 .. 10 19 .. .. .. 12 16 .. 87 8 .. .. 26 5 64 6 21 23 .. 36 10 19 49 72 38 0 7 6 11 79 15 75 30 5 8 7 ..
4 3 4 5 .. .. 2 3 1 0 1 0 .. 6 .. 1 0 .. .. .. .. .. .. 0 .. .. 4 2 14 62 .. 1 2 .. .. 3 .. 36 .. 3 4 18 14 .. 1 2 0 10 1 9 38 3 2 3 6 ..
5 1 1 4 0 .. 0 1 1 0 1 0 1 11 .. 1 .. 23 .. 24 6 .. .. .. 4 1 .. 2 2 .. .. 0 1 3 15 1 4 .. 1 0 4 13 4 1 0 1 0 1 1 2 14 3 0 1 3 ..
1 3 3 44 .. .. 1 1 2 1 0 0 .. 13 .. 1 93 .. .. .. .. .. .. 94 .. .. 1 0 18 .. .. 1 38 .. .. 4 .. 0 .. .. 10 4 0 .. 97 48 92 1 0 0 0 10 2 11 3 ..
0 1 5 24 86 .. 0 0 2 3 0 0 56 31 .. 4 .. 20 .. 1 0 .. .. .. 23 4 .. 0 9 .. .. 0 9 0 1 3 10 .. 1 .. 1 2 2 0 100 61 77 2 6 29 0 8 1 5 2 ..
4 6 5 4 .. .. 1 2 1 10 1 38 .. 3 .. 1 0 .. .. .. .. .. .. 0 .. .. 8 0 2 0 .. 0 6 .. .. 15 .. 2 .. 0 3 6 1 .. 0 10 1 0 1 58 0 47 8 9 3 ..
6 2 4 5 1 .. 0 1 1 10 1 17 18 2 .. 1 .. 6 .. 6 6 .. .. .. 2 8 .. 0 1 .. .. 0 1 2 43 8 55 .. 9 0 2 4 3 56 0 6 1 0 1 51 1 38 1 4 2 ..
9 63 71 35 .. .. 70 87 88 69 96 51 .. 29 .. 94 6 .. .. .. .. .. .. 5 .. .. 14 5 54 2 .. 66 43 .. .. 52 .. 10 .. 83 59 23 8 .. 1 33 5 79 21 10 10 18 38 59 80 ..
26 86 75 54 9 .. 88 93 88 64 93 68 19 24 .. 92 .. 33 .. 59 69 .. .. .. 58 70 .. 10 79 .. .. 73 84 31 36 66 8 .. 52 67 74 28 19 3 0 22 15 85 12 2 15 21 50 82 86 ..
2004 World Development Indicators
199
4.5
Structure of merchandise expor ts Merchandise exports
$ millions 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore b Slovak Republic Slovenia Somalia South Africa c Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia d Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Food
Agricultural raw materials
% of total 2002
4,960 13,869 40,000 106,858 110 56 44,417 73,940 761 1,055 2,929 2,275 138 49 52,730 125,177 6,355 14,367 6,681 9,471 150 145 23,549 29,723 55,642 119,131 1,912 4,699 374 1,850 556 820 57,540 81,137 63,784 87,876 4,212 5,540 .. 738 331 875 23,068 68,853 268 429 2,080 4,594 3,526 6,799 12,959 34,561 .. 2,950 152 442 .. 17,954 23,544 47,275 185,172 279,647 393,592 693,860 1,693 1,861 .. 3,184 17,497 26,890 2,404 16,530 .. .. 692 3,240 1,309 970 1,726 1,760 3,452,501 t 6,454,929 t 101,140 211,197 550,042 1,447,025 317,249 859,842 232,440 587,183 651,141 1,658,222 155,939 606,270 136,412 357,686 143,154 347,667 125,520 184,863 27,728 70,831 68,415 90,905 2,800,647 4,796,707 1,229,213 2,031,196
1990
1 .. .. 1 53 7 .. 5 .. 7 .. 7 15 34 61 .. 2 3 14 .. .. 29 23 5 11 22 .. .. .. 8 7 11 40 .. 2 .. .. 75 .. 44 10 w 16 17 20 14 17 15 .. 26 4 16 .. 8 10
% of total
2002
3 2 56 1 16 .. .. 2 4 4 .. 11 15 21 18 15 3 3 13 4 61 15 23 5 7 10 0 73 13 1 5 8 49 .. 2 .. .. .. 10 26 7w 16 8 8 9 9 7 6 22 4 13 17 7 9
1990
3 .. .. 0 3 .. .. 3 .. 2 .. 3 2 6 38 .. 7 1 4 .. .. 5 21 0 1 3 .. .. .. 1 1 4 21 .. 0 .. .. 10 .. 7 3w 6 4 4 5 5 6 .. 4 1 5 .. 3 2
Fuels
Ores and metals
% of total
2002
3 4 5 0 3 .. .. 0 2 1 .. 3 1 2 6 12 5 0 5 13 13 3 11 0 1 1 10 11 2 .. 0 2 13 .. 0 .. .. .. 3 12 2w 4 2 2 2 2 2 3 3 1 1 6 2 1
1990
18 .. .. 92 12 2 .. 18 .. 3 .. 6 5 1 .. .. 3 0 45 .. .. 1 0 67 17 2 .. .. .. 5 8 3 0 .. 80 .. .. 8 .. 1 8w 23 23 10 39 23 14 .. 24 79 2 .. 5 3
Manufactures
% of total
2002
8 56 0 89 23 .. .. 8 6 1 .. 12 3 0 72 1 3 0 72 14 0 3 1 49 9 2 81 7 9 92 8 2 1 .. 82 .. .. .. 2 1 7w 16 24 21 27 23 8 25 17 75 4 29 4 2
1990
4 .. .. 0 9 7 .. 2 .. 3 .. 9 2 2 0 .. 3 3 1 .. .. 1 45 1 2 4 .. .. .. 39 3 3 0 .. 7 .. .. 7 .. 16 4w 6 6 5 6 6 3 .. 12 2 4 .. 3 2
% of total
2002
4 8 36 0 6 .. .. 1 3 4 .. 11 2 2 0 0 3 4 1 56 9 1 17 0 1 2 0 2 8 4 2 2 0 .. 4 .. .. .. 72 22 2w 5 3 5 2 4 2 5 8 1 3 8 2 2
1990
73 .. .. 7 23 79 .. 72 .. 86 .. 36 75 54 1 .. 83 94 36 .. .. 63 9 27 69 68 .. .. .. 46 79 74 39 .. 10 .. .. 1 .. 31 74 w 49 48 59 35 48 59 .. 34 15 71 .. 79 81
2002
81 22 3 10 51 .. .. 85 85 90 .. 63 78 74 3 76 81 93 7 13 17 74 43 46 82 84 7 8 67 4 79 81 37 .. 13 .. .. .. 14 38 78 w 58 60 60 60 60 79 57 48 19 77 35 82 83
Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Includes re-exports. c. Data on total merchandise exports for 1990 refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland); those for 2002 refer to South Africa only. Data on export commodity shares refer to the South African Customs Union. d. Data for 2002 include the intratrade of the Baltic states and the Commonwealth of Independent States.
200
2004 World Development Indicators
4.5
ECONOMY
Structure of merchandise expor ts About the data
Data on merchandise trade come from customs
reporting practices, data on exports may not be fully
revision 1. Most countries now report using later
reports of goods movement into or out of an econo-
comparable across economies.
revisions of the SITC or the Harmonized System.
my or from reports of the financial transactions relat-
The data on total exports of goods (merchandise) in
Concordance tables are used to convert data report-
ed to merchandise trade recorded in the balance of
this table come from the World Trade Organization
ed in one system of nomenclature to another. The
payments. Because of differences in timing and defi-
(WTO). The WTO uses two main sources, national sta-
conversion process may introduce some errors of
nitions, estimates of trade flows from customs
tistical offices and the IMF’s International Financial
classification, but conversions from later to early sys-
reports are likely to differ from those based on the
Statistics. It supplements these with the COMTRADE
tems are generally reliable. Shares may not sum to
balance of payments. Moreover, several international
database and publications or databases of regional
100 percent because of unclassified trade.
agencies process trade data, each making estimates
organizations, specialized agencies, and economic
to correct for unreported or misreported data, and
groups (such as the Commonwealth of Independent
this leads to other differences in the available data.
States, the Economic Commission for Latin America
Definitions
The most detailed source of data on international
and the Caribbean, Eurostat, the Food and Agri-
• Merchandise exports are the f.o.b. value of goods
trade in goods is the Commodity Trade (COMTRADE)
culture Organization, the Organisation for Economic
provided to the rest of the world, valued in U.S. dol-
database maintained by the United Nations Statistics
Co-operation and Development, and the Organization
lars. • Food corresponds to the commodities in SITC
Division. In addition, the International Monetary Fund
of Petroleum Exporting Countries). It also consults pri-
sections 0 (food and live animals), 1 (beverages and
(IMF) collects customs-based data on exports and
vate sources, such as country reports of the
tobacco), and 4 (animal and vegetable oils and fats)
imports of goods. The value of exports is recorded as
Economist Intelligence Unit and press clippings. In
and SITC division 22 (oil seeds, oil nuts, and oil ker-
the cost of the goods delivered to the frontier of the
recent years country Web sites and direct contacts
nels). • Agricultural raw materials correspond to SITC
exporting country for shipment—the free on board
through email have helped to improve the collection of
section 2 (crude materials except fuels) excluding divi-
(f.o.b.) value. Many countries report trade data in
up-to-date statistics for many countries, reducing the
sions 22, 27 (crude fertilizers and minerals excluding
U.S. dollars. When countries report in local currency,
proportion of estimated figures. The WTO database
coal, petroleum, and precious stones), and 28 (metal-
the United Nations Statistics Division applies the
now covers most of the major traders in Africa, Asia,
liferous ores and scrap). • Fuels correspond to SITC
average official exchange rate for the period shown.
and Latin America, which together with the high-income
section 3 (mineral fuels). • Ores and metals corre-
Countries may report trade according to the gener-
countries account for nearly 90 percent of total world
spond to the commodities in SITC divisions 27, 28,
al or special system of trade (see Primary data docu-
trade. There has also been a remarkable improvement
and 68 (nonferrous metals). • Manufactures corre-
mentation). Under the general system expor ts
in the availability of recent, reliable, and standardized
spond to the commodities in SITC sections 5 (chemi-
comprise outward-moving goods that are (a) goods
figures for countries in Europe and Central Asia.
cals), 6 (basic manufactures), 7 (machinery and
wholly or partly produced in the country; (b) foreign
The shares of exports by major commodity group
goods, neither transformed nor declared for domestic
were estimated by World Bank staff from the COM-
consumption in the country, that move outward
TRADE database. The values of total exports reported
from customs storage; and (c) goods previously
here have not been fully reconciled with the estimates
included as imports for domestic consumption but
of exports of goods and services from the national
subsequently exported without transformation. Under
accounts (shown in table 4.9) or those from the bal-
the special system exports comprise categories a
ance of payments (table 4.15).
and c. In some compilations categories b and c are
The classification of commodity groups is based on
classified as re-exports. Because of differences in
the Standard International Trade Classification (SITC)
4.5a
transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68.
Data sources
Some developing country regions are increasing their share of merchandise exports
The WTO publishes data on world trade in its
Merchandise exports
Annual Report. The IMF publishes estimates of
1990
2002
East Asia & Pacific 5% Europe & Central Asia 4% Latin America & Caribbean 4% Middle East & North Africa 4% Sub-Saharan Africa 2% South Asia 1%
High income 80%
total expor ts of goods in its International East Asia & Pacific 9% Europe & Central Asia 6% Latin America & Caribbean 5% Middle East & North Africa 3% Sub-Saharan Africa 1% South Asia 1%
High income 75%
Financial Statistics and Direction of Trade Statistics, as does the United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Conference on Trade and Development (UNCTAD) publishes data on the structure of exports and imports in its Handbook of
International
Trade
and
Development
Statistics. Tariff line records of exports and The share of developing economies in world merchandise exports increased by 5 percentage points between 1990 and 2002. East Asia and Pacific was the biggest gainer, capturing an additional 4 percentage points.
imports are compiled in the United Nations Statistics Division’s COMTRADE database.
Source: International Monetary Fund data files.
2004 World Development Indicators
201
4.6
Structure of merchandise impor ts Merchandise imports
Food
$ millions 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium a Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
202
936 380 9,780 1,578 4,076 .. 41,985 49,146 .. 3,618 .. 119,702 265 687 360 1,946 22,524 5,100 536 231 164 1,400 123,244 154 285 7,742 53,345 84,725 5,590 1,739 621 1,990 2,097 4,500 4,600 12,880 33,333 3,006 1,861 12,412 1,263 278 .. 1,081 27,001 234,436 918 188 .. 355,686 1,205 19,777 1,649 723 86 332 54,782
Agricultural raw materials
% of total 2002
950 1,516 10,791 3,795 8,988 991 72,689 77,984 1,665 7,914 8,980 210,548 653 1,770 3,425 1,950 49,720 7,897 577 129 1,989 1,796 227,463 110 780 17,093 295,203 207,168 12,738 980 850 7,175 3,075 10,714 4,161 40,756 49,381 8,882 6,431 12,552 5,190 375 5,863 1,594 33,577 329,322 1,080 225 725 493,712 2,790 31,273 6,078 620 82 1,130 112,602
2004 World Development Indicators
Fuels
% of total
1990
2002
.. .. 24 .. 4 .. 5 5 .. 19 .. .. 38 12 .. .. 9 8 .. .. .. 19 6 .. .. 4 9 8 7 .. .. 8 .. 12 .. .. 12 .. 9 32 14 .. .. .. 5 10 .. .. .. 10 11 15 10 .. .. .. 7
.. 20 28 .. 5 21 5 7 14 16 11 9 20 13 .. 14 7 5 15 13 .. 18 6 .. .. 8 3 4 12 .. .. 8 23 9 18 4 12 12 9 28 18 .. 12 11 6 9 18 35 19 7 20 12 13 23 .. .. 4
1990
.. .. 5 .. 4 .. 2 3 .. 5 .. .. 4 2 .. .. 3 3 .. .. .. 0 2 .. .. 2 6 2 4 .. .. 2 .. 4 .. .. 3 .. 3 7 3 .. .. .. 2 3 .. .. .. 3 1 3 2 .. .. .. 5
Ores and metals
% of total
2002
.. 1 3 .. 2 1 1 3 1 7 2 2 5 1 .. 1 2 1 1 3 .. 1 1 .. .. 1 4 1 2 .. .. 1 1 2 1 2 3 2 1 4 2 .. 3 1 3 2 1 1 1 2 2 1 1 1 .. .. 2
Manufactures
% of total
1990
2002
.. .. 1 .. 8 .. 6 6 .. 16 .. .. 1 1 .. .. 27 36 .. .. .. 2 6 .. .. 16 2 2 6 .. .. 10 .. 10 .. .. 7 .. 2 3 15 .. .. .. 12 10 .. .. .. 8 17 8 17 .. .. .. 11
.. 9 1 .. 5 18 8 6 18 5 26 9 17 5 .. 7 15 5 25 12 .. 13 5 .. .. 16 7 2 2 .. .. 7 21 12 20 14 4 23 4 4 13 .. 7 12 12 9 4 12 23 8 9 15 13 19 .. .. 11
1990
.. .. 2 .. 6 .. 1 4 .. 3 .. .. 1 1 .. .. 5 4 .. .. .. 1 3 .. .. 1 3 2 3 .. .. 2 .. 4 .. .. 2 .. 2 2 4 .. .. .. 4 4 .. .. .. 4 0 3 2 .. .. .. 6
% of total
2002
.. 2 1 .. 3 3 1 3 2 2 4 3 1 1 .. 2 3 6 1 2 .. 1 2 .. .. 1 5 2 2 .. .. 1 2 2 1 3 2 1 1 3 1 .. 2 1 5 3 1 1 1 3 2 3 1 0 .. .. 5
1990
2002
.. .. 68 .. 78 .. 84 81 .. 56 .. .. 56 85 .. .. 56 49 .. .. .. 78 81 .. .. 75 80 85 77 .. .. 66 .. 64 .. .. 73 .. 84 56 63 .. .. .. 76 74 .. .. .. 72 70 70 69 .. .. .. 69
.. 68 67 .. 84 57 84 81 65 69 51 77 56 80 .. 72 73 65 58 70 .. 66 84 .. .. 73 80 91 81 .. .. 83 54 75 60 77 77 62 84 51 65 .. 76 74 73 78 75 51 57 71 68 68 71 56 .. .. 76
Merchandise imports
Food
$ millions 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
935 10,340 23,580 21,837 20,322 7,660 20,669 16,793 181,968 1,928 235,368 2,600 .. 2,223 2,930 69,844 3,972 .. 185 .. 2,529 672 570 5,336 .. 1,206 651 575 29,258 602 388 1,618 43,548 .. 924 6,922 878 270 1,163 672 126,098 9,501 638 388 5,627 27,231 2,681 7,411 1,539 1,193 1,352 2,634 13,042 11,570 25,263 ..
Agricultural raw materials
% of total 2002
2,940 37,612 56,595 31,288 22,190 12,000 51,906 35,517 242,957 3,500 337,194 4,962 6,491 3,277 1,718 152,126 8,960 589 431 4,053 6,447 779 675 5,700 7,739 1,921 1,150 674 79,869 928 440 2,168 173,087 1,052 659 11,644 1,340 2,324 1,450 1,419 219,788 15,077 1,795 430 7,547 34,812 6,005 11,233 2,982 1,100 1,770 7,523 35,229 55,113 38,451 ..
Fuels
% of total
1990
2002
10 8 3 5 .. .. 11 8 12 15 15 26 .. 9 .. 6 17 .. .. .. .. .. .. 23 .. .. 11 9 7 26 .. 12 15 .. .. 10 .. 13 .. 15 13 7 19 .. 6 6 19 17 12 18 8 24 10 8 12 ..
16 3 6 11 11 .. 7 6 9 15 13 17 8 12 .. 6 .. 13 .. 13 18 .. .. .. 9 14 .. 12 5 .. .. 19 6 13 18 14 14 .. 13 13 12 9 15 44 20 7 21 12 13 18 12 13 8 6 13 ..
1990
1 4 4 5 .. .. 2 2 6 1 7 2 .. 3 .. 8 1 .. .. .. .. .. .. 2 .. .. 1 1 1 1 .. 3 4 .. .. 6 .. 1 .. 7 2 1 1 .. 1 2 1 4 1 0 0 2 2 3 4 ..
Ores and metals
% of total
2002
1 1 3 6 2 .. 1 1 3 1 3 2 1 2 .. 3 .. 2 .. 2 2 .. .. .. 3 2 .. 2 1 .. .. 2 1 3 1 3 1 .. 1 4 2 1 1 1 1 2 1 5 1 1 1 2 1 2 2 ..
Manufactures
% of total
1990
2002
16 14 27 9 .. .. 6 9 11 20 25 18 .. 20 .. 16 1 .. .. .. .. .. .. 0 .. .. 17 11 5 19 .. 8 4 .. .. 17 .. 5 .. 9 10 8 19 .. 0 4 4 21 16 7 14 12 15 22 11 ..
13 7 33 21 3 .. 3 9 9 18 19 15 13 17 .. 21 .. 26 .. 9 18 .. .. .. 20 14 .. 17 5 .. .. 10 3 22 22 16 16 .. 10 16 9 9 13 13 1 4 2 27 17 22 17 14 9 9 10 ..
1990
1 4 8 4 .. .. 2 3 5 1 9 1 .. 2 .. 7 2 .. .. .. .. .. .. 1 .. .. 1 1 4 1 .. 1 3 .. .. 6 .. 0 .. 2 3 3 1 .. 2 6 1 4 1 1 1 1 3 4 2 ..
ECONOMY
4.6
Structure of merchandise impor ts
% of total
2002
1 2 5 3 2 .. 1 2 4 1 5 2 3 1 .. 6 .. 4 .. 2 2 .. .. .. 1 2 .. 1 3 .. .. 1 2 1 1 2 0 .. 2 3 3 2 0 2 2 5 3 3 1 1 1 1 2 3 2 ..
1990
2002
71 70 51 77 .. .. 76 77 64 61 44 51 .. 66 .. 63 79 .. .. .. .. .. .. 74 .. .. 69 78 82 53 .. 76 75 .. .. 61 .. 81 .. 67 71 81 59 .. 67 82 69 54 70 73 77 61 53 63 71 ..
69 84 52 59 82 .. 81 82 70 63 58 62 75 67 .. 64 .. 55 .. 74 60 .. .. .. 64 44 .. 69 83 .. .. 67 87 61 59 65 47 .. 74 49 74 79 68 40 76 81 69 53 68 58 69 70 56 80 73 ..
2004 World Development Indicators
203
4.6
Structure of merchandise impor ts Merchandise imports
$ millions 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa b Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia c Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Food
Agricultural raw materials
% of total 2002
7,600 17,857 33,100 60,520 288 203 24,069 32,310 1,219 1,560 4,634 6,320 149 264 60,774 116,441 6,670 16,492 6,142 10,937 95 195 18,399 29,267 87,715 154,701 2,688 6,104 618 1,790 663 925 54,264 66,219 69,681 83,672 2,400 5,220 .. 715 1,027 1,687 33,045 64,721 581 650 1,262 4,040 5,513 9,527 22,302 49,663 .. 2,453 288 1,710 .. 16,993 11,199 32,180 222,977 345,321 516,987 1,202,430 1,343 1,964 .. 3,160 7,335 11,834 2,752 19,000 .. .. 1,571 2,590 1,220 1,270 1,847 1,440 3,532,918 t 6,590,272 t 106,125 197,606 502,597 1,364,003 323,769 821,000 179,974 543,003 609,669 1,561,609 160,493 535,235 150,809 371,275 119,568 343,449 104,010 142,093 39,124 84,787 57,515 84,770 2,913,452 5,028,663 1,247,461 1,884,219
1990
12 .. .. 15 29 12 .. 6 .. 9 .. 8 11 19 13 .. 6 6 31 .. .. 5 22 19 11 8 .. .. .. 14 10 6 7 .. 11 .. .. 27 .. 4 9w 8 10 10 10 10 7 .. 11 19 9 .. 9 11
Fuels
% of total
2002
6 23 16 16 26 .. .. 4 5 6 .. 5 10 14 19 20 8 6 16 10 15 5 22 9 10 4 12 14 6 11 8 5 14 .. 13 .. .. .. 14 11 8w 11 9 9 7 9 6 10 9 16 8 10 8 9
1990
4 .. .. 1 2 5 .. 2 .. 4 .. 2 3 2 1 .. 2 2 2 .. .. 5 1 1 4 4 .. .. .. 1 3 2 4 .. 4 .. .. 1 .. 3 3w 4 4 4 2 4 4 .. 3 3 4 .. 3 3
Ores and metals
% of total
2002
1 1 4 1 2 .. .. 0 2 3 .. 1 2 1 1 2 2 1 4 1 2 3 1 1 3 4 0 3 1 1 2 1 4 .. 1 .. .. .. 2 2 2w 4 2 3 1 2 3 2 2 2 3 2 2 2
1990
38 .. .. 0 16 17 .. 16 .. 11 .. 1 12 13 20 .. 9 5 3 .. .. 9 8 11 9 21 .. .. .. 3 6 13 18 .. 3 .. .. 40 .. 16 11 w 17 10 11 8 11 6 .. 13 4 23 .. 11 9
Manufactures
% of total
2002
11 2 16 0 15 .. .. 13 13 7 .. 13 11 14 5 2 9 4 3 37 13 12 15 23 9 14 1 16 39 1 4 10 15 .. 2 .. .. .. 7 8 10 w 24 9 10 7 11 9 12 9 5 30 16 10 9
1990
6 .. .. 3 2 3 .. 2 .. 4 .. 1 4 1 0 .. 3 3 1 .. .. 4 1 6 4 5 .. .. .. 4 4 3 2 .. 4 .. .. 1 .. 2 4w 5 3 3 3 4 3 .. 3 3 6 .. 4 4
% of total
2002
3 2 2 3 2 .. .. 2 3 4 .. 2 3 2 1 1 3 5 3 0 1 3 2 1 2 5 1 1 3 2 2 2 1 .. 2 .. .. .. 2 2 3w 3 3 3 2 3 4 3 2 3 4 1 3 3
1990
39 .. .. 81 51 63 .. 73 .. 67 .. 75 71 65 66 .. 79 84 62 .. .. 75 67 62 72 61 .. .. .. 77 75 73 69 .. 77 .. .. 31 .. 73 71 w 64 71 69 76 70 77 .. 69 70 54 .. 71 72
2002
78 70 62 79 55 .. .. 80 76 79 .. 70 74 68 74 72 75 84 64 51 69 76 60 65 75 68 80 66 48 86 79 78 65 .. 82 .. .. .. 75 76 75 w 57 75 72 81 73 76 72 78 72 54 66 75 74
Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Data on total merchandise imports for 1990 refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland); those for 2002 refer to South Africa only. Data on import commodity shares refer to the South African Customs Union. c. Data for 2002 include the intratrade of the Baltic states and the Commonwealth of Independent States.
204
2004 World Development Indicators
About the data
4.6
ECONOMY
Structure of merchandise impor ts Definitions
Data on imports of goods are derived from the same
The data on total imports of goods (merchandise)
• Merchandise imports are the c.i.f. value of goods
sources as data on exports. In principle, world
in this table come from the World Trade Organization
purchased from the rest of the world valued in U.S.
exports and imports should be identical. Similarly,
(WTO). For further discussion of the WTO’s sources
dollars. • Food corresponds to the commodities in
exports from an economy should equal the sum of
and methodology, see About the data for table 4.5.
SITC sections 0 (food and live animals), 1 (bever-
imports by the rest of the world from that economy.
The shares of imports by major commodity group
ages and tobacco), and 4 (animal and vegetable oils
But differences in timing and definitions result in dis-
were estimated by World Bank staff from the United
and fats) and SITC division 22 (oil seeds, oil nuts,
crepancies in reported values at all levels. For fur-
Nations Statistics Division’s Commodity Trade (COM-
and oil kernels). • Agricultural raw materials corre-
ther discussion of indicators of merchandise trade,
TRADE) database. The values of total imports report-
spond to SITC section 2 (crude materials except
see About the data for tables 4.4 and 4.5.
ed here have not been fully reconciled with the
fuels) excluding divisions 22, 27 (crude fertilizers
The value of imports is generally recorded as the
estimates of imports of goods and services from the
and minerals excluding coal, petroleum, and pre-
cost of the goods when purchased by the importer
national accounts (shown in table 4.9) or those from
cious stones), and 28 (metalliferous ores and
plus the cost of transport and insurance to the fron-
the balance of payments (table 4.15).
scrap). • Fuels correspond to SITC section 3 (miner-
tier of the importing country—the cost, insurance,
The classification of commodity groups is based on
al fuels). • Ores and metals correspond to the com-
and freight (c.i.f.) value, corresponding to the landed
the Standard International Trade Classification (SITC)
modities in SITC divisions 27, 28, and 68
cost at the point of entry of foreign goods into the
revision 1. Most countries now report using later
(nonferrous metals). • Manufactures correspond to
country. A few countries, including Australia, Canada,
revisions of the SITC or the Harmonized System.
the commodities in SITC sections 5 (chemicals), 6
and the United States, collect import data on a free
Concordance tables are used to convert data report-
(basic manufactures), 7 (machinery and transport
on board (f.o.b.) basis and adjust them for freight
ed in one system of nomenclature to another. The
equipment), and 8 (miscellaneous manufactured
and insurance costs. Many countries collect and
conversion process may introduce some errors of
goods), excluding division 68.
report trade data in U.S. dollars. When countries
classification, but conversions from later to early sys-
report in local currency, the United Nations Statistics
tems are generally reliable. Shares may not sum to
Division applies the average official exchange rate
100 percent because of unclassified trade.
for the period shown. Countries may report trade according to the general or special system of trade (see Primary data documentation). Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a country en route to another are excluded.
4.6a Top 10 developing country exporters in 2002 Merchandise exports ($ billions)
Data sources The WTO publishes data on world trade in its
350 1990
Annual Report. The International Monetary Fund
300
2002
(IMF) publishes estimates of total imports of
250
goods in its International Financial Statistics and 200
Direction of Trade Statistics, as does the United 150
Nations Statistics Division in its Monthly Bulletin
100
of Statistics. And the United Nations Conference
50
on Trade and Development (UNCTAD) publishes data on the structure of exports and imports in its
0 China
Mexico
Russian Malaysia Federation
Saudi Arabia
Thailand
Brazil
Indonesia
India
Poland
China led the developing economies in merchandise exports in 2002, followed by Mexico. The top 10 economies accounted for 63 percent of exports of developing economies and 16 percent of world exports. Note: No data are available for the Russian Federation for 1990. Source: World Trade Organization data files.
Handbook of International Trade and Development Statistics. Tariff line records of exports and imports are compiled in the United Nations Statistics Division’s COMTRADE database.
2004 World Development Indicators
205
4.7
Structure of ser vice expor ts Commercial service exports
Transport
Travel
Insurance and financial services
Computer, information, communications, and other commercial services
% of total
% of total
% of total
% of total
services
services
services
$ millions 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium a Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
206
.. 32 479 65 2,264 .. 9,833 22,755 .. 296 .. 26,646 109 133 .. 183 3,706 837 34 7 50 369 18,350 17 23 1,786 5,748 .. 1,548 .. 65 583 425 .. .. .. 12,731 1,086 508 4,812 301 73 200 261 4,562 74,948 214 53 .. 51,545 79 6,514 313 91 4 43
2004 World Development Indicators
2002
.. 552 .. 203 2,878 176 17,443 34,647 321 305 1,276 48,970 133 220 300 .. 8,844 2,553 32 4 593 .. 36,272 .. .. 3,878 39,381 43,333 1,789 .. 158 1,854 506 5,549 .. 7,024 27,182 2,966 917 9,127 749 54 1,979 450 6,400 85,912 .. .. 354 99,622 539 20,125 1,048 43 .. ..
1990
.. 20.0 41.7 48.8 51.1 .. 35.5 6.4 .. 12.9 .. 27.5 33.4 35.8 .. 20.4 36.4 27.5 37.1 38.7 .. 42.6 23.0 50.9 18.4 40.0 47.1 .. 31.3 .. 53.9 16.3 62.4 .. .. .. 32.5 5.6 47.6 50.1 26.2 85.7 74.7 80.6 38.4 21.7 33.4 8.8 .. 28.6 49.2 4.9 7.4 14.2 5.4 19.8
2002
.. 3.4 .. 6.6 24.3 36.5 23.9 16.7 66.1 30.4 55.7 20.8 14.7 27.3 10.0 .. 18.0 30.2 14.6 23.1 15.0 .. 19.0 .. .. 55.7 14.5 30.5 30.1 .. 23.6 13.2 19.0 10.6 .. 24.7 54.5 2.4 36.9 30.6 41.6 18.2 54.4 55.6 24.2 21.9 .. .. 52.0 25.8 21.3 40.0 8.8 20.4 .. ..
1990
.. 11.1 13.4 20.6 39.9 .. 43.2 59.0 .. 6.4 .. 14.0 50.2 43.6 .. 64.1 37.3 38.2 34.1 51.4 .. 14.4 34.7 16.0 34.1 29.8 30.2 .. 26.2 .. 12.9 48.9 12.1 .. .. .. 26.2 66.8 37.0 22.9 25.2 1.0 13.7 2.1 25.8 27.0 1.4 87.9 .. 27.8 5.6 39.7 37.6 32.6 .. 78.9
2002
1990
.. 88.2 .. .. 53.3 35.9 49.2 32.1 15.9 18.6 15.1 15.5 63.4 37.1 37.3 .. 22.6 52.3 61.6 30.6 76.5 .. 29.4 .. .. 18.9 51.8 15.1 53.8 .. 16.1 62.6 10.0 68.7 .. 42.2 21.6 92.2 48.8 41.2 32.8 64.2 28.0 16.0 24.7 38.1 .. .. 35.5 19.3 66.5 49.6 58.6 .. .. ..
.. 2.2 5.9 4.6 0.0 .. 4.2 2.9 .. 0.1 .. 18.2 6.9 10.0 .. 8.2 3.1 3.1 .. 1.6 .. 9.4 .. 18.8 0.2 4.9 3.9 .. 17.1 .. .. 1.5 8.3 .. .. .. 2.3 0.2 9.3 1.0 7.5 .. 0.1 0.7 0.1 14.8 5.7 0.1 .. 1.0 2.7 0.1 1.9 0.1 .. 1.3
services 2002
.. 2.0 .. 13.7 0.2 3.8 5.1 6.4 .. 4.7 0.5 27.8 2.3 16.8 13.8 .. 6.7 1.1 0.4 28.7 .. .. 8.4 .. .. 2.4 0.7 8.3 2.0 .. 2.5 1.2 13.3 1.9 .. 2.8 .. .. 0.2 1.2 4.2 1.0 1.0 1.6 0.2 2.5 .. .. 4.9 11.8 1.1 1.1 5.3 1.7 .. ..
1990
2002
.. 66.7 39.0 26.1 9.0 .. 17.2 31.7 .. 80.6 .. 40.3 9.5 10.6 .. 7.3 23.2 31.2 28.9 8.3 .. 33.6 42.3 14.3 47.3 25.3 18.7 .. 25.5 .. 33.1 34.8 17.2 .. .. .. 39.0 27.3 6.1 26.1 41.1 13.3 11.5 16.6 35.6 36.4 59.4 3.3 .. 42.6 42.6 55.2 53.0 53.1 94.6 0.0
.. 6.4 .. 79.7 22.1 23.8 21.8 44.8 18.0 46.3 28.7 35.9 19.6 18.8 38.9 .. 52.7 16.5 23.4 17.6 8.4 .. 43.1 .. .. 23.0 33.1 46.1 14.1 .. 57.8 23.0 57.7 18.8 .. 30.4 23.9 5.4 14.1 26.9 21.3 16.6 16.6 26.8 51.0 37.5 .. .. 7.6 43.2 11.1 9.3 27.3 77.9 .. ..
Commercial service exports
Transport
Travel
Insurance and financial services
Computer, information, communications, and other commercial services
% of total
% of total
% of total
% of total
services
services
services
$ millions 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
121 2,677 4,610 2,488 343 .. 3,286 4,546 48,579 976 41,384 1,430 .. 774 .. 9,155 1,054 .. 11 290 .. 34 .. 83 .. .. 129 37 3,769 71 14 478 7,222 .. 48 1,871 103 94 106 166 28,478 2,415 34 22 965 12,452 68 1,218 907 198 404 714 2,897 3,200 5,054 ..
2002
463 7,726 24,553 6,517 1,357 .. 28,134 10,825 59,374 1,888 64,909 1,473 1,432 791 .. 27,080 1,372 118 127 1,235 .. 31 .. .. 1,451 220 158 49 14,753 140 .. 1,132 12,474 201 179 4,098 249 405 230 303 54,573 5,041 270 .. .. 19,116 349 1,536 2,254 285 506 1,430 3,029 10,030 9,720 ..
1990
35.1 1.6 20.8 2.8 10.5 .. 31.1 30.8 21.0 18.0 .. 26.0 .. 32.0 .. 34.7 87.5 .. 74.8 94.9 .. 14.1 .. 83.8 .. .. 32.1 46.1 31.8 31.0 35.3 32.9 12.4 .. 41.8 9.6 61.3 10.3 .. 3.6 45.4 43.4 19.2 5.2 3.9 68.7 15.3 59.3 64.9 11.2 18.3 43.4 8.5 57.3 15.6 ..
4.7
ECONOMY
Structure of ser vice expor ts
2002
11.7 8.9 10.3 16.2 49.4 .. 5.7 19.6 15.4 19.5 37.0 19.6 47.6 54.1 .. 48.3 82.3 31.7 18.0 62.5 .. 1.3 .. .. 45.1 36.3 26.8 32.7 19.3 17.0 .. 24.1 9.2 54.4 21.8 19.0 22.4 19.8 .. 15.6 32.4 23.8 9.5 .. .. 56.8 45.5 54.1 55.7 7.5 13.8 19.9 20.8 32.6 18.7 ..
1990
24.0 36.8 33.8 86.5 8.2 .. 44.4 30.7 33.9 77.0 .. 35.7 .. 60.2 .. 34.5 12.5 .. 24.3 2.5 .. 51.2 .. 7.7 .. .. 31.3 42.6 44.7 54.3 64.7 51.1 76.5 .. 10.4 68.4 .. 20.9 81.0 65.6 14.6 42.7 35.5 59.5 2.5 12.6 84.7 12.0 18.9 12.0 21.1 30.4 16.1 11.2 70.4 ..
2002
1990
62.9 42.4 12.3 81.1 36.9 .. 11.0 19.4 45.3 64.0 5.4 53.4 43.4 39.0 .. 19.5 8.6 30.2 82.0 13.1 .. 64.0 .. .. 34.8 17.7 22.9 67.3 48.2 62.9 .. 54.0 71.0 23.2 72.7 64.6 25.6 31.0 95.2 47.5 14.1 57.6 42.0 .. .. 11.4 41.0 6.3 23.4 1.8 11.6 56.1 57.4 43.0 61.4 ..
12.9 0.2 2.7 .. 6.4 .. .. .. 5.5 1.4 .. .. .. 0.7 .. 0.1 .. .. 0.9 0.0 .. .. .. .. .. .. 0.3 0.1 0.1 4.9 .. 0.1 4.6 .. 4.6 0.8 .. 0.5 5.9 .. 0.8 –0.3 .. 13.5 0.3 0.4 .. 1.4 3.8 0.5 .. 11.2 0.5 4.0 0.7 ..
services 2002
3.9 2.1 1.5 0.0 10.7 .. 22.3 0.1 3.3 2.0 4.2 .. 0.8 0.5 .. 3.3 7.9 3.3 .. 5.5 .. –0.0 .. .. 0.7 2.2 0.7 .. 1.4 2.9 .. 1.8 9.7 1.9 0.8 0.7 .. .. 0.6 .. 2.0 0.9 0.9 .. .. 4.7 4.5 2.3 12.6 1.8 5.3 6.8 2.2 3.5 2.3 ..
1990
2002
28.0 61.4 42.7 10.7 74.9 .. 24.5 38.8 39.6 3.6 .. 38.3 .. 7.1 .. 30.7 –0.0 .. .. 2.6 .. 34.7 .. 8.5 .. .. 36.3 11.2 23.5 9.8 –0.0 15.8 6.5 .. 43.2 21.2 38.7 68.3 13.1 30.8 39.2 14.2 45.3 21.8 93.3 18.3 –0.0 27.3 12.4 76.3 60.5 15.0 74.9 27.6 13.3 ..
21.4 46.7 75.9 2.7 2.9 .. 61.0 60.8 36.1 14.4 53.4 27.1 8.2 6.4 .. 28.8 1.2 34.8 .. 18.9 .. 34.7 .. .. 19.4 43.8 49.6 0.0 31.0 17.3 .. 20.0 10.1 20.6 4.7 15.7 52.1 49.2 4.2 36.9 51.4 17.7 47.7 .. .. 27.1 9.0 37.3 8.2 88.9 69.3 17.2 19.5 20.9 17.7 ..
2004 World Development Indicators
207
4.7
Structure of ser vice expor ts Commercial service exports
Transport
Travel
Insurance and financial services
Computer, information, communications, and other commercial services
% of total
% of total
% of total
% of total
services
services
services
$ millions 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
610 2,326 .. 13,453 31 48 3,031 5,184 356 .. .. .. 45 .. 12,719 29,599 .. 2,218 1,219 2,286 .. .. 3,290 4,391 27,649 62,109 425 1,247 134 45 102 113 13,453 23,508 18,233 27,856 740 1,481 .. 60 131 609 6,292 15,232 114 53 322 563 1,575 2,603 7,882 14,738 .. .. .. 230 .. 4,583 .. .. 53,830 123,130 132,880 272,630 460 752 .. .. 1,121 960 .. 2,948 .. .. 82 129 94 114 253 .. 750,300 s 1,511,226 s 14,230 40,966 78,877 225,630 49,966 144,059 28,911 81,571 93,107 266,596 22,049 82,632 15,237 77,656 25,004 46,516 14,513 22,615 6,816 27,994 9,487 10,833 657,193 1,244,630 300,015 480,820
a. Includes Luxembourg.
208
2002
2004 World Development Indicators
1990
2002
1990
50.5 .. 56.1 .. 19.1 .. 9.7 17.5 .. 22.6 .. 21.6 17.2 39.7 14.1 24.5 35.8 16.3 29.7 .. 19.9 21.1 26.9 50.7 23.0 11.7 .. .. .. .. 25.2 28.1 36.9 .. 40.9 .. .. 27.2 68.9 44.3 25.0 w 24.6 26.9 27.0 26.7 26.6 26.1 21.9 28.7 26.7 27.9 28.1 24.8 23.9
41.4 40.8 25.7 .. .. .. .. 38.6 44.9 26.2 .. 23.3 15.1 41.2 36.7 9.4 22.0 11.6 16.6 75.7 10.1 21.4 23.1 36.8 23.5 19.0 .. 16.2 73.8 .. 14.5 17.0 34.6 .. 36.0 .. .. 15.7 37.2 .. 22.4 w 15.7 23.3 24.0 22.2 22.2 17.1 31.4 21.4 19.9 14.2 26.3 22.5 21.0
17.4 .. 32.8 .. 42.7 .. 76.2 36.6 .. 55.0 .. 55.8 67.2 30.2 15.7 29.2 21.7 40.6 43.3 .. 36.4 68.7 50.7 29.4 64.8 40.9 .. .. .. .. 29.0 37.9 51.8 .. 44.2 .. .. 48.8 13.5 25.3 32.9 w 38.3 42.0 42.1 41.8 41.4 48.5 32.8 50.5 30.5 30.1 39.7 31.7 33.2
2002
1990
14.4 31.1 65.3 .. .. .. .. 14.8 19.5 47.4 .. 62.1 54.4 29.1 50.9 23.3 19.1 28.2 73.1 2.7 71.8 51.9 20.4 35.7 58.5 57.5 .. 76.0 17.2 .. 17.2 31.3 46.7 .. 45.7 .. .. 29.3 58.3 .. 30.5 w 28.2 46.9 47.6 45.6 44.2 54.0 41.8 50.2 38.7 13.8 48.9 27.4 32.1
5.6 .. 1.0 .. 0.5 .. 0.1 0.7 .. 1.2 .. 10.8 4.3 4.2 0.5 .. 9.1 23.3 .. .. 0.5 0.2 13.7 .. 1.5 1.7 .. .. .. .. 16.4 3.5 1.0 .. 0.2 .. .. .. 4.1 1.2 6.6 w 2.5 3.2 3.6 2.6 3.1 1.3 1.7 4.5 .. 2.4 5.8 7.1 7.2
services 2002
4.4 1.7 .. .. .. .. .. 2.5 2.2 2.0 .. 5.4 4.2 3.6 0.9 .. 4.3 34.7 .. 1.9 3.9 0.6 2.8 14.0 2.4 1.7 .. 1.6 0.5 .. 23.7 6.9 10.0 .. 0.1 .. .. .. 4.5 .. 8.3 w 1.5 2.5 1.9 3.7 2.4 0.8 2.1 6.2 1.2 1.7 4.2 9.5 8.6
1990
2002
26.6 .. 10.0 .. 37.6 .. 14.1 45.3 .. 21.2 .. 11.9 11.3 25.9 69.7 46.3 33.5 19.7 27.0 .. 43.1 10.0 8.6 19.9 10.7 47.4 .. .. .. .. 29.4 30.5 10.3 .. 14.7 .. .. 24.0 13.4 29.2 36.3 w 35.1 28.5 28.1 29.2 29.5 24.2 44.0 16.5 41.8 39.7 26.8 37.2 35.8
39.7 26.3 9.0 .. .. .. .. 44.0 33.4 24.4 .. 9.1 26.4 26.0 11.5 67.3 54.5 25.5 10.3 19.6 14.3 26.1 53.6 13.6 15.6 21.8 .. 6.2 8.4 .. 44.6 44.9 8.7 .. 18.1 .. .. 55.0 0.1 .. 39.2 w 54.6 27.3 26.6 28.7 31.3 28.1 24.7 22.6 40.5 70.3 19.5 40.9 38.3
About the data
4.7
ECONOMY
Structure of ser vice expor ts Definitions
Balance of payments statistics, the main source of
captured by conventional balance of payments
• Commercial service exports are total service
information on international trade in services, have
statistics is establishment trade—sales in the host
exports minus exports of government services not
many weaknesses. Some large economies—such as
country by foreign affiliates. By contrast, cross-
included elsewhere. International transactions in
the former Soviet Union—did not report data on
border intrafirm transactions in merchandise may be
ser vices are defined by the IMF’s Balance of
trade in services until recently. Disaggregation of
reported as exports or imports in the balance of
Payments Manual (1993) as the economic output of
important components may be limited, and it varies
payments.
intangible commodities that may be produced, trans-
significantly across countries. There are inconsisten-
The data on exports of services in this table and
ferred, and consumed at the same time. Definitions
cies in the methods used to report items. And the
on imports of services in table 4.8, unlike those in
may vary among reporting economies. • Transport
recording of major flows as net items is common (for
editions before 2000, include only commercial serv-
covers all transport services (sea, air, land, internal
example, insurance transactions are often recorded
ices and exclude the category “government services
waterway, space, and pipeline) performed by resi-
as premiums less claims). These factors contribute
not included elsewhere.” The data are compiled by
dents of one economy for those of another and
to a downward bias in the value of the service trade
the IMF based on returns from national sources.
involving the carriage of passengers, movement of
reported in the balance of payments.
Data on total trade in goods and services from the
goods (freight), rental of carriers with crew, and relat-
IMF’s Balance of Payments database are shown in
ed support and auxiliary services. Excluded are
table 4.15.
freight insurance, which is included in insurance
Efforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and
services; goods procured in ports by nonresident
Development, for example, are working together to
carriers and repairs of transport equipment, which
improve the collection of statistics on trade in servic-
are included in goods; repairs of harbors, railway
es in member countries. In addition, the International
facilities, and airfield facilities, which are included in
Monetary Fund (IMF) has implemented the new clas-
construction services; and rental of carriers without
sification of trade in services introduced in the fifth
crew, which is included in other services. • Travel
edition of its Balance of Payments Manual (1993).
covers goods and services acquired from an econo-
Still, difficulties in capturing all the dimensions of
my by travelers in that economy for their own use dur-
international trade in services mean that the record
ing visits of less than one year for business or
is likely to remain incomplete. Cross-border intrafirm
personal purposes. Travel ser vices include the
service transactions, which are usually not captured
goods and services consumed by travelers, such as
in the balance of payments, have increased in
meals, lodging, and transport (within the economy
recent years. One example of such transactions is
visited), including car rental. • Insurance and finan-
transnational corporations’ use of mainframe com-
cial services cover freight insurance on goods
puters around the clock for data processing, exploit-
exported and other direct insurance such as life
ing time zone differences between their home
insurance, financial intermediation services such as
country and the host countries of their affiliates.
commissions, foreign exchange transactions, and
Another important dimension of service trade not
brokerage services; and auxiliary services such as financial market operational and regulatory services.
4.7a
• Computer, information, communications, and
Top 10 developing country exporters of commercial services in 2002
other commercial services include such activities as
Commercial services exports ($ billions)
international telecommunications and postal and courier services; computer data; news-related serv-
40 1990
ice transactions between residents and nonresi2002
30
dents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recre-
20
ational services. 10
0 China
India
Malaysia
Thailand
Turkey
Russian Mexico Federation
Poland
Brazil
Egypt, Arab Rep.
Major exporters of merchandise trade also tend to be major exporters of commercial services. The exceptions are the fuel exporters—Saudi Arabia and Indonesia. These top 10 developing country exporters accounted for 61 percent of commercial services exports of developing economies and 11 percent of world commercial services exports in 2002. Note: No data are available for the Russian Federation for 1990. Source: International Monetary Fund data files.
Data sources The data on exports of commercial services are from the IMF. The IMF publishes balance of payments data in its International Financial Statistics and Balance of Payments Statistics Yearbook.
2004 World Development Indicators
209
4.8
Structure of ser vice impor ts Commercial service imports
Transport
Travel
Insurance and financial services
Computer, information, communications, and other commercial services
% of total
% of total
% of total
% of total
services
services
services
$ millions 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium a Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
210
.. 29 1,155 1,288 2,876 .. 13,388 14,104 .. 554 .. 25,924 113 291 .. 371 6,733 600 196 59 64 1,018 27,479 166 223 1,982 4,113 .. 1,683 .. 748 540 1,518 .. .. .. 10,106 435 755 3,327 296 .. 123 348 7,432 59,560 984 35 .. 84,336 226 2,756 363 243 17 71
2002
.. 561 .. 3,176 4,360 217 17,740 34,416 1,283 1,391 892 42,856 186 500 228 .. 13,631 1,986 135 33 372 .. 41,932 .. .. 4,771 46,080 24,800 3,249 .. 917 1,188 1,341 2,399 .. 6,372 25,116 1,241 1,505 6,013 960 24 1,404 558 8,130 68,171 .. .. 316 149,107 546 10,306 996 156 .. ..
2004 World Development Indicators
1990
2002
.. 26.3 58.1 38.3 32.6 .. 33.9 8.4 .. 71.1 .. 23.3 46.9 61.7 .. 57.5 44.4 40.5 64.7 62.6 24.5 45.3 21.1 49.7 45.1 47.4 78.9 .. 34.9 .. 18.4 41.2 32.1 .. .. .. 38.3 40.0 41.6 44.0 45.9 .. 76.3 76.5 26.1 29.4 23.2 65.1 .. 20.3 55.1 34.0 41.0 57.5 54.5 47.9
.. 22.8 .. 12.4 21.8 60.0 33.8 10.4 13.5 71.2 15.3 19.5 67.9 58.4 59.4 .. 26.6 44.1 65.1 55.2 57.4 .. 21.6 .. .. 45.0 29.5 26.6 37.0 .. 13.1 38.6 39.6 18.7 .. 14.0 47.4 60.3 43.2 29.6 43.4 27.6 54.3 57.2 30.4 26.2 .. .. 33.0 20.4 48.8 46.0 51.5 30.1 .. ..
1990
.. .. 12.9 3.0 40.7 .. 31.5 54.9 .. 14.1 .. 21.1 12.8 20.6 .. 15.0 22.4 31.5 16.6 29.0 .. 27.5 39.8 30.6 31.2 21.5 11.4 .. 27.0 .. 15.2 28.8 11.1 .. .. .. 36.5 33.1 23.2 3.9 20.5 .. 15.4 3.3 37.2 20.7 13.9 23.1 .. 46.3 5.9 39.5 27.4 12.2 19.8 52.1
2002
1990
.. 65.1 .. 2.1 53.4 24.9 34.4 27.5 8.2 14.5 62.6 24.7 9.3 14.8 21.3 .. 17.6 31.0 16.1 42.1 10.3 .. 28.2 .. .. 16.5 33.4 50.1 33.0 .. 7.7 29.0 21.6 32.6 .. 25.1 27.6 23.8 24.2 21.1 19.9 49.5 16.4 8.1 24.2 28.9 .. .. 47.1 35.8 22.0 32.0 26.8 19.7 .. ..
.. 2.9 9.8 2.6 1.7 .. 4.8 4.6 .. 6.6 .. 14.7 5.7 10.0 .. 5.5 2.7 4.5 5.1 6.3 .. 7.2 .. 8.9 4.4 3.3 2.3 .. 13.7 .. 1.6 6.0 4.7 .. .. .. 1.6 4.1 8.1 4.6 12.0 .. 0.3 3.4 1.8 19.2 5.3 9.0 .. 1.0 11.2 5.4 3.4 5.5 5.6 ..
services 2002
.. 9.0 .. 2.9 2.0 5.2 4.5 6.5 1.0 7.4 1.7 19.1 10.2 18.7 12.5 .. 9.2 4.0 14.7 1.4 4.7 .. 11.3 .. .. 8.0 7.2 4.8 13.1 .. 3.9 7.4 11.1 5.5 .. 12.4 .. 9.1 5.1 7.1 13.0 1.9 1.1 5.4 1.2 4.5 .. .. 5.8 3.4 5.4 4.2 11.3 5.8 .. ..
1990
2002
.. 70.8 19.2 56.1 26.7 .. 29.8 32.1 .. 8.3 .. 40.8 34.6 7.6 .. 22.0 30.5 23.5 13.6 2.2 75.5 20.1 39.2 10.7 19.2 27.9 7.4 .. 24.4 .. 64.9 24.0 52.0 .. .. .. 23.6 22.8 27.2 47.5 21.5 .. 8.0 16.8 34.8 30.7 57.6 2.8 .. 32.4 27.8 21.0 28.2 24.8 20.0 ..
.. 3.1 .. 82.6 22.9 9.9 27.4 55.6 77.2 6.8 20.5 36.7 12.6 8.1 6.7 .. 46.6 20.9 4.2 1.3 27.7 .. 38.9 .. .. 30.5 29.8 18.5 17.0 .. 75.4 24.9 27.7 43.2 .. 48.5 25.0 6.8 27.5 42.2 23.7 21.0 28.2 29.3 44.2 40.5 .. .. 14.1 40.4 23.9 17.8 10.4 44.4 .. ..
Commercial service imports
Transport
Travel
Insurance and financial services
Computer, information, communications, and other commercial services
% of total
% of total
% of total
% of total
services
services
services
$ millions 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
213 2,264 5,943 5,898 3,703 .. 5,145 4,825 46,602 667 84,281 1,118 .. 598 .. 10,050 2,805 .. 25 120 .. 48 .. 926 .. .. 172 268 5,394 352 126 407 10,063 .. 155 940 206 73 341 159 28,995 3,251 73 209 1,901 12,247 719 1,863 666 393 361 1,070 1,721 2,847 3,772 ..
2002
601 7,093 18,464 16,779 1,577 .. 40,393 11,269 61,485 1,603 106,612 1,480 3,635 764 .. 35,145 4,880 142 5 698 .. 45 .. .. 878 270 317 222 16,248 414 .. 779 17,031 231 260 1,903 607 364 226 205 56,478 4,682 315 .. .. 16,459 1,678 2,093 1,204 662 294 2,371 4,311 9,089 6,578 ..
4.8
ECONOMY
Structure of ser vice impor ts
1990
2002
45.4 8.8 57.5 47.4 47.3 .. 24.3 39.6 23.7 47.9 .. 52.0 .. 66.2 .. 39.8 31.9 .. 73.0 82.3 .. 67.9 .. 41.9 .. .. 43.5 81.8 46.9 57.4 76.9 51.6 25.0 .. 56.2 58.3 57.7 35.4 46.9 40.8 37.7 40.6 70.7 68.3 33.6 44.6 36.6 67.0 66.6 35.6 61.6 43.5 56.9 52.4 48.4 ..
51.3 14.6 13.7 30.7 72.4 .. 4.4 37.3 22.2 38.3 29.6 48.3 19.1 48.8 .. 30.4 35.8 38.0 99.0 33.3 .. 68.0 .. .. 33.8 37.2 45.4 50.1 36.3 64.0 .. 36.6 11.7 33.0 38.3 45.0 26.0 82.1 37.1 34.9 22.8 35.2 55.1 .. .. 34.2 37.1 66.4 51.1 26.1 59.4 39.7 51.9 20.0 33.1 ..
1990
17.6 25.9 6.6 14.2 9.2 .. 22.6 29.7 22.1 17.0 .. 30.1 .. 6.4 .. 27.5 65.5 .. .. 10.9 .. 24.7 .. 45.7 .. .. 23.4 5.9 26.9 15.8 18.3 23.0 54.9 .. 0.8 19.9 .. 22.6 17.9 28.5 25.4 29.5 20.1 10.4 30.3 30.0 6.5 23.1 14.8 12.8 19.8 27.6 6.4 14.9 23.0 ..
2002
1990
21.7 24.3 18.7 19.6 13.0 .. 9.3 22.6 27.5 16.1 25.0 28.2 20.8 18.7 .. 25.8 61.9 6.9 1.0 32.9 .. 30.7 .. .. 37.1 16.5 28.6 35.2 16.1 8.6 .. 26.2 35.6 37.3 45.8 23.4 18.8 7.6 24.6 38.8 23.0 31.8 22.1 .. .. 30.8 21.9 12.2 14.8 5.8 22.0 26.0 20.2 35.2 34.6 ..
15.0 1.0 5.8 4.0 10.8 .. 1.9 4.4 10.4 6.7 .. 5.2 .. 8.9 .. 0.3 1.2 .. 6.3 4.8 .. 5.6 .. 4.1 .. .. 3.5 8.7 .. 1.9 3.1 5.5 6.2 .. 6.3 6.0 4.3 2.5 6.8 3.2 1.0 2.5 7.9 4.3 3.1 1.7 4.1 1.3 10.2 4.0 11.4 10.9 3.4 1.0 5.1 ..
services 2002
.. 4.4 1.7 1.1 13.5 .. 11.3 3.4 3.9 8.5 4.6 6.8 2.5 10.0 .. 1.7 1.5 15.1 .. 8.7 .. 1.1 .. .. 4.3 3.4 1.5 0.0 3.3 3.5 .. 4.8 39.5 2.3 2.7 2.4 3.2 .. 5.8 .. 4.1 4.0 4.0 .. .. 4.4 7.1 5.7 20.6 7.3 17.4 10.3 7.9 6.1 5.0 ..
1990
2002
22.0 64.3 30.1 34.5 32.8 .. 51.2 26.2 43.8 28.4 .. 12.7 .. 18.5 .. 32.4 1.4 .. 20.6 2.1 .. 1.7 .. 8.3 .. .. 29.5 3.7 26.2 24.9 1.7 19.9 14.0 .. 36.8 15.9 38.1 39.5 28.5 27.5 35.9 27.5 1.4 17.1 32.9 23.6 52.8 8.6 8.4 47.6 7.3 18.0 33.2 31.8 23.5 ..
26.9 56.8 65.9 48.6 1.1 .. 75.0 36.8 46.4 37.1 40.9 16.7 57.5 22.4 .. 42.1 0.8 40.0 .. 25.1 .. 0.1 .. .. 24.7 42.9 24.5 14.7 44.3 23.9 .. 32.4 13.3 27.3 13.2 29.2 52.0 10.3 32.5 26.3 50.1 29.0 18.8 .. .. 30.6 34.0 15.8 13.5 60.8 1.1 23.9 20.1 38.6 27.3 ..
2004 World Development Indicators
211
4.8
Structure of ser vice impor ts Commercial service imports
Transport
Travel
Insurance and financial services
Computer, information, communications, and other commercial services
% of total
% of total
% of total
% of total
services
services
services
$ millions 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
787 2,304 .. 23,577 94 124 12,694 7,159 368 .. .. .. 67 .. 8,575 27,155 .. 1,779 1,034 1,719 .. .. 3,593 5,221 15,197 37,620 620 966 202 784 171 134 16,959 23,732 11,093 16,980 702 1,468 .. 103 288 647 6,160 16,573 217 129 460 339 682 1,353 2,794 6,283 .. .. 195 530 .. 3,143 .. .. 44,713 101,408 97,950 205,580 363 619 .. .. 2,390 3,767 .. 3,698 .. .. 639 883 370 328 460 .. 779,679 s 1,475,405 s 28,313 52,561 92,233 240,279 45,983 154,902 46,250 85,376 120,546 292,840 24,308 104,321 9,321 73,106 32,757 60,676 26,605 18,791 9,176 23,023 18,379 17,855 659,133 1,182,565 293,822 486,299
a. Includes Luxembourg.
212
2002
2004 World Development Indicators
1990
2002
65.5 .. 69.0 18.1 60.1 .. 29.5 41.0 .. 42.5 .. 40.2 30.8 64.2 31.9 6.1 23.2 33.7 54.5 .. 58.0 58.0 56.9 51.7 51.4 32.2 .. 58.3 .. .. 33.2 36.3 48.2 .. 33.5 .. .. 27.6 76.8 51.8 28.3 w 50.4 40.6 50.4 30.8 42.9 56.0 36.0 37.2 33.3 60.4 44.1 25.7 25.4
36.2 12.1 71.9 33.5 .. .. .. 42.6 24.4 21.3 .. 45.6 24.6 34.2 87.5 15.4 14.8 25.6 47.5 79.0 27.3 43.0 71.8 34.4 48.3 30.7 .. .. 15.5 .. 24.2 28.5 41.7 .. 41.1 .. .. 44.2 67.7 .. 26.1 w 28.9 29.1 30.9 25.9 29.1 34.1 19.5 29.3 36.2 23.0 38.0 25.4 20.5
1990
13.1 .. 23.7 .. 12.4 .. 32.7 21.0 .. 27.3 .. 31.5 28.0 11.9 25.4 20.6 37.1 53.0 35.5 .. 7.9 23.3 18.4 26.6 26.2 18.6 .. .. .. .. 41.0 38.9 30.7 .. 42.8 .. .. 9.9 14.6 14.4 28.6 w 13.9 22.5 19.1 25.8 20.5 18.2 19.5 35.9 7.9 11.3 19.2 30.1 31.5
2002
1990
17.2 50.9 19.2 .. .. .. .. 19.2 16.6 35.7 .. 34.6 17.7 27.3 11.7 24.6 30.4 38.9 45.6 1.7 52.2 19.9 3.7 44.5 19.2 29.9 .. .. 20.9 .. 41.4 29.6 28.7 .. 26.0 .. .. 8.8 13.3 .. 28.7 w 18.7 28.8 30.6 25.6 27.1 25.5 35.3 27.6 13.1 18.3 21.0 29.1 27.2
7.3 .. 0.0 2.2 8.8 .. 4.8 9.1 .. 2.5 .. 11.6 6.3 6.8 4.9 .. 7.9 1.4 4.4 .. 6.2 5.5 9.1 9.9 7.3 9.6 .. 6.5 .. .. 2.4 4.5 1.5 .. 4.3 .. .. 5.4 5.3 3.4 6.0 w 4.6 5.2 6.4 3.8 5.1 4.1 2.1 6.0 4.7 4.9 6.3 6.1 8.0
services 2002
7.0 2.9 .. 3.7 .. .. .. 4.3 4.9 2.3 .. 9.7 6.9 2.8 0.1 8.7 3.6 4.8 .. 6.3 4.8 5.9 15.3 2.4 7.5 15.8 .. .. 8.1 .. 6.3 9.2 7.0 .. 7.6 .. .. 7.6 1.7 .. 6.8 w 2.4 9.0 7.1 12.4 7.9 5.4 6.0 17.7 5.4 2.5 6.0 6.5 6.4
1990
2002
14.1 .. 7.3 79.7 18.7 .. 33.0 29.0 .. 27.8 .. 16.7 34.9 17.1 37.8 73.4 31.7 12.0 5.7 .. 27.9 13.2 15.5 11.9 15.0 49.2 .. 35.2 .. .. 23.4 20.4 19.6 .. 19.4 .. .. 57.1 3.3 30.4 38.2 w 31.1 32.4 24.5 40.3 32.1 22.6 43.0 21.5 54.1 23.4 30.6 39.3 35.2
39.5 34.0 8.9 62.7 .. .. .. 33.9 54.1 40.7 .. 10.1 50.8 35.7 0.8 51.2 51.1 30.6 6.9 13.0 15.7 31.2 9.2 18.7 25.0 23.6 .. .. 55.5 .. 28.1 32.7 22.6 .. 25.2 .. .. 39.4 17.3 .. 38.6 w 49.9 33.1 31.5 36.0 35.9 35.1 39.2 25.7 45.3 56.2 35.0 39.3 45.9
About the data
4.8
ECONOMY
Structure of ser vice impor ts Definitions
Trade in ser vices differs from trade in goods
• Commercial service imports are total service
because services are produced and consumed at the
imports minus imports of government services not
same time. Thus services to a traveler may be con-
included elsewhere. International transactions in
sumed in the producing country (for example, use of
ser vices are defined by the IMF’s Balance of
a hotel room) but are classified as imports of the
Payments Manual (1993) as the economic output of
traveler’s country. In other cases services may be
intangible commodities that may be produced, trans-
supplied from a remote location; for example, insur-
ferred, and consumed at the same time. Definitions
ance services may be supplied from one location
may vary among reporting economies. • Transport
and consumed in another. For further discussion of
covers all transport services (sea, air, land, internal
the problems of measuring trade in services, see
waterway, space, and pipeline) performed by resi-
About the data for table 4.7.
dents of one economy for those of another and
The data on exports of services in table 4.7 and on
involving the carriage of passengers, movement of
imports of services in this table, unlike those in edi-
goods (freight), rental of carriers with crew, and relat-
tions before 2000, include only commercial services
ed support and auxiliary services. Excluded are
and exclude the category “government services not
freight insurance, which is included in insurance
included elsewhere.” The data are compiled by the
services; goods procured in ports by nonresident
International Monetary Fund (IMF) based on returns
carriers and repairs of transport equipment, which
from national sources.
are included in goods; repairs of harbors, railway facilities, and airfield facilities, which are included in construction services; and rental of carriers without crew, which is included in other services. • Travel covers goods and services acquired from an economy by travelers in that economy for their own use during visits of less than one year for business or personal purposes. Travel ser vices include the goods and services consumed by travelers, such as meals, lodging, and transport (within the economy visited), including car rental. • Insurance and financial services cover freight insurance on goods imported and other direct insurance such as life insurance, financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxiliary services such as financial market operational and regulatory services. • Computer, information, communications, and other commercial services include such activities as
4.8a
international telecommunications, and postal and
Developing economies are consuming less transport services
courier services; computer data; news-related serv-
Commercial service imports (% of total)
ice transactions between residents and nonresi-
1990
2002
dents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recre-
Other 32% Insurance and financial services 5%
ational services. Other 36%
Transport 43%
Travel 20%
Insurance and financial services 8%
Transport 29%
Travel 27%
Data sources The data on imports of commercial services are from the IMF. The IMF publishes balance of pay-
Between 1990 and 2002 travel, insurance and finance, and other services displaced transport as the most important categories of service imports for developing economies.
ments data in its International Financial Statistics and Balance of Payments Statistics Yearbook.
Source: International Monetary Fund data files.
2004 World Development Indicators
213
4.9
Structure of demand Household final consumption expenditure
General government final consumption expenditure
% of GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
214
% of GDP
1990
2002
1990
.. 61 57 36 77 46 59 55 51 86 47 55 87 77 .. 33 59 60 82 95 91 67 56 86 88 62 50 57 66 79 62 61 72 74 .. 49 49 80 67 73 89 104 62 74 50 55 50 76 65 57 85 72 84 73 87 81
108 93 44 61 61 87 60 58 60 77 61 55 81 75 113 28 58 69 82 92 80 71 56 78 86 61 43 58 66 92 32 68 60 60 70 53 48 76 69 79 90 92 58 78 51 55 52 83 81 59 83 67 85 82 105 103
.. 19 16 34 3 18 19 19 18 4 24 20 11 12 .. 24 19 18 13 11 7 13 23 15 10 10 12 7 9 12 14 18 17 24 .. 23 26 5 11 11 10 22 16 18 22 22 13 14 10 20 9 15 7 9 10 8
2004 World Development Indicators
Gross capital formation
Exports of goods and services
% of GDP
2002
9 8 15 .. a 12 10 18 19 15 5 21 21 13 15 .. a 33 19 18 13 13 6 12 19 12 8 12 13 10 21 4 18 15 11 22 23 21 26 10 10 10 8 38 20 19 22 24 .. a 13 10 19 10 16 8 7 13 .. a
Imports of goods and services
% of GDP
Gross domestic savings
% of GDP
% of GDP
1990
2002
1990
2002
1990
2002
1990
2002
.. 29 29 12 14 47 22 25 27 17 27 22 14 13 .. 37 20 26 18 15 8 18 21 12 16 25 35 28 19 9 16 27 7 10 .. 25 20 25 21 29 14 8 30 12 30 23 22 22 31 24 14 23 14 18 30 13
16 23 31 32 12 21 24 22 33 23 22 19 18 15 20 25 20 20 18 8 22 19 20 15 59 23 40 23 15 7 23 22 10 27 10 28 20 23 28 17 16 26 31 21 20 19 28 21 21 18 20 23 19 17 15 21
.. 15 23 39 10 35 17 40 44 6 46 71 14 23 .. 55 8 33 11 8 6 20 26 15 13 35 18 133 21 30 54 35 32 78 .. 45 36 34 33 20 19 11 60 8 23 21 46 60 40 25 17 18 21 31 10 18
57 19 36 77 28 30 20 52 44 14 70 82 14 22 26 51 16 53 9 7 59 27 44 12 12 36 29 151 20 18 81 42 48 46 16 65 45 26 24 16 27 29 84 16 38 27 59 54 27 35 43 21 16 24 45 13
.. 23 25 21 5 46 17 38 39 14 44 69 26 24 .. 50 7 37 24 28 13 17 26 28 28 31 14 124 15 29 46 41 27 86 .. 43 31 44 32 33 31 45 54 12 24 22 31 72 46 25 26 28 25 31 37 20
89 43 26 70 13 47 22 51 51 19 74 78 26 27 59 37 14 60 22 19 67 28 39 17 65 32 26 142 21 21 54 47 30 55 18 67 39 35 31 23 41 85 94 34 30 25 39 72 39 32 55 27 28 30 77 36
.. 21 27 30 20 36 22 26 31 10 29 24 2 11 .. 43 21 22 5 –5 2 21 21 –1 2 28 38 36 24 9 24 21 11 2 .. 28 25 15 22 16 1 –26 22 7 29 22 37 11 25 24 5 13 10 18 3 11
–16 –1 40 39 27 3 22 23 25 18 18 23 6 10 –13 38 22 13 5 –4 14 18 25 10 6 27 43 32 14 4 50 17 28 18 7 26 26 15 20 10 2 –30 22 2 28 21 48 4 9 22 7 17 7 11 –17 –3
Household final consumption expenditure
% of GDP
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
General government final consumption expenditure
Gross capital formation
% of GDP
1990
2002
66 61 66 59 62 .. 58 56 58 65 53 74 52 67 .. 53 57 71 .. 53 140 121 .. 48 57 72 86 72 52 80 69 64 70 77 58 65 101 89 51 84 50 61 59 84 56 49 27 74 60 59 77 74 72 48 63 65
74 67 65 71 50 .. 47 59 60 67 56 75 60 71 .. 62 56 67 .. 63 95 82 .. 58 62 77 84 88 44 77 79 66 70 86 64 62 59 88 48 78 50 60 78 84 55 67 43 74 63 .. 84 72 69 65 61 ..
1990
14 11 12 9 11 .. 16 30 20 13 13 25 18 19 .. 10 39 25 9 9 25 23 .. 24 19 19 8 15 14 14 26 13 8 .. a 32 15 12 .. a 31 9 23 19 43 15 15 21 38 15 18 25 6 8 10 19 16 14
Exports of goods and services
% of GDP
2002
14 11 13 8 13 .. 15 31 19 20 17 23 12 19 .. 11 26 18 .. 21 14 33 .. 17 21 22 8 18 14 11 19 9 12 17 19 20 11 .. a 28 10 24 19 16 12 27 .. a 23 11 13 .. 8 10 12 19 21 ..
Imports of goods and services
% of GDP
4.9
ECONOMY
Structure of demand
Gross domestic savings
% of GDP
% of GDP
1990
2002
1990
2002
1990
2002
1990
2002
23 25 24 31 29 .. 21 25 22 26 33 32 32 20 .. 38 18 24 .. 40 18 49 .. 19 33 19 17 23 32 23 20 31 23 25 38 25 16 13 34 18 24 20 19 8 15 23 13 19 17 24 23 16 24 26 28 17
28 24 23 14 35 .. 24 18 20 34 26 23 27 14 .. 26 9 19 22 27 18 40 .. 14 22 20 14 12 24 20 31 22 20 23 31 23 45 12 24 25 20 20 32 13 23 19 13 15 25 .. 20 18 19 19 28 ..
36 31 7 25 22 .. 57 35 20 48 10 62 74 26 .. 29 45 29 11 48 18 16 .. 40 52 26 17 24 75 17 46 64 19 49 24 26 8 3 52 11 54 27 25 15 43 40 53 16 38 41 33 16 28 29 33 77
37 64 15 35 31 .. 98 37 27 39 10 46 47 27 .. 40 48 39 .. 45 14 51 .. 48 54 38 16 25 114 32 39 61 27 54 67 32 24 .. 48 16 62 33 23 16 38 41 57 19 28 .. 31 16 49 28 31 81
40 29 9 24 24 .. 52 45 20 52 9 93 75 31 .. 30 58 50 25 49 100 109 .. 31 61 36 28 33 72 34 61 71 20 51 53 32 36 5 67 22 51 27 46 22 29 34 31 23 34 49 39 14 33 22 39 101
53 67 16 29 29 .. 83 46 26 60 10 67 46 30 .. 39 40 43 .. 56 41 107 .. 36 60 57 23 43 97 41 68 57 29 79 81 37 38 .. 49 29 56 32 49 25 44 27 35 19 29 .. 43 17 49 31 41 100
20 28 23 32 27 .. 26 14 22 22 34 1 30 14 .. 37 4 4 .. 39 –64 –44 .. 27 24 9 6 13 34 6 5 23 22 23 9 19 –12 11 18 7 27 20 –2 1 29 30 35 11 21 16 17 18 18 33 21 21
12 22 22 21 37 .. 38 9 21 13 26 3 28 10 .. 27 18 15 .. 17 –9 –15 .. 26 17 0 8 –6 42 12 2 26 18 –3 16 18 30 12 23 12 26 22 6 4 17 33 34 14 24 .. 8 18 19 16 18 ..
2004 World Development Indicators
215
4.9
Structure of demand Household final consumption expenditure
% of GDP 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania b Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
66 49 84 47 76 .. 83 47 54 55 112 57 60 76 .. 62 47 57 69 74 81 57 71 59 58 69 49 92 57 39 63 67 70 61 62 84 .. 74 64 63 59 w 67 59 58 63 61 54 55 65 59 69 63 59 56
General government final consumption expenditure % of GDP
2002
76 51 87 37 76 89 93 42 55 55 .. 62 58 77 79 74 49 76 59 82 77 58 86 69 63 71 49 78 56 .. 66 70 73 58 65 66 79 70 84 72 63 w 69 58 57 61 59 51 61 63 53 68 66 64 57
1990
13 21 10 29 15 .. 8 10 22 19 .. a 20 17 10 .. 18 29 14 14 9 18 9 14 12 16 11 23 8 17 16 20 17 12 25 8 12 .. 17 19 19 17 w 12 15 15 12 14 11 18 13 20 12 18 17 20
Gross capital formation
% of GDP
2002
7 17 12 26 14 18 21 13 21 21 .. 19 18 9 .. a 17 28 .. a 11 9 13 11 10 10 16 13 15 16 20 .. 20 16 13 19 6 6 52 14 12 17 17 w 12 16 16 15 15 12 16 16 18 12 18 18 20
1990
30 30 15 15 14 .. 10 36 33 17 16 17 27 23 .. 19 24 28 17 25 26 41 27 13 32 24 40 13 27 20 20 18 12 32 10 13 .. 15 17 17 24 w 23 25 27 21 25 34 28 19 23 23 17 24 24
Exports of goods and services
% of GDP
2002
23 21 19 20 20 16 9 21 31 23 .. 16 26 21 20 18 17 17 22 23 17 24 22 16 25 16 37 22 19 .. 16 18 12 20 17 32 4 17 17 8 20 w 20 23 25 19 23 32 21 19 23 22 18 19 20
1990
17 18 6 41 25 .. 22 .. 27 84 10 24 16 29 .. 75 29 36 28 28 13 34 33 45 44 13 .. 7 28 65 24 10 24 29 39 36 .. 14 36 23 19 w 17 20 17 28 20 25 23 14 31 9 27 19 27
Imports of goods and services
% of GDP
2002
35 35 8 41 31 21 18 .. 73 58 .. 34 28 36 15 91 43 44 37 58 17 65 33 47 45 30 47 12 56 .. 26 10 22 38 29 56 12 38 29 24 24 w 25 32 29 39 31 41 40 21 34 17 33 22 36
1990
26 18 14 32 30 .. 24 .. 36 74 38 19 20 38 .. 74 28 36 28 35 37 42 45 29 51 18 .. 19 29 40 27 11 18 48 20 45 .. 20 37 23 19 w 19 19 17 25 19 24 24 12 33 12 26 19 27
Gross domestic savings
% of GDP
2002
1990
2002
41 24 25 23 41 44 40 .. 80 56 .. 31 30 43 13 100 37 38 28 72 24 57 50 43 49 30 47 27 52 .. 28 14 20 34 17 60 47 39 42 22 23 w 25 28 26 33 28 37 38 19 29 18 34 22 33
21 30 6 24 9 .. 9 43 24 26 –12 23 23 14 .. 20 25 29 17 17 1 34 15 29 25 20 28 1 26 45 18 16 18 13 29 3 .. 9 17 17 24 w 21 26 27 24 25 34 26 21 21 20 19 24 23
17 32 1 37 10 –7 –14 45 24 25 .. 19 24 14 21 9 23 24 30 10 10 31 5 20 21 16 36 6 24 .. 14 14 14 24 29 28 –31 16 4 11 20 w 19 27 28 25 26 37 23 22 29 20 17 19 22
a. Data on general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. Data cover mainland Tanzania only.
216
2004 World Development Indicators
About the data
4.9
ECONOMY
Structure of demand Definitions
Gross domestic product (GDP) from the expenditure
guidelines are capital outlays on defense establish-
• Household final consumption expenditure is the mar-
side is made up of household final consumption
ments that may be used by the general public, such as
ket value of all goods and services, including durable
expenditure, general government final consumption
schools, airfields, and hospitals, and intangibles such
products (such as cars, washing machines, and home
expenditure, gross capital formation (private and
as computer software and mineral exploration outlays.
computers), purchased by households. It excludes pur-
public investment in fixed assets, changes in inven-
Data on capital formation may be estimated from
tories, and net acquisitions of valuables), and net
direct surveys of enterprises and administrative
exports (exports minus imports) of goods and serv-
records or based on the commodity flow method using
ices. Such expenditures are recorded in purchaser
data from production, trade, and construction activi-
sumption expenditure the expenditures of nonprofit
prices and include net taxes on products.
ties. The quality of data on fixed capital formation by
institutions serving households, even when reported
Because policymakers have tended to focus on fos-
government depends on the quality of government
separately by the country. In practice, household con-
tering the growth of output, and because data on pro-
accounting systems (which tend to be weak in devel-
sumption expenditure may include any statistical dis-
duction are easier to collect than data on spending,
oping countries). Measures of fixed capital formation
crepancy in the use of resources relative to the supply
many countries generate their primary estimate of GDP
by households and corporations—particularly capital
of resources. • General government final consumption
using the production approach. Moreover, many coun-
outlays by small, unincorporated enterprises—are
expenditure includes all government current expendi-
tries do not estimate all the separate components of
usually unreliable.
chases of dwellings but includes imputed rent for owneroccupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. World Development Indicators includes in household con-
tures for purchases of goods and services (including compensation of employees). It also includes most
national expenditures but instead derive some of the
Estimates of changes in inventories are rarely com-
main aggregates indirectly using GDP (based on the
plete but usually include the most important activi-
production approach) as the control total.
ties or commodities. In some countries these
tially have wider public use and are part of government
Household final consumption expenditure (private
estimates are derived as a composite residual along
capital formation. • Gross capital formation consists of
consumption in the 1968 System of National
with household final consumption expenditure.
outlays on additions to the fixed assets of the economy,
Accounts, or SNA) is often estimated as a residual,
According to national accounts conventions, adjust-
net changes in the level of inventories, and net acquisi-
by subtracting from GDP all other known expendi-
ments should be made for appreciation of the value
tions of valuables. Fixed assets include land improve-
tures. The resulting aggregate may incorporate fairly
of inventory holdings due to price changes, but this
ments (fences, ditches, drains, and so on); plant,
large discrepancies. When household consumption
is not always done. In highly inflationary economies
is calculated separately, many of the estimates are
this element can be substantial.
expenditures on national defense and security but excludes government military expenditures that poten-
machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings,
based on household surveys, which tend to be one-
Data on exports and imports are compiled from cus-
year studies with limited coverage. Thus the esti-
toms reports and balance of payments data. Although
mates quickly become outdated and must be
the data from the payments side provide reasonably
unexpected fluctuations in production or sales, and
supplemented by estimates using price- and quantity-
reliable records of cross-border transactions, they may
“work in progress.” • Exports and imports of goods
based statistical procedures. Complicating the
not adhere strictly to the appropriate definitions of val-
and services represent the value of all goods and other
issue, in many developing countries the distinction
uation and timing used in the balance of payments or
market services provided to, or received from, the rest
between cash outlays for personal business and
correspond to the change-of-ownership criterion. This
of the world. They include the value of merchandise,
those for household use may be blurred. World
issue has assumed greater significance with the
freight, insurance, transport, travel, royalties, license
Development Indicators includes in household con-
increasing globalization of international business.
sumption the expenditures of nonprofit institutions
Neither customs nor balance of payments data usu-
serving households.
ally capture the illegal transactions that occur in many
General government final consumption expenditure
countries. Goods carried by travelers across borders
(general government consumption in the 1968 SNA)
in legal but unreported shuttle trade may further dis-
includes expenditures on goods and services for indi-
tort trade statistics.
vidual consumption as well as those on services for
Domestic savings, a concept used by the World
collective consumption. Defense expenditures,
Bank, represent the difference between GDP and
including those on capital outlays (with certain
total consumption. Domestic savings also satisfy the
exceptions), are treated as current spending.
fundamental identity: exports minus imports equal
and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or
fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude labor and property income (factor services in the 1968 SNA) as well as transfer payments. • Gross domestic savings are calculated as GDP less total consumption. Data sources The national accounts indicators for most developing countries are collected from national statistical organizations and central banks by visiting and resident World Bank missions. The data for
Gross capital formation (gross domestic investment
domestic savings minus capital formation. Domestic
in the 1968 SNA) consists of outlays on additions to
savings differ from savings as defined in the nation-
the economy’s fixed assets plus net changes in the
al accounts; the SNA concept of savings represents
files (see the OECD’s National Accounts of OECD
level of inventories. It is generally obtained from
the difference between disposable income and con-
Countries, Detailed Tables 1970–2001, volumes
reports by industry of acquisition and distinguishes
sumption. For further discussion of the problems in
1 and 2). The United Nations Statistics Division
only the broad categories of capital formation. The
compiling national accounts, see Srinivasan (1994),
publishes detailed national accounts for United
1993 SNA recognizes a third category of capital
Heston (1994), and Ruggles (1994). For a classic
Nations member countries in National Accounts
formation: net acquisitions of valuables. Included
analysis of the reliability of foreign trade and nation-
Statistics: Main Aggregates and Detailed Tables
in gross capital formation under the 1993 SNA
al income statistics, see Morgenstern (1963).
and updates in the Monthly Bulletin of Statistics.
high-income economies come from Organisation for Economic Co-operation and Development data
2004 World Development Indicators
217
4.10
Growth of consumption and investment Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
average annual
Per capita average annual
average annual
average annual
% growth
% growth
% growth
$ millions 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil a Bulgaria Burkina Faso Burundi Cambodia a Cameroon Canada Central African Republic a Chad a Chile China Hong Kong, China Colombia Congo, Dem. Rep. a Congo, Rep. a Costa Rica a Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic a Ecuador a Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon a Gambia, The Georgia Germany Ghana Greece Guatemala a Guinea Guinea-Bissau Haiti
218
.. 1,271 35,265 3,674 109,038 1,097 182,448 89,789 3,186 24,988 8,223 109,154 1,602 3,741 .. 1,260 273,952 12,401 2,284 1,070 1,016 7,423 322,564 1,274 1,538 18,759 174,249 42,723 26,357 7,398 1,746 3,502 7,766 13,527 .. 17,195 65,430 5,633 6,988 30,933 4,273 496 2,539 6,382 68,341 672,960 2,961 240 5,231 950,047 5,016 60,164 6,398 2,068 212 2,332
2002
.. 4,496 24,745 .. 62,158 2,121 247,950 117,605 3,587 36,548 8,781 135,445 2,183 5,835 .. 1,537 263,710 10,742 2,556 655 3,287 6,394 391,155 815 1,719 39,211 586,381 93,401 53,046 5,269 955 11,521 7,048 13,483 .. 36,165 82,827 16,408 16,837 71,236 12,847 592 3,727 4,756 66,204 784,209 3,040 296 2,799 1,168,773 5,093 89,446 19,794 2,625 213 3,334
2004 World Development Indicators
1980–90
1990–2002
1980–90
1990–2002
1980–90
.. .. 1.5 –3.6 .. .. 2.9 2.4 .. 3.0 .. 2.0 1.9 1.2 .. 6.3 1.2 3.1 2.6 3.4 .. 3.5 3.2 1.5 2.9 2.0 8.8 6.6 2.6 3.4 2.3 3.6 1.5 .. .. .. 1.4 3.9 1.1 4.6 0.8 .. .. 0.7 3.9 2.2 1.5 –2.4 .. 2.3 2.8 2.0 1.1 .. 0.8 0.9
.. 4.2 0.9 .. 0.5 1.1 3.7 2.3 11.3 2.8 0.9 1.9 3.4 3.4 .. 4.1 4.7 –1.0 3.9 –1.7 4.7 3.5 2.7 .. 2.0 6.2 8.7 3.5 1.8 –2.9 1.7 4.4 3.1 3.0 .. 2.7 1.8 5.6 2.2 4.3 4.8 –0.3 1.2 5.6 2.1 1.6 2.1 5.3 4.6 1.6 1.3 2.4 4.1 3.6 1.5 ..
.. .. –1.4 .. .. .. 1.4 2.2 .. 0.4 .. 1.9 –1.2 –0.9 .. 2.7 –0.7 3.2 0.1 0.5 .. 0.6 2.0 .. 0.2 0.3 7.2 5.2 0.5 0.4 –0.9 0.6 –2.1 .. .. .. 1.4 1.7 –1.5 2.0 –0.2 .. .. –2.4 3.4 1.7 –1.6 –5.9 .. 2.2 –0.6 1.5 –1.4 .. –1.9 ..
.. 4.8 –0.9 .. –0.7 2.5 2.5 2.0 10.3 1.0 1.2 1.6 0.7 0.9 .. 1.5 3.2 –0.3 1.4 –3.7 2.2 0.9 1.7 .. –1.1 4.7 7.6 1.8 –0.1 –5.4 –1.5 2.2 0.2 3.5 .. 2.8 1.4 3.8 0.3 2.3 2.8 –2.9 2.5 3.2 1.8 1.2 –0.6 1.8 5.0 1.3 –1.1 2.0 1.4 1.1 –1.4 ..
.. .. 0.7 8.4 .. .. 3.5 1.4 .. 2.9 .. 1.1 0.5 –3.8 .. 14.9 7.3 5.1 6.2 3.2 .. 6.8 2.4 –1.7 17.0 0.4 9.8 5.3 4.2 0.0 4.3 1.1 –0.1 .. .. .. 0.9 –3.2 –0.7 3.1 0.1 .. .. 4.0 3.2 2.6 –0.6 1.7 .. 1.5 2.4 1.1 2.6 .. 7.2 –4.4
1990–2002
.. 1.6 3.4 .. 1.3 –1.0 3.0 1.6 7.0 4.9 –1.0 1.6 5.8 3.4 .. 7.6 0.2 –5.8 –0.5 –1.6 7.7 2.8 0.5 .. –0.8 3.6 9.0 3.3 8.9 –15.9 –2.6 2.0 0.8 0.1 .. –1.0 2.2 13.6 –1.0 2.5 2.4 14.2 4.4 9.5 1.1 1.9 4.0 0.5 4.3 1.5 4.7 1.6 5.3 4.3 2.1 ..
% growth 1980–90
.. –0.3 –1.8 –5.6 –5.2 .. 3.7 2.4 .. 6.9 .. 2.9 –5.3 0.8 .. 7.6 3.3 .. 8.6 6.9 .. –2.6 5.0 10.0 22.0 6.4 10.8 3.9 1.4 –5.1 –11.6 4.6 –10.4 .. .. .. 4.7 4.5 –1.3 0.0 2.2 .. .. 4.9 3.3 3.3 –5.7 0.0 .. 1.8 3.3 –0.7 –1.8 .. 12.9 –0.6
1990–2002
.. 20.5 0.3 .. 2.5 –6.2 6.4 2.0 8.0 9.0 –6.3 1.9 12.8 5.1 .. 2.6 0.6 .. 7.9 1.2 12.2 2.3 4.6 .. 18.0 6.2 10.7 4.3 0.8 0.3 2.3 5.5 4.5 5.7 .. 4.8 5.2 6.2 1.3 5.5 6.0 10.7 1.8 5.8 1.8 2.0 3.2 2.6 –8.3 0.4 1.1 4.5 6.3 2.3 –12.3 8.7
Household final consumption expenditure
4.10
General government final consumption expenditure
Gross capital formation
average annual
Per capita average annual
average annual
average annual
% growth
% growth
% growth
$ millions 1990
Honduras a Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan a Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania a Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova a Mongolia a Morocco Mozambique a Myanmar Namibia Nepal Netherlands New Zealand Nicaragua a Niger Nigeria Norway Oman Pakistan Panama a Papua New Guinea Paraguay Peru a Philippines Poland a Portugal Puerto Rico
2,026 20,290 215,762 65,010 74,476 .. 27,957 32,112 634,194 2,980 1,618,040 2,978 12,856 5,320 .. 132,113 10,459 1,896 .. 3,365 3,961 746 .. 13,999 5,826 3,021 2,663 1,345 22,806 1,943 705 1,519 182,791 1,780 .. 16,833 2,481 .. 1,204 3,060 145,871 26,632 592 2,079 15,816 57,047 2,810 29,512 3,022 1,902 4,063 19,376 31,566 28,281 44,679 19,827
2002
4,858 42,860 328,706 122,193 54,403 .. 47,973 61,552 713,186 5,859 2,282,911 7,622 14,392 8,819 .. 286,818 19,720 1,083 .. 5,274 16,921 585 .. 10,970 8,577 2,924 3,703 1,665 41,971 2,230 762 2,983 445,791 1,403 744 23,952 2,124 .. 1,377 4,336 209,068 34,955 3,123 1,814 24,135 73,067 8,752 43,936 5,673 .. 4,649 40,717 53,307 123,535 67,078 ..
% growth
1980–90
1990–2002
1980–90
1990–2002
1980–90
1990–2002
1980–90
2.7 1.3 4.2 5.3 2.8 .. 2.2 .. 2.9 .. 3.6 1.9 .. 4.7 .. 7.9 –1.4 .. .. 2.3 .. 1.3 .. .. .. .. –0.7 1.5 3.3 0.6 1.4 6.2 1.1 .. .. 4.3 –1.6 0.6 1.3 .. 1.7 2.1 –3.6 0.0 –2.6 2.2 .. 4.3 2.1 0.4 2.4 0.7 2.6 .. 2.6 3.5
3.1 0.9 4.9 5.8 3.3 .. 5.7 4.2 1.7 .. 1.5 5.2 –5.5 2.2 .. 4.9 .. –4.7 .. –1.6 2.4 –0.4 .. .. 4.9 2.1 2.3 4.9 4.9 3.2 3.9 4.8 2.8 8.6 .. 2.7 1.5 3.9 5.1 .. 2.8 3.1 6.1 1.8 0.2 3.5 .. 4.4 4.1 5.2 3.2 3.6 3.7 4.8 2.8 ..
–0.5 1.7 2.0 3.4 –0.6 .. 1.9 .. 2.8 .. 3.0 –1.9 .. 1.2 .. 6.7 .. .. .. 1.8 .. –0.8 .. .. .. .. –3.4 –1.7 0.4 –1.9 –0.9 5.3 –1.0 .. .. 2.0 –3.1 .. –1.9 .. 1.1 1.2 –6.2 –3.1 –5.5 1.9 .. 1.6 –0.0 –2.1 –0.5 –1.5 0.2 .. 2.4 ..
0.3 1.1 3.1 4.3 1.7 .. 4.8 1.7 1.5 .. 1.2 1.4 –4.6 –0.3 .. 4.0 .. –5.6 .. –0.4 0.7 –1.5 .. .. 5.6 1.4 –0.6 2.9 2.4 0.7 1.1 3.6 1.1 8.9 .. 0.9 –0.7 .. 2.2 .. 2.2 2.0 3.2 –1.7 –2.7 2.9 .. 1.8 2.4 2.6 0.8 1.7 1.4 4.7 2.5 ..
3.3 1.9 7.3 4.6 –5.0 .. –0.3 .. 2.9 .. 3.6 1.9 .. 2.6 .. 5.2 2.2 .. .. 5.0 .. 3.6 .. .. .. .. 0.5 6.3 2.7 7.9 –3.8 3.3 2.4 .. .. 2.1 –1.1 .. 3.7 .. 2.2 1.6 3.4 4.4 –3.5 2.4 .. 10.3 1.2 –0.1 1.5 –0.9 0.6 .. 5.0 5.1
3.7 1.5 6.4 0.9 4.3 .. 4.6 3.0 0.4 .. 3.0 4.1 –4.5 7.5 .. 2.6 .. –6.5 .. 1.8 5.7 6.7 .. .. 1.6 1.5 0.6 –1.5 5.6 5.5 2.0 4.8 1.6 –9.2 .. 3.6 6.2 .. 3.0 .. 2.1 2.6 –2.6 0.8 –1.8 2.7 .. 0.8 2.6 2.2 4.0 4.6 3.3 3.1 2.9 ..
2.9 –0.9 6.2 7.7 –2.5 .. –0.4 .. 2.1 .. 5.5 –1.9 .. 0.4 .. 12.0 –4.5 .. .. 3.4 .. 5.0 .. .. .. .. 4.9 –2.8 3.1 3.6 6.9 10.3 –3.3 .. .. 1.2 3.8 –4.1 –3.2 .. 3.3 3.0 –4.8 –7.1 –8.5 0.9 25.5 5.8 –8.9 –0.9 –0.8 –3.8 –2.1 .. 3.0 6.9
2004 World Development Indicators
1990–2002
5.2 7.6 6.9 –2.1 4.6 .. 9.8 –1.5 1.8 .. –0.1 0.3 –11.8 2.9 .. 1.3 .. –2.2 .. –6.5 4.1 1.0 .. .. 9.3 1.5 4.0 –13.7 3.5 3.5 9.1 4.0 4.6 –12.0 .. 3.9 14.0 15.3 6.5 .. 2.8 5.1 12.6 4.0 5.4 4.5 .. 1.4 8.0 1.3 –1.6 4.9 3.7 8.5 5.4 ..
219
ECONOMY
Growth of consumption and investment
4.10
Growth of consumption and investment Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
average annual
Per capita $ millions 1990
Romania a Russian Federation Rwanda a Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka a Sudan Swaziland a Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania b Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay a Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
2002
25,232 34,785 252,561 177,362 2,162 1,503 54,508 69,666 4,353 3,820 .. 13,915 546 728 17,019 37,360 8,350 13,133 6,917 11,697 .. .. 64,251 64,741 306,953 378,319 6,143 12,736 .. 8,339 547 883 116,475 116,993 130,900 149,886 8,458 12,289 1,940 932 3,526 7,365 48,270 71,743 1,158 1,184 2,975 6,424 7,152 13,152 103,324 130,631 1,616 2,918 4,002 4,528 46,497 23,251 12,726 .. 619,782 1,034,301 3,831,500 7,303,700 6,525 8,836 8,204 4,569 30,170 60,977 5,485 22,780 .. 2,756 3,561 6,882 2,078 3,110 5,543 6,020 12,863,243 t 20,040,617 t 513,634 757,325 1,879,875 2,964,278 1,323,786 1,913,292 558,438 1,046,796 2,389,174 3,715,003 361,814 932,055 604,731 689,168 722,234 1,098,409 238,847 361,722 283,645 432,695 186,935 203,372 10,474,011 16,329,652 3,115,871 3,795,770
average annual
average annual
average annual
% growth
% growth
% growth
1980–90
.. .. 1.2 .. 2.1 .. –2.7 5.8 3.8 .. 1.3 2.4 2.6 4.0 0.0 5.3 2.2 1.6 3.6 .. .. 5.9 4.7 –1.3 2.9 .. .. 2.6 .. 4.6 4.0 3.8 0.7 .. 1.3 .. .. .. 1.8 3.7 3.3 w 3.4 2.4 3.3 .. 2.9 6.4 .. 1.3 .. 4.1 1.5 3.3 2.4
1990–2002
1.9 0.3 2.2 .. 2.9 .. –5.2 5.5 1.8 3.5 .. 2.7 2.5 4.7 .. 3.5 1.6 1.1 2.0 0.9 3.5 3.3 3.6 2.0 4.5 3.1 .. 6.0 –4.7 .. 3.3 3.5 3.2 .. 0.4 5.0 –1.0 3.6 –2.3 0.4 2.8 w 4.4 3.5 3.8 2.6 3.7 6.7 1.2 3.4 .. 4.6 2.6 2.6 1.9
1980–90
1990–2002
.. .. –1.8 .. –0.8 .. –4.7 3.9 3.5 .. .. –0.2 2.3 2.9 .. 2.1 1.9 1.1 0.2 .. .. 4.1 1.3 –2.5 0.3 .. .. –0.6 .. .. 3.8 2.9 0.1 .. –1.2 .. .. .. –1.3 –0.0 1.5 w 1.0 0.8 1.6 .. 0.9 4.7 .. –0.7 .. 1.8 –1.3 2.7 2.1
a. Household final consumption expenditure includes statistical discrepancy. b. Data cover mainland Tanzania only.
220
2004 World Development Indicators
2.2 0.5 0.8 .. 0.2 .. –7.3 2.6 1.6 3.5 .. 0.5 2.0 3.4 .. 0.4 1.3 0.5 –0.8 –0.3 0.7 2.5 0.8 1.5 2.9 1.2 .. 3.0 –4.1 .. 3.1 2.3 2.5 .. –1.7 3.6 –5.1 0.3 –4.5 –1.5 1.4 w 2.4 2.3 2.7 1.3 2.1 5.5 1.1 1.7 .. 2.7 0.1 1.9 1.5
1980–90
.. .. 5.2 .. 3.3 .. –4.7 6.6 4.8 .. 7.0 3.5 4.9 7.3 –0.5 1.4 1.6 3.1 –3.6 4.1 .. 4.2 –1.2 –1.7 3.8 .. .. 2.0 .. –3.9 0.8 3.3 1.8 .. 2.0 .. .. .. –3.4 4.7 3.0 w 5.2 .. 5.5 .. 5.3 6.3 .. 5.6 .. 7.6 2.7 2.8 2.3
1990–2002
0.7 –1.5 0.7 .. 4.1 .. 3.2 9.3 1.4 3.9 .. 0.7 3.0 10.9 .. 3.4 0.5 1.0 0.3 –11.2 1.2 4.5 –2.1 1.3 4.2 4.4 .. 7.1 –3.1 .. 1.4 1.3 1.5 .. 0.4 3.4 13.6 2.2 –6.5 –2.9 1.9 w 3.8 2.1 2.2 2.0 2.3 6.8 –0.3 1.1 .. 5.8 1.4 1.8 1.7
% growth 1980–90
.. .. 4.3 .. 5.2 .. 44.9 3.1 0.0 .. –2.6 –5.3 5.9 0.6 –1.8 –0.4 4.7 3.9 –5.3 –4.3 .. 9.5 2.7 –6.3 –1.8 .. .. 8.0 .. –8.7 6.4 4.0 –6.6 .. –5.3 .. .. .. –4.3 3.6 3.9 w 4.7 1.6 3.4 –2.5 2.1 8.4 .. –0.3 .. 6.0 –3.8 4.2 2.6
1990–2002
–2.7 –13.6 2.7 .. 8.0 .. 2.7 4.5 5.8 9.3 .. 3.1 3.4 5.3 10.8 1.5 2.0 1.0 2.1 –10.1 –0.3 –4.1 1.3 8.4 3.7 1.4 4.3 8.1 –13.9 .. 3.7 6.2 2.3 0.1 2.3 17.2 –22.7 7.5 6.4 –6.2 2.5 w 4.2 1.3 0.3 4.5 1.7 6.9 –6.6 2.6 .. 6.5 3.4 2.7 1.8
About the data
4.10
Definitions
Measures of growth in consumption and capital for-
methods.) Growth rates of household final consump-
• Household final consumption expenditure is the
mation are subject to two kinds of inaccuracy. The first
tion expenditure, household final consumption
market value of all goods and services, including
stems from the difficulty of measuring expenditures at
expenditure per capita, general government final con-
durable products (such as cars, washing machines,
current price levels, as described in About the data for
sumption expenditure, and gross capital formation
and home computers), purchased by households. It
table 4.9. The second arises in deflating current price
are
data.
excludes purchases of dwellings but includes imputed
data to measure volume growth, where results depend
(Consumption and capital formation as shares of
rent for owner-occupied dwellings. It also includes pay-
on the relevance and reliability of the price indexes
GDP are shown in table 4.9.)
ments and fees to governments to obtain permits and
estimated
using
constant
price
and weights used. Measuring price changes is more
To obtain government consumption in constant
licenses. World Development Indicators includes in
difficult for investment goods than for consumption
prices, countries may deflate current values by apply-
household consumption expenditure the expenditures
goods because of the one-time nature of many invest-
ing a wage (price) index or extrapolate from the
of nonprofit institutions serving households, even
ments and because the rate of technological progress
change in government employment. Neither tech-
when reported separately by the country. In practice,
in capital goods makes capturing change in quality dif-
nique captures improvements in productivity or
household consumption expenditure may include any
ficult. (An example is computers—prices have fallen
changes in the quality of government services.
statistical discrepancy in the use of resources relative
as quality has improved.) Several countries estimate
Deflators for household consumption are usually cal-
to the supply of resources. • General government
capital formation from the supply side, identifying cap-
culated on the basis of the consumer price index.
final consumption expenditure includes all govern-
ital goods entering an economy directly from detailed
Many countries estimate household consumption as
ment current expenditures for purchases of goods and
production and international trade statistics. This
a residual that includes statistical discrepancies
services (including compensation of employees). It
means that the price indexes used in deflating pro-
associated with the estimation of other expenditure
also includes most expenditures on national defense
duction and international trade, reflecting delivered or
items, including changes in inventories; thus these
and security but excludes government military expen-
offered prices, will determine the deflator for capital
estimates lack detailed breakdowns of household
ditures that potentially have wider public use and are
formation expenditures on the demand side.
consumption expenditures.
part of government capital formation. • Gross capi-
The data in the table on household final consump-
tal formation consists of outlays on additions to the
tion expenditure (private consumption in the 1968
fixed assets of the economy, net changes in the level
System of National Accounts), in current U.S. dollars,
of inventories, and net acquisitions of valuables.
are converted from national currencies using official
Fixed assets include land improvements (fences,
exchange rates or an alternative conversion factor as
ditches, drains, and so on); plant, machinery, and
noted in Primary data documentation. (For a discus-
equipment purchases; and the construction of
sion of alternative conversion factors, see Statistical
roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and
4.10a
commercial and industrial buildings. Inventories are
Per capita consumption has risen in Asia, fallen in Africa
stocks of goods held by firms to meet temporary or
Per capita household consumption (1995 $)
unexpected fluctuations in production or sales, and “work in progress.”
600
East Asia & Pacific 500
Sub-Saharan Africa
400
Data sources The national accounts indicators for most develop-
South Asia 300
ing countries are collected from national statistical organizations and central banks by visiting and resident World Bank missions. Data for high-income economies come from data files of the Organisation
200
for Economic Co-operation and Development (see the OECD’s National Accounts of OECD Countries, 100
Detailed Tables, 1970–2001, volumes 1 and 2). 1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Starting from slightly lower per capita household consumption in 1980 than South Asia, East Asia and Pacific has raised consumption dramatically and lowered poverty. In Sub-Saharan Africa, by contrast, which had per capita household consumption of well more than twice that in East and South Asia in 1980, per capita household consumption has fallen below that of East Asia and Pacific. Source: World Bank data files.
The United Nations Statistics Division publishes detailed national accounts for United Nations member countries in National Accounts Statistics: Main Aggregates and Detailed Tables and updates in the Monthly Bulletin of Statistics.
2004 World Development Indicators
221
ECONOMY
Growth of consumption and investment
4.11
Central government finances Current revenue a
% of GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
222
Total expenditure
% of GDP
Financing from abroad
Domestic financing
Debt and interest payments Total
% of GDP
% of GDP
Interest
debt
% of
% of
current
GDP
revenue
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
2001
2001
.. .. .. .. 10.4 .. 24.9 34.0 .. .. 30.9 42.7 .. 13.7 .. 50.8 22.8 47.1 9.7 18.2 .. 15.4 21.5 .. 6.7 20.6 6.3 .. 12.6 10.1 22.5 23.0 22.0 33.0 .. .. 37.8 12.0 18.7 23.0 .. .. 26.2 17.3 30.5 39.7 20.6 19.4 .. 25.9 12.5 27.8 .. 16.0 .. ..
.. .. 35.5 .. 13.8 .. 23.9 37.2 17.6 9.3 28.5 .. .. 17.1 .. .. .. 33.0 .. 17.9 .. 15.7 21.1 .. .. 22.8 7.2 .. 12.4 0.0 31.2 22.2 17.0 40.2 .. 33.5 36.6 16.9 .. .. 2.0 .. 29.9 19.0 .. .. .. .. 10.4 .. .. .. .. 11.7 .. 7.9
.. .. .. .. 10.6 .. 23.2 37.6 .. .. 37.3 47.9 .. 16.4 .. 33.6 34.9 55.1 13.3 28.7 .. 21.2 26.1 .. 21.8 20.4 10.1 .. 11.6 18.8 35.6 25.6 24.5 37.6 .. .. 39.0 11.7 15.0 27.8 .. .. 23.7 27.1 30.2 41.8 20.2 23.6 .. 26.5 13.2 52.2 .. 22.9 .. ..
.. .. 31.2 .. 17.1 .. 23.5 40.3 22.6 12.7 29.6 .. .. 26.6 .. .. .. 34.4 .. 26.1 .. 15.5 19.8 .. .. 23.1 10.9 .. 18.8 0.1 25.7 23.6 16.5 45.3 .. 38.2 35.4 16.0 .. .. 2.5 .. 29.9 26.6 .. .. .. .. 10.9 .. .. .. .. 21.0 .. 10.5
.. .. .. .. –0.4 .. 2.0 –4.4 .. .. –4.8 –5.5 .. –1.7 .. 11.2 –5.8 –8.3 –1.2 –3.3 .. –5.9 –4.8 .. –4.7 0.8 –1.9 .. 3.9 –6.5 –14.1 –3.1 –2.9 –4.6 .. .. –0.7 0.6 3.8 –5.7 .. .. 0.4 –9.8 0.2 –2.1 3.2 –0.8 .. –1.5 0.2 –22.9 .. –3.3 .. ..
.. .. 4.0 .. –3.3 .. 1.4 .. –2.5 –2.8 –1.4 .. .. –6.7 .. .. .. 1.9 .. –4.7 .. 0.1 1.3 .. .. –0.3 –2.9 .. –7.0 –0.0 5.8 –1.2 0.9 –2.5 .. –1.9 1.6 1.0 .. .. –0.3 .. 2.5 –5.0 .. .. .. .. 0.1 .. .. .. .. –2.4 .. –2.3
.. .. .. .. 0.2 .. 0.2 0.5 .. .. 2.7 –0.3 .. 0.7 .. 0.0 .. –0.8 .. 4.9 .. 5.2 0.2 .. 5.0 .. 0.8 .. .. 0.0 .. 0.3 4.0 0.0 .. .. .. –0.0 .. –0.7 .. .. 0.0 2.8 0.7 1.1 2.7 .. .. 0.5 1.3 1.6 .. 4.1 .. ..
.. .. –2.6 .. 4.2 .. –0.5 .. .. 0.1 –0.1 .. .. 2.0 .. .. .. –0.2 .. 3.3 .. 0.2 0.6 .. .. 0.7 –0.1 .. 2.1 0.0 2.0 1.4 0.2 1.4 .. –0.2 .. –1.0 .. .. 0.4 .. –0.3 2.8 .. .. .. .. –0.2 0.6 .. .. .. 2.3 .. –0.2
.. .. .. .. 0.2 .. –2.2 3.9 .. .. 2.4 5.8 .. 1.0 .. –11.3 .. 9.1 .. –1.6 .. 1.2 4.6 .. –0.3 .. 1.1 .. .. 6.5 .. 2.8 0.4 4.7 .. .. .. –0.6 .. 6.4 .. .. –0.4 7.0 –0.8 1.0 –5.8 .. .. 1.0 –1.5 21.3 .. –0.8 .. ..
.. .. –1.4 .. –0.9 .. –0.9 .. .. 2.7 1.5 .. .. 4.7 .. .. .. –1.7 .. 1.5 .. –0.3 –1.9 .. .. –0.4 3.0 .. 4.9 0.0 –3.1 –0.2 –1.1 1.1 .. 2.1 .. –0.0 .. .. –0.1 .. –2.2 2.2 .. .. .. .. 0.0 –0.1 .. .. .. 0.2 .. 2.5
.. .. 50.4 .. .. .. 15.4 62.3 .. 40.1 11.4 .. .. 69.3 .. .. .. .. .. 183.9 .. 102.3 58.5 .. .. 15.6 12.7 .. 29.3 .. 160.6 38.4 102.5 .. .. 16.7 .. 20.7 .. .. 3.6 .. 2.6 101.4 .. .. .. .. 67.0 20.0 .. .. .. .. .. ..
.. .. 9.8 .. 27.5 .. 5.3 8.9 2.5 15.7 2.5 .. .. 12.2 .. .. .. 11.2 .. 13.2 .. 19.2 12.4 .. .. 2.1 .. .. 26.8 .. 19.3 17.9 19.5 5.0 .. 2.7 10.8 4.5 .. .. 8.0 .. 0.6 10.8 .. .. .. .. 16.6 .. .. .. .. 37.1 .. 6.1
2004 World Development Indicators
% of GDP
Overall budget balance (including grants)
Current revenue a
% of GDP
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Total expenditure
% of GDP
Overall budget balance (including grants)
% of GDP
Financing from abroad
Domestic financing
4.11 Debt and interest payments Total
% of GDP
% of GDP
Interest
debt
% of
% of
current
GDP
revenue
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
2001
2001
.. 52.9 12.6 18.8 18.1 .. 33.6 39.4 38.2 26.6 14.0 26.1 .. 22.4 .. 17.5 58.7 .. .. .. .. 39.4 .. .. 31.9 .. 11.6 19.8 26.4 .. .. 24.3 15.3 .. 19.6 26.4 .. 10.5 31.3 8.4 45.3 42.1 33.5 .. .. 42.2 38.9 19.1 25.6 25.2 12.3 12.5 16.2 .. 31.3 ..
.. 37.1 13.0 21.2 21.0 .. .. 41.2 41.3 33.9 .. 25.1 11.4 .. .. .. 34.5 16.1 .. 25.8 19.5 .. .. .. 24.7 .. 11.7 .. .. .. .. 20.3 14.8 21.3 30.7 29.6 .. 5.3 32.4 11.2 .. 29.7 18.0 .. .. 40.1 27.0 15.6 22.5 23.0 17.2 15.8 15.3 29.6 .. ..
.. 52.1 16.3 18.4 19.9 .. 37.7 50.7 47.4 24.3 15.3 35.8 .. 27.5 .. 16.2 55.3 .. .. .. .. 51.7 .. .. 28.9 .. 16.0 25.4 29.3 .. .. 24.3 17.9 .. 23.1 28.8 .. 16.0 33.3 17.2 49.8 43.4 72.0 .. .. 41.1 39.5 22.4 23.7 34.7 9.4 20.6 19.6 .. 37.6 ..
.. 41.5 17.3 24.8 21.9 .. .. 47.3 41.9 38.8 .. 32.4 14.6 .. .. .. 44.2 17.7 .. 29.1 35.7 .. .. .. 26.6 .. 17.1 .. .. .. .. 24.5 15.9 22.8 30.7 32.5 .. 8.7 35.9 18.0 .. 29.1 27.3 .. .. 35.4 29.9 21.6 23.5 31.4 18.5 18.5 19.2 35.1 .. ..
.. 0.8 –7.6 0.4 –1.8 .. –2.4 –5.3 –10.2 3.6 –1.5 –3.5 .. –3.8 .. –0.7 .. .. .. .. .. –1.1 .. .. 1.4 .. –1.1 –1.6 –2.0 .. .. –0.4 –2.5 .. –6.4 –2.2 .. –5.1 –1.2 –6.8 –4.3 4.0 –35.6 .. .. 0.5 –0.8 –5.4 3.0 –3.5 2.9 –8.1 –3.5 .. –4.4 ..
.. –3.8 –4.7 –1.2 –0.6 .. .. –3.6 –1.6 –2.7 .. –2.5 –0.4 .. .. .. –9.7 0.4 .. –1.4 –16.2 .. .. .. –0.4 .. –2.4 .. .. .. .. 0.9 –1.3 1.1 –4.0 –2.5 .. –3.4 –3.5 –4.5 .. 0.3 –6.3 .. .. –3.8 –4.2 –4.7 0.3 –2.8 –0.8 –1.8 –4.0 –4.3 .. ..
.. –0.5 0.6 0.7 –0.0 .. .. 0.8 .. .. .. 3.0 .. 1.3 .. –0.2 .. .. .. .. .. 8.0 .. .. .. .. 2.1 .. –0.7 .. .. –0.5 0.3 .. 7.5 3.9 .. 0.0 .. 5.4 –0.3 .. 12.7 .. .. –0.6 –3.9 2.3 –3.4 0.4 –0.9 5.4 0.4 .. –1.3 ..
.. 3.3 0.1 0.5 0.1 .. .. –0.1 .. .. .. 0.2 0.3 .. .. .. .. .. .. 2.2 8.1 .. .. .. 1.0 .. 1.7 .. .. .. .. –2.9 –0.9 –2.7 6.3 –1.5 .. –0.0 .. 1.8 .. .. 3.3 .. .. 3.7 3.1 2.2 1.4 1.7 .. 1.1 0.6 –1.5 .. ..
.. –0.3 7.1 –1.1 1.8 .. .. 4.6 .. .. .. 0.5 .. 4.5 .. 0.9 .. .. .. .. .. –6.9 .. .. .. .. –1.2 .. 2.8 .. .. 0.9 2.3 .. –1.1 –1.6 .. 5.1 .. 1.4 4.6 .. 22.9 .. .. 0.0 4.7 3.1 0.4 3.0 –2.1 2.7 3.1 .. 5.7 ..
.. 0.5 4.6 0.7 0.5 .. .. 3.7 .. .. .. 2.3 0.1 .. .. .. .. .. .. –0.8 8.1 .. .. .. –0.6 .. 0.5 .. .. .. .. 1.9 2.1 1.6 –2.3 4.0 .. 3.4 .. 2.7 .. .. 0.5 .. .. 0.1 1.1 2.5 –1.7 1.0 .. 0.8 3.4 5.7 .. ..
.. 53.1 57.7 45.2 .. .. .. 99.1 .. 142.5 .. 91.9 17.7 .. .. .. .. 99.3 .. 14.8 135.2 .. .. .. 23.2 .. .. .. .. .. .. 32.5 23.2 60.9 83.5 72.8 .. .. .. 63.8 .. 30.4 .. .. .. 19.9 19.9 90.0 .. 63.9 .. 44.3 64.9 38.8 .. ..
.. 12.9 37.1 21.6 0.7 .. .. 12.9 15.5 43.5 .. 13.3 10.0 .. .. .. 4.0 8.8 .. 3.9 74.4 .. .. .. 6.3 .. 12.1 .. .. .. .. 13.6 14.0 19.7 4.6 16.5 .. .. 7.0 10.2 .. 6.9 13.4 .. .. 3.8 4.6 58.4 20.7 19.0 7.5 13.6 31.2 9.5 .. ..
2004 World Development Indicators
223
ECONOMY
Central government finances
4.11
Central government finances Current revenue a
% of GDP
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
% of GDP
Overall budget balance (including grants)
% of GDP
1990
2001
1990
2001
1990
34.4 .. 10.8 .. .. .. 5.6 26.7 .. 39.8 .. 26.3 29.3 21.0 .. 32.7 41.3 20.8 21.9 .. .. 18.5 .. .. 30.7 13.7 .. .. .. 1.6 36.0 18.9 23.8 .. 23.7 .. .. 18.9 .. 24.1 22.3 w 14.4 16.8 16.9 16.3 16.4 11.7 .. 18.6 .. 12.9 21.9 23.5 33.6
26.7 26.8 .. .. 17.8 .. 7.1 24.9 33.3 37.4 .. 27.7 .. 16.4 8.0 28.1 38.0 25.3 23.9 11.5 .. 17.5 .. .. 28.6 29.1 .. 10.9 26.6 3.4 36.0 20.8 25.1 .. 21.2 20.1 .. 23.9 .. .. .. w 16.0 17.7 .. 20.0 17.1 10.8 28.3 .. .. 13.5 23.6 .. ..
33.8 .. 18.9 .. .. .. 8.3 21.3 .. 38.6 .. 30.1 32.6 28.4 .. 25.5 38.1 23.3 21.8 .. .. 14.1 .. .. 34.6 17.4 .. .. .. 11.5 37.5 22.7 23.3 .. 20.7 .. .. 27.8 .. 27.3 25.3 w 17.1 21.4 23.0 17.1 20.7 13.8 .. 25.3 .. 16.4 25.3 26.2 37.2
30.4 24.4 .. .. 21.8 .. 20.9 22.2 39.1 38.9 .. 28.8 .. 26.1 8.5 30.1 37.9 26.6 23.2 11.6 .. 19.7 .. .. 32.0 49.5 .. 21.4 28.9 9.9 35.9 19.5 31.2 .. 25.1 24.3 .. 26.7 .. .. .. w 20.1 21.3 .. 22.3 20.6 15.0 33.0 .. .. 18.3 25.9 .. ..
0.9 .. –5.3 .. .. .. –2.5 10.8 .. 0.3 .. –4.1 –3.1 –7.8 .. 0.0 0.9 –0.9 0.3 .. .. 4.6 .. .. –5.4 –3.0 .. .. .. 0.4 0.6 –3.8 0.3 .. 0.0 .. .. –8.8 .. –5.3 –2.8 w –4.8 –2.7 –3.2 –1.1 –3.0 –0.9 .. –3.5 .. –7.3 –3.5 –2.8 –4.0
a. Excluding grants.
224
Total expenditure
2004 World Development Indicators
2001
–3.0 3.4 .. .. –2.0 .. –8.5 5.2 –3.2 –1.0 .. –1.0 .. –9.8 –0.9 –0.9 0.1 2.9 0.7 0.1 .. –2.8 .. .. –2.6 –19.6 .. –2.2 –0.9 0.0 0.0 1.3 –4.6 .. –4.3 –2.9 .. –3.5 .. .. .. w –3.3 –3.2 .. –1.6 –3.3 –3.7 –3.4 .. .. –4.9 –1.6 .. ..
Financing from abroad
Domestic financing
Debt and interest payments Total
% of GDP 1990
2001
0.0 .. 2.5 .. .. .. 0.5 –0.1 .. 0.1 .. –0.0 0.7 3.6 .. –0.2 –0.3 0.0 .. .. .. –1.5 .. .. 1.8 –0.0 .. .. .. 0.0 0.2 0.2 1.4 .. 1.0 .. .. 3.2 .. 0.9 0.2 m .. .. .. .. 0.3 0.4 .. 0.2 0.9 2.3 0.9 0.2 0.5
0.8 –2.6 .. .. 1.6 .. 1.1 0.0 0.8 0.4 .. 3.4 .. 1.0 0.1 –0.6 –5.3 0.0 2.1 0.2 .. 0.4 .. .. 0.7 –1.9 .. 3.3 0.2 0.0 –0.4 –0.5 3.0 .. 0.3 1.0 .. 1.3 .. .. 1.1 m .. 0.1 0.4 1.0 1.3 1.4 .. .. 1.3 1.4 .. .. ..
% of GDP 1990
–0.9 .. 2.8 .. .. .. 2.0 –10.6 .. –0.4 .. 4.1 2.4 4.2 .. 0.2 –0.7 0.9 .. .. .. –3.1 .. .. 3.6 3.0 .. .. .. –0.4 –0.8 3.6 –1.7 .. –1.0 .. .. 5.6 .. 4.4 0.9 m .. 0.5 1.1 0.2 0.7 2.8 .. 0.1 2.7 3.1 .. 1.0 3.1
2001
2.2 –0.9 .. .. 0.4 .. 7.4 –5.2 2.4 0.6 .. –2.4 .. 8.8 0.8 1.5 5.2 –2.9 –2.8 –0.2 .. 2.4 .. .. 1.8 21.5 .. –1.2 0.7 –0.0 0.3 –0.8 1.6 .. 4.0 1.9 .. 2.2 .. .. 0.8 m .. 1.0 1.2 1.1 0.9 1.4 0.6 0.5 1.7 3.7 .. .. ..
Interest
debt
% of
% of
current
GDP
revenue
2001
2001
.. 48.8 .. .. 72.8 .. 247.4 99.4 42.2 26.4 .. 46.8 .. 103.1 8.7 28.7 .. 26.7 .. 81.4 .. 29.8 .. .. 62.6 99.9 .. 39.6 36.5 .. .. 32.6 .. .. .. .. .. .. .. .. .. m .. 34.7 53.9 23.4 .. 52.3 40.5 .. .. 63.8 .. .. ..
11.5 9.5 .. .. 5.0 .. 81.8 1.3 9.3 4.1 .. 17.5 .. 40.8 9.4 2.0 11.4 3.6 .. 4.8 .. 7.1 .. .. 11.4 85.1 .. 10.7 7.2 .. 7.7 10.8 9.7 .. 13.4 4.3 .. 9.8 .. .. 11.3 m .. 9.8 11.4 9.3 11.8 13.9 9.3 13.4 12.2 38.9 .. 7.5 ..
About the data
4.11
Definitions
Tables 4.11–4.13 present an overview of the size
government activities is usually incomplete. A key
• Current revenue includes all revenue from taxes
and role of central governments relative to national
issue is the failure to include the quasi-fiscal opera-
and current nontax revenues (other than grants), such
economies. The International Monetary Fund’s (IMF)
tions of the central bank. Central bank losses arising
as fines, fees, recoveries, and income from property
Manual on Government Finance Statistics describes
from monetary operations and subsidized financing
or sales. • Total expenditure includes nonrepayable
the government as the sector of the economy
can result in sizable quasi-fiscal deficits. Such
current and capital expenditures. It does not include
responsible for “implementation of public policy
deficits may also result from the operations of other
government lending or repayments to the government
through the provision of primarily nonmarket servic-
financial intermediaries, such as public development
or government acquisition of equity for public policy
es and the transfer of income, supported mainly by
finance institutions. Also missing from the data are
purposes. • Overall budget balance is current and
compulsory levies on other sectors” (1986, p. 3).
governments’ contingent liabilities for unfunded pen-
capital revenue and official grants received, less total
The definition of government generally excludes non-
sion and national insurance plans.
expenditure
financial public enterprises and public financial institutions (such as the central bank).
and
lending
minus
repayments.
Data on government revenues and expenditures are
• Financing from abroad (obtained from nonresi-
collected by the IMF through questionnaires
dents) and domestic financing (obtained from resi-
A second edition of the Manual on Government
distributed to member governments and by the
dents) refer to the means by which a government
Finance Statistics, harmonized with the 1993
Organisation for Economic Co-operation and Develop-
provides financial resources to cover a budget deficit
System of National Accounts, was released in 2001.
ment. Despite the IMF’s efforts to systematize and
or allocates financial resources arising from a budget
The new manual recommends an accrual accounting
standardize the collection of public finance data,
surplus. The data include all government liabilities—
method instead of the earlier cash-based method.
statistics on public finance are often incomplete,
other than those for currency issues or demand, time,
However, most countries still follow the previous
untimely, and not comparable across countries.
manual.
or savings deposits with government—or claims on
Government finance statistics are reported in local
others held by government, and changes in govern-
Units of government meeting this definition exist at
currency. The indicators here are shown as percent-
ment holdings of cash and deposits. They exclude
many levels, from local administrative units to the
ages of GDP. Many countries report government
government guarantees of the debt of others. • Debt
highest level of national government. Inadequate sta-
finance data according to fiscal years; see Primary
is the entire stock of direct government fixed-term
tistical coverage precludes the presentation of sub-
data documentation for the timing of these years. For
contractual obligations to others outstanding on a
national data, however, making cross-countr y
further discussion of government finance statistics,
particular date. It includes domestic debt (such as
comparisons potentially misleading.
see About the data for tables 4.12 and 4.13.
debt held by monetary authorities, deposit money
Central government can refer to one of two
banks, nonfinancial public enterprises, and house-
accounting concepts: consolidated or budgetary. For
holds) and foreign debt (such as debt to international
most countries central government finance data have
development institutions and foreign governments). It
been consolidated into one account, but for others
is the gross amount of government liabilities not
only budgetary central government accounts are
reduced by the amount of government claims against
available. Countries reporting budgetary data are
others. Because debt is a stock rather than a flow, it
noted in Primary data documentation. Because budg-
is measured as of a given date, usually the last day
etary accounts do not necessarily include all central
of the fiscal year. • Interest payments include inter-
government units, the picture they provide of central
est payments on government debt—including longterm bonds, long-term loans, and other debt
4.11a
instruments—to both domestic and foreign residents.
Some developing economies spend a large part of their current revenue on interest payments Central government interest payments as share of current revenue (%) 100
1995 2001
80
Data sources
60
The data on central government finances are from 40
the
IMF’s
Government
Finance
Statistics
Yearbook, 2003 and IMF data files. Each coun20
try’s accounts are reported using the system of common definitions and classifications in the ia
a
es on In d
om
in Co l
nt ge Ar
bi
a
s in e
a ili
pp
ne
a Gu i
di In
ka an Sr iL
Ja
m
ai
ca
ta n
n no
Pa ki s
ba Le
Ph
Si
er
ra
Le
Tu r
on
ke
y
e
0
Note: 2001 data refer to the most recent year for which data are available in 1998–2001. No data are available for Guinea for 1995. Source: International Monetary Fund, Government Finance Statistics data files.
IMF’s Manual on Government Finance Statistics (1986). See these sources for complete and authoritative explanations of concepts, definitions, and data sources.
2004 World Development Indicators
225
ECONOMY
Central government finances
4.12
Central government expenditures Goods and services
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
226
Wages and salaries a
Interest payments
Subsidies and other current transfers
Capital expenditure
% of total
% of total
% of total
% of total
% of total
expenditure
expenditure
expenditure
expenditure
expenditure
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
.. .. .. .. 30 .. 27 25 .. .. 37 19 .. 63 .. 51 16 35 60 34 .. 51 21 .. 41 28 .. .. 26 73 56 57 .. 54 .. .. 20 39 42 42 .. .. 25 77 20 26 63 41 .. 32 50 31 .. 37 .. ..
.. .. 28 .. 18 .. .. 25 31 27 24 .. .. 41 .. .. .. 28 .. 50 .. 52 19 .. .. 27 .. .. 19 47 32 48 56 44 .. 14 22 53 .. .. 62 .. 40 52 .. .. .. .. 36 .. .. .. .. 29 .. 65
.. .. .. .. 23 .. 2 10 .. .. 2 14 .. 36 .. 23 9 3 51 22 .. 39 9 .. 28 18 .. .. 18 23 49 43 .. 22 .. .. 12 29 38 23 .. .. 8 40 10 17 37 21 .. 8 32 21 .. 18 .. ..
.. .. 20 .. 14 .. .. 10 11 18 11 .. .. 24 .. .. .. 8 .. 30 .. 32 9 .. .. 19 .. .. 14 17 20 37 35 24 .. 7 13 41 .. .. 37 .. 9 18 .. .. .. .. 11 .. .. .. .. 19 .. 42
.. .. .. .. 8 .. 8 9 .. .. 2 21 .. 6 .. 2 78 10 6 5 .. 5 20 .. 2 10 .. .. 10 7 22 12 .. 0 .. .. 15 4 23 14 .. .. 0 5 3 5 0 16 .. 5 11 20 .. 7 .. ..
.. .. 11 .. 22 .. 5 8 2 11 2 .. .. 8 .. .. .. 11 .. 9 .. 19 13 .. .. 2 .. .. 18 0 23 17 20 4 .. 2 11 5 .. .. 6 .. 1 8 .. .. .. .. 16 .. .. .. .. 21 .. 5
.. .. .. .. 57 .. 56 57 .. .. 46 56 .. 16 .. 25 39 52 11 10 .. 13 57 .. 3 51 .. .. 42 4 20 20 .. 42 .. .. 61 13 16 26 .. .. 73 9 70 63 6 9 .. 58 20 41 .. 4 .. ..
.. .. 34 .. 54 .. .. 61 50 25 61 .. .. 37 .. .. .. 51 .. 11 .. 15 66 .. .. 56 .. .. 41 35 8 27 12 46 .. 75 64 16 .. .. 15 .. 54 31 .. .. .. .. 48 .. .. .. .. 8 .. 8
.. .. .. .. 5 .. 9 9 .. .. 16 5 .. 15 .. 21 2 3 23 51 .. 26 2 .. 56 11 .. .. 22 16 2 11 .. 3 .. .. 3 44 18 17 .. .. 8 16 7 6 32 34 .. 5 19 8 .. 53 .. ..
.. .. 27 .. 5 .. .. 5 17 23 13 .. .. 14 .. .. .. 11 .. 23 .. 14 2 .. .. 15 .. .. 22 18 37 8 11 6 .. 9 3 22 .. .. 22 .. 6 19 .. .. .. .. 1 .. .. .. .. 36 .. 22
2004 World Development Indicators
Goods and services
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Wages and salaries a
Interest payments
Subsidies and other current transfers
4.12 Capital expenditure
% of total
% of total
% of total
% of total
% of total
expenditure
expenditure
expenditure
expenditure
expenditure
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
.. 27 24 23 53 .. 19 38 17 47 14 55 .. 51 .. 35 62 .. .. .. .. 40 .. .. 12 .. 37 54 41 .. .. 47 25 .. 30 48 .. .. 73 .. 15 19 43 .. .. 19 76 44 64 61 54 30 44 .. 38 ..
.. 18 22 18 68 .. .. 34 20 51 .. 64 38 .. .. .. 58 66 .. 25 30 .. .. .. 46 .. 36 .. .. .. .. 44 24 25 35 46 .. .. 63 .. .. 53 33 .. .. 21 77 23 47 56 52 41 49 16 .. ..
.. 6 11 16 40 .. 14 14 13 21 .. 44 .. 31 .. 13 31 .. .. .. .. 22 .. .. 6 .. 25 23 26 .. .. 37 18 .. 7 35 .. .. 46 .. 9 12 23 .. .. 8 22 .. 49 34 36 17 29 .. 27 ..
.. 9 9 8 52 .. .. 15 16 32 .. 45 8 .. .. .. 35 29 .. 12 23 .. .. .. 16 .. 23 .. .. .. .. 32 17 12 10 36 .. .. 44 .. .. .. 16 .. .. 8 27 4 34 29 46 19 28 8 .. ..
.. 6 22 13 0 .. 21 18 21 29 19 18 .. 19 .. 4 0 .. .. .. .. 11 .. .. .. .. 9 14 20 .. .. 15 45 .. 1 16 .. .. 1 .. 9 15 0 .. .. 6 6 25 8 11 10 37 34 .. 18 ..
.. 12 28 19 1 .. .. 11 15 38 .. 10 8 .. .. .. 3 8 .. 3 41 .. .. .. 6 .. 8 .. .. .. .. 11 13 18 5 15 .. .. 6 6 .. 7 9 .. .. 4 4 42 20 14 7 12 25 8 .. ..
.. 64 43 21 22 .. 54 37 54 1 54 11 .. 10 .. 46 20 .. .. .. .. 5 .. .. 67 .. 9 8 16 .. .. 22 17 .. 56 8 .. .. 10 .. 70 64 14 .. .. 69 7 20 26 18 19 25 7 .. 33 ..
.. 57 41 39 10 .. .. 50 59 0 .. 8 42 .. .. .. 26 15 .. 65 12 .. .. .. 39 .. 6 .. .. .. .. 28 52 54 46 16 .. .. 17 .. .. 37 24 .. .. 70 6 27 24 24 25 36 18 72 .. ..
.. 4 11 43 25 .. 7 6 8 23 13 16 .. 20 .. 15 18 .. .. .. .. 45 .. .. 20 .. 43 24 24 .. .. 17 14 .. 13 28 .. 29 15 .. 6 2 4 .. .. 5 11 12 2 11 17 8 16 .. 12 ..
.. 13 9 24 21 .. .. 6 6 11 .. 18 12 .. .. .. 13 11 .. 6 17 .. .. .. 9 .. 38 .. .. .. .. 17 10 3 14 22 .. 39 14 .. .. 3 34 .. .. 5 13 7 9 6 16 12 8 4 .. ..
2004 World Development Indicators
227
ECONOMY
Central government expenditures
4.12
Central government expenditures Goods and services
Interest payments
Capital expenditure
% of total
% of total
% of total
% of total
% of total
expenditure
expenditure
expenditure
expenditure
2001
26 .. 53 .. .. .. 77 51 .. 40 .. 53 19 33 .. 62 15 31 .. .. .. 60 .. .. 34 52 .. .. .. 88 30 28 35 .. 31 .. .. 64 .. 56 39 m .. 42 43 38 .. 42 .. 35 53 33 .. 25 20
33 37 .. .. 42 .. 60 50 25 41 .. 28 .. 38 74 57 18 28 .. 44 .. 55 .. .. 41 24 .. 30 30 78 29 21 26 .. 26 .. .. 54 .. .. 37 m .. 37 41 26 39 .. 30 41 50 23 .. 29 ..
1990
12 .. 29 .. .. .. 35 27 .. 20 .. 23 13 17 .. 42 6 5 .. .. .. 35 .. .. 28 38 .. .. .. 33 13 10 20 .. 23 .. .. 55 .. 37 23 m .. 25 29 23 .. 27 .. 23 35 .. .. 13 13
2001
12 11 .. .. 24 .. 46 24 14 23 .. 13 .. 21 34 32 6 4 .. 13 .. 30 .. .. 34 18 .. 9 13 35 6 8 15 .. 19 .. .. 39 .. .. 18 m .. 18 24 15 21 .. 12 19 35 9 .. .. ..
1990
2001
0 .. 5 .. .. .. 18 14 .. 1 .. 14 9 23 .. 3 11 3 .. .. .. 13 .. .. 10 18 .. .. .. 0 9 15 8 .. 16 .. .. 8 .. 16 10 m .. 11 13 9 .. 10 .. 10 10 23 .. 11 9
Note: Components include expenditures financed by grants in kind and other cash adjustments to total expenditure. a. Part of goods and services.
228
Subsidies and other current transfers
expenditure 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Wages and salaries a
2004 World Development Indicators
10 10 .. .. 4 .. 28 1 8 4 .. 17 .. 26 9 2 11 3 .. 5 .. 6 .. .. 10 50 .. 5 7 0 8 11 8 .. 11 4 .. 9 .. .. 9m .. 8 10 8 9 11 8 9 11 27 .. 7 ..
1990
57 .. 16 .. .. .. 1 12 .. 52 .. 23 63 23 .. 11 72 61 .. .. .. 9 .. .. 35 16 .. .. .. 10 52 49 50 .. 37 .. .. 6 .. 18 23 m .. 23 21 26 .. 16 .. 25 11 23 .. 56 57
2001
45 44 .. .. 24 .. 6 22 55 49 .. 50 .. 18 7 22 69 64 .. 35 .. 17 .. .. 25 20 .. 17 57 18 59 63 62 .. 42 .. .. 18 .. .. 31 m .. 42 23 54 26 .. 51 36 14 27 .. 59 ..
1990
17 .. 33 .. .. .. 8 24 .. 7 .. 10 9 21 .. 24 2 5 27 .. .. 18 .. .. 22 13 .. .. .. 1 10 8 7 .. 16 .. .. 33 .. 10 13 m .. 16 17 11 .. 21 .. 11 23 12 .. 7 7
2001
12 9 .. .. 29 .. 11 26 12 7 .. 5 .. 18 10 19 2 5 36 17 .. 22 .. .. 23 7 .. 47 6 4 4 5 4 .. 21 34 .. 17 .. .. 13 m .. 12 15 9 16 24 9 14 19 9 .. 5 ..
About the data
4.12
Definitions
Government expenditures include all nonrepayable
by grants in kind and other cash adjustments (which
• Goods and services include all government pay-
payments, whether current or capital, requited or
may be positive or negative) are not shown separate-
ments in exchange for goods and services, whether
unrequited. Total central government expenditure as
ly. For further discussion of government finance sta-
in the form of wages and salaries to employees or
presented in the International Monetary Fund’s (IMF)
tistics, see About the data for tables 4.11 and 4.13.
other purchases of goods and services. • Wages
Government Finance Statistics Yearbook is a more
and salaries consist of all payments in cash, but not
limited measure of general government consumption
in kind (such as food and housing), to employees in
than that shown in the national accounts (see table
return for services rendered, before deduction of
4.10) because it excludes consumption expenditures
withholding taxes and employee contributions to
by state and local governments. At the same time,
social security and pension funds. • Interest pay-
the IMF’s concept of central government expenditure
ments are payments made to domestic sectors and
is broader than the national accounts definition
to nonresidents for the use of borrowed money.
because it includes government gross capital forma-
(Repayment of principal is shown as a financing item,
tion and transfer payments.
and commission charges are shown as purchases of
Expenditures can be measured either by function
services.) Interest payments do not include pay-
(health, defense, education) or by economic type
ments by government as guarantor or surety of inter-
(interest payments, wages and salaries, purchases
est on the defaulted debts of others, which are
of goods and ser vices). Functional data are often
classified as government lending. • Subsidies and
incomplete, and coverage varies by countr y
other current transfers include all unrequited, non-
because functional responsibilities stretch across
repayable transfers on current account to private and
levels of government for which no data are avail-
public enterprises and the cost to the public of
able. Defense expenditures, usually the central gov-
covering the cash operating deficits on sales to the
ernment’s responsibility, are shown in table 5.8.
public by departmental enterprises. • Capital expen-
For more information on education expenditures,
diture is spending to acquire fixed capital assets,
see table 2.10; for more on health expenditures,
land, intangible assets, government stocks, and
see table 2.14.
nonmilitary, nonfinancial assets. Also included are
The classification of expenditures by economic
capital grants.
type can also be problematic. For example, the distinction between current and capital expenditure may be arbitrary, and subsidies to state-owned enterprises or banks may be disguised as capital financing. Subsidies may also be hidden in special contractual pricing for goods and services. Expenditure shares may not sum to 100 percent because adjustments to total expenditures financed
4.12a Interest payments are a large part of government expenditure for some developing economies Central government interest payments as share of total expenditure (%) 50 1995 2001
40
Data sources
30
The data on central government expenditures are 20
from the IMF’s Government Finance Statistics Yearbook, 2003 and IMF data files. Each coun-
10
try’s accounts are reported using the system of common definitions and classifications in the in
re d’ Ivo i
Cô t
e
ea
a Gu
tin en Ar g
Co n
go
pi
,R
ne
ep
.
s
ka lip Ph i
iL
Le o
an
ne Sr
ia Si
er
ra
ai m Ja
In d
ca
n no ba Le
st ki Pa
Tu r
ke y
an
0
Note: Data for 2001 refer to the most recent year for which data are available in 1999–2001. No data are available for Guinea for 1995. Source: International Monetary Fund, Government Finance Statistics data files.
IMF’s Manual on Government Finance Statistics (1986). See these sources for complete and authoritative explanations of concepts, definitions, and data sources.
2004 World Development Indicators
229
ECONOMY
Central government expenditures
4.13
Central government revenues Taxes on income, profits, and capital gains
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
230
Social security taxes
Taxes on goods and services
Taxes on international trade
Other taxes
Nontax revenue
% of total
% of total
% of total
% of total
% of total
% of total
current revenue
current revenue
current revenue
current revenue
current revenue
current revenue
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
.. .. .. .. 2 .. 65 19 .. .. 12 35 .. 5 .. 39 20 30 23 21 .. 18 51 .. 19 12 31 .. 29 27 26 10 16 17 .. .. 37 21 62 19 .. .. 27 29 31 17 24 13 .. 16 23 22 .. 9 .. ..
.. .. 70 .. 18 .. .. 25 22 11 10 .. .. 7 .. .. .. 13 .. 21 .. 21 52 .. .. 20 6 .. 34 15 5 14 20 8 .. 20 35 18 .. .. 15 .. 13 22 .. .. .. .. 4 .. .. .. .. 10 .. ..
.. .. .. .. 44 .. 0 37 .. .. 32 35 .. 9 .. 0 31 23 0 6 .. 6 16 .. 0 8 0 .. 0 1 0 29 7 52 .. .. 4 4 0 15 .. .. 28 0 9 44 1 0 .. 53 0 29 .. 0 .. ..
.. .. 0 .. 23 .. .. 40 22 0 38 .. .. 11 .. .. .. 24 .. 7 .. 0 21 .. .. 7 0 .. 0 0 0 32 9 33 .. 44 4 4 .. .. 14 .. 36 0 .. .. .. .. 20 .. .. .. .. 1 .. ..
.. .. .. .. 20 .. 21 25 .. .. 40 24 .. 31 .. 2 24 18 30 37 .. 21 17 .. 39 43 18 .. 30 18 16 27 27 24 .. .. 41 23 22 14 .. .. 41 25 47 28 23 37 .. 24 30 43 .. 15 .. ..
.. .. 8 .. 36 .. .. 25 40 40 36 .. .. 48 .. .. .. 37 .. 44 .. 26 17 .. .. 46 75 .. 39 22 20 40 21 46 .. 29 45 25 .. .. 36 .. 42 17 .. .. .. .. 62 .. .. .. .. 5 .. ..
.. .. .. .. 14 .. 4 1 .. .. 5 0 .. 7 .. 13 2 2 33 24 .. 14 3 .. 24 12 14 .. 20 46 21 23 29 3 .. .. 0 40 13 14 .. .. 1 15 1 0 18 43 .. 0 39 0 .. 47 .. ..
.. .. 11 .. 4 .. .. 0 9 23 6 .. .. 5 .. .. .. 2 .. 20 .. 28 1 .. .. 5 10 .. 7 32 8 5 42 6 .. 1 0 43 .. .. 6 .. 0 26 .. .. .. .. 6 .. .. .. .. 77 .. ..
.. .. .. .. 10 .. 2 9 .. .. 9 3 .. 11 .. 0 6 1 7 1 .. 4 0 .. 10 3 0 .. 1 1 2 1 11 0 .. .. 3 1 1 11 .. .. 1 2 3 3 2 1 .. 0 0 8 .. 0 .. ..
.. .. 1 .. 9 .. .. 4 2 1 3 .. .. 9 .. .. .. 1 .. 1 .. 4 0 .. .. 4 4 .. 5 23 1 0 5 1 .. 1 4 2 .. .. 11 .. 0 3 .. .. .. .. 0 .. .. .. .. 4 .. ..
.. .. .. .. 10 .. 8 9 .. .. 2 3 .. 38 .. 46 16 27 8 10 .. 28 13 .. 8 21 37 .. 19 7 35 14 9 3 .. .. 15 10 2 27 .. .. 2 30 9 7 32 6 .. 6 8 8 .. 28 .. ..
.. .. 10 .. 9 .. 8 6 5 25 7 .. .. 19 .. .. .. 24 .. 6 .. 20 9 .. .. 18 6 .. 14 8 66 10 4 5 .. 4 12 8 .. .. 18 .. 9 32 .. .. .. .. 9 .. .. .. .. 4 .. ..
2004 World Development Indicators
Taxes on income, profits, and capital gains
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Social security taxes
Taxes on goods and services
Taxes on international trade
Other taxes
Nontax revenue
% of total
% of total
% of total
% of total
% of total
% of total
current revenue
current revenue
current revenue
current revenue
current revenue
current revenue
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
.. 18 15 62 10 .. 37 36 37 36 69 16 .. 30 .. 34 1 .. .. .. .. 11 .. .. 20 .. 13 37 31 .. .. 14 31 .. 24 24 .. 18 34 11 31 53 17 .. .. 16 23 9 17 37 9 5 28 .. 23 ..
.. 21 29 31 17 .. .. 40 36 30 .. 12 24 .. .. .. 1 17 .. 14 11 .. .. .. 11 .. 15 .. .. .. .. 12 34 3 8 24 .. 20 32 18 .. 64 13 .. .. 20 21 23 18 54 9 22 40 17 .. ..
.. 29 0 0 8 .. 15 9 29 0 0 0 .. 0 .. 5 0 .. .. .. .. 0 .. .. 28 .. 0 0 1 .. .. 4 13 .. 14 4 .. 0 0 0 35 0 9 .. .. 24 0 0 20 0 0 7 0 .. 25 ..
.. 31 0 2 9 .. .. 15 30 0 .. 0 0 .. .. .. 6 0 .. 35 0 .. .. .. 31 .. 0 .. .. .. .. 5 10 32 17 5 .. 0 0 0 .. 0 19 .. .. 23 0 0 19 0 0 8 0 31 .. ..
.. 31 36 24 4 .. 38 33 29 30 17 21 .. 43 .. 35 0 .. .. .. .. 21 .. .. 40 .. 19 33 20 .. .. 21 56 .. 31 38 .. 28 25 36 22 27 35 .. .. 34 1 30 17 14 21 50 31 .. 34 ..
.. 34 29 25 6 .. .. 29 24 33 .. 36 53 .. .. .. 0 58 .. 42 20 .. .. .. 48 .. 28 .. .. .. .. 37 62 49 41 36 .. 33 21 36 .. 27 52 .. .. 36 1 38 .. 11 37 52 26 37 .. ..
.. 6 29 6 13 .. 0 2 0 12 1 27 .. 16 .. 12 2 .. .. .. .. 57 .. .. 1 .. 48 16 18 .. .. 46 6 .. 17 18 .. 14 27 31 0 2 19 .. .. 1 2 31 12 25 20 17 25 .. 2 ..
.. 2 18 3 7 .. .. 1 0 7 .. 17 7 .. .. .. 3 3 .. 1 28 .. .. .. 1 .. 52 .. .. .. .. 25 4 6 8 16 .. 4 37 27 .. 2 8 .. .. 0 3 12 .. 27 10 9 17 2 .. ..
.. 0 0 3 4 .. 3 4 2 9 7 7 .. 1 .. 5 0 .. .. .. .. 0 .. .. 3 .. 2 1 3 .. .. 6 2 .. 0 4 .. 0 1 5 3 3 8 .. .. 1 1 0 3 3 24 19 3 .. 4 ..
.. 2 0 3 1 .. .. 3 3 7 .. 10 0 .. .. .. 0 0 .. 0 13 .. .. .. 0 .. 2 .. .. .. .. 6 1 0 1 3 .. 0 1 3 .. 1 .. .. .. 1 2 6 4 4 2 5 4 1 .. ..
.. 16 20 5 60 .. 7 14 3 13 5 29 .. 10 .. 9 97 .. .. .. .. 11 .. .. 8 .. 18 13 28 .. .. 9 11 .. 15 13 .. 41 13 17 9 15 13 .. .. 24 73 30 31 20 25 7 13 .. 12 ..
.. 10 24 36 60 .. .. 12 7 23 .. 24 16 .. .. .. 90 23 .. 7 28 .. .. .. 9 .. 3 .. .. .. .. 15 10 10 25 16 .. 44 8 16 .. 6 8 .. .. 20 73 21 37 5 41 14 13 12 .. ..
2004 World Development Indicators
231
ECONOMY
4.13
Central government revenues
4.13
Central government revenues Taxes on income, profits, and capital gains
Taxes on goods and services
Other taxes
Nontax revenue
% of total
% of total
% of total
% of total
% of total
% of total
current revenue
current revenue
current revenue
current revenue
current revenue
19 .. 18 .. .. .. 31 26 .. 12 .. 51 32 11 .. 30 18 15 31 .. .. 24 .. .. 13 43 .. .. .. 0 39 52 7 .. 64 .. .. 26 .. 45 23 m .. 21 23 17 19 31 .. 17 21 11 .. 32 31
2001
10 9 .. .. 22 .. 26 33 17 14 .. 54 .. 15 15 25 14 16 38 3 .. 28 .. .. 20 35 .. 20 12 0 40 55 15 .. 20 27 .. 18 .. .. 18 m .. 15 20 17 17 25 13 15 19 21 .. 26 ..
1990
23 .. 7 .. .. .. 0 0 .. 47 .. 2 38 0 .. 0 31 51 0 .. .. 0 .. .. 13 0 .. .. .. 2 17 35 27 .. 4 .. .. 0 .. 0 4m .. 4 1 10 0 0 .. 9 2 0 .. 17 35
2001
1990
41 26 .. .. 0 .. 0 0 38 35 .. 2 .. 0 0 0 33 47 0 18 .. 3 .. .. 17 0 .. 0 36 1 17 33 23 .. 3 0 .. 0 .. .. 5m .. 14 3 31 2 0 31 11 0 0 .. 19 ..
Note: Components may not sum to 100 percent as a result of adjustments to tax revenue.
232
Taxes on international trade
current revenue 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Social security taxes
2004 World Development Indicators
33 .. 34 .. .. .. 23 16 .. 27 .. 34 22 46 .. 11 29 23 31 .. .. 41 .. .. 19 32 .. .. .. 36 28 3 36 .. 3 .. .. 10 .. 26 27 m .. 25 28 21 28 24 .. 27 17 36 .. 28 28
2001
30 35 .. .. 33 .. 22 19 31 37 .. 33 .. 59 35 14 27 25 19 53 .. 40 .. .. 38 40 .. 29 29 51 31 3 39 .. 25 34 .. 9 .. .. 34 m .. 37 36 37 35 32 40 39 19 37 .. 27 ..
1990
1 .. 26 .. .. .. 40 2 .. 8 .. 4 2 29 .. 47 1 1 7 .. .. 22 .. .. 28 6 .. .. .. 0 0 2 10 .. 7 .. .. 17 .. 17 13 m .. 14 15 12 17 18 .. 13 15 30 .. 1 0
2001
3 14 .. .. 37 .. 49 2 1 2 .. 3 .. 11 29 52 0 1 10 16 .. 10 .. .. 11 1 .. 50 4 0 0 1 3 .. 7 18 .. 10 .. .. 7m .. 5 9 3 9 9 3 6 14 15 .. 1 ..
1990
15 .. 4 .. .. .. 0 14 .. 0 .. 2 0 5 .. 2 9 3 7 .. .. 4 .. .. 5 3 .. .. .. 0 7 1 12 .. 0 .. .. 5 .. 1 3m .. 3 4 2 3 3 .. 3 5 3 .. 3 3
2001
1 0 .. .. 4 .. 0 9 1 5 .. 3 .. 4 1 4 15 4 6 1 .. 0 .. .. 4 7 .. 1 0 0 7 1 8 .. 3 5 .. 2 .. .. 2m .. 2 3 1 2 2 1 3 3 3 .. 3 ..
1990
10 .. 12 .. .. .. 5 43 .. 5 .. 8 5 10 .. 10 13 7 24 .. .. 8 .. .. 22 15 .. .. .. 62 9 8 5 .. 22 .. .. 43 .. 10 13 m .. 16 16 18 13 20 .. 14 28 18 .. 9 7
2001
14 16 .. .. 4 .. 4 38 11 6 .. 5 .. 12 20 5 11 7 27 9 .. 18 .. .. 9 17 .. 1 18 48 5 6 7 .. 42 16 .. 61 .. .. 12 m .. 14 14 10 12 11 10 18 28 18 .. 9 ..
About the data
Definitions
The International Monetary Fund (IMF) classifies
and duties levied on goods and services). This dis-
• Taxes on income, profits, and capital gains are
government transactions as receipts or payments
tinction may be a useful simplification, but it has no
levied on the actual or presumptive net income of
and according to whether they are repayable or non-
particular analytical significance except with respect
individuals, on the profits of enterprises, and on cap-
repayable. If nonrepayable, they are classified as
to the capacity to fix tax rates.
ital gains, whether realized or not, on land, securities,
capital (meant to be used in production for more
Social security taxes do not reflect compulsory pay-
than a year) or current and as requited (involving
ments made by employers to provident funds or other
eliminated in consolidation. • Social security taxes
payment in return for a benefit or service) or unre-
agencies with a like purpose. Similarly, expenditures
include employer and employee social security contri-
quited. Revenues include all nonrepayable receipts
from such funds are not reflected in government
butions and those of self-employed and unemployed
(other than grants), the most important of which are
expenditure (see table 4.12). The revenue shares
people. • Taxes on goods and services include gen-
taxes. Grants are unrequited, nonrepayable, non-
shown in this table may not sum to 100 percent
eral sales and turnover or value added taxes, selec-
compulsory receipts from other governments or
because adjustments to tax revenues are not shown.
tive excises on goods, selective taxes on services,
from international organizations. Transactions are
For further discussion of taxes and tax policies,
taxes on the use of goods or property, and profits of
generally recorded on a cash rather than an accrual
see About the data for table 5.6. For further discus-
fiscal monopolies. • Taxes on international trade
basis. Measuring the accumulation of arrears on
sion of government revenues and expenditures, see
include import duties, export duties, profits of export
revenues or payments on an accrual basis would
About the data for tables 4.11 and 4.12.
or impor t monopolies, exchange profits, and
or other assets. Intragovernmental payments are
typically result in a higher deficit. Transactions with-
exchange taxes. • Other taxes include employer pay-
in a level of government are not included, but trans-
roll or labor taxes, taxes on property, and taxes not
actions between levels are included. In some cases
allocable to other categories. They may include nega-
the government budget may include transfers used
tive values that are adjustments (for example, for
to finance the deficits of autonomous, extrabud-
taxes collected on behalf of state and local govern-
getary agencies.
ments and not allocable to individual tax categories).
The IMF’s Manual on Government Finance
• Nontax revenue includes requited, nonrepayable
Statistics (1986) describes taxes as compulsory,
receipts for public purposes—such as fines, adminis-
unrequited payments made to governments by indi-
trative fees, or entrepreneurial income from govern-
viduals, businesses, or institutions. Taxes tradition-
ment
ally have been classified as either direct (those
unrequited, nonrepayable receipts other than from
levied directly on the income or profits of individuals
government sources. It does not include proceeds of
and corporations) or indirect (sales and excise taxes
grants and borrowing, funds arising from the repay-
ownership
of
proper ty—and
voluntar y,
ment of previous lending by governments, incurrence
4.13a
of liabilities, and proceeds from the sale of capital
Poor countries rely more on indirect taxes
assets.
Indirect taxes as share of current revenue, 2000–01 (%) 100
80
60
Data sources The data on central government revenues are 40
from the IMF’s Government Finance Statistics Yearbook, 2003 and IMF data files. Each country’s accounts are reported using the system of
20
common definitions and classifications in the IMF’s Manual on Government Finance Statistics (1986). The IMF receives additional information
0 0
1,000
10,000
50,000
GNI per capita ($)
from the Organisation for Economic Co-operation and Development on the tax revenues of some of
Low-income economies
Middle-income economies
High-income economies
Low-income countries tend to rely on indirect taxes on international trade and on goods and services, while high-income countries prefer to tax income, property, and social security contributions. But in all groups there are exceptions.
its members. See the IMF sources for complete and authoritative explanations of concepts, definitions, and data sources.
Source: International Monetary Fund, Government Finance Statistics data files.
2004 World Development Indicators
233
ECONOMY
4.13
Central government revenues
4.14 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria a Azerbaijan Bangladesh Belarus Belgium a Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland a France a Gabon Gambia, The Georgia Germany a Ghana Greece a Guatemala Guinea Guinea-Bissau Haiti
234
Monetar y indicators and prices Money and quasi money
Claims on private sector
Claims on governments and other public entities
GDP implicit deflator
Consumer price index
Food price index
M2
annual growth
annual growth
average annual
average annual
average annual
annual % growth
as % of M2
as % of M2
% growth
% growth
1990
2002
1990
2002
1990
2002
1980–90
1990–2002
1980–90
1990–2002
.. .. 11.4 .. 1,113.3 .. 12.8 .. .. 10.4 .. .. 28.6 52.8 .. –14.0 1,289.2 53.8 –0.5 9.6 .. –1.7 7.8 –3.7 –2.4 24.2 28.9 8.5 33.0 195.4 18.5 27.5 –2.6 .. .. .. 6.5 42.5 48.9 28.7 –17.5 .. 76.5 18.5 .. .. 3.3 8.4 .. .. 13.3 .. 25.8 –17.4 574.6 2.5
.. 5.9 24.8 158.6 19.7 34.0 13.2 .. 14.6 13.3 53.5 .. –7.0 –6.9 9.4 –1.1 23.0 12.2 0.6 29.5 31.1 15.9 5.6 –4.3 26.6 –0.3 19.4 0.5 13.6 40.0 13.1 20.9 30.0 9.6 .. 6.9 4.2 10.3 21.4 12.6 –3.1 20.3 11.2 13.3 .. .. 5.7 35.3 17.9 .. 48.9 .. 11.8 19.7 22.8 22.8
.. .. 12.2 .. 1,444.7 .. 15.3 .. .. 9.2 .. .. –1.3 40.8 .. 12.6 1,566.4 1.9 3.6 15.4 .. 0.9 9.2 –1.6 1.3 21.7 26.5 7.9 107.1 18.0 5.1 7.3 –3.9 .. .. .. 3.0 19.1 17.2 6.3 –24.2 .. 27.6 0.3 .. .. 0.7 7.8 .. .. 4.9 .. 15.0 13.1 90.5 –0.6
.. 2.9 2.6 37.8 –9.5 1.0 10.8 .. 9.7 12.0 35.7 .. 5.5 –1.6 18.6 12.9 11.8 14.7 11.7 29.0 5.6 6.1 5.9 6.2 8.9 14.8 13.4 –2.6 8.2 3.8 –15.7 16.6 –0.3 19.9 .. –12.3 13.9 15.9 26.0 3.4 3.0 3.8 12.7 –0.6 .. .. 7.2 13.9 14.4 .. 13.6 .. 4.7 2.7 –0.3 11.8
.. .. 3.2 .. 1,573.2 .. –2.2 .. .. –0.2 .. .. 12.4 18.0 .. –51.9 3,093.6 83.1 –1.5 –6.9 .. –3.0 0.6 2.3 –17.3 16.3 1.5 –1.0 23.9 429.7 –12.6 8.2 –3.0 .. .. .. –3.1 1.1 –27.4 25.3 10.2 .. –6.8 21.7 .. .. –20.6 –35.4 .. .. 9.9 .. 0.5 2.9 460.7 0.4
.. 2.4 –5.6 28.8 143.2 –6.0 1.0 .. 24.6 0.8 28.8 .. 0.1 8.1 –0.6 117.2 29.9 –1.7 –12.1 7.9 –2.2 –2.4 1.6 2.6 –3.7 0.2 1.4 3.3 8.1 –36.2 3.0 6.8 1.3 3.1 .. 11.6 1.6 3.6 –20.3 11.5 –4.5 17.8 1.2 1.3 .. .. –9.6 0.2 2.7 .. 10.6 .. 3.8 36.0 4.5 11.7
.. –0.4 8.3 5.9 391.1 .. 7.2 3.3 .. 9.8 .. 4.1 1.7 326.9 .. 13.6 284.0 1.8 3.3 4.4 .. 5.6 4.6 7.9 1.4 20.7 5.7 7.8 24.7 62.9 0.5 23.6 2.8 .. .. .. 5.8 21.6 –5.4 13.7 16.3 .. 2.3 3.5 6.7 5.8 1.8 17.9 1.9 2.7 42.1 19.3 14.6 .. 57.4 7.3
.. 28.6 15.7 584.3 4.5 142.2 1.8 1.8 78.6 3.8 283.6 1.9 7.5 7.5 3.4 9.0 139.8 83.8 4.8 12.8 3.7 4.5 1.5 4.1 7.1 7.1 5.5 2.5 19.0 731.4 8.5 15.6 7.8 61.3 1.1 9.9 2.0 8.9 3.8 7.4 6.3 9.6 40.3 5.6 2.0 1.5 5.3 4.6 225.2 1.7 26.4 8.0 9.6 5.0 25.6 20.1
.. .. 9.1 .. 390.6 .. 7.9 3.2 .. .. .. 4.2 .. 322.5 .. 10.0 285.6 6.3 3.4 7.1 .. 8.7 5.3 3.2 0.6 20.6 .. .. 22.7 57.1 0.9 23.0 5.4 304.1 .. .. 5.6 22.4 35.8 17.4 19.6 .. .. 4.0 6.2 5.8 5.1 20.0 .. 2.2 b 39.1 18.7 14.0 .. .. 5.2
.. 21.6 14.0 562.9 7.2 44.7 2.3 2.1 109.1 5.1 258.0 1.9 7.2 7.5 .. 9.8 134.1 94.0 4.9 15.3 4.7 5.5 1.8 4.6 7.7 7.7 6.7 4.1 18.0 691.7 7.9 14.6 6.3 61.3 .. 6.7 2.1 8.3 38.6 7.5 7.2 .. 16.7 4.0 1.6 1.6 4.6 4.0 17.7 2.1 27.4 7.7 9.4 .. 27.3 19.8
2004 World Development Indicators
% growth 1980–90 1990–2002
.. .. 9.7 .. 486.5 .. 7.4 2.7 .. 10.8 2.4 4.0 –3.5 321.8 .. 10.1 314.0 1.8 0.7 6.1 .. .. 4.6 2.0 –5.3 20.7 8.8 6.3 24.6 .. 4.3 16.0 .. 124.6 .. .. 4.8 25.4 40.7 22.0 21.5 .. .. 3.8 5.8 5.7 4.9 20.3 .. .. 33.1 18.0 22.1 .. .. 4.1
.. 31.2 14.7 223.1 6.5 81.5 2.9 1.5 109.8 4.8 141.1 1.2 10.5 6.8 .. 9.6 –15.7 89.9 5.0 .. 4.8 4.1 1.7 5.5 –0.7 6.8 11.3 3.5 16.4 .. 8.1 4.5 .. 58.7 .. 0.8 2.1 7.8 37.8 6.5 7.9 .. –20.1 –3.6 –0.3 1.5 4.3 3.7 15.9 0.5 25.0 6.7 9.2 9.2 .. ..
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland a Israel Italy a Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands a New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal a Puerto Rico
4.14
Money and quasi money
Claims on private sector
Claims on governments and other public entities
GDP implicit deflator
Consumer price index
Food price index
M2
annual growth
annual growth
average annual
average annual
average annual
annual % growth
as % of M2
as % of M2
% growth
% growth
1990
2002
1990
21.4 29.2 15.1 44.6 18.0 .. .. 19.4 .. 21.5 8.2 8.3 .. 20.1 .. 17.2 –100.0 .. 7.8 .. 55.1 8.4 –100.0 19.0 .. .. 4.5 11.1 10.6 –4.9 11.5 21.2 81.9 358.0 31.6 21.5 37.2 37.7 30.3 18.5 .. 12.5 7,677.8 –4.1 32.7 5.6 10.0 11.6 36.6 4.3 54.4 6,384.9 22.4 160.1 .. ..
13.7 14.9 16.8 4.5 27.5 .. .. 6.9 .. 12.0 3.4 8.6 30.1 11.7 .. 11.0 4.8 33.9 37.6 19.9 7.3 8.8 2.0 1.1 16.9 15.7 8.0 20.7 3.1 27.9 8.9 12.5 4.6 38.6 42.0 6.4 21.6 34.6 6.9 3.8 .. 7.7 13.3 –0.5 21.6 7.6 5.2 16.8 –0.3 4.0 3.1 5.1 10.4 –2.8 .. ..
13.0 23.0 5.9 66.9 14.7 .. .. 18.5 .. 12.5 9.7 4.7 .. 8.0 .. 36.1 –89.7 .. 3.6 .. 27.6 6.8 –39.8 2.0 .. .. 23.8 15.5 20.8 0.1 20.2 10.8 48.5 53.3 40.2 44.2 22.0 12.8 15.4 5.7 .. 4.2 4,932.9 –5.1 7.8 5.0 9.6 5.9 0.8 –0.9 32.0 2,123.7 15.6 158.7 .. ..
2002
1990
2002
1980–90
1990–2002
1980–90
1990–2002
4.9 –10.5 13.8 69.7 10.7 10.5 6.3 –6.7 19.2 5.8 .. .. .. .. 8.6 4.9 .. .. 8.5 –16.0 –4.4 1.5 1.7 1.0 33.0 .. 1.6 21.5 .. .. 22.4 –1.2 9.0 –23.0 3.9 .. –0.0 7.0 25.7 .. 1.5 18.5 6.8 –14.9 7.7 –271.0 –0.3 15.0 13.3 .. 3.2 .. 0.3 –14.8 3.7 –12.9 5.8 –1.2 14.1 –13.4 35.2 1.5 5.8 0.8 5.8 13.6 21.9 469.1 28.0 38.5 2.3 –4.9 –0.5 –5.1 16.7 24.2 20.4 –4.2 10.7 7.3 .. .. 9.8 –1.7 9.8 12,679.2 7.2 1.4 8.5 27.1 8.8 –0.6 0.4 –10.9 2.5 7.7 –8.7 –25.7 –3.1 8.8 0.2 –9.2 –0.4 2,129.5 0.5 3.4 2.7 –20.6 .. .. .. ..
–0.5 9.3 4.9 –0.8 7.8 .. .. –3.2 .. 5.6 1.7 3.1 –14.6 5.0 .. –1.1 1.5 12.6 –5.6 5.7 –1.0 15.2 535.8 10.2 –1.2 –14.3 9.8 42.2 3.2 –2.8 –95.2 1.2 9.6 6.9 –7.2 1.1 7.0 18.8 –5.4 8.1 .. 1.0 2.0 3.5 28.8 4.4 –3.6 –1.0 3.1 18.1 9.8 –2.2 4.7 –0.7 .. ..
5.7 8.9 8.2 8.5 14.4 10.3 6.6 101.1 10.0 19.9 1.8 4.3 .. 9.1 .. 6.5 –2.8 .. 37.6 –0.0 .. 12.1 2.9 1.2 .. .. 17.1 15.1 1.7 4.5 8.4 9.4 71.5 .. –1.6 7.1 38.3 12.2 13.7 11.1 1.5 10.5 422.3 1.9 16.7 5.4 –3.6 6.7 1.9 5.3 24.4 220.2 14.9 .. 17.9 3.5
17.1 17.4 7.2 15.6 25.0 .. 3.8 8.9 3.5 19.7 –0.3 2.7 141.0 12.8 .. 4.2 2.6 82.5 28.8 36.1 13.4 9.6 53.8 .. 53.1 56.4 17.0 32.2 3.5 6.7 5.6 6.0 17.3 89.4 45.4 2.5 26.8 24.6 10.3 7.2 2.3 1.7 30.7 5.5 25.0 3.2 2.0 9.1 3.2 7.4 11.3 20.4 8.0 19.8 4.9 3.1
6.3 9.6 8.6 8.3 18.2 .. 6.8 101.7 9.1 15.1 1.7 5.7 .. 11.2 .. 4.9 2.9 .. .. .. .. 13.6 .. 7.5 .. .. 16.6 16.9 2.6 .. 7.1 6.9 73.8 .. .. 7.0 .. 11.5 12.6 10.2 2.0 11.0 535.7 0.7 18.9 7.4 .. 6.3 1.4 5.6 21.9 246.1 13.4 50.9 17.1 ..
17.2 18.1 8.3 14.1 23.6 .. 2.6 8.3 3.4 19.7 0.5 3.1 45.6 13.3 .. 4.7 1.9 18.7 30.0 21.7 .. 8.9 .. 6.3 22.7 6.5 16.8 32.6 3.3 4.6 5.7 6.6 17.7 18.5 39.0 3.3 26.6 25.4 9.5 7.2 2.5 1.9 24.6 5.4 27.8 2.2 –0.1 8.6 1.1 10.0 12.0 20.9 7.7 21.0 4.1 ..
% growth 1980–90 1990–2002
5.2 9.5 8.8 8.7 16.2 .. 6.0 102.4 8.2 16.1 1.5 4.7 .. 10.0 .. 5.0 1.6 .. .. .. .. 13.5 .. .. 2.7 .. 15.7 16.4 2.2 2.7 .. 7.8 73.1 .. .. 6.7 .. 11.9 13.9 10.5 1.3 9.8 69.2 –1.5 22.5 7.8 0.9 6.6 1.5 4.6 24.9 221.8 14.1 52.4 16.7 2.7
2004 World Development Indicators
16.7 17.4 7.9 16.7 24.3 .. 2.9 7.4 2.8 18.6 0.3 3.4 106.1 12.3 .. 4.9 1.5 45.9 .. 18.2 19.8 9.8 .. .. 39.5 .. 19.1 33.2 4.6 4.8 6.3 7.5 17.3 110.5 .. 3.1 .. 27.5 8.8 8.4 1.3 1.7 22.6 6.3 25.9 1.8 0.1 8.8 0.7 9.6 10.5 18.7 6.7 17.9 3.4 9.8
235
ECONOMY
Monetar y indicators and prices
4.14 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain a Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Monetar y indicators and prices Money and quasi money
Claims on private sector
Claims on governments and other public entities
GDP implicit deflator
Consumer price index
Food price index
M2
annual growth
annual growth
average annual
average annual
average annual
annual % growth
as % of M2
as % of M2
% growth
% growth
1990
2002
1990
2002
1990
2002
1980–90
1990–2002
1980–90
1990–2002
26.4 .. 5.6 4.6 –4.8 .. 74.0 20.0 .. 123.0 .. 11.4 .. 19.9 48.8 0.6 0.8 0.8 26.1 .. 41.9 26.7 9.5 6.2 7.6 53.2 .. 60.2 .. –8.2 10.5 4.9 118.5 .. 64.9 .. .. 11.3 47.9 15.1
38.2 33.9 12.6 15.2 8.2 .. 29.6 –0.3 4.1 12.3 .. 14.5 .. 13.4 30.3 13.1 1.9 5.7 18.5 40.5 25.1 1.4 –2.2 5.7 4.4 29.1 83.3 25.0 42.3 11.0 5.1 4.3 28.2 .. 15.8 13.3 .. 17.5 31.1 191.7
.. .. –10.0 –4.5 –8.4 .. 4.9 13.7 .. 96.1 .. 13.7 .. 16.2 12.6 20.5 13.4 11.7 3.4 .. 22.6 30.0 1.8 2.7 5.9 42.9 .. 0.0 .. 1.3 13.1 1.1 56.2 .. 17.6 .. .. 1.4 22.8 13.5
13.3 20.9 6.3 5.7 3.5 .. 7.5 –8.8 8.2 9.1 .. 6.7 .. 10.3 17.9 16.7 12.4 0.4 0.7 26.1 10.2 11.9 –4.0 2.9 5.4 3.3 10.8 5.5 29.2 9.6 10.1 5.7 27.1 .. 0.6 16.7 .. 2.4 2.3 106.2
0.0 .. 26.8 4.2 –5.3 .. 228.7 –4.9 .. –10.4 .. 1.8 .. 4.4 29.4 –13.1 –12.1 1.0 11.4 .. 80.6 –4.0 6.9 –1.9 1.8 0.4 .. –0.9 .. –4.8 1.0 0.6 25.8 .. 45.3 .. .. 10.2 195.2 5.0
1.1 7.2 –8.8 0.2 –8.3 .. –1.8 –4.0 –14.1 –3.7 .. 4.1 .. –1.2 6.4 42.1 2.1 0.2 –0.7 17.8 –4.0 –0.1 –6.4 2.5 –0.0 29.3 59.0 28.1 1.5 –1.6 1.1 2.1 41.5 .. 14.5 2.7 .. –12.4 27.0 45.4
1.5 .. 4.0 –3.8 6.5 .. 60.3 2.0 1.8 .. 49.7 15.5 9.3 11.0 38.4 10.7 7.3 3.4 15.3 2.5 .. 3.9 4.8 2.4 7.4 45.3 .. 113.8 .. 0.8 5.8 3.8 62.7 .. 19.3 222.2 .. .. 42.2 11.6
84.3 121.1 11.7 1.7 4.0 57.1 26.7 0.7 9.7 10.2 .. 9.1 3.8 9.1 51.9 12.1 1.9 1.1 6.9 175.2 18.6 3.6 6.3 5.9 4.1 71.8 266.6 9.5 183.4 2.8 2.8 2.0 25.5 184.2 40.8 12.5 8.8 19.8 44.7 32.3
.. .. 3.9 –0.8 6.2 .. 72.4 1.6 .. .. .. 14.8 9.0 10.9 37.6 14.6 7.0 2.9 23.2 .. 31.0 3.5 2.5 10.7 7.4 44.9 .. 102.5 .. .. 5.8 4.2 61.1 .. 20.9 .. .. .. 48.5 13.8
85.5 75.2 13.4 0.7 4.6 .. 24.5 1.5 8.3 10.8 .. 8.2 3.6 9.8 66.8 9.2 1.8 1.4 4.9 .. 17.8 4.3 7.2 5.4 4.0 75.5 .. 8.5 102.6 .. 2.7 2.6 27.5 .. 43.2 2.9 .. 32.6 42.8 36.1
% growth 1980–90 1990–2002
4.3 .. 6.4 –0.2 5.3 .. .. 1.0 1.6 129.5 .. 15.2 9.3 11.0 .. 13.3 8.2 3.1 25.0 .. 32.0 2.7 1.1 14.6 8.3 18.3 .. .. 2.0 .. 4.5 3.9 62.0 .. 35.1 .. .. .. 48.7 15.1
69.1 122.1 12.7 0.7 5.1 4.1 .. 1.5 14.7 21.3 .. 9.4 3.1 10.3 .. 11.9 –0.0 0.8 3.8 477.3 20.1 4.9 1.7 12.7 4.2 31.8 .. 8.5 99.5 .. 1.7 2.4 25.1 .. 39.5 .. .. ..
Note: The inconsistencies in the growth rates of the GDP deflator and consumer and food price indexes are mainly due to uneven coverage of the time period. a. As members of the European Monetary Union, these countries share a single currency, the euro. b. Data prior to 1990 refer to the Federal Republic of Germany before unification.
236
2004 World Development Indicators
40.1
About the data
4.14
Definitions
Money and the financial accounts that record the
financial derivatives and the net liabilities of the
• Money and quasi money comprise the sum of cur-
supply of money lie at the heart of a country’s finan-
banking system can also be difficult. The quality of
rency outside banks, demand deposits other than those
cial system. There are several commonly used defi-
commercial bank reporting also may be adversely
of the central government, and the time, savings, and
nitions of the money supply. The narrowest, M1,
affected by delays in reports from bank branches,
foreign currency deposits of resident sectors other than
encompasses currency held by the public and
especially in countries where branch accounts are
the central government. This definition of the money
demand deposits with banks. M2 includes M1 plus
not computerized. Thus the data in the balance
supply, often called M2, corresponds to lines 34 and 35
time and savings deposits with banks that require a
sheets of commercial banks may be based on pre-
in the International Monetary Fund’s (IMF) International
notice for withdrawal. M3 includes M2 as well as var-
liminary estimates subject to constant revision. This
Financial Statistics (IFS). The change in money supply is
ious money market instruments, such as certificates
problem is likely to be even more serious for non-
measured as the difference in end-of-year totals relative
of deposit issued by banks, bank deposits denomi-
bank financial intermediaries.
to M2 in the preceding year. • Claims on private sector
nated in foreign currency, and deposits with financial
Controlling inflation is one of the primary goals of
(IFS line 32d) include gross credit from the financial sys-
institutions other than banks. However defined,
monetary policy and is intimately linked to the growth
tem to individuals, enterprises, nonfinancial public enti-
money is a liability of the banking system, distin-
in money supply. Inflation is measured by the rate of
ties not included under net domestic credit, and
guished from other bank liabilities by the special role
increase in a price index, but actual price change can
financial institutions not included elsewhere. • Claims
it plays as a medium of exchange, a unit of account,
also be negative. Which index is used depends on
on governments and other public entities (IFS line
and a store of value.
which set of prices in the economy is being examined.
32an + 32b + 32bx + 32c) usually comprise direct cred-
The banking system’s assets include its net for-
The GDP deflator reflects changes in prices for total
it for specific purposes, such as financing the govern-
eign assets and net domestic credit. Net domestic
gross domestic product. The most general measure
ment budget deficit; loans to state enterprises;
credit includes credit extended to the private sector
of the overall price level, it takes into account
advances against future credit authorizations; and
and general government and credit extended to the
changes in government consumption, capital forma-
purchases of treasury bills and bonds, net of deposits
nonfinancial public sector in the form of investments
tion (including inventory appreciation), international
by the public sector. Public sector deposits with the
in short- and long-term government securities and
trade, and the main component, household final con-
banking system also include sinking funds for the
loans to state enterprises; liabilities to the public
sumption expenditure. The GDP deflator is usually
service of debt and temporary deposits of government
and private sectors in the form of deposits with the
derived implicitly as the ratio of current to constant
revenues. • GDP implicit deflator measures the aver-
banking system are netted out. Net domestic credit
price GDP, resulting in a Paasche index. It is defective
age annual rate of price change in the economy as a
also includes credit to banking and nonbank financial
as a general measure of inflation for use in policy
whole for the periods shown. • Consumer price index
institutions.
because of the long lags in deriving estimates and
reflects changes in the cost to the average consumer of
because it is often only an annual measure.
acquiring a basket of goods and services that may be
Domestic credit is the main vehicle through which changes in the money supply are regulated, with cen-
Consumer price indexes are more current and pro-
fixed or may change at specified intervals, such as year-
tral bank lending to the government often playing the
duced more frequently. They are also constructed
ly. The Laspeyres formula is generally used. • Food
most important role. The central bank can regulate
explicitly, based on surveys of the cost of a defined
price index is a subindex of the consumer price index.
lending to the private sector in several ways—for
basket
example, by adjusting the cost of the refinancing facil-
Nevertheless, consumer price indexes should be
Data sources
ities it provides to banks, by changing market interest
interpreted with caution. The definition of a house-
The monetary, financial, and consumer price index
rates through open market operations, or by control-
hold, the basket of goods chosen, and the geograph-
data are published by the IMF in its monthly
ling the availability of credit through changes in the
ic (urban or rural) and income group coverage of
International Financial Statistics and annual
reserve requirements imposed on banks and ceilings
consumer price surveys can all vary widely across
International Financial Statistics Yearbook. The
on the credit provided by banks to the private sector.
countries. In addition, the weights are derived from
IMF collects data on the financial systems of its
Monetary accounts are derived from the balance
household expenditure surveys, which, for budgetary
member countries. The World Bank receives data
sheets of financial institutions—the central bank,
reasons, tend to be conducted infrequently in devel-
from the IMF in electronic files that may contain
commercial banks, and nonbank financial intermedi-
oping countries, leading to poor comparability over
more recent revisions than the published sources.
aries. Although these balance sheets are usually reli-
time. Although useful for measuring consumer price
The GDP deflator data are from the World Bank’s
able, they are subject to errors of classification,
inflation within a country, consumer price indexes are
national accounts files. The food price index data
valuation, and timing and to differences in account-
of less value in making comparisons across coun-
are from the United Nations Statistics Division’s
ing practices. For example, whether interest income
tries. Food price indexes, like consumer price index-
Statistical Yearbook and Monthly Bulletin of
is recorded on an accrual or a cash basis can make
es, should be interpreted with caution because of the
Statistics. The discussion of monetary indicators
a substantial difference, as can the treatment of
high variability across countries in the items covered.
draws from an IMF publication by Marcello Caiola,
nonper forming assets. Valuation errors typically
The least-squares method is used to calculate the
A Manual for Country Economists (1995). Also see
arise with respect to foreign exchange transactions,
growth rates of the GDP implicit deflator, consumer
the IMF’s Monetar y and Financial Statistics
particularly in countries with flexible exchange rates
price index, and food price index.
Manual (2000) for guidelines for the presentation
or in those that have undergone a currency devalua-
of
consumer
goods
and
ser vices.
of monetary and financial statistics.
tion during the reporting period. The valuation of
2004 World Development Indicators
237
ECONOMY
Monetar y indicators and prices
4.15
Balance of payments current account Goods and services
Net income
Net current transfers
Current account balance
Total reserves a
$ millions Exports 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium b Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
238
.. 354 13,462 3,992 14,800 .. 49,843 63,694 .. 2,064 .. 138,605 364 977 .. 2,005 35,170 6,950 349 89 314 2,508 149,538 220 271 10,221 57,374 .. 8,679 .. 1,488 1,963 3,503 .. .. .. 48,902 1,832 3,262 9,895 973 .. 664 597 31,180 285,389 2,730 168 .. 474,654 983 13,018 1,568 829 26 318 74,172
Imports 2002
.. 915 .. 8,573 28,654 698 82,975 108,865 2,667 6,972 9,264 213,811 555 1,534 1,417 2,651 69,967 8,286 273 39 2,350 .. 301,274 .. .. 22,300 365,395 243,633 14,160 .. 2,454 7,141 5,747 10,545 .. 45,562 82,768 8,238 6,173 16,438 3,799 187 5,504 1,066 51,347 392,362 3,399 .. 975 721,017 2,570 30,091 3,769 976 .. .. 151,058
2004 World Development Indicators
1990
.. 485 10,106 3,385 6,846 .. 53,056 61,580 .. 3,960 .. 135,098 454 1,086 .. 1,987 28,184 8,027 758 318 507 2,475 149,118 410 488 9,166 46,706 .. 6,858 .. 1,282 2,346 3,445 .. .. .. 41,415 2,233 2,519 14,091 1,624 .. 711 1,271 33,456 283,238 1,812 192 .. 428,619 1,506 19,564 1,812 953 88 515 67,015
$ millions 2002
1990
$ millions
$ millions
2002
1990
2002
1990
2002
.. .. .. 2,076 –2 128 .. –2,268 .. 7,796 –765 –1,531 13,011 –4,400 –6,465 1,107 .. 88 88,635 –13,176 –11,541 104,594 –942 –2,082 3,121 .. –385 9,192 –116 –281 9,787 .. –29 203,106 2,316 2,907 790 –25 –17 2,049 –249 –201 4,751 .. 256 2,229 –106 –279 61,863 –11,608 –18,191 9,287 –758 –228 687 0 –26 147 –15 –12 2,693 –21 –168 .. –558 .. 269,721 –19,388 –17,514 .. –22 .. .. –21 .. 20,744 –1,737 –2,536 328,012 1,055 –14,946 230,153 .. 2,806 15,392 –2,305 –2,812 .. .. .. 1,618 –460 –860 7,724 –233 –532 3,869 –1,091 –629 12,709 .. –518 .. .. .. 47,159 .. –3,800 72,394 –5,708 –2,771 10,166 –249 –1,135 7,742 –1,210 –1,306 19,508 –1,022 –267 5,898 –132 –287 552 .. –6 6,119 –13 –331 2,038 –69 –23 39,952 –3,735 –542 365,576 –3,896 12,823 2,022 –617 –718 .. –11 .. 1,398 .. 19 643,327 20,593 –5,997 3,325 –111 –176 41,997 –1,709 –1,957 6,622 –196 –298 999 –149 –69 .. –22 .. .. –18 .. 130,241 4,362 7,353
.. 15 333 –77 998 .. 439 –6 .. 1,613 .. –2,197 97 159 .. 69 799 125 332 174 120 –26 –796 123 192 198 274 .. 1,026 .. 3 192 –181 .. .. .. –408 371 107 7,545 631 450 97 449 –952 –8,199 –134 59 .. –21,954 411 4,718 227 70 39 193 –596
.. 625 .. 91 413 173 –64 –1,615 70 3,242 174 –4,220 126 369 939 –47,313 2,390 549 116 118 447 .. 871 .. .. 426 12,984 –1,896 2,406 .. –10 169 –482 1,076 .. 912 –2,612 2,188 1,654 3,960 2,003 286 144 845 –648 –13,865 –75 .. 174 –25,108 900 3,458 1,958 46 .. .. –2,492
.. –118 1,420 –236 4,552 .. –15,950 1,166 .. –398 .. 3,627 –18 –199 .. –19 –3,823 –1,710 –77 –69 –93 –551 –19,764 –89 –46 –485 11,997 .. 542 .. –251 –424 –1,214 .. .. .. 1,372 –280 –360 2,327 –152 188 36 –294 –6,962 –9,944 168 23 .. 44,674 –223 –3,537 –213 –203 –45 –22 10,923
.. –408 .. –1,431 9,592 –148 –17,264 575 –768 742 –378 9,392 –126 –347 –2,139 –47,169 –7,696 –679 –324 –3 –64 .. 14,909 .. .. –553 35,422 14,390 –1,639 .. –34 –946 767 –1,606 .. –4,485 4,991 –875 –1,222 622 –384 –85 –802 –150 10,205 25,744 584 .. –230 46,586 –31 –10,405 –1,193 –46 .. .. 25,678
$ millions 1990
2002
638 .. .. 866 2,703 25,151 .. 376 6,222 10,492 1 440 19,319 21,567 17,228 13,182 0 722 660 1,722 .. 619 23,789 c 14,698 c 69 616 511 893 .. 1,321 3,331 5,474 9,200 37,835 670 4,846 305 313 112 59 .. 913 37 640 23,530 37,189 123 127 132 223 6,784 15,344 34,476 297,739 24,656 111,919 4,869 10,844 261 .. 10 35 525 1,497 21 1,863 167 5,885 .. .. .. 23,707 11,226 27,719 69 475 1,009 1,004 3,620 14,076 595 1,784 .. 30 198 1,003 55 966 10,415 9,825 68,291 61,697 279 144 55 107 .. 198 104,547 89,143 309 636 4,721 9,432 362 2,373 80 171 18 103 10 82 77,653 166,304
Goods and services
Net income
Net current transfers
Current account balance
4.15 Total reserves a
$ millions Exports 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar d Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
1,032 12,035 22,911 29,295 19,741 .. 26,786 17,312 219,971 2,217 323,692 2,511 .. 2,228 .. 73,295 8,268 .. 102 1,090 511 100 .. 11,468 .. .. 471 443 32,665 420 471 1,722 48,805 .. 493 6,239 229 319 1,220 422 159,304 11,683 392 533 14,550 47,078 5,577 6,835 4,438 1,381 2,514 4,120 11,430 19,037 21,554 ..
Imports 2002
2,451 42,599 77,602 65,826 35,554 .. 114,169 38,505 313,931 3,229 461,293 4,283 11,615 3,295 .. 190,696 17,015 636 477 3,828 2,399 390 163 .. 7,492 1,364 710 472 108,261 1,157 .. 2,965 173,503 871 708 12,199 1,153 2,741 1,309 884 262,898 19,625 909 .. 17,151 79,358 11,423 12,261 7,574 2,098 2,859 9,192 37,439 56,777 36,864 ..
1990
1,127 11,017 29,527 27,511 22,292 .. 24,576 20,228 218,573 2,390 297,306 3,569 .. 2,705 .. 76,360 7,169 .. 212 997 2,836 754 .. 8,960 .. .. 809 549 31,765 830 520 1,916 51,915 .. 1,096 7,783 996 603 1,584 834 147,652 11,699 682 728 6,909 38,910 3,342 10,205 4,193 1,509 2,169 4,087 13,967 15,095 27,146 ..
$ millions 2002
1990
$ millions
$ millions
2002
1990
2002
1990
2002
3,420 –237 –159 44,104 –1,427 –1,586 83,850 –3,257 –3,886 52,706 –5,190 –7,048 31,228 378 –217 .. .. .. 91,385 –4,955 –24,514 42,682 –1,981 –3,599 300,688 –14,712 –14,550 4,828 –430 –606 409,691 22,492 65,769 6,186 –214 111 11,394 .. –1,031 3,670 –418 –122 .. .. .. 183,977 –87 451 14,037 7,738 3,360 697 .. –60 560 –1 –34 4,728 2 –7 7,065 622 817 792 433 161 173 .. –85 .. 174 .. 8,258 .. –183 2,156 .. –31 1,001 –161 –75 795 –80 –38 91,696 –1,872 –6,595 1,211 –37 –241 .. –46 .. 2,805 –23 10 186,339 –8,316 –11,436 1,283 .. 165 946 –44 –5 13,314 –988 –738 1,782 –97 –113 2,968 –192 –367 1,485 37 28 1,446 14 –4 244,133 –620 –2,441 18,770 –1,576 –3,181 1,970 –217 –203 .. –54 .. 15,526 –2,738 –2,090 52,290 –2,700 465 6,988 –254 –588 12,645 –1,084 –2,190 7,724 –255 –217 1,594 –103 –230 2,715 2 34 9,932 –1,733 –1,509 38,295 –872 4,550 63,177 –3,386 –1,887 45,857 –96 –3,135 .. .. ..
280 787 2,837 418 2,500 .. 2,384 5,060 –3,164 291 –4,800 1,045 .. 368 .. 1,150 –4,951 .. 56 96 1,818 286 .. –481 .. .. 234 99 102 225 86 97 3,975 .. 7 2,336 448 39 354 109 –2,943 138 202 14 85 –1,476 –874 2,794 219 156 43 281 714 2,511 5,507 ..
862 447 14,790 1,751 457 .. 805 6,549 –5,434 1,086 –4,923 2,260 113 580 .. –1,078 –2,145 86 .. 260 1,000 121 43 .. 229 498 96 161 –2,780 136 .. 89 10,268 155 138 3,330 217 286 278 390 –6,208 57 377 .. 1,466 –2,385 .. 6,445 213 13 116 1,043 503 3,280 3,315 ..
–51 379 –7,036 –2,988 327 .. –361 163 –16,479 –312 44,078 –227 .. –527 .. –2,003 3,886 .. –55 191 115 65 .. 2,201 .. .. –265 –86 –870 –221 –10 –119 –7,451 .. –640 –196 –415 –436 28 –289 8,089 –1,453 –305 –236 4,988 3,992 1,106 –1,661 209 –76 390 –1,419 –2,695 3,067 –181 ..
–266 –2,644 4,656 7,823 12,645 .. –925 –1,226 –6,741 –1,119 112,447 468 –696 84 .. 6,092 4,192 –35 –82 –647 –2,848 –119 –52 .. –721 –325 –270 –201 7,190 –310 .. 259 –14,004 –92 –105 1,477 –657 –309 130 –165 10,116 –2,269 –888 .. 1,001 25,148 2,315 3,871 –154 286 294 –1,206 4,197 –5,007 –8,813 ..
$ millions 1990
2002
47 1,531 1,185 10,383 5,637 71,608 8,657 32,032 .. .. .. .. 5,362 5,475 6,598 24,083 88,595 55,622 168 1,645 87,828 469,618 1,139 4,116 .. 3,136 236 1,068 .. .. 14,916 121,498 2,929 10,078 .. 317 8 216 .. 1,327 4,210 10,405 72 406 0 3 7,225 15,892 107 2,420 .. 790 92 363 142 170 10,659 34,623 198 594 59 400 761 1,249 10,217 50,671 0 269 23 398 2,338 10,375 233 841 410 549 50 323 354 1,070 34,401 18,948 4,129 3,739 166 453 226 134 4,129 7,567 15,788 21,088 1,784 3,174 1,046 8,796 344 1,183 427 343 675 641 1,891 9,721 2,036 16,136 4,674 29,784 20,579 17,701 .. ..
2004 World Development Indicators
239
ECONOMY
Balance of payments current account
4.15
Balance of payments current account Goods and services
Net income
Net current transfers
Current account balance
Total reserves a
$ millions Exports 1990
Imports 2002
1990
$ millions 2002
Romania 6,380 16,223 9,901 18,825 Russian Federation .. 121,214 .. 85,188 Rwanda 143 132 354 435 Saudi Arabia 47,445 76,862 43,939 49,287 Senegal 1,453 1,549 1,840 2,066 Serbia and Montenegro .. 3,241 .. 6,857 Sierra Leone 210 .. 215 .. Singapore 67,489 158,075 64,953 137,122 Slovak Republic .. 17,174 .. 18,843 Slovenia 7,900 12,764 6,930 12,452 Somalia .. .. .. .. South Africa 27,742 35,571 21,016 32,034 Spain 83,595 188,552 100,870 196,780 Sri Lanka 2,293 5,967 2,965 7,103 Sudan 499 1,996 877 2,971 Swaziland 658 1,072 768 1,177 Sweden 70,560 105,298 70,490 89,903 Switzerland 96,927 129,854 96,388 111,148 Syrian Arab Republic 5,030 8,228 2,955 6,341 Tajikistan 185 708 238 868 Tanzania 538 1,568 1,474 2,224 Thailand 29,229 82,114 35,870 73,741 Togo 663 457 847 683 Trinidad and Tobago 2,289 4,521 1,427 4,183 Tunisia 5,203 9,538 6,039 10,431 Turkey 21,042 54,617 25,652 55,046 Turkmenistan 1,238 3,138 857 2,703 Uganda 178 720 686 1,643 Ukraine .. 23,351 .. 21,494 United Arab Emirates .. .. .. .. United Kingdom 239,226 404,794 264,090 436,634 United States 535,260 974,107 616,120 1,392,145 Uruguay 2,158 2,708 1,659 2,525 Uzbekistan .. 2,985 .. 2,721 Venezuela, RB 18,806 27,716 9,451 17,474 Vietnam .. 19,654 .. 21,458 West Bank and Gaza .. .. .. .. Yemen, Rep. 1,490 3,787 2,170 3,867 Zambia 1,360 1,080 1,897 1,585 Zimbabwe 2,012 .. 2,001 .. World 4,301,369 t 7,985,963 t 4,330,919 t 7,986,659 t Low income 118,587 273,925 135,542 279,942 Middle income 632,588 1,702,940 593,980 1,591,324 Lower middle income 356,798 1,017,575 356,720 952,348 Upper middle income 272,381 683,839 236,047 636,247 Low & middle income 752,042 1,976,803 730,892 1,871,332 East Asia & Pacific 166,647 691,152 165,402 620,489 Europe & Central Asia .. 452,206 .. 443,440 Latin America & Carib. 168,326 403,563 145,500 399,939 Middle East & N. Africa 127,663 210,917 134,989 178,855 South Asia 34,818 104,364 47,813 115,016 Sub-Saharan Africa 79,306 111,723 72,835 110,384 High income 3,542,141 6,007,813 3,583,425 6,113,233 Europe EMU 1,517,749 2,447,680 1,480,370 2,284,894
1990
161 .. –16 7,979 –129 .. –71 1,006 .. –38 .. –4,271 –3,533 –167 –136 59 –4,473 8,746 –401 0 –185 –853 –32 –397 –455 –2,508 0 –48 .. .. –5,154 28,560 –321 .. –774 .. .. –372 –437 –263
$ millions
2002
1990
2002
–459 –6,117 –19 96 –168 –111 .. –1,145 –459 –71 .. –2,691 –9,890 –251 –617 48 –1,907 11,485 –925 –58 –16 –1,340 –33 –510 –984 –4,549 –111 –136 –604 .. 31,255 –3,968 10 –145 –2,654 –721 .. –766 –108 ..
106 .. 143 –15,637 153 .. 7 –421 .. 46 .. –321 2,799 541 141 102 –1,936 –2,329 88 .. 562 213 132 –6 828 4,493 66 293 .. .. –8,794 –26,660 8 .. –302 .. .. 1,790 380 112
1,536 –5 195 –15,975 207 2,343 .. –1,104 120 134 .. –556 2,176 1,123 666 10 –2,864 –4,179 485 184 420 618 77 33 1,131 3,496 68 707 1,921 .. –13,828 –58,852 69 120 –165 1,921 .. 1,384 32 ..
$ millions 1990
–3,254 –1,525 .. 29,905 –85 –126 –4,152 11,696 –363 –478 .. –1,384 –69 .. 3,122 18,704 .. –694 978 375 .. .. 2,134 290 –18,009 –15,942 –298 –264 –372 –926 51 –46 –6,339 10,624 6,955 26,011 1,762 1,062 –53 –34 –559 –251 –7,281 7,650 –84 –169 459 416 –463 –746 –2,625 –1,482 447 –74 –263 –353 .. 3,174 .. .. –38,811 –14,414 –78,960 –480,859 186 262 .. 239 8,279 7,423 .. –604 .. .. 739 538 –594 –553 –140 ..
a. International reserves Including gold valued at London gold price. b. Includes Luxembourg. c. Excludes Luxembourg. d. Data are in fiscal years.
240
2004 World Development Indicators
2002
$ millions 1990
2002
1,374 8,372 .. 48,326 44 244 13,437 22,186 22 637 .. .. 5 85 27,748 82,021 .. 9,196 112 7,063 .. .. 2,583 7,817 57,238 40,303 447 1,652 11 441 216 276 20,324 19,171 61,284 61,276 .. .. .. 90 193 1,529 14,258 38,903 358 205 513 2,049 867 2,365 7,626 28,348 .. .. 44 934 469 4,414 4,891 15,355 43,146 42,819 173,094 157,763 1,446 772 .. .. 12,733 12,107 .. 4,121 .. .. 441 4,428 201 535 295 132
About the data
4.15
Definitions
The balance of payments records an economy’s trans-
residence and ownership, and the exchange rate
• Exports and imports of goods and services com-
actions with the rest of the world. Balance of payments
used to value transactions—contribute to net errors
prise all transactions between residents of an econo-
accounts are divided into two groups: the current
and omissions. In addition, smuggling and other ille-
my and the rest of the world involving a change in
account, which records transactions in goods, servic-
gal or quasi-legal transactions may be unrecorded or
ownership of general merchandise, goods sent for
es, income, and current transfers, and the capital and
misrecorded. For further discussion of issues relat-
processing and repairs, nonmonetary gold, and serv-
financial account, which records capital transfers,
ing to the recording of data on trade in goods and
ices. • Net income refers to receipts and payments
acquisition or disposal of nonproduced, nonfinancial
services, see About the data for tables 4.4–4.8.
of employee compensation for nonresident workers,
assets, and transactions in financial assets and liabili-
The concepts and definitions underlying the data in
and investment income (receipts and payments on
ties. The table presents data from the current account
the table are based on the fifth edition of the
direct investment, portfolio investment, and other
with the addition of gross international reserves.
International Monetary Fund’s (IMF) Balance of
investments and receipts on reserve assets). Income
The balance of payments is a double-entry accounting
Payments Manual (1993). The fifth edition redefined
derived from the use of intangible assets is recorded
system that shows all flows of goods and services into
as capital transfers some transactions previously
under business services. • Net current transfers are
and out of an economy; all transfers that are the coun-
included in the current account, such as debt for-
recorded in the balance of payments whenever an
terpart of real resources or financial claims provided to
giveness, migrants’ capital transfers, and foreign aid
economy provides or receives goods, ser vices,
or by the rest of the world without a quid pro quo, such
to acquire capital goods. Thus the current account
income, or financial items without a quid pro quo. All
as donations and grants; and all changes in residents’
balance now reflects more accurately net current
transfers not considered to be capital are current.
claims on and liabilities to nonresidents that arise from
transfer receipts in addition to transactions in goods,
• Current account balance is the sum of net exports
economic transactions. All transactions are recorded
services (previously nonfactor services), and income
of goods and services, net income, and net current
twice—once as a credit and once as a debit. In princi-
(previously factor income). Many countries maintain
transfers. • Total reserves comprise holdings of mon-
ple the net balance should be zero, but in practice the
their data collection systems according to the fourth
etary gold, special drawing rights, reserves of IMF
accounts often do not balance. In these cases a bal-
edition. Where necessary, the IMF converts data
members held by the IMF, and holdings of foreign
ancing item, net errors and omissions, is included.
reported in such systems to conform to the fifth edi-
exchange under the control of monetary authorities.
tion (see Primary data documentation). Values are in
The gold component of these reserves is valued at
U.S. dollars converted at market exchange rates.
year-end (31 December) London prices ($385.00 an
Discrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure
The data in this table come from the IMF’s Balance
that the data are fully consistent. Sources include
of Payments and International Financial Statistics
customs data, monetary accounts of the banking
databases, supplemented by estimates by World
system, external debt records, information provided
Bank staff for countries for which the IMF does not
by enterprises, surveys to estimate service transac-
collect balance of payments statistics. In addition,
tions, and foreign exchange records. Differences in
World Bank staff make estimates of missing data for
collection methods—such as in timing, definitions of
up to three years prior to the current year.
ounce in 1990, and $342.75 an ounce in 2002).
4.15a Worker remittances are an important source of income for many developing economies Workers’ remittances, 2002
Country $ billions Mexico 10 India 8 Spain 4 Pakistan 4 Portugal 3 Egypt, Arab Rep. 3 Morocco 3 Bangladesh 3 Colombia 2 Serbia and Montenegro 2 Dominican Republic 2 Turkey 2 El Salvador 2
% of merchandise trade 6 17 3 36 13 66 36 47 20 92 37 6 65
Country Jordan Brazil China Guatemala Ecuador Yemen, Rep. Sri Lanka Indonesia Greece Jamaica Poland Tunisia World total
$ billions 2 2 2 2 1 1 1 1 1 1 1 1 76
% of merchandise trade 70 3 1 71 28 40 27 2 11 102 3 16
Data sources More information about the design and compilation of the balance of payments can be found in the IMF’s Balance of Payments Manual, fifth edition (1993), Balance of Payments Textbook (1996a), and Balance of Payments Compilation Guide (1995). The balance of payments data are published in the IMF’s Balance of Payments Statistics Yearbook and International Financial Statistics. The World Bank exchanges data with the IMF through electronic files that in most cases are more timely and cover a longer period than the published sources. The IMF’s International
Remittances accounted for $76 billion in 2002, and 25 countries received more than $1 billion in remittances.
Financial Statistics and Balance of Payments
Source: International Monetary Fund, Balance of Payments data files.
databases are available on CD-ROM.
2004 World Development Indicators
241
ECONOMY
Balance of payments current account
4.16
External debt Total external debt
Long-term debt
Public and publicly guaranteed debt
Private nonguaranteed external debt
Use of IMF credit
$ millions IBRD loans and $ millions
$ millions
Total
IDA credits
$ millions
$ millions
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
Afghanistan .. Albania .. Algeria 28,149 Angola 8,594 Argentina 62,233 Armenia .. Australia .. Austria .. Azerbaijan .. Bangladesh 12,439 Belarus .. Belgium .. Benin 1,292 Bolivia 4,275 Bosnia and Herzegovina .. Botswana 553 Brazil 119,964 Bulgaria .. Burkina Faso 834 Burundi 907 Cambodia 1,845 Cameroon 6,657 Canada .. Central African Republic 698 Chad 524 Chile 19,226 China 55,301 Hong Kong, China .. Colombia 17,222 Congo, Dem. Rep. 10,259 Congo, Rep. 4,947 Costa Rica 3,756 Côte d’Ivoire 17,251 Croatia .. Cuba .. Czech Republic .. Denmark .. Dominican Republic 4,372 Ecuador 12,107 Egypt, Arab Rep. 33,017 El Salvador 2,149 Eritrea .. Estonia .. Ethiopia 8,630 Finland .. France .. Gabon 3,983 Gambia, The 369 Georgia .. Germany .. Ghana 3,837 Greece .. Guatemala 3,080 Guinea 2,476 Guinea-Bissau 692 Haiti 910
.. 1,312 22,800 10,134 132,314 1,149 .. .. 1,398 17,037 908 .. 1,843 4,867 2,515 480 227,932 10,462 1,580 1,204 2,907 8,502 .. 1,066 1,281 41,945 168,255 .. 33,853 8,726 5,152 4,834 11,816 15,347 .. 26,419 .. 6,256 16,452 30,750 5,828 528 4,741 6,523 .. .. 3,533 573 1,838 .. 7,338 .. 4,676 3,401 699 1,248
.. .. 26,688 7,605 48,676 .. .. .. .. 11,657 .. .. 1,218 3,864 .. 547 94,427 .. 750 851 1,683 5,577 .. 624 464 14,687 45,515 .. 15,784 8,994 4,200 3,367 13,223 .. .. .. .. 3,518 10,029 28,438 1,938 .. .. 8,479 .. .. 3,150 308 .. .. 2,772 .. 2,605 2,253 630 772
.. 1,200 21,362 8,883 103,140 941 .. .. 1,037 16,445 711 .. 1,690 4,302 2,303 464 183,710 8,585 1,399 1,095 2,594 7,417 .. 980 1,148 38,188 120,370 .. 30,052 7,391 3,974 3,335 10,369 14,984 .. 15,661 .. 4,206 13,828 27,282 4,837 496 3,151 6,313 .. .. 3,231 504 1,495 .. 6,382 .. 3,744 2,972 662 1,063
.. .. 26,688 7,605 46,876 .. .. .. .. 11,657 .. .. 1,218 3,687 .. 547 87,756 .. 750 851 1,683 5,347 .. 624 464 10,425 45,515 .. 14,671 8,994 4,200 3,063 10,665 .. .. .. .. 3,419 9,865 27,438 1,913 .. .. 8,479 .. .. 3,150 308 .. .. 2,740 .. 2,478 2,253 630 772
.. 1,187 21,255 8,883 74,661 920 .. .. 964 16,445 710 .. 1,690 3,378 2,282 464 96,565 7,474 1,399 1,095 2,594 7,240 .. 980 1,148 6,792 88,531 .. 21,177 7,391 3,974 3,139 9,110 7,679 .. 6,903 .. 4,035 11,243 26,624 4,712 496 482 6,313 .. .. 3,231 504 1,444 .. 6,129 .. 3,641 2,972 662 1,063
.. .. 1,208 0 2,609 .. .. .. .. 4,159 .. .. 326 587 .. 169 8,427 .. 282 398 0 871 .. 265 186 1,874 5,881 .. 3,874 1,161 239 412 1,920 .. .. .. .. 258 848 2,401 164 .. .. 851 .. .. 69 102 .. .. 1,423 .. 293 420 146 324
.. 476 1,203 265 8,513 538 .. .. 314 7,076 89 .. 654 1,320 1,115 16 8,585 958 745 648 306 988 .. 399 632 562 20,677 .. 2,355 1,504 207 93 2,068 588 .. 185 .. 363 847 1,859 385 219 39 2,756 .. .. 50 195 491 .. 3,476 .. 400 1,096 237 501
.. .. 0 0 1,800 .. .. .. .. 0 .. .. 0 177 .. 0 6,671 .. 0 0 0 230 .. 0 0 4,263 0 .. 1,113 0 0 304 2,558 .. .. .. .. 99 164 1,000 26 .. .. 0 .. .. 0 0 .. .. 33 .. 127 0 0 0
.. 13 107 0 28,479 21 .. .. 73 0 1 .. 0 923 21 0 87,145 1,111 0 0 0 177 .. 0 0 31,396 31,839 .. 8,876 0 0 196 1,259 7,305 .. 8,757 .. 171 2,586 658 126 0 2,669 0 .. .. 0 0 51 .. 253 .. 102 0 0 0
.. .. 670 0 3,083 .. .. .. .. 626 .. .. 18 257 .. 0 1,821 .. 0 43 27 121 .. 37 31 1,156 469 .. 0 521 11 11 431 .. .. .. .. 72 265 125 0 .. .. 6 .. .. 140 45 .. .. 745 .. 67 52 5 38
.. 81 1,330 0 14,340 195 .. .. 279 71 56 .. 73 195 139 0 20,827 1,049 127 13 96 307 .. 33 107 0 0 .. 0 571 33 0 491 0 .. 0 .. 27 308 0 0 0 0 143 .. .. 67 32 310 .. 363 .. 0 139 23 31
242
2004 World Development Indicators
Total external debt
Long-term debt
Public and publicly guaranteed debt
Private nonguaranteed external debt
4.16 Use of IMF credit
$ millions IBRD loans and $ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
$ millions
Total
IDA credits
$ millions
$ millions
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
3,718 21,202 83,628 69,872 9,020 .. .. .. .. 4,748 .. 8,333 .. 7,058 .. .. .. .. 1,768 .. 1,779 396 1,849 .. .. .. 3,704 1,558 15,328 2,468 2,113 984 104,442 .. .. 25,017 4,650 4,695 .. 1,640 .. .. 10,745 1,726 33,439 .. 2,736 20,663 6,506 2,594 2,105 20,064 30,580 49,364 .. ..
5,395 34,958 104,429 132,208 9,154 .. .. .. .. 5,477 .. 8,094 17,538 6,031 .. .. .. 1,797 2,664 6,690 17,077 637 2,324 .. 6,199 1,619 4,518 2,912 48,557 2,803 2,309 1,803 141,264 1,349 1,037 18,601 4,609 6,556 .. 2,953 .. .. 6,485 1,797 30,476 .. 4,639 33,672 8,298 2,485 2,967 28,167 61,121 69,521 .. ..
3,487 17,931 72,462 58,242 1,797 .. .. .. .. 4,045 .. 7,202 .. 5,641 .. .. .. .. 1,758 .. 358 378 1,116 .. .. .. 3,335 1,385 13,422 2,337 1,806 910 81,809 .. .. 23,860 4,231 4,466 .. 1,572 .. .. 8,313 1,487 31,935 .. 2,400 16,643 3,855 2,461 1,732 13,959 25,241 39,261 .. ..
4,675 29,289 99,860 100,037 6,797 .. .. .. .. 4,678 .. 7,076 16,355 5,188 .. .. .. 1,593 2,620 2,512 14,530 611 1,065 .. 3,955 1,476 4,137 2,688 40,188 2,487 1,984 911 131,364 1,126 950 16,913 4,039 5,391 .. 2,913 .. .. 5,756 1,658 28,206 .. 3,451 30,100 7,877 2,305 2,481 25,596 53,877 60,637 .. ..
3,420 17,931 70,974 47,982 1,797 .. .. .. .. 4,011 .. 7,202 .. 4,761 .. .. .. .. 1,758 .. 358 378 1,116 .. .. .. 3,335 1,382 11,592 2,337 1,806 762 75,974 .. .. 23,660 4,211 4,466 .. 1,572 .. .. 8,313 1,226 31,545 .. 2,400 16,506 3,855 1,523 1,713 13,629 24,040 39,261 .. ..
4,212 13,551 88,271 70,011 6,578 .. .. .. .. 4,593 .. 7,076 3,209 5,139 .. .. .. 1,394 2,620 1,124 13,829 611 1,065 .. 2,486 1,262 4,137 2,688 26,200 2,487 1,984 832 76,327 846 950 15,001 2,526 5,391 .. 2,913 .. .. 5,576 1,604 28,057 .. 1,979 28,102 6,408 1,488 2,064 20,477 39,575 29,374 .. ..
635 1,512 20,996 10,385 86 .. .. .. .. 672 .. 593 .. 2,056 .. .. .. .. 131 .. 34 112 248 .. .. .. 797 854 1,102 498 264 195 11,030 .. .. 3,138 268 716 .. 668 .. .. 299 461 3,321 .. 52 3,922 462 349 320 1,188 4,044 55 .. ..
1,119 517 26,093 11,523 400 .. .. .. .. 495 .. 1,072 1,178 2,460 .. .. .. 454 474 263 313 255 240 .. 279 432 1,652 1,773 719 1,134 547 107 10,797 331 181 2,573 985 729 .. 1,210 .. .. 811 867 1,951 .. 0 8,143 287 358 241 2,609 3,533 2,385 .. ..
66 0 1,488 10,261 0 .. .. .. .. 34 .. 0 .. 880 .. .. .. .. 0 .. 0 0 0 .. .. .. 0 3 1,830 0 0 148 5,835 .. .. 200 19 0 .. 0 .. .. 0 261 391 .. 0 138 0 938 19 330 1,201 0 .. ..
463 15,738 11,589 30,026 219 .. .. .. .. 86 .. 0 13,146 49 .. .. .. 199 0 1,388 701 0 0 .. 1,469 214 0 0 13,988 0 0 79 55,038 280 0 1,912 1,513 0 .. 0 .. .. 181 54 149 .. 1,471 1,998 1,469 818 417 5,118 14,303 31,263 .. ..
32 330 2,623 494 0 .. .. .. .. 357 .. 94 .. 482 .. .. .. .. 8 .. 0 15 322 .. .. .. 144 115 0 69 70 22 6,551 .. .. 750 74 0 .. 44 .. .. 0 85 0 .. 0 836 272 61 0 755 912 509 .. ..
197 0 0 8,862 0 .. .. .. .. 24 .. 483 0 88 .. .. .. 185 43 16 0 22 304 .. 121 67 150 95 0 166 113 0 0 152 43 0 200 0 .. 4 .. .. 174 106 0 .. 0 2,032 50 116 0 237 1,686 0 .. ..
2004 World Development Indicators
243
ECONOMY
External debt
4.16
External debt Total external debt
Long-term debt
Public and publicly guaranteed debt
Private nonguaranteed external debt
Use of IMF credit
$ millions IBRD loans and $ millions 1990
Romania 1,140 Russian Federation .. Rwanda 712 Saudi Arabia .. Senegal 3,736 Serbia and Montenegro a .. Sierra Leone 1,196 Singapore .. Slovak Republic .. Slovenia .. Somalia 2,370 South Africa .. Spain .. Sri Lanka 5,863 Sudan 14,762 Swaziland 243 Sweden .. Switzerland .. Syrian Arab Republic 17,259 Tajikistan .. Tanzania 6,459 Thailand 28,095 Togo 1,281 Trinidad and Tobago 2,512 Tunisia 7,690 Turkey 49,424 Turkmenistan .. Uganda 2,583 Ukraine .. United Arab Emirates .. United Kingdom .. United States .. Uruguay 4,415 Uzbekistan .. Venezuela, RB 33,171 Vietnam 23,270 West Bank and Gaza .. Yemen, Rep. 6,352 Zambia 6,916 Zimbabwe 3,247 World .. Low income 411,419 Middle income b 940,479 Lower middle income 583,682 Upper middle income b 356,797 Low & middle income b 1,351,898 East Asia & Pacific 234,092 Europe & Central Asia 217,224 Latin America & Carib. 444,227 Middle East & N. Africa 155,134 South Asia 124,395 Sub-Saharan Africa 176,826 High income Europe EMU
2002
$ millions 1990
2002
Total 1990
IDA credits 2002
1990
2002
14,683 230 13,780 223 8,112 0 2,173 147,541 .. 124,738 .. 96,223 .. 6,599 1,435 664 1,305 664 1,305 340 826 .. .. .. .. .. .. .. 3,918 3,000 3,372 2,940 3,339 835 1,578 12,688 .. 8,793 .. 8,514 .. 2,419 1,448 940 1,262 940 1,262 92 479 .. .. .. .. .. .. .. 13,013 .. 8,776 .. 4,295 .. 204 .. .. .. .. .. .. .. 2,688 1,926 1,860 1,926 1,860 419 405 25,041 .. 17,640 .. 9,427 0 13 .. .. .. .. .. .. .. 9,611 5,049 8,805 4,947 8,455 946 1,738 16,389 9,651 9,539 9,155 9,043 1,048 1,192 342 238 274 238 274 44 13 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 21,504 15,108 15,849 15,108 15,849 523 38 1,153 .. 999 .. 912 .. 195 7,244 5,799 6,201 5,787 6,182 1,493 2,874 59,212 19,771 46,902 12,460 22,628 2,530 2,428 1,581 1,081 1,338 1,081 1,338 398 632 2,672 2,055 1,807 1,782 1,697 41 87 12,625 6,880 12,027 6,662 10,641 1,406 1,498 131,556 39,924 94,278 38,870 61,823 6,429 5,456 .. .. .. .. .. .. 31 4,100 2,160 3,690 2,160 3,690 969 2,576 13,555 .. 11,100 .. 8,348 .. 2,233 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 10,736 3,114 7,343 3,045 6,851 359 703 4,568 .. 4,175 .. 3,901 .. 275 32,563 28,159 28,843 24,509 23,264 974 670 13,349 21,378 12,181 21,378 12,181 59 1,715 .. .. .. .. .. .. .. 5,290 5,160 4,563 5,160 4,563 602 1,384 5,969 4,554 4,846 4,552 4,737 813 2,155 4,066 2,649 3,269 2,464 3,123 449 871 s .. s .. s .. s .. s .. s .. s .. s 523,464 351,318 448,932 333,366 399,076 67,061 104,388 1,817,163 749,931 1,469,476 707,793 983,880 70,222 107,517 1,149,118 477,625 925,294 453,753 651,767 49,234 80,019 668,045 272,306 544,182 254,040 332,112 20,988 27,498 2,340,627 1,101,250 1,918,408 1,041,159 1,382,955 137,283 211,905 499,133 194,633 388,064 172,998 272,783 25,306 42,764 545,842 176,378 434,625 171,457 276,350 10,429 30,214 727,944 352,476 613,916 327,447 384,961 35,841 42,072 189,010 120,603 148,851 119,101 142,396 10,074 10,417 168,349 107,527 158,723 105,799 144,785 30,717 44,349 210,350 149,632 174,229 144,355 161,681 24,916 42,089
$ millions 1990
2002
7 5,668 .. 28,514 0 0 .. .. 60 33 .. 280 0 0 .. .. .. 4,481 .. .. 0 0 .. 8,213 .. .. 102 351 496 496 0 0 .. .. .. .. 0 0 .. 87 12 20 7,311 24,274 0 0 273 110 218 1,386 1,054 32,455 .. .. 0 0 .. 2,752 .. .. .. .. .. .. 69 493 .. 274 3,650 5,578 0 0 .. .. 0 0 2 108 185 146 .. s .. s 17,953 49,857 42,138 485,596 23,872 273,527 18,266 212,069 60,091 535,453 21,635 115,281 4,921 158,275 25,029 228,956 1,502 6,455 1,727 13,938 5,276 12,548
$ millions 1990
2002
0 428 .. 6,481 0 85 .. .. 314 253 .. 567 108 169 .. .. .. 0 .. .. 159 152 0 0 .. .. 410 310 956 573 0 0 .. .. .. .. 0 0 .. 94 140 400 1 391 87 52 329 0 176 0 0 22,086 .. 0 282 257 .. 1,876 .. .. .. .. .. .. 101 1,793 .. 62 3,012 0 112 381 .. .. 0 386 949 1,015 7 280 .. s .. s 11,317 20,258 23,334 75,550 7,811 59,160 15,523 16,390 34,651 95,809 2,085 11,618 1,305 34,245 18,297 38,302 1,815 2,219 4,537 2,416 6,612 7,009
a. Data for 1990 refer to the former Socialist Federal Republic of Yugoslavia. Data for 2002 are estimates and reflect borrowings by the former Socialist Federal Republic of of Yugoslavia that are not yet allocated to the successor republics. b. Includes data for Gibraltar not included in other tables.
244
2004 World Development Indicators
About the data
Definitions
Data on the external debt of developing countries are
the IMF’s International Financial Statistics (line ae).
• Total external debt is debt owed to nonresidents
gathered by the World Bank through its Debtor
Flow figures are conver ted at annual average
repayable in foreign currency, goods, or services. It
Reporting System. World Bank staff calculate the
exchange rates (line rf). Projected debt service is
is the sum of public, publicly guaranteed, and private
indebtedness of these countries using loan-by-loan
converted using end-of-period exchange rates. Debt
nonguaranteed long-term debt, use of IMF credit, and
reports submitted by them on long-term public and
repayable in multiple currencies, goods, or services
short-term debt. Short-term debt includes all debt
publicly guaranteed borrowing, along with informa-
and debt with a provision for maintenance of the
having an original maturity of one year or less and
tion on short-term debt collected by the countries or
value of the currency of repayment are shown at
interest in arrears on long-term debt. • Long-term
collected from creditors through the reporting sys-
book value.
debt is debt that has an original or extended maturi-
tems of the Bank for International Settlements and
Because flow data are converted at annual aver-
ty of more than one year. It has three components:
the Organisation for Economic Co-operation and
age exchange rates and stock data at end-of-period
public, publicly guaranteed, and private nonguaran-
Development. These data are supplemented by infor-
exchange rates, year-to-year changes in debt out-
teed debt. • Public and publicly guaranteed debt
mation on loans and credits from major multilateral
standing and disbursed are sometimes not equal to
comprises the long-term external obligations of pub-
banks, loan statements from official lending agen-
net flows (disbursements less principal repayments);
lic debtors, including the national government and
cies in major creditor countries, and estimates by
similarly, changes in debt outstanding, including
political subdivisions (or an agency of either) and
World Bank and International Monetary Fund (IMF)
undisbursed debt, differ from commitments less
autonomous public bodies, and the external obliga-
staff. In addition, the table includes data on private
repayments. Discrepancies are particularly signifi-
tions of private debtors that are guaranteed for
nonguaranteed debt for 80 countries either reported
cant when exchange rates have moved sharply during
repayment by a public entity. • IBRD loans and IDA
to the World Bank or estimated by its staff.
the year. Cancellations and reschedulings of other
credits are extended by the World Bank. The
liabilities into long-term public debt also contribute to
International
the differences.
Development (IBRD) lends at market rates. The
The coverage, quality, and timeliness of debt data vary across countries. Coverage varies for both debt
Bank
for
Reconstruction
and
instruments and borrowers. With the widening spec-
Variations in reporting rescheduled debt also
trum of debt instruments and investors and the
affect cross-country comparability. For example,
vides credits at concessional rates. • Private
expansion of private nonguaranteed borrowing, com-
rescheduling under the auspices of the Paris Club of
nonguaranteed external debt consists of the long-
prehensive coverage of long-term external debt
official creditors may be subject to lags between the
term external obligations of private debtors that are
becomes more complex. Reporting countries differ in
completion of the general rescheduling agreement
not guaranteed for repayment by a public entity.
their capacity to monitor debt, especially private
and the completion of the specific, bilateral agree-
• Use of IMF credit denotes repurchase obligations
nonguaranteed debt. Even data on public and pub-
ments that define the terms of the rescheduled debt.
to the IMF for all uses of IMF resources (excluding
licly guaranteed debt are affected by coverage and
Other areas of inconsistency include country treat-
those resulting from drawings on the reser ve
accuracy in reporting—again because of monitoring
ment of arrears and of nonresident national deposits
tranche). These obligations, shown for the end of the
capacity and sometimes because of unwillingness to
denominated in foreign currency.
year specified, comprise purchases outstanding
International Development Association (IDA) pro-
provide information. A key part often underreported
under the credit tranches (including enlarged access
is military debt.
resources) and all special facilities (the buffer stock,
Because debt data are normally reported in the
compensatory financing, extended fund, and oil facil-
currency of repayment, they have to be converted
ities), trust fund loans, and operations under the
into U.S. dollars to produce summary tables. Stock
structural adjustment and enhanced structural
figures (amount of debt outstanding) are converted
adjustment facilities.
using end-of-period exchange rates, as published in
4.16a Since 2000, GDP has been larger than external debt for the heavily indebted poor countries $ billions
Data sources Total external debt
250
GDP
The main sources of external debt information are reports to the World Bank through its Debtor
200
Reporting System from member countries that have received IBRD loans or IDA credits.
150
Additional information has been drawn from the 100
files of the World Bank and the IMF. Summary tables of the external debt of developing countries
50
are published annually in the World Bank’s Global Development
0 1995
1996
1997
1998
1999
2000
2001
2002
Finance
and
on
its
Global
Development Finance CD-ROM.
Source: World Bank data files.
2004 World Development Indicators
245
ECONOMY
4.16
External debt
4.17
External debt management Indebtedness classification a
2002
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
246
.. L L S S L .. .. L L L .. M L L L S M M S M M .. S S M L .. M S S L S M .. L .. L S L L M S S .. .. S M M .. M .. L M S L
2004 World Development Indicators
Present value of debt
% of GNI 2002
.. 20 42 118 66 34 .. .. 21 22 7 .. 36 b 23 b 34 8 48 79 16 b 110 68 58 b .. 78 37 b 63 14 .. 46 171 228 33 91 76 .. 46 .. .. 95 28 46 40 86 66 b, c .. .. 87 77 b 42 .. 73 b .. 21 47 b 235 b 23
Public and publicly guaranteed debt service
% of exports of goods, services, and income 2002
1990
2002
.. 91 .. 125 393 111 .. .. 46 155 10 .. 155 b 111 b 98 13 342 136 171 b 1,553 114 .. b .. .. .. b 173 50 .. 229 .. 200 69 188 150 .. 62 .. 68 300 150 162 200 89 386 b, c .. .. 107 .. b 144 .. 157 b .. 110 166 b .. ..
.. .. 14.3 3.4 3.6 .. .. .. .. 1.6 .. .. 1.8 5.9 .. 2.9 1.3 .. 0.9 3.6 2.6 3.0 .. 1.1 0.4 5.6 1.6 .. 8.2 1.6 20.4 7.9 5.7 .. .. .. .. 2.1 9.6 5.9 3.7 .. .. 2.3 .. .. 1.9 10.4 .. .. 3.3 .. 2.2 5.6 2.4 0.5
.. 0.7 7.0 8.7 4.3 2.1 .. .. 1.6 1.3 1.1 .. 1.9 2.8 2.0 1.2 5.0 3.8 1.4 2.6 0.2 3.2 .. 0.0 1.2 2.4 1.0 .. 6.4 7.5 0.5 3.7 4.5 7 .. 2.2 .. 2.8 5.7 2.0 2.7 1.2 1.6 1.6 .. .. 9.2 5.3 2.0 .. 2.8 .. 1.5 3.9 6.1 0.4
% of GNI
Multilateral debt service
Short-term debt
% of exports of goods, services, and income 1990 2002
% of public and publicly guaranteed 1990 2002
% of total debt 1990 2002
.. .. 63.3 7.1 28.9 .. .. .. .. 23.3 .. .. 8.6 27.6 .. 4.3 15.7 .. 7.7 40.7 .. 12.6 .. 7.5 2.4 15.1 9.7 .. 34.5 .. 31.6 20.7 14.7 .. .. .. .. 7.2 26.6 23.2 17.7 .. .. 33.1 .. .. 3.8 17.9 .. .. 19.3 .. 10.4 17.7 21.8 4.4
.. .. 5.0 2.2 16.2 .. .. .. .. 22.8 .. .. 95.7 67.6 .. 61.3 43.5 .. 73.0 51.1 .. 43.5 .. 50.0 72.3 35.7 7.6 .. 32.2 49.6 12.7 36.1 77.5 .. .. .. .. 50.3 34.8 18.7 60.2 .. .. 14.5 .. .. 32.6 25.4 .. .. 30.7 .. 36.8 22.1 70.2 69.2
.. .. 2.8 11.5 16.8 .. .. .. .. 1.3 .. .. 4.3 3.6 .. 1.0 19.8 .. 10.1 1.5 7.3 14.4 .. 5.4 5.7 17.6 16.8 .. 8.4 7.2 14.9 10.0 20.8 .. .. .. .. 17.9 15.0 13.5 9.8 .. .. 1.7 .. .. 17.4 4.3 .. .. 8.3 .. 13.3 6.9 8.2 11.1
.. 3.4 .. 9.8 12.8 6.3 .. .. 3.4 8.9 1.7 .. 8.5 13.1 6.6 2.0 29.4 6.8 14.8 47.1 0.3 .. .. .. .. 6.3 3.5 .. 33.4 .. 0.5 8.2 8.6 14 .. 3.0 .. 6.6 20.9 10.6 9.5 4.5 1.7 8.9 .. .. 11.0 .. 6.2 .. 6.6 .. 9.0 12.4 .. ..
.. 37.5 25.2 1.0 80.1 54.6 .. .. 18.0 48.7 31.6 .. 60.0 89.7 57.2 69.5 15.5 26.6 85.5 87.6 60.3 46.3 .. 100.0 77.0 31.3 32.8 .. 26.0 100.0 100.0 53.2 73.6 6 .. 10.7 .. 24.1 40.5 25.5 54.5 58.6 54.1 79.0 .. .. 11.3 51.9 24.4 .. 47.9 .. 61.3 57.0 46.5 41.4
.. 2.4 0.5 12.4 11.2 1.2 .. .. 5.9 3.1 15.6 .. 4.4 7.6 2.9 3.3 10.3 7.9 3.4 8.0 7.4 9.2 .. 4.9 2.0 9.0 28.5 .. 11.2 8.8 22.2 31.0 8.1 2.4 .. 40.7 .. 32.3 14.1 11.3 17.0 5.9 33.5 1.0 .. .. 6.7 6.5 1.8 .. 8.1 .. 19.9 8.5 2.0 12.3
Indebtedness classification a
2002
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
M M L S L .. .. .. .. S .. S M M .. .. .. S S S S L S .. M L L M M M M L L M M M L S .. M .. .. S M S .. L M S S L S M L .. ..
Present value of debt
% of GNI 2002
49 62 17 89 7 .. .. .. .. 82 .. 84 80 40 .. .. .. 93 87 85 102 45 559 .. 50 37 33 b 51 b 57 47 b 66 b 39 26 79 69 51 27 b .. .. 31 .. .. 77 26 b 80 .. 23 45 84 82 42 56 77 38 .. ..
Public and publicly guaranteed debt service
% of exports of goods, services, and income 2002
1990
2002
% of exports of goods, services, and income 1990 2002
121 82 115 191 25 .. .. .. .. 163 .. 165 151 147 .. .. .. 221 .. 176 557 78 1,686 .. 95 87 129 b 183 b 44 134 b .. b 60 86 126 107 147 88 b .. .. 147 .. .. 301 .. b 152 .. .. 238 107 .. 96 319 135 124 .. ..
10.8 11.9 .. 6.8 0.2 .. .. .. .. 11.6 .. 14.5 .. 6.3 .. .. .. .. 1.0 .. 1.1 2 .. .. .. .. 5.2 5.5 8.7 1.8 10.9 3.4 3.1 .. .. 5.9 2.2 .. .. 1.5 .. .. .. 0.7 12.8 .. 6.9 3.5 2.8 8.7 5.6 0.7 6.6 1.5 .. ..
2.6 3.4 .. 4.0 1.2 .. .. .. .. 10.9 .. 5.2 3.7 2.8 .. .. .. 2.9 2.2 1.2 9.5 7 .. .. 4.2 3.9 1.5 1.5 6.4 2.4 5.8 4.2 3.0 6.1 3.9 8.5 1.1 .. .. 1.7 .. .. 2.6 0.7 3.6 .. 4.4 3.5 11.5 5.2 3.4 5.5 6.7 2.0 .. ..
29.1 30.4 .. 24.9 1.3 .. .. .. .. 20.7 .. 21.4 .. 22.7 .. .. .. .. 8.0 .. 3.2 4 .. 3.8 .. .. 31.9 22.4 10.6 9.7 24.8 4.5 15.1 .. .. 23.1 17.2 17.7 0.1 12.1 .. .. 2.4 3.1 22.3 .. 12.0 19.8 2.5 18.2 11.5 4.1 22.2 4.3 .. ..
% of GNI
6.5 4.9 .. 9.8 3.6 .. .. .. .. 22.9 .. 10.1 7.4 10.4 .. .. .. 7.0 .. 2.4 51.8 11 .. .. 7.4 10.5 9.1 5.7 5.1 5.8 .. 6.3 10.7 9.4 6.1 23.9 2.9 .. .. 9.8 .. .. 10.7 .. 8.2 .. .. 16.8 16.4 .. 6.2 31.7 12.3 6.3 .. ..
4.17
Multilateral debt service
Short-term debt
% of public and publicly guaranteed 1990 2002
% of total debt 1990 2002
90.7 8.0 .. 22.5 30.4 .. .. .. .. 38.6 .. 26.8 .. 44.7 .. .. .. .. 53.6 .. 27.8 45 100.0 .. .. .. 23.7 38.2 9.9 54.3 73.8 51.6 26.0 .. .. 39.8 30.6 43.6 .. 36.8 .. .. 21.1 71.3 15.5 .. 5.1 40.3 90.7 23.0 35.9 28.8 28.7 9.2 .. ..
76.1 9.2 .. 42.1 8.1 .. .. .. .. 22.1 .. 47.7 18.2 31.8 .. .. .. 39.9 62.8 70.9 5.4 68 .. .. 18.7 38.5 61.0 100.0 4.4 64.0 69.8 21.8 15.0 40.1 13.4 37.4 61.9 0.7 .. 72.7 .. .. 26.0 89.9 31.3 .. 7.6 56.4 9.5 53.6 68.1 24.1 16.1 8.4 .. ..
5.4 13.9 10.2 15.9 80.1 .. .. .. .. 7.3 .. 12.4 .. 13.2 .. .. .. .. 0.1 .. 79.9 1 22.2 .. .. .. 6.1 3.7 12.4 2.5 11.2 5.3 15.4 .. .. 1.6 7.4 4.9 .. 1.5 .. .. 22.6 8.9 4.5 .. 12.3 15.4 36.6 2.8 17.7 26.7 14.5 19.4 .. ..
2004 World Development Indicators
9.7 16.2 4.4 17.6 25.7 .. .. .. .. 14.2 .. 6.6 6.7 12.5 .. .. .. 1.0 0.0 62.2 14.9 0.6 41.1 .. 34.2 4.7 5.1 4.5 17.2 5.4 9.2 49.5 7.0 5.3 4.3 9.1 8.0 17.8 .. 1.2 .. .. 8.5 1.8 7.4 .. 25.6 4.6 4.5 2.6 16.4 8.3 9.1 12.8 .. ..
247
ECONOMY
External debt management
4.17
External debt management Indebtedness classification a
2002
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
L M S .. M S S .. M .. S L .. M S L .. .. S S L M S L M S M L L .. .. .. S M .. L .. L S M
Present value of debt
% of GNI 2002
37 50 40 b .. 53 b 102 103 b .. 62 .. .. 22 .. 48 136 25 .. .. 114 88 19 b, d 49 92 35 65 77 .. 22 b 35 .. .. .. 65 57 33 35 .. 40 127 ..
% of exports of goods, services, and income 2002
106 122 453 b .. 165 b 421 .. b .. 82 .. .. 66 .. 122 851 26 .. .. 270 124 117 b, d 69 251 61 135 246 .. 175 b 59 .. .. .. 279 136 112 61 .. 90 406 ..
Public and publicly guaranteed debt service
% of GNI 1990
.. .. 0.6 .. 3.8 .. 2.8 .. .. .. 0.8 .. .. 3.6 0.1 4.8 .. .. 9.3 .. 3.4 3.9 3.8 7.3 10.3 4.3 .. 2.0 .. .. .. .. 7.9 .. 8.8 2.4 .. 2.3 5.7 4.3 .. w 3.5 3.4 3.1 3.9 3.4 3.6 .. 3.0 4.2 2.1 ..
2002
4.0 2.6 1.1 .. 3.6 0.7 2.8 .. 3.8 .. .. 1.6 .. 3.2 0.0 1.5 .. .. 0.8 1.5 1.4 6.1 0.1 2.4 6.8 5.6 .. 1.0 2.8 .. .. .. 8.9 7.8 6.6 3.1 .. 1.5 6.4 .. .. w 2.8 3.2 3.0 3.7 3.1 2.3 3.2 4.2 .. 2.4 2.6
% of exports of goods, services, and income 1990 2002
.. .. 10.2 1.2 13.8 .. 7.8 .. .. .. .. .. .. 11.9 4.5 5.6 .. .. 20.3 .. 25.1 10.4 8.6 14.6 23.0 29.6 .. 47.1 .. .. .. .. 29.4 .. 19.4 .. .. 7.1 12.7 18.2 .. w 21.4 14.6 17.3 11.8 15.9 13.7 18.3 17.7 13.3 23.1 ..
11.0 7.1 13.2 .. 11.4 3.1 .. .. 5.0 .. .. 4.2 .. 8.7 0.0 1.6 .. .. 1.9 2.5 7.8 8.9 0.2 4.7 14.1 17.7 .. 7.6 4.9 .. .. .. 33.7 20.2 20.5 5.5 .. 3.5 19.9 .. .. w 10.9 8.9 9.1 8.6 9.2 5.5 7.6 16.1 .. 14.5 6.5
Multilateral debt service
Short-term debt
% of public and publicly guaranteed 1990 2002
% of total debt 1990 2002
.. .. 60.7 .. 39.8 .. 26.1 .. .. .. 100.0 .. .. 13.8 100.0 73.0 .. .. 3.5 .. 52.7 22.1 40.8 4.7 26.0 23.3 .. 37.4 .. .. .. .. 16.2 .. 1.6 3.4 .. 51.0 41.6 24.0 .. w 25.4 19.2 21.2 15.8 20.6 17.5 17.1 26.6 10.8 25.0 30.0
27.1 8.9 61.8 .. 42.9 86.0 43.5 .. 10.4 .. .. 0.4 .. 18.7 100.0 75.7 .. .. 55.9 62.5 39.6 33.7 100.0 44.5 49.4 10.0 .. 83.1 29.8 .. .. .. 26.2 11.6 10.2 2.6 .. 60.0 26.9 16.9 .. 44.3 20.8 22.3 18.1 24.7 27.4 12.7 22.4 26.5 49.2 32.7
79.8 .. 6.6 .. 11.3 .. 12.4 .. .. .. 12.0 .. .. 6.9 28.1 1.9 .. .. 12.5 .. 8.0 29.6 8.8 5.1 8.2 19.2 .. 5.4 .. .. .. .. 27.2 .. 6.0 7.7 .. 18.8 20.4 18.2 .. w 11.9 17.8 16.8 19.3 16.0 16.0 18.2 16.5 21.1 9.9 11.6
3.2 11.1 3.2 .. 7.5 26.2 1.1 .. 32.6 .. 25.1 29.6 .. 5.2 38.3 19.9 .. .. 26.3 5.2 8.9 20.1 12.1 32.4 4.7 11.5 .. 3.7 4.3 .. .. .. 14.9 7.2 11.4 5.9 .. 6.5 1.8 12.7 .. w 10.4 15.0 14.3 16.1 13.9 19.9 14.1 10.4 20.1 4.3 13.8
a. S = severely indebted, M = moderately indebted, L = less indebted. b. Data are from debt sustainability analyses undertaken as part of the Debt Initiative for Heavily Indebted Poor Countries (HIPCs). Present value estimates for these countries are for public and publicly guaranteed debt only. c. As of December 31, 2002, Ethiopia had yet to reach the completion point under the HIPC Debt Initiative. d. Data refer to mainland Tanzania only.
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2004 World Development Indicators
About the data
4.17
Definitions
The indicators in the table measure the relative bur-
Monetary Fund (IMF). When the discount rate is
• Indebtedness classification refers to assessment
den on developing countries of servicing external debt.
greater than the interest rate of the loan, the present
on a three-point scale: severely indebted (S), moder-
The present value of external debt provides a measure
value is less than the nominal sum of future debt
ately indebted (M), and less indebted (L). • Present
of future debt service obligations that can be com-
service obligations.
value of debt is the sum of short-term external debt
pared with the current value of such indicators as
The ratios in the table are used to assess the sus-
plus the discounted sum of total debt service pay-
gross national income (GNI) and exports of goods and
tainability of a country’s debt service obligations, but
ments due on public, publicly guaranteed, and private
services. The table shows the present value of total
there are no absolute rules that determine what val-
nonguaranteed long-term external debt over the life of
debt service both as a percentage of GNI in 2002 and
ues are too high. Empirical analysis of the experi-
existing loans. • Public and publicly guaranteed debt
as a percentage of exports in 2002. The ratios com-
ence of developing countries and their debt service
service is the sum of principal repayments and inter-
pare total debt service obligations with the size of the
performance has shown that debt service difficulties
est actually paid in foreign currency, goods, or servic-
economy and its ability to obtain foreign exchange
become increasingly likely when the ratio of the
es on long-term obligations of public debtors and
through exports. The ratios shown here may differ
present value of debt to exports reaches 200 per-
long-term private obligations guaranteed by a public
from those published elsewhere because estimates of
cent. Still, what constitutes a sustainable debt bur-
entity. • Multilateral debt service is the repayment of
exports and GNI have been revised to incorporate data
den varies from one country to another. Countries
principal and interest to the World Bank, regional
available as of February 1, 2004. Exports refer to
with fast-growing economies and exports are likely to
development banks, and other multilateral and inter-
exports of goods, services, and income. Workers’
be able to sustain higher debt levels.
governmental agencies. • Short-term debt includes
remittances are not included here, though they are
The World Bank classifies countries by their level of
included with income receipts in other World Bank pub-
indebtedness for the purpose of developing debt
lications such as Global Development Finance.
management strategies. The most severely indebted
The present value of external debt is calculated by
countries may be eligible for debt relief under special
discounting the debt service (interest plus amortiza-
programs, such as the HIPC Debt Initiative. Indebted
tion) due on long-term external debt over the life of
countries may also apply to the Paris and London
existing loans. Short-term debt is included at its face
Clubs for renegotiation of obligations to public and
value. The data on debt are in U.S. dollars converted
private creditors. In 2002, countries with a present
at official exchange rates (see About the data for
value of debt service greater than 220 percent of
table 4.16). The discount rate applied to long-term
exports or 80 percent of GNI were classified as
debt is determined by the currency of repayment
severely indebted, countries that were not severely
of the loan and is based on reference rates for
indebted but whose present value of debt service
commercial interest established by the Organisation
exceeded 132 percent of exports or 48 percent of
for Economic Co-operation and Development. Loans
GNI were classified as moderately indebted, and
from the International Bank for Reconstruction and
countries that did not fall into either group were clas-
Development
sified as less indebted.
(IBRD)
and
credits
from
the
all debt having an original maturity of one year or less and interest in arrears on long-term debt.
International Development Association (IDA) are discounted using a special drawing rights (SDR) reference rate, as are obligations to the International
4.17a When the present value of a country’s external debt exceeds 220 percent of exports or 80 percent of GNI the World Bank classifies it as severely indebted
Data sources
Ratio of present value debt to GNI, 2002 (%)
reports to the World Bank through its Debtor Reporting System from member countries that
220% of exports
600 500 400
The main sources of external debt information are
Liberia
have received IBRD loans or IDA credits. Additional information has been drawn from the files of the World Bank and the IMF. The data on GNI and exports of goods and services are from
300
the World Bank’s national accounts files and the
Congo, Rep.
200
Sudan
IMF’s Balance of Payments database. Summary
Burundi
100 80% of GNI
tables of the external debt of developing countries are published annually in the World Bank’s Global
0 0
300
600
900
1,200
1,500
1,800
Development
Finance
and
on
its
Global
Ratio of present value debt to exports, 2002 (%)
Development Finance CD-ROM. Source: World Bank data files.
2004 World Development Indicators
249
ECONOMY
External debt management
5 STATES AND MARKETS
S
uccessful development requires that states complement markets, not substitute for
them. States should focus on providing a good business environment—in which contracts are enforced, markets function, basic infrastructure is provided, and people (especially poor people) are empowered to participate. Government institutions can support the development of markets in many ways—by providing information, fostering competition, enforcing contracts, and helping to make credit available to entrepreneurs. By leveling the playing field, governments create opportunities for poor people to participate in markets and improve their standards of living and give them hope for a better future for their children. Good governance matters for long-term growth, but good policies and effective government spending also have immediate effects on people. Many governments are working with service providers and beneficiaries to improve public service delivery. For example, in Bangalore, India, a civil society group introduced report cards in 1994 rating user experiences with public services. The reports of poor quality and corruption were widely publicized, leading to improvements in service delivery and public governance. This section covers a broad range of indicators showing how effective and accountable government, together with energetic private initiative, produces employment opportunities and services that empower poor people. Its 12 tables cover three cross-cutting development
themes:
private
sector
development,
public
sector
policies,
and
infrastructure, information, and telecommunications. Creating the conditions for private sector development Investment is the foundation of growth, and most investment comes from the private sector. But governments play an important role in providing a predictable environment in which people, ideas, and money work together productively and efficiently. This allows private firms operating in competitive markets to be the engines of growth and job creation, providing opportunities to escape poverty. Governments around the world are expanding opportunities for improved investment and business climates. State-owned enterprises are being privatized, trade barriers are being reduced, and improvements in regulations that enhance business activity are contributing to greater business opportunities and growth.
2004 World Development Indicators
251
Investment in infrastructure—whether in power, transport, housing, telecommunications, or water and sanitation—enables businesses to grow. And when private firms participate in infrastructure, bringing with them capital and know-how, they can improve access to basic infrastructure services, a key to reducing poverty. In developing countries private firms participate mainly in telecommunications and energy, and in many countries investment has been robust. In Chile in 1990–95 investment in telecommunications projects with private participation totaled about $150 million, but in 1996–2002 it increased tenfold, to almost $1,600 million. India also saw a dramatic increase in private participation in energy investment, which soared from $2,888 million in 1990–95 to $9,680 million in 1996–2002. Substantial increases in investment with private participation have also occurred in water and sanitation. In China these investments rose from $68 million in 1990–95 to $3,886 million in 1996–2002 (table 5.1). The case for creating a good investment climate (sound macroeconomic framework, and legal and regulatory framework, good governance to overcome bureaucratic inefficiencies, and access to key financial and infrastructure services) is simple: an economy needs a predictable environment in which people, ideas, and money can work together productively and efficiently. In the context of a sound macroeconomic framework, a good investment climate strengthens governance and overcomes bureaucratic inefficiencies, improves access to key financial and infrastructure services, and provides a sound legal and regulatory framework for enterprises that promotes competition. Countries should focus on improving the investment climate for domestic entrepreneurs, but a better investment climate will also attract foreign investors. And countries that receive more foreign investment—an important conduit for new technologies, management experience, and access to markets—enjoy faster growth and greater poverty reduction. External perceptions of the investment climate are reflected in risk ratings, and changes in sovereign risk ratings may affect country risk and stock returns. One example is the Euromoney creditworthiness ratings, which rank the risk of investing in an economy from 0 (high risk) to 100 (low risk). Although many factors determine the level of foreign investment, countries with high risk, such as the Democratic Republic of Congo, at 18, and Haiti, at 24, have very low foreign direct investment—0.6 percent of gross domestic product (GDP) for the Democratic Republic of Congo and 0.2 percent for Haiti. Countries with low perceived risk, such as the Czech Republic, at 66, and Slovenia, at 76, have much higher levels of foreign direct investment—13.4 percent for the Czech Republic and about 8.5 percent for Slovenia (see table 5.1 for data on foreign direct investment and table 5.2 for credit and risk ratings). Countries with low perceived risk also have large stock markets relative to their GDP. Market capitalization is about 74 percent of GDP in Chile, 93 percent in Australia, and 131 percent in Malaysia (table 5.4). The World Bank’s Doing Business database identifies regulations that enhance or constrain business investment, productivity, and growth, providing indicators of the cost of doing business (see http://rru.worldbank.org/DoingBusiness/default.aspx). The business environment in a country is determined by many factors, including regulation of new entry, access to credit markets, contract
252
2004 World Development Indicators
enforcement, insolvency procedures and cost, and labor regulations (several indicators for these areas are included in table 5.3). A new business environment indicator from the Doing Business database is the employment laws index, constructed by examining the detailed provisions of labor laws (table 5.3). Public sector policies and institutions can improve service delivery—and private sector business activities Improving people’s standard of living by ensuring access to essential services such as health, education, safety, water, sanitation, and electricity is widely viewed as government’s responsibility. An efficient and accountable public sector has institutions that are responsive to citizens, provide information, deliver services efficiently and equitably, and help to enforce people’s rights. Making services work better, especially for poor people who often do not get their fair share of public spending on services, is a challenge that can be met by governments, citizens, and private service providers working together. Measuring the quality of public sector governance is difficult. For example, for public goods, including public service delivery, it is difficult to exclude anyone from benefiting from them, so individuals adopt a “free rider” position, resulting in fewer resources being allocated to public goods. Another example is measurement of some dimensions of governance, such as corruption. Corruption is almost impossible to measure directly because of its illegal and clandestine nature. And although no international benchmarks of good governance have been established, and World Development Indicators does not report on national governance measures, research shows a strong positive correlation between the quality of institutions and economic growth. A related finding is that as countries become richer, institutions and governance do not necessarily improve. But there is a strong positive causal effect going from better governance to higher per capita incomes (Kaufmann and Kraay 2002). Despite the difficulty of measuring the quality of institutions and governance, several international and regional initiatives are under way to identify trends and the links to development: • Country Policy and Institutional Assessments by the World Bank include ratings covering economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions. Public sector management and institutions include measures of property rights and rule-based governance, quality of budgetary and financial management, efficiency of revenue mobilization, quality of public administration, and transparency, accountability, and corruption of the public sector. These assessments are calculated for World Bank member countries that are eligible for lending by the International Development Association (IDA) (see www.worldbank.org/ida). The African Development Bank conducts similar assessments. • Worldwide Governance Indicators from the World Bank Institute measure broad dimensions of governance such as voice and accountability, political instability and violence, government effectiveness, regulatory burden, rule of law, and control of corruption. The database covers 199 countries and territories and draws on 25 sources. Aggregating data from many sources reduces the measurement error from any single source. The database
5a
services, and exports and imports. (Nontax revenue is also important in some economies; see table 4.13.) A comparison of tax levels across countries provides an overview of the fiscal obligations and incentives facing the private sector. Central government tax revenues range from 2–3 percent of GDP in Myanmar to more than 35 percent in Croatia, Israel, and Slovenia (table 5.6). The level and progressivity of taxes on personal and corporate income influence incentives to work and invest. Marginal tax rates on individual income range from 0 percent to 50 percent or more. Most marginal tax rates on corporate income are in the 20–30 percent range (table 5.6).
Higher income economies often have less regulated labor markets than lower income economies Employment laws index, range 0 (less rigid) to 100 (very rigid) 80 70 60 50 40 30 20 10 0 United Kingdom Jamaica
Sri Lanka
India
Peru
Portugal
Factors such as the legal tradition (common law, French legal origin) and other political and efficiency considerations determine every country’s labor regulations. Source: Doing Business database.
includes point estimates and margins of error, to help interpret the estimates (see www.worldbank.org/wbi/governance/ govdata2002/). • Business Environment and Enterprise Performance Surveys are a joint European Bank for Reconstruction and Development and World Bank survey covering 22 countries. Survey questions cover issues related to bureaucratic red tape and corruption (see http://info.worldbank.org/governance/beeps/). • African Peer Review Mechanism (APRM), launched by the New Partnership for Africa’s Development, addresses four dimensions of governance: democracy and political governance, economic governance and management, corporate governance, and socioeconomic development. Sixteen countries have formally joined the APRM (see http://www.touchtech.biz/nepad/). • Code of Good Practices on Fiscal Transparency was adopted by the International Monetary Fund (IMF) in 1998 and updated in 2001. Countries volunteer to prepare a Fiscal Report on Standards and Codes. Key requirements of transparency covered in the reports include roles and responsibilities in government; full information disclosure to the public on fiscal activities; open procedures for budget preparation, execution, and reporting; and fiscal information prepared according to internationally accepted standards of data quality and integrity (see http://www.imf.org/external/np/rosc/rosc.asp). Government functions and policies affect many areas of social and economic life: health and education, natural resources and environmental protection, fiscal and monetary stability, and flows of trade. Data related to these topics are presented in the respective sections. This section provides data on key public sector activities: tax policies, exchange rates, and defense expenditures (tables 5.6–5.8). Taxes are the principle source of revenue for most governments. They are levied mainly on income, profits, capital gains, goods and
Tapping the benefits of infrastructure, information, and telecommunications Infrastructure has become an increasingly important part of the World Bank Group’s development agenda and is central to the Bank’s efforts to help achieve the Millennium Development Goals (tables 1.2–1.4 and World view). There is widespread recognition of the key role that infrastructure plays in helping to achieve these goals. Better quality infrastructure—and better access to it— contribute to the success of manufacturing and agricultural businesses by strengthening employment prospects, productivity, and growth. Roads, rails, power, communication, and water and sanitation systems deliver services that promote better health and education. Better housing increases people’s earning capacity and assets. And good transportation and schooling advance gender equality and the empowerment of women (table 1.5). New information and communications technologies offer vast opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances. Efficient transport is critical to the development of competitive economies (table 5.9). But measuring progress in transport is difficult. Data for most transport sectors are often not strictly comparable across countries that do not consistently follow common definitions and specifications. Moreover, the data do not indicate the quality and level of service, which depend on such factors as maintenance budgets, the availability of trained personnel, geographic and climatic conditions, and incentives and competition to provide the best service at the lowest cost. Recognizing the need for better data on infrastructure for analysis and project planning, World Bank staff are developing a new database on infrastructure. World Development Indicators will report these data as they become available. New information and communications technologies are helping people everywhere improve their quality of life by creating, using, and sharing information and knowledge (tables 5.10 and 5.11). Successful e-government applications such as Citizen Service Centers in Brazil; income tax on line in Brazil, Jordan, Mexico, and Singapore; and new business registration in China, Jamaica, and Jordan have resulted in more convenience, less corruption, lower costs, and greater transparency. The Internet has spread to every corner of the world, starting with only 8 countries online in 1988 to 209 countries by 2003. But many countries still have a long way to go. In some countries, such as Bangladesh, Chad, Ethiopia, Myanmar, and Tajikistan, only 1–2 people per 1,000 have access to the Internet (table 5.11). 2004 World Development Indicators
253
5.1
Private sector investment Domestic credit to private sector
Foreign direct investment
% of GDP
% of GDP
Investment in infrastructure projects with private participation a
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
254
Telecommunications
1990
2002
1990
2002
1990–95
.. .. 44.4 .. 15.6 40.4 64.2 91.6 10.8 16.7 .. 37.0 20.3 24.0 .. 9.4 38.9 7.2 16.8 13.7 .. 26.7 75.9 7.2 7.3 47.2 87.7 163.7 30.8 1.8 15.7 15.8 36.5 .. .. .. 52.2 27.5 13.6 30.6 20.1 .. 20.2 19.5 86.5 96.1 13.0 11.0 .. 90.6 4.9 36.3 14.2 3.5 22.0 12.6
.. 6.8 6.8 4.7 15.3 6.9 89.8 106.4 5.6 28.9 9.1 76.3 11.8 51.4 36.3 18.4 35.5 18.4 13.5 26.1 6.8 10.2 82.2 5.7 4.1 68.1 136.5 150.1 25.1 0.7 2.9 30.1 14.8 51.6 .. 33.4 146.4 40.2 27.9 60.6 40.3 32.4 29.2 26.7 60.0 87.2 12.0 17.3 8.1 118.9 12.0 67.1 19.1 3.8 3.0 18.0
.. 0.0 0.0 –3.3 1.3 .. 2.6 0.4 .. 0.0 .. 4.1 3.4 0.6 .. 2.5 0.2 0.5 0.0 0.1 0.0 –1.0 1.3 0.0 0.5 2.2 1.0 .. 1.2 –0.2 0.0 2.8 0.4 .. .. .. 0.8 1.9 1.2 1.7 0.0 .. 2.1 0.1 0.6 1.1 1.2 0.0 0.0 0.2 0.3 1.2 0.6 0.6 0.8 0.0
.. 2.8 1.9 11.7 0.8 4.7 4.1 0.4 22.9 0.1 1.7 .. 1.5 8.7 5.2 0.7 3.7 3.9 0.3 0.0 1.3 1.0 2.9 0.4 45.0 2.7 3.9 7.9 2.5 0.6 11.0 3.9 2.0 4.4 .. 13.4 3.7 4.4 5.2 0.7 1.5 3.3 4.4 1.2 6.2 3.6 2.5 12.0 4.9 1.9 0.8 0.0 0.5 0.0 0.5 0.2
.. .. .. .. 11,907.0 .. .. .. 14.0 146.0 10.0 .. .. 38.0 .. .. .. 64.0 .. 0.5 31.6 .. .. 1.1 .. 148.9 .. .. 1,551.2 .. 4.6 .. .. .. 371.0 876.0 .. 10.0 51.2 .. .. .. 211.7 .. .. .. .. .. 21.6 .. 25.0 .. 20.0 45.0 .. ..
2004 World Development Indicators
1996–2002
Energy 1990–95
1996–2002
70.0 .. 283.2 .. 501.5 2,300.0 75.3 .. 13,452.2 12,035.1 468.4 .. .. .. .. .. 144.6 .. 594.4 .. 180.3 .. .. .. 90.4 .. 808.9 252.4 .. .. 80.0 .. 70,824.6 613.6 547.3 .. 36.6 .. 15.6 .. 155.7 .. 266.1 .. .. .. .. .. 13.0 .. 1,574.8 2,260.0 13,024.7 6,113.5 .. .. 1,551.0 1,813.2 369.7 .. 111.9 .. .. 76.3 827.4 147.2 1,425.5 .. 60.0 .. 7,960.9 356.0 .. .. 433.2 372.5 728.8 .. 2,895.4 .. 910.7 106.0 40.0 .. 629.0 .. .. .. .. .. .. .. 35.0 .. 6.6 .. 43.8 .. .. .. 436.1 .. .. .. 1,673.3 134.8 75.3 36.4 .. 23.2 19.5 4.7
.. 8.0 .. .. 13,470.3 12.0 .. .. 375.2 1,040.2 500.0 .. .. 2,718.2 .. .. 48,631.8 .. 5.6 .. 123.2 91.9 .. .. .. 6,457.3 14,301.6 .. 5,762.2 .. 325.0 243.1 223.0 375.6 165.0 4,718.9 .. 1,936.3 310.0 1,378.0 879.2 .. 26.5 .. .. .. 624.8 .. 36.0 .. 132.8 .. 1,298.4 .. .. ..
Transport 1990–95
1996–2002
Water and sanitation 1990–95 1996–2002
.. .. .. .. .. .. .. .. .. .. .. .. 5,991.7 8,385.5 5,166.0 .. 50.0 .. .. .. .. .. .. .. .. .. .. .. 25.0 .. .. .. .. .. .. .. .. .. .. .. 185.3 .. .. .. .. .. .. .. 1,349.4 19,577.8 155.3 .. .. .. .. .. .. .. .. .. 120.0 72.2 .. 30.8 95.0 .. .. .. .. .. .. 0.7 .. .. .. 539.9 6,709.6 67.5 6,219.8 15,849.8 104.0 .. .. .. 1,008.8 1,597.4 .. .. .. .. .. .. .. .. 161.0 .. .. 178.0 .. .. 672.2 .. .. .. .. 263.7 126.7 36.5 .. .. .. .. 833.9 .. 12.5 886.8 .. .. 1,057.2 6.0 .. .. .. .. .. .. .. 299.4 .. .. .. .. .. .. .. .. .. .. .. 46.7 .. .. .. .. .. .. .. .. .. .. .. 10.0 .. .. .. .. .. 33.8 .. .. .. .. .. .. .. .. .. ..
.. .. .. .. 3,071.5 .. .. .. .. .. .. .. .. 682.0 .. .. 3,019.0 152.0 .. .. .. .. .. .. .. 3,886.1 1,992.4 .. 330.0 .. .. .. .. 298.7 600.0 314.6 .. .. 550.0 .. .. .. 81.0 .. .. .. .. .. .. .. .. .. .. .. .. ..
Domestic credit to private sector
Foreign direct investment
% of GDP
% of GDP
5.1
STATES AND MARKETS
Private sector investment Investment in infrastructure projects with private participation a
$ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Telecommunications
Energy
1990
2002
1990
2002
1990–95
1996–2002
1990–95
1996–2002
31.1 46.6 25.2 46.9 32.5 .. 47.6 57.6 56.5 36.1 195.1 72.3 .. 32.8 .. 65.5 52.1 .. 1.0 .. 79.4 15.8 30.9 31.0 .. .. 16.9 10.9 108.5 12.8 43.5 35.6 17.5 5.9 19.0 34.0 17.6 4.7 22.6 12.8 80.0 76.0 112.6 12.3 9.4 81.7 22.9 27.7 46.7 28.6 15.8 11.8 22.3 21.1 49.1 ..
40.7 35.3 32.6 22.3 34.3 .. 110.3 97.8 82.3 15.7 175.3 73.5 18.6 23.4 .. 115.6 73.8 4.2 8.4 29.0 90.8 14.3 3.2 18.0 14.2 17.7 9.3 4.1 146.1 17.6 31.7 61.3 12.6 17.6 18.8 54.4 2.1 12.1 48.4 30.7 147.9 118.1 30.8 5.0 17.8 86.3 38.6 27.9 97.6 13.7 24.2 23.1 36.4 28.8 147.9 ..
1.4 0.9 0.1 1.0 –0.3 .. 1.3 0.3 0.6 3.0 0.1 0.9 0.4 0.7 .. 0.3 0.0 0.0 0.7 0.6 0.2 2.8 0.0 .. 0.0 .. 0.7 1.2 5.3 0.2 0.7 1.7 1.0 0.0 .. 0.6 0.4 .. .. 0.0 3.6 4.0 0.0 1.6 2.1 0.9 1.4 0.6 2.6 4.8 1.5 0.2 1.2 0.2 3.7 ..
2.2 1.3 0.6 –0.9 0.0 .. 20.3 1.6 1.2 6.1 0.2 0.6 10.5 0.4 .. 0.4 0.0 0.3 1.5 4.5 1.5 11.3 –11.6 .. 5.2 2.0 0.2 0.3 3.4 3.0 1.2 0.6 2.3 6.8 7.0 1.2 11.3 .. .. 0.2 6.8 1.4 4.3 0.4 2.9 0.5 0.2 1.4 0.5 1.8 –0.4 4.2 1.4 2.2 3.5 ..
.. 3,510.9 720.5 3,549.0 5.0 .. .. .. .. .. .. 43.0 30.0 .. .. 2,650.0 .. .. .. 230.0 100.0 .. .. .. 74.0 .. 5.0 8.0 2,630.0 .. .. .. 18,031.0 .. 13.1 .. .. 4.0 18.0 .. .. .. 9.9 .. .. .. .. 602.0 .. .. 48.1 2,568.7 1,279.0 479.0 .. ..
71.1 5,298.9 14,950.0 9,215.5 28.0 .. .. .. .. 494.0 .. 967.9 1,849.5 107.0 .. 17,600.0 .. 94.0 185.5 894.9 550.9 33.5 .. .. 1,345.0 607.3 10.1 25.5 3,241.6 42.7 99.6 365.6 17,426.2 84.6 20.4 3,643.0 44.0 .. 4.0 45.6 .. .. 162.2 52.7 982.7 .. .. 343.0 1,429.2 .. 204.4 5,412.0 6,700.0 11,070.3 .. ..
95.3 2,156.7 2,888.5 3,202.5 .. .. .. .. .. 289.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6,909.5 0.1 .. .. 1.0 .. .. 2,300.0 .. 394.0 .. 131.4 .. .. .. .. .. .. 204.5 3,417.3 .. .. .. 1,207.8 6,831.3 145.0 .. ..
86.8 1,906.1 9,680.5 7,534.7 .. .. .. .. .. 201.0 .. .. 2,125.0 171.5 .. 2,690.0 .. .. 535.5 177.1 .. .. .. .. 20.0 .. .. .. 2,131.6 747.0 .. 109.3 5,759.1 85.3 .. 4,819.9 1,200.0 .. 5.0 137.2 .. .. 347.4 .. 225.0 .. 998.3 2,519.7 1,064.9 65.0 .. 3,095.7 7,013.1 2,154.8 .. ..
Transport 1990–95
.. 1,004.0 126.9 1,204.9 .. .. .. .. .. 30.0 .. .. .. .. .. 2,280.0 .. .. .. .. .. .. .. .. .. .. .. .. 4,657.6 .. .. .. 7,910.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 299.6 409.9 .. .. 6.6 300.0 3.1 .. ..
1996–2002
Water and sanitation 1990–95 1996–2002
130.5 .. 135.0 10.9 1,969.1 .. 2,314.6 3.8 .. .. .. .. .. .. .. .. .. .. 390.0 .. .. .. 182.0 .. .. .. 53.4 .. .. .. 5,950.0 .. .. .. .. .. 100.0 .. 75.0 .. 200.0 .. .. .. .. .. .. .. .. .. .. .. 20.3 .. 6.0 .. 7,919.0 3,986.7 .. .. .. .. 42.6 .. 5,432.5 312.1 .. .. .. .. .. .. 959.7 .. 50.0 .. 450.0 .. .. .. .. .. .. .. 104.0 .. .. .. 22.8 .. .. .. 546.1 .. 118.7 .. 806.0 .. .. .. 58.0 .. 315.8 .. 2,007.5 .. 705.9 .. .. .. .. ..
2004 World Development Indicators
220.0 167.6 216.0 919.5 .. .. .. .. .. .. .. 209.0 40.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1,105.5 .. .. .. 331.5 .. .. 1,000.0 0.6 .. .. .. .. .. .. 4.9 .. .. .. .. 25.0 175.0 .. 56.0 5,867.7 22.1 .. ..
255
5.1
Private sector investment Domestic credit to private sector
Foreign direct investment
% of GDP
% of GDP
Investment in infrastructure projects with private participation a
$ millions 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
.. .. 6.9 54.7 26.5 .. 3.2 96.8 .. 34.9 .. 81.0 80.2 19.6 4.8 20.7 124.4 167.9 7.5 .. 13.9 83.4 22.6 44.7 66.2 16.7 .. 4.0 2.6 37.4 115.8 93.5 32.4 .. 25.4 2.5 .. 6.1 8.9 23.0 97.5 w 26.5 42.9 50.3 27.3 39.3 74.0 .. 28.4 39.5 24.6 42.4 107.7 79.8
2002
8.3 0.0 17.6 0.0 10.3 0.3 58.2 .. 19.6 1.0 .. .. 3.5 5.0 115.5 15.1 40.6 .. 39.2 0.9 .. 0.6 131.7 .. 111.1 2.7 28.5 0.5 5.0 0.0 14.3 3.4 43.6 0.8 159.0 2.6 8.0 0.6 18.8 0.5 6.3 0.0 102.5 2.9 13.3 1.1 40.7 2.2 68.6 0.6 14.9 0.5 2.3 .. 6.7 0.0 18.0 0.3 55.9 .. 142.6 3.4 140.6 0.8 66.4 0.0 .. 0.1 9.8 0.9 43.1 2.8 .. .. 6.2 –2.7 6.2 6.2 37.0 –0.1 118.1 w 1.0 w 26.5 0.4 62.2 0.9 76.7 0.6 34.5 1.4 55.9 0.8 116.5 1.6 21.9 0.4 24.4 0.7 50.2 0.6 31.8 0.1 53.5 .. 133.1 1.0 102.8 1.1
a. Data refer to total for the period shown.
256
1990
2004 World Development Indicators
2002
Telecommunications 1990–95
1996–2002
Energy 1990–95
1996–2002
2.5 5.0 2,735.0 .. 100.0 0.9 918.0 6,467.2 1,100.0 2,295.3 0.2 .. 15.6 .. .. .. .. .. .. .. 1.9 .. 406.8 .. 124.0 3.0 .. 1,929.5 .. .. 0.6 .. 23.5 .. .. 7.0 .. .. .. .. 16.9 118.6 1,754.1 .. 3,184.6 8.5 .. .. .. .. .. .. 2.0 .. .. 0.7 1,072.8 10,654.8 3.0 1,244.3 3.3 .. .. .. .. 1.5 43.6 849.6 21.7 286.6 4.7 .. 6.0 .. .. 3.8 .. 33.6 .. .. 4.9 .. .. .. .. 1.3 .. .. .. .. 1.1 .. 130.0 .. .. 0.7 .. 1.0 .. .. 2.6 30.1 321.0 6.0 490.0 0.7 4,814.0 5,116.2 2,059.6 6,981.0 5.4 .. 5.0 .. .. 7.6 47.0 146.7 .. 207.0 3.8 .. 277.0 627.0 265.0 0.6 190.3 7,875.4 2,478.0 5,167.2 1.3 .. .. .. .. 2.6 8.8 204.1 .. .. 1.7 100.6 1,299.9 .. 160.0 .. .. .. .. .. 1.8 .. .. .. .. 0.4 .. .. .. .. 1.5 19.0 57.7 86.0 330.0 0.8 2.5 367.4 .. .. 0.7 4,603.3 6,446.7 .. 133.0 4.0 128.0 18.0 .. 2,215.5 .. 65.0 410.6 .. 150.0 1.1 25.0 340.0 .. .. 5.3 .. 56.9 .. 289.4 0.3 .. 46.0 .. 603.0 2.0 w .. s .. s .. s .. s 1.2 5,395.3 31,713.9 10,251.3 29,334.5 2.8 56,414.0 228,094.2 53,083.3 156,285.5 2.7 13,427.6 152,884.0 28,718.7 112,141.9 2.9 42,986.4 75,210.2 24,364.6 44,143.6 2.5 61,809.3 259,808.1 63,334.6 185,620.0 3.1 12,481.7 37,827.2 25,510.4 40,901.2 2.9 6,856.2 55,357.0 6,235.7 23,427.6 2.7 39,482.4 123,980.3 19,482.2 93,198.0 0.9 238.0 9,744.3 5,431.5 7,611.2 0.7 1,512.1 16,852.6 6,458.9 13,664.2 2.5 1,238.9 16,046.7 215.9 6,817.8 1.9 .. .. .. .. 5.0 .. .. .. ..
Transport 1990–95
1996–2002
Water and sanitation 1990–95 1996–2002
.. 23.4 .. 1,040.0 .. 515.4 .. 108.0 .. .. .. .. .. .. .. .. .. .. .. 6.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1,874.1 .. 212.5 .. .. .. .. .. 240.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 23.0 .. .. 2,395.9 546.4 153.0 347.5 .. .. .. .. .. .. .. 120.0 .. .. .. .. .. 724.8 .. 942.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 96.0 621.2 10.0 351.0 .. .. .. .. 100.0 268.0 .. 44.0 10.0 115.0 .. 212.8 .. .. .. 9.5 .. 190.0 .. .. .. .. .. .. 18.0 70.0 .. .. .. s .. s .. s .. s 1,810.2 6,518.8 4.5 1,535.1 32,299.2 80,769.9 10,008.0 27,245.8 11,323.0 47,579.9 418.3 17,377.6 20,976.2 33,190.0 9,589.7 9,868.2 34,109.4 87,288.7 10,012.5 28,780.9 14,908.2 28,974.5 4,247.5 10,620.4 1,270.8 3,327.8 47.4 3,166.0 17,455.1 46,534.7 5,710.9 13,335.7 .. 2,225.3 6.0 1,218.5 426.5 2,352.8 .. 216.0 48.8 3,873.6 0.7 224.3 .. .. .. .. .. .. .. ..
About the data
5.1
STATES AND MARKETS
Private sector investment Definitions
Private sector development and investment—that is,
and extending their delivery to poor people. The
tapping private sector initiative and investment for
privatization trend in infrastructure that began in the
cial resources provided to the private sector—such
socially useful purposes—are critical for poverty
1970s and 1980s took off in the 1990s. Developing
as through loans, purchases of nonequity securities,
reduction. In parallel with public sector efforts, pri-
countries have been at the head of this wave, pio-
and trade credits and other accounts receivable—
vate investment, especially in competitive markets,
neering better approaches to providing infrastructure
that establish a claim for repayment. For some coun-
has tremendous potential to contribute to growth.
services and reaping the benefits of greater compe-
tries
Private markets serve as the engine of productivity
tition and customer focus. In 1990–2002 more than
enterprises. • Foreign direct investment is net
growth, creating productive jobs and higher incomes.
130 developing countries introduced private partici-
inflows of investment to acquire a lasting manage-
And with government playing a complementary role of
pation in at least one infrastructure sector, awarding
ment interest (10 percent or more of voting stock) in
regulation, funding, and provision of services, private
almost 2,500 projects attracting investment commit-
an enterprise operating in an economy other than
initiative and investment can help provide the basic
ments of $750 billion.
that of the investor. It is the sum of equity capital,
services and conditions that empower the poor—by
• Domestic credit to private sector refers to finan-
these
claims
include
credit
to
public
The data on investment in infrastructure projects with
reinvestment of earnings, other long-term capital,
private participation refer to all investment (public and
and short-term capital as shown in the balance of
Credit is an important link in the money transmis-
private) in projects in which a private company
payments. • Investment in infrastructure projects
sion process; it finances production, consumption,
assumes operating risk during the operating period or
with private participation covers infrastructure proj-
and capital formation, which in turn affect the level
assumes development and operating risk during the
ects in telecommunications, energy (electricity and
of economic activity. The data on domestic credit to
contract period. Foreign state-owned companies are
natural gas transmission and distribution), transport,
the private sector are taken from the banking survey
considered private entities for the purposes of this
and water and sanitation that have reached financial
of
(IMF)
measure. The data are from the World Bank’s
closure and directly or indirectly serve the public.
International Financial Statistics or, when data are
Private Participation in Infrastructure (PPI) Project
Incinerators, movable assets, stand-alone solid
unavailable, from its monetary survey. The monetary
Database, which tracks almost 2,500 projects, newly
waste projects, and small projects such as windmills
survey includes monetary authorities (the central
owned or managed by private companies, that
are excluded. The types of projects included are
bank), deposit money banks, and other banking insti-
reached financial closure in low- and middle-income
operation and management contracts, operation and
tutions, such as finance companies, development
economies in 1990–2002. For more information, see
management contracts with major capital expendi-
banks, and savings and loan institutions. In some
http://www.worldbank.org/privatesector/ppi/ppi_
ture, greenfield projects (in which a private entity or
cases credit to the private sector may include credit
database.htm.
a public-private joint venture builds and operates a
improving health, education, and infrastructure.
the
International
Monetar y
Fund’s
to state-owned or partially state-owned enterprises.
new facility), and divestiture.
The statistics on foreign direct investment are based on balance of payments data reported by the IMF, supplemented by data on net foreign direct investment reported by the Organisation for Economic Co-operation and Development and official national sources. (For a detailed discussion of data on foreign direct investment, see About the data for table 6.7). Private participation in infrastructure has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services,
5.1a Foreign direct investment has expanded rapidly in many developing countries, contributing to increased productivity
Data sources The data on domestic credit are from the IMF’s
Net inflows of foreign direct investment (% of GDP)
International Financial Statistics. The data on foreign
20
Slovak Republic
15
direct investment are based on estimates compiled by the IMF in its Balance of Payments Statistics Yearbook, supplemented by World Bank staff
Czech Republic 10 Moldova
estimates. The data on investment in infrastructure projects with private participation are from the
5 Togo 0
World Bank’s Private Participation in Infrastructure (PPI) Project Database (http://www.worldbank.org/
1995
1996
1997
1998
1999
2000
2001
2002
privatesector/ppi/ppi_database.htm).
Source: World Bank data files.
2004 World Development Indicators
257
5.2
Investment climate Credit markets Creditor rights index range 0 (weak) to 4 (strong) January 2003
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
258
.. 3 1 3 1 2 3 3 3 2 2 2 1 2 3 3 1 3 1 1 2 1 1 2 1 2 2 4 0 2 0 1 1 3 .. 3 3 2 1 1 3 .. .. 3 1 0 .. .. .. 3 1 1 1 1 .. 2
Public registry coverage borrowers per 1,000 adults January 2003
.. 0 0 19 202 0 0 10 0 2 .. 82 2 88 0 0 60 6 2 1 0 1 0 1 0 284 4 0 0 0 0 10 2 0 .. 12 0 .. 121 .. 197 .. .. 0 0 15 .. .. 0 6 0 0 0 0 .. 2
2004 World Development Indicators
Private bureau coverage borrowers per 1,000 adults January 2003
.. 0 0 0 645 0 897 366 0 0 0 50 0 213 80 615 602 0 0 0 0 0 976 0 0 1,000 0 242 269 0 0 78 0 0 .. 163 71 617 0 0 192 .. .. 0 842 0 .. .. 0 813 1 100 65 0 .. 0
Composite ICRG risk rating a
Institutional Investor credit rating a
Euromoney country creditworthiness rating a
December 2003
September 2003
September 2003
.. 66.8 66.3 53.3 64.0 62.3 81.8 86.0 69.0 63.0 65.3 85.3 .. 65.8 .. 79.8 65.5 71.8 57.8 .. .. 62.0 85.8 .. .. 77.0 77.3 82.0 63.5 47.0 48.8 72.0 55.8 72.0 60.3 77.8 85.5 60.3 62.5 66.0 69.8 .. 75.0 59.3 86.8 79.0 65.3 67.0 .. 81.8 62.8 76.0 67.0 62.0 46.5 52.0
7.6 17.0 41.6 17.0 18.4 17.9 84.3 90.3 30.4 28.6 17.5 87.2 20.2 27.5 26.0 62.2 37.1 47.0 17.8 10.5 18.7 19.9 90.3 12.8 14.4 65.2 59.9 67.8 37.2 7.3 12.6 44.4 15.7 50.9 12.3 65.6 91.0 36.6 24.2 41.1 46.4 12.0 61.5 16.1 90.6 91.7 22.7 17.8 18.4 86.8 25.8 73.1 32.3 16.5 10.6 15.8
7.8 34.5 41.3 26.9 25.8 33.8 91.7 92.4 43.3 38.3 32.0 90.9 30.9 37.5 35.6 60.3 47.6 50.7 31.0 25.0 35.0 31.3 92.1 26.2 27.7 66.3 61.5 80.6 47.2 18.4 26.7 54.8 26.6 57.1 12.0 66.1 95.3 43.4 36.2 49.2 49.3 26.0 64.5 29.4 93.8 91.1 34.1 32.2 26.5 90.3 35.0 80.7 44.6 27.2 23.0 24.4
Moody’s sovereign long-term debt rating a Foreign currency January 2004
.. .. .. .. Caa1 .. Aaa Aaa .. .. .. Aa1 .. B3 .. A2 B2 Ba2 .. .. .. .. Aaa .. .. Baa1 A2 A1 Ba2 .. .. Ba1 .. Baa3 Caa1 A1 Aaa B2 Caa2 Ba1 Baa3 .. A1 .. Aaa Aaa .. .. .. Aaa .. A1 Ba2 .. .. ..
Domestic currency January 2004
.. .. .. .. B3 .. Aaa Aaa .. .. .. Aa1 .. B3 .. A1 B2 Ba2 .. .. .. .. Aaa .. .. A1 .. Aa3 Baa2 .. .. Ba1 .. Baa1 .. A1 Aaa B2 Caa1 Baa1 Baa2 .. A1 .. Aaa Aaa .. .. .. Aaa .. A1 Ba1 .. .. ..
Standard & Poor’s sovereign long-term debt rating a
Foreign currency January 2004
Domestic currency January 2004
.. .. .. .. SD .. AAA AAA .. .. .. AA+ B+ B– .. A B+ BB+ .. .. .. B AAA .. .. A BBB A+ BB .. .. BB .. BBB– .. A– AAA CCC CCC+ BB+ BB+ .. A– .. AAA AAA .. .. .. AAA B+ A+ BB– .. .. ..
.. .. .. .. SD .. AAA AAA .. .. .. AA+ B+ B– .. A+ BB BBB .. .. .. B AAA .. .. AA .. AA– BBB .. .. BB+ .. BBB+ .. A+ AAA CCC CCC+ BBB– BB+ .. A– .. AAA AAA .. .. .. AAA B+ A+ BB .. .. ..
Credit markets Creditor rights index range 0 (weak) to 4 (strong) January 2003
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
2 2 3 2 2 .. 1 3 1 2 2 1 .. 4 .. 3 2 .. 0 .. 4 2 .. .. .. 3 2 2 2 1 3 .. 0 .. 1 1 2 .. .. 2 3 4 4 1 4 2 0 1 4 2 2 0 1 2 1 1
Public registry coverage borrowers per 1,000 adults January 2003
74 0 0 4 .. .. 0 0 63 0 0 30 0 0 .. 0 0 0 0 0 0 0 .. .. 9 3 3 0 154 1 0 .. 0 0 23 .. 1 .. 0 0 0 0 83 1 0 0 0 1 0 0 0 133 0 0 610 0
Private bureau coverage borrowers per 1,000 adults January 2003
0 17 0 0 0 .. 917 64 482 0 907 0 0 526 .. 672 207 0 0 0 0 0 .. .. 0 0 0 0 676 0 0 .. 562 0 0 0 0 .. .. 0 645 1,000 0 0 0 1,000 0 0 428 0 .. 267 33 665 30 643
Composite ICRG risk rating a
Institutional Investor credit rating a
Euromoney country creditworthiness rating a
December 2003
September 2003
September 2003
62.3 76.5 69.0 60.8 70.5 42.0 87.3 72.5 80.0 69.5 86.5 71.0 72.3 65.8 53.5 80.8 86.3 .. .. 78.3 55.5 .. 36.0 74.0 76.5 .. 60.0 54.0 75.3 58.5 .. .. 71.5 64.5 63.8 75.0 61.3 59.5 76.5 .. 85.0 81.8 54.3 57.5 57.0 90.5 81.0 63.5 71.5 59.0 62.5 68.3 69.3 75.0 78.5 ..
25.3 65.4 48.0 30.3 36.6 8.4 87.5 53.4 83.1 27.8 77.2 38.5 41.4 24.6 7.5 68.5 79.2 16.7 19.8 51.5 25.2 29.5 6.6 34.2 55.6 25.3 15.8 18.8 61.7 18.4 18.6 53.9 54.8 18.7 22.9 49.4 20.6 13.5 39.8 23.8 92.2 81.1 18.0 14.7 20.2 92.9 56.5 26.2 45.0 28.9 22.4 38.3 43.8 61.1 80.4 ..
39.2 68.8 54.9 40.0 45.1 4.3 92.3 68.0 86.9 43.3 90.0 44.1 50.3 36.1 3.3 67.7 73.9 28.1 33.0 62.1 38.1 33.7 11.6 21.9 62.0 36.1 28.0 31.3 62.1 30.4 26.7 54.9 61.1 31.5 37.3 53.8 32.5 20.4 24.5 37.2 93.5 87.1 24.2 30.5 33.5 97.8 61.3 32.0 49.8 37.3 34.7 45.5 51.3 64.0 84.3 ..
Moody’s sovereign long-term debt rating a Foreign currency January 2004
B2 A1 Ba1 B2 .. .. Aaa A2 Aa2 B1 Aa1 Ba2 Baa3 .. .. A3 A2 .. .. A2 B2 .. .. .. A3 .. .. .. Baa1 .. .. Baa2 Baa2 Caa1 .. Ba1 .. .. .. .. Aaa Aaa Caa1 .. .. Aaa Baa2 B2 Ba1 B1 Caa1 Ba3 Ba1 A2 Aa2 ..
Domestic currency January 2004
B2 A1 Ba2 B2 .. .. Aaa A2 Aa2 Ba2 A2 Baa3 Baa1 .. .. A3 A2 .. .. A2 B3 .. .. .. A3 .. .. .. A3 .. .. A2 Baa1 Caa1 .. Ba1 .. .. .. .. Aaa Aaa B3 .. .. Aaa Baa2 B2 .. B1 Caa1 Baa3 Baa3 A2 Aa2 ..
5.2
STATES AND MARKETS
Investment climate
Standard & Poor’s sovereign long-term debt rating a
Foreign currency January 2004
Domestic currency January 2004
.. A– BB B .. .. AAA A– AA B AA– BB BB+ .. .. A– A+ .. .. BBB+ B– .. .. .. BBB+ .. .. .. A– .. .. .. BBB– .. B BB .. .. .. .. AAA AA+ .. .. .. AAA BBB B BB B SD BB– BB BBB+ AA ..
.. A BB+ B+ .. .. AAA A+ AA B+ AA– BBB BBB .. .. A+ A+ .. .. A– B– .. .. .. A– .. .. .. A+ .. .. .. A– .. B BBB .. .. .. .. AAA AAA .. .. .. AAA BBB+ BB– BB B+ CCC BB+ BBB A– AA ..
2004 World Development Indicators
259
5.2
Investment climate Credit markets Creditor rights index range 0 (weak) to 4 (strong) January 2003
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
0 2 1 2 1 2 2 3 .. 3 .. 3 2 2 .. .. 1 1 .. .. 2 3 2 .. 0 2 .. .. 2 2 4 1 .. 2 2 0 .. 0 1 4 2u 2 2 2 2 2 2 2 2 1 2 2 2 2
Public registry coverage borrowers per 1,000 adults January 2003
Private bureau coverage borrowers per 1,000 adults January 2003
1 0 1 0 3 0 0 0 3 16 .. 0 344 12 .. .. 0 0 0 .. 0 0 .. .. 6 10 .. 0 0 15 0 0 65 0 141 3 .. 12 0 0 24 u 4 32 24 46 18 19 2 77 6 3 1 40 103
0 0 0 0 0 0 0 640 0 0 .. 684 55 0 .. .. 592 213 0 .. 0 127 0 .. 0 266 .. 0 0 0 813 1,000 630 0 0 0 .. 0 0 0 182 u 11 168 100 288 93 84 50 293 0 0 57 491 391
Composite ICRG risk rating a
December 2003
70.5 75.0 .. 76.5 64.8 55.3 51.3 87.5 74.3 79.5 45.5 68.8 80.0 63.5 54.3 .. 86.5 91.0 70.3 .. 57.8 76.5 58.3 76.5 72.8 62.8 .. 62.3 68.8 84.5 83.8 75.8 64.5 .. 58.3 69.8 .. 67.0 53.0 34.3 68.9 m 58.8 70.4 68.3 75.0 65.1 66.6 72.0 65.0 70.5 63.5 58.0 83.4 81.8
Institutional Investor credit rating a
Euromoney country creditworthiness rating a
September 2003
September 2003
41.3 45.1 8.2 52.4 29.2 16.1 8.5 84.2 57.8 69.2 6.5 54.6 85.7 34.1 10.5 30.7 89.3 94.0 22.7 14.3 21.8 56.9 17.3 54.2 52.6 32.4 20.8 20.1 32.5 64.7 92.3 92.8 27.3 20.5 27.1 37.7 .. 24.3 15.3 11.0 30.4 m 17.9 39.8 36.6 54.0 25.3 29.6 32.5 30.0 38.5 27.4 17.5 86.3 87.2
49.8 49.0 24.2 65.7 39.6 31.5 22.2 89.1 59.1 76.1 13.2 60.4 87.2 44.3 26.4 33.1 93.8 97.5 33.5 29.9 37.0 59.5 28.1 61.0 57.7 45.2 32.2 37.8 39.0 72.3 93.9 96.6 39.8 33.9 34.6 47.8 .. 33.0 26.3 22.6 39.6 m 30.1 47.2 44.1 60.6 35.6 38.7 44.3 43.3 44.1 37.8 28.7 90.6 90.9
Moody’s sovereign long-term debt rating a Foreign currency January 2004
Ba3 Baa3 .. Baa2 .. .. .. Aaa A3 Aa3 .. Baa2 Aaa .. .. .. Aaa Aaa .. .. .. Baa1 .. Baa3 Baa2 B1 B2 .. B1 A2 Aaa Aaa B3 .. Caa1 B1 .. .. .. ..
Standard & Poor’s sovereign long-term debt rating a
Domestic currency January 2004
Foreign currency January 2004
Domestic currency January 2004
Ba3 Baa3 .. Baa1 .. .. .. Aaa A3 Aa3 .. A2 Aaa .. .. .. Aaa Aaa .. .. .. Baa1 .. Baa1 Baa2 B3 B2 .. B1 .. Aaa Aaa B3 .. Caa1 .. .. .. .. ..
BB BB .. A+ B+ ..
BB+ BB+ .. A+ B+ ..
AAA BBB A+ .. BBB AA+ .. .. .. AA+ AAA .. .. .. BBB .. BBB BBB B+ .. .. B .. AAA AAA B– .. B– BB– .. .. .. ..
AAA A– AA .. A AA+ .. .. .. AAA AAA .. .. .. A .. A– A B+ .. .. B .. AAA AAA B– .. B– BB .. .. .. ..
a. This copyrighted material is reprinted with permission from the following data providers: PRS Group, Inc., 6320 Fly Road, Suite 102, East Syracuse, NY 13057; Institutional Investor Inc., 225 Park Ave. South, New York, NY 10003; Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, UK; Moody’s Investors Service, 99 Church Street, New York, NY 10007; and Standard & Poor’s Rating Services, The McGraw-Hill Companies, Inc., 1221 Avenue of the Americas, New York, NY 10020. Prior written consent from the original data providers cited must be obtained for third-party use of these data.
260
2004 World Development Indicators
About the data
5.2
STATES AND MARKETS
Investment climate Definitions
This year the table includes newly developed meas-
be highly subjective, reflecting external perceptions
• Creditor rights index measures four powers of
ures of the credit market: a creditor rights index, pub-
that do not always capture the actual situation in a
secured lenders in liquidation and reorganization:
lic credit registry coverage, and private credit bureau
country. But these subjective perceptions are the
there are restrictions on entering reorganization,
coverage. The data are from the World Bank’s Doing
reality that policymakers face. Countries not rated by
there is no automatic stay (or asset freeze), secured
Business database.
credit risk rating agencies typically do not attract reg-
creditors are paid first, and management does not
As investment portfolios become increasingly glob-
istered flows of private capital. The risk ratings pre-
stay in reorganization. • Public registry coverage and
al, investors as well as governments seeking to attract
sented here are included for their analytical
private bureau coverage measure the number of bor-
investment must have a good understanding of the
usefulness and are not endorsed by the World Bank.
rowers with records contained in either the public
investment climate. This table includes data on credit
The PRS Group’s International Country Risk Guide
credit registry or private credit bureau, expressed as
market risks and indicators of creditworthiness ratings
(ICRG) collects information on 22 components of
a percentage of the adult population. A score of 0
from several major international rating services.
risk, groups it into three major categories (political,
indicates that a public registry or private bureau does
Lack of access to credit is one of the biggest bar-
financial, and economic), and converts it into a sin-
not operate in the country. The maximum score is
riers entrepreneurs face in starting and operating a
gle numerical risk assessment ranging from 0 to
1,000. • Composite International Country Risk
business. And this in turn affects growth in the econ-
100. Ratings below 50 indicate very high risk, and
Guide (ICRG) risk rating is an overall index, ranging
omy and opportunities for improved livelihoods.
those above 80 very low risk. Ratings are updated
from 0 to 100 (highest risk to lowest), based on 22
Information on credit histories made available in
monthly.
components of risk. • Institutional Investor credit
credit registries is one way for creditors to assess
Institutional Investor country credit ratings are
rating ranks, from 0 to 100 (highest risk to lowest),
risk and allocate credit more efficiently. Information
based on information provided by leading interna-
the chances of a country’s default. • Euromoney
on creditor rights and how well collateral systems
tional banks. Responses are weighted using a for-
country creditworthiness rating ranks, from 0 to 100
facilitate access to credit offers an additional insti-
mula that gives more importance to responses from
(highest risk to lowest), the risk of investing in an
tutional solution to expanding credit. The creditor
banks with greater worldwide exposure and more
economy. • Moody’s sovereign foreign or domestic
rights index is an indicator of the powers of secured
sophisticated country analysis systems. Countries
currency long-term debt rating assesses the risk of
lenders in liquidation and reorganization. This com-
are rated on a scale of 0 to 100 (highest risk to low-
lending to governments. An entity’s capacity to meet
posite index captures information on issues related
est), and ratings are updated every six months.
its senior financial obligations is rated from AAA
to reorganization of insolvent companies, the ability
Euromoney country creditworthiness ratings are
(offering exceptional financial security) to C (usually in
of secured creditors to seize collateral if there is a
based on nine weighted categories (covering debt,
default, with potential recovery values low). Modifiers
reorganization, whether secured creditors are paid
economic performance, political risk, and access to
1–3 are applied to ratings from AA to B, with 1 indi-
first from proceeds from liquidating a bankrupt firm,
financial and capital markets) that assess country
cating a high ranking in the rating categor y.
and whether management remains in power during a
risk. The ratings, also on a scale of 0 to 100 (highest
• Standard & Poor’s sovereign foreign or domestic
reorganization. The index ranges from 0 for weak
risk to lowest), are based on polls of economists and
currency long-term debt rating ranges from AAA
creditor rights to 4 for strong creditor rights. A public
political analysts supplemented by quantitative data
(extremely strong capacity to meet financial commit-
credit registry is a database owned by public author-
such as debt ratios and access to capital markets.
ments) to CCC (currently highly vulnerable). Ratings
ities (usually the central bank or banking superviso-
Moody’s sovereign long-term debt ratings are opin-
from AA to CCC may be modified by a plus or minus
ry) that collect information on the standing of
ions of the capacity of entities to honor senior unse-
sign to show relative standing in the category. An
borrowers in the financial system and make it avail-
cured
obligor rated SD (selective default) has failed to pay
able to financial institutions. A private credit bureau
denominated in foreign currency (foreign currency
is a private firm or nonprofit organization that main-
issuer ratings) or in domestic currency (domestic cur-
tains a database on the standing of borrowers in the
rency issuer ratings).
financial
obligations
and
contracts
one or more financial obligations when due.
financial system. Its primary role is to facilitate
Standard & Poor’s ratings of sovereign long-term
exchange of information among banks and financial
foreign and domestic currency debt are based on
institutions. Coverage of public credit registries and
current information furnished by obligors or obtained
Data sources
private credit bureaus provides an indication of how
by Standard & Poor’s from other sources it considers
The data on credit markets are from the World
many borrowers, as a percentage of the adult popu-
reliable. A Standard & Poor’s issuer credit rating
Bank’s Doing Business project (http://rru.
lation, have information on their payment histories
(one form of which is a sovereign credit rating) is a
worldbank.org/DoingBusiness/). The country risk
available in credit registries. A score of 0 indicates
current opinion of an obligor’s capacity and willing-
and creditworthiness ratings are from the PRS
that a public registry or private bureau does not oper-
ness to pay its financial obligations as they come
Group’s monthly International Country Risk Guide
ate in the country. The maximum score is 1,000.
due (its creditworthiness). This opinion does not
(http://www.ICRGonline.com); the monthly Institu-
Most risk ratings are numerical or alphabetical
apply to any specific financial obligation, as it does
tional Investor; the monthly Euromoney; Moody’s
indexes, with a higher number or a letter closer to the
not take into account the nature and provisions of
Investors Service’s Sovereign, Subnational and
beginning of the alphabet meaning lower risk (a good
obligations, their standing in bankruptcy or liquida-
Sovereign-Guaranteed Issuers; and Standard &
prospect). (For more on the rating processes of the
tion, statutory preferences, or the legality and
Poor’s Sovereign List in Credit Week.
rating agencies, see Data sources.) Risk ratings may
enforceability of obligations.
2004 World Development Indicators
261
5.3
Business environment Entry regulations
Contract enforcement
Insolvency
Cost to Time to
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
262
Time
enforce a
Time to
resolve
Employment
Number of
start
Cost to
Minimum
Procedures
to enforce
contract
resolve
insolvency
laws index
start-up
a business
register
capital
to enforce
a contract
% of
insolvency
% of insol-
range
procedures
days
a business
requirement
a contract
days
GNI per capita
years
vency estate
0 (less rigid) to
January
January
January
January
January
January
January
January
January
100 (very rigid)
2003
2003
2003
2003
2003
2003
2003
2003
2003
January 2003
.. 11 18 14 15 10 2 9 14 7 19 7 9 18 12 10 15 10 15 11 11 12 2 .. 19 10 11 5 19 13 8 11 10 13 .. 10 4 12 14 13 12 .. .. 8 4 10 .. .. 9 9 10 16 13 13 .. 12
.. 47 29 146 68 25 2 29 106 30 118 56 63 67 59 97 152 30 136 17 94 37 3 .. 73 28 46 11 60 215 67 80 77 50 .. 88 4 78 90 43 115 .. .. 44 33 53 .. .. 30 45 84 45 39 71 .. 203
.. 65 32 838 8 9 2 7 17 76 27 11 189 167 52 36 12 8 325 253 554 191 1 .. 395 12 14 2 27 872 271 21 143 18 .. 12 0 48 63 61 130 .. .. 422 3 3 .. .. 26 6 112 70 67 229 .. 199
.. 52 73 174 0 11 0 141 0 0 111 75 378 0 379 0 0 134 652 0 1,826 244 0 .. 652 0 3,856 0 0 321 205 0 235 51 .. 110 52 23 28 789 550 .. .. 1,756 32 32 .. .. 140 104 1 145 37 397 .. 210
.. 37 20 46 32 22 11 20 25 15 19 22 44 44 31 22 16 26 24 62 18 46 17 .. 50 21 20 17 37 55 44 21 18 20 .. 16 14 19 33 19 42 .. .. 24 19 21 .. .. 17 22 21 15 19 41 .. 41
.. 220 387 865 300 65 320 434 115 270 135 365 248 464 630 56 380 410 376 367 210 548 425 .. 604 200 180 180 527 414 500 370 150 330 .. 270 83 495 332 202 240 .. .. 895 240 210 .. .. 180 154 90 315 1,460 150 .. 76
.. 73 13 16 9 15 8 1 3 270 44 9 31 5 21 0 2 6 173 28 269 63 28 0 58 15 32 7 6 92 51 23 83 7 .. 19 4 441 11 31 7 .. .. 35 16 4 .. .. 63 6 24 8 20 40 .. 18
.. 2.0 3.5 .. 2.8 1.9 1.0 1.3 2.7 .. 2.2 0.9 3.2 2.0 1.9 2.2 10.0 3.8 4.0 .. .. 2.0 0.8 .. 10.0 5.8 2.6 1.0 3.0 .. 3.0 2.5 2.2 3.1 .. 9.2 4.2 3.5 3.5 4.3 .. .. .. 2.2 0.9 2.4 .. .. 3.2 1.2 1.6 2.2 4.0 .. .. ..
.. 38 4 .. 18 4 18 18 8 .. 4 4 18 18 8 18 8 18 8 .. .. 18 4 .. 38 18 18 18 1 .. 18 18 18 18 .. 38 8 4 18 18 .. .. .. 8 1 18 .. .. 1 8 18 8 18 .. .. ..
.. 41 46 78 66 57 36 30 63 50 77 48 52 66 49 35 78 53 53 62 54 44 34 62 66 50 47 27 59 60 60 63 53 65 .. 36 25 49 55 59 69 .. .. 51 55 50 .. .. 55 51 35 67 65 60 .. 60
2004 World Development Indicators
% of GNI per capita
Labor regulations
Cost to
Entry regulations
Contract enforcement
Insolvency
Cost to Time to
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
% of GNI per capita
STATES AND MARKETS
5.3
Business environment
Labor regulations
Cost to
Time
enforce a
Time to
resolve
Employment
Number of
start
Cost to
Minimum
Procedures
to enforce
contract
resolve
insolvency
laws index
start-up
a business
register
capital
to enforce
a contract
% of
insolvency
% of insol-
range
procedures
days
a business
requirement
a contract
days
GNI per capita
years
vency estate
0 (less rigid) to
January
January
January
January
January
January
January
January
January
100 (very rigid)
2003
2003
2003
2003
2003
2003
2003
2003
2003
January 2003
14 5 10 11 9 .. 3 5 9 7 11 14 10 11 .. 12 12 9 9 7 6 9 .. .. 9 13 15 11 8 13 11 .. 7 11 8 11 15 .. 10 8 7 3 12 11 10 4 9 10 7 7 18 9 11 12 11 6
80 65 88 168 48 .. 12 34 23 31 31 98 25 61 .. 33 33 26 198 11 46 92 .. .. 26 48 67 45 31 61 73 .. 51 42 31 36 153 .. 85 25 11 3 71 27 44 24 34 22 19 69 73 100 59 31 95 6
73 64 50 15 7 .. 10 5 24 16 11 50 10 54 .. 18 2 13 20 15 130 68 .. .. 6 13 63 125 27 232 110 .. 19 26 12 19 100 .. 19 191 14 0 338 447 92 4 5 47 26 26 156 25 24 20 13 3
165 220 430 303 7 .. 0 0 50 0 71 2,404 35 0 .. 403 911 75 151 93 83 20 .. .. 74 138 31 0 0 598 897 .. 88 86 2,047 763 30 .. 0 0 71 0 0 844 29 33 721 0 0 0 0 0 10 21 43 0
32 17 11 0 23 .. 16 19 16 14 16 32 41 25 .. 23 17 44 .. 19 27 .. .. .. 17 27 29 16 22 27 .. .. 47 36 26 17 18 .. .. 24 21 19 17 29 23 12 17 30 44 22 46 35 28 18 22 55
225 365 365 225 150 .. 183 315 645 202 60 147 120 255 .. 75 195 365 .. 189 721 .. .. .. 74 509 166 108 270 150 .. .. 325 210 224 192 540 .. .. 350 39 50 125 365 730 87 250 365 197 270 188 441 164 1,000 420 365
7 5 95 269 6 .. 7 34 4 42 6 0 8 50 .. 5 4 255 0 8 54 0 .. .. 13 43 120 521 19 7 0 .. 10 14 2 9 9 .. 0 44 1 12 18 57 7 10 5 46 20 41 34 30 104 11 5 21
.. 2.0 11.3 6.0 1.8 .. 0.4 4.0 1.3 1.1 0.6 4.3 3.3 4.6 .. 1.5 4.2 4.0 .. 1.2 4.0 .. .. .. 1.2 3.6 2.2 2.8 2.2 3.5 8.0 .. 2.0 2.8 4.0 1.9 .. .. .. 5.0 2.6 2.0 2.3 5.0 1.6 0.9 7.0 2.8 6.5 .. 3.9 2.1 5.7 1.5 2.6 3.8
.. 38 8 18 8 .. 8 38 18 18 4 8 18 18 .. 4 1 4 .. 4 18 .. .. .. 18 38 18 8 18 18 8 .. 18 8 8 18 .. .. .. 8 1 4 8 18 18 1 4 4 38 .. 8 8 38 18 8 8
56 54 51 57 52 .. 49 38 59 34 37 60 55 34 .. 51 41 64 54 62 46 45 .. .. 64 50 61 52 25 54 59 .. 77 67 50 51 74 .. 43 45 54 32 61 59 43 41 54 58 79 26 73 73 60 55 79 41
2004 World Development Indicators
263
5.3
Business environment Entry regulations
Contract enforcement
Insolvency
Cost to Time to
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
264
Time
enforce a
Time to
resolve
Employment
Number of
start
Cost to
Minimum
Procedures
to enforce
contract
resolve
insolvency
laws index
start-up
a business
register
capital
to enforce
a contract
% of
insolvency
% of insol-
range
procedures
days
a business
requirement
a contract
days
GNI per capita
years
vency estate
0 (less rigid) to
January
January
January
January
January
January
January
January
January
100 (very rigid)
2003
2003
2003
2003
2003
2003
2003
2003
2003
January 2003
12 9 232 131 124 13 1,298 1 10 16 .. 9 19 18 .. .. 1 9 17 .. 199 7 281 .. 16 37 .. 135 27 25 1 1 47 16 19 30 .. 264 24 285 93 u 213 36 38 33 118 73 22 74 67 76 255 9 16
3 30 457 1,611 296 357 0 0 112 89 .. 0 20 0 .. .. 41 34 5,627 .. 0 0 531 .. 352 13 .. 0 451 404 0 0 699 64 0 0 .. 1,717 138 0 297 u 339 369 455 204 355 819 115 90 1,286 86 278 99 65
6 12 9 14 9 10 9 7 10 10 .. 9 11 8 .. .. 3 6 10 .. 13 9 14 .. 10 13 .. 17 14 10 6 5 10 9 14 11 .. 13 6 10 10 u 11 11 12 10 11 10 11 12 12 9 11 7 9
27 29 43 95 58 44 26 8 98 61 .. 38 115 58 .. .. 16 20 42 .. 35 42 63 .. 46 38 .. 36 40 29 18 4 27 33 119 63 .. 96 40 122 57 u 74 57 58 56 65 80 47 78 56 45 72 30 47
2004 World Development Indicators
% of GNI per capita
Labor regulations
Cost to
28 16 0 19 30 40 48 23 26 22 .. 26 20 17 .. .. 19 14 36 .. 14 19 43 .. 14 18 .. 16 20 27 12 17 38 34 41 28 .. 27 16 13 25 u 28 26 27 25 27 20 26 32 23 19 30 19 19
225 160 .. 195 335 1,028 114 50 420 1,003 .. 207 147 440 .. .. 190 224 596 .. 127 210 503 .. 7 105 .. 99 224 559 101 365 360 258 360 120 .. 240 188 197 307 u 304 332 333 329 319 208 317 363 281 358 334 267 287
13 20 87 0 49 20 8 14 13 4 .. 17 11 8 .. .. 8 4 31 .. 4 30 21 .. 4 5 .. 10 11 11 1 0 14 2 47 9 .. 1 16 40 36 u 65 27 32 15 44 77 29 39 14 93 52 8 6
3.2 1.5 .. 3.0 3.0 7.3 2.5 0.7 4.8 3.7 .. 2.0 1.5 2.3 .. .. 2.0 4.6 4.1 .. 3.0 2.6 .. .. 2.5 1.8 .. 2.0 3.0 5.0 1.0 3.0 4.0 3.3 4.0 2.0 .. 2.4 3.7 2.3 3.2 u 3.8 3.4 3.3 3.6 3.6 3.8 3.2 3.7 3.5 5.3 3.5 2.1 1.6
8 4 .. 18 8 38 38 1 18 18 .. 18 8 18 .. .. 8 4 8 .. 8 38 .. .. 8 8 .. 38 18 38 8 4 8 4 38 18 .. 4 8 18 14 u 13 17 15 20 15 23 15 16 11 10 18 10 9
54 61 60 36 54 56 67 20 61 59 .. 36 70 42 .. .. 42 36 45 .. 61 61 57 .. 57 55 .. 42 73 45 28 22 39 55 75 56 .. 43 46 27 53 u 54 56 56 55 55 49 58 62 50 49 53 44 56
About the data
5.3
STATES AND MARKETS
Business environment Definitions
The table presents key indicators on the environment
distressed companies is the insolvency system. Two
• Start-up procedures are those required to start a
for doing business. The indicators, covering entry
indicators measure the time it takes to resolve insol-
business. Procedures are interactions of a company
regulations, contract enforcement, insolvency, and
vency and the associated costs. With effective insol-
with external parties (government agencies, lawyers,
labor regulations, identify regulations that enhance
vency systems, one may expect greater access and
auditors, notaries, and the like), including interactions
or constrain business investment, productivity, and
better allocation of credit.
required to obtain necessary permits and licenses
growth. The data are from the World Bank’s Doing
All economies have labor regulations intended to
and to complete all inscriptions, verifications, and
protect the interests of workers and to guarantee a
notifications to start operations. Data are for busi-
A vibrant private sector is central to promoting
minimum standard of living. These laws and institu-
nesses with specific characteristics of ownership,
growth and expanding opportunities for poor people.
tions encompass four bodies of law: employment
size, and type of production. • Time to start a busi-
But encouraging firms to invest, improve productivity,
laws, industrial relations laws, occupational health
ness is the time, measured in calendar days, needed
and create jobs requires a legal and regulatory envi-
and safety laws, and social security laws. The
to complete the required procedures for legally oper-
ronment that fosters access to credit, protection of
employment laws index is a simple average of the
ating a business. If a procedure can be speeded up
property rights, and efficient judicial, taxation, and
flexibility of hiring index, the conditions of employ-
at additional cost, the fastest procedure, independ-
customs systems. The indicators in the table point to
ment index, and the flexibility of firing index; each
ent of cost, is chosen. Time spent gathering informa-
the administrative and regulatory reforms and insti-
index has values between 0 and 100, with higher val-
tion about the registration process is excluded.
tutions needed to create a favorable environment for
ues indicating more rigid regulation. Flexibility of hir-
• Cost to register a business is normalized by pre-
doing business.
ing covers the availability of part-time, fixed-term,
senting it as a percentage of gross national income
Business database.
When entrepreneurs start a business, the first
and family members’ contracts. Conditions of
(GNI) per capita. • Minimum capital requirement is
obstacles they face are the administrative and legal
employment cover working time requirements, includ-
the amount that an entrepreneur needs to deposit in
procedures required to register the new firm.
ing mandatory minimum daily rest, maximum number
a bank to obtain a company registration number. The
Countries differ widely in how they regulate the entry
of hours in a normal work week, premium for over-
amount is typically specified in the commercial code
of new businesses. In some countries the process is
time work, and restrictions on weekly holidays;
or company law and is often returned to the entre-
straightforward and affordable. But in others the pro-
mandatory payment for nonworking days, which
preneur only when the company is dissolved.
cedures are so burdensome that entrepreneurs may
includes days of annual leave with pay and paid time
• Procedures to enforce a contract are independent
opt to run their business informally.
off for holidays; and minimum wage legislation.
actions, each defined as a procedure (mandated by
The data on entry regulations are derived from a
Flexibility of firing covers workers’ legal protections
law or court regulation) that demands interaction
survey of the procedures that a typical domestic
against dismissal, including the grounds for dis-
between the parties or between them and the judge
limited-liability company must complete before legal-
missal; procedures for dismissal (individual and col-
or court officer. • Time to enforce a contract is the
ly starting operation. The data cover the number and
lective); notice period; and severance payment.
number of calendar days from the time the plaintiff
duration of start-up procedures, the cost to register
To ensure cross-country comparability, several
files the lawsuit in court until the time of final deter-
standard characteristics of a company are defined in
mination and, in appropriate cases, payment. • Cost
Contract enforcement is critical to enable busi-
all surveys, such as size, ownership, location, legal
to enforce a contract includes filing fees, court
nesses to engage with new borrowers or customers.
status, and type of activities undertaken. The data
costs, and estimated attorney fees. • Time to
Without good contract enforcement, trade and credit
were collected through a study of laws and regula-
resolve insolvency is the number of years from the
will be restricted to a small community of people who
tions in each country, surveys of regulators or private
time of filing for insolvency in court until the time of
have developed relationships through repeated deal-
sector professionals on each topic, and cooperative
resolution of distressed assets. • Cost to resolve
ings or through the security of assets. The institution
arrangements with private consulting firms and busi-
insolvency includes filing fees, court costs, attorney
that enforces contracts between debtors and credi-
ness and law associations.
fees, and payments to other professionals (account-
a business, and the minimum capital requirement.
tors, and suppliers and customers, is the court. The efficiency of contract enforcement is reflected
ants, assessors), out of the insolvency estate. The costs are averages of the estimates of survey respon-
in three indicators: the number of judicial procedures
dents, who chose among six options: 0–2 percent,
to resolve a dispute, the time it takes to enforce a
3–5 percent, 6–10 percent, 11–25 percent, 26–50
commercial contract, and the associated costs. The
percent, and more than 50 percent. • Employment
data are derived from structured surveys answered
laws index is a composite index of three aspects of
by attorneys at private law firms. The questionnaires
labor regulations: flexibility of hiring, conditions of
cover the step-by-step evolution of a commercial
employment, and flexibility of firing. The index ranges
case before local courts in the country’s largest city.
from 0 (less rigid) to 100 (more rigid).
The continuing existence of unviable competitors is consistently rated by firms as one of the greatest
Data source
potential barriers to operation and growth. The insti-
All data are from the World Bank’s Doing Business
tution that deals with the exit of unviable companies
project (http://rru.worldbank.org/DoingBusiness/).
and the rehabilitation of viable but financially
2004 World Development Indicators
265
5.4
Stock markets Market capitalization
Market liquidity
Turnover ratio
Listed domestic companies
S&P/IFC Investable index
value of shares $ millions 1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
266
.. .. .. .. 3,270 .. 109,000 11,500 .. 321 .. 65,400 .. .. .. 261 16,400 .. .. .. .. .. 242,000 .. .. 13,600 2,030 83,400 1,420 .. .. 475 549 .. .. .. 39,100 .. 69 1,760 .. .. .. .. 22,700 314,000 .. .. .. 355,000 76 15,200 .. .. .. ..
% of GDP 2003
.. .. .. .. 38,927 .. 380,969 31,664 .. 1,622 .. 127,556 .. 1,560 .. 2,131 234,560 1,755 .. .. .. .. 575,316 .. .. 86,291 681,204 463,108 14,258 .. .. .. 1,650 6,126 .. 17,663 76,788 .. 2,153 27,073 1,520 .. 3,790 .. 138,833 966,962 .. .. .. 685,970 1,426 68,741 232 .. .. ..
2004 World Development Indicators
1990
.. .. .. .. 2.3 .. 35.1 7.1 .. 1.1 .. 33.2 .. .. .. 6.6 3.6 .. .. .. .. .. 42.1 .. .. 44.9 0.5 110.6 3.5 .. .. 5.5 5.1 .. .. .. 29.3 .. 0.6 4.1 .. .. .. .. 16.5 25.8 .. .. .. 21.2 1.2 18.1 .. .. .. ..
value traded
traded as % of
as % of GDP
market capitalization
2002
1990
.. .. .. .. 100.9 .. 93.1 15.5 .. 2.5 .. 52.0 .. 19.4 .. 32.6 27.4 4.7 .. .. .. .. 80.5 .. .. 74.2 36.6 286.7 11.9 .. .. .. 11.4 17.7 .. 22.9 44.4 .. 7.2 29.0 11.0 .. 37.3 .. 105.6 67.6 .. .. .. 34.6 12.0 51.8 1.1 .. .. ..
.. .. .. .. 0.6 .. 12.9 11.5 .. 0.0 .. 3.3 .. .. .. 0.2 1.2 .. .. .. .. .. 12.4 .. .. 2.6 0.2 45.9 0.2 .. .. .. 0.2 .. .. .. 8.3 .. .. 0.3 .. .. .. .. 2.9 9.6 .. .. .. 30.0 .. 4.7 .. .. .. ..
% change in number
price index
2002
1990
2003
1990
2003
.. .. .. .. 1.3 .. 72.0 2.9 .. 1.4 .. 13.8 .. 0.0 .. 1.0 10.7 1.1 .. .. .. .. 56.8 .. .. 4.9 26.3 130.4 0.3 .. .. .. 0.1 0.7 .. 8.8 29.8 .. 0.1 2.8 0.2 .. 3.7 .. 134.2 65.3 .. .. .. 62.1 0.2 18.7 0.0 .. .. ..
.. .. .. .. 33.6 .. 31.6 110.3 .. 1.5 .. .. .. .. .. 6.1 23.6 .. .. .. .. .. 26.7 .. .. 6.3 158.9 43.1 5.6 .. .. 5.8 3.4 .. .. .. 28.0 .. .. .. .. .. .. .. .. .. .. .. .. 139.3 .. 36.3 .. .. .. ..
.. .. .. .. 1.7 .. 77.2 21.3 .. 3.5 .. 247.9 .. 0.1 .. 1.1 3.4 2.0 .. .. .. .. 68.2 .. .. 0.9 11.5 43.5 0.6 .. .. .. 0.6 0.7 .. 6.0 60.3 .. 0.2 1.6 1.5 .. 1.6 .. 106.8 88.0 .. .. .. 140.5 0.2 26.0 3.1 .. .. ..
.. .. .. .. 179 .. 1,089 97 .. 134 .. 182 .. .. .. 9 581 .. .. .. .. .. 1,144 .. .. 215 14 284 80 .. .. 82 23 2 .. .. 258 .. 65 573 .. .. .. .. 73 578 .. .. .. 413 13 145 .. .. .. ..
.. .. .. .. 107 .. 1,355 91 .. 247 .. 143 .. 29 .. 19 367 356 .. .. .. .. 3,756 .. .. 240 1,296 968 114 .. .. .. 38 66 .. 63 201 .. 30 967 32 .. 14 .. 147 772 .. .. .. 715 25 341 10 .. .. ..
2002
2003
.. .. .. .. –51.4 .. .. .. .. –4.2 a .. .. .. .. .. 31.1 a –33.0 62.5 a .. .. .. .. .. .. .. –14.8 –14.5 .. 9.7 a .. .. .. 17.4 a 44.2 a .. 38.9 .. .. 23.4 a –5.8 .. .. 66.3 a .. .. .. .. .. .. .. 27.6 a –31.2 .. .. .. ..
.. .. .. .. 131.4 .. .. .. .. 15.4 a .. .. .. .. .. 25.6 a 105.4 189.2 a .. .. .. .. .. .. .. 79.5 77.7 .. 27.3 a .. .. .. 27.4 a 12.8 a .. 54.4 .. .. 14.6 a 79.3 .. .. 41.5 a .. .. .. .. .. .. .. 65.4 a .. .. .. .. ..
Market capitalization
Market liquidity
Turnover ratio
Listed domestic companies
5.4
STATES AND MARKETS
Stock markets
S&P/IFC Investable index
value of shares $ millions 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
40 505 38,600 8,080 34,300 .. .. 3,320 149,000 911 2,920,000 2,000 .. 453 .. 111,000 .. .. .. .. .. .. .. .. .. .. .. .. 48,600 .. .. 268 32,700 .. .. 966 .. .. 21 .. 120,000 8,840 .. .. 1,370 26,100 1,060 2,850 226 .. .. 812 5,930 144 9,200 ..
% of GDP 2003
.. 16,729 279,093 54,659 9,700 .. 59,938 75,719 477,075 8,500 2,126,075 10,963 1,200 4,178 .. 329,616 .. .. .. 1,141 1,497 .. .. .. 3,510 46 .. 156 168,376 .. 1,090 1,955 122,532 350 .. 13,152 .. .. 308 .. 401,465 21,745 .. .. 9,494 67,300 5,014 16,579 2,600 .. .. 16,055 23,565 37,165 42,846 ..
1990
1.3 1.5 12.2 7.1 .. .. .. 6.3 13.5 19.8 95.6 49.7 .. 5.3 .. 43.9 .. .. .. .. .. .. .. .. .. .. .. .. 110.4 .. .. 11.2 12.4 .. .. 3.7 .. .. 0.7 .. 40.8 20.3 .. .. 4.8 22.5 9.4 7.1 3.4 .. .. 3.1 13.4 0.2 12.9 ..
value traded
traded as % of
as % of GDP
market capitalization
2002
1990
.. 19.9 25.7 17.3 8.5 .. 49.4 43.8 40.3 74.2 53.2 76.2 5.4 11.5 .. 52.2 56.1 .. .. 8.5 8.1 .. .. .. 10.6 1.3 .. 9.2 130.7 .. 113.3 29.3 16.2 23.7 .. 23.8 .. .. 5.9 14.6 96.1 37.1 .. .. 13.2 35.3 19.7 17.3 21.6 .. .. 23.7 50.0 15.2 35.2 ..
.. 0.3 6.9 3.5 .. .. .. 10.5 3.9 0.7 52.5 10.1 .. 0.1 .. 30.1 .. .. .. .. .. .. .. .. .. .. .. .. 24.7 .. .. 0.3 4.6 .. .. 0.2 .. .. .. .. 13.7 4.4 .. .. 0.0 12.1 0.9 0.6 0.0 .. .. 0.4 2.7 0.0 2.4 ..
% change in number
price index
2002
1990
2003
1990
2003
.. 9.0 38.6 7.5 1.0 .. 27.1 53.3 45.6 1.8 39.4 14.4 1.4 0.3 .. 166.2 11.4 .. .. 1.5 0.7 .. .. .. 1.3 0.1 .. 1.3 29.1 .. .. 1.3 4.4 14.2 .. 1.6 .. .. 0.0 0.6 110.6 12.8 .. .. 1.1 25.7 2.6 44.1 0.4 .. .. 2.0 4.0 3.1 16.7 ..
.. 6.3 65.9 75.8 30.4 .. .. 95.8 26.8 3.4 43.8 20.0 .. 2.2 .. 61.3 .. .. .. .. .. .. .. .. .. .. .. .. 24.6 .. .. 1.9 44.0 .. .. .. .. .. .. .. 29.0 17.3 .. .. 0.9 54.4 12.3 8.7 0.9 .. .. 19.3 13.6 89.7 16.9 ..
.. 4.6 14.1 3.8 11.3 .. 50.5 5.9 109.1 0.3 71.0 3.6 26.5 0.7 .. 17.8 .. .. .. 5.0 0.6 .. .. .. 0.8 4.3 .. 13.8 3.3 .. .. 0.3 1.5 60.1 .. 0.9 .. .. 0.0 .. 123.7 38.3 .. .. 0.9 67.8 2.1 40.1 1.7 .. .. 0.5 0.9 2.2 52.4 ..
26 21 2,435 125 97 .. .. 216 220 44 2,071 105 .. 54 .. 669 .. .. .. .. .. .. .. .. .. .. .. .. 282 .. .. 13 199 .. .. 71 .. .. 3 .. 260 171 .. .. 131 112 55 487 13 .. .. 294 153 9 181 ..
.. 49 5,644 333 316 .. 62 576 295 39 3,058 161 31 51 .. 1,563 .. .. .. 56 13 .. .. .. 48 2 .. 8 897 .. 40 40 159 22 .. 53 .. .. 13 .. 180 149 .. .. 200 179 96 701 29 .. .. 197 234 203 63 ..
2002
2003
.. 34.6 6.8 33.3 .. .. .. –26.6 .. 40.0 a –10.1 b –2.1 a .. 42.2 a .. 5.8 .. .. .. –14.1 a 5.7 .. .. .. 25.7 a .. .. .. –2.6 .. .. 22.9 a –16.4 .. .. –8.1 .. .. 22.5 .. .. .. .. .. –0.3 a .. 31.8 112.0 a .. .. .. 33.5 –19.7 2.2 .. ..
.. 28.6 76.5 69.7 .. .. .. 59.5 .. –3.4 a 37.8 b 65.4 a .. 186.2 a .. 33.3 .. .. .. 62.6 a 0.9 a .. .. .. 117.9 a .. .. .. 25.5 .. .. 43.7 a 30.4 .. .. 44.0 .. .. 37.1 a .. .. .. .. .. 57.5 a .. 47.0 a 50.4 a .. .. .. 88.1 41.4 29.5 .. ..
2004 World Development Indicators
267
5.4
Stock markets Market capitalization
Market liquidity
Turnover ratio
Listed domestic companies
S&P/IFC Investable index
value of shares $ millions 1990
Romania .. Russian Federation 244 Rwanda .. Saudi Arabia 48,200 Senegal .. Serbia and Montenegro .. Sierra Leone .. Singapore 34,300 Slovak Republic .. Slovenia .. Somalia .. South Africa 138,000 Spain 111,000 Sri Lanka 917 Sudan .. Swaziland 17 Sweden 97,900 Switzerland 160,000 Syrian Arab Republic .. Tajikistan .. Tanzania .. Thailand 23,900 Togo .. Trinidad and Tobago 696 Tunisia 533 Turkey 19,100 Turkmenistan .. Uganda .. Ukraine .. United Arab Emirates .. United Kingdom 849,000 United States 3,060,000 Uruguay .. Uzbekistan .. Venezuela, RB 8,360 Vietnam .. West Bank and Gaza .. Yemen, Rep. .. Zambia .. Zimbabwe 2,400 World 9,403,525 s Low income 54,623 Middle income 320,160 Lower middle income 212,666 Upper middle income 107,494 Low & middle income 374,783 East Asia & Pacific 86,510 Europe & Central Asia 19,100 Latin America & Carib. 78,169 Middle East & N. Africa 5,259 South Asia 42,688 Sub-Saharan Africa 143,057 High income 9,028,742 Europe EMU 1,183,500
% of GDP 2003
1990
5,584 .. 230,786 0.0 .. .. 157,302 36.7 .. .. .. .. .. .. 101,900 92.9 2,779 .. 5,209 .. .. .. 267,745 123.2 461,559 21.8 2,711 11.4 .. .. 127 1.9 177,065 39.8 553,758 70.0 .. .. .. .. 398 .. 120,887 28.0 .. .. 10,605 13.7 2,464 4.3 68,379 12.7 .. .. 36 .. 4,303 .. 7,881 .. 1,864,134 85.8 11,052,403 53.2 153 .. .. .. 3,820 17.2 .. .. 723 .. .. .. 217 .. 4,975 27.3 23,359,484 s 48.0 w 197,220 9.8 1,639,528 20.0 1,099,924 15.5 539,604 29.6 1,836,748 18.8 702,100 16.4 234,597 2.2 418,720 7.7 124,210 27.4 144,070 10.8 213,051 52.2 21,522,735 51.6 3,485,194 21.7
2002
10.0 35.8 .. 39.7 .. .. .. 117.2 8.0 21.0 .. 177.5 70.7 10.1 .. 10.0 73.7 207.1 .. .. 4.3 36.3 .. 67.6 10.1 18.5 .. 0.6 7.5 11.4 119.0 106.4 0.8 0.6 4.2 .. 17.9 .. 6.0 187.9 74.6 w 22.6 35.3 36.6 33.0 33.3 40.4 22.7 27.4 26.1 22.7 47.3 83.4 52.4
value traded
traded as % of
as % of GDP
market capitalization
1990
2002
.. 0.9 .. 10.4 .. .. 1.7 18.9 .. .. .. .. .. .. 55.0 64.5 .. 3.3 .. 0.5 .. .. 7.3 75.6 8.0 155.3 0.5 1.9 .. .. .. 0.6 7.1 90.9 29.6 245.6 .. .. .. .. .. 0.1 26.8 37.5 .. .. 1.1 1.8 0.2 1.1 3.9 38.5 .. .. .. .. .. 0.3 .. 0.0 28.2 173.7 30.5 244.4 .. 0.0 .. 0.2 4.6 0.1 .. .. .. 1.9 .. .. .. 1.3 0.6 29.9 28.5 w 122.8 w 4.7 27.5 5.2 16.0 9.0 20.8 6.1 7.1 5.2 17.8 6.6 24.4 .. 12.3 2.1 5.4 2.2 6.0 5.6 35.4 .. 32.4 31.4 145.2 14.2 67.4
1990
.. .. .. .. .. .. .. .. .. .. .. .. .. 5.8 .. .. 14.9 .. .. .. .. 92.6 .. 10.0 3.3 42.5 .. .. .. .. 33.4 53.4 .. .. 43.0 .. .. .. .. 2.9 57.1 w 53.8 .. .. 50.3 .. 118.1 .. 29.8 .. 54.0 .. 59.4 ..
2003
% change in number 1990
0.5 .. 3.0 13 .. .. 10.3 59 .. .. .. .. .. .. 39.3 150 1.9 .. 1.4 24 .. .. 3.6 732 211.1 427 1.2 175 .. .. 6.7 1 96.2 258 100.5 182 .. .. .. .. 1.9 .. 18.2 214 .. .. 0.6 30 0.9 13 28.5 110 .. .. .. .. 0.5 .. 3.4 .. 135.4 1,701 202.5 6,599 0.5 36 .. .. 0.6 76 .. .. 10.3 .. .. .. 22.5 .. 1.2 57 123.0 w 25,424 s 139.6 3,446 44.1 4,231 56.3 3,146 23.2 1,085 57.8 7,677 72.7 774 53.6 110 21.7 1,734 19.6 817 180.3 3,231 23.7 1,011 137.9 17,747 106.1 2,630
Note: Aggregates for market capitalization are unavailable for 2003; those shown refer to 2002. a. Data refer to the S&P/IFC Global index. b. Data refer to the Nikkei 225 index. c. Data refer to the FT 100 index. d. Data refer to the S&P 500 index.
268
2004 World Development Indicators
price index 2003
4,484 214 .. 70 .. .. .. 434 306 32 .. 426 2,986 244 .. 5 278 258 .. .. 4 405 .. 35 46 284 .. 2 149 12 1,701 5,685 15 .. 54 .. 24 .. 9 81 47,576 s 7,322 13,307 10,725 2,582 20,629 3,132 6,781 1,381 1,585 6,839 911 26,947 5,843
2002
2003
96.7 a 34.8 .. 3.8 a .. .. .. .. 23.6 a 78.3 a .. 44.9 .. 28.4 a .. .. .. .. .. .. .. 18.3 .. 33.2 a –2.5 a –33.5 .. .. 26.7 a .. –16.5 c –23.4 d .. .. –35.1 a .. .. .. .. 97.9 a
42.5 a 68.5 .. 49.5 a .. .. .. .. 57.2 a 42.1 a .. 37.6 .. 35.6 a .. .. .. .. .. .. .. 147.2 .. 46.7 a 14.9 a 113.2 .. .. 40.3 a .. 26.3 c 26.4 d .. .. 14.3 a .. .. .. .. –74.8 a
About the data
5.4
STATES AND MARKETS
Stock markets Definitions
The development of an economy’s financial markets is
Market liquidity, the ability to easily buy and sell secu-
• Market capitalization (also known as market
closely related to its overall development. Well function-
rities, is measured by dividing the total value traded by
value) is the share price times the number of shares
ing financial systems provide good and easily accessi-
GDP. This indicator complements the market capitaliza-
outstanding. • Market liquidity is the total value
ble information. That lowers transaction costs, which in
tion ratio by showing whether market size is matched by
traded divided by GDP. Value traded is the total value
turn improves resource allocation and boosts economic
trading. The turnover ratio—the value of shares traded
of shares traded during the period. • Turnover ratio
growth. Both banking systems and stock markets
as a percentage of market capitalization—is also a
is the total value of shares traded during the period
enhance growth, the main factor in poverty reduction. At
measure of liquidity as well as of transaction costs.
divided by the average market capitalization for the
low levels of economic development commercial banks
(High turnover indicates low transaction costs.) The
period. Average market capitalization is calculated as
tend to dominate the financial system, while at higher
turnover ratio complements the ratio of value traded to
the average of the end-of-period values for the cur-
levels domestic stock markets tend to become more
GDP, because the turnover ratio is related to the size of
rent period and the previous period. • Listed domes-
active and efficient relative to domestic banks.
the market and the value traded ratio to the size of the
tic companies are the domestically incorporated
Open economies with sound macroeconomic poli-
economy. A small, liquid market will have a high
companies listed on the country’s stock exchanges
cies, good legal systems, and shareholder protection
turnover ratio but a low value traded ratio. Liquidity is an
at the end of the year. This indicator does not include
attract capital and therefore have larger financial mar-
important attribute of stock markets because, in theo-
investment companies, mutual funds, or other col-
kets. Recent research on stock market development
ry, liquid markets improve the allocation of capital and
lective investment vehicles. • S&P/IFC Investable
shows that new communications technology and
enhance prospects for long-term economic growth. A
index price change is the U.S. dollar price change in
increased financial integration have resulted in more
more comprehensive measure of liquidity would include
the stock markets covered by the S&P/IFCI country
cross-border capital flows, a stronger presence of
trading costs and the time and uncertainty in finding a
index, supplemented by the S&P/IFCG country index.
financial firms around the world, and the migration of
counterpart in settling trades.
stock exchange activities to international exchanges.
Standard & Poor’s maintains a series of indexes
Many firms in emerging markets now cross-list on inter-
for investors interested in investing in stock markets
national exchanges, which provides them with lower
in developing countries. At the core of the Standard
cost capital and more liquidity-traded shares. However,
& Poor’s family of emerging market indexes, the
this also means that exchanges in emerging markets
S&P/IFCG index is intended to represent the most
may not have enough financial activity to sustain them,
active stocks in the markets it covers and to be the
putting pressure on them to rethink their operations.
broadest possible indicator of market movements.
The stock market indicators in the table include
The S&P/IFCI index, which applies the same calcula-
measures of size (market capitalization, number of
tion methodology as the S&P/IFCG index, is
listed domestic companies) and liquidity (value traded
designed to measure the returns foreign portfolio
as a percentage of gross domestic product, value of
investors might receive from investing in emerging
shares traded as a percentage of market capitaliza-
market stocks that are legally and practically open to
tion). The comparability of such indicators between
foreign portfolio investment.
countries may be limited by conceptual and statistical
Standard & Poor’s Emerging Markets Data Base,
weaknesses, such as inaccurate reporting and differ-
the source for all the data in the table, provides reg-
ences in accounting standards. The percentage
ular updates on 54 emerging stock markets encom-
change in stock market prices in U.S. dollars, from the
passing more than 2,200 stocks. The S&P/IFCG
Standard & Poor’s Investable (S&P/IFCI) and Global
index includes 34 markets and more than 1,900
(S&P/IFCG) country indexes, is an important measure
stocks, and the S&P/IFCI index covers 30 markets
of overall performance. Regulatory and institutional
and close to 1,200 stocks. These indexes are wide-
factors that can affect investor confidence, such as
ly used benchmarks for international portfolio man-
entry and exit restrictions, the existence of a securi-
agement. See Standard & Poor’s (2001b) for further
Data sources
ties and exchange commission, and the quality of laws
information on the indexes.
The data on stock markets are from Standard &
Because markets included in Standard & Poor’s
Poor’s Emerging Stock Markets Factbook 2003,
emerging markets category vary widely in level of
which draws on the Emerging Markets Data Base,
Stock market size can be measured in a number of
development, it is best to look at the entire category
supplemented by other data from Standard &
ways, and each may produce a different ranking of
to identify the most significant market trends. And it
Poor’s. The firm collects data through an annual
countries. Market capitalization shows the overall
is useful to remember that stock market trends may
survey of the world’s stock exchanges, supple-
size of the stock market in U.S. dollars and as a per-
be distorted by currency conversions, especially when
mented by information provided by its network of
centage of GDP. The number of listed domestic com-
a currency has registered a significant devaluation.
correspondents and by Reuters. The GDP data
to protect investors, may influence the functioning of stock markets but are not included in the table.
panies is another measure of market size. Market
About the data is based on Demirgüç-Kunt and
size is positively correlated with the ability to mobi-
Levine (1996a), Beck and Levine (2001), and
lize capital and diversify risk.
Claessens, Klingebiel, and Schmukler (2002).
are from the World Bank’s national accounts data files.
2004 World Development Indicators
269
5.5
Financial depth and efficiency Domestic credit provided by banking sector
% of GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
270
Liquid liabilities
% of GDP
Quasi-liquid liabilities
Ratio of bank liquid reserves to bank assets
% of GDP
Interest rate spread
Risk premium on lending
Lending minus
Prime lending
deposit rate
rate minus
percentage
treasury bill rate
points
percentage points
%
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
.. .. 74.5 .. 32.4 58.7 71.4 121.4 65.9 23.9 .. 73.1 22.4 30.7 .. –46.0 89.8 118.5 12.1 23.2 .. 31.2 82.3 12.9 11.5 73.0 90.0 154.9 35.9 25.3 29.1 29.9 44.5 .. .. .. 63.0 31.5 15.5 106.8 32.0 .. 66.7 66.8 82.8 104.4 20.0 3.4 .. 104.4 17.5 99.3 17.4 6.0 77.5 34.3
.. 43.6 29.1 5.5 62.4 7.3 93.9 124.3 8.7 40.2 17.5 115.4 5.8 62.3 35.8 –29.6 64.8 23.7 12.7 35.1 6.0 16.3 92.6 13.2 10.9 77.6 166.4 144.5 36.5 0.2 11.4 36.9 20.7 63.8 .. 45.8 156.6 44.8 28.0 109.9 49.4 148.9 49.6 58.0 64.7 105.0 18.8 26.3 19.6 144.7 31.9 109.5 15.7 12.5 16.1 37.3
.. .. 73.5 .. 11.5 79.9 55.0 .. 38.6 23.4 .. .. 26.7 24.5 .. 21.9 26.4 71.9 18.8 18.2 .. 22.6 74.3 15.3 14.6 40.8 79.2 179.4 29.8 12.9 22.0 42.7 28.8 .. .. .. 59.0 28.6 21.1 87.9 30.6 .. 136.0 42.2 54.3 .. 17.8 20.7 .. 69.6 14.1 .. 21.2 9.2 68.9 32.6
.. 61.5 49.0 22.2 27.9 15.6 71.0 .. 13.3 39.1 15.4 .. 26.6 49.1 46.3 28.5 33.1 41.9 18.2 22.3 18.4 20.2 78.4 14.4 13.5 40.0 178.3 238.9 31.8 4.8 13.9 39.8 29.4 65.7 .. 75.5 51.0 39.5 24.8 94.1 42.7 152.5 42.0 52.9 .. .. 17.3 45.1 11.7 .. 30.7 .. 30.6 13.0 60.9 42.8
.. .. 24.8 .. 7.1 42.9 43.2 .. 13.4 16.8 .. .. 5.9 18.0 .. 13.6 18.5 53.6 6.6 6.5 .. 10.1 59.8 1.8 0.6 32.8 41.4 164.7 19.3 2.1 6.1 30.0 10.9 .. .. .. 29.4 13.3 11.6 60.7 19.6 .. 95.2 12.6 .. .. 6.6 8.8 .. .. 3.4 .. 11.8 1.1 4.4 16.6
.. 38.9 19.7 15.3 18.9 7.1 47.6 .. 6.7 29.8 10.1 .. 7.1 40.7 19.1 20.9 25.0 24.8 7.0 7.3 13.2 8.0 54.4 1.4 0.8 30.0 108.9 219.8 21.2 1.8 1.0 25.9 7.9 48.2 .. 39.2 19.4 28.5 15.6 74.5 35.1 86.0 16.8 26.5 .. .. 7.3 20.4 5.6 .. 14.2 .. 18.1 2.3 0.9 28.8
.. .. 1.3 .. 7.4 13.6 1.5 2.1 4.5 12.8 .. 0.2 29.3 18.8 .. 11.0 7.6 10.2 12.7 2.8 .. 3.4 1.6 2.8 3.3 3.6 15.7 0.1 27.4 49.0 2.0 68.5 2.1 .. .. .. 1.1 31.2 22.6 17.1 27.3 .. 43.1 24.0 4.1 1.0 2.0 8.8 .. 3.2 20.2 13.9 31.8 6.2 10.8 74.9
.. 10.5 12.5 14.5 9.5 11.4 1.2 .. 10.2 8.6 7.7 .. 20.9 5.8 10.5 3.8 23.6 8.9 6.8 3.9 71.3 28.0 0.6 2.5 25.2 3.0 12.1 0.2 6.7 6.2 17.0 12.3 6.2 14.3 .. 3.8 1.2 18.8 3.3 17.1 9.5 24.1 9.6 13.6 .. .. 8.9 13.7 14.4 .. 11.2 17.2 22.0 27.5 17.2 40.0
.. 2.1 .. .. .. .. 4.5 .. .. 4.0 .. 6.9 9.0 18.0 .. 1.8 .. 8.9 9.0 .. .. 11.0 4.2 11.0 11.0 8.5 0.7 3.3 8.8 .. 11.0 11.4 9.0 499.3 .. .. 6.2 15.2 –6.0 7.0 3.2 .. .. 3.6 4.1 6.1 11.0 15.2 .. 4.5 .. 8.1 5.1 0.2 13.1 ..
.. 6.8 3.3 48.6 12.4 11.5 5.0 .. 8.7 7.8 10.0 5.1 .. 11.1 8.2 5.7 43.7 6.6 .. .. 13.7 13.0 3.4 13.0 13.0 4.0 3.3 4.7 7.4 .. 13.0 15.0 .. 11.0 .. 4.0 4.7 9.5 9.6 4.5 4.6 .. 4.0 4.6 3.3 3.6 13.0 11.3 22.0 7.0 .. 4.7 9.9 11.9 .. 17.4
.. .. .. .. .. .. 4.0 .. .. .. .. 3.4 .. .. .. .. .. 8.6 .. .. .. .. 1.3 .. .. .. .. 2.7 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.0 .. 0.4 .. .. .. 3.5 .. 3.6 .. .. .. ..
.. 5.8 6.7 .. .. 6.4 3.3 .. 3.3 .. .. 4.5 .. 8.2 .. .. 43.4 6.5 .. .. .. .. 1.6 .. .. .. .. 3.7 .. .. .. .. .. .. .. 3.5 .. .. .. 8.3 .. .. .. 7.4 .. 2.7 .. .. –11.6 6.7 .. 3.9 .. 4.7 .. 18.1
2004 World Development Indicators
Domestic credit provided by banking sector
% of GDP
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Liquid liabilities
% of GDP
Quasi-liquid liabilities
Ratio of bank liquid reserves to bank assets
% of GDP
5.5
STATES AND MARKETS
Financial depth and efficiency Interest rate spread
Risk premium on lending
Lending minus
Prime lending
deposit rate
rate minus
percentage
treasury bill rate
points
percentage points
%
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
40.9 105.5 51.5 45.5 70.8 .. 55.2 106.2 89.4 32.2 259.6 117.9 .. 52.9 .. 65.7 243.0 .. 5.1 .. 132.6 32.8 319.5 104.1 .. .. 26.2 19.7 75.7 13.7 54.7 48.4 36.6 62.8 73.4 60.1 15.6 32.8 20.3 28.9 103.6 80.6 206.6 16.2 23.7 89.0 16.6 50.9 52.7 35.7 14.9 20.2 26.9 19.5 69.4 ..
34.2 53.0 58.5 59.5 47.6 .. 110.6 93.6 99.6 27.6 312.5 90.4 13.0 43.2 .. 116.9 105.8 11.4 12.6 39.6 196.1 10.7 168.7 50.3 18.3 15.9 18.4 14.3 154.2 16.4 –8.3 77.1 26.6 29.7 17.1 84.5 13.2 35.1 49.0 43.2 160.4 118.2 93.0 8.5 25.3 54.0 40.3 43.5 90.7 26.3 29.3 23.9 60.5 36.2 149.9 ..
33.6 43.8 43.1 40.4 57.6 .. 44.5 70.2 70.5 47.2 182.3 131.2 .. 43.3 .. 54.6 192.2 .. 7.2 .. 193.7 39.2 101.9 68.1 .. .. 17.8 20.2 118.0 20.5 28.5 67.9 22.8 70.3 56.2 61.0 26.5 27.9 24.3 32.2 .. 77.0 56.9 19.8 23.6 59.5 28.9 39.8 41.1 35.2 22.3 24.8 37.0 34.0 .. ..
56.8 47.2 63.2 54.9 44.5 .. .. 104.6 .. 49.3 201.5 120.2 19.2 42.6 .. 103.7 89.8 14.7 19.6 36.5 217.9 28.8 8.4 41.3 29.3 28.6 24.3 16.2 128.5 26.6 16.0 87.2 24.5 30.5 37.8 89.4 32.7 33.5 40.2 51.5 .. 89.2 40.3 9.0 30.5 55.7 35.4 54.8 76.4 30.2 36.7 32.4 63.0 42.7 .. ..
18.8 19.0 28.1 29.1 31.1 .. .. 63.6 .. 35.0 155.3 77.8 .. 29.3 .. 45.7 153.9 .. 3.1 .. 170.9 22.6 20.8 13.7 .. .. 5.3 10.8 97.8 5.5 7.0 52.7 16.4 35.4 14.7 18.4 5.2 7.8 14.2 18.5 .. 64.0 23.1 8.3 10.3 26.8 19.3 10.0 33.0 24.0 13.7 11.8 28.4 17.2 .. ..
43.6 27.8 45.7 43.2 25.6 .. .. 96.7 .. 33.7 132.0 85.7 9.1 27.2 .. 93.1 70.6 4.4 16.4 16.3 208.1 9.7 1.5 9.0 12.8 17.3 5.5 7.4 103.0 5.8 5.0 73.9 14.5 14.4 22.7 21.0 19.0 13.1 18.3 34.9 .. 74.4 34.4 2.7 12.4 8.8 25.5 23.9 65.6 15.3 28.0 21.3 51.1 28.1 .. ..
6.7 11.2 14.8 4.2 66.0 .. 4.8 11.9 12.0 37.4 1.6 20.5 .. 9.9 .. 6.3 1.2 .. 3.4 .. 3.9 23.0 67.3 26.4 .. .. 8.5 32.9 5.9 50.8 6.1 8.8 4.3 8.3 2.0 11.3 61.5 286.7 4.4 12.7 0.3 0.8 20.2 42.9 11.9 0.5 6.9 8.9 .. 3.2 31.0 22.0 20.9 20.6 29.0 ..
23.0 5.2 5.6 11.1 26.8 .. .. 8.9 .. 22.3 3.7 27.1 4.4 8.2 .. 2.6 1.1 11.3 26.5 5.9 18.8 6.2 56.3 24.0 10.9 7.5 23.3 23.0 12.5 18.0 4.0 5.1 11.1 16.5 15.8 8.1 14.2 16.8 2.9 22.2 .. 0.5 30.9 19.0 17.9 4.7 4.2 9.0 .. 9.6 24.1 25.4 8.5 5.6 .. ..
8.3 4.1 .. 3.3 .. .. 5.0 12.0 7.3 6.6 3.4 2.2 .. 5.1 .. 0.0 0.0 .. 2.5 .. 23.1 7.4 0.0 1.5 .. .. 5.3 8.9 1.3 9.0 5.0 5.4 .. .. .. 0.5 .. 2.1 10.6 2.5 8.4 4.4 12.5 9.0 5.5 4.6 1.4 .. 3.6 6.9 8.1 2,335.0 4.6 462.5 7.8 ..
8.9 2.8 .. 3.4 .. .. 3.7 3.9 4.3 9.9 1.8 5.8 .. 13.0 .. 1.8 3.3 18.9 23.3 4.7 5.5 11.9 14.0 4.0 5.1 8.8 13.3 22.5 3.2 .. .. 11.1 4.4 9.3 15.2 8.6 8.7 5.5 6.0 2.9 1.2 4.5 15.8 .. 8.1 2.1 5.7 .. 5.6 8.1 15.8 10.5 4.5 5.9 .. ..
.. –1.4 .. .. .. .. 0.4 11.4 1.7 4.3 .. .. .. 4.0 .. .. 0.0 .. .. .. 21.1 4.1 .. 1.5 .. .. .. 8.1 1.1 .. .. .. .. .. .. .. .. .. 6.3 6.5 .. 2.2 .. .. 6.9 .. .. .. .. 4.1 .. .. 0.4 –5.0 8.3 ..
.. 1.3 .. .. .. .. .. 2.5 2.5 3.0 .. .. .. 9.5 .. .. .. 14.7 7.9 4.5 5.7 5.8 .. 1.5 3.1 .. 15.0 8.8 3.7 .. .. .. 1.1 17.6 .. .. –2.0 .. 2.8 2.7 .. 4.3 .. .. 5.7 .. .. .. .. 3.0 .. .. 3.6 3.4 .. ..
2004 World Development Indicators
271
5.5
Financial depth and efficiency Domestic credit provided by banking sector
% of GDP 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
272
79.7 .. 17.1 52.7 33.8 .. 36.3 75.2 .. 36.8 .. 97.8 107.0 38.0 20.4 7.5 135.9 179.0 56.6 .. 34.6 91.1 21.3 58.5 62.5 19.5 .. 17.8 83.2 34.7 121.2 110.8 46.7 .. 37.4 4.7 .. 60.6 67.8 41.7 121.2 w 44.7 65.3 75.0 45.5 60.9 76.4 .. 59.1 70.4 48.8 56.9 132.1 99.5
2002
13.2 26.6 11.1 70.1 22.6 .. 48.4 84.8 52.8 46.0 .. 150.9 129.6 43.6 11.7 5.6 75.2 174.4 26.7 21.3 10.0 116.0 17.0 41.5 74.4 59.3 30.7 15.4 28.1 47.6 145.3 159.4 93.0 .. 15.0 44.8 .. –0.5 46.7 58.7 150.7 w 48.6 82.9 100.7 49.1 76.9 143.8 36.9 42.7 72.9 55.3 65.5 168.5 123.0
2004 World Development Indicators
Liquid liabilities
% of GDP 1990
60.4 .. 14.9 42.9 22.9 .. 18.1 122.7 .. 34.2 .. 44.6 .. 34.9 20.1 28.3 50.7 145.2 54.7 .. 19.9 74.9 36.1 54.6 51.5 24.1 .. 7.6 50.1 46.3 .. 65.5 58.1 .. 38.8 22.7 .. 55.1 21.8 41.8 83.3 w 36.9 42.3 48.8 29.1 41.2 63.1 .. 25.2 59.0 41.0 32.1 92.9 ..
2002
24.7 26.2 17.3 54.0 27.6 .. 22.9 115.8 65.3 55.6 .. 50.1 .. 39.3 15.8 20.9 .. 157.9 79.2 8.4 23.0 114.5 24.3 51.8 60.0 49.8 20.4 20.2 29.1 66.6 .. 70.0 72.5 .. 17.8 53.0 .. 37.4 22.3 61.3 97.5 w 52.1 79.0 97.4 43.8 74.2 150.6 39.3 29.8 67.3 59.8 36.2 105.6 ..
Quasi-liquid liabilities
Ratio of bank liquid reserves to bank assets
% of GDP 1990
32.7 .. 7.0 19.6 9.7 .. 3.6 99.9 .. 25.8 .. 27.2 .. 22.6 2.9 19.8 .. 118.6 10.5 .. 6.3 66.0 19.1 42.7 26.7 16.4 .. 1.4 9.0 37.7 .. 49.4 51.5 .. 29.4 9.3 .. 10.4 10.6 30.3 .. w 22.0 24.5 28.7 16.1 24.0 37.2 .. 17.6 26.9 25.2 16.8 .. ..
2002
19.2 12.4 8.8 25.3 11.6 .. 7.9 92.8 42.5 42.7 .. 18.3 .. 30.5 5.9 14.3 .. 112.0 31.7 3.2 12.4 102.1 8.9 38.7 37.0 44.5 8.9 9.4 10.9 48.6 .. 54.2 67.0 .. 7.8 29.6 .. 19.9 14.0 24.8 68.6 w 35.6 50.3 61.9 28.2 47.7 97.0 24.9 20.2 40.2 42.0 15.4 78.7 ..
Interest rate spread
Risk premium on lending
Lending minus
Prime lending
deposit rate
rate minus
percentage
treasury bill rate
points
percentage points
% 1990
1.2 .. 4.3 5.6 14.1 .. 64.1 3.7 .. 2.7 .. 3.3 8.7 9.9 79.5 21.5 1.9 1.1 46.0 .. 5.3 3.1 59.0 13.5 1.6 16.4 .. 15.2 49.0 4.4 0.5 2.4 31.2 .. 21.9 25.3 .. 121.2 33.7 12.2 10.3 m 12.8 14.6 17.9 9.9 13.2 5.1 .. 22.3 14.2 12.7 11.9 2.0 4.1
2002
61.7 13.9 9.9 10.0 15.9 .. 9.0 2.5 5.2 4.0 .. 2.7 .. 8.1 19.9 7.1 0.4 0.9 9.1 11.0 13.0 3.4 16.9 13.4 3.4 9.0 6.7 9.8 9.0 9.3 0.3 1.1 12.4 .. 23.2 6.3 .. 16.5 22.8 18.8 10.3 m 15.1 9.5 9.3 9.6 11.3 12.1 9.9 18.8 16.5 8.6 13.7 1.2 ..
1990
.. .. 6.3 .. 9.0 .. 12.0 2.7 .. 142.0 .. 2.1 5.4 –6.4 .. 5.8 6.8 –0.9 5.0 .. 0.0 2.2 9.0 6.9 .. .. .. 7.4 .. .. 2.2 .. 76.6 .. 7.7 .. .. .. 9.4 2.9 5.4 m 7.4 5.0 5.6 6.2 6.6 2.2 .. 8.2 2.2 2.5 8.2 4.6 6.5
2002
1990
2002
.. 10.8 .. .. .. .. 13.9 4.5 3.6 4.9 .. 5.0 1.8 4.0 .. 7.2 3.7 3.5 5.0 5.0 13.1 4.9 .. 7.7 .. .. .. 13.5 17.4 .. .. .. 37.4 .. 7.6 2.6 .. 4.7 21.9 18.1 7.0 m 13.0 6.7 8.4 5.6 8.7 4.9 8.2 9.9 5.0 7.3 13.0 3.8 3.7
.. .. .. .. .. .. 5.0 3.7 .. .. .. 3.2 1.8 –1.1 .. 3.4 3.0 –0.9 .. .. .. .. .. 5.4 .. .. .. –2.3 .. .. 0.7 2.5 .. .. .. .. .. .. 9.2 3.3 .. m .. .. .. .. .. .. .. .. .. .. .. 2.4 2.6
.. 3.0 .. .. .. .. 7.0 4.6 .. 4.4 .. 4.6 1.0 0.7 .. 6.7 1.9 3.0 .. .. 12.9 .. .. 7.7 .. .. .. 13.2 .. .. 0.1 3.1 .. .. .. 3.1 .. 6.2 10.7 8.0 .. m .. .. .. .. .. .. .. .. .. .. .. 3.5 3.5
About the data
5.5
STATES AND MARKETS
Financial depth and efficiency Definitions
The organization and performance of financial activities
the earnings on assets—or the interest rate spread.
• Domestic credit provided by banking sector
in a country affect economic growth through their impact
A narrowing of the interest rate spread reduces trans-
includes all credit to various sectors on a gross basis,
on how businesses raise and manage funds. These
action costs, which lowers the overall cost of invest-
with the exception of credit to the central government,
funds come from savings: savers accumulate claims on
ment and is therefore crucial to economic growth.
which is net. The banking sector includes monetary
financial institutions, which pass the funds to their final
Interest rates reflect the responsiveness of financial
authorities, deposit money banks, and other banking
users. But even if a country has savings, growth may not
institutions to competition and price incentives. The
institutions for which data are available (including
materialize—because the financial system may fail to
interest rate spread, also known as the intermedia-
institutions that do not accept transferable deposits
direct the savings to where they can be invested most
tion margin, is a summary measure of a banking sys-
but do incur such liabilities as time and savings
efficiently. Enabling it to do so requires established pay-
tem’s efficiency (although if governments set interest
deposits). Examples of other banking institutions
ments systems, the availability of price information, a
rates, the spreads become less reliable measures of
include savings and mortgage loan institutions and
way to manage uncertainty and control risk, and mecha-
efficiency). The risk premium on lending can be
building and loan associations. • Liquid liabilities are
nisms to deal with problems of asymmetric information
approximated by the spread between the lending rate
also known as broad money, or M3. They include bank
between parties to a financial transaction.
to the private sector (line 60p in the International
deposits of generally less than one year plus curren-
As an economy develops, the indirect lending by
Monetary Fund’s International Financial Statistics, or
cy. Liquid liabilities are the sum of currency and
savers to investors becomes more efficient and grad-
IFS) and the “risk free” treasury bill interest rate (IFS
deposits in the central bank (M0); plus transferable
ually increases financial assets relative to gross
line 60c). A small spread indicates that the market
deposits and electronic currency (M1); plus time and
domestic product (GDP). More specialized savings and
considers its best corporate customers to be low risk.
savings deposits, foreign currency transferable
financial institutions emerge and more financing instru-
Interest rates are expressed as annual averages.
deposits, certificates of deposit, and securities repur-
ments become available, spreading risks and reducing
In some countries financial markets are distorted by
chase agreements (M2); plus travelers’ checks, for-
costs to liability holders. Securities markets mature,
restrictions on foreign investment, selective credit con-
eign currency time deposits, commercial paper, and
allowing savers to invest their resources directly in
trols, and controls on deposit and lending rates. Interest
shares of mutual funds or market funds held by resi-
financial assets issued by firms. Financial systems
rates may reflect the diversion of resources to finance
dents. The ratio of liquid liabilities to GDP indicates
vary widely across countries: banks, nonbank financial
the public sector deficit through statutory reserve
the relative size of these readily available forms of
institutions, and stock markets are larger, more active,
requirements and direct borrowing from the banking sys-
money—money that the owners can use to buy goods
and more efficient in richer countries.
tem. And where state-owned banks dominate the finan-
and services without incurring any cost. • Quasi-liquid
The ratio of domestic credit provided by the banking
cial sector, noncommercial considerations may unduly
liabilities are the M3 money supply less M1. • Ratio
sector to GDP is used to measure the growth of the
influence credit allocation. The indicators in the table pro-
of bank liquid reserves to bank assets is the ratio of
banking system because it reflects the extent to which
vide quantitative assessments of each country’s finan-
domestic currency holdings and deposits with the
savings are financial. In a few countries governments
cial sector, but qualitative assessments of policies, laws,
monetary authorities to claims on other governments,
may hold international reserves as deposits in the
and regulations are needed to analyze overall financial
nonfinancial public enterprises, the private sector,
banking system rather than in the central bank. Since
conditions. Recent international financial crises highlight
and other banking institutions. • Interest rate spread
the claims on the central government are a net item
the risks of weak financial intermediation, poor corporate
is the interest rate charged by banks on loans to
(claims on the central government minus central gov-
governance, and deficient government policies.
prime customers minus the interest rate paid by com-
ernment deposits), this net figure may be negative,
The accuracy of financial data depends on the qual-
mercial or similar banks for demand, time, or savings
resulting in a negative figure for domestic credit pro-
ity of accounting systems, which are weak in some
deposits. • Risk premium on lending is the interest
vided by the banking sector.
developing countries. Some indicators in the table are
rate charged by banks on loans to prime private sec-
Liquid liabilities are a general indicator of the size of
highly correlated, particularly the ratios of domestic
tor customers minus the “risk free” treasury bill inter-
financial intermediaries relative to the size of the econ-
credit, liquid liabilities, and quasi-liquid liabilities to
est rate at which short-term government securities
omy, or an overall measure of financial sector devel-
GDP, because changes in liquid and quasi-liquid liabil-
are issued or traded in the market. In some countries
opment. Quasi-liquid liabilities are long-term deposits
ities flow directly from changes in domestic credit.
this spread may be negative, indicating that the mar-
and assets—such as bonds, commercial paper, and
Moreover, the precise definition of the financial aggre-
ket considers its best corporate clients to be lower
certificates of deposit—that can be converted into cur-
gates presented varies by country.
risk than the government.
rency or demand deposits, but at a cost. The ratio of
The indicators reported here do not capture the activ-
bank liquid reserves to bank assets captures the bank-
ities of the informal sector, which remains an important
ing system’s liquidity. In countries whose banking sys-
source of finance in developing economies. Personal
tem is liquid, adverse macroeconomic conditions
credit or credit extended through community-based
Data sources
should be less likely to lead to banking and financial
pooling of assets may be the only source of credit for
The data on credit, liabilities, bank reserves, and
crises. Data on domestic credit and liquid and quasi-
small farmers, small businesses, and home-based pro-
interest rates are collected from central banks and
liquid liabilities are cited on an end-of-year basis.
ducers. And in financially repressed economies the
finance ministries and reported in the print and
No less important than the size and structure of the
rationing of formal credit forces many borrowers and
electronic editions of the International Monetary
financial sector is its efficiency, as indicated by the
lenders to turn to the informal market, which is very
Fund’s International Financial Statistics.
margin between the cost of mobilizing liabilities and
expensive, or to self-financing and family savings.
2004 World Development Indicators
273
5.6
Tax policies Tax revenue
Taxes on income, profits, and capital gains
Domestic taxes on goods and services
% of
% of
added in industry
Export duties
Import duties
% of
% of
Highest marginal tax rate a
% of value GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
274
total taxes
tax revenue
Corporate
on income %
over $
%
2002
1990
2002
1990
2002
1990
2002
1990
2002
2003
2003
2003
.. .. 32.0 .. 12.5 14.6 .. .. .. .. 26.6 .. .. 13.8 .. .. .. 25.2 .. .. .. .. 19.3 .. 7.2 18.7 .. .. .. 3.9 10.5 20.0 16.3 38.2 .. 32.1 32.3 15.6 .. .. 10.0 .. 27.2 15.3 .. .. .. .. 10.4 .. .. .. .. .. .. ..
.. .. .. .. 2.7 .. 70.9 20.8 .. .. 12.1 36.1 .. 7.9 .. 71.7 24.5 40.6 24.7 23.4 .. 25.1 59.1 .. 20.3 15.8 49.8 .. 36.4 28.5 40.2 11.5 18.1 17.4 .. .. 43.5 23.8 62.9 26.4 .. .. 27.5 40.9 34.5 18.7 35.9 13.7 .. 17.5 25.1 23.3 .. 12.6 .. ..
.. .. 77.9 .. 19.9 18.3 .. .. .. .. 10.3 .. .. 8.7 .. .. .. 17.0 .. .. .. .. 57.3 .. .. 24.7 .. .. .. 16.7 16.0 15.1 21.0 8.7 .. 21.1 40.2 19.6 .. .. 18.6 .. 14.7 39.4 .. .. .. .. 3.8 .. .. .. .. .. .. ..
.. .. .. .. 2.2 .. 5.9 10.0 .. 0.0 17.1 11.5 .. 5.6 .. 1.0 7.1 9.9 4.0 0.0 .. 4.3 4.0 .. 3.9 10.4 1.5 .. 4.8 2.6 0.0 8.7 8.9 9.6 .. .. 18.9 3.1 4.7 4.1 0.0 .. 14.8 9.1 17.5 13.1 5.0 12.2 .. 6.9 6.8 14.5 .. 3.2 .. ..
.. .. 3.4 .. 5.5 5.7 .. .. .. .. 13.5 .. .. 11.2 .. .. .. 16.2 .. .. .. .. .. .. .. 13.0 .. .. .. 1.5 6.5 10.8 4.8 24.9 .. 11.3 19.8 4.8 .. .. 0.8 .. 15.0 5.4 .. .. .. .. 9.4 .. .. .. .. .. .. ..
.. .. .. .. 9.3 .. 0.1 0.0 .. .. 3.6 0.0 .. 0.0 .. 0.0 0.0 0.0 1.1 3.1 .. 1.7 0.0 .. .. .. 0.0 .. 2.0 4.1 0.0 8.0 3.7 0.0 .. .. 0.0 0.1 0.3 0.0 .. .. 0.0 2.8 0.0 0.0 2.8 0.2 .. 0.0 12.4 0.0 .. 51.7 .. ..
.. .. 0.0 .. 0.2 0.0 .. .. .. .. .. .. .. 0.0 .. .. .. 0.0 .. .. .. .. 0.0 .. .. .. .. .. .. 1.0 0.0 0.2 15.3 0.0 .. 0.0 0.0 0.0 .. .. 0.0 .. 0.0 0.4 .. .. .. .. 0.0 .. .. .. .. .. .. ..
.. .. .. .. 2.6 .. 4.4 1.6 .. .. 0.4 0.0 .. 11.1 .. 24.7 2.5 2.5 33.1 23.2 .. 18.9 3.2 .. .. .. 22.1 .. 22.5 45.1 32.3 18.2 28.4 3.6 .. .. 0.1 41.4 12.1 18.9 .. .. 0.8 18.0 1.0 0.0 23.4 45.6 .. 0.0 28.7 0.1 .. 11.2 .. ..
.. .. 12.1 .. 4.5 4.9 .. .. .. .. .. .. .. 6.4 .. .. .. 2.6 .. .. .. .. 1.4 .. .. .. .. .. .. 33.7 23.2 4.2 27.6 6.8 .. 1.4 0.0 44.1 .. .. 7.3 .. 0.2 41.4 .. .. .. .. 7.7 .. .. .. .. .. .. ..
.. .. .. .. 35 20 47 50 35 .. .. 50 .. 13 .. 25 28 29 .. .. 20 .. 29 .. .. 40 45 17 35 50 .. 30 10 45 .. 32 59 25 25 32 .. .. 26 35 36 .. 50 .. .. 49 30 40 31 .. .. ..
.. .. .. .. 36,697 .. 35,149 48,698 12,257 .. .. 28,596 .. .. .. 18,560 7,251 3,982 .. .. 38,462 .. 65,206 .. .. 6,127 12,048 13,462 29,426 6,056 .. 16,860 3,837 35,171 .. 10,988 .. 16,637 54,400 10,823 .. .. 803 .. 52,843 .. .. .. .. 52,659 5,647 22,402 38,028 .. .. ..
2004 World Development Indicators
and services
Individual tax revenue
.. .. .. .. 35 20 30 34 25 .. .. 39 .. 25 .. 15 15 24 .. .. 20 .. 38 .. .. 17 .. 18 39 40 .. 30 35 .. .. 31 30 25 25 40 .. .. 35 30 29 33 .. .. .. 27 33 35 31 .. .. ..
Tax revenue
Taxes on income, profits, and capital gains
Domestic taxes on goods and services
% of
% of
added in industry
Export duties
Import duties
% of
% of
Highest marginal tax rate a
% of value GDP
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
total taxes
and services
Individual tax revenue
STATES AND MARKETS
5.6
Tax policies
Corporate
on income %
over $
%
2002
1990
2002
1990
2002
1990
2002
1990
tax revenue 2002
2003
2003
2003
.. 33.6 9.9 13.6 8.5 .. .. 36.2 .. 26.1 .. 19.0 9.6 .. .. .. .. 12.4 .. 24.0 .. .. .. .. 22.5 33.0 11.3 .. .. .. .. 17.3 13.2 20.5 23.0 .. .. 3.0 29.7 9.6 .. 27.9 16.5 .. .. .. 7.4 12.9 14.1 .. 10.1 13.6 13.3 26.2 .. ..
.. 21.2 18.6 65.4 24.7 .. 39.7 42.4 37.7 41.5 73.0 22.9 .. 32.9 .. 37.5 19.5 .. .. .. .. 12.7 .. .. 22.2 .. 15.7 42.5 42.5 .. .. 15.2 34.2 .. 28.2 27.3 .. 29.8 39.4 13.0 33.6 62.2 20.0 .. .. 21.7 87.6 12.8 24.4 47.0 12.4 5.8 32.5 .. 25.7 ..
.. 23.7 37.4 48.0 41.7 .. .. 45.2 .. 39.0 .. 16.4 28.9 .. .. .. .. 21.9 .. 15.4 .. .. .. .. 11.9 12.8 15.7 .. .. .. .. 14.0 38.1 2.6 10.5 .. .. 34.5 35.3 20.8 .. 68.3 14.7 .. .. .. 77.1 31.1 29.4 .. 16.1 25.1 45.6 18.8 .. ..
.. 22.6 7.4 5.5 1.0 .. 15.5 .. 12.7 0.0 2.4 6.8 .. 15.9 .. 6.7 0.0 .. .. .. .. 13.0 .. .. 16.4 .. 3.4 13.9 6.3 .. .. 7.0 10.2 .. 9.3 12.1 .. 6.8 9.9 6.6 11.5 13.2 16.9 .. .. 16.8 0.3 8.6 4.8 5.0 3.6 8.2 6.4 0.0 13.0 ..
.. 15.2 5.5 6.5 1.6 .. .. .. .. 11.8 .. 10.6 7.1 .. .. .. .. 16.0 .. 12.9 .. .. .. .. 14.3 18.3 5.2 .. .. .. .. 9.2 10.5 18.2 18.2 .. .. 4.0 8.6 7.1 .. .. 11.3 .. .. .. .. 8.4 .. .. 8.1 9.7 4.7 13.1 .. ..
.. 1.3 0.1 0.1 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 .. 0.0 0.0 .. .. .. .. 0.2 .. .. .. .. 8.5 0.0 9.7 .. .. 4.6 0.1 .. 0.0 0.3 .. 0.0 3.6 0.4 0.0 0.0 0.0 .. .. 0.1 0.0 0.0 1.3 2.1 0.0 7.6 0.0 .. 0.0 ..
.. 0.0 0.1 0.3 0.0 .. .. 0.0 .. 0.0 .. 0.0 0.3 .. .. .. .. .. .. 0.0 .. .. .. .. 0.0 .. 0.0 .. .. .. .. 0.0 0.0 0.0 0.4 .. .. 0.0 .. 2.4 .. 0.0 0.0 .. .. .. 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 .. ..
.. 5.6 35.8 6.6 18.6 .. 0.0 1.4 0.0 14.0 1.4 34.7 .. 17.8 .. 13.0 76.8 .. .. .. .. 63.6 .. .. .. .. 50.1 18.7 15.1 .. .. 45.7 6.9 .. 19.6 20.3 .. 23.3 26.9 37.0 0.0 2.5 21.3 .. .. 0.6 7.8 44.4 15.8 29.3 18.8 9.9 28.4 .. 2.6 ..
.. 2.5 24.1 4.6 14.4 .. .. 0.7 .. 9.3 .. 20.4 5.7 .. .. .. .. .. .. 1.3 .. .. .. .. 1.3 7.9 53.5 .. .. .. .. 29.3 4.5 5.6 9.8 .. .. 7.2 .. 31.3 .. 1.8 8.4 .. .. .. 10.3 10.8 .. .. 17.5 10.5 19.6 2.1 .. ..
.. 40 30 35 35 .. 42 50 45 25 37 .. 30 30 .. 36 0 .. .. 25 .. .. .. .. 33 18 .. .. 28 .. .. 25 35 .. .. 44 32 .. 36 .. 52 39 .. .. .. .. 0 35 30 47 0 30 32 40 40 33
.. 5,999 3,139 22,371 125,345 .. 26,805 50,886 67,011 2,363 148,478 .. 39,185 5,720 .. 66,644 .. .. .. .. .. .. .. .. .. .. .. .. 65,789 .. .. 862 61,689 .. .. 5,243 42,583 .. 17,241 .. 47,352 31,561 .. .. .. .. .. 11,111 200,000 24,842 .. 45,863 9,320 18,278 50,045 50,000
2004 World Development Indicators
.. 18 37 30 25 .. 16 36 34 33 30 .. 30 30 .. 27 .. .. .. 19 .. .. .. .. 15 15 .. .. 28 .. .. 25 34 .. .. 35 32 .. 35 .. 35 33 .. .. .. 28 12 45 30 25 30 27 32 27 30 20
275
5.6
Tax policies Tax revenue
Taxes on income, profits, and capital gains
Domestic taxes on goods and services
% of
% of
added in industry
Export duties
Import duties
% of
% of
Highest marginal tax rate a
% of value GDP
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
total taxes
and services
Individual tax revenue
%
over $
%
2002
1990
2002
1990
2002
1990
2002
1990
tax revenue 2002
2003
2003
2003
22.8 22.5 .. .. 17.9 .. .. 15.4 29.6 35.0 .. 26.3 .. 14.5 .. 26.7 .. 23.5 .. 10.5 .. 14.4 .. .. 26.0 24.2 .. 10.8 21.7 .. .. 17.7 23.3 .. 12.2 16.4 .. .. .. ..
21.0 .. 20.0 .. .. .. 33.0 44.6 .. 12.3 .. 55.0 34.0 12.0 .. 33.2 20.6 17.0 40.2 .. .. 26.2 .. .. 16.0 51.2 .. .. .. 0.0 43.2 56.1 7.1 .. 82.2 .. .. 44.9 .. 49.7
12.0 10.8 .. .. 22.8 .. .. 52.7 19.7 15.5 .. 57.0 .. 16.9 .. 26.4 .. 17.7 .. 3.0 .. 34.3 .. .. 22.3 42.2 .. 20.1 14.3 .. .. 55.5 16.5 .. 34.0 32.0 .. .. .. ..
16.0 .. 5.5 .. 0.0 .. 2.1 .. .. 12.7 .. 10.3 7.5 14.7 .. 5.2 14.5 .. 9.6 .. .. 8.8 .. 0.0 7.1 5.9 .. 0.0 .. 0.6 11.3 0.7 9.4 .. 0.8 .. .. 2.5 .. 8.4
10.6 11.3 .. .. 7.4 .. .. 5.1 10.9 16.6 .. 10.3 .. 13.6 .. 6.6 .. 6.7 .. 9.4 .. 7.8 .. .. 12.5 15.4 .. 5.3 10.5 .. .. 0.7 10.4 .. 5.9 9.0 .. .. .. ..
0.0 .. 7.4 .. .. .. 0.4 0.0 .. .. .. 0.0 0.0 4.2 .. 2.0 0.0 0.0 1.3 .. .. 0.2 .. .. 0.4 0.0 .. .. .. .. 0.0 0.0 0.6 .. 0.0 .. .. 0.0 .. 0.0
0.0 11.2 .. .. .. .. .. 0.0 0.0 0.0 .. 0.0 .. 0.0 .. 0.0 .. 0.0 .. 0.0 .. 0.3 .. .. 0.1 0.0 .. 0.0 0.0 .. .. 0.0 0.1 .. 0.0 0.0 .. .. .. ..
0.6 .. 20.7 .. .. .. 41.3 3.5 .. .. .. 3.9 1.7 27.4 .. 50.5 0.6 6.9 8.2 .. .. 23.7 .. .. 35.1 7.3 .. .. .. .. 0.0 1.7 8.1 .. 7.1 .. .. 29.2 .. 18.8
3.4 5.1 .. .. .. .. .. 2.6 1.3 1.8 .. 2.9 .. 12.7 .. 54.7 .. 1.1 .. 17.1 .. 12.3 .. .. 12.5 1.1 .. 50.3 4.4 .. .. 1.0 3.0 .. 12.1 22.8 .. .. .. ..
40 13 .. 0 .. .. .. 22 38 50 .. 40 29 30 .. 33 25 .. .. .. 30 37 .. 30 .. 40 .. 30 40 0 40 39 0 32 34 .. .. .. 30 45
3,743 .. .. .. .. .. .. 184,438 14,087 .. .. 30,380 44,794 3,708 .. 4,215 50,767 .. .. .. 7,074 92,379 .. 7,937 .. 73,417 .. 2,860 3,826 .. 48,413 311,950 .. 561 72,000 .. .. .. 368 26,249
a. These data are from PricewaterhouseCoopers’s Individual Taxes: Worldwide Summaries 2003–2004 and Corporate Taxes: Worldwide Summaries 2003–2004, copyright 2003 by PricewaterhouseCoopers by permission of John Wiley and Sons, Inc.
276
Corporate
on income
2004 World Development Indicators
25 24 .. 0 .. .. .. 22 25 25 .. 30 35 30 .. 30 28 9 .. .. 30 30 .. 30 .. 30 .. 30 30 0 30 35 35 20 34 32 .. .. 35 30
About the data
5.6
STATES AND MARKETS
Tax policies Definitions
Taxes are the main source of revenue for many govern-
because indirect taxes on goods originating from these
• Tax revenue comprises compulsory transfers to the
ments. The sources of the tax revenue received by gov-
sectors are usually negligible. What is missing here is a
central government for public purposes. Compulsory
ernments and the relative contributions of these sources
measure of the uniformity of these taxes across indus-
transfers such as fines, penalties, and most social
are determined by policy choices about where and how
tries and along the value added chain of production.
security contributions are excluded. Refunds and cor-
to impose taxes and by changes in the structure of the
Without such data, no clear inferences can be drawn
rections of erroneously collected tax revenue are treat-
economy. Tax policy may reflect concerns about distribu-
about how neutral a tax system is between subsectors.
ed as negative revenue. • Taxes on income, profits, and
tional effects, economic efficiency (including corrections
“Surplus” revenues raised by some governments by
capital gains are levied on wages, salaries, tips, fees,
for externalities), and the practical problems of adminis-
charging higher prices for goods produced under monop-
commissions, and other compensation for labor servic-
tering a tax system. There is no ideal level of taxation.
oly by state-owned enterprises are not counted as tax rev-
es; interest, dividends, rent, and royalties; profits of
But taxes influence incentives and thus the behavior of
enues. Similarly, losses from charging below-market
businesses, estates, and trusts; and capital gains and
economic actors and the economy’s competitiveness.
prices for products are rarely identified as subsidies.
losses. Social security contributions based on gross
Taxes are compulsory transfers to governments from
Export and import duties are shown separately
pay, payroll, or number of employees are not included,
individuals, businesses, or institutions. They include
because the burden they impose on the economy (and
but taxable portions of social security, pension, and
service fees that are clearly out of proportion to the
thus growth) is likely to be large. Export duties, typically
other retirement account distributions are included.
costs of providing the services but exclude fines, penal-
levied on primary (particularly agricultural) products,
• Domestic taxes on goods and services are all taxes
ties, and compulsory social security contributions.
often take the place of direct taxes on income and prof-
and duties levied by central governments on the pro-
Taxes are considered unrequited because governments
its, but they reduce the incentive to export and encour-
duction, extraction, sale, transfer, leasing, or delivery of
provide nothing specifically in return for them, although
age a shift to other products. High import duties penalize
goods and rendering of services, or on the use of goods
taxes typically are used to provide goods or services to
consumers, create protective barriers—which promote
or permission to use goods or perform activities. These
individuals or communities on a collective basis.
higher priced output and inefficient production—and
include value added taxes, general sales taxes, single-
The level of taxation is typically measured by tax rev-
implicitly tax exports. By contrast, lower trade taxes
stage and multistage taxes (where stage refers to stage
enue as a share of gross domestic product (GDP).
enhance openness—to foreign competition, knowledge,
of production or distribution), excise taxes, motor vehicle
Comparing levels of taxation across countries provides
technologies, and resources—energizing development in
taxes, and taxes on the extraction, processing, or pro-
a quick overview of the fiscal obligations and incentives
many ways. Seeing this pattern, some of the fastest
duction of minerals or other products. • Export duties
facing the private sector. In this table tax data in local
growing economies have lowered import tariffs in recent
are all levies collected on goods at the point of export.
currencies are normalized by scaling values in the
years. The simple mean import tariff in India, for exam-
Rebates on exported goods that are repayments of pre-
same units to ease cross-country comparisons. The
ple, declined from almost 80 percent in 1990 to about
viously paid general consumption taxes, excise taxes, or
table shows only central government data, which may
30 percent in 2001. In some countries, such as mem-
import duties are deducted from the gross amounts
significantly understate the total tax burden, particular-
bers of the European Union, most customs duties are
receivable from these taxes, not from amounts receiv-
ly in countries where provincial and municipal govern-
collected by a supranational authority; these revenues
able from export duties. • Import duties are all levies
ments are large or have considerable tax authority.
are not reported in the individual countries’ accounts.
collected on goods at the point of entry into the country.
Low ratios of tax revenue to GDP may reflect weak
The tax revenues collected by governments are the
They include levies imposed for revenue or protection
administration and large-scale tax avoidance or evasion.
outcomes of systems that are often complex, containing
purposes and determined on a specific or ad valorem
Low ratios may also reflect the presence of a sizable par-
many exceptions, exemptions, penalties, and other
basis as long as they are restricted to imported prod-
allel economy with unrecorded and undisclosed
inducements that affect the incidence of taxes and thus
ucts. • Highest marginal tax rate is the highest rate
incomes. Tax revenue ratios tend to rise with income,
influence the decisions of workers, managers, and entre-
shown on the national level schedule of tax rates
with higher income countries relying on taxes to finance
preneurs. A potentially important influence on both
applied to the annual taxable income of individuals and
a much broader range of social services and social secu-
domestic and international investors is a tax system’s
corporations. Also presented are the income levels for
rity than lower income countries are able to provide.
progressivity, as reflected in the highest marginal tax rate
individuals above which the highest marginal tax rates levied at the national level apply.
As economies develop, their capacity to tax residents
levied at the national level on individual and corporate
directly typically expands and indirect taxes become less
income. Figures for individual marginal tax rates general-
important as a source of revenue. Thus the share of
ly refer to employment income. In some countries the
Data sources
taxes on income, profits, and capital gains is one meas-
highest marginal tax rate is also the basic or flat rate, and
The definitions used here are from the International
ure of an economy’s (and tax system’s) level of develop-
other surtaxes, deductions, and the like may apply. And
Monetary Fund’s (IMF) Manual on Government
ment. In the early stages of development governments
in many countries several different corporate tax rates
Finance Statistics (2002). The data on tax rev-
tend to rely on indirect taxes because the administrative
may be levied, depending on the type of business (min-
enues are from print and electronic editions of the
costs of collecting them are relatively low. The two main
ing, banking, insurance, agriculture, manufacturing), own-
IMF’s Government Finance Statistics Yearbook. The
indirect taxes are international trade taxes (including cus-
ership (domestic or foreign), volume of sales, or whether
data on individual and corporate tax rates are
toms revenues) and domestic taxes on goods and serv-
surtaxes or exemptions are included. The corporate tax
from PricewaterhouseCoopers’s Individual Taxes:
ices. The table shows these domestic taxes as a
rates in the table are mainly general rates applied to
Worldwide Summaries 2003–2004 and Corporate
percentage of value added in industry and services.
domestic companies. For more detailed information, see
Taxes: Worldwide Summaries 2003–2004.
Agriculture and mining are excluded from the denominator
the country’s laws, regulations, and tax treaties.
2004 World Development Indicators
277
5.7
Relative prices and exchange rates Exchange rate arrangements a
Classification 2002
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
278
Official exchange rate
Purchasing power parity (PPP) conversion factor
Structure 2002
local currency units to $ 2002
local currency units to international $ 1990 2002
U U U U U U U U U U U U U U U D U U U U D U U U U U U U U U U U U U .. U U D U U U U U U U U U U U U U U U U U U
3,000.00 140.15 79.68 43.53 3.06 573.35 1.84 1.06 4,860.82 57.89 1,790.92 1.06 696.99 7.17 2.08 6.33 2.92 2.08 696.99 930.75 3,912.08 696.99 1.57 696.99 696.99 688.94 8.28 7.80 2,504.24 346.48 696.99 359.82 696.99 7.87 .. 32.74 7.89 18.61 1.00 4.50 8.75 13.96 16.61 8.57 1.06 1.06 696.99 19.92 2.20 1.06 7,932.70 1.06 7.82 1,975.84 696.99 29.25
MF IF MF MF MF IF IF Euro MF P P Euro EA/Euro P CB/Euro P/Euro IF CB/Euro EA/Euro MF MF EA/Euro IF EA/Euro EA/Euro IF P CB IF IF EA/Euro P EA/Euro MF .. MF P MF EA/Other MF EA/Other P CB/Euro MF Euro Euro EA/Euro MF IF Euro MF Euro MF P EA/Euro MF
2004 World Development Indicators
.. 2.0 5.0 0.0 0.3 .. 1.4 0.9 1.1 9.6 .. 0.9 160.7 1.3 .. 1.2 0.0 0.0 136.3 49.6 .. 171.8 1.3 136.0 106.1 149.5 1.2 6.4 120.6 0.0 387.9 32.8 168.0 .. .. 8.1 8.1 2.6 0.4 0.8 2.4 1.2 0.1 0.7 1.0 1.0 341.2 1.8 .. 1.0 94.9 0.4 1.4 225.0 11.0 1.2
.. 44.5 24.7 17.5 0.8 141.9 1.4 0.9 1,127.8 11.9 465.9 0.9 267.2 2.6 .. 2.4 1.0 0.6 168.0 149.3 610.5 210.9 1.2 162.6 163.6 288.7 1.8 6.9 727.5 58.7 587.7 173.8 324.3 3.9 .. 14.1 8.2 7.0 0.5 1.5 4.0 2.3 6.5 1.0 1.0 0.9 399.5 3.0 0.6 0.9 1,134.2 0.7 3.7 390.8 137.8 7.2
Ratio of PPP Real conversion effective factor to exchange official rate exchange rate
2002
.. 0.3 0.3 0.4 0.2 0.2 0.7 0.9 0.2 0.2 0.3 0.9 0.4 0.4 .. 0.4 0.3 0.3 0.2 0.2 0.2 0.3 0.8 0.2 0.2 0.4 0.2 0.9 0.3 0.2 0.8 0.5 0.5 0.5 .. 0.4 1.0 0.4 0.5 0.3 0.5 0.2 0.4 0.1 1.0 0.9 0.6 0.2 0.3 0.9 0.1 0.7 0.5 0.2 0.2 0.2
index 1995 = 100 2002
.. .. 101.7 .. .. 95.9 96.1 92.1 .. .. .. 90.2 .. 115.4 .. .. .. 135.6 .. 79.1 .. 102.1 97.7 95.4 .. 90.7 121.4 .. 90.4 109.2 .. 109.4 103.7 103.7 .. 133.6 96.7 112.1 113.8 .. .. .. .. 79.4 90.2 89.8 91.4 68.4 .. 86.6 81.0 100.0 .. .. .. ..
Interest rate
Deposit 2002
% Lending 2002
Real 2002
.. 8.5 5.3 48.7 39.2 9.6 3.0 .. 8.7 8.2 26.9 2.6 3.5 9.6 4.5 10.3 19.1 2.8 3.5 .. 2.5 5.0 0.8 5.0 5.0 3.8 2.0 0.3 8.9 .. 5.0 11.5 3.5 1.9 .. 2.2 2.4 16.5 5.5 9.3 9.3 .. 2.7 4.1 1.5 3.0 5.0 12.7 9.8 2.7 16.2 2.8 6.9 7.4 3.5 8.2
.. 15.3 8.5 97.3 51.7 21.1 8.0 .. 17.4 16.0 36.9 7.7 .. 20.6 12.7 16.0 62.9 9.3 .. 19.5 16.2 18.0 4.2 18.0 18.0 7.8 5.3 5.0 16.3 66.8 18.0 26.4 .. 12.8 .. 6.2 7.1 26.1 15.1 13.8 14.0 .. 6.7 8.7 4.8 6.6 18.0 24.0 31.8 9.7 .. 7.4 16.9 19.4 .. 25.7
.. 8.7 7.4 –2.9 16.2 18.5 5.5 .. 16.5 12.4 –3.6 5.8 .. 17.5 10.4 9.9 50.1 5.3 .. 5.8 12.8 17.2 3.2 16.3 13.8 5.0 5.6 8.2 9.7 35.3 18.8 15.8 .. 9.6 .. 3.5 6.2 18.4 2.9 9.4 10.5 .. 2.5 16.9 3.5 4.7 11.4 3.6 23.9 8.0 .. 3.6 8.2 7.4 .. 15.3
Exchange rate arrangements a
Classification 2002
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
P P MF MF MF MF Euro P Euro MF IF P MF MF .. IF P MF MF p P P IF P CB/Euro P IF IF P EA/Euro MF MF IF MF MF P IF MF P P Euro IF P EA/Euro MF IF P MF EA/Other IF MF IF IF IF Euro ..
Structure 2002
U U U U D U U U U U U U U U .. U U U D U U U U U U U U U U U U U U U U U U D U U U U U U M U U U U U U U U U U ..
Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2002
local currency units to international $ 1990 2002
16.43 257.89 48.61 9,311.19 6,906.96 0.31 1.06 4.74 1.06 48.42 125.39 0.71 153.28 78.75 .. 1,251.09 0.30 46.94 10,056.33 0.62 1,507.50 10.54 61.75 1.27 3.68 64.35 6,831.96 76.69 3.80 696.99 271.74 29.96 9.66 13.57 1,110.31 11.02 23,677.96 6.57 10.54 77.88 1.06 2.16 14.25 696.99 120.58 7.98 0.38 59.72 1.00 3.90 5,716.26 3.52 51.60 4.08 1.06 ..
1.3 22.3 4.9 642.6 180.4 .. 0.8 1.8 0.7 4.4 190.2 0.3 .. 9.1 .. 563.7 0.3 .. 174.9 .. 307.0 1.0 .. .. .. .. 516.0 1.4 1.5 141.4 36.5 6.5 1.5 .. 2.9 3.2 321.5 .. 0.9 6.8 0.9 1.6 0.0 122.3 3.8 8.0 0.3 5.8 0.6 0.5 408.1 0.1 5.6 0.2 0.5 0.7
6.1 124.8 8.8 2,357.7 1,963.4 .. 0.9 3.8 0.8 36.6 146.2 0.3 43.2 30.4 .. 738.7 0.3 9.3 1,884.2 0.2 1,346.0 1.7 .. .. 1.4 18.5 2,456.7 23.3 1.6 222.8 42.7 10.5 6.8 3.5 296.8 3.5 4,406.7 .. 2.5 12.7 0.9 1.5 4.3 165.4 46.2 9.2 0.2 12.9 0.7 0.9 1,240.2 1.5 12.1 1.9 0.7 0.7
Ratio of PPP Real conversion effective factor to exchange official rate exchange rate
2002
0.4 0.5 0.2 0.3 0.3 .. 0.9 0.8 0.8 0.8 1.2 0.4 0.3 0.4 .. 0.6 0.9 0.2 0.2 0.4 0.9 0.2 .. .. 0.4 0.3 0.4 0.3 0.4 0.3 0.2 0.3 0.7 0.3 0.3 0.3 0.2 .. 0.2 0.2 0.9 0.7 0.3 0.2 0.4 1.1 0.6 0.2 0.7 0.2 0.2 0.4 0.2 0.5 0.7 ..
index 1995 = 100 2002
.. 130.8 .. .. 198.1 .. 99.0 102.5 110.0 .. 78.9 .. .. .. .. .. .. .. .. .. .. 60.8 .. .. .. 72.6 .. 115.0 91.3 .. .. .. .. 100.2 .. 103.4 .. .. .. .. 95.8 88.9 111.6 .. 90.3 107.9 .. 90.0 .. 81.2 75.6 .. 85.6 133.4 100.6 ..
5.7
STATES AND MARKETS
Relative prices and exchange rates
Interest rate
Deposit 2002
% Lending 2002
Real 2002
13.7 7.4 .. 15.5 .. .. 0.1 6.0 1.4 8.6 0.0 4.4 .. 5.5 .. 4.9 3.2 5.9 6.0 3.2 11.0 5.2 6.2 3.0 1.7 9.6 12.0 28.1 3.2 3.5 .. 9.9 3.8 14.2 13.2 4.5 18.0 9.5 7.8 4.8 2.8 5.3 7.3 3.5 16.7 6.5 2.9 .. 5.0 5.8 22.9 4.2 4.6 6.2 .. ..
22.7 10.2 11.9 18.9 .. .. 3.8 9.9 5.8 18.5 1.9 10.2 .. 18.5 .. 6.8 6.5 24.8 29.3 8.0 16.6 17.1 20.2 7.0 6.8 18.4 25.3 50.5 6.4 .. .. 21.0 8.2 23.5 28.4 13.1 26.7 15.0 13.8 7.7 4.0 9.8 23.2 .. 24.8 8.5 8.5 .. 10.6 13.9 38.7 14.7 9.1 12.1 .. ..
15.5 –0.5 8.7 11.0 .. .. –2.7 5.2 2.9 9.7 3.6 9.7 .. 9.0 .. 5.0 3.0 22.0 18.4 6.1 13.8 7.4 –7.1 .. 6.9 14.3 8.6 28.1 2.7 .. .. 15.1 3.4 14.3 21.1 12.5 13.9 –6.2 4.0 4.3 0.7 8.0 17.0 .. 11.8 10.0 6.6 .. 9.3 1.5 21.0 14.1 4.1 10.3 .. ..
2004 World Development Indicators
279
5.7
Relative prices and exchange rates Exchange rate arrangements a
Classification 2002
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Structure 2002
P MF MF P EA/Euro MF IF MF MF P IF IF Euro MF P P IF IF P MF IF MF EA/Euro MF P IF P IF P P IF IF IF MF P MF .. IF MF P
U U U U U U D U U U D U U U U U U U M U U U U U U U D U U U U U U M U U .. U U U
Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2002
local currency units to international $ 1990 2002
33,055.43 31.35 476.33 3.74 696.99 .. 2,099.03 1.79 45.33 240.25 .. 10.54 1.06 95.66 263.31 10.54 9.74 1.56 11.23 2.76 966.58 42.96 696.99 6.25 1.42 1,507,226.38 5,200.00 1,797.55 5.33 3.67 0.67 1.00 21.26 236.61 1,160.95 15,279.50 .. 175.63 4,398.60 55.04
6.9 .. 31.4 2.9 185.8 .. 30.0 1.9 5.9 .. .. 1.0 0.6 10.3 0.7 0.9 9.9 2.0 10.3 .. 76.0 10.8 94.5 3.1 0.4 1,643.1 .. 111.4 .. 3.4 0.6 1.0 0.6 .. 24.7 644.4 .. 20.4 18.7 1.0
10,344.3 9.2 79.5 2.5 222.2 .. 599.7 1.6 15.5 144.9 .. 2.4 0.8 23.4 59.8 2.5 10.1 1.9 16.9 0.5 444.9 12.6 137.1 4.9 0.5 621,572.8 1,544.1 298.4 0.9 .. 0.7 1.0 10.0 177.4 810.6 2,892.2 .. 108.3 1,893.0 16.4
Ratio of PPP Real conversion effective factor to exchange official rate exchange rate
2002
0.3 0.3 0.2 0.7 0.3 .. 0.3 0.9 0.3 0.6 .. 0.2 0.7 0.2 0.2 0.2 1.0 1.2 1.5 0.2 0.5 0.3 0.2 0.8 0.3 0.4 0.3 0.2 0.2 .. 1.0 1.0 0.5 0.4 0.7 0.2 .. 0.6 0.4 0.3
index 1995 = 100 2002
110.2 109.0 .. 107.2 .. .. 94.1 93.7 105.8 .. .. 62.6 98.3 .. .. .. 89.8 93.1 .. .. .. .. 105.4 126.6 96.2 .. .. 76.7 112.4 .. 130.4 133.6 87.9 .. 132.9 .. .. .. 115.8 ..
Interest rate
Deposit 2002
.. 5.0 8.0 2.2 3.5 .. 8.2 0.9 6.6 8.2 .. 10.8 2.5 9.2 .. 8.0 2.2 0.4 4.0 9.2 3.3 2.0 3.5 4.8 .. 50.5 .. 5.6 7.9 .. .. .. 14.3 .. 29.0 6.4 .. 13.0 23.3 18.4
% Lending 2002
.. 15.7 .. .. .. .. 22.2 5.4 10.2 13.2 .. 15.8 4.3 13.2 .. 15.3 5.8 3.9 9.0 14.2 16.4 6.9 .. 12.5 .. .. .. 19.1 25.3 8.1 4.0 4.7 126.1 .. 36.6 9.1 .. 17.7 45.2 36.5
Real 2002
.. 0.4 .. .. .. .. 17.6 5.2 6.1 4.7 .. 6.6 –0.1 4.5 .. 1.5 4.5 3.5 4.4 –6.4 11.8 6.1 .. 11.6 .. .. .. 23.1 21.4 .. 0.8 3.5 90.3 .. 3.8 4.8 .. 11.9 21.1 –34.2
a. Exchange rate arrangements are given for the end of the year in 2002. Exchange rate classifications include independent floating (IF), managed floating (MF), pegged (P), currency board (CB), and several exchange arrangements (EA): Euro that the currency is pegged to the euro, and other that the currency of another country is used as legal tender. Exchange rate structures include dual exchange rates (D), multiple exchange rates (M), and unitary rate (U).
280
2004 World Development Indicators
About the data
5.7
STATES AND MARKETS
Relative prices and exchange rates Definitions
In a market-based economy the choices households,
For most high-income countries the weights are
• Exchange rate arrangements describe the
producers, and governments make about the alloca-
based on trade in manufactured goods with other
arrangements furnished to the IMF by each member
tion of resources are influenced by relative prices,
high-income countries in 1989–91, and an index of
country under article IV, section 2(a) of the IMF’s
including the real exchange rate, real wages, real
relative, normalized unit labor costs is used as the
Articles of Agreement. • Classification indicates how
interest rates, and a host of other prices in the econ-
deflator. (Normalization smooths a time series by
the exchange rate is determined in the main market
omy. Relative prices also reflect, to a large extent, the
removing short-term fluctuations while retaining
when there is more than one market: floating (man-
choices of these agents. Thus relative prices convey
changes of a large amplitude over the longer eco-
aged or independent), pegged (conventional, within
vital information about the interaction of economic
nomic cycle.) For other countries the weights before
horizontal bands, crawling peg, or crawling band),
agents in an economy and with the rest of the world.
1990 take into account trade in manufactured and
currency board (implicit legislative commitment to
The exchange rate is the price of one currency in
primary products in 1980–82, the weights from
exchange domestic currency for a specified foreign
terms of another. Official exchange rates and
January 1990 onward take into account trade in
currency at a fixed exchange rate), and exchange
exchange rate arrangements are established by gov-
1988–90, and an index of relative changes in con-
arrangement (currency is pegged to the French franc,
ernments. (Other exchange rates fully recognized by
sumer prices is used as the deflator. An increase in
or another country’s currency is used as legal
governments include market rates, which are deter-
the real effective exchange rate represents an appre-
tender). • Structure shows whether countries have a
mined largely by legal market forces, and for coun-
ciation of the local currency. Because of conceptual
unitary exchange rate or dual or multiple rates.
tries maintaining multiple exchange arrangements,
and data limitations, changes in real effective
• Official exchange rate is the exchange rate deter-
principal rates, secondary rates, and tertiary rates.)
exchange rates should be interpreted with caution.
mined by national authorities or the rate determined
Also see Statistical methods for information on
Many interest rates coexist in an economy, reflect-
in the legally sanctioned exchange market. It is cal-
alternative conversion factors used in the Atlas
ing competitive conditions, the terms governing loans
culated as an annual average based on monthly aver-
method of calculating gross national income (GNI)
and deposits, and differences in the position and sta-
ages (local currency units relative to the U.S. dollar).
per capita in U.S. dollars.
tus of creditors and debtors. In some economies
• Purchasing power parity (PPP) conversion factor
The official or market exchange rate is often used
interest rates are set by regulation or administrative
is the number of units of a country’s currency
to compare prices in different currencies. Since
fiat. In economies with imperfect markets, or where
required to buy the same amount of goods and serv-
exchange rates reflect at best the relative prices of
reported nominal rates are not indicative of effective
ices in the domestic market as a U.S. dollar would
tradable goods, the volume of goods and services
rates, it may be difficult to obtain data on interest
buy in the United States. • Ratio of PPP conversion
that a U.S. dollar buys in the United States may not
rates that reflect actual market transactions. Deposit
factor to official exchange rate is the result
correspond to what a U.S. dollar converted to anoth-
and lending rates are collected by the International
obtained by dividing the PPP conversion factor by the
er country’s currency at the official exchange rate
Monetary Fund (IMF) as representative interest rates
official exchange rate. • Real effective exchange
would buy in that country. Since identical volumes of
offered by banks to resident customers. The terms
rate is the nominal effective exchange rate (a meas-
goods and services in different countries correspond
and conditions attached to these rates differ by coun-
ure of the value of a currency against a weighted
to different values (and vice versa) when official
try, however, limiting their comparability. Real interest
average of several foreign currencies) divided by a
exchange rates are used, an alternative method of
rates are calculated by adjusting nominal rates by an
price deflator or index of costs. • Deposit interest
comparing prices across countries has been devel-
estimate of the inflation rate in the economy. A nega-
rate is the rate paid by commercial or similar banks
oped. In this method national currency estimates of
tive real interest rate indicates a loss in the purchas-
for demand, time, or savings deposits. • Lending
GNI are converted to a common unit of account by
ing power of the principal. The real interest rates in
interest rate is the rate charged by banks on loans
using conversion factors that reflect equivalent pur-
the table are calculated as ( i – P ) / ( 1 + P ), where
to prime customers. • Real interest rate is the lend-
chasing power. Purchasing power parity (PPP) con-
i is the nominal interest rate and P is the inflation rate
ing interest rate adjusted for inflation as measured
version factors are based on price and expenditure
(as measured by the GDP deflator).
by the GDP deflator.
surveys conducted by the International Comparison Program and represent the conversion factors applied to equalize price levels across countries. See About the data for table 1.1 for further discussion of
Data sources
the PPP conversion factor.
The information on exchange rate arrangements
The ratio of the PPP conversion factor to the offi-
is from the IMF’s Exchange Arrangements and
cial exchange rate (also referred to as the national
Exchange Restrictions Annual Report, 2003. The
price level) makes it possible to compare the cost of
official and real effective exchange rates and
the bundle of goods that make up gross domestic
deposit and lending rates are from the IMF’s
product (GDP) across countries. These national price
International Financial Statistics. PPP conversion
levels vary systematically, rising with GNI per capita.
factors are from the World Bank. The real interest
Real effective exchange rates are derived by deflat-
rates are calculated using World Bank data on the
ing a trade-weighted average of the nominal
GDP deflator.
exchange rates that apply between trading partners.
2004 World Development Indicators
281
5.8
Defense expenditures and arms transfers Military expenditures
Armed forces personnel
Arms transfers
$ millions % of
% of central
GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
282
government expenditure
Total
% of
thousands
1990 prices
labor force
Exports
Imports
1992
2002
1992
2002
1992
1999
1992
1999
1992
2002
1992
2002
.. 4.6 2.2 12.0 1.4 2.2 2.3 1.0 3.3 1.1 1.5 1.8 .. 2.1 .. 4.3 1.1 2.7 2.3 3.6 4.7 1.5 1.9 1.6 2.7 3.4 2.7 .. 2.4 .. .. .. 1.4 7.6 .. 2.3 1.9 .. 2.7 3.6 2.0 21.4 0.5 2.7 1.9 3.4 .. 1.0 .. 2.1 0.6 4.5 1.3 1.9 0.3 ..
.. 1.2 3.7 3.7 1.2 2.7 1.7 0.8 2.1 1.1 1.4 1.3 .. 1.7 9.5 4.0 1.6 2.7 1.7 7.6 2.7 1.4 1.1 .. 1.4 2.9 2.5 .. 3.7 .. .. .. 0.9 2.5 .. 2.1 1.6 .. 2.1 2.7 0.8 27.5 1.9 5.2 1.2 2.5 0.3 0.9 0.6 1.5 0.6 4.3 0.6 1.7 3.1 ..
.. .. 9.5 .. 12.0 .. 8.9 2.4 12.4 .. 4.1 3.7 .. 10.6 .. 11.7 3.7 6.6 14.0 10.7 .. 8.4 6.9 .. .. 16.2 32.5 .. 15.8 .. .. .. 4.0 19.1 .. 6.2 4.8 .. 16.9 10.5 .. .. 2.2 19.3 4.6 7.6 .. .. .. 6.3 3.6 15.5 .. 9.0 .. ..
.. 3.7 0.0 .. 8.1 .. 7.5 2.0 10.2 11.2 4.5 3.2 .. 6.1 .. .. 5.2 7.9 .. 27.1 .. 10.4 6.2 .. .. 12.4 19.2 .. 18.8 .. .. .. 3.7 5.9 .. 5.4 4.3 .. .. 10.2 31.2 .. 5.6 43.0 4.4 6.4 .. .. 4.9 4.7 .. 15.6 .. 8.5 .. ..
45 65 126 128 65 20 68 44 43 107 102 79 7 32 60 7 296 99 9 13 135 12 82 4 38 92 3,160 .. 139 45 10 8 15 103 175 107 28 22 57 424 49 55 3 120 33 522 7 1 25 442 7 208 44 15 11 8
.. 18 120 100 73 50 55 49 75 110 65 42 8 33 30 8 300 70 9 40 60 15 60 3 30 88 2,400 .. 155 55 10 10 15 60 50 54 27 30 58 430 15 215 7 300 35 421 7 1 14 331 7 204 30 12 7 0
0.6 4.1 1.6 2.8 0.5 1.2 0.8 1.2 1.4 0.2 1.9 1.9 0.3 1.2 3.2 1.2 0.4 2.3 0.2 0.4 2.7 0.2 0.5 0.3 1.3 1.8 0.5 .. 0.9 0.3 0.9 0.6 0.3 4.6 3.5 1.9 1.0 0.7 1.5 2.2 2.4 3.2 0.4 0.5 1.3 2.1 1.4 0.2 0.9 1.1 0.1 4.8 1.4 0.5 2.1 0.3
.. 1.2 1.2 1.8 0.5 3.2 0.6 1.3 2.1 0.2 1.2 1.0 0.3 1.0 1.7 1.1 0.4 1.7 0.2 1.1 1.0 0.2 0.4 0.2 0.8 1.4 0.3 .. 0.9 0.3 0.7 0.7 0.2 2.9 0.9 0.9 0.9 0.8 1.2 1.8 0.6 10.8 0.9 1.1 1.3 1.6 1.2 0.2 0.5 0.8 0.1 4.5 0.7 0.3 1.1 0.0
.. .. .. 20 15 .. 4 13 .. .. 8 20 .. .. .. .. 61 18 .. .. 0 .. 210 .. .. 1 642 .. .. .. .. .. .. .. .. 265 190 .. .. 10 .. .. .. .. 3 845 .. .. .. 1,134 .. 15 .. .. .. ..
.. .. .. 1 3 .. 30 124 .. .. 333 14 .. .. .. .. 18 20 .. .. .. .. 318 .. .. 1 818 .. .. .. .. .. .. 2 .. 85 9 .. .. 25 .. .. .. .. 12 1,617 .. .. 108 745 .. 11 .. .. .. ..
.. .. 16 106 16 8 250 2 64 63 .. 64 .. 24 0 3 66 44 .. .. 2 3 344 1 8 182 1,163 .. 32 2 .. 3 1 24 .. .. 42 .. 14 995 3 14 1 .. 441 387 .. .. 4 969 10 1,994 10 .. 1 ..
31 0 464 5 210 2 614 79 3 21 41 29 .. 1 25 12 154 6 .. 1 22 1 359 .. 15 56 2,307 .. 119 14 0 .. 7 2 .. 53 7 13 1 638 3 180 1 20 24 22 .. .. 80 16 9 567 1 5 .. ..
2004 World Development Indicators
Military expenditures
Armed forces personnel
STATES AND MARKETS
5.8
Defense expenditures and arms transfers
Arms transfers
$ millions % of
% of central
GDP
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
government expenditure
Total
% of
thousands
1990 prices
labor force
Exports
Imports
1992
2002
1992
2002
1992
1999
1992
1999
1992
2002
1992
2002
.. 2.4 2.3 1.7 1.9 .. 1.2 10.5 2.0 .. 0.9 8.2 1.0 1.9 .. 3.4 31.8 0.7 .. 0.8 8.0 2.6 10.6 .. 0.7 .. 1.2 1.4 3.0 2.4 3.5 0.4 0.5 0.5 2.5 4.3 5.1 3.4 4.3 0.9 2.4 1.6 2.4 1.2 0.5 3.0 16.2 6.1 1.2 1.3 1.6 .. 1.3 2.3 2.7 ..
.. 1.8 2.6 1.1 4.8 .. 0.7 8.6 1.9 .. 1.0 8.4 0.9 1.6 .. 2.7 11.2 1.7 2.1 1.8 4.7 3.1 .. .. 2.0 2.8 1.2 0.8 2.1 2.0 1.9 0.2 0.5 0.3 2.3 4.1 2.5 2.3 2.9 1.4 1.6 1.1 1.4 1.1 1.1 1.8 13.0 4.5 1.2 0.8 0.9 1.3 1.0 1.8 2.3 ..
.. 4.3 .. 9.4 11.2 .. 3.0 21.6 3.9 .. 4.5 27.8 .. 7.9 .. 20.6 31.5 3.2 .. 3.4 25.7 5.7 .. .. 3.5 .. 6.6 .. 10.5 .. .. 1.5 3.3 .. 11.6 14.4 .. 30.1 10.6 6.4 4.7 4.3 7.6 .. .. 7.0 40.9 27.7 4.8 4.2 11.8 .. 6.5 5.5 6.2 ..
.. 4.4 .. 4.6 17.2 .. 2.8 16.6 4.8 .. .. 26.5 6.8 5.8 .. 16.6 18.8 9.7 .. 3.9 14.0 6.4 .. .. 6.8 .. 7.1 .. 10.6 .. .. 0.8 3.2 1.2 7.5 12.4 .. 26.6 9.1 8.6 4.0 4.0 2.6 .. .. 5.9 40.7 21.6 4.2 3.3 5.0 9.2 5.1 5.3 5.4 ..
17 78 1,270 283 528 407 13 181 471 3 242 100 15 24 1,200 750 12 12 37 5 37 2 2 85 10 10 21 10 128 12 16 1 175 9 21 195 50 286 8 35 90 11 15 5 76 36 35 580 11 4 16 112 107 270 80 ..
8 51 1,300 296 460 420 14 173 391 3 240 102 33 24 1,000 665 21 12 50 5 58 2 .. 85 12 16 20 5 95 10 11 2 255 11 20 195 8 345 3 35 54 10 12 6 77 33 38 590 13 4 17 115 107 187 71 ..
0.9 1.6 0.3 0.3 3.2 8.2 1.0 8.8 1.9 0.2 0.4 9.8 0.2 0.2 11.3 3.6 2.1 0.6 1.7 0.3 3.1 0.3 0.2 6.6 0.5 1.1 0.4 0.2 1.7 0.3 1.6 0.2 0.5 0.4 2.1 2.1 0.6 1.3 1.3 0.4 1.3 0.6 1.0 0.1 0.2 1.7 6.7 1.4 1.1 0.2 1.0 1.4 0.4 1.4 1.6 ..
0.3 1.1 0.3 0.3 2.4 6.7 0.9 6.6 1.5 0.2 0.4 7.3 0.4 0.2 8.6 2.8 2.5 0.6 2.0 0.4 3.9 0.3 .. 5.8 0.7 1.7 0.3 0.1 1.0 0.2 0.9 0.4 0.6 0.5 1.7 1.7 0.1 1.4 0.4 0.3 0.7 0.5 0.6 0.1 0.2 1.4 6.1 1.2 1.1 0.2 0.9 1.2 0.3 0.9 1.4 ..
.. 21 0 20 1 .. .. 68 368 .. 13 73 .. .. 225 21 .. .. .. 8 .. .. .. 8 .. .. .. .. .. .. .. .. .. 12 .. .. .. .. .. .. 285 4 87 .. .. 5 1 1 .. .. .. .. .. 49 1 ..
.. 24 0 70 0 .. 0 178 490 .. 3 5 9 .. 32 22 82 .. .. .. 45 .. .. 11 3 .. .. 1 8 .. .. .. .. 5 .. .. .. .. .. .. 260 13 .. .. .. 203 .. 8 .. .. .. 5 .. 43 .. ..
.. 1,021 871 47 386 .. 48 1,330 42 .. 1,523 1 .. 3 45 497 897 .. .. 0 38 .. .. .. 74 27 .. 1 16 .. 27 6 12 6 .. 30 .. 52 14 .. 143 61 .. 11 56 317 20 .. 2 10 1 132 59 20 6 ..
.. 14 1,668 51 298 .. 20 226 308 5 154 149 69 61 3 229 27 .. 34 3 4 6 8 145 7 133 .. .. 213 7 9 1 19 .. .. 169 0 208 11 8 236 17 .. 3 2 82 48 .. 12 12 6 4 17 258 103 ..
2004 World Development Indicators
283
5.8
Defense expenditures and arms transfers Military expenditures
Armed forces personnel
Arms transfers
$ millions % of
% of central
GDP
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
government expenditure
Total
% of
thousands
1992
2002
1992
2002
1992
4.3 5.5 4.4 11.7 1.8 .. 2.5 4.8 2.1 2.2 .. 2.9 1.6 3.0 2.5 1.9 2.6 1.8 9.0 0.4 1.9 2.3 2.9 .. 1.9 3.7 1.8 1.6 0.5 4.5 3.8 4.8 2.1 1.5 1.6 3.4 .. 9.1 3.0 3.7 3.0 w 2.4 3.1 3.1 3.0 3.0 2.4 4.5 1.2 7.9 2.7 2.5 3.0 2.3
2.3 4.0 3.6 11.3 1.5 4.9 2.2 5.2 1.9 1.5 .. 1.6 1.2 3.9 3.0 1.5 1.9 1.1 6.1 1.2 1.3 1.4 .. .. 1.6 5.0 3.8 2.4 2.8 2.5 2.4 3.4 1.3 1.1 1.2 .. .. 4.5 0.6 3.2 2.4 w 2.7 2.6 2.7 2.6 2.6 2.3 3.2 1.2 6.9 2.7 1.8 2.4 1.8
10.7 21.1 21.6 .. .. .. 17.7 24.0 .. 5.8 .. 8.8 4.4 11.3 .. .. 5.6 7.0 39.0 .. .. 15.3 .. .. 5.8 18.8 .. .. .. 37.4 8.7 21.1 8.0 .. 8.2 10.6 .. 30.7 .. 11.3 11.3 w 14.5 13.4 15.1 8.5 13.6 23.7 15.8 5.3 .. 16.8 8.4 11.1 5.7
8.1 172 15.4 1,900 .. 30 .. 172 6.8 18 .. 137 .. 8 22.8 56 4.9 33 3.5 15 .. .. 5.4 75 4.2 198 14.7 110 27.4 82 5.2 3 5.4 70 4.2 31 24.2 408 10.1 3 .. 46 7.1 283 .. 8 .. 2 5.2 35 10.0 704 .. 28 10.1 70 9.8 430 30.1 55 7.0 293 16.0 1,920 4.2 25 .. 40 6.1 75 .. 857 .. .. 18.8 64 .. 16 9.4 48 11.0 w 24,533 t 13.0 6,040 11.9 12,071 14.7 10,676 6.1 1,395 12.3 18,111 16.4 6,506 9.6 4,303 6.9 1,443 .. 2,624 14.7 2,152 .. 1,083 11.0 6,422 4.9 2,181
1999
170 900 40 190 13 105 3 60 36 10 .. 68 155 110 105 3 52 39 310 7 35 300 11 2 35 789 15 50 340 65 218 1,490 24 60 75 485 .. 69 17 40 21,198 t 5,869 9,931 8,495 1,436 15,800 5,166 3,192 1,371 2,520 2,153 1,398 5,398 1,768
1990 prices
labor force 1992
1.6 2.5 0.8 3.1 0.5 2.8 0.5 3.4 1.2 1.5 .. 0.5 1.2 1.6 0.8 1.1 1.5 0.8 11.0 0.1 0.3 0.9 0.5 0.4 1.1 2.7 1.8 0.7 1.6 5.2 1.0 1.5 1.8 0.5 1.0 2.4 .. 1.5 0.5 1.0 0.9 w 0.7 1.0 1.0 1.3 0.9 0.7 2.1 0.8 3.3 0.4 0.5 1.4 1.6
1999
Exports 2002
1992
2002
1.6 12 1.2 2,384 1.0 .. 2.9 13 0.3 .. 2.1 24 0.2 .. 3.0 8 1.2 157 1.0 .. .. .. 0.4 83 0.9 88 1.4 .. 0.9 .. 0.8 .. 1.1 182 1.0 283 6.2 38 0.3 .. 0.2 .. 0.8 .. 0.6 .. 0.4 .. 0.9 .. 2.5 .. 0.8 .. 0.5 .. 1.3 232 4.9 .. 0.7 693 1.0 12,108 1.6 .. 0.6 .. 0.8 .. 1.2 .. .. .. 1.3 .. 0.4 .. 0.7 .. 0.7 w 0.6 0.7 0.7 1.1 0.7 0.5 1.3 0.6 2.6 0.4 0.5 1.1 1.3
3 5,941 .. .. .. 7 .. 2 40 .. .. 34 65 .. .. .. 120 11 0 .. .. .. .. .. .. 29 .. .. 270 28 719 3,941 1 170 .. .. .. .. .. ..
160 86 2 1,198 1 0 1 100 181 30 .. 140 187 21 5 .. 47 170 317 24 20 395 3 .. 32 1,347 .. .. .. 204 1,166 198 37 .. 48 .. .. .. .. 57
186 170 14 478 .. 0 13 227 27 0 .. 17 132 9 134 1 45 36 162 .. .. 150 7 1 7 721 .. 6 .. 452 575 346 2 5 50 69 .. 496 27 8
Note: Data for some countries are based on partial or uncertain data or rough estimates; see SIPRI (2003) and U.S. Department of State (2002).
284
2004 World Development Indicators
Imports
1992
5.8
STATES AND MARKETS
Defense expenditures and arms transfers About the data Although national defense is an important function of
the national data provided. Because of the differences in
match. These weapons are assigned a value in an index
government and security from external threats con-
definitions and the difficulty in verifying the accuracy and
that reflects the military resource value of the weapons in
tributes to economic development, high levels of
completeness of data, the data on military spending are
relation to the “core weapons.” These matches are based
defense spending burden the economy and may impede
not strictly comparable across countries.
on such characteristics as size, performance, and type of electronics, and adjustments are made for second-hand
growth. Comparisons of defense spending between
The data on armed forces are from the U.S. Department
countries should take into account the many factors that
of State’s Bureau of Verification and Compliance, which
weapons. More information on SIPRI’s estimation meth-
influence perceptions of vulnerability and risk, including
attributes its data to unspecified U.S. government
ods and sources of arms transfers is available at
historical and cultural traditions, the length of borders
sources. These data refer to military personnel on active
http://projects.sipri.se/armstrade/atmethods.html.
that need defending, the quality of relations with neigh-
duty, including paramilitary forces. These data exclude
bors, and the role of the armed forces in the body politic.
civilians in the defense establishment and so are not con-
Definitions
Data on military expenditures as a share of gross
sistent with the data on military spending on personnel.
• Military expenditures data from SIPRI are derived from
domestic product (GDP) are a rough indicator of the
Moreover, because they exclude personnel not on active
the NATO definition, which includes all current and capital
portion of national resources used for military activities
duty, they underestimate the share of the labor force work-
expenditures on the armed forces, including peacekeep-
and of the burden on the national economy. As an
ing for the defense establishment. Because governments
ing forces; defense ministries and other government agen-
“input” measure, military spending is not directly relat-
rarely report the size of their armed forces, such data typ-
cies engaged in defense projects; paramilitary forces, if
ed to the “output” of military activities, capabilities, or
ically come from intelligence sources.
these are judged to be trained and equipped for military
military security. Data on defense spending from gov-
The data on arms transfers are from SIPRI’s Arms
operations; and military space activities. Such expendi-
ernments are often incomplete and unreliable. Even in
Transfers Project, which reports on international flows
tures include military and civil personnel, including retire-
countries where the parliament vigilantly reviews gov-
of conventional weapons. Data are collected from open
ment pensions of military personnel and social services
ernment budgets and spending, defense spending and
sources, and since publicly available information is
for personnel; operation and maintenance; procurement;
arms transfers often do not receive close scrutiny. For
inadequate for tracking all weapons and other military
military research and development; and military aid (in the
a detailed critique of the quality of such data, see Ball
equipment, SIPRI covers only what it terms major con-
military expenditures of the donor country). Excluded are
(1984) and Happe and Wakeman-Linn (1994).
ventional weapons.
civil defense and current expenditures for previous mili-
This and the previous edition of World Development
SIPRI’s data on arms transfers cover sales of
tary activities, such as for veterans’ benefits, demobiliza-
Indicators use data on military expenditures and arms
weapons, manufacturing licenses, and aid and gifts;
tion, conversion, and destruction of weapons. This
transfers from the Stockholm International Peace
therefore the term arms transfers rather than arms
definition cannot be applied for all countries, however,
Research Institute (SIPRI). The data on military expen-
trade is used. The transferred weapons must be trans-
since that would require much more detailed information
ditures as a percentage of GDP are from SIPRI, and mil-
ferred voluntarily by the supplier, must have a military
than is available about what is included in military budg-
itar y expenditures as a percentage of central
purpose, and must be destined for the armed forces,
ets and off-budget military expenditure items. (For exam-
government expenditure are calculated from SIPRI data
paramilitary forces, or intelligence agencies of another
ple, military budgets might or might not cover civil
on military expenditures and IMF data on central gov-
country. SIPRI data also cover weapons supplied to or
defense, reserves and auxiliary forces, police and para-
ernment expenditures.
from rebel forces in an armed conflict as well as arms
military forces, dual-purpose forces such as military and
SIPRI’s primary source of military expenditure data is
deliveries for which neither the supplier nor the recipi-
civilian police, military grants in kind, pensions for military
official data provided by national governments. These data
ent can be identified with an acceptable degree of cer-
personnel, and social security contributions paid by one
are derived from national budget documents, defense
tainty; these data are available in SIPRI’s database.
part of government to another.) • Armed forces personnel
white papers, and other public documents from official
SIPRI’s estimates of arms transfers, presented in
are active duty military personnel, including paramilitary
government agencies, including governments’ responses
1990 constant price US dollars, are designed as a trend-
forces if these forces resemble regular units in their
to questionnaires sent by SIPRI, the United Nations, or the
measuring device in which similar weapons have similar
organization, equipment, training, or mission. • Arms
Organization for Security and Co-operation in Europe.
values, reflecting both the value and quality of weapons
transfers cover the supply of military weapons through
Secondary sources include international statistics, such
transferred. The trends presented in the tables are
sales, aid, gifts, and those made through manufacturing
as those of the North Atlantic Treaty Organization (NATO)
based on actual deliveries only. SIPRI cautions that
licenses. Data cover major conventional weapons such as
and the International Monetary Fund’s (IMF) Government
these estimated values do not reflect financial value
aircraft, armored vehicles, artillery, radar systems, mis-
Finance Statistics Yearbook. Other secondary sources
(payments for weapons transferred) for three reasons:
siles, and ships designed for military use. Excluded are
include country reports of the Economist Intelligence Unit,
reliable data on the value of the transfer are not avail-
transfers of other military equipment such as small arms
country reports by IMF staff, and specialist journals and
able; even when the value of a transfer is known, it usu-
and light weapons, trucks, small artillery, ammunition,
newspapers. Data on military expenditures presented in
ally includes more than the actual conventional weapons
support equipment, technology transfers, and other serv-
the table may therefore differ from national source data.
such as spares, support systems, and training; and
ices. See About the data for more detail.
Lack of sufficiently detailed data makes it difficult to
even when the value of the transfer is known, details of
apply a common definition of military expenditure global-
the financial arrangements such as credit and loan con-
Data sources
ly, so SIPRI has adopted a definition (derived from the
ditions and discounts are usually not known.
The data on military expenditures and arms trans-
NATO definition) as a guideline (see Definitions). This def-
Given these measurement issues, SIPRI’s method of
fers are from SIPRI’s Yearbook 2003: Armaments,
inition cannot be applied for all countries, however, since
estimating the transfer of military resources includes an
Disarmament and International Security. The data
that would require much more detailed information than
evaluation of the technical parameters of the weapons.
on armed forces personnel are from the Bureau of
is available about what is included in military budgets and
Weapons for which a price is not known are compared
Verification and Compliance’s World Militar y
off-budget military expenditure items. In the many cases
with the same weapons for which actual acquisition
Expenditures and Arms Transfers 2000 (U.S.
where SIPRI cannot make independent estimates, it uses
prices are available (“core weapons”) or for the closet
Department of State 2002).
2004 World Development Indicators
285
5.9
Transpor t infrastructure Roads
Railways Goods
Total road
Rail lines
Air
Traffic
Employee
Ratio of
density
productivity
passenger
Container
traffic units
tariffs to
traffic
Aircraft
Passengers
Air freight
TEU
departures
carried
millions
network
Paved roads
million
Total
Electric
traffic units
km
%
ton-km
km
km
per km
1995–
1995–
1995–
1996–
1996–
1996–
1996–
1996–
thousands
thousands
thousands
ton-km
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001
2002
2002
2002
.. 1,830 .. .. .. 39 .. 16,100 4,836 .. 8,982 17,487 .. .. .. .. .. 168 .. .. 412 .. 84,752 60 .. .. 633,040 .. 31 .. .. 3,070 .. 6,783 .. 40,260 11,696 .. 4,405 31,500 .. .. 4,677 .. 26,500 245,400 .. .. 520 226,982 .. 13,909 .. .. .. ..
.. 440 3,793 .. 28,291 842 .. 5,780 .. 2,768 5,512 3,471 .. 3,163 .. .. 25,652 4,290 .. .. 601 1,006 39,400 .. .. 4,814 58,656 .. 3,154 3,641 900 424 639 2,726 4,667 9,365 2,047 .. .. 5,024 1,202 .. 968 781 5,854 32,515 814 .. 1,562 36,652 953 2,299 .. .. .. ..
.. .. 283 .. 179 784 .. 3,493 .. .. 874 2,705 .. .. .. .. 1,220 2,708 .. .. .. .. .. .. .. 850 14,864 .. .. 858 .. 109 .. 983 132 2,843 625 .. .. 59 503 .. 132 .. 2,372 14,104 .. .. 1,544 19,079 .. .. .. .. .. ..
.. 334 419 .. 318 465 .. 4,261 .. 1,704 7,857 4,445 .. 336 .. .. 1,805 1,846 .. .. 228 1,333 7,479 .. .. 370 30,262 .. .. 169 188 .. 986 1,280 468 2,615 3,648 .. .. 14,308 .. .. 7,999 .. 2,308 3,854 2,087 .. 2,794 4,128 1,778 830 .. .. .. ..
.. 39 230 .. 1,209 80 .. 482 .. 126 630 373 .. 1,381 .. .. 3,970 216 .. .. 69 496 7,600 .. .. 2,162 1,155 .. 1,795 40 55 .. 540 163 81 284 770 .. .. 753 367 .. 1,358 .. 1,056 715 894 .. 276 681 376 182 .. .. .. ..
.. .. .. .. 1.28 0.30 .. 1.14 .. 0.24 .. 1.07 .. 0.31 .. .. .. 0.89 .. .. 0.39 0.34 6.63 .. .. .. 1.19 .. .. .. .. .. 0.67 0.80 .. .. .. .. .. 0.20 .. .. 2.36 .. 2.47 1.54 .. .. 0.37 2.77 .. .. .. .. .. ..
.. .. 338.2 .. 500.2 .. 4,272.0 .. .. 486.3 .. 5,757.6 .. .. .. .. 2,923.1 .. .. .. .. .. 3,299.7 .. .. 1,147.2 55,717.5 b .. 603.1 .. .. 563.8 579.1 .. .. .. 457.3 430.6 462.5 1,223.1 .. .. .. .. 1,091.8 3,278.0 .. .. .. 9,122.3 .. 1,660.5 360.2 .. .. ..
3 4 48 4 94 3 356 136 8 7 6 134 1 21 4 7 628 2 1 .. 5 5 264 1 1 78 932 91 178 5 5 26 1 19 10 47 98 .. 15 42 19 .. 7 28 109 733 8 .. 2 782 4 113 .. .. .. ..
Afghanistan 21,000 Albania 18,000 Algeria 104,000 Angola 51,429 Argentina 215,471 Armenia 15,918 Australia 811,603 Austria 200,000 Azerbaijan 25,013 Bangladesh 207,486 Belarus 75,302 Belgium 149,028 Benin 6,787 Bolivia 53,790 Bosnia and Herzegovina 21,846 Botswana 10,217 Brazil 1,724,929 Bulgaria 37,286 Burkina Faso 12,506 Burundi 14,480 Cambodia 12,323 Cameroon 34,300 Canada 901,903 Central African Republic 23,810 Chad 33,400 Chile 79,605 China 1,698,012 Hong Kong, China 1,831 Colombia 112,988 Congo, Dem. Rep. 157,000 Congo, Rep. 12,800 Costa Rica 35,881 Côte d’Ivoire 50,400 Croatia 28,275 Cuba 60,858 Czech Republic 127,728 Denmark 71,622 Dominican Republic 12,600 Ecuador 43,197 Egypt, Arab Rep. 64,000 El Salvador 10,029 Eritrea 4,010 Estonia 52,038 Ethiopia 31,663 Finland 77,900 France 894,000 Gabon 8,464 Gambia, The 2,700 Georgia 20,229 Germany 230,735 Ghana 46,179 Greece 117,000 Guatemala 14,118 Guinea 30,500 Guinea-Bissau 4,400 Haiti 4,160
286
hauled
Ports
13.3 39.0 68.9 10.4 29.4 96.3 38.7 100.0 92.3 9.5 89.0 78.3 20.0 6.5 52.3 55.0 5.5 94.0 16.0 7.1 16.2 12.5 35.3 2.7 0.8 20.2 91.0 100.0 14.4 .. 9.7 22.0 9.7 84.6 49.0 100.0 100.0 49.4 18.9 78.1 19.8 21.8 19.7 12.0 64.5 100.0 9.9 35.4 93.5 99.1 18.4 91.8 34.5 16.5 10.3 24.3
2004 World Development Indicators
per employee freight tariffs
150 138 3,027 190 5,257 408 32,483 7,070 575 1,544 205 2,342 46 1,509 66 175 35,890 63 53 .. 125 243 23,323 46 46 4,987 83,672 15,636 9,395 47 128 620 46 1,127 589 2,801 6,322 .. 1,292 4,478 1,804 .. 254 1,103 6,414 49,096 366 .. 112 61,043 256 7,579 .. .. .. ..
8 .. 18 51 76 5 1,497 396 76 172 1 655 7 15 1 0 1,540 2 7 .. 4 43 1,578 7 7 1,098 5,014 5,715 540 7 7 13 7 3 40 27 185 .. 6 248 12 .. 1 84 213 4,997 49 .. 2 7,196 19 81 .. .. .. ..
Roads
Railways Goods
Total road
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
hauled
Rail lines
Ports
5.9
STATES AND MARKETS
Transpor t infrastructure
Air
Traffic
Employee
Ratio of
density
productivity
passenger
Container
traffic units
tariffs to
traffic
Aircraft
Passengers
Air freight
TEU
departures
carried
millions
network
Paved roads
million
Total
Electric
traffic units
km
%
ton-km
km
km
per km
1995–
1995–
1995–
1996–
1996–
1996–
1996–
1996–
thousands
thousands
thousands
ton-km
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001
2002
2002
2002
.. 11,398 958 .. .. .. 5,900 .. 219,800 .. 313,118 .. 5,497 .. .. 74,504 .. 1,220 .. 5,359 .. .. .. .. 8,274 2,693 .. .. .. .. .. .. 197,958 964 129 2,952 110 .. .. .. 32,700 .. .. .. .. 12,796 .. 111,323 .. .. .. .. .. 74,403 14,200 ..
.. 7,729 62,759 5,324 6,688 .. 1,915 925 16,499 .. 20,165 293 13,545 2,634 .. 3,123 .. .. .. 2,331 .. .. .. .. 1,905 699 .. 710 1,622 734 .. .. 17,697 .. 1,810 1,907 .. .. 2,382 .. 2,802 3,913 .. .. 3,557 .. .. 7,791 .. .. .. 1,691 491 22,560 2,814 ..
.. 2,628 14,261 131 148 .. 37 .. 10,937 .. 12,080 .. 3,725 .. .. 668 .. .. .. 258 .. .. .. .. 122 233 .. .. 152 .. .. .. 250 .. .. 1,003 .. .. .. .. 2,061 519 .. .. .. .. .. 293 .. .. .. .. .. 11,826 904 ..
.. 2,242 11,725 3,974 3,185 .. 982 2,112 4,102 .. 13,048 2,123 9,981 699 .. 12,456 .. .. .. 5,834 .. .. .. .. 4,171 972 .. 159 1,368 658 .. .. 2,660 .. 2,963 3,425 .. .. 474 .. 6,631 938 .. .. 287 .. .. 2,838 .. .. .. 406 505 3,537 2,066 ..
.. 319 467 610 758 .. 171 1,628 618 .. 1,528 518 1,069 184 .. 1,323 .. .. .. 917 .. .. .. .. 611 162 .. 176 370 322 .. .. 3,925 .. 394 610 .. .. .. .. 752 1,120 .. .. 65 .. .. 232 .. .. .. 363 112 415 465 ..
.. .. 0.31 0.95 .. .. .. .. 1.42 .. .. .. .. .. .. 1.43 .. .. .. .. .. .. .. .. .. 0.39 .. 0.25 0.87 .. .. .. .. .. .. 0.86 .. .. .. .. 2.56 1.46 .. .. 0.10 .. .. 0.28 .. .. .. .. 0.09 0.79 .. ..
13,603 167,839 3,319,644 342,700 167,157 45,550 92,500 16,521 479,688 18,700 1,166,340 7,245 82,638 63,942 31,200 86,990 4,450 18,500 21,716 69,732 7,300 5,940 10,600 83,200 76,573 8,684 49,827 28,400 65,877 15,100 7,660 2,000 329,532 12,691 49,250 57,698 30,400 28,200 62,237 13,223 116,500 92,207 19,032 10,100 194,394 91,443 32,800 257,683 11,643 19,600 29,500 72,900 201,994 364,697 68,732 24,023
20.4 43.7 45.7 46.3 56.3 84.3 94.1 100.0 100.0 70.1 76.6 100.0 93.9 12.1 6.4 74.5 80.6 91.1 44.5 38.6 84.9 18.3 6.2 57.2 91.3 62.0 11.6 18.5 75.8 12.1 11.3 98.0 32.8 86.1 3.5 56.0 18.7 12.2 12.9 30.8 90.0 63.1 11.0 7.9 30.9 77.0 30.0 59.0 34.6 3.5 50.8 12.8 21.0 68.3 86.0 94.0
per employee freight tariffs
406.4 .. 3,243.0 4,539.9 .. .. 775.3 1,461.0 7,918.3 1,065.0 13,501.4 .. .. .. .. 11,542.7 .. .. .. .. 298.9 .. .. .. .. .. .. .. 7,541.7 .. .. .. 1,561.9 .. .. 375.8 .. .. .. .. 6,741.7 1,413.6 .. .. .. .. 1,415.5 965.6 1,248.4 .. .. 537.6 3,270.8 287.4 970.1 1,426.2
.. 33 242 152 90 .. 177 39 351 23 648 16 13 26 1 243 19 4 7 9 11 .. .. 6 10 2 19 5 186 1 2 14 271 4 6 38 8 21 5 13 250 265 1 1 11 271 23 42 22 31 9 31 43 70 114 ..
.. 2,134 18,225 12,114 10,085 .. 19,729 3,731 28,245 2,016 109,247 1,300 593 1,600 84 34,512 2,299 177 220 265 874 .. .. 559 304 166 549 105 16,208 46 106 1,025 19,282 129 270 3,146 282 1,186 222 681 22,931 12,240 61 46 512 13,706 2,104 4,141 1,048 1,235 269 1,879 5,660 2,846 6,894 ..
2004 World Development Indicators
.. 27 550 406 82 .. 116 1,058 1,394 57 8,102 197 15 118 2 7,913 254 6 2 1 81 .. .. 0 2 0 29 1 1,924 7 0 189 311 0 8 51 7 2 21 18 4,204 688 1 7 9 185 130 347 22 24 .. 102 267 67 198 ..
287
5.9
Transpor t infrastructure Roads
Railways Goods
Total road
hauled
Rail lines
Ports
Air
Traffic
Employee
Ratio of
density
productivity
passenger
Container
traffic units
tariffs to
traffic
Aircraft
Passengers
Air freight
TEU
departures
carried
millions
network
Paved roads
million
Total
Electric
traffic units
km
%
ton-km
km
km
per km
1995–
1995–
1995–
1996–
1996–
1996–
1996–
1996–
thousands
thousands
thousands
ton-km
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001 a
2001
2002
2002
2002
Romania 198,603 Russian Federation 537,289 Rwanda 12,000 Saudi Arabia 152,044 Senegal 14,576 Serbia and Montenegro 44,993 Sierra Leone 11,330 Singapore 3,066 Slovak Republic 42,956 Slovenia 20,236 Somalia 22,100 South Africa 362,099 Spain 663,795 Sri Lanka 11,547 Sudan 11,900 Swaziland 3,107 Sweden 212,961 Switzerland 71,176 Syrian Arab Republic 44,575 Tajikistan 27,767 Tanzania 88,200 Thailand 57,403 Togo 7,520 Trinidad and Tobago 8,320 Tunisia 18,997 Turkey 354,373 Turkmenistan 24,000 Uganda 27,000 Ukraine 169,630 United Arab Emirates 1,088 United Kingdom 371,913 United States 6,304,193 Uruguay 8,983 Uzbekistan 81,600 Venezuela, RB 96,155 Vietnam 93,300 West Bank and Gaza .. Yemen, Rep. 67,000 Zambia 91,440 Zimbabwe 18,338 World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
49.5 14,288 67.4 139 8.3 .. 29.9 .. 29.3 .. 62.3 630 7.9 .. 100.0 .. 87.3 20,233 100.0 5,695 11.8 .. 20.3 .. 99.0 98,145 95.0 30 36.3 .. .. .. 78.6 32,000 .. 23,500 21.1 .. 82.7 .. 4.2 .. 98.5 .. 31.6 .. 51.1 .. 65.4 .. 35.5 151,421 81.2 .. 6.7 .. 96.7 16,811 100.0 .. 100.0 150,700 58.8 1,534,430 90.0 .. 87.3 .. 33.6 .. 25.1 .. .. .. 11.5 .. 22.0 .. 47.4 .. 44.0 m 16.0 52.3 52.7 51.1 30.9 25.1 89.0 26.9 63.8 36.9 12.9 92.9 92.9
11,364 86,075 .. 1,390 906 4,058 .. .. 3,662 .. .. 22,657 13,866 1,447 4,599 .. 10,068 .. 1,771 .. 2,722 4,044 .. .. 2,260 8,671 .. 261 22,302 .. 17,067 160,000 3,003 .. 336 3,142 .. .. 1,273 2,759 .. s .. .. .. .. .. .. .. .. .. .. .. .. 124,467
3,929 40,962 .. .. .. 1,103 .. .. 1,536 .. .. 10,430 7,523 .. .. .. 7,405 .. .. .. .. .. .. .. 60 1,752 .. .. 9,170 .. 5,225 484 .. 619 .. .. .. .. .. 311 .. s .. .. .. .. .. .. .. .. .. .. .. .. 63,215
per employee freight tariffs
2,467 267 15,854 1,054 .. .. 799 555 562 339 522 94 .. .. .. .. 3,851 302 2,746 .. .. .. 5,018 2,933 2,295 842 2,271 189 298 98 .. .. 2,492 2,144 .. .. 996 160 .. .. 598 181 3,342 660 .. .. .. .. 1,010 341 1,798 330 .. .. 805 131 9,535 598 .. .. 3,500 2,678 13,800 13,476 127 191 4,830 304 161 180 1,624 154 .. .. .. .. 144 610 1,977 454 .. m .. m .. .. .. 610 .. 610 .. 583 .. .. 2,293 382 .. 304 .. .. .. 555 .. .. .. .. 3,648 770 3,854 618
1.24 0.97 .. .. .. .. .. .. 1.11 .. .. .. .. 0.11 .. .. 2.34 .. .. .. 0.41 0.75 .. .. 1.87 1.20 .. .. .. .. .. 9.28 .. .. 0.21 0.88 .. .. 0.27 0.60 .. m .. .. .. .. .. .. .. .. .. 0.24 .. .. ..
.. 18 961 9 795.7 345 20,892 1,039 .. .. .. .. 1,930.1 109 13,564 862 .. 3 245 7 .. 20 1,186 4 .. 0 14 6 16,986.0 72 17,257 6,772 .. 2 39 1 .. 15 721 5 .. .. .. .. 1,801.6 122 8,167 783 6,669.2 500 40,585 807 1,764.7 11 1,741 203 .. 8 409 33 .. 2 90 0 914.9 201 12,696 267 .. 243 13,292 1,028 .. 13 824 25 .. 6 397 4 .. 5 138 2 3,800.9 98 18,112 1,824 .. 1 46 7 385.2 23 1,269 36 .. 19 1,789 19 1,777.1 106 10,640 381 .. 25 1,464 14 .. 0 41 21 .. 34 1,512 12 5,872.2 55 9,667 2,079 7,059.6 906 71,892 4,941 29,676.9 7,878 c 593,246 c 29,070 c 293.0 8 525 12 .. 23 1,451 69 1,078.0 167 6,370 33 1,290.6 43 4,082 151 .. .. .. .. 388.4 16 869 37 .. 5 47 1 .. 5 251 26 259,736 s 20,481 s 1,615,074 s 116,626 s .. 791 53,966 2,330 94,397 4,365 321,221 17,661 76,710 3,094 236,701 12,669 17,687 1,272 84,520 4,993 104,113 5,156 375,186 19,992 74,871 1,626 144,068 9,726 .. 825 50,903 1,767 12,058 1,626 94,234 3,938 .. 429 42,619 1,750 5,973 319 26,431 1,290 .. 331 16,931 1,520 155,622 15,325 1,239,888 96,634 43,985 3,440 252,823 24,415
a. Data are for the latest year available in the period shown. b. Includes Hong Kong, China. c. Data cover only the carriers designated by the U.S. Department of Transportation as major and national air carriers.
288
2004 World Development Indicators
About the data
5.9
STATES AND MARKETS
Transpor t infrastructure Definitions
Transport infrastructure—highways, railways, ports
million. (Note that kilometers of track may exceed
• Total road network covers motorways, highways,
and waterways, and airports and air traffic control
kilometers of line because of double and triple track-
main or national roads, secondary or regional roads,
systems—and the services that flow from it are cru-
ing, yard tracks, and the like.) Railways whose traffic
and all other roads in a country. • Paved roads are
cial to the activities of households, producers, and
density averages less than 500,000 traffic units per
roads surfaced with crushed stone (macadam) and
governments. Because performance indicators vary
kilometer need to operate at low costs and very high
hydrocarbon binder or bituminized agents, with con-
significantly by transport mode and focus (whether
labor productivity to survive. Labor is the most
crete, or with cobblestones. • Goods hauled by road
physical infrastructure or the services flowing from
expensive factor of production for a railway, and most
are the volume of goods transported by road vehicles,
that infrastructure), highly specialized and carefully
railways have found that improving labor productivity
measured in millions of metric tons times kilometers
specified indicators are required. The table provides
is the most important factor in establishing econom-
selected indicators of the size, extent, and produc-
ic viability. Employee productivity is heavily influ-
tivity of roads, railways, and air transport systems
enced by the balance of passenger and freight
and of the volume of traffic in these modes as well
service, with productivity far lower in passenger serv-
as in ports.
ice. In developing countries a ratio of passenger tar-
Data for transport sectors are not always interna-
iffs to freight tariffs greater than 1 indicates an
tionally comparable. Unlike for demographic statis-
absence of significant cross-subsidies and a poten-
tics, national income accounts, and international
tial to provide higher quality service. This ratio, like
trade data, the collection of infrastructure data has
the other railway indicators, has no normative value
not been “internationalized.” But data on roads are
and is intended for relative analysis only.
traveled. • Total rail lines refer to the track length of the railway lines. • Electric rail lines refer to the length of line with electric traction. This line can include overhead catenary at various direct current or alternating current voltages and third-rail direct current systems. • Railway traffic density is total traffic units divided by total rail lines; total traffic units are the sum of passenger-kilometers (passengers times kilometers traveled) and freight ton-kilometers (metric tons of freight times kilometers traveled) divided by kilometers of line. • Railway employee productivity is annual out-
collected by the International Road Federation (IRF),
Measures of port container traffic, much of it com-
put (in traffic units) per employee. • Ratio of railway
and data on air transport by the International Civil
modities of medium to high value added, give some
passenger tariffs to freight tariffs is the average pas-
Aviation Organization (ICAO).
indication of economic growth in a country. But when
senger fare (total passenger revenue divided by total
National road associations are the primary source
traffic is merely transshipment, much of the eco-
passenger-kilometers) divided by the average freight
of IRF data. In countries where such an association
nomic benefit goes to the terminal operator and
rate (total freight revenue divided by total ton-
is lacking or does not respond, other agencies are
ancillary services for ships and containers rather
kilometers). A ratio of very much less than 1 indicates
contacted, such as road directorates, ministries of
than to the country more broadly. In transshipment
a likelihood of passengers being cross-subsidized by
transport or public works, or central statistical
centers empty containers may account for as much
freight tariffs. • Port container traffic measures the
offices. As a result, the compiled data are of uneven
as 40 percent of traffic.
flow of containers from land to sea transport modes
quality. Even when data are available, they are often
The air transport data represent the total (interna-
and vice versa in twenty-foot-equivalent units (TEUs), a
of limited value because of incompatible definitions
tional and domestic) scheduled traffic carried by the
standard-size container. Data refer to coastal shipping
(for example, in some countries a path used mainly
air carriers registered in a country. Countries submit
as well as international journeys. Transshipment traffic
by animals may be considered a road, while in others
air transport data to ICAO on the basis of standard
is counted as two lifts at the intermediate port (once to
a road must be registered with a state agency
instructions and definitions issued by ICAO. In many
responsible for its maintenance), inappropriate geo-
cases, however, the data include estimates by ICAO
graphic units, lack of timeliness, and variations in
for nonreporting carriers. Where possible, these esti-
the nature of the terrain.
mates are based on previous submissions supple-
Moreover, the quality of transport service (reliability, transit time, and condition of goods delivered) is
mented by information published by the air carriers, such as flight schedules.
off-load and again as an outbound lift) and includes empty units. • Aircraft departures are domestic and international takeoffs of air carriers registered in the country. • Air passengers carried include both domestic and international passengers of air carriers registered in the country. • Air freight is the sum of the metric tons of freight, express, and diplomatic bags
rarely measured, though it may be as important as
The data represent the air traffic carried on sched-
quantity in assessing an economy’s transport sys-
uled services, but changes in air transport regula-
tem. A new initiative is under way in the World Bank
tions in Europe have made it more difficult to classify
to improve data availability and consistency.
traffic as scheduled or nonscheduled. Thus recent
Information covering access, affordability, efficiency,
increases shown for some European countries may
Data sources
quality, and fiscal and institutional aspects of the
be due to changes in the classification of air traffic
The data on roads are from the IRF’s World Road
transport sector will help to measure progress and
rather than actual growth. For countries with few air
Statistics. The data on railways are from a data-
improve performance.
carriers or only one, the addition or discontinuation
base maintained by the World Bank’s Transport
of a home-based air carrier may cause significant
and Urban Development Department, Transport
changes in air traffic.
Division. The data on port container traffic are from
The railways indicators focus on efficiency and productivity. Traffic density is an indication of the inten-
carried on each flight stage (the operation of an aircraft from takeoff to its next landing), multiplied by the stage distance, by air carriers registered in the country.
sity of use of a railway’s largest investment—its
Containerisation International’s Containerisation
track. Traffic densities for branch lines tend to range
International Yearbook. And the data on air trans-
around 500,000 traffic units per kilometer (see
port are from the ICAO’s Civil Aviation Statistics of
Definitions), while those for mainlines range from
the World and ICAO staff estimates.
more than 5 million traffic units per kilometer to 100
2004 World Development Indicators
289
5.10
Power and communications Telephone mainlines a
Electric power Transmission and Consumption distribution per losses capita % kwh of output 2001 2001
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
290
.. 1,123 638 100 2,107 1,127 9,292 7,031 1,846 94 2,676 7,596 66 403 1,444 .. 1,729 3,066 .. .. .. 170 15,385 .. .. 2,557 893 5,541 818 47 75 1,557 .. 2,683 1,069 4,977 6,160 822 631 1,046 595 .. 3,764 22 14,899 6,682 814 .. 718 6,093 341 4,205 358 .. .. 36
.. 51 16 15 14 26 7 5 13 18 14 5 70 12 17 .. 17 14 .. .. .. 26 8 .. .. 7 7 12 22 4 65 7 .. 21 15 7 5 26 25 12 13 .. 16 10 4 6 18 .. 12 4 15 9 23 .. .. 53
2004 World Development Indicators
per 1,000 people 2002
In largest city per 1,000 people 2002
1 71 61 6 219 143 539 489 113 5 299 494 9 68 237 87 223 368 5 3 3 7 635 2 2 230 167 565 179 0 7 251 20 417 51 362 689 110 110 110 103 9 351 5 523 569 25 28 131 651 13 491 71 3 9 16
8 94 124 21 .. 212 .. .. 270 30 397 .. .. 109 502 .. 311 .. 42 .. 19 .. .. .. 8 333 584 577 327 .. .. .. 68 .. 121 666 .. .. 133 .. .. 43 422 60 .. .. .. 97 233 696 83 731 .. .. .. ..
Waiting list thousands 2002
.. 98.5 727.0 240.3 93.1 64.1 0.0 0.0 55.4 199.1 341.5 .. 23.0 .. .. .. 200.0 145.8 12.4 4.7 .. .. 0.0 1.2 .. 32.3 .. 0.0 1,174.7 .. .. 15.8 24.2 0.0 .. 25.1 0.0 .. 14.5 206.1 38.2 38.5 4.1 145.9 0.0 0.0 .. 10.6 138.8 0.0 154.8 7.6 .. 1.4 5.1 ..
Faults per 100 mainlines 2002
per employee 2002
.. 57.2 6.0 .. .. 60.0 8.0 5.7 48.0 .. 26.8 5.9 6.0 .. .. .. 3.0 3.5 19.7 .. .. .. .. .. 60.8 25.0 .. .. 45.5 .. .. 4.2 81.0 12.0 9.6 8.3 8.0 .. 35.3 0.5 14.5 53.3 16.3 .. .. .. .. .. 17.2 .. 67.4 12.1 .. .. 70.5 ..
.. 65 105 38 337 92 136 228 113 29 112 197 48 174 130 83 400 104 51 27 61 50 237 23 16 179 .. 216 229 .. .. 213 91 171 34 153 173 55 275 140 168 56 136 47 124 232 32 34 39 232 57 302 236 33 46 18
Mobile International phones a telecommunications a
Revenue per line $ 2002
.. 1,139 192 1,633 931 151 1,276 1,315 93 593 72 1,343 1,044 742 247 1,238 546 318 984 737 705 .. 1,053 1,196 .. 698 238 1,700 499 .. .. 351 1,186 679 1,370 890 1,091 .. 336 335 903 458 881 295 1,735 944 2,771 760 208 1,084 460 864 593 1,119 .. ..
Cost of local call $ per 3 minutes 2002
per 1,000 people 2002
.. 0.02 0.02 0.09 0.03 0.02 0.12 0.19 0.10 0.03 0.01 0.14 0.28 0.09 0.03 0.02 0.03 0.02 0.10 0.02 0.03 0.06 .. 0.43 0.11 0.10 0.03 0.00 0.03 .. .. 0.03 0.22 0.09 0.09 0.13 0.08 0.06 0.03 0.02 0.07 0.03 0.09 0.02 0.13 0.12 0.22 0.03 0.03 0.09 0.03 0.07 0.08 0.08 .. ..
1 276 13 9 178 19 640 786 107 8 47 786 32 105 196 241 201 333 8 7 28 43 377 3 4 428 161 942 106 11 67 111 62 535 2 849 833 207 121 67 138 0 650 1 867 647 215 73 102 727 21 845 131 12 0 17
Outgoing Cost of traffic call to U.S. minutes per $ per subscriber 3 minutes 2002 2002
.. 282 111 404 53 67 215 312 35 77 81 353 294 69 106 425 21 48 307 127 278 208 254 466 363 79 7 1,039 40 .. .. 125 204 198 65 107 214 245 48 36 243 125 217 36 172 139 854 352 108 190 213 158 172 734 271 ..
.. 2.47 .. 3.11 .. .. 0.68 .. 5.52 2.47 2.25 .. 5.76 .. 3.01 .. .. 1.45 2.58 3.71 .. .. .. 12.93 9.11 2.18 .. 2.62 .. .. .. 1.93 6.38 .. 7.35 0.83 .. .. 1.75 2.57 1.23 3.55 0.74 7.05 1.06 .. .. 3.46 0.68 0.35 1.13 0.67 .. 4.61 .. ..
Telephone mainlines a
Electric power Transmission and Consumption distribution per losses capita % kwh of output 2001 2001
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
508 2,998 365 404 1,570 1,475 5,415 5,841 4,813 2,343 7,237 1,252 2,850 117 .. 5,288 10,251 1,351 .. 1,943 1,824 .. .. 4,020 1,851 .. .. .. 2,731 .. .. .. 1,643 785 .. 461 266 88 .. 61 6,199 8,792 268 .. 82 24,881 3,078 358 1,340 .. 833 692 489 2,490 3,932 ..
21 13 27 13 16 .. 8 3 7 8 4 12 17 21 .. 6 3 34 .. 23 18 .. .. .. 10 .. .. .. 6 .. .. .. 14 47 .. 7 3 20 .. 21 4 11 30 .. 38 7 17 26 22 .. 3 11 12 10 9 ..
per 1,000 people 2002
In largest city per 1,000 people 2002
48 361 40 37 187 28 502 467 481 170 558 127 130 10 21 489 204 77 11 301 199 13 2 118 270 271 4 7 190 5 12 270 147 161 53 38 5 7 65 14 618 448 32 2 6 734 84 25 122 12 47 66 42 295 421 346
.. 588 136 261 381 .. .. .. .. .. 554 183 .. 77 .. 632 46 168 65 500 .. 64 .. .. 427 .. 9 41 .. 24 .. 376 156 350 99 .. .. 32 157 315 .. .. .. 24 12 .. .. .. 284 115 91 .. 265 .. .. ..
Waiting list thousands 2002
Faults per 100 mainlines 2002
342.2 7.8 1,648.8 .. 1,480.5 .. .. .. 0.0 168.6 0.0 1.4 168.3 134.0 .. 0.0 0.0 37.7 5.9 14.3 .. 21.1 .. .. 3.9 .. 1.8 17.4 65.9 .. .. 13.5 .. 107.3 37.8 5.0 12.7 93.5 2.6 317.3 0.0 0.0 .. .. .. 0.0 2.1 214.0 .. 0.2 .. 33.0 .. 501.6 .. ..
3.6 .. 126.0 20.0 .. .. 7.6 .. .. 39.7 .. 10.7 .. 220.9 .. 1.5 .. .. .. 22.7 .. 72.8 .. .. 17.0 .. 42.5 .. 40.0 177.6 .. 56.8 1.9 4.9 28.4 24.8 70.0 169.0 42.2 88.1 .. 30.7 4.6 104.6 .. .. .. .. 30.8 .. 3.4 .. .. 17.2 10.2 ..
per employee 2002
62 176 92 181 258 .. 133 249 358 192 490 108 65 17 .. 437 66 50 45 180 .. 80 .. 43 217 143 25 17 222 37 26 181 139 95 30 74 39 43 81 70 169 325 82 16 58 221 105 58 78 36 25 372 273 159 240 261
5.10
STATES AND MARKETS
Power and communications
Mobile International phones a telecommunications a
Revenue per line $ 2002
1,210 1,015 198 300 104 .. 1,643 1,190 1,288 1,050 1,609 1,128 289 1,482 .. 935 1,778 110 437 338 .. 415 .. .. 472 406 1,614 625 948 1,159 1,330 499 1,134 136 443 1,465 1,533 .. 700 257 1,313 1,127 591 848 715 1,549 2,238 395 1,018 1,221 1,069 690 824 646 1,485 1,534
Cost of local call $ per 3 minutes 2002
per 1,000 people 2002
0.06 0.13 0.02 0.03 0.01 .. 0.14 0.02 0.11 0.07 0.07 0.04 0.00 0.07 .. 0.03 0.00 0.09 0.02 0.11 0.07 0.11 .. .. 0.14 0.01 0.07 0.06 0.03 0.07 0.13 0.04 0.16 0.02 0.02 0.15 0.08 0.05 0.03 0.01 0.11 0.00 0.08 0.10 .. 0.15 0.07 0.02 0.12 0.08 0.09 0.08 0.00 0.08 0.11 ..
49 676 12 55 33 1 763 955 939 535 637 229 64 42 0 679 519 10 10 394 227 42 1 13 475 177 10 8 377 5 92 289 255 77 89 209 14 1 80 1 745 622 38 1 13 844 171 8 189 3 288 86 191 363 825 316
Outgoing Cost of traffic call to U.S. minutes per $ per subscriber 3 minutes 2002 2002
144 66 16 37 21 .. 706 385 169 310 37 294 63 75 .. 45 394 46 138 65 149 64 868 68 36 116 111 435 144 300 394 113 134 75 37 226 274 27 499 102 260 547 108 292 124 165 729 35 120 402 104 82 52 73 124 ..
2004 World Development Indicators
2.85 0.79 3.20 .. 7.70 .. .. .. .. .. 1.67 1.96 .. 5.84 .. 1.74 1.50 8.92 6.37 2.02 .. 2.31 .. .. 2.31 .. 7.41 0.06 2.37 12.28 .. 2.50 3.04 2.21 4.92 1.63 .. 0.36 4.28 .. .. .. 3.20 8.77 .. 0.31 0.79 3.60 4.36 4.32 0.82 .. .. 1.79 0.93 ..
291
5.10
Power and communications Telephone mainlines a
Electric power Transmission and Consumption distribution per losses capita % kwh of output 2001 2001
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
1,620 4,270 .. 5,117 130 .. .. 7,178 4,360 5,535 .. 3,793 4,933 285 67 .. 14,916 7,474 973 2,151 58 1,508 .. 3,829 987 1,391 1,231 .. 2,217 10,787 5,653 11,714 1,918 1,634 2,605 325 .. 109 585 810 2,159 w 317 1,447 1,304 2,505 938 816 2,774 1,493 1,409 331 456 8,421 5,904
13 12 .. 8 19 .. .. 4 4 5 .. 8 9 18 15 .. 7 5 .. 15 25 9 .. 8 11 19 13 .. 20 9 8 6 16 9 25 14 .. 26 3 21 9w 23 11 11 12 13 8 13 16 12 27 11 6 6
per 1,000 people 2002
194 242 3 144 22 233 5 463 268 506 10 107 506 47 21 34 736 744 123 37 5 105 10 250 117 281 77 2 216 314 591 646 280 66 113 48 87 28 8 25 176 w 28 167 164 190 100 131 228 168 107 34 15 585 555
In largest city per 1,000 people 2002
Waiting list thousands 2002
.. 542.1 .. 5,809.6 .. .. 214 73.6 71 9.8 424 143.0 .. .. 463 0.0 665 7.0 .. 0.5 .. .. .. 50.0 .. .. 299 257.7 80 444.0 131 15.6 .. 0.0 .. 0.0 156 2,805.9 133 6.1 20 8.0 452 710.2 35 27.5 .. .. .. 108.7 388 142.9 .. 36.8 .. .. .. 2,158.7 348 0.4 .. 0.0 .. .. 335 0.0 248 38.9 .. .. .. .. .. 0.7 80 704.8 22 11.6 76 158.9 296 w .. s 130 4,517.4 406 .. 524 .. .. .. 270 .. 502 .. .. 10,859.2 .. .. .. 6,099.3 127 2,623.8 .. .. .. .. .. 14.1
Faults per 100 mainlines 2002
23.0 .. .. 26.2 17.3 .. .. 2.4 27.0 22.5 .. 48.2 .. 99.6 .. 160.0 .. .. 50.0 126.0 24.0 19.8 6.2 .. 29.0 37.4 86.4 .. .. 0.3 11.0 12.4 .. 87.4 2.0 .. 97.0 .. 90.8 .. 26.4 m 62.5 28.3 35.3 17.2 40.0 .. 22.8 9.6 10.1 88.1 56.8 8.3 6.8
per employee 2002
114 75 61 155 152 178 19 221 106 227 .. 116 273 72 150 67 304 231 84 48 46 222 57 100 143 297 52 23 86 115 148 170 168 69 192 49 188 100 28 63 105 m 49 133 110 173 80 45 113 161 140 55 56 197 215
Mobile International phones a telecommunications a
Revenue per line $ 2002
410 209 934 1,893 852 146 .. 1,738 604 671 .. 1,102 1,447 379 364 826 1,189 1,771 238 32 1,471 637 823 958 450 275 145 .. 146 1,994 2,087 1,579 751 118 1,033 356 353 266 808 817 890 m 437 700 637 885 545 440 318 709 1,128 387 984 1,343 1,395
Cost of local call $ per 3 minutes 2002
0.11 .. 0.09 0.04 0.10 0.01 0.03 0.02 0.12 0.07 .. 0.09 0.07 0.03 0.03 0.04 0.11 0.15 0.01 0.01 0.12 0.07 0.10 0.04 0.02 0.13 .. 0.21 .. 0.00 0.18 0.00 0.17 0.01 0.04 0.02 0.05 0.02 0.09 0.04 0.06 m 0.07 0.05 0.04 0.09 0.06 0.03 0.06 0.06 0.04 0.02 0.09 0.07 0.13
per 1,000 people 2002
236 120 14 217 55 257 13 796 544 835 3 304 824 49 6 61 889 789 23 2 19 260 35 278 52 347 2 16 84 696 841 488 193 7 256 23 93 21 13 30 110 m 13 149 99 241 62 24 196 126 52 8 16 698 805
Outgoing Cost of traffic call to U.S. minutes per $ per subscriber 3 minutes 2002 2002
50 1.82 34 .. 245 .. 578 2.40 294 1.81 123 2.08 336 .. 1,020 0.68 134 0.79 106 0.52 .. .. 117 0.58 210 .. 58 2.33 80 3.92 657 2.42 188 0.32 481 .. 90 4.81 42 6.96 73 5.28 52 1.54 349 2.15 218 2.22 164 .. 34 2.09 64 .. 125 3.51 36 .. 1,732 1.73 258 .. 217 .. 87 4.88 36 13.95 104 .. 17 .. 132 1.03 81 4.10 178 6.45 309 4.36 155 m 2.09 m 108 3.63 124 2.08 110 2.09 166 2.20 117 2.40 44 4.62 65 2.08 172 2.22 213 2.18 68 2.33 208 3.55 285 0.93 181 0.77
a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report 2003. Please cite the ITU for third-party use of these data.
292
2004 World Development Indicators
About the data
5.10
Definitions
The quality of an economy’s infrastructure, including
generated by primary sources of energy—coal, oil,
• Electric power consumption measures the produc-
power and communications, is an important element in
gas, nuclear, hydro, geothermal, wind, tide and wave,
tion of power plants and combined heat and power
investment decisions for both domestic and foreign
and combustible renewables—where data are avail-
plants less transmission, distribution, and transfor-
investors. Government effort alone will not suffice to
able. Neither production nor consumption data cap-
mation losses and own use by heat and power plants.
meet the need for investments in modern infrastructure;
ture the reliability of supplies, including breakdowns,
• Electric power transmission and distribution loss-
public-private partnerships, especially those involving
load factors, and frequency of outages.
es are losses in transmission between sources of
local providers and financiers, will be critical in lowering
Over the past decade new financing and technology,
supply and points of distribution and in distribution to
costs and delivering value for money. In telecommuni-
along with privatization and liberalization, have
consumers, including pilferage. • Telephone main-
cations, competition in the marketplace, along with
spurred dramatic growth in telecommunications in
lines are telephone lines connecting a customer’s
sound regulation, is lowering costs and improving the
many countries. The table presents some common
equipment to the public switched telephone network.
quality of and access to services around the globe.
per formance indicators for telecommunications,
Data are presented for the entire country and for the
An economy’s production and consumption of elec-
including measures of supply and demand, service
largest city. • Waiting list shows the number of appli-
tricity is a basic indicator of its size and level of
quality, productivity, economic and financial perform-
cations for a connection to a mainline that have been
development. Although a few countries export electric
ance, and tariffs. The quality of data varies among
held up by a lack of technical capacity. • Telephone
power, most production is for domestic consumption.
reporting countries as a result of differences in regu-
mainline faults is the number of reported faults per
Expanding the supply of electricity to meet the grow-
latory obligations for the provision of data.
100 telephone mainlines. • Telephone mainlines per
ing demand of increasingly urbanized and industrial-
Demand for telecommunications is often measured
employee are calculated by dividing the number of
ized economies without incurring unacceptable social,
by the sum of telephone mainlines and registered appli-
mainlines by the number of telecommunications staff
economic, and environmental costs is one of the
cants for new connections. (A mainline is normally iden-
(with part-time staff converted to full-time equiva-
great challenges facing developing countries.
tified by a unique number that is the one billed.) In some
lents) employed by enterprises providing public
Data on electric power production and consumption
countries the list of registered applicants does not
telecommunications services. • Revenue per line is
are collected from national energy agencies by the
reflect real current pending demand, which is often hid-
the revenue received by firms per mainline for provid-
International Energy Agency (IEA) and adjusted by the
den or suppressed, reflecting an extremely short supply
ing telecommunications services. • Cost of local call
IEA to meet international definitions (for data on elec-
that has discouraged potential applicants from applying
is the cost of a three-minute, peak rate, fixed line call
tricity production, see table 3.9). Electricity consump-
for telephone service. And in some countries the wait-
within the same exchange area using the subscriber’s
tion is equivalent to production less power plants’
ing list may overstate demand because applicants have
equipment (that is, not from a public phone).
own use and transmission, distribution, and transfor-
placed their names on the list several times to improve
• Mobile phones refer to portable telephone sub-
mation losses. It includes consumption by auxiliary
their chances. Telephone mainline faults refer to the
scribers to an automatic public mobile telephone
stations, losses in transformers that are considered
number of reported faults per 100 main telephone
service using cellular technology that provides access
integral parts of those stations, and electricity pro-
lines. It is calculated by the total number of reported
to the public switched telephone network, per 1,000
duced by pumping installations. It covers electricity
faults for the year divided by the number of telephone
people. • International telecommunications outgo-
mainlines and multiplied by 100. The definition of fault
ing traffic is the telephone traffic, measured in min-
varies among countries: some operators define faults
utes per subscriber, that originates in the country and
as including malfunctioning customer equipment while
has a destination outside the country. • Cost of call
others include only technical faults. The number of
to U.S. is the cost of a three-minute, peak rate, fixed
mainlines no longer reflects a telephone system’s full
line call from the country to the United States.
5.10a Mobile phone subscribers are approaching (or surpassing) 500 per 1,000 people in some developing and transition economies
capacity because mobile telephones—whose use has
Mobile phone subscribers per 1,000 people
been expanding rapidly in most countries, rich and
600 Croatia
poor—provide an alternative point of access. In addition to waiting list and mainline faults, the
500
table includes two other measures of efficiency in Malaysia
400
300 South Africa 200 Argentina 100 Georgia 0 1995
1998
2002
Source: World Bank data files, based on International Telecommunication Union data.
telecommunications: mainlines per employee and revenue per mainline. Caution should be used in inter-
Data sources
preting the estimates of mainlines per employee
The data on electricity consumption and losses are
because firms often subcontract part of their work.
from the IEA’s Energy Statistics and Balances of
The cross-country comparability of revenue per main-
Non-OECD Countries 2000–2001, the IEA’s Energy
line may also be limited because, for example, some
Statistics of OECD Countries 2000–2001, and the
countries do not require telecommunications providers
United Nations Statistics Division’s Energy Statistics
to submit financial information; the data usually do not
Yearbook. The telecommunications data are from
include revenues from mobile phones or radio, paging,
the International Telecommunication Union’s World
and data services; and there are definitional and
Telecommunication Development Report 2003.
accounting differences between countries.
2004 World Development Indicators
293
STATES AND MARKETS
Power and communications
5.11
The information age Daily newspapers
per 1,000 people 2000
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
294
5 35 27 11 37 5 293 296 27 53 152 160 5 55 152 27 43 116 1 2 2 7 159 2 0 98 .. 792 46 3 8 91 16 114 118 254 283 27 96 31 28 .. 176 0 445 201 30 2 5 305 14 23 33 .. 5 3
Radios
Television a
per 1,000 people 2001
Cable Sets subscribers per 1,000 per 1,000 people people 2002 2002
114 260 244 78 681 264 1,996 763 22 49 199 793 445 667 243 150 433 543 433 220 119 161 1,047 80 233 759 339 686 549 385 109 816 185 339 185 803 1,400 181 422 339 481 464 1,136 189 1,624 950 488 394 568 570 695 478 79 52 178 18
2004 World Development Indicators
14 318 114 52 326 229 731 637 332 59 362 541 12 121 116 44 349 453 79 31 8 75 691 6 2 523 350 504 303 2 13 231 61 293 251 538 859 .. 237 229 233 50 502 6 670 632 308 15 357 661 53 519 145 47 36 6
0.0 2.3 0.0 0.9 162.9 1.2 76.3 132.0 0.6 27.0 77.2 374.7 .. 9.7 19.4 .. 13.8 93.5 0.0 0.0 .. .. 252.9 .. .. 57.4 75.0 90.6 13.6 .. .. .. 0.0 8.1 .. 94.4 201.4 .. 33.8 0.0 49.7 0.0 107.0 0.0 199.7 57.5 11.5 .. 12.4 249.9 0.3 0.0 .. 0.0 .. 4.8
Personal computers
per 1,000 people a 2002
In education number 2002
.. 11.7 7.7 1.9 82.0 15.8 565.1 369.3 .. 3.4 .. 241.4 2.2 22.8 .. 40.7 74.8 51.9 1.6 0.7 2.0 5.7 487.0 2.0 1.7 119.3 27.6 422.0 49.3 .. 3.9 197.2 9.3 173.8 31.8 177.4 576.8 .. 31.1 16.6 25.2 2.5 210.3 1.5 441.7 347.1 19.2 13.8 31.6 431.3 3.8 81.7 14.4 5.5 .. ..
.. .. .. .. 98,635 .. 672,471 196,210 .. .. .. 285,395 .. .. .. .. 774,363 22,078 .. .. .. .. 1,306,715 .. .. 131,024 3,555,157 173,161 167,461 .. .. 12,320 .. .. .. 62,900 276,813 44,792 99,334 48,816 .. .. .. .. 210,163 1,682,650 .. .. .. 2,379,660 .. 117,911 8,310 .. .. ..
Internet
Users per 1,000 people a 2002
0 4 16 3 112 16 482 409 37 2 82 328 7 32 26 30 82 81 2 1 2 4 513 1 2 238 46 430 46 1 2 193 5 180 11 256 513 36 42 28 46 2 328 1 509 314 19 18 15 412 8 155 33 5 4 10
Total monthly price a 20 hours of use % of monthly $ GNI per capita 2003 2003
.. 29 18 79 13 45 18 33 108 20 13 29 46 22 7 27 28 12 45 81 57 52 13 175 69 22 10 4 19 74 121 26 67 17 58 21 18 33 32 5 48 27 14 27 23 14 122 27 26 14 44 38 31 63 105 130
.. 24.8 12.4 143.3 3.9 68.0 1.1 1.7 183.0 66.8 11.3 1.5 146.5 29.8 6.9 10.9 11.8 8.3 247.5 971.3 245.8 110.7 0.7 807.9 375.6 6.1 13.0 0.2 12.2 986.7 207.8 7.6 132.1 4.4 32.2 4.5 0.7 17.1 26.3 4.5 27.8 200.9 3.9 329.1 1.2 0.8 46.9 116.2 48.4 0.7 194.8 3.9 21.4 185.2 840.7 354.5
Information and communications technology expenditures Secure servers number 2003
1 1 4 1 274 2 5,749 1,156 1 1 6 576 1 10 4 .. 1,580 24 .. .. 1 1 10,785 .. .. 233 182 768 105 .. .. 144 1 107 1 229 998 22 23 17 23 .. 89 2 932 2,860 3 .. 4 8,451 5 205 36 .. .. 3
% of GDP 2002
.. .. .. .. 3.9 .. 6.4 5.3 .. .. .. 5.5 .. .. .. .. 8.3 6.9 .. .. .. .. 5.9 .. .. 5.7 5.8 4.6 6.7 .. .. .. .. 7.5 .. 7.2 5.8 .. .. 3.3 .. .. .. .. 5.8 5.2 .. .. .. 5.2 .. 4.8 .. .. .. ..
Per capita $ 2002
.. .. .. .. 95 .. 1,298 1,322 .. .. .. 1,324 .. .. .. .. 205 146 .. .. .. .. 1,352 .. .. 246 58 1,025 114 .. .. .. .. 364 .. 489 1,852 .. .. 38 .. .. .. .. 1,464 1,246 .. .. .. 1,252 .. 604 .. .. .. ..
Daily newspapers
per 1,000 people 2000
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
55 465 60 23 28 19 150 290 104 62 578 75 .. 10 208 393 374 27 4 135 107 8 12 15 29 21 5 3 158 1 0 119 94 13 30 28 2 9 19 12 306 362 30 0 24 569 29 40 62 14 43 0 82 102 32 126
Radios
Television a
per 1,000 people 2001
Cable Sets subscribers per 1,000 per 1,000 people people 2002 2002
411 690 120 159 281 222 695 526 878 795 956 372 411 221 154 1,034 570 110 148 700 182 61 274 273 524 205 216 499 420 180 148 379 330 758 50 243 44 66 134 39 980 992 270 122 200 3,324 621 105 300 86 188 269 161 523 301 761
119 475 83 153 173 83 694 330 494 374 785 177 338 26 162 363 418 49 52 850 357 35 25 137 487 282 25 4 210 33 99 299 282 296 79 167 14 8 269 8 648 557 123 10 103 884 553 150 191 21 218 172 182 422 413 339
21.6 170.1 38.9 0.3 .. .. 143.0 184.0 1.4 .. 183.1 0.3 6.6 0.5 0.0 132.0 .. 3.1 0.0 132.2 29.9 .. .. .. 75.1 .. .. 0.0 0.0 .. .. .. 24.3 13.3 18.5 .. .. .. 16.0 .. 401.4 7.1 10.8 .. 0.5 184.5 0.0 0.2 .. 4.2 21.3 16.6 37.0 91.4 122.1 91.2
Personal computers
per 1,000 people a 2002
In education number 2002
13.6 108.4 7.2 11.9 75.0 8.3 420.8 242.6 230.7 53.9 382.2 37.5 .. 6.4 .. 555.8 120.6 12.7 3.3 171.7 80.5 .. .. 23.4 109.7 .. 4.4 1.3 146.8 1.4 10.8 116.5 82.0 17.5 28.4 23.6 4.5 5.1 70.9 3.7 466.6 413.8 27.9 0.6 7.1 528.3 35.0 4.2 38.3 58.7 34.6 43.0 27.7 105.6 134.9 ..
.. 52,452 347,801 58,593 .. .. 141,360 .. 1,109,182 .. 2,292,417 .. .. .. .. 857,233 .. .. .. .. .. .. .. .. .. .. .. .. 241,392 .. .. .. 302,325 .. .. .. .. .. .. .. 652,319 196,364 .. .. .. 268,861 .. .. 15,253 .. .. 32,308 125,055 109,598 169,230 302,941
Internet
Users per 1,000 people a 2002
25 158 16 38 48 1 271 301 352 229 449 58 16 13 0 552 106 30 3 133 117 10 0 23 144 48 3 3 320 2 4 99 98 34 21 24 2 1 27 3 506 484 17 1 3 503 66 10 41 14 17 93 44 230 194 156
Total monthly price a 20 hours of use % of monthly $ GNI per capita 2003 2003
41 10 9 22 6 .. 28 30 17 44 21 26 34 46 .. 10 25 15 32 58 37 43 .. 19 34 19 67 62 8 58 39 15 23 19 18 25 51 43 33 13 24 13 51 97 85 26 24 16 36 20 36 33 17 16 21 ..
52.9 2.3 21.9 37.6 4.2 .. 1.4 2.1 1.0 18.5 0.8 18.0 27.4 152.4 .. 1.2 2.0 62.1 123.4 20.0 11.1 110.7 .. 3.8 11.2 13.3 336.7 465.0 2.9 289.8 113.1 4.7 4.6 49.6 48.6 25.5 290.2 180.9 22.5 70.3 1.2 1.1 138.6 683.6 353.7 0.8 3.8 45.7 10.7 45.3 37.3 19.2 20.1 4.1 2.3 ..
5.11 Information and communications technology expenditures
Secure servers number 2003
16 139 281 60 1 .. 784 562 1,430 12 11,878 9 3 4 .. 688 38 1 .. 53 16 .. .. .. 29 .. 1 .. 174 1 1 17 416 7 3 15 2 .. 9 2 58 1,276 8 .. 3 726 1 25 85 .. 4 73 97 389 319 63
% of GDP 2002
.. 6.4 2.8 1.5 .. .. 4.0 6.9 4.4 .. 5.3 .. .. .. .. 6.5 .. .. .. .. .. .. .. .. .. .. .. .. 7.3 .. .. .. 4.4 .. .. .. .. .. .. .. 5.8 7.4 .. .. .. 4.1 .. .. .. .. .. .. 4.2 5.2 5.8 ..
2004 World Development Indicators
Per capita $ 2002
.. 420 13 11 .. .. 1,256 1,173 898 .. 1,671 .. .. .. .. 645 .. .. .. .. .. .. .. .. .. .. .. .. 304 .. .. .. 2,097 .. .. .. .. .. .. .. 1,505 1,096 .. .. .. 1,703 .. .. .. .. .. .. 40 256 697 ..
295
STATES AND MARKETS
The information age
5.11
The information age Radios
Television a
per 1,000 people 2000
per 1,000 people 2001
Cable Sets subscribers per 1,000 per 1,000 people people 2002 2002
300 105 0 326 5 107 4 298 131 169 1 32 100 29 26 26 410 373 20 20 4 64 2 123 19 111 7 2 175 156 329 213 293 3 206 4 .. 15 12 18 .. w 40 .. .. 123 .. .. 102 70 33 60 12 284 209
358 418 85 326 128 297 259 672 965 405 60 336 330 215 461 161 2,811 1,002 276 141 406 235 263 534 158 470 279 122 889 330 1,445 2,117 603 456 294 109 .. 65 179 362 419 w 139 360 346 466 257 287 447 410 277 112 198 1,266 813
Daily newspapers
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
697 538 .. 265 78 282 13 303 409 366 14 177 564 117 386 34 965 552 182 357 45 300 123 345 207 423 182 18 456 252 950 938 530 280 186 197 148 308 51 56 275 w 91 326 326 326 190 317 407 289 200 84 69 735 597
152.2 43.6 .. 0.3 0.1 .. .. 84.5 127.3 160.3 .. 0.0 19.9 0.3 0.0 .. 246.0 376.2 0.0 0.1 0.2 12.9 .. .. .. 14.2 .. 0.3 38.6 .. 57.2 255.0 125.9 3.7 36.3 .. 0.0 .. 1.2 2.1 65.5 w 23.7 57.6 58.9 47.1 40.2 70.1 47.6 33.9 .. 37.3 0.3 191.0 158.1
Personal computers
per 1,000 people a 2002
In education number 2002
69.2 36,754 88.7 229,630 .. .. 130.2 .. 19.8 .. 27.1 .. .. .. 622.0 136,000 180.4 27,729 300.6 28,842 .. .. 72.6 364,722 196.0 636,590 13.2 .. 6.1 .. 24.2 .. 621.3 541,805 708.7 405,134 19.4 .. .. .. 4.2 .. 39.8 230,000 30.8 .. 79.5 .. 30.7 .. 44.6 123,907 .. .. 3.3 .. 19.0 .. 129.0 .. 405.7 2,099,346 658.9 19,787,772 110.1 .. .. .. 60.9 104,297 9.8 29,516 36.2 .. 7.4 .. 7.5 .. 51.6 .. 100.8 w 7.5 45.4 37.7 100.5 28.4 26.3 73.4 67.4 38.2 6.8 11.9 466.9 317.5
Internet
Users per 1,000 people a 2002
83 41 3 62 10 60 2 504 160 376 9 68 156 11 3 19 573 351 13 1 2 78 41 106 52 73 2 4 18 337 423 551 119 11 51 18 30 5 5 43 131 u 10 80 46 149 50 44 87 92 37 14 16 364 331
Total monthly price a 20 hours of use % of monthly $ GNI per capita 2003 2003
26 10 67 35 41 13 12 11 21 25 .. 33 21 15 161 21 22 22 55 54 117 7 30 13 17 20 20 97 17 13 24 15 26 20 19 20 25 31 33 23 37 u 57 29 29 30 41 31 26 33 31 30 64 23 24
Information and communications technology expenditures Secure servers number 2003
17.1 30 5.6 233 348.3 1 4.9 26 103.7 3 11.3 6 102.9 1 0.6 732 6.3 48 3.1 96 .. .. 15.4 648 1.7 1,964 21.5 23 550.8 .. 21.0 2 1.1 1,595 0.7 1,931 58.6 1 362.3 .. 501.4 .. 4.2 179 134.9 .. 2.5 13 10.4 13 9.5 496 20.2 .. 464.4 2 26.0 28 0.8 83 1.1 13,540 0.5 138,514 7.3 39 53.8 1 5.7 106 55.4 3 32.8 .. 75.3 1 118.7 .. 58.3 7 88.7 u 217,255 s 246.4 435 18.9 6,686 24.9 3,965 8.6 2,721 114.8 7,121 66.1 720 39.5 1,930 31.8 3,309 29.9 103 58.6 333 268.8 726 1.6 210,134 1.5 18,846
% of GDP 2002
4.3 3.7 .. 4.6 .. .. .. 6.5 5.8 4.9 .. 9.2 4.5 .. .. .. 6.5 6.2 .. .. .. 4.7 .. .. .. 4.6 .. .. .. .. 6.1 6.5 .. .. 4.4 2.4 .. .. .. ..
a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report 2003. Please cite the ITU for third-party use of these data.
296
2004 World Development Indicators
Per capita $ 2002
88 88 .. 369 .. .. .. 1,268 251 556 .. 225 734 .. .. .. 1,765 2,259 .. .. .. 94 .. .. .. 122 .. .. .. .. 1,600 2,358 .. .. 147 10 .. .. .. ..
About the data
5.11
Definitions
The digital and information revolution has changed
The data on Internet users are based on estimates
• Daily newspapers refer to those published at least
the way the world learns, communicates, does busi-
derived from reported counts of Internet service sub-
four times a week and calculated as average circula-
ness, and treats illnesses. New information and
scribers or calculated by multiplying the number of
tion (or copies printed) per 1,000 people. • Radios
communications technologies offer vast opportuni-
Internet hosts by an estimated multiplier. Internet
refer to radio receivers in use for broadcasts to the
ties for progress in all walks of life in all countries—
hosts are computers connected directly to the world-
general public. • Television sets refer to those in use.
opportunities for economic growth, improved health,
wide network, each allowing many computer users to
• Cable television subscribers are households that
better service delivery, learning through distance
access the Internet. This method may undercount
subscribe to a multichannel television service deliv-
education, and social and cultural advances. This
the number of people actually using the Internet, par-
ered by a fixed line connection. Some countries also
table presents indicators of the penetration of the
ticularly in developing countries, where many com-
report subscribers to pay-television using wireless
information economy—newspapers, radios, televi-
mercial subscribers rent out computers connected to
technology or those cabled to community antenna sys-
sions, personal computers, and Internet use—as
the Internet or pre-paid cards are used to access the
tems. • Personal computers are self-contained com-
well as some of the economics of the information
Internet. Although survey methods used to estimate
puters designed to be used by a single individual.
age—Internet access charges, the number of secure
the number of Internet hosts have improved in recent
• Personal computers in education are those
servers, and spending on information and communi-
years, some measurement problems remain (see
installed in primary and secondary schools and uni-
cations technology.
Zook 2000). For detailed analysis of Internet trends
versities. • Internet users are people with access to
The data on the number of daily newspapers in cir-
by country, it is best to use the original source data.
the worldwide network. • Total monthly price refers to
culation and radio receivers in use are from statistical
The table shows the total monthly Internet price,
the sum of ISP and telephone usage charges for 20
surveys by the United Nations Educational, Scientific,
which refers to the sum of Internet service provider
hours of use and as a percentage of monthly GNI per
and Cultural Organization (UNESCO). In some coun-
(ISP) charges and telephone usage charges. The
capita. • Secure servers are servers using encryption
tries definitions, classifications, and methods of enu-
Internet price is also calculated as a percentage of
technology in Internet transactions. • Information
meration do not entirely conform to UNESCO
monthly GNI per capita. Data are generally derived
and communications technology expenditures cover
standards. For example, newspaper circulation data
from the prices listed by the largest ISP and incum-
external spending on information technology (“tangi-
should refer to the number of copies distributed, but
bent telephone company. The number of secure
ble” spending on information technology products
in some cases the figures reported are the number of
servers, from the Netcraft Secure Server Survey,
purchased by businesses, households, govern-
copies printed. In addition, many countries impose
gives an indication of how many companies are con-
ments, and education institutions from vendors or
radio and television license fees to help pay for public
ducting encrypted transactions over the Internet.
organizations outside the purchasing entity), internal
broadcasting, discouraging radio and television own-
The data on information and communications tech-
spending on information technology (“intangible”
ers from declaring ownership. Because of these and
nology expenditures cover the world’s 55 largest buy-
spending on internally customized software, capital
other data collection problems, estimates of the num-
ers of such technology among countries and regions.
depreciation, and the like), and spending on telecom-
ber of newspapers and radios vary widely in reliability
These account for 98 percent of global spending.
munications and other office equipment.
and should be interpreted with caution.
Because of different regulatory requirements for
The data for other electronic communications and
the provision of data, complete measurement of the
information technology are from the International
telecommunications
Telecommunication
Internet
Telecommunications data are compiled through
Software
Union
Consor tium,
(ITU),
Netcraft,
the the
sector
is
not
possible.
World
annual questionnaires sent to telecommunications
Information Technology and Services Alliance, and
authorities and operating companies by the ITU. The
Data sources
the International Data Corporation. The ITU collects
data are supplemented by annual reports and sta-
The data on newspapers and radios are compiled
data on television sets and cable television sub-
tistical yearbooks of telecommunications ministries,
by the UNESCO Institute for Statistics. The data on
scribers through annual questionnaires sent to
regulators, operators, and industry associations. In
television sets, cable television subscribers, per-
national broadcasting authorities and industry asso-
some cases estimates are derived from ITU docu-
sonal computers, Internet users, and Internet
ciations. Some countries require that television sets
ments or other references.
access charges are from the ITU and are reported
be registered. To the extent that households do not
in the ITU’s World Telecommunication Development
register their televisions or do not register all of
Report 2003 and the World Telecommunications
them, the data on licensed sets may understate the
Indicators Database (2003). The data on personal
true number.
computers in education and on information and
The estimates of personal computers are derived
communications technology expenditures are from
from an annual ITU questionnaire, supplemented by
Digital Planet 2002: The Global Information
other sources. In many countries mainframe com-
Economy by the World Information Technology and
puters are used extensively. Since thousands of
Services Alliance (WITSA), and the International
users can be connected to a single mainframe com-
Data Corporation. The data on secure servers are
puter, the number of personal computers under-
from Netcraft (http://www.netcraft.com/).
states the total use of computers.
2004 World Development Indicators
297
STATES AND MARKETS
The information age
5.12
Science and technology Researchers Technicians Scientific Expenditures in R&D in and for R&D technical R&D journal articles per million people 1990–2001 c
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
298
.. .. .. .. 684 1,534 3,439 2,313 2,799 51 1,893 2,953 174 123 .. .. 323 1,167 16 21 .. 3 2,978 47 .. 419 584 1,998 101 .. 33 530 .. 1,187 489 1,466 3,476 .. 83 493 47 .. 1,947 .. 7,110 2,718 .. .. 2,421 3,153 .. 1,400 103 .. .. ..
per million people 1990–2001 c
.. .. .. .. 149 223 792 979 160 32 273 1,157 53 6 .. .. 129 472 15 32 .. 4 1,035 27 .. 307 202 100 48 .. 37 .. .. 347 2,393 712 2,594 .. 72 366 303 .. 387 .. .. 2,878 .. .. 97 1,345 .. 554 111 .. .. ..
2004 World Development Indicators
1999
0 17 162 3 2,361 142 12,525 3,580 66 148 564 4,896 20 33 9 41 5,144 801 23 3 5 61 19,685 4 2 879 11,675 1,817 207 6 13 69 40 545 192 2,005 4,131 6 20 1,198 0 2 261 95 4,025 27,374 20 17 112 37,308 73 2,241 14 2 6 1
% of GDP 1996–2002 c
.. .. .. .. 0.42 0.2 1.53 1.93 0.37 .. .. 1.98 .. 0.34 .. .. 1.05 0.55 0.19 .. .. .. 1.94 .. .. 0.54 1.09 0.44 0.17 .. .. 0.20 .. 0.98 0.65 1.31 2.15 .. 0.09 0.19 0.01 .. 0.66 .. 3.42 2.20 .. .. 0.33 2.50 .. 0.68 .. .. .. ..
High-technology exports
% of $ manufactured millions exports 2002 2002
.. 2 21 .. 583 3 2,945 8,433 10 10 212 15,736 0 15 .. 6 6,007 85 2 0 .. 1 22,662 .. .. 107 68,182 2,688 319 .. .. 1,146 27 432 48 4,494 8,089 .. 34 13 44 .. 375 0 9,139 52,582 4 0 41 86,861 3 524 55 0 .. ..
.. 1 4 .. 7 2 16 15 8 0 4 11 0 7 .. 0 19 3 7 2 .. 1 14 .. .. 3 23 17 7 .. .. 37 3 12 29 14 22 1 7 1 6 .. 12 .. 24 21 7 3 38 17 3 10 7 0 .. ..
Royalty and license fees
Patent applications filed a
Trademark applications filed b
Receipts Payments $ millions $ millions Residents 2002 2002 2001
Nonresidents 2001
Residents 2001
.. .. .. 4 17 .. 304 111 .. 0 1 887 0 2 .. .. 100 4 .. 0 .. .. 1,689 .. .. 6 133 196 4 .. .. 2 0 85 .. 45 .. .. .. 38 2 0 5 0 559 3,241 .. .. 6 3,765 .. 13 0 0 .. ..
.. 129,865 72,257 .. 6,634 75,502 84,929 229,823 75,462 .. 75,750 154,676 .. .. 76,362 56 87,301 77,331 .. .. .. .. 92,752 .. .. 2,879 118,970 8,840 44,882 .. .. 74,360 .. 76,035 75,687 78,648 229,151 .. 28,909 923 .. .. 77,142 4 227,036 153,332 .. 150,081 76,207 212,176 150,194 155,268 260 .. .. 5
.. 0 1,418 .. .. 510 25,159 7,544 0 .. 1,885 21,382 d .. .. 152 .. 85,098 3,508 .. .. 231 .. 17,314 .. .. .. 229,775 5,458 7,265 .. .. .. .. 992 0 8,100 3,646 .. 4,832 0 .. .. 910 .. 2,879 60,513 .. .. 218 63,645 .. 5,879 3,048 .. .. ..
.. .. .. 0 225 .. 1,012 1,053 2 3 3 1,246 1 6 .. .. 1,229 23 0 0 6 .. 3,651 .. .. 345 3,114 491 87 .. .. 51 10 77 .. 119 .. 24 44 171 20 0 14 0 604 1,956 .. .. 11 5,064 0 288 0 1 .. ..
.. 0 52 .. 0 155 10,244 3,358 0 .. 945 1,953 .. .. 52 2 6,706 291 .. .. .. .. 5,737 .. .. 241 30,324 74 63 .. .. 0 .. 456 4 605 3,770 .. 0 464 .. .. 25 3 3,405 21,790 .. 1 257 80,222 2 78 5 .. 0 1
Nonresidents 2001
.. 2,070 3,284 .. .. 2,696 13,893 11,818 2,055 .. 4,846 12,510 d .. .. 4,298 .. 16,415 5,894 .. .. 1,268 .. 21,778 .. .. .. 30,149 15,487 7,096 .. .. .. .. 6,111 2,090 10,949 8,351 .. 5,011 3,216 .. .. 5,617 .. 7,365 14,324 .. .. 3,114 14,235 .. 6,240 5,040 .. .. ..
Researchers Technicians Scientific Expenditures in R&D in and for R&D technical R&D journal articles per million people 1990–2001 c
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
73 1,440 157 .. 590 .. 2,190 1,563 1,128 .. 5,321 1,948 716 .. .. 2,880 212 581 .. 1,078 .. .. .. 361 2,303 387 15 .. 160 .. .. 360 225 329 531 .. .. .. .. .. 2,572 2,197 73 .. .. 4,377 4 69 95 .. 166 229 156 1,473 1,754 ..
per million people 1990–2001 c
256 510 115 .. 174 .. 588 516 808 .. 667 717 293 .. .. 564 53 49 .. 298 .. .. .. 493 492 29 46 .. 45 .. .. 157 183 1,641 116 .. .. .. .. .. .. 776 33 .. .. 1,836 0 12 213 .. 231 1 22 507 506 ..
1999
11 1,958 9,217 142 624 21 1,237 5,025 17,149 44 47,826 204 104 252 1 6,675 260 10 2 153 100 1 1 19 214 36 .. 36 416 11 2 16 2,291 92 8 386 14 10 13 39 10,441 2,375 8 21 397 2,598 73 277 37 36 4 56 164 4,523 1,508 ..
% of GDP 1996–2002 c
.. 0.95 .. .. .. .. 1.16 4.96 1.07 .. 3.09 6.33 0.29 .. .. 2.96 0.20 0.19 .. 0.40 .. .. .. .. 0.63 .. 0.13 .. 0.40 .. .. 0.28 0.43 0.62 .. .. .. .. .. .. 1.95 1.03 0.15 .. .. 1.64 .. .. 0.44 .. 0.00 0.11 .. 0.67 0.78 ..
High-technology exports
% of $ manufactured millions exports 2002 2002
5 7,364 1,788 5,070 64 .. 31,624 5,414 19,872 1 94,730 48 157 35 .. 46,438 .. 6 .. 51 16 .. .. .. 130 9 .. 1 40,912 .. .. 29 28,939 8 1 439 2 .. 6 0 33,667 388 6 0 0 2,863 36 59 1 11 7 24 11,488 915 1,628 ..
2 25 5 16 3 .. 41 20 9 0 24 3 10 10 .. 32 .. 6 .. 4 3 .. .. .. 5 1 .. 3 58 .. .. 2 21 4 0 11 3 .. 1 0 28 10 5 8 0 22 2 1 1 19 3 2 65 3 7 ..
Royalty and license fees
Patent applications filed a
Receipts Payments $ millions $ millions Residents 2002 2002 2001
0 11 350 399 12 350 .. .. 0 0 .. .. 249 10,347 389 450 539 1,273 6 32 10,422 11,021 .. .. 0 19 5 62 .. .. 826 2,979 0 0 3 3 .. .. 3 6 .. .. 11 0 .. .. .. .. 0 11 3 10 0 13 0 0 12 628 0 1 .. .. 0 2 48 720 1 1 0 .. 11 41 0 0 0 0 4 2 .. .. 1,962 2,612 89 347 .. .. .. .. .. .. 171 325 .. .. 2 18 0 41 .. .. 184 1 2 56 1 230 34 557 32 294 .. ..
7 1,019 234 0 691 .. 1,334 2,378 3,819 3 388,390 .. 1,610 2 0 74,001 .. 84 .. 124 0 1 0 .. 70 66 0 2 .. .. .. .. 594 437 106 0 1 .. .. .. 8,107 920 9 .. .. 1,780 0 58 7 .. .. 48 0 2,218 189 ..
5.12 Trademark applications filed b
Nonresidents 2001
Residents 2001
Nonresidents 2001
155 78,181 78,288 77,407 302 .. 155,155 82,027 153,039 66 108,231 .. 75,560 150,443 74,672 116,021 .. 75,489 .. 130,315 104 150,361 76,005 .. 130,287 129,995 76,048 150,687 .. .. .. .. 81,876 75,549 76,133 74,468 146,278 .. .. .. 150,825 82,362 136 .. .. 82,593 2,174 1,168 153 .. .. 944 13,598 78,856 230,719 ..
.. 4,755 .. .. 9,858 .. 918 2,468 0 599 104,655 .. 1,796 0 0 86,408 .. 59 14 1,062 .. 0 0 .. 1,323 440 236 146 .. .. .. .. 43,788 .. 206 0 0 .. .. .. .. 8,382 .. .. .. 3,316 .. 4,852 .. .. .. 6,940 .. 12,434 7,191 ..
.. 10,673 .. .. 1,224 .. 3,038 6,468 11,005 2,394 19,133 .. 3,300 1,442 2,587 20,729 .. 2,382 563 6,133 .. 1,009 1,018 .. 5,994 3,962 336 515 .. .. .. .. 21,147 .. 3,189 3,499 1,162 .. .. .. .. 12,232 .. .. .. 11,767 .. 2,392 .. .. .. 6,983 .. 13,358 9,682 ..
2004 World Development Indicators
299
STATES AND MARKETS
Science and technology
5.12
Science and technology Researchers Technicians Scientific Expenditures in R&D in and for R&D technical R&D journal articles
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
per million people 1990–2001 c
per million people 1990–2001 c
879 3,494 .. .. 2 2,389 .. 4,052 1,774 2,258 .. 992 1,948 191 .. .. 5,186 3,592 29 660 .. 74 102 456 336 306 .. 24 2,118 .. 2,666 4,099 276 1,754 193 274 .. .. .. .. .. w .. 818 810 662 .. 584 2,069 .. .. 158 .. 3,284 2,302
584 551 6 .. 3 515 .. 335 790 877 .. 303 1,019 46 .. .. 3,164 1,399 24 .. .. 74 65 882 32 26 .. 14 594 .. 1,014 .. 52 312 32 .. .. .. .. .. .. w .. .. .. .. .. 202 .. .. .. 113 .. .. 996
1999
785 15,654 4 528 66 546 3 1,653 871 599 0 2,018 12,289 84 43 6 8,326 6,993 55 20 92 470 11 37 237 2,761 0 59 2,194 118 39,711 163,526 144 236 448 98 .. 10 26 85 528,627 s 12,040 64,710 46,694 18,016 76,750 13,055 34,679 12,018 3,617 9,769 3,612 451,877 122,077
% of GDP 1996–2002 c
0.40 1.16 .. .. 0.01 .. .. 2.11 0.62 1.63 .. .. 0.96 0.18 .. .. 4.61 2.64 0.18 .. .. 0.10 .. 0.14 0.45 0.64 .. 0.75 0.95 .. 1.90 2.80 0.24 .. 0.44 .. .. .. .. .. 2.46 w .. 0.66 0.89 0.53 0.57 1.09 0.96 0.52 .. .. .. 2.64 2.13
High-technology exports
% of $ manufactured millions exports 2002 2002
390 2,897 0 30 15 .. .. 63,792 386 488 .. 740 6,777 19 4 3 10,760 17,077 2 37 1 15,234 1 75 177 568 8 4 572 17 71,481 162,345 19 .. 94 .. .. .. 2 21 1,149,146 s .. 182,644 97,450 84,405 .. .. 16,726 38,457 880 .. .. 853,545 278,406
3 13 1 0 4 .. .. 60 3 5 .. 5 7 1 7 1 16 21 1 42 2 31 1 3 4 2 5 12 5 2 31 32 3 .. 3 .. .. .. 2 3 21 w 9 19 17 21 17 32 10 16 2 4 4 23 17
Royalty and license fees
Patent applications filed a
Receipts Payments $ millions $ millions Residents 2002 2002 2001
3 85 147 338 0 0 0 0 .. .. .. .. .. .. .. .. 16 58 7 78 .. .. 43 94 370 1,810 .. .. 0 0 0 46 1,505 888 .. .. .. .. 0 1 0 0 7 1,104 0 1 .. .. 16 6 0 107 .. .. 0 .. 4 110 .. .. 7,701 5,993 44,142 19,258 0 10 .. .. 0 58 .. .. .. .. .. .. .. 0 .. .. 79,611 s 82,187 s 36 420 1,361 10,299 753 7,034 608 3,265 1,397 10,718 153 5,082 695 1,898 407 2,980 65 218 18 371 59 169 78,214 71,469 10,963 25,404
Nonresidents 2001
Trademark applications filed b
Residents 2001
1,148 130,602 5,374 25,046 82,632 39,801 0 4 .. 46 683 .. .. .. .. 470 77,043 971 1 150,465 0 0 79,026 0 260 77,131 2,158 344 130,599 1,009 .. .. .. 175 76,571 .. 3,814 230,729 73,937 0 76,095 .. 5 150,388 0 1 75,091 0 7,133 224,350 6,603 7,323 226,329 7,665 0 0 0 0 75,462 0 2 148,738 0 1,117 4,548 .. .. .. .. 1 76,045 .. 0 195 .. 425 228,914 19,885 0 75,440 0 2 150,406 0 7,234 77,196 6,854 0 75,414 .. 34,500 230,206 50,601 190,907 184,750 181,713 44 572 .. 803 76,432 690 56 2,292 .. 0 76,542 0 .. .. .. .. .. .. 8 3,178 213 2 150,320 1 939,267 s 10,814,596 s 1,263,071 s 2,008 2,642,403 6,866 81,357 3,317,058 505,531 75,937 2,057,922 430,009 5,420 1,259,136 75,522 83,365 5,959,461 512,397 30,430 437,322 230,226 43,800 2,493,388 113,877 7,383 766,888 151,570 1,253 151,002 11,276 292 155,551 4,852 207 1,955,310 596 855,902 4,855,135 750,674 128,297 2,283,274 243,888
Nonresidents 2001
7,208 13,295 .. .. .. 6,022 1,038 3,079 8,958 7,481 .. .. 15,263 .. 1,063 1,054 8,552 17,053 0 1,965 16 .. .. .. .. 8,544 1,803 14 7,320 .. 20,490 34,861 .. 2,723 .. 2,422 .. .. 617 17 630,592 s 33,611 247,653 158,713 88,940 281,264 40,178 151,290 66,176 11,223 3,096 9,301 349,328 105,480
Note: The original information on patent and trademark applications was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. a. Other patent applications filed in 2001 include those filed under the auspices of the African Regional Industrial Property Organization (ARIPO) (5 by residents, 75,101 by nonresidents), European Patent Office (67,330 by residents, 90,960 by nonresidents), and the Eurasian Patent Organization (491 by residents, 75,355 by nonresidents). b. Other trademark applications filed in 2001 include those filed under the auspices of the Office for Harmonization in the Internal Market (30,543 by residents, 18,342 by nonresidents) and ARIPO (6 by residents, 18 by nonresidents). c. Data are for the latest year available. d. Includes Luxembourg and the Netherlands.
300
2004 World Development Indicators
About the data
5.12
Definitions
The best opportunities to improve living standards,
importance. They may also reflect some bias toward
• Researchers in R&D are people engaged in
including new ways of reducing poverty, will come from
English-language journals.
professional R&D activity who have received tertiary level
science and technology. Science, advancing rapidly in
The method used for determining a country’s high
training to work in any field of science. • Technicians in
virtually all fields—particularly in biotechnology—is
technology exports was developed by the Organisation
R&D are people engaged in professional R&D activity
playing a growing economic role: countries able to
for Economic Co-operation and Development in collabo-
who have received vocational or technical training in any
access, generate, and apply relevant scientific knowl-
ration with Eurostat. Termed the “product approach” to
edge will have a competitive edge over those that can-
distinguish it from a “sectoral approach,” the method is
not. And there is greater appreciation of the need for
based on the calculation of R&D intensity (R&D expen-
branch of knowledge or technology. Most such jobs require three years beyond the first stage of secondary education. • Scientific and technical journal articles refer to scientific and engineering articles published in
high-quality scientific input into public policy issues
diture divided by total sales) for groups of products from
such as regional and global environmental concerns.
six countries (Germany, Italy, Japan, the Netherlands,
Technological innovation, often fueled by government-
Sweden, and the United States). Because industrial
led research and development (R&D), has been the
sectors characterized by a few high-technology products
• Expenditures for R&D are current and capital expen-
driving force for industrial growth around the world.
may also produce many low-technology products, the
ditures on creative, systematic activity that increases the
Science and technology cover a range of issues too
product approach is more appropriate for analyzing
stock of knowledge. Included are fundamental and
complex and too broad to be quantified by any single set
international trade than is the sectoral approach. To
applied research and experimental development work
of indicators, but those in the table shed light on coun-
construct a list of high-technology manufactured prod-
leading to new devices, products, or processes. • High-
tries’ “technological base”—the availability of skilled
ucts (services are excluded), the R&D intensity was cal-
technology exports are products with high R&D intensi-
human resources, the number of scientific and techni-
culated for products classified at the three-digit level of
ty, such as in aerospace, computers, pharmaceuticals,
cal articles published, the competitive edge countries
the Standard International Trade Classification revision
scientific instruments, and electrical machinery.
enjoy in high-technology exports, sales and purchases
3. The final list was determined at the four- and five-digit
• Royalty and license fees are payments and receipts
of technology through royalties and licenses, and the
levels. At these levels, since no R&D data were avail-
number of patent and trademark applications filed.
able, final selection was based on patent data and
the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.
between residents and nonresidents for the authorized use of intangible, nonproduced, nonfinancial assets and proprietary rights (such as patents, copyrights, trade-
The United Nations Educational, Scientific, and
expert opinion. This method takes only R&D intensity
Cultural Organization (UNESCO) collects data on scien-
into account. Other characteristics of high technology
tific researchers and technical workers and R&D expen-
are also important, such as know-how, scientific and
ditures
through
technical personnel, and technology embodied in
questionnaires and special surveys as well as from offi-
patents; considering these characteristics would result
patent office for exclusive rights to an invention—a prod-
cial reports and publications, supplemented by infor-
in a different list. (See Hatzichronoglou 1997 for further
uct or process that provides a new way of doing some-
mation from other national and international sources.
details.) Moreover, the R&D for high-technology exports
thing or offers a new technical solution to a problem. A
UNESCO reports either the stock of researchers and
may not have occurred in the reporting country.
patent provides protection for the invention to the owner
marks, franchises, and industrial processes) and for the
from
member
states,
mainly
use, through licensing agreements, of produced originals of prototypes (such as films and manuscripts). • Patent applications filed are applications filed with a national
technicians or the number of economically active people
Most countries have adopted systems that protect
qualified as such. UNESCO supplements these data
patentable inventions. Under most patent legislation
• Trademark applications filed are applications for reg-
with estimates of qualified researchers
by counting
an idea, to be protected by law (patentable), must be
istration of a trademark with a national or regional trade-
people who have completed education at International
new in the sense that it has not already been pub-
mark office. Trademarks are distinctive signs that
Standard Classification of Education (ISCED) levels 6
lished or publicly used; it must be nonobvious (involve
identify goods or services as those produced or provided
and 7; qualified technicians are estimated using the
an inventive step) in the sense that it would not have
number of people who have completed education at
occurred to any specialist in the industrial field had
ISCED level 5. The data are normally calculated in terms
such a specialist been asked to find a solution to the
of full-time-equivalent staff. The information does not
problem; and it must be capable of industrial applica-
reflect the quality of training and education, which varies
tion in the sense that it can be industrially manufac-
Data sources
widely. Similarly, R&D expenditures are no guarantee of
tured or used. Information on patent applications filed
The data on technical personnel and R&D expendi-
progress; governments need to pay close attention to
is shown separately for residents and nonresidents of
tures are from UNESCO’s Statistical Yearbook. The
the practices that make them effective.
the country.
data on scientific and technical journal articles are
of the patent for a limited period, generally 20 years.
by a specific person or enterprise. A trademark provides protection to the owner of the mark by ensuring the exclusive right to use it to identify goods or services or to authorize another to use it in return for payment.
from the National Science Foundation’s Science and
The counts of scientific and technical journal arti-
A trademark provides protection to its owner by
cles include those published in a stable set of about
ensuring the exclusive right to use it to identify goods
5,000 of the world’s most influential scientific and
or services or to authorize another to use it in return
technical journals, tracked since 1985 by the
for payment. The period of protection varies, but a
Institute of Scientific Information’s Science Citation
trademark can be renewed indefinitely by paying addi-
from the International Monetary Fund’s Balance of
Index (SCI) and Social Science Citation Index (SSCI).
tional fees. The trademark system helps consumers
Payments Statistics Yearbook, and the data on
(See Definitions for the fields covered.) The SCI and
identify and purchase a product or service whose
patents and trademarks are from the World Intellectual
SSCI databases cover the core set of scientific jour-
nature and quality, indicated by its unique trademark,
Property Organization’s Industrial Property Statistics.
nals but may exclude some of regional or local
meet their needs.
Engineering Indicators 2002. The information on hightechnology exports is from the United Nations Statistics Division’s Commodity Trade (COMTRADE) database. The data on royalty and license fees are
2004 World Development Indicators
301
STATES AND MARKETS
Science and technology
6 GLOBAL LINKS
I
n the past 20 years the global economy has become increasingly integrated. International
financial flows have grown. More people are on the move. And countries are exchanging more goods and services. In 2002 trade in goods and services as a share of world output reached 54 percent, up from 31 percent in 1980 (figure 6a). Several rounds of tariff reductions and expanding trade in services have spurred growth in trade among highincome economies. Developing economies’ trade has recovered from a slowdown in the 1980s. Since 1992 the share of trade in their output, measured in constant dollars, has been growing as fast or faster than that of the high-income economies.
Still, there are many obstacles to global integration. National policies that protect home industries from competition or subsidize their output distort patterns of trade and prevent developing countries from reaching their full potential. The movement of people, an important mode of trade in services, remains particularly restricted. Risk and uncertainty also inhibit the flow of finance, while development assistance may be directed more by political considerations than by development opportunities. Table 6.1 highlights additional trends in global integration.
Movement of goods High-income countries continue to dominate the global scene. They account for more than three-
6a More than half of world output is globally traded Trade of goods and services as % of GDP (constant US$) 75
quarters of the world’s gross
Developing countries
domestic product (GDP) and for
World
three-quarters of world trade. They
50 High income
also remain the most important markets for low- and middleincome economies. In 2002, 17
25
percent of world trade moved from high-income countries to low- and middle-income economies. Trade between developing economies is still relatively small, but it is
00 1980
1985
1990
1995
2002
Source: World Bank staff estimates.
2004 World Development Indicators
303
growing in importance. In 2002 the movement of goods between low- and middle-income economies accounted for 6 percent of world trade, but in the period 1992–2002 the nominal value of trade between developing economies grew faster than that between high-income countries and between highincome and developing economies (table 6.2). The types of goods traded by developing economies have been shifting. Exports of manufactures have grown at nearly twice the rate of agricultural exports and account for more than half of exports from developing economies. In 2002, 68 percent of imports to high-income Organisation for Economic Cooperation and Development (OECD) countries from middle-income countries were manufactured goods, up from 46 percent in 1992. Low-income economies also saw significant increases, with shares rising from 38 percent in 1992 to 54 percent in 2002. Both middle- and low-income economies experienced declines in the value of exports of agricultural raw materials (table 6.3), during a period when many commodity prices were falling (table 6.4). Yet trade barriers continue to be a significant problem. The World Trade Organization’s Fifth Ministerial Meeting in Cancun in September 2003, which was supposed to move the Doha Round development agenda forward, produced disappointing results. Some 70 percent of the world’s poor live in rural areas and earn incomes from agriculture, while two-thirds of the world’s agricultural trade originates in OECD countries. This occurs in part because rich countries subsidize their producers. Subsidies in OECD countries amount to $330 billion—with some $250 billion going directly to producers. In addition, agricultural exports from developing countries to high-income economies are four to seven times greater than manufacturing exports. Reduced protection in agriculture would account for two-thirds of the gains from full global liberalization of all merchandise trade. Although tariffs on manufactured goods are lower on average in high-income countries than in developing economies, rich countries place substantially lower tariffs on products from other industrial countries than on those from developing economies. But both high-income and developing economies distort trade through tariffs. Latin American exporters of manufactures face tariffs in other markets in the region that are seven times higher than in highincome countries. Tariffs are six times higher in Sub-Saharan Africa than in high-income countries and twice as high in South Asia. Protection also comes through nontariff barriers. Table 6.6 includes new estimates of the ad valorem equivalents of nontariff barriers.
6b Aid after Monterrey Official development assistance (ODA) declined from 0.34 percent of donor countries’ gross national income (GNI) in 1990 to 0.22 percent in 2001 (table 6.9). At the United Nations Conference on Financing for Development in Monterrey, Mexico, in March 2002, donor countries agreed to scale up their commitment on aid to developing economies to help them achieve the Millennium Development Goals. Between 2001 and 2002, ODA flows began to increase, reaching 0.23 percent of donors’ GNI in 2002. In coming years aid flows will continue to rise and by 2006, if countries keep their commitments, aid is expected to reach 0.29 percent of donor GNI. Will aid flows be enough to reach the Monterrey goals?
Net ODA 2002 Country Austria
($ millions)
ODA as % of GNI 2002
2006
520
0.26
0.33
Belgium
1,072
0.43
0.46
Denmark
1,643
0.96
0.83
462
0.35
0.42
France a
5,486
0.38
0.47
Germany
5,324
0.27
0.33
Greece
276
0.21
0.33
Ireland a
398
0.40
0.63
Finland
2,332
0.20
0.33
Luxembourg
Italy
147
0.77
1.00
Netherlands
3,338
0.81
0.80
323
0.27
0.33
1,712
0.26
0.33
Portugal Spain Sweden
1,991
0.83
0.87
United Kingdom
4,924
0.31
0.40
29,949
0.35
0.42
989
0.26
0.26
Canada
2,006
0.28
0.34
Japan
9,283
0.23
0.26
122
0.22
0.26
1,696
0.89
1.00
939
0.32
0.36
United States c
13,290
0.13
0.17
DAC members, total
58,274
0.23
0.29
EU members, total Australia b
New Zealand Norway Switzerland a
Estimates are based on commitments made by donor countries at the United Nations International Conference on Financing for Development in March 2002. a. ODA/GNI ratio for 2006 interpolated between 2002 and year target scheduled to be attained. b. Estimated ODA/GNI of 0.26 percent in 2003/04. Since aid volumes are determined in annual budgets, the same ratio is assumed in forward years. c. For 2006,
Financial flows and aid The downturn in foreign direct investment (FDI) that began in 2000 continued through 2002. World FDI grew from $202 billion in 1990 to a peak of $1.5 trillion in 2000 and then dropped off to $631 billion in 2002. Middle-income economies, which
304
2004 World Development Indicators
assumes additional $5 billion from the Millennium Challenge Account, $2 billion from the Emergency Plan for AIDS Relief, phased spending from Iraq and Afghanistan reconstruction supplements, and 2 percent annual inflation to deflate from 2006 to 2002 prices. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.
receive the largest share of FDI flows to developing countries, were hit hardest. FDI fell from $164 billion in 2001 to $134 billion. Flows to low-income economies increased slightly from $11 billion to $13 billion. The largest drops occurred in Latin America and the Caribbean, Middle East and North Africa, and Sub-Saharan Africa. China’s growth led to an increase in FDI flows in East Asia and Pacific, as did India’s strength in South Asia (table 6.7). Aid—which consists of official development assistance (ODA) and official aid to transition and certain high-income countries—continues to be a major source of financing for developing economies. Net aid flows reached $70 billion in 2002, up from $54 billion in 1997. More than a quarter of net aid flows went to Sub-Saharan Africa, which was equivalent to 32 percent of the region’s gross capital formation, compared with an average of 4.4 percent for all developing economies (table 6.10). The poorest countries are not the only recipients of aid. In 2002, excluding unallocated aid, middle-income countries received almost half of total net aid. In dollar terms the largest aid recipients in 2002 were Pakistan ($2.1 billion), Mozambique ($2.1 billion), Serbia and Montenegro ($1.9 billion), West Bank and Gaza ($1.6 billion), and China ($1.5 billion). The largest recipients of aid per capita were several small island states, as well as West Bank and Gaza ($500), Serbia and Montenegro ($237), Bosnia and Herzegovina ($143), Macedonia, FYR ($136), and Mauritania ($128). Only Mauritania is classified by the World Bank as a low-income economy. Movement of people The movement of people across borders can be important to both high-income and developing economies. Rich countries benefit from access to a larger labor force. And poor countries gain from higher salaries and remittances. In 2001 remittances—current transfers by migrants who are employed or intend to remain employed for more than a year in a country in which they are considered residents—totaled $70 billion, roughly equivalent to net aid flows. The total is even higher when net income flows are included. In addition, workers often bring back skills to their country of origin. Not all migrant flows are well recorded. Records are especially weak for illegal immigration, movements within countries, and flows between developing countries. But migration to OECD
6c Immigrant labor plays an important role in some high-income economies Foreign labor as share of total labor force (%) 15 United States
12
9
France 6 United Kingdom Italy 3
Japan 0 1990
1995
2001
Recent inflows have pushed up the share of the foreign labor force in the United States and Italy. In Japan foreign workers make up less than a quarter of a percent of the labor force. Source: Table 6.13.
countries, seen in table 6.13, has become an important feature of the global labor market. In 2001 some 1 million migrants entered the United States and 350,000 entered Japan. Foreign and foreign-born persons now make up about 38 percent of the population in Luxembourg and 9 percent each in Austria and Germany. International tourism also plays a key role in movements of people. Although most travelers still come from high-income countries, there has been rapid growth in travelers from the developing world. While global tourism receipts have grown at an average annual rate of 5 percent since 1990, in the developing world these have grown more than 9 percent. Global tourism receipts reached $473 billion in 2002, up from $265 billion in 1990. During the same period receipts in the developing world grew from $48 billion to $138 billion (table 6.14). Tourism is a significant export earner and an important factor in the balance of payments of most nations. And it has become an important source of employment.
2004 World Development Indicators
305
6.1
Integration with the global economy Trade in goods
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
306
Ratio of commercial service exports to merchandise exports
% of
% of
GDP
goods GDP
1990
2002
1990
.. 29.0 36.6 53.5 11.6 .. 26.3 55.9 .. 17.6 .. 120.4 30.0 33.1 .. 98.4 11.7 48.9 22.0 27.0 22.4 30.5 43.7 18.4 27.2 53.1 32.5 221.5 30.7 43.5 57.2 60.2 47.9 88.8 .. 83.6 52.6 73.2 44.2 36.8 38.4 37.6 .. 16.0 39.0 37.1 52.5 69.1 .. 46.5 35.7 33.2 36.8 49.5 43.0 17.2
.. 38.2 53.5 101.3 33.7 63.3 33.5 76.8 62.9 29.4 119.4 177.2 37.8 39.5 78.1 84.6 24.3 88.1 23.8 22.1 94.9 38.6 67.1 25.1 48.0 55.2 49.0 252.8 30.6 38.4 101.7 73.8 63.9 69.6 .. 113.9 61.9 65.0 47.1 18.8 57.3 60.4 156.7 33.2 59.6 46.2 73.2 67.3 30.9 55.8 75.2 31.3 35.7 42.7 61.6 41.0
.. 34.5 55.0 91.0 27.0 .. 68.7 140.5 .. .. .. 321.7 60.8 57.0 .. .. .. 70.8 43.3 35.1 33.6 .. 115.1 26.4 54.9 100.5 47.4 772.3 .. 74.5 107.0 .. 86.0 164.8 .. .. 144.1 163.2 .. 72.9 87.6 65.0 .. 25.5 86.3 101.6 97.7 134.4 .. 108.8 58.0 83.5 .. 85.5 53.3 ..
2004 World Development Indicators
%
2002
1990
2002
.. 69.4 82.2 133.4 74.4 94.9 96.5 209.0 .. .. 232.0 542.2 65.1 77.6 .. .. .. 185.9 46.4 .. .. .. .. 37.7 84.7 111.2 73.8 2,020.6 .. 50.6 141.9 .. 137.1 140.3 .. 232.4 171.2 146.0 .. 35.6 146.9 104.4 361.6 62.0 141.0 148.5 .. 116.3 66.7 161.3 129.3 89.1 .. 63.6 86.1 ..
.. 13.7 3.7 1.7 18.3 .. 24.7 55.1 .. 17.7 .. 22.6 38.0 14.3 .. 10.3 11.8 16.6 22.1 8.7 57.8 18.4 14.4 14.5 12.5 21.3 9.3 .. 22.9 .. 6.7 40.3 13.8 .. .. .. 34.5 50.1 18.7 138.4 51.7 484.7 .. 87.4 17.2 34.6 9.7 170.6 .. 12.2 8.8 80.4 26.9 13.6 19.4 26.7
.. 167.3 .. 2.7 11.4 34.7 25.8 44.0 14.8 5.0 15.7 21.8 36.5 16.8 31.6 .. 14.7 44.4 19.5 12.5 39.5 .. 14.4 .. .. 21.1 12.1 22.5 14.9 .. 7.1 35.3 11.5 113.3 .. 18.3 47.7 57.2 18.2 208.3 25.0 386.6 45.6 108.5 14.3 25.9 .. .. 108.5 16.2 29.3 194.4 47.0 5.8 .. ..
Growth in real trade less growth in real GDP
Gross private capital flows
Gross foreign direct investment
percentage
% of
% of
points
GDP
GDP
1990–2002
.. 5.5 –0.5 .. 5.1 –10.4 3.4 4.1 18.3 5.3 –3.4 2.3 –2.0 1.3 –4.0 –1.1 4.8 5.6 –2.7 8.1 8.5 2.4 4.2 .. 4.6 3.2 4.5 3.5 3.3 7.1 1.6 3.8 0.4 4.1 .. 9.4 3.0 –0.3 2.0 –2.1 6.9 1.0 10.0 2.2 5.0 4.0 –1.8 –1.2 13.9 4.0 4.4 3.8 3.2 –1.0 3.8 5.4
1990
2002
1990
2002
.. 18.0 2.6 10.1 8.2 .. 9.3 9.8 .. 0.9 .. 5.1 10.7 3.1 .. 9.0 1.9 39.2 1.0 3.7 3.2 15.5 8.1 2.2 5.6 15.0 2.5 .. 3.1 .. 6.6 7.0 3.5 .. .. .. 15.1 5.0 11.0 6.8 2.0 32.5 3.7 1.6 17.3 20.6 18.0 0.9 .. 9.8 2.7 3.9 2.9 3.9 23.0 1.1
.. 6.3 .. 30.7 39.4 12.3 20.0 41.9 54.3 2.6 7.1 49.3 11.4 17.2 29.5 .. 13.2 16.5 4.3 3.2 5.5 .. 13.4 .. .. 23.6 8.0 92.4 10.8 .. 37.4 10.5 9.8 31.4 .. 28.6 12.1 6.9 21.1 6.6 15.3 9.8 30.1 3.1 38.8 20.2 .. .. 9.6 21.7 4.4 22.6 24.6 2.1 .. ..
.. 0.0 0.0 3.3 1.3 .. 3.7 1.5 .. 0.0 .. 5.1 3.7 0.7 .. 4.4 0.4 0.0 0.0 0.1 1.7 1.1 2.7 0.5 0.0 2.2 1.2 .. 1.3 .. 0.0 2.9 0.4 .. .. .. 2.0 1.9 1.2 1.7 0.8 .. 2.0 0.0 3.6 3.9 8.4 0.0 .. 1.8 0.3 1.2 0.6 0.6 0.0 0.3
.. 2.8 .. 22.7 9.0 4.7 6.3 3.8 49.0 0.1 3.2 10.7 3.9 8.7 5.2 .. 4.4 4.1 0.4 0.0 1.6 .. 7.3 .. .. 5.5 4.7 29.6 3.6 .. 19.4 4.8 2.3 6.7 .. 13.8 6.7 4.6 5.2 0.8 1.6 4.4 8.1 .. 13.4 8.0 .. .. 5.0 5.4 0.8 1.0 9.8 0.0 .. ..
Trade in goods
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Ratio of commercial service exports to merchandise exports
% of
% of
GDP
goods GDP
%
1990
2002
1990
2002
1990
2002
57.9 61.5 .. 41.5 32.9 41.2 93.9 55.0 32.0 67.2 17.1 91.1 .. 38.1 .. 53.4 59.8 .. 30.5 .. 106.5 119.3 374.1 64.2 .. 103.8 31.5 52.7 133.4 39.7 84.1 118.0 32.1 .. .. 43.3 40.8 .. 95.6 24.1 87.6 43.3 .. 27.0 67.5 52.8 77.7 32.6 35.4 73.6 43.9 22.3 47.7 43.9 58.3 ..
64.1 109.3 .. 51.1 43.1 .. 114.1 62.7 41.7 58.5 18.9 82.8 65.8 43.6 .. 66.0 68.9 67.1 43.4 75.4 43.3 149.8 159.3 87.1 96.4 80.0 44.0 60.6 182.4 60.7 76.8 86.6 52.4 105.9 101.5 54.2 56.2 .. 87.7 35.8 111.1 49.0 59.7 33.8 52.0 50.3 84.6 35.8 31.1 94.2 50.8 26.9 91.7 50.9 52.7 ..
106.4 102.4 .. 68.1 61.8 .. 186.7 .. 83.3 162.2 44.1 205.2 .. 68.5 .. 102.7 112.9 .. 40.2 .. .. .. .. .. .. 168.9 53.7 70.6 232.3 63.4 134.0 219.8 78.9 .. .. 86.5 68.9 .. 190.3 .. 230.9 121.0 .. 49.9 90.8 126.6 127.4 .. .. 123.9 82.8 .. 84.7 75.2 140.8 ..
126.7 .. .. 82.6 86.0 .. 255.2 .. 121.1 174.2 64.2 221.3 128.9 100.6 .. 152.0 .. 99.3 .. 202.6 .. .. .. .. 212.0 150.4 91.2 108.9 347.4 87.4 133.9 193.5 148.7 191.0 227.6 116.8 93.3 .. 182.7 .. 332.6 .. 138.9 57.2 95.0 112.9 .. .. .. 147.6 106.6 .. 194.1 121.0 151.0 ..
14.5 26.8 25.7 9.7 1.8 .. 13.8 37.6 28.5 84.2 14.4 134.4 .. 75.0 .. 14.1 15.0 .. 13.5 .. .. 54.9 .. 0.6 .. .. 40.5 8.8 12.8 19.7 3.0 40.0 17.7 .. 7.3 43.9 81.7 29.0 9.7 81.5 21.6 25.7 10.4 7.8 7.1 36.6 1.2 21.7 266.7 16.8 42.1 22.1 35.7 22.3 30.8 ..
36.4 22.5 49.9 11.3 5.6 .. 31.9 36.7 23.7 170.9 15.6 53.7 14.8 37.8 .. 16.7 8.9 24.3 42.5 54.1 .. 7.7 .. .. 26.1 19.8 20.1 10.3 15.8 14.8 .. 64.5 7.8 29.9 35.7 51.7 36.5 13.4 21.0 53.3 22.3 35.1 45.2 .. .. 31.4 3.1 15.5 266.4 18.4 49.1 18.6 8.4 24.5 37.9 ..
Growth in real trade less growth in real GDP
Gross private capital flows
6.1 Gross foreign direct investment
percentage
% of
% of
points
GDP
GDP
1990–2002
–0.4 8.8 .. 0.8 –7.9 .. 7.1 1.4 3.5 –1.1 2.6 –2.6 –3.0 1.4 .. 6.9 .. –2.1 .. 7.3 –2.5 0.6 .. .. 8.8 5.4 1.5 –2.1 3.3 2.7 –1.2 0.1 9.2 11.7 .. 2.7 0.6 .. –0.6 .. 3.6 2.3 .. –2.6 2.1 1.5 .. –1.5 –2.0 0.7 –3.8 3.6 3.1 9.9 3.7 –0.4
GLOBAL LINKS
Integration with the global economy
1990
2002
1990
2002
7.2 4.6 .. 4.1 2.6 .. 22.2 6.5 10.6 8.4 5.4 6.3 .. 3.6 .. 5.6 19.3 .. 3.7 1.7 .. 9.6 .. 7.3 .. .. 1.8 3.2 10.3 2.0 48.8 8.0 9.2 .. .. 5.5 0.4 .. 16.5 3.5 29.8 17.8 .. 2.8 5.9 11.9 3.8 4.2 106.6 5.7 5.4 3.2 4.4 11.0 11.4 ..
5.6 19.3 .. 5.4 2.4 .. 278.2 10.8 13.7 27.1 15.3 7.8 34.2 5.4 .. 7.4 18.9 11.6 1.4 29.5 .. 10.5 .. .. 13.3 14.6 1.2 3.2 19.9 22.9 .. 26.9 6.3 17.8 13.4 3.3 10.0 .. 26.8 3.2 69.1 9.2 9.8 .. .. 38.3 5.0 5.3 69.4 15.8 19.1 10.8 41.2 10.2 37.6 ..
1.4 0.0 .. 1.0 0.0 .. 2.2 0.7 1.3 3.0 1.7 1.7 .. 0.7 .. 0.7 1.3 .. 0.7 0.5 .. 2.8 .. 0.9 .. .. 0.7 0.0 5.3 0.2 0.7 1.7 1.0 .. .. 0.6 0.4 .. 5.0 0.0 8.3 11.5 .. 1.6 2.1 2.1 1.4 0.6 2.6 4.8 1.5 0.2 1.2 0.2 3.9 ..
2.2 3.9 .. 2.1 0.0 .. 47.1 3.0 2.7 7.1 1.4 0.9 12.3 0.0 .. 1.0 0.5 1.4 1.4 5.0 .. 10.3 .. .. 5.3 2.0 0.2 0.3 5.8 12.2 .. 0.6 2.4 6.8 6.8 1.4 7.4 .. 4.8 0.0 15.0 4.0 4.3 .. .. 5.2 0.2 1.4 7.4 2.2 8.3 4.2 1.5 3.7 7.1 ..
2004 World Development Indicators
307
6.1
Integration with the global economy Trade in goods
% of
% of
GDP
goods GDP
1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Ratio of commercial service exports to merchandise exports
32.8 16.5 15.4 58.6 34.7 .. 44.2 307.6 110.8 102.4 26.7 37.4 a 28.1 57.3 4.1 138.2 45.5 58.4 53.7 .. 31.9 65.7 52.1 65.9 73.5 23.4 .. 10.2 .. 101.8 41.2 15.8 32.7 .. 51.1 79.7 .. 46.9 76.9 40.7 32.5 w 26.9 35.2 30.6 45.0 33.4 47.0 28.8 23.1 46.6 16.5 40.8 32.3 44.9
2002
69.3 48.3 14.9 56.4 51.9 54.8 39.7 277.8 130.2 92.9 .. 56.6 a 41.9 65.2 26.5 144.6 61.3 64.1 51.8 119.9 27.3 105.6 78.0 89.7 77.7 45.9 70.4 36.7 84.3 .. 39.9 18.3 31.5 80.0 41.0 101.3 .. 58.4 60.6 38.5 40.3 w 37.3 54.9 49.2 66.2 51.8 63.4 64.3 41.2 50.5 24.2 55.3 37.6 56.3
1990
45.2 35.0 26.9 107.5 90.0 .. .. .. 192.1 196.5 33.2 73.6 a 70.6 .. .. 217.1 112.0 .. 102.4 .. 47.8 132.2 92.6 149.7 161.6 44.5 .. .. .. 159.6 102.6 44.8 85.0 .. 90.8 129.7 .. 90.0 102.3 74.5 80.2 w .. 74.6 63.2 86.4 74.5 78.5 53.3 66.4 84.0 .. 77.1 80.9 112.6
2002
117.7 105.1 23.6 99.8 141.8 .. .. 921.3 302.5 206.2 .. 136.6 a 117.3 .. .. 195.5 167.8 246.7 90.0 152.7 43.0 205.0 126.4 214.8 196.2 105.5 .. .. 145.7 .. 123.9 66.8 104.6 130.5 86.3 .. .. 97.1 113.2 97.5 116.0 w .. 116.8 98.0 146.8 115.0 104.6 132.1 132.0 90.9 .. 119.7 117.2 141.9
% 1990
2002
12.3 .. 28.0 6.8 46.8 .. 32.8 24.1 .. 18.3 .. 14.0 49.7 22.2 35.9 18.3 23.4 28.6 17.6 .. 39.5 27.3 42.6 15.5 44.7 60.8 .. 0.0 .. .. 29.1 33.8 27.2 .. 6.4 .. .. 11.8 7.2 14.7 21.5 14.6 16.5 18.7 13.7 16.2 14.1 29.6 17.5 11.6 24.6 13.9 23.2 24.4
16.8 12.6 84.9 7.0 .. .. .. 23.6 15.4 24.1 .. 14.8 52.1 26.5 2.5 13.8 29.0 31.7 26.7 8.2 69.6 22.1 12.4 12.3 38.3 42.6 .. 52.1 25.5 .. 44.0 39.3 40.4 .. 3.5 17.8 .. 4.0 11.8 .. 23.1 w 19.4 15.6 16.8 13.9 16.1 13.6 21.7 13.4 12.2 39.5 10.1 25.5 23.7
a. Data refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland).
308
2004 World Development Indicators
Growth in real trade less growth in real GDP
Gross private capital flows
Gross foreign direct investment
percentage
% of
% of
points
GDP
GDP
1990–2002
8.5 2.6 0.6 .. 0.1 .. –13.4 .. 8.4 3.1 .. 3.2 6.5 2.6 5.8 –0.6 4.6 3.1 3.6 .. 0.5 2.9 –0.5 2.5 0.3 6.8 3.8 6.8 3.8 .. 3.9 4.5 2.9 –1.2 3.1 .. –3.2 3.1 2.0 4.8
1990
2002
2.9 .. 2.8 8.8 4.8 .. 11.0 54.2 .. 3.4 .. 2.2 11.4 13.1 0.2 10.7 33.1 15.9 18.0 .. 0.2 13.5 9.6 11.4 9.5 4.3 .. 1.1 .. .. 35.3 5.7 12.7 .. 49.9 .. .. 16.2 64.7 1.7 10.1 w 3.0 6.8 4.1 12.2 6.0 5.0 .. 7.9 6.0 1.4 4.9 10.9 14.1
8.5 12.2 0.9 13.9 .. .. .. 47.8 29.6 21.2 .. 10.1 26.9 3.6 7.5 19.6 29.3 59.9 16.8 10.6 3.4 13.6 14.4 20.5 10.6 7.7 .. 4.5 11.8 .. 60.3 9.2 81.7 .. 15.4 5.8 .. 3.6 9.3 .. 20.8 w 4.4 12.4 11.0 15.1 11.1 10.2 13.9 13.7 10.3 3.2 9.6 22.9 49.3
1990
0.0 .. 0.3 1.6 1.3 .. 5.0 20.6 .. 0.9 .. 0.2 3.4 0.5 0.0 5.0 6.8 5.8 0.0 .. 0.0 3.0 1.1 3.1 0.6 0.5 .. 0.0 .. .. 7.4 2.8 0.0 .. 1.7 .. .. 2.7 6.2 0.1 2.7 w 0.5 1.0 0.8 1.5 0.9 1.7 .. 0.9 0.8 0.1 1.0 3.0 2.9
2002
2.5 1.9 0.2 0.5 .. .. .. 11.7 11.8 10.2 .. 1.4 6.2 1.5 4.6 8.5 14.5 9.2 1.5 3.0 2.6 0.8 6.2 11.6 3.8 0.7 .. 2.6 1.7 .. 23.8 2.4 1.7 .. 3.1 4.0 .. 1.1 3.8 .. 6.0 w 1.7 3.7 3.6 3.9 3.3 4.1 3.7 4.0 0.9 0.7 2.2 6.6 14.8
About the data
6.1
GLOBAL LINKS
Integration with the global economy Definitions
The growing integration of societies and economies
Trade and capital flows are converted to U.S. dol-
• Trade in goods as a share of GDP is the sum of
has helped reduce pover ty in many countries.
lars at the International Monetary Fund’s average
merchandise exports and imports divided by the
Between 1990 and 2000 the number of people living
official exchange rate for the year shown. An alter-
value of GDP, all in current U.S. dollars. • Trade in
on less than $1 a day declined by about 137 million.
native conversion factor is applied if the official
goods as a share of goods GDP is the sum of mer-
Although global integration is a powerful force in
exchange rate diverges by an exceptionally large mar-
chandise exports and imports divided by the value of
reducing poverty, more needs to be done—2 billion
gin from the rate effectively applied to transactions
GDP after subtracting value added in services, all in
people are in danger of becoming marginal to the
in foreign currencies and traded products.
current U.S. dollars. • Ratio of commercial service
world economy. All countries have a stake in helping
exports to merchandise exports is total service
developing countries integrate with the global econo-
exports minus exports of government services not
my and gain better access to rich country markets.
included elsewhere over the f.o.b. value of goods
One indication of increasing global economic inte-
provided to the rest of the world, all in current U.S.
gration is the growing importance of trade in the
dollars. • Growth in real trade less growth in real
world economy. Another is the increased size and
GDP is the difference between annual growth in
importance of private capital flows to developing
trade of goods and services and annual growth in
countries that have liberalized their financial mar-
GDP. Growth rates are calculated using constant
kets. This table presents standardized measures of
price series taken from national accounts and are
the size of trade and capital flows relative to gross
expressed as a percentage. • Gross private capital
domestic product (GDP). The numerators are based
flows are the sum of the absolute values of direct,
on gross flows that capture the two-way flow of goods
portfolio, and other investment inflows and outflows
and capital. In conventional balance of payments
recorded in the balance of payments financial
accounting exports are recorded as a credit and
account, excluding changes in the assets and liabili-
imports as a debit. And in the financial account
ties of monetary authorities and general government.
inward investment is a credit and outward invest-
The indicator is calculated as a ratio to GDP in U.S.
ment a debit. Thus net flows, the sum of credits and
dollars. • Gross foreign direct investment is the
debits, represent a balance in which many transac-
sum of the absolute values of inflows and outflows
tions are canceled out. Gross flows are a better
of foreign direct investment recorded in the balance
measure of integration because they show the total
of payments financial account. It includes equity cap-
value of financial transactions during a given period.
ital, reinvestment of earnings, other long-term capi-
Trade in goods (exports and imports) is shown rel-
tal, and short-term capital. This indicator differs from
ative to both total GDP and goods GDP (GDP less
the standard measure of foreign direct investment,
services such as storage, transport, communica-
which captures only inward investment (see table
tions, retail trade, business services, public adminis-
6.7). The indicator is calculated as a ratio to GDP in
tration,
U.S. dollars.
restaurants
and
hotels,
and
social,
community, and personal services). As a result of the growing share of services in GDP, trade as a share of total GDP appears to be declining for some
Data sources
economies. Comparing merchandise trade with GDP
The data on merchandise trade are from the
after deducting value added in services thus provides
World Trade Organization. The data on GDP come
a better measure of its relative size than does com-
from the World Bank’s national accounts files,
paring it with total GDP, although this neglects the
converted from national currencies to U.S. dollars
growing service component of most goods output.
using the official exchange rate, supplemented by
Trade in ser vices (such as transport, travel,
an alternative conversion factor if the official
finance, insurance, royalties, construction, communi-
exchange rate is judged to diverge by an excep-
cations, and cultural services) is an increasingly
tionally large margin from the rate effectively
important element of global integration. The differ-
applied to transactions in foreign currencies and
ence between the growth of real trade in goods and
traded products. The data on real trade and GDP
services and the growth of GDP helps to identify
growth come from the World Bank’s national
economies that have integrated with the global econ-
accounts files. Gross private capital flows and for-
omy by liberalizing trade, lowering barriers to foreign
eign direct investment were calculated using the
investment, and harnessing their abundant labor to
International
gain a competitive advantage in labor-intensive man-
Payments database.
Monetar y
Fund’s
Balance
of
ufactures and services.
2004 World Development Indicators
309
6.2
Direction and growth of merchandise trade
Direction of trade, 2002
High-income importers % of world trade
European
Source of exports High-income economies Industrial economies European Union Japan United States Other industrial economies Other high-income economies Low- and middle-income economies East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & N. Africa South Asia Sub-Saharan Africa World
Other
All
United
Other
All
high
high
industrial
industrial
income
income
50.2 45.0 29.6 3.2 6.0 6.3 5.2 15.5 5.1 3.2 4.1 1.6 0.6 0.9 65.7
7.2 5.1 1.7 1.6 1.3 0.4 2.0 3.6 2.5 0.2 0.2 0.4 0.2 0.1 10.8
57.4 50.1 31.3 4.8 7.3 6.7 7.3 19.1 7.6 3.4 4.3 2.0 0.8 1.0 76.5
Union
Japan
States
29.9 28.4 23.3 1.0 2.3 1.8 1.6 6.4 1.4 2.7 0.6 0.9 0.3 0.5 36.3
2.9 1.8 0.6
11.5 9.3 3.6 1.9
0.8 0.4 1.1 1.9 1.4 0.0 0.1 0.3 0.0 0.1 4.8
5.9 5.5 2.1 0.3 2.9 0.2 0.4 0.8 0.4 0.2 0.1 0.1 0.0 0.0 6.8
3.9 2.2 6.3 1.9 0.2 3.2 0.3 0.3 0.3 17.8
Low- and middle-income importers % of world trade
Source of exports High-income economies Industrial economies European Union Japan United States Other industrial economies Other high-income economies Low- and middle-income economies East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & N. Africa South Asia Sub-Saharan Africa World
310
Europe
Latin
Middle
East Asia
& Central
America
East &
South
Sub-Saharan
& middle-
& Pacific
Asia
& Caribbean
N. Africa
Asia
Africa
income
World
0.8 0.7 0.5 0.1 0.1 0.0 0.1 0.4 0.1 0.0 0.0 0.0 0.0 0.2 1.3
17.2 12.9 6.8 1.8 3.6 0.7 4.3 6.3 1.9 1.6 1.3 0.7 0.3 0.4 23.5
74.6 63.0 38.1 6.5 10.9 7.5 11.6 25.4 9.5 5.1 5.6 2.7 1.1 1.4 100.0
6.3 3.2 0.9 1.3 0.8 0.3 3.1 1.7 0.9 0.2 0.1 0.2 0.1 0.1 8.0
2004 World Development Indicators
3.4 3.3 3.0 0.1 0.2 0.1 0.1 1.6 0.2 1.1 0.0 0.1 0.0 0.0 4.9
1.7 1.5 0.5 0.2 0.8 0.1 0.1 1.0 0.1 0.0 0.7 0.0 0.0 0.0 2.7
1.6 1.4 1.0 0.1 0.2 0.1 0.1 0.6 0.2 0.2 0.1 0.1 0.1 0.0 2.2
All low-
0.7 0.5 0.3 0.1 0.1 0.1 0.2 0.4 0.2 0.0 0.0 0.1 0.1 0.0 1.1
6.2
GLOBAL LINKS
Direction and growth of merchandise trade Nominal growth of trade, 1992–2002
High-income importers annual % growth
European
Source of exports High-income economies Industrial economies European Union Japan United States Other industrial economies Other high-income economies Low- and middle-income economies East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & N. Africa South Asia Sub-Saharan Africa World
Other
All
United
Other
All
high
high
industrial
industrial
income
income
4.2 4.3 3.3 –0.1 5.7 4.2 3.7 9.3 13.0 11.7 10.0 4.0 7.3 4.9 4.7
3.9 3.9 4.0 0.9 3.8 5.4 4.3 8.7 12.0 11.0 9.4 3.0 7.4 6.7 4.9
4.8 3.9 5.6 2.5 3.8 3.5 7.5 8.0 8.0 10.9 9.7 4.4 9.5 16.8 5.7
4.0 3.9 4.0 1.4 3.8 5.3 5.1 8.6 10.5 11.0 9.4 3.3 7.8 7.3 5.0
Union
Japan
States
3.3 3.2 3.5 –0.9 2.9 2.9 4.5 7.0 12.1 11.2 2.4 2.7 6.1 6.6 3.9
2.4 1.6 3.3
6.1 6.5 8.3 2.2
0.7 0.8 3.8 6.9 9.3 0.2 –0.5 3.0 0.0 17.6 3.9
7.7 4.6 11.7 14.4 12.7 12.3 3.7 10.9 5.6 7.7
Low- and middle-income importers annual % growth
Source of exports High-income economies Industrial economies European Union Japan United States Other industrial economies Other high-income economies Low- and middle-income economies East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & N. Africa South Asia Sub-Saharan Africa World
Europe
Latin
Middle
East Asia
& Central
America
East &
South
Sub-Saharan
& middle-
All low-
& Pacific
Asia
& Caribbean
N. Africa
Asia
Africa
income
World
8.7 7.3 7.2 7.1 8.2 6.0 10.5 14.9 16.5 6.3 12.9 16.1 15.5 27.6 9.7
7.5 7.8 8.6 4.0 1.8 2.4 –1.0 3.2 10.7 8.7 6.1 2.9 5.2 17.5 6.0
2.9 3.0 3.1 –0.1 3.9 1.5 2.8 7.1 16.7 7.0 7.0 –2.1 19.0 10.0 4.2
0.9 0.7 1.5 –3.2 –2.0 4.3 1.7 6.3 10.0 5.7 4.8 6.0 4.6 5.7 2.1
5.1 4.0 4.6 –0.4 5.4 4.5 7.5 10.2 14.7 8.3 14.8 4.6 9.8 8.7 6.7
1.3 1.0 1.5 –3.5 1.1 3.1 1.2 9.0 15.8 10.9 5.3 8.7 11.5 11.4 3.3
6.5 5.8 6.5 4.0 6.0 4.2 8.7 9.2 14.3 7.8 8.1 6.7 9.7 13.5 7.1
4.5 4.2 4.4 2.1 4.5 5.2 6.3 8.7 11.2 9.9 9.1 4.1 8.3 8.8 5.4
2004 World Development Indicators
311
6.2
Direction and growth of merchandise trade
About the data
Definitions
The table provides estimates of the flow of trade in
published period average exchange rates (series r f
• Merchandise trade includes all trade in goods; trade
goods between groups of economies. The data are
or rh, monthly averages of the market or official
in services is excluded. • High-income economies are
from the International Monetar y Fund’s (IMF)
rates) for the reporting country or, if those are not
those classified as such by the World Bank (see inside
Direction of Trade database. All high-income coun-
available, monthly average rates in New York.
front cover). • Industrial economies are those classi-
tries and 22 of the 156 developing countries report
Because imports are reported at cost, insurance,
fied as such in the IMF’s Direction of Trade Statistics
trade on a timely basis, covering about 80 percent of
and freight (c.i.f.) valuations, and exports at free on
Yearbook. They include the countries of the European
trade for recent years. Trade by less timely reporters
board (f.o.b.) valuations, the IMF adjusts country
Union, Japan, the United States, and the other indus-
and by countries that do not report is estimated
reports of import values by dividing them by 1.10 to
trial economies listed below. • European Union com-
using reports of partner countries. Because the
estimate equivalent export values. This approxima-
prises Austria, Belgium, Denmark, Finland, France,
largest exporting and importing countries are reliable
tion is more or less accurate, depending on the set
Germany, Greece, Ireland, Italy, Luxembourg, the
reporters, a large portion of the missing trade flows
of partners and the items traded. Other factors
Netherlands, Portugal, Spain, Sweden, and the United
can be estimated from partner reports. Partner
affecting the accuracy of trade data include lags in
Kingdom. • Other industrial economies include
country data may introduce discrepancies due to
reporting, recording differences across countries,
Australia, Canada, Iceland, New Zealand, Norway, and
smuggling, confidentiality, different exchange rates,
and whether the country reports trade according to
Switzerland. • Other high-income economies include
overreporting of transit trade, inclusion or exclusion
the general or special system of trade. (For further
Antigua and Barbuda, Aruba, The Bahamas, Bahrain,
of freight rates, and different points of valuation and
discussion of the measurement of exports and
Barbados, Bermuda, Brunei, Cyprus, Faeroe Islands,
times of recording.
imports, see About the data for tables 4.5 and 4.6.)
French Polynesia, Greenland, Guam, Hong Kong
In addition, estimates of trade within the European
The regional trade flows shown in the table were
(China), Israel, the Republic of Korea, Kuwait, Macao
Union (EU) have been significantly affected by
calculated from current price values. The growth rates
(China), Malta, Netherlands Antilles, New Caledonia,
changes in reporting methods following the creation
presented are in nominal terms; that is, they include
Qatar, Singapore, Slovenia, Taiwan (China), and the
of a customs union. The new system for collecting
the effects of changes in both volumes and prices.
United Arab Emirates. • Low- and middle-income
data on trade between EU members—Intrastat, intro-
regional groupings are based on World Bank classifi-
duced in 1993—has less exhaustive coverage than
cations and may differ from those used by other
the previous customs-based system and has resulted
organizations.
in some problems of asymmetry (estimated imports are about 5 percent less than exports). Despite these issues, only a small portion of world trade is estimated to be omitted from the IMF’s Direction of Trade Statistics Yearbook and Direction of Trade database. Most countries report their trade data in national currencies, which are converted using the IMF’s
6.2a Rich markets for developing country exports Developing economy exports as % of world trade, 2002 10 High income 8
Developing countries
6
4
2
Data sources
0 East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
Intercountry trade flows are published in the IMF’s Direction of Trade Statistics Yearbook and
High-income countries continue to be the principal trading partners of developing countries. Yet trade between and within developing countries continues to grow. At 9.5 percent, East Asia and Pacific is the developing region with the largest exports as a share of world trade. Sub-Saharan Africa’s share, although small, has been growing.
Direction of Trade Statistics Quarterly; the data in the table were calculated using the IMF’s Direction of Trade database.
Source: International Monetary Fund, Direction of Trade database.
312
2004 World Development Indicators
GLOBAL LINKS
6.3
OECD trade with low- and middle-income economies Exports to low-income economies High-income
European
OECD countries
Union
Japan
United States
1992
2002
1992
2002 a
1992
2002
1992
2002
$ billions Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
6.1 2.7 1.9 1.4 1.9 0.3 1.3 57.4 8.1 33.6 15.7 1.5 70.3
8.2 2.2 2.9 2.1 2.1 0.0 1.4 69.6 10.3 38.2 21.1 3.3 88.2
3.4 1.0 0.4 0.6 0.8 0.0 0.8 30.0 4.5 16.4 9.1 0.6 35.8
4.2 0.9 0.7 0.8 0.8 0.0 0.7 34.4 5.6 17.5 11.3 1.2 42.1
0.1 0.0 0.2 0.1 0.1 0.0 0.1 12.9 1.0 9.0 2.9 0.1 13.4
0.1 0.0 0.2 0.2 0.1 0.0 0.1 13.1 1.1 8.5 3.4 0.3 14.1
1.7 1.2 0.6 0.3 0.2 0.0 0.2 6.3 1.3 4.2 0.9 0.3 9.3
2.2 1.0 0.9 0.2 0.2 0.0 0.2 8.7 1.5 5.6 1.7 0.7 12.9
% of total exports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
8.7 3.8 2.8 2.0 2.7 0.4 1.8 81.7 11.6 47.8 22.3 2.2 100.0
9.3 2.5 3.2 2.4 2.4 0.0 1.6 78.9 11.7 43.3 23.9 3.8 100.0
9.5 2.7 1.2 1.6 2.4 0.0 2.3 83.6 12.5 45.7 25.4 1.7 100.0
10.0 2.2 1.7 2.0 1.8 0.0 1.8 81.7 13.2 41.6 26.9 2.8 100.0
0.6 0.1 1.6 0.7 0.6 0.0 0.6 95.9 7.2 67.0 21.7 0.6 100.0
0.4 0.2 1.7 1.8 0.9 0.0 0.7 93.0 8.1 60.4 24.4 2.2 100.0
18.0 13.3 6.7 2.8 1.7 0.0 1.7 67.9 13.5 45.3 9.1 2.8 100.0
17.0 7.5 7.3 1.3 1.3 0.0 1.3 67.8 11.3 43.4 13.1 5.3 100.0
$ billions Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
11.5 0.1 4.4 5.1 29.4 22.2 2.3 31.6 1.3 1.9 28.5 0.4 82.4
17.5 0.4 5.0 5.6 37.0 26.8 3.0 76.0 4.1 9.3 62.5 1.0 142.0
7.1 0.1 2.5 2.0 8.4 7.9 0.3 16.0 0.7 1.0 14.3 0.2 36.2
9.5 0.2 2.8 2.4 9.0 7.7 0.5 32.8 1.6 3.9 27.4 0.4 56.8
2.1 0.0 0.8 2.1 8.8 3.6 0.9 3.9 0.1 0.1 3.6 0.1 17.8
2.8 0.0 0.6 2.3 8.6 3.0 0.7 7.6 0.6 1.9 5.1 0.1 22.1
1.6 0.0 0.7 0.4 9.2 8.8 0.5 8.8 0.2 0.4 8.2 0.1 20.9
3.9 0.1 0.8 0.2 11.8 10.8 1.0 28.9 1.2 2.6 25.1 0.3 45.9
% of total imports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
13.9 0.2 5.4 6.2 35.7 26.9 2.7 38.4 1.5 2.3 34.6 0.5 100.0
12.3 0.3 3.5 3.9 26.1 18.9 2.1 53.5 2.9 6.6 44.0 0.7 100.0
19.6 0.3 6.9 5.6 23.2 21.9 0.9 44.1 1.8 2.9 39.4 0.6 100.0
16.7 0.3 4.9 4.2 15.9 13.6 0.9 57.7 2.8 6.8 48.2 0.7 100.0
11.9 0.0 4.6 11.9 49.5 20.4 5.3 21.7 0.8 0.6 20.3 0.4 100.0
12.6 0.1 2.8 10.4 39.2 13.7 3.2 34.4 2.6 8.8 23.0 0.7 100.0
7.6 0.1 3.5 2.0 44.2 41.9 2.2 42.3 1.0 1.9 39.3 0.4 100.0
8.5 0.2 1.8 0.5 25.7 23.4 2.2 62.8 2.5 5.6 54.6 0.7 100.0
Imports from low-income economies
2004 World Development Indicators
313
6.3
OECD trade with low- and middle-income economies
Exports to middle-income economies High-income
European
OECD countries
Union
Japan
United States
1992
2002
1992
2002 a
1992
2002
1992
2002
$ billions Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
35.8 13.8 7.4 6.4 8.1 0.9 5.4 311.1 38.5 185.0 87.5 10.3 379.0
44.7 10.6 14.1 15.0 14.5 1.2 9.8 619.2 86.5 361.1 171.6 22.6 730.0
16.7 4.7 2.3 2.2 2.8 0.5 2.2 139.4 20.5 77.3 41.6 3.2 166.6
19.9 3.4 4.9 5.8 4.7 0.4 3.7 307.9 45.4 169.9 92.6 6.6 349.8
0.3 0.1 0.5 0.6 0.5 0.0 0.4 60.7 3.4 41.7 15.6 0.6 63.2
0.3 0.0 1.0 2.0 0.5 0.0 0.5 92.5 8.1 62.6 21.8 3.3 99.6
13.0 5.8 3.0 2.0 3.3 0.0 2.2 85.3 10.6 53.9 20.8 5.2 111.8
16.7 5.5 5.1 3.5 5.1 0.0 3.9 153.8 20.0 93.8 39.9 9.1 193.2
% of total exports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
9.5 3.6 2.0 1.7 2.1 0.2 1.4 82.1 10.2 48.8 23.1 2.7 100.0
6.1 1.5 1.9 2.1 2.0 0.2 1.3 84.8 11.9 49.5 23.5 3.1 100.0
10.0 2.8 1.4 1.3 1.7 0.3 1.3 83.7 12.3 46.4 25.0 1.9 100.0
5.7 1.0 1.4 1.7 1.4 0.1 1.0 88.0 13.0 48.6 26.5 1.9 100.0
0.5 0.1 0.8 1.0 0.7 0.0 0.6 96.1 5.3 66.0 24.7 0.9 100.0
0.3 0.0 1.0 2.0 0.5 0.0 0.5 92.9 8.1 62.9 21.9 3.3 100.0
11.6 5.2 2.7 1.8 3.0 0.0 2.0 76.3 9.5 48.2 18.6 4.7 100.0
8.6 2.8 2.6 1.8 2.6 0.0 2.0 79.6 10.4 48.5 20.7 4.7 100.0
Imports from middle-income economies $ billions Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
59.5 2.1 15.2 24.8 122.7 87.9 19.6 196.1 13.9 59.6 122.6 7.4 425.7
81.8 4.4 17.9 39.6 182.3 134.7 24.9 730.2 34.7 333.8 361.6 15.7 1,067.4
30.0 0.4 7.3 12.4 61.1 41.6 9.2 75.6 7.2 17.5 50.9 3.3 189.7
36.5 1.9 9.6 17.3 76.4 52.7 11.6 254.1 15.0 107.5 131.6 2.2 396.1
10.0 0.6 4.2 6.0 20.5 14.5 2.4 19.0 1.8 3.7 13.5 0.6 60.4
14.5 0.5 2.9 7.0 24.1 17.4 1.2 75.6 3.5 32.1 39.9 1.6 125.7
14.2 0.2 1.9 4.2 30.6 23.4 6.8 85.1 3.3 33.6 48.2 3.2 139.2
22.4 0.6 3.8 8.9 61.2 49.7 9.8 335.6 11.3 164.8 159.5 11.5 443.3
% of total imports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Mach. and transport equip. Other Miscellaneous goods Total
14.0 0.5 3.6 5.8 28.8 20.6 4.6 46.1 3.3 14.0 28.8 1.7 100.0
7.7 0.4 1.7 3.7 17.1 12.6 2.3 68.4 3.3 31.3 33.9 1.5 100.0
15.8 0.2 3.9 6.5 32.2 21.9 4.8 39.9 3.8 9.2 26.8 1.7 100.0
9.2 0.5 2.4 4.4 19.3 13.3 2.9 64.1 3.8 27.1 33.2 0.6 100.0
16.6 1.1 7.0 10.0 33.9 24.0 4.0 31.4 3.0 6.1 22.4 1.0 100.0
11.5 0.4 2.3 5.6 19.2 13.8 1.0 60.2 2.8 25.6 31.8 1.3 100.0
10.2 0.2 1.3 3.0 22.0 16.8 4.9 61.1 2.3 24.2 34.6 2.3 100.0
5.1 0.1 0.9 2.0 13.8 11.2 2.2 75.7 2.5 37.2 36.0 2.6 100.0
a. Data for Belgium, Greece, and Luxembourg are for 2001.
314
2004 World Development Indicators
About the data
6.3
GLOBAL LINKS
OECD trade with low- and middle-income economies Definitions
Developing countries are becoming increasingly
goods from developing countries is imposed by other
The product groups in the table are defined in accor-
important in the global trading system. Since the
developing countries). The growing trade between
dance with the Standard International Trade
early 1990s trade between high-income members of
developing countries strengthens the case for reduc-
Classification (SITC) revision 1: food (0, 1, 22, and
the Organisation for Economic Co-operation and
ing these barriers. Despite the growth in trade
4) and cereals (04); agricultural raw materials (2
Development (OECD) and low- and middle-income
between developing countries, high-income OECD
excluding 22, 27, and 28); ores and nonferrous met-
economies has grown faster than trade between
countries remain the developing world’s most impor-
als (27, 28, and 68); fuels (3), crude petroleum
OECD members. The increased trade benefits con-
tant partners.
(331), and petroleum products (332); manufactured
sumers and producers. But as the World Trade
The aggregate flows in the table were compiled
goods (5–8 excluding 68), chemical products (5),
Organization’s (WTO) Ministerial Conference in
from intercountry flows recorded in the United
machinery and transport equipment (7), and other
Doha, Qatar, in October 2001 showed, achieving a
Nations Statistics Division’s Commodity Trade (COM-
manufactured goods (6 and 8 excluding 68); and
more prodevelopment outcome from trade remains
TRADE) database. Partner country reports by high-
miscellaneous goods (9). • Exports are all mer-
a major challenge. Meeting this challenge will
income OECD countries were used for both exports
chandise exports by high-income OECD countries to
require strengthening international consultation.
and imports. Exports are recorded free on board
low-income and middle-income economies as record-
Negotiations after the Doha meetings have been
(f.o.b.); impor ts include insurance and freight
ed in the United Nations Statistics Division’s COM-
launched on services, agriculture, manufactures,
charges (c.i.f.). Because of differences in sources of
TRADE database. • Imports are all merchandise
WTO rules, the environment, dispute settlement,
data, timing, and treatment of missing data, the data
imports by high-income OECD countries from low-
intellectual property rights protection, and disci-
in this table may not be fully comparable with those
income and middle-income economies as recorded
plines on regional integration. These negotiations
used to calculate the direction of trade statistics in
in the United Nations Statistics Division’s COM-
are scheduled to be concluded by 2005.
table 6.2 or the aggregate flows in tables 4.4–4.6.
TRADE database. • High-income OECD countries in
Trade flows between high-income OECD members
For further discussion of merchandise trade statis-
2002 were Australia, Austria, Belgium, Canada,
and low- and middle-income economies reflect the
tics, see About the data for tables 4.4–4.6 and 6.2.
Denmark, Finland, France, Germany, Greece,
changing mix of exports to and imports from devel-
Iceland, Ireland, Italy, Japan, the Republic of Korea,
oping economies. While food imports from middle-
Luxembourg, the Netherlands, New Zealand, Norway,
income countries have continued to fall as a share of
Portugal, Spain, Sweden, Switzerland, the United
OECD imports, food imports from low-income coun-
Kingdom, and the United States. • European Union
tries to high-income countries have increased as a
comprises Austria, Belgium, Denmark, Finland,
share of U.S. and Japanese imports. The share of
France, Germany, Greece, Ireland, Italy, Luxembourg,
manufactures in total goods imports to high-income
the Netherlands, Portugal, Spain, Sweden, and the
countries has grown dramatically for both low- and
United Kingdom.
middle-income countries. Moreover, trade between developing countries has grown substantially over the past decade. This growth has resulted from many factors, including developing countries’ increasing share of world output and the liberalization of their trade. Yet trade barriers remain high (more than 70 percent of the tariff burden faced by manufactured
6.3a Manufactured goods from developing countries dominated imports by OECD countries in 2002 Imports by high-income OECD countries (% of total imports)
Data sources
80 Low income
70
Middle income
COMTRADE data are available in electronic form
60
from the United Nations Statistics Division.
50
Although not as comprehensive as the underlying
40
COMTRADE records, detailed statistics on inter-
30
national trade are published annually in the United
20
Nations Conference on Trade and Development’s
10
(UNCTAD) Handbook of International Trade and Development Statistics and the United Nations
0 Manufactured goods
Fuels
Food
Ores and nonferrous metals
Agricultural raw materials
Statistics Division’s International Trade Statistics Yearbook.
Source: United Nations Statistics Division, COMTRADE database.
2004 World Development Indicators
315
6.4
Primar y commodity prices
1970
World Bank commodity price index (1990 = 100) Non-energy commodities Agriculture Beverages Food Raw materials Fertilizers Metals and minerals Petroleum Steel products a MUV G-5 index
1980
1990
1995
1997
1998
1999
2000
2001
2002
2003
156 163 203 166 130 108 144 19 111 28
159 175 230 177 133 164 120 204 100 79
100 100 100 100 100 100 100 100 100 100
104 112 129 100 116 88 87 64 91 117
114 124 165 112 110 116 87 81 86 104
99 108 141 105 88 123 76 57 75 100
89 93 108 88 89 115 74 80 69 99
89 90 91 87 94 109 85 127 79 97
84 85 76 91 82 105 80 113 71 94
89 93 91 97 89 108 78 117 73 93
91 95 87 96 98 106 82 126 79 100
225 153 154 145 625 3,836
260 319 248 181 503 2,888
182 343 177 86 533 3,392
182 290 218 135 632 2,258
169 275 230 98 641 3,411
145 287 163 72 486 3,349
118 271 188 63 605 3,064
134 283 195 71 614 3,063
112 282 169 64 510 3,185
109 .. 175 83 565 2,947
140 .. 187 106 551 2,643
Beverages (cents/kg) Cocoa Coffee, robustas Coffee, Arabica Tea, avg., 3 auctions
240 330 409 298
330 411 440 211
127 118 197 206
122 237 285 127
156 168 403 199
168 183 299 205
114 150 231 185
93 94 198 193
113 64 146 169
191 71 146 162
175 81 142 152
Energy Coal, Australian ($/mt) Coal, U.S. ($/mt) Natural gas, Europe ($/mmbtu) Natural gas, U.S. ($/mmbtu) Petroleum ($/bbl)
.. .. .. 0.59 4.31
49.67 54.69 4.31 1.97 46.78
39.67 41.67 2.55 1.70 22.88
33.63 33.46 2.33 1.47 14.68
33.90 35.15 2.65 2.40 18.52
29.34 34.51 2.43 2.09 13.12
26.08 33.41 2.15 2.28 18.20
27.01 34.02 3.97 4.44 29.05
34.26 47.56 4.30 4.19 25.82
29.05 42.97 3.28 3.60 26.76
27.83 .. 3.91 5.49 28.90
Commodity prices (1990 prices) Agricultural raw materials Cotton (cents/kg) Logs, Cameroon ($/cu. m) a Logs, Malaysian ($/cu. m) Rubber (cents/kg) Sawnwood, Malaysian ($/cu. m) Tobacco ($/mt)
About the data
Primary commodities—raw or partially processed
the prices paid by importers are used. Annual price
steel products, which are not included in the non-
materials that will be transformed into finished
series are generally simple averages based on high-
energy commodity price index.
goods—are often the most significant exports of
er frequency data. The constant price series in the
The MUV index is a composite index of prices for
developing countries, and revenues obtained from
table is deflated using the manufactures unit value
manufactured exports from the five major (G-5)
them have an important effect on living standards.
(MUV) index for the G-5 countries (see below).
industrial countries (France, Germany, Japan, the
Price data for primary commodities are collected
The commodity price indexes are calculated as
United Kingdom, and the United States) to low- and
from a variety of sources, including trade journals,
Laspeyres index numbers, in which the fixed weights
middle-income economies, valued in U.S. dollars.
international study groups, government market sur-
are the 1987–89 export values for low- and middle-
The index covers products in groups 5–8 of the
veys, newspaper and wire service reports, and com-
income economies, rebased to 1990. Each index
Standard International Trade Classification (SITC)
modity exchange spot and near-term forward prices.
represents a fixed basket of primary commodity
revision 1. To construct the MUV G-5 index, unit
This table is based on frequently updated price
exports. The non-energy commodity price index con-
value indexes for each country are combined using
reports. When possible, the prices received by
tains 37 price series for 31 non-energy commodities.
weights determined by each country’s export share.
exporters are used; if export prices are unavailable,
Separate indexes are compiled for petroleum and for
316
2004 World Development Indicators
1970
Commodity prices (continued) (1990 prices) Fertilizers ($/mt) Phosphate rock TSP
1990
1995
1997
1998
1999
2000
2001
2002
2003
39 152
59 229
40 132
30 128
40 166
43 174
44 156
45 142
44 135
43 143
38 149
1,417 1,350 927 417 367 1,021
855 1,090 740 376 332 758
336 964 290 247 200 447
572 847 536 221 168 534
634 976 527 285 266 545
660 913 674 244 171 628
742 793 439 203 153 430
463 734 319 218 194 348
337 721 303 208 192 375
452 738 419 228 188 488
467 1,242 443 264 211 554
185 208 450 196
164 159 521 219
104 109 271 136
102 105 274 151
106 113 293 154
98 102 305 127
85 91 250 113
91 91 208 117
101 95 183 134
109 107 206 159
107 105 198 146
590 465 599 40 59 29
481 350 496 62 84 80
541 256 531 58 51 28
380 163 454 59 43 25
499 179 443 61 47 24
491 173 444 60 49 20
376 186 434 60 47 14
436 199 374 57 44 19
618 226 631 56 50 20
568 226 606 59 50 16
375 198 682 60 47 16
1,982 5,038 35 108 10,148 1,310 105
1,847 2,769 36 115 8,271 2,128 97
1,639 2,661 32 81 8,864 609 151
1,542 2,508 24 54 7,028 531 88
1,545 2,199 29 60 6,691 545 127
1,363 1,660 31 53 4,647 556 103
1,371 1,584 28 51 6,055 544 108
1,594 1,866 30 47 8,888 559 116
1,531 1,673 32 50 6,303 475 94
1,449 1,674 31 49 7,271 436 84
1,431 1,779 31 51 9,627 489 83
Food Fats and oils ($/mt) Coconut oil Groundnut oil Palm oil Soybeans Soybean meal Soybean oil Grains ($/mt) Grain sorghum Maize Rice Wheat Other food Bananas ($/mt) Beef (cents/kg) Oranges ($/mt) Sugar, EU domestic (cents/kg) Sugar, U.S. domestic (cents/kg) Sugar, world (cents/kg) Metals and minerals Aluminum ($/mt) Copper ($/mt) Iron ore (cents/dmtu) Lead (cents/kg) Nickel ($/mt) Tin (cents/kg) Zinc (cents/kg)
1980
6.4
GLOBAL LINKS
Primar y commodity prices
a. Series not included in the non-energy index.
Definitions
• Non-energy commodity price index covers the 31
iron ore, lead, nickel, tin, and zinc. • Petroleum
and sources, see “Commodity Price Data” (also
non-energy primary commodities that make up the
price index refers to the average spot price of Brent,
known as the “Pink Sheet”) at the Global Prospects
agriculture, fer tilizer, and metals and minerals
Dubai, and West Texas Intermediate crude oils,
Web site (http://www.worldbank.org/prospects).
indexes. • Agriculture includes beverages, food,
equally weighted. • Steel products price index is
and agricultural raw material. • Beverages include
the composite price index for eight steel products
cocoa, coffee, and tea. • Food includes rice, wheat,
based on quotations free on board (f.o.b.) Japan
maize, sorghum, soybeans, soybean oil, soybean
excluding shipments to China and the United
Data sources
meal, palm oil, coconut oil, groundnut oil, bananas,
States, weighted by product shares of apparent
Commodity price data and the G-5 MUV index
beef, oranges, and sugar. • Agricultural raw mate-
combined consumption (volume of deliveries) for
are compiled by the World Bank’s Development
rials include cotton, timber (logs and sawnwood),
Germany, Japan, and the United States. • MUV G-5
Prospects Group. Monthly updates of com-
natural rubber, and tobacco. • Fertilizers include
index is the manufactures unit value index for G-5
modity prices are available on the Web at
phosphate rock and triple superphosphate (TSP).
countr y
http://www.worldbank.org/prospects.
• Metals and minerals include aluminum, copper,
economies. • Commodity prices—for definitions
expor ts
to
low-
and
middle-income
2004 World Development Indicators
317
6.5
Regional trade blocs
Merchandise exports within bloc
$ millions 1970
High-income and lowand middle-income economies APEC a 58,633 CEFTA 1,157 European Union 76,451 NAFTA 22,078 Latin America and the Caribbean ACS 758 Andean Group 97 CACM 287 CARICOM 52 Central American Group of Four 176 Group of Three 59 LAIA 1,263 MERCOSUR 451 OECS .. Africa CEMAC 22 CEPGL 3 COMESA 392 Cross-Border Initiative 209 ECCAS 162 ECOWAS 86 Indian Ocean Commission 23 MRU 1 SADC 483 UDEAC 22 UEMOA 52 Middle East and Asia Arab Common Market 102 ASEAN 1,456 Bangkok Agreement 132 EAEG 9,197 ECO 63 GCC 156 SAARC 99 UMA 60
1980
1990
1995
1997
1998
1999
2000
2001
2002
357,697 7,766 456,857 102,218
901,560 4,235 981,260 226,273
1,688,708 12,118 1,259,699 394,472
1,869,192 13,169 1,159,112 496,423
1,734,386 14,234 1,223,801 521,649
1,896,213 13,226 1,396,574 581,161
2,262,159 15,123 1,407,525 676,440
2,070,710 17,054 1,396,252 639,138
2,166,764 19,180 1,473,375 626,985
4,892 1,161 1,174 576 692 706 10,981 3,424 8
5,398 1,312 667 448 399 1,046 12,331 4,127 29
11,049 4,812 1,594 867 1,026 3,460 35,299 14,199 39
12,032 5,524 1,993 968 1,302 3,944 44,700 20,680 34
12,505 5,408 2,010 1,020 1,230 3,921 42,959 20,352 36
11,252 3,929 2,175 1,136 1,369 2,912 34,785 15,313 37
15,773 4,785 2,418 1,050 1,582 3,544 42,593 17,884 38
14,984 5,461 2,394 1,202 1,546 4,178 40,755 15,244 40
16,937 5,026 2,598 1,221 1,678 3,647 35,755 10,341 43
75 2 609 447 89 692 39 7 617 75 460
139 7 910 613 163 1,557 73 0 1,630 139 621
120 8 1,244 1,002 163 1,936 127 1 3,373 120 560
161 6 1,391 1,144 211 2,244 75 7 4,471 160 707
153 8 1,342 1,156 198 2,350 95 2 3,865 152 752
127 9 1,357 964 179 2,364 91 4 4,224 126 805
102 10 1,556 1,066 196 2,873 105 5 4,452 101 741
120 11 1,639 947 217 2,794 135 4 4,132 119 776
131 12 1,801 1,019 236 3,009 136 5 4,268 130 875
661 13,350 1,464 98,532 15,891 4,632 613 109
911 28,648 4,476 281,067 1,243 6,906 863 958
1,368 81,911 12,066 634,606 4,746 6,832 2,024 1,109
1,146 88,773 13,684 669,833 4,929 8,124 2,174 924
978 72,352 12,851 549,010 4,031 7,358 2,466 881
951 80,415 14,463 612,415 3,903 7,306 2,180 919
1,312 101,848 16,844 772,420 4,485 7,218 2,614 1,104
1,728 90,105 16,733 698,550 4,457 6,959 2,828 1,136
1,857 95,473 18,299 779,364 4,915 6,922 2,999 1,178
Note: Regional bloc memberships are as follows: Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Central European Free Trade Area (CEFTA), Bulgaria, the Czech Republic, Hungary, Poland, Romania, the Slovak Republic, and Slovenia; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom; North American Free Trade Area (NAFTA), Canada, Mexico, and the United States; Association of Caribbean States (ACS), Antigua and Barbuda, the Bahamas, Barbados, Belize, Colombia, Costa Rica, Cuba, Dominica, the Dominican Republic, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, and República Bolivariana de Venezuela; Andean Group, Bolivia, Colombia, Ecuador, Peru, and República Bolivariana de Venezuela; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas (part of the Caribbean Community but not of the Common Market), Barbados, Belize, Dominica, Grenada, Guyana, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Group of Four, El Salvador, Guatemala, Honduras, and Nicaragua; Group of Three, Colombia, Mexico, and República Bolivariana de Venezuela; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and República Bolivariana de Venezuela; Southern Cone Common Market (MERCOSUR), Argentina, Brazil, Paraguay, and Uruguay; Organization of Eastern Caribbean States (OECS), Antigua and Barbuda, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of the Countries of the Great Lakes (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Common Market for Eastern and Southern Africa (COMESA), Angola, Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Tanzania, Zambia,
318
2004 World Development Indicators
6.5
GLOBAL LINKS
Regional trade blocs Merchandise exports within bloc
% of total bloc exports
High-income and lowand middle-income economies APEC a CEFTA European Union NAFTA Latin America and the Caribbean ACS Andean Group CACM CARICOM Central American Group of Four Group of Three LAIA MERCOSUR OECS Africa CEMAC CEPGL COMESA Cross-Border Initiative ECCAS ECOWAS Indian Ocean Commission MRU SADC COMESA UDEAC UEMOA Middle East and Asia Arab Common Market ASEAN Bangkok Agreement EAEG ECO GCC SAARC UMA
1970
1980
1990
1995
1997
1998
1999
2000
2001
2002
57.8 12.9 59.5 36.0
57.9 14.8 60.8 33.6
68.3 9.9 65.9 41.4
71.8 14.6 62.4 46.2
71.6 13.4 55.4 49.1
69.7 13.0 56.8 51.7
71.8 12.1 62.9 54.6
73.1 12.2 61.6 55.7
72.6 12.4 60.8 55.5
73.3 12.2 60.6 56.7
9.6 1.8 26.1 4.2 20.1 1.1 9.9 9.4 ..
8.7 3.8 24.4 5.3 18.1 1.8 13.7 11.6 9.1
8.4 4.1 15.3 8.1 13.7 2.0 10.8 8.9 8.1
8.5 12.0 21.8 12.1 22.2 3.2 17.1 20.3 12.6
7.0 10.8 18.7 14.4 20.2 2.7 17.0 24.8 10.7
7.2 12.8 15.8 17.3 17.1 2.6 16.7 25.0 12.0
5.6 8.8 13.6 16.9 14.6 1.7 12.7 20.6 13.1
6.4 7.9 14.8 14.7 15.1 1.7 12.8 20.8 10.0
6.5 10.3 15.5 14.0 14.8 2.1 12.8 17.2 5.3
7.1 9.5 11.1 12.5 12.8 1.8 11.1 11.6 3.8
4.8 0.4 8.7 9.3 9.6 2.9 8.4 0.2 8.0 4.9 4.9 6.5
1.6 0.1 6.0 8.8 1.4 10.1 3.9 0.8 2.0 1.6 1.6 9.6
2.3 0.5 6.3 10.3 1.4 7.9 4.1 0.0 4.8 2.3 2.3 13.0
2.1 0.5 7.0 11.9 1.5 9.0 6.0 0.1 8.7 2.1 2.1 10.3
2.0 0.4 7.1 12.7 1.5 8.6 3.9 0.5 10.4 2.0 2.0 11.8
2.3 0.6 7.7 13.9 1.8 10.7 4.7 0.1 10.4 2.3 2.3 11.0
1.7 0.8 7.4 12.1 1.3 10.4 4.8 0.4 11.9 1.7 1.7 13.1
1.0 0.8 5.7 10.6 1.1 9.5 4.2 0.4 11.9 1.0 1.0 13.1
1.3 0.8 7.0 10.0 1.3 9.6 5.5 0.3 10.2 1.3 1.3 14.3
1.5 0.7 6.4 10.2 1.3 10.6 5.3 0.2 9.3 1.5 1.5 12.3
2.2 22.9 2.7 28.9 1.5 2.9 3.2 1.4
2.4 18.7 3.7 35.6 73.2 3.0 4.8 0.3
2.7 19.8 3.7 39.7 3.2 8.0 3.2 2.9
6.7 25.4 5.0 47.9 7.9 6.8 4.4 3.8
4.1 24.9 5.1 47.8 7.5 6.5 4.2 2.7
4.8 21.9 5.0 42.0 6.8 8.0 4.8 3.3
3.3 22.4 5.1 43.8 5.8 6.7 4.0 2.5
3.0 23.9 5.1 46.6 5.6 4.5 4.1 2.3
4.5 23.3 5.5 46.6 5.5 4.5 4.3 2.6
4.8 23.7 5.6 48.2 5.9 4.6 4.2 2.7
and Zimbabwe; Cross-Border Initiative, Burundi, Comoros, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of the Congo, the Republic of Congo, Equatorial Guinea, Gabon, Rwanda, and São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Reunion, and Seychelles; Mano River Union (MRU), Guinea, Liberia, and Sierra Leone; Southern African Development Community (SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of the Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Central African Customs and Economic Union (UDEAC; formerly Union Douanière et Economique de l’Afrique Centrale), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo; Arab Common Market, the Arab Republic of Egypt, Iraq, Jordan, Libya, Mauritania, the Syrian Arab Republic, and the Republic of Yemen; Association of South-East Asian Nations (ASEAN), Brunei, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Bangkok Agreement, Bangladesh, India, the Republic of Korea, the Lao People’s Democratic Republic, the Philippines, Sri Lanka, and Thailand; East Asian Economic Caucus (EAEC), Brunei, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, the Philippines, Singapore, Taiwan (China), and Thailand; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; and Arab Maghreb Union (UMA), Algeria, Libya, Mauritania, Morocco, and Tunisia. a. No preferential trade agreement.
2004 World Development Indicators
319
6.5
Regional trade blocs
Total merchandise exports by bloc
% of world exports 1970
High-income and lowand middle-income economies APEC a CEFTA European Union NAFTA Latin America and the Caribbean ACS Andean Group CACM CARICOM Central American Group of Four Group of Three LAIA MERCOSUR OECS Africa CEMAC CEPGL COMESA Cross-Border Initiative ECCAS ECOWAS Indian Ocean Commission MRU SADC UDEAC UEMOA Middle East and Asia Arab Common Market ASEAN Bangkok Agreement EAEC ECO GCC SAARC UMA
320
1980
1990
1995
1997
1998
1999
2000
2001
2002
36.0 3.2 45.6 21.7
33.7 2.9 41.0 16.6
39.0 1.3 44.0 16.2
46.3 1.6 39.7 16.8
47.3 1.8 37.9 18.3
46.1 2.0 39.9 18.7
46.6 1.9 39.2 18.8
48.6 2.0 35.9 19.1
46.5 2.2 37.4 18.8
46.0 2.4 37.9 17.2
2.8 1.9 0.4 0.4 0.3 1.8 4.5 1.7 ..
3.1 1.7 0.3 0.6 0.2 2.1 4.4 1.6 0.0
1.9 0.9 0.1 0.2 0.1 1.5 3.4 1.4 0.0
2.6 0.8 0.1 0.1 0.1 2.1 4.1 1.4 0.0
3.1 0.9 0.2 0.1 0.1 2.7 4.8 1.5 0.0
3.2 0.8 0.2 0.1 0.1 2.8 4.8 1.5 0.0
3.5 0.8 0.3 0.1 0.2 3.0 4.8 1.3 0.0
3.8 1.0 0.3 0.1 0.2 3.3 5.2 1.3 0.0
3.7 0.9 0.3 0.1 0.2 3.2 5.2 1.4 0.0
3.7 0.8 0.4 0.2 0.2 3.1 5.0 1.4 0.0
0.2 0.3 1.6 0.8 0.6 1.1 0.1 0.1 2.2 0.2 0.3
0.3 0.1 0.6 0.3 0.3 0.4 0.1 0.0 1.6 0.3 0.3
0.2 0.0 0.4 0.2 0.3 0.6 0.1 0.1 1.0 0.2 0.1
0.1 0.0 0.4 0.2 0.2 0.4 0.0 0.0 0.8 0.1 0.1
0.1 0.0 0.4 0.2 0.3 0.5 0.0 0.0 0.8 0.1 0.1
0.1 0.0 0.3 0.2 0.2 0.4 0.0 0.0 0.7 0.1 0.1
0.1 0.0 0.3 0.1 0.2 0.4 0.0 0.0 0.6 0.1 0.1
0.2 0.0 0.4 0.2 0.3 0.5 0.0 0.0 0.6 0.2 0.1
0.2 0.0 0.4 0.2 0.3 0.5 0.0 0.0 0.7 0.2 0.1
0.1 0.0 0.4 0.2 0.3 0.4 0.0 0.0 0.7 0.1 0.1
1.6 2.0 1.6 11.3 1.5 1.9 1.1 1.5
1.5 3.7 2.1 15.1 1.2 8.5 0.7 2.3
1.0 4.1 3.5 20.9 1.1 2.5 0.8 1.0
0.4 6.1 4.6 26.1 1.2 2.0 0.9 0.6
0.5 6.6 5.0 25.4 1.2 2.3 0.9 0.6
0.4 5.8 4.6 24.2 1.1 1.7 0.9 0.5
0.5 5.6 4.5 24.7 1.2 1.9 1.0 0.6
0.7 6.9 5.4 26.1 1.3 2.5 1.0 0.8
0.6 6.0 4.7 24.4 1.3 2.5 1.1 0.7
0.6 6.3 5.1 25.2 1.3 2.3 1.1 0.7
2004 World Development Indicators
About the data
6.5
GLOBAL LINKS
Regional trade blocs Definitions
Trade blocs are groups of countries that have estab-
certain groups may be understated. Data on trade
• Merchandise exports within bloc are the sum of
lished special preferential arrangements governing
between developing and high-income countries are
merchandise exports by members of a trade bloc to
trade between members. Although in some cases the
generally complete.
other members of the bloc. They are shown both in
preferences—such as lower tariff duties or exemp-
Membership in the trade blocs shown is based on
U.S. dollars and as a percentage of total merchan-
tions from quantitative restrictions—may be no
the most recent information available, from the
dise exports by the bloc. • Total merchandise
greater than those available to other trading partners,
World Bank Policy Research Report Trade Blocs
exports by bloc as a share of world exports are the
the general purpose of such arrangements is to
(2000a) and from consultation with the World Bank’s
ratio of the bloc’s total merchandise exports (within
encourage exports by bloc members to one another—
international trade unit. Although bloc exports have
the bloc and to the rest of the world) to total mer-
sometimes called intratrade.
been calculated back to 1970 on the basis of current
chandise exports by all economies in the world.
Most countries are members of a regional trade
membership, most of the blocs came into existence
bloc, and more than a third of the world’s trade takes
in later years and their membership may have
place within such arrangements. While trade blocs
changed over time. For this reason, and because sys-
vary widely in structure, they all have the same main
tems of preferences also change over time, intra-
objective: to reduce trade barriers among member
trade in earlier years may not have been affected by
countries. But effective integration requires more than
the same preferences as in recent years. In addition,
reducing tariffs and quotas. Economic gains from com-
some countries belong to more than one trade bloc,
petition and scale may not be achieved unless other
so shares of world exports exceed 100 percent.
barriers that divide markets and impede the free flow
Exports of blocs include all commodity trade, which
of goods, services, and investments are lifted. For
may include items not specified in trade bloc agree-
example, many regional trade blocs retain contingent
ments. Differences from previously published esti-
protections or restrictions on intrabloc trade. These
mates may be due to changes in bloc membership or
include antidumping, countervailing duties, and “emer-
to revisions in the underlying data.
gency protection” to address balance of payments problems or to protect an industry from surges in imports. Other barriers include differing product standards, discrimination in public procurement, and cumbersome and costly border formalities. Membership in a regional trade bloc may reduce the frictional costs of trade, increase the credibility of reform initiatives, and strengthen security among partners. But making it work effectively is a challenge for any government. All sectors of an economy may be affected, and some sectors may expand while others contract, so it is important to weigh the potential costs and benefits that membership may bring. The table shows the value of merchandise intratrade for important regional trade blocs (service expor ts are excluded) as well as the size of intratrade relative to each bloc’s total exports of
Data sources
goods and the share of the bloc’s total exports in
Data on merchandise trade flows are published in
world exports. Although the Asia Pacific Economic
the IMF’s Direction of Trade Statistics Yearbook
Cooperation (APEC) has no preferential arrange-
and Direction of Trade Statistics Quarterly; the
ments, it is included in the table because of the vol-
data in the table were calculated using the IMF’s
ume of trade between its members.
Direction of Trade database. The United Nations
The data on country exports are drawn from the
Conference on Trade and Development (UNCTAD)
International Monetary Fund’s (IMF) Direction of
publishes data on intratrade in its Handbook of
Trade database and should be broadly consistent
International Trade and Development Statistics.
with those from other sources, such as the United
The information on trade bloc membership is from
Nations Statistics Division’s Commodity Trade (COM-
the World Bank Policy Research Report Trade
TRADE) database. However, trade flows between
Blocs (2000a) and the World Bank’s international
many developing countries, particularly in Africa, are
trade unit.
not well recorded. Thus the value of intratrade for
2004 World Development Indicators
321
6.6
Tariff barriers All products
Primary products
Manufactured products
%
Year
Albania Algeria Argentina Australia Bangladesh Belarus Belize Benin Bhutan Bolivia Brazil Burkina Faso Cameroon Canada Central African Republic Chad Chile China Colombia Congo, Rep. Costa Rica Côte d’Ivoire Cuba Czech Republic Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Ethiopia c European Union Gabon Ghana Guatemala Guinea-Bissau Guyana
322
1997 2001 1993 2002 1992 2002 1991 b 2002 b 1989 2002 1996 2002 1996 2001 2001 2002 1996 2002 1993 2001 1989 2002 1993 2002 1994 2002 1989 b 2002 b 1995 2002 1995 b 2002 1992 2002 1992 2001 1991 2002 1994 2002 1995 b 2001 1993 2002 1993 2002 1996 2002 1997 2001 1993 2002 1995 2002 1995 2001 1998 2002 1995 2001 1988 b 2002 b 1995 2002 1993 2000 1995 2001 2001 2002 1996 2001
Binding coverage
Simple mean bound rate
Simple mean tariff
Weighted mean tariff
Share of lines with international peaks
Share of lines with specific rates
Ad valorem equivalent of nontariff barriers a
Simple mean tariff
Weighted mean tariff
Simple mean tariff
Weighted mean tariff
.. 100.0 .. .. .. 100.0 .. 97.0 .. 16.1 .. .. 22.0 98.0 .. 39.4 .. .. .. 100.0 .. 99.9 .. 39.2 .. 13.6 .. 100.0 .. 62.5 .. 13.7 .. 100.0 .. .. .. 100.0 .. 16.3 .. .. .. 33.1 .. 30.9 .. .. .. 100.0 .. 99.8 .. 98.9 .. 100.0 .. .. .. .. .. 100.0 .. 100.0 .. 14.3 .. 34.6 .. 97.7 .. 100.0
.. 7.0 .. .. .. 31.9 .. 9.9 .. 163.8 .. .. 21.1 58.2 .. 28.3 .. .. .. 40.0 .. 31.4 .. 41.9 .. 79.9 .. 5.1 .. 36.2 .. 79.9 .. 25.1 .. .. .. 42.9 .. 27.5 .. .. .. 11.1 .. 21.2 .. .. .. 34.9 .. 21.7 .. 37.2 .. 36.6 .. .. .. .. .. 4.1 .. 21.4 .. 92.4 .. 36.6 .. 48.7 .. 56.7
17.7 11.8 20.9 18.8 14.9 14.6 14.4 5.9 110.5 19.3 11.8 11.1 48.8 12.6 14.5 14.5 16.1 16.3 9.7 9.4 43.5 14.9 25.9 13.1 19.2 18.7 10.8 5.1 17.1 18.3 15.9 16.8 11.0 7.0 41.6 15.1 5.6 12.8 20.8 19.6 10.4 6.6 24.3 12.8 14.1 12.3 6.4 5.0 15.7 9.7 9.4 12.5 23.3 18.4 10.4 7.5 19.4 18.6 35.2 21.3 2.6 3.1 20.5 20.2 15.3 15.2 10.0 7.8 14.1 13.6 22.6 12.2
16.0 12.4 16.1 15.3 13.4 11.9 10.3 3.9 131.0 23.0 7.8 8.0 42.2 13.3 15.5 15.5 14.4 14.3 9.2 8.8 35.6 9.9 23.5 11.2 15.3 15.8 6.4 1.1 14.3 20.0 17.0 14.2 11.0 7.0 35.3 12.8 4.3 10.1 16.6 18.0 8.7 5.8 23.1 12.0 12.3 9.4 5.4 4.1 17.4 10.1 6.4 10.5 17.1 13.4 8.8 6.1 13.3 14.0 16.9 16.5 3.0 2.4 16.1 15.8 10.5 9.5 8.4 5.8 16.1 13.9 20.6 10.6
59.8 40.0 43.7 43.5 36.8 50.3 36.3 8.4 98.9 45.9 30.2 19.4 .. 41.7 57.9 57.6 52.2 45.4 0.0 0.0 92.9 43.1 75.3 48.1 52.7 51.7 18.4 13.0 50.0 51.3 44.2 43.8 0.0 0.0 79.5 41.7 0.3 23.0 61.4 55.2 29.8 0.1 73.0 45.7 30.5 12.5 2.4 3.4 33.1 29.1 19.8 22.8 53.5 44.6 28.0 12.1 56.7 51.0 75.8 57.8 1.7 1.3 60.3 58.6 45.1 47.3 25.8 14.3 55.1 51.8 47.8 40.2
0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.7 1.0 0.0 0.0 1.1 23.0 0.0 0.0 0.0 2.7 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 2.4 3.3 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.6 10.0 0.0 0.0 0.2 0.1 0.2 0.3 12.4 4.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 43.6 0.4
.. 0.6 .. 1.1 .. 4.7 .. 0.6 .. 1.7 .. 0.0 10.5 .. .. .. .. .. .. 0.8 .. 2.4 .. .. .. 0.1 .. 1.5 .. .. .. .. .. 1.0 .. 1.5 .. 3.4 .. .. .. 0.2 .. 2.0 .. .. .. 1.1 .. .. .. .. .. 0.1 .. 6.9 .. .. .. 0.0 .. 1.5 .. 0.2 .. 0.1 .. 0.0 .. .. .. ..
15.7 12.1 22.5 19.7 8.1 10.9 3.0 1.5 79.8 22.4 9.4 11.1 20.4 19.7 15.5 15.5 21.2 24.4 10.0 9.8 31.5 10.9 27.5 15.0 23.9 21.7 4.3 1.9 19.1 24.3 19.0 21.4 11.0 7.0 36.3 16.0 7.0 12.9 24.4 23.7 12.9 10.4 26.5 14.8 12.1 11.5 5.9 5.5 18.0 13.1 9.1 12.3 25.9 18.2 12.8 10.6 24.6 24.1 36.9 22.0 5.8 3.4 24.4 24.1 19.4 21.4 12.6 9.8 17.0 16.3 28.0 20.1
12.8 10.6 8.9 12.8 5.8 8.0 1.7 0.8 53.6 20.1 6.5 7.0 18.8 9.9 12.9 12.9 9.7 14.3 10.0 9.1 18.6 4.8 23.1 15.2 14.9 18.1 2.6 0.5 13.7 25.5 15.9 24.0 11.0 7.0 14.0 19.2 7.5 13.2 20.5 21.3 10.5 7.9 21.6 10.7 7.2 5.8 4.1 3.9 10.4 8.0 6.4 10.8 7.6 6.6 10.2 8.0 23.6 23.0 18.4 6.3 2.7 1.5 20.2 20.2 14.1 28.2 10.2 7.9 18.6 15.2 15.5 14.5
17.2 11.6 21.7 18.3 14.8 14.8 14.1 5.6 108.6 19.3 12.6 11.6
15.2 11.6 18.7 13.1 13.6 12.2 10.5 4.3 109.6 21.1 10.5 10.3
11.6 14.1 14.0 17.0 16.4 9.7 9.3 44.0 15.1 25.5 12.8 18.8 18.0 10.5 4.7 16.8 17.7 15.6 16.5 11.0 7.0 40.6 15.0 5.8 12.7 20.3 18.8 9.9 6.1 24.1 12.6 14.0 12.0 6.5 5.0 15.2 9.4 9.3 12.3 24.0 19.0 9.8 6.9 18.5 17.9 32.3 20.3 2.6 2.9 19.9 19.6 13.8 14.0 9.4 7.2 13.4 12.6 21.0 11.3
11.2 12.4 12.4 16.8 15.0 9.3 8.8 37.1 12.0 20.3 9.2 13.6 13.9 6.6 1.0 13.1 13.4 13.4 13.3 10.9 6.9 35.6 12.8 6.1 10.8 14.6 16.1 8.0 3.7 22.5 10.3 12.9 11.0 6.2 4.3 17.7 9.8 8.3 10.7 22.2 16.4 8.7 6.0 13.6 13.1 18.0 15.2 4.3 2.9 15.2 13.5 9.2 8.9 8.0 5.8 12.4 10.4 19.0 9.8
2004 World Development Indicators
%
%
All products
Primary products
6.6
GLOBAL LINKS
Tariff barriers
Manufactured products
%
Year
Honduras Hungary India Indonesia Jamaica Japan Jordan Kenya Korea, Rep. Kyrgyz Republic Lao PDR Latvia Lebanon Libya Lithuania Madagascar Malawi Malaysia Mali Mauritius Mexico Moldova Morocco Mozambique Nepal New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru
1995 2001 1991 b 2002 1990 b 2001 b 1989 b 2001 b 1996 2001 1988 b 2002 b 2000 2002 1994 b 2001 1988 2002 1995 2002 2000 2001 b 1996 2001 1999 2002 1996 2002 1995 b 2002 b 1995 2001 1994 2001 b 1988 b 2001 b 1995 2002 1995 b 2002 1991 2002 b 1996 2001 1993 2002 1994 2002 b 1993 2002 1992 2002 b 1995 b 2002 b 2001 2002 1988 2002 1988 b 2002 b 1992 2002 1995 2002 1997 2001 1997 2002 1991 2001 1993 2000
Binding coverage
Simple mean bound rate
Simple mean tariff
Weighted mean tariff
Share of lines with international peaks
Share of lines with specific rates
Ad valorem equivalent of nontariff barriers a
Simple mean tariff
% Weighted mean tariff
Simple mean tariff
% Weighted mean tariff
.. 100.0 .. 96.3 .. 73.8 .. 96.6 .. 100.0 .. 99.6 .. 100.0 .. 14.6 .. 94.5 .. 99.9 .. .. .. 100.0 .. .. .. .. .. 100.0 .. 29.7 .. 26.1 .. 83.7 .. 40.1 .. 14.9 .. 99.9 .. .. .. 100.0 .. 100.0 .. .. .. 99.9 .. 100.0 .. 96.8 .. 19.0 .. 100.0 .. 100.0 .. 38.0 .. 100.0 .. 100.0 .. 100.0 .. 100.0
.. 32.5 .. 9.7 .. 49.8 .. 37.5 .. 49.8 .. 2.9 .. 16.3 .. 95.6 .. 16.1 .. 7.4 .. .. .. 12.7 .. .. .. .. .. 9.3 .. 27.4 .. 82.7 .. 14.5 .. 28.8 .. 114.8 .. 34.9 .. .. .. 41.2 .. 99.6 .. .. .. 10.3 .. 41.7 .. 44.3 .. 118.8 .. 3.0 .. 13.8 .. 60.5 .. 23.5 .. 31.7 .. 33.5 .. 30.1
9.8 7.5 12.0 8.3 76.6 31.0 18.7 6.0 21.7 9.1 4.0 2.9 24.0 16.2 32.4 20.0 18.7 7.9 0.0 8.4 8.7 8.6 3.6 3.4 15.3 6.4 21.8 18.8 3.0 0.7 7.9 5.5 32.7 13.8 14.4 7.5 16.5 12.9 35.7 25.1 14.7 16.2 6.4 5.3 64.6 27.7 5.0 12.3 21.8 13.1 10.5 4.3 7.9 4.4 14.6 14.5 25.5 26.6 2.1 0.8 5.2 7.7 50.1 16.9 12.1 7.9 19.1 6.3 16.1 13.9 18.2 13.4
10.3 7.3 9.6 7.5 49.8 21.0 12.0 3.9 21.8 7.8 3.4 2.2 20.7 11.3 25.5 14.4 14.7 5.7 0.0 7.8 14.5 12.2 3.0 2.5 13.1 8.0 17.0 15.9 2.1 0.5 4.8 2.9 29.9 12.5 11.5 4.6 9.5 11.4 22.5 15.8 12.7 4.9 3.3 3.9 47.0 28.2 5.0 9.4 18.1 14.3 8.5 2.8 4.0 2.3 14.1 14.1 20.0 15.8 1.0 0.7 5.1 6.7 45.5 15.2 9.7 5.7 13.4 2.7 13.9 12.5 16.8 12.6
25.6 11.9 15.0 4.8 98.4 94.9 48.5 1.9 46.0 38.9 8.6 6.9 63.7 43.9 91.0 41.3 76.8 3.1 0.0 9.9 8.3 7.9 0.5 0.7 31.2 9.8 57.9 45.7 4.3 1.3 8.0 6.5 89.3 43.0 50.7 19.5 43.1 46.7 63.7 40.2 20.9 43.7 19.7 0.0 97.9 76.9 0.0 36.4 60.8 14.8 37.5 9.8 20.5 0.0 57.5 57.2 63.8 56.3 5.8 1.9 0.8 0.2 93.9 54.9 35.3 0.1 32.2 21.9 44.0 33.2 25.6 14.6
0.0 0.7 0.0 0.0 0.5 0.2 0.1 0.0 45.7 0.2 8.4 1.4 0.4 0.1 0.0 0.1 10.6 0.7 9.6 0.0 0.6 0.0 0.0 0.0 0.1 0.0 0.2 0.7 0.0 0.0 0.0 0.0 0.0 0.0 5.5 0.4 0.0 0.0 0.0 0.1 0.0 0.4 0.0 0.2 0.0 0.0 0.0 0.0 0.1 0.1 1.5 7.1 0.0 0.0 0.0 0.0 0.1 0.7 6.4 7.0 0.0 2.3 3.5 0.0 0.2 0.2 0.4 0.3 0.0 0.0 0.0 0.0
.. 0.0 .. 1.0 .. 3.2 .. 0.5 .. .. .. 1.6 .. 10.2 .. 0.3 .. 0.0 .. .. .. .. .. 0.4 .. 3.7 .. .. .. 0.4 .. 0.6 .. .. .. 1.7 .. .. .. 0.0 .. 1.8 .. .. .. 0.5 .. .. .. .. .. 2.2 .. .. .. .. .. 0.4 .. 0.3 .. 0.9 .. .. .. .. .. .. .. 1.7 .. 1.7
13.0 10.7 13.5 18.0 69.8 32.8 18.1 6.0 23.7 15.3 8.3 5.2 28.0 21.8 32.4 20.9 19.3 12.0 0.0 8.2 15.6 15.9 6.5 8.2 13.1 13.7 24.9 18.1 6.2 3.5 6.3 6.1 29.1 12.8 10.8 4.4 19.5 15.1 26.0 20.1 13.4 14.5 11.3 8.9 55.0 35.7 5.0 14.8 11.8 16.0 5.5 1.7 7.7 6.2 15.1 15.1 33.3 40.1 0.6 2.4 7.2 9.5 43.4 17.9 17.8 11.4 33.2 17.5 14.1 12.8 18.3 15.6
12.9 11.4 5.5 7.2 25.4 22.7 5.9 2.4 14.2 9.5 4.4 2.5 17.0 11.7 17.0 15.3 8.2 6.1 0.0 6.3 14.7 17.3 1.5 5.4 11.2 10.2 9.6 15.7 3.7 1.5 2.9 1.4 12.9 11.1 4.6 2.4 13.4 12.1 25.7 9.0 8.3 7.0 1.5 2.6 30.2 27.7 5.0 11.0 9.3 8.3 4.0 0.5 7.1 3.6 12.9 12.9 32.4 20.6 0.2 2.1 14.2 31.6 24.0 11.2 9.6 5.9 21.8 6.7 3.6 8.2 15.8 13.9
9.2 7.1 12.1 7.7 79.9 30.8 19.2 6.2 20.7 8.5 3.5 2.4 23.3 15.9 31.9 19.6 18.6 7.4 0.0 8.2 8.6 8.8 3.3 2.7 14.4 5.9 22.5 19.9 2.6 0.5 7.6 5.4 31.9 12.8 14.9 8.1 16.0 12.6 37.2 25.8 14.6 15.8 4.7 4.5 65.0 28.0 5.0 11.6 22.9 13.8 10.7 4.2 7.4 4.1 14.2 14.2 25.3 24.9 2.1 0.6 5.1 7.6 51.1 17.5 11.8 7.7 18.5 5.8 15.7 13.6 18.0 13.0
7.5 6.2 11.7 8.0 70.8 28.4 15.1 5.2 20.9 10.1 2.7 1.7 19.8 13.1 23.3 12.5 17.0 4.7 0.0 7.1 12.6 11.9 2.6 1.5 12.7 6.6 25.6 29.0 1.8 0.3 6.3 4.3 26.6 11.7 10.8 4.7 8.5 9.9 22.9 14.4 13.0 4.7 2.3 2.9 55.2 26.2 5.0 8.7 21.0 17.8 9.4 3.6 4.6 2.5 12.7 12.7 21.4 15.5 0.8 0.2 5.4 6.5 50.8 19.1 11.0 7.4 13.7 3.2 14.5 11.9 16.6 12.3
2004 World Development Indicators
323
6.6
Tariff barriers All products
Primary products
Manufactured products
%
Year
Philippines Poland Romania Russian Federation Rwanda Saudi Arabia Senegal Singapore Slovenia South Africa d Sri Lanka Switzerland e Taiwan, China Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United States Uruguay Venezuela, RB Vietnam Zambia Zimbabwe
1988 2002 1991 2003 b 1991 2001 b 1993 2002 1993 2001 b 1994 2000 2001 2002 1989 2002 1999 2001 1988 2001 b 1990 2001 b 1990 2001 1989 2002 1993 2000 1989 2001 b 2001 2002 1991 2002 1990 2002 1993 b 1999 1998 2002 1994 b 2002 b 1995 2002 1989 b 2002 b 1992 2001 b 1992 2000 1994 2001 1993 2002 1996 b 2001
Binding coverage
Simple mean bound rate
Simple mean tariff
Weighted mean tariff
Share of lines with international peaks
Share of lines with specific rates
.. 66.8 .. .. .. 100.0 .. .. .. 100.0 .. .. .. 100.0 .. 69.2 .. 100.0 .. 98.0 .. 22.6 .. 99.8 .. .. .. 13.3 .. 74.7 .. 13.7 .. 100.0 .. 57.4 .. 49.5 .. .. .. 15.7 .. .. .. 100.0 .. 100.0 .. .. .. .. .. 17.1 .. 21.4
.. 25.6 .. .. .. 40.4 .. .. .. 89.3 .. .. .. 30.0 .. 6.9 .. 24.3 .. 17.8 .. 42.7 .. 1.7 .. .. .. 120.0 .. 25.7 .. 80.0 .. 55.7 .. 57.7 .. 28.4 .. .. .. 73.3 .. .. .. 3.6 .. 31.7 .. .. .. .. .. 106.4 .. 94.3
27.7 4.8 11.8 4.0 19.2 11.3 8.9 9.9 39.6 10.0 12.3 12.3 14.0 13.9 0.4 0.0 11.9 11.6 11.4 9.8 27.1 8.4 .. 1.9 11.1 6.9 16.7 19.1 38.7 14.7 14.5 14.5 18.5 9.6 28.6 30.2 7.5 7.1 0.0 5.5 17.5 8.0 8.0 7.9 5.9 4.1 6.7 13.3 17.4 13.5 14.1 15.0 26.2 13.9 41.2 20.4
21.1 2.8 9.5 2.0 10.7 7.3 7.2 8.4 29.6 6.6 11.1 11.4 8.6 8.4 0.2 0.0 10.9 10.2 7.7 3.6 31.5 4.2 .. 0.8 9.7 3.3 19.0 15.4 31.7 8.7 11.5 11.5 11.2 2.9 29.9 27.4 5.7 4.5 0.0 1.7 15.0 6.8 4.3 4.4 5.2 2.6 6.7 6.5 12.9 11.3 13.0 17.4 18.1 8.4 37.3 12.0
74.7 0.3 24.0 7.1 54.8 26.2 2.3 10.0 65.9 12.1 9.6 8.6 53.6 52.9 0.0 0.0 23.7 26.3 31.9 38.0 53.6 18.1 .. .. 13.7 7.5 45.6 74.7 76.5 49.4 58.7 58.4 40.6 38.1 98.1 86.4 6.1 6.4 0.0 12.2 57.3 0.0 11.5 10.5 9.6 7.0 0.0 38.9 48.2 25.1 36.7 37.9 94.1 36.5 95.9 45.7
0.1 0.0 0.0 4.7 0.0 0.0 0.0 16.8 1.1 0.0 0.1 3.5 0.0 0.0 0.3 0.0 2.5 0.0 19.9 1.6 0.8 0.4 51.7 39.2 0.6 2.1 0.0 0.0 18.7 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 3.3 0.0 0.0 0.0 6.9 13.2 7.5 0.0 0.0 0.4 0.0 0.8 0.0 0.0 2.3 0.4 1.4
Ad valorem equivalent of nontariff barriers a
.. 0.4 .. 1.2 .. 2.5 .. .. .. 1.4 .. 0.9 .. 0.0 .. 0.5 .. 0.6 .. 0.5 .. 0.0 .. 1.2 .. .. .. 0.0 .. 0.3 .. .. .. 0.2 .. 0.8 .. 0.2 .. .. .. 0.1 .. 0.0 .. 1.6 .. 1.9 .. 1.4 .. .. .. 0.2 .. ..
%
%
Simple mean tariff
Weighted mean tariff
Simple mean tariff
Weighted mean tariff
29.9 6.7 11.9 12.4 20.0 18.0 3.1 9.7 60.7 13.2 12.0 11.7 14.5 14.4 0.2 0.0 9.5 9.8 4.8 7.5 32.4 13.9 .. 15.0 14.5 9.7 22.7 19.9 30.0 16.2 14.7 14.7 24.8 15.5 25.1 44.7 6.3 16.6 0.0 16.0 19.4 10.0 8.9 7.1 2.5 2.7 7.9 8.9 16.3 13.5 20.9 19.6 30.0 17.3 34.2 20.7
18.5 5.4 8.2 4.0 8.1 11.2 3.9 8.3 24.9 6.8 9.1 7.9 8.3 8.2 2.5 0.0 7.5 7.5 3.6 2.0 32.3 11.3 .. 9.5 8.6 4.1 19.9 13.2 24.3 4.7 10.5 10.5 10.9 5.8 17.4 26.7 7.9 5.5 0.0 13.2 17.4 8.8 2.7 1.5 2.0 1.1 5.8 2.8 14.7 13.6 46.7 20.7 12.4 12.6 40.4 7.0
27.9 5.0 12.2 3.1 18.9 10.6 9.5 10.5 37.4 9.5 12.4 12.2 13.8 13.7 0.4 0.0 11.7 11.2 11.8 9.5 26.6 8.7 .. 1.1 10.8 6.4 15.3 18.4 39.0 14.6 14.2 14.1 17.8 9.2 28.3 28.7 7.4 6.2 0.0 3.7 16.8 7.7 7.3 7.9 5.5 3.8 7.0 13.4 17.1 13.4 13.9 14.7 25.2 13.3 41.3 20.6
23.4 2.6 11.2 1.4 17.9 7.2 7.4 8.9 25.5 5.9 11.5 11.4 10.4 9.9 0.6 0.0 12.1 10.5 12.3 5.8 24.2 5.0 .. 0.2 10.5 3.0 15.0 13.0 34.9 9.7 11.2 11.2 14.1 4.7 28.5 25.5 5.3 5.3 0.0 1.1 12.3 6.1 4.3 6.4 4.1 2.0 5.8 8.1 16.5 13.3 13.1 16.3 20.0 8.3 38.8 14.2
a. Ad valorem equivalents of nontariff barriers are calculated for the year 2000 only. b. Rates are either partially or fully recorded applied rates. All other simple and weighted tariff rates are most favored nation rates. c. Excludes Eritrea. d. Data refer to South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). e. Data for Switzerland include all specific rates converted to their ad valorem equivalents.
324
2004 World Development Indicators
6.6
GLOBAL LINKS
Tariff barriers About the data Poor people in developing countries work primarily in
eign prices. They include administrative price fixing, vol-
Two other measures of tariff coverage are shown:
agriculture and labor-intensive manufactures, sectors
untar y expor t price restraints, variable charges,
the share of tariff lines with international peaks (those
that confront the greatest trade barriers. Removing bar-
antidumping measures, and countervailing measures.
for which ad valorem tariff rates exceed 15 percent)
riers to merchandise trade could increase growth by
Countries typically maintain a hierarchy of trade pref-
and the share of tariff lines with specific duties (those
about 0.5 percent a year in these countries—even more
erences applicable to specific trading partners. The tar-
not covered by ad valorem rates). Some countries—for
if trade in services (retailing, business, financial, and
iff rates used in calculating the indicators in the table
example, Switzerland—apply only specific duties.
telecommunications services) were also liberalized.
are most favored nation rates unless they are specified
The indicators were calculated from data supplied by
In general, tariffs in high-income countries on imports
as applied rates. Effectively applied rates are those in
the United Nations Conference on Trade and Develop-
from developing countries, though low, are four times
effect for partners in preferential trade agreements
ment (UNCTAD) and the World Trade Organization (WTO).
those collected from other high-income countries. But
such as the North American Free Trade Agreement. The
Data are classified using the Harmonized System of
protection is also an issue for developing countries,
difference between most favored nation and applied
trade at the six- or eight-digit level. Tariff line data were
which maintain high tariffs on agricultural commodities,
rates can be substantial. For example, the weighted
matched to Standard International Trade Classification
labor-intensive manufactures, and other products and
average of Slovenia’s 2001 most favored nation rates
(SITC) revision 2 codes to define commodity groups and
services. In some developing regions new trade policies
is 10.2 percent, while the effectively applied rate in
import weights. Import weights were calculated using
could make the difference between achieving important
2000 averaged less than 2 percent. As more countries
the United Nations Statistics Division’s Commodity
Millennium Development Goals—reducing poverty, low-
report their free trade agreements, suspensions of tar-
Trade (COMTRADE) database. Data are shown only for
ering maternal and child mortality rates, improving edu-
iffs, or other special preferences, World Development
the first and last year for which complete data are avail-
cational attainment—and falling far short.
Indicators will include their effectively applied rates.
able. To conserve space, data for the European Union
Countries use a combination of tariff and nontariff
Three measures of average tariffs are shown: the
measures to regulate imports. The most common form
simple and the weighted mean rates and simple
of tariff is an ad valorem duty, based on the value of
bound rates. The most favored nation or applied rates
the import, but tariffs may also be levied on a specific,
are calculated using all traded items, while bound
• Primary products are commodities classified in SITC
or per unit, basis or may combine ad valorem and spe-
rates are based on all products in a country’s tariff
revision 2 sections 0–4 plus division 68 (nonferrous
cific rates. Tariffs may be used to raise fiscal revenues
schedule. Weighted mean tariffs are weighted by the
metals). • Manufactured products are commodities
or to protect domestic industries from foreign competi-
value of the country’s trade with each trading partner.
classified in SITC revision 2 sections 5–8 excluding
tion—or both. Nontariff barriers, which limit the quanti-
Simple averages are often a better indicator of tariff
division 68. • Binding coverage is the percentage of
ty of imports of a particular good, include quotas,
protection than weighted averages, which are biased
product lines with an agreed bound rate. • Simple
prohibitions, licensing schemes, expor t restraint
downward because higher tariffs discourage trade and
mean bound rate is the unweighted average of all the
arrangements, and health and quarantine measures.
reduce the weights applied to these tariffs. Bound
lines in the tariff schedule in which bound rates have
are shown instead of data for individual members.
Definitions
Nontariff barriers are generally considered less
rates have resulted from trade negotiations that are
been set. • Simple mean tariff is the unweighted
desirable than tariffs because changes in an exporting
incorporated into a country's schedule of concessions
average of effectively applied rates or most favored
country’s efficiency and costs no longer result in
and are thus enforceable. If a contracting party raises
nation rates for all products subject to tariffs calculat-
changes in market share in the importing country.
a tariff to a higher level than its bound rate, benefici-
ed for all traded goods. • Weighted mean tariff is the
Further, the quotas or licenses that regulate trade
aries of the earlier binding have a right to receive com-
average of effectively applied rates or most favored
become very valuable, and resources are often wast-
pensation, usually as reduced tariffs on other
nation rates weighted by the product import shares
ed in attempts to acquire these assets. A high per-
products they export to the country. If the beneficiar-
corresponding to each partner country. • Share of
centage of products subject to nontariff barriers
ies are not compensated, they may retaliate by raising
lines with international peaks is the share of lines in
suggests a protectionist trade regime, but the fre-
their own tariffs against an equivalent value of the orig-
the tariff schedule with tariff rates that exceed 15 per-
quency of nontariff barriers does not measure how
inal country's exports. Specific duties (not expressed
cent. • Share of lines with specific rates is the share
much they restrict trade. Moreover, a wide range of
as a proportion of declared value) are not included in
of lines in the tariff schedule that are set on a per unit
domestic policies and regulations (such as health reg-
the table, except for Switzerland. Work is under way to
basis or that combine ad valorem and per unit rates.
ulations) may act as nontariff barriers.
complete the estimations for ad valorem equivalents
• Ad valorem equivalent of nontariff barriers are the
of specific duties for all countries.
simple average of core nontariff barriers transformed
Estimates of ad valorem equivalents of nontariff barriers are given at the six-digit level of the Harmonized
Some countries set fairly uniform tariff rates across
System, which provides the simple averages of core
all imports. Others are more selective, setting high tar-
nontariff barriers, including quantity control measures
iffs to protect favored domestic industries. The stan-
such as nonautomatic licensing, quotas, prohibitions,
dard deviation of tariffs is a measure of the dispersion
and export restraint arrangements but excluding tariff-
of tariff rates around their mean value. Highly dis-
Data sources
quotas and enterprise-specific restrictions; financial
persed rates increase the costs of protection substan-
All indicators in the table were calculated by World
measures, which include advance payment require-
tially. But these nominal tariff rates tell only part of the
Bank staff using the World Integrated Trade
ments, multiple exchange rates, and restrictive official
story. The effective rate of protection—the degree to
Solution (WITS) system. Tariff data were provided
foreign exchange allocation and exclude regulations on
which the value added in an industry is protected—may
by UNCTAD and the WTO. Data on global imports
terms of payment, transfer delays, and queuing; and
exceed the nominal rate if the tariff system systemati-
come from the United Nations Statistics
price control measures, which affect the cost of imports
cally differentiates among imports of raw materials,
Division’s COMTRADE database.
based on differences between domestic prices and for-
intermediate products, and finished goods.
into a price effect using import demand elasticities; they are calculated for traded products only.
2004 World Development Indicators
325
6.7
Global private financial flows Net private capital flows
Foreign direct investment
$ millions
$ millions
Portfolio investment flows
Bank and trade-related lending
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
326
Bonds
Equity
$ millions
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
.. .. –424 235 –216 .. .. .. .. 59 .. .. 62 3 .. 77 666 .. 0 –5 0 –124 .. 0 9 2,216 8,107 .. 345 –27 –100 22 57 .. .. .. .. 129 184 668 7 .. .. –45 .. .. 103 –8 .. .. –5 .. 44 –1 2 0
.. 136 1,023 1,420 681 108 .. .. 1,313 132 227 .. 41 601 299 35 9,861 808 8 –2 54 38 .. 4 900 2,781 47,107 .. 947 32 331 602 117 3,604 .. 10,382 .. 1,351 2,103 437 1,419 21 1,586 71 .. .. 139 42 149 .. 27 .. 61 0 1 6
.. .. 0 –335 1,836 .. 8,111 653 .. 3 .. 8,047 62 27 0 96 989 .. 1 1 0 –113 7,581 1 9 661 3,487 .. 500 –15 0 163 48 .. .. .. 1,132 133 126 734 2 .. .. 12 812 13,183 74 0 .. 3,005 15 1,005 48 18 2 0
.. 135 1,065 1,312 785 111 16,622 886 1,392 47 247 .. 41 677 293 37 16,566 600 8 0 54 86 20,501 4 901 1,713 49,308 12,794 2,023 32 331 662 230 980 .. 9,323 6,410 961 1,275 647 208 21 285 75 8,156 52,020 123 43 165 37,296 50 53 110 0 1 6
.. .. –16 0 –857 .. .. .. .. 0 .. .. 0 0 .. 0 129 .. 0 0 0 0 .. 0 0 –7 –48 .. –4 0 0 –42 –1 .. .. .. .. 0 0 –1 0 .. .. 0 .. .. 0 0 .. .. 0 .. –11 0 0 0
.. 0 0 0 86 0 .. .. 0 0 0 .. 0 0 0 0 1,519 –79 0 0 0 0 .. 0 0 1,614 –1,289 .. 68 0 0 –44 0 –27 .. 180 .. –20 0 0 1,252 0 219 0 .. .. 0 0 0 .. 0 .. –31 0 0 0
.. .. 0 0 0 .. .. .. .. 0 .. .. 0 0 .. 0 103 .. 0 0 0 0 .. 0 0 367 0 .. 0 0 0 0 0 .. .. .. .. 0 0 0 0 .. .. 0 .. .. 0 0 .. .. 0 .. 0 0 0 0
.. 0 0 0 –99 1 .. .. 0 0 0 .. 0 0 0 0 1,981 –23 0 0 0 0 .. 0 0 –317 2,249 .. 17 0 0 0 1 78 .. –265 .. 0 1 –212 0 0 0 0 .. .. 0 0 0 .. 0 .. 0 0 0 0
.. .. –409 570 –1,195 .. .. .. .. 55 .. .. 0 –24 .. –19 –555 .. –1 –6 0 –12 .. –1 –1 1,194 4,668 .. –151 –12 –100 –99 10 .. .. .. .. –3 58 –65 6 .. .. –57 .. .. 29 –8 .. .. –20 .. 7 –19 0 0
.. 1 –42 108 –91 –4 .. .. –79 85 –21 .. 0 –76 6 –2 –10,205 310 0 –2 0 –49 .. 0 –1 –230 –3,161 .. –1,161 0 0 –16 –114 2,573 .. 1,143 .. 410 826 3 –40 0 1,083 –4 .. .. 16 0 –17 .. –23 .. –19 0 0 0
2004 World Development Indicators
Net private capital flows
Foreign direct investment
$ millions
$ millions
Portfolio investment flows
6.7
GLOBAL LINKS
Global private financial flows
Bank and trade-related lending
$ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Bonds
Equity
$ millions
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
75 –147 1,842 2,923 –392 .. .. .. .. 92 .. 252 .. 122 .. .. .. .. 6 .. 13 17 0 .. .. .. 7 26 476 5 5 86 9,600 .. .. 483 35 155 .. –14 .. .. 20 51 467 .. –257 182 129 204 68 59 639 71 .. ..
100 221 4,944 –6,966 816 .. .. .. .. 540 .. –31 4,431 39 .. .. .. –54 25 496 4,803 73 –65 .. 760 113 8 6 4,807 102 16 –43 10,261 77 78 15 381 69 .. 9 .. .. 206 0 639 .. –1,131 379 180 –46 34 3,131 3,549 5,075 .. ..
44 311 237 1,093 –362 .. 627 151 6,411 138 1,777 38 .. 57 .. 788 0 .. 6 .. 7 17 0 .. .. .. 22 23 2,332 6 7 41 2,549 .. .. 165 9 163 .. 0 10,676 1,735 0 41 588 1,003 142 245 136 155 77 41 530 89 2,610 ..
143 854 3,030 –1,513 37 .. 24,697 1,649 14,699 481 9,087 56 2,583 50 .. 1,972 7 5 25 382 257 81 –65 .. 712 77 8 6 3,203 102 12 28 14,622 111 78 428 406 129 .. 10 28,534 823 174 8 1,281 1,008 40 823 57 50 –22 2,391 1,111 4,131 4,235 ..
0 921 147 26 0 .. .. .. .. 0 .. 0 .. 0 .. .. .. .. 0 .. 0 0 0 .. .. .. 0 0 –1,239 0 0 0 661 .. .. 0 0 0 .. 0 .. .. 0 0 0 .. 0 0 –2 0 0 0 395 0 .. ..
0 –742 –272 –406 0 .. .. .. .. 70 .. –11 –50 0 .. .. .. 0 0 0 4,626 0 0 .. –200 0 0 0 1,962 0 0 0 –3,899 –43 0 –31 0 0 .. 0 .. .. 0 0 –452 .. –225 –178 13 0 0 720 1,540 1,307 .. ..
0 0 0 0 0 .. .. .. .. 0 .. 0 .. 0 .. .. .. .. 0 .. 0 0 0 .. .. .. 0 1 0 0 0 0 1,995 .. .. 0 0 0 .. 0 .. .. 0 0 0 .. 0 0 –1 0 0 0 0 0 .. ..
0 –137 967 877 0 .. .. .. .. 0 .. –52 39 0 .. .. .. 0 0 22 4 0 0 .. 6 0 0 0 –250 0 0 0 –104 2 0 –14 0 0 .. 0 .. .. 0 0 0 .. –13 79 0 0 0 –9 410 –830 .. ..
32 –1,379 1,458 1,804 –30 .. .. .. .. –46 .. 214 .. 65 .. .. .. .. 0 .. 6 0 0 .. .. .. –15 2 –617 –1 –1 45 4,396 .. .. 318 26 –8 .. –14 .. .. 20 10 –121 .. –400 –63 –4 49 –9 18 –286 –18 .. ..
–43 247 1,219 –5,924 779 .. .. .. .. –11 .. –24 1,859 –12 .. .. .. –59 0 91 –84 –8 0 .. 242 35 0 0 –110 0 4 –71 –359 8 0 –369 –25 –60 .. 0 .. .. 32 –8 –190 .. –933 –345 110 –96 56 30 488 468 .. ..
2004 World Development Indicators
327
6.7
Global private financial flows Net private capital flows
Foreign direct investment
$ millions
$ millions
Portfolio investment flows
Bank and trade-related lending
$ millions 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
328
4 .. 6 .. 43 .. 36 .. .. .. 6 .. .. 54 0 26 .. .. 63 .. 5 4,371 23 –68 –116 1,836 .. 16 .. .. .. .. –192 .. –126 180 .. 30 194 85 .. s 6,820 36,872 21,964 14,908 43,692 17,179 7,490 13,199 2,266 2,129 1,429 .. ..
2004 World Development Indicators
2002
1990
3,173 0 8,011 .. 3 8 .. .. 94 57 507 .. 5 32 .. 5,575 5,460 .. .. .. 0 6 783 .. .. 13,984 206 43 633 0 45 30 .. 1,982 .. 5,987 224 72 –10 .. 214 0 –1,992 2,444 75 18 736 109 1,625 76 7,582 684 .. .. 149 0 –576 .. .. .. .. 33,504 .. 48,490 107 0 –11 .. –1,639 451 759 180 .. .. 114 –131 186 203 –3 –12 .. s 202,476 s 7,151 2,764 146,679 21,269 98,852 10,180 47,828 11,089 153,831 24,032 47,524 10,512 53,739 1,227 34,544 8,181 5,359 2,604 5,697 536 6,968 972 .. 178,443 .. 60,540
Bonds 2002
1,144 3,009 3 .. 93 475 5 6,097 4,012 1,865 0 739 21,284 242 633 45 11,828 3,599 225 9 240 900 75 736 795 1,037 100 150 693 .. 28,180 39,633 177 65 690 1,400 .. 114 197 26 630,827 s 12,941 134,145 91,104 43,041 147,086 54,834 32,931 44,682 2,653 4,164 7,822 483,741 320,893
Equity
$ millions
1990
2002
1990
2002
1990
0 .. 0 .. 0 .. 0 .. .. .. 0 .. .. 0 0 0 .. .. 0 .. 0 –87 0 –52 –60 597 .. 0 .. .. .. .. –16 .. 345 0 .. 0 0 –30 .. s 142 933 1,270 –336 1,076 –952 1,893 145 –126 147 –31 .. ..
–28 2,745 0 .. 0 0 0 .. –189 .. 0 3,187 .. 0 0 0 .. .. 0 0 0 –1,010 0 0 650 956 .. 0 101 .. .. .. 77 0 –1,066 0 .. 0 0 0 .. s –1,351 14,090 10,259 3,832 12,739 798 4,149 498 5,010 –450 2,735 .. ..
0 .. 0 .. 1 .. 0 .. .. .. 0 .. .. 0 0 –2 .. .. 0 .. 0 440 4 0 5 89 .. 0 .. .. .. .. 0 .. 0 0 .. 0 0 0 .. s 6 2,997 636 2,361 3,004 439 89 2,464 5 1 6 .. ..
21 2,626 0 .. 0 0 0 .. 0 .. 0 –388 .. 0 0 0 .. .. 0 2 0 207 0 0 6 –16 0 0 –1,958 .. .. .. –39 0 75 0 .. 0 0 0 .. s 1,927 3,018 4,887 –1,869 4,945 3,493 –433 1,507 –281 1,046 –387 .. ..
4 .. –2 .. –15 .. 4 .. .. .. 0 .. .. 10 0 –2 .. .. –9 .. 5 1,574 0 –126 –137 466 .. 16 .. .. .. .. –176 .. –922 0 .. 161 –9 127 .. s 3,908 11,673 9,878 1,795 15,581 7,180 4,281 2,408 –217 1,446 482 .. ..
2002
2,037 –370 0 .. 1 32 0 .. 1,637 .. 0 –2,754 .. –36 0 0 .. .. –1 –20 –26 –2,089 0 0 174 5,605 .. –1 588 .. .. .. –108 –76 –1,337 –641 .. 0 –12 –29 .. s –6,365 –4,574 –7,397 2,824 –10,939 –11,601 17,092 –12,143 –2,023 938 –3,202 .. ..
About the data
6.7
GLOBAL LINKS
Global private financial flows Definitions
The data on foreign direct investment are based on
transactions
sources.
• Net private capital flows consist of private debt
balance of payments data repor ted by the
Transactions of public and publicly guaranteed bonds
and nondebt flows. Private debt flows include com-
International Monetary Fund (IMF), supplemented by
are reported through the Debtor Reporting System by
mercial bank lending, bonds, and other private cred-
data on net foreign direct investment reported by the
World Bank member economies that have received
its, as well as foreign direct investment and portfolio
Organisation
either loans from the International Bank for
equity investment. • Foreign direct investment is net
Reconstruction and Development or credits from the
inflows of investment to acquire a lasting manage-
The internationally accepted definition of foreign
International Development Association. Information on
ment interest (10 percent or more of voting stock) in
direct investment is provided in the fifth edition of the
private nonguaranteed bonds is collected from market
an enterprise operating in an economy other than that
IMF’s Balance of Payments Manual (1993). Under
sources, because official national sources reporting
of the investor. It is the sum of equity capital, rein-
this definition foreign direct investment has three
to the Debtor Reporting System are not asked to
vestment of earnings, other long-term capital, and
components: equity investment, reinvested earnings,
report the breakdown between private nonguaranteed
short-term capital, as shown in the balance of pay-
and short- and long-term intercompany loans between
bonds and private nonguaranteed loans. Information
ments. • Portfolio investment flows are net and
parent firms and foreign affiliates. But many coun-
on transactions by nonresidents in local equity mar-
include non-debt-creating portfolio equity flows (the
tries fail to report reinvested earnings, and the defi-
kets is gathered from national authorities, investment
sum of country funds, depository receipts, and direct
nition of long-term loans differs among countries.
positions of mutual funds, and market sources.
purchases of shares by foreign investors) and portfo-
for
Economic
Co-operation
and
Development (OECD) and official national sources.
repor ted
by
market
Foreign direct investment, as distinguished from
The volume of portfolio investment reported by the
other kinds of international investment, is made to
World Bank generally differs from that reported by
investors). • Bank and trade-related lending covers
establish a lasting interest in or effective manage-
other sources because of differences in the sources,
commercial bank lending and other private credits.
ment control over an enterprise in another country. As
in the classification of economies, and in the method
a guideline, the IMF suggests that investments
used to adjust and disaggregate reported informa-
should account for at least 10 percent of voting stock
tion. Differences in reporting arise particularly for
to be counted as foreign direct investment. In prac-
foreign investments in local equity markets because
tice, many countries set a higher threshold.
clarity, adequate disaggregation, and comprehensive
The OECD has also published a definition, in con-
and periodic reporting are lacking in many developing
sultation with the IMF, Eurostat, and the United
economies. By contrast, capital flows through inter-
Nations. Because of the multiplicity of sources and
national debt and equity instruments are well record-
differences in definitions and reporting methods,
ed, and for these the differences in reporting lie
there may be more than one estimate of foreign
primarily in the classification of economies, the
direct investment for a country and data may not be
exchange rates used, whether particular install-
comparable across countries.
ments of the transactions are included, and the
Foreign direct investment data do not give a com-
lio debt flows (bond issues purchased by foreign
treatment of certain offshore issuances.
plete picture of international investment in an economy. Balance of payments data on foreign direct investment do not include capital raised locally, which has become an important source of financing for investment projects in some developing countries. In addition, foreign direct investment data capture only cross-border investment flows involving equity participation and thus omit nonequity crossborder transactions such as intrafirm flows of goods and services. For a detailed discussion of the data issues, see the World Bank’s World Debt Tables 1993–94 (volume 1, chapter 3). Portfolio flow data are compiled from several market
Data sources
and official sources, including Euromoney databases
The data are compiled from a variety of public and
and publications; Micropal; Lipper Analytical Services;
private sources, including the World Bank’s
published reports of private investment houses, cen-
Debtor Reporting System, the IMF’s International
tral banks, national securities and exchange commis-
Financial Statistics and Balance of Payments
sions, and national stock exchanges; and the World
databases, and other sources mentioned in About
Bank’s Debtor Reporting System.
the data. These data are also published in the
Gross statistics on international bond and equity
World Bank’s Global Development Finance 2004.
issues are produced by aggregating individual
2004 World Development Indicators
329
6.8
Net financial flows from Development Assistance Committee members
Net flows to part I countries Official development assistance
Total 2002
$ millions Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total
989 520 1,072 2,006 1,643 462 5,486 5,324 276 398 2,332 9,283 147 3,338 122 1,696 323 1,712 1,991 939 4,924 13,290 58,274
Bilateral grants 2002
774 367 736 1,527 1,019 248 3,874 3,904 107 267 1,083 4,373 116 2,585 92 1,143 183 769 1,242 750 3,384 11,251 39,793
Bilateral loans 2002
.. –2 –25 –24 19 4 –259 –576 .. .. –77 2,320 .. –136 .. 2 3 229 8 15 121 –681 941
Other official flows
Contributions to multilateral institutions 2002
215 156 360 503 605 211 1,871 1,997 169 131 1,326 2,591 31 889 30 551 137 714 741 174 1,419 2,720 17,540
Private flows
Total 2002
2002
31 –36 106 –424 –3 3 635 3,710 .. .. –370 –4,208 .. 229 2 .. –1 54 2 3 –4 227 –45
Foreign direct investment 2002
–433 1,325 86 188 –63 –676 –1,392 –1,124 40 986 –563 –573 .. –5,310 17 131 –150 6,404 199 1,089 13,547 5,173 18,899
–103 1,029 555 829 –63 –5 2,915 1,760 40 .. 639 6,362 .. 281 17 23 –360 6,540 296 1,222 13,940 12,928 48,844
Bilateral portfolio investment 2002
–331 .. .. –604 .. –720 –2,859 –2,496 .. 986 –3,250 –3,077 .. –7,395 .. .. .. .. .. .. 840 –7,930 –26,835
Multilateral portfolio investment 2002
.. .. .. .. .. .. .. –676 .. .. .. –2,804 .. 946 .. .. .. .. .. .. .. –590 –3,124
Private export credits 2002
.. 296 –469 –37 .. 48 –1,448 287 .. .. 2,048 –1,054 .. 859 .. 109 210 –136 –97 –133 –1,233 765 14
Net grants by NGOs
Total net flows
2002
2002
248 57 74 276 .. 10 .. 823 6 86 .. 157 2 257 23 452 .. .. 19 202 353 5,720 8,765
834 1,866 1,337 2,046 1,577 –200 4,729 8,733 322 1,469 1,399 4,659 148 –1,487 164 2,279 171 8,171 2,211 2,234 18,820 24,410 85,893
Net flows to part II countries Official aid
Total 2002
$ millions Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total
7 196 97 104 167 67 1,464 780 16 26 .. 99 10 211 1 45 33 11 107 66 494 2,313 6,317
Bilateral grants 2002
4 142 6 104 90 33 1,083 347 16 1 .. 123 3 138 0 43 1 11 100 57 92 2,418 4,813
Bilateral loans 2002
.. 0 6 .. 5 –1 –20 –81 .. .. .. –66 .. –6 .. .. .. .. 0 1 –4 –173 –342
Note: Data may not sum to totals because of gaps in reporting.
330
2004 World Development Indicators
Other official flows Contributions to multilateral institutions 2002
4 55 85 .. 72 35 401 514 .. 25 .. 43 7 79 0 2 32 .. 7 9 407 69 1,846
2002
13 .. –24 –106 19 –1 21 –505 .. .. 25 –896 .. .. .. 0 –2 .. –2 2 .. –52 –1,508
Private flows
Total 2002
1,747 3,215 –2,527 5,603 431 1,043 4,352 10,980 216 .. –199 6,150 .. –1,061 .. 1,084 71 206 –1,261 1,302 8,121 4,182 43,655
Foreign direct investment 2002
572 3,215 –2,497 5,534 431 390 1,925 7,734 216 .. 197 6,182 .. 2,775 .. 1,082 57 206 –1,288 1,320 5,350 21,372 54,774
Bilateral portfolio investment 2002
1,174 .. 0 76 .. 519 2,626 4,692 .. .. –469 –349 .. –4,066 .. .. .. .. 0 0 2,880 –17,120 –10,036
Net grants by NGOs
Total net flows
Private export credits 2002
2002
2002
.. .. –30 –7 .. 134 –199 –1,446 .. .. 73 318 .. 230 .. 1 14 .. 27 –17 –110 –70 –1,083
248 8 10 .. .. 0 .. 78 1 .. .. .. .. .. .. .. .. .. .. 9 6 3,146 3,508
2,015 3,420 –2,443 5,602 617 1,109 5,837 11,333 234 26 –173 5,353 10 –850 1 1,129 102 218 –1,155 1,379 8,621 9,589 51,972
About the data
6.8
GLOBAL LINKS
Net financial flows from Development Assistance Committee members Definitions
The high-income members of the Development Assist-
and flows reported by the United Nations, all United
• Official development assistance comprises grants and
ance Committee (DAC) of the Organisation for Economic
Nations agencies revised their data to include only reg-
loans (net of repayments of principal) that meet the DAC
Co-operation and Development (OECD) are the main
ular budgetary expenditures since 1990 (except for the
definition of ODA and are made to countries and territo-
source of official external finance for developing coun-
World Food Programme and the United Nations High
ries in part I of the DAC list of aid recipients. • Official aid
tries. This table shows the flow of official and private
Commissioner for Refugees, which revised their data
comprises grants and loans (net of repayments) that
financial resources from DAC members to official and pri-
from 1996 onward).
meet the criteria for ODA and are made to countries and
DAC maintains a list of countries and territories that
territories in part II of the DAC list of aid recipients.
DAC exists to help its members coordinate their devel-
are aid recipients. Part I of the list comprises devel-
• Bilateral grants are transfers of money or in kind for
opment assistance and to encourage the expansion
oping countries and territories considered by DAC
which no repayment is required. • Bilateral loans are
and improve the effectiveness of the aggregate
members to be eligible for ODA. Part II comprises
loans extended by governments or official agencies that
resources flowing to recipient economies. In this capac-
economies in transition: more advanced countries of
have a grant element of at least 25 percent (calculated at
ity DAC monitors the flow of all financial resources, but
Central and Eastern Europe, the countries of the for-
a rate of discount of 10 percent). • Contributions to
its main concern is official development assistance
mer Soviet Union, and certain advanced developing
multilateral institutions are concessional funding
(ODA). DAC has three criteria for ODA: It is undertaken
countries and territories. Flows to these recipients
received by multilateral institutions from DAC members in
by the official sector. It promotes the economic devel-
that meet the criteria for ODA are termed official aid.
the form of grants or capital subscriptions. • Other offi-
opment and welfare of developing countries as a main
The data in the table were compiled from replies by
cial flows are transactions by the official sector whose
objective. And it is provided on concessional terms, with
DAC member countries to questionnaires issued by the
main objective is other than development or whose grant
a grant element of at least 25 percent on loans (calcu-
DAC Secretariat. Net flows of ODA, official aid, and other
element is less than 25 percent. • Private flows consist
lated at a rate of discount of 10 percent).
official resources are defined as gross disbursements of
of flows at market terms financed from private sector
This definition excludes nonconcessional flows from
grants and loans minus repayments of principal on earli-
resources in donor countries. They include changes in
official creditors, which are classified as “other official
er loans. Because the data are based on donor country
holdings of private long-term assets by residents of the
flows,” and military aid, which is not recorded in DAC
reports, they do not provide a complete picture of the
reporting country. • Foreign direct investment is invest-
statistics. The definition includes food aid, capital proj-
resources received by developing and transition
ment by residents of DAC member countries to acquire a
ects, emergency relief, technical cooperation, and post-
economies, for two reasons. First, flows from DAC mem-
lasting management interest (at least 10 percent of vot-
conflict peacekeeping efforts. Also included are
bers are only part of the aggregate resource flows to
ing stock) in an enterprise operating in the recipient coun-
contributions to multilateral institutions, such as the
these economies. Second, the data that record contri-
try. The data reflect changes in the net worth of
United Nations and its specialized agencies, and
butions to multilateral institutions measure the flow of
subsidiaries in recipient countries whose parent company
concessional funding to the multilateral develop-
resources made available to those institutions by DAC
is in the DAC source country. • Bilateral portfolio invest-
ment banks. In 1999, to avoid double counting
members, not the flow of resources from those institu-
ment covers bank lending and the purchase of bonds,
extrabudgetary expenditures reported by DAC countries
tions to developing and transition economies.
shares, and real estate by residents of DAC member
vate recipients in developing and transition economies.
countries in recipient countries. • Multilateral portfolio
6.8a
investment records the transactions of private banks and
Who were the largest donors in 2002?
nonbanks in DAC member countries in the securities
Official development assistance
issued by multilateral institutions. • Private export credits are loans extended to recipient countries by the private sector in DAC member countries to promote trade; they may be supported by an official guarantee. • Net
Spain 3%
Others 15%
Sweden 3%
United States 23%
Canada 3%
grants by NGOs are private grants by nongovernmental organizations, net of subsidies from the official sector. • Total net flows comprise ODA or official aid flows, other official flows, private flows, and net grants by NGOs.
Italy 4% Japan 16%
Netherlands 6%
Data sources The data on financial flows are compiled by DAC
United Kingdom 9% Germany 9%
and published in its annual statistical report, France 9%
Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Cooperation Report. Data are available electronically on the
Disbursements from three countries made up almost half of total net ODA flows in 2002. The top five contributed two-thirds of the total amount. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.
OECD’s International Development Statistics CD-ROM and to registered users at http://www. oecd.org/dataoecd/50/17/5037721.htm.
2004 World Development Indicators
331
6.9
Aid flows from Development Assistance Committee members
Net flows to part I countries Untied aid a
Net official development assistance
$ millions
Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total or average
average annual %
Per capita of
change in volume b
donor country b
% of general
% of bilateral
1996–97 to
$
government disbursement
ODA commitments
% of GNI
1997
2002
1997
2002
1,061 495 764 2,045 1,637 379 6,307 5,857 173 187 1,266 9,358 95 2,947 154 1,306 250 1,234 1,731 911 3,433 6,878 48,465
989 520 1,072 2,006 1,643 462 5,486 5,324 276 398 2,332 9,283 147 3,338 122 1,696 323 1,712 1,991 939 4,924 13,290 58,274
0.27 0.24 0.31 0.34 0.97 0.32 0.45 0.28 0.14 0.31 0.11 0.21 0.55 0.81 0.26 0.84 0.25 0.24 0.79 0.34 0.26 0.09 0.22
0.26 0.26 0.43 0.28 0.96 0.35 0.38 0.27 0.21 0.40 0.20 0.23 0.77 0.81 0.22 0.89 0.27 0.26 0.83 0.32 0.31 0.13 0.23
2001–02
2.4 5.2 7.1 –0.6 2.8 5.1 –2.6 –0.5 9.6 14.3 4.6 3.0 13.5 3.6 3.5 2.8 6.7 9.5 5.2 2.3 6.5 6.8 3.5
1997
2002
1997
2002
1997
2002
43 51 63 65 266 63 89 58 14 47 19 70 198 170 28 291 23 28 151 114 56 28 53
47 61 97 64 286 83 86 60 23 93 37 76 316 190 28 333 28 38 207 118 78 46 65
0.70 0.44 0.61 0.72 1.67 0.55 0.82 0.56 0.30 0.63 0.21 0.61 1.25 1.62 0.56 1.76 0.53 0.53 1.11 .. 0.63 0.24 0.54
0.68 0.49 0.87 0.67 1.71 0.70 0.72 0.55 0.44 0.97 0.41 0.60 1.57 1.68 0.54 1.87 0.57 0.66 1.42 .. 0.77 0.36 0.59
63.1 60.6 49.9 33.4 71.6 76.8 65.1 73.6 .. .. 45.6 99.6 95.1 90.0 .. 91.1 99.0 0.0 74.5 94.9 71.7 .. 83.2
56.7 69.0 .. 61.4 82.1 82.5 91.5 86.6 13.9 100.0 .. 82.8 .. 88.6 76.0 99.1 33.0 59.9 78.5 95.1 100.0 .. 84.8
Net flows to part II countries Net official aid
$ millions
Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total or average
% of GNI
Per capita of donor country b
1996–97 to
1997
2002
1997
2002
2001–02
0 181 59 157 133 71 574 660 9 1 241 84 2 7 0 55 18 3 148 75 337 2,516 5,331
7 196 97 104 167 67 1,464 780 16 26 .. 99 10 211 1 45 33 11 107 66 494 2,313 6,317
0.00 0.09 0.02 0.03 0.08 0.06 0.04 0.03 0.01 0.00 0.02 0.00 0.01 0.00 0.00 0.04 0.02 0.00 0.07 0.03 0.03 0.03 0.02
0.00 0.10 0.04 0.01 0.10 0.05 0.10 0.04 0.01 0.03 .. 0.00 0.05 0.05 0.00 0.02 0.03 0.00 0.04 0.02 0.03 0.02 0.03
10.7 4.8 12.0 –4.3 10.3 3.3 22.2 –1.0 20.6 94.4 .. –4.5 38.1 88.8 86.8 –6.6 13.5 –20.9 –1.7 –2.5 5.4 –3.4 3.3
a. Excluding administrative costs and technical cooperation. b. At 2001 exchange rates and prices.
332
average annual % change in volume b
2004 World Development Indicators
$ 1997
0 19 5 5 22 12 8 7 1 0 4 1 5 0 0 12 2 0 13 9 6 10 6
2002
0 23 9 3 29 12 23 9 1 6 .. 1 22 12 0 9 3 0 11 8 8 8 7
6.9
GLOBAL LINKS
Aid flows from Development Assistance Committee members About the data
Effective aid supports institutional development and
research, stipends and tuition costs for aid-financed
mismanaging aid receipts, but they may also be moti-
policy reforms, which are at the heart of successful
students in donor countries, or payment of experts
vated by a desire to benefit suppliers in the donor
development. To be effective, especially in reducing
hired by donor countries. Second, donors record their
country. The same volume of aid may have different
global poverty, aid requires partnerships among recip-
concessional funding (usually grants) to multilateral
purchasing power depending on the relative costs of
ient countries, aid agencies, and donor countries. It
agencies when they make payments, while the agen-
suppliers in countries to which the aid is tied and the
also requires improvements in economic policies and
cies make funds available to recipients with a time lag
degree to which each recipient’s aid basket is untied.
institutions. Where traditional methods of nurturing
and in many cases in the form of soft loans where
such reforms have failed, aid agencies need to find
donors’ grants have been used to reduce the interest
alternative approaches and new opportunities.
burden over the life of the loan.
Definitions
As part of its work, the Development Assistance
Aid as a share of gross national income (GNI), aid
• Net official development assistance and net offi-
Committee (DAC) of the Organisation for Economic
per capita, and ODA as a share of the general gov-
cial aid record the actual international transfer by the
Co-operation and Development (OECD) assesses the
ernment disbursements of the donor are calculated
donor of financial resources or of goods or services
aid performance of member countries relative to the
by the OECD. The denominators used in calculating
valued at the cost to the donor, less any repayments
size of their economies. As measured here, aid com-
these ratios may differ from corresponding values
of loan principal during the same period. Data are
prises bilateral disbursements of concessional
elsewhere in this book because of differences in tim-
shown at current prices and dollar exchange rates.
financing to recipient countries plus the provision by
ing or definitions.
• Aid as a percentage of GNI shows the donor’s con-
donor governments of concessional financing to mul-
DAC members have progressively introduced the new
tributions of ODA or official aid as a share of its
tilateral institutions. Volume amounts, at constant
United Nations System of National Accounts (adopted
gross national income. • Average annual percentage
prices and exchange rates, are used to measure the
in 1993), which replaced gross national product (GNP)
change in volume and aid per capita of donor coun-
change in real resources provided over time. Aid
with GNI. Because GNI includes items not included in
try are calculated using 2001 exchange rates and
flows to part I recipients—official development
GNP, ratios of ODA to GNI are slightly smaller than the
prices. • Aid as a percentage of general government
assistance (ODA)—are tabulated separately from
previously reported ratios of ODA to GNP.
disbursements shows the donor’s contributions of
those to part II recipients—official aid (see About the
The proportion of untied aid is reported here
ODA as a share of public spending. • Untied aid is
data for table 6.8 for more information on the dis-
because tying arrangements may prevent recipients
the share of ODA that is not subject to restrictions by
tinction between the two types of aid flows).
from obtaining the best value for their money and so
donors on procurement sources.
Measures of aid flows from the perspective of donors
reduce the value of the aid received. Tying arrange-
differ from aid receipts from the perspective of recipi-
ments require recipients to purchase goods and serv-
ents for two main reasons. First, aid flows include
ices from the donor country or from a specified group
expenditure items about which recipients may have no
of countries. They may be justified on the grounds
precise information, such as development-oriented
that they prevent a recipient from misappropriating or
6.9a Official development assistance from selected non-DAC donors, 1998–2002 Net disbursements ($ millions) Donor OECD members (non-DAC) Czech Republic Iceland Korea, Rep. Poland Slovak Republic Turkey Arab countries Kuwait Saudi Arabia United Arab Emirates Other donors Israel Other b Total
1998
1999
2000
2001
2002
16 7 183 19 .. 69
15 8 317 20 7 120
16 9 212 29 6 82
26 10 265 36 8 64
45 13 279 14 7 73
278 288 63
147 185 92
165 295 150
73 490 127
20 2,478 156
87 27 1,037
114 0 1,026
164 a 1 1,128
76 a 2 1,178
Data sources The data on financial flows are compiled by DAC and published in its annual statistical report,
114 a 3 3,201
Note: China also provides aid but does not disclose the amount. a. These figures include $66.8 million in 2000, $50.1 million in 2001, and $87.8 million in 2002 for first-year sustenance expenses for people arriving from developing countries (many of which are experiencing civil war or severe unrest) or who have left their country for humanitarian or political reasons. b. Includes Estonia, Latvia, Lithuania, and Taiwan, China. Source: Organisation for Economic Co-operation and Development.
Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Cooperation Report. Data are available electronically on the OECD’s International Development Statistics CD-ROM and to registered users at http://www. oecd.org/dataoecd/50/17/5037721.htm.
2004 World Development Indicators
333
6.10
Aid dependency Net official development assistance or official aid
$ millions 1997 2002
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
334
Aid per capita
$ 1997
2002
Aid dependency ratios
Aid as % of GNI 1997 2002
Aid as % of gross capital formation 1997 2002
Aid as % of imports of goods and services 1997 2002
Aid as % of central government expenditure 1997 2002
230 166 250 355 105 166
1,285 317 361 421 0 293
10 53 9 31 3 52
46 101 12 32 0 96
.. 7.5 0.5 5.5 0.0 9.6
.. 6.4 0.7 4.3 0.0 12.0
.. 75.8 2.2 18.2 0.2 53.2
.. 28.8 2.1 11.6 0.0 59.2
.. 20.2 .. 5.7 0.2 16.8
.. 15.1 .. 4.5 0.0 25.4
.. 25.3 1.7 .. 0.2 ..
.. .. 1.1 .. 0.3 ..
184 1,011 55
349 913 39
23 8 5
43 7 4
4.7 2.3 0.4
6.1 1.8 0.3
13.6 11.5 1.5
17.5 8.3 1.3
8.6 12.6 0.6
9.9 9.6 0.4
24.2 .. 1.2
.. .. 1.1
221 700 862 122 288 220 368 56 335 499
220 681 587 38 376 381 473 172 487 632
38 89 236 77 2 26 35 9 30 35
34 77 143 22 2 48 40 24 39 40
10.4 9.1 26.1 2.4 0.0 2.2 14.2 6.0 10.1 5.9
8.3 9.0 10.0 0.8 0.1 2.5 15.2 24.2 12.7 7.3
55.7 45.0 59.2 8.3 0.2 21.5 56.5 72.9 66.0 33.9
45.9 59.2 53.4 2.9 0.4 12.5 82.8 303.8 54.7 37.6
27.8 29.7 .. 3.9 0.3 3.5 .. 35.5 25.6 ..
26.3 28.9 12.1 1.3 0.5 3.9 65.1 107.7 16.7 ..
.. 40.0 .. .. 0.1 6.5 .. 24.5 .. ..
.. 34.2 .. .. .. 7.4 .. .. .. ..
91 228 129 2,054 8 196 158 270 –8 446 40 65 117
60 233 –23 1,476 4 441 807 420 5 1,069 166 61 393
26 32 9 2 1 5 3 86 –2 30 9 6 11
16 28 –1 1 1 10 16 115 1 65 37 5 38
9.2 14.5 0.2 0.2 0.0 0.2 5.5 16.2 –0.1 4.1 0.2 0.3 0.2
5.8 11.8 –0.0 0.1 0.0 0.6 14.7 19.1 0.0 9.6 0.8 .. 0.6
92.6 102.0 0.6 0.6 0.0 0.9 102.8 52.0 –0.3 26.4 0.7 3.7 0.7
38.6 19.8 –0.2 0.3 0.0 3.6 199.5 59.7 0.1 87.4 2.7 .. 2.0
.. .. 0.5 1.1 .. 0.9 .. 14.0 –0.1 9.0 0.3 .. 0.3
.. .. –0.1 0.4 0.0 2.3 .. 16.9 0.1 23.0 1.2 .. 0.7
.. .. 0.8 2.8 .. 1.1 26.2 30.8 –0.3 17.4 0.5 .. 0.6
.. .. 0.4 .. .. .. .. 10.5 0.1 9.6 1.3 .. 1.4
71 155 1,985 279 123 66 579
157 216 1,286 233 230 69 1,307
9 13 33 47 33 47 10
18 17 19 36 54 51 19
0.5 0.7 2.6 2.5 14.3 1.5 8.4
0.8 1.0 1.4 1.7 30.8 1.1 21.7
2.3 3.0 14.4 16.5 57.5 4.6 53.3
3.1 3.2 8.5 10.0 135.6 3.4 105.2
0.8 2.1 9.0 6.3 .. 1.5 39.6
1.4 2.4 6.3 3.7 40.7 1.0 63.0
2.8 .. 8.6 .. .. 4.5 38.7
.. .. .. 67.8 .. 4.1 ..
39 39 242
72 61 313
33 33 45
55 44 60
0.8 9.7 6.5
1.7 17.3 9.2
2.4 55.1 58.0
5.1 79.0 43.6
1.4 13.2 16.4
2.5 .. 20.5
.. .. 39.8
.. .. 74.6
494
653
27
32
7.3
10.8
28.9
53.8
17.7
18.6
..
..
264 381 124 325
249 250 59 156
25 55 99 43
21 32 41 19
1.5 10.4 48.9 9.9
1.1 7.9 30.5 4.5
10.9 42.7 212.4 40.3
5.7 46.4 198.7 22.1
5.9 39.9 120.8 35.9
3.5 23.2 .. ..
.. .. .. 93.4
.. .. .. ..
2004 World Development Indicators
Net official development assistance or official aid
$ millions 1997 2002
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Aid per capita
$ 1997
2002
6.10
Aid dependency ratios
Aid as % of GNI 1997 2002
Aid as % of gross capital formation 1997 2002
Aid as % of imports of goods and services 1997 2002
Aid as % of central government expenditure 1997 2002
297 180 1,648 810 200 220
435 471 1,463 1,308 116 116
50 18 2 4 3 10
64 46 1 6 2 5
6.6 0.4 0.4 0.4 0.2 ..
6.8 0.7 0.3 0.8 0.1 ..
19.5 1.5 1.8 1.2 0.9 ..
23.8 3.0 1.3 5.3 0.3 ..
10.6 0.7 2.6 1.1 1.1 ..
11.9 1.0 1.6 2.1 0.4 ..
.. 0.9 2.6 2.1 0.5 ..
.. 1.9 2.1 4.3 .. ..
1,196
754
205
115
1.2
0.7
4.8
4.0
2.8
1.5
2.5
0.3
72
24
28
9
1.1
0.3
3.3
0.9
1.6
0.4
2.7
1.8
462 140 448 88 –160 0 240 329 81 251 92 76 7 104 98 834 344 –240 429 238 43 105 65 251 464 948 50 166 402
534 188 393 267 –82 5 186 278 86 456 76 52 10 147 277 373 377 86 472 355 24 136 142 208 636 2,058 121 135 365
104 9 16 4 –3 0 51 67 33 61 54 26 1 29 49 59 36 –11 43 98 38 1 15 108 17 57 1 96 19
103 13 13 12 –2 2 37 50 37 103 43 16 2 42 136 23 35 4 42 128 20 1 33 85 21 112 2 68 15
6.6 0.6 4.3 .. –0.0 0.0 14.1 19.3 1.4 1.6 6.8 28.8 .. 1.1 2.7 24.1 13.8 –0.3 17.7 22.8 1.0 0.0 3.3 28.1 1.4 29.5 .. 4.1 8.2
5.8 0.8 3.2 .. –0.0 0.0 12.0 17.3 1.0 2.5 8.7 11.0 .. 1.1 7.4 8.6 20.2 0.1 15.1 45.4 0.5 0.0 8.0 18.6 1.8 60.4 .. 4.2 6.6
24.8 4.1 27.1 .. –0.1 0.0 62.5 69.4 6.3 6.4 17.1 .. 0.2 4.2 12.5 183.5 111.3 –0.6 84.1 123.5 3.6 0.1 14.2 96.8 6.7 135.6 .. 22.6 32.2
25.0 2.8 23.5 .. –0.1 0.1 62.7 .. 3.8 14.7 26.7 .. 0.4 4.7 37.0 59.4 160.0 0.4 69.1 116.5 2.4 0.1 38.4 60.8 7.8 127.9 .. 19.3 26.8
8.2 1.6 11.1 .. –0.1 0.0 27.0 44.4 2.3 3.1 7.6 .. 0.1 1.6 5.1 69.8 35.9 –0.2 44.4 42.8 1.6 0.1 4.3 43.6 3.9 80.3 1.9 7.1 20.7
8.1 1.5 10.3 .. –0.0 0.0 24.4 .. 1.7 5.9 9.5 .. .. 1.7 12.4 33.8 44.9 0.1 31.8 .. 0.8 0.1 10.2 21.6 4.4 103.4 .. 8.3 24.2
19.5 3.2 17.2 .. –0.2 0.0 60.6 .. 4.6 4.0 18.1 .. .. 3.9 .. 147.4 .. –1.2 .. .. 4.5 0.2 8.1 112.4 4.5 .. 0.3 12.6 49.5
15.1 4.6 .. .. .. .. 70.0 .. 4.8 .. .. .. .. 4.1 .. .. .. .. .. .. 2.1 .. 33.4 65.7 .. .. .. .. 37.6
411 333 200
517 298 314
88 34 2
97 26 2
24.1 18.3 0.6
13.6 13.8 0.8
66.0 166.1 3.2
40.3 107.7 3.1
21.8 .. 1.1
23.7 .. 1.8
60.4 .. ..
84.9 .. ..
65 596 46 346 108 395 689 861
41 2,144 35 203 57 491 560 1,160
29 5 17 73 22 16 10 22
16 15 12 38 10 18 7 30
0.4 1.0 0.5 7.4 1.1 0.7 0.8 0.6
0.2 3.6 0.3 7.5 1.0 0.9 0.7 0.6
2.3 5.3 1.7 33.5 4.8 2.8 3.4 2.4
1.6 24.7 1.1 .. 3.8 4.7 3.7 3.2
0.9 3.8 0.4 12.6 2.1 2.9 1.3 1.8
.. 14.3 0.4 .. 2.0 4.2 1.3 1.7
1.4 4.5 2.0 24.0 6.8 3.9 4.3 1.5
0.0 15.2 .. .. 4.8 4.6 4.2 1.5
2004 World Development Indicators
335
GLOBAL LINKS
Aid dependency
6.10
Aid dependency Net official development assistance or official aid
$ millions 1997 2002
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Aid per capita
Aid dependency ratios
1997
2002
Aid as % of GNI 1997 2002
$
Aid as % of gross capital formation 1997 2002
Aid as % of imports of goods and services 1997 2002
Aid as % of central government expenditure 1997 2002
219 793 230 11 423 97 119 3 71 99 81 496
701 1,301 356 27 449 1,931 353 7 189 171 194 657
10 5 32 1 48 9 25 1 13 50 10 12
31 9 44 1 46 237 68 2 35 87 21 14
0.6 0.2 12.5 0.0 9.8 .. 14.3 0.0 0.3 0.5 .. 0.3
1.5 0.4 20.8 0.0 9.2 12.4 47.0 0.0 0.8 0.8 .. 0.6
3.0 0.9 89.8 0.0 54.2 4.8 278.9 0.0 1.0 2.3 .. 2.0
6.6 1.8 109.2 0.1 45.3 76.6 514.7 0.0 2.6 3.3 .. 4.0
1.7 0.8 45.8 0.0 24.8 1.9 .. 0.0 0.5 0.9 .. 1.3
3.6 1.3 77.1 0.1 19.9 27.5 .. 0.0 1.0 1.3 .. 1.8
2.0 .. .. .. 50.5 .. 81.3 0.0 0.8 1.4 .. 1.1
5.3 1.5 .. .. 41.0 .. .. 0.0 2.1 1.7 .. 1.3
331 139 28
344 351 25
19 5 29
18 11 23
2.2 1.3 1.8
2.1 2.7 2.0
9.0 6.6 9.6
9.9 13.3 11.6
4.7 8.7 2.1
4.6 9.7 1.9
8.5 .. ..
7.6 .. ..
197 86 945 626 125 33 194 7 12 813 268 2
81 168 1,233 296 51 –7 475 636 41 638 484 4
13 14 30 11 31 26 21 0 3 38 5 1
5 27 35 5 11 –6 49 9 8 26 10 1
1.4 8.0 12.5 0.4 8.5 0.6 1.1 0.0 0.4 13.0 0.5 0.0
0.4 14.6 13.2 0.2 3.8 –0.1 2.4 0.4 .. 11.2 1.2 ..
6.4 39.5 82.5 1.2 51.3 1.6 3.9 0.0 1.2 77.2 2.5 0.0
1.8 61.2 78.7 1.0 17.0 –0.5 9.0 2.1 1.7 50.7 6.1 ..
3.2 9.8 44.4 0.8 16.4 0.9 2.0 0.0 0.7 46.0 1.2 ..
1.1 18.2 53.3 0.4 6.9 –0.1 4.1 1.0 .. 35.4 2.2 ..
1.2 .. .. 2.1 .. .. 3.2 0.0 .. .. .. 0.0
.. .. .. 1.2 .. .. .. 0.2 .. 65.5 4.7 ..
13 189 57 1,277 1,616 584 641 201 69,814 s 29,622 25,382 19,979 4,018 67,945 7,340 12,819 5,108 6,527 6,615 19,406
11 6 0 13 230 22 66 28 9w 9 7 7 8 11 4 15 11 20 3 24
4 7 2 16 500 31 63 15 11 w 12 9 8 12 13 4 27 10 21 5 28
0.2 1.3 0.0 3.8 13.1 5.6 16.5 4.2 0.2 w 2.1 0.4 0.5 0.2 0.9 0.5 0.7 0.3 0.9 0.8 4.5
0.1 2.4 0.1 3.6 42.9 6.3 18.1 .. 0.2 w 2.7 0.5 0.6 0.2 1.1 0.4 1.1 0.3 1.0 1.0 6.3
0.7 3.0 0.0 7.0 .. 9.5 37.3 .. 0.7 w 7.9 1.3 1.8 0.4 3.0 1.4 1.8 1.2 3.2 4.5 12.4
0.5 6.5 0.3 5.7 .. 12.2 36.6 .. 0.7 w 9.5 1.4 1.9 0.6 3.3 1.1 2.7 1.1 3.4 5.3 15.3
0.5 .. 0.0 16.5 .. 16.1 .. 11.1 .. .. .. .. .. .. .. .. .. .. .. ..
0.3 .. 0.1 16.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
34 140 9 998 603 356 610 336 54,482 s 21,534 18,914 15,853 2,578 52,324 6,939 7,121 5,399 5,440 4,313 14,976
1.0 0.9 5.7 11.8 0.0 0.4 13.1 11.3 35.9 1,349.0 20.8 35.1 107.0 99.4 22.0 29.2 0.8 w 1.0 w 8.9 13.1 1.4 1.9 1.7 2.1 0.7 1.2 3.4 4.4 1.3 1.2 2.7 5.3 1.2 1.6 4.5 4.3 3.6 4.9 24.5 32.2
Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region.
336
2004 World Development Indicators
About the data
6.10
Definitions
Ratios of aid to gross national income (GNI), gross
Expenditures on technical cooperation do not always
• Net official development assistance consists of
capital formation, imports, and government spend-
directly benefit the economy to the extent that they
disbursements of loans made on concessional terms
ing provide a measure of the recipient country’s
defray costs incurred outside the country on the
(net of repayments of principal) and grants by official
dependency on aid. But care must be taken in draw-
salaries and benefits of technical experts and the
agencies of the members of DAC, by multilateral
ing policy conclusions. For foreign policy reasons
overhead costs of firms supplying technical services.
institutions, and by non-DAC countries to promote
some countries have traditionally received large
In 1999, to avoid double counting extrabudgetary
economic development and welfare in countries and
amounts of aid. Thus aid dependency ratios may
expenditures reported by DAC countries and flows
territories in part I of the DAC list of aid recipients. It
reveal as much about a donor’s interest as they do
reported by the United Nations, all United Nations
includes loans with a grant element of at least 25
about a recipient’s needs. Ratios in Sub-Saharan
agencies revised their data since 1990 to include
percent (calculated at a rate of discount of 10 per-
Africa are generally much higher than those in other
only regular budgetary expenditures (except for the
cent). • Net official aid refers to aid flows (net of
regions, and they increased in the 1980s. These
World Food Programme and the United Nations High
repayments) from official donors to countries and
high ratios are due only in part to aid flows. Many
Commissioner for Refugees, which revised their data
territories in part II of the DAC list of aid recipients:
African countries saw severe erosion in their terms
from 1996 onward). These revisions have affected
more advanced countries of Central and Eastern
of trade in the 1980s, which, along with weak poli-
net ODA and official aid and, as a result, aid per capi-
Europe, the countries of the former Soviet Union,
cies, contributed to falling incomes, imports, and
ta and aid dependency ratios.
and certain advanced developing countries and terri-
investment. Thus the increase in aid dependency
Because the table relies on information from
tories. Official aid is provided under terms and con-
ratios reflects events affecting both the numerator
donors, it is not consistent with information recorded
ditions similar to those for ODA. • Aid per capita
and the denominator.
by recipients in the balance of payments, which often
includes both ODA and official aid. • Aid dependen-
As defined here, aid includes official development
excludes all or some technical assistance—
cy ratios are calculated using values in U.S. dollars
assistance (ODA) and official aid (see About the data
particularly payments to expatriates made directly by
converted at official exchange rates. For definitions
for table 6.8). The data cover loans and grants from
the donor. Similarly, grant commodity aid may not
of GNI, gross capital formation, imports of goods and
Development Assistance Committee (DAC) member
always be recorded in trade data or in the balance of
services, and central government expenditure, see
countries, multilateral organizations, and non-DAC
payments. Moreover, DAC statistics exclude purely
Definitions for tables 1.1, 4.9, and 4.12.
donors. They do not reflect aid given by recipient
military aid.
countries to other developing countries. As a result,
The nominal values used here may overstate the
some countries that are net donors (such as Saudi
real value of aid to the recipient. Changes in inter-
Arabia) are shown in the table as aid recipients (see
national prices and in exchange rates can reduce the
table 6.9a).
purchasing power of aid. The practice of tying aid,
The data in the table do not distinguish among dif-
still prevalent though declining in importance, also
ferent types of aid (program, project, or food aid;
tends to reduce its purchasing power (see About the
emergency assistance; postconflict peacekeeping
data for table 6.9).
assistance; or technical cooperation), each of which
The values for population, GNI, gross capital for-
may have very different effects on the economy.
mation, imports of goods and services, and central government expenditure used in computing the
6.10a
ratios are taken from World Bank and International
Where did aid go in 2002?
Monetary Fund (IMF) databases. The aggregates
Net aid
also refer to World Bank definitions. Therefore the ratios shown may differ somewhat from those com-
Latin America & Caribbean 9% Middle East & North Africa 11% South Asia 11% East Asia & Pacific 13%
Sub-Saharan Africa 34%
Europe & Central Asia 22%
puted and published by the Organisation for
Data sources
Economic Co-operation and Development (OECD). Aid
The data on financial flows are compiled by DAC
not allocated by country or region—including admin-
and published in its annual statistical report,
istrative costs, research on development issues, and
Geographical Distribution of Financial Flows to
aid to nongovernmental organizations—is included in
Aid Recipients, and in its annual Development
the world total. Thus regional and income group
Cooperation Report. Data are available in electron-
totals do not sum to the world total.
ic format on the OECD’s International Development Statistics CD-ROM and to registered users at http://www.oecd.org/dataoecd/50/17/5037721.
East Asia and Pacific has received a smaller share of total net aid flows, declining from 16 to 13 percent, while flows to Europe and Central Asia increased from 16 to 22 percent.
htm. The data on population, GNI, gross capital for-
Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.
and IMF databases.
mation, imports of goods and services, and central government expenditure are from World Bank
2004 World Development Indicators
337
GLOBAL LINKS
Aid dependency
6.11
Distribution of net aid by Development Assistance Committee members Total
Ten major DAC donors
United
$ millions Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
338
Other DAC donors
United
States
Japan
France
Germany
Kingdom
Netherlands
Canada
Sweden
Norway
Denmark
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
985.9 177.2 122.8 286.4 51.9 171.4
367.6 61.8 5.2 105.6 1.6 114.3
31.7 4.0 –2.2 27.2 12.9 11.4
11.9 3.0 89.6 9.9 11.7 3.8
92.6 24.7 –3.9 16.5 13.1 19.9
130.8 4.9 .. 10.2 .. 1.7
88.3 11.6 0.4 27.7 0.3 7.2
35.8 1.3 0.4 2.6 2.1 0.7
27.5 4.0 0.7 14.1 0.2 1.1
60.9 5.8 3.2 22.2 0.1 3.3
7.8 3.3 0.0 1.0 0.0 0.2
131.2 52.9 29.6 49.4 10.0 7.8
232.2 520.8 26.0
61.5 72.1 8.4
141.8 122.7 0.2
2.9 7.3 2.8
9.8 30.0 6.8
0.5 101.8 0.1
4.1 44.3 1.0
0.8 30.9 0.1
0.4 15.0 2.9
3.5 16.6 0.2
.. 37.3 1.0
6.9 42.7 2.6
140.1 482.2 292.3 36.7 197.6 189.2 229.9 84.7 272.8 436.2
23.4 127.7 75.8 22.4 –37.1 47.5 16.2 21.2 44.4 13.1
4.5 37.5 14.7 –0.1 117.6 36.7 10.0 0.1 98.6 7.5
40.5 33.9 2.4 0.6 20.5 14.9 53.9 7.1 24.6 119.0
24.0 71.9 19.4 4.5 31.9 49.2 19.4 2.7 18.4 67.0
0.1 14.2 7.3 2.2 16.6 7.0 0.3 1.2 13.2 43.5
2.4 62.6 37.3 1.9 14.7 7.9 37.3 9.6 9.3 7.5
2.4 14.6 6.9 0.2 6.0 1.3 8.6 1.8 4.9 80.3
0.1 16.4 27.0 0.6 2.0 0.2 7.5 3.6 14.5 0.0
0.1 3.3 23.8 3.2 2.9 0.5 0.4 10.2 3.1 5.7
23.6 30.6 0.4 0.8 0.4 3.3 23.0 .. 6.6 17.0
19.0 69.4 77.2 0.6 22.1 20.7 53.3 27.2 35.0 75.5
39.6 67.0 –13.8 1,212.8 4.0 426.1 351.0 41.4 4.5 831.1 82.1 49.6 48.5
0.8 7.0 –18.4 17.0 .. 306.3 80.0 5.9 –23.7 53.1 49.5 4.6 2.5
12.9 0.1 –39.6 828.7 2.2 4.3 0.9 0.2 –2.8 5.2 0.5 3.7 1.6
16.5 34.8 11.8 77.2 1.6 13.0 0.8 23.7 4.8 531.3 2.5 2.8 8.3
7.1 13.0 18.7 149.9 0.0 21.4 21.1 2.6 3.1 31.1 2.2 4.3 16.3
0.4 .. 0.3 36.1 .. 3.2 14.9 0.3 –0.1 11.7 2.1 0.6 1.3
0.4 0.8 3.3 17.9 0.1 15.2 135.0 0.2 6.2 24.3 1.6 1.7 1.2
0.1 0.5 1.6 30.0 .. 6.1 9.8 0.4 3.7 78.7 1.1 5.4 0.3
0.2 0.0 0.9 6.4 .. 6.9 7.7 2.2 1.1 0.2 5.5 1.9 1.2
.. .. 0.5 12.2 .. 7.7 12.5 0.4 0.5 0.5 13.2 1.2 0.3
.. .. 0.0 6.3 .. 0.2 .. .. 0.0 0.7 0.1 .. 2.4
1.2 10.8 7.1 31.1 0.1 41.9 68.4 5.6 11.7 94.2 3.7 23.5 13.1
138.2 205.1 1,124.2 217.9 120.7 16.9 489.2
15.7 65.0 845.8 62.0 44.9 –7.7 156.4
42.7 28.3 12.9 32.9 4.3 0.6 50.5
5.9 6.8 100.1 3.0 4.3 1.3 10.2
8.0 16.4 61.9 15.2 3.7 2.4 40.6
25.9 0.6 12.2 11.1 1.2 0.2 43.7
1.4 10.5 17.1 8.4 12.6 0.6 34.8
1.0 9.2 10.3 3.3 1.1 0.3 6.9
0.2 0.6 2.2 5.3 4.2 3.4 21.3
0.4 2.3 0.6 1.7 13.5 0.7 28.5
0.4 4.8 16.1 1.3 10.2 12.2 2.7
36.6 60.6 44.9 73.8 20.8 3.1 93.7
49.5 17.5 209.6
2.3 2.8 133.3
3.8 8.2 18.6
41.0 0.4 1.9
0.5 1.8 21.0
0.2 1.7 3.9
0.3 0.3 8.9
0.9 0.6 0.7
.. 0.5 2.0
.. 0.7 4.4
.. 0.2 0.3
0.5 0.4 14.4
406.2
68.9
23.6
10.2
34.0
123.7
59.6
12.4
1.4
0.7
51.5
20.4
199.6 125.6 25.8 125.4
64.7 47.7 3.8 69.9
29.4 18.6 0.1 9.3
1.4 22.6 4.0 17.2
19.0 15.4 1.4 4.3
0.6 2.7 .. 0.2
20.6 4.0 3.6 4.2
10.2 4.3 0.3 10.2
11.3 0.5 1.8 0.4
11.5 0.6 0.0 1.7
1.9 .. 0.3 0.1
29.0 9.4 10.5 8.1
2004 World Development Indicators
Total
Ten major DAC donors
United
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Other DAC donors
United
States
Japan
France
Germany
Kingdom
Netherlands
Canada
Sweden
Norway
Denmark
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
297.9 40.3 785.3 1,162.0 81.5 85.1
97.3 1.9 –3.8 225.8 0.2 0.0
94.9 6.9 493.6 538.3 17.5 0.1
3.9 7.5 –135.9 44.8 7.9 2.0
13.4 11.5 –26.1 78.4 31.8 18.4
1.9 2.4 343.7 31.7 2.8 13.7
8.9 1.2 59.4 127.3 3.8 15.8
7.1 0.4 16.0 11.6 .. 0.3
11.0 0.2 8.2 1.6 0.0 4.5
0.9 0.3 8.5 6.1 5.3 17.9
12.7 0.3 8.5 1.9 .. ..
46.0 7.8 13.2 94.7 12.1 12.3
749.3
786.8
0.6
6.2
–50.4
..
2.0
..
1.5
..
..
2.6
–3.6
–11.0
–6.6
–0.9
–0.8
7.4
1.6
7.1
0.1
0.6
..
–1.2
370.9 143.9 288.1 187.8 –79.8 3.0 95.2 177.8 26.2 102.4 29.7 27.0 4.4 36.0 179.8 125.9 224.9 85.4 256.8 146.6 3.5 92.6 86.3 141.3 218.7 1,661.0 79.1 84.8 279.4
286.8 74.0 102.4 131.2 –44.6 .. 51.7 8.5 0.8 36.2 6.0 15.1 .. –1.5 50.5 41.7 61.2 1.1 49.2 5.5 0.2 84.0 56.9 20.4 –13.3 159.7 4.8 17.0 32.6
–0.2 30.1 17.4 .. –47.2 0.1 8.1 90.1 0.4 10.1 3.9 0.0 0.2 1.5 3.8 7.6 18.8 54.2 17.0 13.0 0.7 –6.6 5.9 79.0 40.8 69.7 49.4 3.2 97.5
3.2 2.3 17.6 0.5 11.5 1.4 0.4 14.9 1.4 33.2 –0.9 1.7 1.6 2.0 2.0 46.3 5.1 –2.7 63.6 20.0 –0.2 –0.2 2.3 1.0 145.8 431.6 1.5 3.0 –1.9
51.1 13.1 27.1 33.2 –0.2 1.5 11.0 12.0 3.8 7.2 4.7 –2.1 1.7 6.9 16.8 8.6 24.0 4.5 28.0 25.6 1.4 15.0 2.4 23.2 16.9 156.9 1.7 18.3 34.5
5.0 1.1 54.4 3.0 .. .. 4.5 1.0 0.1 0.2 1.7 2.9 .. 0.1 7.6 0.7 50.2 –0.1 6.8 19.4 0.2 2.6 3.3 0.6 .. 48.0 6.5 3.0 36.9
0.3 2.0 12.7 0.6 0.1 0.0 1.7 2.0 0.4 0.4 0.7 2.9 0.1 0.7 17.6 0.4 16.9 0.9 38.2 27.6 0.1 3.3 3.5 2.6 1.2 52.0 4.2 4.8 7.3
3.7 0.7 7.3 0.2 .. .. 0.7 1.5 0.5 1.3 0.2 0.3 .. 0.8 2.0 1.2 8.5 0.7 13.6 2.2 0.2 3.9 0.3 0.9 4.4 9.0 1.3 0.7 4.2
4.2 0.5 14.4 4.3 .. .. 0.8 15.4 5.7 1.1 0.3 1.1 .. 13.3 6.2 0.1 7.7 .. 9.1 0.3 .. 0.4 4.6 2.4 0.8 45.3 0.9 9.4 3.6
2.3 1.8 3.0 3.6 .. .. 1.3 5.7 0.8 5.3 0.4 1.9 .. 1.0 11.7 5.7 15.6 0.3 7.1 0.5 0.3 0.4 1.1 2.6 0.2 38.7 3.9 3.4 13.1
0.1 0.4 9.7 .. .. .. 0.6 5.0 9.8 .. 0.8 0.1 .. 8.9 1.0 0.0 7.8 26.0 0.1 .. .. –0.2 1.5 0.9 –0.9 51.9 0.9 1.9 25.4
14.5 18.0 22.2 11.4 0.7 .. 14.4 21.9 2.5 7.4 11.9 3.1 0.8 2.3 60.6 13.5 9.3 0.6 24.1 32.5 0.6 –9.8 4.6 7.7 22.8 598.2 4.1 20.2 26.3
287.2 114.5 215.0
66.7 16.3 76.1
31.4 13.3 19.1
0.9 34.4 8.8
34.5 14.9 37.7
0.4 0.6 41.7
26.0 1.8 2.8
7.7 5.3 18.1
38.7 0.1 1.5
9.1 2.4 3.1
25.0 6.8 0.0
47.0 18.7 6.2
–0.4 702.5 23.3 197.1 50.8 463.0 509.1 388.6
–4.9 209.0 6.0 0.2 11.2 143.6 78.6 3.1
3.7 301.1 5.3 4.4 26.8 119.6 318.0 –3.8
0.6 2.5 0.8 0.6 0.2 4.9 –2.4 159.6
0.1 76.2 1.7 3.2 3.5 24.3 14.5 37.3
.. 66.9 0.2 .. –0.2 84.4 1.3 3.2
0.0 12.2 0.5 1.3 1.4 12.9 25.9 1.1
.. 7.8 0.8 0.0 0.9 7.9 15.6 70.8
.. 1.6 .. 0.1 1.3 3.9 1.6 4.1
.. 10.3 .. 0.2 0.6 1.4 1.0 1.1
.. –1.2 2.0 .. .. 2.1 2.2 14.3
0.1 16.2 6.0 187.2 5.1 57.9 52.9 97.9
2004 World Development Indicators
339
GLOBAL LINKS
6.11
Distribution of net aid by Development Assistance Committee members
6.11
Distribution of net aid by Development Assistance Committee members Total
Ten major DAC donors
United
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
Other DAC donors
United
States
Japan
France
Germany
Kingdom
Netherlands
Canada
Sweden
Norway
Denmark
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
176.6 1,109.3 199.1 13.4 242.8 1,921.3 225.3 7.1 39.2 2.4 102.4 375.3
54.7 858.8 46.4 .. 37.1 495.4 70.1 .. 4.9 0.3 35.4 89.4
29.6 5.3 0.4 9.0 37.8 0.3 0.1 2.0 3.6 0.3 .. 4.7
23.6 23.8 6.6 3.7 104.5 103.7 3.6 3.2 4.1 1.4 0.4 25.4
29.7 54.1 10.8 0.6 13.2 531.4 15.9 1.5 6.7 –2.9 2.8 42.4
9.7 41.5 52.6 .. 0.6 459.7 54.3 0.1 4.3 0.2 3.1 47.0
6.8 8.2 19.6 0.0 10.4 61.9 20.6 0.0 1.5 0.0 13.1 45.6
2.2 12.8 5.6 0.0 9.8 .. 3.3 0.2 0.7 0.0 0.2 9.5
0.6 31.7 15.6 .. 0.3 24.5 1.7 .. 0.2 0.0 5.5 22.0
1.3 22.7 6.1 0.0 1.4 22.1 10.6 .. 0.3 0.1 25.4 17.5
6.0 13.2 0.5 .. 0.9 8.0 0.4 .. 3.9 .. 1.4 18.8
12.5 37.2 35.0 0.1 26.9 214.2 44.6 0.1 9.0 3.0 15.2 53.2
188.5 232.3 6.6
–11.0 119.6 –0.2
118.9 1.2 4.5
–2.5 2.4 0.0
7.8 14.5 –2.1
7.7 13.5 –1.4
18.6 22.7 1.1
3.5 4.9 0.2
15.0 9.6 0.2
21.5 23.3 0.2
0.2 0.6 –0.2
8.8 19.9 4.3
25.0 128.8 902.8 280.4 39.2 5.7 144.6 99.0 26.0 466.1 358.2 3.7
.. 75.9 85.4 36.4 6.7 0.6 –20.8 144.5 12.1 109.4 255.5 0.4
15.8 27.0 58.2 222.4 0.3 2.7 63.3 –15.9 11.4 8.1 1.6 0.1
13.5 0.2 16.0 –7.1 18.7 0.8 96.6 9.1 0.4 5.5 6.8 2.6
–12.8 10.2 23.2 –4.2 8.1 0.1 –5.2 –71.0 0.8 33.9 44.6 0.5
0.1 3.3 103.2 0.3 0.5 0.2 .. –0.7 0.2 84.0 12.5 0.1
2.3 0.6 138.3 2.4 0.5 0.0 –3.2 0.3 0.0 43.5 2.8 ..
0.2 1.5 8.3 2.5 1.0 1.3 0.8 1.1 0.4 6.4 14.0 ..
0.2 2.0 61.4 3.6 0.1 .. 0.1 1.7 .. 23.4 5.0 ..
0.8 1.4 46.7 1.6 0.2 .. 0.1 4.2 0.2 32.6 0.2 ..
.. 0.1 69.9 8.5 0.1 .. .. 0.0 .. 43.1 5.1 ..
4.9 6.8 292.3 14.1 3.1 0.1 12.9 25.7 0.4 76.3 10.1 0.0
4.1 40.2 3.7 374.7 12.8 6.0 68.4 23.6 6,748.3 s 3,141.6 2,555.7 2,421.5 118.3 6,780.0 2,755.2 401.6 583.2 195.9 1,190.0 579.5
2.4 1.6 5.0 77.8 15.6 4.2 10.1 3.2 4,677.7 s 1,788.8 1,444.8 952.0 433.6 3,904.1 317.8 422.8 174.6 561.7 –118.6 2,129.8
2.0 21.6 3.0 41.7 37.9 28.4 44.2 10.3 3,593.5 s 1,368.2 1,609.6 1,390.9 164.2 3,637.0 378.7 902.6 355.2 243.4 216.3 935.0
6.8 –1.7 152.9 74.3 42.0 10.9 746.0 14.7 410.2 138.1 119.4 24.1 359.5 48.3 177.8 47.0 45,249.8 s 12,814.5 s 17,698.7 3,559.1 15,415.8 5,022.6 13,328.4 4,744.1 1,467.5 179.2 43,667.3 12,042.3 5,742.1 778.1 7,112.1 3,004.4 3,891.7 1,207.3 2,914.9 1,310.6 3,518.0 667.2 11,675.0 2,369.4
0.0 0.1 0.7 0.1 .. .. 1.4 0.7 0.5 0.1 2.6 .. 0.1 0.3 1.4 0.1 0.1 .. 26.5 30.1 20.0 24.4 7.9 48.4 23.8 13.9 8.9 28.0 50.9 5.5 7.8 40.8 0.5 0.4 0.4 .. 28.1 35.5 12.2 19.4 29.1 32.2 28.7 22.3 6.5 8.3 7.2 5.6 3,593.3 s 2,624.9 s 1,607.2 s 1,350.3 s 1,188.3 s 1,133.5 s 1,648.9 1,325.7 496.4 463.6 533.4 541.3 991.4 553.0 447.7 315.0 363.8 268.1 885.7 502.7 218.5 264.9 272.3 156.1 96.4 26.2 95.6 37.9 28.4 80.6 3,592.8 2,528.2 1,606.5 1,348.8 1,187.7 1,133.5 138.3 226.5 96.4 79.8 56.0 107.0 593.2 196.1 233.6 183.9 184.6 143.1 292.4 218.8 143.1 126.0 60.1 89.5 65.7 93.8 31.8 44.2 87.4 20.9 688.2 233.8 99.0 71.3 133.4 88.3 1,022.0 939.5 374.1 404.5 447.8 392.5
Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region.
340
2004 World Development Indicators
–0.9 10.0 17.5 79.9 74.8 7.0 32.1 15.0 5,918.3 s 2,831.6 1,844.3 1,519.7 207.2 5,906.4 808.3 846.1 641.5 259.6 249.1 2,080.8
6.11
About the data
The data in the table show net bilateral aid to low-
research on development issues, and aid to non-
access to information on such aid expenditures as
and middle-income economies from members of the
governmental organizations—is included in the world
development-oriented research, stipends and tuition
Development Assistance Committee (DAC) of the
total; thus regional and income group totals do not
costs for aid-financed students in donor countries, or
Organisation
sum to the world total.
payment of exper ts hired by donor countries.
for
Economic
Co-operation
and
Development (OECD). The DAC compilation of the
In 1999 all United Nations agencies revised their
Moreover, a full accounting would include donor
data includes aid to some countries and territories
data since 1990 to include only regular budgetary
country contributions to multilateral institutions, the
not shown in the table and small quantities of aid to
expenditures (except for the World Food Programme
flow of resources from multilateral institutions to
unspecified economies that are recorded only at the
and the United Nations High Commissioner for
recipient countries, and flows from countries that are
regional or global level. Aid to countries and territo-
Refugees, which revised their data from 1996
not members of DAC.
ries not shown in the table has been assigned to
onward). They did so to avoid double counting extra-
The expenditures that countries report as official
regional totals based on the World Bank’s regional
budgetary expenditures reported by DAC countries
development assistance (ODA) have changed. For
classification system. Aid to unspecified economies
and flows reported by the United Nations.
example, some DAC members have reported as ODA
has been included in regional totals and, when pos-
The data in the table are based on donor country
sible, in income group totals. Aid not allocated by
reports of bilateral programs, which may differ from
country or region—including administrative costs,
reports by recipient countries. Recipients may lack
the aid provided to refugees during the first 12 months of their stay within the donor’s borders. Some of the aid recipients shown in the table are also aid donors. See table 6.9a for a summary of
6.11a
ODA from non-DAC countries.
Top aid recipients from top DAC donors reflect historical alliances and geopolitical events Definitions
Total bilateral aid, 2002 United States
Japan
• Net aid comprises net bilateral official develop-
Russian Federation 7% Egypt, Arab Rep. 7%
China 12%
al official aid to part II recipients (see About the data Indonesia 8%
Israel 6% Serbia and Montenegro 4%
India 7%
for table 6.8). • Other DAC donors are Australia, Austria, Belgium, Finland, Greece, Ireland, Italy, Luxembourg, New Zealand, Portugal, Spain, and
Afghanistan 3% Others 62%
Others 73%
ment assistance to part I recipients and net bilater-
Vietnam 6%
Switzerland.
Phillippines 5%
France
Germany Serbia and Montenegro 15%
Côte d’Ivoire 11% Mozambique 9% Poland 3% Morocco 3% Cameroon 3%
Others 71%
United Kingdom
Mozambique 4%
Others 72%
Netherlands Tanzania 5% Congo, Dem. Rep. 5% Indonesia 5% Afghanistan 3% Bolivia 2%
Serbia and Montenegro 13% India 10%
Others 67%
China 4% Afghanistan 3% Indonesia 2%
Afghanistan 4% Ghana 3% Tanzania 3%
Data sources Data on financial flows are compiled by DAC and published in its annual statistical report,
Others 80%
Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Cooperation Report. Data are available electronically on the OECD’s International Development Statistics
This figure shows the distribution of aid from the top six donors to their top five recipients in 2002. Serbia and Montenegro and Afghanistan drew a large share of aid from donors in 2002.
CD-ROM and to registered users at http://www. oecd.org/dataoecd/50/17/5037721.htm.
Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.
2004 World Development Indicators
341
GLOBAL LINKS
Distribution of net aid by Development Assistance Committee members
6.12
Net financial flows from multilateral institutions International financial institutions
United Nations
Total
$ millions Regional development World Bank
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
342
IMF
banks
Conces-
Non-
Conces-
Non-
$ millions
IDA
IBRD
sional
concessional
sional
concessional
Others
UNDP
UNFPA
UNICEF
WFP
Others
$ millions
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
.. 78.9 0.0 17.9 0.0 66.5
.. 0.0 –129.5 0.0 –928.3 –0.4
.. –2.9 0.0 0.0 0.0 15.0
.. –5.7 –297.5 0.0 –743.0 –7.3
.. 0.0 0.0 –0.3 0.0 0.0
.. 3.5 –33.3 –2.6 –502.3 –6.1
.. 18.7 –155.5 –0.1 0.0 –9.4
9.0 1.4 0.8 1.7 0.3 0.8
9.0 0.4 1.3 2.4 0.3 0.3
9.2 0.7 0.9 5.3 0.6 0.6
2.0 0.5 4.6 30.3 .. 1.3
15.4 3.1 6.2 15.8 2.8 3.5
44.6 98.5 –602.0 70.4 –2,169.5 64.8
56.9 195.0 0.0
0.0 –5.5 –9.8
7.8 –22.9 0.0
–46.4 –65.1 –30.3
0.0 84.6 0.0
0.6 6.2 –15.3
11.2 56.6 –5.7
2.2 14.4 0.2
0.8 10.3 0.2
0.9 11.3 ..
3.3 25.4 ..
3.7 13.3 1.5
40.9 323.7 –59.1
20.1 96.4 96.8 –0.5 0.0 0.0 65.3 25.0 47.2 41.8
0.0 0.0 –23.1 –3.5 337.6 2.1 0.0 0.0 0.0 –21.0
–4.6 0.0 –17.1 0.0 0.0 18.2 0.0 0.0 0.0 11,246.8 0.0 –144.0 6.4 0.0 –2.5 12.5 10.8 –1.4 41.2 0.0
1.1 89.2 0.0 –1.5 0.0 0.0 37.9 0.0 69.9 20.6
–0.3 –54.4 –7.5 –12.0 853.1 –13.7 –1.8 0.0 0.0 –40.2
21.2 76.8 0.5 –12.4 –6.4 31.4 1.8 –0.4 7.0 –11.2
2.8 1.0 1.1 0.5 0.4 0.7 5.0 5.4 3.1 1.7
3.6 3.2 0.1 1.3 0.9 0.2 1.9 1.5 3.6 2.3
1.6 1.2 0.5 1.3 1.2 .. 4.0 2.5 3.5 2.8
1.4 2.8 .. .. .. .. 2.2 5.4 3.2 1.6
3.9 3.2 20.1 3.1 131.0 2.0 4.3 11.5 5.4 3.4
50.8 202.3 106.7 –23.7 12,564.6 –121.4 126.9 60.9 152.4 42.8
0.7 70.3 –0.7 94.7 .. –0.7 275.2 –0.3 –0.2 161.2 0.0 .. 0.0
0.0 0.3 –172.0 –576.9 .. 248.8 –81.5 –6.5 –11.1 –89.9 104.8 .. –41.0
0.0 12.7 0.0 0.0 .. 0.0 358.8 –3.6 0.0 –10.6 0.0 .. 0.0
0.0 0.0 0.0 0.0 .. 0.0 –203.4 –4.7 0.0 0.0 –125.9 .. 0.0
0.0 11.2 –1.3 0.0 .. –13.2 –32.0 0.0 –10.9 34.3 0.0 .. 0.0
–0.2 0.0 –76.2 2.2 .. –424.1 0.0 0.0 –22.5 –109.4 11.5 .. 0.0
0.0 5.7 –0.3 –13.7 .. 33.5 0.0 0.0 –44.7 –1.9 40.6 .. 39.7
3.3 3.7 –4.6 9.7 0.0 0.4 6.2 1.4 0.2 2.5 0.1 0.6 0.1
1.0 2.7 0.2 4.6 .. 0.9 1.7 0.7 0.4 2.0 .. 1.0 ..
2.0 2.4 0.6 11.4 .. 0.8 18.8 1.7 0.6 3.1 .. 0.3 ..
2.7 1.7 .. 12.1 .. 0.7 10.3 0.3 .. 1.6 .. 1.2 ..
4.6 3.8 1.6 12.3 0.0 7.5 42.8 8.8 2.2 8.6 9.9 2.2 1.5
14.1 114.4 –252.7 –443.7 0.0 –145.5 397.0 –2.2 –86.2 1.5 41.0 5.3 0.3
–0.7 –1.1 20.5 –0.8 46.2 0.0 459.5
32.6 –60.0 –46.6 36.0 0.0 –35.2 0.0
0.0 0.0 0.0 0.0 0.0 0.0 33.0
–25.7 97.9 0.0 0.0 0.0 –13.8 0.0
–16.1 –21.8 0.6 –17.6 11.9 0.0 73.3
80.2 –25.4 –65.0 107.8 0.0 –4.3 –19.7
–1.7 8.1 14.4 64.8 7.6 0.7 27.3
0.3 0.2 1.5 0.4 2.7 .. 13.3
1.1 1.5 1.1 1.1 2.0 0.0 3.8
0.6 0.8 2.7 0.7 1.1 .. 14.0
0.4 1.5 3.2 0.1 2.1 .. 23.5
1.6 4.0 7.3 1.1 18.0 0.2 32.2
72.5 5.6 –60.4 193.5 91.6 –52.3 660.4
0.0 14.3 61.3
–5.9 0.0 0.0
0.0 3.7 11.2
–13.1 0.0 –12.0
0.0 4.7 0.0
–12.5 –0.7 4.3
8.9 4.8 0.5
.. 2.6 1.4
0.3 0.5 0.3
0.6 0.7 0.7
0.0 0.9 0.9
3.2 2.3 5.3
–18.5 33.8 73.8
88.7
–1.2
63.4
0.0
36.1
–17.7
6.5
3.1
3.4
3.3
1.0
4.6
191.3
0.0 28.4 3.8 –0.1
69.5 0.0 0.0 0.0
0.0 6.6 –1.2 –1.9
0.0 0.0 –0.3 –7.3
–6.4 0.7 –0.8 1.3
163.3 –12.4 –0.3 0.0
–12.5 –13.7 –1.0 –0.3
0.8 0.8 2.3 2.7
13.5 0.5 0.7 3.3
0.8 2.1 1.1 2.8
3.2 3.1 1.7 3.6
1.8 26.2 1.9 1.4
233.9 42.3 7.9 5.4
2004 World Development Indicators
International financial institutions
United Nations
Total
$ millions Regional development World Bank
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
IMF
banks
Conces-
Non-
Conces-
Non-
$ millions
IDA
IBRD
sional
concessional
sional
concessional
Others
UNDP
UNFPA
UNICEF
WFP
Others
$ millions
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
45.6 –9.7 0.0 –62.9 429.8 –2,383.4 59.8 –706.0 0.0 –56.3 .. ..
–4.2 0.0 0.0 0.0 0.0 ..
–30.8 0.0 0.0 –950.2 0.0 ..
39.4 –19.2 0.0 –18.9 0.0 –1,341.7 8.0 384.1 0.0 0.0 .. ..
7.8 201.3 –24.5 –37.6 0.0 ..
1.1 0.3 21.2 4.1 0.9 0.7
2.0 .. 13.2 6.2 2.4 0.4
1.1 .. 30.4 5.1 1.8 1.7
2.1 .. 9.4 0.4 0.8 1.6
1.6 1.8 35.5 20.3 23.8 13.8
36.8 121.6 –3,210.1 –1,205.8 –26.6 18.2
..
..
..
..
..
..
..
..
..
..
..
0.3
0.3
0.0
39.9
0.0
–18.7
–4.6
92.2
–0.3
0.2
0.3
0.6
..
1.6
111.1
–2.6 0.0 23.6 .. .. .. 33.4 27.2 0.0 0.0 18.5 0.0 .. 0.0 18.2 157.3 45.8 0.0 87.5 40.7 –0.6 0.0 21.9 13.3 –1.4 146.9 0.0 .. 14.5
110.7 35.9 –11.8 .. .. .. 0.0 0.0 –2.8 35.7 –21.3 0.0 .. –9.7 9.6 0.0 –1.6 –70.0 0.0 0.0 19.6 –86.4 –4.4 0.0 –222.3 0.0 0.0 .. 0.0
0.0 0.0 –18.2 .. .. .. –1.4 2.6 0.0 0.0 5.3 0.0 .. 0.0 –1.2 13.1 –7.3 0.0 –9.6 10.0 0.0 0.0 12.0 –7.7 0.0 5.9 0.0 .. –4.5
13.6 0.0 0.0 .. .. .. –7.0 0.0 –9.9 0.0 0.0 –0.3 .. –40.2 –7.6 0.0 22.5 0.0 0.0 0.0 0.0 0.0 –17.8 0.0 0.0 0.0 0.0 .. 0.0
0.0 0.0 –1.0 .. .. .. 27.2 43.7 0.0 0.0 3.7 0.0 .. 0.0 0.0 3.5 17.4 0.0 5.2 11.0 –0.1 0.0 0.0 26.0 2.7 68.1 0.0 .. 1.9
0.0 4.4 –8.1 .. .. .. –7.4 0.0 –12.7 0.0 –1.5 0.0 .. –7.8 –23.9 –5.8 –2.3 –30.3 0.0 2.1 48.3 598.0 –3.1 0.0 –284.4 –1.1 0.0 .. 0.0
–3.3 19.6 –8.2 .. .. .. 6.4 1.8 18.3 –23.0 –1.9 0.0 .. –25.8 33.4 5.7 0.0 –2.4 5.5 19.5 2.1 0.0 –9.6 1.4 193.6 15.0 –0.9 .. 2.9
0.5 0.7 4.6 0.7 0.0 .. 1.5 1.1 0.1 0.5 0.9 0.8 .. 0.2 0.4 5.5 2.8 0.4 3.6 0.6 0.2 0.8 0.8 1.2 1.0 4.0 6.5 –0.2 6.5
0.8 0.6 4.9 1.1 .. .. 0.6 1.8 0.1 0.6 0.5 0.5 .. 0.1 .. 1.7 2.9 0.2 2.2 2.0 0.2 4.7 0.2 2.0 0.9 5.9 1.5 1.2 3.3
0.7 0.9 4.7 2.1 .. .. 0.9 1.5 .. 0.6 1.3 1.5 .. .. 0.6 5.1 4.9 0.4 5.3 1.4 0.5 1.2 0.6 0.9 1.5 6.5 7.4 1.2 3.3
1.7 .. 10.5 0.1 .. .. .. 2.7 .. .. 3.5 5.0 .. .. 0.0 4.2 7.0 .. 4.7 5.0 .. .. .. .. 1.3 5.7 0.8 0.9 7.5
83.3 2.1 30.2 4.1 1.5 0.3 1.7 2.0 0.5 53.5 0.9 11.6 4.7 0.4 5.5 4.7 4.4 1.0 3.2 3.4 1.0 6.3 1.3 3.4 3.0 6.7 11.0 7.3 14.2
205.3 64.2 31.2 7.9 1.5 0.3 56.0 84.5 –6.3 67.8 9.7 19.1 4.7 –82.8 35.0 195.0 96.5 –100.8 107.5 95.8 71.1 524.6 1.9 40.6 –304.1 263.6 26.3 10.4 49.6
71.7 68.3 7.6
0.0 0.0 –176.1
4.7 19.6 0.0
0.0 0.0 0.0
100.8 17.0 27.1
–1.1 0.0 –80.6
20.3 22.7 –1.8
2.2 3.4 12.7
2.0 2.9 6.4
0.7 6.1 18.3
2.1 4.0 ..
1.4 4.3 24.1
204.8 148.3 –162.3
0.0 851.3 0.0 –3.4 –1.4 0.0 –5.2 0.0
–1.4 –208.5 3.2 –14.2 –1.9 –16.8 –143.8 –33.3
0.0 297.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 –222.0 –8.1 0.0 0.0 –173.3 –405.3 0.0
0.0 153.4 –9.5 –2.4 –10.4 –5.0 –1.2 0.0
0.0 106.3 50.3 –2.5 13.8 168.8 –18.6 0.0
–9.9 –173.7 4.0 –0.7 –13.9 307.0 –0.7 0.0
.. 6.4 0.2 1.6 0.2 0.7 2.3 0.3
0.0 4.2 0.5 0.8 0.6 6.4 3.3 0.1
0.4 11.0 0.6 1.1 0.6 1.0 2.8 ..
.. 4.4 .. .. .. 2.1 .. ..
1.5 34.5 1.8 1.9 0.9 9.6 3.7 1.5
–9.4 864.2 43.1 –17.8 –11.6 300.4 –562.7 –31.5
2004 World Development Indicators
343
GLOBAL LINKS
6.12
Net financial flows from multilateral institutions
6.12
Net financial flows from multilateral institutions International financial institutions
United Nations
Total
$ millions Regional development World Bank
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
IMF
banks
Conces-
Non-
Conces-
Non-
$ millions
IDA
IBRD
sional
concessional
sional
concessional
Others
UNDP
UNFPA
UNICEF
WFP
Others
$ millions
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
0.0 0.0 73.2 .. 108.4 159.4 43.0 .. 0.0 .. 0.0 0.0
214.3 –296.3 0.0 .. 0.0 0.0 0.0 .. –21.9 .. 0.0 5.1
0.0 9.0 0.0 –1,486.0 0.7 –2.5 .. .. –10.8 0.0 0.0 0.0 35.5 0.0 .. .. 0.0 0.0 .. .. 0.0 0.0 0.0 0.0
0.0 0.0 6.5 .. 18.2 0.0 14.2 .. 0.0 .. 0.0 0.0
9.9 –15.4 –0.1 .. –14.6 0.0 0.0 .. –1.2 .. 0.0 –0.8
–16.1 0.1 –1.7 .. 32.9 84.2 –0.5 .. 2.1 .. 0.0 0.0
0.5 0.3 2.1 .. 3.4 .. 2.8 .. 0.2 0.1 4.0 1.8
0.4 0.6 1.7 0.0 2.2 2.0 1.1 .. .. .. 0.6 1.5
0.7 .. 3.1 0.6 1.9 0.5 3.0 .. .. .. 4.5 1.6
.. 1.0 4.8 .. 3.7 –0.4 6.5 .. .. .. 1.4 ..
2.0 17.6 10.1 12.8 3.9 1.6 24.8 0.3 1.8 0.8 13.1 6.7
220.7 –1,778.0 98.1 13.4 149.1 247.4 130.3 0.3 –19.0 0.9 23.7 15.9
58.9 –0.3 –0.3
–4.4 0.0 0.1
–50.8 0.0 0.0
70.0 0.0 2.7
37.2 0.0 –2.6
4.1 0.0 2.4
1.7 2.0 0.2
1.2 2.1 0.6
0.7 4.4 1.5
4.2 11.3 0.1
10.2 26.8 2.2
258.2 24.1 6.8
–1.5 9.6 142.8 –3.4 6.8 0.0 –2.1 –5.9 0.0 95.8 0.0 ..
–6.2 0.0 –2.4 –695.8 0.0 –3.1 –36.9 594.3 0.7 0.0 –76.4 ..
0.0 0.0 14.1 0.0 14.2 –0.8 –2.6 –1,274.1 0.0 0.0 –0.1 –19.7 0.0 85.2 0.0 0.0 .. .. 14.7 –0.7 0.0 –26.0 .. ..
–49.6 –7.5 9.4 –74.9 2.4 6.3 69.8 –103.3 .. –4.1 –61.8 ..
1.1 2.4 5.6 0.3 1.6 0.1 0.4 0.9 0.5 4.0 1.4 –0.1
4.0 0.7 7.5 0.3 1.1 0.0 0.4 0.9 0.6 5.4 0.6 ..
0.8 1.2 6.9 0.9 1.6 .. 0.7 0.8 0.9 5.0 .. ..
0.9 4.9 7.7 .. .. .. .. .. .. 14.9 .. ..
33.6 1.8 33.3 9.3 1.7 2.0 1.6 6.3 0.8 20.2 4.4 0.5
–16.9 3.3 271.1 –3,400.1 5.7 –14.5 119.0 6,984.8 3.5 138.0 –340.0 0.5
0.1 0.6 0.7 2.9 1.3 2.8 2.4 1.0 312.5 s 160.2 80.8 67.1 9.8 312.5 32.6 10.3 47.7 17.3 42.3 98.2
0.6 1.9 0.7 4.0 1.6 3.0 3.7 1.8 571.4 s 264.0 63.3 53.1 9.8 571.4 44.3 16.3 21.8 18.6 67.3 168.2
0.0 0.0 0.0 258.9 .. 63.9 140.5 0.0 .. 4,753.6 771.0 758.1 12.8 5,524.6 489.4 597.0 230.1 94.1 1,557.4 2,556.6
158.3 21.5 –169.1 0.0 .. 0.0 –6.4 0.0 s .. s –3,720.2 –2,013.3 –682.5 –1,330.9 –5,733.5 –2,210.6 366.0 –530.2 –352.8 –2,601.9 –404.1
125.2 –22.0 0.0
0.0 0.0 –14.2 –9.7 47.0 0.0 0.0 –1,360.0 –9.4 0.0 0.0 0.0 0.0 0.0 0.0 6,490.9 .. .. –17.0 0.0 0.0 –182.3 .. ..
0.0 0.0 0.0 –9.0 .. 0.0 109.8 –1.1 .. 959.7 –53.9 –53.9 0.0 905.8 –3.3 26.2 –20.4 5.9 218.9 678.4
1,559.8 –1.7 488.2 –3.8 0.3 –21.5 6.8 5.2 0.0 1.2 0.0 0.0 45.7 271.0 0.3 –5.2 175.1 20.0 6.6 4.1 .. .. .. .. 3.5 –17.6 0.0 0.0 –1.3 5.1 0.0 22.8 –11.0 –15.9 2.8 –1.9 0.0 –0.7 1.6 2.3 s .. s .. s .. s .. s 277.9 s –1,583.3 1,166.6 –1,159.7 –6.0 232.1 14,691.3 118.3 –128.9 924.5 46.5 14,083.9 143.4 –664.8 428.7 44.6 607.4 –25.2 535.9 495.8 1.8 13,108.0 1,284.9 –1,288.6 918.6 278.9 –2,722.1 320.6 –919.0 –119.8 38.6 4,609.8 48.1 –95.9 231.0 19.8 11,899.2 134.3 1,526.4 767.1 11.2 –303.8 7.4 –297.5 37.5 16.6 –161.9 327.6 –1,192.0 –121.1 60.7 –213.3 447.0 –310.5 123.8 131.8
.. 1.3 2,203.0 .. 1.4 16.9 .. 3.7 152.9 .. 6.6 464.1 5.4 238.1 249.9 3.5 7.0 66.4 11.0 20.0 279.8 4.2 6.2 13.4 351.6 s 2,156.0 s .. s 281.5 651.4 2,000.0 58.3 920.5 15,478.4 58.3 712.2 14,948.2 0.0 123.1 440.3 351.6 2,149.1 18,383.4 19.2 92.7 –4,937.4 11.4 147.1 5,987.0 19.9 231.0 14,338.1 25.6 543.8 –187.2 55.3 126.4 –1,620.8 213.8 616.1 4,106.0
Note: The aggregates for the regional development banks, United Nations, and total net financial flows include amounts for economies not specified elsewhere.
344
2004 World Development Indicators
About the data
6.12
Definitions
The table shows concessional and nonconcessional
Eligibility is based principally on a country’s per capi-
• Net financial flows in this table are disbursements
financial
ta income and eligibility under IDA, the World Bank’s
of public or publicly guaranteed loans and credits, less
flows
from
the
major
multilateral
institutions—the World Bank, the International
concessional window.
repayments of principal. • IDA is the International
Monetary Fund (IMF), regional development banks,
Regional development banks also maintain conces-
United Nations agencies, and regional groups such
sional windows for funds. Loans from the major region-
dow of the World Bank. • IBRD is the International
as the Commission of the European Communities.
al development banks—the African Development
Bank for Reconstruction and Development, the found-
Much of the data comes from the World Bank’s
Bank, Asian Development Bank, and Inter-American
ing and largest member of the World Bank Group.
Debtor Reporting System.
Development Bank—are recorded in the table accord-
• IMF is the International Monetary Fund. Its noncon-
ing to each institution’s classification.
cessional lending consists of the credit it provides to
The multilateral development banks fund their non-
Development Association, the concessional loan win-
concessional lending operations primarily by selling
In 1999 all United Nations agencies revised their
its members, mainly to meet their balance of pay-
low-interest, highly rated bonds (the World Bank, for
data since 1990 to include only regular budgetary
ments needs. It provides concessional assistance
example, has an AAA rating) backed by prudent lend-
expenditures (except for the World Food Programme
through the Poverty Reduction and Growth Facility and
ing and financial policies and the strong financial sup-
and the United Nations High Commissioner for
the IMF Trust Fund. • Regional development banks
port of their members. These funds are then on-lent
Refugees, which revised their data from 1996
include the African Development Bank, in Abidjan,
at slightly higher interest rates and with relatively long
onward). They did so to avoid double counting extra-
Côte d’Ivoire, which lends to all of Africa, including
maturities (15–20 years) to developing countries.
budgetary expenditures reported by DAC countries
North Africa; the Asian Development Bank, in Manila,
Lending terms vary with market conditions and the
and flows reported by the United Nations.
Philippines, which serves countries in South and
policies of the banks.
Central Asia and East Asia and Pacific; the European
Concessional flows from bilateral donors are
Bank for Reconstruction and Development, in London,
defined by the Development Assistance Committee
United Kingdom, which serves countries in Europe and
(DAC) of the Organisation for Economic Co-operation
Central Asia; the European Development Fund, in
and Development (OECD) as financial flows contain-
Brussels, Belgium, which serves countries in Africa,
ing a grant element of at least 25 percent. The grant
the Caribbean, and the Pacific; and the Inter-American
element of loans is evaluated assuming a nominal
Development Bank, in Washington, D.C., which is the
market interest rate of 10 percent. The grant ele-
principal development bank of the Americas.
ment is nil for a loan carrying a 10 percent interest
Concessional financial flows cover disbursements
rate, and it is 100 percent for a grant, which requires
made
no repayment. Concessional flows from multilateral
Nonconcessional financial flows cover all other dis-
development agencies are credits provided through
bursements. • Others is a residual category in the
their concessional lending facilities. The cost of
World Bank’s Debtor Reporting System. It includes
these loans is reduced through subsidies provided
such institutions as the Caribbean Development Bank
by donors or drawn from other resources available to
and the European Investment Bank. • United Nations
the agencies. Grants provided by multilateral agen-
includes the United Nations Development Programme
cies are not included in the net flows.
(UNDP), United Nations Population Fund (UNFPA),
All concessional lending by the World Bank is carried
out
by
the
International
through
concessional
lending
facilities.
United Nations Children’s Fund (UNICEF), World Food
Development
Programme (WFP), and other United Nations agencies,
Association (IDA). Eligibility for IDA resources is
such as the United Nations High Commissioner for
based on gross national income (GNI) per capita;
Refugees, United Nations Relief and Works Agency for
countries must also meet performance standards
Palestine Refugees in the Near East, and United
assessed by World Bank staff. Since July 1, 2003,
Nations Regular Programme for Technical Assistance.
the GNI per capita cutoff has been set at $735, measured in 2002 using the World Bank Atlas
Data sources
method (see Users guide). In exceptional circum-
The data on net financial flows from international
stances IDA extends eligibility temporarily to countries that are above the cutoff and are undertaking major adjustment efforts but are not creditworthy for lending by the International Bank for Reconstruction and Development (IBRD). An exception has also been made for small island economies. Lending by the International Finance Corporation is not included in this table. The IMF makes concessional funds available through its Poverty Reduction and Growth Facility, which replaced the Enhanced Structural Adjustment
financial institutions come from the World Bank’s Debtor Reporting System. These data are published in the World Bank’s Global Development Finance 2004 and electronically as GDF Online. The data on aid from United Nations agencies come from the DAC annual Development Cooperation Report. Data are available in electronic format on the OECD’s International Development Statistics CD-ROM and to registered users at http://www.oecd.org/ dataoecd/50/17/5037721.htm.
Facility in 1999, and through the IMF Trust Fund. 2004 World Development Indicators
345
GLOBAL LINKS
Net financial flows from multilateral institutions
6.13
Foreign labor and population in selected OECD countries Foreign population a
thousands
Austria Belgium Denmark Finland France Germany Ireland Italy Japan Luxembourg Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom
Foreign labor force b % of total
% of total
Total
population
labor force
thousands c
Asylum seekers thousands
1990
2001
1990
2001
1990
2001
1990
2001
1990
2001
456 905 161 26 3,597 5,343 80 781 1,075 113 692 143 108 279 484 1,100 1,723
764 847 267 99 3,263 7,319 151 1,363 1,778 167 690 186 224 1,109 476 1,419 2,587
5.9 9.1 3.1 0.5 6.3 8.4 2.3 1.4 0.9 29.4 4.6 3.4 1.1 0.7 5.6 16.3 3.2
9.4 8.2 5.0 1.9 5.6 8.9 3.9 2.4 1.4 37.5 4.3 4.1 2.2 2.7 5.3 19.7 4.4
7.4 7.1 2.4 .. 6.2 .. 2.6 1.4 0.1 45.2 e 3.1 2.3 1.0 0.6 5.4 18.9 3.3
11.0 8.9 3.5 1.7 6.2 9.1 e 4.6 3.8 0.2 61.7 e .. 4.9 2.0 3.4 5.1 18.1 4.4
.. 50 15 6 102 d 842 .. .. 224 9 81 16 .. .. 53 101 175
75 66 25 11 141 d 685 28 d 233 d 351 11 95 25 14 d .. 44 100 373
23 13 13 3 55 193 0 5 .. 0 21 4 0 9 29 36 38
30 25 10 2 47 88 10 13 0 1 33 15 0 9 24 21 92
Foreign-born population a
thousands 1990
Australia Canada United States
Inflows of foreign population
3,965 4,343 19,767 f
2001
4,482 5,448 31,811 g
Foreign-born labor force b
Inflows of foreign population
% of total
% of total
Total
population
labor force
thousands c, d
Asylum seekers thousands
1990
2001
1990
2001
1990
2001
1990
2001
22.9 16.1 7.9 f
23.1 18.2 11.1 g
25.7 18.5 9.4
24.2 19.9 13.9
121 214 1,536
88.9 250 1,064
4 37 74
13 42 63
a. Data are from population registers or from registers of foreigners, except for Australia (1991–2001); Canada (1991–2001); France (1990–99); and the United States (censuses); Italy, Portugal, and Spain (residence permits); and Ireland and the United Kingdom (labor force surveys) and refer to the population on December 31 of the year indicated. b. Data include the unemployed, except in Italy, Luxembourg, the Netherlands, Norway, and the United Kingdom. Cross-border and seasonal workers are excluded unless otherwise noted. c. Inflow data are based on population registers and are not fully comparable because the criteria governing who gets registered differ from country to country. Counts for the Netherlands, Norway, and (especially) Germany include substantial numbers of asylum seekers. d. Data are based on residence permits or other sources. e. Includes cross-border workers. f. From the U.S. Census Bureau, 1990 Census of Population. g. From the U.S. Census Bureau, Current Population Report (March 2000).
346
2004 World Development Indicators
About the data
6.13
Definitions
The data in the table are based on national definitions
OECD countries are not the only ones that receive
• Foreign (or foreign-born) population is the number
and data collection practices and are not fully compa-
substantial migration flows. Migrant workers make
of foreign or foreign-born residents in a country.
rable across countries. Japan and the European mem-
up a significant share of the labor force in Gulf
• Foreign (or foreign-born) labor force as a percent-
bers of the Organisation for Economic Co-operation
countries and in southern Africa, and people are dis-
age of total labor force is the share of foreign or
and Development (OECD) have traditionally defined for-
placed by wars and natural disasters throughout the
foreign-born workers in a countr y’s workforce.
eigners by nationality of descent. Australia, Canada,
world. Systematic recording of migration flows is dif-
• Inflows of foreign population are the gross arrivals
and the United States use place of birth, which is
ficult, however, especially in poor countries and
of immigrants in the country shown. The total does not
closer to the concept used in the United Nations’ def-
those affected by civil disorder.
include asylum seekers, except as noted. • Asylum
inition of the immigrant stock. Few countries, however,
seekers are immigrants who apply for permission to
apply just one criterion in all circumstances. For this
remain in a country for humanitarian reasons.
and other reasons, data based on the concept of foreign nationality and data based on the concept of foreign born cannot be completely reconciled. See the notes to the table for other breaks in comparability between countries and over time. Data on the size of the foreign labor force are also problematic. Countries use different permit systems to gather information on immigrants. Some countries issue a single permit for residence and work, while others issue separate residence and work permits. Differences in immigration laws across countries, particularly with respect to immigrants’ access to employment, greatly affect the recording and measurement of migration and reduce the international comparability of raw data. The data exclude temporary visitors and tourists (see table 6.14).
6.13a Migration to OECD countries is growing Foreign population (% of total population)
Luxembourg Australia Switzerland Canada United States Austria Germany Belgium France Sweden Denmark United Kingdom Netherlands Norway Ireland Spain Italy Portugal Finland Japan
1990 2001
Data sources International migration data are collected by the OECD through information provided by national correspondents to the Continuous Repor ting 0
5
10
15
20
25
30
35
40
The proportion of foreigners has increased in most OECD countries over the past 11 years. Only Belgium, France, Sweden, and the Netherlands have shown small declines. Note: Australia, Canada, and the United States refer to foreign born. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee.
System on Migration (SOPEMI) network, which provides an annual overview of trends and policies. The data appear in the OECD’s Trends in International Migration 2003.
2004 World Development Indicators
347
GLOBAL LINKS
Foreign labor and population in selected OECD countries
6.14
Travel and tourism International tourism
International tourism receipts
International tourism expenditures
thousands Inbound tourists
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
348
Outbound tourists
$ millions
% of exports
$ millions
% of imports
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
.. 30 1,137 67 1,930 15 2,215 19,011 77 115 .. 5,147 110 254 .. 543 1,091 1,586 74 109 17 89 15,209 6 9 943 10,484 6,581 813 55 33 435 196 7,049 327 7,278 1,838 1,305 362 2,411 194 169 372 79 1,572 52,497 109 100 .. 17,045 146 8,873 509 .. .. 144
.. 34 988 91 2,820 123 4,841 18,611 834 207 61 6,724 72 308 160 1,037 3,783 3,433 149 36 787 221 20,057 .. 32 1,412 36,803 16,566 541 103 19 1,113 .. 6,944 1,656 4,579 2,010 2,811 654 4,906 951 101 1,360 148 2,875 77,012 212 75 298 17,969 483 14,180 884 43 8 142
.. .. 3,828 .. 2,398 .. 2,170 2,572 .. 388 .. 6,453 418 242 .. 192 1,188 2,395 .. 24 .. .. 20,415 .. 24 768 2,134 2,043 781 .. .. 191 2 .. 12 13,380 2,530 137 181 2,012 525 .. 80 89 1,169 19,430 161 .. .. .. .. 1,651 289 .. .. ..
.. .. 1,257 .. 3,008 110 3,461 3,907 1,130 1,075 1,386 6,773 .. 240 .. .. 1,861 3,188 .. 35 .. .. 17,705 .. 39 1,938 16,600 4,709 1,241 .. .. .. .. .. .. .. .. .. 598 3,074 1,001 .. 1,658 .. 5,824 17,404 .. .. 317 73,300 .. .. 629 .. .. ..
.. 4 64 13 1,131 .. 4,088 13,417 42 11 .. 3,721 55 91 .. 117 1,444 320 11 4 50 53 6,339 3 8 540 2,218 5,032 406 7 8 275 51 1,704 243 419 3,322 900 188 1,100 18 .. 27 25 1,167 20,184 3 26 .. 14,288 81 2,587 185 30 .. 46
.. 487 133 22 2,547 63 8,087 11,237 51 57 193 6,892 60 156 112 309 3,120 1,344 34 1 379 39 9,700 .. .. 845 20,385 10,117 962 .. 25 1,078 50 3,811 1,633 2,941 5,785 2,736 447 3,764 342 73 555 75 1,573 32,329 7 .. 472 19,158 358 9,741 612 31 .. 54
.. 1.1 0.5 0.3 7.6 .. 8.2 21.1 .. 0.5 .. 2.7 15.1 9.3 .. 5.8 4.1 4.6 3.2 4.5 15.9 2.1 4.2 1.4 3.0 5.3 3.9 .. 4.7 .. 0.5 14.0 1.5 .. .. .. 6.8 49.1 5.8 11.1 1.8 .. 4.1 4.2 3.7 7.1 0.1 15.5 .. 3.0 8.2 19.9 11.8 3.6 .. 14.5
.. 53.2 .. 0.3 8.2 9.0 9.7 10.3 1.9 0.8 2.1 3.2 10.8 10.3 7.9 11.7 4.5 16.2 13.1 2.6 16.1 .. 3.2 .. .. 3.8 5.6 4.2 6.8 .. 1.0 15.1 0.9 36.1 .. 6.5 7.0 33.2 7.2 22.9 9.0 39.1 10.1 7.7 3.1 8.2 0.2 .. 48.4 2.7 13.9 32.4 16.2 3.2 .. ..
1 4 149 38 1,505 .. 4,535 7,748 .. 78 .. 5,477 15 130 .. 56 1,559 189 32 17 .. 279 10,931 51 70 426 470 .. 454 16 113 148 169 729 .. 455 3,676 144 175 129 61 .. 19 11 2,791 12,423 137 8 .. 33,771 13 1,090 100 30 .. 37
.. 366 193 66 3,800 54 6,116 9,391 106 202 559 10,435 7 118 49 .. 2,380 616 .. 14 38 .. 9,929 .. .. 793 15,398 12,417 1,072 .. 70 367 290 781 .. 1,575 6,856 295 364 1,278 229 .. 231 45 1,966 19,460 170 .. 174 53,196 120 2,450 267 21 .. ..
.. 0.8 1.5 1.1 22.0 .. 8.5 12.6 .. 2.0 .. 4.1 3.3 12.0 .. 2.8 5.5 2.4 4.2 5.3 .. 11.3 7.3 12.4 14.4 4.6 1.0 .. 6.6 .. 8.8 6.3 4.9 .. .. .. 8.9 6.4 6.9 0.9 3.8 .. 2.7 0.9 8.3 4.4 7.6 4.2 .. 7.9 0.9 5.6 5.5 3.1 .. 7.2
.. 17.6 .. 1.0 13.8 4.9 6.9 9.0 3.4 2.2 5.7 4.8 0.9 6.0 1.0 .. 3.8 6.6 .. 9.5 1.4 .. 3.7 .. .. 3.8 4.7 5.4 7.0 .. 4.3 4.8 7.5 6.1 .. 3.3 9.5 2.9 4.7 6.6 3.9 .. 3.8 2.2 4.9 5.3 8.7 .. 12.4 8.3 3.6 5.8 4.0 2.1 .. ..
2004 World Development Indicators
International tourism
International tourism receipts
6.14
International tourism expenditures
thousands Inbound tourists
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
Outbound tourists
$ millions
% of exports
$ millions
% of imports
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
1990
2002
290 3,693 1,707 2,178 154 748 3,666 1,063 26,679 989 3,236 572 .. 814 115 2,959 15 .. 14 .. 210 171 .. 96 780 562 53 130 7,446 44 .. 292 17,176 226 147 4,024 .. 21 213 255 5,795 976 106 21 190 1,955 149 424 214 41 280 317 1,025 3,400 8,020 2,560
550 3,013 2,384 5,033 1,402 127 6,476 862 39,799 1,266 5,239 1,622 2,832 838 .. 5,347 73 .. 215 848 956 .. .. 174 1,271 99 170 285 13,292 96 30 682 19,667 18 198 4,193 .. 217 670 275 9,595 2,045 472 52 831 3,107 602 498 534 54 250 862 1,933 13,980 11,666 3,087
196 13,596 2,281 688 788 239 1,798 883 .. .. 10,997 1,143 .. 210 .. 1,561 .. .. .. .. .. 254 .. 425 .. .. 34 .. 14,920 .. .. 89 7,357 129 .. 1,202 .. .. .. 82 9,000 717 173 18 56 508 .. .. 151 66 264 329 1,137 22,131 192 996
285 12,966 4,205 .. 2,400 .. 4,634 3,273 25,126 .. 16,523 1,726 2,274 .. .. 7,123 .. .. .. 2,306 .. .. .. .. 3,584 .. .. .. 36,248 .. .. 162 11,948 52 .. 1,533 .. .. .. 200 16,760 1,293 532 .. .. .. .. .. 200 92 141 859 1,968 45,043 .. 1,227
29 824 1,513 2,105 61 55 1,459 1,396 16,458 740 3,578 512 .. 443 .. 3,559 132 2 3 7 .. 17 .. 6 .. 45 40 16 1,667 47 9 244 5,467 .. 5 1,259 .. 9 85 64 4,155 1,030 12 17 25 1,570 69 156 179 41 128 217 1,306 358 3,555 1,366
342 3,273 2,923 4,306 1,122 .. 3,089 1,197 26,915 1,209 3,499 786 621 297 .. 5,277 119 36 113 161 956 20 .. .. 383 23 115 125 6,785 71 .. 612 8,858 47 130 2,152 144 45 404 107 7,706 2,918 116 .. 156 2,738 116 105 679 101 62 801 1,741 4,500 5,919 2,486
2.8 6.8 6.6 7.2 0.3 .. 5.4 8.1 7.5 33.4 1.1 20.4 .. 19.9 .. 4.9 1.6 .. 2.9 0.6 .. 17.0 .. 0.1 .. .. 8.5 3.6 5.1 11.2 1.9 14.2 11.2 .. 1.0 20.2 .. 2.8 7.0 15.2 2.6 8.8 3.1 3.2 0.2 3.3 1.2 2.3 4.0 3.0 5.1 5.3 11.4 1.9 16.5 ..
14.0 7.7 3.8 6.5 3.9 .. 2.7 3.1 8.6 37.4 0.8 18.4 5.3 9.0 .. 2.8 0.7 5.7 21.8 4.2 39.8 5.1 .. .. 6.3 1.6 9.0 26.5 6.3 11.0 .. 20.6 5.1 5.4 18.4 17.6 12.5 1.6 29.6 12.1 2.9 14.9 12.8 .. 0.8 3.5 1.0 0.9 9.0 4.8 2.2 8.7 4.7 7.9 16.1 ..
38 477 393 836 340 .. 1,163 1,442 10,304 114 24,928 336 .. 38 .. 3,166 1,837 .. 1 13 .. 12 .. 424 .. .. 40 16 1,450 62 23 94 5,519 .. 1 184 .. 16 63 45 7,376 958 15 44 576 3,679 47 440 99 50 103 295 111 423 867 630
185 1,722 3,449 3,368 238 .. 3,741 2,547 16,935 258 26,681 416 756 143 .. 7,642 3,021 10 8 230 .. 14 .. .. 218 .. 115 78 2,618 41 .. 204 6,060 86 119 444 296 27 .. 80 12,919 1,480 69 28 700 5,814 367 179 178 .. 65 616 871 3,200 2,274 928
3.4 4.3 1.3 3.0 1.5 .. 4.7 7.1 4.7 4.8 8.4 9.4 .. 1.4 .. 4.1 25.6 .. 0.5 1.3 .. 1.6 .. 4.7 .. .. 4.9 2.9 4.6 7.5 4.4 4.9 10.6 .. 0.1 2.4 .. 2.7 4.0 5.4 5.0 8.2 2.2 6.0 8.3 9.5 1.4 4.3 2.4 3.3 4.7 7.2 0.8 2.8 3.2 ..
5.4 3.9 4.1 6.4 1.1 .. 4.1 6.0 5.6 5.3 6.5 6.7 6.6 3.6 .. 4.2 21.5 1.4 1.4 4.9 .. 1.8 .. .. 3.3 .. 7.8 9.8 2.9 4.4 .. 7.3 3.3 6.7 12.6 3.3 16.6 0.9 .. 4.7 5.3 7.9 3.5 .. 4.9 11.1 5.3 1.4 2.3 .. 2.4 6.2 2.3 5.1 5.0 ..
2004 World Development Indicators
349
GLOBAL LINKS
Travel and tourism
6.14
Travel and tourism International tourism
International tourism receipts
International tourism expenditures
thousands Inbound tourists 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Europe EMU
350
2002
Outbound tourists 1990
2002
$ millions 1990
2002
3,009 3,204 11,247 5,757 106 612 3,009 7,943 4,150 20,343 752 4,188 .. 113 .. .. 10 31 2,209 7,511 .. 7,896 1,884 3,420 246 427 .. .. 167 140 1,186 448 .. .. 419 77 98 28 .. 27 19 12 4,842 6,996 1,237 4,399 4,937 4,932 822 1,399 188 437 70 724 650 1,302 .. 2,055 721 1,083 .. .. .. .. .. .. 1,029 6,550 616 2,794 992 2,728 34,085 51,748 21,878 3,748 18,593 33,609 298 337 297 533 132 253 33 52 219 .. 21 56 263 256 .. .. 30 26 1,900 7,458 8,691 12,300 2,906 4,233 13,200 10,000 9,627 11,427 7,411 7,628 562 1,658 1,041 4,362 320 1,366 .. 4 .. 3 .. 2 153 550 301 .. 65 730 5,299 10,873 883 2,044 4,326 7,902 103 57 .. .. 58 11 195 379 254 .. 95 224 3,204 5,064 1,727 1,669 948 1,422 4,799 12,782 2,917 5,130 3,225 9,010 .. .. .. .. .. .. 69 254 .. 152 10 185 .. 6,326 .. 9,270 .. 2,992 633 5,445 .. .. 169 1,328 18,013 24,180 31,150 59,377 13,762 17,591 39,362 41,892 44,623 56,359 43,007 66,547 1,267 1,258 .. 530 262 318 .. 332 .. 264 .. 68 525 432 309 881 496 468 250 1,599 .. .. 85 .. .. 7 .. .. .. 0 52 76 .. .. 20 38 141 565 .. .. 41 117 605 2,068 352 .. 60 76 448,870 t 692,292 t 380,321 t 692,300 t 264,889 t 472,506 t 10,477 23,235 .. .. 5,892 12,570 115,149 215,705 130,244 273,535 42,455 126,123 55,932 130,729 49,323 98,526 25,307 77,080 58,506 84,172 91,270 .. 17,171 48,887 127,339 242,033 157,717 .. 48,376 138,458 28,191 73,291 21,595 61,131 12,577 43,448 42,782 75,225 124,053 183,289 9,756 36,977 30,253 43,682 16,209 27,174 13,500 28,249 15,665 27,947 16,504 21,501 .. .. 3,054 4,254 3,503 6,964 1,968 3,774 7,217 19,836 .. .. 3,106 7,557 314,680 442,385 223,939 349,895 215,805 336,311 183,210 257,531 .. .. 100,855 160,354
2004 World Development Indicators
% of exports
$ millions
1990
2002
1990
2002
1.7 .. 7.0 4.0 11.5 .. 9.1 7.3 .. 8.5 .. 3.6 22.2 5.8 4.2 4.6 4.1 7.6 6.4 .. 12.1 14.8 8.7 4.2 18.2 15.3 .. 5.6 .. .. 5.8 8.0 12.1 .. 2.6 .. .. 1.3 3.0 3.0 6.1 w 5.0 6.8 7.5 5.9 6.5 7.3 7.4 7.9 4.9 5.7 3.9 6.1 6.6
3.8 103 396 3.5 .. 12,005 23.4 23 24 4.7 .. 7,356 10.9 105 .. 2.4 .. .. .. 4 6 3.1 1,893 5,213 4.2 181 442 8.5 282 614 .. .. .. 7.7 1,117 1,804 17.8 4,254 6,638 4.2 74 253 3.3 51 91 2.4 35 33 4.0 6,286 6,816 5.9 5,873 6,427 16.6 249 610 0.3 .. 2 46.5 23 337 9.6 854 3,303 2.6 40 5 5.0 122 151 14.9 179 260 16.5 520 1,881 .. .. .. 25.7 8 .. 12.8 .. 2,087 .. .. .. 4.3 17,560 40,409 6.8 37,349 58,044 11.7 111 178 2.3 .. .. 1.7 1,023 1,418 .. .. .. .. .. .. 1.0 64 78 11.1 54 44 .. 66 .. 5.9 w 268,743 t 449,218 t 5.5 4,551 10,718 7.4 29,525 79,801 7.7 .. 51,214 6.9 15,183 28,357 7.2 34,183 92,314 6.3 3,947 26,658 8.2 .. 28,911 6.4 12,349 19,019 8.9 3,126 .. 3.6 1,048 4,265 6.7 3,641 5,489 5.5 232,521 357,414 6.8 88,022 141,301
% of imports 1990
2002
1.0 .. 6.5 .. 5.7 .. 1.9 2.9 .. 4.1 .. 5.3 4.2 2.5 5.8 4.6 8.9 6.1 8.4 .. 1.6 2.4 4.7 8.6 3.0 2.0 .. 1.2 .. .. 6.6 6.1 6.7 .. 10.8 .. .. 2.9 2.8 3.3 6.3 w 3.8 5.0 3.1 7.9 4.8 2.4 2.6 9.1 .. 2.1 5.6 6.5 6.0
2.1 14.1 5.5 14.9 .. .. .. 3.8 2.3 4.9 .. 5.6 3.4 3.6 3.1 2.8 7.6 5.8 10.2 0.2 15.2 4.5 0.8 3.8 2.5 3.4 .. .. 9.7 .. 9.3 4.2 7.0 .. 8.1 .. .. 2.0 3.3 .. 5.9 w 4.8 5.6 5.8 5.2 5.5 4.4 7.2 4.1 11.0 3.6 5.4 6.0 6.3
About the data
6.14
Definitions
Tourism is defined as the activities of people traveling
The data in the table are from the World Tourism
• International inbound tourists (overnight visitors)
to and staying in places outside their usual environ-
Organization. The data on international inbound and
are the number of tourists who travel to a country
ment for no more than one consecutive year for
outbound tourists refer to the number of arrivals and
other than that in which they have their usual resi-
leisure, business, and other purposes not related to
departures of visitors within the reference period,
dence, but outside their usual environment, for a peri-
an activity remunerated from within the place visited.
not to the number of people traveling. Thus a person
od not exceeding 12 months and whose main
The social and economic phenomenon of tourism has
who makes several trips to a country during a given
purpose in visiting is other than an activity remuner-
grown substantially over the past quarter of a century.
period is counted each time as a new arrival.
ated from within the country visited. • International
In the past, descriptions of tourism focused on the
International visitors include tourists (overnight visi-
outbound tourists are the number of departures that
characteristics of visitors, such as the purpose of
tors), same-day visitors, cruise passengers, and
people make from their country of usual residence to
their visit and the conditions in which they traveled
crew members.
any other country for any purpose other than a remu-
and stayed. Now, there is a growing awareness of the
Regional and income group aggregates are based
nerated activity in the country visited. • International
direct, indirect, and induced effects of tourism on
on the World Bank’s classification of countries and
tourism receipts are expenditures by international
employment, value added, personal income, govern-
differ from those shown in the World Tourism
inbound visitors, including payments to national carri-
ment income, and the like.
Organization’s publications. Countries not shown in
ers for international transport. These receipts include
Statistical information on tourism is based mainly
the table but for which data are available are includ-
any other prepayment made for goods or services
on data on arrivals and overnight stays along with bal-
ed in the regional and income group totals. World
received in the destination country. They also may
ance of payments information. But these do not com-
totals are no longer calculated by the World Tourism
include receipts from same-day visitors, except in
pletely capture the economic phenomenon of
Organization. The aggregates in the table are calcu-
cases where these are important enough to justify a
tourism. Thus governments, businesses, and citizens
lated using the World Bank’s weighted aggregation
separate classification. Their share in exports is cal-
may not receive the information needed for effective
methodology (see Statistical methods) and differ
culated as a ratio to exports of goods and services
public policies and efficient business operations.
from aggregates provided by the World Tourism
(for definition of exports of goods and services see
Although the World Tourism Organization reports that
Organization.
Definitions for table 4.9). • International tourism
progress has been made in harmonizing definitions
expenditures are expenditures of international out-
and measurement units, differences in national prac-
bound visitors in other countries, including payments
tices still prevent full international comparability. By
to foreign carriers for international transport. These
2005 the World Tourism Organization will improve
expenditures may include those by residents travel-
coverage of tourism expenditure data by adding the
ing abroad as same-day visitors, except in cases
balance of payments category “international passen-
where these are so important as to justify a separate
ger transportation” to “travel.”
classification. Their share in imports is calculated as
Credible data are needed on the scale and signifi-
a ratio to imports of goods and services (for defini-
cance of tourism. Information on the role tourism
tion of imports of goods and services see Definitions
plays in national economies throughout the world is
for table 4.9).
particularly deficient.
6.14a Tourism is highest in high-income countries $ billions, 2002 400
300
Data sources
200
The visitor and expenditure data are available in the World Tourism Organization’s Yearbook of
100
Tourism Statistics and Compendium of Tourism Statistics, 2002. The data in the table were
0 Low income
Middle income
Receipts
High income
Expenditures
Tourism receipts are almost three times larger in highincome economies than in middle-income economies. Expenditures are more than five times the size.
updated from electronic files provided by the World Tourism Organization. The data on exports and imports are from the International Monetary Fund’s International Financial Statistics and World Bank staff estimates.
Source: World Tourism Organization.
2004 World Development Indicators
351
GLOBAL LINKS
Travel and tourism
PRIMARY DATA DOCUMENTATION The World Bank is not a primary data collection agency for most issues other than living standards surveys and debt. As a major user of socioeconomic data, however, the World Bank places particular emphasis on data documentation to inform users of data in economic analysis and policymaking. The tables in this section provide information on the sources, treatment, and currentness of the principal demographic, economic, and environmental indicators in World Development Indicators. Differences in the methods and conventions used by the primary data collectors—usually national statistical agencies, central banks, and customs services—may give rise to significant discrepancies over time both among and within countries. Delays in reporting data and the use of old surveys as the base for current estimates may severely compromise the quality of national data. Although data quality is improving in some countries, many developing countries lack the resources to train and maintain the skilled staff and obtain the equipment needed to measure and report demographic, economic, and environmental trends in an accurate and timely way. The World Bank recognizes the need for reliable data to measure living standards, track and evaluate economic trends, and plan and monitor development projects. Thus, working with bilateral and other multilateral agencies, it continues to fund and participate in technical assistance projects to improve statistical organization and basic data methods, collection, and dissemination. The World Bank is working at several levels to meet the challenge of improving the quality of the data that it collates and disseminates. With a view to strengthening national capacity the Bank conducts technical assistance, training, and surveys at the country level in the following areas: •
Poverty assessments in most borrower member countries.
•
Living standards measurement and other household and farm surveys with national statistical agency partners.
•
National accounts and inflation.
•
Price and expenditure surveys for the International Comparison Program.
•
Projects to improve statistics in the countries of the former Soviet Union.
•
External debt management.
•
Environmental and economic accounting.
2004 World Development Indicators
353
PRIMARY DATA DOCUMENTATION National currency
Fiscal year end
National accounts
Balance of payments and trade
Balance of
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
354
Afghan afghani Albanian lek Algerian dinar Angolan kwanza Argentine peso Armenian dram Australian dollar Euro Azeri manat Bangladesh taka Belarussian rubel Euro CFA franc Boliviano Convertible mark Botswana pula Brazilian real Bulgarian lev CFA franc Burundi franc Cambodian riel CFA franc Canadian dollar CFA franc CFA franc Chilean peso Chinese yuan Hong Kong dollar Colombian peso Congo franc CFA franc Costa Rican colon CFA franc Croatian kuna Cuban peso Czech koruna Danish krone Dominican peso U.S. dollar Egyptian pound Salvadoran colone Eritrean nakfa Estonian kroon Ethiopian birr Euro Euro CFA franc Gambian dalasi Georgian lari Euro Ghanaian cedi Euro Guatemalan quetzal Guinean franc CFA franc Haitian gourde
2004 World Development Indicators
Mar. 20 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Mar. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Jul. 7 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Sep. 30
Alternative
PPP
Payments
Government IMF finance data dissemination standard
Reporting
Base
SNA price
conversion
survey
Manual
External
System
Accounting
period
year
valuation
factor a
year
in use
debt
of trade
concept
1996
BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
FY CY CY CY CY CY FY CY CY FY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY FY CY CY CY FY CY CY CY CY CY CY CY CY CY CY CY FY
1975 1990 b 1980 1997 1993 1996 b, c 1995 b, c 1995 b 2000 b, c 1996 b 1990 b, c 1995 b 1985 1990 b 1996 c 1994 1995 2002 b, c 1990 1980 2000 1990 1995 b 1987 1995 1986 1990 2000 1994 1987 1978 1991 b 1996 1997 b 1984 1995 b 1995 b 1990 2000 1992 1990 1992 2000 b 1981 1995 b 1995 b, c 1991 1987 1996 b 1995 b 1975 1995 b, c 1958 1994 1986 1976
VAB VAB VAB VAP 1991–96 VAB 1971–84 VAB 1990–95 VAB VAB VAB 1992–95 VAB 1960–2002 VAB 1990–95 VAB VAP 1992 VAB 1960–85 VAB VAP VAB VAB 1978–89, 1991–92 VAP 1992–93 VAB VAB VAB 1965–2001 VAB VAB VAB VAB VAP 1978–93 VAB VAB 1992–94 VAP 1999–2001 VAP 1999–2001 VAB VAP VAB VAP VAB VAB VAP VAP VAB 1965–91 VAP 1982–90 VAB VAB 1991–95 VAB 1965–2002 VAB VAB VAP 1993 VAB VAB 1990–95 VAB VAP 1973–87 VAB VAP VAB VAB 1970–86 VAB 1991
1996 2000 2000 2000 2000 1996 2000 2000 1996 1996 1996 1996 2000
1996 2000
1996 1986 1996
1996 1996 2000 2000 2000 1996 1996
2000 2000 2000 1996 2000 2000 2000 1996
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual Estimate Preliminary Actual
G S S S S G S G G G S S S
C B
G S G G S G S G Estimate S Preliminary S Actual S Estimate S G Actual S Preliminary S Estimate S Actual S Estimate S Actual G G Preliminary G G Actual G Estimate S Actual S Actual S Actual Actual G Preliminary G G S Preliminary S Actual G Actual G S Actual G Estimate S Actual S Estimate S Estimate G Preliminary G
B C C C C
Actual Actual Actual Actual Actual Actual Actual Preliminary Actual Actual Preliminary Preliminary Preliminary
C C C C C C C
C C C C B B C C C C C C C C B C B C B C C B B C C B C B C
G G S* S* S* S* G G S* G G G S* S* G G G S* G S* G S* S* G S* G S* S* S* S* S* S* G S* S* G G S* S* G G
PRIMARY DATA DOCUMENTATION Latest population census (including registrationbased censuses)
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia, Rep. Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
1989 1998 1970 2001 2001 2001 2001 1999 2001 1999 2001 2002 2001 1991 2001 2000 2001 1996 1990 1998 1987 2001 1988 1993 2002 2000 2001 1993 1984 1996 2000 1998 2001 2002 2001 2001 2002 2001 1996 1992 1984 2000 1994 2000 1999 1993 2003 2002 1995 2000 2001 2002 1996 1991 2003
Latest demographic, education, or health household survey
MICS, MICS, MICS, MICS,
2000 2000 2000 2000
DHS, 2000
MICS, 2000 Special, 2001
Source of most recent income and expenditure data
Vital registration complete
LSMS, 2002 HLSS, 1995 Priority survey, 1995 EPH, 2002 LSMS, 1996
HBS, 2001 IES, 2000
DHS, 2001 DHS, 2003 MICS, 2000 MICS, 2000 DHS, 1996 DHS, 2003 MICS, 2000 DHS, 2000 DHS, 2004
EH, 2002 LSMS, 2001 HIES, 1993–94 PNAD, 2001 LSMS, 2001 Priority survey, 1998 Priority survey, 1998 SES, 1997 Priority survey, 2001
Yes
Yes Yes Yes Yes Yes Yes Yes
Population, 1995
1990 1996
1990 1990
1992 2000
1976 1994 1990 1992–93
1997
1993 1996
ECV, 2003
CDC, 1993 MICS, 2000
EHPM, 2000 LSMS, 1998 HBS, 2001
MICS, 2000 CDC, 1993
Microcensus 1997
DHS, 2002 CDC, 1999 SPA, 2002 CDC, 1994 DHS, 2002
ENFT, 1998 LSMS, 1998 HECS, 1999 EHPM, 2000
DHS, 2000
ICES, 2000
Yes
Yes Yes Yes Yes Yes
Yes Yes
Yes Yes HHS, 1998
SPA, 2002; DHS, 2003
LSMS, 1998/99
DHS, 1998–99 DHS, 1999 MICS, 2000 DHS, 2000
Yes Yes Yes LSMS, 2000; ENCOVI, 2000 Yes LSMS, 1994 IES, 1993
1987 1995 1995 1987 1995 1994 1985 1991 1995 1990 1990 .. 1994 1987 1995 1992 1992 1988 1992 1987 1987 1987 1991 1987 1987 1987 1993
1972–73 1991
1999 2000 1993
2002 2002 1996
1997 1996
2000 2000 2000 1999
2002 2002
1988 2000 1997 1992 1989
1995 2002 2002 2002 2001 2002 2002 2001 2002 2002 2002
1996 1990 1987 1997 1987 1996 1995 1991 1990 1994 1997 1996 1992
2002 2002 2002 2002 2000 2000 2001 2002 2001 2001 2002 2001 1995 1997
1995 1987 1991 1999 1987 1982 1990 1991 1997 1980 1992 1987 1991 1991
1993
Yes
DHS, 2000 MICS, 2000 MICS, 2000
1997 1981 1997 1991 1995 1995 1996
2002 2000 1991 2002 2002 2002 2002 2002 2001 2002 2001 2001 2002
Latest water withdrawal data
1991
Yes
Yes DHS, 2000 MICS, 2000
1996
Latest trade data
2001 2002 2001 2001 2002
EPI, 1993 CASEN, 2000 HHS (Rural/Urban), 1998
Latest industrial data
1995 1973 1964–65 1988
Yes
Yes MICS, 2000 MICS, 2000
Latest agricultural census
1988 1990 1986 1973 1974–75
.. 1989 1971 1997 1989–90 1970–71 1994 1988–89 1990 1988 1974–75
1993 1984 1993 1979 1996 1988 1971
1991 1984 1999 1996 1998
1996 1999 1995 1995 1993
1995 2000 1988
1996
2002
2004 World Development Indicators
355
PRIMARY DATA DOCUMENTATION National currency
Fiscal year end
National accounts
Balance of payments and trade
Balance of
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Libya Liberia 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
356
Honduran lempira Hungarian forint Indian rupee Indonesian rupiah Iranian rial Iraqi dinar Euro Israeli new shekel Euro Jamaica dollar Japanese yen Jordan dinar Kazakh tenge Kenya shilling Democratic Republic of Korea won Korean won Kuwaiti dinar Kyrgyz som Lao kip Latvian lat Lebanese pound Lesotho loti Libyan dinar Liberian dollar Lithuanian litas Macedonian denar Malagasy franc Malawi kwacha Malaysian ringgit CFA franc Mauritanian ouguiya Mauritian rupee Mexican new peso Moldovan leu Mongolian tugrik Moroccan dirham Mozambican metical Myanmar kyat Namibia dollar Nepalese rupee Euro New Zealand dollar Nicaraguan gold cordoba CFA franc Nigerian naira Norwegian krone Rial Omani Pakistan rupee Panamanian balboa Papua New Guinea kina Paraguayan guarani Peruvian new sol Philippine peso Polish zloty Euro U.S. dollar
2004 World Development Indicators
Dec. 31 Dec. 31 Mar. 31 Mar. 31 Mar. 20 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Mar. 31 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Mar. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Mar. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Mar. 31 Mar. 31 Jul. 14 Dec. 31 Mar. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30
Alternative
PPP
Payments
Government IMF finance data dissemination standard
Reporting
Base
SNA price
conversion
survey
Manual
External
System
Accounting
period
year
valuation
factor a
year
in use
debt
of trade
concept
VAB VAB VAB VAP VAB VAB VAB VAP VAB VAP VAB VAB VAB VAB .. VAP VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAP VAP VAB VAP VAB VAB VAB VAB VAP VAP VAB VAB VAP VAB VAP VAP VAP VAB VAP VAB VAB VAP
1988–89
CY CY FY CY FY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY FY CY CY CY CY CY FY CY FY CY FY CY CY CY CY CY FY CY CY CY CY CY CY CY FY
1978 2000 b 1993 1993 1982 1969 1995 b 2000 b 1995 b 1996 1995 1994 1995 b, c 1982 .. 1995 b 1984 1995 b, c 1990 2000 b 1994 1995 1975 1992 2000 b 1997 b 1984 1994 1987 1987 1985 1998 1993 b 1996 1998 1980 1995 1985 1995 1985 1995 b, c 1995 1998 1987 1987 1995 b, c 1978 1981 1982 c 1983 1982 1994 1985 2002 b, c 1995 b 1954
2000 1960–2002 1980–90
1987–95
1996 1996 2000 2000 2000 1996 2000 1996 2000 1996 2000
1990–95 1991–95
2000 1993 1996
1986 1990–95
1987–95
2000 2000 1996 1996 1993 1996 1996 2000 2000 2000 1996
1992–95
1966–2002
1965–93 1993 1971–98
1972–2002 1989 1982–88 1985–91
1996 2000 2000
1996 2000 1996 1996 1996
1996 1996 2000 2000
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual Actual Preliminary Actual
Actual Actual Actual Actual Actual
S S G S G S G S S G G G G G
S S Actual G Preliminary G Actual S Actual G Actual G G Estimate Actual G Actual G Preliminary S Estimate G Estimate G Actual G Actual G Actual G Actual G Actual G Actual S Preliminary S Estimate S Estimate G Estimate Actual S S G Actual S Preliminary S Estimate G G Actual G Preliminary G Actual S Actual G Actual S Actual S Actual G Actual S S G
C C C C
S* S* S*
C C C C C B C B
S* S* S* G S* G S* G
C C B
S* G G
C C C
S* G G
C
S* G
C B C
C C C C C
G S* G G S* G G
C B C C B C
C B C C B C C B C C
G G S*
G G S* G G G G S* S* S* S*
PRIMARY DATA DOCUMENTATION Latest population census (including registrationbased censuses)
Honduras Hungary India Indonesia Iran, Islamic Republic Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kuwait Kyrgyz Rep. Lao PDR Latvia, Rep. Lebanon Lesotho Libya Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico
2001 2001 2001 2000 1996 1997 2002 1995 2001 2001 2000 1994 1999 1999 1993 2000 1995 1999 1995 2000 1970 2001 1995 2001 2002 1993 1998 2000 1998 2000 2000 2000 1989 2000 1994 1997 1983 2001 2001 2002 2001 1995 2001 1991 2001 2003 1998 2000 2000 2002 1993 2000 2002 2001 2000
Latest demographic, education, or health household survey
CDC, 1994 Benchmark, 1998–2002 DHS, 2002; Special, 2002 Demographic, 1995 MICS, 2000
Source of most recent income and expenditure data
EPHPM, 1999 FBS, 1998 LSMS, 1997–98 d SUSENAS, 2002 SECH, 1998
CDC, 1997; MICS, 2000
LSMS, 2000
DHS, 2002 DHS, 1999 DHS, 2003 MICS, 2000
HIES, 1997 HBS, 2001 WMS II, 1997
FHS, 1996 DHS, 1997 MICS, 2000
HBS, 2001 ECS I, 1997 HBS, 1998
Vital registration complete
Yes
Yes Yes Yes Yes Yes
MICS, 2000; DHS, 2003 EdData, 2002 DHS, 2001 Special, 2003 CDC, 1991 Population, 1995 MICS, 2000 MICS, 2000 DHS, 2003 Interim, 2003 MICS, 2000 DHS, 2000 DHS, 2001
Priority survey, 2001 HHS, 1997 HIBAS, 1997 EMCES, 1994 LSMS, 2000
Yes Yes Yes
Yes Yes
Yes ENIGH, 2002 Yes HBS, 2000 Yes LSMS/Integrated Survey, 1998 LSMS, 1998/99 NHS, 1996/97 NHIES, 1993 LSMS, 1996
LSMS, 2001 LSMS, 1995 NCS, 1996
PIHS, 1998/99 EH, 2000 HGS, 1997 EPH, 2003 ENAHO, 2003 FIES, 2000 HBS, 1998
1996 2000 2000 2000 1993 1992 1999 1996 2000 1996 2000 1997
1981
2000
1991 1970
2000 1999
2002 1999 2002
1996
2002 2001
1999 1994 1999 1989–90 1987 1994 1994 1984 1992–93
Yes Yes Yes
Latest trade data
2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2001 2002
1985 1998
1996 1988 1998 1999
1978 1985
Yes FHS, 1995 RHS, 2000–01 LSMS, 1997 DHS, 1996 CDC, 1998 DHS, 2000 DHS, 2003
1993 1994 1986 1993 1988 1981 1991 1983 1990 1979 1990 1997
Yes
Yes Yes DHS, 2001 MICS, 2000 DHS, 2003
Latest industrial data
Yes
MICS, 2000 MICS, 2000 MICS, 2000 LSMS, 2000
Latest agricultural census
1991
1997 2000
1997
1995 2000
1993 1995 1992 1989 1990 1963 1980 1960 1989 1979 1990 1990
1998 1996 1999 2000 1996 1999
1991 1994 1991 1990 1989 1987
1991 1996 1997 2000 1995 2000
1994 1996 2000 1996
2002 2001 1999 2001 2002 1997 1996 2002 2002 2002 2002 2002 2001 1992 2001 2000 2002 2002 2002 2001 2000 2002 2002 2002 2002 2000 2002 2002 2002 2002 2002
2004 World Development Indicators
Latest water withdrawal data
1992 1991 1990 1990 1993 1990 1980 1997 1998 1993 1992 1993 1993 1990 1987 1994 1994 1994 1987 1994 1996 1987 1999 1987 1995 1996 1984 1994 1995 1987 1985 .. 1998 1992 1993 1998 1992 1987 1991 1994 1991 1991 1998 1988 1987 1985 1991 1991 1990 1987 1987 1992 1995 1991 1990
357
PRIMARY DATA DOCUMENTATION National currency
Fiscal year end
National accounts
Balance of payments and trade
Balance of
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, R.B. Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Romanian leu Russian ruble Rwanda franc Saudi Arabian riyal CFA franc Yugoslav new dinar Sierra Leonean leone Singapore dollar Slovak koruna Slovenian tolar Somali shilling South African rand Euro Sri Lankan rupee Sudanese dinar Lilangeni Swedish krona Swiss franc Syrian pound Tajik somoni Tanzania shilling Thai baht CFA franc Trinidad and Tobago dollar Tunisian dinar Turkish lira Turkmen manat Uganda shilling Ukrainian hryvnia U.A.E. dirham Pound sterling U.S. dollar Uruguayan peso Uzbek sum Venezuelan bolivar Vietnamese dong Israeli new shekel Yemen rial Zambian kwacha Zimbabwe dollar
Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Mar. 31 Dec. 31 Dec. 31 Dec. 31 Mar. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Sep. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30 Dec. 31 Dec. 31 Dec. 31 Sep. 30 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Dec. 31 Jun. 30
Alternative
PPP
Payments
Government IMF finance data dissemination standard
Reporting
Base
SNA price
conversion
survey
Manual
External
System
Accounting
period
year
valuation
factor a
year
in use
debt
of trade
concept
CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY FY CY CY CY CY CY CY CY CY CY CY CY FY CY CY CY CY CY CY CY CY CY CY CY CY
1998 c 2000 b, c 1995 1999 1987 1994 1990 1995 1995 b 2000 b 1985 1995 1995 b 1996 1982 1985 1995 c 1995 1995 1985 b 1992 1988 1978 1985 1990 1987 1987 b 1998 1990 b, c 1985 1995 b 1995 c 1983 1997 c 1984 1994 1998 1990 1994 1990
VAB VAB VAP VAP VAP VAP VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAP VAP VAP VAP VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAP VAB VAP VAB VAB
1987–89, 1992 2000 1987–95 2000
1996 1971–79, 1987 1996 1996 2000 2000 1977–90
BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Estimate Estimate Estimate Preliminary Preliminary Actual
S G G G S
C C C
G
B
G
G G G S
B C C C
G S* S* S*
C C B B B C C C C
S* S* G G G S* S*
2000 2000 1996 1990–95 2000 1996 1991 1996
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4
Actual Actual Estimate Preliminary S S Actual G Estimate G Actual G Estimate S Estimate S Actual G Estimate S Preliminary G Preliminary S Preliminary S Actual G Actual S G Actual G Actual G G G G Actual S Actual G Actual G Preliminary G
1991–96 1996 1990–92 1996 1991, 1998 1996
BPM5 BPM5 BPM5
Actual G Preliminary G Preliminary G
2000 1996 1970–95 2000 2000 1970–2002 1996 1990–95 2000 1996 1996 1996 1996 2000 1987–95 2000 1980–99 1990–95 2000
BPM5 BPM5 BPM5 BPM5
C C C C
2004 World Development Indicators
G S* G S* S*
B C B C C C
G S*
C B
G G
B B C
G G G
Note: For explanation of the abbreviations used in the table see the notes. a. World Bank estimates including adjustments for fiscal year reporting. b. Country uses the 1993 System of National Accounts methodology. c. Original chained constant price data are rescaled. d. For Uttar Pradesh and Bihar.
358
G
S* S* S*
PRIMARY DATA DOCUMENTATION Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, R.B. Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Latest population census (including registrationbased censuses)
Latest demographic, education, or health household survey
Source of most recent income and expenditure data
2002 2002 2002 1992 2002 2002 1985 2000 2001 2002 1987 2001 2001 2001 1993 1997 1990 2000 1994 2000 2002 2000 1981 2000 1994 2000 1995 2002 2001 1995 2001 2000 1996 1989 2001 1999 1997 1994 2000 2002
CDC, 1999 LSMS, 1992 SPA, 2001 Demographic, 1999 DHS, 2004 MICS, 2000 MICS, 2000 General household, 1995
LSMS, 2000 LMS, Round 9, 2000 LSMS, 1998
DHS, 1993 MICS, 2000 MICS, 2000
MICS, 2000 MICS, 2000 AIS, 2003 DHS, 1987 MICS, 2000 MICS, 2000 MICS, 2000 DHS, 1998 DHS, 2000 AIS, 2004 MICS, 2000
Latest agricultural census
1994–95 1984 1983 1960
Latest industrial data
1993 2000 1986
Latest trade data
1997
1995 2002 2002 2002 2002
2002 2002 2002
Latest water withdrawal data
1994 1994 1993 1992 1987
Yes SHEHEA, 1989–90 Yes Yes Yes
1985
1991
1993 2000 1994 1998
Yes Yes
1989 1982
2000 2000 1999
Yes Yes
1981 1990 1981 1994 1995 1993 1996 1982 1961 1991
IES, 1994/95 SES, 1995/96
1998 1993 1997 1990
1981 1995 1995 1997
2002 2002 2002 2002 2002 2002 2002 2002 2000 2001 2001 2002 2002 2002 2002 2000 2002 2002 2001 2002 2002 2002
1997–98 1994 1971 1982–85 1990 1960
1999
2002
1994 1996
1998 2002 2002
SHIES, 1994
LSMS, 1999 LSMS, 1993 SES, 2002
Yes
LSMS, 1992
Yes
LSMS, 2000 LSMS, 1998 NIHS II, 1999 HIES, 1999
Current population, 1997 Special, 2002 MICS, 2000 MICS, 2000; DHS 2002 Demographic, 1995 DHS, 1997 EdData, 2002 DHS, 1999
Yes Yes
ESASM, 1994
Microcensus, 1996 MICS, 2000 DHS, 1998
Vital registration complete
ECH, 2000 FBS, 2000 EHM, 2000 LSMS, 1997/98 HBS, 1998 LCMS II, 1998 ICES, 1995
1995 2000 1997 1998 1999 1994 1995 2000 2000
Yes 1991 Yes Yes Yes Yes Yes Yes
2004 World Development Indicators
1987 1975 1991 1996 1987 1990 1997 1990 1995 .. 1991 1991 1995 1994 1994 1990 1987 1997 1996 1997 1994 1970 1992 1995 1991 1990 1965 1994 1970 1990 1990 1994 1987
359
Primary data documentation notes
• Fiscal year end is the date of the end of the fiscal
Monetary Fund’s (IMF) Balance of Payments Manual
guide member countries in disseminating compre-
year for the central government. Fiscal years for
(1977), and BPM5 to the fifth edition (1993). Since
hensive, timely, accessible, and reliable economic,
other levels of government and the reporting years
1995 the IMF has adjusted all balance of payments
financial, and socio-demographic statistics. IMF
for statistical surveys may differ, but if a country is
data to BPM5 conventions, but some countries con-
member countries voluntarily elect to participate in
designated as a fiscal year reporter in the following
tinue to report using the older system. • External
either the SDDS or the GDDS. Both the GDDS and the
column, the date shown is the end of its national
debt shows debt reporting status for 2000 data.
SDDS are expected to enhance the availability of
accounts reporting period. • Reporting period for
Actual indicates that data are as reported, prelimi-
timely and comprehensive data and therefore con-
national accounts and balance of payments data is
nary that data are preliminary and include an element
tribute to the pursuit of sound macroeconomic poli-
designated as either calendar year (CY) or fiscal year
of staff estimation, and estimate that data are staff
cies; the SDDS is also expected to improve the
(FY). Most economies report their national accounts
estimates. • System of trade refers to the general
functioning of financial markets. • Latest population
and balance of payments data using calendar years,
trade system (G) or the special trade system (S). For
census shows the most recent year in which a census
but some use fiscal years, which straddle two calen-
imports under the general trade system both goods
was conducted and for which at least preliminary
dar years. In World Development Indicators fiscal
entering directly for domestic consumption and goods
results have been released. • Latest demographic,
year data are assigned to the calendar year that con-
entered into customs storage are recorded as
education, or health household survey gives infor-
tains the larger share of the fiscal year. If a country’s
imports at the time of their first arrival; under the spe-
mation on the household surveys used in compiling
fiscal year ends before June 30, the data are shown
cial trade system goods are recorded as imports
demographic, education, and health data presented
in the first year of the fiscal period; if the fiscal year
when declared for domestic consumption whether at
in section 2. CDC is Centers for Disease Control and
ends on or after June 30, the data are shown in the
time of entry or on withdrawal from customs storage.
Prevention, DHS is Demographic and Health Survey,
second year of the period. Balance of payments data
Exports under the general system comprise outward-
FHS is Family Health Sur vey, LSMS is Living
are shown by calendar year and so are not compara-
moving goods: (a) national goods wholly or partly pro-
Standards Measurement Study, MICS is Multiple
ble to national accounts data for countries that
duced in the country; (b) foreign goods, neither
Indicator Cluster Sur vey, RHS is Reproductive
report their national accounts on a fiscal year basis.
transformed nor declared for domestic consumption
Health Sur vey, and SPA is Ser vice Provision
• Base year is the year used as the base period for
in the country, that move outward from customs stor-
Assessment. • Source of most recent income and
constant price calculations in the country’s national
age; and (c) nationalized goods that have been
expenditure data shows household surveys that col-
accounts. Price indexes derived from national
declared from domestic consumption and move out-
lect income and expenditure data. HBS is Household
accounts aggregates, such as the GDP deflator,
ward without having been transformed. Under the
Budget Survey; IECS is Income, Expenditure, and
express the price level relative to prices in the base
special system of trade, exports comprise categories
Consumption Survey; IES is Income and Expenditure
year. Constant price data repor ted in World
(a) and (c). In some compilations categories (b) and
Survey; LSMS is Living Standards Measurement
Development Indicators are rescaled to a common
(c) are classified as re-exports. Direct transit trade,
Study; and SES is Socioeconomic Survey. • Vital
1995 reference year. See About the data for table
consisting of goods entering or leaving for transport
registration complete identifies countries judged to
4.1 for further discussion. • SNA price valuation
purposes only, is excluded from both import and
have complete registries of vital (birth and death) sta-
shows whether value added in the national accounts
export statistics. See About the data for tables 4.5
tistics by the United Nations Department of Economic
is reported at basic prices (VAB) or at producer
and 4.6 for further discussion. • Government finance
and Social Information and Policy Analysis, Statistical
prices (VAP). Producer prices include the value of
accounting concept describes the accounting basis
Division, and repor ted in Population and Vital
taxes paid by producers and thus tend to overstate
for reporting central government financial data. For
Statistics Reports. Countries with complete vital sta-
value added in production. See About the data for
most countries government finance data have been
tistics registries may have more accurate and more
tables 4.1 and 4.2 for further discussion of national
consolidated (C) into one set of accounts capturing all
timely demographic indicators. • Latest agricultural
accounts valuation. • Alternative conversion factor
the central government’s fiscal activities. Budgetary
census shows the most recent year in which an agri-
identifies the countries and years for which a World
central government accounts (B) exclude central gov-
cultural census was conducted and reported to the
Bank–estimated conversion factor has been used in
ernment units. See About the data for tables 4.11,
Food and Agriculture Organization. • Latest industri-
place of the official exchange rate (line rf in the
4.12, and 4.13 for further details. • IMF data dis-
al data refer to the most recent year for which manu-
IMF’s International Financial Statistics). Estimates
semination standard shows the countries that sub-
facturing value added data at the three-digit level of
also include adjustments to correspond to the fiscal
scribe to the IMF’s Special Data Dissemination
the International Standard Industrial Classification
years in which national accounts data have been
Standard (SDDS) or the General Data Dissemination
(revision 2 or revision 3) are available in the United
reported. See Statistical methods for further discus-
System (GDDS). S refers to countries that subscribe
Nations Industrial Development Organization data-
sion of the use of alternative conversion factors.
to the SDDS; S* indicates subscribers that have
base. • Latest trade data shows the most recent
• PPP survey year refers to the latest available sur-
posted data on the Dissemination Standards Bulletin
year for which structure of merchandise trade data
vey year for the International Comparison Program’s
Board Web site [dsbb.imf.org]; while G refers to coun-
are available in the United Nations Statistical
estimates of purchasing power parities (PPPs).
tries that subscribe to the GDDS. The SDDS was
Division’s Commodity Trade (COMTRADE) database.
• Balance of Payments Manual in use refers to the
established by the IMF for member countries that
• Latest water withdrawal data refer to the most
classification system used for compiling and report-
have or that might seek access to international capi-
recent year for which data have been compiled from a
ing data on balance of payments items in table 4.15.
tal markets, to guide them in providing their econom-
variety of sources. See About the data for table 3.5
BPM4 refers to the fourth edition of the International
ic and financial data to the public. The GDDS helps
for more information.
360
2004 World Development Indicators
ACRONYMS AND ABBREVIATIONS Technical terms AIDS BOD CFC c.i.f. COMTRADE CO2 cu. m DHS DMTU DOTS DPT DRS ESAF f.o.b. GDP GEMS GIS GNI ha HIPC HIV ICD ICSE ICT IP ISCED ISIC ISP kg km kwh LIBOR LSMS M0 M1 M2 M3 MICS mmbtu mt MUV NEAP NGO NO2 ODA PC PM10 PPI PPP PRGF R&D SDR SITC SNA SOPEMI SO2 sq. km STD TB TEU TFP ton-km TSP TU
acquired immunodeficiency syndrome biochemical oxygen demand chlorofluorocarbon cost, insurance, and freight United Nations Statistics Division’s Commodity Trade database carbon dioxide cubic meter Demographic and Health Survey dry metric ton unit directly observed treatment, short-course (strategy) diphtheria, pertussis, and tetanus World Bank’s Debtor Reporting System Enhanced Structural Adjustment Facility free on board gross domestic product Global Environment Monitoring System geographic information system gross national income (formerly referred to as gross national product) hectare heavily indebted poor country human immunodeficiency virus International Classification of Diseases International Classification of Status in Employment information and communications technology Internet Protocol International Standard Classification of Education International Standard Industrial Classification Internet service provider kilogram kilometer kilowatt-hour London interbank offered rate Living Standards Measurement Study currency and coins (monetary base) narrow money (currency and demand deposits) money plus quasi money broad money or liquid liabilities Multiple Indicator Cluster Survey millions of British thermal units metric ton manufactures unit value national environmental action plan nongovernmental organization nitrogen dioxide official development assistance personal computer particulate matter smaller than 10 microns private participation in infrastructure purchasing power parity Poverty Reduction and Growth Facility research and development special drawing right Standard International Trade Classification System of National Accounts Continuous Reporting System on Migration sulfur dioxide square kilometer sexually transmitted disease tuberculosis twenty-foot equivalent unit total factor productivity metric ton-kilometer total suspended particulates traffic unit
Organizations ADB AfDB APEC CDC CDIAC CEC DAC EBRD EDF EFTA EIB EMU EU Eurostat FAO G-5 G-7 G-8 GEF IBRD ICAO ICP ICSID IDA IDB IDC IEA IFC ILO IMF IRF ITU IUCN MIGA NAFTA NATO NSF OECD PAHO PARIS21 S&P UIP UIS UN UNAIDS UNCED UNCHS UNCTAD UNDP UNECE UNEP UNESCO UNFPA UNHCR UNICEF UNIDO UNRISD UNSD USAID WCMC WFP WHO WIPO WITSA WTO WWF
Asian Development Bank African Development Bank Asia Pacific Economic Cooperation Centers for Disease Control and Prevention Carbon Dioxide Information Analysis Center Commission of the European Communities Development Assistance Committee of the OECD European Bank for Reconstruction and Development European Development Fund European Free Trade Area European Investment Bank European Monetary Union European Union Statistical Office of the European Communities Food and Agriculture Organization France, Germany, Japan, United Kingdom, and United States G-5 plus Canada and Italy G-7 plus Russian Federation Global Environment Facility International Bank for Reconstruction and Development International Civil Aviation Organization International Comparison Programme International Centre for Settlement of Investment Disputes International Development Association Inter-American Development Bank International Data Corporation International Energy Agency International Finance Corporation International Labour Organization International Monetary Fund International Road Federation International Telecommunication Union World Conservation Union Multilateral Investment Guarantee Agency North American Free Trade Agreement North Atlantic Treaty Organization National Science Foundation Organisation for Economic Co-operation and Development Pan American Health Organization Partnership in Statistics for Development in the 21st Century Standard & Poor’s Urban Indicators Programme UNESCO Institute for Statistics United Nations Joint United Nations Programme on HIV/AIDS United Nations Conference on Environment and Development United Nations Centre for Human Settlements (Habitat) United Nations Conference on Trade and Development United Nations Development Programme United Nations Economic Commission for Europe United Nations Environment Programme United Nations Educational, Scientific, and Cultural Organization United Nations Population Fund United Nations High Commissioner for Refugees United Nations Children’s Fund United Nations Industrial Development Organization United Nations Research Institute for Social Development United Nations Statistics Division U.S. Agency for International Development World Conservation Monitoring Centre World Food Programme World Health Organization World Intellectual Property Organization World Information Technology and Services Alliance World Trade Organization World Wildlife Fund 2004 World Development Indicators
361
STATISTICAL METHODS This section describes some of the statistical procedures used in preparing the
•
Aggregates of ratios are denoted by a w when calculated as weighted averages of
World Development Indicators. It covers the methods employed for calculating
the ratios (using the value of the denominator or, in some cases, another indica-
regional and income group aggregates and for calculating growth rates, and it
tor as a weight) and denoted by a u when calculated as unweighted averages. The
describes the World Bank’s Atlas method for deriving the conversion factor used
aggregate ratios are based on available data, including data for economies not
to estimate gross national income (GNI) and GNI per capita in U.S. dollars. Other
shown in the main tables. Missing values are assumed to have the same average
statistical procedures and calculations are described in the About the data sec-
value as the available data. No aggregate is calculated if missing data account for
tions following each table.
more than a third of the value of weights in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing
Aggregation rules Aggregates based on the World Bank’s regional and income classifications of
values for missing data according to the above rules for computing totals. •
Aggregate growth rates are denoted by a w when calculated as a weighted aver-
economies appear at the end of most tables. The countries included in these
age of growth rates. In a few cases growth rates may be computed from time
classifications are shown on the flaps on the front and back covers of the book.
series of group totals. Growth rates are not calculated if more than half the
Most tables also include aggregates for the member countries of the European
observations in a period are missing. For further discussion of methods of computing growth rates see below.
Monetary Union (EMU). Members of the EMU on 1 January 2004 were Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the
•
Aggregates denoted by an m are medians of the values shown in the table. No
Netherlands, Portugal, and Spain. Other classifications, such as the European
value is shown if more than half the observations for countries with a popula-
Union and regional trade blocs, are documented in About the data for the tables
tion of more than 1 million are missing.
in which they appear.
Exceptions to the rules occur throughout the book. Depending on the judgment
Because of missing data, aggregates for groups of economies should be treat-
of World Bank analysts, the aggregates may be based on as little as 50 percent of
ed as approximations of unknown totals or average values. Regional and income
the available data. In other cases, where missing or excluded values are judged to
group aggregates are based on the largest available set of data, including values
be small or irrelevant, aggregates are based only on the data shown in the tables.
for the 152 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules are intended to yield estimates for
Growth rates
a consistent set of economies from one period to the next and for all indicators.
Growth rates are calculated as annual averages and represented as percentages.
Small differences between sums of subgroup aggregates and overall totals and
Except where noted, growth rates of values are computed from constant price
averages may occur because of the approximations used. In addition, compilation
series. Three principal methods are used to calculate growth rates: least squares,
errors and data reporting practices may cause discrepancies in theoretically iden-
exponential endpoint, and geometric endpoint. Rates of change from one period
tical aggregates such as world exports and world imports.
to the next are calculated as proportional changes from the earlier period.
Five methods of aggregation are used in the World Development Indicators: •
For group and world totals denoted in the tables by a t, missing data are
Least-squares growth rate. Least-squares growth rates are used wherever
imputed based on the relationship of the sum of available data to the total
there is a sufficiently long time series to permit a reliable calculation. No growth
in the year of the previous estimate. The imputation process works forward
rate is calculated if more than half the observations in a period are missing. The
and backward from 1995. Missing values in 1995 are imputed using one of
least-squares growth rate, r, is estimated by fitting a linear regression trend line
several proxy variables for which complete data are available in that year. The
to the logarithmic annual values of the variable in the relevant period. The regres-
imputed value is calculated so that it (or its proxy) bears the same relation-
sion equation takes the form ln Xt = a + bt,
ship to the total of available data. Imputed values are usually not calculated if missing data account for more than a third of the total in the benchmark year. The variables used as proxies are GNI in U.S. dollars, total population,
which is equivalent to the logarithmic transformation of the compound growth
exports and imports of goods and services in U.S. dollars, and value added
equation,
in agriculture, industry, manufacturing, and services in U.S. dollars. •
Xt = Xo (1 + r )t.
Aggregates marked by an s are sums of available data. Missing values are not imputed. Sums are not computed if more than a third of the observations
In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are
in the series or a proxy for the series are missing in a given year.
parameters to be estimated. If b* is the least-squares estimate of b, then the
362
2004 World Development Indicators
average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by
The inflation rate for Japan, the United Kingdom, the United States, and the
100 for expression as a percentage. The calculated growth rate is an average
Euro Zone, representing international inflation, is measured by the change in the
rate that is representative of the available observations over the entire period. It
SDR deflator. (Special drawing rights, or SDRs, are the International Monetary
does not necessarily match the actual growth rate between any two periods.
Fund’s unit of account.) The SDR deflator is calculated as a weighted average of these countries’ GDP deflators in SDR terms, the weights being the amount of
Exponential growth rate. The growth rate between two points in time for cer-
each country’s currency in one SDR unit. Weights vary over time because both
tain demographic indicators, notably labor force and population, is calculated
the composition of the SDR and the relative exchange rates for each currency
from the equation
change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conversion r = ln(pn /p1)/n,
factor is then applied to a country’s GNI. The resulting GNI in U.S. dollars is divided by the midyear population to derive GNI per capita.
where pn and p1 are the last and first observations in the period, n is the num-
When official exchange rates are deemed to be unreliable or unrepresentative
ber of years in the period, and ln is the natural logarithm operator. This growth
of the effective exchange rate during a period, an alternative estimate of the
rate is based on a model of continuous, exponential growth between two points
exchange rate is used in the Atlas formula (see below).
in time. It does not take into account the intermediate values of the series. Nor does it correspond to the annual rate of change measured at a one-year interval,
The following formulas describe the calculation of the Atlas conversion factor for year t :
which is given by (pn – pn – 1)/pn – 1. Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at
and the calculation of GNI per capita in U.S. dollars for year t :
intervals, in which case the compound growth model is appropriate. The average Yt$ = (Yt /Nt)/et*,
growth rate over n periods is calculated as
r = exp[ln(pn /p1)/n] – 1.
where et* is the Atlas conversion factor (national currency to the U.S. dollar) for year t, et is the average annual exchange rate (national currency to the U.S.
Like the exponential growth rate, it does not take into account intermediate val-
dollar) for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S.
ues of the series.
dollar terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, and Nt is the midyear population for
World Bank Atlas method
year t.
In calculating GNI and GNI per capita in U.S. dollars for certain operational purposes, the World Bank uses the Atlas conversion factor. The purpose of the Atlas
Alternative conversion factors
conversion factor is to reduce the impact of exchange rate fluctuations in the
The World Bank systematically assesses the appropriateness of official
cross-country comparison of national incomes.
exchange rates as conversion factors. An alternative conversion factor is used
The Atlas conversion factor for any year is the average of a country’s
when the official exchange rate is judged to diverge by an exceptionally large mar-
exchange rate (or alternative conversion factor) for that year and its exchange
gin from the rate effectively applied to domestic transactions of foreign curren-
rates for the two preceding years, adjusted for the difference between the rate of
cies and traded products. This applies to only a small number of countries, as
inflation in the country and that in Japan, the United Kingdom, the United States,
shown in Primary data documentation. Alternative conversion factors are used in
and the Euro Zone. A country’s inflation rate is measured by the change in its
the Atlas methodology and elsewhere in the World Development Indicators as
GDP deflator.
single-year conversion factors.
2004 World Development Indicators
363
CREDITS This book draws on a wide range of World Bank reports and numerous external
White of the World Resources Institute, Ricardo Quercioli of the International
sources, listed in the bibliography following this section. Many people inside and
Energy Agency, Orio Tampieri of the Food and Agriculture Organization, Laura
outside the World Bank helped in writing and producing World Development
Battlebury of the World Conservation Monitoring Centre, Gerhard Metchies of GTZ,
Indicators. The team would like to particularly acknowledge the help and encour-
and Christine Auclair, Moses Ayiemba, Bildad Kagai, Guenter Karl, Pauline Maingi,
agement of François Bourgignon, Senior Vice President and Chief Economist. The
and Markanley Rai of the Urban Indicators Programme, United Nations Centre for
team is also grateful to those who provided valuable comments on the entire book.
Human Settlements. Mehdi Akhlaghi managed the databases for this section, and
The poverty estimates were prepared by Shaohua Chen of the World Bank’s Poverty
Mayhar Eshragh-Tabary contributed with research and data analysis. The World
Monitoring Group with help from Prem Sangraula and Johan Mistiaen. This note
Bank’s Environment Department and Rural Development Department devoted
identifies those who made specific contributions. Numerous others, too many to
substantial staff resources to the book, for which the team is very grateful. M. H.
acknowledge here, helped in many ways for which the team is extremely grateful.
Saeed Ordoubadi wrote the introduction to the section with valuable comments from Sarwar Lateef, Eric Swanson, and Bruce Ross-Larson, who also edited the
1. World view
text. Other contributions were made by Susmita Dasgupta, Craig Meisner, Kiran
Section 1 was prepared by Eric Swanson and K. M. Vijayalakshmi. Eric Swanson
Pandey, and David Wheeler (air and water pollution) and Giovanni Ruta and Kirk
wrote the introduction with input from Sulekha Patel and Saeed Ordoubadi. Amy
Hamilton (adjusted savings). Valuable comments were also provided by Azamat
Heyman, Masako Hiraga, and Vivienne Wang assisted in developing and prepar-
Abdymomunov, Julian Bandiaks, Zeljko Bogetic, Gohar Gyulumyan, Kirk Hamilton,
ing tables and figures. Valuable suggestions were received from members of the
Aurelien Kruse, Mark Lundell, Evgenij Najdov, Luc Razafimandimby, Giovanni Ruta,
World Bank’s Human Development Network. Yonas Biru and William Prince pro-
Marcin Sasin, Monica Singh, and Jean van Houtte.
vided substantial assistance with the data, preparing the estimates of gross national income in purchasing power parity terms. The team is grateful to Guy
4. Economy
Karsenty and Jurgen Richtering of the World Trade Organization for providing the
Section 4 was prepared by K. M. Vijayalakshmi in close collaboration with the
market access indicators and to Wayne Mitchell of the IMF for providing the HIPC
Macroeconomic Data Team of the World Bank’s Development Data Group, led by
indicators in table 1.4. Vinoda Basnayake assisted in preparing the table.
Soong Sup Lee. Eric Swanson and K. M. Vijayalakshmi wrote the introduction with valuable suggestions from Sarwar Lateef. Contributions to the section were pro-
2. People
vided by Azita Amjadi (trade) and Ibrahim Levent (external debt). The national
Section 2 was prepared by Masako Hiraga in partnership with the World Bank’s
accounts data for low- and middle-income economies were gathered from the
Human Development Network and the Development Research Group in the
World Bank’s regional staff through the annual Unified Survey. Maja Bresslauer,
Development Economics Vice Presidency. Vivienne Wang provided invaluable assis-
Kay Chung, Victor Gabor, and Soong Sup Lee worked on updating, estimating,
tance in data and table preparation. Sulekha Patel wrote the introduction, with input
and validating the databases for national accounts. The national accounts data
from Sarwar Lateef. Contributions to the section were provided by Eduard Bos and
for OECD countries were processed by Mehdi Akhlaghi. The team is grateful to
Emi Suzuki (demography, health, and nutrition); Shaohua Chen and Martin Ravallion
Guy Karsenty and Andreas Maurer of the World Trade Organization and Sanja
(poverty and income distribution); Montserrat Pallares-Miralles (vulnerability and
Blazevic, Arunas Butkevicius, and Aurelie von Wartensleben of the United
security); Barbara Bruns, Saida Mamodova, Raymond Muhula, and Lianqin Wang
Nations Conference on Trade and Development, for providing data on trade in
(education); Lucia Fort and Maria Estela Rivero-Fuentes (gender) and Eldaw Abdalla
goods; to Tetsuo Yamada for help in obtaining the United Nations Industrial
Suliman (social indicators of poverty). The team is also grateful to Rosario Garcia,
Development Organization database; and to C. Patel for helpful comments.
Jens Johansen, and Olivier Labe at the UNESCO Institute for Statistics for their special effort to provide us with the education data. Comments and suggestions at var-
5. States and markets
ious stages of production also came from Eric Swanson.
Section 5 was prepared by David Cieslikowski and Mahyar Eshragh-Tabary in partnership with the World Bank’s Private Sector and Infrastructure Network, its Poverty
3. Environment
Reduction and Economic Management Network, the World Bank Institute, the
Section 3 was prepared by M. H. Saeed Ordoubadi and Mayhar Eshragh-Tabary in
International Finance Corporation, and external partners. David Cieslikowski wrote
partnership with the World Bank’s Environmentally and Socially Sustainable
the introduction to the section. Other contributors include Ada Karina Izaguirre and
Development Network and in collaboration with the World Bank’s Development
Kathy Khuu (privatization and infrastructure projects); Andrew Newby of Euromoney
Research Group and Transportation, Water, and Urban Development Department.
(credit ratings); Simeon Djankov and Caralee McLeish (business environment); Alka
Important contributions were made by Christian Layke, Daniel Prager, and Robin
Banerjee and Isilay Cabuk (Standard & Poor’s emerging stock market indexes);
364
2004 World Development Indicators
Yonas Biru (purchasing power parity conversion factors); Esperanza Magpantay and
overall design direction, editing, and layout, led by Meta de Coquereaumont and
Michael Minges of the International Telecommunication Union (communications and
Bruce Ross-Larson. The editing and production team consisted of Joseph
information); Peter Roberts (transport); Jane Degerlund of Containerisation
Costello, Elizabeth McCrocklin, Christopher Trott, and Elaine Wilson.
International (ports); Maria Helena Capelli Miguel of the United Nations Educational,
Communications Development’s London partner, Grundy & Northedge, provided
Scientific and Cultural Organization Institute for Statistics (culture, research and
art direction and design. Staff from External Affairs oversaw publication and dis-
development, scientists and engineers); Anders Halvorsen of the World Information
semination of the book.
Technology and Services Alliance; Stephen Minton of International Data Corporation (information and communications technology); Dan Gallik of the U.S. Department of
Client services
State (military personnel); Bjorn Hagelin and Petter Stålenheim of the Stockholm
The Development Data Group’s Client Services Team (Azita Amjadi, Richard Fix,
International Peace Research Institute (military expenditures and arms transfers);
Naomi Halewood, Gonca Okur, and William Prince) contributed to the design and
and Lise McLeod of the World Intellectual Property Organization (patents data).
planning of World Development Indicators and The World Bank Atlas and helped coordinate work with the Office of the Publisher.
6. Global links Section 6 was prepared by Amy Heyman in collaboration with Eric Swanson and
Administrative assistance and office technology support
external partners. Substantial input came from Azita Amjadi, Jerzy Rozanski (tar-
Estela Zamora provided administrative assistance and assisted in updating the
iffs), and Ibrahim Levent (financial data). Other contributors include Bernard
databases. Jean-Pierre Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin
Hoekman, Francis Ng, and Marcelo Olarreaga (trade); Betty Dow (commodity
Outadi provided information technology support
prices); Aki Kuwahara of the United Nations Conference on Trade and Development; Cecile Thoreau and Pauline Fron of the Organisation for Economic
Publishing and dissemination
Co-operation and Development (OECD) (migration); Yasmin Ahmad, Brian
The Office of the Publisher, under the direction of Dirk Koehler, provided valuable
Hammond, and Aimee Nichols of the OECD (aid flows); and Antonio Massieu and
assistance throughout the production process. Randi Park coordinated printing
Azucena Pernia of the World Tourism Organization (tourism data). Mehdi Akhlaghi
and supervised marketing and distribution. Chris Neal of the Development
provided valuable technical assistance.
Economics Vice President’s office and Carl Hanlor of External Affairs managed the communications strategy, and the Regional Operations Group headed by Paul
Other parts
Mitchell helped coordinate the overseas release.
Preparation of the maps on the inside covers was coordinated by Jeff Lecksell and Greg Prakas of the World Bank’s Map Design Unit. The Users guide was pre-
World Development Indicators CD-ROM
pared by David Cieslikowski. Statistical methods was written by Eric Swanson.
Programming and testing were carried out by Reza Farivari and his team: Azita
Primary data documentation was coordinated by K. M. Vijayalakshmi, who served
Amjadi, Ying Chi, Ramgopal Erabelly, Nacer Megherbi, Shahin Outadi, and
as database administrator. Gladys Gicker and Estela Zamora assisted in updat-
William Prince. Masako Hiraga produced the social indicators tables. William
ing the Primary data documentation table. Mehdi Akhlaghi was responsible for
Prince coordinated user inter face design and overall production and provided
database updates and aggregation. Acronyms and abbreviations was prepared by
quality assurance.
Amy Heyman. The index was collated by Richard Fix and Gonca Okur.
WDI Online Database management
Design, programming, and testing were carried out by Reza Farivari and his team:
Database management was coordinated by Mehdi Akhlaghi with the cross-team par-
Mehdi Akhlagi, Azita Amjadi, Ying Chi, Shahin Outadi, and Nacer Megherbi.
ticipation of Development Data Group staff to create an integrated World Develop-
William Prince coordinated production and provided quality assurance. Cybèle
ment Indicators database. This database was used to generate the tables for World
Bourgougnon of the Office of the Publisher was responsible for implementation
Development Indicators and related products such as WDI Online, The World Bank
of WDI Online and management of the subscription service.
Atlas, The Little Data Book, and the World Development Indicators CD-ROM.
Client feedback Design, production, and editing
The team is grateful to the many people who have taken the time to provide com-
Richard Fix coordinated all stages of production with Communications
ment on its publications. Their feedback and suggestions have helped improve
Development Incorporated. Communications Development Incorporated provided
this year’s edition.
2004 World Development Indicators
365
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370
2004 World Development Indicators
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2004 World Development Indicators
371
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2004 World Development Indicators
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INDEX OF INDICATORS References are to table numbers.
A
total
6.10
net concessional flows
Agriculture cereal area under production
from international financial institutions
6.12
from United Nations agencies
6.12
3.2
exports as share of total exports
6.3
exports, total
6.3
net official development assistance and official aid by DAC members as share of general government disbursements
imports as share of total imports
6.3
as share of GNI of donor country
1.4, 6.9
imports, total
6.3
average annual change in volume
6.9
yield
3.3
by type
fertilizer
6.9
6.8
from major donors, by recipient
6.11
commodity prices
6.4
for basic social services as share of total ODA commitments
1.4
consumption, per hectare of arable land
3.2
for debt relief as share of total ODA commitments
1.4
food commodity prices
per capita of donor country 6.4
total
exports as share of total exports
6.3
untied aid
exports, total
6.3
imports as share of total imports
6.3
imports, total
6.3
freshwater withdrawals for, as share of total
3.5
labor force as share of total, male and female
2.3
land
6.9 6.8, 6.9, 6.11 6.9
AIDS—see HIV, prevalence of
Air pollution—see Pollution
Air transport
arable, as share of land area
3.1
aircraft departures
5.9
arable, per capita
3.2
air freight
5.9
area under cereal production
3.2
passengers carried
5.9
irrigated, as share of cropland
3.2
permanent cropland as share of land area
3.1
Asylum seekers—see Migration
machinery tractors per 100 square kilometers of arable land
3.2
tractors per 1,000 agricultural workers
3.2
production indexes
B Balance of payments
crop
3.3
current account balance
4.15
food
3.3
exports and imports of goods and services
4.15
livestock
3.3
gross international reserves
4.15
net current transfers
4.15 4.15
value added annual growth of
4.1
net income
as share of GDP
4.2
See also Exports; Imports; Investment; Private capital flows; Trade
per worker
3.3 Bank and trade-related lending
6.7
Aid by recipient
Biological diversity
aid dependency ratios
6.10
assessment, date prepared, by country
per capita
6.10
species
3.14 3.4
2004 World Development Indicators
373
threatened species treaty
3.4
Consumption
3.14
distribution of—see Income, distribution fixed capital
Birds
3.15
government, general
species
3.4
annual growth of
4.10
threatened species
3.4
as share of GDP
4.9
private Birth rate, crude
2.1
annual growth of as share of GDP
Births attended by skilled health staff
Birthweight, low
1.2, 2.6, 2.16
per capita, annual growth of
2.17
1.4, 4.10 4.9 1.2, 4.10
relative price level
4.12
total
4.10
See also Purchasing power parity (PPP)
C
Contraceptive prevalence rate
2.17
Carbon dioxide damage
3.15
Contract enforcement
emissions per capita per 1995 U.S. dollar of GDP total
costs of 1.3, 3.8 3.8
5.3
number of procedures
5.3
time required for
5.3
1.6, 3.8 Country risk
Cities
composite ICRG risk ratings
5.2
air pollution
3.13
Euromoney country creditworthiness ratings
5.2
environment
3.11
Institutional Investor credit ratings
5.2
Moody’s sovereign long-term debt ratings
5.2
Standard & Poor’s sovereign long-term debt ratings
5.2
population in largest city
3.10
in selected cities
3.13
in urban agglomerations of more than one million telephone mainlines in largest city urban population
3.10
Credit, domestic
5.10 3.10, 3.11
from banking sector
5.5
to private sector
5.1
See also Urban environment Credit, markets Commodity prices and price indexes
6.4
creditor rights index
Communications—see Internet, users; Newspapers; Radios;
5.2
private bureau coverage
5.2
public registry coverage
5.2
Telecommunications, international; Television Current account balance Computers
See also Balance of payments
in education
5.11
per 1,000 people
5.11
D DAC (Development Assistance Committee)—see Aid
374
2004 World Development Indicators
4.15
Death rate, crude
2.1
female to male enrollment in primary and secondary schools
See also Mortality rate
Debt, external
2.11
net, by level
2.11
net intake rate, grade 1
2.12
debt service, total
4.17
primary completion rate
long term
4.16
public spending on
2.12
present value of
4.17
as share of GDP
private nonguaranteed
4.16
as share of total government expenditure
public and publicly guaranteed
2.11
gross, by level
2.9
per student, as share of GDP per capita
2.9
debt service
4.17
IBRD loans and IDA credits
4.16
pupil-teacher ratio, primary level
2.10
IMF credit, use of
4.16
repeaters in primary school
2.12
total
4.16
teachers, primary, trained
2.10
ratings
5.2
short term
4.17
total
4.16
Defense armed forces personnel
per student, by level
2.10
2.10
unemployment by level of educational attainment
2.4
Electricity consumption
5.10
distribution losses
5.10
production
as share of labor force
5.8
sources of
3.9
total
5.8
total
3.9
exports
5.8
Employment
imports
5.8
arms transfers
military expenditure
employment laws index
5.3
in agriculture, male and female
2.3 2.3
as share of central government expenditure
5.8
in industry, male and female
as share of GDP
5.8
in informal sector, urban male and female
Deforestation
3.4
2.8
total
2.8
in services, male and female
2.3
Density—see Population, density Endangered species—see Biological diversity, threatened species Development assistance—see Aid Energy Distribution of income or consumption—see Income, distribution
commercial, use of
E Education attainment
annual growth of
3.7
efficiency of
3.8
GDP per unit of
3.8
per capita
3.7
total
share of cohort reaching grade 5, male and female
2.12
depletion as share of GDP
expected years of schooling
2.13
emissions—see Pollution
enrollment ratio
3.7 3.15
imports, net
3.7
2004 World Development Indicators
375
INDEX OF INDICATORS production, commercial
3.7
transport
See also Electricity
travel
4.7 4.7, 6.14
See also Trade Entry regulations for business cost to register a business as share of GNI per capita
5.3
minimum capital requirement as share of GNI per capita
5.3
start-up procedures, number of
5.3
time to start up a business
5.3
F Fertility rate adolescent total
Environmental profile, date prepared
3.14
Environmental strategy, year adopted
3.14
2.16 2.7, 2.16
Financial debt and efficiency—see Liquidity; Monetary indicators
Financial flows, net Euromoney country creditworthiness ratings
5.2
from DAC members
6.8
from multilateral institutions Exchange rates
international financial institutions
6.12
arrangements
5.7
total
6.12
official, local currency units to U.S. dollar
5.7
United Nations
6.12
ratio of PPP conversion factor to official exchange rate
5.7
real effective
5.7
official development assistance and official aid
See also Purchasing power parity (PPP)
Exports arms
5.8
duties on
5.6
goods and services as share of GDP total
6.8
other official flows
6.8
private
6.8
total
6.8
See also Aid
Foreign direct investment, net—see Investment; Private capital flows, net 4.9 4.15
high-technology
Forest area
share of manufactured exports
5.12
as share of total land area
total
5.12
total
merchandise
deforestation, average annual
by high-income OECD countries, by product
6.3
by regional trade blocs
6.5
direction of trade high technology
6.2 5.12
depletion of
3.4 3.4 3.4 3.15
Freshwater annual withdrawals of
structure of
4.5
as share of total resources
3.5
total
4.5
for agriculture
3.5
for domestic use
3.5
for industry
3.5
value, annual growth of volume, annual growth of
4.4, 6.2 4.4
services
376
grants from NGOs
flows
structure of
4.7
internal
3.5
total
4.7
from other countries
3.5
2004 World Development Indicators
resources per capita
3.5
volume of
3.5
revenues, current nontax
See also Water, access to improved source of
Fuel prices
3.12
Gender differences
expected years of schooling
in HIV prevalence, youth in labor force participation
annual growth of
4.10
as share of GDP
4.9
Gross domestic product (GDP)
in education
in employment
4.13, 5.6
Gross capital formation
G enrollment, primary and secondary
4.13
tax, by source
annual growth of 1.2
implicit deflator—see Prices
2.13
per capita, annual growth of
2.3
1.1, 1.6, 4.1
1.1, 1.6
total
4.2
2.18 1.5, 2.2
Gross domestic savings as share of GDP
4.9
in literacy adult
2.13
youth
1.5, 2.13
in life expectancy
1.5
Gross foreign direct investment—see Investment
Gross national income (GNI)
in mortality
per capita
adult
2.19
in 2001 PPP dollars
1.1, 1.6
child
2.19
in 2001 U.S. dollars
1.1, 1.6
in smoking
2.18
in survival to 65
2.19
rank
1.1
rank
unpaid family workers
1.5
in 2001 PPP dollars
1.1
women in parliaments
1.5
in 2001 U.S. dollars
1.1
total Gini index
2.7
in 2001 PPP dollars
1.1, 1.6
in 2001 U.S. dollars
1.1, 1.6
Government, central debt as share of GDP
Gross national savings as share of GNI
3.15
4.11
interest as share of current revenue
4.11
interest as share of total expenditure
4.12
expenditures
H Health care
as share of GDP
4.11
average length of hospital stay
2.14
by economic type
4.12
hospital beds per 1,000 people
2.14
immunization
2.15
military
5.8
financing
inpatient admission rate
2.14
4.11
outpatient visits per capita
2.14
from abroad
4.11
pregnant women receiving prenatal care
overall deficit
4.11
physicians per 1,000 people
revenues as share of GDP
4.11
domestic
1.5 2.14
2004 World Development Indicators
377
INDEX OF INDICATORS reproductive births attended by skilled health staff contraceptive prevalence rate
1.2, 2.6, 2.16 2.16
fertility rate adolescent total low-birthweight babies maternal mortality ratio women at risk of unwanted pregnancy tetanus vaccinations
I Immunization child
2.15
2.16
DPT, share of children under 12 months
2.15
2.16
measles, share of children under 12 months
2.15
2.17 1.2, 2.16
Imports
2.16
arms
5.7
2.15
duties on
5.5
energy, as share of commercial energy use
3.7
tuberculosis DOTS detection rate
2.15
treatment success rate
2.15
goods and services as share of GDP total
Health expenditure
4.9 4.15
merchandise
as share of GDP
2.14
by high-income OECD countries, by product
6.3
per capita
2.14
structure of
4.6
private
2.14
total
public
2.9, 2.14
total
2.14
4.6
value, annual growth of
4.4, 6.2
volume, annual growth of
4.4, 6.2
services Health risks anemia, prevalence of HIV, prevalence of iodized salt consumption malnutrition, child overweight children, prevalence of smoking
2.17 1.3, 2.18
undernourishment, prevalence of
4.8
travel See also Trade
1.3, 2.18 2.17
Income distribution Gini index percentage shares of
Heavily indebted poor countries (HIPCs) completion point
survey year 1.4
decision point
1.4
nominal debt service relief
1.4
HIV, prevalence of
1.3, 2.18
Hospital beds—see Health care
Housing, selected cities 3.11
price to income ratio
3.11
2004 World Development Indicators
urban house price to income ratio, selected cities
Indebtedness classification
2.7 1.2, 2.7 2.7 3.11
4.17
Industry, value added annual growth of
4.1
as share of GDP
4.2
Inflation—see Prices
population with secure tenure
378
4.8 4.8, 6.14
2.17 2.18
tuberculosis, incidence of
4.8
total transport
2.17 1.2, 2.6, 2.17
structure of
Information and communications technology expenditures
Investment
as share of GDP
5.11
foreign direct, gross, as share of GDP
per capita
5.11
foreign direct, net
Insolvency
as share of GDP
5.1
total
6.7
costs to resolve
5.3
government capital expenditure
time to resolve
5.3
infrastructure, private participation in
Institutional Investor credit ratings
5.2
Integration, global economic, indicators of
6.1
6.1
4.12
energy
5.1
telecommunications
5.1
transport
5.1
water and sanitation
5.1
portfolio Interest payments—see Government, central, debt
Interest rates deposit
bonds
6.7
equity
6.7
See also Gross capital formation 5.7
lending
5.7
real
5.7
risk premium on lending
5.5
spreads
5.5
Iodized salt, consumption of
2.17
L Labor force
International Bank for Reconstruction and Development (IBRD)
annual growth of
2.2
IBRD loans and IDA credits
4.16
armed forces
5.8
net financial flows from
6.12
children ages 10–14 in
2.8
female International Country Risk Guide (ICRG) composite risk ratings
5.3
2.2
foreign, in OECD countries
6.13
in agriculture, as share of total, male and female International Development Association (IDA)
2.3
in industry, as share of total, male and female
2.3
IBRD loans and IDA credits
4.16
in services, as share of total, male and female
2.3
net concessional flows from
6.12
maternity leave benefits
1.5
participation International Monetary Fund (IMF) net financial flows from
6.12
use of IMF credit
4.16
Internet
gender differences in
1.5
of population ages 15–64
2.2
total
2.2
women in parliaments
1.5
See also Employment; Migration; Unemployment
costs per 20 hours of use
5.11
as share of monthly GNI per capita
5.11
secure servers
5.11
users
5.11
Labor regulations, employment laws index
5.3
Land area arable—see Agriculture, land of selected cities
3.11
See also Protected areas; Surface area
2004 World Development Indicators
379
INDEX OF INDICATORS Land use
Merchandise
area under cereal production
3.2
by type
3.1
exports agricultural raw materials
4.5
irrigated land
3.2
food
4.5
fuels
4.5
manufactures
4.5
Life expectancy at birth gender differences in total
1.5 1.6, 2.19
ores and metals
4.5
total
4.5
imports Liquidity
agricultural raw materials
4.6
bank liquid reserves to bank assets
5.5
food
4.6
liquid liabilities
5.5
fuels
4.6
quasi-liquid liabilities
5.5
manufactures
4.6
See also Monetary indicators
Literacy rate
ores and metals
4.6
total
4.6
trade
adult, male and female gender differences in total, for other economies youth, male and female
2.13
direction of
1.5
6.2
growth of
4.4, 6.2
1.6 2.13
M
Migration foreign labor force in OECD countries as share of total labor force
6.13
foreign population in OECD countries
6.13
inflows of foreign population
Malnutrition, in children under five
1.2, 2.6, 2.17
asylum seekers
6.13
total
6.13
Mammals species
3.4
threatened species
3.4
Millennium Development Goals, indicators for aid as share of GNI of donor country
Manufacturing structure of
as share of total ODA commitments 4.3
value added
access to improved water source access to improved sanitation facilities
annual growth of
4.1
births attended by skilled health staff
as share of GDP
4.2
carbon dioxide emissions per capita
total
4.3
child malnutrition consumption, national share of poorest quintile
Market access to high-income countries
female to male enrollments, primary and secondary
goods admitted free of tariffs
1.4
support to agriculture
1.4
tariffs on exports from low- and middle-income countries agricultural products
1.4
textiles and clothing
1.4
2004 World Development Indicators
1.4 1.3, 2.15, 3.5 1.3, 2.15, 3.10 1.2, 2.6, 2.16 1.3, 3.8 1.2, 2.6, 2.17 1.2, 2.7 1.2
heavily indebted poor countries (HIPCs) completion point
1.4
decision point
1.4
nominal debt service relief
1.4
HIV, prevalence of, among 15- to 24-year-olds female
380
1.4, 6.9
1.3, 2.18
male maternal mortality ratio net primary enrollment ratio
1.3, 2.18
malnutrition, child
1.2, 2.16
overweight children, prevalence of
2.11
telephone lines
1.3, 5.10
tuberculosis, incidence of
1.3, 2.18
under-five mortality rate
1.2, 2.19
unemployment among 15- to 24-year-olds
1.3, 2.4
1.2, 2.6, 2.17 2.17
undernourishment, prevalence of
2.17
vitamin A supplementation
2.17
O Official aid—see Aid
Minerals, depletion of
3.15 Official development assistance—see Aid
Monetary indicators claims on governments and other public entities
4.14
claims on private sector
4.14
Money and quasi money (M2), annual growth of
Moody’s sovereign long-term debt ratings
4.14
Official flows, other
6.8
P Passenger cars per 1,000 people
3.12
Patent applications filed
5.12
5.2
Mortality rate adult, male and female
2.19
child, male and female
2.19
Pension average, as share of per capita income
2.9
children under five
1.2, 2.19
contributors
2.9
infant
2.6, 2.19
public expenditure on, as share of GDP
2.9
maternal
1.2, 2.16 Physicians—see Health care
Motor vehicles passenger cars
3.12
per kilometer of road
3.12
species
3.4
per 1,000 people
3.12
threatened species
3.4
two-wheelers
3.12
See also Roads; Traffic
Plants, higher
Pollution carbon dioxide damage as share of GDP
N
3.15
carbon dioxide emissions
Nationally protected areas—see Protected areas
per capita
3.8
per PPP dollar of GDP
3.8
total
3.8
Net adjusted savings
3.15
nitrogen dioxide, selected cities
Newspapers, daily
5.11
organic water pollutants, emissions of by industry
Nutrition anemia, prevalence of
2.17
3.13
3.6
per day
3.6
per worker
3.6
breastfeeding
2.17
sulfur dioxide, selected cities
3.13
iodized salt consumption
2.17
suspended particulate matter, selected cities
3.13
2004 World Development Indicators
381
INDEX OF INDICATORS Population
body mass index, women with low
age dependency ratio
2.1
fertility rate
annual growth of
2.1
malnutrition, child
by age group
mortality rate, infant
0–14
2.1
survey year
15–64
2.1
65 and above
2.1
Power—see Electricity, production
rural
3.1
Pregnancy, risk of unwanted
total
1.1, 1.6
2.6 2.6, 2.16 1.2, 2.6, 2.17 2.6, 2.19 2.6
density
female, as share of total foreign, in OECD countries
1.5
Prenatal care
2.16
1.5
6.13
rural
Prices
annual growth of
3.1
commodity prices and price indexes
as share of total
3.1
consumer, annual growth of
4.14
food, annual growth of
4.14
total
1.1, 1.6, 2.1
urban as share of total in largest city in selected cities
6.4
fuel
3.12
3.10
GDP implicit deflator, annual growth of
4.14
3.10
terms of trade
4.4
3.11, 3.13
in urban agglomerations
3.10
total
3.10
See also Migration
Private capital flows gross, as share of GDP
6.1
net bank and trade-related lending
Portfolio investment flows bonds
6.7
equity
6.7
6.7
from DAC members
6.8
foreign direct investment
6.7
portfolio investment
6.7
See also Investment Ports, container traffic in
5.9 Productivity
Poverty
in agriculture, value added per worker
3.3
international poverty line population below $1 a day
2.5
population below $2 a day
2.5
as share of total land area
3.4
poverty gap at $1 a day
2.5
size of
3.4
poverty gap at $2 a day
2.5
survey year
2.5
national poverty line
382
2.5
rural
2.5
survey year
2.5
urban
2.5
2004 World Development Indicators
Purchasing power parity (PPP) conversion factor
population below
social indicators of
Protected areas
gross national income
5.7 1.1, 1.6
R
S
Radios
5.11
Railways
total
5.4
Sanitation
lines electric
S&P/IFC Investable Index
households with sewerage connections, selected cities 5.9
3.11
population with access to
5.9
rural
3.10
productivity of, per employee
5.9
total
1.3, 2.16
tariffs, ratio of passenger to freight
5.9
urban
traffic density
5.9
3.10
Savings Regional development banks, net financial flows from
6.12
Relative prices (PPP)—see Purchasing power parity (PPP)
gross domestic
4.9
gross national
3.15
net
Research and development expenditures for
5.12
researchers
5.12
technicians
5.12
adjusted
3.15
domestic
3.15
Schooling—see Education
Science and engineering Reserves, gross international—see Balance of payments
Risk ratings—see Country risk
researchers in R&D
5.12
scientific and technical journal articles
5.12
See also Research and development
Roads
Services
goods hauled by
5.9
paved, as share of total
5.9
structure of
4.7
total network
5.9
total
4.7
3.12
imports
traffic
Royalty and license fees payments
5.12
receipts
5.12
exports
structure of
4.8
total
4.8
value added annual growth of
4.1
as share of GDP Rural environment access to improved water source access to sanitation
4.2
Sewerage connections, selected cities
3.11
Smoking, prevalence of, male and female
2.19
3.5 3.10
population annual growth of
3.1
as share of total
3.1
density
3.1
Standard & Poor’s sovereign long-term debt ratings
5.2
Stock markets listed domestic companies
5.4
2004 World Development Indicators
383
INDEX OF INDICATORS market capitalization
tax revenue as share of GDP
as share of GDP
5.4
total
5.4
5.6
Technology—see Computers; Exports, merchandise, high technology; Internet,
market liquidity
5.4
users; Research and development; Science and engineering;
S&P/IFC Investable Index
5.4
Telecommunications, international
turnover ratio
5.4 Telecommunications, international
Sulfur dioxide emissions—see Pollution
Surface area
cost of call to United States
5.10
outgoing traffic
5.10
1.1, 1.6
See also Land area
Telephones cost of local call
Suspended particulate matter—see Pollution
fixed line and mobile phone subscribers
5.10 1.3
mainlines
T
faults
5.10
per 1,000 people
Tariffs
in largest city
all products mean tariff
6.6
standard deviation
6.6
manufactured goods
5.10
revenue per line
5.10
waiting list mobile
mean tariff
6.6
standard deviation
6.6
primary products mean tariff
6.6
standard deviation
6.6
See also Taxes and tax policies, duties
Taxes and tax policies
5.10
national
5.10 5.10
Television cable subscribers per 1,000 people
5.11
sets per 1,000 people
5.11
Terms of trade, net barter
4.4
Tetanus vaccinations, pregnant women
2.16
duties on exports
5.6
on imports
5.6
See also Tariffs goods and service taxes, domestic
Threatened species—see Biological diversity
Tourism, international 4.13, 5.6
highest marginal tax rate
expenditures
6.14
inbound tourists, by country
6.14
corporate
5.6
outbound tourists, by country
6.14
individual
5.6
receipts
6.14
income, profit, and capital gains taxes as share of total revenue as share of total taxes
4.13
Trade arms transfers
5.8
4.13
changes in, as share of GDP
6.1
other taxes
4.13
exports plus imports as share of GDP
6.1
social security taxes
4.13
international trade taxes
384
2004 World Development Indicators
5.6
merchandise as share of goods GDP
6.1
direction of, by region
6.2
export value
UNDP, net concessional flows from
6.12
4.4, 6.2
export volume import value
U
4.4
Unemployment
4.4, 6.2
incidence of long term
import volume
4.4
male and female
2.4
nominal growth of, by region
6.2
total
2.4
OECD trade by commodity
6.3
real growth in, less growth in real GDP
6.1
rate
services transport
4.7, 4.8
travel
4.7, 4.8
by level of educational attainment
2.4
for 15- to 24-year-olds
1.3
UNFPA, net concessional flows from
6.12
UNICEF, net concessional flows from
6.12
United Nations agencies, net concessional flows from
6.12
See also Balance of payments; Exports; Imports
Trade blocs, regional exports within bloc
6.5
total exports, by bloc
6.5 Urban environment
Trademark applications filed
5.12
access to sanitation
3.10
population Trade policies—see Tariffs
Traffic accidents, people injured or killed by
3.2
road traffic
3.2
as share of total
3.10
in largest city
3.10
in urban agglomerations of more than one million
3.10
total
3.10
selected cities
See also Roads
area
3.11
households with Transport—see Air transport; Railways; Roads; Traffic; Urban environment
access to potable water
Treaties, participation in
3.11
regular waste collection
3.11
sewerage connections
3.11
biological diversity
3.14
house price to income ratio
CFC control
3.14
population
3.11
climate change
3.14
travel time to work
3.11
work trips by public transportation
3.11
Law of the Sea
3.14
ozone layer
3.14
3.11
See also Pollution; Population; Water, access to improved source of; Sanitation
Tuberculosis incidence of treatment success rate
1.3, 2.18 2.15
V Value added as share of GDP in agriculture
4.2
2004 World Development Indicators
385
INDEX OF INDICATORS in industry
4.2
in manufacturing
4.2
in services
4.2
growth of
3.11
Water, access to improved source of population with, as share of total
1.3, 2.15
in agriculture
4.1
rural
3.5
in industry
4.1
urban
3.5
in manufacturing
4.1
urban households with
in services
4.1
per worker, in agriculture
3.3
total, in manufacturing
4.3
W
6.12
World Bank, net financial flows from
6.12
International Development Association
2004 World Development Indicators
4.12
3.11
WFP, net concessional flows from
See also International Bank for Reconstruction and Development;
Wage, as share of total government expenditure
386
Waste collection, households with access to
The world by region Low- and middle-income economies
Classified according to World Bank analytical grouping
High-income economies
East Asia and Pacific
Middle East and Nor th Africa
OECD
Europe and Central Asia
South Asia
Other
Latin America and the Caribbean
Sub-Saharan Africa
No data
REGION MAP
The world by region
East Asia and Pacific American Samoa Cambodia China Fiji Indonesia Kiribati Korea, Dem. Rep. Lao PDR Malaysia Marshall Islands Micronesia, Fed. Sts. Mongolia Myanmar Northern Mariana Islands Palau Papua New Guinea Philippines Samoa Solomon Islands Thailand Timor-Leste Tonga Vanuatu Vietnam Europe and Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Georgia Hungary Kazakhstan Kyrgyz Republic Latvia Lithuania Macedonia, FYR Moldova Poland Romania Russian Federation Serbia and Montenegro Slovak Republic Tajikistan Turkey Turkmenistan Ukraine Uzbekistan Latin America and the Caribbean Argentina Belize Bolivia
Brazil Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB Middle East and North Africa Algeria Djibouti Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Lebanon Libya Morocco Oman Saudi Arabia Syrian Arab Republic Tunisia West Bank and Gaza Yemen, Rep. South Asia Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana
Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d'Ivoire Equatorial Guinea Eritrea Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mayotte Mozambique Namibia Niger Nigeria Rwanda São Tomé and Principe Senegal Seychelles Sierra Leone Somalia South Africa Sudan Swaziland Tanzania Togo Uganda Zambia Zimbabwe High income OECD Australia Austria * Belgium * Canada Denmark Finland * France * Germany * Greece * Iceland Ireland * Italy * Japan Korea, Rep.
Luxembourg * Netherlands * New Zealand Norway Portugal * Spain * Sweden Switzerland United Kingdom United States Other high income Andorra Antigua and Barbuda Aruba Bahamas, The Bahrain Barbados Bermuda Brunei Cayman Islands Channel Islands Cyprus Faeroe Islands French Polynesia Greenland Guam Hong Kong, China Isle of Man Israel Kuwait Liechtenstein Macao, China Malta Monaco Netherlands Antilles New Caledonia Puerto Rico Qatar San Marino Singapore Slovenia United Arab Emirates Virgin Islands (U.S.)
* Member of the European Monetary Union
