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Human Development Report 2009

Overcoming barriers: Human mobility and development

Published for the United Nations Development Programme (UNDP)

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TeamHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

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TeamHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Team for the preparation of the Human Development Report 2009

Director Jeni Klugman

Research Led by Francisco R. Rodríguez, comprising Ginette Azcona, Matthew Cummins, Ricardo Fuentes Nieva, Mamaye Gebretsadik, Wei Ha, Marieke Kleemans, Emmanuel Letouzé, Roshni Menon, Daniel Ortega, Isabel Medalho Pereira, Mark Purser and Cecilia Ugaz (Deputy Director until October 2008).

Statistics Led by Alison Kennedy, comprising Liliana Carvajal, Amie Gaye, Shreyasi Jha, Papa Seck and Andrew Thornton.

National HDR and network Eva Jespersen (Deputy Director HDRO), Mary Ann Mwangi, Paola Pagliani and Timothy Scott.

Outreach and communications Led by Marisol Sanjines, comprising Wynne Boelt, Jean-Yves Hamel, Melissa Hernandez, Pedro Manuel Moreno and Yolanda Polo.

Production, translation, budget and operations, administration Carlotta Aiello (production coordinator), Sarantuya Mend (operations manager), Fe Juarez-Shanahan and Oscar Bernal.

ForewordHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Foreword

Migration not infrequently gets a bad press. Negative stereotypes portraying migrants as ‘stealing our jobs’ or ‘scrounging off the taxpayer’ abound in sections of the media and public opinion, es- pecially in times of recession. For others, the word ‘migrant’ may evoke images of people at their most vulnerable. This year’s Human Development Report, Overcoming Barriers: Human Mobility and Development, challenges such stereotypes. It seeks to broaden and rebalance perceptions of migration to reflect a more complex and highly variable reality.

This report breaks new ground in applying a human development approach to the study of migration. It discusses who migrants are, where they come from and go to, and why they move. It looks at the multiple impacts of migration for all who are affected by it—not just those who move, but also those who stay.

In so doing, the report’s findings cast new light on some common misconceptions. For ex- ample, migration from developing to developed countries accounts for only a minor fraction of human movement. Migration from one develop- ing economy to another is much more common. Most migrants do not go abroad at all, but in- stead move within their own country.

Next, the majority of migrants, far from being victims, tend to be successful, both before they leave their original home and on arrival in their new one. Outcomes in all aspects of human development, not only income but also education and health, are for the most part posi- tive—some immensely so, with people from the poorest places gaining the most.

Reviewing an extensive literature, the report finds that fears about migrants taking the jobs or lowering the wages of local people, placing an unwelcome burden on local services, or costing the taxpayer money, are generally exaggerated. When migrants’ skills complement those of local people, both groups benefit. Societies as a whole may also benefit in many ways—ranging from ris- ing levels of technical innovation to increasingly diverse cuisine to which migrants contribute.

The report suggests that the policy response to migration can be wanting. Many govern- ments institute increasingly repressive entry regimes, turn a blind eye to health and safety violations by employers, or fail to take a lead in educating the public on the benefits of immigration.

By examining policies with a view to ex- panding people’s freedoms rather than con- trolling or restricting human movement, this report proposes a bold set of reforms. It argues that, when tailored to country-specific contexts, these changes can amplify human mobility’s already substantial contributions to human development.

The principal reforms proposed centre around six areas, each of which has important and complementary contributions to make to human development: opening up existing entry channels so that more workers can emigrate; ensuring basic rights for migrants; lowering the transaction costs of migration; finding solutions that benefit both destination communities and the migrants they receive; making it easier for people to move within their own countries; and mainstreaming migration into national develop- ment strategies.

The report argues that while many of these reforms are more feasible than at first thought, they nonetheless require political courage. There may also be limits to governments’ ability to make swift policy changes while the recession persists.

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentForeword

This is the first Human Development Report for which as Administrator I am writ- ing the foreword. Like all such reports, this is an independent study intended to stimulate debate and discussion on an important issue. It is not a statement of either United Nations or UNDP policy.

At the same time, by highlighting human mobility as a core component of the human development agenda, it is UNDP’s hope that the following insights will add value to ongoing

discourse on migration and inform the work of development practitioners and policy makers around the world.

Helen Clark Administrator United Nations Development Programme

The analysis and policy recommendations of this report do not necessarily reflect the views of the United Nations Development

Programme, its Executive Board or its Member States.

The report is an independent publication commissioned by UNDP. It is the fruit of a collaborative effort by a team of eminent advisers

and the Human Development Report team.

Jeni Klugman, Director of the Human Development Report Office, led the effort.

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Acknowledgements

Acknowledgements

This report is the fruit of the efforts, contribu- tions and support of many people and organiza- tions. I would like to thank Kemal Derviş for the opportunity to take on the daunting task of Director of the Human Development Report, and the new UNDP Administrator, Helen Clark, for advice and support. Coming back to the office after its 20 years of growth and success has been a tremendously rewarding experience, and I would like to especially thank my fam- ily, Ema, Josh and Billy, for their patience and support throughout. The dedication and hard work of the whole HDR team, listed earlier, was critical. Among those who provided important strategic advice and suggestions, which were es- pecially critical in pulling the report together, were Oliver Bakewell, Martin Bell, Stephen Castles, Joseph Chamie, Samuel Choritz, Michael Clemens, Simon Commander, Sakiko Fukuda-Parr, Hein de Haas, Frank Laczko, Loren Landau, Manjula Luthria, Gregory Maniatis, Philip Martin, Douglas Massey, Saraswathi Menon, Frances Stewart, Michael Walton and Kevin Watkins.

Background studies were commissioned on a range of thematic issues and published online in our Human Development Research Papers series, launched in April 2009, and are listed in the bib- liography. A series of 27 seminars that were held between August 2008 and April 2009 likewise provided important stimulus to our thinking and the development of ideas, and we would again thank those presenters for sharing their research and insights. We would also like to acknowledge the contribution of the national experts who par- ticipated in our migration policy assessment.

The data and statistics used in this report draw significantly upon the databases of other organizations to which we were allowed gener- ous access: Andean Development Corporation; Development Research Centre on Migration, University of Sussex; ECLAC; International Migration Institute, Oxford; Inter-Parliamentary Union; Internal Displacement Monitoring Centre; the Department of Statistics and the International Migration Programme of the ILO; IOM; Luxembourg Income Study; OECD; UNICEF; UNDESA, Statistics Division and Population Division; UNESCO Institute for

Statistics; UNHCR; Treaty Section, United Nations Office of Legal Affairs; UNRWA; the World Bank; and WHO.

The report benefited greatly from intel- lectual advice and guidance provided by an academic advisory panel. The panel comprised Maruja Asis, Richard Black, Caroline Brettell, Stephen Castles, Simon Commander, Jeff Crisp, Priya Deshingkar, Cai Fang, Elizabeth Ferris, Bill Frelick, Sergei Guriev, Gordon Hanson, Ricardo Hausmann, Michele Klein-Solomon, Kishore Mahbubani, Andrew Norman Mold, Kathleen Newland, Yaw Nyarko, José Antonio Ocampo, Gustav Ranis, Bonaventure Rutinwa, Javier Santiso, Maurice Schiff, Frances Stewart, Elizabeth Thomas-Hope, Jeffrey Williamson, Ngaire Woods and Hania Zlotnik.

From the outset, the process involved a range of participatory consultations designed to draw on the expertise of researchers, civil society advocates, development practitioners and policy makers from around the globe. This included 11 informal stakeholder consultations held between August 2008 and April 2009 in Nairobi, New Delhi, Amman, Bratislava, Manila, Sydney, Dakar, Rio de Janeiro, Geneva, Turin and Johannesburg, involving almost 300 experts and practitioners in total. The support of UNDP country and regional offices and local partners was critical in enabling these consultations. Several events were hosted by key partners, including the IOM, the ILO and the Migration Policy Institute. Additional aca- demic consultations took place in Washington D.C. and Princeton, and HDRO staff partici- pated in various other regional and global fora, including the Global Forum on Migration and Development (GFMD) in Manila, preparatory meetings for the Athens GFMD, and many con- ferences and seminars organized by other UN agencies (e.g. ILO, UNDESA and UNITAR), universities, think-tanks and non-governmental organizations. Participants in a series of Human Development Network discussions provided wide-ranging insights and observations on the linkages between migration and human devel- opment. More details on the process are avail- able at http://hdr.undp.org/en/nhdr.

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentAcknowledgements

A UNDP Readers Group, comprising repre- sentatives of all the regional and policy bureaux, provided many useful inputs and suggestions on the concept note and report drafts, as did a num- ber of other colleagues who provided inputs and advice. We would especially thank Amat Alsoswa, Carolina Azevedo, Barbara Barungi, Tony Bislimi, Kim Bolduc, Winifred Byanyima, Ajay Chhibber, Samuel Choritz, Pedro Conceição, Awa Dabo, Georgina Fekete, Priya Gajraj, Enrique Ganuza, Tegegnework Gettu, Rebeca Grynspan, Sultan Hajiyev, Mona Hammam, Mette Bloch Hansen, Mari Huseby, Selim Jahan, Bruce Jenks, Arun Kashyap, Olav Kjoren, Paul Ladd, Luis Felipe López-Calva, Tanni Mukhopadhyay, B. Murali, Theodore Murphy, Mihail Peleah, Amin Sharkawi, Kori Udovicki, Mourad Wahba and Caitlin Wiesen for comments.

A team at Green Ink, led by Simon Chater, provided editing services. The design work was carried out by Zago. Guoping Huang developed some of the maps. The production, translation, distribution and promotion of the report ben- efited from the help and support of the UNDP Office of Communications, and particularly of Maureen Lynch. Translations were reviewed by

Luc Gregoire, Madi Musa, Uladzimir Shcherbau and Oscar Yujnovsky. Margaret Chi and Solaiman Al-Rifai of the United Nations Office for Project Services provided critical administra- tive support and management services.

The report also benefited from the dedicated work of a number of interns, namely Shreya Basu, Vanessa Alicia Chee, Delphine De Quina, Rebecca Lee Funk, Chloe Yuk Ting Heung, Abid Raza Khan, Alastair Mackay, Grace Parker, Clare Potter, Limon B. Rodriguez, Nicolas Roy, Kristina Shapiro and David Stubbs.

We thank all of those involved directly or indirectly in guiding our efforts, while acknowl- edging sole responsibility for errors of commis- sion and omission.

Jeni Klugman Director Human Development Report 2009

AcronymesHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Acronyms

Acronyms

CEDAW United Nations Convention on the Elimination of All

Forms of Discrimination against Women

CMW United Nations International Convention on the

Protection of the Rights of All Migrant Workers

and Members of their Families

CRC United Nations Convention on the Rights of the Child

ECD Early childhood development

ECLAC Economic Commission for Latin America and

the Caribbean

ECOWAS Economic Community of West African States

EIU Economist Intelligence Unit

EU European Union

GATS General Agreement on Trade in Services

GDP Gross domestic product

GCC Gulf Cooperation Council

HDI Human Development Index

HDR Human Development Report

HDRO Human Development Report Office

ILO International Labour Organization

IOM International Organization for Migration

MERCOSUR Mercado Común del Sur

( Southern Common Market)

MIPEX Migrant Integration Policy Index

NGO Non-governmental organization

OECD Organisation for Economic Co-operation

and Development

PRS Poverty Reduction Strategy

PRSP Poverty Reduction Strategy Paper

TMBs Treaty Monitoring Bodies

UNDESA United Nations Department of Economic

and Social Affairs

UNDP United Nations Development Programme

UNESCO United Nations Educational, Scientific

and Cultural Organization

UNHCR Office of the United Nations High Commissioner

for Refugees

UNICEF United Nations Children’s Fund

UNODC United Nations Office on Drugs and Crime

UNRWA United Nations Relief and Works Agency for

Palestine Refugees in the Near East

USSR Union of Soviet Socialist Republics

WHO World Health Organization

WTO World Trade Organization

ContentsHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Foreword v Acknowledgements vii Acronyms ix

OVERVIEW 1

How and why people move 1

Barriers to movement 2

The case for mobility 3

Our proposal 3

The way forward 5

CHAPTER 1

Freedom and movement: how mobility can foster human development 9

1.1 Mobility matters 9

1.2 Choice and context: understanding why people move 11

1.3 Development, freedom and human mobility 14

1.4 What we bring to the table 16

CHAPTER 2

People in motion: who moves where, when and why 21

2.1 Human movement today 21

2.2 Looking back 28

2.2.1 The long-term view 28

2.2.2 The 20th century 30

2.3 Policies and movement 33

2.4 Looking ahead: the crisis and beyond 40

2.4.1 The economic crisis and the prospects for recovery 41

2.4.2 Demographic trends 43

2.4.3 Environmental factors 45

2.5 Conclusions 46

CHAPTER 3

How movers fare 49

3.1 Incomes and livelihoods 49

3.1.1 Impacts on gross income 50

3.1.2 Financial costs of moving 53

3.2 Health 55

3.3 Education 57

3.4 Empowerment, civic rights and participation 60

3.5 Understanding outcomes from negative drivers 62

3.5.1 When insecurity drives movement 62

3.5.2 Development-induced displacement 64

3.5.3 Human trafficking 65

3.6 Overall impacts 67

3.7 Conclusions 68

Contents

CHAPTER 4

Impacts at origin and destination 71

4.1 Impacts at places of origin 71

4.1.1 Household level effects 71

4.1.2 Community and national level economic effects 76

4.1.3 Social and cultural effects 79

4.1.4 Mobility and national development strategies 82

4.2 Destination place effects 83

4.2.1 Aggregate economic impacts 84

4.2.2 Labour market impacts 85

4.2.3 Rapid urbanization 86

4.2.4 Fiscal impacts 87

4.2.5 Perceptions and concerns about migration 89

4.3 Conclusions 92

CHAPTER 5

Policies to enhance human development outcomes 95

5.1 The core package 96

5.1.1 Liberalizing and simplifying regular channels 96

5.1.2 Ensuring basic rights for migrants 99

5.1.3 Reducing transaction costs associated with movement 102

5.1.4 Improving outcomes for migrants and destination communities 104

5.1.5 Enabling benefits from internal mobility 106

5.1.6 Making mobility an integral part of national development strategies 108

5.2 The political feasibility of reform 108

5.3 Conclusions 112

Notes 113 Bibliography 119

STATISTICAL ANNEX

Tables 143 Reader’s guide 203 Technical note 208 Definition of statistical terms and indicators 209 Country classification 213

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentContents

BOXES

1.1 Estimating the impact of movement 12

1.2 How movement matters to the measurement of progress 14

1.3 Basic terms used in this report 15

1.4 How do the poor view migration? 16

2.1 Counting irregular migrants 23

2.2 Conflict-induced movement and trafficking 26

2.3 Migration trends in the former Soviet Union 31

2.4 Global governance of mobility 39

3.1 China: Policies and outcomes associated with internal migration 52

3.2 Independent child migrants 59

3.3 The next generation 60

3.4 Enforcement mechanisms in Malaysia 62

4.1 How cell-phones can reduce money transfer costs: the case of Kenya 74

4.2 The 2009 crisis and remittances 75

4.3 Impacts of skills flows on human development 77

4.4 Mobility and the development prospects of small states 80

4.5 Mobility and human development: some developing country perspectives 82

5.1 Opening up regular channels—Sweden and New Zealand 97

5.2 Experience with regularization 98

5.3 Reducing paperwork: a challenge for governments and partners 103

5.4 Recognition of credentials 105

5.5 When skilled people emigrate: some policy options 109

FIGURES

2.1 Many more people move within borders than across them:

Internal movement and emigration rates, 2000–2002 22

2.2 The poorest have the most to gain from moving…

Differences between destination and origin country HDI, 2000–2002 23

2.3 … but they also move less: Emigration rates by HDI and income 25

2.4 An increasing share of migrants come from developing countries: Share

of migrants from developing countries in selected developed countries 32

2.5 Sources and trends of migration into developing countries: Migrants as a

share of total population in selected countries, 1960–2000s 33

2.6 Internal migration rates have increased only slightly: Trends in lifetime

internal migration intensity in selected countries, 1960–2000s 34

2.7 Global income gaps have widened: Trends in real per capita GDP,

1960–2007 35

2.8 Welcome the high-skilled, rotate the low-skilled: Openness to legal

immigration in developed versus developing countries, 2009 36

2.9 Enforcement practices vary: Interventions and procedures regarding

irregular migrants, 2009 37

2.10 Cross-country evidence shows little support for the ‘numbers versus

rights’ hypothesis: Correlations between access and treatment 38

2.11 Unemployment is increasing in key migrant destinations:

Unemployment rates in selected destinations, 2007–2010 41

2.12 Migrants are in places hardest hit by the recession: Immigrants’ location

and projected GDP growth rates, 2009 42

2.13 Working-age population will increase in developing regions:

Projections of working-age population by region, 2010–2050 44

3.1 Movers have much higher incomes than stayers:

Annual income of migrants in OECD destination countries and

GDP per capita in origin countries, by origin country HDI category 50

3.2 Huge salary gains for high-skilled movers: Gaps in average

professional salaries for selected country pairs, 2002–2006 50

3.3 Significant wage gains to internal movers in Bolivia, especially the

less well educated: Ratio of destination to origin wages for internal

migrants in Bolivia, 2000 51

3.4 Poverty is higher among migrant children, but social transfers can help:

Effects of transfers on child poverty in selected countries, 1999–2001 53

3.5 Costs of moving are often high: Costs of intermediaries in selected

corridors against income per capita, 2006–2008 54

3.6 Moving costs can be many times expected monthly earnings:

Costs of movement against expected salary of low-skilled

Indonesian workers in selected destinations, 2008 54

3.7 The children of movers have a much greater chance of surviving:

Child mortality at origin versus destination by origin country

HDI category, 2000 census or latest round 55

3.8 Temporary and irregular migrants often lack access to health care

services: Access to health care by migrant status in developed

versus developing countries, 2009 57

3.9 Gains in schooling are greatest for migrants from low-HDI countries:

Gross total enrolment ratio at origin versus destination by

origin country HDI category, 2000 census or latest round 58

3.10 Migrants have better access to education in developed countries:

Access to public schooling by migrant status in developed versus

developing countries, 2009 58

3.11 Voting rights are generally reserved for citizens: Voting rights in local

elections by migrant status in developed versus developing countries,

2009 61

3.12 School enrolment among refugees often exceeds that of host

communities in developing countries: Gross primary enrolment ratios—

refugees, host populations and main countries of origin, 2007 64

3.13 Significant human development gains to internal movers:

Ratio of migrants’ to non-migrants’ estimated HDI in selected

developing countries, 1995–2005 67

3.14 Migrants are generally as happy as locally-born people: Self-reported

happiness among migrants and locally-born people around the world,

2005/2006 68

4.1 The global recession is expected to impact remittance flows: Projected

trends in remittance flows to developing regions, 2006–2011 75

4.2 Skilled workers move similarly across and within nations: Population

and share of skilled workers who migrate internally and internationally 78

4.3 Support for immigration is contingent on job availability:

Attitudes towards immigration and availability of jobs, 2005/2006 90

4.4 When jobs are limited, people favour the locally born: Public opinion

about job preferences by destination country HDI category, 2005/2006 91

4.5 Many people value ethnic diversity: Popular views about the value

of ethnic diversity by destination country HDI category, 2005/2006 92

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ContentsHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

5.1 Ratification of migrants’ rights convention has been limited:

Ratification of selected agreements by HDI category, as of 2009 100

5.2 Support for opportunity to stay permanently:

Preferences for temporary versus permanent migration, 2008 110

MAPS

1.1 Borders matter: HDI in United States and Mexican border localities, 2000 10

1.2 Migrants are moving to places with greater opportunities: Human

development and inter-provincial migration flows in China, 1995–2000 11

2.1 Most movement occurs within regions: Origin and destination of

international migrants, circa 2000 24

3.1 Conflict as a driver of movement in Africa: Conflict, instability and

population movement in Africa 63

4.1 Remittances flow primarily from developed to developing regions:

Flows of international remittances, 2006–2007 73

TABLES

2.1 Five decades of aggregate stability, with regional shifts:

Regional distribution of international migrants, 1960–2010 30

2.2 Policy makers say they are trying to maintain existing immigration levels:

Views and policies towards immigration by HDI category, 2007 34

2.3 Over a third of countries significantly restrict the right to move:

Restrictions on internal movement and emigration by HDI category 40

2.4 Dependency ratios to rise in developed countries and remain steady in

developing countries: Dependency ratio forecasts of developed versus

developing countries, 2010–2050 45

4.1 PRSs recognize the multiple impacts of migration:

Policy measures aimed at international migration in PRSs, 2000–2008 83

STATISTICAL ANNEX TABLES

A Human movement: snapshots and trends 143

B International emigrants by area of residence 147

C Education and employment of international migrants in OECD countries

(aged 15 years and above) 151

D Conflict and insecurity-induced movement 155

E International financial flows: remittances, official development

assistance and foreign direct investment 159

F Selected conventions related to human rights and migration

(by year of ratification) 163

G Human development index trends 167

H Human development index 2007 and its components 171

I1 Human and income poverty 176

I2 Human and income poverty: OECD countries 180

J Gender-related development index and its components 181

K Gender empowerment measure and its components 186

L Demographic trends 191

M Economy and inequality 195

N Health and education 199

OverviewHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Overview

Consider Juan. Born into a poor family in rural Mexico, his family struggled to pay for his health care and education. At the age of 12, he dropped out of school to help support his family. Six years later, Juan followed his uncle to Canada in pursuit of higher wages and better opportunities. Life expectancy in Canada is five years higher than in Mexico and incomes are three times greater. Juan was selected to work temporarily in Canada, earned the right to stay and eventu- ally became an entrepreneur whose business now employs native-born Canadians. This is just one case out of millions of people every year who find new opportunities and freedoms by migrating, benefiting themselves as well as their areas of ori- gin and destination.

Now consider Bhag yawati. She is a mem- ber of a lower caste and lives in rural Andhra Pradesh, India. She travels to Bangalore city with her children to work on construction sites for six months each year, earning Rs 60 (US$1.20) per day. While away from home, her children do not attend school because it is too far from the construction site and they do not know the local language. Bhagyawati is not entitled to subsidized food or health care, nor does she vote, because she is living outside her registered district. Like millions of other inter- nal migrants, she has few options for improving her life other than to move to a different city in search of better opportunities.

Our world is very unequal. The huge differ- ences in human development across and within countries have been a recurring theme of the Human Development Report (HDR) since it was first published in 1990. In this year’s re- port, we explore for the first time the topic of migration. For many people in developing countries moving away from their home town or village can be the best—sometimes the only—option open to improve their life chances. Human mobility can be hugely effective in rais- ing a person’s income, health and education prospects. But its value is more than that: being able to decide where to live is a key element of human freedom.

When people move they embark on a journey of hope and uncertainty whether within or across international borders. Most people move in search of better opportunities, hoping to combine their own talents with resources in the destination country so as to benefit themselves and their im- mediate family, who often accompany or follow them. If they succeed, their initiative and efforts can also benefit those left behind and the society in which they make their new home. But not all do succeed. Migrants who leave friends and family may face loneliness, may feel unwelcome among people who fear or resent newcomers, may lose their jobs or fall ill and thus be unable to access the support services they need in order to prosper.

The 2009 HDR explores how better poli- cies towards human mobility can enhance human development. It lays out the case for governments to reduce restrictions on move- ment within and across their borders, so as to expand human choices and freedoms. It argues for practical measures that can improve pros- pects on arrival, which in turn will have large benefits both for destination communities and for places of origin.

How and why people move Discussions about migration typically start from the perspective of flows from developing coun- tries into the rich countries of Europe, North America and Australasia. Yet most movement in the world does not take place between develop- ing and developed countries; it does not even take place between countries. The overwhelming ma- jority of people who move do so inside their own country. Using a conservative definition, we esti- mate that approximately 740 million people are internal migrants—almost four times as many as those who have moved internationally. Among people who have moved across national borders,

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentOverview

just over a third moved from a developing to a de- veloped country—fewer than 70 million people. Most of the world’s 200 million international migrants moved from one developing country to another or between developed countries.

Most migrants, internal and international, reap gains in the form of higher incomes, bet- ter access to education and health, and improved prospects for their children. Surveys of migrants report that most are happy in their destination, despite the range of adjustments and obstacles typically involved in moving. Once established, migrants are often more likely than local resi- dents to join unions or religious and other groups. Yet there are trade-offs and the gains from mobility are unequally distributed.

People displaced by insecurity and conflict face special challenges. There are an estimated 14 million refugees living outside their country of citizenship, representing about 7 percent of the world’s migrants. Most remain near the country they fled, typically living in camps until condi- tions at home allow their return, but around half a million per year travel to developed countries and seek asylum there. A much larger number, some 26 million, have been internally displaced. They have crossed no frontiers, but may face spe- cial difficulties away from home in a country riven by conflict or racked by natural disasters. Another vulnerable group consists of people—mainly young women—who have been trafficked. Often duped with promises of a better life, their move- ment is not one of free will but of duress, some- times accompanied by violence and sexual abuse.

In general, however, people move of their own volition, to better-off places. More than three quarters of international migrants go to a country with a higher level of human develop- ment than their country of origin. Yet they are significantly constrained, both by policies that impose barriers to entry and by the resources they have available to enable their move. People in poor countries are the least mobile: for exam- ple, fewer than 1 percent of Africans have moved to Europe. Indeed, history and contemporary evidence suggest that development and migra- tion go hand in hand: the median emigration rate in a country with low human development is below 4 percent, compared to more than 8 per- cent from countries with high levels of human development.

Barriers to movement The share of international migrants in the world ’s population has remained remark- ably stable at around 3 percent over the past 50 years, despite factors that could have been expected to increase f lows. Demographic trends—an aging population in developed countries and young, still-rising populations in developing countries—and growing employ- ment opportunities, combined with cheaper communications and transport, have increased the ‘demand ’ for migration. However, those wishing to migrate have increasingly come up against government-imposed barriers to move- ment. Over the past century, the number of nation states has quadrupled to almost 200, creating more borders to cross, while policy changes have further limited the scale of mi- gration even as barriers to trade fell.

Barriers to mobility are especially high for people with low skills, despite the demand for their labour in many rich countries. Policies generally favour the admission of the better educated, for instance by allowing students to stay after graduation and inviting professionals to settle with their families. But governments tend to be far more ambivalent with respect to low-skilled workers, whose status and treatment often leave much to be desired. In many coun- tries, agriculture, construction, manufacturing and service sectors have jobs that are filled by such migrants. Yet governments often try to ro- tate less educated people in and out of the coun- try, sometimes treating temporary and irregular workers like water from a tap that can be turned on and off at will. An estimated 50 million peo- ple today are living and working abroad with ir- regular status. Some countries, such as Thailand and the United States, tolerate large numbers of unauthorized workers. This may allow those individuals to access better paying jobs than at home, but although they often do the same work and pay the same taxes as local residents, they may lack access to basic services and face the risk of being deported. Some governments, such as those of Italy and Spain, have recognized that unskilled migrants contribute to their societies and have regularized the status of those in work, while other countries, such as Canada and New Zealand, have well designed seasonal migrant programmes for sectors such as agriculture.

Most migrants, internal and international, reap gains in the form of higher incomes, better access to education and health, and improved prospects for their children

OverviewHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

While there is broad consensus about the value of skilled migration to destination coun- tries, low-skilled migrant workers generate much controversy. It is widely believed that, while these migrants fill vacant jobs, they also displace local workers and reduce wages. Other concerns posed by migrant inflows include heightened risk of crime, added burdens on local services and the fear of losing social and cultural cohe- sion. But these concerns are often exaggerated. While research has found that migration can, in certain circumstances, have negative effects on locally born workers with comparable skills, the body of evidence suggests that these effects are generally small and may, in some contexts, be entirely absent.

The case for mobility This report argues that migrants boost eco- nomic output, at little or no cost to locals. Indeed, there may be broader positive effects, for instance when the availability of migrants for childcare allows resident mothers to work out- side the home. As migrants acquire the language and other skills needed to move up the income ladder, many integrate quite naturally, making fears about inassimilable foreigners—similar to those expressed early in the 20th century in America about the Irish, for example—seem equally unwarranted with respect to newcom- ers today. Yet it is also true that many migrants face systemic disadvantages, making it difficult or impossible for them to access local services on equal terms with local people. And these prob- lems are especially severe for temporary and ir- regular workers.

In migrants’ countries of origin, the impacts of movement are felt in higher incomes and consumption, better education and improved health, as well as at a broader cultural and so- cial level. Moving generally brings benefits, most directly in the form of remittances sent to im- mediate family members. However, the benefits are also spread more broadly as remittances are spent—thereby generating jobs for local work- ers—and as behaviour changes in response to ideas from abroad. Women, in particular, may be liberated from traditional roles.

The nature and extent of these impacts de- pend on who moves, how they fare abroad and whether they stay connected to their roots

through flows of money, knowledge and ideas. Because migrants tend to come in large num- bers from specific places—for example, Kerala in India or Fujian Province in China—commu- nity-level effects can typically be larger than na- tional ones. However, over the longer term, the flow of ideas from human movement can have far-reaching effects on social norms and class structures across a whole country. The outflow of skills is sometimes seen as negative, particu- larly for the delivery of services such as educa- tion or health. Yet, even when this is the case, the best response is policies that address underlying structural problems, such as low pay, inadequate financing and weak institutions. Blaming the loss of skilled workers on the workers themselves largely misses the point, and restraints on their mobility are likely to be counter-productive— not to mention the fact that they deny the basic human right to leave one’s own country.

However, international migration, even if well managed, does not amount to a national human development strategy. With few excep- tions (mainly small island states where more than 40 percent of inhabitants move abroad), emigration is unlikely to shape the development prospects of an entire nation. Migration is at best an avenue that complements broader local and national efforts to reduce poverty and improve human development. These efforts remain as critical as ever.

At the time of writing, the world is undergo- ing the most severe economic crisis in over half a century. Shrinking economies and layoffs are af- fecting millions of workers, including migrants. We believe that the current downturn should be seized as an opportunity to institute a new deal for migrants—one that will benefit work- ers at home and abroad while guarding against a protectionist backlash. With recovery, many of the same underlying trends that have been driv- ing movement during the past half-century will resurface, attracting more people to move. It is vital that governments put in place the necessary measures to prepare for this.

Our proposal Large gains to human development can be achieved by lowering the barriers to movement and improving the treatment of movers. A bold vision is needed to realize these gains. This

Large gains to human development can be achieved by lowering the barriers to movement and improving the treatment of movers

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentOverview

report sets out a case for a comprehensive set of reforms that can provide major benefits to mi- grants, communities and countries.

Our proposal addresses the two most im- portant dimensions of the mobility agenda that offer scope for better policies: admissions and treatment. The reforms laid out in our proposed core package have medium- to long- term pay-offs. They speak not only to destina- tion governments but also to governments of origin, to other key actors—in particular the private sector, unions and non-governmental organizations—and to individual migrants themselves. While policy makers face common challenges, they will of course need to design and implement different migration policies in their respective countries, according to na- tional and local circumstances. Certain good practices nevertheless stand out and can be more widely adopted.

We highlight six major directions for re- form that can be adopted individually but that, if used together in an integrated approach, can magnify their positive effects on human devel- opment. Opening up existing entry channels so that more workers can emigrate, ensuring basic rights for migrants, lowering the trans- action costs of migration, finding solutions that benefit both destination communities and the migrants they receive, making it easier for people to move within their own countries, and mainstreaming migration into national development strategies—all have important and complementary contributions to make to human development.

The core package highlights two avenues for opening up regular existing entry channels: • We recommend expanding schemes for

truly seasonal work in sectors such as agri- culture and tourism. Such schemes have al- ready proved successful in various countries. Good practice suggests that this interven- tion should involve unions and employers, together with the destination and source country governments, particularly in design- ing and implementing basic wage guaran- tees, health and safety standards and provi- sions for repeat visits as in the case of New Zealand, for example.

• We also propose increasing the number of visas for low-skilled people, making this

conditional on local demand. Experience suggests that good practices here include: en- suring immigrants have the right to change employers (known as employer portability), offering immigrants the right to apply to extend their stay and outlining pathways to eventual permanent residence, making pro- visions that facilitate return trips during the visa period, and allowing the transfer of accu- mulated social security benefits, as adopted in Sweden’s recent reform. Destination countries should decide on the

desired numbers of entrants through political processes that permit public discussion and the balancing of different interests. Transparent mechanisms to determine the number of en- trants should be based on employer demand, with quotas according to economic conditions.

At destination, immigrants are often treated in ways that infringe on their basic human rights. Even if governments do not ratify the international conventions that protect migrant workers, they should ensure that migrants have full rights in the workplace—to equal pay for equal work, decent working conditions and collective organization, for example. They may need to act quickly to stamp out discrimina- tion. Governments at origin and destination can collaborate to ease the recognition of cre- dentials earned abroad.

The current recession has made migrants par- ticularly vulnerable. Some destination country governments have stepped up the enforcement of migration laws in ways that can infringe on migrants’ rights. Giving laid-off migrants the opportunity to search for another employer (or at least time to wrap up their affairs before departing), publicizing employment outlooks— including downturns in source countries—are all measures that can mitigate the disproportion- ate costs of the recession borne by both current and prospective migrants.

For international movement, the transaction costs of acquiring the necessary papers and meet- ing the administrative requirements to cross na- tional borders are often high, tend to be regressive (proportionately higher for unskilled people and those on short-term contracts) and can also have the unintended effect of encouraging irregular movement and smuggling. One in ten countries have passport costs that exceed 10 percent of per

The two most important dimensions of the mobility agenda that offer scope for better policies are admissions and treatment

OverviewHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

capita income; not surprisingly, these costs are negatively correlated with emigration rates. Both origin and destination governments can simplify procedures and reduce document costs, while the two sides can also work together to improve and regulate intermediation services.

It is vital to ensure that individual migrants settle in well on arrival, but it is also vital that the communities they join should not feel un- fairly burdened by the additional demands they place on key services. Where this poses challenges to local authorities, additional fis- cal transfers may be needed. Ensuring that migrant children have equal access to educa- tion and, where needed, support to catch up and integrate, can improve their prospects and avoid a future underclass. Language training is key—for children at schools, but also for adults, both through the workplace and through spe- cial efforts to reach women who do not work outside the home. Some situations will need more active efforts than others to combat dis- crimination, address social tensions and, where relevant, prevent outbreaks of violence against immigrants. Civil society and governments have a wide range of positive experience in tackling discrimination through, for example, awareness-raising campaigns.

Despite the demise of most centrally planned systems around the world, a surprising number of governments—around a third—maintain de facto barriers to internal movement. Restrictions typically take the form of reduced basic service provisions and entitlements for those not regis- tered in the local area, thereby discriminating against internal migrants, as is still the case in China. Ensuring equity of basic service provi- sion is a key recommendation of the report as regards internal migrants. Equal treatment is important for temporary and seasonal workers and their families, for the regions where they go to work, and also to ensure decent service provi- sion back home so that they are not compelled to move in order to access schools and health care.

While not a substitute for broader develop- ment efforts, migration can be a vital strategy for households and families seeking to diversify and improve their livelihoods, especially in develop- ing countries. Governments need to recognize

this potential and to integrate migration with other aspects of national development policy. A critical point that emerges from experience is the importance of national economic conditions and strong public-sector institutions in enabling the broader benefits of mobility to be reaped.

The way forward Advancing this agenda will require strong, en- lightened leadership coupled with a more deter- mined effort to engage with the public and raise their awareness about the facts around migration.

For origin countries, more systematic consid- eration of the profile of migration and its ben- efits, costs and risks would provide a better basis for integrating movement into national develop- ment strategies. Emigration is not an alternative to accelerated development efforts at home, but mobility can facilitate access to ideas, knowledge and resources that can complement and in some cases enhance progress.

For destination countries, the ‘how and when’ of reforms will depend on a realistic look at economic and social conditions, taking into account public opinion and political constraints at local and national levels.

International cooperation, especially through bilateral or regional agreements, can lead to bet- ter migration management, improved protection of migrants’ rights and enhanced contributions of migrants to both origin and destination coun- tries. Some regions are creating free-movement zones to promote freer trade while enhancing the benefits of migration—such as West Africa and the Southern Cone of Latin America. The expanded labour markets created in these regions can deliver substantial benefits to migrants, their families and their communities.

There are calls to create a new global regime to improve the management of migration: over 150 countries now participate in the Global Forum on Migration and Development. Governments, faced with common challenges, develop com- mon responses—a trend we saw emerge while preparing this report.

Overcoming Barriers fixes human develop- ment firmly on the agenda of policy makers who seek the best outcomes from increasingly com- plex patterns of human movement worldwide.

While not a substitute for broader development efforts, migration can be a vital strategy for households and families seeking to diversify and improve their livelihoods

Freedom and movement: how mobility can foster human development

The world distribution of opportunities is extremely

unequal. This inequality is a key driver of human

movement and thus implies that movement has a

huge potential for improving human development. Yet

movement is not a pure expression of choice—people

often move under constraints that can be severe, while

the gains they reap from moving are very unequally

distributed. Our vision of development as promoting

people’s freedom to lead the lives they choose

recognizes mobility as an essential component of that

freedom. However, movement involves trade-offs for

both movers and stayers, and the understanding and

analysis of those trade-offs is key to formulating

appropriate policies.

1HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

For people who move, the journey almost always entails sacrifices and uncertainty. The possible costs range from the emotional cost of separa- tion from families and friends to high monetary fees. The risks can include the physical dangers of working in dangerous occupations. In some cases, such as those of illegal border crossings, movers face a risk of death. Nevertheless, mil- lions of people are willing to incur these costs or risks in order to improve their living standards and those of their families.

A person’s opportunities to lead a long and healthy life, to have access to education, health care and material goods, to enjoy political free- doms and to be protected from violence are all strongly influenced by where they live. Someone born in Thailand can expect to live seven more years, to have almost three times as many years of education, and to spend and save eight times as much as someone born in neighbouring Myanmar.3 These differences in opportunity create immense pressures to move.

1.1 Mobility matters Witness for example the way in which human development outcomes are distributed near na- tional boundaries. Map 1.1 compares human development on either side of the United States– Mexico border. For this illustration, we use the Human Development Index (HDI)—a sum- mary measure of development used throughout this report to rank and compare countries. A pattern that jumps out is the strong correlation between the side of the border that a place is on

and its HDI. The lowest HDI in a United States border county (Starr County, Texas) is above even the highest on the Mexican side (Mexicali Municipality, Baja California).4 This pattern suggests that moving across national borders can greatly expand the opportunities available for improved well-being. Alternatively, consider the direction of human movements when re- strictions on mobility are lifted. Between 1984 and 1995, the People’s Republic of China pro- gressively liberalized its strict regime of inter- nal restrictions, allowing people to move from one region to another. Massive flows followed, largely towards regions with higher levels of human development. In this case the patterns again suggest that opportunities for improved well-being were a key driving factor (map 1.2).5

These spatial impressions are supported by more rigorous research that has estimated the effect of changing one’s residence on well-being. These comparisons are inherently difficult be- cause people who move tend to have different characteristics and circumstances from those who do not move (box 1.1). Recent academic studies that carefully disentangle these complex relations have nonetheless confirmed very large gains from moving across international borders. For example, individuals with only moderate levels of formal education who move from a typical developing country to the United States can reap an annual income gain of approximately US$10,000— roughly double the average level of per capita income in a developing country.6 Background research commissioned for this report found that

Freedom and movement: how mobility can foster human development

Every year, more than 5 million people cross international borders to go and live in a developed country.1 The number of people who move to a developing nation or within their country is much greater, al- though precise estimates are hard to come by.2 Even larger numbers of people in both destination and source places are affected by the movement of others through flows of money, knowledge and ideas.

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development1

a family who migrates from Nicaragua to Costa Rica increases the probability that their child will be enrolled in primary school by 22 percent.7

These disparities do not explain all movement. An important part of movement occurs in response to armed conflict. Some people emigrate to avoid political repression by authoritarian states. Moving can provide opportunities for people to escape the traditional roles that they were expected to fulfil in their society of origin. Young people often move in search of education and broader horizons, intend- ing to return home eventually. As we discuss in more detail in the next section, there are multiple drivers of, and constraints on, movement that ac- count for vastly different motives and experiences among movers. Nevertheless, opportunity and as- piration are frequently recurring themes.

Movement does not always lead to better human development outcomes. A point that we emphasize throughout this report is that vast inequalities characterize not only the freedom to move but also the distribution of gains from movement. When the poorest migrate, they often do so under conditions of vulnerability that reflect their limited resources and choices. The prior information they have may be limited or misleading. Abuse of migrant female do- mestic workers occurs in many cities and coun- tries around the world, from Washington and London to Singapore and the Gulf Cooperation Council (GCC) states. Recent research in the Arab states found that the abusive and

exploitative working conditions sometimes as- sociated with domestic work and the lack of re- dress mechanisms can trap migrant women in a vicious circle of poverty and HIV vulnerability.8 The same study found that many countries test migrants for HIV and deport those found to carry the virus; few source countries have re-in- tegration programs for migrants who are forced to return as a result of their HIV status.9

Movement across national borders is only part of the story. Movement within national borders is actually larger in magnitude and has enormous potential to enhance human devel- opment. This is partly because relocating to an- other country is costly. Moving abroad not only involves substantial monetary costs for fees and travel (which tend to be regressive—see chapter 3), but may also mean living in a very different culture and leaving behind your network of friends and relations, which can impose a heavy if unquantifiable psychological burden. The lift- ing of what were often severe barriers to internal movement in a number of countries (including but not limited to China) has benefited many of the world’s poorest people—an impact on human development that would be missed if we were to adopt an exclusive focus on interna- tional migration.

The potential of enhanced national and inter- national mobility to increase human well-being leads us to expect that it should be a major focus of attention among development policy makers

Map 1.1 Borders matter HDI in United States and Mexican border localities, 2000

Source: Anderson and Gerber (2007a).

Mexicali: HDI = 0.757

Starr: HDI = 0.766

HDI, 2000

0.636 – 0.700 0.701 – 0.765 0.766 – 0.830 0.831 – 0.895 0.896 – 0.950

1HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

and researchers. This is not the case. The academic literature dealing with the effects of migration is dwarfed by research on the consequences of international trade and macroeconomic poli- cies, to name just two examples.10 While the international community boasts an established institutional architecture for governing trade and financial relations among countries, the governance of mobility has been well character- ized as a non-regime (with the important excep- tion of refugees).11 This report is part of ongoing efforts to redress this imbalance. Building on the recent work of organizations such as the International Organization for Migration (IOM), the International Labour Organization (ILO), the World Bank and the Office of the United Nations High Commissioner for

Refugees (UNHCR), and on discussions in such arenas as the Global Forum on Migration and Development, we argue that migration deserves greater attention from governments, interna- tional organizations and civil society.12 This is not only because of the large potential gains to the world as a whole from enhanced movement, but also because of the substantial risks faced by many who move—risks that could be at least partly offset by better policies.

1.2 Choice and context: understanding why people move There is huge variation in the circumstances sur- rounding human movement. Thousands of Chin have emigrated to Malaysia in recent years to es- cape persecution by Myanmar’s security forces,

Map 1.2 Migrants are moving to places with greater opportunities Human development and inter-provincial migration flows in China, 1995–2000

Source: UNDP (2008a) and He (2004).

HDI, 1995

0.000 – 0.600 0.601 – 0.700 0.701 – 0.800 0.801 – 1

> 2,500,000

Number of migrants, 1995–2000

No data

1,000,000– 2,500,000

150,000–1,000,000

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development1

but live under constant fear of detection by civil- ian paramilitary groups.13 More than 3,000 people are believed to have drowned between 1997 and 2005 in the Straits of Gibraltar while trying to enter Europe illegally on makeshift boats.14 These experiences contrast with those of hundreds of poor Tongans who have won a lottery to settle in New Zealand, or of the hundreds of thousands of Poles who moved to better paid jobs in the United Kingdom under the free mobility regime of the European Union introduced in 2004.

Our report deals with various types of move- ment, including internal and international, tem- porary and permanent, and conflict-induced. The usefulness of casting a broad net over all of these cases might be questioned. Are we not talking about disparate phenomena, with widely different causes and inherently dissimilar out- comes? Wouldn’t our purpose be better served if we limited our focus to one type of migration and studied in detail its causes, consequences and implications?

We don’t think so. While broad types of human movement do vary significantly in their drivers and outcomes, this is also true of more spe- cific cases within each type. International labour

migration, to take one example, covers cases rang- ing from Tajik workers in the Russian Federation construction industry, impelled to migrate by harsh economic conditions in a country where most people live on less than US$2 a day, to highly coveted East Asian computer engineers recruited by the likes of Motorola and Microsoft.

Conventional approaches to migration tend to suffer from compartmentalization. Distinctions are commonly drawn between mi- grants according to whether their movement is classed as forced or voluntary, internal or interna- tional, temporary or permanent, or economic or non-economic. Categories originally designated to establish legal distinctions for the purpose of governing entry and treatment can end up play- ing a dominant role in conceptual and policy thinking. Over the past decade, scholars and pol- icy makers have begun to question these distinc- tions, and there is growing recognition that their proliferation obscures rather than illuminates the processes underlying the decision to move, with potentially harmful effects on policy-making.15

In nearly all instances of human movement we can see the interaction of two basic forces, which vary in the degree of their influence. On

Box 1.1 Estimating the impact of movement

Key methodological considerations affect the measurement of both

returns to individuals and effects on places reported in the exten-

sive literature on migration. Obtaining a precise measure of impacts

requires a comparison between the well-being of someone who mi-

grates and their well-being had they stayed in their original place.

The latter is an unknown counterfactual and may not be adequately

proxied by the status of non-migrants. Those who move internation-

ally tend to be better educated and to have higher levels of initial

income than those who do not, and so can be expected to be better

off than those who stay behind. There is evidence that this phenom-

enon—known technically as migrant selectivity—is also present in

internal migration (see chapter 2). Comparisons of groups with similar

observable characteristics (gender, education, experience, etc.) can

be more accurate but still omit potentially important characteristics,

such as attitudes towards risk.

There are a host of other methodological problems. Difficulties

in identifying causality plague estimates of the impact of remittances

on household consumption. Understanding how migration affects

labour markets in the destination place is also problematic. Most

studies have tried to look at the impact on wages at the regional level

or on particular skill groups. These may still be subject to selection

bias associated with individual choices of location. A key issue, dis-

cussed in chapter 4, is whether the migrants’ skills substitute for or

complement those of local people; determining this requires accurate

measures of these skills.

One increasingly popular approach seeks to exploit quasi- or

manufactured randomization to estimate impacts. For example, New

Zealand’s Pacific Access Category allocated a set of visas randomly,

allowing the impact of migration to be assessed by comparing lottery

winners with unsuccessful applicants.

There is also an important temporal dimension. Migration has

high upfront costs and the gains may take time to accrue. For ex-

ample, returns in the labour market tend to improve significantly

over time as country-specific skills are learned and recognized. A

migrant’s decision to return is an additional complication, affecting

the period over which impacts should be measured.

Finally, as we discuss in more detail in the next chapter, migration

analysis faces major data constraints. Even in the case of rich coun-

tries, comparisons are often difficult to make for very basic reasons,

such as differences in the definition of migrants.

Source: Clemens, Montenegro and Pritchett (2008), McKenzie, Gibson and Stillman (2006).

1HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

the one hand we have individuals, families and sometimes communities, who decide to move of their own free will in order to radically alter their circumstances. Indeed, even when people are im- pelled to move by very adverse conditions, the choices they make almost always play a vital role. Research among Angolan refugees settling in northwest Zambia, for example, has shown that many were motivated by the same aspirations that impel those who are commonly classified as economic migrants.16 Similarly, Afghans fleeing conflict go to Pakistan or Iran via the same routes and trading networks established decades ago for the purposes of seasonal labour migration.17

On the other hand, choices are rarely, if ever, unconstrained. This is evident for those who move to escape political persecution or economic deprivation, but it is also vital for understanding decisions where there is less compulsion. Major factors relating to the structure of the economy and of society, which are context-specific but also change over time, frame decisions to move as well as to stay. This dynamic interaction between indi- vidual decisions and the socio-economic context in which they are taken—sometimes labelled in sociological parlance the ‘agency–structure inter- action’—is vital for understanding what shapes human behaviour. The evolution over time of key structural factors is dealt with in chapter 2.

Consider the case of the tens of thousands of Indonesian immigrants who enter Malaysia every year. These flows are driven largely by the wide income differentials between these countries. But the scale of movement has also grown steadily since the 1980s, whereas the income gap be- tween the two countries has alternately widened and narrowed over the same period.18 Broader socio-economic processes have clearly played a part. Malaysian industrialization in the 1970s and 1980s generated a massive movement of Malays from the countryside to the cities, creat- ing acute labour scarcity in the agricultural sector at a time when the commercialization of farming and rapid population growth were producing a surplus of agricultural labour in Indonesia. The fact that most Indonesians are of similar ethnic, linguistic and religious backgrounds to Malays doubtless facilitated the flows.19

Recognition of the role of structural factors in determining human movement has had a deep impact on migration studies. While early attempts

to conceptualize migration flows focused on dif- ferences in living standards, in recent years there has been growing understanding that these differ- ences only partly explain movement patterns.20 In particular, if movement responds only to income differentials, it is hard to explain why many suc- cessful migrants choose to return to their country of origin after several years abroad. Furthermore, if migration were purely determined by wage dif- ferences, then we would expect to see large move- ments from poor to rich countries and very little movement among rich countries—but neither of these patterns holds in practice (chapter 2).

These observed patterns led to several strands of research. Some scholars recognized that a focus on the individual distracts from what is typically a family decision and indeed strategy (as when some family members move while oth- ers stay at home).21 The need to go beyond the assumption of perfectly competitive markets also became increasingly evident. In particu- lar, credit markets in developing countries are highly imperfect, while household livelihoods often depend on such volatile sectors as agri- culture. Sending a family member elsewhere allows the family to diversify against the risk of bad outcomes at home.22 Other researchers emphasized how structural characteristics and long-run trends in both origin and destination places—often labelled ‘push’ and ‘pull’ factors— shape the context in which movement occurs. Movement, for example, can result from grow- ing concentration in the ownership of assets such as land, making it difficult for people to subsist through their traditional modes of production.23 It was also recognized that the opportunities available to migrants are constrained by barriers to entry, as we discuss in chapters 2 and 3, and by the way in which labour markets function, as shown by the considerable evidence that both in- ternational and internal migrants are channelled into lower-status and worse-paid occupations.

Most importantly, theories that empha- size purely economic factors fail to capture the broader social framework in which decisions are taken. For example, young men among the lower caste Kolas in the Central Gujarat region of India commonly seek factory jobs outside their village in order to break away from subordinate caste relations. This occurs despite the fact that fac- tory wages are not higher, and in some cases are

Theories that emphasize purely economic factors fail to capture the broader social framework in which decisions to migrate are taken

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development1

lower, than what they would earn as agricultural day labourers at home.24 Escaping traditional hi- erarchies can be an important factor motivating migration (chapter 3).

Moreover, the relationship between move- ment and economics is far from unidirectional. Large-scale movements of people can have pro- found economic consequences for origin and destination places, as we will discuss in detail in chapter 4. Even the way in which we think about basic economic concepts is affected by the move- ment of people, as can be illustrated by the issues raised for the measurement of per capita incomes and economic growth (box 1.2).

1.3 Development, freedom and human mobility Our attempt to understand the implications of human movement for human development be- gins with an idea that is central to the approach of this report. This is the concept of human

development as the expansion of people’s free- doms to live their lives as they choose. This con- cept—inspired by the path-breaking work of Nobel laureate Amartya Sen and the leadership of Mahbub ul Haq and also known as the ‘ca- pabilities approach’ because of its emphasis on freedom to achieve vital ‘beings and doings’— has been at the core of our thinking since the first Human Development Report in 1990, and is as relevant as ever to the design of effective policies to combat poverty and deprivation.25 The capabilities approach has proved powerful in reshaping thinking about topics as diverse as gender, human security and climate change.

Using the expansion of human freedoms and capabilities as a lens has significant implications for how we think about human movement. This is because, even before we start asking whether the freedom to move has significant effects on in- comes, education or health, for example, we rec- ognize that movement is one of the basic actions

Box 1.2 How movement matters to the measurement of progress

Attempts to measure the level of development of a country rely on

various indicators designed to capture the average level of well-

being. While a traditional approach uses per capita income as a proxy

for economic development, this report has promoted a more com-

prehensive measure: the Human Development Index (HDI). However,

both of these approaches are based on the idea of evaluating the

well-being of those who reside in a given territory.

As researchers at the Center for Global Development and

Harvard University have recently pointed out, these approaches to

measuring development prioritize geographical location over people

in the evaluation of a society’s progress. Thus, if a Fijian moves to

New Zealand and her living standards improve as a result, traditional

measures of development will not count that improvement as an in-

crease in the development of Fiji. Rather, that person’s well-being will

now be counted in the calculation of New Zealand’s indicator.

In background research carried out for this report, we dealt with

this problem by proposing an alternative measure of human devel-

opment. We refer to this as the human development of peoples (as

opposed to the human development of countries), as it captures the

level of human development of all people born in a particular country.

For instance, instead of measuring the average level of human devel-

opment of people who live in the Philippines, we measure the aver-

age level of human development of all individuals who were born in

the Philippines, regardless of where they now live. This new measure

has a significant impact on our understanding of human well-being.

In 13 of the 100 nations for which we can calculate this measure, the

HDI of their people is at least 10 percent higher than the HDI of their

country; for an additional nine populations, the difference is between

5 and 10 percent. For 11 of the 90 populations for which we could

calculate trends over time, the change in HDI during the 1990–2000

period differed by more than 5 percentage points from the average

change for their country. For example, the HDI of Ugandans went up

by nearly three times as much as the HDI of Uganda.

Throughout the rest of this report, we will continue to adopt

the conventional approach for reasons of analytical tractability and

comparability with the existing literature. We also view these two

measures as complements rather than substitutes: one captures

the living standards of people living in a particular place, the other

of people born in a particular place. For example, when we anal-

yse human development as a cause of human movement, as we

do throughout most of this report, then the country measure will be

more appropriate because it will serve as an indicator of how living

standards differ across places. For the purposes of evaluating the

success of different policies and institutions in generating well-being

for the members of a society, however, there is a strong case for

adopting the new measure.

Source: Ortega (2009 ) and Clemens and Pritchett (2008).

1HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

that individuals can choose to take in order to realize their life plans. In other words, the ability to move is a dimension of freedom that is part of development—with intrinsic as well as potential instrumental value.

The notion that the ability to change one’s place of residence is a fundamental component of human freedom has been traced back to clas- sical philosophy in several intellectual traditions. Confucius wrote that “good government obtains when those who are near are made happy, and those who are far off are attracted to come,”26 while Socrates argued that “anyone who does not like us and the city, and who wants to em- igrate to a colony or to any other city, may go where he likes, retaining his property.”27 In 1215, England’s Magna Carta guaranteed the freedom “to go out of our Kingdom, and to return safely and securely, by land or water.” More recently, American philosopher Martha Nussbaum ar- gued that mobility is one of a set of basic human functional capabilities that can be used to assess the effective freedom that individuals have to carry out their life plans.28

Yet world history is replete with the experi- ences of societies that severely limited human development by restricting movement. Both feudalism and slavery were predicated on the physical restriction of movement. Several re- pressive regimes in the 20th century relied on the control of internal movement, including the Pass Laws of South African apartheid and the propiska system of internal passports in Soviet Russia. The subsequent demise of such restric- tions contributed to dramatic expansions in the freedoms enjoyed by these countries’ peoples.

Our report seeks to capture and examine the full set of conditions that affect whether individu- als, families or communities decide to stay or to move. These conditions include people’s resources or entitlements as well as the way in which dif- ferent constraints—including those associated with policies, markets, security, culture and val- ues—determine whether movement is an option for them. People’s ability to choose the place they call home is a dimension of human freedom that we refer to as human mobility. Box 1.3 defines this and other basic terms used in this report.

The distinction between freedoms and actions is central to the capabilities approach. By referring to the capability to decide where to

live as well as the act of movement itself, we rec- ognize the importance of the conditions under which people are, or are not, able to choose their place of residence. Much conventional analysis of migration centres on studying the effect of movement on well-being. Our concern, however, is not only with movement in itself but also with the freedom that people have to decide whether to move. Mobility is a freedom—movement is the exercise of that freedom.29

We understand human mobility as a posi- tive and not only a negative freedom. In other words, the absence of formal restrictions on the movement of people across or within borders does not in itself make people free to move if

Box 1.3 Basic terms used in this report

Human Development Index (HDI) A composite index measuring average

achievement in three basic dimensions of human development: a long

and healthy life, access to knowledge and a decent standard of living.

Developed/developing We call countries that have achieved an HDI of 0.9

or higher developed, and those that have not developing.

Low/medium/high/very high HD A classification of countries based on

the value of the HDI according to the most recent data. The ranges are

0–0.499 for low HDI, 0.500–0.799 for medium HDI, 0.800–0.899 for high

HDI and greater than 0.900 for very high HDI.

Internal migration Human movement within the borders of a country,

usually measured across regional, district or municipal boundaries.

International migration Human movement across international borders,

resulting in a change of country of residence.

Migrant An individual who has changed her place of residence either by

crossing an international border or by moving within her country of origin

to another region, district or municipality. An emigrant is a migrant viewed

from the perspective of the origin country, while an immigrant is a migrant

viewed from the perspective of the destination country. While sometimes

the term ‘migrant’ (as opposed to ’immigrant’) has been reserved for

temporary migration, we do not adopt such a distinction in this report.

Human mobility The ability of individuals, families or groups of people to

choose their place of residence.

Human movement The act of changing one’s place of residence.

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development1

they lack the economic resources, security and networks necessary to enjoy a decent life in their new home, or if informal constraints such as dis- crimination significantly impede the prospects of moving successfully.

Let us illustrate the implications of this ap- proach with a couple of examples. In the case of human trafficking, movement comes together with brutal and degrading types of exploita- tion. By definition, trafficking is an instance of movement in which freedoms become restricted by means of force, deception and/or coercion. Commonly, a trafficked individual is not free to choose to abort the trip, to seek alternative employment once she gets to her destination, or to return home. A trafficked person is physically moving, but doing so as a result of a restriction on her ability to decide where to live. From a capa- bilities perspective, she is less, not more, mobile.

Alternatively, consider the case of some- one who has to move because of the threat of political persecution or because of degraded environmental conditions. In these cases exter- nal circumstances have made it more difficult,

perhaps impossible, for her to remain at home. These circumstances restrict the scope of her choices, reducing her freedom to choose where to live. The induced movement may very well coincide with a further deterioration in her liv- ing conditions, but this does not mean that the movement is the cause of that deterioration. In fact, if she were not able to move, the outcome would probably be much worse.

If it is tempting to view the distinction be- tween mobility and movement as somewhat academic, we should take this opportunity to emphasize that freedom to choose where to live emerged as an important theme in research to find out what poor people think about migration (box 1.4). In the end, their views matter more than those of the experts, since it is they who must take the difficult decision as to whether or not to risk a move.

1.4 What we bring to the table Putting people and their freedom at the centre of development has implications for the study of human movement. In the first place, it requires

Box 1.4 How do the poor view migration?

In recent years there has been growing interest in the use of qualita-

tive methods to understand how people living in poverty view their

situation, as indicated by the landmark World Bank study Voices of

the Poor, published in 2000. In preparing the current report we com-

missioned research to investigate relevant findings of Participatory

Poverty Assessments—large-scale studies that combine qualitative

and quantitative research methods to study poverty from the point of

view of the poor. What emerged is that moving is commonly described

by the poor as both a necessity—part of a coping strategy for families

experiencing extreme hardship—and an opportunity—a means of ex-

panding a household’s livelihoods and ability to accumulate assets.

In Niger, two thirds of respondents indicated that in order to cope

with lack of food, clothing or income they had left their homes and

looked for livelihoods elsewhere. Some households reported mem-

bers leaving in search of paid work, particularly to reduce pressures

on dwindling food supplies in times of scarcity. In the villages of Ban

Na Pieng and Ban Kaew Pad, Thailand, participants described mi-

gration as one of the ways in which a family’s socio-economic status

could be enhanced. For these communities, remittances from abroad

enabled those left behind to invest in commercial fishing and thus

expand the family’s standing and influence.

Seasonal internal migration was the most common type of mi-

gration discussed in focus groups with the poor. When international

migration was discussed, it was described as something for the bet-

ter off. For instance, participants in the Jamaica study said that the

better off, unlike the poor, have influential contacts that help them

acquire the necessary visas to travel and work abroad. Similarly, in

Montserrat participants described how the more educated and finan-

cially better off were able to leave the country after the 1995 volcano

eruption, while the less well off stayed on despite the devastation.

Participatory Poverty Assessments give us a good picture of

how poor people see movement but may be uninformative about

how others have managed to move out of poverty, as these assess-

ments are by design limited to people who are still poor. A more re-

cent study of 15 countries carried out by the World Bank examines

pathways out of poverty. In these studies, the ability to move evolved

as a common theme in conversations about freedom. In Morocco,

young women expressed frustration with traditional restrictions that

limit women’s ability to travel without a male escort or search for

employment outside the home. Men described the ability to migrate

as both a freedom and a responsibility, because with the freedom to

move comes the responsibility to remit.

Source: Azcona (2009 ), Narayan, Pritchett, and Kapoor (2009 ), World Bank (2000 ), World Bank (2003), and ActionAid International (2004).

1HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

us to understand what makes people less or more mobile. This means considering why people choose to move and what constraints encourage them or deter them from making that choice. In chapter 2, we look at both choices and constraints by studying the macro patterns of human move- ment over space and time. We find that these patterns are broadly consistent with the idea that people move to enhance their opportunities, but that their movement is strongly constrained by policies—both in their place of origin and at their destinations—and by the resources at their disposal. Since different people face different constraints, the end result is a process character- ized by significant inequalities in opportunities to move and returns from movement.

We explore how these inequalities interact with policies in chapter 3. While, as we have emphasized in this introductory chapter, there is considerable intrinsic value to mobility, its instrumental value for furthering other dimen- sions of human development can also be of enor- mous significance. But while people can and do expand other freedoms by moving, the extent to which they are able to do so depends greatly on the conditions under which they move. In chapter 3 we look at the outcomes of migration in different dimensions of human development, including incomes and livelihoods, health, edu- cation and empowerment. We also review the cases in which people experience deteriorations in their well-being during movement—when this is induced by trafficking or conflict, for ex- ample—and argue that these cases can often be traced back to constraints on the freedom of in- dividuals to choose where they live.

A key point that emerges in chapter 3 is that human movement can be associated with trade- offs—people may gain in some and lose in other dimensions of freedom. Millions of Asian and Middle Eastern workers in the GCC states ac- cept severe limitations on their rights as a condi- tion for permission to work. They earn higher pay than at home, but cannot be with their families, obtain permanent residence or change employ- ers. Many cannot even leave, as their passports are confiscated on entry. For many people around the world the decision to move involves leaving their children behind. In India, seasonal workers are in practice excluded from voting in elections when these are scheduled during the peak period of

internal movements.30 People living and working with irregular status are often denied a whole host of basic entitlements and services and lead their lives in constant fear of arrest and deportation. Understanding the effects of movement requires the systematic analysis of these multiple dimen- sions of human development in order to gain a better sense of the nature and extent of these trade- offs, as well as the associated policy implications.

More complex trade-offs occur when mov- ers have an effect on the well-being of non- movers. Indeed, the perception that migration generates losses for those in destination coun- tries has been the source of numerous debates among policy makers and academics. Chapter 4 focuses on these debates. The evidence we present strongly suggests that fears about the negative effects of movement on stayers (both at source and destination) are frequently over- stated. However, sometimes these concerns are real and this has significant implications for the design of policy.

If movement is constrained by policies and resources, yet enhanced mobility can signifi- cantly increase the well-being of movers while often also having positive effects on stayers, what should policy towards human movement look like? In chapter 5, we argue that it should look very different from what we see today. In par- ticular, it should be redesigned to open up more opportunities for movement among low-skilled workers and to improve the treatment of movers at their destinations.

We do not advocate wholesale liberalization of international mobility. This is because we recognize that people at destination places have a right to shape their societies, and that borders are one way in which people delimit the sphere of their obligations to those whom they see as mem- bers of their community. But we also believe that people relate to each other in myriad ways and that their moral obligations can operate at differ- ent levels. This is primarily because individuals don’t belong to just one society or group. Rather than being uniquely or solely defined by their re- ligion, race, ethnicity or gender, individuals com- monly see themselves through the multiple prisms of a set of identities. As Amartya Sen has power- fully put it, “A Hutu labourer from Kigali… is not only a Hutu, but also a Kigalian, a Rwandan, an African, a labourer and a human being.”31

While there is considerable intrinsic value to mobility, its instrumental value for furthering other dimensions of human development can also be of enormous significance

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development1

The responsibilities of distributive justice are overlapping and naturally intersect national boundaries; as such, there is no contradiction between the idea that societies may design insti- tutions with the primary purpose of generating just outcomes among their members, and the idea that the members of that same society will share an obligation to create a just world with and for their fellow humans outside that soci- ety. There are many ways in which these obli- gations are articulated: the creation of charities and foundations, the provision of development aid, assistance in building national institutions, and the reform of international institutions so as to make them more responsive to the needs of poor countries are just some of them. However, our analysis, which informs the recommenda- tions in chapter 5, suggests that reducing restric- tions on the entry of people—in particular of low-skilled workers and their families—into better-off developed and developing countries is one relatively effective way of discharging these obligations.

Our report’s policy recommendations are not only based on our view of how the world should be. We recognize that the formulation of policies towards human movement must contend with what can at times look like formidable political opposition to greater openness. However, hav- ing considered issues of political feasibility, we argue that a properly designed programme of lib- eralization—designed so as to respond to labour market needs in destination places while also addressing issues of equity and non-discrimina- tion—could generate significant support among voters and interest groups.

Our analysis builds on the contributions to thinking about human development that have been made since the concept was introduced in the 1990 HDR. That report devoted a full chap- ter to urbanization and human development, reviewing the failed experiences of policies de- signed to reduce internal migration and con- cluding: “[A]s long as differences exist between rural and urban areas, people will move to try to take advantage of better schools and social services, higher income opportunities, cultural amenities, new modes of living, technological in- novations and links to the world.”32 Like other HDRs, this one begins with the observation that

the distribution of opportunities in our world is highly unequal. We go on to argue that this fact has significant implications for understanding why and how people move and how we should reshape policies towards human movement. Our critique of existing policies towards migration is directed at the way in which they reinforce those inequalities. As noted in the 1997 HDR, it is precisely because “the principles of free global markets are applied selectively” that “the global market for unskilled labour is not as free as the market for industrial country exports or capi- tal”.33 Our emphasis on how migration enhances cultural diversity and enriches people’s lives by moving skills, labour and ideas builds on the analysis of the 2004 HDR, which dealt with the role of cultural liberty in today’s diverse world.34

At the same time, the agenda of human de- velopment is evolving, so it is natural for the treatment of particular topics to change over time. This report strongly contests the view— held by some policy makers and at times echoed in past reports—that the movement of people should be seen as a problem requiring corrective action.35 In contrast, we see mobility as vital to human development and movement as a natural expression of people’s desire to choose how and where to lead their lives.

While the potential of increased mobility for increasing the well-being of millions of people around the world is the key theme of this re- port, it is important to stress at the outset that enhanced mobility is only one component of a strategy for improving human development. We do not argue that it should be the central one, nor are we arguing that it should be placed at the same level in the hierarchy of capabilities as, say, adequate nourishment or shelter. Neither do we believe mobility to be a replacement for national development strategies directed toward invest- ing in people and creating conditions for people to flourish at home. Indeed, the potential of mo- bility to improve the well-being of disadvantaged groups is limited, because these groups are often least likely to move. Yet while human mobility is not a panacea, its largely positive effects both for movers and stayers suggest that it should be an important component of any strategy to generate sustained improvements in human development around the world.

We see mobility as vital to human development and movement as a natural expression of people’s desire to choose how and where to lead their lives

People in motion: who moves where, when and why

This chapter examines human movement across the

world and over time. The patterns are consistent with

the idea that people move to seek better opportunities,

but also that their movement is strongly constrained by

barriers—most importantly, by policies at home and at

destination and by lack of resources. Overall, the share

of people going to developed countries has increased

markedly during the past 50 years, a trend associated

with growing gaps in opportunities. Although these

flows of people are likely to slow temporarily during

the current economic crisis, underlying structural

trends will persist once growth resumes and are likely

to generate increased pressures for movement in the

coming decades.

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

People in motion: who moves where, when and why

The aim of this chapter is to characterize human movement gen- erally—to give an overview of who moves, how, why, where and when. The picture is complex and our broad brushstrokes will in- evitably fail to capture specifics. Nevertheless, the similarities and commonalities that emerge are striking, and help us understand the forces that shape and constrain migration.

We start by examining the key features of movement—its magnitude, composition and directions—in section 2.1. Section 2.2 considers how movement today resembles or differs from movement in the past. Our examination sug- gests that movement is largely shaped by policy constraints, an issue that we discuss in detail in the third section (2.3). In the last section (2.4), we turn to the future and try to understand how movement will evolve in the medium to longer term, once the economic crisis that started in 2008 is over.

2.1 Human movement today Discussions about migration commonly start with a description of flows between developing and developed countries, or what sometimes are loosely—and inaccurately—called ‘South– North’ flows. However, most movement in the world does not take place between developing and developed countries. Indeed, it does not even take place between countries. The overwhelming majority of people who move do so within the borders of their own country.

One of the reasons why this basic reality of human movement is not better known lies in se- vere data limitations. Background research con- ducted for this report sought to overcome this knowledge gap by using national censuses to cal- culate the number of internal migrants on a con- sistent basis for 24 countries covering 57 percent of the world’s population (figure 2.1).1 Even with a conservative definition of internal migration, which counts movement across only the largest zonal demarcations in a country, the number of people who move internally in our sample is six

times greater than those who emigrate.2 Using the regional patterns found in these data, we estimate that there are about 740 million inter- nal migrants in the world—almost four times as many as those who have moved internationally.

By comparison, the contemporary figure for international migrants (214 million, or 3.1 per- cent of the world’s population) looks small. Of course this global estimate is dogged by a num- ber of methodological and comparability issues, but there are good reasons to believe that the order of magnitude is right.3 Box 2.1 deals with one of the most frequently voiced concerns about the international data on migration, namely the extent to which they capture irregular migration is discussed below.

Even if we restrict attention to international movements, the bulk of these do not occur be- tween countries with very different levels of devel- opment. Only 37 percent of migration in the world is from developing to developed countries. Most migration occurs within countries in the same category of development: about 60 percent of mi- grants move either between developing or between developed countries (the remaining 3 percent move from developed to developing countries).4

This comparison relies on what is inevitably a somewhat arbitrary distinction between coun- tries that have achieved higher levels of develop- ment and those that have not. We have classified countries that have attained an HDI greater than or equal to 0.9 (on a scale of 0 to 1) as de- veloped and those that have not as developing (see box 1.3). We use this demarcation throughout this report, without intending any judgement of the merits of any particular economic or political

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

system or seeking to obscure the complex inter- actions involved in increasing and sustaining human well-being. The countries and territories thereby classified as developed feature many that would normally be included in such a list (all Western European countries, Australia, Canada, Japan, New Zealand and the United States), but also several that are less frequently labelled as de- veloped (Hong Kong (China), Singapore and the Republic of Korea, in East Asia; Kuwait, Qatar and the United Arab Emirates, in the Gulf re- gion). However, most Eastern European econo- mies, with the exception of the Czech Republic and Slovenia, are not in the top HDI category (see Statistical Table H).

One obvious reason why there is not more movement from developing to developed coun- tries is that moving is costly, and moving long distances is costlier than undertaking short journeys. The higher expense of international movement comes not only from transport costs but also from the policy-based restrictions on crossing international borders, which can be overcome only by those who have enough re- sources, possess skills that are sought after in the new host country, or are willing to run very high risks. Nearly half of all international migrants move within their region of origin and about 40 percent move to a neighbouring country. The proximity between source and destination coun- tries, however, is not solely geographical: nearly 6 out of 10 migrants move to a country where the major religion is the same as in their country of birth, and 4 out of 10 to a country where the dominant language is the same.5

The pattern of these inter- and intra-regional movements is presented in map 2.1, where the absolute magnitudes are illustrated by the thick- ness of the arrows, the size of each region has been represented in proportion to its population, and the colouring of each country represents its HDI category. Intra-regional movement dominates. To take one striking example, intra-Asian migration accounts for nearly 20 percent of all international migration and exceeds the sum total of move- ments that Europe receives from all other regions.

The fact that flows from developing to de- veloped countries account for only a minor- ity of international movement does not mean that differences in living standards are unim- portant. Quite the contrary: three quarters of

Figure 2.1 Many more people move within borders than across them Internal movement and emigration rates, 2000–2002

Ghana

Kenya

Rwanda

South Africa

Uganda

Argentina

Brazil

Chile

Colombia

Costa Rica

Ecuador

Mexico

Panama

United States

Venezuela

Cambodia

China

India

Indonesia

Malaysia

Philippines

Belarus

Portugal

Spain

A fric

a A

m e

ric a

s E

u ro

p e

A s

Lifetime internal migration intensity (%)

Emigration rate (%)

| | | | | | 0 5 10 15 20 25

Source: Bell and Muhidin (2009 ) and HDR team estimates based on Migration DRC (2007) database. Note: All emigration data are from the Migration DRC (2007) database and cover 2000–2002. The internal migration rates are based on census data from 2000 to 2002, except for Belarus (1999 ), Cambodia (1998), Colombia (2005), Kenya (1999 ) and the Philippines (1990 ).

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

international movers move to a country with a higher HDI than their country of origin; among those from developing countries, this share ex- ceeds 80 percent. However, their destinations are often not developed countries but rather other developing countries with higher living stan- dards and/or more jobs.

The difference between human development at origin and destination can be substantial. Figure 2.2 illustrates this difference—a magni- tude that we loosely call the human development ‘gains’ from migration—plotted against the ori- gin country’s HDI.6 If migrants were on average emigrating to countries with the same level of human development as their origin countries, this magnitude would be zero. In contrast, the difference is positive and generally large for all but the most developed countries. The fact that the average gain diminishes as human develop- ment increases shows that it is people from the poorest countries who, on average, gain the most from moving across borders.

That movers from low-HDI countries have the most to gain from moving internation- ally is confirmed by more systematic studies. Background research commissioned for this

report compared the HDI of migrants at home and destination and found that the differences— in both relative and absolute terms—are inversely related to the HDI of the country of origin.7

Box 2.1 Counting irregular migrants

The only comprehensive estimates of the number of foreign-born people

in the world come from the United Nations Department of Economic and

Social Affairs (UNDESA) and cover approximately 150 United Nations

(UN) member states. These estimates are primarily based on national

censuses, which attempt to count the number of people residing in a par-

ticular country at a given moment, where a resident is defined as a per-

son who “has a place to live where he or she normally spends the daily

period of rest.” In other words, national censuses attempt to count all

residents, regardless of whether they are regular or irregular.

However, there are good reasons to suspect that censuses sig-

nificantly undercount irregular migrants, who may avoid census inter-

viewers for fear that they will share information with other government

authorities. House owners may conceal the fact that they have illegal

units rented to irregular migrants. Migrants may also be more mobile

and thus harder to count.

Studies have used a variety of demographic and statistical meth-

ods to assess the magnitude of the undercount. In the United States,

the Pew Hispanic Center has developed a set of assumptions con-

sistent with census-based studies and historical demographic data

from Mexico that estimate the undercount to be approximately 12

percent. Other researchers estimated under-coverage rates in Los

Angeles during the 2000 Census at 10–15 percent. Thus it appears

that the official count in the United States misses 1–1.5 million irregu-

lar migrants, or 0.5 percent of the country’s population.

Few studies of the undercount of migrants have been conducted

in developing countries. One exception is Argentina, where a recent

study found an underestimation of the migrant stock equivalent to

1.3 percent of the total population. In other developing countries,

the undercount rates could be much higher. Estimates of the num-

ber of irregular migrants for a number of countries—including the

Russian Federation, South Africa and Thailand—range from 25 to 55

percent of the population. However, there is huge uncertainty about

the true number. According to the migration experts surveyed by the

HDR team, irregular migration was estimated to average around one

third of all migration for developing countries. An upper bound for

the number of migrants omitted from international statistics can be

obtained by assuming that none of these migrants are captured by

country censuses (that is, an undercount of 100 percent); in that case,

the resulting underestimation in the global statistics for developing

countries would be around 30 million migrants.

Source: UN (1998), Passel and Cohn (2008), Marcelli and Ong (2002), Comelatto, Lattes, and Levit (2003). See Andrienko and Guriev (2005) for the Russian Federation, and Sabates-Wheeler (2009 ) for South Africa and Martin (2009b) for Thailand.

Figure 2.2 The poorest have the most to gain from moving… Differences between destination and origin country HDI, 2000–2002

0.3

0.2

0.1

–0.1A ve

ra g

e d

if fe

re n

c e a

t d

e st

in a ti

o n b

y re

g io

Africa

Europe

Latin America and the Caribbean

Asia

Source: HDR team estimates based on Migration DRC (2007) database. Note: Averages estimated using Kernel density regressions.

North America

OceaniaOrigin country HDI

| | | | | 0.2 0.4 0.6 0.8 1

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

Migrants from low-HDI countries had the most to gain—and indeed on average saw a 15-fold in- crease in income (to US$15,000 per annum), a doubling in education enrolment rate (from 47 to 95 percent) and a 16-fold reduction in child mortality (from 112 to 7 deaths per 1,000 live births). Using comparable surveys in a number of developing countries, the study also found that self-selection—the tendency for those who move to be better off and better educated—accounted for only a fraction of these gains. Analysis of bilateral migration flows across countries, pre- pared as background research for this report, confirmed the positive effect on emigration of all components of human development at destina- tion, while finding that income differences had the most explanatory power.8 These patterns are discussed in detail in the next chapter.

Paradoxically, despite the fact that people moving out of poor countries have the most to gain from moving, they are the least mobile. For example, despite the high levels of attention given to emigration from Africa to Europe, only 3 percent of Africans live in a country different

from where they were born and fewer than 1 per- cent of Africans live in Europe. Several scholars have observed that if we correlate emigration rates with levels of development, the relation- ship resembles a ‘hump’, whereby emigration rates are lower in poor and rich countries than among countries with moderate levels of devel- opment.9 This is illustrated in figure 2.3, which shows that the median emigration rate in coun- tries with low levels of human development is only about one third the rate out of countries with high levels of human development.10 When we restrict the comparison to out-migration to developed countries, the relationship is even stronger: the median emigration rate among countries with low human development is less than 1 percent, compared to almost 5 percent out of countries with high levels of human de- velopment. Analysis of bilateral migration flows prepared as background research for this report confirmed that this pattern holds, even when controlling for characteristics of origin and des- tination countries such as life expectancy, years of schooling and demographic structure.11

Map 2.1 Most movement occurs within regions Origin and destination of international migrants, circa 2000

Source: HDR team estimates based on Migration DRC (2007) database.

Human Development Index, 2007 Very high High Medium Low The size of countries is proportional to 2007 population.

Regions Number of migrants (in millions) North America

Europe Oceania

Latin America and the Caribbean Asia

Africa

Intra- regional

migration

Europe

Asia

Oceania

Africa

Latin America and the Caribbean

North America

0.01

0.02

0.31

0.25 0.13

0.08

0.75

0.35

0.30

19.72

1.33 1.34

15.69

0.35

0.06

2.44

0.14

0.73

13.18

35.49

1.29

0.53

8.53

9.578.22

1.65

0.22

7.25

3.13

1.30

3.54

31.52

0.84

1.07

3.1 1.24

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Evidence that poverty is a constraint to emi- gration has also been found in household-level analysis: a study of Mexican households, for ex- ample, found that the probability of migration increased with higher income levels for house- hold incomes lower than US$15,000 per annum (figure 2.3, panel B). A commissioned study found that during the Monga or growing sea- son in Bangladesh, when people’s cash resources are lowest, a randomized monetary incentive significantly increased the likelihood of migrat- ing.12 The magnitude of the effect was large: giv- ing emigrants an amount equivalent to a week’s wages at destination increased the propensity to migrate from 14 to 40 percent. These results shed strong doubts on the idea, often promoted in policy circles, that development in countries of origin will reduce migratory flows.

While many migrant families do improve their standard of living by moving, this is not al- ways the case. As discussed in chapter 3, move- ment often coincides with adverse outcomes when it occurs under conditions of restricted choice. Conflict-induced migration and traffick- ing are not a large proportion of overall human movement, but they affect many of the world’s poorest people and are thus a special source of concern (box 2.2).

Another key fact about out-migration pat- terns is their inverse relation to the size of a country’s population. For the 48 states with populations below 1.5 million—which include 1 low-, 21 medium-, 12 high- and 11 very high- HDI countries—the average emigration rate is 18.4 percent, considerably higher than the world average of 3 percent. Indeed, the top 13 emigra- tion countries in the world are all small states, with Antigua and Barbuda, Grenada, and Saint Kitts and Nevis having emigration rates above 40 percent. The simple correlation between size and emigration rates is –0.61. In many cases, it is re- moteness that leads people born in small states to move in order to take advantage of opportunities elsewhere—the same factor that drives much of the rural to urban migration seen within coun- tries. Cross-country regression analysis confirms that the effect of population size on emigration is higher for countries that are far from world mar- kets—the more remote a small country is, the more people decide to leave.13 The implications of these patterns are discussed in box 4.4.

The aggregate facts just surveyed tell us where migrants come from and go to, but they do not tell us who moves. While severe data limitations impede presentation of a full global profile of mi- grants, the existing data nonetheless reveal some interesting patterns.

Approximately half (48 percent) of all in- ternational migrants are women. This share has been quite stable during the past five decades: it stood at 47 percent in 1960. This pattern con- trasts with that of the 19th century, when the majority of migrants were men.14 Yet despite recent references to the ‘feminization’ of migra- tion, it appears that numerical gender balance was largely reached some time ago. However, the aggregate stability hides trends at the regional level. While the share of women going to the European Union has increased slightly from 48

Figure 2.3 … but they also move less Emigration rates by HDI and income

Panel A: Median emigration rates by origin country HDI group

Panel B: Probability of emigration by income level in Mexican households

Low HDI

Medium HDI

High HDI

Very high HDI

To developing countries

To developed countries

| | | | | | 0 2 4 3 8 10

Median emigration rate (%)

Source: HDR team estimates based on Migration DRC (2007) and UN (2009e).

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0P ro

b a

b ili

ty o

f m

ig ra

ti o

n ( %

)

| | | | | 0 5 10 15 20

Income per capita (US$ thousands)

Source: Meza and Pederzini (2006).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

to 52 percent, that same share has dropped from 47 to 45 percent in Asia.

Of course, the relatively equal gender shares of the migrant population may hide significant dif- ferences in the circumstances of movement and the opportunities available.15 At the same time, a growing literature has challenged conventional views about the subordinate role of women in migration decisions.16 For example, a qualitative study of decisions taken by Peruvian couples mov- ing to Argentina found that many of the women moved first by themselves, because they were able to secure jobs more rapidly than their partners, who would later follow with the children.17

The data also show very large temporary flows of people. In the countries of the Organisation for Economic Co-operation and Development (OECD), temporary migrants typically repre- sent more than a third of arrivals in a given year. However, since most leave after a short period while others transit towards more permanent arrangements, the number of people on tempo- rary visas at any given moment is much smaller than the aggregate flows suggest. Indeed, 83 per- cent of the foreign-born population in OECD countries has lived there for at least five years.18 Almost all temporary migrants come for work- related reasons. Some enter into ‘circular’ ar- rangements, whereby they repeatedly enter and leave the destination country to carry out sea- sonal or temporary work, effectively maintaining two places of residence.19

It is important not to overemphasize the dis- tinction between categories of migrants, as many migrants shift between categories. Indeed, the mi- gration regime in many countries can perhaps best be understood through the analogy of the multi- ple doors of a house. Migrants can enter the house through the front door (permanent settlers), the side door (temporary visitors and workers) or the back door (irregular migrants). However, once inside a country, these channels often merge, as when temporary visitors become immigrants or slip into unauthorized status, those with irregular status gain authorization to remain, and people with permanent status decide to return.

This analogy is particularly useful for un- derstanding irregular migration. Overstaying is an important channel through which migrants become irregular, particularly in developed coun- tries. In fact, the distinction between regular and irregular is much less clear-cut than is often as- sumed. For example, it is common for people to enter a country legally, then work despite lacking a permit to do so.20 In some island states, such as Australia and Japan, overstaying is practically the only channel to irregular entry; even in many European countries, overstay appears to account for about two thirds of unauthorized migra- tion. In OECD countries, people with irregular residence or work status tend to be workers with low levels of formal education.21 The best esti- mates of the number of irregular migrants in the United States amount to about 4 percent of the

Box 2.2 Conflict-induced movement and trafficking

People affected by conflict and insecurity can suffer some of the

worst human development outcomes of all migrants. The number of

people who move as a result of conflict is significant: at the begin-

ning of 2008, there were around 14 million refugees falling under the

mandate of either UNHCR or the United Nations Relief and Works

Agency for Palestine Refugees in the Near East (UNRWA), accounting

for roughly 7 percent of all international migration. The vast majority

of refugees originate in and relocate to the poorer countries of the

world: in Asia and Africa refugees account respectively for 18 and 13

percent of all international migrants.

Even more individuals displaced by violence and conflict relo-

cate within the borders of their country. It is estimated that, in 2009,

internally displaced persons number some 26 million, including 4.9

million in Sudan, 2.8 million in Iraq and 1.4 million in the Democratic

Republic of the Congo.

It is much harder to ascertain the magnitude of human traffick-

ing. In fact, there are no accurate estimates of the stocks and flows

of people who have been trafficked. Among the reasons for this are

the fact that trafficking data are commonly mixed with data on other

forms of illegal migration or migrant exploitation, the inherent chal-

lenges in distinguishing between what is voluntary and forced, and

the very nature of human trafficking as a clandestine and criminal

activity. Many of the frequently cited figures are disputed by the coun-

tries concerned, and there is a significant gap between estimated

numbers and identified cases.

Source: IDMC (2009b), Carling (2006), Kutnick, Belser, and Danailova-Trainor (2007), de Haas (2007) and Lazcko (2009 ).

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

population or 30 percent of total migrants.22 A recent research project funded by the European Commission estimated that in 2005 irregular migrants accounted for 6–15 percent of the total stock of migrants, or about 1 percent of the popu- lation of the European Union.23 Some of these migrants are counted in official estimates of mi- gration, but many are not (box 2.1).

The over-representation of skilled, working- age people in migrant populations is one aspect of migrant selectivity. Not only do migrants tend to have higher income-earning capacity than non-migrants but they often also appear to be healthier and more productive than natives of the destination country with equivalent educa- tional qualifications. Migrant selectivity usually reflects the effect of economic, geographical or policy-imposed barriers that make it harder for low-skilled people to move. This is most evident in terms of formal education. Tertiary graduates, for example, make up 35 percent of working- age immigrants to the OECD but only about 6 percent of the working-age population in non- OECD countries.24 Immigrants to the OECD from developing countries tend to be of working age: for example, over 80 percent of those from sub-Saharan Africa fall into this group.25

What do we know about migrant selectivity in developing countries? When the migration process is more selective, individuals of work- ing age (who have higher earning capacity than those out of the labour force) form a large pro- portion of movers. Using census data, we com- pared the age profiles of migrants to people in their countries of origin in 21 developing and 30 developed countries. We found a significant dif- ference between the age profile of immigrants in developed countries and that of their countries of origin: 71 percent of migrants in developed countries are of working age, as opposed to 63 percent of the population in their origin coun- tries; in contrast, the difference is negligible in developing countries (63 versus 62 percent).

New evidence on internal migration paints a more complex picture of migrant selectivity. In Kenya, for example, commissioned research found a positive relationship between measures of human capital and migration,26 which tends to diminish with successive cohorts of migrants over time,27 a result that is consistent with the development of social and other networks that

facilitate movement. In other words, poorer people may decide to take the risk of migrat- ing as they hear news of others’ success and become more confident that they will receive the support they need in order to succeed them- selves. Other commissioned research generated education profiles for internal migrants across 34 developing countries. This showed that mi- grants were more likely than non-migrants to complete secondary school, reflecting both se- lectivity and better outcomes among migrant children (chapter 3).28

What else do we know about the relation- ship between internal and international migra- tion? Internal migration, particularly from rural to urban areas, can be a first step towards inter- national migration, as found by some studies in Mexico, Thailand and Turkey, but this is far from being a universal pattern.29 Rather, emigra- tion may foster subsequent internal migration in the home country. In Albania, migration flows to Greece in the early 1990s generated remittances, which helped to finance internal migration to urban centres; in India, international movers from the state of Kerala have freed up positions in their areas of origin and their remittances spurred a construction boom that has attracted low-skilled migrants from surrounding areas.30

Comparisons between internal and interna- tional migration can yield useful insights into the causes and implications of human movement. For example, background research for this report analysed the relationship between the size of the place of origin (as measured by its population) and skilled labour flows and found that the pat- terns were broadly similar across countries as well as within them. In particular, emigration rates for skilled workers are higher in small localities than in large ones, just as they are higher in small coun- tries than in large ones.31 These patterns reflect the importance of human interaction in driving movement. Movement both within and between nations is predominantly driven by the search for better opportunities, and in many cases—in par- ticular those involving skilled labour—oppor- tunities will be greater in places where there are other people with complementary skills. This is one of the reasons why people gravitate to urban centres, and why high-skilled professionals often move to cities and places where their profession is already well established.32

Movement both within and between nations is predominantly driven by the search for better opportunities

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

Despite our ability to establish these broad contours of movement, what we know is dwarfed by what we don’t know. Unfortunately, migra- tion data remain weak. It is much easier for policy makers to count the international move- ments of shoes and cell-phones than of nurses and construction workers. Most of our informa- tion is based on censuses, but these do not pro- vide time series of migration flows that would enable trends to be recognized nor key data for assessing the impact of migration, such as the income and other characteristics of migrants at the time of admission. Population registers can produce such time series, but very few countries have registers with that capacity. Policy makers typically require information about migrant admissions by type (e.g. contract workers, train- ees, family members, skilled professionals, etc.), so administrative data reflecting the number of visas and permits granted to different types of migrants are important. Yet none of these data sources can answer questions about the social or economic impact of international migration.

Advances have been made in recent years. The OECD, the UN, the World Bank and other agencies have compiled and published census and administrative databases that shed new light on some aspects of global flows of people. But pub- lic data still cannot answer basic questions, such as: how many Moroccans left France last year? What are the occupations of Latin Americans who took up United States residency in 2004? How has the number of Zimbabweans going to South Africa changed in recent years? How much return or circular migration occurs glob- ally, and what are the characteristics of those mi- grants? For the most part, migration data remain patchy, non-comparable and difficult to access. Data on trade and investment are vastly more de- tailed. Many aspects of human movement simply remain a blind spot for policy makers.

While some data limitations are difficult to overcome—including the problem of accurately estimating the number of irregular migrants— others should be surmountable. A logical first step is to ensure that national statistics offices fol- low international guidelines, such that every cen- sus contains core migration questions.33 Existing surveys could be slightly expanded, or existing administrative data compiled and disseminated, to increase public information on migration

processes. Adding questions on country of birth or country of previous residence to the national census would be a low-cost way forward for many countries. Another would be the public release of existing labour force data, including coun- try of birth, as Brazil, South Africa, the United States and some other countries already do. Yet another would be the inclusion of standard mi- gration questions in household surveys in coun- tries where migration has grown in importance. These improvements are worthy of government attention and increased development assistance.

2.2 Looking back We now consider how human movement has shaped world history. Doing so sheds light on the extent to which earlier movements differed from or were similar to those of today. It will also reveal the role of migration in the structural transformation of societies, the forces that drive migration and the constraints that frustrate it. We then present a more detailed discussion of the evolution of internal and international move- ments during the 20th century, with a focus on the post-World War II era. The analysis of trends during the past 50 years is key to understanding the factors causing recent changes in migration patterns and how we can expect these to con- tinue evolving in the future.

2.2.1 The long-term view Despite the widespread perception that inter- national migration is associated with the rise of globalization and trade in the late 20th cen- tury, large-scale long-distance movements were prevalent in the past. At the peak of Iberian rule in the Americas, more than half a million Spaniards and Portuguese and about 700,000 British subjects went to the colonies in the Americas.34 Through the brutal use of force, 11–12 million Africans were sent as slaves across the Atlantic between the 15th and late 19th cen- turies. Between 1842 and 1900, some 2.3 mil- lion Chinese and 1.3 million Indians travelled as contract labourers to South-East Asia, Africa and North America.35 At the close of the 19th century the fraction of foreign-born residents in many countries was higher than today.36

Going back further in time, we find human movement has been a pervasive phenomenon throughout history, present in nearly every

Unfortunately, migration data remain weak. It is much easier for policy makers to count the international movements of shoes and cell-phones than of nurses and construction workers

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

community for which historical or archaeo- logical evidence is available. Recent DNA tests support previous fossil evidence that all human beings evolved from a common ancestor from equatorial Africa, who crossed the Red Sea into Southern Arabia approximately 50,000 years ago.37 While encounters among different societ- ies often led to conflict, the peaceful coexistence of immigrants in foreign lands is also recorded. An ancient Babylonian tablet from the 18th century BCE, for example, talks about a com- munity of migrants from Uruk who fled their homes when their city was raided and, in their new home, met little resistance to their cultural practices, with their priests being allowed to inhabit the same quarters as those venerating local gods.38 The idea that migrants should be treated according to basic norms of respect is found in many ancient religious texts. The Old Testament, for example, states that “the alien living with you must be treated as one of your native-born,” whereas the Koran requires the faithful to move when their beliefs are in danger and to give aman (refuge) to non-Muslims, even if they are in conflict with Muslims.39

Population movements have played a vital role in the structural transformation of economies throughout history, thereby contributing greatly to development. Genetic and archaeological evi- dence from the Neolithic period (9500–3500 BCE) suggests that farming practices spread with the dispersal of communities after they had mas- tered the techniques of cultivation.40 The British Industrial Revolution both generated and was fuelled by rapid urban growth, driven mainly by movement from the countryside.41 The share of rural population has declined markedly in all economies that have become developed, falling in the United States from 79 percent in 1820 to below 4 percent by 1980, and even more rapidly in the Republic of Korea, from 63 percent in 1963 to 7 percent in 2008.42

An interesting episode from the standpoint of our analysis was that of the large flows from Europe to the New World during the second half of the 19th century. By 1900, more than a million people were moving out of Europe each year, spurred by the search for better conditions in the face of hunger and poverty at home. The size of these flows is staggering by contemporary standards. At its peak in the 19th century, total

emigrants over a decade accounted for 14 percent of the Irish population, 1 in 10 Norwegians, and 7 percent of the populations of both Sweden and the United Kingdom. In contrast, the number of lifetime emigrants from developing countries today is less than 3 percent of the total popula- tion of these countries. This historical episode was partly driven by falling travel costs: between the early 1840s and the late 1850s, passenger fares from Britain to New York fell by 77 per- cent in real terms.43 There were other determin- ing factors in particular cases, such as the potato famine in Ireland. These population movements had sizeable effects on both source and destina- tion countries. Workers moved from low-wage labour-abundant regions to high-wage labour- scarce regions. This contributed to significant economic convergence: between the 1850s and World War I, real wages in Sweden rose from 24 to 58 percent of those of the United States, while, over the same period, Irish wages rose from 61 to 92 percent of those in Great Britain. According to economic historians, more than two thirds of the wage convergence across countries that oc- curred in the late 19th century can be traced to the equalizing effect of migration.4 4

Remittances and return migration were also very important in the past. Remittances were sent by courier and through transfers and notes via immigrant banks, mercantile houses, postal services and, after 1900, by telegraph wire. It is estimated that the average British remitter in the United States in 1910 sent up to a fifth of his income back home, and that about a quar- ter of European migration to the United States around that time was financed through remit- tances from those already there.45 Return migra- tion was often the norm, with estimated rates of return from the United States ranging as high as 69 percent for Bulgaria, Serbia and Montenegro and 58 percent for Italy.46 In Argentina, Italian immigrants were often referred to as golondrinas (swallows) because of their tendency to return, and a contemporary observer wrote that “the Italian in Argentina is no colonist; he has no house, he will not make a sustenance… his only hope is a modest saving.”47

These population movements were enabled by a policy stance that was not only receptive to migration but in many cases actively encouraged it. This is as true of origin countries, which often

Population movements have played a vital role in the structural transformation of economies throughout history

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

subsidized passage in order to reduce pressures at home, as it was of destination governments, which invited people to come in order to consolidate set- tlements and take advantage of natural resources. For example, by the 1880s about half of migrants to Argentina received a travel subsidy, while a law passed in Brazil in 1850 allotted land to migrants free of charge.48 More generally, the late 19th cen- tury was marked by the absence of the plethora of mechanisms to control international flows of people that subsequently emerged. Until the pas- sage of restrictive legislation in 1924, for example, there was not even a visa requirement to settle per- manently in the United States, and in 1905, only 1 percent of the one million people who made the transatlantic journey to Ellis Island were denied entry into the country.49

One key distinction between the pre-World War I period and today lies in the attitudes of destination governments. While anti-immigrant sentiment could run high and often drove the erection of barriers to specific kinds of move- ment, the prevailing view among governments was that movement was to be expected and was ultimately beneficial to both origin and destina- tion societies.50 This is all the more remarkable

in societies where intolerance of minorities was prevalent and socially accepted to a far larger extent than today.51 It is also a useful reminder that the barriers to migration that characterize many developed and developing countries today are much less an immutable reality than might at first be supposed.

2.2.2 The 20th century The pro-migration consensus was not to last. Towards the end of the 19th century, many coun- tries introduced entry restrictions. The causes were varied, from the depletion of unsettled land to labour market pressures and popular senti- ment. In countries such as Argentina and Brazil the policy shift occurred through the phasing out of subsidies; in Australia and the United States it came through the imposition of entry barriers.52 Despite the introduction of these restrictions, estimates from the early 20th century indicate that the share of international migrants in the world’s population was similar if not larger than it is today. This is especially striking given the relatively high transport costs at that time.53

There was nothing in the area of migration policy even remotely resembling the rapid mul- tilateral liberalization of trade in goods and movements of capital that characterized the post-World War II period.54 Some countries en- tered bilateral or regional agreements to respond to specific labour shortages, such as the United States’ 1942 Mexican Farm Labour (Bracero) Program, which sponsored 4.6 million contracts for work in the United States over a 22-year pe- riod,55 the 1947 United Kingdom–Australia Assisted Passage Agreement, or the flurry of European labour movement agreements and guest-worker programmes.56 But early enthusi- asm for guest-worker programmes had fizzled out by the 1970s. The United States phased out its Bracero Program in 1964, and most Western European countries that had heavily relied on guest-worker programmes ceased recruitment during the 1970s oil shock.57

This lack of liberalization is consistent with the observed stability in the global share of mi- grants. As shown in table 2.1, this share (which excludes Czechoslovakia and the former Soviet Union for comparability reasons—see below) has inched up from 2.7 to 2.8 percent between 1960 and 2010. The data nonetheless reveal a

Table 2.1 Five decades of aggregate stability, with regional shifts Regional distribution of international migrants, 1960–2010

Share of population

1960 2010

Share of world migrants

Total migrants (millions)

World 74.1 2.7% 188.0 2.8% (excluding the former Soviet Union and former Czechoslovakia)

BY REGION Africa 9.2 12.4% 3.2% 19.3 10.2% 1.9% Northern America 13.6 18.4% 6.7% 50.0 26.6% 14.2% Latin America and the Caribbean 6.2 8.3% 2.8% 7.5 4.0% 1.3% Asia 28.5 38.4% 1.7% 55.6 29.6% 1.4%

GCC states 0.2 0.3% 4.6% 15.1 8.0% 38.6% Europe 14.5 19.6% 3.5% 49.6 26.4% 9.7% Oceania 2.1 2.9% 13.5% 6.0 3.2% 16.8%

BY HUMAN DEVELOPMENT CATEGORY Very high HDI 31.1 41.9% 4.6% 119.9 63.8% 12.1%

OECD 27.4 37.0% 4.2% 104.6 55.6% 10.9% High HDI 10.6 14.2% 3.2% 23.2 12.3% 3.0% Medium HDI 28.2 38.1% 1.7% 35.9 19.1% 0.8% Low HDI 4.3 5.8% 3.8% 8.8 4.7% 2.1%

Source: HDR team estimates based on UN (2009d). Note: Estimates exclude the former Soviet Union and former Czechoslovakia.

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

remarkable shift in destination places. The share in developed countries more than doubled, from 5 percent to more than 12 percent.58 An even larger increase—from 5 to 39 percent of the population—occurred in the GCC countries, which have experienced rapid oil-driven growth. In the rest of the world, however, the fraction of foreign-born people has been stable or declining. The declines are most marked in Latin America and the Caribbean, where international migra- tion has more than halved, but are also evident in Africa and the rest of Asia.

An important caveat is that these trends exclude two sets of countries for which it is dif- ficult to construct comparable time series on international migrants, namely the states of the former Soviet Union, and the two components of former Czechoslovakia. The independence of these new nations generated an artificial increase in the number of migrants, which should not be interpreted as a real increase in the prevalence of international movement (box 2.3).59

Where are recent migrants to developed countries coming from? We do not have a full

picture of bilateral flows over time, but figure 2.4 displays the evolution of the share of people from developing countries in eight developed econo- mies that have comparable information. In all but one case (the United Kingdom), there were double-digit increases in the share of migrants from developing countries.60 In many European countries, this shift is driven by the increase in migrants from Eastern European countries classed as developing according to their HDI. For example, during the 1960s only 18 percent of developing country immigrants into Germany came from Eastern Europe; 40 years later that ratio was 53 percent.

In developing countries, the picture is more mixed, although data are limited. We can com- pare the source of migrants today and several decades ago for a few countries, revealing some interesting contrasts (figure 2.5). In Argentina and Brazil, the decline in the share of foreign- born people was driven by a fall in those com- ing from the poorer countries of Europe, as those countries experienced dramatic post- war growth while much of Latin America

Box 2.3 Migration trends in the former Soviet Union

When the Soviet Union broke up in 1991, 28 million people be-

came international migrants overnight—even if they hadn’t moved

an inch. This is because statistics define an international migrant

as a person who is living outside their country of birth. These peo-

ple had moved within the Soviet Union before 1991 and were now

classified as foreign-born. Without their knowing it, they were now

‘statistical migrants’.

At one level, the reclassification makes sense. A Russian in Minsk

was living in the country of her birth in 1990; by the end of 1991, she

was technically a foreigner. But interpreting the resulting increase in

the number of migrants as an increase in international movement,

as some authors have done, is mistaken. Hence we have excluded

them, together with migrants in the former Czechoslovakia, from the

calculation of trends in table 2.1.

Has human movement increased in the former Soviet Union since

1991? On the one hand, the relaxation of propiska controls increased

human mobility. On the other, the erection of national boundaries may

have reduced the scope for movement. The picture is further com-

plicated by the fact that many movements after 1991 were returns to

the region of origin: for example, people of Russian origin returning

from central Asia.

Any attempt to understand trends in the former Soviet Union

should use comparable territorial entities. One way to do this is to

consider inter-republic migration before and after the break-up. In this

approach, anyone who moved between two republics that would later

become independent nations would be considered an international

migrant. Thus, a Latvian in St. Petersburg would be classified as an

international migrant both before and after 1991.

In background research for this report, Soviet census data were

used to construct such a series. Thus defined, the share of foreign-born

people in the republics of the USSR rose slightly from 10 percent in 1959

to 10.6 percent in 1989. After 1990, there were divergent trends across

the different states. In the Russian Federation, which became some-

thing of a magnet in the region, the migrant stock increased from 7.8 to

9.3 percent of the population. For Ukraine and the three Baltic states,

migrant shares declined, as large numbers of foreign-born people left.

In all the other states of the former Soviet Union, the absolute number of

migrants declined until 2000 and in most cases the migrant share of the

population also declined. Thus, while 30.3 million foreign-born people

lived in the territory of the Soviet Union at the time of its dissolution, the

aggregate number fell to 27.4 million in 2000 and to 26.5 million in 2005,

as many in the post-Soviet space chose to return home.

Source: Heleniak (2009 ), UN (2002), Zlotnik (1998), and Ivakhnyuk (2009 ).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

stagnated. In contrast, the rise in the immigra- tion rate in Costa Rica was driven by large flows of Nicaraguan migrants, while the reduction in Mali reflects significant declines in immigration from Burkina Faso, Guinea and Mauritania.

Many countries have experienced increases in internal migration, as shown in figure 2.6. However, this trend is far from uniform. For the 18 countries for which we have comparable information over time, there is an increasing trend in 11 countries, no clear trend in four, and a decline in two developed countries. The average rate of increase for this set of countries is around 7 percent over a decade. However, our research has also found that the share of recent migrants (defined as those who have moved between regions in the past five years) has not increased in most countries in our sample, indi- cating a possible stabilization of internal migra- tion patterns.

A levelling off or even a decline in internal migration flows is to be expected in developed and high-HDI countries, where past flows were associated with rapid urbanization that has now abated. But in many developing countries urbanization has not slowed and is expected to continue. In fact, estimates from UNDESA sug- gest that the urban share of the world’s popula- tion will nearly double by 2050 and will increase from 40 percent to over 60 percent in Africa. Urbanization is spurred in part by natural popu- lation growth in urban areas, alongside migration from rural areas and from abroad. Although it is difficult to determine the precise contributions of these different sources, it is clear that migration is an important factor in many countries.61

Urbanization can be associated with major challenges to city dwellers and the government authorities responsible for urban planning and service provision. The most visible of these chal- lenges is the 2 billion people—40 percent of urban residents—who are expected to be living in slums by 2030.62 As is well known, living con- ditions are often very poor in the slums, with in- adequate access to safe water and sanitation and insecure land tenure. As we discuss in chapters 4 and 5, it is important that urban local authori- ties be accountable to residents and adequately financed to tackle these challenges, since local planning and programmes can play a critical role in improving matters.

In sum, the period since 1960 has been marked by a growing concentration of migrants in developed countries against a background of aggregate stability in overall migration. How do we explain these patterns? Our research shows that three key factors—trends in income, popu- lation and transport costs—tended to increase movement, which simultaneously faced an in- creasingly significant constraint: growing legal and administrative barriers.

Divergence in incomes across regions, com- bined with a general increase in incomes around most of the world, is a major part of the expla- nation of movement patterns. The evolution of income inequalities shows remarkable diver- gence between most developing and developed regions, even if the East Asia–Pacific and South Asia regions have seen a mild convergence (figure 2.7, panel A).63 China presents an exception to the broad pattern of lack of convergence, with

Figure 2.4 An increasing share of migrants come from developing countries

Share of migrants from developing countries in selected developed countries

1960–1969

1990–2004

Australia

Belgium

Canada

Germany

New Zealand

Sweden

United Kingdom

United States

| | | | | | | | | 0 10 20 30 40 50 60 70 80

Share of all migrants (%)

Source: HDR team estimates based on UN (2006a).

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

national per capita income rising from 3 to 14 percent of the developed country average be- tween 1960 and 2007.64 Overall, the data indi- cate that income incentives to move from poor to rich countries have strongly increased.65

Attempts to account for this divergence have generated a vast literature, in which differences in labour and capital accumulation, technologi- cal change, policies and institutions have all been investigated.66 Whatever the ultimate driving forces, one of the key contributing factors has been differing population growth rates. As is well known, between 1960 and 2010 the spatial demographic composition of the world popula- tion shifted: of the additional 2.8 billion work- ing-aged people in the world, 9 out of 10 were in developing countries. Because labour became much more abundant in developing countries, wage differentials widened. This meant that moving to developed countries became more at- tractive and patterns of movement shifted as a result, despite—as we shall see—the raising of high barriers to admission. At the same time, average income levels in the world as a whole were increasing, as shown in panel B of figure 2.7 (even if some developing regions also saw pe- riods of decline). Since poverty is an important constraint on movement, higher average incomes made long-distance movement more feasible. In other words, as incomes rose, poorer countries moved up the ‘migration hump’, broadening the pool of potential migrants to developed countries.

Recent declines in transport and communi- cation costs have also increased movement. The real price of air travel fell by three fifths between 1970 and 2000, while the cost of communica- tions fell massively.67 The real cost of a 3-minute telephone call from Australia to the United Kingdom fell from about US$350 in 1926 to US$0.65 in 2000—and, with the advent of in- ternet telephony, has now effectively fallen to zero.68 Such trends have made it easier than ever before for people to reach and establish them- selves in more distant destinations.

Given these drivers, we would expect to see significant growth in international migration in recent decades. However, this potential has been constrained by increased policy barriers to move- ment, especially against the entry of low-skilled applicants. We turn now to a more in-depth

examination of the role that these barriers play in shaping and constraining movement today.

2.3 Policies and movement Since the emergence of modern states in the 17th century, the international legal system has been built on the bedrock of two principles: sovereignty and territorial integrity. Within this system, which includes a series of norms and constraints imposed by international law, governments police their country’s borders and enforce their right to restrict entry. This section discusses the different ways in which government policy determines how many people to admit, where these people come from, and what status is accorded to them.

Figure 2.5 Sources and trends of migration into developing countries Migrants as a share of total population in selected countries, 1960–2000s

Argentina

Brazil

Costa Rica

India

Indonesia

Thailand

Turkey

Mali

Rwanda

| | | | | 0 2 4 6 8

Share of population (%)

From developing countries From developed countries

Origin unknown

1970 2001

1960 2000

1961 2001

1971 1990

1970 2000

1965 2000

1976 1998

1978 2002

Source: HDR team estimates based on Minnesota Population Center (2008) and national census data for indicated years.

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

While there is a wealth of qualitative coun- try-level analysis of policies—especially for developed countries—severe data limitations impede comparisons of policy across countries. Measurement is intrinsically difficult because the rules take many forms and are enforced in dif- ferent ways and to varying degrees, with results that are generally not amenable to quantification. In contrast to most aspects of economic policy, for example, national statistical bureaux do not measure the effects of migration policy in ways that are consistent across countries. Most of the measures used in this report have been developed by international research and non-governmental organizations (NGOs), not by national public- sector agencies.

The measure that covers the largest number of countries and the longest time span comes from a periodic survey of policy makers conducted by UNDESA, in which governments report their views and responses to migration. The survey covers 195 countries and reflects the views of policy makers regarding the level of immigration and whether their policy is to lower, maintain or raise future levels. While it is a self-assessment, and official intentions rather than practice are in- dicated, some interesting patterns emerge (table 2.2). In 2007, some 78 percent of respondent governments viewed current immigration levels as satisfactory, while 17 percent felt them to be too high and 5 percent too low. A similar picture emerges when governments are asked to describe their policies. On both questions, developed country governments appear to be more restric- tive than those of developing countries.

These patterns indicate a significant gap between the policies that the public appears to favour in most countries—namely greater re- strictions on immigration—and actual policies, which in fact allow for significant amounts of immigration.69 While explanations for this gap are complex, several factors likely come into play.

The first is that opposition to immigration is not as monolithic as first appears, and voters often have mixed views. As we show below, in many countries, concerns about adverse employment or fiscal effects are mixed with the recognition that tolerance of others and ethnic diversity are posi- tive values. Second, organized groups such as la- bour unions, employer organizations and NGOs can have a significant effect on the formulation

Figure 2.6 Internal migration rates have increased only slightly Trends in lifetime internal migration intensity in selected countries, 1960–2000s

Table 2.2 Policy makers say they are trying to maintain existing immigration levels Views and policies towards immigration by HDI category, 2007

VERY HIGH HDI No. of Countries 7 26 6 39 7 24 7 1 39 Percent (%) 18 67 15 100 18 62 18 3 100

HIGH HDI No. of Countries 6 40 1 47 9 37 1 0 47 Percent (%) 13 85 2 100 19 79 2 0 100

MEDIUM HDI No. of Countries 17 62 4 83 18 47 3 15 83 Percent (%) 20 75 5 100 22 57 4 18 100

LOW HDI No. of Countries 4 22 0 26 4 6 0 16 26 Percent (%) 15 85 0 100 15 23 0 62 100

TOTAL No. of Countries 34 150 11 195 38 114 11 32 195 Percent (%) 17 77 6 100 19 58 6 16 100

Total No inter- vention

Raise levels

Maintain levels

Lower levelsTotalToo low

Satis- factoryToo high

Policy on immigrationGovernment’s view on immigration

HDI categories

Source: UN (2008b).

| | | | | | 1960 1970 1980 1990 2000 2010

0L if e ti

m e m

ig ra

ti o

n in

te n

si ty

( %

)

United States

Malaysia Costa Rica Mexico

Brazil

Kenya

Rwanda

India

Source: Bell and Muhidin (2009 ).

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

of public policies; in many cases these groups do not advocate for tight restrictions to immigra- tion. Third, many governments implicitly toler- ate irregular migration, suggesting that policy makers are aware of the high economic and social costs of a crackdown. For example, in the United States employers are not required to verify the authenticity of immigration documents, but must deduct federal payroll taxes from migrants’ pay: through this mechanism, illegal immigrant workers provide around US$7 billion annually to the US Treasury.70

For the purposes of this report, we sought to address existing gaps in knowledge by working with national migration experts and the IOM to conduct an assessment of migration policies in 28 countries.71 The key value added of this exercise lies in the coverage of developing countries (half the sample), which have typically been excluded from such assessments in the past, and the rich information we collected on aspects such as ad- missions regimes, treatment and entitlements, and enforcement.

Comparing the migration policy regimes of developed and developing countries reveals strik- ing differences as well as similarities. Some of the restrictions commonly noted (and criticized) in developed countries are also present in many developing countries (figure 2.8). The regimes in both groups of countries are biased in favour of high-skilled workers: 92 percent of developing and all of developed countries in our sample were open to temporary skilled migrants; for perma- nent skilled migration, the corresponding figures were 62 and 93 percent. In our country sample, 38 percent of developing and half of developed countries were closed to permanent migration of unskilled workers.72

Temporary regimes have long been used and most countries provide such permits. These programmes stipulate rules for the time-bound admission, stay and employment of foreign workers. The H1B visas of the United States, for instance, grant temporary admission to high- skilled workers for up to six years, while H2B visas are available for low-skilled seasonal work- ers for up to three. Similarly, Singapore’s im- migration policy has Employment Passes—for skilled professionals—and a Work Permit or R-Pass for unskilled or semi-skilled workers.73 Among the countries in our policy assessment,

developing countries were much more likely to have temporary regimes for low-skilled workers.

Rules concerning changes in visa status and family reunion differ widely across coun- tries.74 Some temporary schemes offer a path to

Figure 2.7 Global income gaps have widened Trends in real per capita GDP, 1960–2007

R a ti

o o

f re

g io

n a

l t o

d e ve

lo p

e d

p e

r c

a p

it a G

D P

( %

)

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Panel A: Ratio of income of developing countries to income of developed countries

Eastern Europe

Latin America and the Caribbean

China Asia, excl. China Africa Oceania

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

G D

P p

e r

c a

p it

a (

U S

$ t

h o

u s a

n d

s )

Eastern Europe

Latin America and the Caribbean

China

Asia, excl. China

Africa

Oceania

Panel B: Real per capita income of developing countries by region

Source: HDR team estimates based on World Bank (2009b) and Heston, Summers, and Aten (2006).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

long-term or even permanent residence and allow foreign workers to bring in their dependents. An example is the US’s H2B visas, although their annual number is capped at a low level and the dependents are not entitled to work. Other gov- ernments explicitly prohibit status change and family reunion, or severely restrict them.

The temporary worker or kafala (literally meaning ‘guaranteeing and taking care of ’ in Arabic) programmes of the GCC countries are a special case.75 Under these programmes, foreign migrant workers receive an entry visa and resi- dence permit only if a citizen of the host country sponsors them. The khafeel, or sponsor-employer, is responsible financially and legally for the worker, signing a document from the Labour Ministry to that effect.76 If the worker is found to have breached the contract, they have to leave the country immediately at their own expense.

Kafala programmes are restrictive on several counts, including family reunification. Human rights abuses—including non-payment of wages and sexual exploitation of domestic workers— are well documented, especially among the in- creasing share of migrants originating in the Indian subcontinent.77

In recent years, some countries in the region have taken moderate steps in the direction of reforming their immigration regimes. Saudi Arabia recently passed a series of regulations facilitating the transfer of workers employed by companies providing services (e.g. maintenance) to government departments.78 Other initiatives have also been implemented to monitor the liv- ing and working conditions of foreign migrants. In the United Arab Emirates, the Ministry of Labour has introduced a hotline to receive com- plaints from the general public. In 2007, the authorities inspected 122,000 establishments, resulting in penalties for almost 9,000 violations of workers’ rights and of legislation on working conditions. However, more ambitious proposals for reform, such as Bahrain’s proposal in early 2009 to abolish the kafala system, have floun- dered, reportedly in the face of intense political opposition by business interests.79

In some developed countries—including Australia, Canada and New Zealand—the pref- erence for high-skilled workers is implemented through a points system. The formulae take into account such characteristics as education, occu- pation, language proficiency and age. This con- fers some objectivity to what otherwise might seem an arbitrary selection process, although other countries attract large numbers of gradu- ates without a point-based system.80

Points systems are uncommon in developing countries. Formal restrictions on entry include requirements such as a previous job offer and, in some cases, quotas. One aspect on which devel- oping countries appear to be relatively restrictive is family reunification. About half the develop- ing countries in our sample did not allow the family members of temporary immigrants to come and work—as opposed to one third of de- veloped countries.

Family reunification and marriage migration represent a significant share of inflows into virtu- ally all OECD countries. Indeed, some countries are dominated by flows linked to family ties, as in

Figure 2.8 Welcome the high-skilled, rotate the low-skilled Openness to legal immigration in developed versus developing countries, 2009

| | | | | | 0 20 40 60 80 100

Share of countries in sample (%)

Developed

Developing

Developed

Developing

Developed

Developing

Developed

Developing

High-skilled

Low-skilled

Open Partially closed Totally closed

Panel A: Permanent immigration

Panel B: Temporary immigration

Source: Klugman and Pereira (2009 ).

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

France and the United States, where these account for 60 and 70 percent of annual flows respectively. While it is common to distinguish between fam- ily reunification and labour migration, it is impor- tant to note that family migrants often either have or can acquire authorization to work.

Of course the stated policy may differ from what happens in practice. Significant variations exist in migration law enforcement across coun- tries (figure 2.9). In the United States, research has found that border enforcement varies over the economic cycle, increasing during recessions and easing during expansions.81 In South Africa, deportations more than doubled between 2002 and 2006 without a change in legislation, as the police force became more actively involved in enforcement.82 Our policy assessment suggested that while developing countries were somewhat less likely to enforce border controls and less likely to detain violators of immigration laws, other aspects of enforcement including raids by law enforcement agencies and random checks, as well as fines, were at least as frequent as in de- veloped countries. Lower institutional capacity may explain part of this variation. Even after detection, developing countries are reportedly more likely to do nothing or simply to impose fines on irregular migrants. In some countries, courts weigh family unity concerns and the strength of an immigrant’s ties to a country in deportation procedures.83 Further discussion of the role of enforcement in immigration policies is provided in chapter 5.

One question that emerges from these rules on entry and treatment, which can be investi- gated using cross-country data, is whether there is a ‘numbers versus rights’ trade-off. It is pos- sible that countries will open their borders to a larger number of immigrants only if access to some basic rights is limited. This could arise if, for example, immigration is seen to become too costly, so that neither voters nor policy makers will support it.84 Data on the treatment of im- migrants allow us to empirically examine this question. The Economist Intelligence Unit (EIU) has created an accessibility index for 61 countries (34 developed, 27 developing) that summarizes official policy in terms of ease of hiring, licensing requirements, ease of fam- ily reunification and official integration pro- grammes for migrants. The Migrant Integration

Policy Index (MIPEX) measures policies to in- tegrate migrants in six policy areas (long-term residence, family reunion, citizenship, political participation, anti-discrimination measures and labour market access).

Our analysis suggests that there is no system- atic relation between various measures of rights and migrant numbers (figure 2.10). Comparison with the EIU index (panel A), which has a broader sample of developed and developing countries, reflects essentially no correlation be- tween the number of migrants and their access to basic rights, suggesting that the various regimes governing such access are compatible with both high and low numbers of migrants. Restricting the analysis to the smaller sample of countries covered by the MIPEX allows us to take advan- tage of OECD data, which distinguish the share of immigrants with low levels of formal educa- tion from developing countries. Again, we find essentially no correlation (panel B). For example,

Figure 2.9 Enforcement practices vary Interventions and procedures regarding irregular migrants, 2009

Interventions to detect irregular migrants

Procedure after detecting irregular migrants

Border control

None

Raids by law enforcement agencies

Migrant is fined

Migrant is detained

Random police checks

Employer is fined

Migrant is deported

Information gained from service providers

(e.g. schools)

| | | | | 0 1 2 3 4

Average score (1 = Never or rare; 5 = Almost always)

Developed countries Developing countries

Source: Klugman and Pereira (2009 ).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

countries like Poland and Ireland have very low shares of low-skilled workers from developing countries, yet score poorly in the MIPEX. We have also found that countries that have seen in- creases in their migrant shares over time did not curtail the rights and entitlements provided to immigrants.85 For example, between 1980 and 2005 the share of immigrants in Spain increased from 2 to 11 percent; during the same period the Spanish government extended the provision of emergency and non-emergency health care to ir- regular migrants.86

Similar results were found in our policy as- sessment, which allowed us to distinguish be- tween different components of migration policy. In fact, if there was any indication of a correla- tion, it was often the opposite of that proposed by the numbers versus rights hypothesis. What the data reveal is that, in general, across many measures, developing countries have lower me- dian shares of foreign-born workers and lower protection of migrant rights. Developed coun- tries, which have more migrants, also tend to have rules that provide for better treatment of migrants. For example, India has the lowest score on provision of entitlements and services to in- ternational migrants in our assessment, but has an immigrant share of less than 1 percent of the population; Portugal has the highest score while having an immigrant share of 7 percent.

Policies towards migration are not deter- mined solely at the national level. Supra-national agreements, which can be bilateral or regional in nature, can have significant effects on mi- gration flows. Regional agreements have been established under various political unions, such as the Economic Community of West African States (ECOWAS), the European Union and the Mercado Común del Sur (MERCOSUR), while a good example of a bilateral agreement is that of the Trans-Tasman Travel Arrangement between Australia and New Zealand. These agreements have had significant effects on migration flows between signatory countries. They are most likely to allow freedom of movement when par- ticipating member states have similar economic conditions and when there are strong political or other motivations for socio-economic integra- tion. For the countries in our policy assessment, about half of the special mobility agreements of developed countries were with other developed

Figure 2.10 Cross-country evidence shows little support for the ‘numbers versus rights’ hypothesis Correlations between access and treatment

Source: UN (2009d), The Economist Intelligence Unit (2008), OECD (2009a) and Migration Policy Group and British Council (2007).

Argentina

Australia

Belgium

Brazil

Canada

Chile

China Côte d’Ivoire

Estonia

France

Ghana

Greece

Hong Kong (China)

Hungary

India

Iran

Ireland

Israel

Italy

Japan

Jordan

Kazakhstan

Republic of Korea

Kuwait

Latvia

Malaysia

New Zealand

Nigeria

Peru Poland

Portugal

Qatar

Romania Russian Federation

Saudi Arabia

Singapore

South Africa

Switzerland

Thailand

Turkey

Ukraine

United Arab Emirates

United Kingdom

United States

Venezuela

Spain

Mexico

Germany

Austria

Belgium Canada

Czech Republic

Denmark

Finland

France Germany

Greece

Hungary

Ireland

Italy

Luxembourg

Netherlands

Norway

Poland

Portugal

Slovakia

Spain

Sweden

Switzerland

United Kingdom

Argentina

Australia

Belgium

Brazil

Canada

Chile

China Côte d’Ivoire

Estonia

France

Ghana

Greece

Hong Kong (China)

Hungary

India

Iran

Ireland

Israel

Italy

Japan

Jordan

Kazakhstan

Republic of Korea

Kuwait

Latvia

Malaysia

New Zealand

Nigeria

Peru Poland

Portugal

Qatar

Romania Russian Federation

Saudi Arabia

Singapore

South Africa

Switzerland

Thailand

Turkey

Ukraine

United Arab Emirates

United Kingdom

United States

Venezuela

Spain

Mexico

Germany

Austria

Belgium Canada

Czech Republic

Denmark

Finland

France Germany

Greece

Hungary

Ireland

Italy

Luxembourg

Netherlands

Norway

Poland

Portugal

Slovakia

Spain

Sweden

Switzerland

United Kingdom

Share of migrants in population (%)

Low-skilled migrants from developing countries as share of population (%)

35In d

e x

o f

m ig

ra n

t in

te g

ra ti

o n (

M IP

E X

)

| | | | | 0 20 40 60 80

| | | | | | 0 2 4 6 8 10

Panel A: Foreign-born migrants and EIU accessibility score, 2008

Panel B: Low-skilled foreign-born migrants in OECD and MIPEX aggregate score

M ig

ra n

t a c

c e

s si

b ili

ty s

c o

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

countries, while more than two thirds of those of developing countries were with other de- veloping countries. There are examples where mobility is granted only to some workers, such as the higher skilled. For example, the migra- tion system of the North American Free Trade Agreement (NAFTA) covers only nationals of Canada, Mexico and the United States who have a B.A. degree and a job offer in another member country. Box 2.4 briefly overviews the multilat- eral arrangements related to human movement.

However, there can be large differences be- tween the letter of these agreements and actual practice, particularly in countries where the rule of law is weak. For example, despite the provisions for comprehensive rights of entry, residence and establishment provided for in the ECOWAS agreement signed in 1975 (which were to be implemented in three phases over a 15-year period), only the first phase of the proto- col—elimination of the need for visas for stays up to 90 days—has been achieved. The reasons

for slow implementation range from inconsis- tency between the protocol and national laws, regulations and practices to border disputes and full-scale wars which have often led to the expul- sion of foreign citizens.87

We also find restrictions on human move- ment within nations as well as on exit. One source of data on these restrictions is the NGO Freedom House, which has collected informa- tion on formal and informal restrictions on foreign and internal travel as a component of its assessment of the state of freedom in the world.88 The results are striking, particularly given that the Universal Declaration of Human Rights guarantees the right to move freely within one’s country and to exit and return to one’s own country: over a third of countries in the world impose significant restrictions on these freedoms (table 2.3).

Formal restrictions on internal movement are present in many countries with a legacy of central planning, including Belarus, China, Mongolia,

Box 2.4 Global governance of mobility

Beyond a well-established convention on refugees, international

mobility lacks a binding multilateral regime. The ILO has long had

conventions on the rights of migrant workers, but they are heavily

undersubscribed (chapter 5). The IOM has expanded beyond its his-

toric role in the post-war repatriation of refugees towards a more gen-

eral mission to improve migration management and has increased

its membership, but it is outside the UN system and remains largely

oriented towards service provision to member states on a project

basis. Under the General Agreement on Trade in Services (GATS) of

the World Trade Organization (WTO), some 100 member states have

made commitments to the temporary admission of foreign nationals

who provide services, but these mostly involve business visitor visas

for up to 90 days and fixed-term intra-company transfers involving

high-skilled professionals.

The lack of multilateral cooperation on migration has been at-

tributed to several related factors. In contrast to trade negotiations,

where countries negotiate over the reciprocal reduction of barriers to

each other’s exports, developing countries are in a weaker bargain-

ing position on the migration front. Most migrants from developed

countries go to other developed countries, so there is little pressure

from developed country governments to open channels for entering

developing countries. This asymmetry, as well as the political sen-

sitivity of the migration issue in most developed destination coun-

tries, has led to a lack of leadership from these states in international

negotiations. International discussions have also been characterized

by lack of cooperation among sending countries. These obstacles

have so far defied the best efforts of international organizations and

a handful of governments to promote cooperation and binding inter-

national commitments.

Further liberalization is currently being canvassed in the Doha

Round of trade negotiations, which began in 2000 but have long

since stalled. Existing commitments under GATS are limited, refer-

ring mainly to high-skilled workers. GATS also excludes “measures

affecting natural persons seeking access to the employment market

of another country [or] measures regarding citizenship, residence, or

employment on a permanent basis”. Nor does GATS apply to perma-

nent migration: most WTO members limit service providers to less

than five years in their country.

During the Doha Round it became clear that developing countries

want to liberalize the movement of natural persons, whereas indus-

trial countries prefer trade in services. It could be argued that the

importance of GATS to labour migration does not lie in the relatively

small amount of additional mobility facilitated thus far, but rather in

the creation of an institutional framework for future negotiations.

However, better progress might be made if the WTO took a more

inclusive and people-centred approach, which allowed greater par-

ticipation by other stakeholders and linked more closely with existing

legal regimes for the protection of human rights.

Source: Castles and Miller (1993), Neumayer (2006), Leal-Arcas (2007), Charnovitz (2003), p.243, Mattoo and Olarreaga (2004), Matsushita, Schoenbaum, and Mavroidis (2006), Solomon (2009 ), and Opeskin (2009 ).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

the Russian Federation and Viet Nam.89 These restrictions are costly, time-consuming and cumbersome to maintain, as are informal bar- riers, albeit to a lesser extent. Although many people in these countries are able to travel without the proper documentation, they later find that they cannot access services and jobs without them. In several countries, corruption is a key impediment to internal movement. Checkpoints on local roads, where bribes are levied, are commonplace in parts of sub-Saha- ran Africa. For instance, in Côte d’Ivoire, peo- ple living in northern areas controlled by rebel groups were routinely harassed and forced to pay US$40–60 when attempting to travel south to government-controlled areas.90 Examples of corruption were also reported from Myanmar, the Russian Federation and Viet Nam, where bribes were required to process applications for changes in place of residence. In several South Asian countries, migrants living in urban slums face constant threats of clearance, evic- tion and rent-seeking from government offi- cials.91 Internal movement is also impeded by regulations and administrative procedures that

exclude migrants from access to the public ser- vices and legal rights accorded to local people (chapter 3).

Countries can limit exit by nationals from their territory by several means, ranging from formal prohibitions to practical barriers cre- ated by fees and administrative requirements. Exorbitant passport fees can make it all but impossible for a poor person to leave the coun- try through regular channels: a recent study found that 14 countries had passport fees that exceeded 10 percent of annual per capita income.92 In many countries, a labyrinth of procedures and regulations, often exacerbated by corruption, causes excessive delays and compounds the costs of leaving. For example, Indonesian emigrants have to visit numerous government offices in order to acquire the nec- essary paperwork to leave. Not surprisingly, these exit restrictions are negatively correlated with emigration rates.93

A handful of countries have formal restric- tions on exit. These are strictly enforced in Cuba and the Democratic People’s Republic of Korea, and are in place in China, Eritrea, Iran, Myanmar, and Uzbekistan.94 Eritrea, for example, requires exit visas for citizens and foreign nationals and has reportedly denied the exit visas of children whose parents (living abroad) had not paid the 2 percent tax on for- eign income.95 Twenty countries restrict the exit of women—including Myanmar, Saudi Arabia and Swaziland—while eight impose age-specific restrictions related to the travel of citizens of military service age.96

2.4 Looking ahead: the crisis and beyond The future of the global economy is a central concern for policy makers. Like everyone else, we hold no crystal ball, but we can examine the impacts and implications of the current crisis as the basis for identifying probable trends for the coming decades. Demographic trends, in particular, can be expected to continue to play a significant role in shaping the pressures for movement between regions, as we have seen over the past half-century. But new phenomena such as climate change are also likely to come into play, with effects that are much more dif- ficult to predict.

Table 2.3 Over a third of countries significantly restrict the right to move Restrictions on internal movement and emigration by HDI category

VERY HIGH HDI No. of Countries 0 3 1 3 31 38 Percent (%) 0 8 3 8 81 100

HIGH HDI No. of Countries 2 4 4 10 27 47 Percent (%) 4 9 9 21 57 100

MEDIUM HDI No. of Countries 2 13 24 27 16 82 Percent (%) 2 16 29 33 20 100

LOW HDI No. of Countries 2 5 13 5 0 25 Percent (%) 8 20 52 20 0 100

TOTAL No. of Countries 6 25 42 45 74 192 Percent (%) 3 13 22 23 39 100

Total Least

restrictive321 Most

restrictiveHDI categories

Restrictions on mobility, 2008

Source: Freedom House (2009 ).

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

2.4.1 The economic crisis and the prospects for recovery Many people are now suffering the consequences of the worst economic recession in post-war his- tory. At the time of writing, world GDP was expected to fall by approximately 1 percent in 2009, marking the first contraction of global output in 60 years.97 This year’s contraction in developed countries is much larger, approach- ing 4 percent. However, initial optimism that emerging economies might be able to ‘decouple’ from the financial crisis has been dampened by mounting evidence that they too are, or will be, hard hit. Asian countries have suffered from col- lapsing export demand, while increases in the cost of external credit have adversely affected Central and Eastern Europe. African countries are battling with collapsing commodity prices, the drying up of capital liquidity, a sharp de- cline in remittances and uncertainty concern- ing future flows of development aid. Some of the largest emerging economies, such as Brazil and the Russian Federation, will dip into negative growth, while others, notably China and India, will see severe slowdowns.98

Typical recessions do not have a large impact on long-run economic trends.99 However, it is now clear that this is anything but a typical recession. As such it is likely to have long-lasting and maybe even permanent effects on incomes and employ- ment opportunities, which are likely to be expe- rienced unequally by developing and developed countries.100 For example, the recession set off by the Federal Reserve’s increase of interest rates in 1980 lasted just 3 years in the United States, but the ensuing debt crisis led to a period of stagna- tion that became known as the ‘lost decade’ in Africa and Latin America, as the terms of trade of countries in these regions deteriorated by 25 and 37 percent respectively. As commodity prices have fallen significantly from the peak levels of 2008, a similar scenario is probable this time round.

The financial crisis has quickly turned into a jobs crisis (figure 2.11). The OECD unem- ployment rate is expected to hit 8.4 percent in 2009.101 That rate has already been exceeded in the United States, which by May 2009 had lost nearly six million jobs since December 2007, with the total number of jobless rising to 14.5 million.102 In Spain, the unemployment rate climbed as high as 15 percent by April 2009 and

topped 28 percent among migrants.103 The places hit hardest by the crisis thus far are those where most migrants live—the more developed econo- mies. The negative correlation between numbers of immigrants and economic growth suggests that migrants are likely to be badly affected not only in OECD countries but also in the Gulf, East Asia and South Africa (figure 2.12).104

A jobs crisis is generally bad news for mi- grants. Just as economies tend to call on people from abroad when they face labour shortages, so they tend to lay off migrants first during times of recession. This is partly because, on average, migrants have a profile typical of workers who are most vulnerable to recessions—that is, they are younger, have less formal education and less work experience, tend to work as temporary labourers and are concentrated in cyclical sec- tors.105 Even controlling for education and gen- der, labour force analysis in Germany and the United Kingdom found that migrants are much more likely to lose their job during a downturn than non-migrants.106 Using quarterly GDP and unemployment data from 14 European coun- tries between 1998 and 2008, we also found that, in countries that experienced recessions,

Figure 2.11 Unemployment is increasing in key migrant destinations Unemployment rates in selected destinations, 2007–2010

| | | | 2007 2008 2009* 2010*

0U n

e m

p lo

ym e

n t

ra te

( %

)

Germany France United States Canada Italy

Australia

United Kingdom

Hong Kong (China)

* Forecasts

Source: Consensus Economics (2009a,b).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

the unemployment rate of migrants tended to increase faster than that of other groups. Within the OECD, migrants were concentrated in highly cyclical sectors that have suffered the largest job losses—including manufacturing, construction, finance, real estate, hotels and res- taurants—sectors that employ more than 40 per- cent of immigrants in almost every high-income OECD country.107 The decline in remittances from migrants is likely to have adverse effects on family members in countries of origin, as we discuss in greater detail in chapter 4.

Several factors come into play in determin- ing how the crisis affects—and will affect—the movement of people. They include immediate prospects at home and abroad, the perceived risks of migrating, staying or returning, and the increased barriers that are likely to come into place. Several major destination countries have introduced incentives to return (bonuses, tickets, lump sum social security benefits) and increased restrictions on entry and stay. Some govern- ments are discouraging foreign recruitment and

reducing the number of visa slots, especially for low-skilled workers but also for skilled work- ers. In some cases these measures are seen as a short-term response to circumstances and have involved marginal adjustments rather than out- right bans (e.g. Australia plans to reduce its an- nual intake of skilled migrants by 14 percent).108 But there is also a populist tone to many of the announcements and provisions. For example, the United States economic stimulus pack- age restricts H1B hires among companies that receive funds from the Troubled Asset Relief Program;109 the Republic of Korea has stopped issuing new visas through its Employment Permit System; and Malaysia has revoked more than 55,000 visas for Bangladeshis in order to boost job prospects for locals.110

There is some evidence of a decline of flows into developed countries during 2008, as the crisis was building. In the United Kingdom, applications for National Insurance cards from foreign-born people fell by 25 percent.111 Data from surveys carried out by the US Census

Figure 2.12 Migrants are in places hardest hit by the recession Immigrants’ location and projected GDP growth rates, 2009

| | | | 10 12 14 16

–4

–8

–12P ro

je c

te d

p e

r c

a p

it a G

D P

g ro

w th

r a te

, 2

0 0

Total number of immigrants (log scale)

Source: HDR team estimates based on Consensus Economics (2009a,b,c,d) and UN (2009d).

Belgium

Canada France

Germany Italy

Japan

Netherlands

Norway

Spain Switzerland

United Kingdom

United States

Bolivia

Chile

Colombia Dominican Republic

EI Salvador

Guatemala Honduras

Mexico

Paraguay

Peru

Uruguay

Venezuela Australia

Hong Kong (China)

India

Indonesia

Malaysia

Singapore

Republic of Korea

Thailand

Bulgaria

Czech Republic

Estonia

Hungary

Latvia

Lithuania

Poland

Romania Russian Federation

Turkey

Ukraine

ArgentinaBrazil

Ecuador

China

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Bureau show a 25 percent decline in the flow of Mexican migrants to the United States in the year ending in August 2008.112 These trends can be expected to continue in 2009 and 2010, as the full effect of the crisis plays out in rising domestic unemployment. There are reasons to be sceptical, however, that major return flows will emerge. As the experience of European guest-worker programmes in the 1970s demonstrates, the size of return flows are affected by the prospects of re-entry to the host country, the generosity of the host country’s welfare system, the needs of family members and conditions back home—all of which tend to encourage migrants to stay put and ride out the recession.

Whether this crisis will have major structural effects on migration patterns is not yet clear. Evidence from previous recessions shows that the outcomes have varied. A historical review of several countries—Argentina, Australia, Brazil, Canada, the United States and the United Kingdom—showed that, between 1850 and 1920, declines in domestic wages led to tighter restrictions on immigration.113 Several schol- ars have argued that the 1973 oil crisis, which heralded a prolonged period of economic stag- nation, structural unemployment and lower de- mand for unskilled workers in Europe, affected migration patterns as a wealthier Middle East emerged as the new destination hub.114 During the 1980s, the collapse of Mexican import sub- stitution set in motion an era of mass migration to the United States that was unintentionally ac- celerated by the 1986 United States immigration reform.115 In contrast, there is little evidence that the East Asian financial crisis of the late 1990s had a lasting impact on international migration flows.116

At this stage it is impossible to predict the type and magnitude of the structural changes that will emerge from the current crisis with any confidence. Some commentators have argued that the origin of the crisis and its fierce con- centration in certain sectors in developed coun- tries may strengthen the position of developing countries, particularly in Asia, even leading to a radically different configuration of the global economy.117 However, there are also reasons for expecting a revival of pre-crisis economic and structural trends once growth resumes. It is cer- tainly true that deeper long-term processes, such

as the demographic trends, will persist regardless of the direction taken by the recession.

2.4.2 Demographic trends Current forecasts are that the world’s population will grow by a third over the next four decades. Virtually all of this growth will be in developing countries. In one in five countries—including Germany, Japan, the Republic of Korea and the Russian Federation—populations are expected to shrink; whereas one in six countries—all of them developing and all but three of them in Africa—will more than double their popula- tions within the next 40 years. Were it not for migration, the population of developed coun- tries would peak in 2020 and fall by 7 percent in the following three decades. The trend evident over the past half century—the fall in the share of people living in Europe and the increase in Africa—is likely to continue.118

Aging of populations is a widespread phe- nomenon. By 2050, the world as a whole and every continent except Africa are projected to have more elderly people (at least 60 years of age) than children (below 15). This is a natural consequence of the decline in death rates and the somewhat slower decline in birth rates that has occurred in most developing countries, a well-known phenomenon known as the ‘demo- graphic transition’. By 2050, the average age in developing countries will be 38 years, compared to 45 years in developed countries. Even this seven-year difference will have marked effects. The global working-age population is expected to increase by 1.1 billion by 2050, whereas the working-age population in developed countries, even assuming a continuation of current migra- tion flows, will decline slightly. Over the next 15 years, new entrants to the labour force in de- veloping countries will exceed the total number of working-age people currently living in devel- oped countries (figure 2.13). As in the past, these trends will put pressure on wages and increase the incentives for moving among potential employees in poor countries—and for seeking out workers from abroad among employers in rich countries.

This process affects the dependency ratio— that is, the ratio of elderly and young to the working-age population (table 2.4). For every 100 working-age people in developed countries, there are currently 49 who are not of working

Current forecasts are that the world’s population will grow by a third over the next four decades

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

age, roughly half of whom are children or elderly. In contrast, in developing countries, the ratio is higher, at 53, but three quarters of the depen- dents are children. Over the next 40 years, as the effect of lower birth rates is felt and the pro- portion of children falls as they reach working age, the dependency ratio will remain roughly stable in developing countries, reaching just 55 by 2050. However, the proportion of elderly will rise markedly in developed countries, so that there will then be 71 non-working-age people for every 100 of working age, a significantly higher fraction than today. These dependency ratios would increase even more rapidly without the moderate levels of immigration included in these scenarios: if developed countries were to become completely closed to new immigration, the ratio would rise to 78 by 2050.

As is well known, this scenario makes it much more difficult for developed countries to pay for the care of their children and old people. Publicly funded education and health systems are paid with taxes levied on the working popu- lation, so that as the share of potential taxpayers

shrinks it becomes more difficult to maintain expenditure levels.

These demographic trends argue in favour of relaxing the barriers to the entry of migrants. However, we do not suggest that migration is the only possible solution to these challenges. Greater labour scarcity can lead to a shift in specialization towards high-technolog y and capital-intensive industries, and technological innovations are possible for services that were traditionally labour-intensive, such as care of the old. The sustainability of pensions and health care systems can also be addressed, at least in part, by increases in the retirement age and in social security contributions.119 Growing depen- dency ratios will occur sooner or later in all coun- tries undergoing demographic transitions—and migrants themselves grow old. Nevertheless, the growing labour abundance of developing coun- tries suggests that we are entering a period when increased migration to developed countries will benefit not only migrants and their families but will also be increasingly advantageous for the populations of destination countries.

Figure 2.13 Working-age population will increase in developing regions Projections of working-age population by region, 2010–2050

2010 2050

Source: HDR team calculations based on UN (2009e).

North America

0.23 0.27 billion +16%

Europe

0.50 0.38 billion –23%

Asia

2.80 3.40 billion +22%

Africa

0.58 1.3 billion +125%

Oceania

0.02 0.03 billion +31%

Latin America and the Carribean

0.39 0.49 billion +26%

2HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

2.4.3 Environmental factors The environment can be a key driver of human movement. From nomadic pastoralists, who fol- low the favourable grazing conditions that arise after rain, to the people displaced by natural di- sasters such as the Indian Ocean tsunami and Hurricane Katrina, environmental conditions have been intimately linked to movements of people and communities throughout human his- tory. Some are now expecting that the continu- ing warming of the earth will generate massive population shifts.

Climate change is projected to increase en- vironmental stress in already marginal lands and to raise the frequency of natural hazards. Continued greenhouse gas emissions are likely to be associated with changes in rainfall pat- terns, desertification, more frequent storms and rises in sea level, all of which have implications for human movement.120 Changing rainfall pat- terns, for example, will affect the availability of water and hence the production of food, possibly increasing food prices and the risk of famine.

Existing estimates indicate that several de- veloping areas will be strongly affected by cli- mate change, although the range of estimates is still very wide and predictions are subject to considerable uncertainty. At one extreme, by 2020 it is expected that the yields from rainfed agriculture in Southern Africa could be halved by drought.121 Over the medium term, as glacial water banks run down, river flows are expected to diminish, severely affecting irrigated agricul- ture, especially around major massifs such as the Himalayas.

Rises in sea level most directly affect people in coastal areas. One scenario suggests that 145 million people are presently at risk from a rise of one meter, three quarters of whom live in East and South Asia.122 In some cases, rises will imply the relocation of entire communities. The government of the Maldives, for example, is con- sidering buying land in other countries as a safe haven, given the probability that their archipel- ago will become submerged.123

Some estimates of the numbers of people who will be forced to move as a result of climate change have been presented, ranging from 200 million to 1 billion.124 Regrettably, there is little hard science backing these numbers. For the most part, they represent the number of people

exposed to the risk of major climatic events and do not take into account the adaptation mea- sures that individuals, communities and gov- ernments may undertake.125 It is thus difficult to know whether such inevitably crude estimates facilitate or obstruct reasoned public debate.

The effect of climate change on human set- tlement depends partly on how change comes about—as discrete events or a continuous pro- cess. Discrete events often come suddenly and dramatically, forcing people to move quickly to more secure places. Continuous processes, on the other hand, are associated with slow-onset changes like sea level rise, salinization or erosion of agricultural lands and growing water scarcity. In many cases, continuous change leads commu- nities to develop their own adaptation strategies, of which migration—whether seasonal or per- manent—may be only one component. Under these conditions movement typically takes the form of income diversification by the house- hold, with some household members leaving and others staying behind.126 This pattern has been observed, for example, among Ethiopian house- holds hit by severe and recurrent droughts.127

Given the uncertainty as to whether climate change will occur through a continuous process or discrete events, the extent and type of result- ing adaptation and movement are difficult to predict. Moreover, environmental factors are not the sole determinants of movement but interact with livelihood opportunities and public policy responses. It is often the case that natural disas- ters do not lead to out-migration of the most vul- nerable groups, because the poorest usually do

Table 2.4 Dependency ratios to rise in developed countries and remain steady in developing countries Dependency ratio forecasts of developed versus developing countries, 2010–2050

No Migration scenarioNo Migration scenario Baseline scenarioBaseline scenario

Developing countriesDeveloped countries

2010 49 50 53 53 2020 55 56 52 52 2030 62 65 52 52 2040 68 74 53 53 2050 71 78 55 54

Year

Source: UN (2009e).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development2

not have the means to move and natural disasters further impair their ability to do so. Empirical studies in Mexico have found that the effects of changes in rainfall on migration patterns are determined by socio-economic conditions and the ability to finance the cost of moving.128 Background research on migration patterns in Nicaragua during Hurricane Mitch, carried out for this report, found that rural families in the bottom two wealth quintiles were less likely to migrate than other families in the aftermath of Hurricane Mitch.129

More fundamentally, what happens in the future is affected by the way we consume and use our natural resources today. This was the key message of the 2007/2008 HDR (Fighting climate change: Human solidarity in a divided world): catastrophic risks for future generations can be avoided only if the international commu- nity acts now. The demand for increased energy in developing countries, where many people still lack access to electricity, can be met while re- ducing total carbon emissions. The use of more energy-efficient technologies that already exist in developed countries needs to be expanded in developing countries, while creating the next generation of still more efficient technologies and enabling developing countries to leapfrog through to these better solutions. At the same time, energ y consumption in developed coun- tries needs to be rationalized. The policy options for encouraging a transition to a low-carbon en- ergy mix include market-based incentives, new standards for emissions, research to develop new technologies and improved international cooperation.130

2.5 Conclusions Three key findings have emerged from this chap- ter’s analysis of global trends in human move- ment. First, movement largely reflects people’s need to improve their livelihoods. Second, this movement is constrained by policy and eco- nomic barriers, which are much more difficult for poor people to surmount than for the rela- tively wealthy. Third, the pressure for increased flows will grow in the coming decades in the face of divergent economic and demographic trends.

Ultimately, how these structural factors will affect the flow of people in the future depends critically on the stance taken by policy makers,

especially those in host countries. At present, policy makers in countries with large migrant populations face conflicting pressures: signifi- cant levels of resistance to increased immigration in public opinion on the one hand, and sound economic and social rationales for the relaxation of entry barriers on the other.

How can we expect policies to evolve in the next few decades? Will they evolve in ways that enable us to realize the potential gains from mo- bility, or will popular pressures gain the upper hand? Will the economic crisis raise protection- ist barriers against immigration, or will it serve as an opportunity to rethink the role of move- ment in fostering social and economic progress? History and contemporary experience provide contrasting examples. Acute labour scarcity made the Americas very open to migration dur- ing the 19th century and allowed rapid rates of economic development despite widespread in- tolerance and xenophobia. This is analogous in some ways to the situation in the GCC states today. However, the tendency to blame outsiders for society’s ills is accentuated during economic downturns. Recent incidents across a range of countries—from the Russian Federation to South Africa to the United Kingdom—could presage a growing radicalization and closing off to people from abroad.131

Yet none of these outcomes is predetermined. Leadership and action to change the nature of public debate can make a crucial difference. Shifting attitudes towards internal migrants in the United States during the Great Depression provide us with a compelling example. As a result of severe drought in the nation’s south- ern Midwest region, an estimated 2.5 million people migrated to new agricultural areas dur- ing the 1930s. There they met fierce resistance from some residents, who saw these migrants as threats to their jobs and livelihoods. It was in this context that John Steinbeck wrote The Grapes of Wrath, one of the most powerful indictments of the mistreatment and intolerance of internal migrants ever written. Steinbeck’s novel sparked a national debate, leading to a congressional in- vestigation into the plight of migrant workers and ultimately to a landmark 1941 decision by the Supreme Court establishing that states had no right to interfere with the free movement of people within the United States.132

Movement largely reflects people’s need to improve their livelihoods… this movement is constrained by policy and economic barriers

How movers fare

Movers can reap large gains from the opportunities

available in better-off places. These opportunities are

shaped by their underlying resources—skills, money and

networks—and are constrained by barriers. The policies

and laws that affect decisions to move also affect the

process of moving and the outcomes. In general, and

especially for low-skilled people, the barriers restrict

people’s choices and reduce the gains from moving.

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

How movers fare

People are motivated to move by the prospects of improved access to work, education, civil and political rights, security and health care. The majority of movers end up better off—sometimes much better off—than before they moved. The gains are potentially high- est for people who move from poor to the wealthiest countries, but this type of movement is only a small share of total flows. Available evidence suggests that people who move to emerging and develop- ing countries, as well as within countries, also tend to gain.

However, movement does not necessarily yield a direct positive impact on the well-being of everyone. Moving is risky, with uncertain out- comes and with the specific impacts determined by a host of contextual factors. For both inter- nal and international mobility, different as- pects of the process—including the proximate causes of moving and the resources and capa- bilities that people start out with—profoundly affect outcomes. Those who are forced to flee and leave behind their homes and belongings often go into the process with limited freedom and very few resources. Likewise, those who are moving in the face of local economic crisis, drought or other causes of desperate poverty, may not know what capabilities they will have; they only know that they cannot remain. Even migrants who end up well off after a move often start out with very restricted capabilities and high uncertainty.

The human development outcomes of moving are thus profoundly affected by the conditions under which people move. These conditions determine what resources and ca- pabilities survive the move. Those who go to an embassy to collect a visa, buy a plane ticket and take up a position as a student in, say, the United Kingdom, arrive at their destination in much better shape than someone who is traf- ficked—arriving with no papers, no money and in bondage. The distance travelled (geo- graphical, cultural and social) is also impor- tant. Travelling to a countr y where one does not speak the language immediately devalues one’s knowledge and skills.

This chapter examines how movement affects those who move, why gains are unevenly distrib- uted and why some people win while others lose out. There may well be trade-offs, such as loss of civic rights, even where earnings are higher. The costs of moving also need to be taken into ac- count. We review evidence about these impacts in turn, to highlight the main findings from a vast literature and experience.

The key related question of how moving af- fects those who don’t move, in source and des- tination places, is addressed in chapter 4. These distinct areas of focus are of course inextricably linked—successful migrants tend to share their success with those who stay at home, while the policy responses of destination places affect how non-movers, as well as movers, fare. Home and host-country impacts are interconnected. Socio- economic mobility in a host country and the ability to move up the ladder in the homeland are often two sides of the same coin.

3.1 Incomes and livelihoods It is important to recall at the outset that esti- mating the impacts of migration is fraught with difficulties, as we saw in box 1.1. The main prob- lem is that movers may differ from non-movers in their basic characteristics, so straight compari- sons can be misleading and the identification of causal relationships is problematic.

That said, the most easily quantifiable im- pacts of moving can be seen in incomes and consumption. We begin with these, then turn to review the costs of moving, which must be subtracted from the gross benefits.

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

3.1.1 Impacts on gross income The evidence consistently reflects very large av- erage income gains for movers. Commissioned research found large differences in income be- tween stayers and movers to OECD countries, with the biggest differences for those moving from low-HDI countries (figure 3.1). Migrant workers in the United States earn about four times as much as they would in their develop- ing countries of origin,1 while Pacific Islanders in New Zealand increased their net real wages by a factor of three.2 Evidence from a range of countries suggests that income gains increase over time, as the acquisition of language skills leads to better integration in the labour market.3

Gains arise not only when people move to OECD countries. Thai migrants in Hong Kong (China) and Taiwan (Province of China), for ex- ample, are paid at least four times as much as they would earn as low-skilled workers at home.4 In Tajikistan, when the average monthly wage was only US$9, seasonal earnings of US$500–700 in the Russian Federation could cover a family’s annual household expenses in the capital city, Dushanbe.5 However, these average gains are unevenly distributed, and the costs of moving also detract from the gross gains.

Gains can be large for the high-skilled as well as the low-skilled. The wages of Indian soft- ware engineers in the late 1990s, for example, were less than 30 percent of their United States counterparts, so those who were able to relocate to this country reaped large gains.6 Figure 3.2 illustrates the wage gaps, adjusted for purchas- ing power parity, between high-skilled profes- sionals in selected pairs of countries. A doctor from Côte d’Ivoire can raise her real earnings by a factor of six by working in France. Beyond salaries, many are also often motivated by fac- tors such as better prospects for their children, improved security and a more pleasant working environment.7

Internal migrants also tend to access bet- ter income-earning opportunities and are able to diversif y their sources of livelihood. Commissioned research found that internal migrants in Bolivia experienced significant real income gains, with more than fourfold in- creases accruing to workers with low education levels moving from the countryside to the cities (figure 3.3). We also found that in 13 out of 16

Figure 3.2 Huge salary gains for high-skilled movers Gaps in average professional salaries for selected country pairs, 2002–2006

Figure 3.1 Movers have much higher incomes than stayers Annual income of migrants in OECD destination countries and GDP per capita in origin countries, by origin country HDI category

GDP per capita at origin Migrants’ income in OECD destination countries

Low HDI

Medium HDI

High HDI

Very high HDI

(US$ thousands)

Difference: US$13,736

Difference: US$12,789

Difference: US$9,431

Difference: US$2,480

Source: Ortega (2009 ).

| | | | | | | 0 5 10 15 20 25 30

Annual salary (US$ thousands)

| | | | | | | 0 20 40 65 80 100 120

Origin country Destination country

Côte d’Ivoire France

Zambia Canada

Physician

Malawi South Africa

Ghana United Kingdom

Nurse

India United States

Software engineer – manager

India United States

Software engineer – developer

India United Kingdom

Professor – top level

China Australia

Professor – entry level

Source: Source: Clemens (2009b).

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

countries internal migrants had higher incomes than non-migrants.8 In Brazil and Panama, a se- ries of studies controlling for education found income gains for indigenous groups who move.9 Studies across a range of countries suggest that internal migration has enabled many house- holds to lift themselves out of poverty, as dis- cussed further in the next chapter.

The segmentation of labour markets in de- veloping countries affects how movers fare. Sometimes this can be traced to administrative restrictions, as in the hukou system in China (box 3.1) and the ho khau system in Viet Nam. However, segmentation is also widespread in other regions, including South Asia, Africa and Latin America, through barriers that, while not imposed by law, are nonetheless deeply en- trenched through social and cultural norms.10 For example, rural–urban migrants in India are predominantly employed in industries such as construction, brick kilns, textiles and mining, which entail hard physical labour and harsh working and living environments; in Mongolia, rural–urban migrants typically work in infor- mal activities which are temporary, strenuous and without legal protection.11 In Asia, recent low-skilled migrants from rural areas tend to oc- cupy the lowest social and occupational rungs of urban society and are treated as outsiders.

As we saw in chapter 2, most movers from low-HDI countries are living and working in other low- or medium-HDI countries, in part because barriers to admission are often lower and the costs of moving are less. At the same time, the conditions may well be more difficult than in rich countries and there are risks of both exploitation and expulsion.

Labour market opportunities for migrant women from developing countries tend to be highly concentrated in care activities, paid do- mestic work and the informal sector.12 Such women may become trapped in enclaves. For example, in New York City, Hispanic-owned firms were found to provide low wages, few benefits and limited career opportunities to Dominican and Colombian women, reinforc- ing their social disadvantages.13 Similar results were found among Chinese migrant women workers.14 Most Peruvian and Parag uayan women in Argentina (69 and 58 percent respec- tively) work for low pay on an informal basis

in the personal ser vice sector.15 Difficulties are compounded where migrant women are excluded from normal worker protections, as is the case for domestic workers in the GCC states.16 A lthough practices are changing in some countries (e.g. Saudi Arabia and the United Arab Emirates), migrants are legally prohibited from joining local unions, and even when this is allowed, they may face resistance and hostility from other workers.17 NGOs may provide services and protection to migrants, but their coverage tends to be limited.

Labour market discrimination can be a major obstacle to migrants. This is reflected in low call-back rates to job applications where the applicant has a foreign-sounding surname.18 Yet the picture is often complex, and ethnicity, gen- der and legal status may all come into play. In the United Kingdom, some studies have found dis- crimination in hiring migrants in terms of lower employment rates and payments, whereas other studies found that people with Chinese, Indian and Irish backgrounds tended to have employ- ment situations at least as good as white British workers.19 Our analysis of the 2006 European Social Survey reveals that the vast majority of migrants (more than 75 percent) in this region did not report feeling discriminated against. However, in the much larger country sample provided by the World Values Survey, there was widespread support among locally born people for the proposition, “Employers should give priority to natives when jobs are scarce”, albeit with considerable differences across countries (see section 4.2.5).

Figure 3.3 Significant wage gains to internal movers in Bolivia, especially the less well educated Ratio of destination to origin wages for internal migrants in Bolivia, 2000

Ratio of destination to origin wages

Source: Molina and Yañez (2009 ).

Rural to urban migrants Urban to urban migrants

5 years of schooling

11 years of schooling

16 years of schooling

| | | | | | 0 1 2 3 4 5

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

One problem facing many migrants on ar- rival is that their skills and credentials go un- recognized.20 Coupled with language and other social barriers, this means that they tend to earn much less than similarly qualified local residents.21 The extent of this problem seems to vary across sectors. Information technology firms tend to be more flexible on credentials, for example, whereas public-sector organizations are often more closed. The failure to fully deploy their skills can cause new immigrants to incur significant costs. The Migration Policy Institute recently estimated that up to 20 percent of col- lege-educated migrants in the United States were unemployed or working in low-skilled jobs, and

in Canada, despite the points system, this prob- lem is estimated to drain US$1.7 billion a year from the economy.22 In response, the Canadian government has launched programmes to speed up the recognition of credentials earned abroad.

Incomes do not depend solely on labour market earnings. In countries with established welfare systems, social transfers reduce poverty rates among disadvantaged groups through un- employment benefits, social assistance and pen- sions. Whether or not a programme benefits migrant families depends on the design and rules of the system. There are obvious differ- ences across countries in the generosity of these programmes, as their scale tends to be more

Box 3.1 China: Policies and outcomes associated with internal migration

Modelled after the Soviet propiska system, albeit with roots dating

back to ancient times, China’s Residence Registration System oper-

ates through a permit (hukou), needed to gain access to farmland in

agricultural areas and to social benefits and public services in urban

areas. Until the mid-1980s, the system was administered strictly and

movement without a hukou was forbidden. Since then, China has lib-

eralized movement but formally maintained the hukou system.

As in other areas of reform, China chose a gradual and partial

approach. Beginning in the mid-1880s, it allowed people to work out-

side their place of residence without a hukou, but did not allow them

access to social benefits, public services or formal-sector jobs. A

two-tier migration system analogous to the points system in some

developed countries was designed: changes in permanent residency

are permitted for the well educated, but only temporary residence

is granted for less-educated rural migrants. Many city governments

have offered ‘blue-stamp’ hukou to well-off migrants who were able

to make sizeable investments.

The evidence suggests that the human development gains for

internal migrants and their families have been limited by the persis-

tence of the hukou system, along the dimensions illustrated below:

Income gains. In 2004, on average, rural–urban migrants earned

RMB780 (US$94) per month, triple the average rural farm income.

However, due to the segmentation created by the hukou system, tem-

porary migrants typically move to relatively low-paid jobs, and their

poverty incidence is double that of urban residents with hukou.

Working conditions. Low-skilled migrants tend to work in informal

jobs that have inadequate protection and benefits. According to one

survey in three provinces, migrants’ work hours are 50 percent longer

than locals, they are often hired without a written contract and fewer

than 1 in 10 have old-age social security and health insurance, com-

pared to average coverage of over 70 percent in China as a whole.

Occupational hazards are high—migrants accounted for about 75

percent of the 11,000 fatalities in 2005 in the notoriously dangerous

mining and construction industries.

Access to services. Children who move with temporary sta-

tus pay additional fees and are denied access to elite schools. An

estimated 14–20 million migrant children lack access to schooling

altogether. Their drop-out rates at primary and secondary schools

exceed 9 percent, compared to close to zero for locals. Access to

basic health services is limited. Even in Shanghai, one of the better

cities in terms of providing social services to migrants, only two thirds

of migrant children were vaccinated in 2004, compared to universal

rates for local children. When migrants fall ill, they often move back

to rural areas for treatment, due to the costs of urban health care.

Participation. Many migrants remain marginalized in destination

places due to institutional barriers. They have few channels for ex-

pressing their interests and protecting their rights in the work place.

Almost 8 out of 10 have no trade union, workers’ representative con-

ference, labour supervisory committees or other labour organization,

compared to one fifth of locally born people. Long distances also

hinder participation: in a survey of migrants in Wuhan City, only 20

percent had voted in the last village election, mainly because they

lived too far away from polling stations.

Discussions about hukou reform are reportedly ongoing, while some

regional governments have further liberalized their systems. Legislative

reforms in 1997 significantly improved the rights of all workers—includ-

ing migrants, and measures to provide portable pensions for migrant

workers were announced in 2008. Other signs of change come from

Dongguan, Guangdong, for example, where migrants are now referred

to as ‘new residents’ and the Migrants and Rental Accommodation

Administration Office was relabelled the ‘Residents Service Bureau’.

Source: Avenarius (2007), Gaige (2006), Chan, Liu, and Yang (1999 ), Fan (2002), Meng and Zhang (2001), Cai, Du, and Wang (2009 ), Huang (2006), Ha, Yi, and Zhang (2009b), Fang and Wang (2008), and Mitchell (2009 ).

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

limited in developing countries due to budget- ary constraints. Since most developing coun- tries do not have extensive systems in place, the question of equality of access does not arise. The focus here is therefore on developed countries.

Our policy assessment found that nearly all developed countries in the sample granted per- manent migrants access to unemployment ben- efits and family allowances. However, people with temporary status are less likely to be able to access assistance. Some countries, includ- ing Australia and New Zealand, have imposed waiting periods before various benefits can be accessed. And in efforts to avoid welfare depen- dency, countries such as France and Germany require that applications for family reunifica- tion demonstrate that the applicant has stable and sufficient income to support all family members without relying on state benefits.

The Luxembourg Income Study and the European Survey of Income and Living Conditions allow estimates of the effects of social transfers on poverty among families with chil- dren.23 For all 18 countries in the sample, migrant families are more likely to be poor than locally born families. Based on market incomes before social transfers, poverty rates among children ex- ceed 50 and 40 percent among migrant families

in France and the United Kingdom respectively. The redistributive effect of social welfare in these countries is significant, since transfers more than halve these rates for both migrant and locally born children (figure 3.4).24 In contrast, in the United States the poverty-reducing effect of so- cial transfers for both local and migrant families is negligible, because transfers overall are rela- tively small. At the same time it is notable that in Australia, Germany and the United States rates of market–income poverty are much lower than in France and the United Kingdom, suggesting that migrant families are doing better in the labour market in those countries.

3.1.2 Financial costs of moving The gross income gains reported in the litera- ture typically do not account for the monetary costs of moving. These costs arise from various sources, including official fees for documents and clearances, payments to intermediaries, travel expenses and, in some cases, payments of bribes. The costs appear regressive, in that fees for unskilled workers are often high relative to expected wages abroad, especially for those on temporary contracts.25

Substantial costs may arise for those with- out basic documents. Around the world, an

Figure 3.4 Poverty is higher among migrant children, but social transfers can help Effects of transfers on child poverty in selected countries, 1999–2001

Source: Smeeding, Wing, and Robson (2008).

Share of migrant children in poverty before social transfers (%)

Share of migrant children in poverty after social transfers (%)

Share of non-migrant children in poverty after social transfers (%)

France

Germany

United Kingdom

United States

Australia

16 15

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

estimated 48 million children, often from very poor families, lack a birth certificate. The main reason is the fee for obtaining such documents and related factors such as distance to the regis- tration centre.26

Lengthy application processes and, in some countries, payments of bribes for routine ser- vices can make applying for vital records and basic travel documents very expensive.27 In the Democratic Republic of the Congo passport applicants can expect to pay up to US$500 (70 percent of average annual income) in bribes.28 Other countries with limited bureaucratic ca- pacity and corruption in the issuance of travel documents reportedly include Azerbaijan, India and Uzbekistan.29

Intermediaries, also known as ‘middlemen’, perform a specific function in the global labour market. They help to overcome information gaps and meet administrative requirements (such as having a job offer prior to visa application) and sometimes lend money to cover the upfront costs of the move. There are a large number of agen- cies: in the Philippines alone there are nearly 1,500 licensed recruitment agencies, while India has close to 2,000.30 The cost of intermediary services appears to vary enormously, but often exceeds per capita income at home (figure 3.5).

The example of Indonesia illustrates how the costs can vary by destination, with moves to Malaysia and Singapore costing about six months’ expected salary and to Taiwan a full year (figure 3.6). Legal caps on fees charged by recruiters are generally ignored, as migrants routinely pay much more.31 The difference between wages at home and expected wages abroad is perhaps the most important determinant of the price of in- termediary services. Where relatively few jobs are available, intermediaries who are in a position to allocate these slots are able to charge additional rents. There are cases of abuse and fraud, where prospective movers pay high recruitment fees only to find later on (at the destination) that the work contract does not exist, there have been unilat- eral changes to the contract, or there are serious violations related to personal safety and working conditions.32 Some migrants report that employ- ers confiscate their passports, mistreat their em- ployees and deny access to medical care.33

Extensive regulations and official fees can encourage irregularity. For Russian employers,

Figure 3.5 Costs of moving are often high Costs of intermediaries in selected corridors against income per capita, 2006–2008

Figure 3.6 Moving costs can be many times expected monthly earnings Costs of movement against expected salary of low-skilled Indonesian workers in selected destinations, 2008

Source: Bangladesh to Saudi Arabia: Malek (2008); China to Australia: Zhiwu (2009 ); Colombia to Spain: Grupo de Investigación en Movilidad Humana (2009 ); Philippines to Singapore: TWC (2006); Viet Nam to Japan: van Thanh (2008).

Source: The Institute for ECOSOC Rights (2008).

Viet Nam to Japan (6 years, 5 months and 4 days)

Bangladesh to Saudi Arabia (5 years, 2 months and 3 days)

China to Australia (3 years, 10 months and 16 days)

Colombia to Spain (1 year, 8 months and 3 days)

India to United Kingdom (1 year, 3 months)

Philippines to Singapore (8 months and 26 days)

= Origin country annual GNI per capita

12 m

o n

th s

Hong Kong (China)

Taiwan (Province of China)

Malaysia

Singapore

= Monthly expected salary

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

the administrative procedure to apply for a li- cense to hire a foreign worker is reportedly so time-consuming and corrupt that it frequently leads to evasion and perpetuates irregular em- ployment practices.34 In Singapore, employers of low-skilled migrants must pay a levy, which they in turn deduct from workers’ wages.35 Under agreements between Thailand, Cambodia and the Lao People’s Democratic Republic, recruit- ment fees are equivalent to 4–5 months’ salary, processing time averages about four months and 15 percent of wages are withheld pending the migrant’s return home. In contrast, smugglers in these corridors reportedly charge the equivalent of one month’s salary. Given these cost differ- ences, it is not surprising that only 26 percent of migrant workers in Thailand were registered in 2006.36

3.2 Health This section reviews the impacts of movement on the health of those who move. Gaining bet- ter access to services, including health care, may be among the key motivations for moving. Among top high-school graduates from Tonga and Papua New Guinea, ‘health care’ and ‘chil- dren’s education’ were mentioned more often than ‘salary’ as reasons for migrating, and an- swers such as ‘safety and security’ were almost

as frequent.37 However, the links between migration and health are complex. Migrants’ health depends on their personal history be- fore moving, the process of moving itself, and the circumstances of resettlement. Destination governments often rigorously screen applicants for work visas, so successful applicants tend to be healthy.38 Nevertheless, irregular migrants may have specific health needs that remain unaddressed.

Moving to more developed countries can improve access to health facilities and profes- sionals as well as to health-enhancing factors such as potable water, sanitation, refrigeration, better health information and, last but not least, higher incomes. Evidence suggests that migrant families have fewer and healthier children than they would have had if they had not moved.39 Recent research conducted in the United States using panel data, which tracks the same indi- viduals over time, found that health outcomes improve markedly during the first year after immigration.40

Our commissioned study found a 16-fold re- duction in child mortality (from 112 to 7 deaths per 1,000 live births) for movers from low-HDI countries (figure 3.7). Of course these gains are partly explained by self-selection.41 Nonetheless, the sheer size of these differences suggests that

Figure 3.7 The children of movers have a much greater chance of surviving Child mortality at origin versus destination by origin country HDI category, 2000 census or latest round

Source: Ortega (2009 ).

Child mortality at origin Child mortality at destination

Low HDI

(100 versus 7 per thousand)

Medium HDI

(50 versus 7 per thousand)

Very high HDI

(5 versus 7 per thousand)

High HDI

(16 versus 7 per thousand)

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

similar outcomes would have been very difficult to realize at home. For comparison, as reported in the 2006 HDR, families in the richest quin- tile in Burkina Faso had a child mortality rate of about 150 deaths per 1,000 live births.

Not surprisingly, given the poor health ser- vices, water quality and sanitation in rural areas, studies suggest that migrants to urban centres significantly improve their chances of survival relative to rural residents.42 The size of this effect has been correlated with duration of stay, which was itself associated with higher incomes and improved knowledge and practices. Sometimes migrants use health care services more than urban locals, suggesting that the availability of these may have motivated their move in the first place. However, the health outcomes associated with urbanization are variable: a broader study found that internal migrants’ outcomes were worse than those of urban natives, due to their socio-economic disadvantage, and our commis- sioned research found that internal migrants had higher life expectancy than non-migrants in only half of the countries studied.43

Detailed studies in a number of OECD countries have found that migrants’ initial health advantage tends to dissipate over time.4 4 This is believed to reflect the adoption of poorer health behaviour and lifestyles as well as, for some, exposure to the adverse working, hous- ing and environmental conditions that often characterize low-income groups in industrial countries. Separation from family and social networks and uncertainty regarding job secu- rity and living conditions can affect health. In several studies, migrants have reported higher incidence of stress, anxiety and depression than residents,45 outcomes that were correlated with worse economic conditions, language barriers, irregular status and recent arrival. Conversely, other studies have found positive effects of mi- gration on mental health, associated with better economic opportunities.46

Poor housing conditions and risky occupa- tions can increase accidents and compromise health, which may be worse for irregular mi- grants.47 There are well-documented inequali- ties in health care and status between vulnerable migrant groups and host populations in devel- oped countries.48 The health of child migrants can also be affected by their type of work, which

may be abusive and/or hazardous.49 In India, for example, many internal migrants work in dan- gerous construction jobs, while working condi- tions in the leather industry expose the mainly migrant workers to respiratory problems and skin infections.50 Yet these jobs are well paid compared to what was available at home, and interviews in rural Bihar indicate that such jobs are highly sought after.51

Not all types of migrants have the same ac- cess to health care.52 Permanent migrants often have greater access than temporary migrants, and the access of irregular migrants tends to be much more restricted (figure 3.8). Movement sometimes deprives internal migrants of ac- cess to health services if eligibility is linked to authorized residence, as in China. In contrast, permanent migrants, especially the high-skilled, tend to enjoy relatively good access, while in some countries health care is open to all mi- grants, regardless of their legal status, as is the case in Portugal and Spain. In the United Arab Emirates coverage varies by emirate, but both Abu Dhabi and Dubai have compulsory insur- ance schemes to which employers must contrib- ute on behalf of their workers. In Canada all residents are entitled to national health insur- ance, and the provincial authorities determine who qualifies as a resident.

In practice, barriers to health services arise due to financial constraints as well as status, cultural and language differences,53 especially for irregular migrants. In France, Germany and Sweden there is a ‘responsibility to report’ the treatment of an irregular migrant, which can lead to a lack of trust between providers and pa- tients and deter migrants from seeking care.54 If single female migrants in the GCC states are found to be pregnant, they are deported.55

In less-wealthy destination countries there is a tension between the ideal of granting health care access to irregular migrants and the real- ity of resource constraints. In South Africa many non-nationals report not being able to access antiretroviral drugs against AIDS be- cause facilities deny treatment on the basis of ‘being foreign’ or not having a national identity booklet.56 Given that South Africa has one of the highest HIV prevalence rates in the world, combined with improved but still limited ac- cess to antiretrovirals, it is not surprising that

Barriers to health services arise due to financial constraints as well as status, cultural and language differences

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

irregular migrants represent a low priority. But more positive examples are found in other parts of the world. Thailand, for example, pro- vides antiretroviral treatment to migrants from Cambodia and Myanmar, with support from the Global Fund on AIDS, Tuberculosis and Malaria. Thailand also provides migrants with access to health insurance, and efforts are under way to reach irregular migrants.

3.3 Education Education has both intrinsic value and brings instrumental gains in income-earning potential and social participation. It can provide the lan- guage, technical and social skills that facilitate economic and social integration and intergener- ational income gains. Movement is likely to en- hance educational attainment, especially among children. Many families move with the specific objective of having their children attend better and/or more advanced schools. In many rural areas in developing countries education is avail- able only at primary level and at a lower quality than in urban areas, providing an additional motive for rural–urban migration.57 Similarly, international migration for educational pur- poses—school migration—is rising.58

In this section we review the evidence con- cerning school completion levels at places of origin and at destinations, whether migrant children can access state schools and how well they perform relative to children born locally.

School enrolments can change for a number of reasons when a family relocates. Higher in- comes are part of the story, but other factors, such as the availability of teachers and schools, the quality of infrastructure and the cost of transport, may be important as well. A natu- ral starting point when measuring education gains is a comparison of enrolment rates. These present a striking picture of the advantages of moving (figure 3.9), with the crude differences being largest for children from low-HDI coun- tries. Two familiar notes of caution should be sounded, however: these results may be over- estimated due to positive selection; and mere enrolment guarantees neither a high-quality education nor a favourable outcome from schooling.59

The importance of early stimulation to the physical, cognitive and emotional development

Figure 3.8 Temporary and irregular migrants often lack access to health care services Access to health care by migrant status in developed versus developing countries, 2009

Source: Klugman and Pereira (2009 ).

Share of countries in sample (%)

| | | | | | 0 20 40 60 80 100

Panel A: Preventive care

Panel B: Emergency care

Permanent

Temporary

Irregular

Humanitarian

Developed countries

Developing countries

Only available for citizens or not available

Available for migrants with conditions

Immediately available for migrants

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

of children, and the associated importance of early childhood development (ECD) pro- grammes, is well established.60 Research from Germany indicates that ECD can bring the children of migrants to par with native children with the same socio-economic background.61

However, due to traditional norms, language and cultural barriers and sometimes uncertain legal status, these children are generally less likely to enrol in formal ECD programmes, de- spite the fact that authorities in Europe and the United States often actively reach out to migrant children.62 Thailand is among those developing countries that seek to extend informal ECD to migrants, in border areas in the north. Similar arrangements can be found in some other coun- tries; programmes in the Dominican Republic serve Haitian children, for example.

In some countries migrant children may not have access to state schools or their parents may be asked to pay higher fees. Our policy assess- ment found that developed countries are more likely to allow immediate access to schooling for all types of migrant—permanent, temporary, humanitarian and irregular (figure 3.10). Yet a third of developed countries in our sample, in- cluding Singapore and Sweden,63 did not allow access to children with irregular status, while the same was true for over half the developing countries in the sample, including Eg ypt and India. Some specific cases: in the United Arab Emirates children with irregular migrant sta- tus do not have access to education services; in Belgium education is free and a right for every person, but not compulsory for irregular chil- dren; in Poland education for children between 6 and 18 years is a right and is compulsory, but children with irregular status cannot be counted for funding purposes, which may lead the school to decline to enrol such children.64

Poverty and discrimination (formal and in- formal) can inhibit access to basic services. Even if children with irregular status have the right to attend a state school, there may be barriers to their enrolment. In several countries (e.g. France, Italy, the United States), fears that their irregular situation will be reported have been found to deter enrolment.65 In South Africa close to a third of school-age non-national children are not enrolled, for a combination of reasons including inability to pay for fees, transport, uniforms and books, and exclusion by school administrators, while those in school regularly report being subjected to xenophobic comments by teachers or other students.66

The steepest challenges appear to be faced by two groups: children who migrate alone,

Figure 3.10 Migrants have better access to education in developed countries Access to public schooling by migrant status in developed versus developing countries, 2009

Figure 3.9 Gains in schooling are greatest for migrants from low-HDI countries Gross total enrolment ratio at origin versus destination by origin country HDI category, 2000 census or latest round

Source: Klugman and Pereira (2009 ).

Share of countries in sample (%)

| | | | | | 0 20 40 60 80 100

Permanent

Temporary

Irregular

Humanitarian

Developed countries

Developing countries

Only available for citizens or not available

Available for migrants with conditions

Immediately available for migrants

Source: Ortega (2009 ).

Note: Gross total enrolment includes primary, secondary and tertiary education.

Enrolment ratio at origin Enrolment ratio at destination

Low HDI

(47% versus 95%)

Medium HDI

(66% versus 92%)

High HDI

(77% versus 92%)

Very high HDI

(92% versus 93%)

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

who tend to have irregular status (box 3.2), and children who migrate within and between developing countries with their parents, on a temporary basis. The first group is unlikely to be able to access education at all, due to social and cultural isolation, strenuous and hazardous work, extreme poverty, poor health conditions and language barriers.67 As regards the second group, qualitative studies in Viet Nam and Pakistan have found that seasonal migration disrupts their education.68 For instance, the Rac Lai minority in Viet Nam migrate with their children to isolated mountainous areas during the harvest season and their children do not at- tend school during this period.69

Even if migrant children gain access to bet- ter schools than would have been available to them at origin, they do not all perform well in examinations in comparison with their lo- cally born peers. In the 21 OECD and 12 non- OECD countries covered by the Programme for International Student Assessment,70 which tested performance in science, pupils who were migrants tended to perform worse in this sub- ject than locally born children. However, for- eign-born pupils perform as well as their native peers in Australia, Ireland and New Zealand, as well as in Israel, Macao (China) the Russian Federation and Serbia. Likewise, pupils from the same country of origin performed differently across even neighbouring countries: for example, migrant pupils from Turkey perform better in mathematics in Switzerland than in Germany.71 The next generation—children of migrants who are born in the destination place—generally do better, but with exceptions, including Denmark, Germany and the Netherlands.

Part of the educational disadvantage of chil- dren in migrant families can be traced to low parental education and low income. Children whose parents have less than full second- ary completion—which tends to be the case in migrant households in France, Germany, Switzerland and the United States—typically complete fewer years of school. However, while many migrant families live away from relatives and social networks, a study of migrant children in eight developed countries found that they are generally more likely than local children to grow up with both parents.72 This counters a belief sometimes found in the literature that migrant

children are often disadvantaged by the absence of a parent.

In OECD countries migrant pupils generally attend schools with teachers and educational resources of similar quality to those attended by locally-born pupils, although there are some exceptions, including Denmark, Greece, the Netherlands and Portugal. In some cases, the quality of schools that migrant children attend is below national standards, but this is more often related to local income levels generally than to migrant status in particular. Studies on school segregation in the United States suggest that children from migrant families have worse test scores if they attend minority, inner-city schools.73 Studies from the Netherlands and Sweden find that clustering migrant children and separating them from other children is det- rimental to school performance.74 Even if they are not at a disadvantage with regard to instruc- tional materials and equipment, migrant pupils may need special services, such as local language instruction.

Our interest in schooling is partly due to its value in improving the prospects of future

Box 3.2 Independent child migrants

Trafficking and asylum-seeking are often depicted as accounting for most of the

independent movement of children. However, evidence with a long historic record

confirms that children also move in search of opportunities for work and education.

The Convention on the Rights of the Child goes some way to recognizing children as

agents, decision makers, initiators and social actors in their own right. However, the

literature and policy responses to children’s mobility have largely focused on welfare

and protection from harm, and tended to neglect policies of inclusion, facilitation and

non-discrimination.

As for other types of movement, the effect of independent child migration is

context-specific. Some studies have found a significant link between non-attendance

at school and the propensity to migrate to work among rural children, while others

find that migration is positively associated with education. A recent study using cen-

sus data in Argentina, Chile and South Africa shows that independent child migrants

had worse shelter at destination, whereas dependent child migrants were similar to

non-migrants in their type of shelter. Over a fifth of international independent child

migrants aged 15–17 years in these countries were employed, compared to fewer than

4 percent of non-migrant dependent children. Many live with relatives or employers,

but shelter and security can be important concerns. Children may be less able than

adults to change jobs, find it harder to obtain documents even when eligible, may be

more likely to suffer employer violence or encounters with the police, and may be more

easily cheated by employers and others.

Source: Bhabha (2008) and Yaqub (2009 ).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

generations. Some evidence on the extent to which this happens is presented in box 3.3.

3.4 Empowerment, civic rights and participation Moving has the potential to affect not only mate- rial well-being but also such things as bargaining power, self-respect and dignity. Empowerment, defined as the freedom to act in pursuit of per- sonal goals and well-being,75 can be enhanced through movement. However, the reception in the host country obviously matters, especially when migrants face local hostility, which can even lead to outbreaks of violence.

Human development is concerned with the full range of capabilities, including social free- doms that cannot be exercised without political and civic guarantees. These form part of the di- mension of freedom that some philosophers have labelled “the social bases of self-respect”.76 They can be just as important as gains in income and may be associated with these gains, but are often held in check by deep-seated social, class and ra- cial barriers. In many countries the attitude to- wards migration is negative, which can diminish migrants’ sense of dignity and self-respect. This is not a new phenomenon: in the 19th century, the Irish faced the same prejudices in the United Kingdom, as did the Chinese in Australia.

Movement can allow rural women to gain autonomy. Empowerment tends to occur when migration draws women from rural to urban areas, separating them from other family mem- bers and friends and leading them to take paid work outside the home.77 Qualitative studies in Ecuador, Mexico and Thailand have dem- onstrated such effects. For the women in these studies, returning to the old rural way of life was an unthinkable proposition.78 Higher labour force participation and greater autonomy have also been found among Turkish women who emigrated.79 It is not only women who seek to challenge traditional roles when they move: young migrant men can be similarly empow- ered to challenge patriarchal structures within the family.80

But such positive outcomes are not inevita- ble. Some migrant communities become caught in a time warp, clinging to the cultural and so- cial practices that prevailed in the home country at the time of migration, even if the country has since moved on.81 Or the migrant communi- ties may develop radically conservative ideas and practices, as a way to isolate them from the host culture. This can lead to alienation and, occasionally, to extremism. There is a complex dynamic between cultural and community traditions, socio-economic circumstances and

Box 3.3 The next generation

People who move are often motivated by the prospect of better lives

for their children. And indeed the children of migrants can represent

a key population group requiring the attention of policy makers. In

Brussels, for example, they represent over 40 percent of the school-

age population, while in New York they are half and in Los Angeles

County almost two thirds.

Obtaining a good education is critical to future prospects.

Evidence suggests that the children of migrants typically perform

better than their parents, but do not fully catch up with children with-

out a migrant background, even after controlling for socio-economic

characteristics. There are exceptions, however, including Australia

and Canada, where school performance is close to or exceeds that

of native peers. Countries with education systems that involve early

streaming, such as Germany and the Netherlands, appear to have

the biggest gaps in school performance.

How the children of migrants fare in the labour market also tends

to differ across countries and groups. Recent findings suggest that

they have higher employment rates compared to migrants in the

same age group, but they are at a disadvantage compared to those

without a migrant background. In some European countries youth

unemployment rates are worse among the children of migrants.

Limited access to informal networks and discrimination (whether

origin- or class-based) can contribute to these disparities.

Some children of migrants encounter racism, often linked to

limited job opportunities. Studies in the United States, for exam-

ple, have suggested that there is a risk of ‘segmented assimilation’,

meaning that the contacts, networks and aspirations of children of

immigrants are limited to their own ethnic group, but also that this

risk varies across groups. Teenage children of Mexican migrants

have been found to be at higher risk of dropping out of school, going

to prison or becoming pregnant. The same studies suggest that eco-

nomic and social resources at the family and community levels can

help to overcome these risks and avert the rise of an underclass of

disaffected youth.

Source: Crul (2007), OECD (2007), Castles and Miller (1993), and Portes and Zhou (2009 ).

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

public policies. Recent micro-analysis for 10 Latin American countries found that internal migrants of indigenous origin still faced dis- crimination in urban areas, even while they gained greater access to services than they had in their rural area.82 Another study found that Bolivian women in Argentina were discrimi- nated against, had only limited employment opportunities and continued to occupy subor- dinate social positions.83

Participation and civic engagement are im- portant aspects of empowerment. Our analysis using the World Values Survey suggests that people with a migrant background are more likely to participate in a range of civic associa- tions. Compared to people who do not have a migrant parent, they are more likely to be a member of, and also tend to have more confi- dence in, a range of organizations, such as sport, recreational, art and professional organizations. Research also suggests that political participa- tion increases with the ability to speak the host country’s language, with duration of stay, educa- tion in the destination country, connections to social networks and labour markets, and when institutional barriers to registering and voting are lower.84

Institutional factors matter, especially civic and electoral rights. Our policy assessment found that voting in national elections was largely restricted to citizens, although several developed countries allow foreigners to vote in local elections (figure 3.11). The Migrant Integration Policy Index (MIPEX), which as- sesses the opportunities for migrants to par- ticipate in public life in terms of collective associations, voting in, and standing for local elections and support provided to migrant asso- ciations, found policies in Western Europe to be favourable to participation, but those in Central, Eastern and South-Eastern Europe were less so. In Sweden any legal resident who has lived in the country for three years can vote in regional and local elections and stand for local elections, while in Spain foreigners can vote in local elec- tions as long as they are registered as residents with their local authority.

Many people move at least partly to enjoy greater physical and personal security, and to places where the rule of law and government ac- countability are better. This is obviously the case

for many refugees fleeing from conflict, even if their legal situation remains tenuous while they are seeking asylum. Our analysis of determi- nants of flows between pairs of countries shows that the level of democracy in a country has a positive, significant effect on migrant inflows.85

Yet even countries with strong legal tradi- tions are tested when routine police work in- volves the enforcement of migration law. As we saw in chapter 2, countries vary in their enforcement practices. In some countries, ir- regular migrants may be seen as easy targets by corrupt officials. In South Africa police hoping to extort bribes often destroy or refuse to rec- ognize documents in order to justify arrest.86 Mongolian migrants in the Czech Republic also report paying fines during police raids, regard- less of whether they are authorized or not.87 In Malaysia migrants have sometimes been subject to informal enforcement mechanisms, which have led to complaints of abuse (box 3.4).

As we shall see in chapter 4, people in des- tination places often have concerns about the economic, security and cultural impacts of im- migration. In some cases, xenophobia arises. This appears to be most likely where extrem- ists foment fears and insecurities. Outbreaks of violence towards migrants can erupt—such

Figure 3.11 Voting rights are generally reserved for citizens Voting rights in local elections by migrant status in developed versus developing countries, 2009

Source: Klugman and Pereira (2009 ).

Share of countries in sample (%)

| | | | | | 0 20 40 60 80 100

Permanent

Temporary

Developed countries

Permanent

Temporary

Developing countries

Only available for citizens or not available

Available for migrants with conditions

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

as those in Malaysia and South Africa in 2008 and Northern Ireland in 2009, for example— with serious repercussions for both the indi- viduals involved and the societies as a whole.88 Experience suggests that such outbreaks typi- cally occur where political vacuums allow unscrupulous local actors to manipulate under- lying social tensions.89

Ironically, although intolerance often results in resistance to social contact, evidence suggests that increased social contact between migrants and non-migrants can improve levels of toler- ance for migrant groups and counter existing biases.90 Clearly, moderate politicians, govern- ment authorities and NGOs all have a critical part to play in designing and delivering policies and services that facilitate integration and avert escalated tensions. Having legislation on the books is not enough: it must be accompanied by leadership, accountability and informed public debate (chapter 5).

3.5 Understanding outcomes from negative drivers Some people move because their luck im- proves—they win the green card lottery, or a friend or relative offers a helping hand to take up a new opportunity in the city. But many others move in response to difficult circum- stances—economic collapse and political unrest in Zimbabwe, war in Sudan, natural disasters

such as the Asian tsunami. Moving under these circumstances can expose people to risk, in- crease their vulnerability and erode their capa- bilities. But of course in these cases it is not the migration per se but the underlying drivers that cause such deterioration in outcomes. This sec- tion reviews the outcomes associated with three broad drivers: conflict, development-induced displacement and trafficking.

3.5.1 When insecurity drives movement People who flee insecurity and violence typically see an absolute collapse in their human devel- opment outcomes. But migration nonetheless protects them from the greater harm they would doubtless come to if they were to stay put. Several forms of protection are available for refugees, es- pecially for those covered by the 1951 Refugee Convention—which defines the criteria under which individuals may be granted asylum by its signatory countries and sets out their associated rights—and thus under the UNHCR mandate. This protection has allowed millions of people to move to new safe and secure environments.

Contemporary conflicts are increasingly as- sociated with large population movements, in- cluding deliberate displacement of civilians as a weapon of war.91 While some are able to flee to more distant places in North America, Western Europe and Australasia, most displaced people relocate within or near their country of origin. Even if camps host only about a third of those displaced by conflicts,92 these settlements have come to symbolize the plight of people in poor, conflict-affected regions. A contemporary ex- ample is the people of Darfur, Sudan, who fled their villages in the wake of attacks that de- stroyed their cattle and crops, wells and homes, to join what was already the largest displaced population in the world in the wake of the long- running war in southern Sudan.

When the poor and destitute flee combat zones, they run severe risks. Conflict weakens or destroys all forms of capital and people are cut off from their existing sources of income, services and social networks, heightening their vulnerability. After flight, those displaced may have escaped the most direct physical threats, but still face a range of daunting challenges. Security concerns and local hostility rank high

Box 3.4 Enforcement mechanisms in Malaysia

As one of the most robust economies in South-East Asia, Malaysia has attracted

many migrant workers (officially measured at around 7 percent of the population in

2005). The Malaysian labour force at the end of 2008 was almost 12 million, about

44 percent of the 27 million residents, and included about 2.1 million legal migrants

from Bangladesh, Indonesia and other Asian countries. The Malaysian government

has tended to tolerate unauthorized migration, while regularizations have sometimes

been coupled with a ban on new entries and stepped up enforcement.

Since 1972, Malaysia’s People’s Volunteer Corps (Ikatan Relawan Rakyat or

RELA) has helped to enforce laws, including immigration laws. RELA volunteers, who

number about 500,000, are allowed to enter workplaces and homes without warrants,

to carry firearms and to make arrests after receiving permission from RELA leaders.

Migrant activists say that RELA volunteers have become vigilantes, planting evidence

to justify arrests of migrants and using excessive force in their policing. The govern-

ment has recently announced its intention to curb abuses and is currently looking into

ways of improving RELA by providing training to its members.

Source: Crush and Ramachandran (2009 ), Vijayani (2008) and Migration DRC (2007).

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

among their problems, especially in and around camps.93 In civil wars, the internally displaced may face harassment from government and ani- mosity from local people.

Nevertheless, it is important to bear in mind that conflict and insecurity drive only a small share of all movement—about one tenth of in- ternational movement and around one twen- tieth of internal movement. There are regional differences: Africa has been more extensively af- fected, conflict being associated with about 13 percent of international movement on the con- tinent. Map 3.1 shows the location of conflicts and major flows of people displaced within and across borders in Africa. While the map paints a sombre picture, we underline that the vast majority of migration in Africa is not conflict- induced and that most Africans move for the same reasons as everyone else.94

Beyond continuing insecurity, trying to earn a decent income is the single greatest chal- lenge that displaced people encounter, especially where they lack identity papers.95 In commis- sioned case studies,96 Uganda was the only one of six countries where refugees were legally al- lowed to move around freely, to accept work and to access land. About 44 percent of Uganda’s working-age camp population was employed, whereas in all five other countries the figure was below 15 percent. Even if the displaced are per- mitted to work, opportunities are often scarce.

The human development outcomes of those driven to move by insecurity vary considerably. While the UN Guiding Principles on Internal Displacement have raised awareness, internally displaced people—80 percent of whom are women and children—do not benefit from the same legal rights as refugees.97 Roughly half the world ’s estimated 26 million internally displaced people receive some support from UNHCR, IOM and others, but sovereignty is often invoked as a justification for restrict- ing international aid efforts. In 2007, Sudan, Myanmar and Zimbabwe each had more than 500,000 crisis-affected people who were be- yond the reach of any humanitarian assistance.98 Even in less extreme cases, malnutrition, poor access to clean water and health care, and lack of documentation and property rights are typi- cal among the internally displaced. However, some governments have made concerted efforts

Map 3.1 Conflict as a driver of movement in Africa Conflict, instability and population movement in Africa

Source: UNHCR (2008) and IDMC (2008).

Note: This map illustrates refugee flows based on official UNHCR data and misses important flows associated with instability, as in the case of Zimbabweans fleeing to South Africa for example.

Recent conflict zones

Ongoing UN peacekeeping missions (2009)

Refugee flows in 2007 (in thousands)

Number of refugees (end of 2008)

0–1,000

1,000–10,000

10,000–100,000

100,000–523,032

23.8

Tunisia

Libyan Arab Jamahiriya

Algeria

Mali

Benin

Togo Ghana

Mauritania

Senegal

Niger

Nigeria

Chad

Congo Gabon

Equatorial Guinea

Sao Tome and Principe

Madagascar

South Africa

Lesotho

Swaziland

Zimbabwe

Uganda Kenya

Zambia MozambiqueMalawi

Tanzania

Burundi

Rwanda

Botswana

Namibia

Angola

Sudan

Congo, (Dem. Rep. of the)

Cameroon

Guinea Guinea- Bissau

Sierra Leone

Liberia Côte

d’Ivoire

Burkina Faso

Morocco

Western Sahara

Egypt

Ethiopia

Somalia

Djibouti

Eritrea

Central African Republic

16.6 0.6

0.6

20.0

24.9

3.5 2.5 2.7

9.4

2.6

3.7

0.1

Gambia

7.0

0.3

1.2

0.3

23.8

0.3

0.4

Internally displaced persons (end of 2008)

Burundi 100,000

Central African Republic 108,000

Chad 180,000

Congo Up to 7,800

Congo, DRC 1,400,000

Côte d’Ivoire At least 621,000

Ethiopia 200,000–300,000

Kenya 300,000–600,000

Liberia Undetermined

Rwanda Undetermined

Senegal 10,000–70,000

Somalia 1,300,000

Sudan 4,900,000

Uganda 869,000

Zimbabwe 570,000–1,000,000

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

to improve the rights and living conditions of their internally displaced populations.99

The situation of international refugees also varies, but can be bleak, especially in cases of protracted conflict, such as Palestine. Such cases account for roughly half of all refugees. Our commissioned analysis confirmed overall weak human development outcomes, alongside some heterogeneity across groups and countries. The incidence of sexual and gender-based vio- lence is high. Paradoxically, however, women in Burundi and Sri Lanka were reportedly empow- ered as they adopted new social roles as protec- tors and providers for their families.100

Education and health indicators in refugee camps are sometimes superior to those of sur- rounding local populations. Our study found that the share of births attended by skilled medical personnel in camps surveyed in Nepal, Tanzania and Uganda was significantly higher than among these countries’ population as a whole. Similarly, education indicators—such as gross primary enrolment ratios and pupil-to- teacher ratios—were better among camp-based

refugees than for the general population (figure 3.12). These patterns reflect both the effects of humanitarian assistance in camps and the gen- erally poor human development conditions and indicators prevailing in countries that host the bulk of refugees.

As noted above, most refugees and internally displaced people do not end up in camps at all, or at least not for long. For example, less than a third of Palestinian refugees live in UNRWA- administered camps.101 On average, those who relocate to urban centres seem to be younger and better educated, and may enjoy better human de- velopment outcomes than those living in camps. Others, usually the better off, may be able to flee to more distant and wealthier countries, some- times under special government programmes.

Only a minority of asylum seekers succeed in obtaining either refugee status or residency, and those whose request is denied can face pre- carious situations.102 Their experience depends on the policies of the destination country. Developed countries in our policy assessment allowed humanitarian migrants access to emer- gency services, but more restricted access to preventive services, whereas in the developing countries in our sample, access to public health services was even more restricted (figure 3.8).

Finding durable long-term solutions to the problem in the form of sustainable return or successful local integration has proved a major challenge. In 2007, an estimated 2.7 million in- ternally displaced people and 700,000 refugees, representing about 10 and 5 percent of stocks respectively, returned to their areas of origin.103 Perhaps the Palestinian case, more than any other, illustrates the hardships faced by refu- gees when conflict is protracted, insecurity is rampant and local economic opportunities are almost non-existent.104

In other cases, gradual integration into local communities, sometimes through naturaliza- tion, has taken place in a number of develop- ing and developed countries, although refugees tend to be relatively disadvantaged, especially as regards labour market integration.105

3.5.2 Development-induced displacement Outcomes may also be negative when people are displaced by development projects. The classic

Figure 3.12 School enrolment among refugees often exceeds that of host communities in developing countries Gross primary enrolment ratios: refugees, host populations and main countries of origin, 2007

Source: de Bruijn (2009 ), UNHCR (2008) and UNESCO Institute for Statistics (2008b).

Gross enrolment ratio (%)

| | | | | | | | | 0 20 40 60 80 100 120 140 160

Kenya

Uganda

Bangladesh

Tanzania

Nepal

Thailand

Refugees

Population of main origin country

Population of asylum country

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

case of this occurs when large dams are built to provide urban water supplies, generate elec- tricity or open up downstream areas for irriga- tion. Agricultural expansion is another major cause, as when pastoralists lose traditional riv- erine grazing lands when these are developed for irrigated cash crops. Infrastructure projects such as roads, railways or airports may also dis- place people, while the energy sector—mining, power plants, oil exploration and extraction, pipelines—may be another culprit. Parks and forest reserves may displace people when man- aged in a top-down style rather than by local communities.

These types of investment generally expand most people’s opportunities—in terms of pro- viding yield-increasing technolog y, links to markets and access to energy and water, among other things.106 But how the investments are designed and delivered is critical. By the 1990s it was recognized that such interventions could have negative repercussions for the minority of people directly affected, and were criticized on social justice and human rights grounds.107 One vocal critic has been the World Commission on Dams, which has stated that, “impoverishment and disempowerment have been the rule rather than the exception with respect to resettled people around the world,”108 and that these out- comes have been worst for indigenous and tribal peoples displaced by big projects.

Among the impacts observed in indigenous communities are loss of assets, unemployment, debt bondage, hunger and cultural disintegra- tion. There are many such examples, which have been well documented elsewhere.109 The India Social Institute estimates that there are about 21 million development-induced dis- placed persons in India, many of whom belong to scheduled castes and tribal groups. In Brazil the construction of the Tucuruí Dam displaced an estimated 25,000 to 30,000 people and sig- nificantly altered the lifestyle and livelihood means of the Parakanã, Asurini and Parkatêjê indigenous groups. Poor resettlement planning split up communities and forced them to relo- cate several times, often in areas that lacked the necessary infrastructure to serve both the needs of a growing migrant population (pulled in by construction jobs) and those displaced by the project.110

This issue was addressed in the Guiding Principles on Internal Displacement mentioned above. The principles provide that, during the planning stage, the authorities should explore all viable options for avoiding displacement. Where it cannot be avoided, it is up to the au- thorities to make a strong case for it, stating why it is in the best interests of the public. The sup- port and participation of all stakeholders should be sought and, where applicable, agreements should stipulate the conditions for compensa- tion and include a mechanism for resolving disputes. In all instances, displacement should not threaten life, dignity, liberty or security, and should include long-term provisions for ad- equate shelter, safety, nutrition and health for those displaced. Particular attention should be given to the protection of indigenous peoples, minorities, smallholders and pastoralists.

These principles can help inform devel- opment planners as to the social, economic, cultural and environmental problems that large- and even small-scale development proj- ects can create. Incorporating such analysis in planning processes, as has been done for some major sources of development finance—includ- ing the World Bank, which has an Involuntary Resettlement Policy—has been an important step forward.111 Such policies allow for rights of appeal by aggrieved parties through inspection panels and other mechanisms. Approaches of this kind can enable favourable human develop- ment outcomes for the majority while helping to mitigate the risks borne by the displaced minor- ity, though the challenges remain large.

3.5.3 Human trafficking The images associated with trafficking are often horrendous, and attention tends to focus on its association with sexual exploitation, organized crime, violent abuse and economic exploitation. Human trafficking not only adversely affects individuals but can also undermine respect for whole groups. However, the increasing focus on this phenomenon has not yet provided a reliable sense of either its scale or its relative importance in movements within and across borders (chap- ter 2).

Above all, trafficking is associated with re- strictions on human freedom and violations of basic human rights. Once caught in a trafficking

Above all, trafficking is associated with restrictions on human freedom and violations of basic human rights

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

network, people may be stripped of their travel documents and isolated, so as to make escape difficult if not impossible. Many end up in debt bondage in places where language, social and physical barriers frustrate their efforts to seek help. In addition, they may be reluctant to iden- tify themselves, since they risk legal sanctions or criminal prosecution. People trafficked into sex work are also at high risk of infection from HIV and other sexually transmitted diseases.112

One basic constraint in assessing the im- pacts of trafficking relates to data. The IOM’s Counter Trafficking Module database contains data on fewer than 14,000 cases that are not a representative sample, and the same applies to the database of the United Nations Office on Drugs and Crime (UNODC).113 The picture that emerges from these data, alongside existing studies and reports, suggests that most people who are trafficked are young women from mi- nority ethnic groups. This is confirmed by other sources—for example a study in South-eastern Europe, which found that young people and ethnic minorities in the rural areas of post-con- flict countries were vulnerable to trafficking, as they tended to experience acute labour market exclusion and disempowerment.114 However, this picture may be biased, since it is possible that males are less willing to self-report for fear they will be refused victim status. In addition to social and economic exclusion, violence and exploitation at home or in the home community increase vulnerability to trafficking. So too does naïve belief in promises of well-paid jobs abroad.

Sexual exploitation is the most commonly identified form of human trafficking (about 80 percent of cases in the UNODC database), with economic exploitation comprising most of the balance. For women, men and children traf- ficked for these and other exploitative purposes, bonded labour, domestic servitude, forced mar- riage, organ removal, begging, illicit adoption and conscription have all been reported.

Alongside the lack of power and assets of the individuals involved, the negative human devel- opment outcomes of trafficking can be partly associated with the legal framework of destina- tion countries. Restrictive immigration controls mean that marginalized groups tend to have ir- regular status and so lack access to the formal la- bour market and the protections offered by the

state to its citizens and to authorized migrant workers.115 More generally, of course, trafficking can be most effectively combated through better opportunities and awareness at home—the abil- ity to say ‘no’ to traffickers is the best defence.

Difficulties in distinguishing trafficking from other types of exploitation, as well as chal- lenges involved in defining exploitative practices, further complicate the rights of trafficked peo- ple. Problems can arise over enforcement. It ap- pears that trafficking is sometimes very broadly interpreted to apply to all migrant women who engage in sex work. This can be used to justify their harassment and deportation, making them even more vulnerable to exploitation. And once identified, they are virtually always deported or referred to assistance programmes conditional on cooperation with law enforcement.

Anti-trafficking initiatives have burgeoned in recent years. Interventions to reduce vulner- ability in potential source communities, such as awareness campaigns and livelihood projects, have been undertaken. Assistance programmes have also provided counselling, legal aid and support for return and reintegration. Some of these programmes are proving successful, such as the use of entertainment and personal stories as community awareness tools in Ethiopia and Mali, or door-to-door mass communication campaigns as in the Democratic Republic of the Congo.116 Other initiatives, however, have led to counterproductive and sometimes even disastrous outcomes, including prejudicial limi- tations on women’s rights. In Nepal, for exam- ple, prevention messages discouraged girls and women from leaving their villages, while HIV awareness campaigns stigmatized returnees.117 Anti-trafficking initiatives clearly raise very complex and difficult challenges, which need to be carefully handled.

The lines between traffickers on the one hand and recruiters and smugglers on the other can be blurred. For example, the business of re- cruitment expands to include numerous layers of informal sub-agents. These sub-agents, work- ing under the umbrella of legitimate recruiters can reduce accountability and increase costs. The risks of detention and deportation are high. Smuggling costs in some cases include bribing corrupt border officials and manufacturing false documents.118

Trafficking can be most effectively combated through better opportunities and awareness at home— the ability to say ‘no’ to traffickers is the best defence

3HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

3.6 Overall impacts We have studied the discrete impacts of migra- tion on incomes, health, education and aspects of empowerment and agency—and looked at the negative outcomes that can occur when people move under duress. Differences in the HDI are a simple way to capture overall changes.

Our background research found very large average differences between the HDI of mi- grants and that of non-migrants, moving in- ternally and across borders. We found that, on average, migrants to OECD countries had an HDI about 24 percent higher than that of people who stayed in their respective countries of origin.119 But the gains are large not only for those who move to developed countries: we also found substantial differences between inter- nal migrants and non-migrants.120 Figure 3.13 shows that, in 14 of the 16 developing countries covered by this analysis, the HDI for internal migrants is higher than that of non-migrants.

In some cases the differences are substantial. For internal movers in Guinea, for example, the HDI for migrants is 23 percent higher than for non-migrants—only one percentage point lower than for migrants to OECD countries. If these migrants were thought of as a separate country, they would be ranked about 25 places higher than non-migrants in the global HDI.

There are two major exceptions to the over- all pattern of improved well-being from in- ternal movement: in Guatemala and Zambia internal migrants appear to do worse than non- migrants. Both these cases underline the risks that accompany migration. In Guatemala most movers were displaced by violence and civil war in the 1980s and early 1990s, while in Zambia migrants faced extreme urban poverty follow- ing the successive economic shocks that have hit this country over the past 20 years. In a few other cases—Bolivia and Peru, for example— the overall human development outcome ap- pears marginal despite sizeable income gains, suggesting poor access to services as a factor in- hibiting well-being. However, these exceptional cases serve to emphasize the norm, which is that most movers are winners.

These findings for international movers are borne out by evidence on migrants’ own sense of well-being (figure 3.14). We analysed data for 52 countries in 2005 and found that self-reported

levels of happiness and health were very similar among migrants and non-migrants: 84 percent of migrants felt happy (compared to 83 percent of non-migrants), while 72 percent felt that their health was good or very good (compared to 70 percent of non-migrants); only 9 percent were ‘not satisfied’ with life (compared to 11 percent of non-migrants). The share of migrants reporting that they felt quite or very happy was highest in developed countries. Similar shares of foreign and locally born respondents—more than 70 percent—felt that they have ‘freedom and choice over their lives’.121

Figure 3.13 Significant human development gains to internal movers Ratio of migrants’ to non-migrants’ estimated HDI in selected developing countries, 1995–2005

HDI ratio

| | | | | | 0.6 0.8 1.0 1.2 1.4 1.6

Source: Harttgen and Klasen (2009 ).

Guinea

Madagascar

Uganda

Indonesia

Viet Nam

Côte d’Ivoire

Ghana

Kyrgyzstan

Paraguay

Cameroon

Bolivia

Nicaragua

Colombia

Peru

Zambia

Guatemala

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development3

3.7 Conclusions The complex effects associated with movement are difficult to summarize simply. The broad findings presented in this chapter underline the role of movement in expanding human free- doms that was outlined in chapter 1. We saw that people who move generally do enhance their opportunities in at least some dimensions, with gains that can be very large. However, we also saw that the gains are reduced by policies at home and destination places as well as by the constraints facing individuals and their fami- lies. Since different people face different oppor- tunities and constraints, we observed significant inequalities in the returns to movement. The cases in which people experience deteriorations in their well-being during or following the pro- cess of movement—conflict, trafficking, natural disasters, and so on—were associated with con- straints that prevent them from choosing their place in life freely.

A key point that emerged is that human movement can also be associated with trade- offs—people may gain in some and lose in other dimensions of freedom. However, the losses can be alleviated and even offset by better policies, as we will show in the final chapter.

Figure 3.14 Migrants are generally as happy as locally-born people Self-reported happiness among migrants and locally-born people around the world, 2005/2006

Source: HDR team estimates based on WVS (2006).

Percent of responses

| | | | | | 0 20 40 60 80 100

Total Locally born

Foreign born

“All things considered, would you say you are:”

Very happy Quite happy Not very happy Not at all happy

Total

Low HDI

Medium HDI

High HDI

Very high HDI

Impacts at origin and destination

Movement has multiple impacts on other people besides

those who move—impacts that critically shape its overall

effects. This chapter explores impacts in the country

of origin and in the host country while underlining their

interconnectedness. Families with members who have

moved elsewhere in the country or abroad tend to

experience direct gains, but there can also be broader

benefits, alongside concerns that people’s departure

is a loss to origin communities. As regards impacts on

places of destination, people often believe that these are

negative—because they fear that newcomers take jobs,

burden public services, create social tensions and even

increase criminality. The evidence suggests that these

popular concerns are exaggerated and often unfounded.

Still, perceptions matter—and these warrant careful

investigation to help frame the discussion of policy.

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Impacts at origin and destination

Among people who do not move but can be affected by movement are the families of movers and communities at places of origin and destination. The multiple impacts of movement in these differ- ent places are critical in shaping the overall human development effects of movement; this chapter addresses each in turn.

At places of origin, impacts can be seen on in- come and consumption, education and health, and broader cultural and social processes. These impacts are mostly favourable, but the concern that communities lose out when people move needs to be explored. Our review of the evi- dence shows that impacts are complex, context- specific and subject to change over time. The nature and extent of impacts depend on who moves, how they fare abroad and their procliv- ity to stay connected, which may find expression in flows of money, knowledge and ideas, and in the stated intention to return at some date in the future. Because migrants tend to come in large numbers from specific places—e.g. Kerala in India and Fujian Province in China—impacts on local communities may be more pronounced than national impacts. Yet the flow of ideas can also have far-reaching effects on social norms and class structures, rippling out to the broader community over the longer term. Some of these impacts have traditionally been seen as negative, but a broader perspective suggests that a more nuanced view is appropriate. In this light we also examine the extent to which national develop- ment plans, such as poverty reduction strategies (PRSs), reflect and frame efforts of developing countries to promote gains from mobility.

Much academic and media attention has been directed to the impacts of migrants on places of destination. One widespread belief is that these impacts are negative—newcomers are seen as ‘taking our jobs’ if they are employed, liv- ing off the taxpayer by claiming welfare benefits if they are not employed, adding an unwanted extra burden to public services in areas such as health and education, creating social tensions with local people or other immigrant groups and even increasing criminal behaviour. We

investigate the vast empirical literature on these issues, which reveals that these fears are exagger- ated and often unfounded. Nevertheless, these perceptions matter because they affect the po- litical climate in which policy decisions about the admission and treatment of migrants are made—fears may stoke the flames of a broader hostility to migrants and allow political extrem- ists to gain power. Indeed, historical and con- temporary evidence suggests that recessions are times when such hostility can come to the fore. We end this chapter by tackling the thorny issue of public opinion, which imposes constraints on the policy options explored in the final chapter.

4.1 Impacts at places of origin Typically, only a small share of the total population of an origin country will move. The exceptions— countries with significant shares abroad—are often small states, including Caribbean nations such as Antigua and Barbuda, Grenada, and Saint Kitts and Nevis. In these cases the share can ex- ceed 40 percent. The higher the share, the more likely it is that impacts on people who stay will be more pervasive and more profound. While the discussion below focuses on developing countries, it is important to bear in mind that, as shown in chapter 2, emigration rates for low-HDI countries are the lowest across all country groupings.

In general, the largest impacts at places of origin are felt by the households with an absent migrant. However, the community, the region and even the nation as a whole may be affected. We now look at each of these in turn.

4.1.1 Household level effects In many developing countries, movement is a household strategy aimed at improving not only the mover’s prospects but those of the extended

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

family as well. In return for supporting the move, the family can expect financial remit- tances when the migrant is established—trans- fers that typically far outweigh the initial outlay or what the mover might have hoped to earn in the place of origin. These transfers can in turn be used to finance major investments, as well as immediate consumption needs.

Despite these financial rewards, separation is typically a painful decision incurring high emo- tional costs for both the mover and those left behind. In the words of Filipina poet Nadine Sarreal:

Your loved ones across that ocean Will sit at breakfast and try not to gaze Where you would sit at the table Meals now divided by five Instead of six, don’t feed an emptiness.1

The fact that so many parents, spouses and partners are willing to incur these costs gives an idea of just how large they must perceive the re- wards to be.

Financial remittances are vital in improving the livelihoods of millions of people in developing countries. Many empirical studies have confirmed the positive contribution of international remit- tances to household welfare, nutrition, food, health and living conditions in places of origin.2 This con- tribution is now well recognized in the literature on migration and reflected in the increasingly accurate data on international remittances published by the World Bank and others, illustrated in map 4.1. Even those whose movement was driven by conflict can be net remitters, as illustrated at various points in history in Bosnia and Herzegovina, Guinea- Bissau, Nicaragua, Tajikistan and Uganda, where remittances helped entire war-affected communi- ties to survive.3

In some international migration corridors, money transfer costs have tended to fall over time, with obvious benefits for those sending and receiving remittances.4 Recent innovations have also seen significant falls in costs at the na- tional level, as in the case of Kenya described in box 4.1. With the reduction in money transfer costs, families who once relied on relatives and close family friends or who used informal ave- nues such as the local bus driver to remit are now opting to send money through banks, money transfer companies and even via cell-phones.

An important function of remittances is to di- versify sources of income and to cushion families against setbacks such as illness or larger shocks caused by economic downturns, political con- flicts or climatic vagaries.5 Studies in countries as diverse as Botswana, El Salvador, Jamaica and the Philippines have found that migrants respond to weather shocks by increasing their remittances, although it is difficult to establish whether these effectively serve as insurance. Recent examples include the 2004 Hurricane Jeanne in Haiti, the 2004 tsunami in Indonesia and Sri Lanka and the 2005 earthquake in Pakistan.6 In a sample of poor countries, increased remittances were found to offset some 20 percent of the hurricane damage experienced,7 while in the Philippines about 60 percent of declines in income due to rainfall shocks were offset.8 In El Salvador crop failure caused by weather shocks increased the probability of households sending a migrant to the United States by 24 percent.9

Migrants can provide this kind of protection if their incomes are large enough and do not vary in tandem with their families’. This depends on the nature and breadth of the shock, as well as the location of the migrant. For example, remit- tances may not provide much insurance against the effects of the current global economic reces- sion, as migrant workers almost everywhere suf- fer retrenchment just when their families most need support (box 4.2). Remittances to develop- ing countries are expected to fall from US$308 billion in 2008 to US$293 billion in 2009.10

Even when the total volume of remittances is large, their direct poverty-reducing impact depends on the socio-economic background of those who moved. Within the Latin America region, for example, a recent study found that in Mexico and Paraguay remittance-receiving households were primarily from the bottom of the income and education distribution, whereas the opposite pattern was found in Peru and Nicaragua.11 More generally, however, restric- tions imposed by the limited opportunities of the low-skilled to move across borders mean that remittances do not tend to flow directly to the poorest families,12 nor to the poorest countries.13 Take China, for example: because migrants generally do not come from the poorest house- holds, the aggregate poverty impact of internal migration is limited (an estimated 1 percent

Despite these financial rewards, separation is typically a painful decision incurring high emotional costs for both the mover and those left behind

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

reduction), although this still translates into al- most 12 million fewer poor.14 At the same time, some migrants do come from poor households and significant remittances sometimes flow to non-family members, which allows for broader benefits—as has been found for Fiji and Jamaica, for example.15

The poverty-reducing effects of internal mi- gration, which have been demonstrated by stud- ies in a diverse range of national situations, may be even more significant. In Andhra Pradesh and Madhya Pradesh in India poverty rates in house- holds with a migrant fell by about half between 2001/02 and 2006/07,16 and similar results were found for Bangladesh.17 Large gains have also been reported from panel data, tracking individ- uals over time, in the Kagera region of Tanzania between 1991 and 2004.18 Research conducted

for this report, using panel data and controlling for selection bias, examined the cases of Indonesia between 1994 and 2000 and Mexico between 2003 and 2005. In Indonesia, where almost half of all households had an internal migrant, poverty rates for non-migrants were essentially stable for the period (which included the East Asian financial crisis), falling slightly from 40 to 39 percent, but declined rapidly for migrants, from 34 to 19 percent. In Mexico, where about 9 percent of households had an internal migrant, poverty rates rose sharply from 25 to 31 percent for non-migrants for the period (which included the 2001/02 recession), but only slightly, from 29 to 30 percent, for migrants. In both countries, at the outset households with a migrant made up less than half of the top two wealth quintiles, but over time this share rose to nearly two thirds.19

Map 4.1 Remittances flow primarily from developed to developing regions Flows of international remittances, 2006–2007

Source: HDR team data based on Ratha and Shaw (2006) and World Bank (2009b).

Remittances as a share of GDP, 2007

Regions Remittances, 2006 (in US$ billions)

North America

Europe

Oceania

Latin America and the Caribbean

Asia

Africa

no data

0.0%–0.4%

0.5%–0.9%

1.0%–4.9%

5.0%–9.9%

10.0%–14.9%

15.0%–19.9%

20.0%–24.9%

25.0%–29.9%

>30%

Intra- regional

remittances

1.1

0.10.9 3.6 0.08

0.01

0.5 0.02

0.02

0.3

4.0

0.3

2.9

3.1

1.5

1.6

0.3

0.9

10.3

1.9 2.2

2.8

0.4

42.0 52.5

0.2

30.1

17.3

36.3

5.3

15.9

1.2

4.4

0.02

2.2

Asia

Europe North America

Latin America & Caribbean

Africa

Oceania

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

One dimension of movement that appears to affect remittance flows is gender. Evidence sug- gests that women tend to send a larger propor- tion of their incomes home, on a more regular basis, though their lower wages often mean that the absolute amounts are smaller.20

There is also a temporal dimension to these flows. Over time, the knock-on effects of remit- tances may substantially broaden the impacts on poverty and inequality.21 The poor may gain when remittances are spent in ways that gener- ate local employment, such as building houses, or when businesses are established or expanded.22 Some studies have found that remittance re- cipients exhibit greater entrepreneurship and a higher marginal propensity to invest than house- holds without a migrant.23 Positive investment effects can take decades to materialize in full, however, and are complex and far from auto- matic. The lag may reflect delays in the sending of remittances as migrants adapt to their new homes, or political and economic conditions in places of origin—such as a poor climate for investment—which can inhibit or deter trans- fers.24 Lastly, remittances can also create a store of capital to fund further migration, years after the first family member has left.

Some commentators discount the impor- tance of remittances because they are partly spent on consumption. This critique is mis- taken, for two broad reasons. First, consumption can be inherently valuable and often has long- term, investment-like effects, especially in poor

communities. Improvements in nutrition and other basic consumption items greatly enhance human capital and hence future incomes. 25 Similarly, spending on schooling is often a pri- ority for families receiving remittances, because it increases the earning power of the next genera- tion. Second, most types of spending, especially on labour-intensive goods and services such as housing and other construction, will benefit the local economy and may have multiplier effects.26 All of these effects are positive.

Families with migrants appear more likely to send their children to school, using cash from remittances to pay fees and other costs. This re- duces child labour. And, once there, the children of migrants are more likely to finish school, as the better prospects associated with migration affect social norms and incentives.27 In Guatemala in- ternal and international migration is associated with increased educational expenditures (45 and 48 percent respectively), especially on higher lev- els of schooling.28 In rural Pakistan temporary migration can be linked with increased enrol- ment rates and declines in school dropout rates that exceed 40 percent, with larger effects for girls than for boys.29 In our own commissioned research, similar results were found in Mexico, where children in households with an internal migrant had a 30–45 percent higher probability of being in an appropriate grade for their age.30

The prospect of moving can strengthen in- centives to invest in education.31 This has been predicted in theory and shown in practice in

Box 4.1 How cell-phones can reduce money transfer costs: the case of Kenya

For many people in remote rural areas of developing countries, the

costs of receiving money remain high: recipients typically have to

travel long distances to a regional or national capital to collect cash,

or the cash has to be hand-delivered by an intermediary, who may

take a sizeable margin.

The rapid diffusion of cell-phone technology over the past de-

cade has led to the development of innovative money transfer sys-

tems in several countries. For example, in Kenya, a leading cell-phone

company, Safaricom, teamed up with donors to pilot a system that

subsequently led to the launch in 2007 of M-PESA (meaning ‘Mobile-

Cash’). Anyone with a cell-phone can deposit money in an account

and send it to another cell-phone user, using M-PESA agents distrib-

uted across the country.

A recent survey of users across Kenya found that, in just two

years, M-PESA has expanded rapidly. It is now used by some 6

million people or 17 percent of the population—out of 26 percent

who are cell-phone owners—and is supported by a network of

more than 7,500 agents. Transfers can be made from the port city

of Mombasa to Kisumu on the shores of Lake Victoria, or from

Nairobi in the south to Marsabit in the north—both two-day bus

trips—with the push of a few buttons and at a cost of less than a

dollar. By mid-2008, the volume of money sent had reached some

8 percent of GDP, mostly in the form of a large number of relatively

small transactions.

Source: Jack and Suri (2009 ).

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

some countries. Emigration of Fijians to high- skilled jobs in Australia, for example, has en- couraged the pursuit of higher education in Fiji. This effect is so large that, while roughly a third of the Indo-Fijian population has emigrated in the past three decades and skilled workers are over-represented among emigrants, the absolute number of skilled Indo-Fijian workers in Fiji has greatly increased.32 A number of governments, including the Philippines, have deliberately sought to promote work abroad in part by facili- tating the generation of skills at home.33

The impacts of migration prospects on schooling incentives are shaped by the con- text and the prospects themselves. In Mexico, for instance, where low-sk i l led, of ten ir- reg ular migration predominates, boys were more likely to drop out of school to take up this option.3 4 In our commissioned study of Chinese census data at the provincial level, investments in schooling in rural source com- munities responded to the skills needed for job opportunities outside the province. Thus, where internal migrants had secondary educa- tion, this generally encouraged the completion of higher levels by children remaining in the community, whereas in provinces where mi- grants tended to have completed only middle school, this was associated with lower high school completion rates.35

The health outcomes of people who do not move may be affected by migration, through effects on nutrition, living conditions, higher incomes and the transmission of knowledge and practices. There is evidence that the higher incomes and better health knowledge associ- ated with migration have a positive influence on infant and child mortality rates.36 However, in Mexico at least, it was found that longer term health outcomes may be adversely affected, be- cause levels of preventive health care (e.g. breast feeding and vaccinations) were lower when at least one parent had migrated.37 This may be associated with the higher work burden and/ or reduced levels of knowledge associated with single parenting or families with fewer adults. Moreover, when infectious diseases can be con- tracted in destination places, return travel can bring significant health risks to families at home. The risks of HIV and other sexually transmitted diseases can be especially high.38

Offsetting the potential gains in consump- tion, schooling and health, children at home can be adversely affected emotionally by the process of migration. One in five Paraguayan mothers residing in Argentina, for example, has

Box 4.2 The 2009 crisis and remittances

The 2009 economic crisis, which began in major destination countries and has now

gone global, has shrunk flows of remittances to developing countries. There is already

evidence of significant declines in flows to countries that depend heavily on remit-

tances, including Bangladesh, Egypt, El Salvador and the Philippines.

Countries and regions vary in their exposure to the crisis via remittance effects.

Remittances to Eastern European and Central Asian countries are forecast to suffer

the biggest drop in both relative and absolute terms, partly reflecting the reversal of

the rapid expansion that had followed European Union accession and the economic

boom in the Russian Federation. In Moldova and Tajikistan, where remittance shares

of GDP are the highest in the world (45 and 38 percent respectively), flows are pro-

jected to shrink by 10 percent in 2009. El Salvador is facing a significant decline in

remittances, which account for over 18 percent of its GDP.

About three quarters of remittances to sub-Saharan Africa come from the United

States and Europe, which have been badly affected by the downturn (chapter 2). It

remains to be seen whether these sources will prove more or less resilient than official

development aid and private investment flows.

Figure 4.1 The global recession is expected to impact remittance flows Projected trends in remittance flows to developing regions, 2006–2011

East Asia and the Pacific South Asia Latin America and the Carribean

Central and Eastern Europe and the CIS

Arab States

Sub-Saharan Africa

| | | | | | 2006 2007 2008* 2009** 2010** 2011**

10R e

m it

ta n

c e fl

o w

s (i n U

S $ b

ill io

n s )

* Estimate ** Forecasts

Source: Ratha and Mohapatra (2009b) and The Economist Intelligence Unit (2009 ).

Note: These regional groupings include all developing countries as per UNDP Regional Bureaux’ classification. For the complete list of countries in each region see

‘Classification of Countries’ in the Statistical Annex.

Source: Ratha and Mohapatra (2009a,b).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

young children in Paraguay.39 Studies investigat- ing the possible impacts have found that these depend on the age of the child when the separa- tion occurs (in the first years of life the impact may be greater), on the familiarity and attitude of the adult in whose care the child is left, and on whether the separation is permanent or tem- porary.40 The advent of cheap and easy commu- nication, for example by cell-phone and Skype, has eased the separation of family members and has greatly helped the maintenance of ties and relationships in recent years.

Movement can affect gender relations at home.41 When women move, this can change tra- ditional roles, especially those surrounding the care of children and the elderly.42 When men mi- grate, rural women can be empowered by their ab- sence: field studies conducted in Ecuador, Ghana, India, Madagascar and Moldova all found that, with male migration, rural women increased their participation in community decision-making.43 Norms adopted in a migrant’s new home—such as a higher age of marriage and lower fertility, greater educational expectations of girls, and labour force participation—can filter back to the place of ori- gin. This diffusion process may be accelerated in cases where the social and cultural gap between sending and receiving countries is large.44 This has been confirmed by recent findings regarding the transfer of fertility norms from migrants to the extended family and friends at places of origin: lower numbers of children at the national level become the norm in both places.45

Overall, however, the evidence about impacts on traditional gender roles is mixed. For example, where the lives of migrants’ wives at home remain largely confined to housekeeping, child-rearing and agricultural work, little may change—except that their workloads increase. Gains in authority may be temporary if male migrants resume their position as head of the household on return, as has been reported from Albania and Burkina Faso, for example.46

The transmission of norms may extend to par- ticipation in civic affairs. Recent studies in six Latin American countries have found that individuals with greater connections to international migrant networks participate more in local community af- fairs, are more supportive of democratic principles and are also more critical of their own country’s democratic performance.47

4.1.2 Community and national level economic effects Beyond its direct impacts on families with mi- grants, movement may have broader effects. Migration-driven processes of social and cultural change can have significant impacts on entrepre- neurship, community norms and political trans- formations—impacts that are often felt down the generations. For example, Kenya, and indeed most of Africa, may be affected today and in the future by Barack Obama Senior’s decision, taken five decades ago, to study in the United States. Most of these effects are highly positive. However, one concern that needs to be addressed is the outflow of skills from source communities.

Fears that the mobility of the skilled harms the economy of origin countries have long been voiced, though the debate has become more nu- anced in recent years.48 The concerns surface regularly in a range of small states and poorer countries, but also extend to such countries as Australia, which sees many of its graduates go abroad. This issue has, over the past few decades, spawned a range of proposals, which are reviewed in chapter 5. But an important underlying point is that mobility is normal and prevalent, even in prosperous societies (chapter 2). Skilled people, like everyone else, move in response to a perceived lack of opportunities at home and/or better op- portunities elsewhere, for both themselves and their children. Attempts to curtail these move- ments without addressing underlying structural causes are unlikely to be effective. There are also reasons to believe that the effects of skills flows are less detrimental for origin communities than is often assumed, as argued in box 4.3.

One traditional concern has been that the departure of able-bodied youth leads to labour shortages and declines in output, particularly in agriculture.49 In Indonesia, for example, com- munities faced shortages of labour for coopera- tive farm work.50 However, in many developing countries, movements of labour from agriculture to urban areas can be an important part of struc- tural transformation. And to the extent that a shortage of capital, not labour, constrains growth in most developing countries, remittances can be an important source of rural investment finance.

Migration can be a strong force for conver- gence in wages and incomes between source and destination areas. This is because, as mobility

The effects of skills flows are less detrimental for origin communities than is often assumed

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

increases between two regions, their labour mar- kets become more integrated and large differences in wages become more difficult to sustain. There is considerable historical evidence, reviewed in chapter 2, that enhanced mobility is associated with the reduction of wage disparities between countries. Inequalities within countries can fol- low a bell-shaped pattern over time: progress in some areas creates wealth and thus increases in- equality, which encourages migration, which over time in turn tends to reduce inequality. Studies have associated greater internal labour mobility with a reduction in inter-regional income dispari- ties in Brazil, India, Indonesia and Mexico.51

Interestingly, emigration rates for skilled workers are substantially higher among women than men in most developing countries.52 Women with tertiary degrees are at least 40 per- cent more likely than male graduates to emigrate to OECD countries from a wide range of coun- tries, including Afghanistan, Croatia, Ghana, Guatemala, Malawi, Papua New Guinea, Togo, Uganda and Zambia. While this could reflect various factors, structural and/or cultural barri- ers to professional achievement at home seem the most likely explanation.53

The movement of skilled people happens not only across but also within borders, as peo- ple move towards better opportunities. This is illustrated in figure 4.2, which compares move- ment within Brazil, Kenya, Philippines and United States to international rates. The strik- ing result is that we find very similar patterns of migration of skilled workers within and across nations. In particular, the tendency for a higher proportion of skilled workers to emigrate from small states is echoed in a similar tendency to migrate more from small localities. This sug- gests that the policy options explored in discus- sions of local development—such as increased incentives and improved working conditions— may also be relevant to policy-making related to the emigration of skilled professionals abroad.

More broadly, the economic effects of mi- gration at the national level in countries of origin are complex and, for the most part, dif- ficult to measure. Networks may arise that fa- cilitate the diffusion of knowledge, innovation and attitudes and so promote development in the medium to longer term. There is a host of anecdotal evidence indicating that migrants support productive activities in their countries

Box 4.3 Impacts of skills flows on human development

The emigration of people with university degrees has attracted much

popular and academic attention, especially because the shortage of

skills is acute in many poor countries. The evidence suggests that

improving local working conditions in order to make staying at home

more attractive is a more effective strategy than imposing restric-

tions on exit.

It is important to recognize that the dreadful quality of key service

provision in some poor countries cannot be causally traced to the

emigration of professional staff. Systematic analysis of a new data-

base on health worker emigration from Africa confirms that low health

staffing levels and poor public health conditions are major problems,

but tend to reflect factors unrelated to the international movement

of health professionals—namely weak incentives, inadequate re-

sources, and limited administrative capacity. Migration is more accu-

rately portrayed as a symptom, not a cause, of failing health systems.

The social cost associated with skilled emigration should not be

overestimated. Where graduate unemployment is high, as it often is

in poor countries, the opportunity cost of departure may not be large.

If a highly productive but modestly paid worker leaves a community,

it suffers a significant loss; but if an equally skilled but unproductive

worker leaves, the community is hardly affected. If, for example,

teachers often do not show up to work, the direct impacts of their de-

parture are unlikely to be large. While this should not weaken the drive

to address these underlying sources of inefficiency and waste, the

fact that staff may not currently be serving their communities is not a

point that can simply be wished away in the debate about skills flows.

Like other migrants, skilled people abroad often bring benefits to

their countries of origin, through remittances and the development of

networks. As shown in figure 3.2, the absolute gain in income from

migration can be huge, so that if only a fraction of the difference is re-

mitted, the benefits to the home country can be considerable. Some

research has suggested that the share of foreign direct investment in

a developing country is positively correlated with the number of that

country’s graduates present in the investing country. Other studies

have found that the more high-skilled emigrants from one country live

in another, the more trade occurs between those countries.

Last but not least, significant numbers of skilled emigrants do re-

turn—a recent estimate suggested that about half do so, usually after

about five years. Recent literature has also emphasized the increas-

ing importance of circular movement as transnational networks grow.

Source: Clemens (2009b), Banerjee and Duflo (2006), Javorcik, Ozden, Spatareanu, and Neagu (2006), Rauch (1999 ), Felbermayr and Toubal (2008), Findlay and Lowell (2001) and Skeldon (2005).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

of origin, through technolog y transfer, the re- patriation of enhanced skills and exposure to better working and management practices.5 4 The Chinese government has pursued links with Chinese studying abroad to help pro- mote academic excellence in its universities. Similarly, India’s ‘argonauts’—young graduates who helped fuel the country’s high-tech boom in the early 2000s—brought to their jobs the ideas, experience and money they had accu- mulated in the United States and elsewhere.55 The entire software industry model changed as firms increasingly outsourced production to India or based themselves there. In this case, skilled migration brought significant external and dynamic effects, which benefit both work- ers and the industry in the place of origin.

The spread of new industries via international networks of skilled professionals can be rapid and unpredictable, can find niches even amidst otherwise low levels of overall development, and depends crucially on the openness of the busi- ness and political environment at home. It ap- pears that countries such as the Islamic Republic of Iran, Viet Nam and the Russian Federation, which have more closed systems, have benefited less in high-tech business formation via their

skilled workers abroad than have India and Israel, for example.56

A lmost all the quantitative macro studies on effects at the national level have focused more narrowly on the scale and contribution of remittances. In 2007 the volume of officially recorded remittances to developing countries was about four times the size of total official development aid.57 At this scale, remittances are likely to be making a strong contribution to foreign exchange earnings relative to other sources in individual countries. In Senegal, for instance, remittances in 2007 were 12 times larger than foreign direct investment. Remittances represent a significant share of GDP in a range of small and poor states, with Tajikistan topping the list at 45 percent; for all the countries in the top 20 remittance receiv- ing countries, the share exceeded 9 percent in 2007; and in more than 20 developing coun- tries, remittances exceed the earnings from the main commodity export.

However, two major qualifications should be attached to these findings. First, the vast bulk of these flows do not go to the poorest countries. Of the estimated inflows of remit- tances in 2007, less than 1 percent went to

Figure 4.2 Skilled workers move similarly across and within nations Population and share of skilled workers who migrate internally and internationally

Source: Clemens (2009b).

Note: Shares represented using Kernel density regressions.

0.8

0.6

0.4

0.2

0S h

a re

o f

sk ill

e d

w o

rk e

rs w

h o

m ig

ra te

d ( %

)

| | | | | | | | | | | 0 15 20

US states

Filipino provinces Brazilian states

Kenyan districts

Countries

Total population (in millions, log scale)

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

countries in the low-HDI category. So, for this group, remittances are only about 15 percent of their official development aid. By contrast, in Latin America and the Caribbean remit- tances in 2007 amounted to about 60 percent of the combined volume of all foreign direct investment and aid. Second, studies that have sought to trace the impacts of remittances on the long-term growth of the recipient country suggest that these impacts are generally small, although the findings are mixed.58 This stems in part from the fact that the development impact of remittances is ultimately contingent on local institutional structures.59

Concerns have been expressed that remit- tances create a form of ‘resource curse’, contrib- uting to undesirable currency appreciation and thereby hampering competitiveness. Here again, however, the evidence is mixed.60 Moreover, re- mittances go to individuals and families and are thus distributed more widely than rents from natural resources, which flow only to govern- ments and a handful of companies and thus can tend to exacerbate corruption. One positive macroeconomic feature of remittances is that they tend to be less volatile than either official development aid or foreign direct investment, although still subject to cyclical fluctuations, as seen in 2009 (box 4.2).61

In general, ‘remittance-led development’ would not appear to be a robust growth strat- egy. Like flows of foreign aid, remittances alone cannot remove the structural constraints to economic growth, social change and better gov- ernance that characterize many countries with low levels of human development. That said, for some small states, particularly those facing addi- tional challenges related to remoteness, mobility may be integral to an effective overall strategy for human development (box 4.4).

4.1.3 Social and cultural effects Mobility can have profound consequences for social, class and ethnic hierarchies in origin communities if lower status groups gain ac- cess to substantially higher income streams. This is illustrated by the cases of the Maya in Guatemala62 and the Haratin, a group of mainly black sharecroppers, in Morocco.63 These are welcome changes, which can disrupt traditional, caste-like forms of hereditary inequality based

on such things as kinship, skin colour, ethnic group or religion, which are associated with un- equal access to land and other resources.

The ideas, practices, identities and social capital that flow back to families and communi- ties at origin are known as social remittances.64 These remittances can arise through visits and through rapidly improving communications. The case of the Dominican village of Miraflores, where two thirds of families sent members to Boston in the 1990s, shows the impacts on gen- der dynamics. Women’s roles changed, not only in Boston, where they went out to work, but also in the Dominican Republic, where they enjoyed a more equal distribution of household tasks and greater empowerment generally. Another exam- ple comes from Pakistanis at the Islamic Center of New England in the United States, where women pray and run the mosque alongside men. News of these changes has travelled back to Karachi in Pakistan, where some women still prefer traditional approaches but others are try- ing to create new spaces where women can pray and study together. Health is another area where social remittances have an impact. As a result of exposure abroad, visiting or returning migrants may bring back practices such as drinking safe water, keeping animals out of living spaces, or going for annual medical check-ups.

The social and cultural effects of migration are not always positive, however. A counter- example is the deportation of youth from the United States back to Central America, which has been likened to the export of gangs and gang cultures.65 Although detailed data and analysis are not available, a recent regional report found that the distinction between home-grown gangs (pandillas) and those exported from the United States (maras) is not always clear.66 In either case, programmes that target at-risk individuals and communities with a view to preventing youth and gang violence are needed, alongside inter- governmental cooperation and greater support and funding for reintegration programmes.67

For many young people all over the world, spending time abroad is considered a normal part of life experience and migration marks the transition to adulthood. Field studies in Jordan, Pakistan, Thailand and Viet Nam have found that migration was a means of enhancing a fam- ily’s social status in the local community. It is

The ideas, practices, identities and social capital that flow back to families and communities at origin are known as social remittances

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

thus not surprising that the probability of mi- gration increases for those with links to people already abroad.

Sometimes a ‘culture of migration’ emerges, in which international migration is associated with personal, social and material success while staying home smacks of failure.68 As the social network grows, the culture is further engrained and migration becomes the norm, particularly among the young and able. This has been ob- served in cases where there has been large-scale

out-migration, such as the Philippines, as well as in West and Southern Africa. A study in Nigeria found that two out five undergraduate students were more interested in leaving Nigeria as a way of gaining social status than in seeking gainful employment at home.69 This can also be seen with respect to internal migration: a recent study from Ethiopia suggests that shifting preferences and aspirations as a result of education could lead people to migrate out of rural areas, irrespective of the earning potential that migration may

Box 4.4 Mobility and the development prospects of small states

As noted in chapter 2, it is striking that the countries with the high-

est rates of emigration are small states. These rates often coincide

with underdevelopment. For poorer small states, the disadvantages

of being small include over-dependence on a single commodity or

sector and vulnerability to exogenous shocks. Small countries cannot

easily take advantage of economies of scale in economic activity and

in the provision of public goods, and often face high production costs

and consumer prices. In the case of small island states, remoteness

is an additional factor, raising transport costs and times and making

it difficult to compete in external markets. All these factors encour-

age out-migration.

The financial benefits associated with migration are relatively

large for small states. In 2007, remittances averaged US$233 per cap-

ita, compared to a developing country average of US$52. The annual

highest flows relative to GDP are found in the Caribbean, with remit-

tances accounting for 8 percent of GDP. However, most small states

are not among the countries with the highest GDP shares of remit-

tances, so they are not especially exposed to shocks from this source.

At the same time, the benefits of migration for small states go well

beyond the monetary value of remittances. Moving opens up oppor-

tunities for labour linkages, which can enhance integration with eco-

nomic hubs. Temporary labour migration can be a way of balancing

the economic needs of both the origin and destination sides, of pro-

viding opportunities for low-skilled workers and of enabling broader

benefits at home through the repatriation of skills and business ideas.

To the extent that smallness overlaps with fragility and, in some coun-

tries, instability, migration can be a safety valve to mitigate the risk

of conflict, as well as a diversification strategy over the longer term.

Some small states have integrated emigration into their develop-

ment strategies, mainly to meet the challenge of job creation. Our com-

missioned review of PRSs showed that many small states (Bhutan,

Cape Verde, Dominica, Guinea-Bissau, Sao Tome and Principe, and

Timor-Leste) mention positive elements of international migration in

terms of impact on development and/or poverty reduction. Among

the goals in Timor-Leste’s Poverty Reduction Strategy Paper (PRSP)

(2003) was that of developing a plan for 1,000 workers to go abroad an-

nually. However, others (Djibouti, Gambia, Guyana and Maldives) refer

to emigration only as a problem. Some see negative aspects, such

as exposure to downturns in remittances (Cape Verde) and increased

inequality (Bhutan). Dominica’s PRS saw emigration both as a cause

of poverty and as contributing to poverty reduction.

Small states can make migration a strategic element of develop-

ment efforts in several ways, some of which involve regional agree-

ments. Some countries focus on temporary employment abroad.

Others emphasize the creation of skills, sometimes in concert with

neighbours. Mauritius has actively encouraged temporary employ-

ment abroad as a way of acquiring skills and capital that migrants

can use to set up their own business on return. Supported by do-

nors, the government has established a programme that provides

technical and financial support to returning migrants. The Lesotho

Development Vision 2020 focuses on generating jobs at home by

attracting foreign direct investments, while recognizing the role of

work abroad, especially in neighbouring South Africa. Its PRS sets

out reform measures that include automation and decentralization

of immigration services, establishment of a one-stop shop for ef-

ficient processing of immigration and work permits, and anti-cor-

ruption measures in the Department of Immigration. Development

strategies can take broader measures to deal with the challenges of

remoteness. For example, in the South Pacific, regional universities

and vocational training have facilitated mobility, and several states

have entered into migration agreements with their neighbours.

Emigrants from small states have similar profiles to migrants gen-

erally, in that they tend to have more skills and resources than people

who stay. In Mauritius, for example, the total emigration rate is 12.5

percent, but about 49 percent for graduates. Overall, however, there

is no significant difference in the net supply of skills, measured by the

number of doctors per 10,000 population, between small and large

states. In terms of simple averages, the number of doctors is actually

higher for small states, at 23 per 10,000 compared to 20 per 10,000

on average for all countries.

Source: Luthria (2009 ), Winters and Martin (2004), Black and Sward (2009 ), Seewooruthun (2008), Government of Lesotho (2004), Winters, Walmsley, Wang, and Grynberg (2003), Amin and Mattoo (2005), Koettl (2006) and Pritchett (2006).

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

provide.70 The culture can acquire its own self- perpetuating momentum, as illustrated by the Irish, who continued to emigrate at the height of the Celtic Tiger boom.

In West Africa, migration is often not merely a vehicle for economic mobility but is also consid- ered a process through which a boy attains matu- rity.71 For some groups in Mali, Mauritania and Senegal, migration is a rite of passage: it is through the knowledge and experience acquired from travel that young adolescent males become men.72 In the Soninke village of Kounda in Mali mobil- ity distinguishes males and females.73 Masculinity involves the freedom to move, whereas women in the village are to a large extent fixed inside the household. Men who do not migrate and remain economically dependent on their kin are consid- ered to be immature youngsters and women refer to them with a derogatory term, tenes, which means ‘being stuck like glue’. In Mali, the collo- quial French term used to describe migration is aller en aventure, literally, to go on adventure. For the Soninke, being ‘on adventure’ implies being ‘on the path to adulthood’.

The effect of migration on income distribution and social inequality is primarily a function of se- lection—that is, who moves (see chapter 2).74 In general, money flows associated with international migration tend to go to the better off, whereas, at least in the longer term, remittances from internal migrants tend to be more equalizing.75 This type of pattern has been found for Mexico and Thailand, for example.76 Our commissioned research on China also found that inequality initially rose with internal remittances, then fell.77

If it is the better off who tend to migrate, then an appropriate response is to ensure access to basic services and opportunities at home as well as to facilitate the mobility of the poor. As we argue in chapter 5, poor people should not have to move in order to be able to send their children to decent schools: they should have options at home, alongside the possibility of moving.

Collective remittances sent through home- town associations and other community groups have arisen in recent decades.78 These usually take the form of basic infrastructure projects, such as the construction of roads and bridges, the installation of drinking water and drainage systems, the sinking of wells, the bringing of electricity and telephone lines, and other public

goods such as local church or soccer field resto- rations. Sometimes these are co-financed—the most famous example being Mexico’s Tres Por Uno programme, which aims to increase col- lective remittances by assuring migrant asso- ciations that, for every peso they invest in local development projects, the federal, municipal and local government will put in three. The amount transferred as collective remittances remains only a fraction of that sent back individually to families, so the potential development impact of such programmes should not be overstated.79 For example, it has been estimated that, since 1990, Filipinos in the United States have do- nated US$44 million in financial and material assistance to charitable organizations in the Philippines, an amount equivalent to only 0.04 percent of GDP in 2007.80

Mobility can affect social and political life in countries of origin in a broader sense. Migrants and their descendants may return and become directly involved in civic and political activities. Alternatively, business investments, frequent re- turn visits and/or collective initiatives can affect patterns of participation by others at home. For example, in Lebanon, new political forces were formed, particularly after the 1989 Ta’ef Accord, as returning migrants used the wealth earned abroad to engage in politics.81

Evidence that emigrants have spurred the im- provement of political institutions in their home countries is accumulating. Democratic reform has been found to progress more rapidly in developing countries that have sent more students to universi- ties in democratic countries.82 Knowledge and ex- pectations brought home by a group of Moroccans returning from France have been found to shape basic infrastructure investments by the govern- ment in their home region.83 However, if emi- gration serves simply as a safety valve, releasing political pressure, the incentives of the established political elite to reform are diminished.84

Just as migrants enrich the social fabric of their adopted homes, so too they can act as agents of political and social change if they re- turn with new values, expectations and ideas shaped by their experiences abroad. Sometimes this has taken the form of supporting civil wars, as in the case of Sri Lanka’s diaspora, but in most cases engagement is more constructive.85 Contemporary high-profile examples include

Evidence that emigrants have spurred the improvement of political institutions in their home countries is accumulating

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

Ellen Johnson-Sirleaf, President of Liberia and Africa’s first female head of state, and Joaquim Chissano, former President of Mozambique and now a respected elder statesman. Recognizing the potential benefits of diaspora engagement, some governments have begun to actively reach out.86 For example, Morocco and Turkey have extended political and economic rights to emi- grants and allowed dual citizenship.87 However, whether these policies of engagement benefit non-migrants or simply subsidize an elite group outside the country remains an open question. By improving its investment climate (presently ranked first in Africa by the World Bank’s Doing Business Index), Mauritius has also attracted mi- grants back; similar patterns have been seen in India and Turkey, among other countries.

4.1.4 Mobility and national development strategies To date, national development and poverty re- duction strategies in developing countries have tended not to recognize the potential of mobil- ity, nor integrated its dynamics into planning and monitoring. This is in part due to the range of other pressing priorities facing these coun- tries, from improving systems of service deliv- ery, through building basic infrastructure, to promoting broad-based growth.

Country-level perspectives on the links be- tween mobility and development can be gleaned

from recent National Human Development Reports. The highlights are summarized in box 4.5.

To gain insights into the link between na- tional development strategies and migration in a larger sample of countries, we commis- sioned a study to review the role of migration in Poverty Reduction Strategies (PRSs). These strategies are statements of development objec- tives and policy, prepared by poorer countries whose views are often neglected in migration debates. PRSs are of interest since they also in- volve contributions from, or partnerships with, civil society actors, are intended to be based on quantitative and participatory assessments of poverty, and provide a sense of government pri- orities.88 They are also important important be- cause international partners have committed to aligning their assistance to these national strate- gies, given the importance of country ownership in development.

To date, Bangladesh’s PRS has perhaps the most comprehensive treatment of migration and development linkages. The most recent PRSs for Albania, the Kyrgyzstan and Sri Lanka also re- flect a major focus on migration-related issues. Many African countries acknowledge the role of remittances, the advantages of return and circu- lar migration of skilled expatriates and the value of knowledge transfer from such people. Several strategies intend to attract development invest- ments from wealthy members of the diaspora.

Box 4.5 Mobility and human development: some developing country perspectives

Several recent National Human Development Reports (NHDRs), in-

cluding those of Albania, El Salvador and Mexico, have focused on

the development implications of mobility. In other countries NHDRs

have considered how mobility influences selected aspects of de-

velopment, such as the role of civil society (Egypt), rural develop-

ment (Uganda), economic growth (Moldova), social cohesion (Côte

d’Ivoire) and inequality (China).

Mexico’s NHDR identifies inequality as the most robust deter-

minant of migratory flows, and movement as a factor that modi-

fies the availability of opportunities to others, including stayers.

Drawing on the National Employment Survey, the average Mexican

migrant is found to have slightly above-average schooling and in-

termediate income levels but comes from a marginalized munici-

pality, suggesting an initial set of capabilities coupled with lack of

opportunities as major driving factors. The report finds that the

overall human development impacts of migration in Mexico are

complex and conditional on the profile and resources of different

groups. For example, while migration tends to reduce education

inequality, especially for girls, it can also discourage investment in

higher education in communities where most migrants traditionally

go abroad for low-skilled jobs.

Different insights come from El Salvador, where emigrants repre-

sent 14 percent of the population and the impact of migration is more

visible at the macro level. The recent acceleration of migration is seen

to have contributed to the country’s transition to a service economy,

which has relied heavily on remittances and a mosaic of small busi-

nesses specialized in delivering goods and services to migrants and

their families, including nostalgia products and communications. The

report suggests that migration allows some relatively poor people a

degree of upward mobility through their links to the global economy.

Source: UNDP (2000; 2004a; 2005a,b; 2006a; 2007c,e; 2008c).

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Earlier analysis of the treatment of interna- tional migration in PRSs was based in part on the number of mentions of the word ‘migration’.89 While simple, this indicator is not very meaning- ful. It is nonetheless striking that there is no sig- nificant correlation in PRSs between the number of references to migration and various measures of its possible importance for national development, such as share of the population living abroad, level of remittances and rate of urbanization.90

PRSs have laid out a wide range of migra- tion-related policy initiatives, although these are often not explicitly based on prior analy- sis. In many cases the state of knowledge about the relationship between the proposed initia- tive and its expected development impact is weak, underlining the importance of better data and analysis.

In general, PRSs appear to recognize the complexity of international migration, acknowl- edging both its advantages—opportunities for development and poverty reduction—and its possible negative effects. Some tend to stress the positive—for example the most recent PRSs of Ethiopia, Nepal, Senegal and Uzbekistan frame emigration as an opportunity, without mentioning possible downsides. Most recent strategies emphasize the role of remittances, including those of Bangladesh, Democratic Republic of the Congo, Ghana, the Lao People’s Democratic Republic, Liberia, Pakistan, Timor- Leste and Uzbekistan.

Several strategies articulate policies towards migration. We can distinguish between poli- cies that are broadly ‘proactive/facilitative’ and those focused on ‘regulation/control’ (table 4.1). Combating trafficking, preventing irregular migration and modernizing and strengthen- ing immigration and customs services feature frequently. It is striking how some of these policies echo those promoted by rich country governments.

To sum up, while the PRS framework gen- erally has not been geared towards addressing migration policy per se, it could provide a useful tool for integrating migration and development issues. Fitting this dimension into an overall national strategy for development will require investments in data and analysis and in broad stakeholder consultation. These challenges are discussed further in chapter 5.

4.2 Destination place effects Debates about migration often dwell on the eco- nomic and social impacts on rich destination countries. This report has deliberately sought to redress this imbalance, by beginning with the migrants and their families, then focusing on the places they came from. However, that is not to say that the impacts on people in destination communities are unimportant.

In many developed countries, the percentage of migrants in the total population has risen rap- idly over the past 50 years. It is now estimated to be in double figures in more than a dozen OECD countries.91 As noted in chapter 2 and shown in detail in Statistical Table A, the highest shares are found in Oceania (16 percent)—which includes Australia and New Zealand, North America (13 percent) and Europe (8 percent). The shares range between only 1 and 2 percent in the three major developing regions of Africa, Asia, and Latin America and the Caribbean. The highest country shares are recorded in the GCC states and in South-East Asia, including 63 percent in Qatar, 56 percent in the United Arab Emirates, 47 percent in Kuwait and 40 percent in Hong Kong (China). The real and perceived impacts of immigration are critical, not least because these perceptions shape the political climate in which policy reforms are debated and determined.

We begin this section by reviewing the eco- nomic impacts of immigration as a whole, then focus more narrowly on the labour market and

Table 4.1 PRSs recognize the multiple impacts of migration Policy measures aimed at international migration in PRSs, 2000–2008

Export labour 10 Facilitate remittances 9 Combat trafficking 19 Encourage female migration 1 Encourage legal remittance channels 3 Modernise customs 18 Promote student mobility 3 Engage diasporas 17 Strengthen border control 17 Sign bilateral agreements 9 Promote investment by diasporas 8 Combat illegal migration 12 Improve labour conditions abroad 6 Import skills 4 Promote refugee return 10 Pre-departure training 6 Participate in regional Tackle the ‘brain drain’ 9

cooperation programmes 8 Develop consular services 3 Promote more research/monitoring 8 Support return 7 Regulate recruitment industry 2 Build institutional capacity 5 Sign readmission agreements 2 Facilitate portability of pensions 2 Combat HIV/AIDS amongst migrants 7 Promote refugee integration 7 Re-integrate trafficking victims 5

No. of countriesProactive/facilitative

No. of countriesRegulation/control

Source: Adapted from Black and Sward (2009 ). Note: 84 PRSs reviewed.

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

fiscal impacts. For each of these types of impact there are important distributional issues—while there are overall gains, these are not evenly distributed.

4.2.1 Aggregate economic impacts The impact of migration on aggregate growth rates of destination countries has been much discussed, but robust measurement is difficult. The data requirements and methodological complexities, including the need to disentangle direct and indirect effects and work out their timing, all present challenges (see box 1.1).

Economic theory predicts that there should be significant aggregate gains from movement, both to movers and to destination countries. This is because migration, like international trade, allows people to specialize and take ad- vantage of their relative strengths. The bulk of the gains accrue to the individuals who move, but some part goes to residents in the place of destination as well as to those in the place of ori- gin via financial and other flows. In background research commissioned for this report, estimates using a general equilibrium model of the world economy suggested that destination countries would capture about one-fifth of the gains from a 5 percent increase in the number of migrants in developed countries, amounting to US$190 billion dollars.92

To complement our review of the country- level studies, we commissioned research to construct a new dataset on migration flows and stocks, including consistent annual data on nature of employment, hours worked, capi- tal accumulation and changes in immigration laws for 14 OECD destination countries and 74 origin countries for each year over the pe- riod 1980–2005.93 Our research showed that immigration increases employment, with no evidence of crowding out of locals, and that in- vestment also responds vigorously. These results imply that population growth due to migration increases real GDP per capita in the short run, one-for-one (meaning that a 1 percent increase in population due to migration increases GDP by 1 percent). This finding is reasonable, since in most instances annual migration flows are only a fraction of a percentage point of the labour force of the receiving country. Moreover, these flows are largely predictable, implying that the

full adjustment of per capita investment levels is plausible even in the short run.

At the individual country level, at least in the OECD countries, similar results have been found—that is, increased migration has neu- tral or marginally positive effects on per capita income. For example, simulations following the European Union accessions of 2004 sug- gest that output levels in the United Kingdom and Ireland, which allowed large-scale inflows from the new member states of Eastern Europe, would be 0.5–1.5 percent higher after about a decade.94 In countries where migrants account for a much higher share of the population and labour force—for example in the GCC states— the aggregate and sectoral contributions to the economy can be expected to be larger. However, detailed empirical analysis is unfortunately not available.

Migrants can bring broader economic ben- efits, including higher rates of innovation. Productivity gains in a number of destination places have been traced to the contributions of foreign students and scientists to the knowledge base. Data from the United States show that be- tween 1950 and 2000, skilled migrants boosted innovation: a 1.3 percent increase in the share of migrant university graduates increased the number of patents issued per capita by a massive 15 percent, with marked contributions from sci- ence and engineering graduates and without any adverse effects on the innovative activity of local people.95

Countries explicitly compete for talent at the global level and the share of graduates among mi- grants varies accordingly.96 The United States, in particular, has been able to attract migrant tal- ent through the quality of its universities and research infrastructure and its favourable patent- ing rules.97 In Ireland and the United Kingdom the share of migrants with tertiary education exceeds 30 percent, while in Austria, Italy and Poland it is below 15 percent.98 Countries offer- ing more flexible entry regimes and more prom- ising long-term opportunities have done better in attracting skilled people, whereas restrictions on duration of stay, visa conditions and career development, as in Germany for example, limit uptake. This has led to discussions about a blue card or European Union-wide employment permit—an idea that has received preliminary

Migrants can bring broader economic benefits, including higher rates of innovation

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

backing from the European Parliament and ap- proval by the European Council.99 Singapore and Hong Kong (China), have explicit policies to welcome foreign high-skilled professionals. These policies range from allowing immigrants to bring their families, through facilitating per- manent residence after defined waiting periods (two years for Singapore, seven for Hong Kong (China)), to the option of naturalization.100

Programmes to attract skilled labour can be developed using a general points-based ap- proach, linked to labour market tests and/or employer requirements (chapter 2). A centralized ‘manpower’ planning approach can be difficult to implement, especially in the face of structural change and economic shocks. Points-based schemes, which have the virtue of simplicity, have been used by destination governments to fa- vour high-skilled migrants or to attract workers for occupations in short supply on the national labour market, as in Australia’s General Skilled Migration programme.

Migration can stimulate local employment and businesses, but such effects are likely to be context-specific. Migrants also affect the level and composition of consumer demand, for ex- ample in favour of nostalgia goods, as well as locally available goods and services that are close to homes and work-places. Our commis- sioned study of such effects in California found evidence suggesting that an influx of immigrants over the decade to 2000 into specific areas (se- lected to capture the potential pool of custom- ers for different firms) was positively correlated with higher employment growth in some sectors, especially in education services. The impact on the composition of demand was mixed: a higher share of migrants was associated with fewer small firms and stand-alone retail stores, but more large-scale discount retailers. At the same time, consistent with expectations, the study found that increased immigration was associated with increased ethnic diversity of restaurants.101

4.2.2 Labour market impacts There is controversy around the effects of mi- gration on employment and wages in the des- tination country, especially for those with low levels of formal education. Public opinion polls show that there is significant concern that im- migration lowers wages.102 There have also been

lively academic debates on the subject, notably in the United States. Yet it is striking that most empirical studies in the OECD draw similar conclusions, namely that the aggregate effect of immigration on the wages of local workers may be positive or negative but is fairly small in the short and long run.103 In Europe, both multi- and single-country studies find little or no impact of migration on the average wages of local people.104

At the same time it must be recognized that wage responses to immigration are unlikely to be distributed evenly across all workers and will be most pronounced where locally born work- ers compete with immigrants. The debates have clarified that it is not just the total number of migrants that matter but their skill mix as well. The kinds of skill that migrants bring affect the wages and employment opportunities of differ- ent segments of the local population, sometimes in subtle ways. If the skills of migrant workers complement those of locally born workers, then both groups will benefit.105 If the skills match exactly, then competition will be heightened, creating the possibility that locally born workers will lose out. However, this is not a foregone con- clusion: often the results are mixed, with some individuals in both groups gaining while others lose. Assessing these effects is problematic, be- cause measuring the degree to which different groups’ skills complement or substitute for one another is difficult, particularly across interna- tional borders.106

One striking example of complementarity is how migrants can facilitate higher labour force participation among locally born females.107 The availability of low-cost child care can free up young mothers, enabling them to go out and find a job. There is consensus in the literature that low-skilled migrant labour generally comple- ments local labour in Europe.108 This may arise in part because migrants are more mobile than locally born workers—as in Italy, for example.109 More importantly, migrants are often willing to accept work that locals are no longer prepared to undertake, such as child care, care of the elderly (much in demand in aging societies), domestic work, and restaurant, hotel and other hospitality industry work.

As noted, the small average effect on pay may mask considerable variation across types of local workers. There is a vast empirical literature on

Migrants can facilitate higher labour force participation among locally born females

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

the effect of immigration on the distribution of wages in developed countries. In the United States, estimates of the effect on the wages of un- skilled workers range from –9 to +0.6 percent.110 Locals with low levels of formal schooling may still have advantages over migrants due not only to language but also to knowledge of local insti- tutions, networks and technology, which enables them to specialize in complementary and better- paid tasks.111

The imperfect substitutability of migrant and local labour is consistent with recent evidence suggesting that the workers affected most by the entry of new migrants are earlier migrants. They feel the brunt of any labour market adjust- ment, since newcomers primarily compete with them. In the United Kingdom, for example, heightened competition among migrants in the early 2000s may have increased the difference between the wages of locals and migrants by up to 6 percent.112

While the evidence about employment im- pacts is less extensive, the pattern is similar. Detailed investigations have not established a systematic relationship between immigration and unemployment. This is in part because of labour market segmentation, as low-skilled mi- grants accept jobs that are less attractive to lo- cals, enabling the latter to move to other sectors and jobs. The massive inflows associated with European Union accession led neither to the displacement of local workers nor to increased unemployment in Ireland and the United Kingdom. Recent experience in Europe thus supports the idea that migrant labour does not have a large effect on the employment of locals. One European study found that a 10 percent in- crease in the share of migrants in total employ- ment would lower the employment of residents by between 0.2 and 0.7 percent.113

These econometric results should also be in- terpreted in the light of the evidence concern- ing the labour market disadvantage of migrants that was reviewed in chapter 3. Legal and insti- tutional factors—both their design and their enforcement—matter. If migrant workers fall through the net of the formal arrangements that protect wages and working conditions, un- fair competition with locally born workers could well follow. A similar outcome can be expected where people are excluded from unions or where

the enforcement of regulations is weak. Even in countries with well-regulated labour markets, workers with irregular status often tend to fall ‘under the radar’—the drowning of Chinese cockle gatherers in Morecambe Bay in the United Kingdom was a notorious case of lack of enforcement of health and safety standards. Recent British research found that more general structural trends, particularly the increasing use of agency (temporary) labour contracts, which are associated with fewer rights for workers, are significant factors shaping the pay and working conditions of migrant workers. There is wide- spread evidence of payment below the legal mini- mum wage, especially for younger migrants.114

Among emerging and developing econo- mies, empirical evidence on the labour market impacts of immigration is sparse. A recent study of Thailand, which investigated whether places with higher concentrations of migrants had lower wages, found that a 10 percent increase in migrants reduced the wages of Thai locals by about 0.2 percent but did not lower employment or reduce internal migration.115 Simulations con- ducted for Hong Kong (China), found that even large increases in new immigrants (a 40 percent increase) would lower wages by no more than 1 percent.116 To the extent that migrants can find employment only in the informal labour mar- ket, their arrival will have a larger effect on lo- cals who themselves operate informally. In many developing countries, informality is ubiquitous, so migrants are likely to join an already large seg- ment of the market.

4.2.3 Rapid urbanization Rapid urban growth, which can be partly at- tributed to internal migration, can pose major challenges. While people may be attracted by the better opportunities available in cities, it is nonetheless true that local services and amenities may come under severe strain. This can be seen in large cities, such as Calcutta and Lagos, as well as the myriad medium-sized cities, from Colombo to Guayaquil to Nairobi. Many newcomers and their families in developing countries end up in shanty towns and slums, typically on the out- skirts of large cities. Residents in these areas often face high service costs. They may also be at risk from flooding and landslides, not to mention ha- rassment from the authorities and violence, theft

Legal and institutional factors—both their design and their enforcement—matter

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

or extortion at the hands of criminals. When movement is driven by falling living

standards and weak support services in places of origin, the rate of migration to urban centres can exceed the demand for labour and the provision of services there.117 Under these conditions the outcome is high structural unemployment and underemployment. Moreover, where local au- thorities are ill prepared for population growth and face severe institutional and financial con- straints, the result is likely to be rapidly increas- ing disparities in incomes and well-being and segmentation of the city into areas that are rela- tively prosperous and safe, with good services, and ‘no-go’ areas where living conditions are fall- ing apart. In contrast, when people are attracted to cities because of employment opportunities, net benefits are likely to accrue as the concentra- tion of ideas, talent and capital lead to positive spillovers. This has been found in the Republic of Korea, for example.118

These contrasting scenarios underline the im- portance of good urban governance, which can be defined as the sum of the many ways individu- als and institutions—public and private—plan and manage city life. Among the most important aspects of urban governance for migrants are: ad- equate financial resources, which must often be generated through local taxation; equitable pric- ing policies for basic social services and utilities; the extension of services to areas where migrants live; even-handed regulation of the informal sec- tor; outreach and support services (such as lan- guage classes) targeted to migrant groups; and accountability, through such mechanisms as rep- resentation on local authorities, the publication of performance standards for key services, and the regular independent audit and publication of municipal accounts.

Field research provides useful insights into how city authorities are handling flows of people and the more general challenges of urban pov- erty. The findings suggest that decentralization and democratization can allow the poor more opportunities to lobby and to make incremental gains, at least in terms of infrastructure provi- sion.119 Having a voice—and having that voice heard—seems to work in terms of protecting the poor from the worst excesses of bad governance, particularly from harassment and removal of informal traders.120 There are clearly echoes of

Amartya Sen’s argument about the positive ef- fects of democratic processes and a free press.121

Clearly, however, some municipal govern- ments have wielded levers with negative reper- cussions for migrants. For instance, a review of urbanization experiences in Asia, commissioned for this report, finds that a number of govern- ments continue to pursue policies aimed at de- celerating in-migration. Several countries were found to have forcibly cleared slums, pushing the poor into periphery areas void of services.122 In Dhaka, Bangladesh, some 29 slum areas, home to 60,000 people, were cleared by the authorities in early 2007. In Jakarta, Indonesia, the ‘closed city’ policy requires migrants to present proof of employment and housing, making it diffi- cult for them to stay legally, while a law passed in September 2007 makes squatter settlements on river banks and highways illegal. Sometimes this kind of intervention can lead to unrest, as in Bangladesh, for example, following evictions in Agargoan and other settlements.123 It appears that mass evictions are more likely when democ- racy and accountability are weak, as the shanty- town clearances around Harare in Zimbabwe during 2005 demonstrate.

One final point: popular perceptions among local people in Europe and the United States as well as South Africa, for example, associate migrants with price increases in certain private markets, such as the rental market for housing. To the best of our knowledge, no studies estab- lish the existence of such an effect.

4.2.4 Fiscal impacts A popular measure of the impact of migration, though not one that necessarily reflects its true economic and social effects, is the perception of the changes it brings to the government’s fiscal position.124 People across the political spectrum often share concerns about the implications of migration for the welfare state. Our analysis of the European Social Survey of 2002 suggested that up to 50 percent of the region’s population worry about migrants being a net fiscal burden, with those most concerned tending to be less well educated, older and/or unemployed. The concerns are most acute in the Czech Republic, Greece, Hungary and Ireland, much less so in Italy, Luxembourg, Portugal and Sweden. Some people are worried about increased costs, others

People across the political spectrum often share concerns about the implications of migration for the welfare state

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

about sustainability in the face of reduced so- cial cohesion. Some governments have sought to address these concerns by introducing waiting periods for becoming eligible to receive benefits, as in Australia, New Zealand and the United Kingdom, for example.

Do migrants ‘take more than they give,’ or vice versa? This is a highly contentious issue, and one that we believe has garnered unwarranted attention. Estimating migrants’ use of public services is fraught with measurement difficul- ties, while calculating their offsetting tax con- tributions adds another layer of complexity. A migrant whose child attends state school may also provide childcare services that facilitate the entry of a high-skilled woman into the labour force—and both pay taxes.

In practice, there is wide variation across countries in both the existence and generosity of welfare benefits and the eligibility of migrants. Studies in the United States, which has low levels of benefits for a rich country, have found a range of estimates, but the general picture is consistent: first-generation migrants tend to generate net fiscal costs whereas later genera- tions tend to produce large fiscal surpluses.125 At the same time, taxes paid by migrants may not accrue to the levels of government providing services to migrants. Especially where migrants are under-counted and where fiscal transfers are made to local authorities on a per capita or needs basis, it may be that the localities facing the largest burdens in extending basic services to migrants also lack adequate resources to do so.

Local government typically accounts for a significant share of total government spend- ing and often bears the burden of financing basic services, including services for migrants. According to the International Monetar y Fund,126 the share of spending in 2007 by sub- national authorities in developed countries ranged from 63 percent for Denmark to 6 per- cent for Greece. The share is significant in a number of other major destination countries, in- cluding the Russian Federation (51 percent) and South Africa (47 percent). But there are excep- tions—for example Thailand, where the share is below 15 percent. Thus, depending on the struc- ture of public finances, migrants could impose net fiscal costs on one level of government while being net contributors to total public revenue.

For example, the costs of providing educational and health services, which may include special programmes such as language courses, may be concentrated in local authorities, while income taxes accrue to the central government.

In the United States, fiscal concerns appear to affect the immigration policy preferences of different groups. One study found that locals tend to be in favour of curbing immigration if they live in states that have large migrant popu- lations and provide migrants with generous wel- fare benefits.127 This opinion is strongest among locals with high earnings potential, who tend to be in higher tax brackets. Similar results were obtained using a sample of over 20 countries in Europe.128

In countries with progressive tax systems and welfare benefits, low-skilled migrants, refu- gees and those entering under family reunifica- tion programmes are associated with higher net fiscal costs. In some European countries mi- grants, after accounting for their demographic characteristics, appear to be more dependent on welfare programmes than locals, but this is certainly not the case in all countries.129 The dif- ference can be traced back at least partly to the relative generosity of the welfare systems.

In the 2008/09 recession, rising unem- ployment and hardship among migrants can be expected to impose additional costs on public finances, although the degree to which this happens in practice remains to be seen. Determining factors in each country will be the share of migrants among the unemployed and the structure of unemployment benefits, par- ticularly the eligibility rules. Even in countries with well-developed welfare systems, the access of migrants to benefits may be limited. A recent study predicted that, among European coun- tries, Estonia, France and Latvia were likely to face a higher public finance burden due to the costs of migrants’ welfare benefits during the 2009 downturn, whereas Austria, Finland, Germany, Ireland and Spain would register less-marked increases.130 In many developing countries, the issue of rising fiscal costs during a time of recession typically does not arise, be- cause welfare benefits are simply unavailable to anyone.

Migration is sometimes touted as a solution to the looming fiscal crisis associated with rapid

A migrant whose child attends state school may also provide childcare services that facilitate the entry of a high-skilled woman into the labour force—and both pay taxes

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

aging in many developed countries (chapter 2). This would require that migrants be net contrib- utors to the fiscal system in the short to medium term. The longer term costs when migrants themselves retire need also to be taken into ac- count. Both imply the need either to continu- ally expand immigration or, more realistically, to raise social security contributions from the increased numbers of working migrants while introducing structural changes to the design of social security and retirement systems.

Whether positive or negative, the net fiscal impacts of immigration are not large. Putting the various effects together, relative to GDP, most estimates for the United States and Europe place the net fiscal impact of immigration in the range of ± 1 percent of GDP.131 For example, the figure for the United Kingdom is ± 0.65 percent of GDP.132 These estimates indicate that the fis- cal consequences of migration should not gener- ally be a key factor in designing policy.

Some destination governments impose ad- ditional fees on migrants, based on the prin- ciple that individuals receiving a benefit over and above the services enjoyed by the local tax- payer should contribute more. In 1995, Canada introduced a R ight of Permanent Residence Fee equivalent to US$838, to be paid before a visa can be issued (but refundable if the client is refused or chooses not to proceed). Several amendments over time have sought to mitigate negative impacts with a loan option, flexibility in the timing of payment, elimination of the fee for refugees, protected persons and depen- dent children—and then halving of the fee in 2006. In addition to the fee, there is a US$430 administrative charge for adults (US$86 for de- pendents). However, in the Canadian and other similar cases, there is no direct link between the revenues generated from this fee and fund- ing for integration programmes. The United Kingdom recently introduced a landing fee, at a more symbolic level of UK £50 (US$93). Both these examples seem oriented more towards as- suaging popular concerns than towards raising revenue to cover fiscal costs.

4.2.5 Perceptions and concerns about migration Migration is a controversial issue in many coun- tries. The mere presence of newcomers from

different backgrounds can pose challenges, es- pecially in societies that were traditionally ho- mogeneous. Broadly speaking, three interlinked types of concern can be distinguished, related to security and crime, socio-economic factors, and cultural factors.133 We end this chapter by addressing each of these aspects in turn.

Following the attacks on the United States in 2001, security concerns rose to the top of the political agenda. A major issue was the associa- tion, real or imagined, of foreigners with a lack of loyalty and the threat of terrorism. Such fears are far from new, having characterized many historical instances of anti-immigration senti- ment. Examples include the ethnic Chinese in Indonesia, who were suspected of political sub- version on behalf of Communist China during the 1960s, and the ethnic Russian populations in the Baltic states who were suspected of un- dermining the states’ newly won independence after the collapse of the Soviet Union in the early 1990s. These concerns normally abate some- what over time, only to resurface in new forms at times of political instability and change.

Security concerns also derive from the per- ceived links between immigration and crime, which are often cited in popular debates about migration. We found that more than 70 percent of respondents to the European Social Survey of 2002 believed that immigrants worsen a coun- try’s crime problems, with the figure rising to more than 85 percent in Germany, the Czech Republic and Norway. As exemplified by the film The Godfather, stereotype images associ- ating immigrants with crime have long been propagated through the popular media, which often feature violence perpetrated by a range of immigrant groups including the Italian mafia, Chinese triads and Central American gangs such as the Salvadoran Mara Salvatrucha.

The data do not confirm these stereotypes. However, they do reveal significant variation in immigrant crime rates across countries. Data from the 2000 US census show that, for every ethnic group, incarceration rates among young men are lowest for immigrants, even those who are the least educated. On average, among men aged 18 to 39 (who comprise the vast majority of the prison population), the incarceration rate of the locally born in 2000 was 3.5 percent, five times higher than the 0.7 percent rate of the

Whether positive or negative, the net fiscal impacts of immigration are not large

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

foreign-born.134 Earlier studies for the United States yielded similar findings.135 However, the picture in Europe is more varied. Data from the Council of Europe on 25 countries show that on average there are more than twice as many foreign-born people in prison than locally born. A study on six European countries found that, in Austria, Germany, Luxembourg, Norway and Spain, offense rates are higher for foreign- ers, while this was not the case in Greece, for example.136

Fears that migrants will undermine the socio-economic status of local people have been tested empirically. As already indicated, the ef- fects can be positive for some individuals and groups and negative for others, but are seldom very large. However, the 2008/09 economic recession represents a severe shock to many workers in destination (and other) countries, possibly the worst since the Great Depression of the 1930s. While there is no serious sugges- tion that this shock has been caused by migrant labour, it has nevertheless stoked the flames of anti-immigrant rhetoric, as local workers search for ways of saving their own jobs. Governments are under enormous pressure—and often fail to withstand it. Opinions are shifting, even in cases where migration has been broadly welcomed by the public thus far—for example, in the United Kingdom against Eastern Europeans, despite the successful experience of large-scale inflows during the long boom.137

People’s views about migration are condi- tioned by the availability of jobs. In the majority of the 52 countries covered in the latest World Values Survey, most respondents endorsed re- strictions on immigration, but many empha- sized that these restrictions should be clearly linked to the availability of jobs (figure 4.3).138 The demographic and economic projections presented in chapter 2 suggest that, beyond the current recession, structural features will lead to the re-emergence of job vacancies and hence new opportunities for migrants.

Even in normal times, many feel that pref- erence should be given to locally born people (figure 4.4). Our regression analysis found that this view prevailed more among people who were older, had lower incomes, lived in small towns and did not have a migrant background. Interestingly, however, people were more likely

Figure 4.3 Support for immigration is contingent on job availability Attitudes towards immigration and availability of jobs, 2005/2006

Let anyone come who wants to Let people come as long as there are jobs available Limit/prohibit immigration

Source: Kleemans and Klugman (2009 ).

| | | | | | | | | | | 0 10 20 30 40 50 60 70 80 90 100

Percent of responses

Rwanda

Burkina Faso

Mali

Viet Nam

Andorra

Ukraine

Switzerland

Peru

Sweden

China

Morocco

Ethiopia

Bulgaria

Romania

Republic of Moldova

Slovenia

Republic of Korea

Australia

Argentina

Mexico

Ghana

Chile

Italy

Brazil

Spain

Hong Kong (China)

Cyprus

Turkey

Germany

Finland

Poland

New Zealand

Japan

India

United States

Zambia

Serbia

Trinidad and Tobago

Taiwan (Province of China)

Jordan

Egypt

South Africa

Iran (Islamic Republic of)

Thailand

Indonesia

Malaysia

“How about people from other countries coming here to work. Which one of the following do you think the government should do?”

4HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

to favour equal treatment of migrants in coun- tries where the stock of migrants was relatively high.

Economic and security concerns can some- times reinforce each other, in what becomes a vicious circle. Migrants who are marginalized— due, for example, to temporary or irregular sta- tus or high levels of unemployment—may resort to anti-social or criminal behaviour, confirming the security fears of locals. If this leads to fur- ther discrimination in the labour market and in policy formation, such migrants may turn away from the new society back to the old, possibly forming gangs or other anti-social organiza- tions that threaten local populations. This type of patholog y has been observed among some young Maghrebians in France and some Central American groups in the United States.

Where labour market disadvantage leads to social exclusion, repercussions for social co- hesion can quickly follow. Recent research in seven developed countries has highlighted bar- riers to socialization encountered by children in immigrant families.139 These families are often concentrated in certain locations, such as par- ticular low-income urban localities. This fosters educational and socio-economic segregation: residence in segregated neighbourhoods limits contacts with locally born people, a separation reinforced by attendance at schools that are de facto segregated. A study we commissioned on Latino immigrant identity in the United States suggested that restrictive migration policies and increasingly adverse public opinion over time, alongside mixed human development outcomes, have affected people’s sense of self. The study, based on interviews with immigrants and their children from several Latin American coun- tries, suggests that immigrants have formative experiences that engender group solidarity but promote a rejection of American identity, re- lated to the realities of the labour market during a period of rising inequality.140

Concerns are also expressed about the pos- sible impacts of immigration on the political climate.141 However, in most countries, the relative size of the migrant population is too small to have a direct effect on national elec- toral politics, particularly since migrants come from a diversity of backgrounds and will have a diversity of political views. In any case migrants

are generally not permitted to vote in national elections. Their preferences may be more sig- nificant in local elections, where granting of voting rights to first-generation immigrants is more common.142 Over time, as economic, social and cultural assimilation deepens, the effects of migrants on voting patterns become even less predictable.143

Last but not least, in sufficient numbers, migrants can affect the ethnic and cultural di- versity of a society, literally changing the face of a nation. Several countries that today are highly prosperous were historically founded by migrants. Australia, Canada, New Zealand and United States have continued to welcome large inflows over time, in successive waves from dif- ferent countries of origin, and generally have been highly successful in absorbing migrants and giving them a common sense of belonging to the new nation despite their cultural differ- ences.14 4 In countries with a long and proud history of independence and a strong sense of national identity, the arrival of newcomers may pose more challenges.

Of course, some cultural attributes are more easily adopted by locals than others. For example, many societies welcome new cuisines (probably the most resistant are the French and Italians, who think they have figured it out already). This confirms Paul Krugman’s thesis that a taste for variety, combined with

Figure 4.4 When jobs are limited, people favour the locally born Public opinion about job preferences by destination country HDI category, 2005/2006

Source: Kleemans and Klugman (2009 ).

“When jobs are scarce, employers should give priority to [natives] over immigrants”

Disagree Agree Neither

Total

Low HDI

Medium HDI

High HDI

Very high HDI

Percent of responses

| | | | | | 0 20 40 60 80 100

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development4

economies of scale, does more to explain inter- national trade patterns than any other factor. But some find it harder to open the door to new religious and social customs such as the wear- ing of headscarves by women and the payment of dowries.

While specific issues can arise, the evidence suggests that people are generally tolerant of minorities and have a positive view of ethnic diversity (figure 4.5). People who are less well- educated, older, unemployed and without a migrant background are less likely to value ethnic diversity.145 At the same time, more than 75 percent of respondents in the 2005/2006 World Values Survey did not object to having a migrant as their neighbour. These attitudes point to clear opportunities for building a broad consensus around better treatment of migrants, a policy option that we explore in the next chapter.

Insecurity and adverse reactions may arise when migrant communities are seen to repre- sent alternative and competing social norms and structures, implicitly threatening the local culture. This is associated with the view that ethnic identities compete with each other and vary considerably in their commitment to the nation state, implying that there is a zero sum

game between recognizing diversity and unify- ing the state. Yet individuals can and do have multiple identities that are complementary— in terms of ethnicity, language, religion, race and even citizenship (chapter 1). Thus when migrants integrate more fully and more dif- fusely with their adopted homeland, which in turn becomes even more diverse, they have a better chance of being valued as enriching so- ciety and introducing complementary cultural traits.

4.3 Conclusions This chapter has explored the impacts of mobil- ity on those who do not move. We began with places of origin and focused on developing countries (although by far the highest regional rates of out-migration are observed for Europe and the lowest for Africa). The greatest impacts are at the household level, for those who have family members who have moved, and these are largely positive for income, consumption, education and health. However, the poverty impacts are limited because those who move are mainly not the poorest. Broader commu- nity and national effects can also be observed, although these patterns are often complex, con- text-specific and subject to change over time.

Given the global recession of 2008/09, it is especially important to assess the impact of migration on host communities and countries. There is no evidence of significant adverse eco- nomic, labour market or fiscal impacts, and there is evidence of gains in such areas as so- cial diversity and capacity for innovation. Fears about migrants are generally exaggerated.

These findings, alongside those in the pre- ceding chapter, suggest the possibility of creat- ing virtuous circles through policy measures that enhance and broaden the benefits of mo- bility. This would increase migrants’ economic and social contributions to both destination and origin communities and countries.

The public policies that people encounter when they move play a large part in shaping their futures. Designing these policies well is in the interests of migrants themselves, the communities they leave behind and the other residents in their adopted homes. It is to this topic that we turn in the final chapter of this report.

Figure 4.5 Many people value ethnic diversity Popular views about the value of ethnic diversity by destination country HDI category, 2005/2006

Source: Kleemans and Klugman (2009 ).

“Turning to the question of ethnic diversity, with which of the following views do you agree?”

Ethnic diversity compromises a country’s unity Neither Ethnic diversity helps enrich life

Total

Low HDI

Medium HDI

High HDI

Very High HDI

Percent of responses

| | | | | | 0 20 40 60 80 100

Policies to enhance human development outcomes

This final chapter proposes reforms that will allow

mobility to contribute to a fuller enhancement of

people’s freedoms. At present, many people who

move have at best only precarious rights and face

uncertain futures. The policy mismatch between

restrictive entry and high labour demand for low-

skilled workers needs to be addressed. We propose

a core package of reforms that will improve

outcomes for individual movers and their families,

their origin communities and host places. The

design, timing and acceptability of reforms depend

on a realistic appraisal of economic and social

conditions and a recognition of public opinion and

political constraints.

5HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Policies to enhance human development outcomes

The foregoing analysis has shown that large gains to human devel- opment would flow from improved policies towards movers. These would benefit all groups affected by migration. A bold vision is needed to realize these gains—a vision that embraces reform be- cause of its potential pay-offs, while recognizing the underlying challenges and constraints.

We have also shown that the entry policies that have prevailed in many destination coun- tries over recent decades can be largely charac- terized by denial and delay on the one hand, and heightened border controls and illegal stays on the other. This has worsened the situation of people lacking legal status and, especially dur- ing the recession, has created uncertainty and frustration among the wider population.

The factors driving migration—includ- ing disparate opportunities and rapid demo- graphic transitions—are expected to persist in the coming decades. Lopsided demographic patterns mean that nine tenths of the growth in the world’s labour force since 1950 has been in developing countries, while developed coun- tries are aging. These trends create pressures for people to move, but the regular channels allowing movement for low-skilled people are very restricted. Demographic projections to the year 2050 predict that these trends will con- tinue, even if the demand for labour has been temporarily attenuated by the current economic crisis. This implies a need to rethink the policy of restricting the entry of low-skilled workers, which ill accords with the underlying demand for such workers. This chapter tackles the major challenge of how governments can prepare for the resumption of growth, with its underlying structural trends.

Our proposal consists of a core package of reforms with medium- to long-term pay-offs. The package consists of six ‘pillars’. Each pillar is beneficial on its own, but together they offer the best chance of maximizing the human devel- opment impacts of migration:

1. Liberalizing and simplifying regular chan- nels that allow people to seek work abroad;

2. Ensuring basic rights for migrants; 3. Reducing transaction costs associated with

migration; 4. Improving outcomes for migrants and desti-

nation communities; 5. Enabling benefits from internal mobility; and 6. Making mobility an integral part of national

development strategies. Our proposal involves new processes and

norms to govern migration, but does not pre- scribe any particular levels of increased admis- sions, since these need to be determined at the country level.

Our agenda is largely oriented towards the longer-term reforms needed to enhance the gains from movement, while recognizing the major challenges in the short term. In the midst of what is shaping up to be the worst economic crisis since the Great Depression, unemployment is rising to record highs in many countries. As a result, many migrants find themselves doubly at risk: suffering unemployment, insecurity and so- cial marginalization, yet at the same time often portrayed as the source of these problems. It is important that the current recession must not become an occasion for scapegoating, but rather be seized as an opportunity to institute a new deal for migrants—one that will benefit work- ers at home and abroad while guarding against a protectionist backlash. Forging that new deal and selling it to the public will require political vision and committed leadership.1

Open dialogue is critical if progress is to be made in the public debate about migration. In

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development5

this debate, the benefits should not be overplayed and the concerns about distributional effects— especially among low-skilled workers—need to be recognized and taken into account. The po- litical economy of reform is directly addressed below.

Because this is a global report with diverse stakeholders—governments in origin, destina- tion and transit countries; donors and interna- tional organizations; the private sector; and civil society, including migrant groups and diaspora associations, academia and the media—the pol- icy directions we outline are inevitably pitched at a general level. Our intention is to stimulate debate and follow-up in discussing, adapting and implementing these recommendations. At the country level, much more detailed analysis will be needed to ensure relevance to local cir- cumstances and allow for political realities and practical constraints.

5.1 The core package We will now explore the policy entry points outlined above. Our focus is limited to selected aspects out of the much broader menu of op- tions that have been discussed and implemented around the world.2 In defining a priority agenda we have been motivated by a focus on the disad- vantaged, a realistic consideration of the political constraints and an awareness that trade-offs are inevitable. Whenever possible, we illustrate with examples of good practice.

5.1.1 Liberalizing and simplifying regular channels Overly restrictive barriers to entry prevent many people from moving and mean that millions who do move have irregular status—an estimated one quarter of the total. This has created uncertainty and frustration, both in the migrant community and among the wider population, especially dur- ing the current recession.

When growth resumes, the demand for mi- grant labour will likewise rebound, since the demographic and economic conditions that cre- ated that demand in the first place will still be in place. The need for working-age people in devel- oped countries has been largely structural, and is long-term—not temporary—in nature. This is true even for high-turnover jobs in such sectors as care, construction, tourism and food processing.

If the demand for labour is long-term, then, from the perspective of both migrants and their desti- nation communities and societies, it is better to allow people to come legally. And provided mi- grants can find and keep jobs, it is better to offer them the option of extending their stay than to limit them to temporary permits. The longer people stay abroad, the greater the social and eco- nomic mobility they and their children are likely to enjoy. When the presence of migrants is de- nied or ignored by host governments, the risk of segmentation is greatly increased, not only in the labour market and economy but also in society more generally. This is one lesson that emerged clearly from the German guest-worker experi- ence. We see it again today, in destinations as di- verse as the GCC states, the Russian Federation, Singapore, South Africa and Thailand.

So what would a liberalization and simplifi- cation of migration channels look like? There are two broad avenues where reform appears both desirable and feasible: seasonal or circular pro- grammes, and entry for unskilled people, with conditional paths to extension. The difficult issue of what to do about people with irregular status is a third area in which various options for change are possible and should be consid- ered. In each case, the specific design of new measures will need to be discussed and debated at the national level through political processes that permit the balancing of different interests (section 5.2). As high-skilled people are already welcomed in most countries, reforms need to focus on the movement of people without ter- tiary degrees.

The first avenue, already explored by a num- ber of countries, is to expand schemes for truly seasonal work in sectors such as agriculture and tourism. Key elements when planning and imple- menting reforms include consultation with source country governments, union and employer in- volvement, basic wage guarantees, health and safety protection, and provision for repeat visits. These elements are the basis for schemes that have been successfully operating for decades in Canada, for example, and have more recently been introduced in New Zealand (box 5.1). Workers in formal schemes of this kind are typically accorded better protection than those with irregular status. From a human development point of view, that is one of their major advantages.

Open dialogue is critical if progress is to be made in the public debate about migration

5HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

The second avenue, which involves more fundamental reforms, is to expand the number of visas for low-skilled people—conditional on employer demand. As is currently the case, the visas can initially be temporary. Issuance can be made conditional on a job offer, or at least experience of, or willingness to work in, a sector that is known to face labour shortages.

Expanding regular entry channels involves taking decisions on the following key issues:

Setting annual inflow numbers. These must be responsive to local conditions and there are several ways of ensuring this. Numbers can be based on employer demand—such that an individual is required to have a job offer prior to arrival—or on the recommendations of a technical committee or similar body that considers projections of demand and submis- sions from unions, employers and community groups. The United K ingdom’s Migration Advisory Committee, set up in late 2007 to provide advice on the designation of so-called

‘shortage occupations’, is a good example. The disadvantages of requiring a job offer are that the decision is effectively delegated to individ- ual employers, transaction costs for individual migrants may be higher, and portability can become an issue. Caution should be exercised in relation to employers’ stated ‘needs’ for mi- grants. These could arise because migrants are willing to work longer hours and/or because they are more skilled. Employers should not use migrant labour as a stratagem for evading their legal obligations to provide basic health and safety protection and to guarantee minimum standards in working conditions, which should be accorded to all workers, regardless of origin.

Employer portability. Tying people to spe- cific employers prevents them from finding better opportunities and is therefore both eco- nomically inefficient and socially undesirable. Our policy assessment found that governments typically allow employment portability for permanent high-skilled migrants, but not for

Box 5.1 Opening up regular channels—Sweden and New Zealand

Two countries have recently introduced reforms in line with the direc-

tions suggested by this report, although both are too new to evaluate

in terms of impact.

In late 2008, Sweden introduced a major labour migration re-

form. The initiative came from the Swedish parliament and began

with the appointment of a parliamentary committee with a mandate

to propose changes. This was during a period of rapid economic

growth and widespread labour shortages. Parliamentary and media

debates focused on the risk of displacement of local workers and on

whether unsuccessful asylum seekers could apply. A scheme was

thus designed that met union concerns about undercutting of wages

and labour standards.

Among the scheme’s key elements is the provision that employ-

ers are the primary judges of needs (self-assessment), with a role for

the Swedish Migration Board to ensure consistency with collective

agreements and allow for union comment. Portability across employ-

ers is allowed after two years, and if individuals change jobs during

this initial period they must apply for a new work permit. The duration

is initially for two years, extendable to four, after which permanent

residence can be granted. During the first quarter of operation, there

were 24,000 applications, representing about 15 percent of total ap-

plications to come to Sweden.

New Zealand’s Recognised Seasonal Employer Scheme (RSE)

was launched in April 2007 as part of the government’s growth and

innovation agenda, to address the acute problems experienced by the

horticulture and viticulture industries in finding workers during sea-

sonal labour peaks. It provides a number of seasonal jobs, set annually.

RSE was designed to avoid some of the downsides of the low-

wage temporary work cycle, which was seen as unsustainable for

both employers and workers, many of whom were irregular migrants.

Transiting to RSE shook out existing irregular workers from the system

and brought new employers into contact with the government. During

the transition period employers were allowed to retain workers already

in New Zealand for a limited period and under certain conditions.

Central to the objectives of both the New Zealand government

and the union movement, and critical to public acceptance, was

to ensure that employers recruit and train New Zealand workers

first, before they recruit offshore. However, the scheme allows

Pacific Island countries to find a continuing market for their low-

skilled labour, provided that they put in place appropriate selection

and facilitation processes and help to ensure return. Their workers

have the opportunity to be trained and properly remunerated, and

to broaden their experience and contacts. So far, no serious prob-

lems have been reported.

RSE is not a low-cost scheme. It will not be economically sus-

tainable unless the industries involved can realize productivity and

quality gains in partnership with a known group of workers, who can

be relied on to return to specific orchards and vineyards each year.

Sources: Government of Sweden (2008) and World Bank (2006a).

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development5

temporary low-skilled workers. However, there are signs of change. The United Arab Emirates has begun to offer transferable employment sponsorships in response to complaints of abuse from migrants.3 Sweden’s recent labour immi- gration reform, described in box 5.1, is perhaps the most comprehensive example of employ- ment and benefits portability to date, as work permits are transferable and migrants who lose their jobs—for whatever reason—have three months to find work before the visa is revoked.4 An employer who has gone abroad to recruit will typically seek some period of non-porta- bility—but even in these cases there are ways of building in a degree of flexibility: for example, allowing the migrant or another employer who wants to employ her to pay a fee reimbursing the original employer for recruitment costs.

Right to apply for extension and pathways to permanence. This will be at the discretion of the host government and, as at present, is usually subject to a set of specific conditions. Nevertheless, extension of temporary permits is possible in many developed countries (e.g. Canada, Portugal, Sweden, United Kingdom and United States), and some developing coun- tries (e.g. Ecuador and Malaysia). Whether the permit is renewed indefinitely may depend on bilateral agreements. Some countries grant the opportunity for migrants to convert temporary into permanent status after several years of regu- lar residence (e.g. in Italy after six years, and in Portugal and the United Kingdom after five). This may be conditional on, for example, the mi- grant’s labour market record and lack of criminal convictions.5

Provisions to facilitate circularity. The free- dom to move back and forth between host and source country can enhance benefits for mi- grants and their origin countries. Again, this can be subject to discretion or to certain conditions. Portability of accumulated social security ben- efits is a further advantage that can encourage circularity.

The issue of irregular status inevitably crops up in almost any discussion of immigration. Various approaches have been used by govern- ments to address the issue. Amnesty schemes are announced and remain open for a finite pe- riod—these have been used in various European countries as well as in Latin America. Ongoing administrative mechanisms may grant some type of legal status on a discretionary basis—for ex- ample, on the basis of family ties, as is possible in the United States. Forced returns to the country of origin have also been pursued. None of these measures is uncontroversial. Box 5.2 summarizes recent regularization experiences.6

So-called ‘earned regularizations’, as tried in a number of countries, may be the most viable way forward.7 These provide irregular migrants with a provisional permit to live and work in the host country, initially for a finite period, which can be extended or made permanent through the fulfilment of various criteria, such as language acquisition, maintaining stable employment and paying taxes. There is no initial amnesty but rather a conditional permission to transit to full residence status. This approach has the

Box 5.2 Experience with regularization

Most European countries have operated some form of regularization programme,

albeit for a range of motives and, in some cases, despite denying that regularization

takes place (Austria and Germany). A recent study estimated that in Europe over 6

million people have applied to transit from irregular to legal status over the decade to

2007, with an approval rate of 80 percent. The numbers in each country vary hugely—

Italy having the highest (1.5 million), followed by Spain and Greece.

Regularization programmes are not limited to the OECD. A regional agreement in

Latin America, MERCOSUR, means that Argentina, for example, has legislated that

any citizen of a MERCOSUR country without a criminal background can obtain legal

residence. In South Africa efforts are underway to regularize irregular Zimbabweans,

beginning with a temporary residence permit that grants them access to health care

and education and the right to stay and work for at least six months. In Thailand

135,000 migrants were regularized in early 2008, although in the past periods of regu-

larization were followed by stepped-up rates of deportation.

The pros and cons of regularization have been hotly debated. The benefits for

the destination country relate to security and the rule of law, while the individuals and

families who are regularized may be better placed to overcome social and economic

exclusion. Among the disadvantages are concerns about encouraging future flows,

the undermining of formal admissions programmes and fraudulent applications. At

the same time, the benefits of regularization are highly dependent on context. For

example, in the United States many irregular immigrants already pay taxes, so the

revenue benefits are much lower than in countries with large informal economies,

where taxes are avoided on a much larger scale. Surveys of country experiences

have tended to conclude that the socio-economic impacts of regularization have

been mixed, with the expected positive impacts on wages, mobility and integration

not always materializing.

Source: ICMPD (2009 ), Cerrutti (2009 ) and Martin (2009b).

5HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

attraction of potentially garnering broad public acceptance.

The varied European experience suggests that among the key ingredients of successful regular- izations are the involvement of civil society or- ganizations, migrant associations and employers in planning and implementation; guarantee against expulsion during the process; and clear qualifying criteria (for example, length of resi- dence, employment record and family ties).8 Among the challenges faced in practice are long delays. With locally administered schemes, as in France, variable treatment across locations may be an issue.

Forced returns are especially controversial. Their number has been rising sharply in some countries, surpassing 350,000 in the United States and 300,000 in South Africa in 2008 alone. Pushed enthusiastically by rich coun- try governments, forced returns also feature in the European Union’s mobility partnerships.9 Many origin states cooperate with destination countries by signing readmission agreements, although some, for example South Africa, have so far declined to sign.

What should humane enforcement policies look like? Most people argue that there need to be some sanctions for breaches of border control and work rules and that, alongside discretionary regularization, forced returns have a place in the policy armoury. But implementing this sanc- tion raises major challenges, especially in cases where the individuals concerned have lived and worked in the country for many years and may have family members who are legally resident. For example, a recent survey of Salvadorian de- portees found that one quarter had resided in the United States for more than 20 years, and that about four fifths were working at the time of their deportation, many with children born in the United States.10 In various countries, includ- ing the United Kingdom, the media have occa- sionally taken up cases of threatened deportation that have seemed particularly inhumane.

It is clearly important that, where individu- als with irregular status are identified, enforce- ment procedures should follow the rule of law and basic rights should be respected. There is a need to establish the accountability of employ- ers who engage workers with irregular status. This has been a topic of debate in the United

States, for example. Formal processes to de- termine whether or not individuals have the legal right to stay in the country are clearly bet- ter than summary or mass expulsions, which have been observed in the past (e.g. Malaysia’s expulsion of irregular Indonesian workers in early 2005)11, although some procedural as- pects, such as the right to counsel, may repre- sent an unwelcome burden on the public purse in developing countries. The United Kingdom Prison Inspectorate has published Immigration Detention Expectations based on international human rights standards. But mere publication does not, of course, ensure that the standards are met. In some countries, NGOs work to im- prove living conditions in detention camps—the Ukrainian Red Cross is an example. The recent European Union directive on the procedures for return appears to be a step towards transparency and harmonization of regulations, with an em- phasis on standard procedures either to expel people with irregular status or to grant them definite legal status. The directive has, however, been criticized as inadequate in guaranteeing respect for human rights.12

5.1.2 Ensuring basic rights for migrants This report has focused on mobility through the lens of expanding freedoms. But not all migrants achieve all the freedoms that migration prom- ises. Depending on where they come from and go to, people frequently find themselves having to trade off one kind of freedom against another, most often in order to access higher earnings by working in a country where one or more funda- mental human rights are not respected. Migrants who lack resources, networks, information and avenues of recourse are more likely to lose out in some dimensions, as too are those who face racial or other forms of discrimination. Major problems can arise for those without legal status and for those in countries where governance and accountability structures are weak.

Refugees are a distinct legal category of migrants by virtue of their need for interna- tional protection. They have specific rights, set out in the 1951 Refugee Convention and 1967 Protocols, which have been ratified by 144 states (figure 5.1).13 These agreements provide critical protection to those fleeing across international borders to escape persecution.

Where individuals with irregular status are identified, enforcement procedures should follow the rule of law and basic rights should be respected

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More generally, the six core international human rights treaties, which have been ratified by 131 countries around the globe, all contain strong non-discrimination clauses ensuring the applicability of many provisions to migrants.14 These instruments are universal and apply to both citizens and non-citizens, including those who have moved or presently stay, whether their status is regular or irregular. Of particular rel- evance are the rights to equality under the law and to be free from discrimination on grounds of race, national origin or other status. These are important legal constraints on state action.15

Recently, protocols against the trafficking and smuggling of people have rapidly garnered broad support, building on existing instruments with 129 ratifications.16 These protocols, which seek to criminalize trafficking, focus more on suppressing organized crime and facilitating or- derly migration than on advancing the human rights of the individuals (mainly women) in- volved.17 Many states have enacted these prin- ciples into national legislation: of the 155 states surveyed in 2008, some 80 percent had intro- duced a specific offence of trafficking in persons and more than half had created a special anti- trafficking police unit.18 Progress on this front is

clearly welcome, although some observers have noted that increasingly harsh immigration poli- cies have also tended to promote trafficking and smuggling.19

By way of contrast, the series of ILO con- ventions adopted throughout the 20th century, which seek to promote minimum standards for migrant workers, have not attracted wide en- dorsement. The causes are several, including the scope and comprehensiveness of the conventions versus the desire for unfettered state discretion in such matters. In 1990, the UN International Convention on the Protection of the Rights of All Migrant Workers and Members of their Families (CMW) reiterated the core principles of the human rights treaties, but also went fur- ther, for example in defining discrimination more broadly, in providing stronger safeguards against collective and arbitrary expulsion and in ensuring the right of regular migrants to vote and be elected. However, there are only 41 sig- natories to date, of which only five are net im- migration countries and none belong to the very high-HDI category (figure 5.1).

Looking behind figure 5.1 to examine the migration profiles of ratif ying countries, we found that most have immigration and emigra- tion rates below 10 percent. Among the coun- tries where the share of the population who are either migrants or emigrants exceeds 25 percent, the rates of signing are still low—only 3 out of 64 have signed up to the CMW, for example, al- though 22 have signed the six core human rights treaties. Even among countries with net out- migration rates exceeding 10 percent of their population—which have strong incentives to sign in order to protect their workers abroad— ratification rates of the CMW are low. Only 20 percent of high-emigration country govern- ments have signed the CMW over the almost two decades of its existence, whereas half have ratified the six core human rights treaties and 59 percent are signatories to the more recent traf- ficking protocol.

Countries that have not ratified the CMW are still obliged to protect migrant workers, through other core human rights treaties. Treaty Monitoring Bodies (TMBs) under existing con- ventions are now supplemented by periodic re- view by UNHCR. Recent analysis of a decade of deliberations by TMBs reveals that the relevant

Figure 5.1 Ratification of migrants’ rights convention has been limited Ratification of selected agreements by HDI category, as of 2009

| | | | | | | | | 0 20 40 60 80 100 120 140 160

Source: UNODC (2004) and UN (2009b).

Convention on Status of Refugees (1951)

25 54 34 31

19 47 33 32

16 44 41 28

7 22 12

Protocol on Trafficking (2000)

Convention on Rights of Migrant Workers and their Families (1990)

Low HDI Medium HDI High HDI Very high HDI

Number of ratifying countries

Six core human rights treaties (1965–1989)

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provisions of other core human rights treaties can highlight problems and protect the rights of migrants, and have increasingly done so over time.20 Even if each country naturally seeks to portray its human rights record in the best light, TMBs can, despite the lack of enforcement mechanisms, influence through ‘naming and shaming’, highlighting egregious cases and seek- ing moral or political suasion.

Ensuring the rights of migrants has been a recurrent cry in all global forums, as exemplified by the statements made by civil society organiza- tions at the 2008 Global Forum on Migration and Development in Manila. Yet it is also clear that the main challenge is not the lack of a legal framework for the protection of rights—as a se- ries of conventions, treaties and customary law provisions already exist—but rather their effec- tive implementation. In this spirit, in 2005 the ILO developed a Multilateral Framework on Labour Migration, which provides guidelines and good practices within a non-binding frame- work that recognizes the sovereign right of all states to determine their own migration policies. This ‘soft law’ type of approach accommodates the inherent differences between states and al- lows for gradual implementation.21

Even if there is no appetite to sign up to for- mal conventions, there is no sound reason for any government to deny such basic migrant rights as the right to: • Equal remuneration for equal work, decent

working conditions and protection of health and safety;

• Organize and bargain collectively; • Not be subject to arbitrary detention, and

be subject to due process in the event of deportation;

• Not be subject to cruel, inhumane or degrad- ing treatment; and

• Return to countries of origin. These should exist alongside basic human rights of liberty, security of person, freedom of be- lief and protection against forced labour and trafficking.

One argument against ensuring basic rights has been that this would necessarily reduce the numbers of people allowed to enter. However as we showed in chapter 2, this trade-off does not generally hold such and an argument is in any case not justifiable on moral grounds.

The prime responsibility for ensuring basic rights while abroad lies with host governments. Attempts by source country governments, such as India and the Philippines, to mandate minimum wages paid to emigrants have typi- cally failed due to the lack of jurisdiction over this matter. Source country governments can nonetheless provide support in terms of advis- ing about migrants’ rights and responsibilities through migrant resource centres and pre-de- parture orientation about what to expect while abroad.

Consular services can play an important part in providing a channel for complaints and pos- sible recourse, while bilateral agreements can establish key principles. However, a collective and coordinated effort by countries of origin to raise standards is more likely to be effective than isolated national efforts.

Employers, unions, NGOs and migrant as- sociations also have a role. Employers are the main source of breaches of basic rights—hence their behaviour is paramount. Some employers have sought to set a good example by develop- ing codes of conduct and partnering with the Business for Social Responsibility programme for migrant workers’ rights, which focuses on sit- uations where there are no effective mechanisms for enforcing existing labour laws.22 Among the measures available to unions and NGOs are: informing migrants about their rights, working more closely with employers and government of- ficials to ensure that these rights are respected, unionizing migrant workers and advocating for regularization. One active NGO, is the Collectif de défense des travailleurs étrangers dans l’agriculture (CODESTR AS), which seeks to improve the situation of seasonal workers in the South of France through awareness-raising, information, dissemination and legal support.23

The role of trade unions is particularly important. Over time, unions have accorded greater attention to migrants’ rights. The World Values Survey of 2005/2006, covering 52 coun- tries, suggests that rates of union membership are higher among people with a migrant back- ground: 22 percent of those who have a mi- grant parent are members of a labour union, compared to 17 percent of those who do not. This difference is especially large in low-HDI countries.24

The prime responsibility for ensuring basic rights while abroad lies with host governments... Employers, unions, NGOs and migrant associations also have a role

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Last but not least, migrants themselves can affect the way destination communities and soci- eties perceive immigration. Sometimes, negative public opinion partly reflects past incidents of unlawful behaviour associated with migrants. By supporting more inclusive societies and communities, where everyone—including mi- grants—understands and respects the law and pursues peaceful forms of participation and, if necessary, protest, migrants can alleviate the risk of such negative reactions. Civil society and local authorities can help by supporting migrant net- works and communities.25

5.1.3 Reducing transaction costs associated with movement Moving across borders inevitably involves trans- action costs. Distance complicates job matching, both within countries and, more acutely, across national borders, because of information gaps, language barriers and varying regulatory frame- works. This creates a need for intermediation and facilitation services. Given the magnitude of income differences between low- and very high- HDI countries, it is not surprising that there is a market for agents who can match individuals with jobs abroad and help navigate the adminis- trative restrictions associated with international movement.

Under current migration regimes, the major cost is typically the administrative requirement that a job offer be obtained from a foreign em- ployer before departure. Especially in Asia, many migrant workers rely on commercial agents to organize the offer and make all the practical ar- rangements. Most agents are honest brokers and act through legal channels, but some lack adequate information on the employers and/or the workers or smuggle people through borders illegally.

This market for intermediation services can be problematic, however. In the worst cases it can result in trafficking and years of bondage, vio- lent abuse and sometimes even death. A much more common problem is high fees, especially for low-skilled workers. Intermediation often generates surplus profits for recruiters, due to the combination of restrictive entry and high labour demand for low-skilled workers, who frequently lack adequate information and have unequal bargaining power. The costs also appear to be regressive, rising as the level of skills falls,

meaning that, for example, few migrant nurses pay recruitment fees but most domestic helpers do. Asian migrants moving to the Gulf states often pay 25–35 percent of what they expect to earn over two or three years in recruitment and other fees.26 In some cases, corruption im- poses additional costs. Extensive administrative regulation can be counterproductive in that it is more likely to expose migrants to corruption and creates rents for middlemen, officials and others who grease the wheels of the system.

Governments can help to reduce transaction costs for migrant workers in several ways. Six areas deserve priority consideration:

Opening corridors and introducing re- gimes that allow free movement. Because of MERCOSUR, for example, Bolivian work- ers can travel relatively freely to Argentina, as well as learn about jobs and opportunities from friends and relatives through deepening social networks. The same dynamic was observed on an accelerated basis following European Union enlargement in 2004. Another example is fa- cilitated access for seasonal workers across the Guatemala–Mexico border.

Reducing the cost of and easing access to offi- cial documents, such as birth certificates and pass- ports. Rationalizing ‘paper walls’ in countries of origin is an important part of reducing the bar- riers to legal migration.27 Analysis at the level of the country and migration corridor is needed to identify the types and amounts of upfront costs, which can range from travelling multiple times from the village to the capital to apply for a pass- port, to the fees for other pre-departure require- ments such as health checks, police clearances, insurance fees and bank guarantees. Prospective migrants in the Mexico–Canada programme go to the capital city six times on average—a requirement that prompted the government to offer a stipend to cover travel costs (although rationalizing the administrative requirements would have been more efficient).28 Some costs arise from destination country requirements. For example, the Republic of Korea requires that migrants learn the language before arrival: while language training increases earnings and promotes integration, it also increases pre-arrival debt.29 A number of countries have attempted to speed up paperwork for migrants, with varying degrees of success (box 5.3).

Rationalizing ‘paper walls’ in countries of origin is an important part of reducing the barriers to legal migration

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Empowerment of migrants, through access to information, rights of recourse abroad and stron- ger social networks. The latter, in particular, can do much to plug the information gap between migrant workers and employers, limiting the need for costly recruitment agencies and en- abling migrants to pick and choose among a wider variety of employment opportunities.30 In Malaysia migrant networks allow Indonesians to learn about new job openings before the news even reaches local residents.31 Similarly, improved telecommunication has helped pro- spective migrants in Jamaica become better in- formed.32 Information centres, such as the pilot launched by the European Union in Bamako, Mali in 2008, can provide potential migrants with accurate (if disappointing!) information about opportunities for work and study abroad.

Regulation of private recruiters to prevent abuses and fraud. Prohibitions do not tend to work, in part because bans in destination places do not apply to recruiters in source areas.33 Yet some regulations can be effective, for example joint liability between employers and recruit- ers, which can help to avert fraud and deceit. In the Philippines recruitment agencies are treated as ‘co-employers’, liable jointly and separately for failure to comply with a given contract. An agency found to be at fault risks having its license revoked, although suspension is often avoided by payment of a fine. Self-regulation through indus- try associations and codes of conduct is another means of promoting ethical standards. Industry associations can collect and disseminate infor- mation on high-risk agencies and best practices. Many such associations exist in South and East Asia, although none has emerged as a self-regu- latory body similar to those found in developed countries, since most have focused on ensuring that government policy on migration is friendly to the recruitment industry—as, for example, in Bangladesh, the Philippines and Sri Lanka.34 Such associations could develop over time to play a more effective role in assuring the quality of services and, where necessary, censuring mem- bers for lax standards.

Direct administration of recruitment by public agencies. In Guatemala, for example, the IOM administers a programme that sends sea- sonal farm workers to Canada at no charge to the worker. However there is debate about the

appropriate role for government agencies. In most poor countries, the capacity of national employment agencies to match workers with suitable jobs at home, let alone abroad, is very weak.35 Some bilateral agreements, such as those signed by the Republic of Korea, require mi- grants to use government agencies, prompting complaints from recruiters and workers about high costs and lack of transparency. The fees charged by public recruiters are sometimes lower, but the costs in terms of time can be significant and can discourage prospective migrants from using regular channels.36

Intergovernmental cooperation. This can play an important role. The Colombo Process and the Abu Dhabi Dialogue are two recent intergovernmental initiatives designed to co- operatively address transactions costs and other issues. This process, which took place for the first time in January 2008, involved almost a dozen source and several destination countries in the GCC states and South East Asia, with the United Arab Emirates and IOM serving as the co-hosts. It focuses on developing key partner- ships between countries of origin and destina- tion around the subject of temporary contractual labour to, among other things, develop and share

Box 5.3 Reducing paperwork: a challenge for governments and partners

A prime example of streamlined deployment despite extensive administrative re-

quirements is the Philippine Overseas Employment Administration, which regulates

all aspects of recruitment and works closely with other agencies to ensure the pro-

tection of its workers abroad. Indonesia has attempted to follow suit, establishing

the National Agency for the Placement and Protection of Indonesian Migrant Work-

ers (BNP2TKI) in 2006, although low bureaucratic capacity and weak intergovern-

mental coordination have reportedly compromised BNP2TKI’s effectiveness. Other

countries have attempted to address issues related to delays and costs, but few

have succeeded. In Gabon the government instituted a 3-day limit on the waiting

time for passports, but the delays remain long and the process arduous. Similarly,

the Myanmar government recently instituted a policy for passports to be issued

within one week, but continuing complaints suggest that delays and demands for

bribes remain common.

Development assistance programmes could support and finance administrative

improvements for vital records registration with shorter processing times and lower

costs. This would allow governments to offer their citizens proper travel documents

at affordable prices. Bangladesh, which has a birth registration rate of below 10 per-

cent, has partnered with the United Nations Children’s Fund (UNICEF) on this front.

Source: Agunias (2008), Tirtosudarmo (2009 ), United States Department of State (2009e), Koslowski (2009 ), and UNICEF (2007).

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knowledge on labour market trends, prevent il- legal recruitment, and promote welfare and protection measures for contractual workers. The ministerial consultation is intended to take place every two years. A pilot project followed where under the initiative of the governments of India, the Philippines and the United Arab Emirates there will be a test and identification of best practices in different aspects of temporary and circular migration, beginning with a group of Filipino and Indian workers in the sectors of construction, health and hospitality.37

5.1.4 Improving outcomes for migrants and destination communities While the weight of evidence shows that the aggregate economic impact of migration in the long run is likely to be positive, local people with specific skills or in certain locations may expe- rience adverse effects. To a large extent these can be minimized and offset by policies and programmes that recognize and plan for the presence of migrants, promoting inclusion and ensuring that receiving communities are not un- duly burdened. It is important to recognize the actual and perceived costs of immigration at the community level, and consider how these might be shared.

Inclusion and integration are critical from a human development perspective, since they have positive effects not only for individual movers and their families but also for receiving commu- nities. The ways in which the status and rights of immigrants are recognized and enforced will de- termine the extent of such integration. In some developing countries, support for integration could be an appropriate candidate for develop- ment assistance.

Yet institutional and policy arrangements may often be more important than targeted mi- grant integration policies. For example, the qual- ity of state schooling in poor neighbourhoods is likely to be critical—and not only for migrants. Within this broader context, the policy priori- ties for improving outcomes for migrants and destination communities are as follows:

Provide access to basic services—particularly schooling and health care. These services are not only critical to migrants and their families, but also have broader positive externalities. Here the key is equity of access and treatment. Our review

suggests that access is typically most restricted for temporary workers and people with irregular status. Access to schooling should be provided on the same basis and terms as for locally born residents. The same applies for health care—both emergency care in the case of accidents or severe illness and preventive services such as vaccina- tions, which are typically also in the best inter- ests of the whole community and highly effective in the long term. Some developing countries, for example Costa Rica, grant migrants access to public health facilities regardless of status.38

Help newcomers acquire language proficiency. Services in this area can contribute greatly to labour market gains and inclusion more gener- ally. They need to be designed with the living and working constraints faced by migrants in mind. The needs of adults vary, depending on whether or not they are working outside the home, while children can access school-based programmes. Among good practice examples are Australia, which provides advanced language training to migrants and indigenous popula- tions.39 Examples of targeted language learning for children include the Success for All pro- gramme in the United States, which combines group instruction and individual tutoring at the pre-school and primary school levels.40 Several European countries provide language courses for newcomers through programmes offered by central government, state schools, municipalities and NGOs, such as the Swedish for Immigrants programme that dates back to 1965, the Portugal Acolhe programme offered since 2001, and the Danish Labour Market programme introduced in 2007.

Allow people to work. This is the single most important reform for improving human de- velopment outcomes for migrants, especially poorer and more vulnerable migrants. Access to the labour market is vital not just because of the associated economic gains but also because em- ployment greatly increases the prospects for so- cial inclusion. Restrictions on seeking paid work, as have traditionally been applied to asylum seek- ers and refugees in many developed countries, are damaging both to short- and medium-term outcomes, since they encourage dependency and destroy self-respect. They should be abol- ished. Allowing people to move among employ- ers is a further basic principle of well-designed

Inclusion and integration are critical from a human development perspective

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programmes, which are concerned with the in- terests of migrants and not solely with those of employers. In many countries, high-skilled new- comers also face problems in accreditation of the qualifications they bring from abroad (box 5.4).

Support local government roles. Strong local government, accountable to local users, is essen- tial for the delivery of services such as primary health and education. However, in some coun- tries, government officials implicitly deny the existence of migrants by excluding them from development plans and allowing systematic dis- crimination to thrive. Improving individual and community outcomes associated with migration requires local governments that aim to41: • Promote inclusive local governance structures

to enable participation and accountability; • Avoid institutional practices that contribute

to discrimination; • Ensure that law and order plays a facilitating

role, including an effective and responsive police service;

• Provide relevant information for the public and for civil society organizations, including migrants’ associations;42 and

• Ensure equitable land use planning, consis- tent with the needs of the poor—for exam- ple, options to alleviate tenure insecurity and related constraints. Address local budget issues, including fiscal

transfers to finance additional local needs. Often, responsibility for the provision of basic services such as schools and clinics lies with local authori- ties, whose budgets may be strained by growing populations and who may lack the tax base to address their responsibilities for service delivery. Where subnational governments have an impor- tant role in financing basic services, redistribu- tive fiscal mechanisms can help offset imbalances between revenue and expenditure allocations. Intergovernmental transfers are typically made across states and localities on the basis of at least two criteria: need (such as population, poverty rates, and so on) and revenue-generating capacity (so as not to discourage local taxation efforts). Since circumstances and objectives differ from country to country, no single pattern of transfers is universally appropriate. Per capita grants re- quire that all people present, including irregular migrants and their families, should be counted. Transfers may also be used to reimburse specific

costs, especially in social services, where there is a strong argument for equalization of access. Well- designed transfer systems do not rely heavily on earmarking, and the grants should be made in as simple, reliable and transparent a way as possible.43

Address discrimination and xenophobia. Appropriate interventions by governments and civil society can foster tolerance at the commu- nity level. This is especially important where there is a risk of violence, although in practice policy responses tend to emerge ex post. In re- sponse to violence in Côte d’Ivoire, for example, an Anti-Xenophobia Law was passed in August 2008 to impose sanctions on conduct that incites such violence.44 Civil society can also work to en- gender tolerance and protect diversity, as dem- onstrated recently in South Africa, where the ‘No to Xenophobia’ emergency mobile phone

Box 5.4 Recognition of credentials

Many migrants, especially from poorer countries, are well qualified yet unable to use

their skills abroad. Accreditation of skills is rarely practised in Europe, for example,

even where there are institutional arrangements in place that are supposed to facili-

tate recognition.

There are reasons why immediate accreditation is not allowed. For example, it

may be difficult to judge the quality of overseas qualifications, and there may be a

premium on local knowledge (e.g. lawyers, with respect to applicable legislation).

Among the strategies available to promote the use of skills and qualifications held

by foreigners are the following:

• Mutual recognition agreements. These are most common between countries with

similar systems of education and levels of economic development, as in the Euro-

pean Union.

• Prior vetting. Both source and destination governments can vet the credentials

of potential migrants before they leave. Australia has pioneered this approach.

However, if an individual’s goal is to enhance her human development via migra-

tion, the wait for credential recognition may be more costly than trying her luck

in some other country, especially if she is unable to practise her profession at

home or works there for a low wage.

• Fast-track consideration. Governments can facilitate fast-track consideration of

credentials and establish national offices to expedite recognition. Mentors and

short courses abroad can help migrants fill any gaps. Some states in the United

States have established ‘New Americans’ offices to help newcomers navigate

what can be a maze, even for internal migrants.

• Recognition of on-the-job skills. Many skills are learned on the job and mechanisms

for recognizing such informally learned skills may be lacking. Developing the ca-

pacity to recognize and certify on-the-job skills could make it easier for workers to

have their skills recognized abroad.

Source: Iredale (2001).

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SMS network was initiated after the violence of May 2008.45 Another example is the Campaign for Diversity, Human Rights and Participation, organized by the Council of Europe in partner- ship with the European Commission and the European Youth Forum. This emphasized the role of the media in combating prejudice against Muslim and Roma peoples, and offered awards for municipalities that actively advance protec- tion and inclusion.46 Of course, where discrimi- nation and tensions are deep-seated and have erupted in violence, and especially where the rule of law is weak, it will take time as well as much effort and goodwill for such efforts to bear fruit.

Ensure fair treatment during recession. This has assumed some urgency in 2009, which has brought reports of backlashes and deportations around the world. Among the provisions that can protect migrant workers against undue hard- ship are to47:

• Allow those laid off to look for a new job, at least until their existing work and residence permits expire;

• Ensure that those who are laid off before the end of their contracts can claim severance payments and/or unemployment benefits when entitled to do so;

• Step up labour law enforcement so as to mini- mize abuses (e.g. wage arrears) where workers are fearful of layoffs;

• Ensure continued access to basic services (health and education) and to job search services;

• Support institutions in origin countries that help laid-off workers to return and provide training grants and support; and

• Improve disaggregated data—including data on layoffs and wages, by sector and gender— so that origin governments and communities can become aware of changes in employment prospects. If governments take these types of measures,

the economic crisis could become an oppor- tunity to promote better treatment and avoid conflict.

It is important to give credit where it is due. There are examples where state and local govern- ments have embraced migration and its broader social and cultural implications. The recent West Australian Charter on Multiculturalism is an interesting example of a state-level commitment

to the elimination of discrimination and the promotion of cohesion and inclusion among individuals and groups.48 Many of the foregoing recommendations are already standard policy in some OECD countries, although there tends to be plenty of variability in practice. The boldest reforms are needed in a number of major desti- nation countries, including, for example, South Africa and the United Arab Emirates, where current efforts to enable favourable human de- velopment outcomes for individuals and com- munities fall far short of what is needed.

5.1.5 Enabling benefits from internal mobility In terms of the number of people involved, in- ternal migration far exceeds external migration. An estimated 136 million people have moved in China alone, and 42 million in India, so the totals for just these two countries approach the global stock of people who have crossed frontiers. This reflects the fact that mobility is not only a natural part of human history but a continuing dimension of development and of modern societ- ies, in which people seek to connect to emerging opportunities and change their circumstances accordingly.

Given these realities, government policies should seek to facilitate, not hinder, the pro- cess of internal migration. The policies and programmes in place should not adversely af- fect those who move. By the same token, they should not require people to move in order to ac- cess basic services and livelihood opportunities. These two principles lead to a series of recom- mendations that are entirely within the jurisdic- tion of all national governments to implement:

Remove the barriers to internal mobility. To ensure full and equal civic, economic and social rights for all, it is vital to lift legal and admin- istrative constraints to mobility and to combat discrimination against movers. As reviewed in chapter 2, administrative barriers are less com- mon since the demise of central planning in large parts of the world—but some are remark- ably persistent, despite typically failing to curb mobility to any marked degree. Such barriers are contrary to international law. They are also costly and time-consuming to maintain for gov- ernment and to negotiate for movers. Many opt to travel without the proper documentation,

It is critical to ensure fair treatment of migrants during recessions

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only to find later that they cannot access key services. Internal migrants should have equal access to the full range of public services and benefits, especially education and health, but also pensions and social assistance where these are provided.

Freedom of movement is especially impor- tant for seasonal and temporary workers, who are typically among the poorest migrants and have often been neglected or actively discrimi- nated against. These types of migration flows can present acute challenges for local authorities responsible for the provision of services, which need to learn to cater to more fluid populations. Partial reforms that allow migrants to work but not to access services on an equal basis (as is the case in China) are not enough. Reforms have been introduced in some states in India—for example, allowing seasonal migrants to obtain temporary ration cards—but implementation has been slow.49

Provide appropriate support to movers at des- tination. Just as they should do for people com- ing from abroad, governments should provide appropriate support to people who move in- ternally. This may be done in partnership with local communities and NGOs. Some people who move are disadvantaged—due to lack of education, prejudice against ethnic minorities and linguistic differences—and therefore need targeted support programmes. Support could be provided in areas ranging from job search to lan- guage training. Access to social assistance and other entitlements should be ensured. Above all, it is vital to ensure that basic health care and education needs are met. India has examples of NGO-run children’s hostels to help children of migrants access accommodation, schooling and extra classes to catch up.

Redistribute tax revenues. Intergovernmental fiscal arrangements should ensure the redistri- bution of revenues so that poorer localities, where internal migrants often live, do not bear a disproportionate burden in providing ade- quate local public services. The same principles as apply to fiscal redistribution to account for the location of international migrants also apply here.

Enhance responsiveness. This may sound obvi- ous and should by now go without saying, but it is vital to build the capacity of local government

and programmes to respond to people’s needs. Inclusive and accountable local government can play a central role not only in service provision but also in averting and alleviating social ten- sions. Proactive urban planning, rather than de- nial, is needed to avoid the social and economic marginalization of migrants.

The Mi l lennium Development Goa ls (MDGs) call for action plans to create ‘Cities without Slums’ to, inter alia, improve sanitation and secure land tenure. However, progress has been slow: according to the most recent global MDG report, more than a third of the world’s urban population lives in slum conditions, ris- ing to over 60 percent in sub-Saharan Africa.50

Governments sometimes respond to con- cerns about slums by seeking to curb inflows of migrants to cities, as revealed by the review of PR Ss presented in chapter 4. However, a more constructive policy approach would be to meet the needs of a growing and shifting population by addressing the serious water and sanitation challenges that tend to prevail in slum areas. With proactive planning and sufficient resources, it is possible to ensure that growing cities can provide decent living condi- tions. Some cities, recognizing the importance of sustainable urban development, have come up with innovative solutions for improving the lives of city dwellers. Singapore’s experi- ence with urban renewal is widely cited as a best practice example: virtually all of its squat- ter settlements were replaced with high-rise public housing, complemented by expanded public transport and improved environmental management. A more recent example comes from A lexandria, Eg ypt, where participatory approaches have been used to develop me- dium- and long-term plans for economic de- velopment, urban upgrading of slum areas and environmental regeneration.51

Last but not least, many rural migrants de- scribe being pushed rather than pulled to urban areas because of inadequate public facilities in their place of origin. The universal provision of services and infrastructure should extend to places experiencing net out-migration. This will provide opportunities for people to develop the skills to be productive and to compete for jobs in their place of origin, while also preparing them for jobs elsewhere if they so choose.

Inclusive and accountable local government can play a central role not only in service provision but also in averting and alleviating social tensions

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development5

5.1.6 Making mobility an integral part of national development strategies A central theme of the 2009 Global Forum on Migration and Development, hosted by Greece, is the integration of migration into national development strategies. This raises the broader question of the role of mobility in strategies for improving human development. Our analysis of PRSs since 2000 helped to identify current policy attitudes and constraints, while recog- nizing that migration has played a major role in national visions of development at different moments and periods in history.

The links between mobility and develop- ment are complex, in large part because mobility is best seen as a component of human develop- ment rather than an isolated cause or effect of it. The relationship is further complicated by the fact that, in general, the largest devel- opmental gains from mobility accrue to those who go abroad—and are thus beyond the realm of the territorial and place-focused approaches that tend to dominate policy thinking.

Migration can be a vital strateg y for house- holds and families seeking to diversify and im- prove their livelihoods, especially in developing countries. Flows of money have the potential to improve well-being , stimulate economic growth and reduce poverty, directly and indi- rectly. However, migration, and remittances in particular, cannot compensate for an institu- tional environment that hinders economic and social development more generally. A critical point that emerges from experience is the im- portance of national economic conditions and strong public-sector institutions in enabling the broader benefits of mobility to be reaped.

We have seen that the mobility choices of the poor are often constrained. This can arise from underlying inequalities in their skills, but also from policy and institutional barri- ers. Needed now is country-specific identifica- tion of the constraints surrounding people’s choices, using quantitative and qualitative data and analysis. Improvements in data, alongside such recent initiatives as the development of migration profiles (supported by the European Commission and other partners), will be cru- cial to this effort. This would highlight barri- ers and inform attempts to improve national strategies.

Some development strategies—8 of the 84 PRSs prepared between 2000 and 200852—raise concerns about the exit of graduates. There is broad agreement that coercive policies to limit exit, as well as being contrary to international law, are not the right way to proceed, for both ethical and eco- nomic reasons.53 However, there is less agreement as to what alternative policies should look like. Box 5.5 looks at the merits of different options.

Finally, while this topic is beyond the focus of this report, we underline the importance of sus- tained efforts to promote human development at home.54 A comprehensive investigation of the sources of human development success and fail- ure and its implications for national development strategies will be a major theme of the next HDR, which marks the 20th anniversary of the global report.

5.2 The political feasibility of reform Against a background of popular scepticism about migration, a critical issue is the political feasibility of our proposals. This section argues that reform is possible, but only if steps are taken to address the concerns of local people, so that they no longer view immigration as a threat, either to themselves individually or to their society.

While the evidence on mobility points to significant gains for movers and, in many cases, benefits also for destination and origin coun- tries, any discussion of policy must recognize that in many destination countries, both de- veloped and developing, attitudes among the local population towards migration are at best mildly permissive and often quite negative. An array of opinion polls and other surveys suggest that residents see controls on immigration as essential and most would prefer to see existing rules on entry tightened rather than relaxed. Interestingly, however, attitudes to migration appear to be more positive in countries where the migrant population share in 1995 was large and where rates of increase over the past decade have been high.55 In terms of the treatment of migrants, the picture is more positive, as people tend to support equitable treatment of migrants already within their borders.

We begin with the vexed issue of liberalizing entry. The evidence suggests that opposition to liberalization is widespread, but the picture is

Migration can be a vital strategy for households and families seeking to diversify and improve their livelihoods

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5HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

not as monochrome as it initially appears. There are four main reasons why this is so.

First, as mentioned in chapter 4, many peo- ple are willing to accept immigration if jobs are available. Our proposal links future liberaliza- tion to the demand for labour, such that inflows of migrants will respond to vacancy levels. This alleviates the risk that migrants will substitute for or undercut local workers. Indeed, conditions of this kind are already widely applied by govern- ments, particularly in the developed economies,

to the entry of skilled migrants. Our proposal is that this approach be extended to low-skilled workers, with an explicit link to the state of the national labour market, and sectoral needs.

Second, our focus on improving the trans- parency and efficiency of the pathways to per- manence for migrants can help address the persistent impression, shared by many local people, that a significant part of cross-border migration is irregular or illegal. Certainly, in the United States the size of the irregular migrant

Box 5.5 When skilled people emigrate: some policy options

Taxing citizens abroad—sometimes termed a Bhagwati tax—has

been a longstanding proposal and is an established feature of the

United States tax system. It can be justified by the notion that citizen-

ship implies responsibilities, including the payment of tax, especially

by the better off. If entry barriers create a shortage of skilled labour

in destination countries and hence higher incomes for those who do

manage to move, taxing these rents is non-distortionary and would

not affect the global allocation of labour.

However, there are several arguments against imposing a sur-

charge on nationals abroad, who may already be paying tax to their

new host countries. First, implementation would either be on a vol-

untary basis or through bilateral tax agreements. But people do not

like paying taxes—and there is no consensus among governments

as to the desirability of migrant taxation, largely because it is admin-

istratively costly. Second, while some emigrants will have benefited

from attending a public university at home, others will have been

educated abroad or privately. Third, through remittances, investment

and other mechanisms, migrants often generate substantial benefits

back home. Taxation could discourage these flows and persuade em-

igrants to relinquish their citizenship in favour of their new homeland.

Hence implementation of such taxes has been very limited. The

Philippines tried, but experience was very mixed and the approach

was shelved nearly a decade ago. Today most governments, includ-

ing the Philippines, grant tax holidays to emigrants.

An alternative way to compensate for skill losses could be direct

transfers between governments. Whether self-standing or part of an

official development aid package, these have the advantage of sim-

plicity and relatively low transaction costs. However, skill loss is hard

to measure. And such transfers would not address the underlying is-

sues that stimulated exit in the first place, such as low-quality educa-

tional and health services and/or thin markets for skilled individuals.

Aid is largely fungible, as many studies have shown, so even aid

that is earmarked to support the higher education system mostly sup-

ports whatever the government is spending money on.

There may still be a case for policy to address skilled emigration

in those sectors, such as health and education, where there are po-

tentially large divergences between private and public benefits and

costs. Which policy approach has merit depends on local circum-

stances. For example:

• Targeted incentives in the form of wage supplements for public-

sector workers. Such an approach would have to be carefully

calibrated, given its possible effects on labour supply. A major

constraint here is that the wage differentials are often too great

to lie within the fiscal capacity of poor governments.

• Training tailored to skills that are useful in origin countries but less

tradable across borders. For example, while an international mar-

ket for doctors exists, training in paramedic skills may promote

better retention of skilled people as well as being more relevant

to local health care needs.

• Reform of education financing. This would allow private-sector

provision so that people seeking training as a way of moving

abroad do not rely on public funding. The Philippines has been

taking this route for training its nurses.

• Investment in alternative technologies. Distance services, dis-

pensed by cell-phone, internet telephony or websites, can allow

skills that are in short supply to benefit larger numbers of people.

• Targeted development assistance. Where loss of talent is as-

sociated with lack of innovation and investment—for example,

in agriculture—development assistance could prioritize regional

and national research institutions.

Providing incentives for skilled migrants to return has also been

tried, but experience has been mixed and it is not clear that this is

the best use of scarce public funds. Effectiveness depends partly

on the strength of the home institution to which the migrant would

return but also, and perhaps more importantly, on the performance

and prospects of the whole country. Evidence suggests that returns

occur anyway when countries offer sufficiently attractive opportuni-

ties. China, India and Mauritius are recent cases in point.

Sources: Clemens (2009b), Bhagwati (1979 ), Clemens (2009a), Pomp (1989 ) and World Bank (1998).

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development5

labour force is a major political issue, on which a policy consensus has yet to be reached. Irregular migration is also prominent in other destina- tion countries, both developed and developing. Interestingly, recent data suggest that there is considerable support in developed countries for permanent migration, with over 60 percent of respondents feeling that legal migrants should be given an opportunity to stay permanently (figure 5.2).

To translate this support into action will re- quire the design of policies for legal migration that are explicitly linked to job availability—and the marketing of this concept to the public so as to build on existing levels of support. Parallel measures to address the problem of irregular mi- gration will also need to be designed and imple- mented, so that the policy vacuum in this area is no longer a source of concern to the public. Large-scale irregular migration, although often convenient for employers and skirted around by policy makers, tends not only to have ad- verse consequences for migrants themselves (as documented in chapter 3) but also to weaken the acceptability of—and hence the overall case for—further liberalization of entry rules.

Sustainable solutions would have to include in- centives for employers to hire regular migrants, as well as incentives for migrants to prefer regu- lar status.

Third, some of the resistance to migration is shaped by popular misperceptions of its conse- quences. Many believe, for example, that immi- grants have a negative impact on the earnings of existing residents or that they are responsible for higher crime levels. These concerns again tend to be more pronounced in relation to irregular migrants, not least because their status is asso- ciated with an erosion of the rule of law. There are several broad approaches to these issues that have promise. Public information campaigns and awareness-raising activities are vital. Because migration is a contentious issue, information is often used selectively at present, to support the arguments of specific interest groups. While this is a natural and usually desirable feature of democratic discussion, it can come at the cost of objectivity and factual understanding. For ex- ample, a recent review of 20 European countries found that, in every case, the perceived number of immigrants greatly exceeded the actual num- ber, often by a factor of two or more.56

To address such vast gaps between percep- tions and reality, there is a need to provide the public with more impartial sources of informa- tion and analysis on the scale, scope and con- sequences of migration. A recurring feature of the migration debate is the pervasive mistrust of official statistics and interpretation. Because migration is so vexed a policy issue, more atten- tion needs to be paid to informing public debate on it in ways that are recognized and respected for their objectivity and reliability. Governments can benefit significantly from technical ad- vice given by expert bodies, such as the United Kingdom’s Migration Advisory Committee. These should be deliberately kept at arm’s length from the administration, so that they are seen as impartial.

Fourth, migration policy is normally formed through the complex interaction of a multi- tude of players, who form different interest groups and belong to different political par- ties. Organized groups can and do mobilize to bring about reform, often forming coalitions to pursue change in areas where their interests coincide.57 For example, employer groups have

Figure 5.2 Support for opportunity to stay permanently Preferences for temporary versus permanent migration, 2008

Source: Transatlantic Trends (2008).

| | | | | | 0 20 40 60 80 100

“Do you think immigrants should be:”

Allowed the opportunity to stay permanently

Only admitted temporarily, then required to return to their country of origin

Total

United States

Europe

of which

France

Germany

United Kingdom

Italy

Netherlands

Poland

Percent of respondents (%)

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5HUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

often been in the vanguard of calls for changes in entry rules in response to labour and/or skill shortages. Destination countries should decide on the design of migration policies and target numbers of migrants through political processes that permit public debate and the balancing of different interests. Further, what may be feasible at the national level needs to be discussed and debated locally, and the design further adapted to meet local constraints. Partly out of fear that debate over migration will take on racist over- tones, discussion of migration among main- stream political parties and organizations has often been more muted than might have been expected. While the reasons for caution are laud- able, there is a danger that self-censorship will be counter-productive.

How migrants are treated is a further area of policy in which reform may turn out to be easier than at first expected. Equitable treatment of migrants not only accords with basic notions of fairness but can also bring instrumental benefits for destination communities, associated with cultural diversity, higher rates of innovation and other aspects explored in chapter 4. Indeed, the available evidence suggests that people are generally quite tolerant of minorities and have a positive view of ethnic diversity. These attitudes suggest that there are opportunities for building a broad consensus around the better treatment of migrants.

The protection of migrants’ rights is in- creasingly in the interest of the major destina- tion countries that have large numbers of their own nationals working abroad.58 By 2005, more than 80 countries had significant shares of their populations—in excess of 10 percent—as either immigrants or emigrants. For these countries, observance of the rights of migrants is obviously an important policy objective. This suggests that bilateral or regional arrangements that enable reciprocity could have an important role to play in enacting reforms in a coordinated manner.

While there is clear scope for improving the quality of public debates and of resulting poli- cies, our proposals also recognize that there are very real and important choices and trade-offs to be made. In particular, our proposals have been designed in such a way as to ensure that the gains from further liberalization can be used in part to offset the losses suffered by particular groups

and individuals. Further, while the fiscal costs of migration are not generally significant (as shown in chapter 3), there may be a political case for measures that help improve the perception of burden sharing. For example, Canada has had administrative fees in place for over a decade; other countries, such as the United Kingdom, have followed this approach.

Moreover, the design of policy has to address the potential costs associated with migration. The suggested design of the reform package al- ready ensures that the number of entrants is re- sponsive to labour demand, and helps assure that migrants have regular status. Further measures could include compensation for communities and localities that bear a disproportionate share of the costs of migration in terms of providing ac- cess to public services and welfare benefits. This will help to dispel resentments against migrants among specific groups and reduce the support for extremist political parties in areas where im- migration is a political issue. An example of this can be found in the case of financial transfers to schools with high migrant pupil numbers, a mea- sure taken in a number of developed countries.

Another important measure to minimize disadvantages to local residents lies in the ob- servance of national and local labour standards. This is a core concern of unions and also of the public, whose distress at the exploitation and abuse of migrants is commendable and a clear sign that progressive reform will prove accept- able. Contemporary examples of union involve- ment in scheme design and implementation include Barbados, New Zealand and Sweden, which have thereby improved the design and acceptability of their programmes.

Lastly, it should go without saying (but often does not) that participation in decision- making increases the acceptance of reform. This is perhaps the most important measure that governments can take to ensure that changes to migration policy are negotiated with and agreed by different stakeholder groups. The Netherlands is an example where the govern- ment has undertaken regular consultations with migrant organizations. Similarly, in New Zealand, ‘Kick-Start Forums’ have success- fully been used to bring together stakeholders to resolve problems in the Recognised Seasonal Employment Scheme.59

Equitable treatment of migrants not only accords with basic notions of fairness but can also bring instrumental benefits for destination communities

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development5

5.3 Conclusions We began this report by pointing to the extraor- dinarily unequal global distribution of oppor- tunities and how this is a major driver of the movement of people. Our main message is that mobility has the potential to enhance human de- velopment—among movers, stayers and the ma- jority of those in destination societies. However, processes and outcomes can be adverse, some- times highly so, and there is therefore scope for significant improvements in policies and institu- tions at the national, regional and international levels. Our core package calls for a bold vision and identifies an ambitious long-term agenda for capturing the large unrealized gains to human development from current and future mobility.

Existing international forums—most no- tably the Global Forum on Migration and Development—provide valuable opportuni- ties to review challenges and share experiences. Consultations at this level need to be matched by action at other levels. Even on a unilateral basis, governments can take measures to im- prove outcomes for both international and in- ternal movers. Most of the recommendations

we have made are not conditional on new in- ternational agreements. The key reforms with respect to the treatment of migrants and the improvement of destination community out- comes are entirely within the jurisdiction of national governments. In some cases actions are needed at subnational levels—for example to ensure access to basic ser vices. Unilateral action needs to be accompanied by progress in bilateral and regional arrangements. Many governments, both at origin and destination, as well as countries of transit, have signed bilateral agreements. These are typically used to set quo- tas, establish procedures and define minimum standards. Regional agreements can play an es- pecially important role, especially in establishing free movement corridors.

Our suggested reforms to government poli- cies and institutions could bring about sizeable human development gains from mobility at home and abroad. Advancing this agenda will require committed leadership, extensive consul- tation with stakeholders and bold campaigning for changes in public opinion to move the de- bates and policy discussions forward.

Mobility has the potential to enhance human development—among movers, stayers and the majority of those in destination societies

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NotesHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

113

NotesHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Chapter 1

1 OECD (2009a).

2 Few developing countries have data on flows of

migrants. However, the sum of the stock of

internal migrants and international migrants in

developing countries is considerably larger than

the stock of migrants in developed countries (see

section 2.1).

3 See the Statistical Tables for life expectancy and

income, and Barro and Lee (2001) for years of

education.

4 For a discussion of the reasons behind the poor living

conditions in the Lower Rio Grande Valley, see

Betts and Slottje (1994). Anderson and Gerber

(2007b) provide an overview of living conditions

along both sides of the border and their evolution

over time. Comprehensive data and analysis on

human development within the United States can

be found in Burd-Sharps, Lewis, and Martins

(2008).

5 The number of Chinese who changed their district

of residence over the period 1979–2003 is

estimated to exceed 250 million ( Lu and Wang,

2006). Inter-provincial flows (corresponding

to the definition of internal migration we use in

the report—see box 1.3) accounted for about a

quarter of these movements.

6 Clemens, Montenegro, and Pritchett (2008).

7 Clemens, Montenegro, and Pritchett (2008), Ortega

(2009 ).

8 UNDP (2008d).

9 The practice of compulsory testing of immigrants

is not unique to the Arab states. For example,

the United States severely restricts the entry of

HIV-positive travellers and bars HIV-positive non-

citizens from obtaining permanent residence. See

U.S.Citizenship and Immigration Services (2008).

10 A search for scholarly articles on international

migration using the Social Sciences Citation Index

yielded only 1,441 articles—less than a fifth of

those dealing with international trade (7,467)

and less than one twentieth of those dealing with

inflation (30,227).

11 Koslowski (2008).

12 IOM (2008b), World Bank (2006b), ILO (2004),

and GFMD (2008).

13 Aliran (2007).

14 Branca (2005).

15 In particular, questioning of the distinction between

voluntary and involuntary migration led to terms

like ‘mixed migration’ and the ‘migration–asylum

nexus’. The use of some of these terms is not

uncontroversial, as recognition of economic

motives among asylum seekers can have

implications for admissions and treatment.

See Richmond (1994), van Hear (2003), van

Hear, Brubaker, and Bessa (2009 ), and UNHCR

(2001).

16 Bakewell (2008) shows that the return to Angola

of many of these migrants since the end of the

civil war in 2002 coincided with the attempt by

many Zambians to move to Angola in order to

participate in expected improvements in social

and economic conditions. This suggests that

economic motives were at least as important

among expatriate Angolans as the desire to return

to their country of origin.

17 Van Hear, Brubaker, and Bessa (2009 ) and Van

Engeland and Monsutti (2005).

18 An interesting example of migration flows being

disconnected from economic growth differentials

was the 1985/86 recession, when Malaysian

per capita GDP shrank by 5.4 percent while

the Indonesian economy was unaffected, yet

migration flows between the two countries

continued unabated. See Hugo (1993).

19 This does not mean that migrants in Malaysia are

free from discrimination. See Hugo (1993).

20 Attempts to develop a conceptual framework for

understanding migration go back at least to

Ravenstein (1885), who proposed a set of ‘laws

of migration’ and emphasized the development of

cities as ‘poles of attraction’. Within neoclassical

economic theory, initial expositions include Lewis

(1954), and Harris and Todaro (1970 ), while

the tradition of Marxist studies was initiated by

discussion of the ‘agrarian question’ by Kautsky

(1899 ).

21 Stark and Bloom (1985), Stark (1991).

22 Mesnard (2004), Yang (2006).

23 Massey (1988).

24 Gidwani and Sivaramakrishnan (2003).

25 See Nussbaum (1993) on the origins of this idea.

26 Huan-Chang (1911).

27 Plato (2009 ).

28 Nussbaum (2000 ).

29 This definition is consistent with more conventional

usage. For example, the Oxford English Dictionary

defines mobility as “the ability to move or to be

moved; capacity for movement or change of

place; ... ” (Oxford University Press, 2009 ). The

idea of labour mobility as indicating the absence

of restrictions on movement, as distinguished

from the action of movement itself, also has a

long tradition in international economics; see

Mundell (1968).

30 Sainath (2004).

31 Sen (2006), p.4.

32 UNDP (1990 ), p.89.

33 UNDP (1997).

34 UNDP (2004b).

35 See, for example, the idea of using international

transfers to reduce emigration pressures in

poor countries, which was featured in the 1994

Human Development Report, UNDP (1994).

Chapter 2

1. Bell and Muhidin (2009 ).

2. Less conservative definitions raise the estimates

significantly. For example, while our estimate

of 42 million internal migrants (4 percent of the

population) in India includes all those who have

moved between states, there are 307 million

people (28 percent of the population) who live

in a different city from where they were born

( Deshingkar and Akter, 2009 ). Montenegro

and Hirn (2008) use an intermediate zonal

denomination and calculate an average internal

migration rate of 19.4 percent for 34 developing

countries. Seasonal migration is excluded

from both of these estimates. To the best of

our knowledge, no comparable cross-country

estimates of seasonal migration exist, although

country-specific research suggests that it is often

high.

3. Immigrants, for example, are defined on the basis

of place of birth in 177 countries but on the basis

of citizenship in 42 countries. A few countries

(including China) do not have information on

either their foreign-born or foreign citizens, which

means that these countries must be dropped

from the sample or that their immigrant share

must be estimated. The UN (2009e) estimates

used throughout this report do the latter.

4. Migration DRC (2007).

5. HDR team calculations based on Migration DRC

(2007) and CEPII (2006).

6. The destination country HDI is calculated

as the weighted average of the HDI of all

destination countries, where the weights are

the shares in the population of migrants. The

magnitude presented in figure 2.2 is only a rough

approximation of the human development gains

from international migration, because the human

development of migrants may be different from

the average of populations at both home and

destination countries, and because the HDI itself

is only a partial measure of human development.

Box 1.1 and chapter 3 provide a more detailed

discussion of the methodological problems

inherent in estimating individual gains from

migration.

7. Ortega (2009 ).

8. Cummins, Letouze, Purser, and Rodríguez

(2009 ). These authors use the Migration DRC

(2007) database on bilateral stocks of migrants

to develop the first gravity (bilateral flows) model

covering both OECD and non-OECD countries.

Other findings include large and statistically

significant effects of characteristics such as land

area, population structures, a common border

and geographic distance, as well as former

colonial ties and having a common language.

9. Martin (1993) observed that development in

poor countries typically went hand in hand

with increasing rather than decreasing rates of

emigration and hypothesized that there may be

a non-linear inverted-U relationship between

migration and development. The theory has since

been discussed by several authors including

Martin and Taylor (1996), Massey (various) and

Hatton and Williamson (various). The first cross-

country test of the theory using data on bilateral

flows was carried out by de Haas (2009 ).

10. A similar figure was first presented by de Haas

(2009 ).

11. Cummins, Letouze, Purser, and Rodríguez

(2009 ).

12. Mobarak, Shyamal, and Gharad (2009 ).

Notes

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentNotes

13. HDR team analysis based on UN (2009e),

Migration DRC (2007) and CEPII (2006). These

regressions control for a linear and quadratic

term in HDI as well as for linear terms and a

multiplicative interaction of size and remoteness.

Remoteness is measured by the average

distance to OECD countries, as calculated by

the CEPII (2006). Size is measured by the log of

population.

14. For example, female migrants accounted for less

than a third of immigrants into the United States

200 years ago (Hatton and Williamson (2005),

p.33).

15. See Ramírez, Domínguez, and Morais (2005) for

a comprehensive discussion of the key issues.

16. Nava (2006).

17. Rosas (2007).

18. OECD (2008b).

19. Newland (2009 ) provides a comprehensive

survey of the key issues involved in circular

migration.

20. Sabates-Wheeler (2009 ).

21. OECD (2008b).

22. Passel and Cohn (2008).

23. Vogel and Kovacheva (2009 ).

24. Docquier and Marfouk (2004). If we use a

broader definition of the labour force and count

as economically active all individuals over the age

of 15, we find that 24 percent of immigrants to

the OECD have a tertiary degree, as opposed to 5

percent of the population of non-OECD countries.

25. OECD (2009a).

26. Miguel and Hamory (2009 ).

27. Sun and Fan (2009 ).

28. Background research carried out by the HDR

team in collaboration with the World Bank. This

profile of internal migrants also found that those

with lower levels of formal education were more

likely to migrate in the upper middle-income

countries of Latin America. This result suggests

that when the average level of income of a

country is sufficiently high, even relatively poor

people are able to move.

29. King, Skeldon, and Vullnetari (2008).

30. Skeldon (2006) on India and Pakistan, and King,

Skeldon, and Vullnetari (2008) on Italy, Korea

and Japan.

31. Clemens (2009b).

32. See Jacobs (1970 ) and Glaeser, Kallal,

Scheinkman, and Shleifer (1992). For a

comprehensive discussion of the relationship

between agglomeration economies, economic

development and flows of international and

internal migration; see World Bank (2009e).

33. These guidelines are outlined in OECD (2008b).

34. Altman and Horn (1991).

35. Sanjek (2003).

36. In 1907 alone, almost 1.3 million people or

1.5 percent of the population were granted

permanent resident status in the United States; a

century later, in 2007, both the absolute number

and fraction were lower: 1.05 million and only 0.3

percent of the population ( DHS, 2007). Hatton

and Williamson (2005) estimated for a sample of

countries—Denmark, France, Germany, Norway,

Sweden, United Kingdom and six ‘New World’

countries (Argentina, Australia, Brazil, Canada,

New Zealand and United States) —that the stock

of foreign-born migrants in 1910–1911 was

around 23 million, or about 8 percent of their

population.

37. Linz et al. (2007).

38. van Lerberghe and Schoors (1995).

39. Rahaei (2009 ).

40. Bellwood (2005).

41. Williamson (1990 ).

42. Lucas (2004); 2008 figure from OECD (2008a).

43. By the late 19th century, the cost of steerage

passage from the United Kingdom to the United

States had fallen to one tenth of average annual

income, making the trip feasible for many more

people. However, the costs from elsewhere

were much higher: for example, from China to

California in 1880, it cost approximately six times

Chinese per capita income. See Hatton and

Williamson (2005) and Galenson (1984).

44. Taylor and Williamson (1997) and Hatton and

Williamson (2005). For the Ireland–Great Britain

comparison the period is 1852–1913, while for

Sweden–United States it is 1856–1913.

45. Magee and Thompson (2006) and Baines

(1985).

46. Gould (1980 ).

47. Cinel (1991), p.98.

48. Nugent and Saddi (2002).

49. Foner (2002).

50. For example, Canada’s open policy towards

immigration following confederation was seen

as a pillar of the national policy to generate

economic prosperity through population growth.

See Kelley and Trebilcock (1998).

51. See, e.g. Ignatiev (1995).

52. See Timmer and Williamson (1998), who find

evidence of tightening between 1860 and 1930

in Argentina, Australia, Brazil, Canada and the

United States.

53. A report by the ILO counted 33 million foreign

nationals in 1910, equivalent to 2.5 percent of

the population covered by the study (which was

76 percent of the world population at the time).

In contrast to modern statistics, it counted those

with a different nationality than their country

of residence as foreigners, thereby probably

underestimating the share of foreign-born people

( International Labour Office (1936), p. 37). It is

also important to note that, since the number of

nations has increased significantly during the

past century, the rate of international migration

could be expected to have increased even if no

genuine increases in movement has taken place.

54. Since 1960, world trade as a share of global GDP

has more than doubled, increasing at an average

rate of 2.2 percent a year.

55. García y Griego (1983).

56. Appleyard (2001).

57. The German restrictions appear to have started

before the oil shock but gained intensity after it.

See Martin (1994).

58. These percentages refer to the migrants in

countries that are developed according to the

most recent HDI (See box 1.3). We might expect

these patterns to be different if we instead

calculated the share of migrants in the countries

that were developed in 1960, but in fact the

share of migrants in the 17 most developed

countries in 1960 (covering 15 percent of the

world population, the same share covered by

developed countries today) was 6.2 percent, not

very different from our 5 percent figure.

59. Czechoslovakia and the Soviet Union were not the

only cases where new nations emerged during

this period. However, in background analysis

carried out for this report, we studied the patterns

of changes in the share of migrants that occurred

after reunifications or break-ups since 1960 and

in other cases (e.g. Germany, former Yugoslavia),

the changes in the migrant share were not large

enough to have a significant impact on aggregate

trends.

60. The exception is the United Kingdom, where

large shares of immigrants from developing

Commonwealth countries took place during the

1960s.

61. UN-HABITAT (2003).

62. UN (2008c) and UN-HABITAT (2003).

63. This divergence has not occurred for other

dimensions of human development, such as

health and education (school enrolment rates).

These dimensions are critical, although income

appears to have a larger impact on the propensity

to move (see Cummins, Letouze, Purser, and

Rodríguez, 2009 ).

64. Moreover, China was different from other

developing regions during the 1960s because

of the restrictions on exit, which also affect

comparisons of migration flows over time.

65. Since our exercise compares countries classified

according to their current HDI levels, it does

not take into account the convergence of some

fast-growing developing countries, which moved

into the top HDI category. Our method seems

better suited to understanding the growing

concentration of migrants in the subset of

countries that are developed today. Furthermore,

if we do the comparison for the group of countries

classed as developing in 1960, we get very

similar patterns (see endnote 58).

66. For a comprehensive survey of this literature see

UN (2006b). The debate on divergence is related

to the discussion on whether world inequality has

been increasing, although the latter depends also

on the evolution of inequality within countries.

67. Doganis (2002).

68. Department of Treasury and Finance (2002).

69. Facchini and Mayda (2009 ) find that, while

greater public opposition towards immigration is

associated with higher policy restrictions, there is

still a significant gap between the policies desired

by most voters and those that are adopted by

policy makers. See also Cornelius, Tsuda, Martin,

and Hollifield (2004).

70. Hanson (2007).

71. The assessment evaluated several dimensions

of migration policy, including admissions criteria,

integration policies, the treatment of authorized

migrants and the situation of irregular migrants.

The openness of each regime was assessed

through subjective evaluation by respondents as

well as according to a set of objective criteria,

such as the existence of numerical limits, entry

requirements and international agreements

115

NotesHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

on free movement. The developing countries

covered were Chile, China (internal mobility

only), Costa Rica, Côte d’Ivoire, Ecuador, Egypt,

India, Kazakhstan, Malaysia, Mexico, Morocco,

Russian Federation, Thailand and Turkey. The

developed countries were Australia, Canada,

France, Germany, Italy, Japan, Portugal, Republic

of Korea, Singapore, Spain, Sweden, United Arab

Emirates, United Kingdom and United States.

Further details of the assessment are provided in

Klugman and Pereira (2009 ).

72. Governments often differ in the criteria they use

to classify workers as skilled. In order to achieve

some degree of homogeneity across countries,

we classified as skilled all workers coming under

regimes requiring a university degree. When

the classification was based on occupation, we

tried to match the type of occupation with the

education level typically required to perform the

job. When there was no explicit distinction in visa

regimes based on education level or occupation,

we either made a distinction based on information

on the most common workers in each visa class,

or, in the case of clearly mixed flows, we treated

the regulation as applying to both high-skilled and

low-skilled workers.

73. Ruhs (2005) and Singapore Government Ministry

of Manpower (2009 ).

74. Ruhs (2002) and OECD (2008b).

75. This concept originated as a mechanism in Arab

countries’ legislation—which typically does not

recognize adoption—whereby adults pledged

to take care of orphaned or abandoned children.

See Global Legal Information Network (2009 ).

76. Longva (1997), pp. 20–21.

77. See, for example, Bahrain Center for Human

Rights (2008) and UNDP (2008d).

78. Under the new regulation, the Labor Ministry

will transfer the sponsorship of the workers from

previous government contractors to new ones and

the state will bear their iqama (residence permit)

and sponsorship transfer fees. See Thaindian

News (2009 ) and Arab News (2009 ).

79. Khaleej Times (2009 ).

80. Jasso and Rosenzweig (2009 ).

81. Hanson and Spilimbergo (2001).

82. Lawyers for Human Rights (2008).

83. Human Rights Watch (2007a).

84. Ruhs and Martin (2008) and Ruhs (2009 ).

85. See Cummins and Rodríguez (2009 ). These

authors also address potential issues of reverse

causation by using the predicted immigration

shares from a bilateral gravity model as an

exogenous source of cross-national variation.

Their results still point to a statistically

insignificant correlation between numbers and

rights; indeed, in most of their instrumental

variable estimates the correlation turns positive,

shedding further doubts on the numbers versus

rights hypothesis.

86 Muñoz de Bustillo and Antón (2009 ).

87 Adepoju (2005).

88 Freedom House (2009 ).

89 United States Department of State (2009b),

Wang (2005), National Statistics Office (2006),

Ivakhnyuk (2009 ), and Anh (2005).

90. United States Department of State (2009d).

91. Kundu (2009 ).

92. McKenzie (2007).

93. Tirtosudarmo (2009 ).

94. On Cuba, see Human Rights Watch (2005a) and

Amnesty International (2009 ). On the Democratic

People’s Republic of Korea, see Freedom House

(2005). For other countries, see United States

Department of State (2009a), Immigration and

Refugee Board of Canada (2008) and IATA

(2006).

95. Human Rights Watch (2007b).

96. United States Department of State (2009a) and

McKenzie (2007).

97. IMF (2009a).

98. See IMF (2009c), Consensus Economics

(2009a), Consensus Economics (2009c),

Consensus Economics (2009d).

99. Recessions in developed countries tend to last

two years, after which trend economic growth is

re-established: Chauvet and Yu (2006). However,

the mean duration and intensity of recessions

is much longer in developing countries. See

Hausmann, Rodríguez, and Wagner (2008).

100. See Perron (1989 ) and Perron and Wada (2005),

who find evidence of persistent effects of the oil

shock and the Great Depression on incomes.

101. OECD (2009b).

102. United States Bureau of Labor Statistics (2009 ).

103. INE (2009 ).

104. The correlation is statistically significant at

5 percent. The Asian Development Bank has

projected contractions in the key migrant

destinations of the region, ranging up to 5

percent in Singapore. In South Africa, home

to 1.2 million migrants, the EIU expects the

economy to contract by 0.8 percent in 2009,

and the economy of the United Arab Emirates

is projected to contract by 1.7 percent in 2009.

Business Monitor International (2009 ).

105. Betcherman and Islam (2001).

106. Dustmann, Glitz, and Vogel (2006).

107. OECD (2008a).

108. Taylor (2009 ).

109. Kalita (2009 ).

110. The Straits Times (2009 ) and Son (2009 ).

111. Local Government Association (2009 ).

112. Preston (2009 ).

113. Timmer and Williamson (1998).

114. de Haas (2009 ).

115. See Martin (2003) and Martin (2009a).

116. Skeldon (1999 ) and Castles and Vezzoli (2009 ).

There were deportations in order to demonstrate

support for local workers, but once governments

realized that locals were not interested in

migrants’ jobs, these restrictions were reversed.

117. See for example Rodrik (2009 ) and Castles and

Vezzoli (2009 ).

118. While all forecasts are inherently uncertain,

population projections tend to be quite accurate.

The UN has produced 12 different estimates

of the 2000 world population since 1950,

and all but one of these estimates were within

4 percentage points of the actual number

( Population Reference Bureau, 2001). One recent

study found average prediction errors of the

order of 2 percent even for age sub-groups of the

population.

119. However, these alternative solutions are in

themselves costly: technological innovation to

substitute for a globally abundant factor uses

up resources, and raising retirement ages or

contributions reduces leisure or consumption.

120 Barnett and Webber (2009 ).

121 IPCC (2007), chapter 9.

122 Anthoff, Nicholls, Richard, and Vafeidis (2009 ).

123 Revkin (2008).

124 Myers (2005) and Christian Aid (2007).

125 Barnett and Webber (2009 ).

126 Stark (1991).

127 Ezra and Kiros (2001).

128 Black et al. (2008).

129 Carvajal and Pereira (2009 ).

130 UNDP (2007a) and UNDP (2008e).

131 See Friedman (2005).

132 Steinbeck (1939 ). On the Great Dust Bowl

Migration see Worster (1979 ) and Gregory

(1989 ). For the landmark 1941 US Supreme

Court decision in the case of California vs.

Edwards see ACLU (2003).

Chapter 3

1. Clemens, Montenegro, and Pritchett (2008).

2. McKenzie, Gibson, and Stillman (2006).

3. Chiswick and Miller (1995).

4. Sciortino and Punpuing (2009 ).

5. Maksakova (2002).

6. Commander, Chanda, Kangasniemi, and Winters

(2008).

7. Clemens (2009b).

8. Harttgen and Klasen (2009 ). Migrants had

lower income in two countries (Guatemala and

Zambia) and there was no statistically significant

difference in one ( Viet Nam). See section 3.6.

9. Del Popolo, Oyarce, Ribotta, and Rodríguez

(2008).

10. Srivastava and Sasikumar (2003), Ellis and Harris

(2004) and ECLAC (2007).

11. See Deshingkar and Akter (2009 ) on India and

MOSWL, PTRC, and UNDP (2004) on Mongolia.

12. Ghosh (2009 ).

13. Gilbertson (1995).

14. Zhou and Logan (1989 ).

15. Cerrutti (2009 ).

16. UNDP (2008d).

17. Castles and Miller (1993) and ICFTU (2009 ).

18. Bursell (2007) and Bovenkerk, Gras, Ramsoedh,

Dankoor, and Havelaar (1995).

19. Clark and Drinkwater (2008) and Dustmann and

Fabbri (2005).

20. Iredale (2001).

21. Chiswick and Miller (1995).

22. Reitz (2005).

23. The social transfer programmes included in this

analysis are all forms of universal and social

insurance benefits, minus income and payroll

taxes and social assistance (including all forms

of targeted income-tested benefits). The poverty

line is defined as half the median income. See

Smeeding, Wing, and Robson (2009 ).

24. These estimates may over- or underestimate

the effect of transfers on poverty because the

endogenous response of labour supply decisions

to transfers is not factored in.

25. Martin (2005) and Kaur (2007).

116

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentNotes

26. UNICEF (2005a).

27. Koslowski (2009 ).

28. McKenzie (2007) and United States Department

of State (2006).

29. United States Department of State (2009a).

30. Agunias (2009 ) and Martin (2005).

31. Martin (2005).

32. Agunias (2009 ) and Martin (2005).

33. UNFPA (2006).

34. Ivakhnyuk (2009 ).

35. Martin (2009b).

36. Martin (2009b).

37. Gibson and McKenzie (2009 ).

38. The so-called ‘healthy migrant effect’ has been

well documented; see, for example, Fennelly

(2005).

39. Rossi (2008).

40. Jasso, Massey, Rosenzweig, and Smith (2004),

using the US Citizenship and Immigration

Service’s New Immigrant Survey.

41. Ortega (2009 ).

42. Brockerhoff (1990 ).

43. Brockerhoff (1995) and Harttgen and Klasen

(2009 ).

44. See Chiswick and Lee (2006), and Antecol and

Bedard (2005). Another factor clouding these

estimates is the possibility that ‘regression to

the mean’ may account for part of the apparent

deterioration in health. In particular, if not being

ill is an important condition enabling migration,

then those who migrate may include people who

are not inherently healthier but who nevertheless

have had the good luck not to fall ill. These people

will also be more likely to fall ill after migrating

than those whose lack of illness is due truly to

good health.

45. Garcia-Gomez (2007) on Catalonia, Spain;

Barros and Pereira (2009 ) on Portugal.

46. Stillman, McKenzie, and Gibson (2006), Steel,

Silove, Chey, Bauman, and Phan T. (2005) and

Nazroo (1997).

47. McKay, Macintyre, and Ellaway (2003).

48. Benach, Muntaner, and Santana (2007).

49. Whitehead, Hashim, and Iversen (2007).

50. Tiwari (2005).

51. Deshingkar and Akter (2009 ).

52. Some migrants gain access to services over

time. For example, in many countries, asylum

seekers who apply for refugee status often do

not have access unless and until their application

is successful. In other countries, Australia for

example, payment of limited income support is

available to some asylum seekers living in the

community who have reached a certain stage in

visa processing and meet other criteria (such as

passing a means test).

53. Carballo (2007) and Goncalves, Dias, Luck,

Fernandes, and Cabral (2003).

54. PICUM (2009 ).

55. Kaur (2007).

56. Landau and Wa Kabwe-Segatti (2009 ).

57. Hashim (2006) and Pilon (2003)

58. OECD (2008b).

59. Our commissioned research of HDI differences

between internal migrants and non-migrants

in 16 countries found that the educational

level of migrants was higher in 10 countries,

not significantly different in 4 and lower in 2

countries.

60. UNICEF (2008). Other studies find similar

returns. For a comprehensive review of the

evidence on early childhood interventions, see

Heckman (2006).

61. Clauss and Nauck (2009 ).

62. For example, Norwegian authorities are obliged to

inform refugee families about the importance and

availability of ECD within three months of arrival.

63. For further information on undocumented

migrants in Sweden, see PICUM (2009 ).

64. PICUM (2008a).

65. PICUM (2008a).

66. Landau and Wa Kabwe-Segatti (2009 ).

67. Rossi (2008).

68. Government of Azad Jammu and Kashmir (2003)

and Poverty Task Force (2003).

69. Poverty Task Force (2003).

70. The Programme for International Student

Assessment is a triennial survey of pupils aged

15 years.

71. OECD (2007). The Programme for International

Student Assessment study focuses on science

but also assesses reading and mathematics,

which yielded similar comparisons.

72. Australia, France, Germany, Italy, Netherlands,

Switzerland, United Kingdom and United States.

See Hernandez (2009 ).

73. Portes and Rumbaut (2001).

74. Karsten et al. (2006), Nordin (2006) and Szulkin

and Jonsson (2007).

75. Sen (1992).

76. Rawls (1971).

77. Hugo (2000 ).

78. Petros (2006), Zambrano and Kattya (2005) and

Mills (1997).

79. Içduygu (2009 ).

80. Piper (2005).

81. Ghosh (2009 ) and Kabeer (2000 ).

82. Del Popolo, Oyarce, Ribotta, and Rodríguez

(2008).

83. Cerrutti (2009 ).

84. Uhlaner, Cain, and Kiewiet (1989 ), Cho (1999 ),

Rosenstone and Hansen (1993), Wolfinger and

Rosenstone (1980 ) and Ramakrishnan and

Espenshade (2001).

85. A standard deviation increase of 1 in destination

country democracy, as measured by the Polity

IV index, leads to an 11 log point increase

in immigration, significant at 1 percent. See

Cummins, Letouze, Purser, and Rodríguez

(2009 ).

86. Landau (2005).

87. Ministry of Social Welfare and Labour, United

Nations Population Fund, and Mongolian

Population and Development Association (2005).

88. Crush and Ramachandran (2009 ).

89. Misago, Landau, and Monson (2009 ).

90. Pettigrew and Tropp (2005) and Pettigrew

(1998).

91. Human Security Centre (2005) and Newman and

van Selm (2003).

92. UNHCR (2008). There is no reliable estimate of

the share of internally displaced people living in

camps, but 70 percent are estimated to live with

host-country relatives, families and communities.

93. IDMC (2008).

94. Bakewell and de Haas (2007).

95. van Hear, Brubaker, and Bessa (2009 ) and Crisp

(2006).

96. Camps located in Bangladesh, Kenya, Nepal,

Tanzania, Thailand and Uganda: de Bruijn

(2009 ).

97. ECOSOC (1998). Presented to the UN

Commission on Human Rights by the

Representative of the Secretary General in 1998,

the Guiding Principles on Internal Displacement

set the basic standards and norms to guide

governments, international organizations and

all other relevant actors in providing assistance

and protection to internally displaced persons

in internal conflict situations, natural disasters

and other situations of forced displacement

worldwide.

98. Estimates in this paragraph come from IDMC

(2008).

99. IDMC (2008) lists Azerbaijan, Bosnia and

Herzegovina, Côte d’Ivoire, Croatia, Georgia,

Lebanon, Liberia, Turkey and Uganda in this

category. Noteworthy efforts include financial

compensation as part of Turkey’s return

programme and specific efforts towards property

restitution across the Balkans, which had largely

been completed by 2007.

100. Ghosh (2009 ).

101. UNRWA (2008).

102. Gibney (2009) and Hatton and Williamson (2005).

In the United Kingdom, for example, only 19 out

of every 100 people who applied for asylum in

2007 were recognized as refugees and had their

applications granted, while another nine who applied

for asylum but did not qualify were given permission

to stay for humanitarian or other reasons.

103. UNHCR (2008).

104. UNRWA-ECOSOC (2008).

105. UNHCR (2002).

106. See, for example, UNECA (2005).

107. Robinson (2003).

108. Bartolome, de Wet, Mander, and Nagraj (2000 ),

p. 7.

109. See IIED and WBCSD (2003), Global IDP Project

and Norwegian Refugee Council (2005) and

Survival International (2007).

110. La Rovere and Mendes (1999 ).

111. For World Bank, CIEL (2009 ); there are other

examples: for ADB, see Asian Development Bank

(2009 ); for IDB, see IDB (2009 ).

112. UNDP (2007b).

113. UNODC (2009 ).

114. Clert, Gomart, Aleksic, and Otel (2005).

115. See, for example, Carling (2006).

116. USAID (2007).

117. Laczko and Danailova-Trainor (2009 ) .

118. Koser (2008).

119. Ortega (2009 ).

120. Harttgen and Klasen (2009 ).

121. These numbers are taken from the 2005/2006

World Values Survey. The survey records whether

at least one parent is a migrant, which we use

as a proxy for migrant status. These particular

results are consistent with data from the 1995

World Values Survey, which show whether or not

the respondent is foreign-born.

117

NotesHUMAN DEVELOPMENT REPORT 2009Overcoming barriers: Human mobility and development

Chapter 4

1. Sarreal (2002).

2. Yang (2009 ).

3. UNDP (2008b).

4. For a list of least and most costly international

corridors, see World Bank (2009c).

5. Stark (1991).

6. Savage and Harvey (2007).

7. Yang (2008a).

8. Yang and Choi (2007).

9. Halliday (2006).

10. Ratha and Mohapatra (2009a). This is the

‘base case’ scenario, which assumes that new

migration flows to major destination countries

will be zero, implying that the stock of existing

migrants will remain unchanged.

11. Fajnzylber and Lopez (2007).

12. Schiff (1994).

13. Kapur (2004).

14. Zhu and Luo (2008).

15. Lucas and Chappell (2009 ).

16. Deshingkar and Akter (2009 ).

17. Rayhan and Grote (2007).

18. Beegle, De Weerdt, and Dercon (2008).

19. Deb and Seck (2009 ).

20. Murison (2005). For example, Bangladeshi

women working in the Middle East remit up to

72 percent of their earnings on average, and

Colombian women working in Spain remit more

than men ( 68 versus 54 percent).

21. Docquier, Rapoport, and Shen (2003) and Stark,

Taylor, and Yitzhaki (1986).

22. Adelman and Taylor (1988) and Durand, Kandel,

Emilio, and Massey (1996).

23. Yang (2009 ).

24. Massey et al. (1998), Taylor et al. (1996) and

Berriane (1997).

25. Behrman et al. (2008).

26. Adelman and Taylor (1988), Durand, Kandel,

Emilio, and Massey (1996) and Stark (1980 )

(1980 ).

27. Adams Jr. (2005), Cox Edwards and Ureta

(2003) and Yang (2008b).

28. Adams Jr. (2005).

29. Mansuri (2006).

30. Deb and Seck (2009 ).

31. Fan and Stark (2007) and Stark, Helmenstein,

and Prskawetz (1997).

32. Chand and Clemens (2008).

33. Castles and Delgado Wise (2008).

34. McKenzie and Rapoport (2006).

35. Ha, Yi, and Zhang (2009a).

36. Frank and Hummer (2002).

37. Hildebrandt, McKenzie, Esquivel, and

Schargrodsky (2005).

38. Wilson (2003).

39. Cerrutti (2009 ).

40. Bowlby (1982), Cortes (2008), Smith, Lalaonde,

and Johnson (2004) and Suarez-Orozco,

Todorova, and Louie (2002).

41. For a review of gender empowerment and

migration see Ghosh (2009 ).

42. King and Vullnetari (2006).

43. See Deshingkar and Grimm (2005).

44. Fargues (2006).

45. Beine, Docquier, and Schiff (2008).

46. Hampshire (2006) and King, Skeldon, and

Vullnetari (2008).

47. Cordova and Hiskey (2009 ). The countries

covered were Dominican Republic, El Salvador,

Guatemala, Honduras, Mexico and Nicaragua.

48. See the review of this literature in Clemens

(2009b).

49. Lipton (1980 ) and Rubenstein (1992).

50. Tirtosudarmo (2009 ).

51. World Bank (2009e), p. 165.

52. Docquier and Rapoport (2004) and Dumont,

Martin, and Spielvogel (2007).

53. An analogy can be drawn with the sharp decline

in the skills and qualifications of schoolteachers

in the United States over the past half century,

which is attributed to the fact that skilled

women now have a much broader range of

career choices available to them than teaching

(Corcoran, William, and Schwab, 2004).

54. Saxenian (2002).

55. Commander, Chanda, Kangasniemi, and Winters

(2008).

56. Saxenian (2006).

57. The World Bank, which has been closely tracking

flows, estimates that unrecorded flows would add

at least 50 percent to the total remittance figure.

58. Chami, Fullenkamp, and Jahjah (2005) and

Leon-Ledesma and Piracha (2004).

59. Eckstein (2004) and Ahoure (2008).

60. World Bank (2006b) and Kireyev (2006).

61. Buch, Kuckulenz, and Le Manchec (2002) and de

Haas and Plug (2006).

62. Taylor, Moran-Taylor and Ruiz (2006).

63. de Haas (2006).

64. Levitt (1998) and Levitt (2006).

65. Quirk (2008).

66. World Bank (2009a).

67. World Bank (2009a).

68. Massey, Arango, Hugo, Kouaouci, Pellegrino and

Taylor (1993) and Thomas-Hope (2009 ).

69. Adesina (2007).

70. Ali (2009 ).

71. Bakewell (2009 ).

72. Ba, Awumbila, Ndiaye, Kassibo, and Ba (2008).

73. Jonsson (2007).

74. Black, Natali and Skinner (2005).

75. If the incomes and consumption of those abroad

were included in these measures of inequality

the distribution would widen considerably, since

incomes abroad are so much higher.

76. Taylor, Mora, Adams, and Lopez-Feldman (2005)

for Mexico; Yang (2009 ) for Thailand.

77. Ha, Yi, and Zhang (2009b).

78. Goldring (2004) and Lacroix (2005).

79. Orozco and Rouse (2007) and Zamora (2007).

80. HDR team estimates based on figures cited in

Anonuevo and Anonuevo (2008).

81. Tabar (2009 ).

82. Spilimbergo (2009 ).

83. Iskander (2009 ).

84. Castles and Delgado Wise (2008).

85. Massey et al. (1998).

86. Eckstein (2004), Massey et al. (1998), Newland

and Patrick (2004) and van Hear, Pieke, and

Vertovec (2004).

87. Gamlen (2006) and Newland and Patrick (2004).

88. IMF and World Bank (1999 ).

89. Jobbins (2008) and Martin (2008).

90. Black and Sward (2009 ).

91. These countries are Australia, Austria, Belgium,

Canada, France, Germany, Ireland, Luxembourg,

Netherlands, New Zealand, Spain, Sweden,

Switzerland and United States; see Statistical

Table A. The share of foreign-born migrants in

the United Kingdom was estimated at about 9

percent at that time.

92. Van der Mensbrugghe and Roland-Holst (2009 ).

These simulations extend and update those

presented in World Bank (2006b).

93. Ortega and Peri (2009 ).

94. See Barrell, Fitzgerald, and Railey (2007). In

the United States, Borjas (1999 ) estimated the

aggregate effect to be positive but small, at 0.1

percent of GDP.

95. Hunt and Gauthier-Loiselle (2008).

96. See, for example, the Council of the European

Union (2009 ).

97. See, inter alia, Baumol, Litan, and Schramm

(2007) and Zucker and Darby (2008).

98. OECD (2008b).

99. EurActiv.com News (2008).

100. Martin (2009b).

101. This finding must be qualified because of

the inability to distinguish the labour supply

(immigrants tend to work in these restaurants)

from labour demand effects (if they consume

there); see Mazzolari and Neumark (2009 ).

102. For example, 38 percent of Britons believe this

is the case: Dustmann, Frattini, and Preston

(2008a).

103. For instance, see Longhi, Nijkamp, and Poot

(2005), Ottaviano and Peri (2008), and Münz,

Straubhaar, Vadean, and Vadean (2006).

104. For Spain, see Carrasco, Jimeno, and Ortega

(2008), for France, Constant (2005), for the

United Kingdom, Dustmann, Frattini, and Preston

(2008).

105. See, for example, Borjas (1995). A substitute is

when an increased supply of one input lowers the

price of the other input, while a complement is

when an increased supply raises the price of the

other input.

106. For example, in the United States, workers with

less than high-school education may in most

respects be perfect substitutes for high-school

graduates, throwing doubt on the assumption that

completion per se matters; see Card (2009 ).

107. Kremer and Watt (2006) and Castles and Miller

(1993).

108. For a survey, see Münz, Straubhaar, Vadean, and

Vadean (2006).

109. Reyneri (1998).

110. The first estimate comes from Borjas (2003), for

the period 1980–2000, while the second comes

from Ottaviano and Peri (2008) and refers to the

1990–2006 period. Using Borjas’s methodology

for the 1990–2006 period gives an estimate

of -7.8 percent (Ottaviano and Peri (2008), p.

59 ). The approaches differ in their assumptions

regarding the substitutability between high-

school dropouts and high-school graduates.

See also Card (1990 ) and Borjas, Grogger, and

Hanson (2008).

111. Peri, Sparber, and Drive (2008); Amuedo-

Dorantes and de la Rica (2008) for Spain.

118

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentNotes

112. Manacorda, Manning, and Wadsworth (2006).

113. Angrist and Kugler (2003).

114. Jayaweera and Anderson (2009 ).

115. Bryant and Rukumnuaykit (2007).

116. Suen (2002).

117. A comprehensive discussion of this issue can be

found in World Bank (2009e).

118. Henderson, Shalizi, and Venables (2001).

119. Amis (2002).

120. The Cities Alliance (2007).

121. Dreze and Sen (1999 ).

122. Kundu (2009 ).

123. See Hossain, Khan, and Seeley (2003) and Afsar

(2003).

124. Hanson (2009 ).

125. For example, Borjas (1995) and Lee and Miller

(2000 ).

126. IMF (2009b).

127. Hanson, Scheve, and Slaughter (2007).

128. Facchini and Mayda (2008).

129. Brucker et al. (2002). Countries with greater

migrant dependence on welfare included

Austria, Belgium, Denmark, Finland, France and

Netherlands, while those with less dependence

included Germany, Greece, Spain, Portugal and

United Kingdom.

130. Vasquez, Alloza, Vegas, and Bertozzi (2009 ).

131. Rowthorn (2008).

132. Alternative estimates could be derived by

considering the entire future stream of taxes and

spending associated with immigrants and their

dependents, plus future generations. However,

estimating the net present value would be very

difficult given all the assumptions needed about

people’s future behaviour (fertility, schooling,

employment prospects, and so on), so in practice

a static approach is used: see Rowthorn (2008).

Some authors have estimated the net present

fiscal value of an immigrant in the United States

and have found largely positive estimates; see

Lee and Miller (2000 ).

133. Lucassen (2005).

134. IPC (2007).

135. Butcher and Piehl (1998).

136. Australian Institute of Criminology (1999 ).

137. Savona, Di Nicola, and Da Col (1996).

138. However, particularly in medium-HDI countries

(such as Egypt, Indonesia, Islamic Republic

of Iran, Jordan, South Africa and Thailand), a

significant proportion did favour more restrictions

on access. Similarly, in countries with higher

income inequality, people were more likely to

favour limiting migration and said that employers

should give priority to local people when jobs are

scarce. See Kleemans and Klugman (2009 ).

139. Zimmermann (2009 ).

140. Massey and Sánchez R. (2009 ).

141. O’Rourke and Sinnott (2003).

142. Earnest (2008).

143. Several studies have investigated the long-run

effects of immigration on political values, with

differing results. Bueker (2005) finds significant

differences in turnout and participation among US

voters of different immigrant backgrounds, while

Rodríguez and Wagner (2009 ) find that the well-

documented patterns of civic engagement and

attitudes towards redistribution across different

regions of Italy are not reflected in the political

behaviour of Italians from these regions who are

living in Venezuela.

144. Castles and Miller (1993).

145. Kleemans and Klugman (2009 ).

Chapter 5

1. Scheve and Slaughter (2007).

2. This chapter does not provide a comprehensive

review of policies that are relevant to migration,

since these have been well documented

elsewhere: see OECD (2008b), IOM (2008a),

Migration Policy Group and British Council (2007)

and ILO (2004).

3. Agunias (2009) and Klugman and Pereira (2009).

4. Government of Sweden (2008).

5. Khoo, Hugo, and McDonald (2008) and Klugman

and Pereira (2009 ).

6. See ICMPD (2009 ) for an excellent review.

7. Papademetriou (2005).

8. ICMPD (2009 ), p. 47.

9. For example, in the United Kingdom the Foreign

and Commonwealth Office team working on

promoting the return of irregular migrants and

failed asylum seekers is currently five times

larger than the team focused on migration and

development in the Department for International

Development. See Black and Sward (2009 ).

10. Hagan, Eschbach, and Rodriguez (2008).

11. Migrant Forum in Asia (2006) and Human Rights

Watch (2005b).

12. See European Parliament (2008); on criticisms,

see, for example, Amnesty International (2008).

13. UNHCR (2007).

14. See international conventions on Economic,

Social and Cultural Rights ( ICESCR 1966), on

Civil and Political Rights ( ICCPR 1966), on the

Elimination of All Forms of Racial Discrimination

( ICERD 1966), on the Elimination of All Forms of

Discrimination against Women (CEDAW 1979 ),

Against Torture, and Other Cruel, Inhuman or

Degrading Treatment or Punishment (CAT 1984),

and on the Rights of the Child (CRC 1989 ). The

ratification rates are lowest among Asian and

Middle Eastern states (47 percent) and stand at

58 and 70 percent for Latin America and Africa

respectively. While 131 countries have ratified

all six core human rights treaties, some of these

treaties have more than 131 signatories. The total

number of parties for individual treaties can be

found in the Statistical Annex.

15. ICCPR Art 2, 26; ICESCR Art 2; see Opeskin

(2009 ).

16. The European Community, which is listed as a

separate signatory, is not included here.

17. IOM (2008b), p. 62.

18. UNODC (2009 ).

19. See for example Carling (2006) (on trafficking

from Nigeria) and de Haas (2008).

20. December 18 vzw (2008).

21. Alvarez (2005) and Betts (2008).

22. Martin and Abimourchad (2008).

23. PICUM (2008b).

24. Kleemans and Klugman (2009 ).

25. For examples of such activities, see the Joint

Initiative of the European Commission and the

United Nations ( EC-UN Joint Migration and

Development Initiative, 2008). The joint initiative

has at its heart a knowledge management

platform of activities related to remittances,

communities, capacities and rights led by civil

society and local authorities. See GFMD (2008).

26. Martin (2009b) and Agunias (2009 ).

27. McKenzie (2007).

28. Martin (2005), p. 20.

29. Martin (2009a), p. 47.

30. Hamel (2009 ).

31. Martin (2009a).

32. Horst (2006).

33. The 1997 ILO Convention on Private Employment

Agencies prohibits the charging of fees to

workers, but this has been ratified by only 21

countries.

34. Agunias (2008), Ruhunage (2006) and Siddiqui

(2006).

35. Betcherman, Olivas, and Dar (2004) review

the effectiveness of active labour market

programmes, drawing on 159 evaluations in

developing and developed countries.

36. Martin (2009b) and Sciortino and Punpuing

(2009 ).

37. See Colombo Process (2008).

38. Marquette (2006).

39. Christensen and Stanat (2007).

40. Success for All Foundation (2008).

41. Misago, Landau, and Monson (2009 ).

42. This might include, for example, leaflets

explaining who does what and where to go to

complain.

43. World Bank (2002).

44. Zamble (2008).

45. One World Net (2008).

46. Council of Europe (2006).

47. Martin (2009a).

48. Government of Western Australia (2004).

49. Deshingkar and Akter (2009 ), pp. 38-40.

50. UN (2008a).

51. The Cities Alliance (2007).

52. Black and Sward (2009 ).

53. For example, in Myanmar, college graduates must

reimburse the government for the cost of their

education before they can receive a passport;

United States Department of State (2009c).

54. As Ranis and Stewart (2000 ) note, while there

are many paths to good human development

performance, in general successes have been

characterized by initiatives that give priority to

girls and women (education, incomes), effective

expenditure policies (e.g. Chile) and good

economic performance (e.g. Viet Nam).

55. Kleemans and Klugman (2009 ).

56. Sides and Citrin (2007).

57. Facchini and Mayda (2009 ).

58. Ghosh (2007).

59. Bedford (2008).

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Statistical annex

143

HUMAN DEVELOPMENT REPORT 2009

TABLE AHuman movement: snapshots and trends

Stock of immigrants

(000)

1960 1990 2005 2010a 1960–2005 1960 2005 2005 2000–2002 2000–2002 1990–2005 1990–20051960

Immigration Emigration Lifetime internal

migrationb

International migration Internal migration

Share of population

(%)

Annual rate of growth

(%)

Proportion female

(%)

Emigration rate (%)

International movement

rate (%)

Total migrants

(000)

Migration rate (%)

VERY HIGH HUMAN DEVELOPMENT 1 Norway 61.6 195.2 370.6 485.4 4.0 1.7 8.0 54.3 51.1 3.9 11.0 .. .. 2 Australia 1,698.1 3,581.4 4,335.8 4,711.5 2.1 16.5 21.3 44.3 50.9 2.2 22.5 .. .. 3 Iceland 3.3 9.6 22.6 37.2 4.3 1.9 7.6 52.3 52.0 10.6 16.4 .. .. 4 Canada 2,766.3 4,497.5 6,304.0 7,202.3 1.8 15.4 19.5 48.1 52.0 4.0 21.5 .. .. 5 Ireland 73.0 228.0 617.6 898.6 4.7 2.6 14.8 51.7 49.9 20.0 28.1 .. .. 6 Netherlands 446.6 1,191.6 1,735.4 1,752.9 3.0 3.9 10.6 58.8 51.6 4.7 14.2 .. .. 7 Sweden 295.6 777.6 1,112.9 1,306.0 2.9 4.0 12.3 55.1 52.2 3.3 15.0 .. .. 8 France 3,507.2 5,897.3 6,478.6 6,684.8 1.4 7.7 10.6 44.5 51.0 2.9 13.1 .. .. 9 Switzerland 714.2 1,376.4 1,659.7 1,762.8 1.9 13.4 22.3 53.3 49.7 5.6 26.0 .. .. 10 Japan 692.7 1,075.6 1,998.9 2,176.2 2.4 0.7 1.6 46.0 54.0 0.7 1.7 .. .. 11 Luxembourg 46.4 113.8 156.2 173.2 2.7 14.8 33.7 53.8 50.3 9.5 38.3 .. .. 12 Finland 32.1 63.3 171.4 225.6 3.7 0.7 3.3 56.3 50.6 6.6 9.0 .. .. 13 United States 10,825.6 23,251.0 39,266.5 42,813.3 2.9 5.8 13.0 51.1 50.1 0.8 12.4 44,400 c 17.8 c

14 Austria 806.6 793.2 1,156.3 1,310.2 0.8 11.5 14.0 56.6 51.2 5.5 17.2 .. .. 15 Spain 210.9 829.7 4,607.9 6,377.5 6.9 0.7 10.7 52.2 47.7 3.2 8.3 8,600 c 22.4 c

16 Denmark 94.0 235.2 420.8 483.7 3.3 2.1 7.8 64.3 51.9 4.3 10.7 .. .. 17 Belgium 441.6 891.5 882.1 974.8 1.5 4.8 8.5 45.1 48.9 4.4 14.6 .. .. 18 Italy 459.6 1,428.2 3,067.7 4,463.4 4.2 0.9 5.2 57.3 53.5 5.4 8.1 .. .. 19 Liechtenstein 4.1 10.9 11.9 12.5 2.4 24.6 34.2 53.8 48.8 12.6 42.0 .. .. 20 New Zealand 333.9 523.2 857.6 962.1 2.1 14.1 20.9 47.1 51.9 11.8 27.3 .. .. 21 United Kingdom 1,661.9 3,716.3 5,837.8 6,451.7 2.8 3.2 9.7 48.7 53.2 6.6 14.3 .. .. 22 Germany 2,002.9 d 5,936.2 10,597.9 10,758.1 3.7 2.8 d 12.9 35.1 d 46.7 4.7 15.3 .. .. 23 Singapore 519.2 727.3 1,494.0 1,966.9 2.3 31.8 35.0 44.0 55.8 6.3 19.1 .. .. 24 Hong Kong, China (SAR) 1,627.5 2,218.5 2,721.1 2,741.8 1.1 52.9 39.5 48.0 56.5 9.5 45.6 .. .. 25 Greece 52.5 412.1 975.0 1,132.8 6.5 0.6 8.8 46.1 45.1 7.8 17.2 .. .. 26 Korea (Republic of) 135.6 572.1 551.2 534.8 3.1 0.5 1.2 47.7 51.4 3.1 3.4 .. .. 27 Israel 1,185.6 1,632.7 2,661.3 2,940.5 1.8 56.1 39.8 49.5 55.9 13.1 40.3 .. .. 28 Andorra 2.5 38.9 50.3 55.8 6.7 18.7 63.1 44.2 47.4 9.7 79.6 .. .. 29 Slovenia .. 178.1 167.3 163.9 .. .. 8.4 .. 46.8 5.2 7.6 .. .. 30 Brunei Darussalam 20.6 73.2 124.2 148.1 4.0 25.1 33.6 42.0 44.8 4.9 33.4 .. .. 31 Kuwait 90.6 1,585.3 1,869.7 2,097.5 6.7 32.6 69.2 25.6 30.0 16.6 54.5 .. .. 32 Cyprus 29.6 43.8 116.2 154.3 3.0 5.2 13.9 50.3 57.1 18.4 23.4 .. .. 33 Qatar 14.4 369.8 712.9 1,305.4 8.7 32.0 80.5 25.8 25.8 2.3 60.7 .. .. 34 Portugal 38.9 435.8 763.7 918.6 6.6 0.4 7.2 58.4 50.6 16.1 21.4 1,200 c 12.8 c

35 United Arab Emirates 2.2 1,330.3 2,863.0 3,293.3 15.9 2.4 70.0 15.0 27.7 3.3 55.1 .. .. 36 Czech Republic 60.1 e 424.5 453.3 453.0 4.5 0.4 e 4.4 59.5 e 53.8 3.5 7.7 .. .. 37 Barbados 9.8 21.4 26.2 28.1 2.2 4.2 10.4 59.8 60.1 29.8 36.6 90 f 31.1 f

38 Malta 1.7 5.8 11.7 15.5 4.3 0.5 2.9 59.7 51.6 22.3 24.0 .. ..

HIGH HUMAN DEVELOPMENT 39 Bahrain 26.7 173.2 278.2 315.4 5.2 17.1 38.2 27.9 31.9 15.9 47.3 .. .. 40 Estonia .. 382.0 201.7 182.5 .. .. 15.0 .. 59.6 12.2 28.5 .. .. 41 Poland 2,424.9 1,127.8 825.4 827.5 -2.4 8.2 2.2 53.9 59.0 5.1 7.1 .. .. 42 Slovakia .. 41.3 124.4 130.7 .. .. 2.3 .. 56.0 8.2 10.3 .. .. 43 Hungary 518.1 347.5 333.0 368.1 -1.0 5.2 3.3 53.1 56.1 3.9 6.6 .. .. 44 Chile 104.8 107.5 231.5 320.4 1.8 1.4 1.4 43.7 52.3 3.3 4.5 3,100 c 21.3 c

45 Croatia .. 475.4 661.4 699.9 .. .. 14.9 .. 53.0 12.0 23.8 800 g 26.6 g

46 Lithuania .. 349.3 165.3 128.9 .. .. 4.8 .. 56.6 8.6 13.9 .. .. 47 Antigua and Barbuda 4.9 12.0 18.2 20.9 2.9 8.9 21.8 50.2 55.1 45.3 56.1 24,000 f 28.4 f

48 Latvia .. 646.0 379.6 335.0 .. .. 16.6 .. 59.0 9.1 33.0 .. .. 49 Argentina 2,601.2 1,649.9 1,494.1 1,449.3 -1.2 12.6 3.9 45.4 53.4 1.6 5.6 6,700 c 19.9 c

50 Uruguay 192.2 98.2 84.1 79.9 -1.8 7.6 2.5 47.8 54.0 7.0 9.5 800 f 24.1 f

51 Cuba 143.6 34.6 15.3 15.3 -5.0 2.0 0.1 30.6 29.0 8.9 9.6 1,800 f 15.2 f

52 Bahamas 11.3 26.9 31.6 33.4 2.3 10.3 9.7 43.7 48.5 10.8 19.3 .. .. 53 Mexico 223.2 701.1 604.7 725.7 2.2 0.6 0.6 46.2 49.4 9.0 9.5 17,800 c 18.5 c

54 Costa Rica 32.7 417.6 442.6 489.2 5.8 2.5 10.2 44.2 49.8 2.6 9.7 700 c 20.0 c

55 Libyan Arab Jamahiriya 48.2 457.5 617.5 682.5 5.7 3.6 10.4 49.0 35.5 1.4 11.5 .. .. 56 Oman 43.7 423.6 666.3 826.1 6.1 7.7 25.5 21.2 20.8 0.7 28.0 .. .. 57 Seychelles 0.8 3.7 8.4 10.8 5.1 1.9 10.2 35.4 42.5 17.0 21.6 .. .. 58 Venezuela (Bolivarian Republic of) 509.5 1,023.8 1,011.4 1,007.4 1.5 6.7 3.8 37.9 49.9 1.4 5.3 5,200 c 23.8 c

59 Saudi Arabia 63.4 4,743.0 6,336.7 7,288.9 10.2 1.6 26.8 36.4 30.1 1.1 24.8 .. ..

HDI rank

A HUMAN DEVELOPMENT REPORT 2009

144

Human movement: snapshots and trends

Stock of immigrants

(000)

1960HDI rank 1990 2005 2010a 1960–2005 1960 2005 2005 2000–2002 2000–2002 1990–2005 1990–20051960

Immigration Emigration Lifetime internal

migrationb

International migration Internal migration

Share of population

(%)

Annual rate of growth

(%)

Proportion female

(%)

Emigration rate (%)

International movement

rate (%)

Total migrants

(000)

Migration rate (%)

60 Panama 68.3 61.7 102.2 121.0 0.9 6.1 3.2 42.7 50.2 5.7 8.2 600 c 20.6 c

61 Bulgaria 20.3 21.5 104.1 107.2 3.6 0.3 1.3 57.9 57.9 10.5 11.6 800 g 14.3 g

62 Saint Kitts and Nevis 3.5 3.2 4.5 5.0 0.5 6.9 9.2 48.6 46.3 44.3 49.3 .. .. 63 Romania 330.9 142.8 133.5 132.8 -2.0 1.8 0.6 54.8 52.1 4.6 5.0 2,300 g 15.1 g

64 Trinidad and Tobago 81.0 50.5 37.8 34.3 -1.7 9.6 2.9 49.8 53.9 20.2 22.8 .. .. 65 Montenegro .. .. h 54.6 42.5 .. .. 8.7 .. 60.9 .. h .. h .. .. 66 Malaysia 56.9 1,014.2 2,029.2 2,357.6 7.9 0.7 7.9 42.2 45.0 3.1 10.1 4,200 c 20.7 c

67 Serbia 155.4 e 99.3 674.6 525.4 3.3 0.9 e 6.8 56.9 e 56.1 13.6 18.7 .. .. 68 Belarus .. 1,249.0 1,106.9 1,090.4 .. .. 11.3 .. 54.2 15.2 26.1 900 c 10.8 c

69 Saint Lucia 2.4 5.3 8.7 10.2 2.8 2.7 5.3 50.1 51.3 24.1 27.9 30 f 18.5 f

70 Albania 48.9 66.0 82.7 89.1 1.2 3.0 2.7 53.7 53.1 21.0 21.4 500 g 24.1 g

71 Russian Federation 2,941.7 e 11,524.9 12,079.6 12,270.4 3.1 1.4 e 8.4 47.9 e 57.8 7.7 15.3 .. .. 72 Macedonia (the Former Yugoslav Rep. of) .. 95.1 120.3 129.7 .. .. 5.9 .. 58.3 11.3 12.8 .. .. 73 Dominica 2.4 2.5 4.5 5.5 1.4 4.0 6.7 50.9 46.2 38.3 41.6 .. .. 74 Grenada 4.0 4.3 10.8 12.6 2.2 4.5 10.6 51.2 53.3 40.3 45.0 .. .. 75 Brazil 1,397.1 798.5 686.3 688.0 -1.6 1.9 0.4 44.4 46.4 0.5 0.8 17,000 c 10.1 c

76 Bosnia and Herzegovina .. 56.0 35.1 27.8 .. .. 0.9 .. 49.8 25.1 27.0 1,400 g 52.5 g

77 Colombia 58.7 104.3 110.0 110.3 1.4 0.4 0.3 43.9 48.3 3.9 4.1 8,100 c 20.3 c

78 Peru 66.5 56.0 41.6 37.6 -1.0 0.7 0.1 44.3 52.4 2.7 2.9 6,300 f 22.4 f

79 Turkey 947.6 1,150.5 1,333.9 1,410.9 0.8 3.4 1.9 48.1 52.0 4.2 6.0 .. .. 80 Ecuador 24.1 78.7 123.6 393.6 3.6 0.5 0.9 45.5 49.1 5.3 5.9 2,400 c 20.2 c

81 Mauritius 10.2 8.7 40.8 42.9 3.1 1.6 3.3 39.3 63.3 12.5 13.1 .. .. 82 Kazakhstan .. 3,619.2 2,973.6 3,079.5 .. .. 19.6 .. 54.0 19.4 35.8 1,000 g 9.3 g

83 Lebanon 151.4 523.7 721.2 758.2 3.5 8.0 17.7 49.2 49.1 12.9 27.1 .. ..

MEDIUM HUMAN DEVELOPMENT 84 Armenia .. 658.8 492.6 324.2 .. .. 16.1 .. 58.9 20.3 28.1 500 g 24.5 g

85 Ukraine .. 6,892.9 5,390.6 5,257.5 .. .. 11.5 .. 57.2 10.9 23.8 .. .. 86 Azerbaijan .. 360.6 254.5 263.9 .. .. 3.0 .. 57.0 14.3 15.8 1,900 g 33.2 g

87 Thailand 484.8 387.5 982.0 1,157.3 1.6 1.8 1.5 36.5 48.4 1.3 2.0 .. .. 88 Iran (Islamic Republic of) 48.4 4,291.6 2,062.2 2,128.7 8.3 0.2 2.9 50.6 39.7 1.3 4.7 .. .. 89 Georgia .. 338.3 191.2 167.3 .. .. 4.3 .. 57.0 18.3 22.1 .. .. 90 Dominican Republic 144.6 291.2 393.0 434.3 2.2 4.3 4.1 25.9 40.1 9.1 10.4 1,700 f 17.7 f

91 Saint Vincent and the Grenadines 2.5 4.0 7.4 8.6 2.4 3.1 6.8 50.6 51.8 34.4 39.0 .. .. 92 China 245.7 376.4 590.3 685.8 1.9 0.0 0.0 47.3 50.0 0.5 0.5 73,100 c 6.2 c

93 Belize 7.6 30.4 40.6 46.8 3.7 8.2 14.4 46.1 50.5 16.5 27.4 40 f 14.2 f

94 Samoa 3.4 3.2 7.2 9.0 1.6 3.1 4.0 45.9 44.9 37.2 39.4 .. .. 95 Maldives 1.7 2.7 3.2 3.3 1.4 1.7 1.1 46.3 44.8 0.4 1.5 .. .. 96 Jordan 385.8 1,146.3 2,345.2 2,973.0 4.0 43.1 42.1 49.2 49.1 11.6 45.3 .. .. 97 Suriname 22.5 18.0 34.0 39.5 0.9 7.7 6.8 47.4 45.6 36.0 36.9 .. .. 98 Tunisia 169.2 38.0 34.9 33.6 -3.5 4.0 0.4 51.0 49.5 5.9 6.3 .. .. 99 Tonga 0.1 3.0 1.2 0.8 5.0 0.2 1.1 45.5 48.7 33.7 34.7 .. ..

100 Jamaica 21.9 20.8 27.2 30.0 0.5 1.3 1.0 48.4 49.4 26.7 27.0 .. .. 101 Paraguay 50.0 183.3 168.2 161.3 2.7 2.6 2.8 47.4 48.1 6.9 9.8 1,600 f 26.4 f

102 Sri Lanka 1,005.3 458.8 366.4 339.9 -2.2 10.0 1.9 46.6 49.8 4.7 6.6 .. .. 103 Gabon 20.9 127.7 244.6 284.1 5.5 4.3 17.9 42.9 42.9 4.3 22.8 .. .. 104 Algeria 430.4 274.0 242.4 242.3 -1.3 4.0 0.7 50.1 45.2 6.2 6.9 .. .. 105 Philippines 219.7 159.4 374.8 435.4 1.2 0.8 0.4 43.9 50.1 4.0 5.6 6,900 c 11.7 c

106 El Salvador 34.4 47.4 35.9 40.3 0.1 1.2 0.6 72.8 52.8 14.3 14.6 1,200 f 16.7 f

107 Syrian Arab Republic 276.1 690.3 1,326.4 2,205.8 3.5 6.0 6.9 48.7 48.9 2.4 7.4 .. .. 108 Fiji 20.1 13.7 17.2 18.5 -0.3 5.1 2.1 37.6 47.9 15.0 16.6 .. .. 109 Turkmenistan .. 306.5 223.7 207.7 .. .. 4.6 .. 57.0 5.3 9.8 .. .. 110 Occupied Palestinian Territories 490.3 910.6 1,660.6 1,923.8 2.7 44.5 44.1 49.2 49.1 23.9 61.3 .. .. 111 Indonesia 1,859.5 465.6 135.6 122.9 -5.8 2.0 0.1 48.0 46.0 0.9 1.0 8,100 c 4.1 c

112 Honduras 60.0 270.4 26.3 24.3 -1.8 3.0 0.4 45.4 48.6 5.3 5.9 1,200 f 17.2 f

113 Bolivia 42.7 59.6 114.0 145.8 2.2 1.3 1.2 43.4 48.1 4.3 5.3 1,500 f 15.2 f

114 Guyana 14.0 4.1 10.0 11.6 -0.8 2.5 1.3 42.2 46.5 33.5 33.6 .. .. 115 Mongolia 3.7 6.7 9.1 10.0 2.0 0.4 0.4 47.4 54.0 0.3 0.6 200 g 9.7 g

116 Viet Nam 4.0 29.4 54.5 69.3 5.8 0.0 0.1 46.4 36.6 2.4 2.4 12,700 g 21.9 g

117 Moldova .. 578.5 440.1 408.3 .. .. 11.7 .. 56.0 14.3 24.6 .. .. 118 Equatorial Guinea 19.4 2.7 5.8 7.4 -2.7 7.7 1.0 30.2 47.0 14.5 14.7 .. ..

145

ATABLE

Stock of immigrants

(000)

1960 1990 2005 2010a 1960–2005 1960 2005 2005 2000–2002 2000–2002 1990–2005 1990–20051960

Immigration Emigration Lifetime internal

migrationb

International migration Internal migration

Share of population

(%)

Annual rate of growth

(%)

Proportion female

(%)

Emigration rate (%)

International movement

rate (%)

Total migrants

(000)

Migration rate (%)

119 Uzbekistan .. 1,653.0 1,267.8 1,175.9 .. .. 4.8 .. 57.0 8.5 13.4 .. .. 120 Kyrgyzstan .. 623.1 288.1 222.7 .. .. 5.5 .. 58.2 10.5 20.6 600 g 16.2 g

121 Cape Verde 6.6 8.9 11.2 12.1 1.2 3.4 2.3 50.4 50.4 30.5 32.1 .. .. 122 Guatemala 43.3 264.3 53.4 59.5 0.5 1.0 0.4 48.3 54.4 4.9 5.2 1,500 f 11.1 f

123 Egypt 212.4 175.6 246.7 244.7 0.3 0.8 0.3 47.8 46.7 2.9 3.1 .. .. 124 Nicaragua 12.4 40.8 35.0 40.1 2.3 0.7 0.6 46.6 48.8 9.1 9.6 800 f 13.3 f

125 Botswana 7.2 27.5 80.1 114.8 5.4 1.4 4.4 43.8 44.3 0.9 3.8 .. .. 126 Vanuatu 2.8 2.2 1.0 0.8 -2.2 4.4 0.5 39.0 46.5 2.0 2.7 .. .. 127 Tajikistan .. 425.9 306.4 284.3 .. .. 4.7 .. 57.0 11.4 16.1 400 g 9.9 g

128 Namibia 27.2 112.1 131.6 138.9 3.5 4.5 6.6 36.9 47.3 1.3 8.7 .. .. 129 South Africa 927.7 1,224.4 1,248.7 1,862.9 0.7 5.3 2.6 29.0 41.4 1.7 3.9 6,700 c 15.4 c

130 Morocco 394.3 57.6 51.0 49.1 -4.5 3.4 0.2 51.5 49.9 8.1 8.5 6,800 g 33.4 g

131 Sao Tome and Principe 7.4 5.8 5.4 5.3 -0.7 11.6 3.5 46.4 47.9 13.5 17.9 .. .. 132 Bhutan 9.7 23.8 37.3 40.2 3.0 4.3 5.7 18.5 18.5 2.2 3.8 .. .. 133 Lao People’s Democratic Republic 19.6 22.9 20.3 18.9 0.1 0.9 0.3 48.9 48.1 5.9 6.2 .. .. 134 India 9,410.5 7,493.2 5,886.9 5,436.0 -1.0 2.1 0.5 46.0 48.6 0.8 1.4 42,300 c 4.1 c

135 Solomon Islands 3.7 4.7 6.5 7.0 1.2 3.1 1.4 45.6 44.0 1.0 1.7 .. .. 136 Congo 26.3 129.6 128.8 143.2 3.5 2.6 3.8 51.6 49.6 14.7 20.0 .. .. 137 Cambodia 381.3 38.4 303.9 335.8 -0.5 7.0 2.2 48.3 51.3 2.3 3.9 1,300 c 11.7 c

138 Myanmar 286.6 133.5 93.2 88.7 -2.5 1.4 0.2 44.9 47.7 0.7 0.9 .. .. 139 Comoros 1.5 14.1 13.7 13.5 4.9 0.8 2.2 46.6 53.1 7.7 10.7 .. .. 140 Yemen 159.1 343.5 455.2 517.9 2.3 3.0 2.2 38.3 38.3 3.0 4.3 .. .. 141 Pakistan 6,350.3 6,555.8 3,554.0 4,233.6 -1.3 13.0 2.1 46.4 44.8 2.2 4.8 .. .. 142 Swaziland 16.9 71.4 38.6 40.4 1.8 4.9 3.4 48.5 47.4 1.1 4.8 .. .. 143 Angola 122.1 33.5 56.1 65.4 -1.7 2.4 0.3 41.7 51.1 5.5 5.8 .. .. 144 Nepal 337.6 430.7 818.7 945.9 2.0 3.5 3.0 64.1 69.1 3.9 6.2 .. .. 145 Madagascar 126.3 46.1 39.7 37.8 -2.6 2.5 0.2 49.2 46.1 0.9 1.3 1,000 g 9.3 g

146 Bangladesh 661.4 881.6 1,031.9 1,085.3 1.0 1.2 0.7 46.4 13.9 4.5 5.1 .. .. 147 Kenya 59.3 163.0 790.1 817.7 5.8 0.7 2.2 37.1 50.8 1.4 2.3 3,500 c 12.6 c

148 Papua New Guinea 20.2 33.1 25.5 24.5 0.5 1.0 0.4 43.3 37.6 0.9 1.3 .. .. 149 Haiti 14.5 19.1 30.1 35.0 1.6 0.4 0.3 50.5 43.2 7.7 8.0 1,000 g 17.5 g

150 Sudan 242.0 1,273.1 639.7 753.4 2.2 2.1 1.7 47.2 48.3 1.7 3.8 .. .. 151 Tanzania ( United Republic of) 477.0 576.0 797.7 659.2 1.1 4.7 2.0 45.0 50.2 0.8 3.3 .. .. 152 Ghana 529.7 716.5 1,669.3 1,851.8 2.6 7.8 7.6 36.4 41.8 4.5 7.3 3,300 c 17.8 c

153 Cameroon 175.4 265.3 211.9 196.6 0.4 3.2 1.2 44.3 45.6 1.0 1.9 .. .. 154 Mauritania 12.1 93.9 66.1 99.2 3.8 1.4 2.2 41.1 42.1 4.1 6.3 400 g 24.2 g

155 Djibouti 11.8 122.2 110.3 114.1 5.0 13.9 13.7 41.8 46.5 2.2 5.8 .. .. 156 Lesotho 3.2 8.2 6.2 6.3 1.5 0.4 0.3 50.5 45.7 2.6 2.8 .. .. 157 Uganda 771.7 550.4 652.4 646.5 -0.4 11.4 2.3 41.3 49.9 0.7 2.7 1,300 c 5.2 c

158 Nigeria 94.1 447.4 972.1 1,127.7 5.2 0.2 0.7 36.2 46.5 0.8 1.4 .. ..

LOW HUMAN DEVELOPMENT 159 Togo 101.3 162.6 182.8 185.4 1.3 6.5 3.1 51.8 50.4 3.7 6.8 .. .. 160 Malawi 297.7 1,156.9 278.8 275.9 -0.1 8.4 2.0 51.2 51.6 1.2 3.4 200 g 2.7 g

161 Benin 34.0 76.2 187.6 232.0 3.8 1.5 2.4 48.5 46.0 7.5 8.8 .. .. 162 Timor-Leste 7.1 9.0 11.9 13.8 1.1 1.4 1.2 46.0 52.6 2.6 3.2 .. .. 163 Côte d’Ivoire 767.0 1,816.4 2,371.3 2,406.7 2.5 22.3 12.3 40.8 45.1 1.0 13.8 .. .. 164 Zambia 360.8 280.0 287.3 233.1 -0.5 11.9 2.4 47.0 49.4 2.2 5.6 .. .. 165 Eritrea 7.7 11.8 14.6 16.5 1.4 0.5 0.3 41.9 46.5 12.5 12.8 .. .. 166 Senegal 168.0 268.6 220.2 210.1 0.6 5.5 2.0 41.7 51.0 4.4 7.0 .. .. 167 Rwanda 28.5 72.9 435.7 465.5 6.1 1.0 4.8 53.9 53.9 2.7 3.7 800 c 10.4 c

168 Gambia 31.6 118.1 231.7 290.1 4.4 9.9 15.2 42.7 48.7 3.6 16.4 .. .. 169 Liberia 28.8 80.8 96.8 96.3 2.7 2.7 2.9 37.8 45.1 2.7 7.8 .. .. 170 Guinea 11.3 241.1 401.2 394.6 7.9 0.4 4.4 48.0 52.8 6.3 14.3 .. .. 171 Ethiopia 393.3 1,155.4 554.0 548.0 0.8 1.7 0.7 41.9 47.1 0.4 1.4 .. .. 172 Mozambique 8.9 121.9 406.1 450.0 8.5 0.1 1.9 43.6 52.1 4.2 6.0 900 g 8.1 g

173 Guinea-Bissau 11.6 13.9 19.2 19.2 1.1 2.0 1.3 50.0 50.0 8.6 9.9 .. .. 174 Burundi 126.3 333.1 81.6 60.8 -1.0 4.3 1.1 46.0 53.7 5.4 6.5 .. .. 175 Chad 55.1 74.3 358.4 388.3 4.2 1.9 3.6 44.0 48.0 3.2 3.7 .. .. 176 Congo (Democratic Republic of the) 1,006.9 754.2 480.1 444.7 -1.6 6.5 0.8 49.8 52.9 1.5 2.9 8,500 g 27.1 g

177 Burkina Faso 62.9 344.7 772.8 1,043.0 5.6 1.3 5.6 52.3 51.1 9.8 17.9 .. ..

HDI rank

A HUMAN DEVELOPMENT REPORT 2009

146

Human movement: snapshots and trends

Stock of immigrants

(000)

1960 1990 2005 2010a 1960–2005 1960 2005 2005 2000–2002 2000–2002 1990–2005 1990–20051960

Immigration Emigration Lifetime internal

migrationb

International migration Internal migration

Share of population

(%)

Annual rate of growth

(%)

Proportion female

(%)

Emigration rate (%)

International movement

rate (%)

Total migrants

(000)

Migration rate (%)

178 Mali 167.6 165.3 165.4 162.7 0.0 3.3 1.4 50.0 47.8 12.5 12.9 .. .. 179 Central African Republic 43.1 62.7 75.6 80.5 1.2 2.9 1.8 49.6 46.6 2.7 4.2 .. .. 180 Sierra Leone 45.9 154.5 152.1 106.8 2.7 2.0 3.0 35.6 45.7 2.0 3.0 600 g 19.0 g

181 Afghanistan 46.5 57.7 86.5 90.9 1.4 0.5 0.4 43.6 43.6 10.6 10.8 .. .. 182 Niger 55.0 135.7 183.0 202.2 2.7 1.7 1.4 50.0 53.6 4.0 5.0 .. ..

OTHER UN MEMBER STATES Iraq 87.8 83.6 128.1 83.4 0.8 1.2 0.5 40.9 31.1 4.1 4.6 .. .. Kiribati 0.6 2.2 2.0 2.0 2.6 1.8 2.2 38.2 48.8 4.0 6.7 .. .. Korea (Democratic People’s Rep. of) 25.1 34.1 36.8 37.1 0.9 0.2 0.2 47.3 52.0 2.0 2.2 .. .. Marshall Islands 0.8 1.5 1.7 1.7 1.5 5.8 2.9 41.0 41.0 17.7 20.1 .. .. Micronesia (Federated States of) 5.8 3.7 2.9 2.7 -1.6 13.1 2.6 40.9 46.4 18.6 21.0 1 g 1.2 g

Monaco 15.4 20.1 22.6 23.6 0.9 69.5 69.8 57.5 51.3 39.3 82.6 .. .. Nauru 0.4 3.9 4.9 5.3 5.5 9.3 48.7 5.1 45.0 9.3 50.4 .. .. Palau 0.3 2.9 6.0 5.8 6.5 3.3 30.0 34.9 40.2 39.3 58.7 .. .. San Marino 7.5 8.7 11.4 11.7 0.9 48.9 37.7 53.5 53.5 18.1 45.0 .. .. Somalia 11.4 633.1 21.3 22.8 1.4 0.4 0.3 41.9 46.5 6.5 6.7 .. .. Tuvalu 0.4 0.3 0.2 0.2 -1.6 6.1 1.9 42.2 45.4 15.4 18.2 .. .. Zimbabwe 387.2 627.1 391.3 372.3 0.0 10.3 3.1 24.1 37.8 2.3 7.4 .. ..

Africa 9,175.9 T 15,957.6 T 17,678.6 T 19,191.4 T 1.7 3.2 1.9 43.1 47.8 2.9 .. .. .. Asia 28,494.9T 50,875.7 T 55,128.5 T 61,324.0 T 0.7 1.7 1.4 46.6 47.1 1.7 .. .. .. Europe 17,511.7 T 49,360.5 T 64,330.1 T 69,744.5 T 2.9 3.0 8.8 49.0 52.9 7.3 .. .. .. Latin America and the Caribbean 6,151.4 T 7,130.3 T 6,869.4 T 7,480.3 T 0.2 2.8 1.2 44.6 48.4 5.0 .. .. .. Northern America 13,603.5 T 27,773.9 T 45,597.1 T 50,042.4 T 2.8 6.7 13.6 50.8 50.3 1.1 .. .. .. Oceania 2,142.6 T 4,365.0 T 5,516.3 T 6,014.7 T 1.7 13.5 16.4 44.3 48.2 4.9 .. .. .. OECD 31,574.9 T 61,824.3 T 97,622.8 T 108,513.7 T 2.6 4.1 8.4 48.7 51.1 3.9 .. .. .. European Union (EU27) 13,555.3 T 26,660.0 T 41,596.8 T 46,911.3 T 2.8 3.5 8.5 49.1 51.4 5.7 .. .. .. GCC 241.0T 8,625.2 T 12,726.6 T 15,126.6 T 10.2 4.9 37.1 33.5 29.1 3.2 .. .. .. Very high human development 31,114.9 T 66,994.9 T 107,625.9 T 120,395.2 T 3.1 4.6 11.1 48.6 50.9 3.4 .. .. .. Very high HD: OECD 27,461.0 T 58,456.2 T 94,401.4 T 105,050.9 T 3.1 4.1 10.0 48.6 50.9 3.2 .. .. .. Very high HD: non-OECD 3,653.8 T 8,538.7 T 13,224.6 T 15,344.3 T 4.7 41.5 46.5 47.4 50.3 11.6 .. .. .. High human development 13,495.1 T 34,670.2 T 38,078.0 T 40,383.6 T 1.1 2.8 3.8 47.2 50.5 6.0 .. .. .. Medium human development 28,204.2 T 44,870.0 T 40,948.6 T 44,206.5 T 0.6 1.7 0.8 46.1 46.8 1.9 .. .. .. Low human development 4,265.7 T 8,928.0 T 8,467.5 T 8,812.0 T 1.6 3.9 2.3 45.0 48.9 3.9 .. .. .. World (excluding the former Soviet 74,078.1 T 125,389.2 T 168,780.5 T 187,815.1 T 1.1 2.7 2.7 46.8 47.8 2.4 .. .. .. Union and Czechoslovakia) World 77,114.7 Ti 155,518.1 Ti 195,245.4 Ti 213,943.8 Ti 1.1 2.6 i 3.0 i 47.0 i 49.2 i 3.0 i .. .. ..

NOTES

a 2010 projections are based on long-run tendencies

and may not accurately predict the effect of

unexpected short-term fluctuations such as the 2009

economic crisis. See UN (2009d) for further details.

b Due to differences in definition of the underlying data,

cross country comparisons should be made with

caution. Data are from different censuses and surveys

and refer to different time periods and so are not

strictly comparable.

c Data are estimates based on censuses from Bell and

Muhidin (2009 ). Internal migrants are expressed as a

percentage of the total population.

d Estimates for 1960 for Germany refer to the former

Federal Republic of Germany and the former German

Democratic Republic.

SOURCES

Columns 1–4 and 6–9 : UN (2009d).

Column 5: calculated based on data from UN (2009d).

Column 10: calculated based on data from Migration

DRC 2007 and population data from UN (2009e).

Column 11: calculated based on data from Migration

DRC (2007).

Column 12–13: various (as indicated).

e Estimates for 1960 for the Czech Republic, the

Russian Federation and Serbia refer to the former

states of Czechoslovakia, the Soviet Union and

Yugoslavia respectively.

f Data are estimates based on censuses from ECLAC

(2007). Internal migrants are expressed as a

percentage of the total population.

g Data are estimates based on household surveys

from the World Bank (2009e). Internal migrants

are expressed as a percentage of the working age

population only.

h Data for Montenegro are included with those for

Serbia.

i Data are aggregates from original data source.

HDI rank

147

TABLE

HUMAN DEVELOPMENT REPORT 2009

BTABLE International emigrants by area of residence

Africa AfricaVery highAsia AsiaHighEurope EuropeMedium Low

Latin America and the

Caribbean

Latin America and the

Caribbean Northern America

Northern AmericaOceania Oceania

Continent of residence 2000–2002

(% of total emigrant stocks)

Human develoment category of countries of residencea

2000–2002 (% of total emigrant stocks)

Share of continents’ immigrants from country 2000–2002

(% of total immigrant stocks in the continent)

Areas of residenceB

VERY HIGH HUMAN DEVELOPMENT 1 Norway 1.7 9.3 62.1 1.0 23.3 2.6 87.0 5.1 7.1 0.8 0.02 0.03 0.19 0.03 0.11 0.10 2 Australia 2.5 10.9 46.9 0.9 21.9 17.1 83.4 3.6 12.1 0.9 0.07 0.10 0.35 0.06 0.24 1.47 3 Iceland 1.7 4.3 61.4 0.7 30.3 1.6 92.4 2.7 4.1 0.8 0.00 0.00 0.04 0.00 0.03 0.01 4 Canada 1.3 5.8 15.2 2.2 72.7 2.7 91.6 3.0 4.8 0.7 0.11 0.15 0.34 0.48 2.35 0.70 5 Ireland 1.6 3.4 69.2 0.6 19.4 5.8 93.4 2.6 3.3 0.8 0.10 0.07 1.16 0.10 0.47 1.13 6 Netherlands 2.0 7.1 46.5 2.3 28.6 13.5 88.0 7.0 4.2 0.9 0.10 0.11 0.62 0.30 0.56 2.10 7 Sweden 3.3 6.3 65.5 1.7 20.6 2.6 87.2 6.3 4.7 1.9 0.06 0.04 0.34 0.09 0.15 0.16 8 France 16.0 6.5 54.5 4.6 15.9 2.4 70.4 13.0 9.7 6.9 1.79 0.24 1.67 1.37 0.71 0.85 9 Switzerland 2.5 6.9 68.4 2.7 16.4 3.2 86.8 7.1 5.3 0.9 0.07 0.06 0.50 0.19 0.18 0.27 10 Japan 1.3 12.9 13.4 8.6 59.5 4.3 78.8 10.9 9.7 0.6 0.07 0.23 0.20 1.26 1.30 0.76 11 Luxembourg 1.6 3.2 87.2 0.7 6.9 0.4 92.9 3.3 3.1 0.7 0.00 0.00 0.07 0.01 0.01 0.00 12 Finland 1.8 4.4 80.5 0.7 10.2 2.4 91.2 4.1 4.0 0.8 0.04 0.03 0.50 0.04 0.09 0.17 13 United States 2.7 20.1 28.3 32.2 12.6 4.2 45.7 35.7 17.3 1.4 0.38 0.91 1.08 11.97 0.70 1.89 14 Austria 1.9 9.1 63.0 1.8 19.8 4.4 84.7 8.8 5.7 0.8 0.06 0.09 0.50 0.14 0.23 0.41 15 Spain 1.8 3.4 61.2 23.5 9.1 1.0 70.4 24.8 3.9 0.8 0.15 0.09 1.43 5.34 0.31 0.27 16 Denmark 2.1 6.9 63.8 1.1 21.7 4.4 88.3 5.2 5.8 0.8 0.03 0.03 0.26 0.05 0.13 0.21 17 Belgium 2.0 6.3 75.6 1.6 13.3 1.2 88.4 6.1 4.6 0.9 0.06 0.06 0.61 0.12 0.16 0.11 18 Italy 2.0 3.5 51.1 10.7 26.0 6.7 82.9 12.4 3.9 0.8 0.42 0.23 2.86 5.81 2.12 4.38 19 Liechtenstein 1.5 3.1 92.0 0.6 2.5 0.2 93.1 3.2 3.0 0.7 0.00 0.00 0.01 0.00 0.00 0.00 20 New Zealand 1.1 6.6 16.6 0.3 6.9 68.6 92.1 1.6 5.7 0.5 0.03 0.07 0.15 0.03 0.09 7.17 21 United Kingdom 2.2 9.9 22.1 1.2 34.6 30.0 87.2 3.7 8.1 1.0 0.57 0.84 1.58 0.87 3.60 24.92 22 Germany 2.3 17.0 41.0 1.6 35.2 2.9 75.6 17.2 6.4 0.9 0.59 1.40 2.85 1.07 3.55 2.35 23 Singapore 0.9 51.2 21.9 0.2 12.3 13.5 49.1 34.4 16.0 0.5 0.02 0.29 0.10 0.01 0.09 0.74 24 Hong Kong, China (SAR) 1.0 3.9 20.5 0.4 63.2 11.0 94.8 1.5 3.2 0.5 0.04 0.06 0.25 0.05 1.12 1.55 25 Greece 1.9 14.4 42.6 1.0 27.4 12.7 83.4 10.5 5.3 0.8 0.11 0.27 0.68 0.15 0.63 2.33 26 Korea (Republic of) 0.9 35.7 7.4 1.6 50.3 4.2 86.5 2.4 10.6 0.5 0.09 1.08 0.19 0.38 1.86 1.23 27 Israel 1.0 76.1 6.8 0.7 14.6 0.8 24.8 4.3 70.4 0.4 0.06 1.47 0.11 0.12 0.35 0.14 28 Andorra 10.2 3.2 84.4 0.8 1.2 0.2 84.5 3.1 11.3 1.1 0.00 0.00 0.01 0.00 0.00 0.00 29 Slovenia 1.7 3.4 68.6 0.8 19.1 6.3 72.1 23.9 3.2 0.8 0.01 0.01 0.13 0.01 0.05 0.14 30 Brunei Darussalam 1.4 25.3 31.9 0.2 28.3 12.9 73.3 1.5 24.7 0.4 0.00 0.01 0.01 0.00 0.01 0.05 31 Kuwait 5.0 84.1 3.6 0.2 6.5 0.6 13.4 28.1 58.2 0.3 0.15 0.83 0.03 0.01 0.08 0.06 32 Cyprus 1.0 10.8 68.1 0.2 9.0 10.9 87.6 8.2 3.8 0.5 0.01 0.04 0.21 0.01 0.04 0.39 33 Qatar 7.6 59.3 12.6 0.2 18.4 1.9 35.2 7.3 57.2 0.4 0.01 0.02 0.00 0.00 0.01 0.01 34 Portugal 5.6 3.2 59.6 12.1 18.7 0.8 78.3 13.8 3.3 4.5 0.70 0.13 2.01 3.97 0.92 0.32 35 United Arab Emirates 6.6 71.9 8.3 0.2 11.5 1.5 21.6 6.2 71.6 0.5 0.05 0.18 0.02 0.00 0.04 0.04 36 Czech Republic 2.0 7.1 66.9 0.8 21.0 2.1 69.2 26.0 4.0 0.8 0.05 0.05 0.42 0.05 0.19 0.15 37 Barbados 1.1 3.4 25.6 4.7 64.9 0.4 90.7 5.0 3.7 0.5 0.01 0.01 0.05 0.08 0.17 0.01 38 Malta 1.8 3.4 35.9 0.5 16.5 42.0 93.9 1.9 3.4 0.8 0.01 0.01 0.07 0.01 0.05 0.94

HIGH HUMAN DEVELOPMENT 39 Bahrain 4.7 86.1 5.3 0.2 3.1 0.7 11.4 5.4 82.8 0.4 0.04 0.22 0.01 0.00 0.01 0.02 40 Estonia 1.6 6.7 81.1 0.2 9.1 1.4 47.2 42.0 10.1 0.7 0.02 0.03 0.26 0.01 0.04 0.05 41 Poland 1.7 8.9 53.3 1.4 31.8 2.9 74.8 18.0 6.4 0.8 0.22 0.37 1.88 0.46 1.63 1.20 42 Slovakia 1.7 4.7 83.1 0.6 9.2 0.7 84.9 10.7 3.5 0.8 0.05 0.05 0.68 0.05 0.11 0.07 43 Hungary 1.7 6.7 48.6 1.5 35.6 5.9 86.6 8.8 3.8 0.8 0.04 0.05 0.34 0.10 0.36 0.47 44 Chile 1.1 3.6 20.2 50.1 20.6 4.5 45.3 49.5 4.7 0.5 0.04 0.04 0.19 4.49 0.28 0.48 45 Croatia 1.6 3.2 72.2 0.5 13.4 9.0 87.0 9.1 3.2 0.8 0.06 0.04 0.75 0.05 0.20 1.08 46 Lithuania 1.7 8.7 76.4 0.4 11.6 1.2 28.2 62.0 9.0 0.8 0.03 0.06 0.42 0.02 0.09 0.08 47 Antigua and Barbuda 1.0 46.6 8.4 11.4 32.5 0.0 41.1 11.7 46.7 0.5 0.00 0.06 0.01 0.13 0.05 0.00 48 Latvia 1.6 7.8 71.6 0.3 15.7 3.0 35.3 52.2 11.8 0.8 0.02 0.04 0.29 0.01 0.09 0.14 49 Argentina 1.1 10.6 28.6 34.6 23.3 1.8 59.1 21.2 19.1 0.5 0.04 0.13 0.30 3.58 0.36 0.22 50 Uruguay 1.1 3.5 17.2 61.4 13.0 3.8 34.0 60.4 5.1 0.5 0.02 0.02 0.07 2.55 0.08 0.19 51 Cuba 1.1 3.5 9.0 4.2 82.2 0.0 91.3 3.8 4.3 0.5 0.07 0.08 0.17 0.75 2.21 0.01 52 Bahamas 1.1 3.5 8.2 1.9 84.7 0.6 93.7 2.5 3.2 0.5 0.00 0.00 0.01 0.01 0.08 0.00 53 Mexico 1.1 3.9 1.6 0.8 92.5 0.0 94.8 1.2 3.4 0.5 0.68 0.80 0.28 1.39 23.24 0.07 54 Costa Rica 1.1 3.8 6.2 16.7 71.9 0.3 78.8 10.0 10.8 0.5 0.01 0.01 0.01 0.31 0.20 0.01 55 Libyan Arab Jamahiriya 16.3 39.8 26.7 0.4 14.7 2.0 68.1 7.7 18.9 5.3 0.08 0.06 0.04 0.01 0.03 0.03 56 Oman 8.6 60.4 17.6 0.2 10.7 2.5 33.1 8.6 57.9 0.3 0.01 0.02 0.01 0.00 0.00 0.01 57 Seychelles 39.7 2.7 32.1 0.2 10.4 14.9 57.0 1.6 30.7 10.7 0.04 0.00 0.01 0.00 0.00 0.05 58 Venezuela ( Bolivarian Republic of) 1.0 3.4 37.1 22.5 35.6 0.4 72.7 21.6 5.2 0.5 0.02 0.02 0.22 1.32 0.31 0.02 59 Saudi Arabia 8.3 66.5 8.0 0.8 15.5 0.8 26.8 10.4 62.3 0.4 0.13 0.33 0.03 0.03 0.09 0.04

HDI rank

HUMAN DEVELOPMENT REPORT 2009

148

B International emigrants by area of residence

Africa AfricaVery highAsia AsiaHighEurope EuropeMedium Low

Latin America and the

Caribbean

Latin America and the

Caribbean Northern America

Northern AmericaOceania Oceania

Continent of residence 2000–2002

(% of total emigrant stocks)

Human develoment category of countries of residencea

2000–2002 (% of total emigrant stocks)

Share of continents’ immigrants from country 2000–2002

(% of total immigrant stocks in the continent)

Areas of residence

60 Panama 1.1 3.5 4.5 10.2 80.6 0.1 85.5 10.0 4.0 0.5 0.01 0.01 0.01 0.31 0.37 0.00 61 Bulgaria 1.5 68.3 24.3 0.6 4.9 0.4 24.2 57.8 17.2 0.7 0.09 1.28 0.38 0.09 0.11 0.07 62 Saint Kitts and Nevis 1.0 3.1 29.1 29.4 37.3 0.1 66.2 30.0 3.3 0.5 0.00 0.00 0.02 0.18 0.03 0.00 63 Romania 1.7 19.7 57.4 1.0 19.0 1.3 74.9 19.2 5.1 0.8 0.11 0.42 1.03 0.17 0.50 0.28 64 Trinidad and Tobago 1.1 3.4 9.7 4.0 81.4 0.4 91.6 3.9 3.9 0.6 0.02 0.02 0.05 0.22 0.67 0.03 65 Montenegro 1.6b 11.3b 72.3b 0.4b 10.8b 3.5b 76.2b 19.0b 4.0b 0.8b 0.17b 0.38b 2.07b 0.12b 0.45b 1.16 66 Malaysia 1.4 66.8 10.7 0.2 9.4 11.6 78.8 1.0 19.6 0.5 0.07 1.06 0.14 0.03 0.18 1.79 67 Serbia 1.6b 11.3b 72.3b 0.4b 10.8b 3.5b 76.2b 19.0b 4.0b 0.8b 0.17b 0.38b 2.07b 0.12b 0.45b 1.16

68 Belarus 1.8 8.6 86.8 0.2 2.6 0.1 7.7 67.4 24.1 0.8 0.20 0.31 2.64 0.05 0.11 0.04 69 Saint Lucia 1.1 3.3 21.3 40.4 33.8 0.1 55.1 38.5 5.8 0.5 0.00 0.00 0.02 0.34 0.04 0.00 70 Albania 1.6 3.9 88.2 0.5 5.6 0.2 89.6 6.2 3.4 0.7 0.08 0.06 1.23 0.06 0.11 0.04 71 Russian Federation 1.9 35.3 58.9 0.3 3.4 0.2 13.0 31.7 54.5 0.8 1.44 8.63 12.14 0.51 1.03 0.45 72 Macedonia (the Former Yugoslav Rep. of) 1.6 17.9 52.8 0.4 10.2 17.1 75.7 18.8 4.8 0.8 0.03 0.09 0.23 0.02 0.07 0.87 73 Dominica 1.0 3.6 25.9 23.9 45.5 0.0 71.5 24.3 3.7 0.5 0.00 0.00 0.02 0.17 0.05 0.00 74 Grenada 1.1 3.4 18.4 20.1 56.9 0.2 75.4 20.0 4.0 0.5 0.00 0.00 0.02 0.23 0.10 0.00 75 Brazil 1.0 30.4 23.8 18.9 25.3 0.6 69.3 8.8 21.4 0.5 0.06 0.59 0.39 3.00 0.60 0.11 76 Bosnia and Herzegovina 1.7 3.5 82.7 0.3 10.0 2.0 57.1 38.9 3.2 0.8 0.13 0.09 1.78 0.05 0.31 0.49 77 Colombia 1.1 3.5 18.9 43.3 33.0 0.3 52.2 43.8 3.5 0.5 0.11 0.12 0.53 11.80 1.35 0.09 78 Peru 1.0 9.4 20.0 27.4 41.3 0.8 66.6 26.7 6.2 0.5 0.05 0.14 0.25 3.36 0.76 0.12 79 Turkey 0.9 10.2 84.0 0.2 3.7 1.0 85.4 9.8 4.4 0.5 0.17 0.62 4.32 0.11 0.27 0.61 80 Ecuador 1.0 3.3 41.7 8.5 45.3 0.2 86.7 9.6 3.2 0.5 0.04 0.05 0.50 0.99 0.79 0.03 81 Mauritius 32.8 2.6 49.7 0.2 4.9 9.8 63.7 1.7 24.4 10.2 0.36 0.01 0.15 0.01 0.02 0.34 82 Kazakhstan 1.0 13.6 84.8 0.2 0.4 0.0 6.2 73.6 19.7 0.5 0.22 0.99 5.19 0.11 0.04 0.03 83 Lebanon 10.3 18.6 22.7 4.8 31.2 12.5 67.2 16.7 11.6 4.4 0.37 0.22 0.22 0.46 0.45 1.42

MEDIUM HUMAN DEVELOPMENT 84 Armenia 1.0 11.3 78.2 0.2 9.2 0.1 17.7 65.4 16.4 0.5 0.05 0.18 1.04 0.03 0.18 0.02 85 Ukraine 1.8 12.1 79.7 0.2 5.9 0.3 14.5 76.6 8.1 0.8 0.65 1.44 7.98 0.21 0.86 0.34 86 Azerbaijan 1.0 23.3 74.3 0.2 1.2 0.0 6.9 67.6 24.9 0.5 0.08 0.65 1.73 0.04 0.04 0.01 87 Thailand 1.0 60.1 13.0 0.2 22.3 3.4 43.7 30.3 25.5 0.5 0.06 1.04 0.19 0.03 0.47 0.57 88 Iran (Islamic Republic of) 5.1 17.9 34.9 0.3 39.6 2.3 82.8 6.6 10.1 0.5 0.30 0.33 0.55 0.04 0.91 0.41 89 Georgia 1.0 15.7 81.8 0.2 1.2 0.1 15.5 63.5 20.5 0.5 0.06 0.33 1.44 0.03 0.03 0.01 90 Dominican Republic 1.1 3.8 10.7 6.4 77.9 0.0 88.8 6.3 4.3 0.5 0.06 0.07 0.17 0.97 1.75 0.00 91 Saint Vincent and the Grenadines 1.1 3.4 16.5 27.1 51.9 0.1 68.5 27.5 3.4 0.5 0.00 0.00 0.02 0.25 0.07 0.00 92 China 1.1 64.0 7.2 0.9 23.3 3.5 79.5 6.5 13.5 0.5 0.41 7.53 0.71 0.89 3.35 3.99 93 Belize 1.1 3.5 4.4 7.6 83.3 0.1 88.1 4.0 7.3 0.5 0.00 0.00 0.00 0.07 0.11 0.00 94 Samoa 0.8 5.4 1.5 0.3 16.6 75.3 76.5 1.1 21.9 0.5 0.01 0.01 0.00 0.00 0.04 1.57 95 Maldives 1.4 38.9 34.5 0.7 4.8 19.8 60.6 3.1 35.8 0.5 0.00 0.00 0.00 0.00 0.00 0.00 96 Jordan 5.9 81.3 3.7 0.3 8.2 0.6 15.8 27.5 56.3 0.5 0.25 1.10 0.04 0.03 0.14 0.07 97 Suriname 1.0 3.1 82.2 11.0 2.7 0.0 83.7 3.9 12.0 0.5 0.02 0.02 0.38 0.49 0.02 0.00 98 Tunisia 9.3 9.9 78.3 0.2 2.3 0.1 81.1 6.8 8.7 3.4 0.35 0.12 0.81 0.02 0.03 0.01 99 Tonga 0.8 5.5 2.2 0.9 35.8 54.8 90.2 1.6 7.7 0.5 0.00 0.01 0.00 0.01 0.04 0.55 100 Jamaica 1.1 3.4 19.8 2.6 73.0 0.1 92.9 3.5 3.1 0.5 0.06 0.07 0.32 0.41 1.72 0.02 101 Paraguay 1.1 3.9 2.9 87.4 4.6 0.1 8.2 87.1 4.2 0.5 0.03 0.03 0.02 5.99 0.05 0.01 102 Sri Lanka 0.9 54.1 25.7 0.2 12.7 6.5 46.4 18.0 35.1 0.5 0.05 1.02 0.41 0.03 0.29 1.18 103 Gabon 69.9 2.1 26.1 0.2 1.7 0.0 27.6 1.2 59.8 11.4 0.25 0.00 0.03 0.00 0.00 0.00 104 Algeria 9.5 6.8 81.6 0.2 1.8 0.1 83.7 5.2 7.6 3.5 1.23 0.28 2.88 0.06 0.09 0.02 105 Philippines 0.9 35.4 8.7 0.2 49.9 4.9 66.5 25.4 7.6 0.5 0.20 2.43 0.50 0.14 4.20 3.30 106 El Salvador 1.1 3.5 2.4 5.1 86.8 1.0 90.5 2.9 6.1 0.5 0.07 0.07 0.04 0.84 2.15 0.19 107 Syrian Arab Republic 7.7 49.5 19.5 4.6 17.0 1.7 40.9 38.3 19.8 1.0 0.20 0.42 0.14 0.32 0.18 0.14 108 Fiji 0.8 5.0 4.4 0.3 38.0 51.6 92.5 1.1 5.9 0.5 0.01 0.01 0.01 0.01 0.13 1.46 109 Turkmenistan 1.0 12.1 86.2 0.2 0.5 0.0 10.2 71.7 17.6 0.5 0.02 0.06 0.38 0.01 0.00 0.00 110 Occupied Palestinian Territories 11.1 85.4 2.3 0.3 0.6 0.3 6.4 14.9 78.3 0.4 0.74 1.84 0.04 0.06 0.02 0.06 111 Indonesia 1.0 77.5 13.7 0.2 4.8 2.9 25.5 60.3 13.7 0.5 0.11 2.87 0.43 0.07 0.22 1.04 112 Honduras 1.1 3.6 3.4 10.8 81.1 0.1 84.9 3.7 10.9 0.5 0.02 0.03 0.02 0.65 0.73 0.00 113 Bolivia 1.1 4.9 8.2 70.5 15.1 0.2 24.4 70.7 4.4 0.5 0.03 0.04 0.05 4.56 0.15 0.02 114 Guyana 1.1 3.4 8.8 8.0 78.6 0.2 87.6 7.7 4.2 0.6 0.03 0.03 0.06 0.51 0.74 0.01 115 Mongolia 0.9 21.0 40.7 0.4 35.1 1.8 75.8 17.4 6.3 0.4 0.00 0.00 0.01 0.00 0.01 0.00 116 Viet Nam 0.9 15.1 18.3 0.2 57.4 8.0 85.0 2.7 11.8 0.5 0.12 0.61 0.63 0.07 2.86 3.16 117 Moldova 1.8 7.7 86.7 0.2 3.5 0.1 12.0 50.1 37.1 0.8 0.07 0.10 0.98 0.02 0.06 0.02 118 Equatorial Guinea 77.9 3.0 18.3 0.2 0.6 0.0 18.7 1.1 72.0 8.2 0.46 0.01 0.03 0.00 0.00 0.00

HDI rank

149

TABLE B

Africa AfricaVery highAsia AsiaHighEurope EuropeMedium Low

Latin America and the

Caribbean

Latin America and the

Caribbean Northern America

Northern AmericaOceania Oceania

Continent of residence 2000–2002

(% of total emigrant stocks)

Human develoment category of countries of residencea

2000–2002 (% of total emigrant stocks)

Share of continents’ immigrants from country 2000–2002

(% of total immigrant stocks in the continent)

Areas of residence

119 Uzbekistan 1.0 39.7 57.9 0.2 1.2 0.0 8.5 49.9 41.1 0.5 0.14 1.88 2.31 0.08 0.07 0.02 120 Kyrgyzstan 1.0 10.4 87.8 0.2 0.6 0.0 6.9 80.7 11.9 0.5 0.04 0.13 0.89 0.02 0.01 0.00 121 Cape Verde 33.8 3.0 49.1 0.2 14.0 0.0 62.3 1.7 10.8 25.2 0.42 0.01 0.17 0.01 0.07 0.00 122 Guatemala 1.1 3.7 3.0 9.1 83.0 0.1 86.4 5.6 7.5 0.5 0.04 0.05 0.03 0.91 1.25 0.01 123 Egypt 10.5 70.5 9.7 0.3 7.4 1.6 21.8 54.5 20.3 3.5 1.43 3.10 0.36 0.11 0.40 0.69 124 Nicaragua 1.1 3.5 2.5 48.4 44.4 0.1 47.3 46.0 6.2 0.5 0.04 0.04 0.02 4.23 0.58 0.02 125 Botswana 60.3 2.7 21.3 0.2 10.8 4.7 36.6 1.3 43.2 18.9 0.06 0.00 0.01 0.00 0.00 0.02 126 Vanuatu 0.8 5.3 25.4 0.3 2.8 65.4 57.2 1.6 40.8 0.4 0.00 0.00 0.00 0.00 0.00 0.05 127 Tajikistan 1.0 42.8 55.6 0.2 0.4 0.0 6.3 50.3 42.9 0.5 0.05 0.70 0.77 0.03 0.01 0.00 128 Namibia 77.8 2.5 11.3 0.2 5.4 2.7 19.5 1.1 36.6 42.8 0.12 0.00 0.00 0.00 0.00 0.01 129 South Africa 38.6 3.3 30.5 0.3 13.8 13.5 57.5 1.6 12.5 28.4 1.89 0.05 0.41 0.04 0.27 2.09 130 Morocco 9.1 13.2 74.5 0.2 2.8 0.1 82.8 5.8 7.8 3.5 1.48 0.69 3.29 0.09 0.18 0.03 131 Sao Tome and Principe 27.2 3.0 69.0 0.2 0.6 0.0 68.5 2.0 20.1 9.4 0.04 0.00 0.03 0.00 0.00 0.00 132 Bhutan 0.7 89.3 6.4 0.2 2.8 0.5 10.5 0.9 87.9 0.6 0.00 0.02 0.00 0.00 0.00 0.00 133 Lao People’s Democratic Republic 0.9 15.6 17.4 0.2 62.9 3.0 84.2 1.3 14.0 0.5 0.02 0.11 0.10 0.01 0.55 0.21 134 India 1.7 72.0 9.7 0.2 15.0 1.3 47.9 20.4 30.7 1.0 0.97 13.18 1.49 0.35 3.37 2.41 135 Solomon Islands 0.9 5.6 11.4 0.3 4.5 77.3 60.4 1.3 37.9 0.4 0.00 0.00 0.00 0.00 0.00 0.06 136 Congo 80.1 2.1 16.5 0.2 1.1 0.0 17.5 1.1 73.8 7.6 2.74 0.02 0.15 0.02 0.01 0.00 137 Cambodia 0.9 13.1 26.3 0.2 50.5 8.9 86.5 1.5 11.5 0.5 0.02 0.08 0.14 0.01 0.39 0.55 138 Myanmar 0.8 77.6 5.9 0.2 11.8 3.7 23.1 0.9 75.4 0.5 0.02 0.49 0.03 0.01 0.09 0.23 139 Comoros 42.0 4.8 52.4 0.2 0.6 0.0 52.2 4.5 37.8 5.5 0.13 0.00 0.04 0.00 0.00 0.00 140 Yemen 6.1 85.4 4.6 0.2 3.6 0.1 17.5 65.9 16.2 0.4 0.23 1.04 0.05 0.02 0.05 0.01 141 Pakistan 1.4 72.5 16.4 0.2 9.1 0.4 27.7 24.1 47.4 0.9 0.30 5.02 0.96 0.11 0.78 0.28 142 Swaziland 72.5 3.2 14.9 0.2 7.1 2.1 24.0 1.9 25.8 48.4 0.05 0.00 0.00 0.00 0.00 0.00 143 Angola 65.8 3.8 28.6 0.8 1.0 0.0 29.2 2.0 33.7 35.2 3.62 0.07 0.43 0.11 0.02 0.01 144 Nepal 0.7 95.0 2.4 0.2 1.3 0.3 5.6 2.2 91.6 0.6 0.05 1.99 0.04 0.03 0.03 0.07 145 Madagascar 28.2 3.0 65.8 0.5 2.4 0.1 67.2 15.3 8.7 8.9 0.27 0.01 0.17 0.01 0.01 0.00 146 Bangladesh 0.7 92.4 4.7 0.2 1.8 0.2 7.7 8.4 83.2 0.6 0.31 12.76 0.55 0.17 0.30 0.25 147 Kenya 41.5 4.2 37.9 0.2 14.4 1.8 53.6 1.6 39.8 5.0 1.18 0.04 0.29 0.02 0.16 0.16 148 Papua New Guinea 0.8 8.9 4.9 0.3 4.4 80.7 59.1 1.1 39.3 0.5 0.00 0.01 0.00 0.00 0.01 0.81 149 Haiti 1.1 3.4 5.5 25.7 64.3 0.0 70.0 12.1 17.3 0.5 0.05 0.05 0.07 3.19 1.20 0.00 150 Sudan 42.9 45.9 5.7 0.2 4.6 0.8 12.5 38.8 42.0 6.7 1.72 0.60 0.06 0.02 0.07 0.10 151 Tanzania ( United Republic of) 67.5 2.8 17.4 0.2 11.4 0.7 29.4 1.3 45.7 23.7 1.21 0.02 0.09 0.01 0.08 0.04 152 Ghana 74.8 3.4 12.2 0.2 9.1 0.2 21.6 1.0 16.5 60.8 4.48 0.07 0.20 0.03 0.22 0.05 153 Cameroon 48.9 3.2 38.8 0.2 8.9 0.1 47.2 1.5 36.7 14.6 0.52 0.01 0.11 0.01 0.04 0.00 154 Mauritania 75.9 4.5 17.1 0.2 2.3 0.0 19.3 3.6 18.9 58.2 0.55 0.01 0.03 0.00 0.01 0.00 155 Djibouti 41.7 5.0 48.0 0.2 4.7 0.5 52.4 4.5 11.5 31.5 0.04 0.00 0.01 0.00 0.00 0.00 156 Lesotho 93.5 2.3 2.8 0.1 1.1 0.2 4.2 0.9 23.6 71.3 0.30 0.00 0.00 0.00 0.00 0.00 157 Uganda 37.5 3.7 43.9 0.2 13.9 0.9 58.1 1.6 31.8 8.5 0.40 0.01 0.13 0.01 0.06 0.03 158 Nigeria 62.3 4.4 18.1 0.2 14.8 0.2 33.0 2.3 44.5 20.2 4.06 0.09 0.32 0.04 0.38 0.04

LOW HUMAN DEVELOPMENT 159 Togo 83.8 2.7 11.3 0.2 2.0 0.0 13.2 0.9 51.4 34.5 1.12 0.01 0.04 0.01 0.01 0.00 160 Malawi 83.7 2.5 11.6 0.2 1.7 0.4 13.6 1.1 43.4 41.9 0.79 0.01 0.03 0.00 0.01 0.01 161 Benin 91.6 3.1 4.6 0.2 0.5 0.0 5.2 0.8 43.5 50.4 3.30 0.04 0.05 0.02 0.01 0.00 162 Timor-Leste 0.8 39.5 18.2 0.2 0.2 41.0 59.8 1.2 38.5 0.4 0.00 0.02 0.01 0.00 0.00 0.19 163 Côte d’Ivoire 47.7 3.1 43.4 0.2 5.6 0.1 48.4 1.6 10.4 39.6 0.53 0.01 0.13 0.01 0.02 0.00 164 Zambia 78.3 2.9 13.2 0.2 3.8 1.6 18.5 1.1 53.8 26.5 1.21 0.01 0.06 0.01 0.02 0.08 165 Eritrea 78.2 11.5 5.6 0.2 4.3 0.3 10.4 9.4 13.1 67.1 2.78 0.13 0.05 0.02 0.06 0.03 166 Senegal 55.7 3.0 38.1 0.2 2.9 0.0 40.6 1.5 24.7 33.2 1.67 0.03 0.31 0.02 0.03 0.00 167 Rwanda 85.2 3.2 9.1 0.2 2.3 0.0 11.4 1.0 79.7 8.0 1.28 0.02 0.04 0.01 0.01 0.00 168 Gambia 44.7 2.9 39.7 0.2 12.4 0.1 51.6 1.5 16.5 30.4 0.14 0.00 0.03 0.00 0.02 0.00 169 Liberia 34.9 4.4 11.5 0.2 48.8 0.2 60.4 1.1 24.9 13.6 0.19 0.01 0.02 0.00 0.10 0.00 170 Guinea 90.3 3.0 5.1 0.2 1.4 0.0 6.6 0.8 10.2 82.4 3.29 0.04 0.05 0.02 0.02 0.00 171 Ethiopia 8.6 37.5 21.4 0.2 30.7 1.5 75.1 10.0 10.5 4.4 0.15 0.22 0.10 0.01 0.22 0.08 172 Mozambique 83.8 2.5 12.8 0.3 0.6 0.1 13.3 1.2 50.1 35.4 4.44 0.04 0.18 0.04 0.01 0.01 173 Guinea-Bissau 65.0 2.8 31.3 0.2 0.6 0.0 31.5 1.3 13.1 54.1 0.52 0.01 0.07 0.00 0.00 0.00 174 Burundi 90.8 3.2 4.6 0.2 1.1 0.0 5.8 0.9 84.2 9.1 2.21 0.03 0.03 0.01 0.01 0.00 175 Chad 90.7 5.5 3.1 0.2 0.5 0.0 3.8 3.7 74.3 18.1 1.72 0.03 0.02 0.01 0.00 0.00 176 Congo ( Dem. Republic of the) 79.7 2.6 15.3 0.2 2.2 0.0 17.4 1.1 48.6 32.8 4.09 0.04 0.21 0.02 0.04 0.01 177 Burkina Faso 94.0 3.0 2.4 0.2 0.3 0.0 2.9 0.8 8.9 87.5 7.93 0.08 0.06 0.04 0.01 0.00

HDI rank

HUMAN DEVELOPMENT REPORT 2009

150

B International emigrants by area of residence

Africa AfricaVery highAsia AsiaHighEurope EuropeMedium Low

Latin America and the

Caribbean

Latin America and the

Caribbean Northern America

Northern AmericaOceania Oceania

Continent of residence 2000–2002

(% of total emigrant stocks)

Human develoment category of countries of residencea

2000–2002 (% of total emigrant stocks)

Share of continents’ immigrants from country 2000–2002

(% of total immigrant stocks in the continent)

Areas of residence

178 Mali 91.1 3.1 5.1 0.2 0.5 0.0 5.7 0.9 17.5 76.0 8.99 0.10 0.14 0.05 0.02 0.00 179 Central African Republic 84.1 2.1 13.0 0.2 0.6 0.1 13.5 1.0 70.9 14.6 0.58 0.00 0.02 0.00 0.00 0.00 180 Sierra Leone 40.9 3.0 31.5 0.2 24.0 0.5 55.4 1.4 11.1 32.1 0.24 0.01 0.05 0.00 0.06 0.01 181 Afghanistan 0.8 91.4 4.4 0.2 2.7 0.5 11.0 4.6 84.0 0.4 0.14 4.82 0.20 0.08 0.17 0.25 182 Niger 93.3 3.0 3.0 0.2 0.5 0.0 3.6 0.8 20.6 75.0 2.90 0.03 0.02 0.02 0.01 0.00

OTHER UN MEMBER STATES Iraq 5.1 59.2 22.1 0.2 10.7 2.7 44.2 6.6 48.7 0.4 0.35 1.33 0.42 0.03 0.29 0.59 Kiribati 0.8 5.5 7.9 0.3 28.6 57.0 62.6 1.2 35.8 0.4 0.00 0.00 0.00 0.00 0.00 0.04 Korea ( Dem. People’s Rep. of) 0.9 47.5 2.0 0.9 48.6 0.0 85.9 1.5 12.2 0.5 0.03 0.46 0.02 0.07 0.58 0.00 Marshall Islands 0.8 25.1 3.5 1.0 64.2 5.4 69.1 4.0 26.4 0.5 0.00 0.01 0.00 0.00 0.02 0.01 Micronesia ( Federated States of) 0.8 23.1 3.9 1.1 30.4 40.7 35.7 30.2 33.6 0.5 0.00 0.01 0.00 0.00 0.02 0.20 Monaco 2.0 5.9 87.9 0.6 3.4 0.2 90.1 2.9 6.3 0.7 0.00 0.00 0.03 0.00 0.00 0.00 Nauru 0.7 5.6 6.9 4.2 11.1 71.5 86.3 4.7 8.7 0.4 0.00 0.00 0.00 0.00 0.00 0.01 Palau 0.7 55.3 3.3 1.6 17.6 21.6 22.3 12.7 64.5 0.5 0.00 0.01 0.00 0.00 0.01 0.05 San Marino 1.5 3.1 86.2 1.1 8.0 0.1 92.9 3.4 3.0 0.7 0.00 0.00 0.01 0.00 0.00 0.00 Somalia 50.8 9.6 27.5 0.2 10.8 1.0 39.2 8.2 11.7 41.0 1.71 0.10 0.25 0.02 0.14 0.11 Tuvalu 0.7 5.1 17.0 0.3 1.6 75.3 83.0 4.3 12.3 0.3 0.00 0.00 0.00 0.00 0.00 0.03 Zimbabwe 61.8 3.0 24.1 0.2 5.7 5.1 34.7 1.5 28.2 35.7 1.12 0.02 0.12 0.01 0.04 0.29

Africa 52.6 12.5 28.9 0.2 4.9 0.9 35.9 8.3 25.7 30.0 82.39T 6.31T 12.34T 0.97T 3.07T 4.41 Asia 1.7 54.7 24.5 0.5 16.4 2.2 41.7 23.2 34.5 0.6 6.83T 72.37T 27.34T 5.62T 26.57T 28.68 Europe 2.5 16.0 59.0 2.5 15.4 4.6 52.6 28.1 18.1 1.2 8.39T 17.25T 53.66T 21.75T 20.39T 48.18 Latin America and the Caribbean 1.1 5.1 10.3 13.4 69.8 0.3 81.7 12.1 5.6 0.5 1.77T 2.73T 4.69T 59.05T 46.01T 1.70 Northern America 2.2 14.7 23.6 21.0 34.9 3.7 62.8 23.5 12.6 1.1 0.49T 1.07T 1.44T 12.46T 3.09T 2.60 Oceania 1.4 8.7 20.1 0.6 22.5 46.7 84.3 2.8 12.3 0.6 0.13T 0.28T 0.54T 0.16T 0.87T 14.44

OECD 2.4 9.0 36.4 4.8 41.2 6.2 83.1 9.7 6.0 1.2 6.84T 8.22T 28.10T 35.99T 46.29T 55.89 European Union (EU27) 3.1 10.7 49.1 4.4 24.6 8.0 77.4 14.9 6.2 1.5 5.47T 6.04T 23.25T 20.41T 16.91T 43.70 GCC 6.1 77.9 5.9 0.3 9.1 0.8 18.0 17.6 63.9 0.4 0.39T 1.60T 0.10T 0.05T 0.23T 0.17 Very high human development 3.0 14.3 39.2 6.3 28.2 9.0 76.7 11.9 9.9 1.4 6.08T 9.43T 21.71T 34.20T 22.75T 57.60 Very high HD: OECD 3.1 10.7 41.4 7.0 28.5 9.3 79.4 12.1 7.0 1.5 5.68T 6.32T 20.60T 33.87T 20.67T 53.47 Very high HD: non-OECD 1.9 46.4 19.6 0.6 25.3 6.3 53.8 10.4 35.3 0.5 0.39T 3.11T 1.11T 0.33T 2.08T 4.14 High human development 1.7 16.5 43.8 4.4 32.4 1.3 56.4 23.9 18.9 0.7 5.53T 17.75T 39.74T 38.67T 42.85T 13.42 Medium human development 7.4 43.3 27.8 2.1 17.6 1.8 42.6 25.3 28.9 3.2 35.37T 66.96T 36.26T 26.71T 33.33T 27.88 Low human development 64.1 21.9 10.2 0.2 3.2 0.4 15.0 2.6 40.8 41.6 53.02T 5.85T 2.29T 0.42T 1.07T 1.10 World (excluding the former Soviet 10.8 29.2 24.8 4.2 27.4 3.5 59.6 13.3 21.1 6.0 96.81T 84.39T 60.44T 98.72T 97.03T 98.57 Union and Czechoslovakia)

World 9.1 28.2 33.4 3.4 23.0 2.9 51.1 20.7 23.3 5.0 100.00T 100.00T 100.00T 100.00T 100.00T 100.00

NOTES

a Percentages may not sum to 100% due to movements

to areas not classified by human development

categories.

b Data refer to Serbia and Montenegro prior to its

separation into two independent states in June 2006.

SOURCES

All columns: calculated based on data from Migration

DRC (2007).

HDI rank

151

HUMAN DEVELOPMENT REPORT 2009

TABLE CEducation and employment of international migrants in OECD countries (aged 15 years and above)

(thousands) (% of all migrants) (%) (% of all migrants) (% of labour force)

Stock of international migrants in

OECD countries

less than upper

secondary

Low Low

upper secondary or

post-secondary non-tertiary

Medium Medium

tertiary

High High

Tertiary emigration

rate

Labour force participation

rateb

(both sexes)

Total unemployment

rateb

(both sexes)

less than upper

secondary

upper secondary or

post-secondary non-tertiary tertiary

Educational attainment levels of international migrantsa By level of educational attainmenta

Economic activity status of international migrants

Unemployment rates of international migrantsC

VERY HIGH HUMAN DEVELOPMENT 1 Norway 123.3 21.7 38.1 31.7 4.5 45.1 5.7 8.5 6.8 3.8 2 Australia 291.9 16.6 36.1 42.3 2.5 73.4 6.1 10.7 7.5 3.7 3 Iceland 22.7 15.3 39.0 33.5 18.0 65.2 4.8 9.0 4.4 3.9 4 Canada 1,064.1 18.3 40.7 39.4 3.0 58.3 4.1 7.9 5.1 2.5 5 Ireland 788.1 37.8 25.3 22.4 22.1 55.2 5.1 7.7 4.8 3.0 6 Netherlands 583.4 25.9 36.6 31.8 6.2 55.7 4.5 6.8 4.5 3.4 7 Sweden 201.5 18.0 37.5 36.8 4.6 62.4 7.2 15.4 8.8 3.9 8 France 1,135.6 32.0 30.7 32.2 4.2 60.2 7.7 13.2 7.6 4.6 9 Switzerland 427.2 34.6 40.0 24.0 9.8 60.3 10.4 14.8 9.9 6.3 10 Japan 565.4 10.4 38.9 49.0 1.1 57.7 4.4 8.5 5.3 3.2 11 Luxembourg 31.3 39.0 32.4 23.7 .. 50.4 8.8 13.2 8.9 4.8 12 Finland 257.2 30.4 42.5 23.5 6.1 53.6 4.7 5.8 5.0 3.3 13 United States 840.6 19.6 29.3 46.6 0.4 60.3 5.7 9.6 7.8 3.9 14 Austria 383.1 23.4 45.0 27.3 9.8 55.3 3.2 5.1 3.0 2.3 15 Spain 757.6 51.7 26.8 17.6 2.4 52.7 7.5 9.4 7.6 4.7 16 Denmark 159.5 20.3 38.3 33.3 6.3 54.2 5.0 7.8 5.5 3.7 17 Belgium 350.8 34.5 32.4 30.8 5.8 54.7 8.7 14.4 9.8 4.6 18 Italy 2,357.1 57.5 26.3 11.5 3.8 48.4 8.0 11.0 6.5 3.6 19 Liechtenstein 3.5 27.5 46.9 19.5 .. 59.6 3.7 5.1 3.4 2.8 20 New Zealand 413.1 30.6 34.7 26.5 8.2 76.4 6.9 10.4 6.5 3.7 21 United Kingdom 3,241.3 25.7 36.7 33.1 10.3 59.7 5.4 9.5 5.6 3.3 22 Germany 3,122.5 26.6 43.0 27.4 7.1 57.2 7.9 14.2 7.9 4.6 23 Singapore 106.6 19.7 32.2 43.5 12.9 63.9 5.9 7.0 7.4 4.4 24 Hong Kong, China (SAR) 388.4 27.9 31.4 37.9 16.8 61.7 6.8 7.1 9.0 5.4 25 Greece 685.8 55.3 26.0 15.1 7.9 49.6 6.3 8.8 3.9 4.6 26 Korea (Republic of) 975.3 16.4 39.3 43.6 .. 58.8 5.5 8.8 6.1 4.3 27 Israel 162.7 18.3 37.0 42.7 5.4 65.6 6.2 11.2 7.4 4.0 28 Andorra 3.4 46.3 27.2 25.6 .. 47.7 11.9 12.8 11.9 10.8 29 Slovenia 78.4 47.3 39.1 11.4 .. 39.1 6.3 7.4 6.2 4.5 30 Brunei Darussalam 8.9 19.1 41.1 37.7 .. 63.3 6.3 5.8 9.2 4.3 31 Kuwait 37.1 16.7 36.9 44.2 6.5 53.8 9.6 18.9 12.3 6.3 32 Cyprus 140.5 41.0 28.4 23.0 24.8 54.4 6.8 8.9 7.0 4.7 33 Qatar 3.3 16.1 37.0 43.9 .. 45.7 10.7 14.5 15.8 6.9 34 Portugal 1,260.2 67.2 23.4 6.2 6.3 71.0 7.7 8.5 6.7 5.3 35 United Arab Emirates 14.4 21.0 50.2 24.2 .. 40.8 14.9 18.8 17.1 10.6 36 Czech Republic 242.5 22.6 51.6 23.7 .. 55.9 11.0 30.5 10.9 3.6 37 Barbados 88.4 30.0 40.2 26.3 47.3 66.0 6.3 9.2 6.5 4.0 38 Malta 98.0 53.2 24.5 13.5 .. 54.0 4.9 5.8 4.6 3.2

HIGH HUMAN DEVELOPMENT 39 Bahrain 7.2 15.8 40.6 40.2 5.3 61.7 7.9 6.1 10.1 6.7 40 Estonia 36.0 26.6 36.6 30.6 .. 37.2 11.4 15.4 13.8 7.5 41 Poland 2,112.6 30.6 46.2 21.1 12.3 59.5 10.7 15.8 11.1 6.1 42 Slovakia 361.5 40.7 45.5 12.9 .. 48.8 15.7 34.8 10.8 3.9 43 Hungary 331.5 25.6 44.1 27.4 8.4 46.6 6.5 11.1 6.2 5.0 44 Chile 207.9 25.1 41.8 29.9 3.8 65.8 8.8 12.6 9.2 6.1 45 Croatia 488.9 45.7 39.4 12.4 .. 56.7 8.4 15.9 3.6 3.6 46 Lithuania 134.4 35.8 39.6 21.8 .. 28.9 11.6 19.3 13.6 6.1 47 Antigua and Barbuda 24.3 29.7 41.4 26.6 .. 68.0 8.1 12.8 8.9 3.9 48 Latvia 54.8 19.5 36.1 35.8 .. 39.7 6.5 11.0 7.3 5.2 49 Argentina 322.3 31.1 34.8 32.6 2.0 62.8 9.9 13.6 9.8 7.6 50 Uruguay 74.4 34.7 37.0 26.3 5.1 67.3 9.5 12.5 9.4 6.6 51 Cuba 924.6 40.8 35.1 23.9 .. 52.5 8.0 12.0 7.5 5.2 52 Bahamas 30.1 23.3 46.9 29.4 .. 63.8 9.7 16.8 11.2 4.6 53 Mexico 8,327.9 69.6 24.7 5.7 6.5 60.1 9.4 10.6 7.7 5.2 54 Costa Rica 75.7 31.5 43.7 24.4 3.9 64.8 6.6 10.4 6.1 3.8 55 Libyan Arab Jamahiriya 64.8 44.3 30.6 23.6 .. 51.2 7.6 8.0 6.9 7.4 56 Oman 2.6 13.6 44.6 37.5 .. 34.4 7.7 7.5 10.4 6.1 57 Seychelles 8.1 42.6 31.5 17.3 .. 60.3 9.7 12.6 8.4 7.4 58 Venezuela (Bolivarian Republic of) 233.3 27.0 35.8 36.7 3.8 64.3 11.3 15.0 12.7 8.1 59 Saudi Arabia 34.1 22.8 38.8 35.8 .. 43.5 11.8 18.4 13.2 8.2

HDI rank

HUMAN DEVELOPMENT REPORT 2009

152

C Education and employment of international migrants in OECD countries (aged 15 years and above)

60 Panama 139.8 16.9 50.0 32.9 11.1 65.5 6.1 13.3 6.8 3.3 61 Bulgaria 604.4 51.0 31.3 13.0 .. 59.2 9.3 8.9 10.1 8.7 62 Saint Kitts and Nevis 20.0 33.0 35.5 26.6 .. 66.8 6.6 10.5 6.1 4.2 63 Romania 1,004.6 32.7 43.9 22.3 .. 59.8 8.8 12.1 8.8 5.9 64 Trinidad and Tobago 274.2 23.3 46.2 29.7 66.4 70.2 7.1 11.5 7.6 4.1 65 Montenegro ..c 52.1d 30.2d 10.6d .. 55.9d 13.6d 16.3d 12.2d 7.8d

66 Malaysia 214.3 18.4 28.8 47.6 11.3 65.7 6.2 8.3 9.0 4.3 67 Serbia 1,044.4 52.1d 30.2d 10.6d .. 55.9d 13.6d 16.3d 12.2d 7.8d

68 Belarus 151.1 37.1 37.3 25.0 .. 29.1 10.4 14.7 13.9 6.4 69 Saint Lucia 24.5 37.9 37.0 20.3 .. 65.6 9.0 12.6 8.4 5.5 70 Albania 524.1 54.0 34.6 8.7 .. 68.8 10.0 10.3 9.3 10.6 71 Russian Federation 1,524.4 33.9 37.9 27.1 .. 58.0 15.7 19.6 15.7 13.0 72 Macedonia (the Former Yugoslav Rep. of) 175.7 57.1 24.4 7.4 .. 59.6 10.0 11.0 8.1 8.0 73 Dominica 25.7 40.4 34.0 21.7 .. 64.3 9.9 13.1 9.9 6.4 74 Grenada 46.4 34.2 39.6 23.3 .. 69.0 8.3 12.3 7.9 4.7 75 Brazil 544.1 30.6 38.8 25.9 1.6 70.9 6.8 9.0 6.2 5.7 76 Bosnia and Herzegovina 569.9 44.3 42.0 9.6 .. 68.3 11.0 14.2 9.0 7.8 77 Colombia 691.7 33.9 40.5 24.8 5.8 63.9 11.5 16.3 10.2 8.3 78 Peru 415.1 24.7 44.8 28.6 3.0 67.7 8.4 12.0 8.0 6.8 79 Turkey 2,085.5 69.0 21.6 6.7 3.2 58.1 19.6 23.2 15.9 5.2 80 Ecuador 503.7 48.8 35.8 15.0 5.8 69.8 10.9 12.6 9.9 8.1 81 Mauritius 91.4 42.9 27.9 24.4 48.5 69.3 11.7 16.2 12.6 4.8 82 Kazakhstan 415.7 35.1 48.0 16.6 .. 60.0 13.0 17.9 12.4 8.9 83 Lebanon 335.5 33.8 31.6 30.9 .. 56.9 10.4 15.3 11.0 6.9

MEDIUM HUMAN DEVELOPMENT 84 Armenia 79.4 27.3 41.5 30.3 .. 56.6 14.4 21.4 13.8 11.4 85 Ukraine 773.0 36.8 34.8 27.0 .. 36.1 9.8 12.3 10.9 7.9 86 Azerbaijan 30.1 25.2 33.0 39.8 .. 57.1 16.9 21.2 16.8 14.8 87 Thailand 269.7 34.8 31.9 27.6 1.5 58.7 9.0 13.5 8.5 5.3 88 Iran (Islamic Republic of) 616.0 17.2 34.4 45.9 8.3 62.5 8.6 19.4 9.5 6.2 89 Georgia 84.7 35.8 35.4 24.8 .. 58.6 16.9 19.6 16.1 15.1 90 Dominican Republic 695.3 53.2 34.2 12.3 9.8 56.7 13.3 17.1 11.3 7.2 91 Saint Vincent and the Grenadines 34.8 34.4 38.6 24.5 .. 68.1 8.9 11.8 9.5 5.5 92 China 2,068.2 31.0 25.1 39.4 3.0 58.5 6.1 7.8 6.9 4.9 93 Belize 42.6 30.5 48.7 20.4 .. 66.0 8.4 11.2 8.5 5.7 94 Samoa 71.5 31.1 44.1 8.7 .. 62.0 13.5 15.9 12.6 7.8 95 Maldives 0.4 25.8 40.5 30.0 .. 30.0 13.1 18.2 4.7 14.5 96 Jordan 63.9 20.0 37.8 41.0 4.6 61.9 7.9 12.0 8.5 6.2 97 Suriname 7.1 23.9 43.2 30.9 .. 61.0 6.9 15.6 6.2 3.5 98 Tunisia 427.5 55.5 27.8 15.9 14.3 57.0 20.6 26.4 18.8 10.3 99 Tonga 40.9 34.6 44.8 9.5 .. 62.0 11.3 14.1 9.9 6.5

100 Jamaica 789.7 33.1 39.6 24.2 72.6 68.9 7.9 11.9 7.9 4.3 101 Paraguay 20.1 37.1 37.5 23.9 1.9 69.3 6.9 7.5 6.9 6.3 102 Sri Lanka 316.9 32.7 34.4 26.4 19.4 67.8 10.5 13.5 10.9 7.0 103 Gabon 10.8 29.9 33.1 35.9 .. 49.7 23.1 32.6 24.3 17.2 104 Algeria 1,313.3 55.4 27.8 16.4 15.4 53.0 21.9 29.0 20.3 11.7 105 Philippines 1,930.3 17.4 35.1 45.9 7.4 68.7 4.9 8.9 5.6 3.5 106 El Salvador 835.6 62.9 29.2 7.7 14.1 64.7 8.4 9.6 6.9 5.7 107 Syrian Arab Republic 130.2 33.0 30.3 33.3 3.8 55.3 10.5 13.7 10.5 8.6 108 Fiji 119.0 30.8 41.5 21.4 38.3 69.9 7.5 9.6 7.4 5.3 109 Turkmenistan 4.9 25.4 48.4 24.8 .. 45.8 16.3 17.3 17.0 14.6 110 Occupied Palestinian Territories 15.5 23.5 28.2 40.5 .. 46.7 12.1 13.9 13.6 10.9 111 Indonesia 339.4 24.8 38.3 34.5 1.8 48.8 4.4 3.4 4.4 4.5 112 Honduras 275.6 57.2 32.2 10.6 12.0 63.7 10.0 12.0 8.5 5.5 113 Bolivia 76.8 24.9 44.1 29.4 3.3 66.6 8.5 11.0 8.9 6.3 114 Guyana 303.6 31.0 42.9 25.0 76.9 68.6 6.6 10.2 6.4 4.0 115 Mongolia 4.3 16.5 35.1 45.7 .. 58.6 9.7 9.2 7.6 11.3 116 Viet Nam 1,518.1 40.7 34.8 22.9 .. 64.6 7.7 10.5 7.2 4.7 117 Moldova 41.4 26.8 37.4 34.6 .. 63.7 12.3 16.9 11.4 10.3 118 Equatorial Guinea 12.1 52.0 25.5 22.4 .. 63.3 22.3 26.9 20.9 15.0

HDI rank (thousands) (% of all migrants) (%) (% of all migrants) (% of labour force)

Stock of international migrants in

OECD countries

less than upper

secondary

Low Low

upper secondary or

post-secondary non-tertiary

Medium Medium

tertiary

High High

Tertiary emigration

rate

Labour force participation

rateb (both sexes)

Total unemployment

rateb (both sexes)

less than upper

secondary

upper secondary or

post-secondary non-tertiary tertiary

Educational attainment levels of international migrantsa By level of educational attainmenta

Economic activity status of international migrants

Unemployment rates of international migrants

153

TABLE C

119 Uzbekistan 45.2 25.0 40.0 33.9 .. 59.0 12.5 16.0 12.7 10.5 120 Kyrgyzstan 34.1 33.5 47.9 18.4 .. 58.8 12.8 17.3 12.3 9.7 121 Cape Verde 87.9 73.7 19.1 5.9 .. 70.5 9.4 9.7 9.7 5.1 122 Guatemala 485.3 63.6 27.9 8.4 11.2 63.5 8.2 9.1 7.4 5.4 123 Egypt 308.7 18.8 30.7 47.3 3.7 59.9 8.3 12.9 9.7 6.5 124 Nicaragua 221.0 40.7 41.1 18.1 14.3 61.6 8.7 12.0 8.0 5.2 125 Botswana 4.1 12.3 46.3 37.1 4.2 45.3 14.3 10.6 17.6 10.6 126 Vanuatu 1.7 27.8 39.1 27.2 .. 63.4 12.6 16.6 10.1 12.1 127 Tajikistan 8.9 30.4 45.1 24.1 .. 57.5 12.4 18.0 12.3 8.5 128 Namibia 3.1 15.3 34.8 45.9 .. 70.3 6.0 10.6 6.1 4.8 129 South Africa 351.7 14.6 34.6 44.8 6.8 74.2 5.5 10.1 6.6 3.7 130 Morocco 1,505.0 61.1 23.1 13.9 .. 60.9 19.8 22.6 19.0 12.2 131 Sao Tome and Principe 11.6 72.2 16.9 10.7 .. 73.7 9.3 9.8 9.9 5.8 132 Bhutan 0.7 39.1 30.6 23.7 .. 57.4 14.1 13.4 12.7 14.1 133 Lao People’s Democratic Republic 264.2 49.5 35.7 14.2 .. 63.0 9.6 12.4 8.4 6.0 134 India 1,952.0 25.5 19.5 51.2 3.5 66.6 5.9 9.8 7.0 4.3 135 Solomon Islands 1.8 25.3 29.5 36.8 .. 63.5 10.8 18.3 15.0 5.7 136 Congo 68.7 27.1 34.2 34.9 25.7 72.4 26.4 37.4 28.3 18.5 137 Cambodia 239.1 52.4 30.8 15.2 .. 62.2 11.2 14.6 9.5 6.4 138 Myanmar 61.2 25.0 26.2 40.9 2.5 61.7 5.8 8.2 6.5 4.5 139 Comoros 17.6 63.6 25.6 10.7 .. 66.8 40.8 45.4 36.1 25.7 140 Yemen 31.9 47.0 30.2 19.3 .. 56.3 9.1 8.8 10.6 6.8 141 Pakistan 669.0 43.6 21.4 30.3 9.8 55.2 10.9 15.1 10.6 7.3 142 Swaziland 1.8 19.8 32.9 42.9 3.2 69.6 7.4 12.2 6.6 6.1 143 Angola 196.2 52.9 26.5 19.5 .. 77.0 9.7 11.4 10.2 4.9 144 Nepal 23.9 21.3 33.0 39.2 3.0 72.0 6.3 6.2 7.2 5.8 145 Madagascar 76.6 33.3 34.6 31.7 .. 67.2 17.7 25.0 18.3 11.9 146 Bangladesh 285.7 46.2 22.3 27.2 3.2 54.8 12.5 17.9 12.0 7.5 147 Kenya 198.1 26.0 32.7 36.9 27.2 73.6 6.1 8.2 7.0 4.1 148 Papua New Guinea 25.9 28.0 33.8 31.2 15.1 70.3 8.7 13.2 9.5 4.9 149 Haiti 462.9 39.3 40.6 20.0 67.5 66.2 11.3 15.2 10.8 6.6 150 Sudan 42.1 23.4 32.9 39.7 4.6 59.4 16.2 25.1 14.8 13.9 151 Tanzania ( United Republic of) 70.2 25.1 30.4 40.7 15.6 69.9 5.9 8.1 7.4 4.2 152 Ghana 165.6 26.5 38.4 31.3 33.7 75.7 9.6 14.2 9.7 6.4 153 Cameroon 58.5 23.3 32.3 41.9 12.5 68.9 21.8 32.6 24.5 15.9 154 Mauritania 15.2 63.1 19.1 17.2 .. 72.0 22.2 23.1 24.8 15.8 155 Djibouti 5.4 34.1 34.7 29.7 .. 56.5 24.9 37.4 23.2 16.8 156 Lesotho 0.9 18.3 31.6 45.8 3.8 62.5 6.0 .. 9.9 3.8 157 Uganda 82.1 27.4 29.0 39.0 24.2 72.9 6.9 9.0 8.1 5.0 158 Nigeria 261.0 15.5 28.4 53.1 .. 75.4 11.2 20.7 13.9 7.9

LOW HUMAN DEVELOPMENT 159 Togo 18.4 27.9 34.1 35.8 11.8 71.9 21.3 28.0 22.2 16.2 160 Malawi 14.9 32.5 28.5 34.8 15.5 70.4 7.2 10.2 7.7 4.7 161 Benin 14.4 25.8 30.5 42.2 11.3 70.9 19.7 26.9 22.8 14.3 162 Timor-Leste 11.1 57.1 23.4 12.4 .. 62.6 12.1 14.8 11.6 4.5 163 Côte d’Ivoire 62.6 38.1 34.2 26.4 .. 70.7 22.7 28.0 22.9 16.1 164 Zambia 34.9 14.2 34.4 47.9 15.5 77.1 6.3 11.9 7.7 4.1 165 Eritrea 48.0 36.0 39.3 20.7 .. 65.2 11.3 14.8 10.3 7.8 166 Senegal 133.2 56.6 23.6 19.1 18.6 74.8 18.5 20.4 19.2 12.3 167 Rwanda 14.8 25.4 32.6 34.9 20.8 59.0 26.4 37.4 27.3 21.5 168 Gambia 20.9 47.9 30.9 16.5 44.6 67.9 15.0 20.3 12.1 7.5 169 Liberia 41.0 20.6 44.8 33.5 24.7 73.7 9.3 20.8 9.2 5.0 170 Guinea 21.3 49.6 25.4 22.4 .. 68.2 24.6 31.6 20.2 15.7 171 Ethiopia 124.4 24.3 43.6 29.2 .. 68.4 9.5 14.9 8.9 7.0 172 Mozambique 85.7 44.2 28.8 26.4 53.6 77.9 6.7 8.9 7.0 3.5 173 Guinea-Bissau 30.0 66.3 20.5 12.8 71.5 76.5 16.7 18.0 16.3 11.2 174 Burundi 10.6 24.3 28.7 38.0 .. 60.5 24.5 37.0 26.5 18.1 175 Chad 5.8 22.7 33.1 42.2 .. 73.5 20.5 30.6 20.6 16.5 176 Congo (Democratic Republic of the) 100.7 25.0 32.5 35.5 9.6 66.5 21.8 31.9 24.4 15.1 177 Burkina Faso 8.3 46.9 22.6 28.5 .. 72.3 15.3 16.8 13.9 13.8

HDI rank (thousands) (% of all migrants) (%) (% of all migrants) (% of labour force)

Stock of international migrants in

OECD countries

less than upper

secondary

Low Low

upper secondary or

post-secondary non-tertiary

Medium Medium

tertiary

High High

Tertiary emigration

rate

Labour force participation

rateb (both sexes)

Total unemployment

rateb (both sexes)

less than upper

secondary

upper secondary or

post-secondary non-tertiary tertiary

Educational attainment levels of international migrantsa By level of educational attainmenta

Economic activity status of international migrants

Unemployment rates of international migrants

HUMAN DEVELOPMENT REPORT 2009

154

178 Mali 45.2 68.3 18.7 12.6 14.6 74.9 24.9 27.1 24.4 14.4 179 Central African Republic 9.8 33.4 33.1 32.7 9.1 69.1 24.2 35.6 23.6 17.8 180 Sierra Leone 40.2 23.5 37.4 33.7 34.5 71.8 10.7 19.1 10.5 6.5 181 Afghanistan 141.2 44.7 28.9 19.4 6.4 47.3 13.6 13.9 13.1 12.5 182 Niger 4.8 26.6 34.3 37.5 5.8 68.1 18.5 27.8 17.8 14.1

OTHER UN MEMBER STATES Iraq 335.5 38.9 26.9 26.6 8.4 49.5 17.8 27.4 12.5 12.6 Kiribati 1.7 38.3 33.9 20.2 .. 57.5 8.4 7.7 11.6 4.8 Korea (Democratic People’s Rep. of) 1.2 21.7 32.1 38.6 .. 58.3 6.5 8.3 4.7 6.7 Marshall Islands 5.3 34.9 54.1 10.9 .. 58.1 19.9 27.9 20.5 4.8 Micronesia (Federated States of) 6.5 26.9 59.7 13.3 .. 68.9 11.5 17.9 11.1 4.6 Monaco 12.3 41.4 35.1 23.0 .. 50.8 11.1 16.4 12.3 5.7 Nauru 0.5 35.3 34.7 21.6 .. 62.4 8.2 22.2 6.0 2.4 Palau 2.1 12.7 58.9 28.3 .. 71.5 8.1 12.1 9.2 5.1 San Marino 2.8 61.6 25.7 12.4 .. 44.3 4.3 6.2 2.7 3.6 Somalia 125.1 44.0 30.6 12.5 .. 42.0 28.2 37.0 24.0 18.9 Tuvalu 0.9 38.9 27.2 6.2 .. 57.2 16.1 19.2 13.0 6.8 Zimbabwe 77.4 14.9 39.9 40.6 9.4 73.4 7.0 11.0 8.6 4.4

Africa 6,555.3T 44.6 28.6 24.5 9.3 63.4 16.5 22.8 15.7 9.0 Asia 17,522.0T 33.0 29.8 34.3 3.6 60.9 9.0 14.6 8.6 5.0 Europe 27,318.1T 38.6 35.7 21.6 7.0 56.5 8.8 12.6 8.5 5.3 Latin America and the Caribbean 18,623.0T 53.8 31.9 13.8 6.0 61.4 9.4 11.6 8.3 5.7 Northern America 1,923.8T 18.8 35.8 42.5 0.7 59.3 4.8 8.6 6.1 3.2 Oceania 1,098.2T 26.6 38.7 27.4 4.0 71.4 7.8 11.8 7.9 4.2 OECD 33,500.2T 44.5 32.3 20.3 2.9 58.3 8.5 12.2 7.7 4.1 European Union (EU27) 20,514.2T 37.1 35.9 23.0 7.0 56.7 7.6 11.5 7.6 4.3 GCC 98.6T 19.2 40.0 37.9 6.3 48.1 11.0 17.6 13.4 7.3 Very high human development 21,480.5T 33.4 34.5 27.9 2.7 57.9 6.6 10.4 6.7 3.9 Very high HD: OECD 20,281.1T 33.5 34.6 27.6 2.6 57.8 6.6 10.5 6.6 3.8 Very high HD: non-OECD 1,199.3T 30.6 33.2 32.2 12.2 59.3 6.6 8.2 7.9 4.8 High human development 28,213.0T 49.4 33.2 15.7 5.1 59.3 10.9 14.0 9.8 6.6 Medium human development 22,102.2T 37.8 30.4 29.2 5.2 61.8 10.3 15.2 9.9 6.0 Low human development 1,244.8T 37.7 32.1 25.8 12.8 65.9 16.1 21.5 15.2 10.4 World (excluding the former Soviet 69,018.3T 41.4 32.3 23.5 3.7 60.3 9.3 13.3 8.7 5.2 Union and Czechoslovakia) World 75,715.9Te 41.0 32.7 23.5 3.7 59.7 9.5 13.6 9.0 5.5

NOTES

a. Percentages may not sum to 100% as those whose

educational attainment levels are unknown are

excluded.

b. Persons whose economic activity status is unknown

are excluded.

c. Data for Montenegro are included with those from

Serbia.

d. Data refer to Serbia and Montenegro prior to its

separation into two independent states in June 2006.

e. Data are aggregates from original data source.

SOURCES

Columns 1–4 and 8–10: OECD (2009a).

Columns 5: OECD (2008a).

Columns 6 and 7: calculated based on data

from OECD (2009a).

Education and employment of international migrants in OECD countries (aged 15 years and above)

HDI rank (thousands) (% of all migrants) (%) (% of all migrants) (% of labour force)

Stock of international migrants in

OECD countries

less than upper

secondary

Low Low

upper secondary or

post-secondary non-tertiary

Medium Medium

tertiary

High High

Tertiary emigration

rate

Labour force participation

rateb (both sexes)

Total unemployment

rateb (both sexes)

less than upper

secondary

upper secondary or

post-secondary non-tertiary tertiary

Educational attainment levels of international migrantsa By level of educational attainmenta

Economic activity status of international migrants

Unemployment rates of international migrants

155

HUMAN DEVELOPMENT REPORT 2009

DTABLE Conflict and insecurity-induced movement

Total (thousands)

2007

Share of international

emigrant stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

Total (thousands)

2008

Total (thousands)

2007

Share of international immigrant

stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

InternalInternational

By country of origin

International

By country of asylum

Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

Internally displaced peopled Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

VERY HIGH HUMAN DEVELOPMENT 1 Norway 0.0 0.0 0.0 0.0 0.0 .. 34.5 9.3 0.2 0.0 6.7 2 Australia 0.1 0.0 0.0 0.0 0.0 .. 22.2 0.5 0.2 0.0 1.5 3 Iceland 0.0 0.0 0.0 0.0 .. .. 0.0 0.2 0.0 0.0 0.0 4 Canada 0.5 0.0 0.0 0.0 0.1 .. 175.7 2.8 1.2 0.0 37.5 5 Ireland 0.0 0.0 0.0 0.0 0.0 .. 9.3 1.5 0.1 0.0 4.4 6 Netherlands 0.0 0.0 0.0 0.0 0.0 .. 86.6 5.0 0.6 0.0 5.8 7 Sweden 0.0 0.0 0.0 0.0 0.0 .. 75.1 6.7 0.5 0.0 27.7 8 France 0.1 0.0 0.0 0.0 0.1 .. 151.8 2.3 1.1 0.0 31.1 9 Switzerland 0.0 0.0 0.0 0.0 0.0 .. 45.7 2.8 0.3 0.0 10.7 10 Japan 0.5 0.1 0.0 0.0 0.0 .. 1.8 0.1 0.0 0.0 1.5 11 Luxembourg 0.0 0.0 0.0 0.0 .. .. 2.7 1.8 0.0 0.0 0.0 12 Finland 0.0 0.0 0.0 0.0 .. .. 6.2 3.6 0.0 0.0 0.7 13 United States 2.2 0.1 0.0 0.0 1.1 .. 281.2 0.7 2.0 0.0 83.9 14 Austria 0.0 0.0 0.0 0.0 0.0 .. 30.8 2.7 0.2 0.0 38.4 15 Spain 0.0 0.0 0.0 0.0 0.0 .. 5.1 0.1 0.0 0.0 0.0 16 Denmark 0.0 0.0 0.0 0.0 0.0 .. 26.8 6.4 0.2 0.0 0.6 17 Belgium 0.1 0.0 0.0 0.0 0.0 .. 17.6 2.0 0.1 0.0 15.2 18 Italy 0.1 0.0 0.0 0.0 0.0 .. 38.1 1.2 0.3 0.0 1.5 19 Liechtenstein 0.0 0.0 0.0 0.0 .. .. 0.3 2.4 0.0 0.0 0.0 20 New Zealand 0.0 0.0 0.0 0.0 0.0 .. 2.7 0.3 0.0 0.0 0.2 21 United Kingdom 0.2 0.0 0.0 0.0 0.0 .. 299.7 5.1 2.1 0.0 10.9 22 Germany 0.1 0.0 0.0 0.0 0.1 .. 578.9 5.5 4.0 0.0 34.1 23 Singapore 0.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 24 Hong Kong, China (SAR) 0.0 0.0 0.0 0.0 0.0 .. 0.1 0.0 0.0 0.0 1.9 25 Greece 0.1 0.0 0.0 0.0 0.0 .. 2.2 0.2 0.0 0.0 28.5 26 Korea (Republic of) 1.2 0.1 0.0 0.0 0.4 .. 0.1 0.0 0.0 0.0 1.2 27 Israel 1.5 0.2 0.0 0.0 0.9 150–420 b 1.2 0.0 0.0 0.0 5.8 28 Andorra 0.0 0.1 0.0 0.0 0.0 .. .. .. .. .. .. 29 Slovenia 0.1 0.0 0.0 0.0 0.0 .. 0.3 0.2 0.0 0.0 0.1 30 Brunei Darussalam 0.0 0.0 0.0 0.0 .. .. .. .. .. .. .. 31 Kuwait 0.7 0.2 0.0 0.0 0.1 .. 0.2 0.0 0.0 38.0 0.7 32 Cyprus 0.0 0.0 0.0 0.0 0.0 .. 1.2 1.0 0.0 0.0 11.9 33 Qatar 0.1 0.4 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 34 Portugal 0.0 0.0 0.0 0.0 0.0 .. 0.4 0.0 0.0 0.0 0.0 35 United Arab Emirates 0.3 0.2 0.0 0.0 0.0 .. 0.2 0.0 0.0 0.0 0.1 36 Czech Republic 1.4 0.4 0.0 0.0 0.1 .. 2.0 0.4 0.0 0.0 2.2 37 Barbados 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. .. 38 Malta 0.0 0.0 0.0 0.0 0.0 .. 3.0 25.7 0.0 0.0 0.9

HIGH HUMAN DEVELOPMENT 39 Bahrain 0.1 0.1 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 40 Estonia 0.3 0.1 0.0 0.0 0.1 .. 0.0 0.0 0.0 0.0 0.0 41 Poland 2.9 0.1 0.0 0.0 0.2 .. 9.8 1.2 0.1 0.0 5.9 42 Slovakia 0.3 0.1 0.0 0.0 0.1 .. 0.3 0.2 0.0 0.0 0.6 43 Hungary 3.4 0.8 0.0 0.0 0.1 .. 8.1 2.4 0.1 0.0 1.6 44 Chile 1.0 0.2 0.0 0.0 0.1 .. 1.4 0.6 0.0 0.0 0.5 45 Croatia 100.4 16.5 0.7 0.0 0.1 3 c 1.6 0.2 0.0 0.0 0.1 46 Lithuania 0.5 0.1 0.0 0.0 0.1 .. 0.7 0.4 0.0 0.0 0.0 47 Antigua and Barbuda 0.0 0.0 0.0 0.0 .. .. .. .. .. .. .. 48 Latvia 0.7 0.3 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 49 Argentina 1.2 0.2 0.0 0.0 0.1 .. 3.3 0.2 0.0 0.0 1.1 50 Uruguay 0.2 0.1 0.0 0.0 0.0 .. 0.1 0.2 0.0 0.0 0.0 51 Cuba 7.1 0.7 0.0 0.4 1.1 .. 0.6 4.0 0.0 0.0 0.0 52 Bahamas 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. .. 53 Mexico 5.6 0.1 0.0 0.0 14.8 6 1.6 0.3 0.0 0.0 0.0 54 Costa Rica 0.4 0.3 0.0 0.0 0.1 .. 11.6 2.6 0.1 5.6 0.5 55 Libyan Arab Jamahiriya 2.0 2.5 0.0 0.0 0.6 .. 4.1 0.7 0.0 0.0 2.8 56 Oman 0.0 0.2 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 57 Seychelles 0.1 0.3 0.0 0.0 0.0 .. .. .. .. .. .. 58 Venezuela (Bolivarian Republic of) 5.1 1.4 0.0 0.0 1.8 .. 0.9 0.1 0.0 200.0 9.6 59 Saudi Arabia 0.8 0.3 0.0 0.0 0.0 .. 240.7 3.8 1.7 0.0 0.3

HDI rank

HUMAN DEVELOPMENT REPORT 2009

156

D Conflict and insecurity-induced movement

Total (thousands)

2007

Share of international

emigrant stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

Total (thousands)

2008

Total (thousands)

2007

Share of international immigrant

stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

InternalInternational

By country of origin

International

By country of asylum

Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

Internally displaced peopled Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

60 Panama 0.1 0.1 0.0 0.0 0.0 .. 1.9 1.8 0.0 15.0 0.5 61 Bulgaria 3.3 0.4 0.0 0.0 0.4 .. 4.8 4.6 0.0 0.0 1.0 62 Saint Kitts and Nevis 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. .. 63 Romania 5.3 0.5 0.0 0.0 0.6 .. 1.8 1.3 0.0 0.0 0.2 64 Trinidad and Tobago 0.2 0.1 0.0 0.0 0.2 .. 0.0 0.1 0.0 0.0 0.1 65 Montenegro 0.6 .. 0.0 0.0 0.3 .. 8.5 15.6 0.1 0.0 0.0 66 Malaysia 0.6 0.1 0.0 0.0 0.1 .. 32.2 1.6 0.2 0.4 6.9 67 Serbia 165.6 9.8 1.2 0.1 14.2 248 d 98.0 14.5 0.7 0.0 0.0 68 Belarus 5.0 0.3 0.0 0.0 1.2 .. 0.6 0.1 0.0 0.0 0.0 69 Saint Lucia 0.2 0.4 0.0 0.0 0.2 .. 0.0 0.0 0.0 0.0 0.0 70 Albania 15.3 1.9 0.1 0.0 1.6 .. 0.1 0.1 0.0 0.0 0.0 71 Russian Federation 92.9 0.8 0.6 0.0 17.6 18–137 e 1.7 0.0 0.0 0.0 3.1 72 Macedonia (the Former Yugoslav Rep. of) 8.1 3.1 0.1 0.0 1.1 1 1.2 1.0 0.0 0.1 0.2 73 Dominica 0.1 0.1 0.0 0.0 0.0 .. .. .. .. .. .. 74 Grenada 0.3 0.4 0.0 0.0 0.1 .. .. .. .. .. .. 75 Brazil 1.6 0.2 0.0 0.0 0.3 .. 3.8 0.6 0.0 17.0 0.4 76 Bosnia and Herzegovina 78.3 6.2 0.5 0.0 1.1 125 7.4 21.0 0.1 0.0 0.6 77 Colombia 70.1 4.3 0.5 481.6 43.1 2,650-4,360 c 0.2 0.2 0.0 0.0 0.1 78 Peru 7.7 1.0 0.1 0.0 3.1 150 c 1.0 2.4 0.0 0.0 0.5 79 Turkey 221.9 7.4 1.6 0.0 9.2 954-1,200 7.0 0.5 0.0 0.0 5.2 80 Ecuador 1.3 0.2 0.0 0.0 0.3 .. 14.9 12.1 0.1 250.0 27.4 81 Mauritius 0.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 82 Kazakhstan 5.2 0.1 0.0 0.0 0.5 .. 4.3 0.1 0.0 0.0 0.1 83 Lebanon 13.1 2.3 0.1 0.0 2.6 90–390 f 466.9g 64.7g 3.3g 0.1 0.6

MEDIUM HUMAN DEVELOPMENT 84 Armenia 15.4 2.0 0.1 0.0 4.0 8 c 4.6 0.9 0.0 0.0 0.1 85 Ukraine 26.0 0.4 0.2 0.0 2.4 .. 2.3 0.0 0.0 5.0 1.3 86 Azerbaijan 15.9 1.2 0.1 0.0 1.9 573 h 2.4 0.9 0.0 0.0 0.1 87 Thailand 2.3 0.3 0.0 0.0 0.4 .. 125.6 12.8 0.9 0.0 13.5 88 Iran (Islamic Republic of) 68.4 7.4 0.5 0.0 10.4 .. 963.5 46.7 6.7 0.0 1.2 89 Georgia 6.8 0.7 0.0 5.0 4.1 0 i 1.0 0.5 0.0 0.0 0.0 90 Dominican Republic 0.4 0.0 0.0 0.0 0.1 .. .. .. .. .. .. 91 Saint Vincent and the Grenadines 0.6 1.1 0.0 0.0 0.5 .. .. .. .. .. .. 92 China 149.1 2.6 1.0 0.0 15.5 .. 301.1 51.0 2.1 0.0 0.1 93 Belize 0.0 0.0 0.0 0.0 0.0 .. 0.4 0.9 0.0 0.0 0.0 94 Samoa 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. .. 95 Maldives 0.0 1.6 0.0 0.0 0.0 .. .. .. .. .. .. 96 Jordan 1.8 0.3 0.0 0.0 0.7 .. 2,431.0g .. 17.0g 0.0 0.4 97 Suriname 0.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 98 Tunisia 2.5 0.4 0.0 0.0 0.3 .. 0.1 0.3 0.0 0.0 0.1 99 Tonga 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. ..

100 Jamaica 0.8 0.1 0.0 0.0 0.2 .. .. .. .. .. .. 101 Paraguay 0.1 0.0 0.0 0.0 0.0 .. 0.1 0.0 0.0 0.0 0.0 102 Sri Lanka 134.9 14.5 0.9 0.0 6.0 500 0.2 0.0 0.0 0.0 0.2 103 Gabon 0.1 0.2 0.0 0.0 0.0 .. 8.8 3.6 0.1 0.0 4.3 104 Algeria 10.6 0.5 0.1 0.0 1.4 .. j 94.1 38.8 0.7 0.0 1.6 105 Philippines 1.5 0.0 0.0 0.0 0.8 314 k 0.1 0.0 0.0 0.0 0.0 106 El Salvador 6.0 0.6 0.0 0.0 18.6 .. 0.0 0.1 0.0 0.0 0.0 107 Syrian Arab Republic 13.7 3.2 0.1 0.0 6.9 433 1,960.8g .. 13.7g 0.0 5.9 108 Fiji 1.8 1.3 0.0 0.0 0.2 .. 0.0 0.0 0.0 0.0 0.0 109 Turkmenistan 0.7 0.3 0.0 0.0 0.1 .. 0.1 0.1 0.0 0.0 0.0 110 Occupied Palestinian Territories 4,953.4g .. 34.6g 6.0 2.4 25–115 c,l 1,813.8g .. 12.7g 0.0 0.0 111 Indonesia 20.2 1.1 0.1 0.3 2.4 150–250 c 0.3 0.2 0.0 0.0 0.2 112 Honduras 1.2 0.3 0.0 0.0 0.7 .. 0.0 0.1 0.0 0.0 0.0 113 Bolivia 0.4 0.1 0.0 0.0 0.4 .. 0.6 0.6 0.0 0.0 0.2 114 Guyana 0.7 0.2 0.0 0.0 0.2 .. .. .. .. .. .. 115 Mongolia 1.1 14.5 0.0 0.0 2.0 .. 0.0 0.1 0.0 0.0 0.0 116 Viet Nam 327.8 16.3 2.3 0.0 1.8 .. 2.4 4.3 0.0 0.0 0.0 117 Moldova 4.9 0.7 0.0 0.0 0.9 .. 0.2 0.0 0.0 0.0 0.1 118 Equatorial Guinea 0.4 0.4 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0

HDI rank

157

TABLE D

Total (thousands)

2007

Share of international

emigrant stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

Total (thousands)

2008

Total (thousands)

2007

Share of international immigrant

stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

InternalInternational

By country of origin

International

By country of asylum

Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

Internally displaced peopled Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

119 Uzbekistan 5.7 0.2 0.0 0.0 1.8 3 1.1 0.1 0.0 0.0 0.0 120 Kyrgyzstan 2.3 0.4 0.0 0.0 0.4 .. 0.4 0.1 0.0 0.4 0.7 121 Cape Verde 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. .. 122 Guatemala 6.2 1.0 0.0 0.0 15.0 .. 0.4 0.7 0.0 0.0 0.0 123 Egypt 6.8 0.3 0.0 0.0 1.6 .. 97.6 39.5 0.7 0.0 14.9 124 Nicaragua 1.9 0.4 0.0 0.0 0.8 .. 0.2 0.5 0.0 0.0 0.0 125 Botswana 0.0 0.1 0.0 0.0 0.1 .. 2.5 3.1 0.0 0.0 0.0 126 Vanuatu 0.0 0.0 0.0 0.0 .. .. 0.0 0.1 0.0 0.0 0.0 127 Tajikistan 0.5 0.1 0.0 0.4 0.1 .. 1.1 0.4 0.0 0.0 0.1 128 Namibia 1.1 4.6 0.0 0.0 0.0 .. 6.5 5.0 0.0 0.0 1.2 129 South Africa 0.5 0.1 0.0 0.0 0.1 .. 36.7 2.9 0.3 0.0 170.9 130 Morocco 4.0 0.2 0.0 0.0 0.5 .. 0.8 1.5 0.0 0.0 0.7 131 Sao Tome and Principe 0.0 0.1 0.0 0.0 .. .. 0.0 0.0 0.0 0.0 0.0 132 Bhutan 108.1 .. 0.8 2.5 1.6 .. .. .. .. .. .. 133 Lao People’s Democratic Republic 10.0 2.8 0.1 0.0 0.2 .. 0.0 0.0 0.0 0.0 0.0 134 India 20.5 0.2 0.1 0.0 7.1 500 k 161.5 2.7 1.1 0.0 2.4 135 Solomon Islands 0.0 1.1 0.0 0.0 0.0 .. .. .. .. .. .. 136 Congo 19.7 3.6 0.1 0.0 6.1 8 c 38.5 29.9 0.3 0.0 4.8 137 Cambodia 17.7 5.7 0.1 0.0 0.4 .. 0.2 0.1 0.0 0.0 0.2 138 Myanmar 191.3 60.8 1.3 0.1 19.0 503 m 0.0 0.0 0.0 0.0 0.0 139 Comoros 0.1 0.2 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 140 Yemen 1.6 0.3 0.0 0.0 0.3 25–35 117.4 25.8 0.8 0.0 0.7 141 Pakistan 31.9 0.9 0.2 0.0 8.6 .. n 887.3 25.0 6.2 1,147.8 3.1 142 Swaziland 0.0 0.2 0.0 0.0 0.1 .. 0.8 2.0 0.0 0.0 0.3 143 Angola 186.2 21.2 1.3 0.0 0.8 20 c,o 12.1 21.5 0.1 0.0 2.9 144 Nepal 3.4 0.3 0.0 0.0 2.1 50–70 128.2 15.7 0.9 2.5 1.6 145 Madagascar 0.3 0.2 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 146 Bangladesh 10.2 0.1 0.1 0.0 7.3 500 c 27.6 2.7 0.2 0.0 0.1 147 Kenya 7.5 1.7 0.1 0.0 1.7 400 p 265.7 33.6 1.9 0.0 5.8 148 Papua New Guinea 0.0 0.1 0.0 0.0 0.0 .. 10.0 39.2 0.1 0.0 0.0 149 Haiti 22.3 3.0 0.2 0.0 10.3 .. 0.0 0.0 0.0 0.0 0.0 150 Sudan 523.0 81.4 3.7 0.0 19.4 6,000 q 222.7 34.8 1.6 0.0 7.3 151 Tanzania ( United Republic of) 1.3 0.4 0.0 0.0 2.9 .. 435.6 54.6 3.0 0.0 0.3 152 Ghana 5.1 0.5 0.0 0.0 1.7 .. 35.0 2.1 0.2 0.0 0.4 153 Cameroon 11.5 6.8 0.1 0.0 3.0 .. 60.1 28.4 0.4 0.0 2.2 154 Mauritania 33.1 28.3 0.2 0.0 1.0 .. 1.0 1.5 0.0 29.5 0.0 155 Djibouti 0.6 3.8 0.0 0.0 0.0 .. 6.7 6.0 0.0 0.0 0.5 156 Lesotho 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 157 Uganda 21.3 12.5 0.1 0.0 3.2 869 r 229.0 35.1 1.6 0.0 5.8 158 Nigeria 13.9 1.3 0.1 0.0 9.7 .. 8.5 0.9 0.1 0.0 0.7

LOW HUMAN DEVELOPMENT 159 Togo 22.5 10.5 0.2 0.0 1.3 2 c 1.3 0.7 0.0 0.0 0.1 160 Malawi 0.1 0.1 0.0 0.0 8.2 .. 2.9 1.1 0.0 0.0 6.8 161 Benin 0.3 0.0 0.0 0.0 0.2 .. 7.6 4.1 0.1 0.0 0.5 162 Timor-Leste 0.0 0.0 0.0 0.0 0.0 30 0.0 0.0 0.0 0.0 0.0 163 Côte d’Ivoire 22.2 12.6 0.2 0.0 7.4 621 24.6 1.0 0.2 0.0 1.8 164 Zambia 0.2 0.1 0.0 0.0 0.5 .. 112.9 39.3 0.8 0.0 0.0 165 Eritrea 208.7 36.7 1.5 0.0 12.2 32 c 5.0 34.4 0.0 0.0 2.0 166 Senegal 15.9 3.3 0.1 0.0 0.9 10–70 20.4 9.3 0.1 0.0 2.5 167 Rwanda 81.0 33.7 0.6 0.0 8.2 .. 53.6 12.3 0.4 0.0 0.7 168 Gambia 1.3 2.5 0.0 0.0 1.0 .. 14.9 6.4 0.1 0.0 0.0 169 Liberia 91.5 .. 0.6 0.0 3.5 .. 10.5 10.8 0.1 0.0 0.1 170 Guinea 8.3 1.4 0.1 0.0 1.9 .. 25.2 6.3 0.2 0.0 4.0 171 Ethiopia 59.8 21.0 0.4 0.0 29.5 200 c 85.2 15.4 0.6 0.0 0.2 172 Mozambique 0.2 0.0 0.0 0.0 0.7 .. 2.8 0.7 0.0 0.0 4.2 173 Guinea-Bissau 1.0 0.8 0.0 0.0 0.3 .. 7.9 40.9 0.1 0.0 0.3 174 Burundi 375.7 96.7 2.6 0.0 7.1 100 24.5 30.0 0.2 0.0 7.5 175 Chad 55.7 18.4 0.4 0.0 2.7 186 294.0 82.0 2.1 0.0 0.0 176 Congo (Democratic Republic of the) 370.4 45.1 2.6 0.0 36.3 1,400 s 177.4 36.9 1.2 0.0 0.1 177 Burkina Faso 0.6 0.0 0.0 0.0 0.3 .. 0.5 0.1 0.0 0.0 0.6

HDI rank

HUMAN DEVELOPMENT REPORT 2009

158

D Conflict and insecurity-induced movement

Total (thousands)

2007

Share of international

emigrant stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

Total (thousands)

2008

Total (thousands)

2007

Share of international immigrant

stock (%)

Share of world refugees

(%) 2007

Total (thousands)

2007

Total (thousands)

2007

InternalInternational

By country of origin

International

By country of asylum

Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

Internally displaced peopled Stock of refugees

People in refugee-like situations

Stock of asylum seekers

(pending cases)

178 Mali 1.0 0.1 0.0 3.5 0.6 .. 9.2 5.6 0.1 0.0 1.9 179 Central African Republic 98.1 89.5 0.7 0.0 1.3 108 7.5 10.0 0.1 0.0 2.0 180 Sierra Leone 32.1 34.0 0.2 0.0 4.7 .. 8.8 5.8 0.1 0.0 0.2 181 Afghanistan 1,909.9 73.2 13.4 1,147.8 16.1 200 t 0.0 0.0 0.0 0.0 0.0 182 Niger 0.8 0.2 0.0 0.0 0.3 .. 0.3 0.2 0.0 0.0 0.0

OTHER UN MEMBER STATES Iraq 2,279.2 .. 15.9 30.0 27.7 2,842 v 42.4 33.1 0.3 0.0 2.4 Kiribati 0.0 1.0 0.0 0.0 .. .. .. .. .. .. .. Korea (Democratic People’s Rep. of) 0.6 0.1 0.0 0.0 0.2 .. .. .. .. .. .. Marshall Islands 0.0 0.0 0.0 0.0 .. .. .. .. .. .. .. Micronesia (Federated States of) 0.0 0.0 0.0 0.0 .. .. 0.0 0.1 0.0 0.0 0.0 Monaco 0.0 0.0 0.0 0.0 .. .. .. .. .. .. .. Nauru 0.0 0.3 0.0 0.0 0.0 .. .. .. .. .. .. Palau 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. .. San Marino 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. .. Somalia 455.4 84.5 3.2 2.0 16.4 1,100 0.9 4.2 0.0 0.0 8.7 Tuvalu 0.0 0.1 0.0 0.0 .. .. .. .. .. .. .. Zimbabwe 14.4 5.0 0.1 0.0 34.3 880–960 4.0 1.0 0.0 0.0 0.5

Africa 2,859.7T 11.4 20.0T 31.6T 234.2T .. 2,468.8T 14.0 17.3T 29.5T 272.3T

Asia 10,552.2T 16.1 73.8T 1,192.1T 166.4T .. 9,729.8T 17.6 68.1T 1,189.1T 69.3T

Europe 516.0T 0.9 3.6T 0.1T 42.7T .. 1,564.1T 2.4 10.9T 5.1T 234.2T

Latin America and the Caribbean 142.9T 0.5 1.0T 482.0T 112.2T .. 43.0T 0.6 0.3T 487.6T 41.2T

Northern America 2.7T 0.1 0.0T 0.0T 1.2T .. 457.0T 1.0 3.2T 0.0T 121.4T

Oceania 2.0T 0.1 0.0T 0.0T 0.3T .. 34.9T 0.6 0.2T 0.0T 1.7T

OECD 240.9T 0.5 1.7T 0.0T 26.4T .. 1,924.1T 2.0 13.5T 0.0T 357.7T

European Union (EU27) 19.0T 0.1 0.1T 0.0T 2.0T .. 1,363.3T 3.3 9.5T 0.0T 223.3T

GCC 2.0T 0.2 0.0T 0.0T 0.2T .. 241.1T 1.9 1.7T 38.0T 1.2T

Very high human development 9.7T 0.0 0.1T 0.0T 3.2T .. 1,903.7T 1.8 13.3T 38.0T 365.7T

Very high HD: OECD 6.8T 0.0 0.0T 0.0T 2.0T .. 1,897.3T 2.0 13.3T 0.0T 344.4T

Very high HD: non-OECD 2.9T 0.1 0.0T 0.0T 1.2T .. 6.4T 0.0 0.0T 38.0T 21.3T

High human development 828.8T 1.5 5.8T 482.1T 117.2T .. 941.1T 2.5 6.6T 488.1T 70.1T

Medium human development 9,410.0T 12.3 65.8T 70.3T 240.6T .. 10,550.7T 25.8 73.8T 1,185.1T 259.2T

Low human development 3,827.1T 28.9 26.8T 1,153.3T 195.9T .. 902.1T 10.7 6.3T 0.0T 45.0T

World (excluding the former Soviet 13,891.2T 9.6 97.2T 1,700.3T 521.4T .. 14,274.8T 8.5 99.8T 1,705.9T 731.6T

Union and Czechoslovakia) World 14,297.5T 7.3 100.0T 1,711.3Tu 740.0Tu 26,000 Tu 14,297.5T 7.3 100.0T 1,711.3Tu 740.0Tu

NOTES

a Estimates maintained by the IDMC are based on

various sources and are associated with high levels of

uncertainty.

b Higher figure includes an estimate of internally

displaced Bedouin.

c Data refer to a year or period other than that specified.

d Figure includes 206,000 registered IDPs in Serbia plus

an estimated 20,000 unregistered Roma displaced in

Serbia and 21,000 IDPs in Kosovo.

e Figure includes forced migrants registered in

Ingushetia and Chechnya.

f Figure includes 32,000 Palestinian refugees displaced

as a result of fighting between Lebanese forces and

Fatah al Islam in May–August 2007.

g Including Palestinian refugees under the responsibility

of UNRWA (2008).

h Figure refers to those displaced from Nagorno

Karabakh and seven occupied territories.

i Some 59,000 people displaced since the August

2008 crisis have not been able to return. There are

some 221,597 IDPs based on the result of a survey

conducted by UNHCR and the government but these

are yet to be endorsed.

j There are no reliable estimates but in 2002, the EU

estimated the number to be 100,000.

k Figures are suspected to be underestimate.

l Lower figure relates to IDPs evicted by home

demolitions in Gaza between 2000 and 2004 whilst

higher figure is cumulative since 1967.

m Figure relates to the eastern border areas only.

n Exact IDP numbers are unknown but conflict induced

displacement has taken place in the North-West

Frontier province, Baluchistan and Waziristan.

o Figure refers to IDPs in Cabinda region only.

p Figure takes into account the government’s return

programme which claims that some 172,000 displaced

due to the post-election violence returned in May

2008.

q Figures are based on separate estimates for Darfur,

Khartoum and Southern Sudan.

r Excludes IDPs in urban areas.

s Figure includes an estimated 250,000 civilians who

fled their homes in North Kiva due to fighting between

the national army and CNDP rebels.

t It is believed that there are more than 200,000 IDPs.

u Data are aggregates from the original data source.

v Figure is cumulative since 2001 and includes 1.5

million people displaced due to a rise in inter-

communal violence since February 2006.

SOURCES

Columns 1, 3, 4, 7, 9 and 10: UNHCR (2009b).

Column 2: calculated based on data from UNHCR

(2009b) and Migration DRC (2007).

Columns 5 and 11: UNHCR (2009a).

Column 6: IDMC (2009a).

Column 8: calculated based on UNHCR (2009b) and

UN (2009d).

HDI rank

159

HUMAN DEVELOPMENT REPORT 2009

ETABLE International financial flows: remittances, official development assistance and foreign direct investment

E Inflows

total (US$

millions)

2007

Outflows total (US$

millions)

Outflows per

migrant (US$)

per capita (US$)

as % of net ODA receipts

as % of GDP

ratio of remittances

to FDI Africa

(% of total remittance inflows)

Asia Europe

Latin American and the

Caribbean Northern America Oceania

Remittances Relative size of remittance inflows Remittance inflows by continent of origin

ODA received (net

disbursements) per capita

(US$)

VERY HIGH HUMAN DEVELOPMENT 1 Norway 613 3,642 10,588 .. 130 .. 0.2 0.2 0.0 4.2 66.2 0.7 26.3 2.7 2 Australia 3,862 3,559 869 .. 186 .. 0.4 0.1 0.7 6.7 49.3 0.8 25.7 16.8 3 Iceland 41 100 4,333 .. 137 .. 0.2 0.0 0.0 0.5 63.4 0.3 34.1 1.6 4 Canada .. .. .. .. .. .. .. .. .. .. .. .. .. .. 5 Ireland 580 2,554 4,363 .. 135 .. 0.2 0.0 0.0 0.2 70.6 0.1 22.9 6.1 6 Netherlands 2,548 7,830 4,780 .. 155 .. 0.3 0.0 0.0 3.4 51.5 1.8 30.4 12.9 7 Sweden 775 1,142 1,022 .. 85 .. 0.2 0.1 0.6 3.2 69.4 1.4 22.9 2.6 8 France 13,746 4,380 677 .. 223 .. 0.5 0.1 13.5 3.8 58.8 4.7 16.8 2.3 9 Switzerland 2,035 16,273 9,805 .. 272 .. 0.4 0.0 0.1 3.2 75.4 2.3 16.2 2.8 10 Japan 1,577 4,037 1,971 .. 12 .. 0.0 0.1 0.1 8.8 15.8 9.0 62.3 4.0 11 Luxembourg 1,565 9,281 53,446 .. 3,355 .. 3.3 0.0 0.0 0.2 90.7 0.2 8.5 0.4 12 Finland 772 391 2,506 .. 146 .. 0.3 0.1 0.2 1.0 83.7 0.2 12.3 2.6 13 United States 2,972 45,643 1,190 .. 10 .. 0.0 0.0 0.7 12.0 31.2 38.2 13.4 4.5 14 Austria 2,945 2,985 2,420 .. 352 .. 0.8 0.1 0.0 3.7 73.6 1.2 17.9 3.5 15 Spain 10,687 14,728 3,075 .. 241 .. 0.7 0.2 0.1 0.3 63.8 24.2 10.8 1.0 16 Denmark 989 2,958 7,612 .. 182 .. 0.3 0.1 0.3 2.6 67.4 0.7 24.6 4.5 17 Belgium 8,562 3,192 4,438 .. 819 .. 1.9 0.1 0.2 2.4 79.7 1.3 15.3 1.2 18 Italy 3,165 11,287 4,481 .. 54 .. 0.2 0.1 0.1 0.2 56.2 9.8 27.4 6.3 19 Liechtenstein .. .. .. .. .. .. .. .. .. .. .. .. .. .. 20 New Zealand 650 1,207 1,880 .. 155 .. 0.5 0.2 0.1 2.1 16.5 0.1 8.2 73.0 21 United Kingdom 8,234 5,048 933 .. 135 .. 0.3 0.0 0.3 4.4 26.2 0.7 38.4 29.9 22 Germany 8,570 13,860 1,366 .. 104 .. 0.3 0.2 0.2 12.1 44.3 1.5 39.1 2.8 23 Singapore .. .. .. .. .. .. .. .. .. .. .. .. .. .. 24 Hong Kong, China (SAR) 348 380 127 .. 48 .. 0.2 0.0 0.0 2.5 17.7 0.2 68.9 10.8 25 Greece 2,484 1,460 1,499 .. 223 .. 0.7 1.3 0.0 8.2 58.1 0.4 23.6 9.7 26 Korea (Republic of) 1,128 4,070 7,384 .. 23 .. 0.1 0.7 0.0 36.1 6.9 1.3 52.0 3.7 27 Israel 1,041 2,770 1,041 .. 150 .. 0.6 0.1 0.0 70.0 7.8 0.8 20.5 0.9 28 Andorra .. .. .. .. .. .. .. .. .. .. .. .. .. .. 29 Slovenia 284 207 1,236 .. 142 .. 0.7 0.2 0.0 0.1 77.0 0.5 17.1 5.2 30 Brunei Darussalam .. 405 3,263 .. .. .. .. .. .. .. .. .. .. .. 31 Kuwait .. 3,824 2,291 .. .. .. .. .. .. .. .. .. .. .. 32 Cyprus 172 371 3,195 .. 201 .. .. 0.1 0.0 6.3 69.8 0.0 11.5 12.4 33 Qatar .. .. .. .. .. .. .. .. .. .. .. .. .. .. 34 Portugal 3,945 1,311 1,717 .. 371 .. 1.8 0.7 3.1 0.3 62.4 12.1 21.2 0.8 35 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. .. .. 36 Czech Republic 1,332 2,625 5,790 .. 131 .. 0.8 0.1 0.0 4.1 70.2 0.4 23.3 2.0 37 Barbados 140 40 1,534 46 476 1,025.6 .. .. .. .. .. .. .. .. 38 Malta 40 54 5,011 .. 99 .. .. 0.0 0.0 0.1 36.1 0.0 19.3 44.5

HIGH HUMAN DEVELOPMENT 39 Bahrain .. 1,483 5,018 .. .. .. .. .. .. .. .. .. .. .. 40 Estonia 426 96 474 .. 319 .. 2.3 0.2 0.0 4.5 81.5 0.1 12.3 1.6 41 Poland 10,496 1,278 1,818 .. 276 .. 2.6 0.5 0.0 5.5 54.2 1.0 36.4 2.9 42 Slovakia 1,483 73 588 .. 275 .. 2.0 0.4 0.0 1.8 85.4 0.1 12.0 0.7 43 Hungary 413 235 742 .. 41 .. 0.3 0.0 0.0 3.4 52.4 0.9 37.8 5.5 44 Chile 3 6 25 7 0 2.1 0.0 0.0 0.0 0.0 25.7 42.0 27.2 5.1 45 Croatia 1,394 86 129 36 306 850.8 2.9 0.3 0.0 0.0 77.8 0.3 13.7 8.1 46 Lithuania 1,427 566 3,424 .. 421 .. 3.8 0.7 0.0 6.8 74.2 0.3 17.2 1.5 47 Antigua and Barbuda 24 2 113 49 276 560.9 2.0 0.1 0.0 14.2 11.7 10.6 63.3 0.1 48 Latvia 552 45 100 .. 242 .. 2.1 0.2 0.0 5.9 67.4 0.2 22.7 3.7 49 Argentina 604 472 315 2 15 737.0 0.2 0.1 0.0 6.5 41.1 24.5 26.2 1.7 50 Uruguay 97 4 42 10 29 285.6 0.4 0.1 0.0 0.1 29.2 48.4 17.9 4.5 51 Cuba .. .. .. 8 .. .. .. .. .. .. .. .. .. .. 52 Bahamas .. 171 5,397 .. .. .. .. .. .. .. .. .. .. .. 53 Mexico 27,144 .. .. 1 255 22,416.0 3.0 1.1 0.0 0.0 0.8 0.3 98.9 0.0 54 Costa Rica 635 271 616 12 142 1,205.1 2.3 0.3 0.0 0.2 6.5 11.8 81.2 0.3 55 Libyan Arab Jamahiriya 16 762 1,234 3 3 84.1 .. 0.0 14.3 34.0 32.1 0.1 17.4 2.0 56 Oman 39 3,670 5,847 .. 15 .. 0.1 0.0 .. .. .. .. .. .. 57 Seychelles 11 21 4,309 32 129 402.5 1.9 0.0 7.6 0.2 51.2 0.0 17.7 23.3 58 Venezuela (Bolivarian Republic of) 136 598 592 3 5 191.0 0.1 0.2 0.0 0.1 47.1 14.7 37.8 0.3 59 Saudi Arabia .. 16,068 2,526 .. .. .. .. .. .. .. .. .. .. ..

HDI rank

HUMAN DEVELOPMENT REPORT 2009

160

E International financial flows: remittances, official development assistance and foreign direct investment

Inflows total (US$

millions)

2007

Outflows total (US$

millions)

Outflows per

migrant (US$)

per capita (US$)

as % of net ODA receipts

as % of GDP

ratio of remittances

to FDI Africa

(% of total remittance inflows)

Asia Europe

Latin American and the

Caribbean Northern America Oceania

Remittances Relative size of remittance inflows Remittance inflows by continent of origin

ODA received (net

disbursements) per capita

(US$)

60 Panama 180 151 1,476 .. 54 .. 0.8 0.1 0.0 0.1 3.9 8.1 87.8 0.1 61 Bulgaria 2,086 86 822 .. 273 .. 5.7 0.2 0.0 53.8 37.2 0.1 8.5 0.5 62 Saint Kitts and Nevis 37 6 1,352 57 739 1,289.0 .. .. .. .. .. .. .. 63 Romania 8,533 351 2,630 .. 398 .. 5.6 0.9 0.0 15.0 61.3 0.4 22.0 1.3 64 Trinidad and Tobago 92 .. .. 14 69 503.0 0.4 .. 0.0 0.0 8.0 2.0 89.6 0.4 65 Montenegro .. .. .. 177 .. .. .. .. .. .. .. .. .. .. 66 Malaysia 1,700 6,385 3,895 8 64 851.4 1.0 0.2 0.0 80.3 6.0 0.0 6.7 7.0 67 Serbia .. .. .. 85 .. .. .. .. .. .. .. .. .. .. 68 Belarus 354 109 92 9 37 425.4 0.8 0.2 0.0 6.1 88.4 0.0 5.4 0.1 69 Saint Lucia 31 4 488 143 188 131.5 3.5 0.1 .. .. .. .. .. .. 70 Albania 1,071 7 85 96 336 350.9 10.1 2.2 0.0 0.4 91.2 0.0 8.2 0.2 71 Russian Federation 4,100 17,716 1,467 .. 29 .. 0.3 0.1 0.0 31.3 61.8 0.1 6.5 0.2 72 Macedonia (the Former Yugoslav Rep. of) 267 18 147 105 131 124.9 3.6 0.8 0.0 6.1 71.0 0.1 9.5 13.3 73 Dominica 26 0 37 288 385 133.8 8.0 0.6 0.0 0.3 27.5 13.3 58.9 0.0 74 Grenada 55 4 329 215 524 244.3 .. 0.4 0.0 0.0 17.6 12.6 69.6 0.2 75 Brazil 4,382 896 1,396 2 23 1,475.0 0.3 0.1 0.0 31.9 27.3 11.2 29.1 0.5 76 Bosnia and Herzegovina 2,520 65 1,601 113 640 568.6 .. 1.2 0.0 0.1 85.1 0.1 12.7 2.0 77 Colombia 4,523 95 775 16 98 618.9 3.0 0.5 0.0 0.2 29.1 26.7 43.7 0.3 78 Peru 2,131 137 3,294 9 76 810.2 1.9 0.4 0.0 7.5 26.7 16.4 48.7 0.8 79 Turkey 1,209 106 80 11 16 151.7 0.2 0.1 0.0 3.7 92.4 0.0 3.2 0.7 80 Ecuador 3,094 83 726 16 232 1,436.6 6.9 16.9 0.0 0.0 52.7 3.9 43.3 0.2 81 Mauritius 215 12 557 59 170 288.3 2.9 0.6 1.0 0.2 75.1 0.0 8.2 15.5 82 Kazakhstan 223 4,303 1,720 13 14 110.1 0.2 0.0 0.0 9.6 89.6 0.0 0.8 0.0 83 Lebanon 5,769 2,845 4,332 229 1,407 614.1 24.4 2.0 2.1 11.0 33.1 4.0 36.9 12.9

MEDIUM HUMAN DEVELOPMENT 84 Armenia 846 176 749 117 282 240.6 9.0 1.2 0.0 6.2 72.7 0.0 20.9 0.2 85 Ukraine 4,503 42 6 9 97 1,111.1 3.9 0.5 0.0 9.1 77.0 0.1 13.4 0.5 86 Azerbaijan 1,287 435 2,395 27 152 571.4 4.4 .. 0.0 16.3 80.1 0.0 3.5 0.0 87 Thailand 1,635 .. .. .. 26 .. 0.7 0.2 0.0 32.4 25.3 0.0 37.8 4.5 88 Iran (Islamic Republic of) 1,115 .. .. 1 16 1,094.5 0.5 1.5 0.0 9.5 40.1 0.1 48.1 2.2 89 Georgia 696 28 148 87 158 182.0 6.8 0.4 0.0 10.4 86.3 0.0 3.2 0.1 90 Dominican Republic 3,414 28 180 13 350 2,674.2 9.3 2.0 0.0 0.1 12.7 2.9 84.4 0.0 91 Saint Vincent and the Grenadines 31 7 702 545 254 46.6 6.7 0.3 .. .. .. .. .. .. 92 China 32,833 4,372 7,340 1 25 2,282.3 1.1 0.2 0.1 61.9 7.4 0.4 27.3 3.0 93 Belize 75 22 555 81 260 319.4 5.3 0.7 0.0 0.0 2.8 4.9 92.2 0.1 94 Samoa 120 13 1,422 197 640 324.3 .. 48.1 0.0 0.0 0.0 0.0 26.9 73.1 95 Maldives 3 103 30,601 122 10 8.0 .. 0.2 0.0 37.5 38.5 0.4 5.3 18.4 96 Jordan 3,434 479 215 85 580 680.8 22.7 1.9 0.0 74.2 7.6 0.1 17.1 0.9 97 Suriname 140 65 12,233 329 305 92.7 .. .. 0.0 0.0 89.0 7.3 3.8 0.0 98 Tunisia 1,716 15 402 30 166 553.2 5.0 1.1 8.9 4.3 84.0 0.0 2.6 0.1 99 Tonga 100 12 10,525 304 992 326.8 .. 3.6 0.0 0.2 1.3 0.5 48.0 50.0

100 Jamaica 2,144 454 25,724 10 790 8,231.9 19.4 2.5 0.0 0.0 17.3 1.3 81.3 0.1 101 Paraguay 469 .. .. 18 77 434.1 3.2 2.4 0.0 1.1 4.6 82.9 11.3 0.2 102 Sri Lanka 2,527 314 853 31 131 429.1 8.1 4.2 0.0 26.2 45.7 0.0 19.4 8.6 103 Gabon 11 110 451 36 8 22.8 0.1 0.0 33.5 0.0 61.5 0.0 4.8 0.2 104 Algeria 2,120 .. .. 12 63 543.9 1.6 1.3 0.7 2.3 94.7 0.0 2.2 0.1 105 Philippines 16,291 35 93 7 185 2,567.7 11.6 5.6 0.0 20.1 9.6 0.0 66.2 4.1 106 El Salvador 3,711 29 1,213 13 541 4,211.6 18.4 2.4 0.0 0.0 1.1 2.7 95.3 0.9 107 Syrian Arab Republic 824 235 239 4 41 1,099.7 2.2 .. 4.7 33.0 31.9 2.7 25.7 2.0 108 Fiji 165 32 1,836 69 197 287.9 5.0 0.6 0.0 0.3 3.5 0.0 46.2 50.0 109 Turkmenistan .. .. .. 6 .. .. .. .. .. .. .. .. .. .. 110 Occupied Palestinian Territories 598 16 9 465 149 32.0 .. .. .. .. .. .. .. .. 111 Indonesia 6,174 1,654 10,356 3 27 776.1 1.5 0.9 0.0 65.1 20.3 0.0 9.9 4.6 112 Honduras 2,625 2 94 65 369 565.4 24.5 3.2 0.0 0.1 2.6 4.3 93.0 0.0 113 Bolivia 927 72 621 50 97 194.4 6.6 4.5 0.0 2.0 16.7 49.3 31.7 0.3 114 Guyana 278 61 54,887 168 377 224.6 23.5 1.8 0.0 0.0 7.0 2.9 90.0 0.1 115 Mongolia 194 77 8,443 87 74 85.1 .. 0.6 0.0 11.0 63.2 0.1 24.8 1.0 116 Viet Nam 5,500 .. .. 29 63 220.3 7.9 0.8 0.0 4.1 17.9 0.0 70.6 7.5 117 Moldova 1,498 87 197 71 395 556.6 38.3 3.0 0.0 6.4 83.2 0.0 10.2 0.2 118 Equatorial Guinea .. .. .. 62 .. .. .. .. .. .. .. .. .. ..

HDI rank

161

TABLE E

Inflows total (US$

millions)

2007

Outflows total (US$

millions)

Outflows per

migrant (US$)

per capita (US$)

as % of net ODA receipts

as % of GDP

ratio of remittances

to FDI Africa

(% of total remittance inflows)

Asia Europe

Latin American and the

Caribbean Northern America Oceania

Remittances Relative size of remittance inflows Remittance inflows by continent of origin

ODA received (net

disbursements) per capita

(US$)

119 Uzbekistan .. .. .. 6 .. .. .. .. .. .. .. .. .. .. 120 Kyrgyzstan 715 220 763 51 134 261.1 19.0 3.4 0.0 8.6 89.2 0.0 2.0 0.1 121 Cape Verde 139 6 537 308 262 85.0 9.2 1.1 12.7 0.0 62.0 0.0 25.2 0.0 122 Guatemala 4,254 18 347 34 319 945.6 10.6 5.9 0.0 0.0 1.9 5.1 92.9 0.0 123 Egypt 7,656 180 1,082 14 101 706.6 6.0 0.7 12.5 58.6 13.3 0.1 13.1 2.3 124 Nicaragua 740 .. .. 149 132 88.7 12.1 1.9 0.0 0.0 1.7 32.5 65.6 0.2 125 Botswana 141 120 1,495 56 75 135.2 1.2 .. 76.2 0.1 12.9 0.0 7.8 2.9 126 Vanuatu 5 18 17,274 251 22 8.8 1.2 0.1 0.0 0.2 39.6 0.0 5.6 54.6 127 Tajikistan 1,691 184 600 33 251 764.0 45.5 4.7 0.0 28.6 69.2 0.0 2.1 0.0 128 Namibia 17 16 112 99 8 8.2 0.2 0.1 48.9 0.0 29.9 0.1 14.9 6.2 129 South Africa 834 1,186 1,072 16 17 105.0 0.3 0.1 23.6 0.6 38.3 0.1 20.4 17.0 130 Morocco 6,730 52 394 35 216 617.8 9.0 2.4 0.2 8.0 88.4 0.0 3.3 0.1 131 Sao Tome and Principe 2 1 92 228 13 5.6 .. 0.1 8.4 0.0 90.5 0.0 1.1 0.0 132 Bhutan .. .. .. 135 .. .. .. .. .. .. .. .. .. .. 133 Lao People’s Democratic Republic 1 1 20 68 0 0.3 0.0 0.0 0.0 6.3 12.5 0.0 79.2 2.1 134 India 35,262 1,580 277 1 30 2,716.2 3.1 1.5 0.3 58.2 12.8 0.0 26.9 1.8 135 Solomon Islands 20 3 854 500 41 8.2 .. 0.5 0.0 0.5 16.2 0.0 8.9 74.3 136 Congo 15 102 355 34 4 11.7 0.2 0.0 25.8 0.4 67.7 0.0 6.1 0.1 137 Cambodia 353 157 517 46 24 52.5 4.2 0.4 0.0 4.6 22.7 0.0 64.4 8.3 138 Myanmar 125 32 270 4 3 65.9 .. 0.3 .. .. .. .. .. .. 139 Comoros 12 .. .. 53 14 27.0 2.6 15.0 10.8 0.1 88.1 0.0 0.9 0.1 140 Yemen 1,283 120 455 10 57 569.1 6.1 1.4 0.2 84.7 6.5 0.0 8.5 0.1 141 Pakistan 5,998 3 1 13 37 271.1 4.2 1.1 0.2 45.2 32.2 0.0 21.6 0.7 142 Swaziland 99 8 180 55 86 156.9 3.5 2.6 94.3 0.1 3.2 0.0 1.9 0.5 143 Angola .. 603 10,695 14 .. .. .. .. .. .. .. .. .. .. 144 Nepal 1,734 4 5 21 61 289.8 15.5 302.1 0.0 75.3 10.2 0.0 12.4 2.1 145 Madagascar 11 21 338 45 1 1.2 0.1 0.0 5.8 0.1 90.3 0.1 3.7 0.1 146 Bangladesh 6,562 3 3 9 41 436.9 9.5 10.1 0.0 69.7 18.4 0.0 11.2 0.7 147 Kenya 1,588 16 47 34 42 124.5 5.4 2.2 8.8 0.4 61.0 0.0 27.2 2.6 148 Papua New Guinea 13 135 5,301 50 2 4.2 0.2 0.1 0.0 0.7 6.1 0.0 8.5 84.7 149 Haiti 1,222 96 3,208 73 127 174.3 20.0 16.4 0.0 0.0 4.1 6.1 89.7 0.0 150 Sudan 1,769 2 3 55 46 84.1 3.7 0.7 16.7 55.5 12.5 0.0 13.3 2.0 151 Tanzania ( United Republic of) 14 46 59 69 0 0.5 0.1 0.0 11.0 0.5 49.3 0.0 37.3 1.9 152 Ghana 117 6 4 49 5 10.2 0.8 0.1 29.7 0.7 38.8 0.0 30.2 0.6 153 Cameroon 167 103 750 104 9 8.7 0.8 0.4 30.0 0.1 56.1 0.0 13.8 0.0 154 Mauritania 2 .. .. 116 1 0.5 0.1 0.0 37.1 0.5 54.3 0.0 8.1 0.0 155 Djibouti 28 5 233 135 34 25.3 .. 0.1 .. .. .. .. .. .. 156 Lesotho 443 21 3,567 65 221 342.3 28.7 3.4 98.3 0.0 1.0 0.0 0.6 0.1 157 Uganda 849 364 702 56 27 49.1 7.2 1.8 4.3 0.5 69.0 0.0 25.0 1.3 158 Nigeria 9,221 103 106 14 62 451.5 6.7 1.5 15.2 2.0 42.9 0.0 39.5 0.4

LOW HUMAN DEVELOPMENT 159 Togo 229 35 193 18 35 189.4 8.4 3.3 38.1 0.0 54.8 0.0 7.0 0.0 160 Malawi 1 1 4 53 0 0.1 0.0 0.0 28.0 0.0 59.1 0.0 10.8 2.2 161 Benin 224 67 383 52 25 47.7 4.1 4.7 81.2 0.0 17.0 0.0 1.8 0.0 162 Timor-Leste .. .. .. 241 .. .. .. .. .. .. .. .. .. .. 163 Côte d’Ivoire 179 19 8 9 9 108.7 0.9 0.4 13.9 0.1 74.1 0.0 11.7 0.1 164 Zambia 59 124 451 88 5 5.7 0.5 0.1 .. .. .. .. .. .. 165 Eritrea .. .. .. 32 .. .. .. .. .. .. .. .. .. .. 166 Senegal 925 96 296 68 75 109.8 8.5 11.9 20.0 0.1 73.5 0.0 6.2 0.1 167 Rwanda 51 68 562 73 5 7.2 1.9 0.8 40.6 0.1 43.8 0.0 15.2 0.2 168 Gambia 47 12 52 42 28 65.4 6.9 0.7 5.4 0.0 73.1 0.0 21.4 0.1 169 Liberia 65 0 5 186 17 9.3 .. 0.5 .. .. .. .. .. .. 170 Guinea 151 119 294 24 16 67.2 3.0 1.4 65.8 0.2 25.8 0.0 8.2 0.0 171 Ethiopia 359 15 26 29 4 14.8 2.0 1.6 4.7 24.1 28.7 0.0 41.0 1.5 172 Mozambique 99 45 111 83 5 5.6 1.3 0.2 63.7 0.0 34.0 0.2 1.8 0.3 173 Guinea-Bissau 29 5 280 73 17 23.5 8.3 4.1 17.7 0.0 80.5 0.0 1.8 0.0 174 Burundi 0 0 2 55 0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 175 Chad .. .. .. 33 .. .. .. .. .. .. .. .. .. .. 176 Congo (Democratic Republic of the) .. .. .. 19 .. .. .. .. .. .. .. .. .. .. 177 Burkina Faso 50 44 57 63 3 5.4 0.7 0.1 91.6 0.0 7.8 0.0 0.7 0.0

HDI rank

HUMAN DEVELOPMENT REPORT 2009

162

E International financial flows: remittances, official development assistance and foreign direct investment

Inflows total (US$

millions)

2007

Outflows total (US$

millions)

Outflows per

migrant (US$)

per capita (US$)

as % of net ODA receipts

as % of GDP

ratio of remittances

to FDI Africa

(% of total remittance inflows)

Asia Europe

Latin American and the

Caribbean Northern America Oceania

Remittances Relative size of remittance inflows Remittance inflows by continent of origin

ODA received (net

disbursements) per capita

(US$)

178 Mali 212 57 1,234 82 17 20.8 3.3 0.6 74.1 0.0 23.8 0.0 2.0 0.0 179 Central African Republic .. .. .. 41 .. .. .. .. .. .. .. .. .. .. 180 Sierra Leone 148 136 1,140 91 25 27.7 9.4 1.6 1.5 0.0 55.1 0.0 42.9 0.5 181 Afghanistan .. .. .. 146 .. .. .. .. .. .. .. .. .. .. 182 Niger 78 29 237 38 5 14.4 1.9 2.9 82.7 0.0 14.3 0.0 3.0 0.0

OTHER UN MEMBER STATES Iraq .. 781 27,538 314 .. .. .. .. .. .. .. .. .. .. Kiribati 7 .. .. 285 74 25.9 .. .. 0.0 0.3 34.0 0.0 34.0 31.6 Korea (Democratic People’s Rep. of) .. .. .. 4 .. .. .. .. .. .. .. .. .. .. Marshall Islands .. .. .. 879 .. .. .. .. .. .. .. .. .. .. Micronesia (Federated States of) .. .. .. 1,034 .. .. .. .. .. .. .. .. .. .. Monaco .. .. .. .. .. .. .. .. .. .. .. .. .. .. Nauru .. .. .. 2,518 .. .. .. .. .. .. .. .. .. .. Palau .. .. .. 1,100 .. .. .. .. .. .. .. .. .. .. San Marino .. .. .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. 44 .. .. .. .. .. .. .. .. .. .. Tuvalu .. .. .. 1,115 .. .. .. .. .. .. .. .. .. .. Zimbabwe .. .. .. 35 .. .. .. .. .. .. .. .. .. ..

Africa 36,850 T 4,754 T 324 36 44 .. .. .. 12.2 16.4 57.4 0.0 12.5 1.5 Asia 141,398 T 62,220 T 1,448 9 36 .. .. .. 0.3 45.8 17.3 0.5 32.8 3.4 Europe 119,945 T 126,169 T 1,990 .. 160 .. .. .. 2.2 6.3 62.0 4.2 20.4 4.8 Latin America and the Caribbean 63,408 T 3,947 T 798 10 114 .. .. .. 0.0 2.7 9.7 6.2 81.2 0.2 Northern America 2,972 T 45,643 T .. .. .. .. .. .. .. .. .. .. .. .. Oceania 6,193 T 5,090 T .. .. .. .. .. .. .. .. .. .. .. .. OECD 124,520 T 165,254 T 1,884 .. 108 .. .. .. 2.0 3.6 44.1 5.2 39.5 5.6 European Union (EU27) 96,811 T 88,391 T 2,208 .. 196 .. .. .. 2.7 5.9 58.5 5.1 22.5 5.4 GCC 39 T 25,044 T 2,797 .. .. .. .. .. .. .. .. .. .. .. Very high human development 86,313 T 172,112 T 1,845 .. 92 .. .. .. 2.7 5.0 55.3 6.8 22.8 7.5 Very high HD: OECD 83,776 T 163,562 T 1,919 .. 91 .. .. .. 2.8 4.6 55.5 6.9 22.7 7.5 Very high HD: non-OECD 2,537 T 8,550 T .. .. .. .. .. .. .. .. .. .. .. .. High human development 92,453 T 59,434 T 1,705 9 101 .. .. .. 0.2 9.1 35.8 3.4 49.4 2.2 Medium human development 189,093 T 15,403 T 446 12 44 .. .. .. 1.6 37.8 21.3 1.0 35.9 2.3 Low human development 2,907 T 874 T 133 51 11 .. .. .. 34.7 2.5 53.0 0.0 9.6 0.2 World (excluding the former Soviet 349,632 T 221,119 T 1,540 14 57 .. .. .. 1.8 21.4 33.2 3.4 36.4 3.8 Union and Czechoslovakia) World 370,765 Ta 248,283 Ta 1,464 14 58 .. .. .. 1.8 21.1 34.7 3.2 35.4 3.7

NOTES

a Data are aggregates from original data source.

SOURCES

Columns 1, 2 and 7: World Bank (2009b).

Column 3: calculated based on data on remittances and

stocks of migrants from World Bank (2009b).

Column 4: calculated based on data on ODA from

OECD-DAC (2009 ) and population data from UN

(2009e).

Column 5: calculated based on data on remittances

from World Bank (2009b) and population data from UN

(2009e).

Column 6: calculated based on data on remittances

from World Bank (2009b) and on ODA from OECD-DAC

(2009 ).

Column 8: calculated based on data on remittances and

FDI from World Bank (2009b).

Columns 9–14: calculated based on data from Ratha

and Shaw (2006).

HDI rank

163

HUMAN DEVELOPMENT REPORT 2009

FTABLE Selected conventions related to human rights and migration (by year of ratification)

International Convention on

the Protection of the Rights of All Migrant Workers

and Members of their Families

1990

Protocol to Prevent, Suppress and

Punish Trafficking in Persons,

Especially Women and Children,

supplementing the UN Convention

against Transnational

Organized Crime 2000

International Covenant on Economic, Social and

Cultural Rights 1966

International Convention on the Elimination of All Forms of

Racial Discrimination

1966

Convention against Torture

and Other Cruel, Inhuman or Degrading Treatment or Punishment

1984

Convention relating to the

Status of Refugees

1951

Convention on the Elimination of All Forms of Discrimination against Women

1979

International Covenant on

Civil and Political Rights

1966

Convention on the Rights of

the Child 1989

VERY HIGH HUMAN DEVELOPMENT 1 Norway .. 2003 1953 1970 1972 1972 1981 1986 1991 2 Australia .. 2005 1954 1975 1980 1975 1983 1989 1990 3 Iceland .. 2000 1955 1967 1979 1979 1985 1996 1992 4 Canada .. 2002 1969 1970 1976 1976 1981 1987 1991 5 Ireland .. 2000 1956 2000 1989 1989 1985 2002 1992 6 Netherlands .. 2005 1956 1971 1978 1978 1991 1988 1995 7 Sweden .. 2004 1954 1971 1971 1971 1980 1986 1990 8 France .. 2002 1954 1971 1980 1980 1983 1986 1990 9 Switzerland .. 2006 1955 1994 1992 1992 1997 1986 1997 10 Japan .. 2002 1981 1995 1979 1979 1985 1999 1994 11 Luxembourg .. 2009 1953 1978 1983 1983 1989 1987 1994 12 Finland .. 2006 1968 1970 1975 1975 1986 1989 1991 13 United States .. 2005 .. 1994 1992 1977 1980 1994 1995 14 Austria .. 2005 1954 1972 1978 1978 1982 1987 1992 15 Spain .. 2002 1978 1968 1977 1977 1984 1987 1990 16 Denmark .. 2003 1952 1971 1972 1972 1983 1987 1991 17 Belgium .. 2004 1953 1975 1983 1983 1985 1999 1991 18 Italy .. 2006 1954 1976 1978 1978 1985 1989 1991 19 Liechtenstein .. 2008 1957 2000 1998 1998 1995 1990 1995 20 New Zealand .. 2002 1960 1972 1978 1978 1985 1989 1993 21 United Kingdom .. 2006 1954 1969 1976 1976 1986 1988 1991 22 Germany .. 2006 1953 1969 1973 1973 1985 1990 1992 23 Singapore .. .. .. .. .. .. 1995 .. 1995 24 Hong Kong, China (SAR) .. .. .. .. .. .. .. .. .. 25 Greece .. 2000 1960 1970 1997 1985 1983 1988 1993 26 Korea (Republic of) .. 2000 1992 1978 1990 1990 1984 1995 1991 27 Israel .. 2008 1954 1979 1991 1991 1991 1991 1991 28 Andorra .. .. .. 2006 2006 .. 1997 2006 1996 29 Slovenia .. 2004 1992 1992 1992 1992 1992 1993 1992 30 Brunei Darussalam .. .. .. .. .. .. 2006 .. 1995 31 Kuwait .. 2006 .. 1968 1996 1996 1994 1996 1991 32 Cyprus .. 2003 1963 1967 1969 1969 1985 1991 1991 33 Qatar .. 2009 .. 1976 .. .. 2009 2000 1995 34 Portugal .. 2004 1960 1982 1978 1978 1980 1989 1990 35 United Arab Emirates .. 2009 .. 1974 .. .. 2004 .. 1997 36 Czech Republic .. 2002 1993 1993 1993 1993 1993 1993 1993 37 Barbados .. 2001 .. 1972 1973 1973 1980 .. 1990 38 Malta .. 2003 1971 1971 1990 1990 1991 1990 1990

HIGH HUMAN DEVELOPMENT 39 Bahrain .. 2004 .. 1990 2006 2007 2002 1998 1992 40 Estonia .. 2004 1997 1991 1991 1991 1991 1991 1991 41 Poland .. 2003 1991 1968 1977 1977 1980 1989 1991 42 Slovakia .. 2004 1993 1993 1993 1993 1993 1993 1993 43 Hungary .. 2006 1989 1967 1974 1974 1980 1987 1991 44 Chile 2005 2004 1972 1971 1972 1972 1989 1988 1990 45 Croatia .. 2003 1992 1992 1992 1992 1992 1992 1992 46 Lithuania .. 2003 1997 1998 1991 1991 1994 1996 1992 47 Antigua and Barbuda .. .. 1995 1988 .. .. 1989 1993 1993 48 Latvia .. 2004 1997 1992 1992 1992 1992 1992 1992 49 Argentina 2007 2002 1961 1968 1986 1986 1985 1986 1990 50 Uruguay 2001 2005 1970 1968 1970 1970 1981 1986 1990 51 Cuba .. .. .. 1972 2008 2008 1980 1995 1991 52 Bahamas .. 2008 1993 1975 2008 2008 1993 2008 1991 53 Mexico 1999 2003 2000 1975 1981 1981 1981 1986 1990 54 Costa Rica .. 2003 1978 1967 1968 1968 1986 1993 1990 55 Libyan Arab Jamahiriya 2004 2004 .. 1968 1970 1970 1989 1989 1993 56 Oman .. 2005 .. 2003 .. .. 2006 .. 1996 57 Seychelles 1994 2004 1980 1978 1992 1992 1992 1992 1990 58 Venezuela (Bolivarian Republic of) .. 2002 .. 1967 1978 1978 1983 1991 1990 59 Saudi Arabia .. 2007 .. 1997 .. .. 2000 1997 1996

HDI rank

HUMAN DEVELOPMENT REPORT 2009

164

F Selected conventions related to human rights and migration (by year of ratification)

International Convention on

the Protection of the Rights of All Migrant Workers

and Members of their Families

1990

Protocol to Prevent, Suppress and

Punish Trafficking in Persons,

Especially Women and Children,

supplementing the UN Convention

against Transnational

Organized Crime 2000

International Covenant on Economic, Social and

Cultural Rights 1966

International Convention on the Elimination of All Forms of

Racial Discrimination

1966

Convention against Torture

and Other Cruel, Inhuman or Degrading Treatment or Punishment

1984

Convention relating to the

Status of Refugees

1951

Convention on the Elimination of All Forms of Discrimination against Women

1979

International Covenant on

Civil and Political Rights

1966

Convention on the Rights of

the Child 1989

60 Panama .. 2004 1978 1967 1977 1977 1981 1987 1990 61 Bulgaria .. 2001 1993 1966 1970 1970 1982 1986 1991 62 Saint Kitts and Nevis .. 2004 2002 2006 .. .. 1985 .. 1990 63 Romania .. 2002 1991 1970 1974 1974 1982 1990 1990 64 Trinidad and Tobago .. 2007 2000 1973 1978 1978 1990 .. 1991 65 Montenegro 2006 2006 2006 2006 2006 2006 2006 2006 2006 66 Malaysia .. 2009 .. .. .. .. 1995 .. 1995 67 Serbia 2004 2001 2001 .. .. .. 2001 .. 2001 68 Belarus .. 2003 2001 1969 1973 1973 1981 1987 1990 69 Saint Lucia .. .. .. 1990 .. .. 1982 .. 1993 70 Albania 2007 2002 1992 1994 1991 1991 1994 1994 1992 71 Russian Federation .. 2004 1993 1969 1973 1973 1981 1987 1990 72 Macedonia (the Former Yugoslav Rep. of) .. 2005 1994 1994 1994 1994 1994 1994 1993 73 Dominica .. .. 1994 .. 1993 1993 1980 .. 1991 74 Grenada .. 2004 .. 1981 1991 1991 1990 .. 1990 75 Brazil .. 2004 1960 1968 1992 1992 1984 1989 1990 76 Bosnia and Herzegovina 1996 2002 1993 1993 1993 1993 1993 1993 1993 77 Colombia 1995 2004 1961 1981 1969 1969 1982 1987 1991 78 Peru 2005 2002 1964 1971 1978 1978 1982 1988 1990 79 Turkey 2004 2003 1962 2002 2003 2003 1985 1988 1995 80 Ecuador 2002 2002 1955 1966 1969 1969 1981 1988 1990 81 Mauritius .. 2003 .. 1972 1973 1973 1984 1992 1990 82 Kazakhstan .. 2008 1999 1998 2006 2006 1998 1998 1994 83 Lebanon .. 2005 .. 1971 1972 1972 1997 2000 1991

MEDIUM HUMAN DEVELOPMENT 84 Armenia .. 2003 1993 1993 1993 1993 1993 1993 1993 85 Ukraine .. 2004 2002 1969 1973 1973 1981 1987 1991 86 Azerbaijan 1999 2003 1993 1996 1992 1992 1995 1996 1992 87 Thailand .. 2001 .. 2003 1996 1999 1985 2007 1992 88 Iran (Islamic Republic of) .. .. 1976 1968 1975 1975 .. .. 1994 89 Georgia .. 2006 1999 1999 1994 1994 1994 1994 1994 90 Dominican Republic .. 2008 1978 1983 1978 1978 1982 1985 1991 91 Saint Vincent and the Grenadines .. 2002 1993 1981 1981 1981 1981 2001 1993 92 China .. .. 1982 1981 1998 2001 1980 1988 1992 93 Belize 2001 2003 1990 2001 1996 2000 1990 1986 1990 94 Samoa .. .. 1988 .. 2008 .. 1992 .. 1994 95 Maldives .. .. .. 1984 2006 2006 1993 2004 1991 96 Jordan .. .. .. 1974 1975 1975 1992 1991 1991 97 Suriname .. 2007 1978 1984 1976 1976 1993 .. 1993 98 Tunisia .. 2003 1957 1967 1969 1969 1985 1988 1992 99 Tonga .. .. .. 1972 .. .. .. .. 1995

100 Jamaica 2008 2003 1964 1971 1975 1975 1984 .. 1991 101 Paraguay 2008 2004 1970 2003 1992 1992 1987 1990 1990 102 Sri Lanka 1996 2000 .. 1982 1980 1980 1981 1994 1991 103 Gabon 2004 .. 1964 1980 1983 1983 1983 2000 1994 104 Algeria 2005 2004 1963 1972 1989 1989 1996 1989 1993 105 Philippines 1995 2002 1981 1967 1986 1974 1981 1986 1990 106 El Salvador 2003 2004 1983 1979 1979 1979 1981 1996 1990 107 Syrian Arab Republic 2005 2000 .. 1969 1969 1969 2003 2004 1993 108 Fiji .. .. 1972 1973 .. .. 1995 .. 1993 109 Turkmenistan .. 2005 1998 1994 1997 1997 1997 1999 1993 110 Occupied Palestinian Territories .. .. .. .. .. .. .. .. .. 111 Indonesia 2004 2000 .. 1999 2006 2006 1984 1998 1990 112 Honduras 2005 2008 1992 2002 1997 1981 1983 1996 1990 113 Bolivia 2000 2006 1982 1970 1982 1982 1990 1999 1990 114 Guyana 2005 2004 .. 1977 1977 1977 1980 1988 1991 115 Mongolia .. 2008 .. 1969 1974 1974 1981 2002 1990 116 Viet Nam .. .. .. 1982 1982 1982 1982 .. 1990 117 Moldova .. 2005 2002 1993 1993 1993 1994 1995 1993 118 Equatorial Guinea .. 2003 1986 2002 1987 1987 1984 2002 1992

HDI rank

165

TABLE F

International Convention on

the Protection of the Rights of All Migrant Workers

and Members of their Families

1990

Protocol to Prevent, Suppress and

Punish Trafficking in Persons,

Especially Women and Children,

supplementing the UN Convention

against Transnational

Organized Crime 2000

International Covenant on Economic, Social and

Cultural Rights 1966

International Convention on the Elimination of All Forms of

Racial Discrimination

1966

Convention against Torture

and Other Cruel, Inhuman or Degrading Treatment or Punishment

1984

Convention relating to the

Status of Refugees

1951

Convention on the Elimination of All Forms of Discrimination against Women

1979

International Covenant on

Civil and Political Rights

1966

Convention on the Rights of

the Child 1989

119 Uzbekistan .. 2008 .. 1995 1995 1995 1995 1995 1994 120 Kyrgyzstan 2003 2003 1996 1997 1994 1994 1997 1997 1994 121 Cape Verde 1997 2004 .. 1979 1993 1993 1980 1992 1992 122 Guatemala 2003 2004 1983 1983 1992 1988 1982 1990 1990 123 Egypt 1993 2004 1981 1967 1982 1982 1981 1986 1990 124 Nicaragua 2005 2004 1980 1978 1980 1980 1981 2005 1990 125 Botswana .. 2002 1969 1974 2000 .. 1996 2000 1995 126 Vanuatu .. .. .. .. 2008 .. 1995 .. 1993 127 Tajikistan 2002 2002 1993 1995 1999 1999 1993 1995 1993 128 Namibia .. 2002 1995 1982 1994 1994 1992 1994 1990 129 South Africa .. 2004 1996 1998 1998 1994 1995 1998 1995 130 Morocco 1993 .. 1956 1970 1979 1979 1993 1993 1993 131 Sao Tome and Principe 2000 2006 1978 2000 1995 1995 2003 2000 1991 132 Bhutan .. .. .. 1973 .. .. 1981 .. 1990 133 Lao People’s Democratic Republic .. 2003 .. 1974 2000 2007 1981 .. 1991 134 India .. 2002 .. 1968 1979 1979 1993 1997 1992 135 Solomon Islands .. .. 1995 1982 .. 1982 2002 .. 1995 136 Congo 2008 2000 1962 1988 1983 1983 1982 2003 1993 137 Cambodia 2004 2007 1992 1983 1992 1992 1992 1992 1992 138 Myanmar .. 2004 .. .. .. .. 1997 .. 1991 139 Comoros 2000 .. .. 2004 2008 2008 1994 2000 1993 140 Yemen .. .. 1980 1972 1987 1987 1984 1991 1991 141 Pakistan .. .. .. 1966 2008 2008 1996 2008 1990 142 Swaziland .. 2001 2000 1969 2004 2004 2004 2004 1995 143 Angola .. .. 1981 .. 1992 1992 1986 .. 1990 144 Nepal .. .. .. 1971 1991 1991 1991 1991 1990 145 Madagascar .. 2005 1967 1969 1971 1971 1989 2005 1991 146 Bangladesh 1998 .. .. 1979 2000 1998 1984 1998 1990 147 Kenya .. 2005 1966 2001 1972 1972 1984 1997 1990 148 Papua New Guinea .. .. 1986 1982 2008 2008 1995 .. 1993 149 Haiti .. 2000 1984 1972 1991 .. 1981 .. 1995 150 Sudan .. .. 1974 1977 1986 1986 .. 1986 1990 151 Tanzania ( United Republic of) .. 2006 1964 1972 1976 1976 1985 .. 1991 152 Ghana 2000 .. 1963 1966 2000 2000 1986 2000 1990 153 Cameroon .. 2006 1961 1971 1984 1984 1994 1986 1993 154 Mauritania 2007 2005 1987 1988 2004 2004 2001 2004 1991 155 Djibouti .. 2005 1977 2006 2002 2002 1998 2002 1990 156 Lesotho 2005 2003 1981 1971 1992 1992 1995 2001 1992 157 Uganda 1995 2000 1976 1980 1995 1987 1985 1986 1990 158 Nigeria .. 2001 1967 1967 1993 1993 1985 2001 1991

LOW HUMAN DEVELOPMENT 159 Togo 2001 2009 1962 1972 1984 1984 1983 1987 1990 160 Malawi .. 2005 1987 1996 1993 1993 1987 1996 1991 161 Benin 2005 2004 1962 2001 1992 1992 1992 1992 1990 162 Timor-Leste 2004 .. 2003 2003 2003 2003 2003 2003 2003 163 Côte d’Ivoire .. .. 1961 1973 1992 1992 1995 1995 1991 164 Zambia .. 2005 1969 1972 1984 1984 1985 1998 1991 165 Eritrea .. .. .. 2001 2002 2001 1995 .. 1994 166 Senegal 1999 2003 1963 1972 1978 1978 1985 1986 1990 167 Rwanda 2008 2003 1980 1975 1975 1975 1981 2008 1991 168 Gambia .. 2003 1966 1978 1979 1978 1993 1985 1990 169 Liberia 2004 2004 1964 1976 2004 2004 1984 2004 1993 170 Guinea 2000 2004 1965 1977 1978 1978 1982 1989 1990 171 Ethiopia .. .. 1969 1976 1993 1993 1981 1994 1991 172 Mozambique .. 2006 1983 1983 1993 .. 1997 1999 1994 173 Guinea-Bissau 2000 2007 1976 2000 2000 1992 1985 2000 1990 174 Burundi .. 2000 1963 1977 1990 1990 1992 1993 1990 175 Chad .. .. 1981 1977 1995 1995 1995 1995 1990 176 Congo (Democratic Republic of the) .. 2005 1965 1976 1976 1976 1986 1996 1990 177 Burkina Faso 2003 2002 1980 1974 1999 1999 1987 1999 1990

HDI rank

HUMAN DEVELOPMENT REPORT 2009

166

F Selected conventions related to human rights and migration (by year of ratification)

International Convention on

the Protection of the Rights of All Migrant Workers

and Members of their Families

1990

Protocol to Prevent, Suppress and

Punish Trafficking in Persons,

Especially Women and Children,

supplementing the UN Convention

against Transnational

Organized Crime 2000

International Covenant on Economic, Social and

Cultural Rights 1966

International Convention on the Elimination of All Forms of

Racial Discrimination

1966

Convention against Torture

and Other Cruel, Inhuman or Degrading Treatment or Punishment

1984

Convention relating to the

Status of Refugees

1951

Convention on the Elimination of All Forms of Discrimination against Women

1979

International Covenant on

Civil and Political Rights

1966

Convention on the Rights of

the Child 1989

178 Mali 2003 2002 1973 1974 1974 1974 1985 1999 1990 179 Central African Republic .. 2006 1962 1971 1981 1981 1991 .. 1992 180 Sierra Leone 2000 2001 1981 1967 1996 1996 1988 2001 1990 181 Afghanistan .. .. 2005 1983 1983 1983 2003 1987 1994 182 Niger 2009 2004 1961 1967 1986 1986 1999 1998 1990

OTHER UN MEMBER STATES Iraq .. 2009 .. 1970 1971 1971 1986 .. 1994 Kiribati .. 2005 .. .. .. .. 2004 .. 1995 Korea ( Democratic People’s Rep. of) .. .. .. .. 1981 1981 2001 .. 1990 Marshall Islands .. .. .. .. .. .. 2006 .. 1993 Micronesia (Federated States of) .. .. .. .. .. .. 2004 .. 1993 Monaco .. 2001 1954 1995 1997 1997 2005 1991 1993 Nauru .. 2001 .. 2001 2001 .. .. 2001 1994 Palau .. .. .. .. .. .. .. .. 1995 San Marino .. 2000 .. 2002 1985 1985 2003 2006 1991 Somalia .. .. 1978 1975 1990 1990 .. 1990 2002 Tuvalu .. .. 1986 .. .. .. 1999 .. 1995 Zimbabwe .. .. 1981 1991 1991 1991 1991 .. 1990

Total state parties  41 129 144 173 164 160 186 146 193 Treaties signed, not yet ratified  15 21 0 6 8 6 1 10 2 Africa  16 36 48 49 50 48 51 43 52  9 5 0 3 3 3 0 5 1 Asia  8 25 19 41 35 38 45 33 47  3 6 0 1 3 0 0 2 0 Europe  2 37 42 44 43 42 43 44 44  2 5 0 0 0 0 0 0 0 Latin America and the Caribbean  15 26 27 31 29 27 33 22 33  1 3 0 1 1 2 0 2 0 Northern America  0 2 1 2 2 1 1 2 1  0 0 0 0 0 1 1 0 1 Oceania  0 3 7 6 5 4 12 2 16  0 1 0 1 1 0 0 1 0

Very high human development  0 26 31 37 34 32 36 36 38  0 8 0 0 0 1 1 0 1 High human development  12 41 34 43 39 39 47 37 47  2 1 0 1 1 1 0 1 0 Medium human development  22 44 54 68 66 64 77 52 83  8 11 0 4 6 4 0 7 0 Low human development  7 15 25 25 25 25 25 21 25  5 3 0 1 1 0 0 2 1

NOTES

Data refer to the year of ratification, accession or

succession unless otherwise specified. All these

stages have the same legal effect. Bold signifies signature not yet followed by ratification. Data are as

of June 2009.

 Total state parties  Treaties signed, but not yet ratified.

SOURCES

All columns: UN (2009b).

HDI rank

167

HUMAN DEVELOPMENT REPORT 2009

GTABLE Human development index trends

1980 1985 1990 1995 2000 2005 2006 2006 2006–2007 1980–2007 1990–2007 2000–20072007

Average annual growth rates

(%)

Rank Change in

rank Long term

Medium term

Short term

VERY HIGH HUMAN DEVELOPMENT 1 Norway 0.900 0.912 0.924 0.948 0.961 0.968 0.970 0.971 1 0 0.28 0.29 0.16 2 Australia 0.871 0.883 0.902 0.938 0.954 0.967 0.968 0.970 2 0 0.40 0.43 0.24 3 Iceland 0.886 0.894 0.913 0.918 0.943 0.965 0.967 0.969 3 0 0.33 0.35 0.39 4 Canada 0.890 0.913 0.933 0.938 0.948 0.963 0.965 0.966 4 0 0.31 0.21 0.27 5 Ireland 0.840 0.855 0.879 0.903 0.936 0.961 0.964 0.965 5 0 0.52 0.55 0.44 6 Netherlands 0.889 0.903 0.917 0.938 0.950 0.958 0.961 0.964 7 1 0.30 0.30 0.21 7 Sweden 0.885 0.895 0.906 0.937 0.954 0.960 0.961 0.963 6 -1 0.32 0.36 0.14 8 France 0.876 0.888 0.909 0.927 0.941 0.956 0.958 0.961 11 3 0.34 0.32 0.30 9 Switzerland 0.899 0.906 0.920 0.931 0.948 0.957 0.959 0.960 9 0 0.25 0.25 0.19 10 Japan 0.887 0.902 0.918 0.931 0.943 0.956 0.958 0.960 10 0 0.29 0.26 0.25 11 Luxembourg .. .. .. .. .. 0.956 0.959 0.960 8 -3 .. .. .. 12 Finland 0.865 0.882 0.904 0.916 0.938 0.952 0.955 0.959 13 1 0.38 0.35 0.32 13 United States 0.894 0.909 0.923 0.939 0.949 0.955 0.955 0.956 12 -1 0.25 0.21 0.11 14 Austria 0.865 0.878 0.899 0.920 0.940 0.949 0.952 0.955 16 2 0.37 0.35 0.23 15 Spain 0.855 0.869 0.896 0.914 0.931 0.949 0.952 0.955 15 0 0.41 0.37 0.36 16 Denmark 0.882 0.891 0.899 0.917 0.936 0.950 0.953 0.955 14 -2 0.29 0.36 0.28 17 Belgium 0.871 0.885 0.904 0.933 0.945 0.947 0.951 0.953 17 0 0.34 0.31 0.13 18 Italy 0.857 0.866 0.889 0.906 0.927 0.947 0.950 0.951 19 1 0.39 0.40 0.36 19 Liechtenstein .. .. .. .. .. .. 0.950 0.951 18 -1 .. .. .. 20 New Zealand 0.863 0.874 0.884 0.911 0.930 0.946 0.948 0.950 20 0 0.36 0.42 0.30 21 United Kingdom 0.861 0.870 0.891 0.929 0.932 0.947 0.945 0.947 21 0 0.35 0.36 0.24 22 Germany 0.869 0.877 0.896 0.919 .. 0.942 0.945 0.947 22 0 0.32 0.33 .. 23 Singapore 0.785 0.805 0.851 0.884 .. .. 0.942 0.944 24 1 0.68 0.61 .. 24 Hong Kong, China (SAR) .. .. .. .. .. 0.939 0.943 0.944 23 -1 .. .. .. 25 Greece 0.844 0.857 0.872 0.874 0.895 0.935 0.938 0.942 25 0 0.41 0.45 0.73 26 Korea (Republic of) 0.722 0.760 0.802 0.837 0.869 0.927 0.933 0.937 26 0 0.97 0.92 1.08 27 Israel 0.829 0.853 0.868 0.883 0.908 0.929 0.932 0.935 28 1 0.44 0.44 0.42 28 Andorra .. .. .. .. .. .. 0.933 0.934 27 -1 .. .. .. 29 Slovenia .. .. 0.853 0.861 0.892 0.918 0.924 0.929 29 0 .. 0.51 0.58 30 Brunei Darussalam 0.827 0.843 0.876 0.889 0.905 0.917 0.919 0.920 30 0 0.39 0.29 0.22 31 Kuwait 0.812 0.826 .. 0.851 0.874 0.915 0.912 0.916 31 0 0.44 .. 0.67 32 Cyprus .. .. 0.849 0.866 0.897 0.908 0.911 0.914 32 0 .. 0.43 0.26 33 Qatar .. .. .. .. 0.870 0.903 0.905 0.910 34 1 .. .. 0.64 34 Portugal 0.768 0.789 0.833 0.870 0.895 0.904 0.907 0.909 33 -1 0.63 0.52 0.23 35 United Arab Emirates 0.743 0.806 0.834 0.845 0.848 0.896 0.896 0.903 37 2 0.72 0.47 0.91 36 Czech Republic .. .. 0.847 0.857 0.868 0.894 0.899 0.903 36 0 .. 0.38 0.56 37 Barbados .. .. .. .. .. 0.890 0.891 0.903 39 2 .. .. .. 38 Malta .. 0.809 0.836 0.856 0.874 0.897 0.899 0.902 35 -3 0.50a 0.45 0.45 HIGH HUMAN DEVELOPMENT 39 Bahrain 0.761 0.784 0.829 0.850 0.864 0.888 0.894 0.895 38 -1 0.60 0.45 0.50 40 Estonia .. .. 0.817 0.796 0.835 0.872 0.878 0.883 40 0 .. 0.46 0.80 41 Poland .. .. 0.806 0.823 0.853 0.871 0.876 0.880 42 1 .. 0.52 0.45 42 Slovakia .. .. .. 0.827 0.840 0.867 0.873 0.880 44 2 .. .. 0.66 43 Hungary 0.802 0.813 0.812 0.816 0.844 0.874 0.878 0.879 41 -2 0.34 0.47 0.58 44 Chile 0.748 0.762 0.795 0.822 0.849 0.872 0.874 0.878 43 -1 0.59 0.58 0.48 45 Croatia .. .. 0.817 0.811 0.837 0.862 0.867 0.871 45 0 .. 0.38 0.58 46 Lithuania .. .. 0.828 0.791 0.830 0.862 0.865 0.870 46 0 .. 0.29 0.68 47 Antigua and Barbuda .. .. .. .. .. .. 0.860 0.868 48 1 .. .. .. 48 Latvia .. .. 0.803 0.765 0.810 0.852 0.859 0.866 50 2 .. 0.44 0.96 49 Argentina 0.793 0.797 0.804 0.824 .. 0.855 0.861 0.866 47 -2 0.33 0.44 .. 50 Uruguay 0.776 0.783 0.802 0.817 0.837 0.855 0.860 0.865 49 -1 0.40 0.45 0.47 51 Cuba .. .. .. .. .. 0.839 0.856 0.863 51 0 .. .. .. 52 Bahamas .. .. .. .. .. 0.852 0.854 0.856 52 0 .. .. .. 53 Mexico 0.756 0.768 0.782 0.794 0.825 0.844 0.849 0.854 54 1 0.45 0.52 0.50 54 Costa Rica 0.763 0.770 0.791 0.807 0.825 0.844 0.849 0.854 53 -1 0.42 0.45 0.48 55 Libyan Arab Jamahiriya .. .. .. .. 0.821 0.837 0.842 0.847 56 1 .. .. 0.44 56 Oman .. .. .. .. .. 0.836 0.843 0.846 55 -1 .. .. .. 57 Seychelles .. .. .. .. 0.841 0.838 0.841 0.845 57 0 .. .. 0.06 58 Venezuela (Bolivarian Republic of) 0.765 0.765 0.790 0.793 0.802 0.822 0.833 0.844 62 4 0.37 0.39 0.74 59 Saudi Arabia .. .. 0.744 0.765 .. 0.837 0.840 0.843 58 -1 .. 0.74 ..

HDI rank

HUMAN DEVELOPMENT REPORT 2009

168

Human development index trends

1980 1985 1990 1995 2000 2005 2006 2006 2006–2007 1980–2007 1990–2007 2000–20072007

Average annual growth rates

(%)

Rank Change in

rank Long term

Medium term

Short term

60 Panama 0.759 0.769 0.765 0.784 0.811 0.829 0.834 0.840 61 1 0.38 0.55 0.50 61 Bulgaria .. .. .. .. 0.803 0.829 0.835 0.840 59 -2 .. .. 0.65 62 Saint Kitts and Nevis .. .. .. .. .. 0.831 0.835 0.838 60 -2 .. .. .. 63 Romania .. .. 0.786 0.780 0.788 0.824 0.832 0.837 64 1 .. 0.37 0.87 64 Trinidad and Tobago 0.794 0.791 0.796 0.797 0.806 0.825 0.832 0.837 63 -1 0.19 0.30 0.53 65 Montenegro .. .. .. .. 0.815 0.823 0.828 0.834 65 0 .. .. 0.34 66 Malaysia 0.666 0.689 0.737 0.767 0.797 0.821 0.825 0.829 66 0 0.81 0.69 0.56 67 Serbia .. .. .. .. 0.797 0.817 0.821 0.826 67 0 .. .. 0.51 68 Belarus .. .. 0.795 0.760 0.786 0.812 0.819 0.826 69 1 .. 0.22 0.70 69 Saint Lucia .. .. .. .. .. 0.817 0.821 0.821 68 -1 .. .. .. 70 Albania .. .. .. .. 0.784 0.811 0.814 0.818 70 0 .. .. 0.61 71 Russian Federation .. .. 0.821 0.777 .. 0.804 0.811 0.817 73 2 .. -0.03 .. 72 Macedonia (the Former Yugoslav Rep. of) .. .. .. 0.782 0.800 0.810 0.813 0.817 72 0 .. .. 0.30 73 Dominica .. .. .. .. .. 0.814 0.814 0.814 71 -2 .. .. .. 74 Grenada .. .. .. .. .. 0.812 0.810 0.813 74 0 .. .. .. 75 Brazil 0.685 0.694 0.710 0.734 0.790 0.805 0.808 0.813 75 0 0.63 0.79 0.41 76 Bosnia and Herzegovina .. .. .. .. .. 0.803 0.807 0.812 76 0 .. .. .. 77 Colombia 0.688 0.698 0.715 0.757 0.772 0.795 0.800 0.807 82 5 0.59 0.71 0.63 78 Peru 0.687 0.703 0.708 0.744 0.771 0.791 0.799 0.806 83 5 0.59 0.76 0.63 79 Turkey 0.628 0.674 0.705 0.730 0.758 0.796 0.802 0.806 78 -1 0.93 0.79 0.87 80 Ecuador 0.709 0.723 0.744 0.758 .. .. 0.805 0.806 77 -3 0.48 0.47 .. 81 Mauritius .. .. 0.718 0.735 0.770 0.797 0.801 0.804 79 -2 .. 0.67 0.63 82 Kazakhstan .. .. 0.778 0.730 0.747 0.794 0.800 0.804 81 -1 .. 0.20 1.05 83 Lebanon .. .. .. .. .. 0.800 0.800 0.803 80 -3 .. .. ..

MEDIUM HUMAN DEVELOPMENT 84 Armenia .. .. 0.731 0.693 0.738 0.777 0.787 0.798 85 1 .. 0.51 1.12 85 Ukraine .. .. .. .. 0.754 0.783 0.789 0.796 84 -1 .. .. 0.76 86 Azerbaijan .. .. .. .. .. 0.755 0.773 0.787 88 2 .. .. .. 87 Thailand 0.658 0.684 0.706 0.727 0.753 0.777 0.780 0.783 86 -1 0.64 0.61 0.57 88 Iran (Islamic Republic of) 0.561 0.620 0.672 0.712 0.738 0.773 0.777 0.782 87 -1 1.23 0.89 0.83 89 Georgia .. .. .. .. 0.739 0.765 0.768 0.778 91 2 .. .. 0.73 90 Dominican Republic 0.640 0.659 0.667 0.686 0.748 0.765 0.771 0.777 89 -1 0.72 0.90 0.54 91 Saint Vincent and the Grenadines .. .. .. .. .. 0.763 0.767 0.772 93 2 .. .. .. 92 China 0.533 0.556 0.608 0.657 0.719 0.756 0.763 0.772 99 7 1.37 1.40 1.00 93 Belize .. .. 0.705 0.723 0.735 0.770 0.770 0.772 90 -3 .. 0.54 0.70 94 Samoa .. 0.686 0.697 0.716 0.742 0.764 0.766 0.771 96 2 0.53a 0.59 0.55 95 Maldives .. .. .. 0.683 0.730 0.755 0.765 0.771 97 2 .. .. 0.78 96 Jordan 0.631 0.638 0.666 0.656 0.691 0.764 0.767 0.770 95 -1 0.73 0.85 1.55 97 Suriname .. .. .. .. .. 0.759 0.765 0.769 98 1 .. .. .. 98 Tunisia .. 0.605 0.627 0.654 0.678 0.758 0.763 0.769 100 2 1.09 a 1.20 1.79 99 Tonga .. .. .. .. 0.759 0.765 0.767 0.768 94 -5 .. .. 0.16

100 Jamaica .. .. .. .. 0.750 0.765 0.768 0.766 92 -8 .. .. 0.29 101 Paraguay 0.677 0.677 0.711 0.726 0.737 0.754 0.757 0.761 101 0 0.43 0.40 0.45 102 Sri Lanka 0.649 0.670 0.683 0.696 0.729 0.752 0.755 0.759 102 0 0.58 0.62 0.57 103 Gabon .. .. .. 0.748 0.735 0.747 0.750 0.755 103 0 .. .. 0.39 104 Algeria .. 0.628 0.647 0.653 0.713 0.746 0.749 0.754 104 0 0.83a 0.90 0.79 105 Philippines 0.652 0.651 0.697 0.713 0.726 0.744 0.747 0.751 105 0 0.53 0.44 0.49 106 El Salvador 0.573 0.585 0.660 0.691 0.704 0.743 0.746 0.747 106 0 0.99 0.73 0.85 107 Syrian Arab Republic 0.603 0.625 0.626 0.649 0.715 0.733 0.738 0.742 109 2 0.77 1.00 0.53 108 Fiji .. .. .. .. .. 0.744 0.744 0.741 107 -1 .. .. .. 109 Turkmenistan .. .. .. .. .. .. 0.739 0.739 108 -1 .. .. .. 110 Occupied Palestinian Territories .. .. .. .. .. 0.736 0.737 0.737 110 0 .. .. .. 111 Indonesia 0.522 0.562 0.624 0.658 0.673 0.723 0.729 0.734 111 0 1.26 0.95 1.25 112 Honduras 0.567 0.593 0.608 0.623 0.690 0.725 0.729 0.732 112 0 0.94 1.09 0.84 113 Bolivia 0.560 0.577 0.629 0.653 0.699 0.723 0.726 0.729 113 0 0.98 0.87 0.62 114 Guyana .. .. .. .. .. 0.722 0.721 0.729 114 0 .. .. .. 115 Mongolia .. .. .. .. 0.676 0.713 0.720 0.727 116 1 .. .. 1.02 116 Viet Nam .. 0.561 0.599 0.647 0.690 0.715 0.720 0.725 115 -1 1.16a 1.13 0.71 117 Moldova .. .. 0.735 0.682 0.683 0.712 0.718 0.720 117 0 .. -0.12 0.77 118 Equatorial Guinea .. .. .. .. 0.655 0.715 0.712 0.719 118 0 .. .. 1.33

HDI rank

169

TABLE G

1980 1985 1990 1995 2000 2005 2006 2006 2006–2007 1980–2007 1990–2007 2000–20072007

Average annual growth rates

(%)

Rank Change in

rank Long term

Medium term

Short term

119 Uzbekistan .. .. .. .. 0.687 0.703 0.706 0.710 119 0 .. .. 0.48 120 Kyrgyzstan .. .. .. .. 0.687 0.702 0.705 0.710 120 0 .. .. 0.46 121 Cape Verde .. .. 0.589 0.641 0.674 0.692 0.704 0.708 121 0 .. 1.08 0.71 122 Guatemala 0.531 0.538 0.555 0.621 0.664 0.691 0.696 0.704 123 1 1.05 1.40 0.85 123 Egypt 0.496 0.552 0.580 0.631 0.665 0.696 0.700 0.703 122 -1 1.30 1.13 0.81 124 Nicaragua 0.565 0.569 0.573 0.597 0.667 0.691 0.696 0.699 124 0 0.79 1.17 0.67 125 Botswana 0.539 0.579 0.682 0.665 0.632 0.673 0.683 0.694 126 1 0.94 0.10 1.34 126 Vanuatu .. .. .. .. 0.663 0.681 0.688 0.693 125 -1 .. .. 0.62 127 Tajikistan .. .. 0.707 0.636 0.641 0.677 0.683 0.688 127 0 .. -0.16 1.03 128 Namibia .. .. 0.657 0.675 0.661 0.672 0.678 0.686 129 1 .. 0.26 0.53 129 South Africa 0.658 0.680 0.698 .. 0.688 0.678 0.680 0.683 128 -1 0.14 -0.13 -0.10 130 Morocco 0.473 0.499 0.518 0.562 0.583 0.640 0.648 0.654 130 0 1.20 1.37 1.63 131 Sao Tome and Principe .. .. .. .. .. 0.639 0.645 0.651 131 0 .. .. .. 132 Bhutan .. .. .. .. .. 0.602 0.608 0.619 133 1 .. .. .. 133 Lao People’s Democratic Republic .. .. .. 0.518 0.566 0.607 0.613 0.619 132 -1 .. .. 1.26 134 India 0.427 0.453 0.489 0.511 0.556 0.596 0.604 0.612 134 0 1.33 1.32 1.36 135 Solomon Islands .. .. .. .. .. 0.599 0.604 0.610 135 0 .. .. .. 136 Congo .. .. 0.597 0.575 0.536 0.600 0.603 0.601 136 0 .. 0.04 1.65 137 Cambodia .. .. .. .. 0.515 0.575 0.584 0.593 137 0 .. .. 2.01 138 Myanmar .. 0.492 0.487 0.506 .. 0.583 0.584 0.586 138 0 0.79a 1.08 .. 139 Comoros 0.447 0.461 0.489 0.513 0.540 0.570 0.573 0.576 139 0 0.94 0.96 0.92 140 Yemen .. .. .. 0.486 0.522 0.562 0.568 0.575 141 1 .. .. 1.36 141 Pakistan 0.402 0.423 0.449 0.469 .. 0.555 0.568 0.572 142 1 1.30 1.42 .. 142 Swaziland 0.535 0.587 0.619 0.626 0.598 0.567 0.569 0.572 140 -2 0.24 -0.47 -0.63 143 Angola .. .. .. .. .. 0.541 0.552 0.564 143 0 .. .. .. 144 Nepal 0.309 0.342 0.407 0.436 0.500 0.537 0.547 0.553 144 0 2.16 1.81 1.46 145 Madagascar .. .. .. .. 0.501 0.532 0.537 0.543 145 0 .. .. 1.14 146 Bangladesh 0.328 0.351 0.389 0.415 0.493 0.527 0.535 0.543 148 2 1.86 1.96 1.39 147 Kenya .. .. .. .. 0.522 0.530 0.535 0.541 147 0 .. .. 0.51 148 Papua New Guinea 0.418 0.427 0.432 0.461 .. 0.532 0.536 0.541 146 -2 0.95 1.32 .. 149 Haiti 0.433 0.442 0.462 0.483 .. .. 0.526 0.532 149 0 0.77 0.83 .. 150 Sudan .. .. .. .. 0.491 0.515 0.526 0.531 150 0 .. .. 1.12 151 Tanzania ( United Republic of) .. .. 0.436 0.425 0.458 0.510 0.519 0.530 151 0 .. 1.15 2.09 152 Ghana .. .. .. .. 0.495 0.512 0.518 0.526 154 2 .. .. 0.88 153 Cameroon 0.460 0.498 0.485 0.457 0.513 0.520 0.519 0.523 152 -1 0.48 0.44 0.26 154 Mauritania .. .. .. .. 0.495 0.511 0.519 0.520 153 -1 .. .. 0.71 155 Djibouti .. .. .. .. .. 0.513 0.517 0.520 155 0 .. .. .. 156 Lesotho .. .. .. .. 0.533 0.508 0.511 0.514 156 0 .. .. -0.52 157 Uganda .. .. 0.392 0.389 0.460 0.494 0.505 0.514 158 1 .. 1.59 1.57 158 Nigeria .. .. 0.438 0.450 0.466 0.499 0.506 0.511 157 -1 .. 0.91 1.31

LOW HUMAN DEVELOPMENT 159 Togo 0.404 0.387 0.391 0.404 .. 0.495 0.498 0.499 159 0 0.78 1.44 .. 160 Malawi .. 0.379 0.390 0.453 0.478 0.476 0.484 0.493 161 1 1.20a 1.38 0.44 161 Benin 0.351 0.364 0.384 0.411 0.447 0.481 0.487 0.492 160 -1 1.25 1.46 1.37 162 Timor-Leste .. .. .. .. .. 0.488 0.484 0.489 162 0 .. .. .. 163 Côte d’Ivoire .. .. 0.463 0.456 0.481 0.480 0.482 0.484 163 0 .. 0.26 0.08 164 Zambia .. .. 0.495 0.454 0.431 0.466 0.473 0.481 164 0 .. -0.17 1.57 165 Eritrea .. .. .. .. 0.431 0.466 0.467 0.472 165 0 .. .. 1.29 166 Senegal .. .. 0.390 0.399 0.436 0.460 0.462 0.464 166 0 .. 1.02 0.88 167 Rwanda 0.357 0.361 0.325 0.306 0.402 0.449 0.455 0.460 167 0 0.94 2.04 1.90 168 Gambia .. .. .. .. .. 0.450 0.453 0.456 168 0 .. .. .. 169 Liberia 0.365 0.370 0.325 0.280 0.419 0.427 0.434 0.442 169 0 0.71 1.81 0.77 170 Guinea .. .. .. .. .. 0.426 0.433 0.435 170 0 .. .. .. 171 Ethiopia .. .. .. 0.308 0.332 0.391 0.402 0.414 171 0 .. .. 3.13 172 Mozambique 0.280 0.258 0.273 0.310 0.350 0.390 0.397 0.402 172 0 1.34 2.28 1.97 173 Guinea-Bissau 0.256 0.278 0.320 0.349 0.370 0.386 0.391 0.396 174 1 1.62 1.25 0.99 174 Burundi 0.268 0.292 0.327 0.299 0.358 0.375 0.387 0.394 175 1 1.43 1.10 1.38 175 Chad .. .. .. 0.324 0.350 0.394 0.393 0.392 173 -2 .. .. 1.61 176 Congo (Democratic Republic of the) .. .. .. .. 0.353 0.370 0.371 0.389 177 1 .. .. 1.41 177 Burkina Faso 0.248 0.264 0.285 0.297 0.319 0.367 0.384 0.389 176 -1 1.67 1.82 2.85

HDI rank

HUMAN DEVELOPMENT REPORT 2009

170

G Human development index trends

1980 1985 1990 1995 2000 2005 2006 2006 2006–2007 1980–2007 1990–2007 2000–20072007

Average annual growth rates

(%)

Rank Change in

rank Long term

Medium term

Short term

178 Mali 0.245 0.239 0.254 0.267 0.316 0.361 0.366 0.371 179 1 1.53 2.23 2.30 179 Central African Republic 0.335 0.344 0.362 0.347 0.378 0.364 0.367 0.369 178 -1 0.36 0.12 -0.33 180 Sierra Leone .. .. .. .. .. 0.350 0.357 0.365 180 0 .. .. .. 181 Afghanistan .. .. .. .. .. 0.347 0.350 0.352 181 0 .. .. .. 182 Niger .. .. .. .. 0.258 0.330 0.335 0.340 182 0 .. .. 3.92

NOTES

The human development index values in this table were

calculated using a consistent methodology and data

series. They are not strictly comparable with those

published in earlier Human Development Reports. See

the Reader’s guide for more details.

a Average annual growth rate between 1985 and 2007.

SOURCES

Columns 1–8: calculated based on data on life

expectancy from UN (2009e); data on adult literacy

rates from UNESCO Institute for Statistics (2003) and

(2009a); data on combined gross enrolment ratios from

UNESCO Institute for Statistics (1999 ) and (2009b);

and data on GDP per capita (2007 PPP US $) from World

Bank (2009d).

Column 9 : calculated based on revised HDI values for

2006 in column 7.

Column 10: calculated based on revised HDI ranks for

2006 and new HDI ranks for 2007.

Column 11: calculated based on the HDI values for 1980

and 2007.

Column 12: calculated based on the HDI values for 1990

and 2007.

Column 13: calculated based on the HDI values for 2000

and 2007.

HDI rank

171

HUMAN DEVELOPMENT REPORT 2009

HTABLE Human development index 2007 and its components

Human development

index value

Adult literacy rate

(% aged 15 and above)

GDP per capita (PPP US$) GDP index

Life expectancy at

birth (years)

Combined gross

enrolment ratio in education

(%) Education

index

Life expectancy

index

GDP per capita rank minus HDI

rankb

H 2007 1999–2007a 2007 20072007 2007 20072007 2007HDI rank

VERY HIGH HUMAN DEVELOPMENT 1 Norway 0.971 80.5 .. c 98.6 d 53,433 e 0.925 0.989 1.000 4 2 Australia 0.970 81.4 .. c 114.2 d,f 34,923 0.940 0.993 0.977 20 3 Iceland 0.969 81.7 .. c 96.0 d 35,742 0.946 0.980 0.981 16 4 Canada 0.966 80.6 .. c 99.3 d,g 35,812 0.927 0.991 0.982 14 5 Ireland 0.965 79.7 .. c 97.6 d 44,613 e 0.911 0.985 1.000 5 6 Netherlands 0.964 79.8 .. c 97.5 d 38,694 0.914 0.985 0.994 8 7 Sweden 0.963 80.8 .. c 94.3 d 36,712 0.930 0.974 0.986 9 8 France 0.961 81.0 .. c 95.4 d 33,674 0.933 0.978 0.971 17 9 Switzerland 0.960 81.7 .. c 82.7 d 40,658 0.945 0.936 1.000 4 10 Japan 0.960 82.7 .. c 86.6 d 33,632 0.961 0.949 0.971 16 11 Luxembourg 0.960 79.4 .. c 94.4 h 79,485 e 0.906 0.975 1.000 -9 12 Finland 0.959 79.5 .. c 101.4 d,f 34,526 0.908 0.993 0.975 11 13 United States 0.956 79.1 .. c 92.4 d 45,592 e 0.902 0.968 1.000 -4 14 Austria 0.955 79.9 .. c 90.5 d 37,370 0.915 0.962 0.989 1 15 Spain 0.955 80.7 97.9 i 96.5 d 31,560 0.929 0.975 0.960 12 16 Denmark 0.955 78.2 .. c 101.3 d,f 36,130 0.887 0.993 0.983 1 17 Belgium 0.953 79.5 .. c 94.3 d 34,935 0.908 0.974 0.977 4 18 Italy 0.951 81.1 98.9 j 91.8 d 30,353 0.935 0.965 0.954 11 19 Liechtenstein 0.951 .. k .. c 86.8 d,l 85,382 e,m 0.903 0.949 1.000 -18 20 New Zealand 0.950 80.1 .. c 107.5 d,f 27,336 0.919 0.993 0.936 12 21 United Kingdom 0.947 79.3 .. c 89.2 d,g 35,130 0.906 0.957 0.978 -1 22 Germany 0.947 79.8 .. c 88.1 d,g 34,401 0.913 0.954 0.975 2 23 Singapore 0.944 80.2 94.4 j .. n 49,704 e 0.920 0.913 1.000 -16 24 Hong Kong, China (SAR) 0.944 82.2 .. o 74.4 d 42,306 0.953 0.879 1.000 -13 25 Greece 0.942 79.1 97.1 j 101.6 d,f 28,517 0.902 0.981 0.944 6 26 Korea (Republic of) 0.937 79.2 .. c 98.5 d 24,801 0.904 0.988 0.920 9 27 Israel 0.935 80.7 97.1 l 89.9 d 26,315 0.928 0.947 0.930 7 28 Andorra 0.934 .. k .. c 65.1 d,l 41,235 e,p 0.925 0.877 1.000 -16 29 Slovenia 0.929 78.2 99.7 c,j 92.8 d 26,753 0.886 0.969 0.933 4 30 Brunei Darussalam 0.920 77.0 94.9 j 77.7 50,200 e 0.867 0.891 1.000 -24 31 Kuwait 0.916 77.5 94.5 i 72.6 d 47,812 d,e 0.875 0.872 1.000 -23 32 Cyprus 0.914 79.6 97.7 j 77.6 d,l 24,789 0.910 0.910 0.920 4 33 Qatar 0.910 75.5 93.1 i 80.4 74,882 d,e 0.841 0.888 1.000 -30 34 Portugal 0.909 78.6 94.9 j 88.8 d 22,765 0.893 0.929 0.906 8 35 United Arab Emirates 0.903 77.3 90.0 i 71.4 54,626 d,e,q 0.872 0.838 1.000 -31 36 Czech Republic 0.903 76.4 .. c 83.4 d 24,144 0.856 0.938 0.916 1 37 Barbados 0.903 77.0 .. c,o 92.9 17,956 d,q 0.867 0.975 0.866 11 38 Malta 0.902 79.6 92.4 r 81.3 d 23,080 0.910 0.887 0.908 1

HIGH HUMAN DEVELOPMENT 39 Bahrain 0.895 75.6 88.8 j 90.4 d,g 29,723 d 0.843 0.893 0.950 -9 40 Estonia 0.883 72.9 99.8 c,j 91.2 d 20,361 0.799 0.964 0.887 3 41 Poland 0.880 75.5 99.3 c,j 87.7 d 15,987 0.842 0.952 0.847 12 42 Slovakia 0.880 74.6 .. c 80.5 d 20,076 0.827 0.928 0.885 3 43 Hungary 0.879 73.3 98.9 j 90.2 d 18,755 0.805 0.960 0.874 3 44 Chile 0.878 78.5 96.5 j 82.5 d 13,880 0.891 0.919 0.823 15 45 Croatia 0.871 76.0 98.7 j 77.2 d 16,027 0.850 0.916 0.847 7 46 Lithuania 0.870 71.8 99.7 c,j 92.3 d 17,575 0.780 0.968 0.863 3 47 Antigua and Barbuda 0.868 .. k 99.0 r .. n 18,691 q 0.786 0.945 0.873 0 48 Latvia 0.866 72.3 99.8 c,j 90.2 d 16,377 0.788 0.961 0.851 3 49 Argentina 0.866 75.2 97.6 j 88.6 d 13,238 0.836 0.946 0.815 13 50 Uruguay 0.865 76.1 97.9 i 90.9 d 11,216 0.852 0.955 0.788 20 51 Cuba 0.863 78.5 99.8 c,j 100.8 6,876 d,s 0.891 0.993 0.706 44 52 Bahamas 0.856 73.2 .. o 71.8 d,g 20,253 d,s 0.804 0.878 0.886 -8 53 Mexico 0.854 76.0 92.8 i 80.2 d 14,104 0.850 0.886 0.826 5 54 Costa Rica 0.854 78.7 95.9 j 73.0 d,g 10,842 q 0.896 0.883 0.782 19 55 Libyan Arab Jamahiriya 0.847 73.8 86.8 j 95.8 d,g 14,364 q 0.814 0.898 0.829 2 56 Oman 0.846 75.5 84.4 j 68.2 22,816 d 0.841 0.790 0.906 -15 57 Seychelles 0.845 .. k 91.8 r 82.2 d,l 16,394 q 0.797 0.886 0.851 -7 58 Venezuela (Bolivarian Republic of) 0.844 73.6 95.2 i 85.9 l 12,156 0.811 0.921 0.801 7 59 Saudi Arabia 0.843 72.7 85.0 j 78.5 d,l 22,935 0.794 0.828 0.907 -19

HUMAN DEVELOPMENT REPORT 2009

172

H Human development index 2007 and its components

Human development

index value

Adult literacy rate

(% aged 15 and above)

GDP per capita (PPP US$) GDP index

Life expectancy at

birth (years)

Combined gross

enrolment ratio in education

(%) Education

index

Life expectancy

index

GDP per capita rank minus HDI

rankb

2007 1999–2007a 2007 20072007 2007 20072007 2007HDI rank

60 Panama 0.840 75.5 93.4 j 79.7 d 11,391 q 0.842 0.888 0.790 7 61 Bulgaria 0.840 73.1 98.3 j 82.4 d 11,222 0.802 0.930 0.788 8 62 Saint Kitts and Nevis 0.838 .. k 97.8 t 73.1 d,g 14,481 q 0.787 0.896 0.830 -6 63 Romania 0.837 72.5 97.6 j 79.2 d 12,369 0.792 0.915 0.804 1 64 Trinidad and Tobago 0.837 69.2 98.7 j 61.1 d,g 23,507 q 0.737 0.861 0.911 -26 65 Montenegro 0.834 74.0 96.4 r,u 74.5 d,u,v 11,699 0.817 0.891 0.795 1 66 Malaysia 0.829 74.1 91.9 j 71.5 d 13,518 0.819 0.851 0.819 -5 67 Serbia 0.826 73.9 96.4 r,u 74.5 d,u,v 10,248 w 0.816 0.891 0.773 8 68 Belarus 0.826 69.0 99.7 c,j 90.4 10,841 0.733 0.961 0.782 6 69 Saint Lucia 0.821 73.6 94.8 x 77.2 9,786 q 0.810 0.889 0.765 8 70 Albania 0.818 76.5 99.0 c,j 67.8 d 7,041 0.858 0.886 0.710 23 71 Russian Federation 0.817 66.2 99.5 c,j 81.9 d 14,690 0.686 0.933 0.833 -16 72 Macedonia (the Former Yugoslav Rep. of) 0.817 74.1 97.0 j 70.1 d 9,096 0.819 0.880 0.753 8 73 Dominica 0.814 .. k 88.0 x 78.5 d,g 7,893 q 0.865 0.848 0.729 10 74 Grenada 0.813 75.3 96.0 x 73.1 d,g 7,344 q 0.838 0.884 0.717 18 75 Brazil 0.813 72.2 90.0 i 87.2 d 9,567 0.787 0.891 0.761 4 76 Bosnia and Herzegovina 0.812 75.1 96.7 y 69.0 d,z 7,764 0.834 0.874 0.726 11 77 Colombia 0.807 72.7 92.7 i 79.0 8,587 0.795 0.881 0.743 4 78 Peru 0.806 73.0 89.6 i 88.1 d,g 7,836 0.800 0.891 0.728 7 79 Turkey 0.806 71.7 88.7 i 71.1 d,g 12,955 0.779 0.828 0.812 -16 80 Ecuador 0.806 75.0 91.0 r .. n 7,449 0.833 0.866 0.719 11 81 Mauritius 0.804 72.1 87.4 j 76.9 d,g 11,296 0.785 0.839 0.789 -13 82 Kazakhstan 0.804 64.9 99.6 c,j 91.4 10,863 0.666 0.965 0.782 -10 83 Lebanon 0.803 71.9 89.6 i 78.0 10,109 0.781 0.857 0.770 -7

MEDIUM HUMAN DEVELOPMENT 84 Armenia 0.798 73.6 99.5 c,j 74.6 5,693 0.810 0.909 0.675 16 85 Ukraine 0.796 68.2 99.7 c,j 90.0 6,914 0.720 0.960 0.707 9 86 Azerbaijan 0.787 70.0 99.5 c,i 66.2 d,aa 7,851 0.751 0.881 0.728 -2 87 Thailand 0.783 68.7 94.1 j 78.0 d,g 8,135 0.728 0.888 0.734 -5 88 Iran (Islamic Republic of) 0.782 71.2 82.3 i 73.2 d,g 10,955 0.769 0.793 0.784 -17 89 Georgia 0.778 71.6 100.0 c,ab 76.7 4,662 0.777 0.916 0.641 21 90 Dominican Republic 0.777 72.4 89.1 j 73.5 d,g 6,706 q 0.790 0.839 0.702 7 91 Saint Vincent and the Grenadines 0.772 71.4 88.1 x 68.9 d 7,691 q 0.774 0.817 0.725 -2 92 China 0.772 72.9 93.3 j 68.7 d 5,383 0.799 0.851 0.665 10 93 Belize 0.772 76.0 75.1 x 78.3 d,g 6,734 q 0.851 0.762 0.703 3 94 Samoa 0.771 71.4 98.7 j 74.1 d,g 4,467 q 0.773 0.905 0.634 19 95 Maldives 0.771 71.1 97.0 j 71.3 d,g 5,196 0.768 0.885 0.659 9 96 Jordan 0.770 72.4 91.1 i 78.7 d 4,901 0.790 0.870 0.650 11 97 Suriname 0.769 68.8 90.4 j 74.3 d,g 7,813 q 0.729 0.850 0.727 -11 98 Tunisia 0.769 73.8 77.7 j 76.2 d 7,520 0.813 0.772 0.721 -8 99 Tonga 0.768 71.7 99.2 c,j 78.0 d,g 3,748 q 0.778 0.920 0.605 21

100 Jamaica 0.766 71.7 86.0 j 78.1 d,g 6,079 q 0.778 0.834 0.686 -2 101 Paraguay 0.761 71.7 94.6 i 72.1 d,g 4,433 0.778 0.871 0.633 13 102 Sri Lanka 0.759 74.0 90.8 i 68.7 d,g 4,243 0.816 0.834 0.626 14 103 Gabon 0.755 60.1 86.2 j 80.7 d,g 15,167 0.584 0.843 0.838 -49 104 Algeria 0.754 72.2 75.4 j 73.6 d,g 7,740 q 0.787 0.748 0.726 -16 105 Philippines 0.751 71.6 93.4 j 79.6 d 3,406 0.777 0.888 0.589 19 106 El Salvador 0.747 71.3 82.0 r 74.0 5,804 q 0.771 0.794 0.678 -7 107 Syrian Arab Republic 0.742 74.1 83.1 j 65.7 d,g 4,511 0.818 0.773 0.636 5 108 Fiji 0.741 68.7 .. o 71.5 d,g 4,304 0.728 0.868 0.628 7 109 Turkmenistan 0.739 64.6 99.5 c,j .. n 4,953 d,q 0.661 0.906 0.651 -3 110 Occupied Palestinian Territories 0.737 73.3 93.8 i 78.3 .. d,ac 0.806 0.886 0.519 111 Indonesia 0.734 70.5 92.0 i 68.2 d 3,712 0.758 0.840 0.603 10 112 Honduras 0.732 72.0 83.6 i 74.8 d,g 3,796 q 0.783 0.806 0.607 7 113 Bolivia 0.729 65.4 90.7 i 86.0 d,g 4,206 0.673 0.892 0.624 4 114 Guyana 0.729 66.5 .. o 83.9 2,782 q 0.691 0.939 0.555 13 115 Mongolia 0.727 66.2 97.3 j 79.2 3,236 0.687 0.913 0.580 10 116 Viet Nam 0.725 74.3 90.3 r 62.3 d,g 2,600 0.821 0.810 0.544 13 117 Moldova 0.720 68.3 99.2 c,j 71.6 2,551 0.722 0.899 0.541 14 118 Equatorial Guinea 0.719 49.9 87.0 y 62.0 d,g 30,627 0.415 0.787 0.955 -90

173

TABLE H

Human development

index value

Adult literacy rate

(% aged 15 and above)

GDP per capita (PPP US$) GDP index

Life expectancy at

birth (years)

Combined gross

enrolment ratio in education

(%) Education

index

Life expectancy

index

GDP per capita rank minus HDI

rankb

2007 1999–2007a 2007 20072007 2007 20072007 2007HDI rank

119 Uzbekistan 0.710 67.6 96.9 y 72.7 2,425 q 0.711 0.888 0.532 14 120 Kyrgyzstan 0.710 67.6 99.3 c,j 77.3 2,006 0.710 0.918 0.500 20 121 Cape Verde 0.708 71.1 83.8 j 68.1 3,041 0.769 0.786 0.570 5 122 Guatemala 0.704 70.1 73.2 j 70.5 4,562 0.752 0.723 0.638 -11 123 Egypt 0.703 69.9 66.4 r 76.4 d,g 5,349 0.749 0.697 0.664 -20 124 Nicaragua 0.699 72.7 78.0 r 72.1 d,g 2,570 q 0.795 0.760 0.542 6 125 Botswana 0.694 53.4 82.9 j 70.6 d,g 13,604 0.473 0.788 0.820 -65 126 Vanuatu 0.693 69.9 78.1 j 62.3 d,g 3,666 q 0.748 0.728 0.601 -4 127 Tajikistan 0.688 66.4 99.6 c,j 70.9 1,753 0.691 0.896 0.478 17 128 Namibia 0.686 60.4 88.0 j 67.2 d 5,155 0.590 0.811 0.658 -23 129 South Africa 0.683 51.5 88.0 j 76.8 d 9,757 0.442 0.843 0.765 -51 130 Morocco 0.654 71.0 55.6 j 61.0 4,108 0.767 0.574 0.620 -12 131 Sao Tome and Principe 0.651 65.4 87.9 j 68.1 1,638 0.673 0.813 0.467 17 132 Bhutan 0.619 65.7 52.8 r 54.1 d,g 4,837 0.678 0.533 0.647 -24 133 Lao People’s Democratic Republic 0.619 64.6 72.7 r 59.6 d 2,165 0.659 0.683 0.513 2 134 India 0.612 63.4 66.0 j 61.0 d 2,753 0.639 0.643 0.553 -6 135 Solomon Islands 0.610 65.8 76.6 l 49.7 d 1,725 q 0.680 0.676 0.475 10 136 Congo 0.601 53.5 81.1 j 58.6 d,g 3,511 0.474 0.736 0.594 -13 137 Cambodia 0.593 60.6 76.3 j 58.5 1,802 0.593 0.704 0.483 6 138 Myanmar 0.586 61.2 89.9 y 56.3 d,g,aa 904 d,q 0.603 0.787 0.368 29 139 Comoros 0.576 64.9 75.1 j 46.4 d,g 1,143 0.666 0.655 0.407 20 140 Yemen 0.575 62.5 58.9 j 54.4 d 2,335 0.624 0.574 0.526 -6 141 Pakistan 0.572 66.2 54.2 i 39.3 d 2,496 0.687 0.492 0.537 -9 142 Swaziland 0.572 45.3 79.6 y 60.1 d 4,789 0.339 0.731 0.646 -33 143 Angola 0.564 46.5 67.4 y 65.3 d 5,385 0.359 0.667 0.665 -42 144 Nepal 0.553 66.3 56.5 j 60.8 d,g 1,049 0.688 0.579 0.392 21 145 Madagascar 0.543 59.9 70.7 y 61.3 932 0.582 0.676 0.373 21 146 Bangladesh 0.543 65.7 53.5 j 52.1 d 1,241 0.678 0.530 0.420 9 147 Kenya 0.541 53.6 73.6 y 59.6 d,g 1,542 0.477 0.690 0.457 2 148 Papua New Guinea 0.541 60.7 57.8 j 40.7 d,v 2,084 q 0.594 0.521 0.507 -10 149 Haiti 0.532 61.0 62.1 j .. n 1,155 q 0.600 0.588 0.408 9 150 Sudan 0.531 57.9 60.9 y,ad 39.9 d,g 2,086 0.548 0.539 0.507 -13 151 Tanzania ( United Republic of) 0.530 55.0 72.3 j 57.3 1,208 0.500 0.673 0.416 6 152 Ghana 0.526 56.5 65.0 j 56.5 1,334 0.525 0.622 0.432 1 153 Cameroon 0.523 50.9 67.9 i 52.3 2,128 0.431 0.627 0.510 -17 154 Mauritania 0.520 56.6 55.8 j 50.6 d,l 1,927 0.526 0.541 0.494 -12 155 Djibouti 0.520 55.1 .. o 25.5 d 2,061 0.501 0.554 0.505 -16 156 Lesotho 0.514 44.9 82.2 i 61.5 d,g 1,541 0.332 0.753 0.457 -6 157 Uganda 0.514 51.9 73.6 j 62.3 d,g 1,059 0.449 0.698 0.394 6 158 Nigeria 0.511 47.7 72.0 j 53.0 d,g 1,969 0.378 0.657 0.497 -17

LOW HUMAN DEVELOPMENT 159 Togo 0.499 62.2 53.2 y 53.9 788 0.620 0.534 0.345 11 160 Malawi 0.493 52.4 71.8 j 61.9 d,g 761 0.456 0.685 0.339 12 161 Benin 0.492 61.0 40.5 j 52.4 d,g 1,312 0.601 0.445 0.430 -7 162 Timor-Leste 0.489 60.7 50.1 ae 63.2 d,g 717 q 0.595 0.545 0.329 11 163 Côte d’Ivoire 0.484 56.8 48.7 y 37.5 d,g 1,690 0.531 0.450 0.472 -17 164 Zambia 0.481 44.5 70.6 j 63.3 d,g 1,358 0.326 0.682 0.435 -12 165 Eritrea 0.472 59.2 64.2 j 33.3 d,g 626 q 0.570 0.539 0.306 12 166 Senegal 0.464 55.4 41.9 i 41.2 d,g 1,666 0.506 0.417 0.469 -19 167 Rwanda 0.460 49.7 64.9 y 52.2 d,g 866 0.412 0.607 0.360 1 168 Gambia 0.456 55.7 .. o 46.8 d,g 1,225 0.511 0.439 0.418 -12 169 Liberia 0.442 57.9 55.5 j 57.6 d 362 0.548 0.562 0.215 10 170 Guinea 0.435 57.3 29.5 y 49.3 d 1,140 0.538 0.361 0.406 -10 171 Ethiopia 0.414 54.7 35.9 i 49.0 779 0.496 0.403 0.343 0 172 Mozambique 0.402 47.8 44.4 j 54.8 d,g 802 0.380 0.478 0.348 -3 173 Guinea-Bissau 0.396 47.5 64.6 j 36.6 d,g 477 0.375 0.552 0.261 5 174 Burundi 0.394 50.1 59.3 y 49.0 341 0.418 0.559 0.205 6 175 Chad 0.392 48.6 31.8 j 36.5 d,g 1,477 0.393 0.334 0.449 -24 176 Congo (Democratic Republic of the) 0.389 47.6 67.2 y 48.2 298 0.377 0.608 0.182 5 177 Burkina Faso 0.389 52.7 28.7 i 32.8 1,124 0.462 0.301 0.404 -16

HUMAN DEVELOPMENT REPORT 2009

174

H Human development index 2007 and its components

Human development

index value

Adult literacy rate

(% aged 15 and above)

GDP per capita (PPP US$) GDP index

Life expectancy at

birth (years)

Combined gross

enrolment ratio in education

(%) Education

index

Life expectancy

index

GDP per capita rank minus HDI

rankb

2007 1999–2007a 2007 20072007 2007 20072007 2007HDI rank

178 Mali 0.371 48.1 26.2 i 46.9 1,083 0.385 0.331 0.398 -16 179 Central African Republic 0.369 46.7 48.6 y 28.6 d,g 713 0.361 0.419 0.328 -5 180 Sierra Leone 0.365 47.3 38.1 j 44.6 d 679 0.371 0.403 0.320 -5 181 Afghanistan 0.352 43.6 28.0 y 50.1 d,g 1,054 d,ag 0.310 0.354 0.393 -17 182 Niger 0.340 50.8 28.7 i 27.2 627 0.431 0.282 0.307 -6

OTHER UN MEMBER STATES Iraq .. 67.8 74.1 y 60.5 d,g .. 0.714 0.695 .. .. Kiribati .. .. k .. 75.8 d,g 1,295 q 0.699 .. 0.427 .. Korea (Democratic People’s Rep. of) .. 67.1 .. .. .. 0.702 .. .. .. Marshall Islands .. .. k .. 71.1 d,g .. 0.758 .. .. .. Micronesia (Federated States of) .. 68.4 .. .. 2,802 q 0.724 .. 0.556 .. Monaco .. .. k .. c .. .. 0.948 .. .. .. Nauru .. .. k .. 55.0 d,g .. 0.906 .. .. .. Palau .. .. k 91.9 d,r 96.9 d,g .. 0.758 0.936 .. .. San Marino .. .. k .. c .. .. 0.940 .. .. .. Somalia .. 49.7 .. .. .. 0.412 .. .. .. Tuvalu .. .. k .. 69.2 d,g .. 0.683 .. .. .. Zimbabwe .. 43.4 91.2 j 54.4 d,g .. 0.306 0.789 .. ..

Arab States 0.719 68.5 71.2 66.2 8,202 0.726 0.695 0.736 .. Central and Eastern Europe and the CIS 0.821 69.7 97.6 79.5 12,185 0.745 0.916 0.802 .. East Asia and the Pacific 0.770 72.2 92.7 69.3 5,733 0.786 0.849 0.676 .. Latin America and the Caribbean 0.821 73.4 91.2 83.4 10,077 0.806 0.886 0.770 .. South Asia 0.612 64.1 64.2 58.0 2,905 0.651 0.621 0.562 .. Sub-Saharan Africa 0.514 51.5 62.9 53.5 2,031 0.441 0.597 0.503 .. OECD 0.932 79.0 .. 89.1 32,647 0.900 .. 0.966 .. European Union (EU27) 0.937 79.0 .. 91.0 29,956 0.899 .. 0.952 .. GCC 0.868 74.0 86.8 77.0 30,415 0.816 0.835 0.954 .. Very high human development 0.955 80.1 .. 92.5 37,272 0.918 .. 0.988 .. Very high HD: OECD .. 80.1 .. 92.9 37,122 0.919 .. 0.988 .. Very high HD: non-OECD .. 79.7 .. .. 41,887 0.912 .. 1.000 .. High human development 0.833 72.4 94.1 82.4 12,569 0.790 0.902 0.807 .. Medium human development 0.686 66.9 80.0 63.3 3,963 0.698 0.744 0.614 .. Low human development 0.423 51.0 47.7 47.6 862 0.434 0.477 0.359 .. World 0.753 67.5 af 83.9 af 67.5 9,972 0.708 0.784 0.768 ..

175

TABLE H

NOTES

a Data refer to national literacy estimates from

censuses or surveys conducted between 1999 and

2007, unless otherwise specified. Due to differences

in methodology and timeliness of underlying data,

comparisons across countries and over time should

be made with caution. For more details, see http://

www.uis.unesco.org/.

b A positive figure indicates that the HDI rank is higher

than the GDP per capita ( PPP US $) rank; a negative

figure, the opposite.

c For the purposes of calculating the HDI, a value of

99.0% was applied.

d Data refer to a year other than that specified.

e For the purposes of calculating the HDI, a value of

40,000 ( PPP US $) was applied.

f For the purposes of calculating the HDI, a value of

100% was applied.

g UNESCO Institute for Statistics estimate.

h Statec (2008). Data refer to nationals enrolled both

in the country and abroad and thus differ from the

standard definition.

i Data are from a national household survey.

j UNESCO Institute for Statistics estimates based on its

Global Age-specific Literacy Projections model, April

2009.

k For the purposes of calculating the HDI unpublished

estimates from UN (2009e) were used: Andorra

80.5, Antigua and Barbuda 72.2, Dominica 76.9,

Liechtenstein 79.2, Saint Kitts and Nevis 72.2 and the

Seychelles 72.8.

l National estimate.

m HDRO estimate based on GDP from UN (2009c) and

the PPP exchange rate for Switzerland from World

Bank (2009d).

n Because the combined gross enrolment ratio was

unavailable, the following HDRO estimates were used:

Antigua and Barbuda 85.6, Ecuador 77.8, Haiti 52.1,

Singapore 85.0 and Turkmenistan 73.9.

o In the absence of recent data, estimates for 2005

from UNESCO Institute for Statistics (2003), based

on outdated census or survey information, were used

and should be interpreted with caution: the Bahamas

95.8, Barbados 99.7, Djibouti 70.3, Fiji 94.4, the

Gambia 42.5, Guyana 99.0 and Hong Kong, China

(SAR ) 94.6.

p HDRO estimate based on GDP from UN (2009c).

q World Bank estimate based on regression.

r Data are from a national census of population.

s Heston, Summers and Aten (2006). Data differ from

the standard definition.

t Data are from the Secretariat of the Organization of

Eastern Caribbean States, based on national sources.

u Data refer to Serbia and Montenegro prior to its

separation into two independent states in June 2006.

Data exclude Kosovo.

v UNESCO Institute for Statistics (2007).

w Data exclude Kosovo.

x Data are from the Secretariat of the Caribbean

Community, based on national sources.

y Data are from UNICEF’s Multiple Indicator Cluster

Survey.

z UNDP (2007d).

aa UNESCO Institute for Statistics (2008a).

ab UNICEF (2004).

ac In the absence of an estimate of GDP per capita

( PPP US $), an HDRO estimate of 2,243 ( PPP US $)

was used, derived from the value of GDP for 2005

in US $ and the weighted average ratio of PPP US $

to US $ in the Arab States. The value is expressed in

2007 prices.

ad Data refer to North Sudan only.

ae UNDP (2006b).

af Data are aggregates provided by original data source.

ag Calculated on the basis of GDP in PPP US $ for 2006

from World Bank (2009d) and total population for the

same year from UN (2009e).

SOURCES

Column 1: calculated based on data in columns 6–8.

Column 2: UN (2009e).

Column 3: UNESCO Institute for Statistics (2009a).

Column 4: UNESCO Institute for Statistics (2009b).

Column 5: World Bank (2009d).

Column 6: calculated based on data in column 2.

Column 7: calculated based on data in columns 3 and 4.

Column 8: calculated based on data in column 5.

Column 9 : calculated based on data in columns 1 and 5.

HUMAN DEVELOPMENT REPORT 2009

176

TABLE Human and income poverty

Rank

Human poverty index (HPI-1)

Value (%)

(% of cohort) 2005–2010

Probability of not surviving to age 40a,†

(% aged 15 and above) 1999–2007

Adult illiteracy rateb,†

(%) 2006

Population not using an

improved water source†

(% aged under 5)

2000–2006c

Children under weight

for age

$1.25 a day 2000–2007c

$2 a day 2000–2007c

National poverty line 2000–2006c

HPI-1 rank minus income poverty rankd

Population below income poverty line (%)

VERY HIGH HUMAN DEVELOPMENT 23 Singapore 14 3.9 1.6 5.6 i 0 f 3 .. .. .. .. 24 Hong Kong, China (SAR) .. .. 1.4 .. k .. .. .. .. .. .. 26 Korea (Republic of) .. .. 1.9 .. e 8 j .. <2 f,g <2 f,g .. .. 27 Israel .. .. 1.9 2.9 l 0 .. .. .. .. .. 29 Slovenia .. .. 1.9 0.3 e,i .. .. <2 <2 .. .. 30 Brunei Darussalam .. .. 2.6 5.1 i .. .. .. .. .. .. 31 Kuwait .. .. 2.5 5.5 h .. 10 g .. .. .. .. 32 Cyprus .. .. 2.1 2.3 i 0 .. .. .. .. .. 33 Qatar 19 5.0 3.0 6.9 h 0 6 g .. .. .. .. 35 United Arab Emirates 35 7.7 2.3 10.0 h 0 14 g .. .. .. .. 36 Czech Republic 1 1.5 2.0 .. e 0 1 g <2 g <2 g .. 0 37 Barbados 4 2.6 3.0 .. e,k 0 6 g,m .. .. .. .. 38 Malta .. .. 1.9 7.6 n 0 .. .. .. .. ..

HIGH HUMAN DEVELOPMENT 39 Bahrain 39 8.0 2.9 11.2 i 0 f 9 g .. .. .. .. 40 Estonia .. .. 5.2 0.2 e,i 0 .. <2 <2 8.9 g .. 41 Poland .. .. 2.9 0.7 e,i 0 f .. <2 <2 14.8 .. 42 Slovakia .. .. 2.7 .. e 0 .. <2 g <2 g .. .. 43 Hungary 3 2.2 3.1 1.1 i 0 2 g,m <2 <2 17.3 g 2 44 Chile 10 3.2 3.1 3.5 i 5 1 <2 2.4 17.0 g 6 45 Croatia 2 1.9 2.6 1.3 i 1 1 g <2 <2 .. 1 46 Lithuania .. .. 5.7 0.3 e,i .. .. <2 <2 .. .. 47 Antigua and Barbuda .. .. .. 1.1 n 9 j 10 g,m .. .. .. .. 48 Latvia .. .. 4.8 0.2 e,i 1 .. <2 <2 5.9 .. 49 Argentina 13 3.7 4.4 2.4 i 4 4 4.5 f 11.3 f .. -18 50 Uruguay 6 3.0 3.8 2.1 h 0 5 <2 f 4.2 f .. 4 51 Cuba 17 4.6 2.6 0.2 e,i 9 4 .. .. .. .. 52 Bahamas .. .. 7.3 .. k 3 j .. .. .. .. .. 53 Mexico 23 5.9 5.0 7.2 h 5 5 <2 4.8 17.6 16 54 Costa Rica 11 3.7 3.3 4.1 i 2 5 g 2.4 8.6 23.9 -13 55 Libyan Arab Jamahiriya 60 13.4 4.0 13.2 i 29 j 5 g .. .. .. .. 56 Oman 64 14.7 3.0 15.6 i 18 j 18 g .. .. .. .. 57 Seychelles .. .. .. 8.2 n 13 j 6 g,m .. .. .. .. 58 Venezuela (Bolivarian Republic of) 28 6.6 6.7 4.8 h 10 j 5 3.5 10.2 .. -5 59 Saudi Arabia 53 12.1 4.7 15.0 i 10 j 14 g .. .. .. .. 60 Panama 30 6.7 5.9 6.6 i 8 7 g 9.5 17.8 37.3 g -15 61 Bulgaria .. .. 3.8 1.7 i 1 .. <2 2.4 12.8 .. 62 Saint Kitts and Nevis .. .. .. 2.2 o 1 .. .. .. .. .. 63 Romania 20 5.6 4.3 2.4 i 12 3 <2 3.4 28.9 13 64 Trinidad and Tobago 27 6.4 8.4 1.3 i 6 6 4.2 g 13.5 g 21.0 g -7 65 Montenegro 8 3.1 3.0 3.6 n,p 2 3 .. .. .. .. 66 Malaysia 25 6.1 3.7 8.1 i 1 8 <2 7.8 .. 17 67 Serbia 7 3.1 3.3 3.6 n,p 1 2 .. .. .. .. 68 Belarus 16 4.3 6.2 0.3 e,i 0 1 <2 <2 18.5 11 69 Saint Lucia 26 6.3 4.6 5.2 q 2 14 g,m 20.9 g 40.6 g .. -35 70 Albania 15 4.0 3.6 1.0 e,i 3 8 <2 7.8 25.4 10 71 Russian Federation 32 7.4 10.6 0.5 e,i 3 3 g <2 <2 19.6 24 72 Macedonia (the Former Yugoslav Rep. of) 9 3.2 3.4 3.0 i 0 6 g <2 3.2 21.7 5 73 Dominica .. .. .. 12.0 q 3 j 5 g,m .. .. .. .. 74 Grenada .. .. 3.2 4.0 q 6 j .. .. .. .. .. 75 Brazil 43 8.6 8.2 10.0 h 9 6 g 5.2 12.7 21.5 1 76 Bosnia and Herzegovina 5 2.8 3.0 3.3 r 1 2 <2 <2 19.5 3 77 Colombia 34 7.6 8.3 7.3 h 7 7 16.0 27.9 64.0 g -21 78 Peru 47 10.2 7.4 10.4 h 16 8 7.9 18.5 53.1 0 79 Turkey 40 8.3 5.7 11.3 h 3 4 2.7 9.0 27.0 6 80 Ecuador 38 7.9 7.3 9.0 n 5 9 4.7 12.8 46.0 g 0 81 Mauritius 45 9.5 5.8 12.6 i 0 15 g .. .. .. .. 82 Kazakhstan 37 7.9 11.2 0.4 e,i 4 4 3.1 17.2 15.4 3 83 Lebanon 33 7.6 5.5 10.4 h 0 4 .. .. .. ..

HDI rank

177

TABLE I1

Rank

Human poverty index (HPI-1)

Value (%)

(% of cohort) 2005–2010

Probability of not surviving to age 40a,†

(% aged 15 and above) 1999–2007

Adult illiteracy rateb,†

(%) 2006

Population not using an

improved water source†

(% aged under 5)

2000–2006c

Children under weight

for age

$1.25 a day 2000–2007c

$2 a day 2000–2007c

National poverty line 2000–2006c

HPI-1 rank minus income poverty rankd

Population below income poverty line (%)

MEDIUM HUMAN DEVELOPMENT 84 Armenia 12 3.7 5.0 0.5 e,i 2 4 10.6 43.4 50.9 -30 85 Ukraine 21 5.8 8.4 0.3 e,i 3 1 <2 <2 19.5 14 86 Azerbaijan 50 10.7 8.6 0.5 e,h 22 7 <2 <2 49.6 38 87 Thailand 41 8.5 11.3 5.9 i 2 9 <2 11.5 13.6 g 30 88 Iran (Islamic Republic of) 59 12.8 6.1 17.7 h 6 j 11 g <2 8.0 .. 44 89 Georgia 18 4.7 6.7 0.0 e,s 1 3 g 13.4 30.4 54.5 -29 90 Dominican Republic 44 9.1 9.4 10.9 i 5 5 5.0 15.1 42.2 3 91 Saint Vincent and the Grenadines .. .. 5.8 11.9 q .. .. .. .. .. .. 92 China 36 7.7 6.2 6.7 i 12 7 15.9 t 36.3 t 2.8 -19 93 Belize 73 17.5 5.6 24.9 q 9 j 7 .. .. .. .. 94 Samoa .. .. 5.6 1.3 i 12 .. .. .. .. .. 95 Maldives 66 16.5 6.0 3.0 i 17 30 .. .. .. .. 96 Jordan 29 6.6 5.3 8.9 h 2 4 <2 3.5 14.2 21 97 Suriname 46 10.1 10.0 9.6 i 8 13 15.5 g 27.2 g .. -9 98 Tunisia 65 15.6 4.1 22.3 i 6 4 2.6 12.8 7.6 g 26 99 Tonga .. .. 5.4 0.8 e,i 0 .. .. .. .. .. 100 Jamaica 51 10.9 9.9 14.0 i 7 4 <2 5.8 18.7 39 101 Paraguay 49 10.5 8.9 5.4 h 23 5 6.5 14.2 .. 5 102 Sri Lanka 67 16.8 5.5 9.2 h 18 29 14.0 39.7 22.7 7 103 Gabon 72 17.5 22.6 13.8 i 13 12 4.8 19.6 .. 24 104 Algeria 71 17.5 6.4 24.6 i 15 4 6.8 g 23.6 g 22.6 g 19 105 Philippines 54 12.4 5.7 6.6 i 7 28 22.6 45.0 25.1 g -19 106 El Salvador 63 14.6 10.7 18.0 n 16 10 11.0 20.5 37.2 8 107 Syrian Arab Republic 56 12.6 3.9 16.9 i 11 10 .. .. .. .. 108 Fiji 79 21.2 6.2 .. k 53 8 g .. .. .. .. 109 Turkmenistan .. .. 13.0 0.5 e,i .. 11 24.8 g 49.6 g .. .. 110 Occupied Palestinian Territories 24 6.0 4.3 6.2 h 11 3 .. .. .. .. 111 Indonesia 69 17.0 6.7 8.0 h 20 28 .. .. 16.7 .. 112 Honduras 61 13.7 9.3 16.4 h 16 11 18.2 29.7 50.7 -3 113 Bolivia 52 11.6 13.9 9.3 h 14 8 19.6 30.3 65.2 -10 114 Guyana 48 10.2 12.8 .. k 7 14 7.7 g 16.8 g 35.0 g 2 115 Mongolia 58 12.7 10.3 2.7 i 28 6 22.4 49.0 36.1 -15 116 Viet Nam 55 12.4 5.8 9.7 n 8 25 21.5 48.4 28.9 -13 117 Moldova 22 5.9 6.2 0.8 e,i 10 4 8.1 28.9 48.5 -21 118 Equatorial Guinea 98 31.9 34.5 13.0 r 57 19 .. .. .. .. 119 Uzbekistan 42 8.5 10.7 3.1 r 12 5 46.3 76.7 27.5 -46 120 Kyrgyzstan 31 7.3 9.2 0.7 e,i 11 3 21.8 51.9 43.1 -34 121 Cape Verde 62 14.5 6.4 16.2 i 20 j 14 g 20.6 40.2 .. -6 122 Guatemala 76 19.7 11.2 26.8 i 4 23 11.7 24.3 56.2 15 123 Egypt 82 23.4 7.2 33.6 n 2 6 <2 18.4 16.7 58 124 Nicaragua 68 17.0 7.9 22.0 n 21 10 15.8 31.8 47.9 g 6 125 Botswana 81 22.9 31.2 17.1 i 4 13 31.2 g 49.4 g .. -8 126 Vanuatu 83 23.6 7.1 21.9 i 41 j 20 g,m .. .. .. .. 127 Tajikistan 74 18.2 12.5 0.4 e,i 33 17 21.5 50.8 44.4 -2 128 Namibia 70 17.1 21.2 12.0 i 7 24 49.1 g 62.2 g .. -29 129 South Africa 85 25.4 36.1 12.0 i 7 12 g 26.2 42.9 .. -2 130 Morocco 96 31.1 6.6 44.4 i 17 10 2.5 14.0 .. 50 131 Sao Tome and Principe 57 12.6 13.9 12.1 i 14 9 .. .. .. .. 132 Bhutan 102 33.7 14.2 47.2 n 19 19 g 26.2 49.5 .. 13 133 Lao People’s Democratic Republic 94 30.7 13.1 27.3 n 40 40 44.0 76.8 33.0 -6 134 India 88 28.0 15.5 34.0 i 11 46 41.6 t 75.6 t 28.6 -10 135 Solomon Islands 80 21.8 11.6 23.4 l 30 21 g,m .. .. .. .. 136 Congo 84 24.3 29.7 18.9 i 29 14 54.1 74.4 .. -27 137 Cambodia 87 27.7 18.5 23.7 i 35 36 40.2 68.2 35.0 -10 138 Myanmar 77 20.4 19.1 10.1 r 20 32 .. .. .. .. 139 Comoros 78 20.4 12.6 24.9 i 15 25 46.1 65.0 .. -20 140 Yemen 111 35.7 15.6 41.1 i 34 46 17.5 46.6 41.8 g 35 141 Pakistan 101 33.4 12.6 45.8 h 10 38 22.6 60.3 32.6 g 16 142 Swaziland 108 35.1 47.2 20.4 r 40 10 62.9 81.0 69.2 -15 143 Angola 118 37.2 38.5 32.6 r 49 31 54.3 70.2 .. 2

HDI rank

HUMAN DEVELOPMENT REPORT 2009

178

I1 TABLE

Rank

Human poverty index (HPI-1)

Value (%)

(% of cohort) 2005–2010

Probability of not surviving to age 40a,†

(% aged 15 and above) 1999–2007

Adult illiteracy rateb,†

(%) 2006

Population not using an

improved water source†

(% aged under 5)

2000–2006c

Children under weight

for age

$1.25 a day 2000–2007c

$2 a day 2000–2007c

National poverty line 2000–2006c

HPI-1 rank minus income poverty rankd

Population below income poverty line (%)

144 Nepal 99 32.1 11.0 43.5 i 11 39 55.1 t 77.6 t 30.9 -16 145 Madagascar 113 36.1 20.8 29.3 r 53 42 67.8 89.6 71.3 g -14 146 Bangladesh 112 36.1 11.6 46.5 i 20 u 48 49.6 v 81.3 v 40.0 2 147 Kenya 92 29.5 30.3 26.4 r 43 20 19.7 39.9 52.0 g 16 148 Papua New Guinea 121 39.6 15.9 42.2 i 60 35 g,m 35.8 g 57.4 g 37.5 g 23 149 Haiti 97 31.5 18.5 37.9 j,n 42 22 54.9 72.1 .. -16 150 Sudan 104 34.0 23.9 39.1 r,w 30 41 .. .. .. .. 151 Tanzania ( United Republic of) 93 30.0 28.2 27.7 i 45 22 88.5 96.6 35.7 -37 152 Ghana 89 28.1 25.8 35.0 i 20 18 30.0 53.6 28.5 0 153 Cameroon 95 30.8 34.2 32.1 h 30 19 32.8 57.7 40.2 4 154 Mauritania 115 36.2 21.6 44.2 i 40 32 21.2 44.1 46.3 32 155 Djibouti 86 25.6 26.2 .. k 8 29 18.8 41.2 .. 12 156 Lesotho 106 34.3 47.4 17.8 h 22 20 43.4 62.2 68.0 g 3 157 Uganda 91 28.8 31.4 26.4 i 36 20 51.5 75.6 37.7 -17 158 Nigeria 114 36.2 37.4 28.0 i 53 29 64.4 83.9 34.1 g -11

LOW HUMAN DEVELOPMENT 159 Togo 117 36.6 18.6 46.8 r 41 26 38.7 69.3 .. 18 160 Malawi 90 28.2 32.6 28.2 i 24 19 73.9 90.4 65.3 g -35 161 Benin 126 43.2 19.2 59.5 i 35 23 47.3 75.3 29.0 g 19 162 Timor-Leste 122 40.8 18.0 49.9 x 38 46 52.9 77.5 .. 9 163 Côte d’Ivoire 119 37.4 24.6 51.3 r 19 20 23.3 46.8 .. 29 164 Zambia 110 35.5 42.9 29.4 i 42 20 64.3 81.5 68.0 -14 165 Eritrea 103 33.7 18.2 35.8 i 40 40 .. .. 53.0 g .. 166 Senegal 124 41.6 22.4 58.1 h 23 17 33.5 60.3 33.4 g 28 167 Rwanda 100 32.9 34.2 35.1 r 35 23 76.6 90.3 60.3 -28 168 Gambia 123 40.9 21.8 .. k 14 20 34.3 56.7 61.3 26 169 Liberia 109 35.2 23.2 44.5 i 36 26 g 83.7 94.8 .. -24 170 Guinea 129 50.5 23.7 70.5 r 30 26 70.1 87.2 40.0 g 1 171 Ethiopia 130 50.9 27.7 64.1 h 58 38 39.0 77.5 44.2 30 172 Mozambique 127 46.8 40.6 55.6 i 58 24 74.7 90.0 54.1 -3 173 Guinea-Bissau 107 34.9 37.4 35.4 i 43 19 48.8 77.9 65.7 -1 174 Burundi 116 36.4 33.7 40.7 r 29 39 81.3 93.4 68.0 g -16 175 Chad 132 53.1 35.7 68.2 i 52 37 61.9 83.3 64.0 g 11 176 Congo (Democratic Republic of the) 120 38.0 37.3 32.8 r 54 31 59.2 79.5 .. 0 177 Burkina Faso 131 51.8 26.9 71.3 h 28 37 56.5 81.2 46.4 12 178 Mali 133 54.5 32.5 73.8 h 40 33 51.4 77.1 63.8 g 22 179 Central African Republic 125 42.4 39.6 51.4 r 34 29 62.4 81.9 .. 3 180 Sierra Leone 128 47.7 31.0 61.9 i 47 30 53.4 76.1 70.2 14 181 Afghanistan 135 59.8 40.7 72.0 r 78 39 .. .. .. .. 182 Niger 134 55.8 29.0 71.3 h 58 44 65.9 85.6 63.0 g 8

OTHER UN MEMBER STATES Iraq 75 19.4 10.0 25.9 r 23 8 .. .. .. .. Kiribati .. .. .. .. 35 13 g .. .. .. .. Korea (Democratic People’s Rep. of) .. .. 10.0 .. 0 23 .. .. .. .. Marshall Islands .. .. .. .. 12 j .. .. .. .. .. Micronesia (Federated States of) .. .. 8.8 .. 6 15 g .. .. .. .. Nauru .. .. .. .. .. .. .. .. .. .. Palau .. .. .. 8.1 j,n 11 .. .. .. .. .. Somalia .. .. 34.1 .. 71 36 .. .. .. .. Tuvalu .. .. .. .. 7 .. .. .. .. .. Zimbabwe 105 34.0 48.1 8.8 i 19 17 .. .. 34.9 g ..

HDI rank

Human and income poverty

179

TABLE I1

NOTES

† Denotes indicators used to calcualte the human

poverty index ( HPI-1). For further details, see

Technical note 1: www.hdr.undp.org/en/statistics/tn1

a Data refer to the probability at birth of not surviving to

age 40, multiplied by 100.

b Data refer to national illiteracy estimates from

censuses or surveys conducted between 1999 and

2007, unless otherwise specified. Due to differences

in methodology and timeliness of underlying data,

comparisons across countries and over time should be

made with caution. For more details, see http://www.

uis.unesco.org/.

c Data refer to the most recent year available during the

period specified.

d Income poverty refers to the share of the population

living on less than $1.25 a day. All countries with an

income poverty rate of less than 2% were given equal

rank. The rankings are based on countries for which

data are available for both indicators. A positive figure

indicates that the country performs better in income

poverty than in human poverty, a negative the opposite.

e For the purposes of calculating the HPI-1 a value of

1% was assumed.

f Estimates cover urban areas only.

g Data refer to an earlier year outside the range of years

specified.

h Data are from a national household survey.

i UNESCO Institute for Statistics estimates based on its

Global Age-specific Literacy Projections model, April

2009.

j Data refer to an earlier year than that specified.

k In the absence of recent data, estimates for 2005

from UNESCO Institute for Statistics (2003), based

on outdated census or survey information, were used

and should be interpreted with caution: Bahamas 4.2,

Barbados 0.3, Djibouti 29.7, Fiji 5.6, Gambia 57.5,

Guyana 1.0 and Hong Kong, China (SAR ) 5.4.

l National estimate.

m UNICEF (2005b).

n Data are from a national census of population.

o Data are from the Secretariat of the Organization of

Eastern Caribbean States, based on national sources.

p Data refer to Serbia and Montenegro prior to its

separation into two independent states in June 2006.

Data exclude Kosovo.

q Data are from the Secretariat of the Caribbean

Community, based on national sources.

r Data are from UNICEF’s Multiple Indicator Cluster

Survey.

s UNICEF (2004).

t Estimates are weighted averages of urban and rural

values.

u Estimates have been adjusted for arsenic

contamination levels based on national surveys

conducted and approved by the government.

v Estimates are adjusted by spatial consumer price

index information.

w Data refer to North Sudan only.

x UNDP (2006b).

SOURCES

Column 1: determined on the basis of HPI-1 values.

Column 2: calculated on the basis of data in columns

3–6.

Column 3: UN (2009e).

Column 4: UNESCO Institute for Statistics (2009a).

Columns 5 and 6: UN (2009a) based on a joint effort by

UNICEF and WHO.

Columns 7–9 : World Bank (2009d).

Column 10: calculated based on HPI-1 values and the

income poverty measures.

1 Czech Republic 2 Croatia 3 Hungary 4 Barbados 5 Bosnia and Herzegovina 6 Uruguay 7 Serbia 8 Montenegro 9 Macedonia (the Former Yugoslav Rep. of) 10 Chile 11 Costa Rica 12 Armenia 13 Argentina 14 Singapore 15 Albania 16 Belarus 17 Cuba 18 Georgia 19 Qatar 20 Romania 21 Ukraine 22 Moldova 23 Mexico 24 Occupied Palestinian Territories 25 Malaysia 26 Saint Lucia 27 Trinidad and Tobago 28 Venezuela (Bolivarian Republic of) 29 Jordan 30 Panama 31 Kyrgyzstan 32 Russian Federation 33 Lebanon 34 Colombia 35 United Arab Emirates

36 China 37 Kazakhstan 38 Ecuador 39 Bahrain 40 Turkey 41 Thailand 42 Uzbekistan 43 Brazil 44 Dominican Republic 45 Mauritius 46 Suriname 47 Peru 48 Guyana 49 Paraguay 50 Azerbaijan 51 Jamaica 52 Bolivia 53 Saudi Arabia 54 Philippines 55 Viet Nam 56 Syrian Arab Republic 57 Sao Tome and Principe 58 Mongolia 59 Iran (Islamic Republic of) 60 Libyan Arab Jamahiriya 61 Honduras 62 Cape Verde 63 El Salvador 64 Oman 65 Tunisia 66 Maldives 67 Sri Lanka 68 Nicaragua 69 Indonesia 70 Namibia

71 Algeria 72 Gabon 73 Belize 74 Tajikistan 75 Iraq 76 Guatemala 77 Myanmar 78 Comoros 79 Fiji 80 Solomon Islands 81 Botswana 82 Egypt 83 Vanuatu 84 Congo 85 South Africa 86 Djibouti 87 Cambodia 88 India 89 Ghana 90 Malawi 91 Uganda 92 Kenya 93 Tanzania ( United Republic of) 94 Lao People’s Democratic Republic 95 Cameroon 96 Morocco 97 Haiti 98 Equatorial Guinea 99 Nepal 100 Rwanda 101 Pakistan 102 Bhutan 103 Eritrea 104 Sudan 105 Zimbabwe

106 Lesotho 107 Guinea-Bissau 108 Swaziland 109 Liberia 110 Zambia 111 Yemen 112 Bangladesh 113 Madagascar 114 Nigeria 115 Mauritania 116 Burundi 117 Togo 118 Angola 119 Côte d’Ivoire 120 Congo (Democratic Republic of the) 121 Papua New Guinea 122 Timor-Leste 123 Gambia 124 Senegal 125 Central African Republic 126 Benin 127 Mozambique 128 Sierra Leone 129 Guinea 130 Ethiopia 131 Burkina Faso 132 Chad 133 Mali 134 Niger 135 Afghanistan

HPI-1 RANKS FOR 135 COUNTRIES AND AREAS

HUMAN DEVELOPMENT REPORT 2009

180

TABLE Human and income poverty: OECD countries

Rank Value (%)

Probability at birth of not surviving to

age 60a† (% of cohort) 2005–2010

People lacking functional literacy

skillsb†

(% aged 16–65) 1994–2003

Long-term unemployment†

(% of labour force)

2007

Population living below 50% of

median income† 2000–2005c

HPI-2 rank minus income poverty rankd

Human poverty index (HPI-2)

VERY HIGH HUMAN DEVELOPMENT 1 Norway 2 6.6 6.6 7.9 0.2 7.1 -6 2 Australia 14 12.0 6.4 17.0 e 0.7 12.2 -4 3 Iceland .. .. 5.4 .. 0.1 .. .. 4 Canada 12 11.2 7.3 14.6 0.4 13.0 -8 5 Ireland 23 15.9 6.9 22.6 e 1.4 16.2 0 6 Netherlands 3 7.4 7.1 10.5 e 1.3 4.9f 1 7 Sweden 1 6.0 6.3 7.5 e 0.7 5.6 -3 8 France 8 11.0 7.7 .. g 3.1 7.3 -1 9 Switzerland 7 10.6 6.4 15.9 1.5 7.6 -3 10 Japan 13 11.6 6.2 .. g 1.2 11.8 f,h -4 11 Luxembourg 10 11.2 7.8 .. g 1.3 8.8 -4 12 Finland 5 7.9 8.2 10.4 e 1.5 6.5 -1 13 United States 22 15.2 9.7 20.0 0.5 17.3 -2 14 Austria 9 11.0 7.6 .. g 1.2 7.7 -2 15 Spain 17 12.4 7.1 .. g 2.0 14.2 -4 16 Denmark 4 7.7 9.2 9.6 e 0.7 5.6 1 17 Belgium 15 12.2 8.0 18.4 e,i 3.8 8.1 3 18 Italy 25 29.8 6.8 47.0 2.8 12.8 6 20 New Zealand .. .. 7.6 18.4 e 0.2 .. .. 21 United Kingdom 21 14.6 7.8 21.8 e 1.3 11.6 5 22 Germany 6 10.1 7.6 14.4 e 4.8 8.4 -7 25 Greece 18 12.5 7.0 .. g 4.1 14.3 -4 26 Korea (Republic of) .. .. 8.1 .. 0.0 .. .. 34 Portugal .. .. 8.7 .. 3.7 .. .. 36 Czech Republic 11 11.2 10.2 .. g 2.8 4.9 f 10 HIGH HUMAN DEVELOPMENT 41 Poland 19 12.8 13.2 .. g 4.4 11.5 4 42 Slovakia 16 12.4 13.3 .. g 7.8 7.0 f 9 43 Hungary 20 13.2 16.4 .. g 3.5 6.4 f 15 53 Mexico 24 28.1 13.0 43.2 j 0.1 18.4 -1 79 Turkey .. .. 14.9 .. 3.1 .. ..

NOTES

† Denotes indicators used to calculate the HPI-2. For

further details see Technical note 1.

a Data refer to the probability at birth of not surviving to

age 60, multiplied by 100.

b Based on scoring at level 1 on the prose literacy

scale of the IALS. Data refer to the most recent year

available during the period specified.

c Data refer to the most recent year available during the

period specified.

d Income poverty refers to the share of the population

living on less than 50% of the median adjusted

disposable household income. A positive figure

indicates that the country performs better in income

poverty than in human poverty, a negative the

opposite.

e OECD and Statistics Canada (2000 ).

f Data refer to an earlier year than the period specified.

g For calculating HPI-2 an estimate of 16.4%, the

unweighted average of countries with available data,

was applied.

h Smeeding (1997).

i Data refer to Flanders only.

j Data refer to the state of Nuevo Leon only.

SOURCES

Column 1: Determined on the basis of HPI-2 values in

column 2.

Column 2: calculated based on data in columns 3–6.

Column 3: UN (2009e).

Column 4: OECD and Statistics Canada (2005), unless

otherwise specified.

Column 5: calculated on the basis of data on long-term

unemployment and labour force from OECD (2009c).

Column 6: LIS (2009 ).

Column 7: calculated based on data in columns 1 and 6.

HDI rank

181

HUMAN DEVELOPMENT REPORT 2009

TABLE JGender-related development index and its components

Gender-related development index (GDI)

2007

Life expectancy at birth (years) 2007

Adult literacy ratea (% aged 15 and above)

1999–2007

Combined gross enrolment

ratio in educationb (%)

2007

Estimated earned incomec

(PPP US$) 2007

Rank MaleValue Female as a % of HDI value MaleFemale FemaleMale MaleFemale

HDI rank minus GDI

rankd

VERY HIGH HUMAN DEVELOPMENT 1 Norway 2 0.961 98.9 82.7 78.2 .. e .. e 102.7 f,g 94.7 f,g 46,576 g 60,394 g -1 2 Australia 1 0.966 99.6 83.7 79.1 .. e .. e 115.7 f,g 112.8 f,g 28,759 g 41,153 g 1 3 Iceland 3 0.959 99.0 83.3 80.2 .. e .. e 102.1 f,g 90.1 f,g 27,460 g 43,959 g 0 4 Canada 4 0.959 99.2 82.9 78.2 .. e .. e 101.0 f,g,h 97.6 f,g,h 28,315 g,i 43,456 g,i 0 5 Ireland 10 0.948 98.2 82.0 77.3 .. e .. e 99.1 f 96.2 f 31,978 g,i 57,320 g,i -5 6 Netherlands 7 0.954 98.9 81.9 77.6 .. e .. e 97.1 f 97.9 f 31,048 46,509 -1 7 Sweden 5 0.956 99.3 83.0 78.6 .. e .. e 99.0 f 89.8 f 29,476 g,i 44,071 g,i 2 8 France 6 0.956 99.4 84.5 77.4 .. e .. e 97.4 f 93.5 f 25,677 g 42,091 g 2 9 Switzerland 13 0.946 98.5 84.1 79.2 .. e .. e 81.4 f 84.0 f 31,442 g 50,346 g -4 10 Japan 14 0.945 98.4 86.2 79.0 .. e .. e 85.4 f 87.7 f 21,143 g 46,706 g -4 11 Luxembourg 16 0.943 98.2 82.0 76.5 .. e .. e 94.7 j 94.0 j 57,676 g,i 101,855 g,i -5 12 Finland 8 0.954 99.5 82.8 76.0 .. e .. e 105.1 f,g 97.9 f,g 29,160 g 40,126 g 4 13 United States 19 0.942 98.5 81.3 76.7 .. e .. e 96.9 f 88.1 f 34,996 g,i 56,536 g,i -6 14 Austria 23 0.930 97.4 82.5 77.0 .. e .. e 92.1 f 89.0 f 21,380 g 54,037 g -9 15 Spain 9 0.949 99.4 84.0 77.5 97.3 98.6 99.9 f 93.3 f 21,817 g,i 41,597 g,i 6 16 Denmark 12 0.947 99.2 80.5 75.9 .. e .. e 105.3 f,g 97.6 f,g 30,745 g 41,630 g 4 17 Belgium 11 0.948 99.4 82.4 76.5 .. e .. e 95.9 f 92.8 f 27,333 g 42,866 g 6 18 Italy 15 0.945 99.3 84.0 78.1 98.6 99.1 94.7 f 89.1 f 20,152 g,i 41,158 g,i 3 19 Liechtenstein .. .. .. .. k ..k .. e .. e 79.6 f,l 94.0 f,l .. .. .. 20 New Zealand 18 0.943 99.3 82.1 78.1 .. e .. e 113.4 f,g 102.0 f,g 22,456 32,375 1 21 United Kingdom 17 0.943 99.5 81.5 77.1 .. e .. e 92.8 f,h 85.9 f,h 28,421 g 42,133 g 3 22 Germany 20 0.939 99.2 82.3 77.0 .. e .. e 87.5 88.6 25,691 g,i 43,515 g,i 1 23 Singapore .. .. .. 82.6 77.8 91.6 97.3 .. .. 34,554 g,i 64,656 g,i .. 24 Hong Kong, China (SAR) 22 0.934 98.9 85.1 79.3 .. m .. m 73.4 f 75.4 f 35,827 g 49,324 g 0 25 Greece 21 0.936 99.4 81.3 76.9 96.0 98.2 103.2 f,g 100.1 f,g 19,218 i 38,002 i 2 26 Korea (Republic of) 25 0.926 98.8 82.4 75.8 .. e .. e 90.6 f,g 105.8 f,g 16,931 i 32,668 i -1 27 Israel 26 0.921 98.5 82.7 78.5 88.7f 95.0f 92.1 f 87.8 f 20,599 i 32,148 i -1 28 Andorra .. .. .. .. k .. k .. e .. e 66.3 f,h 64.0 f,g .. .. .. 29 Slovenia 24 0.927 99.7 81.7 74.4 99.6 99.7 98.1f 87.7 f 20,427 i 33,398 i 2 30 Brunei Darussalam 29 0.906 98.5 79.6 74.9 93.1 96.5 79.1 76.5 36,838 g,i 62,631 g,i -2 31 Kuwait 34 0.892 97.4 79.8 76.0 93.1 95.2 77.8 f 67.8 f 24,722 f,g,i 68,673 f,g,i -6 32 Cyprus 27 0.911 99.7 81.9 77.3 96.6 99.0 77.8 f,l 77.3 f,l 18,307 31,625 2 33 Qatar 35 0.891 97.9 76.8 74.8 90.4 93.8 87.7 74.2 24,584 g,i 88,264 g,i -5 34 Portugal 28 0.907 99.7 81.8 75.3 93.3 96.6 91.6 f 86.2 f 17,154 28,762 3 35 United Arab Emirates 38 0.878 97.2 78.7 76.6 91.5 89.5 78.7 h 65.4 h 18,361 g,i 67,556 g,i -6 36 Czech Republic 31 0.900 99.7 79.4 73.2 .. e .. e 85.1 f 81.9 f 17,706 i 30,909 i 2 37 Barbados 30 0.900 99.7 79.7 74.0 .. g,m .. g,m 100.2 g 85.8 g 14,735 f,i 22,830 f,i 4 38 Malta 32 0.895 99.3 81.3 77.7 93.5f 91.2f 81.7 f 81.0 f 14,458 31,812 3 HIGH HUMAN DEVELOPMENT 39 Bahrain 33 0.895 99.9 77.4 74.2 86.4 90.4 95.3 f,h 85.8 f,h 19,873 f 39,060 f 3 40 Estonia 36 0.882 99.8 78.3 67.3 99.8 g 99.8 g 98.2 f 84.6 f 16,256 i 25,169 i 1 41 Poland 39 0.877 99.6 79.7 71.3 99.0 99.6 91.4 f 84.2 f 11,957 i 20,292 i -1 42 Slovakia 40 0.877 99.7 78.5 70.7 .. e .. e 83.1 f 77.9 f 14,790 i 25,684 i -1 43 Hungary 37 0.879 99.9 77.3 69.2 98.8 99.0 94.0 f 86.6 f 16,143 21,625 3 44 Chile 41 0.871 99.2 81.6 75.5 96.5 96.6 82.0 f,h 83.0 f,h 8,188 i 19,694 i 0 45 Croatia 43 0.869 99.7 79.4 72.6 98.0 99.5 79.4 f 75.2 f 12,934 19,360 -1 46 Lithuania 42 0.869 99.9 77.7 65.9 99.7 99.7 97.6 f 87.2 f 14,633 20,944 1 47 Antigua and Barbuda .. .. .. .. k .. k 99.4 98.4 .. .. .. .. .. 48 Latvia 44 0.865 99.8 77.1 67.1 99.8 g 99.8 g 97.5 f 83.2 f 13,403 19,860 0 49 Argentina 46 0.862 99.5 79.0 71.5 97.7 97.6 93.3 f 84.0 f 8,958 i 17,710 i -1 50 Uruguay 45 0.862 99.7 79.8 72.6 98.2 97.4 96.3 f 85.6 f 7,994 i 14,668 i 1 51 Cuba 49 0.844 97.7 80.6 76.5 99.8 99.8 110.7 g 91.5 g 4,132 f,i,n 8,442 f,i,n -2 52 Bahamas .. .. .. 76.0 70.4 .. m .. m 72.2 f,h 71.4 f,h .. .. .. 53 Mexico 48 0.847 99.2 78.5 73.6 91.4 94.4 79.0 f 81.5 f 8,375 i 20,107 i 0 54 Costa Rica 47 0.848 99.4 81.3 76.4 96.2 95.7 74.4 f,h 71.6 f,h 6,788 14,763 2 55 Libyan Arab Jamahiriya 54 0.830 98.0 76.8 71.6 78.4 94.5 98.5 f,h 93.1 f,h 5,590 i 22,505 i -4 56 Oman 56 0.826 97.7 77.3 74.1 77.5 89.4 68.3 68.1 7,697 i 32,797 i -5 57 Seychelles .. .. .. .. k .. k 92.3 91.4 83.6 f,l 80.9 f,l .. .. .. 58 Venezuela (Bolivarian Republic of) 55 0.827 97.9 76.7 70.7 94.9 95.4 75.7 f 72.7 f 7,924 i 16,344 i -3 59 Saudi Arabia 60 0.816 96.7 75.1 70.8 79.4 89.1 78.0 f 79.1 f 5,987 i 36,662 i -7

HDI rank

HUMAN DEVELOPMENT REPORT 2009

182

60 Panama 51 0.838 99.7 78.2 73.0 92.8 94.0 83.5 f 76.1 f 8,331 14,397 3 61 Bulgaria 50 0.839 99.9 76.7 69.6 97.9 98.6 82.9 f 81.8 f 9,132 13,439 5 62 Saint Kitts and Nevis .. .. .. .. k .. k .. .. 74.1 f 72.1 f .. .. .. 63 Romania 52 0.836 99.9 76.1 69.0 96.9 98.3 81.7 f 76.7 f 10,053 14,808 4 64 Trinidad and Tobago 53 0.833 99.5 72.8 65.6 98.3 99.1 62.2 f,h 59.9 f,h 16,686 i 30,554 i 4 65 Montenegro .. .. .. 76.5 71.6 94.1 f,o 98.9 f,o .. .. 8,611 i,p 14,951 i,p .. 66 Malaysia 58 0.823 99.2 76.6 71.9 89.6 94.2 73.1 f 69.8 f 7,972 i 18,886 i 0 67 Serbia .. .. .. 76.3 71.6 94.1 f,o 98.9 f,o .. .. 7,654 i,p 12,900 i,p .. 68 Belarus 57 0.824 99.8 75.2 63.1 99.7 g 99.8 g 93.8 87.1 8,482 13,543 2 69 Saint Lucia .. .. .. 75.5 71.7 .. .. 80.6 73.8 6,599 i 13,084 i .. 70 Albania 61 0.814 99.5 79.8 73.4 98.8 g 99.3 g 67.6 f 68.0 f 4,954 i 9,143 i -1 71 Russian Federation 59 0.816 99.9 72.9 59.9 99.4 99.7 86.1 f 78.0 f 11,675 i 18,171 i 2 72 Macedonia (the Former Yugoslav Rep. of) 62 0.812 99.4 76.5 71.7 95.4 98.6 71.1 f 69.1 f 5,956 i 12,247 i 0 73 Dominica .. .. .. .. k .. k .. .. 82.7 f,h 74.5 f,h .. .. .. 74 Grenada .. .. .. 76.7 73.7 .. .. 73.8 f,h 72.4 f,h .. .. .. 75 Brazil 63 0.810 99.7 75.9 68.6 90.2 89.8 89.4 f 85.1 f 7,190 12,006 0 76 Bosnia and Herzegovina .. .. .. 77.7 72.4 94.4 99.0 .. .. 5,910 i 9,721 i .. 77 Colombia 64 0.806 99.9 76.5 69.1 92.8 92.4 80.9 77.2 7,138 10,080 0 78 Peru 65 0.804 99.7 75.8 70.4 84.6 94.9 89.9 f,h 86.4 f,h 5,828 i 9,835 i 0 79 Turkey 70 0.788 97.7 74.2 69.4 81.3 96.2 66.3 f,h 75.7 f,h 5,352 i 20,441 i -4 80 Ecuador .. .. .. 78.0 72.1 89.7 92.3 .. .. 4,996 i 9,888 i .. 81 Mauritius 67 0.797 99.1 75.7 68.5 84.7 90.2 75.7 f,h 78.0 f,h 6,686 i 15,972 i 0 82 Kazakhstan 66 0.803 99.8 71.2 59.1 99.5 99.8 95.1 87.8 8,831 i 13,080 i 2 83 Lebanon 71 0.784 97.7 74.1 69.8 86.0 93.4 80.3 75.7 4,062 i 16,404 i -2

MEDIUM HUMAN DEVELOPMENT 84 Armenia 68 0.794 99.5 76.7 70.1 99.3 99.7 77.8 71.6 4,215 7,386 2 85 Ukraine 69 0.793 99.7 73.8 62.7 99.6 99.8 93.2 l 87.0 l 5,249 8,854 2 86 Azerbaijan 73 0.779 99.0 72.3 67.6 99.2 g 99.8 g .. .. 4,836 11,037 -1 87 Thailand 72 0.782 99.8 72.1 65.4 92.6 95.9 79.6 f,h 76.6 f,h 6,341 i 10,018 i 1 88 Iran (Islamic Republic of) 76 0.770 98.4 72.5 69.9 77.2 87.3 73.0 f,h 73.4 f,h 5,304 i 16,449 i -2 89 Georgia .. .. .. 75.0 68.1 .. .. 77.7 h 75.8 h 2,639 6,921 .. 90 Dominican Republic 74 0.775 99.7 75.2 69.8 89.5 88.8 76.7 f 70.4 f 4,985 i 8,416 i 1 91 Saint Vincent and the Grenadines .. .. .. 73.6 69.4 .. .. 70.3 f 67.6 f 5,180 i 10,219 i .. 92 China 75 0.770 99.8 74.7 71.3 90.0 96.5 68.5 f 68.9 f 4,323 i 6,375 i 1 93 Belize .. .. .. 78.0 74.2 .. .. 79.2 f,h 77.4 f,h 4,021 9,398 .. 94 Samoa 80 0.763 99.0 74.7 68.4 98.4 98.9 76.3 f,h 72.0 f,h 2,525 i 6,258 i -3 95 Maldives 77 0.767 99.5 72.7 69.7 97.1 97.0 71.4 f,h 71.3 f,h 3,597 i 6,714 i 1 96 Jordan 87 0.743 96.5 74.3 70.7 87.0 95.2 79.9 f 77.5 f 1,543 8,065 -8 97 Suriname 79 0.763 99.3 72.5 65.3 88.1 92.7 79.3 f,h 69.4 f,h 4,794 i 10,825 i 1 98 Tunisia 84 0.752 97.8 76.0 71.8 69.0 86.4 78.9 f,h 73.6 f,h 3,249 i 11,731 i -3 99 Tonga 78 0.765 99.6 74.6 69.0 99.3 99.2 78.8 f,h 77.2 f,h 2,705 i 4,752 i 4

100 Jamaica 81 0.762 99.5 75.1 68.3 91.1 80.5 82.0 f,h 74.3 f,h 4,469 i 7,734 i 2 101 Paraguay 82 0.759 99.8 73.8 69.6 93.5 95.7 72.2 f,h 72.1 f,h 3,439 i 5,405 i 2 102 Sri Lanka 83 0.756 99.6 77.9 70.3 89.1 92.7 69.9 f,h 67.5 f,h 3,064 5,450 2 103 Gabon 85 0.748 99.1 61.5 58.7 82.2 90.2 75.0 f 79.8 f 11,221 i 19,124 i 1 104 Algeria 88 0.742 98.4 73.6 70.8 66.4 84.3 74.5 f,h 72.8 f,h 4,081 i 11,331 i -1 105 Philippines 86 0.748 99.6 73.9 69.4 93.7 93.1 81.6 f 77.8 f 2,506 i 4,293 i 2 106 El Salvador 89 0.740 99.0 75.9 66.4 79.7 84.9 74.8 73.3 3,675 i 8,016 i 0 107 Syrian Arab Republic 98 0.715 96.4 76.0 72.2 76.5 89.7 63.9 f,h 67.5 f,h 1,512 i 7,452 i -8 108 Fiji 90 0.732 98.7 71.0 66.5 .. m .. m 73.2 f,h 70.0 f,h 2,349 i 6,200 i 1 109 Turkmenistan .. .. .. 68.8 60.6 99.3 99.7 .. .. 3,594 i 5,545 i .. 110 Occupied Palestinian Territories .. .. .. 74.9 71.7 90.3 97.2 80.8 75.9 .. .. .. 111 Indonesia 93 0.726 99.0 72.5 68.5 88.8 95.2 66.8 f,h 69.5 f,h 2,263 i 5,163 i -1 112 Honduras 95 0.721 98.4 74.4 69.6 83.5 83.7 78.3 f,h 71.3 f,h 1,951 i 5,668 i -2 113 Bolivia 91 0.728 99.8 67.5 63.3 86.0 96.0 83.6 f 89.7 f 3,198 i 5,222 i 3 114 Guyana 96 0.721 98.9 69.6 63.7 .. g,m .. g,m 83.0 84.7 1,607 i 3,919 i -1 115 Mongolia 92 0.727 100.0 69.6 63.0 97.7 96.8 84.9 73.7 3,019 3,454 4 116 Viet Nam 94 0.723 99.7 76.1 72.3 86.9 93.9 60.7 f,h 63.9 f,h 2,131 i 3,069 i 3 117 Moldova 97 0.719 99.8 72.1 64.5 98.9 99.6 74.6 l 68.6 l 2,173 i 2,964 i 1 118 Equatorial Guinea 102 0.700 97.3 51.1 48.7 80.5 93.4 55.8 f 68.2 f 16,161 i 45,418 i -3

Gender-related development index and its components

HDI rank

Gender-related development index (GDI)

2007

Life expectancy at birth (years) 2007

Adult literacy ratea (% aged 15 and above)

1999–2007

Combined gross enrolment

ratio in educationb (%)

2007

Estimated earned incomec

(PPP US$) 2007

Rank MaleValue Female as a % of HDI value MaleFemale FemaleMale MaleFemale

HDI rank minus GDI

rankd

183

TABLE J

119 Uzbekistan 99 0.708 99.7 70.9 64.5 95.8 98.0 71.4 74.0 1,891 i 2,964 i 1 120 Kyrgyzstan 100 0.705 99.4 71.4 63.9 99.1 99.5 79.7 74.9 1,428 i 2,600 i 1 121 Cape Verde 101 0.701 98.9 73.5 68.2 78.8 89.4 69.7 66.6 2,015 i 4,152 i 1 122 Guatemala 103 0.696 98.9 73.7 66.7 68.0 79.0 67.8 73.2 2,735 i 6,479 i 0 123 Egypt .. .. .. 71.7 68.2 57.8 74.6 .. .. 2,286 8,401 .. 124 Nicaragua 106 0.686 98.2 75.9 69.8 77.9 78.1 72.7 f,h 71.5 f,h 1,293 i 3,854 i -2 125 Botswana 105 0.689 99.3 53.3 53.2 82.9 82.8 71.3 f,h 70.0 f,h 9,961 i 17,307 i 0 126 Vanuatu 104 0.692 99.9 72.0 68.1 76.1 80.0 60.3 f,h 64.2 f,h 2,970 i 4,332 i 2 127 Tajikistan 107 0.686 99.6 69.3 63.7 99.5 99.8 64.6 77.2 1,385 i 2,126 i 0 128 Namibia 108 0.683 99.5 61.2 59.3 87.4 88.6 68.2 f 66.3 f 4,006 i 6,339 i 0 129 South Africa 109 0.680 99.6 53.2 49.8 87.2 88.9 77.3 f 76.3 f 7,328 i 12,273 i 0 130 Morocco 111 0.625 95.7 73.3 68.8 43.2 68.7 55.1 f,h 64.0 f,h 1,603 i 6,694 i -1 131 Sao Tome and Principe 110 0.643 98.8 67.3 63.5 82.7 93.4 68.6 67.7 1,044 i 2,243 i 1 132 Bhutan 113 0.605 97.7 67.6 64.0 38.7 65.0 53.7 f,h 54.6 f,h 2,636 i 6,817 i -1 133 Lao People’s Democratic Republic 112 0.614 99.3 65.9 63.2 63.2 82.5 54.3 f 64.8 f 1,877 i 2,455 i 1 134 India 114 0.594 97.1 64.9 62.0 54.5 76.9 57.4 f 64.3 f 1,304 i 4,102 i 0 135 Solomon Islands .. .. .. 66.7 64.9 .. .. 47.8 f 51.4 f 1,146 i 2,264 i .. 136 Congo 115 0.594 98.8 54.4 52.5 71.8 f 90.6 f 55.2 f,h 62.0 f,h 2,385 i 4,658 i 0 137 Cambodia 116 0.588 99.2 62.3 58.6 67.7 85.8 54.8 h 62.1 h 1,465 i 2,158 i 0 138 Myanmar .. .. .. 63.4 59.0 86.4 93.9 .. .. 640 i 1,043 i .. 139 Comoros 117 0.571 99.2 67.2 62.8 69.8 80.3 42.3 f,h 50.4 f,h 839 i 1,446 i 0 140 Yemen 122 0.538 93.6 64.1 60.9 40.5 77.0 42.3 f 65.9 f 921 i 3,715 i -4 141 Pakistan 124 0.532 93.0 66.5 65.9 39.6 67.7 34.4 f 43.9 f 760 i 4,135 i -5 142 Swaziland 118 0.568 99.3 44.8 45.7 78.3 80.9 58.4 f 61.8 f 3,994 i 5,642 i 2 143 Angola .. .. .. 48.5 44.6 54.2 82.9 .. .. 4,212 i 6,592 i .. 144 Nepal 119 0.545 98.4 66.9 65.6 43.6 70.3 58.1 f,h 63.4 f,h 794 i 1,309 i 2 145 Madagascar 120 0.541 99.6 61.5 58.3 65.3 76.5 60.2 62.5 774 1,093 2 146 Bangladesh 123 0.536 98.7 66.7 64.7 48.0 58.7 52.5 f 51.8 f 830 i 1,633 i 0 147 Kenya 121 0.538 99.4 54.0 53.2 70.2 77.7 58.2 f,h 61.0 f,h 1,213 i 1,874 i 3 148 Papua New Guinea .. .. .. 63.0 58.7 53.4 62.1 .. .. 1,775 i 2,383 i .. 149 Haiti .. .. .. 62.9 59.1 64.0 f 60.1 f .. .. 626 i 1,695 i .. 150 Sudan 127 0.516 97.0 59.4 56.3 51.8 71.1 37.6 f,h 42.2 f,h 1,039 i 3,119 i -2 151 Tanzania ( United Republic of) 125 0.527 99.4 55.8 54.2 65.9 79.0 56.2 h 58.4 h 1,025 i 1,394 i 1 152 Ghana 126 0.524 99.5 57.4 55.6 58.3 71.7 54.5 h 58.3 h 1,133 i 1,531 i 1 153 Cameroon 129 0.515 98.6 51.4 50.3 59.8 77.0 47.7 l 56.7 l 1,467 i 2,791 i -1 154 Mauritania 128 0.516 99.1 58.5 54.7 48.3 63.3 50.5 f,l 50.7 f,l 1,405 i 2,439 i 1 155 Djibouti 130 0.514 98.8 56.5 53.7 .. m .. m 21.9 f 29.0 f 1,496 i 2,627 i 0 156 Lesotho 132 0.509 99.1 45.5 43.9 90.3 73.7 62.3 f,h 60.6 f,h 1,315 i 1,797 i -1 157 Uganda 131 0.509 99.2 52.4 51.4 65.5 81.8 61.6 f,h 62.9 f,h 861 i 1,256 i 1 158 Nigeria 133 0.499 97.7 48.2 47.2 64.1 80.1 48.1 f,h 57.9 f,h 1,163 i 2,777 i 0

LOW HUMAN DEVELOPMENT 159 Togo .. .. .. 63.9 60.4 38.5 68.7 .. .. 494 i 1,088 i .. 160 Malawi 134 0.490 99.4 53.4 51.3 64.6 79.2 61.7 f,h 62.1 f,h 646 i 877 i 0 161 Benin 135 0.477 97.0 62.1 59.8 27.9 53.1 44.5 f,h 60.1 f,h 892 1,726 0 162 Timor-Leste .. .. .. 61.5 59.8 .. .. 62.1 f,h 64.2 f,h 493 i 934 i .. 163 Côte d’Ivoire 137 0.468 96.6 58.3 55.7 38.6 60.8 31.3 f,h 43.7 f,h 852 i 2,500 i -1 164 Zambia 136 0.473 98.3 45.0 44.0 60.7 80.8 60.7 f,h 66.0 f,h 980 i 1,740 i 1 165 Eritrea 138 0.459 97.3 61.4 56.8 53.0 76.2 27.6 f,h 39.1 f,h 422 i 839 i 0 166 Senegal 140 0.457 98.5 56.9 53.9 33.0 52.3 39.0 f,h 43.3 f,h 1,178 i 2,157 i -1 167 Rwanda 139 0.459 99.8 51.4 47.9 59.8 71.4 52.4 f 52.0 f 770 i 970 i 1 168 Gambia 141 0.452 99.1 57.3 54.1 .. m .. m 47.2 f,h 46.4 f,h 951 i 1,499 i 0 169 Liberia 142 0.430 97.3 59.3 56.5 50.9 60.2 48.6 f 66.5 f 240 i 484 i 0 170 Guinea 143 0.425 97.7 59.3 55.3 18.1 42.6 41.5 f 56.9 f 919 i 1,356 i 0 171 Ethiopia 144 0.403 97.3 56.2 53.3 22.8 50.0 44.0 h 54.0 h 624 i 936 i 0 172 Mozambique 145 0.395 98.3 48.7 46.9 33.0 57.2 50.2 f,h 59.4 f,h 759 i 848 i 0 173 Guinea-Bissau 148 0.381 96.2 49.1 46.0 54.4 75.1 28.8 f,h 44.5 f,h 301 i 658 i -2 174 Burundi 146 0.390 99.1 51.4 48.6 52.2 67.3 46.2 h 51.8 h 296 i 387 i 1 175 Chad 149 0.380 96.8 49.9 47.3 20.8 43.0 27.5 f,h 45.5 f,h 1,219 i 1,739 i -1 176 Congo (Democratic Republic of the) 150 0.370 95.1 49.2 46.1 54.1 80.9 40.5 l 55.9 l 189 i 410 i -1 177 Burkina Faso 147 0.383 98.4 54.0 51.4 21.6 36.7 29.2 36.3 895 i 1,354 i 3

HDI rank

Gender-related development index (GDI)

2007

Life expectancy at birth (years) 2007

Adult literacy ratea (% aged 15 and above)

1999–2007

Combined gross enrolment

ratio in educationb (%)

2007

Estimated earned incomec

(PPP US$) 2007

Rank MaleValue Female as a % of HDI value MaleFemale FemaleMale MaleFemale

HDI rank minus GDI

rankd

HUMAN DEVELOPMENT REPORT 2009

184

178 Mali 153 0.353 95.2 48.8 47.4 18.2 34.9 37.5 f,h 51.0 f,h 672 i 1,517 i -2 179 Central African Republic 151 0.354 95.8 48.2 45.1 33.5 64.8 22.9 f,h 34.4 f,h 535 i 900 i 1 180 Sierra Leone 152 0.354 97.1 48.5 46.0 26.8 50.0 37.6 f,h 51.7 f,h 577 i 783 i 1 181 Afghanistan 154 0.310 88.0 43.5 43.6 12.6 43.1 35.4 f,h 63.6 f,h 442 f,i,q 1,845 f,i,q 0 182 Niger 155 0.308 90.8 51.7 50.0 15.1 42.9 22.1 32.3 318 i 929 i 0

OTHER UN MEMBER STATES Iraq .. .. .. 71.8 64.2 64.2 84.1 52.1 f,h 68.5 f,h .. .. .. Kiribati .. .. .. .. k .. k .. .. 77.9 f,h 73.8 f,h .. .. .. Korea (Democratic People’s Rep. of) .. .. .. 69.1 64.9 .. .. .. .. .. .. .. Marshall Islands .. .. .. .. k .. k .. .. 71.2 f,h 71.1 f,h .. .. .. Micronesia (Federated States of) .. .. .. 69.2 67.6 .. .. .. .. .. .. .. Monaco .. .. .. .. k .. k .. .. .. .. .. .. .. Nauru .. .. .. .. k .. k .. .. 56.1 f,h 54.0 f,h .. .. .. Palau .. .. .. .. k .. k 90.5 f 93.3 f 91.2 f,h 82.4 f,h .. .. .. San Marino .. .. .. .. k .. k .. e .. e .. .. .. .. .. Somalia .. .. .. 51.2 48.3 .. .. .. .. .. .. .. Tuvalu .. .. .. .. k .. k .. .. 70.8 f,h 67.8 f,h .. .. .. Zimbabwe .. .. .. 43.6 42.6 88.3 94.1 53.4 f,h 55.5 f,h .. .. ..

NOTES

a Data refer to national literacy estimates from censuses

or surveys conducted between 1999 and 2007,

unless otherwise specified. Due to differences in

methodology and timeliness of underlying data,

comparisons across countries and over time should be

made with caution. For more details, see http://www.

uis.unesco.org/.

b Data for some countries may refer to national or

UNESCO Institute for Statistics estimates. For details,

see http://www.uis.unesco.org/.

c Because of the lack of gender-disaggregated

income data, female and male earned income are

crudely estimated on the basis of data on the ratio

of the female nonagricultural wage to the male

nonagricultural wage, the female and male shares of

the economically active population, the total female

and male population and GDP per capita in PPP US $

(see http://hdr.undp.org/en/statistics/tn1). The wage

ratios used in this calculation are based on data for the

most recent year available between 1999 and 2007.

d The HDI ranks used in this calculation are recalculated

for the countries with a GDI value. A positive figure

indicates that the GDI rank is higher than the HDI rank;

a negative figure, the opposite.

e For the purposes of calculating the HDI, a value of

99.0% was applied.

f Data refer to an earlier year than that specified.

g For the purpose of calculating the GDI, the female

and male values appearing in this table were scaled

downward to reflect the maximum values for adult

literacy (99%), gross enrolment ratios (100%), and

GDP per capita (40,000 ( PPP US $)). For more details,

see http://hdr.undp.org/en/statistics/tn1.

h UNESCO Institute for Statistics estimate.

i No wage data are available. For the purposes of

calculating the estimated female and male earned

income, a value of 0.75 was used for the ratio

of the female nonagricultural wage to the male

nonagricultural wage.

j Statec (2008). Data refer to nationals enrolled both

in the country and abroad and thus differ from the

standard definition.

k For the purposes of calculating the HDI unpublished

estimates from UN (2009e) were used: Andorra

84.3 (for females) and 77.5 (for males), Antigua and

Barbuda 74.6 and 69.7, Dominica 80.3 and 73.7,

Liechtenstein 82.4 and 76.0, Saint Kitts and Nevis

74.6 and 69.8 and the Seychelles 77.7 and 68.4.

l National estimate from the UNESCO Institute for

Statistics.

m In the absence of recent data, estimates for 2005

from UNESCO Institute for Statistics (2003), based on

outdated census or survey information, were used and

should be interpreted with caution: the Bahamas 96.7

(for females) and 95.0 (for males), Barbados 99.8 and

99.7, Djibouti 61.4 and 79.9, Fiji 92.9 and 95.9, the

Gambia 35.4 and 49.9, Guyana 98.7 and 99.2 and

Hong Kong, China (SAR ) 91.4 and 97.3.

n Heston, Summers and Aten (2006). Data differ from

the standard definition.

o Data refer to Serbia and Montenegro prior to its

separation into two independent states in June 2006.

Data exclude Kosovo.

p Earned income is estimated using data on the

economic activity rate for Serbia and Montenegro prior

to its separation into two independent states in June

2006.

q Calculated on the basis of GDP in PPP US $ for 2006

from World Bank (2009d) and total population for the

same year from UN (2009e).

SOURCES

Column 1: determined on the basis of the GDI values.

Column 2: calculated based on data in columns 4–11.

Column 3: calculated based on GDI and HDI values.

Columns 4 and 5: UN (2009e).

Columns 6 and 7: UNESCO Institute for Statistics (2009a.)

Columns 8 and 9 : UNESCO Institute for Statistics (2009b).

Columns 10 and 11: calculated based on data on GDP

(in PPP US$) and population from the World Bank (2009d),

data on wages and economically active population from

ILO (2009b).

Column 12: calculated based on recalculated HDI ranks

and GDI ranks in column 1.

Gender-related development index and its components

HDI rank

Gender-related development index (GDI)

2007

Life expectancy at birth (years) 2007

Adult literacy ratea (% aged 15 and above)

1999–2007

Combined gross enrolment

ratio in educationb (%)

2007

Estimated earned incomec

(PPP US$) 2007

Rank MaleValue Female as a % of HDI value MaleFemale FemaleMale MaleFemale

HDI rank minus GDI

rankd

185

TABLE J

1 Australia 2 Norway 3 Iceland 4 Canada 5 Sweden 6 France 7 Netherlands 8 Finland 9 Spain 10 Ireland 11 Belgium 12 Denmark 13 Switzerland 14 Japan 15 Italy 16 Luxembourg 17 United Kingdom 18 New Zealand 19 United States 20 Germany 21 Greece 22 Hong Kong, China (SAR) 23 Austria 24 Slovenia 25 Korea (Republic of) 26 Israel 27 Cyprus 28 Portugal 29 Brunei Darussalam 30 Barbados 31 Czech Republic 32 Malta 33 Bahrain 34 Kuwait 35 Qatar 36 Estonia 37 Hungary 38 United Arab Emirates 39 Poland 40 Slovakia

41 Chile 42 Lithuania 43 Croatia 44 Latvia 45 Uruguay 46 Argentina 47 Costa Rica 48 Mexico 49 Cuba 50 Bulgaria 51 Panama 52 Romania 53 Trinidad and Tobago 54 Libyan Arab Jamahiriya 55 Venezuela (Bolivarian Republic of) 56 Oman 57 Belarus 58 Malaysia 59 Russian Federation 60 Saudi Arabia 61 Albania 62 Macedonia (the Former Yugoslav Rep. of) 63 Brazil 64 Colombia 65 Peru 66 Kazakhstan 67 Mauritius 68 Armenia 69 Ukraine 70 Turkey 71 Lebanon 72 Thailand 73 Azerbaijan 74 Dominican Republic 75 China 76 Iran (Islamic Republic of) 77 Maldives 78 Tonga 79 Suriname 80 Samoa

81 Jamaica 82 Paraguay 83 Sri Lanka 84 Tunisia 85 Gabon 86 Philippines 87 Jordan 88 Algeria 89 El Salvador 90 Fiji 91 Bolivia 92 Mongolia 93 Indonesia 94 Viet Nam 95 Honduras 96 Guyana 97 Moldova 98 Syrian Arab Republic 99 Uzbekistan 100 Kyrgyzstan 101 Cape Verde 102 Equatorial Guinea 103 Guatemala 104 Vanuatu 105 Botswana 106 Nicaragua 107 Tajikistan 108 Namibia 109 South Africa 110 Sao Tome and Principe 111 Morocco 112 Lao People’s Democratic Republic 113 Bhutan 114 India 115 Congo 116 Cambodia 117 Comoros 118 Swaziland 119 Nepal 120 Madagascar

121 Kenya 122 Yemen 123 Bangladesh 124 Pakistan 125 Tanzania ( United Republic of) 126 Ghana 127 Sudan 128 Mauritania 129 Cameroon 130 Djibouti 131 Uganda 132 Lesotho 133 Nigeria 134 Malawi 135 Benin 136 Zambia 137 Côte d’Ivoire 138 Eritrea 139 Rwanda 140 Senegal 141 Gambia 142 Liberia 143 Guinea 144 Ethiopia 145 Mozambique 146 Burundi 147 Burkina Faso 148 Guinea-Bissau 149 Chad 150 Congo (Democratic Republic of the) 151 Central African Republic 152 Sierra Leone 153 Mali 154 Afghanistan 155 Niger

GDI RANKS FOR 155 COUNTRIES AND AREAS

HUMAN DEVELOPMENT REPORT 2009

186

TABLE Gender empowerment measure and its components

K Seats in

parliament held by womena

(% of total)

Gender empowerment measure

(GEM) Female legislators,

senior officials and managersb (% of total)

Female professional and technical

workersb

(% of total)

Year women received right tod

Women in ministerial positionsf

(% of total)Rank Value

Ratio of estimated female to

male earned incomec vote

stand for election

Year a woman became

Presiding Officer of

parliament or of one of its

houses for the first timee

VERY HIGH HUMAN DEVELOPMENT 1 Norway 2 0.906 36 g 31 51 0.77 1913 1907, 1913 1993 56 2 Australia 7 0.870 30 g 37 57 0.70 1902, 1962 1902, 1962 1987 24 3 Iceland 8 0.859 33 g 30 56 0.62 1915, 1920 1915, 1920 1974 36 4 Canada 12 0.830 25 g 37 56 0.65 1917, 1960 1920, 1960 1972 16 5 Ireland 22 0.722 15 g 31 53 0.56 1918, 1928 1918, 1928 1982 21 6 Netherlands 5 0.882 39 g 28 50 0.67 1919 1917 1998 33 7 Sweden 1 0.909 47 g 32 51 0.67 1919, 1921 1919, 1921 1991 48 8 France 17 0.779 20 g 38 48 0.61 1944 1944 .. 47 9 Switzerland 13 0.822 27 g 30 46 0.62 1971 1971 1977 43

10 Japan 57 0.567 12 9 h 46 h 0.45 1945, 1947 1945, 1947 1993 12 11 Luxembourg .. .. 23 g .. .. 0.57 1919 1919 1989 14 12 Finland 3 0.902 42 29 55 0.73 1906 1906 1991 58 13 United States 18 0.767 17 g 43 56 0.62 1920, 1965 1788 j 2007 24 14 Austria 20 0.744 27 g 27 48 0.40 1918 1918 1927 38 15 Spain 11 0.835 34 g 32 49 0.52 1931 1931 1999 44 16 Denmark 4 0.896 38 g 28 52 0.74 1915 1915 1950 37 17 Belgium 6 0.874 36 g 32 49 0.64 1919, 1948 1921 2004 23 18 Italy 21 0.741 20 g 34 47 0.49 1945 1945 1979 24 19 Liechtenstein .. .. 24 .. .. .. 1984 1984 .. 20 20 New Zealand 10 0.841 34 40 54 0.69 1893 1919 2005 32 21 United Kingdom 15 0.790 20 g 34 47 0.67 1918, 1928 1918, 1928 1992 23 22 Germany 9 0.852 31 g 38 50 0.59 1918 1918 1972 33 23 Singapore 16 0.786 24 31 45 0.53 1947 1947 .. 0 24 Hong Kong, China (SAR) .. .. .. 30 42 0.73 .. .. .. .. 25 Greece 28 0.677 15 g 28 49 0.51 1952 1952 2004 12 26 Korea (Republic of) 61 0.554 14 g 9 40 0.52 1948 1948 .. 5 27 Israel 23 0.705 18 g 30 52 0.64 1948 1948 2006 12 28 Andorra .. .. 25 .. .. .. 1970 1973 .. 38 29 Slovenia 34 0.641 10 g 34 56 0.61 1946 1946 .. 18 30 Brunei Darussalam .. .. .. 35 h 37 h 0.59 — — .. 7 31 Kuwait .. .. 3 .. .. 0.36 2005 2005 .. 7 32 Cyprus 48 0.603 14 g 15 48 0.58 1960 1960 .. 18 33 Qatar 88 0.445 0 7 25 0.28 2003 k 2003 .. 8 34 Portugal 19 0.753 28 g 32 51 0.60 1931, 1976 1931, 1976 .. 13 35 United Arab Emirates 25 0.691 23 10 21 0.27 2006 l 2006 l .. 8 36 Czech Republic 31 0.664 16 g 29 53 0.57 1920 1920 1998 13 37 Barbados 37 0.632 14 43 52 0.65 1950 1950 .. 28 38 Malta 74 0.531 9 g 19 41 0.45 1947 1947 1996 15

HIGH HUMAN DEVELOPMENT 39 Bahrain 46 0.605 14 13 h 19 h 0.51 1973, 2002 1973, 2002 .. 4 40 Estonia 30 0.665 21 34 69 0.65 1918 1918 2003 23 41 Poland 38 0.631 18 g 36 60 0.59 1918 1918 1997 26 42 Slovakia 32 0.663 19 g 31 58 0.58 1920 1920 .. 13 43 Hungary 52 0.590 11 g 35 60 0.75 1918, 1945 1918, 1945 1963 21 44 Chile 75 0.526 13 g 23 h 50 h 0.42 1949 1949 2002 41 45 Croatia 44 0.618 21 g 21 51 0.67 1945 1945 1993 24 46 Lithuania 40 0.628 18 g 38 70 0.70 1919 1919 .. 23 47 Antigua and Barbuda .. .. 17 45 55 .. 1951 1951 1994 9 48 Latvia 33 0.648 20 41 66 0.67 1918 1918 1995 22 49 Argentina 24 0.699 40 g 23 54 0.51 1947 1947 1973 23 50 Uruguay 63 0.551 12 g 40 53 0.55 1932 1932 1963 29 51 Cuba 29 0.676 43 31 h 60 h 0.49 1934 1934 .. 19 52 Bahamas .. .. 25 43 63 .. 1961, 1964 1961, 1964 1997 8 53 Mexico 39 0.629 22 g 31 42 0.42 1947 1953 1994 16 54 Costa Rica 27 0.685 37 g 27 43 0.46 1949 1949 1986 29 55 Libyan Arab Jamahiriya .. .. 8 .. .. 0.25 1964 1964 .. 0 56 Oman 87 0.453 9 9 33 0.23 1994, 2003 1994, 2003 .. 9 57 Seychelles .. .. 24 .. .. .. 1948 1948 .. 20 58 Venezuela (Bolivarian Republic of) 55 0.581 19 g 27 h 61 h 0.48 1946 1946 1998 21 59 Saudi Arabia 106 0.299 0 10 29 0.16 — — .. 0

HDI rank

187

TABLE K

Seats in parliament

held by womena

(% of total)

Gender empowerment measure

(GEM) Female legislators,

senior officials and managersb (% of total)

Female professional and technical

workersb

(% of total)

Year women received right tod

Women in ministerial positionsf

(% of total)Rank Value

Ratio of estimated female to

male earned incomec vote

stand for election

Year a woman became

Presiding Officer of

parliament or of one of its

houses for the first timee

60 Panama 47 0.604 17 g 44 52 0.58 1941, 1946 1941, 1946 1994 23 61 Bulgaria 45 0.613 22 31 61 0.68 1937, 1945 1945 .. 24 62 Saint Kitts and Nevis .. .. 7 .. .. .. 1951 1951 2004 .. 63 Romania 77 0.512 10 g 28 56 0.68 1929, 1946 1929, 1946 2008 0 64 Trinidad and Tobago 14 0.801 33 g 43 53 0.55 1946 1946 1991 36 65 Montenegro 84 0.485 11 20 60 0.58 1946 m 1946 m .. 6 66 Malaysia 68 0.542 15 23 41 0.42 1957 1957 .. 9 67 Serbia 42 0.621 22 g 35 55 0.59 1946 m 1946 m 2008 17 68 Belarus .. .. 33 .. .. 0.63 1918 1918 .. 6 69 Saint Lucia 51 0.591 17 52 56 0.50 1951 1951 2007 .. 70 Albania .. .. 7 g .. .. 0.54 1920 1920 2005 7 71 Russian Federation 60 0.556 11 39 64 0.64 1918 1918 .. 10 72 Macedonia (the Former Yugoslav Rep. of) 35 0.641 28 g 29 53 0.49 1946 1946 .. 14 73 Dominica .. .. 19 48 55 .. 1951 1951 1980 21 74 Grenada .. .. 21 49 53 .. 1951 1951 1990 50 75 Brazil 82 0.504 9 g 35 53 0.60 1932 1932 .. 11 76 Bosnia and Herzegovina .. .. 12 g .. .. 0.61 1946 1946 2009 0 77 Colombia 80 0.508 10 g 38 h 50 h 0.71 1954 1954 .. 23 78 Peru 36 0.640 29 g 29 47 0.59 1955 1955 1995 29 79 Turkey 101 0.379 9 8 33 0.26 1930 1930 .. 4 80 Ecuador 41 0.622 28 g,n 28 49 0.51 1929 1929 .. 35 81 Mauritius 71 0.538 17 20 45 0.42 1956 1956 .. 10 82 Kazakhstan 73 0.532 12 g 38 67 0.68 1924, 1993 1924, 1993 .. 6 83 Lebanon .. .. 5 g .. .. 0.25 1952 1952 .. 5

MEDIUM HUMAN DEVELOPMENT 84 Armenia 93 0.412 8 g 24 65 0.57 1918 1918 .. 6 85 Ukraine 86 0.461 8 39 64 0.59 1919 1919 .. 4 86 Azerbaijan 100 0.385 11 5 53 0.44 1918 1918 .. 7 87 Thailand 76 0.514 13 g 30 53 0.63 1932 1932 .. 10 88 Iran (Islamic Republic of) 103 0.331 3 13 34 0.32 1963 1963 .. 3 89 Georgia 95 0.408 6 34 62 0.38 1918, 1921 1918, 1921 2001 18 90 Dominican Republic 64 0.550 17 g 31 51 0.59 1942 1942 1999 14 91 Saint Vincent and the Grenadines .. .. 18 .. .. 0.51 1951 1951 .. 21 92 China 72 0.533 21 g 17 52 0.68 1949 1949 .. 9 93 Belize 81 0.507 11 41 50 0.43 1954 1954 1984 18 94 Samoa 89 0.431 8 29 39 0.40 1948, 1990 1948, 1990 .. 23 95 Maldives 90 0.429 12 14 49 0.54 1932 1932 .. 14 96 Jordan .. .. 8 g .. .. 0.19 1974 1974 .. 15 97 Suriname 58 0.560 25 28 h 23 0.44 1948 1948 1997 17 98 Tunisia .. .. 20 g .. .. 0.28 1959 1959 .. 7 99 Tonga 102 0.363 3 o 27 43 0.57 1960 1960 .. .. 100 Jamaica .. .. 14 .. .. 0.58 1944 1944 1984 11 101 Paraguay 79 0.510 14 g 35 50 0.64 1961 1961 .. 19 102 Sri Lanka 98 0.389 6 g 24 46 0.56 1931 1931 .. 6 103 Gabon .. .. 17 .. .. 0.59 1956 1956 2009 17 104 Algeria 105 0.315 6 g 5 35 0.36 1962 1962 .. 11 105 Philippines 59 0.560 20 g 57 63 0.58 1937 1937 .. 9 106 El Salvador 70 0.539 19 g 29 48 0.46 1939 1961 1994 39 107 Syrian Arab Republic .. .. 12 .. 40 h 0.20 1949, 1953 1953 .. 6 108 Fiji .. .. .. p 51 h 9 0.38 1963 1963 .. 8 109 Turkmenistan .. .. .. .. .. 0.65 1927 1927 2006 7 110 Occupied Palestinian Territories .. .. .. g 10 34 .. .. .. .. .. 111 Indonesia 96 0.408 12 g 14 h 48 h 0.44 1945, 2003 1945 .. 11 112 Honduras 54 0.589 23 g 41 h 52 h 0.34 1955 1955 .. .. 113 Bolivia 78 0.511 15 g 36 40 0.61 1938, 1952 1938, 1952 1979 24 114 Guyana 53 0.590 30 g 25 59 0.41 1953 1945 .. 26 115 Mongolia 94 0.410 4 48 54 0.87 1924 1924 .. 20 116 Viet Nam 62 0.554 26 22 51 0.69 1946 1946 .. 4 117 Moldova 66 0.547 22 g 40 68 0.73 1924, 1993 1924, 1993 2001 11 118 Equatorial Guinea .. .. 6 g .. .. 0.36 1963 1963 .. 14

HDI rank

HUMAN DEVELOPMENT REPORT 2009

188

Seats in parliament

held by womena

(% of total)

Gender empowerment measure

(GEM) Female legislators,

senior officials and managersb (% of total)

Female professional and technical

workersb

(% of total)

Year women received right tod

Women in ministerial positionsf

(% of total)Rank Value

Ratio of estimated female to

male earned incomec vote

stand for election

Year a woman became

Presiding Officer of

parliament or of one of its

houses for the first timee

119 Uzbekistan .. .. 16 g .. .. 0.64 1938 1938 2008 5 120 Kyrgyzstan 56 0.575 26 g 35 62 0.55 1918 1918 .. 19 121 Cape Verde .. .. 18 .. .. 0.49 1975 1975 .. 36 122 Guatemala .. .. 12 g .. .. 0.42 1946 1946, 1965 1991 7 123 Egypt 107 0.287 4 g 11 32 0.27 1956 1956 .. 6 124 Nicaragua 67 0.542 18 g 41 51 0.34 1950 1955 1990 33 125 Botswana 65 0.550 11 g 33 51 0.58 1965 1965 .. 28 126 Vanuatu .. .. 4 .. .. 0.69 1975, 1980 1975, 1980 .. 8 127 Tajikistan .. .. 20 .. .. 0.65 1924 1924 .. 6 128 Namibia 43 0.620 27 g 36 52 0.63 1989 1989 .. 25 129 South Africa 26 0.687 34 g,q 34 55 0.60 1930, 1994 1930, 1994 1994 45 130 Morocco 104 0.318 6 g 12 35 0.24 1959 1963 .. 19 131 Sao Tome and Principe .. .. 7 .. .. 0.47 1975 1975 1980 25 132 Bhutan .. .. 14 .. .. 0.39 1953 1953 .. 0 133 Lao People’s Democratic Republic .. .. 25 .. .. 0.76 1958 1958 .. 11 134 India .. .. 9 g .. .. 0.32 1935, 1950 1935, 1950 2009 10 135 Solomon Islands .. .. 0 .. .. 0.51 1974 1974 .. 0 136 Congo .. .. 9 .. .. 0.51 1947, 1961 1963 .. 13 137 Cambodia 91 0.427 16 14 41 0.68 1955 1955 .. 7 138 Myanmar .. .. .. r .. .. 0.61 1935 1946 .. 0 139 Comoros .. .. 3 .. .. 0.58 1956 1956 .. .. 140 Yemen 109 0.135 1 4 15 0.25 1967, 1970 1967, 1970 .. 6 141 Pakistan 99 0.386 21 g 3 25 0.18 1956 1956 2008 4 142 Swaziland .. .. 22 .. .. 0.71 1968 1968 2006 19 143 Angola .. .. 37 g .. .. 0.64 1975 1975 .. 6 144 Nepal 83 0.486 33 g 14 20 0.61 1951 1951 .. 20 145 Madagascar 97 0.398 9 22 43 0.71 1959 1959 .. 13 146 Bangladesh 108 0.264 6 g,s 10 h 22 h 0.51 1935, 1972 1935, 1972 .. 8 147 Kenya .. .. 10 g .. .. 0.65 1919, 1963 1919, 1963 .. .. 148 Papua New Guinea .. .. 1 .. .. 0.74 1964 1963 .. 4 149 Haiti .. .. 5 g .. .. 0.37 1957 1957 .. 11 150 Sudan .. .. 17 g .. .. 0.33 1964 1964 .. 6 151 Tanzania ( United Republic of) 69 0.539 30 g 16 38 0.74 1959 1959 .. 21 152 Ghana .. .. 8 g .. .. 0.74 1954 1954 2009 16 153 Cameroon .. .. 14 g .. .. 0.53 1946 1946 .. 12 154 Mauritania .. .. 20 g .. .. 0.58 1961 1961 .. 12 155 Djibouti .. .. 14 g .. .. 0.57 1946 1986 .. 9 156 Lesotho 50 0.591 26 g 52 58 0.73 1965 1965 2000 32 157 Uganda 49 0.591 31 g 33 35 0.69 1962 1962 .. 28 158 Nigeria .. .. 7 .. .. 0.42 1958 1958 2007 23

LOW HUMAN DEVELOPMENT 159 Togo .. .. 11 .. .. 0.45 1945 1945 .. 10 160 Malawi .. .. 13 g .. .. 0.74 1961 1961 .. 24 161 Benin .. .. 11 .. .. 0.52 1956 1956 .. 22 162 Timor-Leste .. .. 29 g .. .. 0.53 .. .. .. 25 163 Côte d’Ivoire .. .. 9 g .. .. 0.34 1952 1952 .. 13 164 Zambia 92 0.426 15 19 h 31 h 0.56 1962 1962 .. 17 165 Eritrea .. .. 22 g .. .. 0.50 1955 t 1955 t .. 18 166 Senegal .. .. 29 g .. .. 0.55 1945 1945 .. 18 167 Rwanda .. .. 51 g .. .. 0.79 1961 1961 2008 17 168 Gambia .. .. 9 .. .. 0.63 1960 1960 2006 28 169 Liberia .. .. 14 g .. .. 0.50 1946 1946 2003 20 170 Guinea .. .. .. u .. .. 0.68 1958 1958 .. 16 171 Ethiopia 85 0.464 21 g 16 33 0.67 1955 1955 1995 10 172 Mozambique .. .. 35 g .. .. 0.90 1975 1975 .. 26 173 Guinea-Bissau .. .. 10 .. .. 0.46 1977 1977 .. 25 174 Burundi .. .. 32 g .. .. 0.77 1961 1961 2005 30 175 Chad .. .. 5 .. .. 0.70 1958 1958 .. 17 176 Congo (Democratic Republic of the) .. .. 8 .. .. 0.46 1967 1970 .. 12 177 Burkina Faso .. .. 15 g .. .. 0.66 1958 1958 .. 14

HDI rank

Gender empowerment measure and its components

189

TABLE K

Seats in parliament

held by womena

(% of total)

Gender empowerment measure

(GEM) Female legislators,

senior officials and managersb (% of total)

Female professional and technical

workersb

(% of total)

Year women received right tod

Women in ministerial positionsf

(% of total)Rank Value

Ratio of estimated female to

male earned incomec vote

stand for election

Year a woman became

Presiding Officer of

parliament or of one of its

houses for the first timee

178 Mali .. .. 10 g .. .. 0.44 1956 1956 .. 23 179 Central African Republic .. .. 10 .. .. 0.59 1986 1986 .. 13 180 Sierra Leone .. .. 13 g .. .. 0.74 1961 1961 .. 14 181 Afghanistan .. .. 26 g .. .. 0.24 1963 1963 .. 4 182 Niger .. .. 12 g .. .. 0.34 1948 1948 .. 26

OTHER UN MEMBER STATES Iraq .. .. 25 g .. .. .. 1980 1980 .. 10 Kiribati .. .. 4 27 h 44 h .. 1967 1967 .. 8 Korea ( Democratic People’s Rep. of) .. .. 20 g .. .. .. 1946 1946 .. 0 Marshall Islands .. .. 3 19 h 36 h .. 1979 1979 .. 10 Micronesia (Federated States of) .. .. 0 .. .. .. 1979 1979 .. 14 Monaco .. .. 25 .. .. .. 1962 1962 .. 0 Nauru .. .. 0 .. .. .. 1968 1968 .. 0 Palau .. .. 7 36 h 44 h .. 1979 1979 .. 0 San Marino .. .. 15 19 52 .. 1959 1973 1981 20 Somalia .. .. .. g .. .. .. 1956 1956 .. .. Tuvalu .. .. 0 25 50 .. 1967 1967 .. 0 Zimbabwe .. .. 18 g .. .. .. 1919, 1957 1919, 1978 2005 16

NOTES

a Data are as of 28 February 2009, unless otherwise

specified. Where there are lower and upper houses,

data refer to the weighted average of women’s shares

of seats in both houses.

b Data refer to the most recent year available between

1999 and 2007. Estimates for countries that have

implemented the International Standard Classification

of Occupations ( ISCO-88) are not strictly comparable

with those for countries using the previous

classification ( ISCO-68).

c Calculated on the basis of data in columns 10 and

11 in Table J. Estimates are based on data for the

most recent year available between 1996 and 2007.

Following the methodology implemented in the

calculation of the GDI, the income component of

the GEM has been scaled downward for countries

whose income exceeds the maximum goalpost GDP

per capita value of 40,000 ( PPP US $). For more

details, For more details see http://hdr.undp.org/en/

statistics/tn1

d Data refer to the year in which the right to vote or

stand for national election on a universal and equal

basis was recognized. Where two years are shown,

the first refers to the first partial recognition of the

right to vote or stand for election. In some countries,

women were granted the right to vote or stand at local

elections before obtaining these rights for national

elections; however, data on local election rights are

not included in this table.

e Date at which, for the first time in the country’s

parliamentary history, a woman became speaker/

presiding officer of parliament or of one of its houses.

As of May 2009, women occupy only 12.6% of the

total number of 269 posts of Presiding Officers of

parliament or of one of its houses.

f Data are as of January 2008. The total includes

deputy prime ministers and ministers. Prime ministers

were also included when they held ministerial

portfolios. Vice-presidents and heads of governmental

or public agencies are not included.

g Countries with established quota systems for women.

Quota systems aim at ensuring that women constitute

at least a ‘critical minority’ of 30 or 40 percent. Today

women constitute 16 percent of the members of

parliaments around the world.

h Data follow the ISCO-68 classification.

i The total refers to all voting members of the House.

j No information is available on the year all women

received the right to stand for election. As the

country’s constitution does not mention gender with

regard to this right.

k According to the new constitution approved in 2003,

women are granted suffrage. To date, no legislative

elections have been held.

l In December 2006, the Federal National Council was

renewed. Men and women were entitled to vote, under

similar conditions. One woman was elected to the

Council and 7 subsequently appointed.

m Serbia and Montenegro separated into two

independent states in June 2006. Women received

the right to vote and to stand for elections in 1946,

when Serbia and Montenegro were part of the former

Yugoslavia.

n The 2008 Constitution provides that the National

Congress shall be replaced by a 124-member National

Assembly. Elections to that body are due to take place

on 26 April 2009. During the transitional period, a

Legislative and Oversight Commission, comprising the

members of the Constituent Assembly, assumes the

legislative and oversight functions. The date refers to

the date when the Commission held its first session.

o No woman candidate was elected in the 2008

elections. One woman was appointed to the cabinet. As

cabinet ministers also sit in parliament, there was one

woman out of a total of 32 members in October 2008.

p The parliament was dissolved following a coup d’état in

December 2006.

q The figures on the distribution of seats do not include

the 36 special rotating delegates appointed on an ad

hoc basis, and all percentages given are therefore

calculated on the basis of the 54 permanent seats.

r The parliament elected in 1990 has never been

convened nor authorized to sit, and many of its

members were detained or forced into exile.

s Forty five seats reserved for women are yet to be filled.

t In November 1955, Eritrea was part of Ethiopia. The

Constitution of sovereign Eritrea adopted on 23 May

1997 stipulates that “All Eritrean citizens, of eighteen

years of age or more, shall have the right to vote”.

u The parliament was dissolved following a coup d’état in

December 2008.

SOURCES

Column 1: detemined on the basis of GEM values in column 2.

Column 2: calculated on the basis of data in columns

3–6; see Technical note 1 for details (http://hdr.undp.

org/en/statistics/tn1).

Column 3: calculated on the basis of data on

parliamentary seats from IPU (2009 ).

Column 4: calculated on the basis of occupational data

from ILO (2009b).

Column 5: calculated on the basis of occupational data

from ILO (2009b).

Column 6: calculated on the basis of data in columns 10

and 11 of table J.

Columns 7 and 8: IPU (2009 ).

Columns 9 and 10: IPU (2009 ).

HDI rank

HUMAN DEVELOPMENT REPORT 2009

190

1 Sweden 2 Norway 3 Finland 4 Denmark 5 Netherlands 6 Belgium 7 Australia 8 Iceland 9 Germany

10 New Zealand 11 Spain 12 Canada 13 Switzerland 14 Trinidad and Tobago 15 United Kingdom 16 Singapore 17 France 18 United States 19 Portugal 20 Austria 21 Italy 22 Ireland 23 Israel 24 Argentina 25 United Arab Emirates 26 South Africa 27 Costa Rica 28 Greece

29 Cuba 30 Estonia 31 Czech Republic 32 Slovakia 33 Latvia 34 Slovenia 35 Macedonia (the Former Yugoslav Rep. of) 36 Peru 37 Barbados 38 Poland 39 Mexico 40 Lithuania 41 Ecuador 42 Serbia 43 Namibia 44 Croatia 45 Bulgaria 46 Bahrain 47 Panama 48 Cyprus 49 Uganda 50 Lesotho 51 Saint Lucia 52 Hungary 53 Guyana 54 Honduras 55 Venezuela (Bolivarian Republic of) 56 Kyrgyzstan

57 Japan 58 Suriname 59 Philippines 60 Russian Federation 61 Korea (Republic of) 62 Viet Nam 63 Uruguay 64 Dominican Republic 65 Botswana 66 Moldova 67 Nicaragua 68 Malaysia 69 Tanzania ( United Republic of) 70 El Salvador 71 Mauritius 72 China 73 Kazakhstan 74 Malta 75 Chile 76 Thailand 77 Romania 78 Bolivia 79 Paraguay 80 Colombia 81 Belize 82 Brazil 83 Nepal 84 Montenegro

85 Ethiopia 86 Ukraine 87 Oman 88 Qatar 89 Samoa 90 Maldives 91 Cambodia 92 Zambia 93 Armenia 94 Mongolia 95 Georgia 96 Indonesia 97 Madagascar 98 Sri Lanka 99 Pakistan 100 Azerbaijan 101 Turkey 102 Tonga 103 Iran (Islamic Republic of) 104 Morocco 105 Algeria 106 Saudi Arabia 107 Egypt 108 Bangladesh 109 Yemen

GEM RANKS FOR 109 COUNTRIES OR AREAS

Gender empowerment measure and its components

191

HUMAN DEVELOPMENT REPORT 2009

LTABLE Demographic trends

L Total population

(millions)

Rate of natural increase

(%) Urban populationa

(% of total)

Net international migration rate

(%) Child dependency

ratio Total fertility rate

(births per woman)

Old age dependency

ratio

1990 20102010

2005 to

20102020b 20102010

2005 to

2010

2005 to

2007 19901995

1990 to

1995

1990 to

1995

1990 to

19901990

VERY HIGH HUMAN DEVELOPMENT 1 Norway 4.2 4.7 5.2 0.4 0.4 0.2 0.6 72.0 77.6 29.3 28.4 25.2 22.7 1.9 1.9 2 Australia 17.1 20.9 23.7 0.7 0.6 0.4 0.5 85.4 89.1 32.9 28.1 16.8 20.7 1.9 1.8 3 Iceland 0.3 0.3 0.4 1.1 0.9 -0.1 1.3 90.8 92.3 38.7 29.8 16.5 17.4 2.2 2.1 4 Canada 27.7 32.9 37.1 0.7 0.3 0.5 0.6 76.6 80.6 30.4 23.5 16.6 20.3 1.7 1.6 5 Ireland 3.5 4.4 5.1 0.5 0.9 0.0 0.9 56.9 61.9 44.6 30.6 18.5 16.7 2.0 2.0 6 Netherlands 15.0 16.5 17.1 0.4 0.3 0.3 0.1 68.7 82.9 26.5 26.3 18.6 22.9 1.6 1.7 7 Sweden 8.6 9.2 9.7 0.3 0.2 0.3 0.3 83.1 84.7 27.9 25.3 27.7 28.1 2.0 1.9 8 France 56.8 61.7 64.9 0.3 0.4 0.1 0.2 74.1 77.8 30.5 28.4 21.6 26.2 1.7 1.9 9 Switzerland 6.7 7.5 7.9 0.3 0.1 0.7 0.3 73.2 73.6 24.9 22.4 21.3 25.5 1.5 1.5 10 Japan 123.2 127.4 123.7 0.3 -0.1 0.1 0.0 63.1 66.8 26.3 20.5 17.2 35.1 1.5 1.3 11 Luxembourg 0.4 0.5 0.5 0.3 0.3 1.1 0.8 80.9 82.2 25.1 25.7 19.4 20.5 1.7 1.7 12 Finland 5.0 5.3 5.5 0.3 0.2 0.2 0.2 61.4 63.9 28.7 25.0 19.9 25.9 1.8 1.8 13 United States 254.9 308.7 346.2 0.7 0.6 0.5 0.3 75.3 82.3 33.0 30.3 18.7 19.4 2.0 2.1 14 Austria 7.7 8.3 8.5 0.1 0.0 0.6 0.4 65.8 67.6 25.8 21.8 22.1 25.9 1.5 1.4 15 Spain 38.8 44.1 48.6 0.1 0.2 0.2 0.8 75.4 77.4 29.8 22.0 20.5 25.3 1.3 1.4 16 Denmark 5.1 5.4 5.6 0.1 0.1 0.2 0.1 84.8 87.2 25.3 27.6 23.2 25.6 1.7 1.8 17 Belgium 9.9 10.5 11.0 0.1 0.2 0.2 0.4 96.4 97.4 27.0 25.4 22.3 26.4 1.6 1.8 18 Italy 57.0 59.3 60.4 0.0 -0.1 0.1 0.6 66.7 68.4 24.0 21.7 22.2 31.3 1.3 1.4 19 Liechtenstein 0.0 0.0 0.0 .. .. .. .. 16.9 14.2 .. .. .. .. .. .. 20 New Zealand 3.4 4.2 4.7 0.9 0.7 0.8 0.2 84.7 86.8 35.1 30.3 16.9 19.4 2.1 2.0 21 United Kingdom 57.2 60.9 65.1 0.2 0.2 0.1 0.3 88.7 90.1 29.1 26.3 24.1 25.1 1.8 1.8 22 Germany 79.4 82.3 80.4 -0.1 -0.2 0.7 0.1 73.1 73.8 23.3 20.2 21.7 30.9 1.3 1.3 23 Singapore 3.0 4.5 5.2 1.3 0.3 1.5 2.2 100.0 100.0 29.4 21.0 7.7 13.8 1.8 1.3 24 Hong Kong, China (SAR) 5.7 6.9 7.7 0.7 0.2 1.0 0.3 99.5 100.0 30.7 15.3 12.1 17.0 1.3 1.0 25 Greece 10.2 11.1 11.3 0.1 -0.1 0.9 0.3 58.8 61.4 28.7 21.1 20.4 27.2 1.4 1.4 26 Korea (Republic of) 43.0 48.0 49.5 1.0 0.4 -0.3 0.0 73.8 81.9 36.9 22.3 7.2 15.2 1.7 1.2 27 Israel 4.5 6.9 8.3 1.5 1.5 2.0 0.2 90.4 91.7 52.5 44.4 15.2 16.4 2.9 2.8 28 Andorra 0.1 0.1 0.1 .. .. .. .. 94.7 88.0 .. .. .. .. .. .. 29 Slovenia 1.9 2.0 2.1 0.0 0.0 0.4 0.2 50.4 48.0 30.9 19.8 16.3 23.5 1.4 1.4 30 Brunei Darussalam 0.3 0.4 0.5 2.5 1.7 0.3 0.2 65.8 75.7 54.9 37.5 4.3 4.9 3.1 2.1 31 Kuwait 2.1 2.9 3.7 1.9 1.6 -6.2 0.8 98.0 98.4 58.9 31.3 1.9 3.2 3.2 2.2 32 Cyprus 0.7 0.9 1.0 1.0 0.4 0.4 0.6 66.8 70.3 40.8 25.2 17.3 19.0 2.4 1.5 33 Qatar 0.5 1.1 1.7 1.8 1.0 0.6 9.4 92.2 95.8 38.9 19.2 1.6 1.3 4.1 2.4 34 Portugal 10.0 10.6 10.8 0.1 0.0 0.0 0.4 47.9 60.7 30.8 22.7 20.3 26.7 1.5 1.4 35 United Arab Emirates 1.9 4.4 5.7 2.1 1.3 3.2 1.6 79.1 78.0 43.4 24.0 1.8 1.3 3.9 1.9 36 Czech Republic 10.3 10.3 10.6 0.0 0.0 0.0 0.4 75.2 73.5 32.4 19.9 19.0 21.6 1.7 1.4 37 Barbados 0.3 0.3 0.3 0.6 0.4 -0.8 -0.1 32.7 40.8 36.4 23.5 15.1 14.4 1.6 1.5 38 Malta 0.4 0.4 0.4 0.7 0.1 0.3 0.2 90.4 94.7 35.5 21.7 15.8 21.2 2.0 1.3 HIGH HUMAN DEVELOPMENT 39 Bahrain 0.5 0.8 1.0 2.3 1.6 0.9 0.5 88.1 88.6 47.5 36.2 3.4 3.1 3.4 2.3 40 Estonia 1.6 1.3 1.3 -0.3 -0.1 -1.4 0.0 71.1 69.5 33.5 22.7 17.5 25.2 1.6 1.6 41 Poland 38.1 38.1 37.5 0.3 0.0 0.0 -0.1 61.3 61.2 38.8 20.6 15.5 18.8 1.9 1.3 42 Slovakia 5.3 5.4 5.4 0.4 0.0 0.0 0.1 56.5 56.8 39.2 20.9 16.0 16.9 1.9 1.3 43 Hungary 10.4 10.0 9.8 -0.3 -0.4 0.2 0.1 65.8 68.3 30.5 21.4 20.1 23.8 1.7 1.4 44 Chile 13.2 16.6 18.6 1.6 1.0 0.1 0.0 83.3 89.0 46.7 32.5 9.6 13.5 2.6 1.9 45 Croatia 4.5 4.4 4.3 0.0 -0.2 0.7 0.0 54.0 57.8 30.1 22.1 16.6 25.6 1.5 1.4 46 Lithuania 3.7 3.4 3.1 0.2 -0.4 -0.5 -0.6 67.6 67.2 33.9 21.2 16.4 23.7 1.8 1.3 47 Antigua and Barbuda 0.1 0.1 0.1 .. .. .. .. 35.4 30.3 .. .. .. .. .. .. 48 Latvia 2.7 2.3 2.2 -0.3 -0.4 -1.0 -0.1 69.3 68.2 32.1 20.1 17.7 25.4 1.6 1.4 49 Argentina 32.5 39.5 44.3 1.3 1.0 0.1 0.0 87.0 92.4 50.2 38.6 15.3 16.6 2.9 2.3 50 Uruguay 3.1 3.3 3.5 0.8 0.6 -0.1 -0.3 89.0 92.5 41.7 35.4 18.7 21.8 2.5 2.1 51 Cuba 10.6 11.2 11.2 0.8 0.4 -0.2 -0.3 73.4 75.7 32.8 24.6 12.7 17.5 1.7 1.5 52 Bahamas 0.3 0.3 0.4 1.8 1.1 0.1 0.1 79.8 84.1 51.9 36.8 7.0 10.3 2.6 2.0 53 Mexico 83.4 107.5 119.7 2.2 1.4 -0.3 -0.5 71.4 77.8 67.4 42.7 7.6 10.0 3.2 2.2 54 Costa Rica 3.1 4.5 5.2 2.1 1.3 0.4 0.1 50.7 64.3 60.6 37.1 8.4 9.5 2.9 2.0 55 Libyan Arab Jamahiriya 4.4 6.2 7.7 2.0 1.9 0.0 0.1 75.7 77.9 79.7 45.9 4.7 6.6 4.1 2.7 56 Oman 1.8 2.7 3.5 3.1 1.9 0.2 0.1 66.1 71.7 81.8 46.8 3.6 4.7 6.3 3.1 57 Seychelles 0.1 0.1 0.1 .. .. .. .. 49.3 55.3 .. .. .. .. .. .. 58 Venezuela (Bolivarian Republic of) 19.7 27.7 33.4 2.2 1.6 0.0 0.0 84.3 94.0 65.3 45.4 6.4 8.7 3.3 2.5 59 Saudi Arabia 16.3 24.7 31.6 2.9 2.0 -0.6 0.1 76.6 82.1 75.1 49.1 4.1 4.6 5.4 3.2

HDI rank

HUMAN DEVELOPMENT REPORT 2009

192

Total population (millions)

Rate of natural increase

(%) Urban populationa

(% of total)

Net international migration rate

(%) Child dependency

ratio Total fertility rate

(births per woman)

Old age dependency

ratio

1990 20102010

2005 to

20102020b 20102010

2005 to

2010

2005 to

2007 19901995

1990 to

1995

1990 to

1995

1990 to

19901990

60 Panama 2.4 3.3 4.0 2.0 1.6 0.1 0.1 53.9 74.8 58.8 45.0 8.4 10.4 2.9 2.6 61 Bulgaria 8.8 7.6 7.0 -0.3 -0.5 -0.8 -0.1 66.4 71.7 30.5 19.6 19.7 25.5 1.5 1.4 62 Saint Kitts and Nevis 0.0 0.1 0.1 .. .. .. .. 34.6 32.4 .. .. .. .. .. .. 63 Romania 23.2 21.5 20.4 0.0 -0.2 -0.5 -0.2 53.2 54.6 35.7 21.8 15.8 21.3 1.5 1.3 64 Trinidad and Tobago 1.2 1.3 1.4 1.1 0.7 -0.4 -0.3 8.5 13.9 56.8 28.3 9.2 9.5 2.1 1.6 65 Montenegro 0.6 0.6 0.6 0.7 0.2 0.5 -0.2 48.0 59.5 40.2 28.3 12.7 18.8 1.8 1.6 66 Malaysia 18.1 26.6 32.0 2.3 1.6 0.3 0.1 49.8 72.2 63.5 44.0 6.2 7.3 3.5 2.6 67 Serbia 9.6 9.8 9.8 0.4 0.0 0.9 0.0 50.4 52.4 34.6 25.9 14.3 21.1 2.0 1.6 68 Belarus 10.3 9.7 9.1 0.0 -0.5 0.0 0.0 66.0 74.3 34.8 20.4 16.1 18.6 1.7 1.3 69 Saint Lucia 0.1 0.2 0.2 1.8 1.1 -0.6 -0.1 29.3 28.0 65.4 38.3 13.4 10.1 3.2 2.0 70 Albania 3.3 3.1 3.3 1.7 0.9 -2.6 -0.5 36.4 48.0 53.0 34.0 8.6 14.4 2.8 1.9 71 Russian Federation 148.1 141.9 135.4 -0.2 -0.4 0.3 0.0 73.4 72.8 34.3 20.8 15.1 17.9 1.5 1.4 72 Macedonia (the Former Yugoslav Rep. of) 1.9 2.0 2.0 0.8 0.2 -0.3 -0.1 57.8 67.9 39.4 25.0 11.2 16.9 2.1 1.4 73 Dominica 0.1 0.1 0.1 .. .. .. .. 67.7 74.6 .. .. .. .. .. .. 74 Grenada 0.1 0.1 0.1 1.7 1.3 -0.9 -1.0 32.2 31.0 73.2 41.9 14.8 10.6 3.5 2.3 75 Brazil 149.6 190.1 209.1 1.6 1.0 0.0 0.0 74.8 86.5 58.5 37.7 7.4 10.2 2.6 1.9 76 Bosnia and Herzegovina 4.3 3.8 3.7 0.3 -0.1 -5.4 -0.1 39.2 48.6 34.7 21.4 8.8 19.6 1.5 1.2 77 Colombia 33.2 44.4 52.3 2.0 1.5 -0.1 -0.1 68.3 75.1 61.8 43.8 7.2 8.6 3.0 2.5 78 Peru 21.8 28.5 32.9 2.2 1.6 -0.3 -0.4 68.9 71.6 66.3 46.7 6.9 9.3 3.6 2.6 79 Turkey 56.1 73.0 83.9 1.8 1.2 0.0 0.0 59.2 69.6 60.5 39.0 6.8 8.8 2.9 2.1 80 Ecuador 10.3 13.3 15.4 2.2 1.6 -0.1 -0.5 55.1 66.9 68.5 48.8 7.4 10.6 3.4 2.6 81 Mauritius 1.1 1.3 1.4 1.5 0.7 -0.1 0.0 43.9 42.6 43.7 31.5 7.1 10.7 2.3 1.8 82 Kazakhstan 16.5 15.4 16.7 1.1 0.9 -1.9 -0.1 56.3 58.5 50.2 34.5 9.3 10.0 2.6 2.3 83 Lebanon 3.0 4.2 4.6 1.8 0.9 1.4 -0.1 83.1 87.2 60.5 36.4 8.8 10.8 3.0 1.9

MEDIUM HUMAN DEVELOPMENT 84 Armenia 3.5 3.1 3.2 1.1 0.7 -3.0 -0.5 67.5 63.7 47.4 29.4 8.8 16.1 2.4 1.7 85 Ukraine 51.6 46.3 42.9 -0.2 -0.6 0.0 0.0 66.8 68.1 32.3 19.7 18.3 22.1 1.6 1.3 86 Azerbaijan 7.2 8.6 9.8 1.8 1.2 -0.3 -0.1 53.7 52.2 55.7 34.4 6.9 9.5 2.9 2.2 87 Thailand 56.7 67.0 71.4 1.2 0.6 0.0 0.1 29.4 34.0 45.9 30.3 7.1 10.9 2.1 1.8 88 Iran (Islamic Republic of) 56.7 72.4 83.7 2.2 1.3 -0.4 -0.1 56.3 69.5 86.7 33.4 6.2 6.8 4.0 1.8 89 Georgia 5.5 4.4 4.0 0.6 0.0 -2.1 -1.2 55.1 52.9 37.2 24.2 14.1 20.7 2.1 1.6 90 Dominican Republic 7.4 9.8 11.5 2.3 1.7 -0.3 -0.3 55.2 70.5 66.6 49.5 6.6 9.8 3.3 2.7 91 Saint Vincent and the Grenadines 0.1 0.1 0.1 1.7 1.0 -1.5 -0.9 40.6 47.8 67.9 39.7 11.0 10.0 2.9 2.1 92 China 1,142.1 c 1,329.1 c 1,431.2 c 1.2 0.7 0.0 0.0 27.4 44.9 42.9 27.7 8.3 11.4 2.0 1.8 93 Belize 0.2 0.3 0.4 3.1 2.1 -0.1 -0.1 47.5 52.7 82.6 56.3 7.4 6.7 4.3 2.9 94 Samoa 0.2 0.2 0.2 2.4 1.8 -1.6 -1.8 21.2 23.4 74.0 68.6 7.1 8.6 4.7 4.0 95 Maldives 0.2 0.3 0.4 2.8 1.4 0.0 0.0 25.8 40.5 94.0 39.6 5.2 6.4 5.3 2.1 96 Jordan 3.3 5.9 7.5 2.9 2.2 2.7 0.8 72.2 78.5 93.6 54.4 6.3 5.9 5.1 3.1 97 Suriname 0.4 0.5 0.6 1.5 1.2 -0.2 -0.2 68.3 75.6 53.7 44.0 7.6 9.9 2.6 2.4 98 Tunisia 8.2 10.1 11.4 1.8 1.0 -0.1 0.0 57.9 67.3 66.5 32.4 8.0 9.6 3.1 1.9 99 Tonga 0.1 0.1 0.1 2.4 2.2 -1.8 -1.7 22.7 25.3 70.1 66.0 8.0 10.3 4.5 4.0

100 Jamaica 2.4 2.7 2.8 1.8 1.2 -0.9 -0.7 49.4 53.7 61.2 45.7 12.5 12.2 2.8 2.4 101 Paraguay 4.2 6.1 7.5 2.6 1.9 -0.1 -0.1 48.7 61.5 75.9 54.7 7.4 8.4 4.3 3.1 102 Sri Lanka 17.3 19.9 21.7 1.4 1.2 -0.3 -0.3 17.2 15.1 51.1 35.7 8.9 11.4 2.5 2.3 103 Gabon 0.9 1.4 1.8 2.7 1.8 0.4 0.1 69.1 86.0 77.9 59.2 10.6 7.2 5.1 3.4 104 Algeria 25.3 33.9 40.6 2.3 1.6 0.0 -0.1 52.1 66.5 80.6 39.5 6.8 6.8 4.1 2.4 105 Philippines 62.4 88.7 109.7 2.5 2.0 -0.3 -0.2 48.8 66.4 72.6 53.8 5.8 6.9 4.1 3.1 106 El Salvador 5.3 6.1 6.6 2.3 1.4 -0.9 -0.9 49.2 61.3 75.0 51.5 8.6 12.0 3.7 2.3 107 Syrian Arab Republic 12.7 20.5 26.5 2.9 2.5 -0.1 0.8 48.9 54.9 98.9 56.1 5.4 5.2 4.9 3.3 108 Fiji 0.7 0.8 0.9 2.1 1.5 -0.9 -0.8 41.6 53.4 64.1 48.2 5.3 7.7 3.4 2.8 109 Turkmenistan 3.7 5.0 5.8 2.4 1.4 0.3 -0.1 45.1 49.5 72.6 43.4 6.8 6.2 4.0 2.5 110 Occupied Palestinian Territories 2.2 4.0 5.8 3.9 3.2 0.0 0.0 67.9 72.1 93.6 84.6 6.8 5.5 6.5 5.1 111 Indonesia 177.4 224.7 254.2 1.6 1.2 -0.1 -0.1 30.6 53.7 59.3 39.7 6.3 9.0 2.9 2.2 112 Honduras 4.9 7.2 9.1 3.1 2.3 -0.5 -0.3 40.3 48.8 88.9 62.5 6.6 7.3 4.9 3.3 113 Bolivia 6.7 9.5 11.6 2.6 2.0 -0.3 -0.2 55.6 66.5 74.0 60.2 6.8 8.0 4.8 3.5 114 Guyana 0.7 0.8 0.7 1.6 1.0 -1.3 -1.0 29.5 28.5 62.1 45.0 7.8 9.5 2.6 2.3 115 Mongolia 2.2 2.6 3.0 2.0 1.2 -1.5 -0.1 57.0 57.5 76.8 36.4 7.4 5.8 3.5 2.0 116 Viet Nam 66.2 86.1 98.0 2.2 1.2 -0.2 0.0 20.3 28.8 70.6 36.6 8.4 9.3 3.3 2.1 117 Moldova 4.4 3.7 3.4 0.4 -0.1 -0.6 -0.9 46.8 41.2 43.8 23.0 13.0 15.4 2.1 1.5 118 Equatorial Guinea 0.4 0.6 0.9 2.8 2.3 0.7 0.3 34.7 39.7 68.4 72.2 7.6 5.1 5.9 5.4

HDI rank

Demographic trends

193

TABLE L

Total population (millions)

Rate of natural increase

(%) Urban populationa

(% of total)

Net international migration rate

(%) Child dependency

ratio Total fertility rate

(births per woman)

Old age dependency

ratio

1990 20102010

2005 to

20102020b 20102010

2005 to

2010

2005 to

2007 19901995

1990 to

1995

1990 to

1995

1990 to

19901990

119 Uzbekistan 20.5 26.9 31.2 2.5 1.4 -0.3 -0.3 40.1 36.9 74.3 42.7 7.3 6.6 3.9 2.3 120 Kyrgyzstan 4.4 5.3 6.2 2.1 1.5 -1.2 -0.3 37.8 36.6 65.4 44.1 8.7 7.7 3.6 2.6 121 Cape Verde 0.4 0.5 0.6 2.9 1.9 -0.5 -0.5 44.1 61.1 97.8 58.7 9.0 6.8 4.9 2.8 122 Guatemala 8.9 13.4 18.1 3.1 2.8 -0.8 -0.3 41.1 49.5 88.5 76.8 6.6 8.2 5.5 4.2 123 Egypt 57.8 80.1 98.6 2.2 1.9 -0.2 -0.1 43.5 42.8 78.4 50.8 6.9 7.3 3.9 2.9 124 Nicaragua 4.1 5.6 6.7 2.9 2.0 -0.5 -0.7 52.3 57.3 90.4 56.6 6.2 7.5 4.5 2.8 125 Botswana 1.4 1.9 2.2 2.5 1.3 0.2 0.2 41.9 61.1 85.9 52.1 5.0 6.1 4.3 2.9 126 Vanuatu 0.1 0.2 0.3 2.9 2.5 -0.1 0.0 18.7 25.6 83.7 65.4 6.8 5.7 4.8 4.0 127 Tajikistan 5.3 6.7 8.4 2.8 2.2 -1.1 -0.6 31.7 26.5 81.4 60.6 7.2 6.0 4.9 3.5 128 Namibia 1.4 2.1 2.6 2.9 1.9 -0.2 0.0 27.7 38.0 82.6 60.7 6.3 6.1 4.9 3.4 129 South Africa 36.7 49.2 52.7 1.9 0.7 0.5 0.3 52.0 61.7 67.2 46.6 5.5 7.1 3.3 2.6 130 Morocco 24.8 31.2 36.2 2.0 1.5 -0.3 -0.3 48.4 56.7 70.6 42.1 6.8 8.1 3.7 2.4 131 Sao Tome and Principe 0.1 0.2 0.2 2.8 2.5 -0.8 -0.9 43.6 62.2 95.2 72.2 8.9 6.9 5.2 3.9 132 Bhutan 0.5 0.7 0.8 2.3 1.4 -3.8 0.3 16.4 36.8 79.2 45.8 6.1 7.5 5.4 2.7 133 Lao People’s Democratic Republic 4.2 6.1 7.7 2.8 2.1 -0.1 -0.2 15.4 33.2 82.7 61.9 6.7 6.1 5.8 3.5 134 India 862.2 1,164.7 1,367.2 2.0 1.4 0.0 0.0 25.5 30.1 64.9 47.9 6.6 7.7 3.9 2.8 135 Solomon Islands 0.3 0.5 0.7 2.9 2.5 0.0 0.0 13.7 18.6 87.6 66.4 5.8 5.4 5.5 3.9 136 Congo 2.4 3.6 4.7 2.7 2.2 -0.1 -0.3 54.3 62.1 84.1 71.8 7.2 6.8 5.2 4.4 137 Cambodia 9.7 14.3 17.7 2.9 1.6 0.3 0.0 12.6 22.8 84.8 51.0 5.2 5.6 5.5 3.0 138 Myanmar 40.8 49.1 55.5 1.5 1.1 -0.1 -0.2 24.9 33.9 62.6 39.1 8.4 8.1 3.1 2.3 139 Comoros 0.4 0.6 0.8 2.5 2.6 -0.1 -0.3 27.9 28.2 91.1 64.7 5.9 5.2 5.1 4.0 140 Yemen 12.3 22.3 31.6 3.7 3.0 0.9 -0.1 20.9 31.8 111.8 79.8 4.2 4.4 7.7 5.3 141 Pakistan 115.8 173.2 226.2 2.8 2.3 -0.4 -0.2 30.6 37.0 82.1 61.7 7.0 6.9 5.7 4.0 142 Swaziland 0.9 1.2 1.4 3.1 1.4 -0.8 -0.1 22.9 25.5 97.8 67.1 5.5 5.9 5.3 3.6 143 Angola 10.7 17.6 24.5 3.0 2.6 0.2 0.1 37.1 58.5 95.3 84.5 5.2 4.7 7.1 5.8 144 Nepal 19.1 28.3 35.3 2.6 1.9 -0.1 -0.1 8.9 18.2 78.1 59.8 5.9 6.8 4.9 2.9 145 Madagascar 11.3 18.6 25.7 3.0 2.7 0.0 0.0 23.6 30.2 85.7 78.0 6.1 5.6 6.1 4.8 146 Bangladesh 115.6 157.8 185.6 2.1 1.5 -0.1 -0.1 19.8 28.1 79.8 47.4 5.6 6.1 4.0 2.4 147 Kenya 23.4 37.8 52.0 3.0 2.7 0.2 -0.1 18.2 22.2 101.2 78.5 5.6 4.8 5.6 5.0 148 Papua New Guinea 4.1 6.4 8.5 2.6 2.4 0.0 0.0 15.0 12.5 74.4 68.0 3.9 4.3 4.7 4.1 149 Haiti 7.1 9.7 11.7 2.4 1.9 -0.4 -0.3 28.5 49.6 81.3 60.2 7.2 7.3 5.2 3.5 150 Sudan 27.1 40.4 52.3 2.7 2.1 -0.1 0.1 26.6 45.2 83.1 67.0 5.7 6.4 5.8 4.2 151 Tanzania ( United Republic of) 25.5 41.3 59.6 2.8 3.0 0.4 -0.1 18.9 26.4 89.5 85.8 5.2 6.0 6.1 5.6 152 Ghana 15.0 22.9 29.6 2.8 2.1 0.0 0.0 36.4 51.5 83.4 65.5 5.7 6.3 5.3 4.3 153 Cameroon 12.2 18.7 24.3 2.8 2.3 0.0 0.0 40.7 58.4 88.7 73.2 7.0 6.4 5.7 4.7 154 Mauritania 2.0 3.1 4.1 2.8 2.3 -0.1 0.1 39.7 41.4 84.5 67.5 5.2 4.6 5.7 4.5 155 Djibouti 0.6 0.8 1.0 2.7 1.8 -0.5 0.0 75.7 88.1 82.1 58.2 4.5 5.4 5.9 3.9 156 Lesotho 1.6 2.0 2.2 2.5 1.2 -1.0 -0.4 14.0 26.9 88.6 67.9 8.5 8.4 4.7 3.4 157 Uganda 17.7 30.6 46.3 3.2 3.3 0.1 -0.1 11.1 13.3 97.7 99.9 5.5 5.2 7.1 6.4 158 Nigeria 97.3 147.7 193.3 2.5 2.4 0.0 0.0 35.3 49.8 89.2 77.7 5.7 5.8 6.4 5.3

LOW HUMAN DEVELOPMENT 159 Togo 3.9 6.3 8.4 3.0 2.5 -0.6 0.0 30.1 43.4 90.2 69.5 6.1 6.3 6.0 4.3 160 Malawi 9.5 14.4 20.5 3.3 2.8 -1.9 0.0 11.6 19.8 92.4 90.1 5.3 6.1 6.8 5.6 161 Benin 4.8 8.4 12.2 3.1 3.0 0.4 0.1 34.5 42.0 89.4 79.7 7.0 6.1 6.6 5.5 162 Timor-Leste 0.7 1.1 1.6 2.7 3.1 0.0 0.2 20.8 28.1 68.7 85.4 3.5 5.8 5.7 6.5 163 Côte d’Ivoire 12.6 20.1 27.0 2.9 2.4 0.5 -0.1 39.7 50.1 85.1 72.6 5.2 7.0 5.9 4.6 164 Zambia 7.9 12.3 16.9 2.8 2.6 0.0 -0.1 39.4 35.7 88.6 91.0 5.4 6.0 6.3 5.9 165 Eritrea 3.2 4.8 6.7 2.6 2.9 -2.3 0.2 15.8 21.6 90.7 74.1 5.1 4.5 6.1 4.7 166 Senegal 7.5 11.9 16.2 3.0 2.8 -0.2 -0.2 39.0 42.9 92.3 79.8 4.9 4.4 6.5 5.0 167 Rwanda 7.2 9.5 13.2 -0.1 2.6 -5.3 0.0 5.4 18.9 102.1 76.8 5.4 4.5 6.2 5.4 168 Gambia 0.9 1.6 2.2 2.9 2.6 0.9 0.2 38.3 58.1 79.0 76.4 5.0 5.2 6.0 5.1 169 Liberia 2.2 3.6 5.3 2.9 2.8 -5.1 1.3 45.3 61.5 87.0 78.2 5.7 5.7 6.4 5.1 170 Guinea 6.1 9.6 13.5 2.9 2.9 1.0 -0.6 28.0 35.4 85.4 78.8 6.2 6.1 6.6 5.5 171 Ethiopia 48.3 78.6 108.0 3.0 2.7 0.3 -0.1 12.6 17.6 86.5 80.5 5.5 6.0 7.0 5.4 172 Mozambique 13.5 21.9 28.5 2.4 2.3 0.9 0.0 21.1 38.4 92.7 83.0 6.4 6.2 6.1 5.1 173 Guinea-Bissau 1.0 1.5 2.1 2.3 2.4 0.4 -0.2 28.1 30.0 74.7 79.0 6.5 6.4 5.9 5.7 174 Burundi 5.7 7.8 10.3 2.5 2.1 -0.8 0.8 6.3 11.0 87.9 63.9 6.0 4.7 6.5 4.7 175 Chad 6.1 10.6 14.9 3.1 2.9 0.0 -0.1 20.8 27.6 90.7 88.4 6.7 5.5 6.6 6.2 176 Congo ( Democratic Republic of the) 37.0 62.5 87.6 3.3 2.8 0.6 0.0 27.8 35.2 94.1 91.0 5.5 5.2 7.1 6.1 177 Burkina Faso 8.8 14.7 21.9 3.0 3.5 -0.3 -0.1 13.8 20.4 94.6 90.0 5.1 3.9 6.7 5.9

HDI rank

HUMAN DEVELOPMENT REPORT 2009

194

Total population (millions)

Rate of natural increase

(%) Urban populationa

(% of total)

Net international migration rate

(%) Child dependency

ratio Total fertility rate

(births per woman)

Old age dependency

ratio

1990 20102010

2005 to

20102020b 20102010

2005 to

2010

2005 to

2007 19901995

1990 to

1995

1990 to

1995

1990 to

19901990

178 Mali 8.7 12.4 16.8 2.5 2.7 -0.6 -0.3 23.3 33.3 86.2 82.2 5.4 4.3 6.3 5.5 179 Central African Republic 2.9 4.3 5.3 2.4 1.9 0.2 0.0 36.8 38.9 81.4 72.3 7.5 6.9 5.7 4.8 180 Sierra Leone 4.1 5.4 7.3 1.8 2.4 -2.2 0.2 32.9 38.4 77.2 79.5 5.1 3.4 5.5 5.2 181 Afghanistan 12.6 26.3 39.6 2.9 2.7 4.3 0.7 18.3 24.8 89.5 88.5 4.5 4.3 8.0 6.6 182 Niger 7.9 14.1 22.9 3.3 3.9 0.0 0.0 15.4 16.7 100.7 104.7 4.1 4.1 7.8 7.1

OTHER UN MEMBER STATES Iraq 18.1 29.5 40.2 3.1 2.6 -0.2 -0.4 69.7 66.4 89.0 72.5 6.6 5.8 5.8 4.1 Kiribati 0.1 0.1 0.1 .. .. .. .. 35.0 44.0 .. .. .. .. .. .. Korea (Democratic People’s Rep. of) 20.1 23.7 24.8 1.5 0.4 0.0 0.0 58.4 63.4 37.9 30.6 6.8 14.2 2.4 1.9 Marshall Islands 0.0 0.1 0.1 .. .. .. .. 65.1 71.8 .. .. .. .. .. .. Micronesia (Federated States of) 0.1 0.1 0.1 2.6 1.9 -0.4 -1.6 25.8 22.7 84.3 61.2 6.8 6.1 4.8 3.6 Monaco 0.0 0.0 0.0 .. .. .. .. 100.0 100.0 .. .. .. .. .. .. Nauru 0.0 0.0 0.0 .. .. .. .. 100.0 100.0 .. .. .. .. .. .. Palau 0.0 0.0 0.0 .. .. .. .. 69.6 82.7 .. .. .. .. .. .. San Marino 0.0 0.0 0.0 .. .. .. .. 90.4 94.3 .. .. .. .. .. .. Somalia 6.6 8.7 12.2 2.5 2.8 -2.7 -0.6 29.7 37.4 84.5 85.7 5.6 5.2 6.5 6.4 Tuvalu 0.0 0.0 0.0 .. .. .. .. 40.7 50.4 .. .. .. .. .. .. Zimbabwe 10.5 12.4 15.6 2.6 1.4 -0.3 -1.1 29.0 38.3 90.3 70.0 5.8 7.3 4.8 3.5

Arab States 638.6 T 964.5 T 1,276.1 T 2.6 d 2.3 d -0.1 d -0.1 d 4.6 4.6 85.5 d 71.5 d 5.9 d 6.1 d 5.6 d 4.6 d

Central and Eastern Europe 3,178.8 T 4,029.3 T 4,596.3 T 1.7 d 1.2 d 0.0 d 0.0 d 2.4 2.4 55.2 d 39.0 d 7.8 d 10.0 d 3.0 d 2.4 d

and the CIS East Asia and the Pacific 720.8 T 730.7 T 732.8 T 0.0 d -0.1 d 0.1 d 0.2 d 1.5 1.5 30.7 d 22.5 d 19.1 d 23.8 d 1.6 d 1.5 d

Latin America and the Caribbean 442.3 T 569.7 T 645.5 T 1.9 d 1.3 d -0.1 d -0.2 d 2.3 2.3 61.4 d 42.3 d 8.3 d 10.6 d 3.0 d 2.3 d

South Asia 282.7 T 341.7 T 383.4 T 0.7 d 0.6 d 0.5 d 0.4 d 2.0 2.0 32.7 d 29.6 d 18.5 d 19.5 d 2.0 d 2.0 d

Sub-Saharan Africa 26.9 T 34.5 T 40.3 T 1.2 d 1.0 d 0.3 d 0.3 d 2.4 2.4 41.4 d 37.2 d 14.3 d 16.6 d 2.5 d 2.4 d

OECD 1,048.6 T 1,189.0 T 1,269.7 T 0.6 0.4 0.2 0.2 71.8 76.8 34.6 27.7 17.5 22.1 1.9 1.8 European Union (EU27) 471.6 T 493.2 T 505.3 T 0.1 0.0 0.2 0.3 71.5 74.0 29.1 23.2 20.8 26.2 1.6 1.5 GCC 23.1 T 36.5 T 47.1 T 2.7 1.8 -0.5 0.7 78.5 82.8 70.2 43.1 3.6 3.9 5.1 2.9 Very high human development 877.3 T 986.5 T 1,051.0 T 0.4 0.3 0.3 0.3 73.7 78.4 29.8 25.5 19.0 24.3 1.7 1.7 Very high HD: OECD 855.4 T 954.9 T 1,013.4 T 0.4 0.3 0.3 0.3 73.3 78.0 29.6 25.5 19.2 24.7 1.7 1.7 Very high HD: non-OECD 22.0 T 31.6 T 37.6 T 1.2 0.8 0.9 1.2 88.5 89.7 40.1 26.4 10.5 12.4 2.2 1.8 High human development 784.2 T 918.4 T 996.0 T 1.2 0.8 -0.1 -0.1 69.4 76.5 51.4 35.0 10.6 12.7 2.5 2.0 Medium human development 3,388.5 T 4,380.5 T 5,090.6 T 1.8 1.3 -0.1 -0.1 30.3 41.1 61.0 44.3 7.3 8.8 3.3 2.6 Low human development 240.2 T 385.1 T 536.8 T 2.9 2.7 0.1 0.0 22.7 29.7 89.9 83.6 5.5 5.5 6.7 5.6 World 5,290.5 Td 6,670.8 Td 7,674.3 Td 1.5 d 1.2 d 0.0 d 0.0 d 2.6 2.6 53.8 d 41.2 d 10.0 d 11.6 d 3.1 d 2.6 d

NOTES

a Because data are based on national definitions of what

constitutes a city or metropolitan area, cross-country

comparisons should be made with caution.

b Data refer to medium variant projections.

c Population estimates include Taiwan, Province of

China.

d Data are aggregates provided by original data source.

SOURCES

Columns 1–7 and 10–15: UN (2009e).

Columns 8 and 9 : UN (2008c).

HDI rank

Demographic trends

195

HUMAN DEVELOPMENT REPORT 2009

MTABLE Economy and inequality

GDP GDP per capita

Average annual change in consumer price index

(%)

Share of income or expenditureb

(%) Inequality measures

US$ billions 2007 1990–2007

PPP US$ billions 2007 2006–2007

US$ 2007

Poorest 10%

Annual growth rate at constant

prices (%)

1990–2007 Richest

10%

Highest value in

the period 1980–2007 2007 PPP

US$a

Richest 10% to poorest 10%c

Year of highest value Gini indexd

VERY HIGH HUMAN DEVELOPMENT 1 Norway 388.4 251.6 82,480 2.6 53,433 2007 2.1 0.7 3.9 e 23.4 e 6.1 25.8 2 Australia 821.0 733.9 39,066 2.4 34,923 2007 2.5 2.3 2.0 e 25.4 e 12.5 35.2 3 Iceland 20.0 11.1 64,190 2.5 35,742 2007 3.5 5.1 .. .. .. .. 4 Canada 1,329.9 1,180.9 40,329 2.2 35,812 2007 2.0 2.1 2.6 e 24.8 e 9.4 32.6 5 Ireland 259.0 194.8 59,324 5.8 44,613 2007 3.0 4.9 2.9 e 27.2 e 9.4 34.3 6 Netherlands 765.8 633.9 46,750 2.1 38,694 2007 2.4 1.6 2.5 e 22.9 e 9.2 30.9 7 Sweden 454.3 335.8 49,662 2.3 36,712 2007 1.5 2.2 3.6 e 22.2 e 6.2 25.0 8 France 2,589.8 2,078.0 41,970 1.6 33,674 2007 1.6 1.5 2.8 e 25.1 e 9.1 32.7 9 Switzerland 424.4 307.0 56,207 0.8 40,658 2007 1.2 0.7 2.9 e 25.9 e 9.0 33.7 10 Japan 4,384.3 4,297.2 34,313 1.0 33,632 2007 0.2 0.1 4.8 e 21.7 e 4.5 24.9 11 Luxembourg 49.5 38.2 103,042 3.3 79,485 2007 2.1 2.3 3.5 e 23.8 e 6.8 30.8 12 Finland 244.7 182.6 46,261 2.8 34,526 2007 1.5 2.5 4.0 e 22.6 e 5.6 26.9 13 United States 13,751.4 13,751.4 45,592 2.0 45,592 2007 2.6 2.9 1.9 e 29.9 e 15.9 40.8 14 Austria 373.2 310.7 44,879 1.8 37,370 2007 2.0 2.2 3.3 e 23.0 e 6.9 29.1 15 Spain 1,436.9 1,416.4 32,017 2.4 31,560 2007 3.4 2.8 2.6 e 26.6 e 10.3 34.7 16 Denmark 311.6 197.3 57,051 1.9 36,130 2007 2.1 1.7 2.6 e 21.3 e 8.1 24.7 17 Belgium 452.8 371.2 42,609 1.8 34,935 2007 1.9 1.8 3.4 e 28.1 e 8.2 33.0 18 Italy 2,101.6 1,802.2 35,396 1.2 30,353 2007 2.9 1.8 2.3 e 26.8 e 11.6 36.0 19 Liechtenstein .. .. .. .. .. .. .. .. .. .. .. .. 20 New Zealand 135.7 115.6 32,086 2.1 27,336 2007 2.0 2.4 2.2 e 27.8 e 12.5 36.2 21 United Kingdom 2,772.0 2,143.0 45,442 2.4 35,130 2007 2.7 4.3 2.1 e 28.5 e 13.8 36.0 22 Germany 3,317.4 2,830.1 40,324 1.4 34,401 2007 1.7 2.1 3.2 e 22.1 e 6.9 28.3 23 Singapore 161.3 228.1 35,163 3.8 49,704 2007 1.2 2.1 1.9 e 32.8 e 17.7 42.5 24 Hong Kong, China (SAR) 207.2 293.0 29,912 2.4 42,306 2007 2.0 2.0 2.0 e 34.9 e 17.8 43.4 25 Greece 313.4 319.2 27,995 2.7 28,517 2007 5.9 2.9 2.5 e 26.0 e 10.2 34.3 26 Korea (Republic of) 969.8 1,201.8 20,014 4.5 24,801 2007 4.0 2.5 2.9 e 22.5 e 7.8 31.6 27 Israel 164.0 188.9 22,835 1.7 26,315 2007 5.7 0.5 2.1 e 28.8 e 13.4 39.2 28 Andorra .. .. .. .. .. .. .. .. .. .. .. .. 29 Slovenia 47.2 54.0 23,379 3.5 26,753 f 2007 8.2 3.6 3.4 g 24.6 g 7.3 31.2 30 Brunei Darussalam 11.5 h 19.5 30,032 h -0.3 83,688 1980 1.2 f 0.1 h .. .. .. .. 31 Kuwait 112.1 121.1 h 42,102 1.8 47,812 f 2006 2.0 5.5 .. .. .. .. 32 Cyprus 21.3 21.2 24,895 2.5 24,789 2007 3.2 2.4 .. .. .. .. 33 Qatar 52.7 56.3 64,193 h .. .. .. 3.4 13.8 .. .. .. .. 34 Portugal 222.8 241.5 20,998 1.9 22,765 2007 3.6 2.8 2.0 e 29.8 e 15.0 38.5 35 United Arab Emirates 163.3 226.1 38,436 h -0.1 101,057 f 1980 .. .. .. .. .. .. 36 Czech Republic 175.0 249.5 16,934 2.4 24,144 f 2007 4.6 2.9 4.3 e 22.7 e 5.3 25.8 37 Barbados 3.0 h 5.0 h 10,427 h .. .. .. 2.5 4.0 .. .. .. .. 38 Malta 7.4 9.4 18,203 2.6 23,080 2007 2.7 1.3 .. .. .. ..

HIGH HUMAN DEVELOPMENT 39 Bahrain 15.8 h 20.3 h 21,421 h 2.4 29,723 f 2005 0.5 -5.5 .. .. .. .. 40 Estonia 20.9 27.3 15,578 5.3 20,361 2007 10.3 6.6 2.7 g 27.7 g 10.4 36.0 41 Poland 422.1 609.4 11,072 4.4 15,987 f 2007 13.6 2.4 3.0 g 27.2 g 9.0 34.9 42 Slovakia 75.0 108.4 13,891 3.4 20,076 f 2007 7.3 2.8 3.1 e 20.8 e 6.8 25.8 43 Hungary 138.4 188.6 13,766 3.3 18,755 2007 13.4 7.9 3.5 g 24.1 g 6.8 30.0 44 Chile 163.9 230.3 9,878 3.7 13,880 2007 5.7 4.4 1.6 e 41.7 e 26.2 52.0 45 Croatia 51.3 71.1 11,559 3.0 16,027 f 2007 32.4 2.9 3.6 g 23.1 g 6.4 29.0 46 Lithuania 38.3 59.3 11,356 3.0 17,575 f 2007 11.8 5.7 2.7 g 27.4 g 10.3 35.8 47 Antigua and Barbuda 1.0h 1.6 h 11,664 h 1.8 19,085 2006 .. .. .. .. .. .. 48 Latvia 27.2 37.3 11,930 4.7 16,377 2007 13.3 10.1 2.7 g 27.4 g 10.3 35.7 49 Argentina 262.5 522.9 6,644 1.5 13,238 2007 7.3 8.8 1.2 e 37.3 e 31.6 50.0 50 Uruguay 23.1 37.3 6,960 1.5 11,216 2007 19.7 8.1 1.7 e 34.8 e 20.1 46.2 51 Cuba .. .. .. .. .. .. .. .. .. .. .. .. 52 Bahamas 6.6 .. 19,844 .. .. .. 1.9 2.5 .. .. .. .. 53 Mexico 1,022.8 1,484.9 9,715 1.6 14,104 2007 13.2 4.0 1.8 g 37.9 g 21.0 48.1 54 Costa Rica 26.3 48.4 5,887 2.6 10,842 2007 13.1 9.4 1.5 e 35.5 e 23.4 47.2 55 Libyan Arab Jamahiriya 58.3 88.4 9,475 .. .. .. 1.2 f 3.4 h .. .. .. .. 56 Oman 35.7 56.6 14,031 h 2.3 22,816 f 2006 .. 6.0 .. .. .. .. 57 Seychelles 0.7 1.4 8,560 1.4 16,771 2000 2.5 5.3 .. .. .. .. 58 Venezuela (Bolivarian Republic of) 228.1 334.1 8,299 -0.2 12,233 1980 34.3 18.7 1.7 e 32.7 e 18.8 43.4 59 Saudi Arabia 381.7 554.1 15,800 0.3 36,637 1980 0.5 4.2 .. .. .. ..

HDI rank

HUMAN DEVELOPMENT REPORT 2009

196

GDP GDP per capita

Average annual change in consumer price index

(%)

Share of income or expenditureb

(%) Inequality measures

US$ billions 2007 1990–2007

PPP US$ billions 2007 2006–2007

US$ 2007

Poorest 10%

Annual growth rate at constant

prices (%)

1990–2007 Richest

10%

Highest value in

the period 1980–2007 2007 PPP

US$a

Richest 10% to poorest 10%c

Year of highest value Gini indexd

60 Panama 19.5 38.1 5,833 2.6 11,391 2007 1.1 4.2 0.8 e 41.4 e 49.9 54.9 61 Bulgaria 39.5 86.0 5,163 2.3 11,222 2007 55.7 8.4 3.5 g 23.8 g 6.9 29.2 62 Saint Kitts and Nevis 0.5 0.7 10,795 2.8 14,481 2007 3.2 4.4 .. .. .. .. 63 Romania 166.0 266.5 7,703 2.3 12,369 2007 56.4 4.8 3.3 g 25.3 g 7.6 31.5 64 Trinidad and Tobago 20.9 31.3 15,668 5.0 23,507 2007 5.2 7.9 2.1 e 29.9 e 14.4 40.3 65 Montenegro 3.5 7.0 5,804 3.8 11,699 f 2007 .. .. .. .. .. .. 66 Malaysia 186.7 358.9 7,033 3.4 13,518 2007 2.8 2.0 2.6 e 28.5 e 11.0 37.9 67 Serbia 40.1 75.6 5,435 0.0 13,137f 1990 36.4 6.4 .. .. .. .. 68 Belarus 44.8 105.2 4,615 3.4 10,841f 2007 114.2 8.4 3.6 g 22.0 g 6.1 27.9 69 Saint Lucia 1.0 1.6 5,834 1.3 9,786 2007 2.6 2.5 2.0 e 32.5 e 16.2 42.6 70 Albania 10.8 22.4 3,405 5.2 7,041 2007 13.0 2.9 3.2 g 25.9 g 8.0 33.0 71 Russian Federation 1,290.1 2,087.4 9,079 1.2 14,690 f 2007 44.4 9.0 2.6 g 28.4 g 11.0 37.5 72 Macedonia (the Former Yugoslav Rep. of) 7.7 18.5 3,767 0.4 9,096 f 2007 4.8 3.5 2.4 g 29.5 g 12.4 39.0 73 Dominica 0.3 h 0.6 h .. 1.4 7,893 f 2006 1.6 3.1 .. .. .. .. 74 Grenada 0.6 0.8 5,724 2.4 7,557 2005 2.1 4.2 .. .. .. .. 75 Brazil 1,313.4 1,833.0 6,855 1.2 9,567 2007 67.6 3.6 1.1 e 43.0 e 40.6 55.0 76 Bosnia and Herzegovina 15.1 29.3 4,014 11.2 7,764 f 2007 .. .. 2.8 27.4 g 9.9 35.8 77 Colombia 207.8 377.7 4,724 1.2 8,587 2007 13.6 5.4 0.8 45.9 e 60.4 58.5 78 Peru 107.3 218.6 3,846 2.7 7,836 2007 12.5 1.8 1.5 37.9 e 26.1 49.6 79 Turkey 655.9 957.2 8,877 2.2 12,955 2007 56.5 8.8 1.9 33.2 g 17.4 43.2 80 Ecuador 44.5 99.4 3,335 1.2 7,449 2007 30.1 2.3 1.2 43.3 e 35.2 54.4 81 Mauritius 6.8 14.2 5,383 3.7 11,296 2007 6.2 8.8 .. .. .. .. 82 Kazakhstan 104.9 168.2 6,772 3.2 10,863 f 2007 24.3 10.8 3.1 25.9 g 8.5 33.9 83 Lebanon 24.4 41.4 5,944 2.4 10,137 f 2004 .. .. .. .. .. ..

MEDIUM HUMAN DEVELOPMENT 84 Armenia 9.2 17.1 3,059 5.8 5,693 f 2007 21.1 4.4 3.7 28.9 g 7.9 33.8 85 Ukraine 141.2 321.5 3,035 -0.7 9,137 f 1989 50.6 12.8 3.8 22.5 g 6.0 28.2 86 Azerbaijan 31.2 67.2 3,652 2.9 7,851 f 2007 52.1 16.7 6.1 17.5 g 2.9 36.5 87 Thailand 245.4 519.2 3,844 2.9 8,135 2007 3.6 2.2 2.6 33.7 g 13.1 42.5 88 Iran (Islamic Republic of) 286.1 778.0 4,028 2.5 10,955 2007 20.1 17.2 2.6 29.6 g 11.6 38.3 89 Georgia 10.2 20.5 2,313 1.8 7,604 1985 11.4 9.2 1.9 30.6 g 15.9 40.8 90 Dominican Republic 36.7 65.2 3,772 3.8 6,706 2007 11.0 6.1 1.5 38.7 e 25.3 50.0 91 Saint Vincent and the Grenadines 0.6 0.9 4,596 3.0 7,691 2007 1.9 7.0 .. .. .. .. 92 China 3,205.5 7,096.7 2,432 8.9 5,383 2007 4.4 4.8 2.4 31.4 g 13.2 41.5 93 Belize 1.3 2.0 4,200 2.3 6,796 2006 1.9 2.3 .. .. .. .. 94 Samoa 0.5 0.8 2,894 2.9 4,467 f 2007 4.1 5.6 .. .. .. .. 95 Maldives 1.1 1.6 3,456 5.1 5,196 f 2007 .. 7.4 .. .. .. .. 96 Jordan 15.8 28.0 2,769 2.0 4,901 2007 2.9 5.4 3.0 30.7 g 10.2 37.7 97 Suriname 2.2 3.6 4,896 1.8 7,813 2007 50.4 6.7 1.0 40.0 e 40.4 52.9 98 Tunisia 35.0 76.9 3,425 3.4 7,520 2007 3.5 3.1 2.4 31.6 g 13.3 40.8 99 Tonga 0.3 0.4 2,474 1.7 3,772 f 2006 5.7 5.9 .. .. .. ..

100 Jamaica 11.4 16.3 4,272 0.6 6,587 2006 15.4 9.3 2.1 35.6 g 17.0 45.5 101 Paraguay 12.2 27.1 1,997 -0.3 4,631 1981 10.7 8.1 1.1 42.3 e 38.8 53.2 102 Sri Lanka 32.3 84.9 1,616 3.9 4,243 2007 9.6 15.8 2.9 33.3 g 11.7 41.1 103 Gabon 11.6 20.2 8,696 -0.7 18,600 1984 2.7 5.0 2.5 32.7 g 13.3 41.5 104 Algeria 135.3 262.0 3,996 1.4 7,740 2007 9.2 3.5 2.8 26.9 g 9.6 35.3 105 Philippines 144.1 299.4 1,639 1.7 3,406 2007 6.4 2.8 2.4 33.9 g 14.1 44.0 106 El Salvador 20.4 39.8 2,973 1.8 5,804 2007 5.5 4.6 1.0 37.0 e 38.6 49.7 107 Syrian Arab Republic 37.7 89.7 1,898 1.5 4,511 2007 4.1 3.9 .. .. .. .. 108 Fiji 3.4 3.6 4,113 1.6 4,632 2006 3.0 4.8 .. .. .. .. 109 Turkmenistan 12.9 22.6 2,606 .. .. .. .. .. 2.5 31.8 g 12.9 40.8 110 Occupied Palestinian Territories 4.0 .. 1,160 h .. .. .. 4.1 f 3.5 .. .. .. .. 111 Indonesia 432.8 837.6 1,918 2.3 3,712 2007 12.8 6.4 3.0 32.3 g 10.8 39.4 112 Honduras 12.2 27.0 1,722 1.5 3,796 2007 16.2 6.9 0.7 42.2 e 59.4 55.3 113 Bolivia 13.1 40.0 1,379 1.3 4,206 2007 5.9 8.7 0.5 44.1 g 93.9 58.2 114 Guyana 1.1 2.1 1,462 2.9 2,782 2007 5.8 12.3 1.3 34.0 e 25.5 44.6 115 Mongolia 3.9 8.4 1,507 2.2 3,236 f 2007 17.2 9.0 2.9 24.9 g 8.6 33.0 116 Viet Nam 68.6 221.4 806 6.0 2,600 f 2007 4.1 8.9 3.1 29.8 g 9.7 37.8 117 Moldova 4.4 9.7 1,156 -1.3 4,208 1989 15.6 12.4 3.0 28.2 g 9.4 35.6 118 Equatorial Guinea 9.9 15.5 19,552 21.1 30,627 f 2007 7.6 .. .. .. .. ..

HDI rank

Economy and inequality

197

TABLE M

GDP GDP per capita

Average annual change in consumer price index

(%)

Share of income or expenditureb

(%) Inequality measures

US$ billions 2007 1990–2007

PPP US$ billions 2007 2006–2007

US$ 2007

Poorest 10%

Annual growth rate at constant

prices (%)

1990–2007 Richest

10%

Highest value in

the period 1980–2007 2007 PPP

US$a

Richest 10% to poorest 10%c

Year of highest value Gini indexd

119 Uzbekistan 22.3 65.1 830 1.2 2,425 f 2007 .. .. 2.9 29.5 g 10.3 36.7 120 Kyrgyzstan 3.7 10.5 715 -0.4 2,652 f 1990 11.3 10.2 3.6 25.9 g 7.3 32.9 121 Cape Verde 1.4 1.6 2,705 3.3 3,041 f 2007 3.5 4.4 1.9 40.6 g 21.6 50.5 122 Guatemala 33.9 60.9 2,536 1.4 4,562 2007 8.3 6.5 1.3 42.4 e 33.9 53.7 123 Egypt 130.5 403.7 1,729 2.5 5,349 2007 6.5 9.3 3.9 27.6 g 7.2 32.1 124 Nicaragua 5.7 14.4 1,022 1.9 2,955 1981 .. 11.1 1.4 41.8 e 31.0 52.3 125 Botswana 12.3 25.6 6,544 4.3 13,604 2007 9.1 7.1 1.3 51.2 g 40.0 61.0 126 Vanuatu 0.5 0.8 2,001 -0.4 3,877 1998 2.5 4.0 .. .. .. .. 127 Tajikistan 3.7 11.8 551 -2.2 3,685 f 1988 .. 13.1 3.2 26.4 g 8.2 33.6 128 Namibia 7.0 10.7 3,372 1.8 5,155 2007 .. 6.7 0.6 65.0 e 106.6 74.3 129 South Africa 283.0 466.9 5,914 1.0 9,757 2007 7.0 7.1 1.3 44.9 g 35.1 57.8 130 Morocco 75.1 126.8 2,434 2.0 4,108 2007 2.6 2.0 2.7 33.2 g 12.5 40.9 131 Sao Tome and Principe 0.1 0.3 916 .. .. .. .. .. .. .. .. .. 132 Bhutan 1.1 3.2 1,668 5.2 4,837 2007 6.6 5.2 2.3 37.6 g 16.3 46.8 133 Lao People’s Democratic Republic 4.1 12.7 701 4.2 2,165 f 2007 25.7 4.5 3.7 27.0 g 7.3 32.6 134 India 1,176.9 3,096.9 1,046 4.5 2,753 2007 6.8 6.4 3.6 31.1 g 8.6 36.8 135 Solomon Islands 0.4 0.9 784 -1.5 2,149 1995 9.5 7.7 .. .. .. .. 136 Congo 7.6 13.2 2,030 -0.2 4,496 1984 5.9 2.7 2.1 37.1 g 17.8 47.3 137 Cambodia 8.3 26.0 578 6.2 1,802 f 2007 3.9 5.9 3.0 34.2 g 11.5 40.7 138 Myanmar .. 41.0 .. 6.8 904 f 2005 24.6 35.0 .. .. .. .. 139 Comoros 0.4 0.7 714 -0.4 1,361 1984 .. .. 0.9 55.2 g 60.6 64.3 140 Yemen 22.5 52.3 1,006 1.6 2,335 f 2007 17.6 10.0 2.9 30.8 g 10.6 37.7 141 Pakistan 142.9 405.6 879 1.6 2,496 2007 7.3 7.6 3.9 26.5 g 6.7 31.2 142 Swaziland 2.9 5.5 2,521 0.9 4,789 2007 8.5 f 5.3 1.8 40.8 g 22.4 50.7 143 Angola 61.4 91.3 3,623 2.9 5,385 f 2007 308.1 12.2 0.6 44.7 g 74.6 58.6 144 Nepal 10.3 29.5 367 1.9 1,049 2007 6.5 6.1 2.7 40.4 g 14.8 47.3 145 Madagascar 7.4 18.3 375 -0.4 1,297 1980 14.0 10.3 2.6 41.5 g 15.9 47.2 146 Bangladesh 68.4 196.7 431 3.1 1,241 2007 5.4 9.1 4.3 26.6 g 6.2 31.0 147 Kenya 24.2 57.9 645 0.0 1,542 2007 11.2 9.8 1.8 37.8 g 21.3 47.7 148 Papua New Guinea 6.3 13.2 990 -0.6 2,551 1994 9.4 0.9 1.9 40.9 g 21.5 50.9 149 Haiti 6.7 11.1 699 -2.1 2,258 1980 19.1 8.5 0.9 47.8 e 54.4 59.5 150 Sudan 46.2 80.4 1,199 3.6 2,086 2007 35.5 8.0 .. .. .. .. 151 Tanzania ( United Republic of) 16.2 48.8 400 1.8 1,208 f 2007 12.6 7.0 3.1 27.0 g 8.9 34.6 152 Ghana 15.1 31.3 646 2.1 1,334 2007 24.0 10.7 2.0 32.8 g 16.1 42.8 153 Cameroon 20.7 39.4 1,116 0.6 2,979 1986 4.3 0.9 2.4 35.5 g 15.0 44.6 154 Mauritania 2.6 6.0 847 0.6 1,940 2006 6.0 7.3 2.5 g 29.6 g 11.6 39.0 155 Djibouti 0.8 1.7 997 -2.1 2,906 f 1990 .. .. 2.4 g 30.9 g 12.8 40.0 156 Lesotho 1.6 3.1 798 2.4 1,541 2007 8.2 8.0 1.0 g 39.4 g 39.8 52.5 157 Uganda 11.8 32.7 381 3.5 1,059 f 2007 6.7 6.1 2.6 g 34.1 g 13.2 42.6 158 Nigeria 165.5 291.4 1,118 1.1 1,969 2007 21.3 5.4 2.0 g 32.4 g 16.3 42.9

LOW HUMAN DEVELOPMENT 159 Togo 2.5 5.2 380 -0.2 1,147 1980 5.1 1.0 3.3 g 27.1 g 8.3 34.4 160 Malawi 3.6 10.6 256 0.4 800 1980 26.1 8.0 3.0 g 31.9 g 10.5 39.0 161 Benin 5.4 11.8 601 1.3 1,312 2007 5.0 1.3 2.9 g 31.0 g 10.8 38.6 162 Timor-Leste 0.4 0.8 373 .. .. .. .. 10.3 2.9 g 31.3 g 10.8 39.5 163 Côte d’Ivoire 19.8 32.6 1,027 -0.7 2,827 1980 4.9 1.9 2.0 g 39.6 g 20.2 48.4 164 Zambia 11.4 16.2 953 0.1 1,660 1981 35.5 10.7 1.3 g 38.9 g 29.5 50.7 165 Eritrea 1.4 3.0 284 -0.7 900 f 1997 .. .. .. .. .. .. 166 Senegal 11.2 20.7 900 1.1 1,666 2007 3.3 5.9 2.5 g 30.1 g 11.9 39.2 167 Rwanda 3.3 8.4 343 1.1 872 1983 10.5 9.1 2.1 g 37.8 g 18.1 46.7 168 Gambia 0.6 2.1 377 0.3 1,225 2007 5.2 f 2.1 h 2.0 g 36.9 g 18.9 47.3 169 Liberia 0.7 1.3 198 1.9 1,910 1980 .. .. 2.4 g 30.1 g 12.8 52.6 170 Guinea 4.6 10.7 487 1.3 1,147 2006 .. .. 2.4 g 34.4 g 14.4 43.3 171 Ethiopia 19.4 61.6 245 1.9 779 f 2007 4.8 17.2 4.1 g 25.6 g 6.3 29.8 172 Mozambique 7.8 17.1 364 4.2 802 2007 20.0 8.2 2.1 g 39.2 g 18.5 47.1 173 Guinea-Bissau 0.4 0.8 211 -2.6 753 1997 17.0 4.6 2.9 g 28.0 g 9.5 35.5 174 Burundi 1.0 2.9 115 -2.7 525 1991 12.8 8.3 4.1 g 28.0 g 6.8 33.3 175 Chad 7.1 15.9 658 2.4 1,555 2005 4.8 -9.0 2.6 g 30.8 g 11.8 39.8 176 Congo (Democratic Republic of the) 9.0 18.6 143 -4.3 794 1980 318.3 16.9 2.3 g 34.7 g 15.1 44.4 177 Burkina Faso 6.8 16.6 458 2.5 1,124 2007 3.8 -0.2 3.0 g 32.4 g 10.8 39.6

HDI rank

HUMAN DEVELOPMENT REPORT 2009

198

GDP GDP per capita

Average annual change in consumer price index

(%)

Share of income or expenditureb

(%) Inequality measures

US$ billions 2007 1990–2007

PPP US$ billions 2007 2006–2007

US$ 2007

Poorest 10%

Annual growth rate at constant

prices (%)

1990–2007 Richest

10%

Highest value in

the period 1980–2007 2007 PPP

US$a

Richest 10% to poorest 10%c

Year of highest value Gini indexd

178 Mali 6.9 13.4 556 2.2 1,086 2006 3.4 1.4 2.7 g 30.5 g 11.2 39.0 179 Central African Republic 1.7 3.1 394 -0.8 990 1982 3.7 .. 2.1 g 33.0 g 15.7 43.6 180 Sierra Leone 1.7 4.0 284 -0.3 855 1982 17.8 11.7 2.6 g 33.6 g 12.8 42.5 181 Afghanistan 8.4 h 26.1 h .. .. .. .. .. 17.0 .. .. .. .. 182 Niger 4.2 8.9 294 -0.6 980 1980 4.0 0.1 2.3 g 35.7 g 15.3 43.9

OTHER UN MEMBER STATES Iraq .. .. .. .. .. .. .. .. .. .. .. .. Kiribati 0.1 0.1 817 2.1 1,520 2002 .. .. .. .. .. .. Korea (Democratic People’s Rep. of) .. .. .. .. .. .. .. .. .. .. .. .. Marshall Islands 0.1 .. 2,559 .. .. .. .. .. .. .. .. .. Micronesia (Federated States of) 0.2 0.3 2,126 -0.4 3,279 f 1993 .. .. .. .. .. .. Monaco .. .. .. .. .. .. .. .. .. .. .. .. Nauru .. .. .. .. .. .. .. .. .. .. .. .. Palau 0.2 .. 8,148 .. .. .. .. .. .. .. .. .. San Marino 1.7 .. 55,681 .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. Tuvalu .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe 3.4 .. 261 h .. .. .. 105.6 .. 1.8 g 40.3 g 22.0 50.1

Arab States 1,347.1 T 2,285.8 .. .. .. .. .. .. .. .. .. .. Central and Eastern Europe and the CIS 3,641.3 T 5,805.0 .. .. .. .. .. .. .. .. .. .. East Asia and the Pacific 5,661.6 T 11,184.6 .. .. .. .. .. .. .. .. .. .. Latin America and the Caribbean 3,610.5 T 5,576.6 .. .. .. .. .. .. .. .. .. .. South Asia 1,727.5 T 4,622.5 .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa 804.0 T 1,481.7 .. .. .. .. .. .. .. .. .. .. OECD 40,378.6 T 38,543.3 .. .. .. .. .. .. .. .. .. .. European Union (EU27) 16,843.0 T 14,811.7 .. .. .. .. .. .. .. .. .. .. GCC 761.4T 1,034.4 .. .. .. .. .. .. .. .. .. .. Very high human development 39,078.8 Ti 36,438.4 39,821 i 1.8 i .. .. .. .. .. .. .. .. Very high HD: OECD .. T 35,194.8 .. .. .. .. .. .. .. .. .. .. Very high HD: non-OECD .. T 1,243.6 .. .. .. .. .. .. .. .. .. .. High human development 7,929.2 Ti 11,321.4 8,470 i 2.1 i .. .. .. .. .. .. .. .. Medium human development 7,516.8 Ti 16,837.5 1,746 i 4.8 i .. .. .. .. .. .. .. .. Low human development 147.4 Ti 312.4 380 i 0.0 i .. .. .. .. .. .. .. .. World 54,583.8 Ti 64,909.7 8,257 i 1.6 i .. .. .. .. .. .. .. ..

NOTES

a Expressed in 2007 constant prices.

b Because the underlying household surveys differ in

method and type of data collected, cross-country

comparisons should be made with caution as the the

distribution data are not strictly comparable across

countries.

c Data show the ratio of the income or expenditure

share of the richest group to that of the poorest.

d The Gini index lies between 0 and 100. A value of

0 represents absolute equality and 100 absolute

inequality.

e Data refer to income shares by percentiles of the

population, ranked by per capita income.

f Data refer to a period shorter than that specified.

g Data refer to expenditure shares by percentiles of the

population, ranked by per capita expenditure.

h Data refer to an earlier year than that specified.

i Aggregates calculated for HDRO by the World Bank.

SOURCES

Columns 1–3 and 9–12: World Bank (2009d).

Column 4: calculated for HDRO by the World Bank

based on World Bank (2009d) using the least squares

method.

Columns 5 and 6: calculated based on GDP per capita

( PPP US $) time series from World Bank (2009d).

Columns 7 and 8: calculated based on consumer price

index data from World Bank (2009d).

HDI rank

Economy and inequality

199

HUMAN DEVELOPMENT REPORT 2009

NTABLE Health and education

Public expenditure on health

Public expenditure on education

Educational attainment levelsb (% of the population aged 25 and above)

Under-five mortality rate (per 1,000 live births)

Wealth quintile Educational level of

mother

per capita PPP US$

2006

per pupil in primary education PPP US$

2003–2006

as % of total aid

2007

Aid allocated to social sectorsa

as % of

total government expenditure

2006

as % of total

government expenditure

2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007

upper secondary or post-

secondary non-tertiary

Medium

less than upper

secondary

Low

tertiary

High

lowest

lowest (no

education)highest

highest (secondary or higher)

Healthy life

expectancy at birthc (years)

2007

Unhealthy life

expectancy as a % of total life

expectancyd

2007

VERY HIGH HUMAN DEVELOPMENT 1 Norway 3,780 17.9 7,072 16.7 .. 14.5 53.8 31.7 .. .. .. .. 74 8 2 Australia 2,097 17.2 5,181 13.3 .. .. .. .. .. .. .. .. 75 8 3 Iceland 2,758 18.1 7,788 18.0 .. 37.4 30.3 27.6 .. .. .. .. 75 8 4 Canada 2,585 17.9 .. 12.5 .. 23.7 38.1 38.2 .. .. .. .. 75 7 5 Ireland 2,413 17.3 5,100 13.9 .. 40.0 31.2 26.4 .. .. .. .. 74 7 6 Netherlands 2,768 16.4 5,572 11.5 .. 34.8 38.6 26.0 .. .. .. .. 74 7 7 Sweden 2,533 13.4 8,415 12.9 .. 20.7 51.1 27.0 .. .. .. .. 75 7 8 France 2,833 16.7 5,224 10.6 .. 42.6 35.9 19.8 .. .. .. .. 76 6 9 Switzerland 2,598 19.6 7,811 13.0 .. 21.4 52.3 26.2 .. .. .. .. 76 7 10 Japan 2,067 17.7 .. 9.5 .. 26.1 43.9 30.0 .. .. .. .. 78 6 11 Luxembourg 5,233 16.8 9,953 .. .. 39.0 39.7 21.3 .. .. .. .. 75 5 12 Finland 1,940 12.1 5,373 12.5 .. 30.9 38.8 30.3 .. .. .. .. 75 6 13 United States 3,074 19.1 .. 13.7 .. 14.8 49.0 36.2 .. .. .. .. 72 9 14 Austria 2,729 15.5 7,596 10.9 .. 26.2 57.9 15.9 .. .. .. .. 74 7 15 Spain 1,732 15.3 4,800 11.0 .. 58.6 17.8 23.6 .. .. .. .. 76 6 16 Denmark 2,812 15.6 7,949 15.5 .. 25.8 43.7 30.3 .. .. .. .. 73 7 17 Belgium 2,264 13.9 6,303 12.1 .. 42.3 31.0 26.8 .. .. .. .. 74 7 18 Italy 2,022 14.2 6,347 9.2 .. 59.5 30.4 10.1 .. .. .. .. 76 6 19 Liechtenstein .. .. .. .. .. .. .. .. .. .. .. .. .. .. 20 New Zealand 1,905 18.6 4,831 15.5 .. 28.7 40.1 25.9 .. .. .. .. 74 8 21 United Kingdom 2,434 16.5 5,596 12.5 .. .. .. .. .. .. .. .. 73 8 22 Germany 2,548 17.6 4,837 9.7 .. 21.5 57.1 21.4 .. .. .. .. 75 6 23 Singapore 413 5.4 .. .. .. 41.2 39.2 19.6 .. .. .. .. 75 6 24 Hong Kong, China (SAR) .. .. .. 23.2 .. 45.9 38.9 15.2 .. .. .. .. .. .. 25 Greece 1,317 11.5 3,562 9.2 .. 51.0 25.7 23.3 .. .. .. .. 74 6 26 Korea (Republic of) 819 11.9 3,379 15.3 .. 36.2 40.4 23.4 .. .. .. .. 74 7 27 Israel 1,477 11.1 5,135 13.7 .. 23.9 33.1 39.7 .. .. .. .. 74 8 28 Andorra 2,054 22.7 .. .. .. 48.0 34.8 16.1 .. .. .. .. 76 .. 29 Slovenia 1,507 13.5 5,206 12.7 .. 26.4 55.5 18.1 .. .. .. .. 74 5 30 Brunei Darussalam 314 5.1 .. 9.1 .. .. .. .. .. .. .. .. 67 13 31 Kuwait 422 4.9 2,204 12.9 .. 74.4 17.3 8.3 .. .. .. .. 69 11 32 Cyprus 759 6.4 .. 14.5 .. 41.3 33.8 24.9 .. .. .. .. 71 11 33 Qatar 1,115 9.7 .. 19.6 .. 59.0 20.1 20.9 .. .. .. .. 66 13 34 Portugal 1,494 15.5 4,908 11.3 .. 77.4 11.4 11.2 .. .. .. .. 73 7 35 United Arab Emirates 491 8.7 1,636 28.3 .. .. .. .. .. .. .. .. 68 12 36 Czech Republic 1,309 13.6 2,242 9.5 .. 14.5 73.0 12.5 .. .. .. .. 72 6 37 Barbados 722 11.9 .. 16.4 94.8 75.7 23.1 1.1 .. .. .. .. 69 10 38 Malta 1,419 14.7 2,549 10.5 .. 77.2 12.0 10.8 .. .. .. .. 74 7

HIGH HUMAN DEVELOPMENT 39 Bahrain 669 9.5 .. .. .. 50.3 38.4 11.2 .. .. .. .. 66 13 40 Estonia 734 11.3 2,511 14.6 .. 27.9 42.3 27.5 .. .. .. .. 71 3 41 Poland 636 9.9 3,155 12.7 .. .. .. .. .. .. .. .. 70 7 42 Slovakia 913 13.8 2,149 10.8 .. 19.2 67.6 13.2 .. .. .. .. 70 6 43 Hungary 978 10.4 4,479 10.9 .. 36.5 48.9 14.7 .. .. .. .. 69 6 44 Chile 367 14.1 1,287 16.0 34.0 .. .. .. .. .. .. .. 72 8 45 Croatia 869 13.9 2,197 10.0 72.3 40.2 45.4 13.9 .. .. .. .. 70 8 46 Lithuania 728 13.3 2,166 14.7 .. 23.5 50.8 25.7 .. .. .. .. 68 5 47 Antigua and Barbuda 439 11.3 .. .. 91.3 .. .. .. .. .. .. .. 66 .. 48 Latvia 615 10.2 .. 14.2 .. 19.7 60.0 20.3 .. .. .. .. 68 6 49 Argentina 758 14.2 1,703 13.1 54.7 65.7 23.2 11.1 .. .. .. .. 69 8 50 Uruguay 430 9.2 .. 11.6 51.4 75.3 15.1 9.6 .. .. .. .. 70 8 51 Cuba 329 10.8 .. 14.2 77.5 59.6 31.0 9.4 .. .. .. .. 71 10 52 Bahamas 775 13.9 .. 19.7 .. 28.9 70.2 0.3 .. .. .. .. 68 7 53 Mexico 327 11.0 1,604 25.6 67.7 69.7 15.3 14.9 .. .. .. .. 69 9 54 Costa Rica 565 21.5 1,623 20.6 26.2 64.7 18.5 15.0 .. .. .. .. 71 10 55 Libyan Arab Jamahiriya 189 6.5 .. .. 51.6 .. .. .. .. .. .. .. 66 11 56 Oman 321 5.4 .. 31.1 22.8 .. .. .. .. .. .. .. 67 11 57 Seychelles 602 8.8 2,399 12.6 39.4 51.8 36.8 7.4 .. .. .. .. 65 .. 58 Venezuela (Bolivarian Republic of) 196 9.3 583 .. 71.0 63.9 21.7 12.8 .. .. .. .. 68 8 59 Saudi Arabia 468 8.7 .. 27.6 78.8 65.8 19.2 14.9 .. .. .. .. 64 12

HDI rank

HUMAN DEVELOPMENT REPORT 2009

200

Public expenditure on health

Public expenditure on education

Educational attainment levelsb (% of the population aged 25 and above)

Under-five mortality rate (per 1,000 live births)

Wealth quintile Educational level of

mother

per capita PPP US$

2006

per pupil in primary education PPP US$

2003–2006

as % of total aid

2007

Aid allocated to social sectorsa

as % of

total government expenditure

2006

as % of total

government expenditure

2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007

upper secondary or post-

secondary non-tertiary

Medium

less than upper

secondary

Low

tertiary

High

lowest

lowest (no

education)highest

highest (secondary or higher)

Healthy life

expectancy at birthc (years)

2007

Unhealthy life

expectancy as a % of total life

expectancyd

2007

60 Panama 495 11.5 .. 8.9 47.1 66.0 23.1 10.4 .. .. .. .. 68 10 61 Bulgaria 443 11.9 2,045 6.2 .. 40.4 41.3 18.0 .. .. .. .. 69 6 62 Saint Kitts and Nevis 403 9.5 .. 12.7 58.7 .. .. .. .. .. .. .. 67 .. 63 Romania 433 12.4 941 8.6 .. 47.3 43.6 9.0 .. .. .. .. 68 6 64 Trinidad and Tobago 438 6.9 .. 13.4 69.9 .. .. .. .. .. .. .. 64 8 65 Montenegro 93 20.1 .. .. 50.8 22.6 61.4 16.1 .. .. .. .. 66 11 66 Malaysia 226 7.0 1,324 25.2 30.9 61.3 27.1 8.0 .. .. .. .. 66 11 67 Serbia 373 14.3 .. .. 60.6 .. .. .. .. .. .. .. 66 11 68 Belarus 428 10.2 1,196 9.3 85.4 .. .. .. .. .. .. .. 66 4 69 Saint Lucia 237 10.2 949 19.1 14.7 .. .. .. .. .. .. .. 69 6 70 Albania 127 11.3 .. 8.4 67.2 63.0 29.6 7.4 .. .. .. .. 64 16 71 Russian Federation 404 10.8 .. 12.9 .. .. .. .. .. .. .. .. 65 2 72 Macedonia (the Former Yugoslav Rep. of) 446 16.5 .. 15.6 57.4 52.2 35.6 12.2 .. .. .. .. 66 11 73 Dominica 311 9.2 .. .. 4.9 88.8 5.7 5.0 .. .. .. .. 67 .. 74 Grenada 387 9.5 766 12.9 18.4 .. .. .. .. .. .. .. 62 18 75 Brazil 367 7.2 1,005 14.5 46.3 70.4 21.2 8.1 99 e 33 e 119 e 37 e 66 9 76 Bosnia and Herzegovina 454 14.0 .. .. 73.2 .. .. .. .. .. .. .. 68 9 77 Colombia 534 17.0 1,257 14.2 61.6 64.7 25.4 9.7 39 16 51 20 69 5 78 Peru 171 13.1 446 15.4 38.5 53.7 26.0 16.3 .. .. .. .. 67 8 79 Turkey 461 16.5 1,059 .. 49.9 76.8 14.7 8.5 .. .. .. .. 67 7 80 Ecuador 130 7.3 .. 8.0 65.4 .. .. .. .. .. .. .. 66 12 81 Mauritius 292 9.2 1,205 12.7 43.8 79.2 17.7 2.6 .. .. .. .. 65 10 82 Kazakhstan 214 10.4 .. 12.1 32.8 29.5 56.1 14.4 .. .. .. .. 60 8 83 Lebanon 285 11.3 402 9.6 33.8 .. .. .. .. .. .. .. 64 11

MEDIUM HUMAN DEVELOPMENT 84 Armenia 112 9.7 .. 15.0 54.6 18.4 61.2 20.4 52 23 .. .. 63 14 85 Ukraine 298 8.8 .. 19.3 64.0 25.6 36.0 38.0 .. .. .. .. 64 6 86 Azerbaijan 67 3.6 356 17.4 45.7 16.5 70.2 13.3 .. .. 68 58 60 14 87 Thailand 223 11.3 .. 25.0 36.5 .. .. .. .. .. .. .. 65 5 88 Iran (Islamic Republic of) 406 9.2 927 19.5 71.7 .. .. .. .. .. .. .. 62 13 89 Georgia 76 5.6 .. 9.3 40.7 16.3 57.8 25.8 .. .. .. .. 67 6 90 Dominican Republic 140 9.5 644 16.8 57.7 .. .. .. 53 28 57 29 64 12 91 Saint Vincent and the Grenadines 289 9.3 1,227 16.1 9.3 .. .. .. .. .. .. .. 66 8 92 China 144 9.9 .. .. 56.4 .. .. .. .. .. .. .. 68 7 93 Belize 254 10.9 846 18.1 32.6 74.2 13.6 10.9 .. .. .. .. 63 17 94 Samoa 188 10.5 .. 13.7 70.8 .. .. .. .. .. .. .. 63 12 95 Maldives 742 14.0 .. 15.0 29.7 .. .. .. .. .. .. .. 64 10 96 Jordan 257 9.5 695 .. 67.0 .. .. .. 30 27 .. .. 64 12 97 Suriname 151 8.0 .. .. 15.1 .. .. .. .. .. .. .. 64 7 98 Tunisia 214 6.5 1,581 20.8 52.2 .. .. .. .. .. .. .. 67 9 99 Tonga 218 11.1 .. 13.5 51.7 25.9 66.2 7.9 .. .. .. .. 62 14

100 Jamaica 127 4.2 547 8.8 26.6 .. .. .. .. .. .. .. 66 8 101 Paraguay 131 13.2 518 10.0 37.0 72.6 23.6 3.7 57 e 20 e 78 e 29 e 66 8 102 Sri Lanka 105 8.3 .. .. 27.5 .. .. .. .. .. .. .. 65 12 103 Gabon 198 13.9 .. .. 49.6 .. .. .. 93 55 112 87 53 12 104 Algeria 146 9.5 692 .. 56.1 92.1 7.6 .. .. .. .. .. 63 13 105 Philippines 88 6.4 418 15.2 23.1 62.6 26.4 8.4 66 21 105 29 64 11 106 El Salvador 227 15.6 478 20.0 53.6 75.6 13.8 10.6 .. .. .. .. 63 12 107 Syrian Arab Republic 52 5.9 611 .. 79.6 89.6 5.1 5.3 22 20 .. .. 65 12 108 Fiji 199 9.1 1,143 20.0 72.5 .. .. .. .. .. .. .. 64 7 109 Turkmenistan 172 14.9 .. .. 79.9 .. .. .. 106 70 133 88 57 12 110 Occupied Palestinian Territories .. .. .. .. 58.4 68.8 12.8 18.4 .. .. .. .. .. .. 111 Indonesia 44 5.3 .. 17.2 33.6 .. .. .. 77 22 90 37 61 13 112 Honduras 116 15.0 .. .. 47.4 .. .. .. 50 20 55 20 64 11 113 Bolivia 128 11.6 435 18.1 57.3 61.6 23.8 14.0 105 32 145 48 59 10 114 Guyana 223 8.3 752 15.5 67.7 .. .. .. .. .. .. .. 55 17 115 Mongolia 124 11.0 261 .. 56.8 46.6 41.1 12.2 .. .. .. .. 62 6 116 Viet Nam 86 6.8 .. .. 34.9 .. .. .. 53 16 66 29 66 11 117 Moldova 107 11.8 .. 19.8 52.5 .. .. .. 29 17 .. .. 63 8 118 Equatorial Guinea 219 7.0 .. 4.0 84.5 .. .. .. .. .. .. .. 46 8

HDI rank

Health and education

201

TABLE N

Public expenditure on health

Public expenditure on education

Educational attainment levelsb (% of the population aged 25 and above)

Under-five mortality rate (per 1,000 live births)

Wealth quintile Educational level of

mother

per capita PPP US$

2006

per pupil in primary education PPP US$

2003–2006

as % of total aid

2007

Aid allocated to social sectorsa

as % of

total government expenditure

2006

as % of total

government expenditure

2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007

upper secondary or post-

secondary non-tertiary

Medium

less than upper

secondary

Low

tertiary

High

lowest

lowest (no

education)highest

highest (secondary or higher)

Healthy life

expectancy at birthc (years)

2007

Unhealthy life

expectancy as a % of total life

expectancyd

2007

119 Uzbekistan 89 8.0 .. .. 69.4 .. .. .. 72 42 .. .. 60 11 120 Kyrgyzstan 55 8.7 .. 18.6 54.4 23.0 62.1 14.9 .. .. .. .. 59 13 121 Cape Verde 227 13.2 1,052 16.4 44.7 .. .. .. .. .. .. .. 64 10 122 Guatemala 98 14.7 390 .. 38.6 84.8 11.2 3.7 78 e 39 e 79 e 42 e 62 12 123 Egypt 129 7.3 .. 12.6 28.1 .. .. .. 75 25 68 31 62 11 124 Nicaragua 137 16.0 331 15.0 46.1 .. .. .. 64 19 72 25 66 9 125 Botswana 487 17.8 1,158 21.0 72.2 .. .. .. .. .. .. .. 48 10 126 Vanuatu 90 10.9 .. 26.7 54.5 .. .. .. .. .. .. .. 62 11 127 Tajikistan 16 5.5 106 18.2 53.4 21.0 68.3 10.6 .. .. .. .. 57 14 128 Namibia 218 10.1 944 21.0 68.9 .. .. .. 92 29 .. .. 53 12 129 South Africa 364 9.9 1,383 17.4 62.8 73.0 18.1 8.9 .. .. .. .. 48 7 130 Morocco 98 5.5 1,005 26.1 54.2 .. .. .. 78 26 63 27 63 11 131 Sao Tome and Principe 120 12.2 .. .. 49.0 .. .. .. .. .. .. .. 54 17 132 Bhutan 73 7.3 .. 17.2 46.8 .. .. .. .. .. .. .. 56 15 133 Lao People’s Democratic Republic 18 4.1 61 14.0 41.8 .. .. .. .. .. .. .. 54 16 134 India 21 3.4 .. 10.7 46.6 .. .. .. 101 34 .. .. 57 10 135 Solomon Islands 99 12.6 .. .. 84.2 .. .. .. .. .. .. .. 60 9 136 Congo 13 4.0 39 8.1 39.5 .. .. .. 135 85 202 101 49 8 137 Cambodia 43 10.7 .. 12.4 59.1 .. .. .. 127 43 136 53 55 9 138 Myanmar 7 1.8 .. 18.1 57.9 .. .. .. .. .. .. .. 52 15 139 Comoros 19 8.0 .. 24.1 68.8 .. .. .. 129 e 87 e 121 e 75 e 58 11 140 Yemen 38 5.6 .. 32.8 77.4 .. .. .. 118 37 .. .. 55 12 141 Pakistan 8 1.3 .. 11.2 53.0 76.7 17.1 6.3 121 60 102 62 55 17 142 Swaziland 219 9.4 484 .. 56.8 .. .. .. 118 101 150 95 42 7 143 Angola 61 5.0 .. .. 78.4 .. .. .. .. .. .. .. 47 .. 144 Nepal 24 9.2 119 14.9 51.8 .. .. .. 98 47 93 32 55 17 145 Madagascar 21 9.2 57 16.4 28.6 .. .. .. 142 49 149 65 53 12 146 Bangladesh 26 7.4 115 14.2 50.0 82.9 12.9 4.2 121 72 114 68 55 16 147 Kenya 51 6.1 237 17.9 54.0 .. .. .. 149 91 127 63 48 10 148 Papua New Guinea 111 7.3 .. .. 58.9 .. .. .. .. .. .. .. 57 6 149 Haiti 65 29.8 .. .. 56.0 .. .. .. 125 55 123 65 55 10 150 Sudan 23 6.3 .. .. 24.1 .. .. .. .. e .. e 152 e 84 e 50 14 151 Tanzania ( United Republic of) 27 13.3 .. .. 31.0 98.4 0.7 0.9 137 93 160 76 45 18 152 Ghana 36 6.8 300 .. 45.6 .. .. .. 128 88 125 85 50 12 153 Cameroon 23 8.6 107 17.0 11.5 .. .. .. 189 88 186 93 45 12 154 Mauritania 31 5.3 224 10.1 37.8 .. .. .. 98 79 111 86 52 8 155 Djibouti 75 13.4 .. 22.4 46.5 .. .. .. .. .. .. .. 50 9 156 Lesotho 88 7.8 663 29.8 64.0 .. .. .. 114 82 161 82 41 9 157 Uganda 39 10.0 110 18.3 50.8 93.5 1.6 4.8 172 108 164 91 44 15 158 Nigeria 15 3.5 .. .. 38.9 .. .. .. 257 79 269 107 42 12

LOW HUMAN DEVELOPMENT 159 Togo 20 6.9 .. 13.6 75.9 .. .. .. 150 62 145 64 52 16 160 Malawi 51 18.0 90 .. 48.4 94.8 4.7 0.5 183 111 181 86 44 16 161 Benin 25 13.1 120 17.1 51.6 85.6 12.2 2.2 151 83 143 78 50 18 162 Timor-Leste 150 16.4 .. .. 72.2 .. .. .. .. .. .. .. 55 9 163 Côte d’Ivoire 15 4.1 .. 21.5 55.3 .. .. .. .. .. .. .. 48 16 164 Zambia 29 10.8 55 14.8 57.5 .. .. .. 192 92 198 121 40 10 165 Eritrea 10 4.2 99 .. 56.1 .. .. .. 100 65 121 59 56 5 166 Senegal 23 6.7 299 26.3 52.0 .. .. .. 183 64 152 60 52 6 167 Rwanda 134 27.3 109 19.0 53.9 .. .. .. 211 122 210 95 44 11 168 Gambia 33 8.7 .. 8.9 72.5 .. .. .. 158 72 140 66 53 5 169 Liberia 25 16.4 .. .. 43.9 .. .. .. 138 117 151 119 49 15 170 Guinea 14 4.7 .. 25.6 53.8 .. .. .. 217 113 194 92 48 16 171 Ethiopia 13 10.6 130 23.3 53.9 .. .. .. 130 92 139 54 51 7 172 Mozambique 39 12.6 156 21.0 46.2 .. .. .. 196 108 201 86 42 12 173 Guinea-Bissau 10 4.0 .. .. 34.8 .. .. .. .. .. .. .. 43 9 174 Burundi 4 2.3 132 17.7 30.8 .. .. .. .. .. .. .. 43 14 175 Chad 14 9.5 54 10.1 26.1 .. .. .. 176 187 200 143 40 18 176 Congo (Democratic Republic of the) 7 7.2 .. .. 38.4 .. .. .. 184 97 209 112 46 3 177 Burkina Faso 50 15.8 328 15.4 35.1 .. .. .. 206 144 198 108 43 18

HDI rank

HUMAN DEVELOPMENT REPORT 2009

202

Public expenditure on health

Public expenditure on education

Educational attainment levelsb (% of the population aged 25 and above)

Under-five mortality rate (per 1,000 live births)

Wealth quintile Educational level of

mother

HDI rank

per capita PPP US$

2006

per pupil in primary education PPP US$

2003–2006

as % of total aid

2007

Aid allocated to social sectorsa

as % of

total government expenditure

2006

as % of total

government expenditure

2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007 2000–2007

upper secondary or post-

secondary non-tertiary

Medium

less than upper

secondary

Low

tertiary

High

lowest

lowest (no

education)highest

highest (secondary or higher)

Healthy life

expectancy at birthc (years)

2007

Unhealthy life

expectancy as a % of total life

expectancyd

2007

178 Mali 34 12.2 183 16.8 39.6 .. .. .. 233 124 223 102 43 11 179 Central African Republic 20 10.9 88 .. 22.5 .. .. .. 223 112 187 107 42 10 180 Sierra Leone 20 7.8 .. .. 28.7 .. .. .. .. .. 279 164 37 22 181 Afghanistan 8 4.4 .. .. 49.0 .. .. .. .. .. .. .. 36 17 182 Niger 14 10.6 178 17.6 37.4 .. .. .. 206 157 222 92 45 11

OTHER UN MEMBER STATES Iraq 90 3.4 .. .. 22.7 .. .. .. .. .. 49 37 58 15 Kiribati 268 13.0 .. .. 41.7 .. .. .. .. .. .. .. 60 .. Korea ( Democratic People’s Rep. of) 42 6.0 .. .. 19.0 .. .. .. .. .. .. .. 61 9 Marshall Islands 589 15.1 .. 15.8 42.4 .. .. .. .. .. .. .. 53 .. Micronesia ( Federated States of) 444 18.9 .. .. 42.5 .. .. .. .. .. .. .. 62 9 Monaco 5,309 15.6 .. .. .. .. .. .. .. .. .. .. 76 .. Nauru 444 25.0 .. .. 48.5 .. .. .. .. .. .. .. 57 .. Palau 1,003 16.4 .. .. 11.0 .. .. .. .. .. .. .. 67 .. San Marino 2,765 13.3 .. .. .. .. .. .. .. .. .. .. 76 .. Somalia 8 4.2 .. .. 23.8 .. .. .. .. .. .. .. 46 7 Tuvalu 189 16.1 .. .. 60.1 .. .. .. .. .. .. .. 58 .. Zimbabwe 77 8.9 .. .. 50.7 89.5 8.8 1.5 72 57 69 68 38 12

NOTES

a Refers to allocation of aid to social infrastructure

and services including health, education, water

and sanitation, government and civil society and

other services. Out of the total, an estimated 50%

is allocated to health and education. Differences in

allocation of funds exist between countries.

b Percentages may not sum to 100% as those whose

educational attainment levels are unknown are

excluded.

c Average number of years that a person can expect to

live in ‘full health’ by taking into account years lived in

less than full health due to disease and/or injury.

d Refers to the difference between life expectancy and

healthy life expectancy, expressed in percentage

terms.

e Data refer to a year other than that specified.

SOURCES

Columns 1–2 and 9–13: WHO (2009 ).

Columns 3 and 4: UNESCO Institute for Statistics

(2009c).

Column 5: OECD-DAC (2009 ).

Columns 6–8: UNESCO Institute for Statistics. (2008b).

Column 14: calculated based on data on healthy

life expectancy from WHO (2009 ) and data on life

expectancy from UN (2009e).

Health and education

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Reader’s guide

Human development indicators The human development indicators provide an assessment of country achievements in different areas of human development. Where possible the tables include data for 192 UN member states along with Hong Kong Special Administrative Region of China, and the Occupied Palestinian Territories.

In the tables, countries and areas are ranked by their human development index (HDI) value. To locate a country in the tables, refer to the Key to countries on the inside back cover of the Report, where countries with their HDI ranks are listed alphabetically. Most of the data in the tables are for 2007 and are those available to the Human Development Report Office (HDRO) as of 10 June 2009, unless otherwise specified.

This year the Statistical Annex begins with a series of tables A–F related to the main theme of the report—migration. They are followed by tables G–K on the human development compos- ite indices: the HDI and its trends; the Human Poverty Index (HPI), the Gender-related Development Index (GDI) and the Gender Empowerment Measure (GEM). Finally there are three tables (L–N) on demographic trends, the economy and inequality, and education and health. Additional selected human development indicators—including time series data and re- gional aggregates—will be available at http:// hdr.undp.org/en/statistics.

All of the indicators published in the tables are available electronically and free of charge in several formats: individually, in pre-defined ta- bles or via a query tool that allows users to design their own tables. Interactive media, including maps of all the human development indices and many of the migration-related data and selected animations, are also provided. There are also more descriptive materials such as country fact- sheets, as well as further technical details on how to calculate the indices. All of these materials are available in three languages: English (at http:// hdr.undp.org/en/statistics), French (at http://hdr. undp.org/fr/statistiques) and Spanish (http://hdr. undp.org/es/estadisticas).

Sources and definitions HDRO is primarily a user, not a producer, of statistics. It relies on international data agencies with the mandate, resources and expertise to col- lect and compile international data on specific statistical indicators. Sources for all data used in compiling the indicator tables are given at the end of each table. These correspond to full references in the Bibliography. In order to allow replication, the source notes also show the origi- nal data components used in any calculations by HDRO. Indicators for which short, meaning- ful definitions can be given are included in the Report’s Definition of statistical terms and indi- cators. Other relevant information appears in the notes at the end of each table. For more detailed technical information about these indicators, please consult the relevant websites of the source agencies, links to which can be found at http:// hdr.undp.org/en/statistics.

Comparisons over time and across edi- tions of the Report The HDI is an important tool for monitoring long-term trends in human development. To facilitate trend analyses across countries, the HDI is calculated at five-year intervals for the period 1980–2007. These estimates, presented in Table G, are based on a consistent methodol- og y using the data available when the Report is prepared.

As international data agencies continually improve their data series, including updating historical data periodically, the year-to-year changes in the HDI values and rankings across editions of the Human Development Report often reflect revisions to data—both specific to a country and relative to other countries—rather than real changes in a country. In addition, oc- casional changes in country coverage could affect the HDI ranking of a country. For example, a country’s HDI rank could drop considerably be- tween two consecutive Reports, but when com- parable revised data are used to reconstruct the HDI for recent years, the HDI rank and value may actually show an improvement.

For these reasons HDI trend analysis should not be based on data from different editions of

Reader’s guide

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentReader’s guide

the Report. Table G provides up-to-date HDI trends based on consistent data time series and methodology.

Inconsistencies between national and international estimates When compiling international data series, inter- national data agencies apply international stan- dards and harmonization procedures to national data improve comparability across countries. When data for a country are missing, an inter- national agency may produce an estimate if other relevant information can be used. In some cases, international data series may not incorporate the most recent national data. All these factors can lead to substantial differences between national and international estimates.

When data inconsistencies have arisen, HDRO has helped to link national and inter- national data authorities to address these incon- sistencies. In many cases this has led to better statistics becoming available. HDRO continues to advocate improving international data and plays an active role in supporting efforts to en- hance data quality. It works with national agen- cies and international bodies to improve data consistency through more systematic reporting and monitoring of data quality.

Country groupings and aggregates In addition to country-level data, a number of aggregates are shown in the tables. These are generally weighted averages that are calculated for the country groupings as described below. In general, an aggregate is shown for a country grouping only when data are available for at least half the countries and represent at least two-thirds of the available weight in that clas- sification. HDRO does not impute missing data for the purpose of aggregation. Therefore, un- less otherwise specified, aggregates for each clas- sification represent only the countries for which data are available. Occasionally aggregates are totals rather than weighted averages (and are in- dicated by the symbol T).

The country groupings used include: human development levels (very high, high, medium and low), the world and at least one geographic grouping—either the continents (in the migra- tion tables) or UNDP Regional Bureaux groups (in the remaining tables).

Human development classif ications. A l l countries or area s included in the HDI are classified into one of four categories of achievement in human development. For the first time, we have introduced a new cate- gory—very high human development (with an HDI of 0.900 or above)—and throughout the Report we have referred to this group as ‘de- veloped countries’. The remaining countries are referred to as ‘developing countries’ and are classified into three groups: high human development (HDI va lue of 0.80 0 – 0.899), medium human development (HDI of 0.500– 0.799) and low human development (HDI of less than 0.500). See box 1.3.

Continents To assist the analysis of migra- tion movements, this year’s HDR has classi- fied the world into six continents: Africa, Asia, Europe, Latin America and the Caribbean, Northern America and Oceania, based on the Composition of Macro Geographical Regions compiled by the Statistical Division of the United Nations Department of Economic and Social Affairs (see http://unstats.un.org/unsd/ methods/m49/m49regin.htm).

UNDP Regional Bureaux As in past Reports, for the majority of our tables we present the UNDP Regional Bureaux geographic groups: Arab States, Central and Eastern Europe and the Commonwealth of Independent States, East Asia and the Pacific, Latin America and the Caribbean, South Asia, and sub-Saharan Africa.

Country notes Unless otherwise noted, data for China do not include Hong Kong Special Administrative Region of China, Macao Special Administrative Region of China, or Taiwan Province of China. Data for Sudan are often based on information collected from the northern part of the country only. While Serbia and Montenegro became two independent States in June 2006, data for the union of the two States have been used where data do not yet exist separately for the indepen- dent States. Where this is the case, a note has been included to that effect. In the migration tables, data prior to 1990 for the Czech Republic refer to the former Czechoslovakia, those for the Russian Federation refer to the former Soviet Union and those for Serbia refer to the former Republic of Yugoslavia.

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Reader’s guide

Symbols A dash between two years, such as in 2005–2010 indicates that the data presented are estimates for the entire period, unless otherwise indicated. Growth rates are usually average annual rates of growth between the first and last years of the period shown.

The following symbols are used in the tables:

.. data not available

0 or 0.0 nil or neglegible

— not applicable

< less than

T total

Primary international data sources Life expectancy at birth. The life expectancy at birth estimates are taken from World Population Prospects 1950–2050: The 2008 Revision (UN 2009e), the official source of UN population estimates and projections. They are prepared biennially by the United Nations Department of Economic and Social Affairs Population Division using data from national vital registra- tion systems, population censuses and surveys.

In the 2008 Revision, countries where HIV prevalence among persons aged 15 to 49 was ever equal to or greater than one percent dur- ing 1980–2007 are considered affected by the HIV epidemic, and their mortality is projected by modelling the course of the epidemic and pro- jecting the yearly incidence of HIV infection. Also considered among the affected countries are those where HIV prevalence has always been lower than one percent but whose population is so large that the number of people living with HIV in 2007 surpassed 500,000. These include Brazil, China, India, the Russian Federation and the United States. This brings the number of countries considered to be affected by HIV to 58.

For more details on World Population Prospects 1950–2050: The 2008 Revision, see www.un.org/esa/population/unpop.htm.

Adult literacy rate. This Report uses data on adult literacy rates from the United Nations Educational, Scientific and Cultural

Orga nization (U N E SCO) Institute for Statistics (U IS)(U NE SCO Institute for Statistics 2009a) that combine direct national estimates with recent estimates based on its global age-specific literacy projections model, which was developed in 2007. The national es- timates, made available through targeted efforts by UIS to collect recent literacy data from coun- tries, are obtained from national censuses or surveys between 1995 and 2007. Where recent estimates are not available, older UIS estimates have been used.

Many developed countries, having attained high levels of literacy, no longer collect basic lit- eracy statistics and thus are not included in the UIS data. In calculating the HDI, a literacy rate of 99.0% is assumed for these countries if they do not report adult literacy information.

Many countries estimate the number of lit- erate people based on self-reported data. Some use educational attainment data as a proxy, but measures of school attendance or grade comple- tion may differ. Because definitions and data col- lection methods vary across countries, literacy estimates should be used with caution.

The UIS, in collaboration with partner agen- cies, is actively pursuing an alternative meth- odolog y for generating more reliable literacy estimates, known as the Literacy Assessment and Monitoring Programme (LAMP). LAMP seeks to go beyond the current simple categories of ‘literate’ and ‘illiterate’ by providing informa- tion on a continuum of literacy skills.

Combined gross enrolment ratios in primary, sec- ondary and tertiary education. Gross enrolment ra- tios are produced by the UIS (UNESCO Institute for Statistics 2009b) based on enrolment data col- lected from national governments (usually from ad- ministrative sources) and population data from the World Population Prospects 1950–2050: The 2006 Revision (UN 2007). The ratios are calculated by dividing the number of students enrolled in pri- mary, secondary and tertiary levels of education by the total population in the theoretical age group corresponding to these levels. The theoretical age group for tertiary education is assumed to be the five-year age group immediately following the end of upper secondary school in all countries.

Combined gross enrolment ratios do not re- flect the quality of educational outcomes. Even when used to capture access to educational

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentReader’s guide

opportunities, combined gross enrolment ratios can hide important differences among countries because of differences in the age range corre- sponding to a level of education and in the dura- tion of education programmes. Grade repetition and dropout rates can also distort the data.

As currently defined, the combined gross en- rolment ratio measures enrolment in the country of study and therefore excludes from the na- tional figure students who are studying abroad. For many smaller countries, where pursuit of a tertiary education abroad is common, access to education or educational attainment of the pop- ulation could be underestimated.

GDP per capita (PPP US$). GDP per capita data are provided by the World Bank and re- leased in its World Development Indicators database. In comparing standards of living across countries, economic statistics must be converted into purchasing power parity (PPP) terms to eliminate differences in national price levels. The current estimates are based on price data from the latest round of the International Comparison Program (ICP), which was con- ducted in 2005 and covers a total of 146 coun- tries and areas. For many countries not included in the ICP surveys, the World Bank derives estimates through econometric regression. For countries not covered by the World Bank, PPP estimates provided by the Penn World Tables of the University of Pennsylvania (Heston, Summers and Aten 2006) are used.

The new PPP estimates were released for the first time during 2008 and represented sub- stantial revisions to those used in our Reports published in 2007 and earlier years that were based on the prior round of ICP surveys—con- ducted in the early 1990s—covering only 118 countries. The new data indicated that price levels in many countries (especially develop- ing countries) were higher than previously thought. For 70 countries, per capita incomes were revised downwards by at least 5 percent. Many of these are in sub-Saharan Africa, in- cluding seven of the eight countries where the downward revision was at least 50 percent. By contrast, for around 60 countries there was an upward revision of at least 5 percent, includ- ing many oil-producing countries where revi- sions exceeded 30 percent and four countries where the values were doubled. Such massive

revisions in GDP per capita clearly affect HDI values and also HDI ranks. A halving (or dou- bling) of GDP per capita changes the HDI value by 0.039.

Consequently, at the end of 2008, we released a short report entitled Human Development Indices: A statistical update 2008 explaining the reasons for this revision and its effects on the HDI and our other composite indices. More details can be found at http://hdr.undp.org/en/ statistics/data/hdi2008. For details on the ICP and the PPP methodology, see the ICP website at www.worldbank.org/data/icp.

Migration data. Migration data in this report have been sourced from different agencies.

The main source for trends in international migrant stocks is the Population Division of the United Nations Department for Social and Economic Affairs (UNDESA). The data are from Trends in Total Migrant Stocks: The 2008 Revision (UN 2009d) and are based on data from population censuses conducted between 1955 and 2008. This source provides broad data (sex and type) over time on migrants according to their countries of destination.

As far as possible, international migrants are defined as foreign-born. In countries where data on place of birth were not available, country of citizenship provided the basis for the identifica- tion of international migrants.

For data on countries of origin (as well as des- tination) of the international migrant stock, we have used the Global Migrant Origin Database (version 4) compiled by the Development Research Centre on Migration, Globalisation and Poverty based at the University of Sussex, England (Migration DRC 2007). The estimates are based national censuses conducted during the 2000 round of censuses and provide an esti- mate for the period 2000–2002. It is important to note that the database presents data on mi- grant stocks—i.e. the total number of migrants both by country of origin and country of desti- nation—and not the annual (or periodic) flows of migrants between countries. The stocks are the cumulative effect of flows over a much longer period of time than a year and hence are gener- ally much greater than the annual flows would be. For details see http://www.migrationdrc.org/ research/typesofmigration/global_migrant_ori- gin_database.html

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Reader’s guide

For more detailed data on the characteristics of international migrants we used the OECD Database on Immigrants in OECD Countries (OECD 2009b). This database has been com- piled from data collected during the 2000 round of censuses, supplemented in some cases by data from labour force surveys. As far as possible in- ternational migrants are defined as the foreign- born, although for some countries of destination the definitions may differ slightly from those that were used by the UN Population Division. We have chosen to present results according to the countries of origin of these migrants; therefore it is not possible to make a direct com- parison with the estimates from the other two sources. We have presented data on education levels and economic activity, as well as highly- skilled (tertiary) emigration rates according to the countries of origin of migrants aged 15 years and above in OECD countries.

Cross-nationally comparable data on in- ternal migrants (i.e. people who move within the borders of a country) are not readily avail- able. For this reason, during the preparation of this report we commissioned analyses from (Bell and Muhudin 2009) based on national censuses that produced comparable estimates for 24 countries of the percentage of the total population that has moved. These data have been supplemented by estimates compiled by the UN Statistics Division (UNSD) in col- laboration with the Economic Commission for Latin America and the Caribbean (ECLAC 2007), which are based also on censuses and total population, as well as by World Bank data based on household surveys and the population of working age (World Bank 2009e). Because of the differences in definitions across these three sources, comparisons should be treated with caution. Where estimates were available from more than one source for a country, we have

given precedence to the estimates of Bell and Muhudin over the other two sources.

Data on conflict-induced migration are from several sources, depending on the type of mi- grant: those who have moved across international borders (refugees and asylum-seekers) and those who have moved within a country (internally displaced people). Data on refugees are from the United Nations High Commission for Refugees (UNHCR 2009b), with the exception of refu- gees from Palestine, who fall mainly under the mandate of United Nations Relief and Work Agency for Palestine Refugees in the Near East (UNRWA 2008). Data are compiled from various sources, including national censuses and surveys. However, routine registration, which is created to establish a legal or administrative record or to ad- minister entitlements and deliver services, consti- tutes the main source of refugee data. UNHCR also provides estimates for 27 developed coun- tries that have no dedicated registers. These estimates are based on the recognition of asylum- seekers and estimated naturalization rates over a 10-year period. The most notable challenges of this estimation method pertain to its underlying assumption that all recognised asylum seekers are indeed refugees and the harmonization of its cut-off period to 10 years. This is particularly true for the ‘traditional’ immigration countries where it takes less than 10 years for migrants— including refugees—to obtain citizenship. Data on internally displaced persons are sourced from the Internally Displaced Monitoring Centre (IDMC 2009a). They are compiled from differ- ent sources, including the United Nations Office for the Coordination of Humanitarian Affairs (OCHA), estimates from UNHCR and from national governments. Because of the difficulty in tracking IDPs, estimates are associated with high levels of uncertainty and should therefore be interpreted with caution.

208

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentTechnical note

Calculating the human development indices The diagrams here summarize how the five human development indices are constructed, highlighting both their similarities and their differences.

Full details of the methods of calculation can be found at: www.hdr.undp.org/en/statistics/tn1

209

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Statistical terms

Asylum The grant, by a state, of protection on its territory to

individuals or groups of people from another state fleeing

persecution or serious danger.

Asylum seekers Individuals or groups of people who apply

for asylum in a country other than their own. They retain

the status of asylum-seeker until their applications are

considered and adjudicated.

Child dependency ratio The population aged under 15 years

expressed as a percentage of the population of working

age (15–64 years of age).

Conflict-induced movement Human movement resulting

in a change of usual place of residence in response to

an ongoing or imminent violent or armed conflict that

threatens lives or livelihoods.

Consumer price index, average annual change in Reflects

changes in the cost to the average consumer of acquiring a

standard or fixed basket of goods and services.

Country of origin The country from which an international

migrant originally moves to another country, with the

intention of settling temporarily or indefinitely.

Country of destination The country to which an international

migrant moves, from another country, with the intention of

settling temporarily or indefinitely.

Earned income (PPP US $), estimated Derived on the basis

of the ratio of the female non-agricultural wage to the

male non-agricultural wage, the female and male shares

of the economically active population, total female and

male population and total GDP (in purchasing power parity

terms in US dollars; see PPP (purchasing power parity).

The estimated earned income is used in the calculation of

both the Gender-related Development Index and the Gender

Empowerment Measure. For details of this estimation,

seehttp://hdr.undp.org/en/ technicalnote1.pdf.

Earned income, ratio of estimated female to male The ratio

of estimated female earned income to estimated male

earned income. See Earned income ( PPP US $), estimated.

Economically active population (or the labour force ) All

persons aged 15 years and above who, during a given

reference period, were either employed or did not have a

job but were actively looking for one. See Labour force.

Education expenditure per pupil in primary education

Public current expenditure on primary education in PPP

US $ at constant 2005 prices divided by the total number

of pupils enrolled in primary education.

Education expenditure as percentage of total government

expenditure Total public expenditure on the education

sector expressed as a percentage of total public

expenditure by all levels of government.

Education index One of the three indices on which the

human development index is built. It is based on the adult

literacy rate and the combined gross enrolment ratio for

primary, secondary and tertiary schools. See Literacy rate,

adult, and Enrolment ratio, gross combined, for primary,

secondary and tertiary schools.

Education levels Categorized as pre-primary ( ISCED 0 ),

primary ( ISCED 1), secondary ( ISCED 2 and 3), post-

secondary ( ISCED 4) and tertiary ( ISCED 5 and 6) in

accordance with the International Standard Classification

of Education ( ISCED).

Educational attainment Percentage distribution of population

of a given age group according to the highest level of

education attained or completed, with reference to

education levels defined by ISCED. Typically expressed as

high ( ISCED 5 and 6), medium ( ISCED 2, 3 and 4) and low

(less than ISCED 2) levels of attainment. It is calculated

by expressing the number of persons in the given age

group with a particular highest level of attainment as a

percentage of the total population of the same age group.

Emigrant An individual from a given country of origin (or birth)

who has changed their usual country of residence to

another country.

Emigration rate The stock of emigrants from a country at a

particular point in time expressed as a percentage of the

sum of the resident population in the country of origin and

the emigrant population.

Enrolment ratio, gross combined, for primary, secondary

and tertiary education The number of students enrolled

in primary, secondary and tertiary levels of education,

regardless of age, expressed as a percentage of the

population of theoretical school age for the three levels.

See Education levels.

Fertility rate, total The number of children that would be born

to each woman if she were to live to the end of her child-

bearing years and bear children at each age in accordance

with prevailing age-specific fertility rates in a given year/

period, for a given country, territory or geographical area.

Foreign direct investment, net inflows of Net inflows of

investment to acquire a lasting management interest (10%

Definition of statistical terms and indicators

210

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentStatistical terms

or more of voting stock) in an enterprise operating in an

economy other than that of the investor. It is the sum of

equity capital, reinvestment of earnings, other long-term

capital and short-term capital.

GDP (gross domestic product) The sum of value added by all

resident producers in the economy plus any product taxes

(less subsidies) not included in the valuation of output. It is

calculated without making deductions for depreciation of

fabricated capital assets or for depletion and degradation

of natural resources. ‘Value added’ is the net output of

an industry after adding up all outputs and subtracting

intermediate inputs.

GDP (US $) Gross domestic product converted to US dollars

using the average official exchange rate reported by the

International Monetary Fund. An alternative conversion

factor is applied if the official exchange rate is judged to

diverge by an exceptionally large margin from the rate

effectively applied to transactions in foreign currencies and

traded products. See GDP (gross domestic product).

GDP index One of the three indices on which the human

development index is built. It is based on gross domestic

product per capita (in purchasing power parity terms in US

dollars; see PPP ).

GDP per capita (PPP US $) Gross domestic product (in

purchasing power parity terms in US dollars) divided by

mid-year population. See GDP (gross domestic product),

PPP (purchasing power parity) and Population, total.

GDP per capita (US $) Gross domestic product in US dollar

terms divided by mid-year population. See GDP (US$) and

Population, total.

GDP per capita annual growth rate Least squares annual

growth rate, calculated from constant price GDP per capita

in local currency units.

Gender empowerment measure (GEM) A composite index

measuring gender inequality in three basic dimensions

of empowerment—economic participation and decision-

making, political participation, and decision-making and

power over economic resources.

Gender-related development index (GDI) A composite index

measuring average achievement in the three basic dimensions

captured in the human development index—a long and

healthy life, knowledge and a decent standard of living—

adjusted to account for inequalities between men and women.

Gini index Measures the extent to which the distribution

of income (or consumption) among individuals or

households within a country deviates from a perfectly

equal distribution. A Lorenz curve plots the cumulative

percentages of total income received against the

cumulative number of recipients, starting with the poorest

individual or household. The Gini index measures the

area between the Lorenz curve and a hypothetical line

of absolute equality, expressed as a percentage of the

maximum area under the line. A value of 0 represents

absolute equality, a value of 100 absolute inequality.

Health expenditure per capita (PPP US $) Public expenditure

on health by all levels of government (in purchasing power

parity US dollars), divided by the mid-year population.

Health expenditure includes the provision of health services

(preventive and curative), family planning activities,

nutrition activities and emergency aid designated for

health, but excludes the provision of water and sanitation.

Health expenditure, public as percentage of total

government expenditure Public expenditure on health by

all levels of government expressed as a percentage of total

government spending.

Healthy life expectancy at birth Average number of years

that a person can expect to live in ’full health’ by taking

into account years lived in less than full health due to

disease and/or injury.

Human development index (HDI) A composite index

measuring average achievement in three basic dimensions

of human development—a long and healthy life, access to

knowledge and a decent standard of living.

Human poverty index (HPI-1) A composite index measuring

deprivations in the three basic dimensions captured in the

human development index—a long and healthy life, access

to knowledge and a decent standard of living.

Human poverty index for OECD countries (HPI-2) A

composite index measuring deprivations in the three basic

dimensions captured in the human development index—a

long and healthy life, access to knowledge and a decent

standard of living—and also capturing social exclusion.

Illiteracy rate, adult Calculated as 100 minus the adult literacy

rate. See Literacy rate, adult.

Immigrant An individual residing in a given host country (country

of destination) that is not their country of origin (or birth).

Income or expenditure, shares of The shares of income

or expenditure (consumption) accruing to subgroups

of population, based on national household surveys

covering various years. Expenditure or consumption

surveys produce results showing lower levels of inequality

between poor and rich than do income surveys, as poor

people generally consume a greater share of their income.

Because data come from surveys covering different years

and using different methodologies, comparisons between

countries must be made with caution.

Income poverty line, population below The percentage of

the population living below the specified poverty line:

US$1.25 a day and US$2 a day — at 2005 international prices

adjusted for purchasing power parity;

National poverty line —the poverty line deemed appropriate for

a country by its authorities. National estimates that are

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Statistical terms

based on population-weighted subgroup estimates from

household surveys;

50% of median income —50% of the median adjusted

disposable household income.

Internal migration Human movement within the borders of

a country usually measured across regional, district or

municipality boundaries resulting in a change of usual

place of residence.

Internally Displaced Persons (IDPs) Individuals or groups

of people who have been forced to leave their homes or

places of usual residence, in particular as a result of or in

order to avoid the effects of armed conflict, situations of

generalized violence, violations of human rights or natural

or human-made disasters, and who have not crossed an

international border.

International migration Human movement across international

borders resulting in a change of country of usual residence.

International migrants as a percentage of the population

Estimated number of international migrants expressed as a

percentage of the total population.

International movement rate The sum of total stock of

immigrants into and emigrants from a particular country,

expressed as a percentage of the sum of that country’s

resident population and its emigrant population.

Labour force All people employed (including people above a

specified age who, during the reference period, were in

paid employment, either at work, self-employed or with

a job but not at work) and unemployed (including people

above a specified age who, during the reference period,

were without work, currently available for work and actively

seeking work). See Economically active population.

Labour force participation rate A measure of the proportion

of a country’s working-age population that engages

actively in the labour market, either by working or actively

looking for work. It is calculated by expressing the number

of persons in the labour force as a percentage of the

working-age population. The working-age population is the

population above 15 years of age (as used in this Report).

See Labour force and Economically active population.

Legislators, senior officials and managers, female

Women’s share of positions defined according to the

International Standard Classification of Occupations

( ISCO-88 ) to include legislators, senior government

officials, traditional chiefs and heads of villages, senior

officials of special-interest organizations, corporate

managers, directors and chief executives, production and

operations department managers and other department

and general managers.

Life expectancy at birth The number of years a newborn

infant could expect to live if prevailing patterns of age-

specific mortality rates at the time of birth were to stay the

same throughout the child’s life.

Life expectancy index One of the three indices on which the

human development index is built.

Literacy rate, adult The proportion of the adult population aged

15 years and older which is literate, expressed as a percentage

of the corresponding population (total or for a given sex) in a

given country, territory, or geographic area, at a specific point

in time, usually mid-year. For statistical purposes, a person is

literate who can, with understanding, both read and write a

short simple statement on their everyday life.

Medium-variant projection Population projections by the United

Nations Population Division assuming medium-fertility path,

normal mortality and normal international migration. Each

assumption implies projected trends in fertility, mortality and

net migration levels, depending on the specific demographic

characteristics and relevant policies of each country or

group of countries. In addition, for the countries highly

affected by the HIV epidemic, the impact of HIV is included

in the projection. The United Nations Population Division

also publishes low- and high-variant projections. For more

information, see http://esa.un.org/unpp/assumptions.html.

Migrant An individual who has changed their usual place of

residence, either by crossing an international border or

moving within their country of origin to another region,

district or municipality.

Migrant stock, annual rate of growth Estimated average

exponential growth rate of the international migrant stock

over each period indicated, expressed in percentage terms.

Migrant stock as a share of population Estimated number

of international migrants, expressed as a percentage of the

total population.

Mortality rate, under-five The probability of dying between

birth and exactly five years of age, expressed per 1,000

live births.

Natural increase, annual rate of The portion of population

growth (or decline) determined exclusively by births

and deaths.

Net international migration rate The total number of

immigrants to a country minus the number of emigrants

over a period, divided by the person-years lived by the

population of the receiving country over that period. It is

expressed as net number of migrants per 1,000 population

or as a percentage.

Official development assistance (ODA), net Disbursements

of loans made on concessional terms (net of repayments

of principal) and grants by official agencies of the members

of the Development Assistance Committee ( DAC ), by

multilateral institutions and by non-DAC countries to

promote economic development and welfare in countries

and territories in Part I of the DAC List of Aid Recipients.

For more details see www.oecd.org/dac/stats/daclist.

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentStatistical terms

Official development assistance (ODA) allocated to basic

social services Aid funds allocated to social infrastructure

and services (including health, education, water and

sanitation, government and civil society and other services)

expressed as a percentage of total official development

assistance (ODA).

Old age dependency ratio The population aged 65 years

and above expressed as a percentage of the population of

working age (15–64 years of age).

Population, annual growth rate The average annual

exponential growth rate of the population for the period

indicated. See Population, total.

Population, total The de facto population in a country, area

or region as of 1 July of the year indicated. The de facto

population includes those who are usually present,

including visitors but excluding residents, who are

temporarily absent from the country, area or region.

Population, urban The de facto population living in areas

classified as urban according to the criteria used by each

area or country. Data refer to 1 July of the year indicated.

See Population, total.

PPP (purchasing power parity) A rate of exchange that

accounts for price differences across countries, allowing

international comparisons of real output and incomes. At

the PPP US $ rate (as used in this Report), PPP US $1 has

the same purchasing power in the domestic economy as

US $1 has in the United States.

Probability at birth of not surviving to a specified age

Calculated as 100 minus the probability (expressed

as a percentage) of surviving to a specified age for

a given cohort. See Probability at birth of surviving to a

specified age.

Probability at birth of surviving to a specified age The

probability of a newborn infant surviving to a specified age

if subject to prevailing patterns of age-specific mortality

rates, expressed as a percentage.

Professional and technical workers, female Women’s

share of positions defined according to the International

Standard Classification of Occupations ( ISCO-88 )

to include physical, mathematical and engineering

science professionals (and associate professionals),

life science and health professionals (and associate

professionals), teaching professionals (and associate

professionals) and other professionals and

associate professionals.

Refugees Individuals or groups of people who have fled their

country of origin because of a well-founded fear of being

persecuted for reasons of race, religion, nationality,

political opinion or membership of a particular social group

and who cannot or do not want to return.

Remittances are earnings and material resources transferred

by international migrants or refugees to recipients in

their country of origin or countries in which the migrant

formerly resided.

Seats in parliament held by women Seats held by women in

a lower or single house and, where relevant, in an upper

house or senate.

Tertiary emigration rate Total number of emigrants aged

15 years and older from a particular country with tertiary

education, expressed as a percentage of the sum of

all persons of the same age with tertiary education in

the origin country and the emigrants population with

tertiary education.

Treaties, ratification of In order to enact an international

treaty, a country must ratify it, often with the approval of

its legislature. Ratification implies not only an expression

of interest as indicated by the signature, but also the

transformation of the treaty’s principles and obligations

into national law.

Unemployed All people above a specified age who are not in

paid employment or self-employed, but who are available

for work and have taken specific steps to seek paid

employment or self-employment.

Unemployment, long-term rate People above a specified

age who have been unemployed for at least 12 months,

expressed as a percentage of the labour force (those

employed plus the unemployed). See Unemployed and

Labour force.

Unemployment rate The unemployed, expressed as a

percentage of the labour force (those employed plus the

unemployed). See Unemployed and Labour force.

Water source, improved, population not using Calculated

as 100 minus the percentage of the population using an

improved water source. Improved sources include household

connections, public standpipes, boreholes, protected dug

wells, protected springs, and rainwater collection.

Women in government at ministerial level Includes deputy

prime ministers and ministers. Prime ministers are

included if they hold ministerial portfolios. Vice-presidents

and heads of ministerial-level departments or agencies are

also included if they exercise a ministerial function in the

government structure.

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HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Country classification

Human development categories

Very high human development (HDI 0.900 and above)

Andorra

Australia

Austria

Barbados

Belgium

Brunei Darussalam

Canada

Cyprus

Czech Republic

Denmark

Finland

France

Germany

Greece

Hong Kong, China (SAR)

Iceland

Ireland

Israel

Italy

Japan

Korea (Republic of)

Kuwait

Liechtenstein

Luxembourg

Malta

Netherlands

New Zealand

Norway

Portugal

Qatar

Singapore

Slovenia

Spain

Sweden

Switzerland

United Arab Emirates

United Kingdom

United States

(38 countries or areas)

High human development (HDI 0.800–0.899)

Albania

Antigua and Barbuda

Argentina

Bahamas

Bahrain

Belarus

Bosnia and Herzegovina

Brazil

Bulgaria

Chile

Colombia

Costa Rica

Croatia

Cuba

Dominica

Ecuador

Estonia

Grenada

Hungary

Kazakhstan

Latvia

Lebanon

Libyan Arab Jamahiriya

Lithuania

Macedonia (the Former Yugoslav Rep. of)

Malaysia

Mauritius

Mexico

Montenegro

Oman

Panama

Peru

Poland

Romania

Russian Federation

Saint Kitts and Nevis

Saint Lucia

Saudi Arabia

Serbia

Seychelles

Slovakia

Trinidad and Tobago

Turkey

Uruguay

Venezuela (Bolivarian Republic of)

(45 countries or areas)

Medium human development (HDI 0.500–0.799)

Algeria

Angola

Armenia

Azerbaijan

Bangladesh

Belize

Bhutan

Bolivia

Botswana

Cambodia

Cameroon

Cape Verde

China

Comoros

Congo

Djibouti

Dominican Republic

Egypt

El Salvador

Equatorial Guinea

Fiji

Gabon

Georgia

Ghana

Guatemala

Guyana

Haiti

Honduras

India

Indonesia

Iran (Islamic Republic of)

Jamaica

Jordan

Kenya

Kyrgyzstan

Lao People’s Democratic Republic

Lesotho

Madagascar

Maldives

Mauritania

Moldova

Mongolia

Morocco

Myanmar

Namibia

Nepal

Nicaragua

Nigeria

Occupied Palestinian Territories

Pakistan

Papua New Guinea

Paraguay

Philippines

Saint Vincent and the Grenadines

Samoa

Sao Tome and Principe

Solomon Islands

South Africa

Sri Lanka

Sudan

Suriname

Swaziland

Syrian Arab Republic

Tajikistan

Tanzania (United Republic of)

Thailand

Tonga

Tunisia

Turkmenistan

Uganda

Ukraine

Uzbekistan

Vanuatu

Viet Nam

Yemen

(75 countries or areas)

Low human development (HDI below 0.500)

Afghanistan

Benin

Burkina Faso

Burundi

Central African Republic

Chad

Congo (Democratic Republic of the)

Côte d’Ivoire

Eritrea

Ethiopia

Gambia

Guinea

Guinea-Bissau

Liberia

Malawi

Mali

Mozambique

Niger

Rwanda

Senegal

Sierra Leone

Timor-Leste

Togo

Zambia

(24 countries or areas)

214

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentCountry classification

Continents

Africa Algeria

Angola

Benin

Botswana

Burkina Faso

Burundi

Cameroon

Cape Verde

Central African Republic

Chad

Comoros

Congo

Congo (Democratic Republic of the)

Côte d’Ivoire

Djibouti

Egypt

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

Liberia

Libyan Arab Jamahiriya

Madagascar

Malawi

Mali

Mauritania

Mauritius

Morocco

Mozambique

Namibia

Niger

Nigeria

Réunion

Rwanda

Saint Helena

Sao Tome and Principe

Senegal

Seychelles

Sierra Leone

Somalia

South Africa

Sudan

Swaziland

Tanzania (United Republic of)

Togo

Tunisia

Uganda

Western Sahara

Zambia

Zimbabwe

(56 countries or areas)

Asia Afghanistan

Armenia

Azerbaijan

Bahrain

Bangladesh

Bhutan

Brunei Darussalam

Cambodia

China

Cyprus

Georgia

Hong Kong, China (SAR)

India

Indonesia

Iran (Islamic Republic of)

Iraq

Israel

Japan

Jordan

Kazakhstan

Korea (Democratic People’s Rep. of)

Korea (Republic of)

Kuwait

Kyrgyzstan

Lao People’s Democratic Republic

Lebanon

Macao, China (SAR)

Malaysia

Maldives

Mongolia

Myanmar

Nepal

Occupied Palestinian Territories

Oman

Pakistan

Philippines

Qatar

Saudi Arabia

Singapore

Sri Lanka

Syrian Arab Republic

Taiwan Province of China

Tajikistan

Thailand

Timor-Leste

Turkey

Turkmenistan

United Arab Emirates

Uzbekistan

Viet Nam

Yemen

(51 countries or areas)

Europe Albania

Andorra

Austria

Belarus

Belgium

Bosnia and Herzegovina

Bulgaria

Croatia

Czech Republic

Denmark

Estonia

Faeroe Islands

Finland

France

Germany

Gibraltar

Greece

Holy See

Hungary

Iceland

Ireland

Isle of Man

Italy

Latvia

Liechtenstein

Lithuania

Luxembourg

Macedonia (the Former Yugoslav Rep. of)

Malta

Moldova

Monaco

Montenegro

Netherlands

Norway

Poland

Portugal

Romania

215

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Country classification

Russian Federation

San Marino

Serbia

Slovakia

Slovenia

Spain

Svalbard and Jan Mayen Islands

Sweden

Switzerland

Ukraine

United Kingdom

(49 countries or areas)

Latin America and the Caribbean Antigua and Barbuda

Argentina

Bahamas

Barbados

Belize

Bolivia

Brazil

Chile

Colombia

Costa Rica

Cuba

Dominica

Dominican Republic

Ecuador

El Salvador

Grenada

Guatemala

Guyana

Haiti

Honduras

Jamaica

Mexico

Nicaragua

Panama

Paraguay

Peru

Saint Kitts and Nevis

Saint Lucia

Saint Vincent and the Grenadines

Suriname

Trinidad and Tobago

Uruguay

Venezuela (Bolivarian Republic of)

(33 countries or areas)

Northern America Canada

United States

(2 countries or areas)

Oceania Australia

Fiji

Kiribati

Marshall Islands

Micronesia (Federated States of)

Nauru

New Zealand

Palau

Papua New Guinea

Samoa

Solomon Islands

Tonga

Tuvalu

Vanuatu

(14 countries or areas)

UNDP regional bureaux

Arab States Algeria

Bahrain

Djibouti

Egypt

Iraq

Jordan

Kuwait

Lebanon

Libyan Arab Jamahiriya

Morocco

Occupied Palestinian Territories

Oman

Qatar

Saudi Arabia

Somalia

Sudan

Syrian Arab Republic

Tunisia

United Arab Emirates

Yemen

(20 countries or areas)

Central and Eastern Europe and the Commonwealth of Independent States (CIS) Albania

Armenia

Azerbaijan

Belarus

Bosnia and Herzegovina

Bulgaria

Croatia

Cyprus

Czech Republic

Estonia

Georgia

Hungary

Kazakhstan

Kyrgyzstan

Latvia

Lithuania

Macedonia (the Former Yugoslav Rep. of))

Malta

Moldova

Montenegro

Poland

216

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and developmentCountry classification

Romania

Russian Federation

Serbia

Slovakia

Slovenia

Tajikistan

Turkey

Turkmenistan

Ukraine

Uzbekistan

(31 countries or areas)

East Asia and the Pacific Brunei Darussalam

Cambodia

China

Fiji

Hong Kong, China (SAR)

Indonesia

Kiribati

Korea (Democratic People’s Republic of)

Korea (Republic of)

Lao People’s Democratic Republic

Malaysia

Marshall Islands

Micronesia (Federated States of)

Mongolia

Myanmar

Nauru

Palau

Papua New Guinea

Philippines

Samoa

Singapore

Solomon Islands

Thailand

Timor-Leste

Tonga

Tuvalu

Vanuatu

Viet Nam

(28 countries or areas)

Latin America and Caribbean Antigua and Barbuda

Argentina

Bahamas

Barbados

Belize

Bolivia

Brazil

Chile

Colombia

Costa Rica

Cuba

Dominica

Dominican Republic

Ecuador

El Salvador

Grenada

Guatemala

Guyana

Haiti

Honduras

Jamaica

Mexico

Nicaragua

Panama

Paraguay

Peru

Saint Kitts and Nevis

Saint Lucia

Saint Vincent and the Grenadines

Suriname

Trinidad and Tobago

Uruguay

Venezuela (Bolivarian Republic of)

(33 countries or areas)

Sub-Saharan Africa Angola

Benin

Botswana

Burkina Faso

Burundi

Cameroon

Cape Verde

Central African Republic

Chad

Comoros

Congo

Congo (Democratic Republic of the)

Côte d’Ivoire

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mali

Mauritania

Mauritius

Mozambique

Namibia

Niger

Nigeria

Rwanda

Sao Tome and Principe

Senegal

Seychelles

Sierra Leone

South Africa

Swaziland

Tanzania (United Republic of)

Togo

Uganda

Zambia

Zimbabwe

(45 countries or areas)

South Asia Afghanistan

Bangladesh

Bhutan

India

Iran (Islamic Republic of)

Maldives

Nepal

Pakistan

Sri Lanka

(9 countries or areas)

217

HUMAN DEVELOPMENT REPORT 2009 Overcoming barriers: Human mobility and development Country classification

Other country groupings

Gulf Cooperation Council (GCC) Bahrain

Kuwait

Qatar

Oman

Saudi Arabia

United Arab Emirates

(6 countries or areas)

European Union (EU27) Austria

Belgium

Bulgaria

Cyprus

Czech Republic

Denmark

Estonia

Finland

France

Germany

Greece

Hungary

Ireland

Italy

Latvia

Lithuania

Luxembourg

Malta

Netherlands

Poland

Portugal

Romania

Slovakia

Slovenia

Spain

Sweden

United Kingdom

(27 countries or areas)

Organisation of Economic Cooperation and Development (OECD) Australia

Austria

Belgium

Canada

Czech Republic

Denmark

Finland

France

Germany

Greece

Hungary

Iceland

Ireland

Italy

Japan

Korea (Republic of)

Luxembourg

Mexico

Netherlands

New Zealand

Norway

Poland

Portugal

Slovakia

Spain

Sweden

Switzerland

Turkey

United Kingdom

United States

(30 countries or areas)