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38426
MIGRATION
REMITTANCES
Eastern Europe and the
Former Soviet Union
Edited by
Ali Mansoor
Bryce Quillin
This report is part of a series undertaken by the Europe and Central Asia Region of the World Bank.
The series covers the following countries:
Albania
Armenia
Azerbaijan
Belarus
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Estonia
FYR Macedonia
Georgia
Hungary
Kazakhstan
Kyrgyz Republic
Latvia
Lithuania
Moldova
Poland
Romania
Russian Federation
Serbia and Montenegro
Slovak Republic
Slovenia
Tajikistan
Turkey
Turkmenistan
Ukraine
Uzbekistan
MIGRATION AND
REMITTANCES
MIGRATION AND
REMITTANCES
Eastern Europe and the
Former Soviet Union
Edited by
Ali Mansoor
Bryce Quillin
Europe and Central Asia Region
2006
©2007 The International Bank for Reconstruction and Development/The World Bank
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Washington DC 20433
Telephone: 202-473-1000
Internet: www.worldbank.org
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All rights reserved
1 2 3 4 5 10 09 08 07 06
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ISBN-10: 0-8213-6233-X
ISBN-13: 978-0-8213-6233-4
eISBN: 0-8213-6234-8
DOI: 10.1596/978-0-8213-6233-4
Cover photo: Karen Robinson ©Panos Pictures.
Inc.
Library of Congress Cataloging-in-Publication Data
Mansoor, Ali M.
Migration and remittances : Eastern Europe and the former Soviet Union / [Ali Mansoor,
Bryce Quillin].
p. cm.
Includes bibliographical references and index.
ISBN-13: 978-0-8213-6233-4 (alk. paper)
ISBN-10: 0-8213-6233-X (alk. paper)
ISBN-13: 978-0-8213-6234-1
ISBN-10: 0-8213-6234-8
1. Migrant labor—Europe, Eastern. 2. Migrant labor—Former Soviet republics. 3. Migrant
remittances—Europe, Eastern. 4. Migrant remittances—Former Soviet republics. I. Quillin, Bryce,
1976– II. World Bank. Europe and Central Asia Region. III. Title.
HD5856.E852M36 2007
304.80947—dc22
Contents
Foreword
xi
Acknowledgments
xv
Abbreviations and Glossary
Overview
Nature and Evolution of Migration, 1990–2006
Migrant Remittances
Determinants of Migration
Regulatory Framework for International
Labor Migration
Methodology
The Report in Perspective
1. Overview of Migration Trends in Europe and Central Asia,
1990–2004
Problems with Measuring Migration in ECA
Migration and Population Change
Major Migration Flows in the ECA Region
Refugees and Internally Displaced Persons
Transit and Undocumented Migration in the
ECA Region
Major Migration Partners of the ECA Countries
Future Migration Trends in the Region
xvii
1
3
6
8
13
19
19
23
26
30
32
37
41
46
50
v
vi
Contents
2. Migrants’ Remittances
Data
Impact of Remittances on Development
Economic Impact of Remittances
3. Determinants of Migration
Incentives for Migration: A Theoretical Perspective
Incentives for Migration: Empirical Evidence from
Eastern Europe and the Former Soviet Union
Incentives for Migration: Lessons from Southern
European Countries and Ireland
Simulating the Determinants of Migration
4. International Regulatory Framework
Current Regime
A Proposal for an Alternative Framework
57
58
60
63
75
77
79
86
92
97
98
107
Appendixes
1.1
1.2
2.1
2.2
3.1
3.2
4.1
4.2
4.3
4.4
4.5
4.6
Survey Methodology
Migration Statistics
Remittance Data
Estimations of the Impact of International
Remittances on Macroeconomic Growth
Estimating the Determinants of Migration
in ECA
Computable General Equilibrium Model of
Migration
The Impact of Migrants and the Receiving
Society: Integration Policies
Transitional Arrangement for the Free
Movement of Workers from the New
Member States
Undocumented Immigration and
Vulnerabilities
Incentives for Criminality in Migration
Migrants, Their Families, and Communities
“Left Behind”
Brain Drain in the ECA Region
113
115
125
127
139
147
157
163
167
173
177
181
Bibliography
191
Index
207
vii
Contents
Boxes
1
2
2.1
3.1
3.2
3.3
4.1
4.2
Possible Costs and Externalities of Illegal
Immigration
Methodology
Estimating the Impact of Remittances on
Macroeconomic Growth
Estimating the Determinants of Migration
in ECA
Irish Migration Dynamics
Portuguese Migration Dynamics
Possible Costs and Externalities of Illegal
Immigration
Social Externalities Generated by Migration
16
20
68
83
89
90
106
108
Figures
1
2
3
4
5
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
Remittances as a Portion of GDP in Eastern
Europe and the Former Soviet Union, 2004
Percent Distribution of Remittances and
Population by Location in 2002
Disparities in GDP per Capita in the CEE-CIS
States, 1990–2002
Postwar Emigration from Southern Europe,
1960–88
Migrants’ Preferences for Short versus
Long-Term Migration
Transition of the Migration System in the
Europe and Central Asia Region
Migration in Top 10 Sending and Receiving
Countries and by Region, 2003
Natural Increase (Decrease) and Net Migration
in the ECA Region, 2000–03
Net Migration in Western ECA and the CIS
Largest Migration Flows Involving a CIS
Country, 2000–03
Largest Migration Flows Involving a Western
ECA Country, 2000–03
Main Displaced Populations from the Former
Yugoslavia, December 1995
Main Displaced Population from the Former
Soviet Union, Mid–1990s
Refugees and Internally Displaced Persons in
the ECA Region, 1989–2003
6
9
10
12
18
24
25
31
33
36
37
38
39
39
viii
Contents
1.10 Largest Numbers of Refugees, IDPs, and
Others of Concern in the ECA Region, 2004
1.11 Russia, Net Migration by Country,
1989–2003
1.12 Major Migration Partners of the CIS Countries
1.13 Poland: Net Migration by Country,
1992–2003
1.14 Major Migration Partners of Selected
Western ECA Countries
1.15 Population Size of Western Europe,
Western ECA, and Turkey, 1950 to 2050
1.16 Population Size of the Northern and
Southern FSU States, 1950 to 2050
1.17 Russia: Net Migration and Natural Increase,
1980–2015
2.1 Leading 20 Remittance-Receiving Countries
in the World
2.2 Remittances as a Portion of GDP in Eastern
Europe and the Former Soviet Union, 2004
2.3 Growth Rate of Remittances in ECA: 1995–98,
2001–04
2.4 Remittances as a Share of Exports in 2003
2.5 Expenditure Patterns from Remittances in
Six ECA Countries
2.6 Remittances as Share of Total Household
Expenditure in 2004
2.7 Distribution of Remittances by Location
in 2002
2.8 Distribution of Population by Location in 2002
3.1 Nationality Composition of Migration to Russia,
1989 to 2003
3.2 Disparities in GDP per Capita in the CEE-CIS
States, 1990–2002
3.3 Net Migration in Selected Immigration
Countries in ECA, 1989–2003
3.4 Net Migration in Selected Emigration Countries
in ECA, 1989–2003
3.5 Postwar Emigration in Southern Europe,
1960–88
3.6 Percentage Decrease in Total Migration Flows
into the EU Owing to Improvements in
Quality of Life
40
47
48
50
51
52
54
55
58
59
60
63
64
65
70
71
80
81
85
85
87
93
ix
Contents
3.7
4.1
4.2
Percentage Increase in Migration Outflows
from EU-15 to ECA Countries Owing to
Improvement in Quality of Life in ECA
Migrants’ Preferences for Short- versus
Long-Term Migration
Percentage Decrease in Illegal Migration
into the EU Owing to Increase in Penalty for
Hiring Illegally
94
110
111
Tables
1.1
1.2
Population by Place of Birth in the USSR, 1989 29
Migration Flows Involving ECA Countries,
35
2000–03
1.2a Total Migration Flows Involving ECA Countries
and Major Partners, 2000–03
35
1.2b Percent of Total Emigration
35
1.2c Percent of Total Immigration
35
1.3 Estimated Irregular Migrants
45
2.1 Remittance Flows by Subregion, 2003
61
2.2 Annual Consumption and Remittances
per Capita by Quintile
72
3.1 Motivations for Migration
78
4.1 Regional Composition of Bilateral Agreements 100
4.2 Geographical Distribution of Bilateral
Migration Agreements between CEEC and
101
EU-15
4.3 Bilateral Migration Agreements between
102
the EU and CEECs by Country and Type
4.4 Number of Registered Foreigners and
Estimated Number of Aliens Living Irregularly
in Some CIS Countries, 2000
104
Foreword
The countries of Eastern Europe and the former Soviet Union have
been reintegrating into the world economy following the dissolution
of the Soviet economic network. The Europe and Central Asia Region
of the World Bank has undertaken a multivolume analysis of the
processes that have influenced this transition period. This volume,
Migration and Remittances: Eastern Europe and the Former Soviet Union,
focuses on international migration. The core of the report documents
the history of migration and remittances since transition and discusses the determinants of migration. A final chapter lays out some
tentative policy interventions that might enhance the gains from
migration and remittances for net immigration and emigration countries and for migrants and their families.
Migration is important for the economies of this region because
many of the world’s largest international migration flows emanate
from and flow to the countries of Eastern Europe and the former
Soviet Union. The distinctive patterns of migration experienced since
transition will continue to exert an important impact on growth and
development in the near future.
The early years of transition witnessed high levels of cross-border
migration as populations that were previously unable to move due to
Soviet restrictions relocated to their ethnic or cultural homelands.
These “diaspora” flows emerged simultaneously with refugee move-
xi
xii
Foreword
ments that resulted from the eruption of civil and transborder conflicts among the newly emergent countries of the area. However, as
conflicts abated and economic reforms took root in the last five to
seven years, economic motivations became the key driver of migratory flows.
The result of these trends has been a broad biaxial pattern of migration flows among the transition economies: one axis from the western part of the region to the European Union (EU) and another axis
from the southern to the northern countries of the Commonwealth
of Independent States (CIS). However, this broad generalization
should not obscure the more complex patterns of movement.
Although the majority of migrants from the poorer CIS countries
travel to the middle-income CIS countries, many also move west in
search of higher earnings, toward the EU and Turkey. A number of
CIS migrants may spend short or long periods in Central and Eastern
European countries or Turkey in the hope of moving to Western
Europe.
Migration creates challenges and opportunities for sending and
receiving countries. For many net emigration countries in ECA,
household income and national output are strongly tied to the
incomes of migrants living and working abroad. Cross-country
growth studies conducted for this report indicate that remittances
have a positive impact on long-term economic growth. Migration can
allow migrants to learn new skills and can facilitate cross-border trade
and investment linkages. Moreover, labor-importing CIS economies
and the neighboring EU rely on migrant labor from the region to
maintain rates of economic growth and standards of living.
Yet, working abroad can expose migrants to risks of abuse or trafficking, particularly those that work abroad illegally and do not have
recourse to legal channels. Migration can also create social dislocation
by separating families for long periods. For the sending countries,
large-scale migration can deprive the economy of needed skills. For
the receiving country, migration can create social friction and possibly
security risks.
This study finds that the benefits that sending countries and
migrants secure from migration and associated remittances are at
least partly conditional on the quality of economic, social, and political institutions and policies in those countries. Improvements in the
overall quality of life in sending countries have the potential to (a)
reduce out-migration rates, (b) induce migrants in the diaspora to
return home, and (c) provide incentives for migrants to use the
human and financial capital, including remittances, accumulated
abroad at home.
xiii
Foreword
Migration sending and receiving countries could more closely
coordinate migration policy so that the supply of international
migrant labor can better meet demand through legal channels that
respect the rights of migrants and are politically and socially acceptable to migrant-receiving countries. Though bilateral labor agreements represent a promising route for enhancing the gains to
migration in this region, the nature and content of these agreements
need to reflect the actual demand for migrant labor.
In particular, managed-migration programs between sending and
receiving countries might combine short-term migration with incentives for return or circular migration. Circular migration programs
may be an important step in resolving a key migration paradox: there
is demand for migrant labor yet often little public support for permanent migration—particularly unskilled migration—in the many
European and middle-income CIS countries in demographic decline.
Moreover, circular migration may have the potential to facilitate
development in migration-sending countries by increasing migrants’
human and financial capital, facilitating international skills transfers,
building cross-border trade and investment, and preventing the
long-term separation of families.
There are no ready-made solutions for migration reform in the
Europe and Central Asia region. The complexity of migration and the
poor data on migration and remittances require that policy recommendations be qualified. The exact mix of international and domestic
policies needed to balance supply and demand varies according to the
demographic and economic characteristics of the countries in question. In addition to the benefits that a stable and equitable business
and social climate and good quality of governance have for economic
growth and poverty reduction generally, such policies will improve
the returns to migration for migration-sending and receiving countries and migrants themselves.
The study, part of a new series of regional studies, is intended as a
contribution to the World Bank’s goal to work more effectively with
clients and partners in the Region to reduce poverty and foster economic growth by enhancing gains from international labor migration. It complements recent studies on growth, poverty, and
inequality, job opportunities, and on trade and integration in the
Region. I hope that these studies stimulate debate, promote better
understanding, and spur action to bring about prosperity for all.
Shigeo Katsu
Vice President
Europe and Central Asia Region
Acknowledgments
This study was prepared by a core team led by Ali Mansoor and Bryce
Quillin, who were the main authors, and comprising Anders Danielson, Timothy Heleniak, Kathleen Kuehnast, Theodore Lianos, Rainer
Münz, Maria Stoilkova, Philippe Wanner, and Alessandra Venturini.
It also draws on the inputs of Pritam Banerjee, Natalia Catrinescu,
Taras Chernetsky, Carine Clert, Betsy Cooper, Shushanik Hakobyan,
Elena Kantarovich, Elaine Kelly, Ben Klemens, Marek Kupiszewski,
Marianne Kurtzweil, Miguel Leon-Ledesma, Diana Marginean, Margaret Osdoby-Katz, Eric Livny, Panagiota Papaconstantinou, Chris
Parsons, Marina Lutova, Matloob Piracha, Sherman Robinson,
Makiko Shirota, Valerie Stadlbauer, and Saltanat Sulaimanova.
The study was supported by the essential guidance of Pradeep
Mitra, Chief Economist of the Europe and Central Asia Region. The
team gratefully acknowledges suggestions and comments from Arup
Banerji, Nora Dudwick, Willem van Eeghen, Alan Gelb, Daniela Gressani, Ellen Hamilton, Jariya Hoffman, Robert Holzmann, Nadir
Mohammed, Fernando Montes-Negret, Jaime de Melo, Dominique
van der Mensbrugghe, Irena Omelaniuk, Caglar Ozden, Pierella Paci,
Martin Raiser, Dilip Ratha, Maurice Schiff, Dennis de Tray, Merrell
Tuck-Primdahl, Alan Winters, and Ruslan Yemtsov. The team benefited from advice and comments provided by Yuri Andrienko (Center
of Economic and Financial Research, Russia), Lev Palei (International
xv
xvi
Acknowledgments
Monetary Fund), Louka Katseli (Organisation for Economic Cooperation and Development), Gregory Maniatis (Migration Policy
Institute), Demetrios Papademetriou (Migration Policy Institute),
Alexandros Zavos (Hellenic Migration Policy Institute, Greece),
Alexander Sarris (Food and Agriculture Organization of the United
Nations), and Thomas Timberg (Nathan Associates Inc.). The team
thanks participants of the 2005 Migration Policy Institute–Hellenic
Migration Policy Institute conference on “Capturing the Benefits of
Migration in Southeastern Europe” held in Athens October 11–12.
Helpful comments and suggestions were provided during the
presentation of earlier drafts of this report in 2005 to the European
Commission; the Organisation for Economic Co-operation and
Development; the Kennan Institute at the Woodrow Wilson International Center for Scholars; the International Organization for
Migration; the International Labor Organization; the U.K. Department for International Development; the Centre on Migration, Policy and Society at Oxford University; and the Development Studies
Institute at the London School of Economics.
Book design, editing, and production were coordinated by the
World Bank’s Office of the Publisher. Ian McDonald edited the
manuscript.
Abbreviations and
Glossary
BOP
CEECs
CES
CGE
CIS
CPIA
DPD
EBRD
ECA
balance of payments
Central and Eastern European Countries, consisting
of Albania, Bosnia and Herzegovina, Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia,
Lithuania, the former Yugoslav Republic of Macedonia, Moldova, Poland, Romania, Serbia and Montenegro, the Slovak Republic, and Slovenia
constant elasticity of substitution
computable general equilibrium
Commonwealth of Independent States
country policy and institutional assessment
dynamic panel data
European Bank for Reconstruction and
Development
The Europe and Central Asia region of the World
Bank is an administrative regional country grouping.
It consists of Albania, Armenia, Azerbaijan, Belarus,
Bosnia and Herzegovina, Bulgaria, Croatia, the Czech
Republic, Estonia, Georgia, Hungary, Kazakhstan,
Kyrgyz Republic, Latvia, Lithuania, the former
Yugoslav Republic of Macedonia, Moldova, Poland,
Romania, the Russian Federation, Serbia and Montenegro, the Slovak Republic, Slovenia, Tajikistan,
Turkey, Turkmenistan, Ukraine, and Uzbekistan.
xvii
xviii
Abbreviations and Glossary
EU
EU-15
EU-8
FSU
FYR
GATS
GDP
GMM
GNP
GTAP
IDP
ILO
IMF
IOM
OECD
PPP
UN
UNHCR
WTO
Western ECA
European Union
Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Sweden, Spain, and the United
Kingdom
The Czech Republic, Poland, Hungary, the Slovak
Republic, Slovenia, Latvia, Lithuania, and Estonia
Former Soviet Union
Former Yugoslav Republic (of Macedonia)
General Agreement on Trade in Services
gross domestic product
generalized method of moments
Gross National Product
Global Trade Analysis Project
internally displaced persons
International Labour Organization
International Monetary Fund
International Organization for Migration
Organisation for Economic Co-operation and Development
purchasing power parity
United Nations
United Nations High Commission for Refugees
World Trade Organization
The Czech Republic, Poland, Hungary, the Slovak
Republic, Slovenia, Latvia, Lithuania, Estonia,
Romania, Bulgaria, Bosnia and Herzegovina, Serbia
and Montenegro, Albania, Croatia, and FYR Macedonia
Overview
Migration has been an important part of the transition process in
Europe and Central Asia (ECA),1 and continues to be relevant as
these countries move beyond transition. Labor migration is likely to
gain in importance in view of the aging of populations in Europe and
some parts of the former Soviet Union.
Migration in the region is unique and significant: ECA accounts for
one-third of all developing country emigration and Russia is the second largest immigration country worldwide. Migrants’ remittances,
as a portion of gross domestic product, are also large by world standards in many countries of the region.
Economic motivations currently drive migration flows in ECA.
This was not the case in the initial transition period, which unlocked
large flows reflecting the return of populations to ethnic or cultural
homelands, the creation of new borders, political conflict, and the
unwinding of restrictions placed on movement by the Soviet system.
Nor will it be the case in about a decade, when demographics will
begin to dominate motivations for migration. However, for now market opportunities and the reintegration of ECA countries into the
world economy spur labor migration.
Incentives for permanent and large quantities of undocumented
migration may exist because of the structure of many of the immigration policies governing migration from ECA to Western Europe
1
2
Migration and Remittances: Eastern Europe and the Former Soviet Union
and the migration-receiving countries of the Commonwealth of
Independent States (CIS). Immigration policies distinguish
between skilled and unskilled labor and the policies increasingly
recognize the value of skilled labor, which is partly covered by the
World Trade Organization’s General Agreement on Trade in Services (GATS). However, policies on unskilled labor often focus too
heavily on controlling a very large supply through border controls
without looking to efficiently match this supply with the domestic
demand for low-skilled migrant workers. As a result, such policies
can fail to contain a large and growing population of undocumented migrants. The report focuses, where distinctions are relevant, on the case of unskilled labor migration because existing
international migration policies often poorly address this form of
cross-border movement.
Migration-sending countries can contribute to the slowing of outmigration by accelerating economic and political reforms and thus
the associated expectation that the quality of life will rapidly
improve. Receiving countries could increase the payoff from migration by accepting and factoring the demand side of the equation into
policies designed to minimize undocumented migration. In doing so,
the negative consequences of undocumented migration—including
the inefficient distribution of resources, hindrances to sending remittances, and the inhibiting of circular migration patterns—could be
avoided.
The core focus of this report is on documenting the trends of international migration and remittances in this region since the period of
transition (chapters 1 and 2) and discussing the determinants of
migration in this region (chapter 3). A final chapter (chapter 4)
reviews the organization of international migration policy in the
region. It details the nature and types of bilateral migration schemes
in place between ECA countries and between ECA and Western
Europe and identifies some of their limitations. The final section of
chapter 4 suggests some avenues through which bilateral migration
agreements could be improved. The ambition of this section is
explore how bilateral migration agreements could reduce the incentives for undocumented migration while minimizing the cultural
and social frictions from increased migration in the receiving country. The viability of this proposal has not been tested so it is suggested
that this proposal could form the basis for pilot programs in the
future.
This overview chapter summarizes the main findings that are
developed in much greater detail in later chapters of Migration and
Remittances: Eastern Europe and the Former Soviet Union.
Overview
Nature and Evolution of Migration, 1990–2006
Migration in Eastern Europe and the CIS is large by international
standards. If movements between industrial countries are excluded,
ECA accounts for over one-third of total world emigration and immigration. There are 35 million foreign-born residents in ECA countries.
Overall, several ECA countries are among the top 10 sending and
receiving countries for migrants worldwide. Russia is home to the
second largest number of migrants in the world after the United
States; Ukraine is fourth after Germany; and Kazakhstan and Poland
are respectively ninth and tenth.
The collapse of communism encouraged a massive increase in
geographic migration in the ECA region, including internal movements, cross-border migration within ECA, outflows from ECA, and
some inflows from other regions. The formation of many new countries following the breakup of the Soviet Union “created” many statistical migrants—long-term, foreign-born residents who may not
have physically moved, but were defined as migrants under UN
practice.
Migration flows in ECA tend to move in a largely bipolar pattern.
Much of the emigration in western ECA (42 percent) is directed
toward Western Europe, while much emigration from the CIS countries remains within the CIS (80 percent). Germany is the most
important destination country outside ECA for migrants from the
region, while Israel was an important destination in the first half of
the 1990s. Russia is the main intra-CIS destination. The United Kingdom, in particular, is becoming a destination for migrants from the
ECA countries of the European Union (EU) who are temporarily
barred from legal access to many of the other EU-15 labor markets.
The number of undocumented migrants from ECA countries in
Western Europe and the CIS is believed to be large but, by definition,
is difficult to quantify. Currently, there are estimated to be upward of
3 million undocumented immigrants in the EU, and between 3 million and 3.5 million in Russia.
Migration and Population Change
ECA countries display significant variation in terms of the direction of
migration flows and their impact on net population changes. From
2000 to 2003, ECA countries were about evenly split between those
that registered a natural decline in population—in which the number
of deaths exceeded births (13)—and those that registered population
increases (14). In the EU, both Germany and Italy already have
3
4
Migration and Remittances: Eastern Europe and the Former Soviet Union
declining populations and many other EU countries are expected to
show natural decreases in the future as their populations age.
Of the 14 ECA countries with a natural increase in population,
• Nine countries registered net emigration during 2000–03 with
Turkey achieving near parity (that is, having nearly equal amounts
of emigrants and immigrants). We anticipate that within this group
migration pressures will persist unless economic reforms can lead
to rapid increases in the quality of life and standard of living.
• Three countries appear to have an increase in population not only
due to demographic causes, but also owing to a positive net migration balance.
Of the 13 ECA countries with a natural decline in population,
• One group of seven comprises countries experiencing population
declines owing to both more deaths than births and more emigration than immigration. This group includes Bulgaria, Latvia,
Lithuania, Moldova, Poland, Romania, and Ukraine.
• A final group comprises net-immigration countries with declining
populations, in which immigration is insufficient to offset the natural population decline. This group includes Belarus, Russia, and
the Central European countries that are new EU members.
Internal displacement continues to be substantial within the ECA
region. Internal displacement refers to migration within the country
owing to strife or economic motivation. In 2003, the largest concentrations of internal displacement resulting from conflict were in Azerbaijan (576,000) and Georgia (262,000). These numbers are down
only slightly from peaks in the mid-1990s as the conflicts that gave
rise to them continue to persist without any permanent settlement.
Internal displacement for economic reasons can also have substantial repercussions. Concentrations of direct foreign investment, trade,
and other economic opportunities leading to greater urban agglomeration can draw in large numbers of people, leaving other parts of the
country somewhat depopulated. For example, according to the 2002
Russian census, Moscow has grown from 1.5 million inhabitants at
the start of transition to 10.4 million. This growth arises because the
bulk of both domestic and foreign investment, overall job growth,
and job creation in sectors of the “new economy” are concentrated in
Russia’s capital. At the other end of the urban spectrum are a large
number of “ghost towns”—population settlements where census takers expected to find people but on census day discovered they were
completely depopulated.
Overview
In recent years, migration may have declined for many ECA countries compared with the period following transition. Immigration
countries, such as Russia, receive less net immigration, while emigration countries register lower outflows. This is consistent with the view
that the early period of transition was marked by ethnic and conflictdriven migration, while later, as the situation stabilized, migration
became mainly economically motivated. The one exception is
Ukraine, where transit migration may have increased.
The total population of the EU-8 accession countries and the
Balkans declined overall by 1.1 million and by more than 2.7 million,
respectively. This decline is related both to a natural population
decrease and to migration. While all these countries had negative net
natural-population growth, in the Czech Republic and Slovenia the
total population grew because of net gains from migration. Labor
migration in these states is still relatively small when compared with
both population size and the size of the workforce. Furthermore, the
great majority of migrant workers come from neighboring countries
and regions. EU membership and the rise in sustained foreign investment, however, will create the demand for additional, most probably
foreign, labor.
With the breakup of the Soviet Union in 1991, there was a rapid
shift in the causes and patterns of migration. Russia gained 3.7 million persons through migration and became a net recipient of migration from all the other states of the CIS and the Baltics, except for
Belarus. At the same time, 15 percent or more of the populations of
Armenia, Albania, Georgia, Kazakhstan, and Tajikistan migrated permanently, many of them the better-educated and younger elements
of society.
Future Trends
While economic factors will continue to be important drivers of
migration (see chapter 3), demographic patterns will also play an
increasingly important role. Migration flows that are generated in the
short term may be unsustainable in a decade owing to the mediumterm population dynamics in most of the ECA region. With the exception of Albania and Turkey, all Central and Eastern European
countries are forecast to experience population declines, many of
them greater than in the destination countries.
The decline in the working-age population will create a demand
for workers that can only be sourced from abroad. The more prosperous EU-8 countries and middle-income CIS countries may be able to
obtain some of these workers from the rest of the region. However,
5
6
Migration and Remittances: Eastern Europe and the Former Soviet Union
for the region as a whole, demand will have to be met from elsewhere, probably from Africa and Asia. Whether these flows are legal
or undocumented will depend on future immigration legislation.2
Migrant Remittances
Relative to GDP, remittances are significant in many ECA countries.
In 2004, officially recorded remittances to the ECA region totaled
over US$19 billion, amounting to 8 percent of the global total for
remittances (US$232.3 billion) and over 12 percent of remittances
received by developing countries (US$ 160.4 billion).3
For many ECA countries, remittances are the second most important
source of external financing after foreign direct investment. For many of
the poorest countries in the region, they are the largest source of outside
income and have served as a cushion against the economic and political
turbulence brought about by transition. Migrants’ funds represent over
20 percent of GDP in Moldova and Bosnia and Herzegovina, and over
10 percent in Albania, Armenia, and Tajikistan (figure 1).
FIGURE 1
Remittances as a Portion of GDP in Eastern Europe and the Former Soviet Union, 2004
Source: IMF, Balance of Payment Statistics.
Notes: 1. Received remittances = received compensation of employee + received worker’s remittances + received migrants’ transfer.
2. Albania and Slovak Republic are 2003 data, other countries 2004 data.
3. GDP is $ converted current price.
Overview
Generally remittance flows in ECA follow the same two-bloc pattern
as migration. The EU and the resource-rich CIS are the main sources of
remittances, with the EU accounting for three-quarters of the total and
the rich CIS countries for 10 percent. The amount contributed by the
EU-8 and accession countries is also significant, just below the 10
percent level.
Remittances recorded in the balance of payments undercount
transfers between migrants and their families. According to surveys
with returned migrants prepared for this study, between one-third and
two-thirds of migrants, depending on their country of origin, used
informal channels—or methods outside of the formal financial system
such as bank transfers—to transmit remittances at some point.4 Specifically, the surveys indicated that an average of 41 percent of ECA
migrants reported using an informal channel to transfer remittances,
such as public transportation drivers, friends, or family. Only two
countries in ECA—Moldova and Russia—attempt to capture remittances sent through these informal channels in the balance of payments statistics.5 Thus, official remittances figures tend to undercount
the actual flows by the amount sent through these informal networks
in most instances.
Remittances can exert a positive impact on macroeconomic
growth. Cross-country regressions indicate that remittances can have
a positive, although relatively mild, impact on long-term growth.
Moreover, remittances have a positive impact on poverty reduction
for the poorest households. Household budget surveys indicate that
remittances constitute over 20 percent of the expenditure of households in the poorest quintile.
Remittances represent an important source of foreign exchange for
several ECA countries.
• The high-migration countries earn from remittances over 10 percent of the amount exports of goods and services bring in.
• In Moldova and Serbia and Montenegro, remittances bring in foreign exchange equivalent to almost half of export earnings.
• For Albania and Bosnia and Herzegovina, the contribution of
remittances is almost as large as that of exports.
At the same time, the inflow of remittances may serve to raise the
real exchange rate, harming competitiveness.
Unrecorded remittances appear to be crucial in explaining the continued high current-account deficit in many ECA high-migration
countries. For Albania, Bosnia and Herzegovina, Moldova, Serbia and
Montenegro, and Tajikistan, the current account was large but
7
8
Migration and Remittances: Eastern Europe and the Former Soviet Union
unrecorded remittances were estimated to be significantly larger than
the negative balances on the current account.
Because they are a significant source of foreign exchange, remittances can improve creditworthiness and access to international capital markets for many ECA countries. For example, if remittances are
included as a potential source of foreign exchange, the ratio of debt to
exports falls by close to 50 percent for Albania and Bosnia and Herzegovina. Unlike capital flows, remittances do not create debt servicing
or other obligations. As such, they can provide financial institutions
with access to better financing than might otherwise be available.
Among ECA countries, Turkey has been in the lead in using such
remittance securitization, but Kazakhstan has also used this instrument to raise financing (World Bank 2006).
Because remittances per se do not lower anyone’s income, the impact
on poverty is beneficial. A recent analysis by Adams and Page (2003)
finds that a 10 percent increase in the share of migrants in a country’s
population will lead to a 1.9 percent decline in the share of people living
on less than US$1 a day. A review of the urban-rural distribution of
remittances for selected ECA countries indicates that different countries
are characterized by different patterns. Information from Household
Budget Surveys suggests that in Central Asian countries, most remittances go to rural areas, while in the Caucasus the bulk go to metropolitan areas and cities. The pattern is dictated by the different regions from
which migrants originate (figure 2). In the Caucasus, it appears that
families that receive a higher income as a result of remittances tend to
move to urban areas, which are considered safer and more convenient.6
Remittances to the ECA region have the potential to improve
income levels and standards of living for both individuals and nations.
The greatest potential benefit is enhanced economic growth, driven
by consumption and investment. Increasing the volume of remittances sent through formal channels involves lowering the cost of
regular payments. The extent to which increased remittance flows
can deliver sustainable economic growth will depend partly on the
quality of institutions and institutional development in the migrants’
home countries. It is, therefore, crucial to address institutional weaknesses and governance if remittance income is to be translated into
sustained advances in economic development.
Determinants of Migration
Despite the great variation in the migration patterns across the region
and the extremely complex combination of economic and social moti-
9
Overview
FIGURE 2
Percent Distribution of Remittances and Population by Location in 2002
100
percent
75
50
25
0
Albania
capital city
Armenia
other urban areas
Georgia
Kyrgyz Rep.
Tajikistan
rural areas
Source: World Bank, Household Data Archive for Europe and Central Asia.
Note: Tajikistan data are from 2003.
vations for migration, a number of similar motivations seem to underpin the decisions to migrate. International migration is often
explained by a basic push-and-pull model: economic conditions,
demographic pressures, and unemployment (“push factors”) in the
sending countries work in coordination with higher wages, demand
for labor, and family reunification (“pull factors”) in the migrationreceiving countries (Smith 1997).
Disparities in GDP per capita have widened considerably in the
ECA. One simple explanation for migration trends among the ECA
countries, based on traditional migration theory, is that widening disparities in GDP per capita drive migrants from lower-income to
higher-income countries. Countries such as those of the former Soviet
Union have attempted to equalize incomes among social groups and
also among regions, which was accomplished through a massive and
elaborate system of subsidies, transfers, and controlled prices. With
independence and economic transition, levels of GDP per capita have
10
Migration and Remittances: Eastern Europe and the Former Soviet Union
widened considerably among the ECA countries, and have become a
factor driving migration where this was not the case previously.
According to figure 3, the coefficient of variation in per capita GDP
among the ECA countries for the period 1990–2002 increased from
0.43 in 1990 to 0.70 in 1997, before declining slightly.
Yet, GDP per capita disparities do not fully explain migration trends
in ECA. The links between flows and income differentials are too
weak to make such differentials a viable explanation without additional qualifiers such as ethnic and political considerations, expectations of quality of life at home, and geography. Though the above
data are illustrative of the widening income levels among ECA countries during transition, they are somewhat misleading because the
two countries with the highest and lowest per capita GDPs in 2002
were Slovenia and Tajikistan. Given the distance between the two
countries and various other factors, there is not expected to be much
migration from Tajikistan to Slovenia. More telling are the income
disparities between migration spaces of geographically adjacent
groups of countries, in this case the CIS and Europe, the latter including both Eastern and Western Europe.
The perceptions of (potential) migrants of economic possibilities at
home and abroad contribute to population movements. What
emerges from this study is a complex picture indicating that expected
income differences, the expected probability of finding employment
abroad, and expected quality of life at home play a strong role in
FIGURE 3
Disparities in GDP per Capita in the CEE-CIS States, 1990–2002
(PPP current international dollars)
25
0.80
0.70
coefficient of variation
0.60
0.50
15
ratio of country with highest to
country with lowest GDP
10
0.40
0.30
0.20
5
0.10
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Source: World Bank, World Development Indicators.
Note: CEE = Central and Eastern European; PPP = purchasing power parity.
0.00
coefficient of variation
ratio of highest to lowest
20
Overview
many cases but a role tempered by the influence of numerous other
variables. Evidence for the importance of these noneconomic drivers
of migration is partly given by statistical tests, yet the poor nature of
migration data in the region of the period since transition may cast
doubt on the utility of these results. More robust information on the
drivers of current trends and forecasts of the future is provided
through looking at the history of migration from the Southern European countries and Ireland and through simulations.
Experience of Southern Europe and Ireland
The migration histories of Southern Europe and Ireland—which realized a shift from being net emigration to net immigration countries
during the post–World War Two period—are useful for understanding
and predicting patterns of migration for the Central and Eastern European countries. First, these western ECA countries, like Ireland and
all Southern European countries, are geographically near the EU.
This proximity is not only physical but also cultural—languages and
social traditions are comparable. Additionally, Southern European
countries and Ireland, as we see with ECA countries now, were
poorer than their destination countries. While there are clearly distinctions between the Southern European countries and Ireland and
the ECA countries, the similarities are sufficient that a study of the
migration history of the former may provide a reasonable amount of
evidence about current and future trends.
The history of migration from the Southern European countries
and Ireland to the wealthier European Community members during
the period of the 1960s through the 1980s suggests the importance of
expected income differentials and expected improvements in domestic policy in motivating migration. In Southern Europe and Ireland,
for example, emigration rates initially accelerated as these countries
became more integrated into the regional economy, as has occurred
for many ECA countries since transition. However, this increase was
also associated with a shift from long-term to shorter-term migration,
suggesting greater interest in return migration which, in fact, then
materialized.
Looking at the patterns illustrated in figure 4, the surge in Italian emigration to the United States at the beginning of the last century was due
not to an increase in poverty but to an increase in income and employment growth at the beginning of Italian industrialization (Hatton and
Williamson 1994). The surge of Spanish emigration to other European
countries in the period 1960–74 was the result of a growth rate higher
than in the other European countries.7 The peak of Portuguese emigra-
11
12
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 4
Postwar Emigration from Southern Europe, 1960–88
gross emigration (thousands)
20
15
10
5
0
1960
1962
Greece
1964
1966
Italy
1968
1970
Portugal
1972
1974
1976
1978
1980
1982
1984
1986
1988
Spain
Source: Venturini 2004.
tion in the 1970s also took place during a growth phase, and Greece’s
emigration rates rose during the economic boom of the 1960s.
Possibilities of EU membership may also influence the desire to
migrate. The slowing emigration from Southern Europe in the second
half of the 1970s was the result of lower incentives to migrate owing in
part to the large investments made by the EU in these countries before
their accession (figure 4). Such investments in turn led to expectations
of a higher quality of life in these countries. Membership in the EU also
played a role in Italy’s turnaround from a net emigration to a net immigration country. First, in the period before Italy’s entry into the EU, the
country implemented reforms that increased the quality of life and
facilitated the development of its goods market. Second, transfers from
the European Structural Fund after entry were an additional source of
growth and improvement in the quality of life and delivery of public
services. This growth also increased domestic demand for labor in Italy.
Third, expectations of future growth may have been as important as
current jobs in modifying the expectations of potential migrants.
Fourth, the freedom to move can actually reduce migration in the short
term because potential migrants are free to put off the move until later.
Simulations
The results from a simulation of the determinants of migration suggest that an improving quality of life at home can slow out-migration
Overview
even when income differentials between countries exist. In other
words, the policies of migration-sending countries create the incentives for migration and return migration.
The results show that with an increase in the quality of life in sending countries, migration flows into the EU are reduced from all ECA
regions. For western ECA countries, legal migration flows fell
between 0.6 and 1 percent. Migration also fell for the countries of the
former Soviet Union and Turkey though by a reduced amount.
The model also suggests that the possibility of improvements in the
quality of life increased return migration or circular migration—the
process in which migrants return home for short periods before
migrating again. An improvement in the quality of life in ECA countries led to increased flows from the EU-15 to all ECA countries.
Migration flows from the EU-15 into western ECA increased around
1 percent and around 0.5 percent for the former Soviet Union and
Turkey.
Regulatory Framework for International Labor Migration
Multilateral efforts to address migration have been related almost
exclusively to the Mode 4 framework of the General Agreement on
Trade in Services (GATS). Mode 4 addresses the provision of services
through the cross-border movements of citizens of the World Trade
Organization (WTO) member countries. Its introduction generated
initial optimism that a broader liberalization of labor markets could
follow. A commitment to deepen the coverage of Mode 4, however,
has not yet emerged. Even though services represent over 70 percent
of the GDP of developed economies, only a very small portion of
international migrants qualify as “service providers” by WTO standards. WTO provisions currently focus on extending freedom of passage to a limited subset of international migrants in multinational
firms. Thus, the provisions and any proposed revisions to them have
little consequence for unskilled migrants at present.
Unlike trade liberalization in products and other services, providing for the free movement of people generates a number of negative
externalities stemming from the values, rights, responsibilities, and
risks that migrants may pose. As a result, GATS protections are only
extended to “natural persons” who intend to relocate temporarily or
provide a service abroad.
Most legal labor migration is facilitated by direct agreements
between migration-sending and receiving countries. The current system is a series of several types of bilateral agreements that appear
13
14
Migration and Remittances: Eastern Europe and the Former Soviet Union
largely uncoordinated between recipient countries. Only a few countries account for most of the agreements on both the sending and the
receiving sides in ECA.
Like the migration flows they regulate, bilateral agreements have a
strong bipolar regional orientation. Most of the agreements involving
western ECA (82 percent) are with Eastern European countries. Likewise, a large majority (64 percent) of CIS bilateral agreements are
with other CIS members, particularly Russia. The overall number of
bilateral agreements increased rapidly in the 1990s, largely as a result
of the collapse of the Soviet Union and the breakup of Yugoslavia. Of
the existing 92 agreements, 75 percent were signed after 1989. On
the EU side, half of the existing bilateral agreements covering labor
migration have been signed by Germany, the largest destination for
western ECA migrants. Of the EU-15 as a whole, 14 countries have
bilateral agreements with the western ECA countries (Denmark is the
only exception).
The need for bilateral agreements between the countries of Western and Eastern Europe will expire as the former obtain membership
in the EU’s single labor market. Since the accession of the EU-8 countries to the EU in May 2004, only eight countries have opened their
labor markets to the new member states. Ireland, Sweden, and the
United Kingdom never had restrictions on workers from the EU-8.
Greece, Finland, Spain, and Portugal lifted restrictions in May 2006.
Italy ended the transitional arrangements in July 2006. France, Belgium, and Luxembourg softened their restrictions on workers from
the EU-8. The transitional arrangements following the enlargement
of the EU8 allow the EU-15 to postpone the opening of their labor
markets for up to seven years. As a result, bilateral agreements may
retain some importance in facilitating intra-European migration for
the short term.
The current regulatory framework of legal migration flows in the
CIS is characterized by a series of regional and bilateral agreements on
labor activity and social protection of citizens working outside of their
countries. The main regional agreement is the “Agreement on cooperation in the field of labor migration and the social protection of migrant
workers,” accepted in 1994 by all of the CIS states. This agreement,
however, did not come into force because it must be implemented
through bilateral agreements, which were never signed (IOM 2002).
Russia has concluded the most bilateral agreements (with nine out
of the eleven CIS member states). Belarus has concluded the next
largest number of bilateral agreements, with six other CIS countries.
Kazakhstan and Ukraine have concluded four each. Kazakhstan, the
main receiving country in Central Asia, has no agreements with its
Overview
Central Asian neighbors except for an agreement with the Kyrgyz
Republic on the labor activities and the social protection of labor
migrants working in the agricultural sector in the border areas.
The bilateral agreement frameworks may fail to meet their stated
objectives in many instances. To the degree that the objective of these
agreements is to facilitate legal international migration, these do not
appear to be always successful as indicated by the high levels of
undocumented migration in the region (chapter 1). Large amounts of
irregular migration can impose significant social, economic, and
national security costs on receiving and sending countries (see box 1).
Moreover, undocumented migrants are more likely to be subject to
abuse.9
The failure of these agreements to stem undocumented migration
may reflect several weaknesses. First, there may be high bureaucratic
costs for migrants to bear in applying for many of these programs.
Also, the high demand for undocumented labor in the receiving
countries in the EU and CIS suggest that these agreements may have
insufficient quotas.
Finally, most agreements do not contain mechanisms to encourage
temporary or circular migration. If it is costly for potential migrants to
apply for a space on a temporary migration program, they may well
have an incentive to remain abroad—even if through illegal channels
by overstaying their visas—for longer periods than they prefer. Surveys with returned migrants conducted for this report found that
most migrants would prefer to spend shorter times abroad then
return home. Agreements that facilitate this temporary migration
while opening up the option to migrate abroad at a later stage with
relatively low transactions costs might represent an improvement
over the current system.
The Role for International Public Policy:
The Contours of a Policy Proposal
The final section of the report identifies some general means through
which bilateral migration agreements could be improved, yet all policy suggestions must be heavily qualified. As the United Nation’s
Global Commission on International Migration detailed, migration
involves a complex series of political, economic, and social factors.10
Given the complexity of migration, it is difficult to provide a “one size
fits all” selection of policies to better match the supply and demand
for international labor. Further study and perhaps policy experimentation is required to better understand how to improve upon the limitations of the existing framework. Policies will need to be strongly
15
16
Migration and Remittances: Eastern Europe and the Former Soviet Union
BOX 1
Possible Costs and Externalities of Illegal Immigration
1. With the exception of sales tax, the income earned by illegal immigrants is not taxable. This
represents forgone fiscal revenue.
2. Illegal migrants offer an unfair competitive advantage to firms that employ them over firms
that do not.
3. Irregular migrants are not covered by a minimum wage or national and industry wage agreements. They are therefore more likely to undercut the wages of the low skilled.
4. Whether entry is legal or illegal may affect the quality of migrants, even if the legal migration
scheme does not select on the basis of skill. Skilled workers or professionals are much more
likely to enter if there is a legal channel, even if their qualifications are not a condition of entry.
5. Employers may decide not to abide by health and safety regulations, leading to the potential
for migrant death and injury. Police and health services may be called upon to rescue or treat
the injured, to investigate the reasons for death, or to bury the dead.
6. Illegal migrants are not screened for diseases and viruses upon arrival, and have little access
to health services during their stay. At the same time, they risk having been exposed to illnesses on their journey, especially if they have been smuggled or trafficked. This has the potential to generate large public health externalities because diseases can spread to the native
population. Particularly important examples include tuberculosis, which seems to be
reemerging in parts of Europe, and HIV, as many trafficked women become involved in the
sex industry. By way of illustration, in 2002–03, those apprehended on the Slovak–Ukraine
border were found to be suffering from respiratory tract infections, tuberculosis, and scabies.
7. Forced to live underground, and with little access to legitimate employment, migrants are
more likely to be exposed to the world of crime.
8. Stigmatization of illegal migrants can undermine social cohesion if it spreads to cover those
who entered legally.
9. Illegal migrants may be encouraged to stay longer than they might desire and to remain even
when unemployed because of the risks of detection and associated costs of entering and
leaving.
Source: World Bank staff.
tailored to the migration-sending and receiving countries in question.
Here we detail some elements that could be included in international
migration policy to improve the returns to migration for sending and
receiving countries and migrants and their families.
Overview
The findings of this report suggest that the international governance of migration could be more coherent, and involve closer coordination between migration-sending and -receiving countries.
Revised bilateral migration agreements could recognize, organize,
and facilitate unskilled labor migration, while acting on both demand
and supply to limit undocumented migration. The outcome could be
an improvement in the protection given to temporary workers while
still offering migration-receiving countries needed labor.
Given variations in national attributes and preferences, such a temporary framework could take a variety of different forms and be
organized bilaterally, regionally, or internationally. Yet there are a
number of common elements that such policies might include:
• Recognize that the labor market, like any other market, needs to
balance supply as well as demand. The framework could explicitly
target measures at the supply of low-skilled labor as well as the
demand for such labor.
• The new regime could channel migrant labor to sectors or subsectors with little native labor to ensure that migrants are complements to and not substitutes for domestic labor.
• On the demand side, receiving countries need policies that limit
the employment of undocumented migrants by offering employers
the means to hire legally the workers they need. To promote development and coordinate with the preferences of many ECA
migrants to go abroad temporarily, an alternative regime could
emphasize circular migration. World Bank surveys for this report
found that the majority of migrants would prefer to spend shorter
times abroad and then return home (see figure 5).
• To ensure that employment under the new regime is temporary
and not permanent, the incentives could be designed to encourage
return home when not employed. For example, unemployment
and pension benefits could both be portable and only payable in
the country of origin.
• Policies should respect the rights of migrants to be treated with dignity while abroad, including clear and transparent rules regarding
remuneration, work conditions, or dismissal procedures. Moreover, migrants’ rights to appeal to receiving country authorities to
adjudicate disputes and protect themselves from crime could be
communicated and enforced.
Bilateral migration agreements that include some or all of these features could have a number of advantages over many existing policies:
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18
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 5
Migrants’ Preferences for Short versus Long-Term Migration
80
70
percent response
60
50
40
30
20
10
0
Tajikistan
Kyrgyz Rep.
Bulgaria
Georgia
leave temporarily and return fairly soon
leave temporarily without plan to return
leave for a long time and return
leave permanently
Romania
Bosnia and Herzegovina
Source: World Bank surveys with returned migrants.
• Agreements could stimulate circular migration, allow employers in
receiving countries to obtain affordable nontraded services while
respecting the law and reduce incentives for potential migrants to
use illegal means of entry.
• Such an approach seems commensurate with migrants’ preferences to spend shorter periods abroad and the need for receiving
countries to obtain labor services but not necessarily absorb a permanent population of migrants.
• Moreover, in the sending country, increased circular migration,
encouraged by the lowering of transportation costs, could reduce
many of the negative social effects that result from the separation
of families during long-term migration11 and reduce the incidence
and degree of ‘brain drain” from migration-sending countries in
ECA.12
• For undocumented migrants, a regime with these features—with
creative incentives for legal migration—could strengthen the rights
that migrants receive in the receiving country and allow them to
obtain social protection benefits that are out of reach today. Undocumented migrants have no access to adjudicative processes when
abroad and hence have no legal recourse to oppose abuse. By drying
Overview
up the incentives and opportunities for undocumented hiring, legal
protections for large stocks of foreign workers could be expanded.
Methodology
Like all studies on migration, the analysis in this report is supported by
a relatively poor and inconsistent base of underlying data and information. The problems with counting international migrants and measuring workers’ remittances are notoriously difficult. Official estimates
are known to contain very large errors in both overstating and understating actual stocks and flows. Such problems are exacerbated by the
prevalence of undocumented migration and, as an artifact unique to
the ECA, by the problem that many people who had lived permanently in one location suddenly were counted as “foreign-born” and
hence as migrants when national boundaries were adjusted after the
dissolution of the Soviet Union, Yugoslavia, and Czechoslovakia. These
limitations make it difficult to document migration, draw inferences
on its impact, and prescribe policies to optimize the role of migration
in enhancing growth and poverty reduction.
This report addresses the data problem by employing a multidimensional approach that draws conclusions and inferences from several different methods (see box 2). Findings rely on cumulative evidence from
the various elements that each alone suffers from weaknesses but when
combined provide some degree of confidence in the results.
The Report in Perspective
This report is part of a series of World Bank studies that take stock of
the state of the transition economies of Eastern Europe and the former Soviet Union as well as Turkey almost 15 years after the start of
the transition. It is designed to advance understanding, promote
debate, and initiate a dialogue on the role that policy could play in
optimizing the returns from migration13 for (a) the migration-sending
countries; (b) the receiving countries; and (c) migrants themselves by
• Assessing the importance and characteristics of migration in Eastern Europe and Central Asia and documenting the trends of the
last 15 years;
• Explaining the economic, political, and social drivers of labor
migration and how they may impact migration in the near term
(next 10–15 years) before demographic influences dominate;
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Migration and Remittances: Eastern Europe and the Former Soviet Union
BOX 2
Methodology
The report relies on five different methodologies:
1. Cross-country statistical analyses of migration flow and stock levels and rates. In collecting a
database of migration statistics, several different sources are drawn upon:
a. Administrative data obtained from national population estimates
b. Decennial population censuses
2. Comparative historical analyses of the Southern European countries’ experiences with international migration to develop some insight into migration from ECA countries.
3. Statistical estimations of the determinants of migration and the economic impact of
remittances.
4. Model-based simulations of the impact of adjusting economic and labor-market policies on
creating the incentive for circular migration while drying up the market for undocumented
migration.
5. The results of on-the-ground surveys with returned migrants in six ECA countries: Bosnia and
Herzegovina, Bulgaria, Georgia, the Kyrgyz Republic, Romania, and Tajikistan.a
Each of these methods has fairly well-established strengths and weaknesses. The poorness of
migration and remittance data makes statistical testing difficult. Comparative historical analysis
may yield valuable qualitative insights, yet the past may not be a reliable guide to understanding
the future, particularly in a volatile transitioning environment. Model-based simulations are a useful and flexible tool but themselves rely on the underlying migration data and a set of assumptions regarding the expectations of how international labor markets behave. Finally, the surveys
of returned migrants provide a rich base of information yet the surveys may not be representative of all migrants.
When two or more of these methods indicate a particular conclusion or inference, however,
some confidence is lent to the results. This report attempts, wherever possible, to draw conclusions when more than one method supports the statements and to report those instances
where the application of more than one method produces contradictory evidence. In this way, it
hopes to establish as firm an empirical base as possible for the conclusions drawn.
a. Further information on the survey methodology and the data will be made available through the Web site for the Europe
and Central Asia Region of the World Bank (www.worldbank.org/ECA).
• Evaluating the current framework of programs to manage international labor flows among the ECA economies and between these
economies and Western Europe and the key migration-receiving
countries in the CIS; and
Overview
• Suggesting the broad contours of reforms to enhance the gains
from migration by modifying international agreements and
strengthening the policies and institutions of the migrationsending countries.
Endnotes
1. This report uses the World Bank’s delineation of the zone of formerly
centrally planned economies in Europe and Central Asia. Countries
included in this region include Albania, Armenia, Azerbaijan, Belarus,
Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia,
FYR Macedonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic,
Latvia, Lithuania, Moldova, Poland, Romania, Russian Federation, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine, and Uzbekistan. Although the Czech Republic and
Slovenia graduated from World Bank borrower status in 2005 and 2004,
respectively, they are included in this analysis because we analyze trends
spanning the entire transition process. The glossary spells out terminology, including country groupings associated with the different names
used.
2. A full statistical appendix is found in appendix 1.2.
3. World Bank 2006.
4. See appendix 1.1 for a discussion of the survey methodology.
5. See De Luna Martinez (2005).
6. Studies using household survey data in Mexico suggest that while both
internal and international remittances have a positive impact on incomes
in rural areas, international migration has a greater impact. These studies also suggest that remittances tend to have an equalizing effect (in
terms of income inequality) in high-migration areas but not so in lowmigration areas. For more information see Ozden and Schiff (2006),
which refers to Mora and Taylor (2004), and Lopez Cordoba (2004).
7. The rapid growth rate produced a reduction of 1,900,000 persons active
in agriculture, and 800,000 emigrants (INE).
8. According to the transitional arrangements (2+3+2 regulation) the EU15 can apply national rules on access to their labor markets for the first
two years after enlargement. The diverse national measures have
resulted in several legally different migration regimes. In May 2006, the
second phase of the transitional period started, which allowed member
states to continue national measures for up to another three years. At
the end of this period (2009) all member states will be invited to open
their labor markets entirely. Only if countries can show serious disturbances in the labor market, or a threat of such disturbances, will they be
allowed to resort to a safeguard clause for a maximum period of two
years. From 2011 all member states will have to comply with European
Commission rules regulating the free movement of labor.
9. See appendix 4.3 for further information on undocumented migration
and some of the risks that it poses to migration sending and receiving
countries and migrants themselves.
21
22
Migration and Remittances: Eastern Europe and the Former Soviet Union
10. UN 2005.
11. For further information on the impact of longer-term migration on communities left behind, see appendix 4.4.
12. To date, there is not a good understanding of the prevalence and impact
of brain drain in the ECA region. For a summary of the existing state of
knowledge, see appendix 4.5.
13. This report considers anyone who is not native born to be a migrant,
owing to the limitations of UN data.
CHAPTER 1
Overview of Migration Trends in
Europe and Central Asia, 1990–2004
Some of the trends and motivations for migration in the Europe and
Central Asia (ECA) region are similar to those found elsewhere in the
world. However, many of the migration movements that have taken
place since 1990 are unique to the region, given the circumstances of
economic transition, political and social liberalization, and the
breakup of three federal states. Figure 1.1 shows how the factors
influencing migration have changed from the communist period to
the present. This chapter provides an overview of some of the main
migration trends that have taken place across the region over the past
15 years, with a focus on international movements among countries.
Migration in the ECA region is both large by international standards and unique in that the region is both a major receiver and
sender of migrants. Figure 1.2 exhibits the ECA region and selected
ECA countries in terms of their shares of foreign-born populations.
Excluding movements between industrial countries, ECA accounts
for over one-third of world emigration and immigration. There are 35
million foreign-born residents in ECA countries, including 13 million
in the Russian Federation, 7 million in Ukraine, 3 million in Kazakhstan, 3 million in Poland, and 1.5 million in Turkey. Furthermore,
several ECA countries are among the top 10 sending and receiving
countries of migrants worldwide. Russia is home to the second largest
number of migrants in the world after the United States; Ukraine is
23
24
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 1.1
Transition of the Migration System in the Europe and Central Asia Region
Migration under Central Planning in the Europe and
Central Asia Region
Migration during the Transition Period in the Europe
and Central Asia Region
Eight countries in the region (only five remain in their pretransition borders)
Twenty-seven countries following the breakup of three federal states
Migration was very tightly controlled
Much less control over migration
Prices were administratively set and wages and income were not very
differentiated across sectors or regions
Prices are market determined and income is increasingly distributed
among people, sectors, and regions
A massive and elaborate system of subsidies caused certain sectors and
regions to be “over-valued” and others to be “under-valued”
Wages and prices have adjusted to their market-clearing value
Migration control efforts were aimed mainly at keeping people in a country
Migration control is aimed at both keeping people in and outside a
country and, in general, migration control systems are poorly developed
Little involvement in international institutions and foreign trade
Open economies, involvement with international institutions, and
“globalization”
Source: World Bank staff.
fourth after Germany; and Kazakhstan and Poland are respectively
ninth and tenth.
Migration patterns in the region follow a broad biaxial pattern: on
one axis a migration system developed among the countries of Western, Central, and Eastern Europe and on the other a system of movement arose among the countries of the Commonwealth of
Independent States (CIS). However, this system is not exclusively bipolar. Though the majority of migrants from Central and Eastern
European countries move into Western Europe, the same is true for
many migrants from the poorer CIS economies, particularly Moldova.
While the majority of migrants from Central Asia travel to the
resource-rich CIS countries (particularly Russia and Kazakhstan)
many move west in search of higher earnings, toward the European
Union (EU) and Turkey.
The creation of many new countries following the breakup of the
Soviet Union produced “new” migrants (long-term, foreign-born residents) who may not have physically moved, but were defined as
migrants under UN practice. In addition to the issue of these “statistical” migrants, there are numerous other problems in analyzing migration trends across the region based on available data. This chapter and
the report in general are an attempt to pull together and analyze all
available migration data to gain as complete a picture as possible of
migration trends over the past 15 years; thus, the issue of the veracity of migration data is a constant theme.
The chapter begins with a description of some of the problems
involved in measuring migration among the ECA countries during
25
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.2
Migration in Top 10 Sending and Receiving Countries and by Region, 2003
Immigration into the top 10 receiving countries and by Region, 2003 (stock of immigrants)
Regions
Developing Regions
North America
ECA
Europe
Middle East and North Africa
South and West Asia
Sub-Saharan Africa
East and South East Asia
Others
Latin America
ECA
Middle East and North Africa
Sub-Saharan Africa
Latin America
South and West Asia
East and South East Asia
Countries
United Arab Emirates
Estonia
Latvia
Switzerland
Australia
Saudi Arabia
Kazakhstan
Ukraine
Belarus
United States
Countries
United States
Russian Fed.
Germany
Ukraine
France
India
Saudi Arabia
Australia
Kazakhstan
Poland
0
50
0
100 150 200 250 300 350 400
100
thousands
200
300
400
500
600
700
per thousand population
Emigration from the top 10 sending countries and by Region, 2003 (stock of immigrants)
Regions
Developing Regions
ECA
Latin America
Europe
East and South East
South and West Asia
Middle East and North Africa
Others
Sub-Saharan Africa
North America
ECA
Latin America
Middle East and North Africa
Sub-Saharan Africa
East and South East Asia
South and West Asia
Countries
Jamaica
Bosnia and Herzegovina
Albania
Slovenia
Armenia
Kazakhstan
Belarus
El Salvador
Georgia
Moldova
Countries
Russian Fed.
Mexico
Ukraine
India
China
Germany
Kazakhstan
Turkey
Philippines
Uzbekistan
0
100
200
300
400
thousands
ECA
Western Europe
500
0
50
100 150 200 250 300 350 400
per thousand population
others
Sources: UN Population Division 2003 and Walmsley, Ahmed, and Parsons 2005.
26
Migration and Remittances: Eastern Europe and the Former Soviet Union
the transition period. Then using the data that are available, it analyzes the impact of migration on overall levels of population change
in the ECA countries. The next section provides a broad overview of
migration flows across the ECA region during the period 2000–03, a
recent period when most of the ethnic migration had already taken
place and flows were dominated by the economic migration flows
that are expected to predominate in the future. Following this are discussions of refugee and internally displaced population movements,
and transit and irregular migration. A further section looks at the
main migration partners of each ECA country. Finally, the chapter
looks at possible future migration trends in the region.1
Problems with Measuring Migration in ECA
There are three main sources for migration data in the ECA countries,
as well as in countries outside the region. These are population censuses, usually conducted once a decade; administrative statistics of
persons crossing international borders; and surveys. This final category includes surveys targeted directly at migrant populations, as well
as surveys designed for other purposes where migration-related questions are asked.
Population censuses usually include questions that measure lifetime
migration. For instance, the last Soviet census, conducted in January
1989, included questions on place of birth, whether the respondent
had been living in his or her present residence continuously since birth,
and if not, when he or she had migrated to that place. All of the ECA
countries conducted population censuses between the years 1989 and
1992 and most conducted another census between 1999 and 2002. The
more recent round of censuses typically included a question on citizenship, though this question was frequently not posed in the censuses
conducted around 1990. Some also included questions about persons
temporarily absent. The 2002 Russian census also included a set of
questions for those persons temporarily residing in Russia, although
the total of a quarter million persons enumerated were thought to significantly underestimate the true figure.
Whereas censuses attempt to count stocks of migrants, administrative statistics are counts of flows of migrants. In most cases, data on
total international border crossings also record information on the
age, sex, and country of previous residence or intended destination,
and other characteristics of migrants. It is the change, and in some
cases breakdown, of systems for measuring migration flows where
the ECA countries have suffered the most.
Overview of Migration Trends in Europe and Central Asia, 1990–2004
Surveys are useful for obtaining qualitative information about
migrants and to serve as a check on the veracity of flow statistics from
administrative sources. An increasing number of surveys of migrants
have been conducted across the ECA region, both by the countries
themselves as well as by international organizations such as the International Organization for Migration (IOM).
Several reasons make migration flows in ECA challenging to capture. First, the type, direction, and magnitude of the flows in the region
have changed dramatically since the beginning of economic transition,
liberalization of societies (including increased freedom of movement),
and the emergence of 22 new states. What had previously been internal boundaries have now become international borders. Migration in
ECA, which was once subject to considerable state control within several self-contained migration spaces, now rests in the hands of individuals who have the ability to transit across new and rather porous
international boundaries. In the former Soviet Union, the propiska or
resident permit system required persons to register before being
allowed to migrate to a new location. However, the visa-free travel
among the CIS countries for most of the 1990s contributed to an environment of porous borders, which made the recording of migration
flows difficult. The extent to which the successor states have instituted
systems to properly measure total migration flows and to disaggregate
these flows by age, gender, nationality, and other characteristics useful
for analysis and policy making varies considerably.
The previous systems for measuring migration in the centrally
planned countries of the ECA are wholly inadequate for capturing
movements across the newly independent states. In their initial years,
the newly independent states had to erect the elements of government apparatus, including independent statistical systems to measure
social and economic trends such as migration movements. With other
elements of state building causing greater concern, building systems
for measuring migration often received low priority. Many of these
issues in migration measurement are unique to the newly independent states of the ECA region.
A second set of problems with proper migration measurement is
endemic to all countries. Definitions, underlying concepts, sources,
and reporting systems differ significantly between countries, making
available migration statistics fragmentary. The boundaries between
extended travel, seasonal work, and economic migration are blurred.
In most cases it is not clear whether an individual reported as
“migrant” is a long-term mover, a temporary mover, a seasonal
worker, someone on the move to another destination, an individual
transitioning through a territory, a returning migrant, a member of a
27
28
Migration and Remittances: Eastern Europe and the Former Soviet Union
family already residing abroad with no intention to work, a student
(who may or may not undertake part-time employment), a refugee,
a member of the staff of a foreign company in the country, or some
other category of migrant.
Third, undocumented migration plays an important role in today’s
migrant flows to, from, and within ECA, as well as in many other parts
of the world. Reported data refers to legal migrants, based most often
on residence or work permits. Even countries in the region with seemingly well-developed statistical systems often are not able to record
migration completely. Decennial population censuses are used to adjust
and calibrate population totals. For instance, in Lithuania, there was a
downward adjustment of the population by over 200,000, or more
than 5 percent of the population, following the census conducted there
in April 2001. Roughly the same magnitude of adjustment took place
in Estonia following its March 2000 census, when it adjusted the population total downward by 67,000, or about 5 percent. Similar postcensus adjustments downsizing the resident population were made in
the Czech Republic, Poland, and the Slovak Republic. Among the surprises in the Russian census conducted in October 2002 was that the
total population was 1.2 million higher than the previous estimate,
mainly because of an undercount of migration.
These differences between population estimates and census figures
in the ECA countries are worth comparing to the experience of the
United States, long a traditional migration destination. Before the
2000 census in the United States, the population was estimated at
275 million. That census revealed a count of 281 million, a difference
of 6 million, almost all attributable to an undercount of the huge
migration into the United States during the 1990s.2 The United States
has long grappled with an issue that the ECA states are only beginning to deal with in trying to estimate temporary or circular migration. Until recently, most of the ECA states recorded only long-term
or permanent moves and much of the movements over the past
decade are of a temporary or circular nature.
The breakup of the Soviet Union, Yugoslavia, and Czechoslovakia
created a large number of “statistical migrants.” The commonly
accepted UN definition describes a “migrant” as a person living outside his or her country of birth. As used here, statistical migrants
refers to persons who migrated internally while those countries
existed, thus not qualifying as a migrant under the UN definition at
the time, but who began to be counted as migrants when those countries broke apart even though they did not move again. Having a large
number of these statistical migrants has hampered analysis of migration patterns across the ECA region because of the difficulty of sepa-
29
Overview of Migration Trends in Europe and Central Asia, 1990–2004
rating those who moved during the communist period, before the
start of transition and independence, and those who moved later for
ethnic or economic reasons. However, with data that are available
from population censuses, it is possible to get a fairly good idea of the
total number of statistical migrants and changes in their numbers
since the breakup of the countries.
Table 1.1 shows the population of the Soviet Union by place of birth
in 1989, at the time of the last Soviet census. At that time, 2.4 million
persons or 0.8 percent of the population had been born outside the
Soviet Union.3 This low figure is not surprising because for most of the
period between the end of World War II and the breakup of the Soviet
Union, there was little migration either into or out of the Soviet Union
and little shifting of international borders. In fact, the listed figure of
the Soviet population being classified as migrants is likely a considerable overestimate because it also includes those not indicating their
place of birth. If similar data from the 2002 Russian census is any
guide, about one-quarter had actually been born outside the former
Soviet Union and about three-quarters did not indicate their place of
birth. Thus, the true figure of the migrant population was likely less
than 1 million or only about 0.3 percent of the population.
TABLE 1.1
Population by Place of Birth in the USSR, 1989
(thousands)
Place of
permanent
residence
USSR
RSFSR
Ukrainskaia SSR
Belorusskaia SSR
Uzbekskaia SSR
Kazakhskaia SSR
Gruzinskaia SSR
Azerbaidzhanskaia SSR
Litovskaia SSR
Moldavskaia SSR
Latviiskaia SSR
Kirgizskaia SSR
Tadzhikskaia SSR
Armianskaia SSR
Turkmenskaia SSR
Estonskaia SSR
Born in
republic of
current
residence
Born
elsehwere
in USSR
Born
outside
USSR
Total
population
255,409
135,550
44,332
8,883
18,108
12,715
5,039
6,604
3,299
3,739
1,975
3,586
4,650
2,570
3,205
1,155
27,955
10,478
6,665
1,213
1,649
3,536
349
398
356
579
678
638
433
267
311
403
2,378
994
455
55
53
214
13
19
19
18
14
34
9
467
7
8
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Source: Eastview Publications and CIS Statistical Committee; USSR Census Results 1989 CD-ROM.
Note: Data are as of January 1989.
Born in
republic of
current
residence
89.4
92.2
86.2
87.5
91.4
77.2
93.3
94.1
89.8
86.2
74.0
84.2
91.3
77.8
91.0
73.7
Born
elsehwere
in USSR
9.8
7.1
13.0
12.0
8.3
21.5
6.5
5.7
9.7
13.3
25.4
15.0
8.5
8.1
8.8
25.7
Born
outside
USSR
0.8
0.7
0.9
0.5
0.3
1.3
0.2
0.3
0.5
0.4
0.5
0.8
0.2
14.1
0.2
0.5
30
Migration and Remittances: Eastern Europe and the Former Soviet Union
However, there was considerable migration among the states of
the former Soviet Union. In 1989, there were 28 million persons who
were residing in a republic other than the one in which they were
born. This figure amounted to 9.8 percent of the Soviet population,
which should be regarded as the number of “statistical migrants” that
were created by the breakup of the Soviet Union, greatly contributing
to the increase in the world stock of migrants. The bulk of these individuals were in the three Slavic states, Uzbekistan, and Kazakhstan.
In percentage terms, the countries with the largest migrant stock populations were Estonia, Latvia, and Kazakhstan. All of these countries
were prime destinations for Russian and Russian-speaking migrants
during the period after World War II.
Migration and Population Change
An analysis of migration and population change among the ECA
states begins at a broad level by dividing the countries into groups
according to their recent patterns of migration and natural increase in
population (figure 1.3; data underlying this figure are in appendix 2).
Natural increase is the difference between the number of births and
deaths and is a function of the age structure of the population and
levels of fertility and mortality. As will be discussed below, differential
rates of natural increase among countries are a major driver of migration within the ECA region and elsewhere. A positive natural increase
occurs where the number of births exceeds the number of deaths,
which is the situation in nearly all countries in the world. Negative
natural increase is where the number of deaths in a population
exceeds the number of births. The 14 ECA countries shown below
with a negative natural increase or a natural decrease, along with
Italy and Germany, are among a small group of countries where this
is occurring. So many ECA countries are part of this group because
fertility levels have fallen steeply during the transition period, to 1.3
children per woman or less; such levels are unsustainable for natural
population increase.4 These figures are compared to net migration,
which is the difference between the number of immigrants to a country and emigrants from a country.
There are two countries in the ECA region that have both a natural increase and positive net migration; however, neither truly
belongs in this category because both suffer from data problems that
affect their migration counts. Turkmenistan has some rather unrealistically high population estimates, which cause net migration figures
to appear unrealistically high. Bosnia and Herzegovina suffers from
31
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.3
Natural Increase (Decrease) and Net Migration in the ECA Region, 2000–03
10
positive natural increase,
negative net migration
positive natural increase,
positive net migration
Tajikistan
natural increase/decrease (percent change)
8
Uzbekistan
Turkmenistan
6
Kyrgyz Rep.
4
Albania
Azerbaijan
Kazakhstan
2
Macedonia
Armenia
Georgia
Poland
0
Romania
⫺2
Bosnia and
Serbia and Montenegro Herzegovina
Slovak Rep.
Slovenia
Czech Rep.
Moldova
Croatia
Hungary
Lithuania
Bulgaria
Latvia
Belarus
Ukraine
negative natural increase,
negative net migration
Russian Fed.
negative natural increase,
positive net migration
⫺4
⫺6
⫺5
⫺4
⫺3
⫺2
⫺1
0
net migration (percent change)
Source: National statistical office of the ECA countries and UNICEF, TransMONEE Database.
incomplete and inconsistent counts of migration, with some years
showing emigration and some immigration. Furthermore, in recent
years, there has been an undetermined amount of return migration
of some of the refugee populations that left during the mid-1990s.
Based on this evidence, both of these countries should probably be
grouped in the category of countries with positive natural increase
and negative net migration.
There are 10 ECA countries that combine natural increase and net
emigration (12 if the two mentioned above are included). This is the
pattern for most of the world’s countries. This includes the countries
of Central Asia, the Caucasus, and many of the former Yugoslav
states. With their faster-growing populations, especially youth populations, migration pressures in these countries will likely persist into
the future.
A third group of countries comprises those that combine having
more deaths than births and more immigrants than emigrants. These
are Russia and Belarus in the CIS and four of the smaller new EU
member states. While all have had more immigrants than emigrants
over recent years, in all but Russia, the population increases as a result
1
2
32
Migration and Remittances: Eastern Europe and the Former Soviet Union
of net migration are small, amounting to less than 1 percent of their
populations. As pointed out elsewhere in this report, Russia has
become a major migration magnet within the CIS, with a measured
population increase from migration of 4 percent since 1990 and perhaps an equal amount of undocumented migration.
A fourth group are nine ECA countries where populations are
declining because they experience both more deaths than births and
more emigrants than immigrants. This includes Ukraine and
Moldova, the three Baltic states, and four Central European countries, including the largest, Poland. In all of these countries, both
trends are expected to continue well into the future, causing large
population declines as well as rapid aging of their populations.
Figures 1.4a and 1.4b show the net population change from migration over the period 1989–2003 for the CIS and western ECA countries, respectively.5 From this figure, one part of the region’s bipolar
migration story of the past decade and a half can be clearly seen, with
Russia showing by far the largest population gain from migration. The
impact on those other few countries with population gains from
migration has been minimal. Most of the migrants into Russia consist
of persons migrating from the other states of the former Soviet Union,
which show large population declines from migration. There have
been several countries in the region that have transitioned from net
emigration to net immigration including Belarus, Slovenia, Hungary,
Croatia, and Serbia and Montenegro.
Of the five ECA countries with population declines of over 15 percent, four are in the southern tier of the former Soviet Union. The
three Baltic states have had considerable out-migration in large part
because of the emigration of large numbers of Russians and Russianspeakers in the years immediately following the breakup of the Soviet
Union. In southeast Europe, Albania and Bulgaria have also had emigrations of large portions of their populations.
These figures are based on counts of the long-term, permanent
migration of the populations and do not include short-term or undocumented counts of population movements. These figures also understate the potential impact of migration because it is usually the
better-educated segments of the population and those in the early
stages of their working lives who migrate in the largest numbers.
Major Migration Flows in the ECA Region
As mentioned often throughout this report, proper measurement of
migration is difficult, even for high-income countries with well-devel-
33
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.4
Net Migration in Western ECA and the CIS
a. Net migration in the CIS, 1989–2003
Russian Fed.
Belarus
Ukraine
Azerbaijan
Moldova
Lithuania
population gains
from migration
population losses
from migration
Uzbekistan
Latvia
Kyrgyz Rep.
Estonia
Tajikistan
Armenia
Georgia
Kazakhstan
⫺25
⫺20
⫺15
⫺10
⫺5
0
5
10
net migration as percent of population at beginning of period
b. Net migration in Western ECA, 1989–2003
Czech Rep.
Poland
Slovak Rep.
Slovenia
Hungary
population losses
from migration
FYR Macedonia
population gains
from migration
Croatia
Serbia and Montenegro
Romania
Bulgaria
Albania
⫺30
⫺25
⫺20
⫺15
⫺10
⫺5
0
5
net migration as a percent of population at beginning of period
1989–99
2000–03
Source: National statistical offices and UNICEF, TransMONEE Database.
oped statistical systems. For the ECA countries, measuring migration
during this period of rapid social, economic, and political change has
been especially difficult. However, by compiling migration data from
several different sources and triangulating, a fairly complete picture
of the major flows taking place within the region can be obtained. It
34
Migration and Remittances: Eastern Europe and the Former Soviet Union
is helpful to keep in mind that international migration involves a flow
between two countries and that when a person migrates, that person
ideally should be recorded twice, by both the sending and receiving
country. Even so, there is considerable variation in how countries
record migrants; some countries track movements of people by place
of previous or next residence, some by citizenship, and some by various other methods.
Table 1.2 shows the migration flows among major blocs of ECA
countries and origins and destinations of flows outside the region for
the years 2000 to 2003. This was a period after much of the ethnicinduced migration associated with the breakup of the Soviet Union,
Yugoslavia, and Czechoslovakia had already taken place and the magnitude of migration flows had settled into a more “normal” pattern
influenced primarily by economic incentives. The table was compiled
by collecting all available data on migration by origin and destination
country according to both residence and citizenship definitions; this
was followed by calculating a “maximum” matrix of the highest of
each pair of flows. Migration data for 52 countries were collected,
comprising the 28 ECA countries, 21 countries in Western Europe,
plus Canada, Israel, and the United States. Sufficient data were available to fill about 90 percent of the matrix. Most of the cells that were
not able to be filled represented flows between pairs of countries for
which there is not known to be substantial migration (for example,
between Iceland and Turkmenistan). Thus, the assembled data are
thought to be a fairly complete representation of migration involving
ECA countries during this period.
The data partially support the story that two major migration blocs
have developed involving migration of the ECA countries. As suspected
by other and anecdotal evidence, there has been considerable migration
from western ECA to Western Europe and considerable migration from
the rest of the CIS into Russia. At the same time, there are other flows
developing that were not suspected and not that readily apparent from
other data. About equal percentages of migrants from the CIS countries
other than Russia (other CIS) travel to Russia as to Western Europe,
with Ukraine and Kazakhstan being the major sending countries and
Germany the major receiver. Over 70 percent of migrants from western
ECA go to Western Europe. At the same time, there is also considerable
flow from Western Europe to western ECA. Flows between Germany
and three countries make up the bulk of this overall total, that is, flows
from Germany to Poland, Serbia and Montenegro, and Turkey. These
figures not only represent the return of persons who had previously
migrated but also indicate considerable “churning,” as for each of these
three flows, there are also large flows in the opposite direction.
35
Overview of Migration Trends in Europe and Central Asia, 1990–2004
TABLE 1.2
Migration Flows Involving ECA Countries, 2000–03
TABLE 1.2A
Total Migration Flows Involving ECA Countries and Major Partners, 2000–03
To
From
Russia
Other CIS
Western ECA
Western Europe
U.S., Canada,
Israel
Total
(emigration)
Russia
Other CIS
Western ECA
Western Europe
U.S., Canada, Israel
Total (immigration)
0
319,514
22,896
74,460
8,466
425,336
272,929
159,652
32,820
82,705
6,342
554,448
17,882
85,104
274,762
640,052
16,973
1,034,773
85,468
280,843
1,300,289
2,808,366
457,664
4,932,630
53,539
90,265
149,045
269,253
142,762
704,864
429,818
935,378
1,779,812
3,874,837
632,207
Western ECA
Western Europe
U.S., Canada,
Israel
Total
(emigration)
4
9
15
17
3
20
30
73
72
72
12
10
8
7
23
100
100
100
100
100
Western ECA
Western Europe
U.S., Canada,
Israel
2
8
27
62
2
100
2
6
26
57
9
100
8
13
21
38
20
100
TABLE 1.2B
Percent of Total Emigration
To
From
Russia
Other CIS
Western ECA
Western Europe
U.S., Canada, Israel
Russia
Other CIS
0
34
1
2
1
63
17
2
2
1
TABLE 1.2C
Percent of Total Immigration
To
From
Russia
Other CIS
Western ECA
Western Europe
U.S., Canada, Israel
Total (immigration)
Russia
0
75
5
18
2
100
Other CIS
49
29
6
15
1
100
Source: See text for explanation of how data were compiled.
Note: “Other CIS” consists of Armenia, Azerbaijan, Belarus, Georgia, Kyrgyz Republic, Kazakhstan, Moldova, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.
“Western ECA” consists of Albania, Bosnia and Herzegovina, Bulgaria, Serbia and Montenegro, the Czech Republic, Estonia, Croatia, Hungary, Lithuania, Latvia, FYR
Macedonia, Poland, Romania, the Slovak Republic, Slovenia, and Turkey. “Western Europe” consists of Austria, Belgium, Switzerland, Cyprus, Germany, Denmark,
Spain, Finland, France, Greece, Ireland, Iceland, Italy, Liechtenstein, Luxembourg, Malta, the Netherlands, Norway, Portugal, Sweden, and the United Kingdom.
On the immigration side, Russia receives 75 percent of its immigrants from other CIS countries. There are minimal flows from the CIS
states in the western ECA, with over half consisting of migrants from
Ukraine to the Czech Republic and from Moldova into Romania;
Ukraine and Moldova are thus unique in having significant migrant
36
Migration and Remittances: Eastern Europe and the Former Soviet Union
flows both to Western Europe and to resource-rich CIS countries. The
largest flows into Western Europe are from other Western European
countries, making up about half of the total. However, flows into Western Europe from western ECA make up about one-third of the total.
Figure 1.5 shows the largest country-to-country migration streams
involving a CIS country for the period 2000–03. Much of this is driven
by the gravity of proximity and population size; thus, it is not surprising that Russia is either a source or destination of most of these flows.
The largest flows that do not include Russia are flows from Kazakhstan to Germany and Ukraine to Germany. The flow from Ukraine
to Germany can be explained by proximity, population size, and large
differences in per capita income, while the flow from Kazakhstan to
Germany can be explained by the fact that Kazakhstan was home to
the largest concentration of Germans in the former Soviet Union and,
initially, Germany had a rather liberal law for the return of the
Aussiedler. The pull of Russia from the other CIS countries is clearly
evident from the map.
Figure 1.6 shows the largest country-to-country migration streams
involving a western ECA country for the same period. A quite different pattern emerges than among CIS states, with a country outside
FIGURE 1.5
Largest Migration Flows Involving a CIS Country, 2000–03
Source: World Bank staff estimates based on analysis of migration statistics from a variety of sending and receiving countries.
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.6
Largest Migration Flows Involving a Western ECA Country, 2000–03
Source: World Bank staff estimates based on analysis of migration statistics from a variety of sending and receiving countries.
the region, Germany, being the major driver of migration for these
countries. Again the gravity of migration encompassing proximity,
population size, and the size of the German economy explains many
of the notable patterns. None of the largest flows involved two countries within the region because there are only two countries, Turkey
and Poland, that can be considered sizable (or at least medium-sized
comparable to the largest Western European countries). What is
interesting is that all of the largest flows involving Germany are twoway flows with large amounts of return migration.
Refugees and Internally Displaced Persons
Each of the ECA countries is an ethnic homeland. However, many
other ethnic homelands exist at the subnational level. The boundaries
of many of these were drawn arbitrarily by outside authorities and do
not necessarily coincide with what different ethnic groups regard as
their rightful homelands. During the communist period, there was
considerable migration of different ethnic groups to regions or countries outside of their homelands. When Yugoslavia and the Soviet
37
38
Migration and Remittances: Eastern Europe and the Former Soviet Union
Union broke apart, they did so along their ethnic seams. Most of this
occurred peacefully but was accompanied by some diaspora migration. However, in some cases these cleavages instigated considerable
ethnoterritorial conflict; as a result, forced migration became the predominant form of migration in some parts of the region. Figure 1.7
shows the major displacements that took place in the former
Yugoslavia in 1995 at about the peak of the conflict there. Figure 1.8
shows the same for the former Soviet Union for the mid-1990s.
Figure 1.9 shows the temporal trends in the numbers of refugees
and internally displaced persons (IDPs) across the ECA region
between 1989 and 2003.6 The figure shows a combination of actual
and statistical trends. During the late communist period, the numbers
of refugees and IDPs were rather small. However, estimates rely on
imperfect data counting measures; none of these countries had
acceded to the 1951 Geneva Convention on Refugees and hence did
not have mechanisms in place for recognizing and counting refugees.
As the newly independent states in the region and others began to
erect institutions capable of enumerating refugees and asylum seekers, their numbers began to increase. Thus, part of the rise from 1989
to the mid-1990s is statistical. However, a large part of the increase is
real, brought about by the increase in the number of persons disFIGURE 1.7
Main Displaced Populations from the Former Yugoslavia, December 1995
Source: Humanitarian Issues Working Group HIWG06/6, December 11, 1996.
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.8
Main Displaced Population from the Former Soviet Union, Mid–1990s
Source: Based on IOM, CIS Migration Report 1996.
Note: Map is designed to broadly illustrate major refugee and IDP flows at the time, based upon best available information, and is not intended to be authoritative or precise.
FIGURE 1.9
Refugees and Internally Displaced Persons in the ECA Region, 1989–2003
1,400,000
1,200,000
persons
1,000,000
800,000
600,000
400,000
200,000
0
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
refugees
Source: UNICEF, TransMONEE database.
IDPs
39
40
Migration and Remittances: Eastern Europe and the Former Soviet Union
placed as a result of the breakup of the Soviet Union and Yugoslavia
and the resultant ethnoterritorial disputes.
The number of refugees increased from 145,000 in 1989 to over a
half million during the years 1992 to 1997 (with the exception of
1994), but fell to about 237,000 in 2003. It should be kept in mind that
these figures refer to the numbers of refugees and IDPs within each of
the ECA countries, not from the countries. Refugees, by definition,
have crossed an international border, whereas IDPs have not. If the
number of refugees from the ECA countries were counted instead, the
number would certainly be higher because many of those from the
former Yugoslav states fled to Western Europe. Partly for these reasons, the number of IDPs is comparatively much higher than for the
number of refugees, rising from about 100,000 in 1989 to over a million during the years 1993 to 1997 before declining slightly to 927,000
in 2003. In 2003, the largest concentrations of IDPs were in Azerbaijan (576,000) and Georgia (262,000). These numbers are down only
slightly from peaks in the mid-1990s because the conflicts that gave
rise to them continue to persist without any permanent settlement.
Figure 1.10 shows the countries in the ECA region with the largest
concentrations of refugees, IDPs, and “others of concern” at the end of
2004, according to the UNHCR. Overall, the ECA region accounts for
7.4 percent of the world population in total but contains 19 percent of
FIGURE 1.10
Largest Numbers of Refugees, IDPs, and Others of Concern in the ECA Region, 2004
Russian Fed.
Serbia and Montenegro
Azerbaijan
Latvia
Bosnia and Herzegovina
Georgia
Armenia
Estonia
Ukraine
Kazakhstan
0
100
200
300
thousands
refugees
IDPs
other
Source: UNHCR, 2004 Global Refugee Trends (http://www.unhcr.org).
400
500
600
700
Overview of Migration Trends in Europe and Central Asia, 1990–2004
the total number of asylum seekers, refugees, and others of concern. In
particular, the ECA region accounts for a disproportionate share of the
world’s total number of IDPs (32 percent), because of past or ongoing
conflicts in Russia, Georgia, and Azerbaijan in the CIS, and in Serbia
and Montenegro and Bosnia and Herzegovina in the former Yugoslavia.
Substantial proportions of ECA migrants also fall into the category of
“others of concern,” which generally includes asylum seekers, returned
refugees, returned IDPs, and various other categories of (usually forced)
migrants. In ECA countries, this includes various categories of stateless
persons and, in Latvia and Estonia, the large Russian-speaking groups
of noncitizens. Aside from those two countries, the ECA countries with
the largest numbers of persons of concern are mostly those where there
has been or continues to be conflict. The region also accounts for a disproportionate share of others of concern because of the large number
of stateless or noncitizens living in various countries. Many of the original ethnoterritorial conflicts that gave rise to these groups of forced
migrants remain unresolved more than a decade after they first arose.
Transit and Undocumented Migration in the ECA Region
With the opening up of the ECA countries to the rest of the world and
the liberalization of migration, transit, illegal, and undocumented
migration has become an issue for countries in the region, and particularly for those that were not previously under communist rule.
Some migrants (from within and outside ECA) hoping to migrate to
the United States, Japan, or Western Europe seek transit through
ECA countries. Some transit migrants then conclude that this hope is
unrealistic and settle in the transit country, which typically is poorer
than the West but more developed than their home country. Russia is
emerging as a transit as well as a key sending and receiving country.
Ukraine, Romania, and Azerbaijan are examples of other countries in
the ECA region that have significant transit migrant populations. This
section first considers the motivations of migrants who come to the
ECA. It then considers the experience of the host countries from two
perspectives: the statistical frequency of undocumented migration (a
figure notably difficult to calculate), and the policy decisions of ECA
states for regulating this phenomenon.
Migration Experiences
The decision to migrate, as well as the choice of destination, reflects a
careful calculation of relative risks and income-earning potential for
41
42
Migration and Remittances: Eastern Europe and the Former Soviet Union
those who end up in ECA countries. A U.K. Economic and Social
Research Council survey of Fujianese Chinese finds that Europe was
the second choice for refugees unable to get to Japan or the United
States but who wanted to make money abroad within a set time.
Fujianese migrants choose their preferred migration destination
based on the likelihood of successfully getting there, expected
income, and the presence of relatives or friends. Availability of legal
residence status seems to be less important, although visa requirements, perceived ease of obtaining refugee status, and amnesties for
undocumented migrants are all important in directing Fujianese (and
other Chinese) migrants to particular countries at particular times
(Economic and Social Research Council 2002).
A survey conducted from May to October 2003 of transit migrants
in Azerbaijan (IOM 2004) also determines that the motivations for
migration are the result of careful contemplation. Most such transit
migrants depart from developing countries in Asia and the Middle
East and aim to settle in North America or Western Europe. Some
would like to return home when the political and economic situations in their home countries stabilize. Some entered and reside in
Azerbaijan legally, while others migrated illegally. Most undocumented entries were through Iran, and were frequently assisted by
middlemen. “Push factors”—including conflict and economic difficulties in the countries of origin—were the main motivations for migration. For many, Azerbaijan was attractive owing to its geographical
proximity to and cultural similarities with their homeland.
Countries with generous immigration provisions, such as Ukraine,
also have the potential to become important crossroads for the transportation of undocumented migrants. A Kennan Institute study
(Kennan Institute 2004) focusing on nontraditional immigrants from
Asia and Africa identified a set of migrants heading for Western
Europe who took advantage of the relatively open immigration system in Ukraine (at least before 1999). They entered both legally and
illegally, and hoped to stay a short time before crossing to Western
Europe. Some had been duped by traffickers who promised safe passage to Western Europe and then dumped them in Ukraine. In this
case as well, migration decisions were greatly influenced by available
information from government, extended family, business ties, friends
who had studied in Ukraine during Soviet times, communities of
compatriots in Ukraine, and organizers of undocumented migration.
The majority of Chinese immigrants stated that they relied primarily
on small business owners and traders, individuals who were first to
take advantage of favorable conditions for entering Ukraine after the
breakup of the Soviet Union. Many such migrants had legalized their
Overview of Migration Trends in Europe and Central Asia, 1990–2004
status and launched businesses, especially in the food industry and
trading at Kiev markets. In contrast, many African migrants were
informed about Ukraine as an “easy” transit country to Western
Europe by countrymen who had studied in Ukraine during Soviet
times.
Profiles of undocumented migrants demonstrate that young, middle-level educated men are more likely to migrate illegally. Most
respondents to the Azerbaijan survey were between the ages of 18
and 34 and the majority had completed secondary or vocational
schools (with legal migrants having more education on average than
irregular migrants) and had worked as low-skilled workers. Among
legal migrants, men and women were about equally numerous,
whereas most irregular migrants were men (Economic and Social
Research Council 2002). The survey of undocumented transit
migrants in Ukraine found that about 15,000 such migrants, many
young Muslim men, may be located in Kiev. Many were married to
Ukrainian women. Two-thirds had a high level of education and had
lived in large cities or capitals in their home countries before migrating (Kennan Institute 2004).
Despite the careful calculations made in decisions to migrate, the
migration process is long and difficult for most transit migrants. Those
interviewed in Azerbaijan had all spent at least one year there, and
most were uncertain how much longer they would stay in transit.
Few expected to depart for their final destinations within the next
year and 11 percent had decided to stay in Azerbaijan if possible.
Transit migrants faced a number of difficulties—including shortages
of finance, unemployment, poor access to housing and health care,
and language barriers—yet were largely satisfied with the overall attitudes of government officials and the local population. More irregular migrants had employment in Azerbaijan than did legal migrants
(Economic and Social Research Council 2002).
A major factor inhibiting the further movement of so-called transit migrants was their lack of information. The intended final destinations of most irregulars were the United States, Canada, and Western
Europe, whereas most legal migrants intended either to return home
(especially to Russia) or to continue on to Western Europe. Most were
poorly informed about the rules and regulations for entry to their
planned destination countries and living conditions there. Furthermore, illegal migrants who intended to return home were often
dependent on outside assistance to do so. Most legal migrants planned
to leave Azerbaijan on their own, while most irregular migrants were
hoping for assistance from humanitarian organizations, travel agencies, and middlemen (Economic and Social Research Council 2002).
43
44
Migration and Remittances: Eastern Europe and the Former Soviet Union
Thus, clearly the migration experience is substantially influenced by
the legal status of those who undertake it.
ECA Country Experiences and Policies
Undocumented immigration is by definition difficult to quantify. Currently, there are estimated to be upward of 3 million undocumented
immigrants in the EU, and between 1,300 and 1,500 in Russia. The
International Organization for Migration reports that “99 percent of
labor migration in the Eurasian Economic Union formed of Tajikistan,
Kyrgyz Republic, Kazakhstan, the Russian Federation, and Belarus is
irregular. Due to their irregular situation, most labor migrants do not
benefit from the same protection rights other regular citizens enjoy
and are thus more vulnerable to exploitation by underground
employers” (IOM 2001, p. 11). Legal status not only affects the relative migration costs and expected benefits, but also changes the
underlying economic incentives. Table 1.3 provides a range of estimates of undocumented migration in selected ECA countries, Western Europe, and the United States.
ECA countries act as source, host, and transit countries for undocumented migrants. The concerns associated with the illicit movement,
transit, and trade in people are therefore salient across the region. The
major host is Russia, most of whose undocumented workers are from
the rest of the CIS. However, following accession of the EU-8 to the EU,
undocumented migration from western CIS, Russia, the Balkans, and
Turkey is becoming an increasing issue for the EU-8 and other countries
along its borders. Demographic change is generating a demand for workers in certain sectors and regions, while other migrants are becoming
“stuck” as they fail to cross the EU-15 borders. The status of the EU-8 is
in transition, but the slowdown in westward emigration in most countries, as well as the opening up of labor markets in some parts of the EU15, is increasingly regularizing flows. In fact, the expansion of the
Schengen Agreement to cover the EU-8 is extending the problem eastward, as irregular migrants are now becoming stuck in the Ukraine.7
Turkey hosts a number of undocumented workers mainly from ECA,
but also from the Middle East. Taking into account these factors and the
role of the ECA as the main overland route to Western Europe, the
whole of the region is a major transit route. Transit migrants may come
from the region itself, or from the Middle East, Africa, or Asia. It is
thought that of the 500,000 trafficked women in Eastern Europe,
300,000 originated in or were transported through the Balkans.
The growth of undocumented migration in the ECA region may be
closely tied to the migration policies used to regulate it, and particu-
45
Overview of Migration Trends in Europe and Central Asia, 1990–2004
TABLE 1.3
Estimated Irregular Migrants
(thousands)
Country
North America and Canada
United States
Canada
High-income Europe
Greece
Portugal
Italy
United Kingdom
Spain
Belgium
Germany
Switzerland
Netherlands
France
Ireland
Finland
Total
ECA countries
Poland
Ukraine
Tajikistan
Czech Republic
Slovak Republic
Turkey
Russia
Kazakhstan
Belarus
Kyrgyz Republic
Uzbekistan
Lithuania
Total
number of
migrants
Estimated number of
irregular migrants
Max
Min
Year of
estimation
Average %
of total
migrants
34,988
5,826
10,300
200
—
100
2004
2003
29.44
3.43
534
233
1,634
4,029
1,259
879
7,349
1,801
1,576
6,277
310
134
26,015
320
100
500
1,000
280
150
1,000
180
163
400
10
1
4,104
—
—
—
—
—
—
—
—
112
—
—
—
—
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
59.87
42.96
30.59
24.82
22.24
17.06
13.61
9.99
8.72
6.37
3.23
0.75
15.78
2,088
6,947
330
236
51
1,503
13,259
3,028
1,284
572
1,367
339
600
1,600
60
40
8
200
1,500
300
150
30
30
2
—
—
—
—
—
—
1,300
220
50
—
—
—
2000
2000
2002
2003
1998
2001
2000
2002
2000
1998
2000
1997
28.73
23.03
18.16
16.98
15.69
13.31
11.31
9.91
11.68
5.24
2.19
0.59
Sources: Pew Hispanic Center; IOM; ILO; World Bank; ISTAT; Home Office in United Kingdom; Jimenez (2003); Center on
Migration, Policy and Society of the University of Oxford; EU Business Council of Europe; Ministry of Labor in Finland;
Sadovskaya (2002); Migration Policy Group; Jandl (2003).
Note: — = not available. Estimation methods are different for each country. Total number of migrants is at the point in
2000 and is estimated by UN (2003).
larly policies in the EU-15 that cap supply of labor below demand.
The flow of labor under existing migration agreements is regulated
through quotas, as well as a maximum residency period allowed in
the receiving country. Quotas often appear small both in relation to
the perceived need for labor and in relation to the actual flow of labor
migrants. Thus, for instance, Jandl (2003) notes that while 1.11 million foreigners had valid residence permits in Spain in 2000, the 2001
census counted 1.57 million foreigners and the Organisation for Eco-
46
Migration and Remittances: Eastern Europe and the Former Soviet Union
nomic Co-operation and Development (OECD) (OECD 2005) estimates that roughly 1 million irregular migrants (around 6 percent of
the labor force) will be affected by the recent amnesty. In the United
Kingdom, Migration Watch estimates that the number of irregular
migrants—including disappeared asylum seekers, visa overstayers,
and clandestine entries—is over 100,000 a year; other sources put the
figure as high as 500,000.8 Jandl (2003) estimates that the stock of
irregular migrants in Europe is somewhere between 2.6 million and
6.4 million and the annual number of border apprehensions in EU-15
is close to 300,000.
In light of these numbers, and assuming that most clandestine
migrants succeed in finding work, the quotas for labor migration in
the bilateral agreements between EU-15 and Central Europe and the
Balkans are very small. For example, the Italian agreement on seasonal migration concluded in 1997 with Albania allows 3,000
migrants a year; Germany’s quota for guest workers is 15,500 a year
(though there are approximately 200,000 seasonal agricultural workers), and the United Kingdom allows an annual inflow of 25,000 from
all countries outside of the European Economic Area (OECD 2004).
Between the time of EU enlargement in May 2004 and November
2005, there has been an inflow of 156,165 workers from the EU into
the United Kingdom and 107,024 into Ireland. Through December
2004, there was a flow of 3,514 workers into Sweden.
Major Migration Partners of the ECA Countries
An important aspect of migration management is understanding the
patterns of migration for any particular country. Such an exercise is
similar to investigating a country’s major foreign trade partners,
though usually fewer countries are major senders and receivers of
migrants to any particular country than are significant trade partners.
Furthermore, the problems with obtaining migration data in many
countries in the region make this a somewhat inexact exercise. Fortunately, the largest country in the region, Russia, which is also the
main migration partner of most of the other former Soviet Union
(FSU) states, has a fairly complete set of migration data, although it
does not include the undocumented migrants in the country. Figure
1.11 shows that Russia has been a net recipient of migration from all
of the other FSU states except for Belarus, and a net sender to the “far
abroad” or to countries outside of the FSU (data underlying these figures are in table 1.6 of appendix 1). The countries from which Russia
has received the largest numbers of migrants are those from which
47
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.11
Russia, Net Migration by Country, 1989–2003
Kazakhstan
Uzbekistan
Georgia
Azerbaijan
Tajikistan
Ukraine
Kyrgyz Republic
Armenia
Turkmenistan
Latvia
Moldova
Estonia
Lithuania
Sweden
Poland
Australia
Finland
Canada
Belarus
Other
United States
Israel
Germany
Migration gains from all FSU states
(except Belarus)
Migration losses to all
states outside FSU
⫺1,000
⫺500
0
500
1,000
net migration (thousands)
Source: Goskomstat Rossii (selected publications).
there has been a large return of ethnic Russians—Kazakhstan,
Ukraine, and Uzbekistan. However, since 1994, there has been a net
immigration to Russia of many other nationalities. If undocumented
migrants were included, the numbers representing non-Russians
would be even larger.
Three countries outside Russia are the primary destinations for
Russian migrants: Germany, Israel, and the United States. Those who
migrate consist primarily of Germans, Jews, and Russians, reflecting a
combination of ethnic and economic factors driving their decisions to
migrate.
The trends shown in the data from Belarus, Moldova, and Ukraine
(see figure 1.12) are roughly consistent with the data that appear in
the data from Russia. Ukraine had net migration losses to Russia
while Belarus overall gained migrants. Moldova had net overall losses
and net migration losses to the FSU countries, though it did gain
migrants from all FSU countries except Russia, Ukraine, and Belarus.
All three of these countries are net recipients of migrants from all of
the other FSU states. As was the case for Russia, the same three countries outside the FSU—Germany, Israel, and the United States—are
the primary destinations of migrants from Ukraine, Belarus, and
Moldova. There is anecdotal evidence that an increasing number of
1,500
2,000
48
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 1.12
Major Migration Partners of the CIS Countries
Baltics: net migration by country
Ukraine, Belarus, and Moldova: net migration by country
Russian Fed.
Ukraine
Belarus
Moldova
Latvia
Lithuania
Estonia
Armenia
Azerbaijan
Georgia
Kazakhstan
Kyrgyz Rep.
Tajikistan
Turkmenistan
Uzbekistan
Russian Fed.
Ukraine
Belarus
Moldova
Latvia
Lithuania
Estonia
Germany
Israel
United States
Poland
Finland
Other
Germany
Israel
United States
Other
⫺250
⫺200
⫺150
⫺100
⫺50
0
50
100
⫺90 ⫺80 ⫺70 ⫺60 ⫺50 ⫺40 ⫺30 ⫺20 ⫺10 0
150
net migration (thousands)
Ukraine
Belarus
10
net migration (thousands)
Latvia
Moldova
Transcaucasus states: net migration by country
Lithuania
Estonia
Central Asia: net migration by country
Russian Fed.
Ukraine
Belarus
Azerbaijan
Georgia
Kazakhstan
Kyrgyz Rep.
Tajikistan
Turkmenistan
Uzbekistan
Germany
Israel
United States
Other
Russian Fed.
Ukraine
Belarus
Moldova
Armenia
Azerbaijan
Georgia
Kazakhstan
Kyrgyz Rep.
Tajikistan
Turkmenistan
Uzbekistan
Germany
Israel
United States
⫺300 ⫺250 ⫺200 ⫺150 ⫺100 ⫺50
net migration (thousands)
Armenia
Azerbaijan
Georgia
0
50
100
⫺1,200 ⫺1,000 ⫺800
⫺600 ⫺400
⫺200
0
200
net migration (thousands)
Kazakhstan
Kyrgyz Rep.
Turkmenistan
Uzbekistan
Tajikistan
Source: National statistical offices of the ECA countries.
labor migrants from Ukraine and Moldova are departing for the countries of Western Europe.
For the three Baltic states (Latvia, Lithuania, and Estonia), mainly
titular members of these states have migrated to Russia and the other
Slavic states. The data do not demonstrate the fact that this ethnic
migration peaked in 1992–93, just after the breakup of the Soviet
Union, or that it has declined substantially since then as Russians and
other minorities in the Baltics have remained as a result of faster
growing economies and impending EU membership. As in other FSU
countries, Germany, Israel, and the United States are the primary destinations for migrants from the Baltic states to countries outside the
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FSU, although there may have been a broader dispersion of destinations after these states became EU members in 2004.
For the three Caucasus countries (Armenia, Azerbaijan, and Georgia), Russia has been the dominant migration destination. There is
considerable evidence that these figures represent only a fraction of a
much larger undocumented and circular migration from these countries to Russia. This is especially the case with Georgia, where the data
on net migration by country only cover the period 1990 to 1992. In
contrast, the 2002 population census in Georgia revealed a net migration loss of 1.1 million persons or 20 percent of the population. The
migration of Armenians from Nagorno-Karabakh and the surrounding regions in Azerbaijan is shown in this data set, although such
movement was confined to the early 1990s. The United States is the
primary destination outside the FSU for migrants from Armenia, with
most of these joining the already large Armenian diaspora community there, while Israel remains a top Azerbaijani destination.
For the five Central Asian countries (Kazakhstan, the Kyrgyz
Republic, Tajikistan, Turkmenistan, and Uzbekistan), Russia again
dominates as a migration destination, as migration turnover to other
FSU states is rather minimal. There is, however, some tentative evidence that Kazakhstan is becoming a favored migration destination
for persons from the other Central Asian countries. From both Kazakhstan and the Kyrgyz Republic, there were large migrations of ethnic Germans to Germany. From Kazakhstan, over 800,000 Germans
left and from the Kyrgyz Republic, nearly 100,000. These movements
were the remnants of both voluntary and forced migrations of Germans to Central Asia during the Soviet period.
Figure 1.13 shows the major migration patterns of the largest western ECA country, Poland. As can be seen, Poland is losing people to
many developed countries (albeit to varying degrees) and remains a
net emigration country. Its largest losses are to neighboring Germany,
the United States, and Canada, where there are already large Polish
diaspora populations as a result of past migrations. The figure for Germany is likely an underestimate because many Poles can travel rather
easily to Germany. This figure encompasses the period before Poland
became an EU member and thus does not include Poles working in
the United Kingdom, Ireland, and Sweden. Many of them would not
likely be included in these totals, because such labor migrants generally do not view their departure from Poland to be permanent.
Figure 1.14 provides data on the main migration partners of Hungary, Romania, the Czech Republic, and the Slovak Republic. According to these data, Hungary is a net recipient of migrants from nearly all
listed countries, with especially large numbers coming from Romania,
49
50
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 1.13
Poland: Net Migration by Country, 1992–2003
Hungary
United States
Germany
Canada
Austria
Netherlands
Sweden
Belgium
United Kingdom
Denmark
Australia
Greece
Norway
Russian Fed.
Czech Rep.
Finland
Italy
⫺200,000
⫺150,000
⫺100,000
⫺500,00
0
50,000
net migration
Source: Migration Policy Institute; OECD SOPEMI 2003; and German Federal Statistical Office.
Yugoslavia, and other countries that housed ethnic Hungarians after
present-day Hungary was carved out of the Austro-Hungarian Empire.
Romania shows population losses to nearly every other country, with
especially large losses to Germany, where many Romanians have gone
for work. The only country from which Romania is gaining migrants
is its close ethnic neighbor, Moldova. The Czech Republic has been a
net recipient of people from other countries, with the bulk of in-migration coming from the Slovak Republic (which had been a part of
Czechoslovakia until 1993). The Slovak Republic itself is a net recipient from all listed countries except the Czech Republic.
Future Migration Trends in the Region
One of the themes of this report is that both economic and demographic incentives affect the motivation to migrate for ECA and
neighboring countries. This section describes the demographic implications for future migration flows in this region.
Future Migration Patterns in the EU and Neighboring
Countries
A combination of income convergence and demographic change suggests that the potential for large-scale migration from western ECA to
51
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.14
Major Migration Partners of Selected Western ECA Countries
Hungary, net migration by country, 1989–98
Romania, net migration by country, 1990–98
Moldova
Ukraine
Slovakia
Yugoslavia
Czech Rep.
Denmark
United Kingdom
Switzerland
Greece
Australia
Belgium
Sweden
Israel
France
Others
Italy
Canada
Austria
United States
Hungary
Germany
Romania
Others
Yugoslavia
Ukraine
United States
Germany
Russia
Greece
Slovakia
United Kingdom
Croatia
Israel
Austria
France
Italy
Bulgaria
Czech Rep.
Uzbekistan
0
10
20
30
40
50
60
70
80
90
net migration (thousands)
Slovakia, net migration by country, 1988–98
Slovakia
Others
Others
Ukraine
Ukraine
Yugoslavia
Germany
Romania
Russia
Russia
United States
United States
Canada
Poland
Poland
Bulgaria
Yugoslavia
Germany
Bulgaria
Canada
Austria
Austria
Romania
Czech Rep.
5
10
15
20
25
30
net migration (thousands)
20
net migration (thousands)
Czech Republic, net migration by country, 1988–98
0
0
⫺160 ⫺140⫺120⫺100⫺80⫺60 ⫺40 ⫺20
35
⫺10 ⫺8
⫺6 ⫺4
⫺2
0
2
net migration (thousands)
Source: Walmsley, Ahmed, and Parsons (2005).
the EU and other neighboring countries is limited. The richest countries in western ECA have already begun to be net immigration countries. This suggests that the experience of most Western European
countries that are net recipients of migrants is likely to become the
norm in most western ECA countries with income convergence and
EU membership. Even with no convergence, changes in migration
patterns appear inevitable.
4
6
40
52
Migration and Remittances: Eastern Europe and the Former Soviet Union
With the exception of Albania, all western ECA countries are forecast to experience population declines between now and 2050. The
total population of these countries peaked in 1990 at 130 million and
is projected to decline by 19 percent to 104 million by mid-century.
As shown in figure 1.15, western ECA source countries are often projected to have larger population declines than those in Western
Europe. The population of Western Europe is expected to increase
from its current size of 397 million to a peak of 407 million in 2030
before declining to 400 million in 2050. For western ECA, a decline in
the working-age population and a corresponding increase in those
over age 65 will create a demand for workers from abroad. The more
prosperous western ECA countries may be able to source some of
these workers from the rest of the region. However, for the region as
a whole, demand will have to be met from elsewhere, probably CIS,
Africa, and Asia. Whether these flows are legal or undocumented will
depend on immigration legislation.
While the total population of Western Europe is expected to rise
slightly between now and mid-century as a result of the current age
structure of these countries and expected demographic trends, the
working-age population in these countries is expected to decline substantially. Of course, the largest variable in future European migration
FIGURE 1.15
Population Size of Western Europe, Western ECA, and Turkey, 1950 to 2050
450
400
population (millions)
350
300
250
200
150
100
50
0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Western Europe
Western ECA
Turkey
Source: United Nations Population Division, World Population Prospects: The 2004 Revision (http://www.un.org/esa/population/unpop.html).
Overview of Migration Trends in Europe and Central Asia, 1990–2004
patterns in both Western Europe and western ECA is Turkey, in which
most of the future population growth and additions to the labor force
in Europe are expected to take place. Because of its younger age structure and higher fertility rates, Turkey is expected to grow by 33 million
between now and 2050 to a total of 101 million, nearly the size of the
other western ECA countries combined. Turkey, with an increase of 16
million in its working-age population, could produce sufficient migration to cover the 12 million person population deficit in the EU.
Future Migration Patterns in the Former Soviet Union
Economic factors such as differences in per capita income drive migration patterns among the post-Soviet states in the short term. These
will continue to be important, but demographic factors also will play
an important role. Figure 1.16 shows the population and expected
population of the FSU states over the period 1950–2050. The countries are grouped into the northern FSU—the Slavic and Baltic states
and Moldova, and the southern FSU states—Central Asia and the
Caucasus. The northern states as a group are characterized by continued low fertility, aging populations, an excess of deaths over births,
and declining populations. The group’s population peaked in 1990
and is expected to decline over the next half century by about onethird to 149 million. By contrast, the southern FSU states have
younger populations, above replacement-level fertility, and continued growing populations. As a group, these countries nearly tripled in
size, from 25 million in 1950 to 72 million in 2000. While growth is
declining, the momentum built into the age structure of these populations will cause their continued growth to 93 million in 2050.
Differential rates of population growth (or decline) do not necessarily imply that there will be migration from the high-growth to
low-growth areas but do present a precondition to that effect. While
the northern FSU states will have declining working-age populations
in even greater numbers than their overall population declines, most
of the southern FSU states, with their “youth bulges,” will have growing working-age populations with economies not growing fast
enough to supply jobs. Given their geographic proximity and common historical legacy, it would be only natural that the youth of the
southern FSU would look north for jobs, and as mentioned above,
there is ample evidence that they are doing so. Furthermore, historical legacy contributes to the selection of migration destinations. The
Soviet Union was an almost self-contained migration space; the interconnectedness of FSU countries may cause people to favor destinations in that area over others.
53
54
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 1.16
Population Size of the Northern and Southern FSU States, 1950 to 2050
population (millions)
250,000
200,000
150,000
100,000
50,000
0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Northern FSU
Southern FSU
Source: United Nations Population Division, World Population Prospects: The 2002 Revision Population Database (http://www.un.org/esa/population/unpop.htm).
Note: The northern FSU consists of Russia, Ukraine, Belarus, Moldova, Latvia, Lithuania, and Estonia. The southern FSU consists of Armenia, Azerbaijan, Georgia,
Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan.
A recent United Nations study examined the issue of using
“replacement migration” as a policy measure to address declining and
aging populations.9 The EU and Russia were included in the study, as
were other countries—including France, Germany, Italy, Japan, the
Republic of Korea, the United Kingdom, and the United States—that
face similar trends of declining and aging populations. The population
declines projected by 2050 in these countries range from 17 percent
(Moldova) to 52 percent (Estonia). Countries with aging and declining populations face a number of policy dilemmas, including appropriate retirement ages, pension system reform, and health care for the
elderly; support levels and ratios between working and pension-age
populations; labor force participation; and possible replacement
migration and the integration of immigrant populations. In contrast
to these other possibilities, replacement migration refers to the principle of using international migration to offset declines in total population, working-age population, or population aging.
Figure 1.17 shows the combination of natural increase (the difference between births and deaths) and net migration for Russia for the
period 1980–2015. During the 1980s, Russia’s population was growing as a result of both demographic and migratory factors. Starting in
1992 and expected to continue for the foreseeable future, the number of deaths has exceeded the number of births. Migration into Rus-
55
Overview of Migration Trends in Europe and Central Asia, 1990–2004
FIGURE 1.17
Russia: Net Migration and Natural Increase, 1980–2015
1,500
1,000
thousands
500
0
⫺500
⫺1,000
⫺1,500
1980
1985
natural increase
1990
1995
2000
2005
2010
net migration
Source: Goskomstat Rossii.
Note: Data are actuals from 1980 to 2003 and projected from 2004 to 2015.
sia spiked sharply in the 1990s following the breakup of the Soviet
Union and has declined sharply since then (at least documented
migration). If these trends continue, Russia’s population will decline
and age rapidly. For Russia to maintain the size of its total and working-age populations, allowing migration seems to be the only policy
option.
Under the medium-variant scenario used in the study, the EU is
projected to have a net migration of 13.5 million and Russia to have
a net migration of 5.4 million between 2000 and 2050. To maintain
the population size as it was in 1995 using migration alone would
require a net migration of 47.9 million into the EU and 24.9 million
into Russia during that period. Maintaining the same size workingage population would require a net migration of 79 million into the
EU and 35.8 million into Russia. For comparison’s sake, there was a
net migration of about 8.8 million into the EU and about 3.3 million
into Russia during the 1990s. Furthermore, for Russia this was a
period of extraordinary change and unprecedented migration that is
not likely to be repeated.
2015
2020
2025
56
Migration and Remittances: Eastern Europe and the Former Soviet Union
For the EU, Russia, and the other large aging and declining populations in the UN study, it is obvious that the needed replacement
migration levels are far above levels that are politically and socially
plausible. Even low levels of migration will require very careful political and social balancing acts in Russia, the other northern FSU countries, and other major migration destinations. Policies must be
designed to accommodate these new migration realities in both destination and originating countries, and, most importantly, the dynamic
fluctuations between the two. There is evidence that Russia and some
of the other FSU states are facing up to this new migration reality in
the region and taking steps to regularize it.
Endnotes
1. Much of the migration data upon which this chapter is based is contained in appendix 1.
2. Estimates as of March 2004 are that there are 10.3 million undocumented migrants in the United States and each year another 700,000 to
800,000 unauthorized enter the country, which is about the same size as
those who migrate legally to the United States (Passel 2005).
3. The figure for Armenia, which includes those not indicating their place
of birth, is likely a large overestimate because of the problems with the
census, which was conducted in January 1989, just after the devastating
earthquake in December 1988.
4. For more on the fertility decline in the ECA region, see Heleniak (2005).
5. Turkmenistan and Bosnia and Herzegovina are not included because of
the suspected migration data problems mentioned above.
6. To ensure comparability, the data are taken from one source, UNICEF’s
TransMONEE database, which collects data from the national statistical
offices of the 27 transition ECA countries, not including Turkey.
7. The Schengen Agreement originally was a state treaty to end internal
border checkpoints and controls among European countries. Today the
Schengen system is part of EU legislation regulating border control, visa
and admission and nonadmission standards, as well as the joint Schengen Information System. The 15 current Schengen countries include
Austria, Belgium, Denmark, Finland, France, Germany, Iceland, Italy,
Greece, Luxembourg, the Netherlands, Norway, Portugal, Spain, and
Sweden. All these countries except Norway and Iceland are EU members. The name “Schengen” originates from the small town in Luxembourg where the agreement was signed in 1985.
8. Data from the U.K. Home Office. Source at http://www.timesonline.
co.uk/article/0,,2087-1572533,00.html, retrieved June 22, 2005.
9. United Nations Population Division 2001. The study uses the 1998 Revision of UN population projections as a baseline. The European Union
defined in the report was the EU-15.
CHAPTER 2
Migrants’ Remittances
For most countries in the Europe and Central Asia (ECA) region,
remittances are the second most important source of external financing after foreign assistance and foreign direct investment. For many
of the poorest countries in the region they are the largest source and
have served as a cushion against the economic and political turbulence brought about by transition.
The situation is substantially different in the new European Union
(EU) member countries (EU-8). Income levels are higher, cross-country income differentials are lower, and there is less need for workers
living abroad to support their families’ consumption. Moreover, the
current and improving opportunities at home mean that there can be
large gains from accumulating human and financial capital abroad,
although as the economic situation at home improves, the incentives
to migrate may themselves decrease.
Yet, relative to GDP, remittances are significant in many ECA countries (figure 2.1).1 Four of the world’s largest recipients of remittances
as a portion of GDP are in ECA (Moldova, Bosnia and Herzegovina,
Albania, and Armenia). In 2004, officially recorded remittances to the
ECA region amounted to over US$19 billion, the equivalent of about
8 percent of the global total (US$232.3 billion) and 12 percent of
remittances received by developing countries (US$160.4 billion).
The first section of this chapter seeks to complement chapter 1 in providing a statistical overview of migrants’ remittances in ECA (figure 2.2).
57
58
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 2.1
Leading 20 Remittance-Receiving Countries in the World
(percentage of GDP in 2004)
Tonga
Haiti
Moldova
Lesotho
Lebanon
Bosnia and Herzegovina
Jamaica
El Salvador
Honduras
Albania
Mongolia
Dominican Republic
Nepal
Tajikistan
Cape Verde
Yemen, Rep.
West Bank and Gaza
Guinea-Bissau
Armenia
Guatemala
0
5
10
15
20
25
30
35
40
45
50
percent
Source: IMF Balance of Payment Statistics:, World Bank.
Note: Received remittances = received compensation of employee + received worker’s remittances + received migrants’ transfer. Lighter bars in the graph are ECA
countries.
As before, the problems of data quality are pervasive because of the difficulties of measuring remittances sent outside of the formal financial
sector are very difficult to quantity. Further complicating these data
problems are that large year-on-year increases in remittances may reflect
improvements in central banks’ remittance recording systems rather
than changes in migrants’ behaviors.
Data
While remittances have increased dramatically in a number of countries, they have slowed for others. A review of remittance flows over
the past nine years demonstrates this pattern (figure 2.3). Interestingly, while remittances from migrants who have lived out of their
59
Migrants’ Remittances
FIGURE 2.2
Remittances as a Portion of GDP in Eastern Europe and the Former Soviet Union, 2004
Moldova
Bosnia and Herzegovina
Albania
Tajikistan
Armenia
Kyrgyz Rep.
Georgia
Macedonia, FYR
Hungary
Croatia
Azerbaijan
Lithuania
Estonia
Latvia
Slovak Rep.
Poland
Belarus
Slovenia
Ukraine
Russian Fed.
Bulgaria
Kazakhstan
Turkey
Romania
Czech Rep.
0
5
10
15
20
25
30
percent
Source: IMF Balance of Payments Statistics.
Note: Received remittances = received compensation of employee + received worker’s remittances + received migrants’ transfers. Albania and Slovak Republic are
2003 data, other countries are 2004 data. GDP is $ converted current price.
home countries for more than one year represent the largest share of
inflows, remittances from migrants who have lived abroad for less
than a year represent an increasingly large share.
Not all migrants, however, send remittances, particularly in those
cases where the stay in destination countries is short. Surveys conducted for this report found that in Bulgaria, 80 percent did not; in
Bosnia and Herzegovina, 37 percent; and in Romania, 62 percent.
Generally remittance flows in ECA follow the same two-bloc pattern
as migration (table 2.1). The EU and the middle-income Commonwealth of Independent States (CIS) countries are the main sources of
60
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 2.3
Growth Rate of Remittances in ECA: 1995–98, 2001–04
(percent)
Turkmenistan
Hungary
Bulgaria
Slovak Rep.
Kazakhstan
Turkey
Slovenia
Poland
Azerbaijan
Macedonia, FYR
Georgia
Serbia and Montenegro
Albania
Bosnia and Herzegovina
0
0.05
0.1
0.15
0.2
0.25
0.3
percent
1995–98
Source: IMF Balance of Payments Yearbook.
2001–04
Note: Remittances defined as the sum of received workers’ remittances, compensation of employees, and migrants’ transfers.
remittances, with the EU accounting for three-quarters of the total and
the rich CIS countries for 10 percent. The amount contributed by the
EU-8 and accession countries is also significant, just below 10 percent.
Impact of Remittances on Development
The theoretical and empirical record on the economic impact of remittances is far from clear. Remittances can reduce poverty and fuel high
rates of household savings and investment (Rapoport and Docquier
forthcoming; Roberts 2004). At the same time, however, remittances
may exert upward pressure on the real exchange rate and reduce the
competitiveness of exports (similar to arguments about the Dutch disease). Some have found that remittances can also create incentives that
reduce the domestic work effort (Chami, Fullenkamp, and Jahjah 2003).
This section explores the development impact of remittances in ECA.
Considering each in turn, we find that remittances are often an impor-
61
Migrants’ Remittances
TABLE 2.1
Remittance Flows by Subregion, 2003
EU-15
New and
accession EU
Balkans
Sending
Russia and
Moldova Non-resourceresource-rich CIS and Ukraine
rich CIS
Receiving
New and accession EU
Balkans
Russia and resource-rich CIS
Moldova and Ukraine
Non-resource-rich CIS
Total
Total
($ million)
2,813
1,322
357
223
428
5,143
244
168
85
23
35
555
1
0.1
1
0.2
0.4
2
46
2
183
165
340
736
18
0.3
200
29
8
255
36
2
61
3
54
156
3,159
1,495
886
443
865
6,848
23
1
39
2
35
100
46
22
13
6
13
100
1
0
7
1
6
2
100
100
100
100
100
100
(percent for sending subregion)
New and accession EU
Balkans
Russia and resource-rich CIS
Moldova and Ukraine
Non-resource-rich CIS
Total
55
26
7
4
8
100
44
30
15
4
6
100
35
5
30
10
20
100
6
0
25
22
46
100
7
0
78
11
3
100
(percent for receiving subregion)
New and accession EU
Balkans
Russia and resource-rich CIS
Moldova and Ukraine
Non-resource-rich CIS
Total
89
88
40
50
49
75
8
11
10
5
4
8
0
0
0
0
0
0
1
0
21
37
39
11
1
0
23
7
1
4
Source: World Bank staff calculations from migration and remittance data in chapters 1 and 4.
Note: Remittances are defined as workers’ remittances and compensation of employees. Cell contents refer to the total remittance flows or percentage flows into
the receiving region from the sending region. Shaded areas are 10 percent or more of receiving or sending subregion or 5 percent or more of ECA flows.
tant source of foreign exchange, domestic consumption, and investment. Unlike other international transfers, remittances may be countercyclical. Remittances also are an important and stable source of income
for many households in the region, especially in the rural areas. Though
the underlying remittances data are poor, our estimations of the broader,
macroeconomic impact of remittances suggest that they exert a mild
positive impact on long-term patterns of macroeconomic growth, while
evidence on their impact on the distribution of poverty is mixed.
Remittances as a Stable Source of Foreign Exchange
Remittances often serve as a key source of foreign exchange for the
countries in the region. For example, remittances have represented a
key source of foreign exchange for Albania and helped to finance its
rapidly mounting deficit on trade in goods and services since 1990. In
62
Migration and Remittances: Eastern Europe and the Former Soviet Union
contrast, both official and private financial inflows on capital account
have played a relatively small role, although some increase in direct
investments in Albania since the turn of the millennium has occurred.
Remittances financed more than 70 percent of the deficit since 1995
(Lucas 2005). A recent World Bank study found that remittances provided similar financing of the trade deficit in Moldova since the late
1990s (World Bank 2005). In general, remittances have played an
increasingly important role in the foreign exchange flows to the
poorer countries in the ECA.
Figure 2.4 depicts shares of total remittances to exports of goods
and services for selected ECA countries. Taking into account that in
many cases exports are the major source of foreign exchange into the
country, this ratio can be a good approximation of the importance of
migrants’ transfers for the foreign exchange revenues of the country.
Being a significant source of foreign exchange, remittances can
serve as a pillar to support and improve creditworthiness and access
to international capital markets for many countries in the ECA region.
The ratio of external debt to exports, a common indebtedness indicator, declines substantially for some ECA countries if remittances are
also included as a potential source of foreign exchange.
Because they are a significant source of foreign exchange, remittances can improve creditworthiness and access to international capital markets for many ECA countries. For example, if remittances are
included as a potential source of foreign exchange, the ratio of debt to
exports falls by close to 50 percent for Albania and for Bosnia and
Herzegovina. Unlike capital flows, remittances do not create debt
servicing or other obligations. Thus, they can provide financial institutions with access to better financing than might otherwise be available. Among ECA countries, Turkey has been in the lead in using
such remittance securitization, but Kazakhstan has also used this
instrument to raise financing.
Remittances are one of the defining factors of exchange rate
dynamics and, as a consequence, macroeconomic policy in the small
open economies. Lucas (2005) observed that from 1992 to 2002, the
Albanian lek depreciated by some 7.6 percent per year on average
against the U.S. dollar. Because this is less than the rate of inflation,
this means a real appreciation of the lek, and this rate of real appreciation has continued at more than 7 percent on average in the five
years to 2002. No doubt exports would have been stronger in the
absence of this real appreciation. Even so, U.S. dollar earnings from
merchandise exports grew on average by almost 20 percent in the
decade to 2002, outstripping import growth even though exports
started from a much smaller base (Lucas 2005).
63
Migrants’ Remittances
FIGURE 2.4
Remittances as a Share of Exports in 2003
(percent)
Bosnia and Herzegovina
Albania
Moldova
Georgia
Armenia
Tajikistan
Hungary
Macedonia, FYR
Kyrgyz Rep.
Croatia
Azerbaijan
Poland
Latvia
Belarus
Slovak Rep.
Lithuania
Estonia
Ukraine
Turkey
Kazakhstan
Czech Rep.
Russian Fed.
Bulgaria
Romania
0
10
20
30
40
percent
Source: IMF Balance of Payment Statistics, World Bank.
Note: Received remittances = received compensation of employee + received worker’s remittances + received migrants’ transfer.
Economic Impact of Remittances
The economic consequences of remittances are hard to disentangle—
they can affect growth through a variety of channels. Lucas (2005)
divides the discussion of remittances in two: the effects on poverty
and inequality (which are considered in the subsequent section of
this report); and the influences upon investment, growth, and macroeconomic stability, which are considered here.
50
60
64
Migration and Remittances: Eastern Europe and the Former Soviet Union
Remittances augment national income and aggregate demand as a
whole. Figure 2.2 provided estimates of the income received from
friends and relatives abroad as a proportion of the national income. The
leaders in this respect are Moldova, Bosnia and Herzegovina, Albania,
Tajikistan, Armenia, and Kyrgyz Republic. It is interesting to note that
in Moldova, for example, earnings abroad constitute almost one-quarter of the national income.
Like any income, remittances are partially spent on household
consumption and partially saved and invested. If we subscribe to a
traditional macroeconomic model, the expansionary effect of remittances will be greater if they are spent on investment or saved in the
formal financial sector. Results from surveys with returned migrants
in ECA found that the majority of remittances are utilized for funding
consumption of food and clothing but that large quantities are also
used for education and savings (over 10%). Smaller amounts are
spent on business investment (less than 5%) (see figure 2.5).
Figure 2.6 provides the share of total remittances compared with
total household expenditure for selected ECA countries in 2003. It is
not surprising that the results are well correlated with GDP shares,
given that consumption is a main component of GDP. If the propensity to consume from remittances is similar to other income, it can be
FIGURE 2.5
Expenditure Patterns from Remittances in Six ECA Countries
Food and clothing
Education
Home repair
Savings
Property purchase
Medical expenses
Business investment
Special events
Other
Car purchase
Land purchase
Charity
0
5
10
15
20
25
30
35
percent
Source: Results from a World Bank survey with returned migrants in Bosnia and Herzegovina, Bulgaria, Georgia, Kyrgyz Republic, Romania, and Tajikistan. See appendix 1.1 for further information on the survey.
65
Migrants’ Remittances
FIGURE 2.6
Remittances as Share of Total Household Expenditure in 2004
Moldova
Bosnia and Herzegovina
Albania
Armenia
Tajikistan
Kyrgyz Rep.
Georgia
Hungary
Croatia
Macedonia, FYR
Azerbaijan
Lithuania
Estonia
Latvia
Slovak Rep.
Belarus
Poland
Slovenia
Ukraine
Russian Fed.
Kazakhstan
Bulgaria
Turkey
Romania
Czech Rep.
0
5
10
15
20
25
30
35
percent
Sources: IMF, Balance of Payments Statistics, World Economic Outlook; World Bank.
Note: Received remittances = received compensation of employee + received workers’ remittances + received migrants’ transfer. Albania and Slovak Republic are
2003 data. Otherwise, in 2004 data. Household expenditures is $ converted current price.
concluded that, for some countries, remittances spurred a significant
portion of total consumption. For example, in Moldova or Albania,
every fifth dollar spent in 2003 came from remittances.
There is a debate over the extent to which remittances actually
boost the economy of the migrant-source country, because, as the discussion above demonstrates, a substantial portion of income has been
used for consumption purposes and not saved or invested (Drinkwater, Levine, and Lotti 2002). Recent strands of literature, however,
66
Migration and Remittances: Eastern Europe and the Former Soviet Union
indicate that remittances can lead to economic growth simply by
increasing the migrant’s household income, regardless of whether this
additional income is spent on consumption or savings. For example,
Ratha (2003) indicated that if remittances are invested, they contribute to output growth, but they generate positive multiplier effects
if consumed. Research on Moldova corroborates this information, as
economic growth has been strongly driven by a spike in gross national
disposable income since the late 1990s, a period characterized by high
levels of international remittances (World Bank 2005).
Furthermore, significant empirical evidence indicates that remittances lead to positive economic growth, whether through increased
consumption, savings, or investment. Lucas (2005) cites several case
studies that show signs that remittances may indeed have accelerated
investment in Morocco, Pakistan, and India. Glytsos (2002) models
the direct and indirect effects of remittances on incomes and hence
on investment in seven Mediterranean countries, and finds that
investment rises with remittances in six out of the seven countries.
Additionally, the results of the analysis conducted by León-Ledesma
and Piracha (2001) for 11 transition economies of Eastern Europe
during 1990–99 show support for the view that remittances have a
positive impact on productivity and employment, both directly and
indirectly through their effect on investment. A recent study by
Roberts (2004) on remittances in Armenia concludes that, overall,
empirical evidence suggests that the propensity to save out of remittance income is high (almost 40 percent) and remarkably consistent
across studies.
There is also evidence of important multiplier effects from remittance spending, particularly from housing construction (Roberts 2004;
Lucas 2005, citing Glytsos 1993; Adelman and Taylor 1990; Zarate
2002). The multiplier effect can be high—Durand, Parrado, and Massey
(1996) find that every “migradollar” that enters a local economy generates as much as $4 in demand for goods and services, though such
analyses may rely on extreme assumptions. Moreover, Desai et al.
(2003) indicate that additional consumption increases indirect tax
receipts, thus also increasing government consumption or savings.
Therefore, there is evidence that remittances have enabled economic growth through greater rates of investment. Even more certainly, remittances have important multiplier effects, raising income
levels in the economy beyond the households of remittance recipients.
There are, nevertheless, at least two points of reservation regarding
these optimistic conclusions. One is the possibility that countries can
face a situation similar to the Dutch disease, in which the inflow of
remittances causes a real appreciation, or postpones depreciation, of
Migrants’ Remittances
the exchange rate, restricting export performance and hence possibly
limiting output and employment (Lucas 2005). More importantly,
research by Chami, Fullenkamp, and Jahjah (2003) ascertained that
income from remittances may be plagued by a moral-hazard problem,
permitting the migrant’s family members to reduce their work effort.
Part of the explanation for these distinct findings may be that the
studies suffer from an omitted variable bias: the role of institutions. We
hypothesize that the impact on remittances of macroeconomic growth
and development is conditioned by the quality of the recipient country’s political and economic policies and institutions. The quality of
institutions might play an important role in determining the exact
effect of remittances on economic growth, because institutions exert
substantial influence on the volume and efficiency of investment.
Overall, estimations conducted with dynamic-panel methods find
that remittances have a positive impact on macroeconomic growth.
Moreover, the results are not inconsistent with the argument that
institutions play a role in conditioning this relationship (see box 2.1).
Distribution, Poverty, and Inequality
In addition to absolute indicators of growth and macroeconomic stability (Lucas 2005), remittances may have distributive effects on
poverty and inequality. Of the two factors, the effect of remittances
on poverty seems much less controversial, because remittances per se
do not lower anyone’s income. Remittances contribute to household
income and thus, in the short run, reduce poverty. Recent analysis by
Adams and Page (2003) confirms that a 10 percent increase in the
share of international migrants in a country’s population will lead to
a 1.9 percent decline in the share of people living on less than $1 per
person per day. In addition, Adams finds that international remittances have a negative statistically significant effect on three poverty
measures (poverty headcount measure, poverty gap, and squared
poverty gap measure) (Adams and Page 2003).
When it comes to the overall impact of remittances on income
inequality, Ratha (2003) finds the evidence mixed. Some find that
remittances sharpen inequality (Stark, Taylor, and Yitzhaki 1986;
Adams 1991), while others claim that in the long run, income distribution becomes more equal as a result of the liquidity provided for
capital accumulation, or through trickle-down effects in the labor
market (Taylor and Wyatt 1996).
Richard Adams in his “The Effects of International Remittances on
Poverty, Inequality, and Development in Rural Egypt” (1991) finds
67
68
Migration and Remittances: Eastern Europe and the Former Soviet Union
BOX 2.1
Estimating the Impact of Remittances on Macroeconomic Growth
This dynamic-panel investigation estimates the impact of workers’ remittances on per capita
GDP growth in a sample of developed and developing economies (for information on the estimations and alternative specifications, see appendix 2.2). The estimator used in most of the
sample equations below is the Anderson and Hsiao (1981) method. The results of using the
GMM estimator are also relevant because we do not have specific Monte Carlo evidence on the
appropriateness of each estimator for our panel settings. In all the estimations we have used the
Worker Remittances and Growth: Dynamic Panel Estimation (1970–2003)
(dependent variable: growth of GDP per capita; endogenous variable: log (remittances/GDP)
Growth GDPpc (t-1)
Log(remittances/GDP growth)
(i)
AH
(ii)
AH
(iii)
AH
(iv)
AH
0.233***
(0.015)
⫺0.002
(⫺0.003)
0.203***
(0.018)
0.001
(0.002)
0.041***
(0.011)
0.315***
(0.076)
0.024***
(0.008)
⫺0.010
(⫺0.048)
⫺0.003
(⫺0.004)
0.051
(0.352)
0.053
(0.045)
⫺0.161
(⫺0.250)
⫺0.019
(⫺0.012)
⫺0.037
(⫺0.039)
⫺1.711
(⫺1.257)
1926
121
0.000
0.358
0.000
0.532
⫺0.003
(⫺0.010)
1660
108
0.000
0.443
0.000
0.406
0.001
(0.003)
566
90
0.000
0.452
0.000
0.254
0.035***
(0.012)
150
51
0.088
0.867
0.140
0.854
0.055
(0.054)
Log(GCF/GDP)
Log(NPCF/GDP)
TI corruption index
UNHDI
Voice and accountability
Political stability
Government efficiency
Regulatory quality
Rule of law
Corruption
Observations
Number of ID
Wald
Sargan
AR(1)
AR(2)
Long-run remittances coefficient
Source: World Bank Staff calculations.
Note: Specifications (1) to (6) were obtained using the Anderson-Hsiao estimator (AH). Specifications (7) to (9) were obtained
using the 2-steps GMM estimator of Arellano and Bond (1991) with robust standard errors.
Standard errors in parentheses.
* significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.
69
Migrants’ Remittances
logarithm of the remittances/GDP ratio as our independent variable, as well as the control variables described in further detail in appendix 2.2. Finally, we provide the long-run dynamic solution for the coefficient on remittances, which is to be interpreted as the impact of remittances
on growth in equilibrium.
According to the results below, remittances appear to have a positive and statistically significant
impact on growth in four out of six of these specifications. We could safely conclude that we can
reject the existence of a negative impact of remittances on growth and that there is some indication of a positive, albeit mild, impact.
(v)
AH
(vi)
AH
(vii)
GMM
(viii)
GMM
(ix)
GMM
0.939
(1.432)
⫺0.032
(⫺0.114)
0.336
(0.441)
⫺0.007
(⫺0.106)
0.585
(1.344)
0.05
(0.177)
⫺0.372
(⫺1.719)
0.002
(0.181)
⫺0.033
(⫺0.096)
⫺1.262
(⫺12.510)
0.011
(1.450)
0.020
(0.708)
0.353
(1.296)
⫺0.147
(⫺0.148)
0.431
(1.668)
0.081
(0.779)
150
51
0.782
0.845
0.646
0.967
0.12
(0.691)
0.293***
(0.071)
0.028*
(0.016)
0.012
(0.018)
⫺0.003
(⫺0.003)
0.001
(0.005)
⫺0.034
(⫺0.034)
0.018
(0.020)
⫺0.023
(⫺0.014)
⫺0.018
(⫺0.016)
⫺0.005
(⫺0.015)
0.025
(0.022)
0.0114
(0.016)
150
51
0.000
0.216
0.017
0.127
0.040**
(0.021)
0.164**
(0.069)
0.023*
(0.012)
0.062***
(0.016)
⫺0.001
(⫺0.002)
0.061
(0.090)
0.043*
(0.023)
0.047***
(0.014)
0.688
(0.950)
⫺0.590
(⫺0.676)
0.100
(0.519)
⫺0.221
(⫺0.329)
0.285
(0.782)
0.047
(0.514)
344
77
0.936
0.998
0.367
0.369
⫺0.536
(⫺13.800)
0.008
(0.019)
⫺0.006
(⫺0.007)
0.009
(0.012)
⫺0.025**
(⫺0.012)
0.024
0.018)
⫺0.027**
(⫺0.012)
334
77
0.000
0.51
0.000
0.242
0.027**
(0.014)
⫺0.023*
(⫺0.016)
⫺0.0001
(⫺0.002)
530
93
0.001
0.973
0.000
0.346
0.045**
(0.023)
70
Migration and Remittances: Eastern Europe and the Former Soviet Union
that when remittances are included in predicted per capita household
income, the Gini coefficient increases by 24.5 percent. He explains
this by the fact that the poorest quintile of households produces a
proportionate share of still-abroad migrants, the richest 40 percent of
households produce more than their share, but the second and third
quintiles are under represented. “It is these variations in the number
of migrants produced by different income groups—and not differences in either migrant earnings abroad or marginal propensities to
remit—that cause international remittances to have a negative effect
on rural income distribution” (Adams 1991, p. 74).
The distribution of remittances across urban and rural as well as
capital-city areas for the abovementioned case studies is presented in
figure 2.7. As can be seen from the figure, different countries are
characterized by different patterns. For example, in Tajikistan and
Albania the bulk of remittances goes to the rural areas (almost 70 percent), while in Armenia and Georgia the pattern is the opposite—
almost 70 percent of remittances channeled into the countries go to
large metropolitan areas and other cities. There appears to be a link
FIGURE 2.7
Distribution of Remittances by Location in 2002
(percent)
100
percent
75
50
25
0
Tajikistan
Kyrgyz Rep.
capital city
other urban areas
Georgia
Armenia
rural areas
Source: Authors’ calculations; World Bank, Household Data Archive for Europe and Central Asia.
Note: Data for Tajikistan are for 2003.
Albania
71
Migrants’ Remittances
between such findings and population distributions; figure 2.8
demonstrates that Armenia and Georgia have proportionally less of
their populations living in rural areas.
The relationship between remittances and inequality becomes
even more evident when we look at the specific areas from which
international migration is more prevalent. In the case of Albania, it is
the poor regions in the north and other rural areas in the country that
send international migrants.2 In Armenia or Georgia, most households that report receiving remittances (and thus have relatives or
other acquaintances abroad) hail from urban areas; the majority share
of remittances reported by households goes into urban areas as well.
There are two explanations for the trend toward remittances to
urban areas. First, individuals may find it relatively difficult to migrate
abroad from rural areas. Second, most households that receive remittances might move into cities as a result of their newfound wealth.
The latter situation is of special relevance to Armenia, where some
portion of households may receive relatively high amounts of income
from remittances from the so-called “old diaspora” on a regular basis.
FIGURE 2.8
Distribution of Population by Location in 2002
(percent)
100
percent
75
50
25
0
Tajikistan
Kyrgyz Rep.
capital city
other urban areas
Georgia
Armenia
rural areas
Source: Authors’ calculations; World Bank, Household Data Archive for Europe and Central Asia.
Note: Data for Tajikistan are for 2003.
Albania
72
Migration and Remittances: Eastern Europe and the Former Soviet Union
As a result, their incomes increase by a substantial amount, thus
enabling a move to urban or capital areas, which are considered safer
and more convenient to live in, though more expensive.
Table 2.2 presents estimates of average remittances and consumption per quintile for receiving and all households for the selected ECA
countries. One of the key findings of the table is that richer households receive more remittances as a proportion of all households. This
tendency is prevalent for all countries in the investigation, where
data quality allows such investigation.
There can be several explanations for this migration bias skewed
toward better-off families. First, movement internationally may be
costly. Fixed costs of migration include transportation, as well as visa
and work-permit fees. Furthermore, migrants likely support themselves for the first months of living abroad. Such expenditures may be
relatively expensive once the differences in prices between host and
sending countries are taken into account. Second, richer households
have better access to information: they can employ expensive conTABLE 2.2
Annual Consumption and Remittances per Capita by Quintile
(US$)
Quintile
Albania (2002)
Consumption per capita (all households)
Share of receiving households (percent)
Remittances per capita (receiving households)
Remittances/consumption (receiving households; percent)
Armenia (2003)
Consumption per capita (all households)
Share of receiving households (percent)
Remittances per capita (receiving households)
Remittances/consumption (receiving households; percent)
Georgia (2002)a
Consumption per capita (all households)
Share of receiving households (percent)
Remittances per capita (receiving households)
Remittances/consumption (receiving households; percent)
Kyrgyz Republic (2003)
Consumption per capita (all households)
Share of receiving households (percent)
Remittances per capita (receiving households)
Remittances/consumption (receiving households; percent)
Tajikistan (2003)
Consumption per capita (all households)
Share of receiving households (percent)
Remittances per capita (receiving households)
Remittances/consumption (receiving households; percent)
1
2
3
4
283.66
16.87
147.58
52.03
425.76
13.23
186.59
43.82
560.02
18.08
261.76
46.74
761.15
24.31
294.35
38.67
1,403.13
28.37
541.85
38.62
135.39
16.51
67.88
50.13
194.02
16.30
105.36
54.31
244.81
16.40
74.30
30.35
312.24
17.61
112.47
36.02
547.30
21.20
167.51
30.61
24.73
2.58
35.83
144.88
46.66
2.15
35.76
76.63
67.38
1.83
35.18
52.21
96.06
1.91
50.49
52.56
193.85
2.53
76.57
39.50
78.31
0.84
7.73
9.87
115.55
1.63
7.14
6.18
148.32
1.38
10.80
7.28
198.93
3.41
41.76
20.99
337.12
7.04
46.02
13.65
67.20
8.01
23.56
35.07
103.88
9.82
28.12
27.07
139.03
9.33
34.25
24.63
188.13
8.96
41.85
22.25
344.35
7.66
55.68
16.17
Sources: Authors’ calculations; World Bank, Household Data Archive for Europe and Central Asia.
a. Quarterly.
5
Migrants’ Remittances
sulting services and on average have higher education levels, factors
that may facilitate migration. Third, existing social relationships help
facilitate migration. Richer households with better opportunities to
move initially may also pass on the knowledge and networks they
obtain to households that interact with them—households that are
most likely to be from the same or neighboring quintile. Finally,
remittances received have an effect on household income—some
households are likely in the top quintiles of income distribution precisely because they receive remittances.3 Even so, it is likely that over
time the difference in shares of remittances received for every quintile equalizes and even reverses; migrants who moved earlier on may
return home to start their own businesses. Furthermore, the costs of
moving will decrease in the long run through a reduction in the fees
of consulting companies for migrants.4
Another finding of table 2.2 is that richer households receive greater
remittances on average in per capita terms than poor households.
Migrants in many cases remit two or three times as much to rich
households. It is worth noting that this situation is present for all countries under our investigation, even those where only tiny proportions
of the households surveyed report actually receiving remittances.
One of the explanations for this finding can be, as mentioned above,
better access to information for richer households than for poor ones.
Richer households can pay for costly consulting services to help them
find better jobs, a cost that in many cases poor households cannot
afford. Decisions made throughout the migration process are another
reason for this phenomenon. Given expected future earnings at home
and abroad, the cost of moving, and the time spent apart from family,
migrants from rich households may have greater discretion over which
job offers to accept than one who represents a household from a poorer
quintile. It is possible that migrants from poor households have on
average worse paid jobs than migrants from rich ones, at least at the
beginning. A further explanation relies on connections to the “old diaspora.” For example, in Armenia relatively large values of remittances
are sent abroad by distant relatives or friends from the West.5 If richer
households have more connections within the old diaspora, they may
have greater networks through which to receive remittances.
The third key finding from table 2.2 is that remittances constitute
a considerable proportion of household expenditure and a higher
portion of consumption per capita for the poor households than for
the rich, suggesting that remittances are more important for poor
than for rich households.
73
74
Migration and Remittances: Eastern Europe and the Former Soviet Union
Endnotes
1. More detailed remittances data, including more extensive international
comparisons, are presented in appendix 2.1.
2. For more evidence on migration patterns in Albania see Albania Poverty
Assessment 2003 and A. Sarris (2004).
3. For more information on this topic, see Adams (2004).
4. Consulting companies or most of the so-called travel agencies in ECA countries assist migrants with visa documents, work permits, traveling, job search,
and so forth. For many migrants this assistance is crucial in their decision to
move. At the beginning of the migration era, this array of services was provided by just a few companies, which could result in price-setting power.
5. For more information on the Armenian diaspora and its role in remittances, see Roberts (2004).
CHAPTER 3
Determinants of Migration
Migration is driven by perceived differences in the utility of living or
working in two geographical locations. Over time, such perceptions
have changed in Eastern Europe and the former Soviet Union (FSU).
In the aftermath of transition, migration was stimulated not only by
economic motivations but also by the desire to escape conflict and
relocate to ethnic homelands in many instances. As much of the diaspora migration ran its course and security risks diminished—with
some exceptions such as in southern Russia—migration flows began
“normalizing” and much current migration reflects perceived expectations about differences in income and the quality of life.
Despite the great variation in the migration patterns across the
region and the extremely complex combination of microeconomic
and social motivations for migration, similar motivations seem to
underpin the decisions to migrate throughout the region. The most
recent labor flows in Europe and Central Asia (ECA) region seem
largely to be a response to poorly functioning labor markets, insufficient productive capital, the low quality of life in a number of migration sending countries, and a rising demand for unskilled labor for the
nontraded services sector in the labor-importing economies in the
European Union (EU) and Commonwealth of Independent States
(CIS). As the neoclassic or Harris-Todaro approach argues, differences
in real income or expected income clearly drive the supply of migra-
75
76
Migration and Remittances: Eastern Europe and the Former Soviet Union
tion in a large number of cases. Yet, income differentials explain only
a portion of the story. There is evidence that migration between two
countries with unequal average real wages can remain low when
there is an expectation that aggregate “quality of life” is improving in
the lower-income country. Significant portions of any country’s
workforce may, all else being equal, prefer to remain at home rather
than take on the risks of moving abroad and leave family and friends.
Yet, many households agree to leave their familiar surroundings
when their home countries do not provide for their physical protection from attack or abuse, or have poor public-service delivery and
governance at the local and national level, an uncertain business
investment environment, or high unemployment.
On the demand side, the migration of unskilled labor to the EU
and the resource-rich CIS primarily reflects a need for labor in nontraded services resulting from rising incomes, the growth of the middle class, and the increasing number of women participating in the
labor force. This demand for labor can be met by migrants for whom
the market-clearing wage is superior to opportunities back home. As
per capita incomes and mandated wages rise, unskilled local workers
are increasingly priced out of the market, while the large excess supply of migrant labor sustains demand and the prevailing wage.
This chapter seeks to understand the motivations driving migration in ECA using three methods. The first section lays out the theoretical perspective for the chapter—it undertakes a literature review
of existing research on the determinants of migration, and raises the
possibility that overall quality of life expectations, in addition to wage
differentials, may drive migration.
The next two sections contain a comparative historical analysis of
the migration experiences of the countries of the FSU in one case and
of the Southern and “cohesion” European countries from the 1960s
to the 1980s. These countries’ experiences in moving from net emigration to immigration countries over this period provide insights
into the configurations of migrants’ expectations and economic and
quality-of-life motivations that shape broader national migration patterns. A key goal of this section is to provide a more refined understanding of the migration “hump” that some have observed
characterizes migration from Southern Europe and other regions, as
well as to identify the role that migrants’ expectations play in shaping
such hump patterns. Coming to grips with these countries’ experiences may be instructive for understanding how migration may
evolve in ECA in the future.
A final section employs an economic model to simulate international labor markets and thus judge the impact of improving quality
Determinants of Migration
of life in the receiving countries on patterns of migration. The simulation finds that improvements in the sending countries’ policies and
institutions can slow out-migration and perhaps enhance the incentives for circular migration, a form of migration where the migrant
spends intermittent time at home and abroad.
Incentives for Migration: A Theoretical Perspective
The motivations for migration may be stylistically described as combinations of social, ethnic, and politically related push and pull factors
(table 3.1). Yet, as chapter 1 discussed, labor migration is becoming
the chief motive for migration for the majority of migrants in Central
and Eastern European and Central Asian countries. This labor migration has generally been understood to be driven by differences in
returns to labor, or expected returns, across markets.1
The simplest economic models of migration highlight that migration streams result from actual wage differentials across markets, or
countries for our purposes, that emerge from heterogeneous degrees
of labor market tightness. Todaro (1968, 1969) and Harris and Todaro
(1970) refined this simple model into the more widely applied explanation that migration is driven by expected rather than actual wage
differentials. Though their model was designed to understand internal migration in less-developed economies, their approach of explicitly modeling expected wage differentials has been widely generalized
in formal explanations of international migration because it reflects
the uncertainty that migrants will be able to successfully locate better
paying jobs in another location. As Todaro (1969, p. 140) explained,
“[a] 70 per cent …real wage premium, for example, might be of little
consequence to the prospective migrant if his chances of actually
securing a job are, say, one in fifty.”
Yet as Bauer and Zimmermann (1999) observed, the predictions
made by this simple economic model have had mixed success in
explaining and predicting migration across a variety of regions. These
authors found that in a number of studies, wage and also employment differentials (which are linked to the probability of locating a
position abroad) were statistically significant predictors of migration
in the expected directions only about half the time. In a number of
cases, these differentials seemed to produce the opposite of the
expected effect.
To some extent, these uneven results reflect the differential drivers
of migration across countries at different points in time, as well as the
extreme complexity of the migration process. They might also reflect
77
78
Migration and Remittances: Eastern Europe and the Former Soviet Union
TABLE 3.1
Motivations for Migration
Push factors
Economic and demographic
Political
Social and cultural
Poverty
Unemployment
Low wages
High fertility rates
Lack of basic health and education
Conflict, insecurity, violence
Poor governance
Corruption
Human rights abuses
Discrimination based on ethnicity,
gender, religion, and the like
Pull factors
Prospects of higher wages
Potential for improved standard of living
Personal or professional development
Safety and security
Political freedom
Family reunification
Ethnic (diaspora migration) homeland
Freedom from discrimination
Source: World Bank staff.
the poor and noisy qualities of migration data. Yet, there are a number
of empirical anomalies to the Harris-Todaro framework that suggest a
more fundamental weakness. For example, the accession of Greece
(1981), Portugal (1986), and Spain (1986) to the European Community (EC) was accompanied by predictions of massive waves of economic migration from these Southern European countries to Western
and Northern Europe as barriers to free labor movements were phased
out. The income differentials between these new member states and
the majority of the EC raised fears that wages would be depressed and
unemployment of indigenous workers would result in the older EC
states while domestic social security systems would be placed under
enormous pressure. Similar “doomsday” scenarios resulted when EU
membership expanded into Central and Eastern Europe in 2004
(European Commission 2006). However, in both instances, the most
extreme of these fears were exaggerated because migration levels were
not as elastic to wage and employment differentials as some empirical
estimations of the Harris-Todaro model would predict.
These anomalies indicate the importance of including broader quality-of-life considerations in the home country as an explanatory variable. Differences in political stability, human rights situations, and the
general rule of law may also affect migration, because these factors
serve as a proxy for the level of individually perceived insecurity. Thus,
it is possible to hypothesize that broad, quality-of-life considerations
drive or even inhibit migration. Though the decision to migrate for
more productive and lucrative jobs is certainly related to the search for
a higher-quality life, wage and unemployment differentials alone will
not explain as much migration as when combined with these broad
quality-of-life concerns. Risk-averse individuals and households may
Determinants of Migration
be less motivated to exploit spreads in earnings across countries if their
day-to-day lifestyle is comfortable and stable. Yet, differentials in the
pursuit of security may motivate those who would otherwise stay at
home to search for a better and more secure life. This suggests that
migration might be kept low even when income differentials are high
if growth is rapid or the adoption of better institutions is underway (as
with EU candidates adopting the Acquis Communautaire), but might
increase when change is not occurring.
Thus, the policies that improve the incentives for business investment, financial deepening, and the exercise of entrepreneurship
might be the same as those that reduce the incentives for migration.
If “quality of life” policies are understood as a broad range of economic structural, social equity, and governance factors, then improving these policies creates the incentives necessary to maximize the
benefits from existing migration flows.
Incentives for Migration: Empirical Evidence from
Eastern Europe and the Former Soviet Union
As discussed above, neoclassical economic theory posits that it is differentials in wages among regions, or countries, that cause people to
move from low-wage, high-unemployment regions to high-wage,
low-unemployment regions. Extensions of neoclassical theory, called
“the new economics of migration,” use households, families, or other
groups of related people, rather than markets themselves, as their
unit of analysis. These units operate collectively to maximize income
and minimize risk. Thus, they often send one or more family members to other parts of the country, usually a larger city, or abroad to
increase overall family income while others remain behind earning
lower but more stable incomes.
The complex system of ethnic homelands that make up the ECA
countries further complicates migration patterns in several ways. For
instance, when the Soviet Union broke apart, there were 53 different
ethnic homelands, 15 of which became independent sovereign states.
Across ECA, there were large diaspora populations living outside their
ethnic homelands. Many thought that “return migration” to ethnic
homelands of diaspora groups would dominate migration patterns
during the early part of the transition period.
It appears from available data that these ethnic causes of migration, namely “diaspora” migration, did dominate trends in the early
1990s, but that economic motives are now becoming the major factor
influencing migration. Much diaspora migration was accompanied by
79
80
Migration and Remittances: Eastern Europe and the Former Soviet Union
ethnic violence, resulting in large refugee and internally displaced
populations. Appendix table 1.3 shows the nationality composition of
the ECA countries based upon the 1990 and 2000 population censuses. In all but one of the 15 countries of the FSU, the titular population increased its share of the total population. The lone exception
was Russia, where the percentage of the Russian population fell
slightly, likely owing to the high rate of natural decrease of the ethnic
Russian population. In the eight countries of the western ECA region
where data are available from both censuses, the titular population
increased in only three. This result is explained in part by increases in
Roma populations resulting from ethnic reidentification.
Figure 3.1 shows the ethnic composition of migration into Russia
since 1989. The share that ethnic Russians contributed to total migration into Russia peaked in 1992—the first year after the breakup of
the Soviet Union—at two-thirds of total immigration. The Russian
share has since declined to only half of total immigration into Russia
as, presumably, those Russians who were going to leave the nonRussian states of the FSU did so in the early 1990s. As the number of
Russians migrating to Russia has declined, total migration to Russia
has declined and the number of non-Russians going to Russia has
increased, presumably for economic reasons. The share of non-Russians would presumably be even higher if undocumented and temporary migration were included.
FIGURE 3.1
Nationality Composition of Migration to Russia, 1989 to 2003
800
net migration (thousands)
700
600
500
400
300
200
100
0
1989
1990
Russians
1991
1992
Armenians
1993
1994
1995
Tatars
Source: Goskomstat Rossii, Demographic Yearbook of Russia (selected years).
1996
Ukrainians
1997
1998
Others
1999
2000
2001
2002
2003
81
Determinants of Migration
One rather simple theoretical explanation for the migration trends
among the ECA countries is the widening disparities in GDP per
capita. Within countries such as the Soviet Union there was an
attempt to equalize incomes among both social groups and geographic regions, which was accomplished through a massive and
elaborate system of subsidies, transfers, and controlled prices. With
independence and economic transition, levels of GDP per capita have
widened considerably among the ECA countries, and now act as a
factor. Figure 3.2 shows the coefficient of variation and the high-low
ratio of per capita GDP among the ECA countries for the period
1990–2002. The coefficient of variation increased from 0.43 in 1990
to 0.70 in 1997, before declining slightly. The ratio of the country
with the highest GDP to the lowest showed a similar trend, increasing
from 4.9 in 1990 to 21.6 in 1999, before declining slightly.
Though illustrative of the widening income levels among ECA
countries during transition, these coefficients are somewhat misleading because the two countries with the highest and lowest per capita
GDPs in 2002 were Slovenia and Tajikistan. Given the distance
between the two and various other factors, there is not expected to be
a lot of migration from Tajikistan to Slovenia. More telling are the
income disparities between migration spaces of geographically adjacent groups of countries, in this case the CIS and Europe, which
includes both Eastern and Western Europe. Appendix table 1.4 shows
FIGURE 3.2
Disparities in GDP per Capita in the CEE-CIS States, 1990–2002
(PPP current international dollars)
25
0.80
0.70
coefficient of variation
0.60
0.50
15
ratio of country with highest to
country with lowest GDP
10
0.40
0.30
0.20
5
0.10
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Source: World Bank, World Development Indicators.
Note: CEE = Central and Eastern European; PPP = purchasing power parity.
0.00
coefficient of variation
ratio of highest to lowest
20
82
Migration and Remittances: Eastern Europe and the Former Soviet Union
the income differentials between western ECA countries and Western
Europe and appendix table 1.5 shows these differentials among CIS
countries. Among western ECA countries, even the country with the
highest income, Slovenia, has an income less than two-thirds of the
Western European average. Similarly, within the CIS, the two countries with the second highest incomes, Kazakhstan and Belarus, still
have incomes only about two-thirds that of Russia, while Russian
GDP per capita is eight times that of Tajikistan.
The relative influence of ethnic versus economic factors partially
explains the temporal trends in migration that took place across the
ECA region since 1990. Yet, clearly the motivations for migration
across the region have been complex and, for periods in the early
1990s, were partly driven by the dissolution of the Soviet Union. This
complexity combined with the poorness of the data used for measuring these flows make the statistical estimation of the determinants
very difficult. What emerges from such studies is a complex picture
indicating that expected income differences, the expected probability
of finding employment abroad, and expected quality of life at home
play a strong role in the decision to migrate in many cases but can
also be tempered by the influence of numerous other variables and
the patterns vary considerably across countries (see box 3.1).2
Figures 3.3 and 3.4 show the trends in net migration rates for
selected immigration and emigration countries, respectively. For
nearly all immigration countries, net migration was much higher in
the early 1990s than after 2000. As shown in figure 3.3, in Russia the
net migration rate went from 0.1 per thousand in 1991, the last year
of the Soviet Union’s existence, to 5.4 in 1994 before falling back to
almost the pretransition rate of 0.2 in 2003. Most of the other ECA
countries that are now net recipients of migrants experienced a similar trend of either larger immigration or emigration in the early and
mid-1990s as a result of ethnic reshuffling. However, much of the
migration as a result of ethnic factors, whether voluntary or forced
(or somewhere between the two), seems to have been a one-time
event brought about by the increase in the number of states. Most of
those who found themselves outside their ethnic homelands and who
would migrate “back” home already have done so.
A similar pattern is seen among emigration countries in figure 3.4,
where the large outflow of the early 1990s slowed considerably after
2000. Of the total migration of ethnic Russians to Russia over the
period 1989 to 2002, over half took place in the first four years after
the breakup of the Soviet Union—1992 to 1995. In the three Baltic
states, which all had large Russian populations, three-quarters of
return migration took place during this period. Now that these three
83
Determinants of Migration
BOX 3.1
Estimating the Determinants of Migration in ECA
In this investigation of the determinants of migration in ECA, the model of migration developed
by Hatton (1995) is used as a starting point (further information on the model and estimations is
presented in appendix 3.1). This model, based on the concepts of individual utility maximization
and migration as a form of investment in human capital, is delineated as follows:
(1)
Ut = ln(w d )t + γ ln(ed )t − ln(w h )t − η ln(eh )t − zt
where wd, wh, ed, eh are the income and probability of employment in the countries of destination and origin, respectively, and z is the cost of migration.
The formation of expectations of the future utility of migration follows a geometric series of past
values; the most recent utility streams are given greater weight.
Ut* = λUt + λ 2Ut −1 + λ 3Ut −2 + ...,
0 < λ <1
(2)
or
Ut* = λUt + λUt*−1
Furthermore, the immigration rate (Mt) is assumed to be a function of current and net present
value levels of utility from immigration.
(3)
Mt = β (Ut* + αUt ), α > 1
where β stands for the aggregation parameter, and α for the extra weight given to the current
utility.
Extending this basic migration model and following Zoubanov (2004) to account for the nonlinear relationship between the cost of migration and current stock of immigrants, we incorporate
the squared current stock of immigrants (MST) from a given country of origin into the equation.
To account for quality-of-life considerations, the same adaptive expectations structure is used as
above. The European Bank for Reconstruction and Development transition index is used to account for the quality of life in the origin country. As such, the final specification is as follows:
∆Mt = β (α + λ ) ∆ ln(w d / w h )t + γ∆ ln(ed )t − η∆ ln(eh )t − ε1∆MSTt − ε 2 ∆MSTt 2 + ∆EBRDt +
+ β (α + λ − λα ) ε 0 + ln((w d / w h )t −1 + γ ln(ed )t −1 − η ln(eh )t −1 + ε1MSTt −1 + ε 2MSTt 2−1 + EBRDt −1 (4)
−(1− λ )Mt −1
The dependent variable here is the change in gross migration rates (inflows from origin to destination country divided by the population stock of origin country). Explanatory variables in the
model are transformed to one-year differences and 1-year lagged levels to capture short and
(Continues on the following page.)
84
Migration and Remittances: Eastern Europe and the Former Soviet Union
BOX 3.1
Estimating the Determinants of Migration in ECA (continued)
longer-term dynamics. The real wages wd and wh are approximated by per capita income data
(with purchasing power parity calculations applied) of the destination and origin countries, respectively. Ignoring the labor market participation, the employment rates ed and eh are proxied
by 100 percent minus unemployment rate in destination and origin countries, respectively. The
model also incorporates distance between the capitals of destination and origin countries as a
dependent variable, as well as the EBRD transition index discussed above. A summary of the results is in the table below:
Migration Rates and Current Stock of Immigrants:
Extended Basic Migration Model, 1991–2003
Migration to
Russia
Germany
United Kingdom
Austria
Sweden
Denmark
PCI ratio
Changes
E in d
MST
⫹
0
⫺
0
0
0
⫺
⫹
⫺
0
⫹
0
⫺
⫹
0
⫺
0
0
EBRD
⫹
0
0
0
0
0
PCI ratio
⫹
0
⫺
⫺
0
⫹
Lagged levels
E in d
MST
0
⫹
0
⫹
⫹
⫹
⫺
⫺
0
0
⫹
⫺
EBRD
⫺
⫺
⫹
⫹
⫺
⫺
M
⫺
⫺
0
⫺
⫺
⫺
D
⫺
⫺
0
⫺
⫺
⫺
Source: World Bank staff estimates.
Note: ⫹ indicates that the coefficient was positive and significant at less than 10 percent; ⫺ indicates that the coefficient was negative and significant at less than 10 percent; 0 indicates that the coefficient was not statistically significant.
PCI ratio: Ratio of GDP per capita of host country to GDP per capita of home country.
E in D: Employment rate in destination country.
MST: Squared current level of migrants in host country (capturing network effects).
EBRD: EBRD Transition Index capturing quality of life related issues.
The model suggests that wage and employment differentials were statistically significant predictors of migration in the expected directions only about half the time. In a number of cases,
these differentials seemed to produce the opposite of the expected effect.
In general, the results for the Russian model are broadly in line with our hypothesis that the migration rate is positively correlated with expected income differentials and negatively correlated
with the expectations of improving quality of life at home. The significant negative effect of the
stock of migrants seems to reject the commonly referred “network” effect in the models for
Russia, Austria, and Denmark, suggesting instead that the existence of factors such as increased competition in the labor market of the destination country, anti-immigration policy, racial
intolerance, and other factors may make migrant stock a poor predictor of future migrant flows.
As was expected, distance is negatively correlated with the migration rate in all models.
Source: World Bank Staff estimates.
85
Determinants of Migration
FIGURE 3.3
Net Migration in Selected Immigration Countries in ECA, 1989–2003
8
net migration rates (per thousand)
6
4
2
0
⫺2
⫺4
⫺6
⫺8
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Belarus
Czech Rep.
Russia
Slovak Rep.
Slovenia
Source: World Bank Staff estimates.
FIGURE 3.4
Net Migration in Selected Emigration Countries in ECA, 1989–2003
5
net migration rates (per thousand)
0
⫺5
⫺10
⫺15
⫺20
⫺25
⫺30
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Azerbaijan
Source: World Bank Staff estimates.
Kazakhstan
Latvia
Poland
Romania
86
Migration and Remittances: Eastern Europe and the Former Soviet Union
countries have joined the EU and their economies are growing, net
migration of Russians to Russia is less than 1,000 a year; for comparison, over 60,000 Russians left in 1992 alone. It appears that income
differentials among countries will be the primary factor driving migration in the ECA region in the medium term, while demographic factors will play a role in the longer term (see chapter 1).
Despite this evidence, the temporal dimensions of these patterns
do not clearly match up to those that might emerge if migrants’ motivations were driven solely by cross-national income differences. The
income disparities that persist fail to explain contemporary migration
patterns in the ECA. The following section considers alternative
explanations for determinants of migration, using the experiences of
Southern Europe and Ireland as test cases.
Incentives for Migration: Lessons from Southern European
Countries and Ireland
The migration histories of Ireland and Southern Europe—countries
that saw many of their citizens emigrate during the postwar period—
are especially useful for interpreting and forecasting patterns of emigration for the countries of Central and Eastern Europe. First, ECA
countries, like Ireland and all Southern European countries, are close
to their respective destination countries. This proximity is not only
physical but also cultural—languages and social traditions are comparable. Additionally, Southern European countries and Ireland, as we
see with ECA countries now, were poorer than their destination
countries. However, in both cases the differential (especially in the
last century) in fact was not extreme, particularly if the quality of
human capital is the measure employed, as opposed to per capita
gross national product (GNP) at purchasing power parity. Thus, while
there are obvious differences between the Southern European and
Irish countries and the ECA countries,3 the similarities are sufficient
that a study of the migration history of the former may provide a reasonable amount of evidence about current and future trends.4
To begin, some have observed that migration patterns in Southern
Europe evolved as a “hump.” This pattern of migration, as figure 3.5
illustrates, refers to a scenario in which emigration rates accelerate as
a country’s wealth increases and more households are able to fund
migration. Yet as a country develops further, the motives for migration diminish and emigration rates drop.
Looking at the patterns illustrated in figure 3.5, the surge in Italian
emigration during the 1960s to early 1970s was due not to an increase
87
Determinants of Migration
FIGURE 3.5
Postwar Emigration in Southern Europe, 1960–88
gross emigration (thousands)
20
15
10
5
0
1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988
Greece
Italy
Portugal
Spain
Source: Venturini 2004.
in poverty but to an increase in income and employment growth at
the beginning of Italian industrialization (Hatton and Williamson
1994). The surge of Spanish emigration to other European countries
in the period 1960–74 was the result of a growth rate higher than in
the other European countries.5 The peak of Portuguese emigration in
the 1970s also took place during a growth phase, and Greece’s emigration rates rose during the economic boom of the 1960s.
After World War II, even if the gains from intercontinental emigration were greater (given the lowering of international travel costs
during this period), emigrants were affluent enough to choose a closer
destination, which they viewed as a more temporary emigration solution with numerous emigrants returning home. Faini and Venturini
(1993, 2001) have tested these hypotheses using gross emigration
flows from Spain, Greece, Portugal, and Turkey between World War
II and the end of 1980s. During that period, the per capita income differentials between these countries of origin and European destination
countries were relatively stable, and increases and decreases in migration flows were due to the effects of other variables—labor market
factors, the per capita income in the origin country, and the absence
of a competitive business or investment environment at home. The
turning point in this inverted U shape effect of the annual per capita
income on the migration decision in these countries was estimated at
about $3,500, after which additional economic growth discouraged
emigration decisions. Similarly, Irish emigrants ceased to prefer the
88
Migration and Remittances: Eastern Europe and the Former Soviet Union
United States to Britain as a consequence of the Great Depression—
80 percent of total flows went to Britain in the late 1940s—but they
did not change preferences when the American economy recovered.
Nor were their flows sensitive to the reduction in travel costs, which
again corroborates the dominance of the effect of income after World
War II (Barrett 1999).
Looking at the downward slope of migration rates in figure 3.5,
Italian outflows declined to a fractional value during the 1960s, at
which time the wage differential between Italy and the main destination countries was approximately 30 percent. This can be called the
“cost” of migration: people no longer emigrate if the return on the
investment in migration is not 30 percent higher than the wage that
they can earn in the country of origin. Yet, as was discussed above,
though wage differentials are a good first indicator with which to
understand emigration patterns, they must be combined with
employment and quality of life expectations, which are a function of
the future prospects of the economy and income levels, and these are
not always included in empirical estimates. It could be argued that
Italians reached the level of income that, all other things equal, yields
no migration incentive. The halting of Spanish and Greek emigration
to Germany in the second part of the 1970s was also the result of
lower incentives (the GDP per capita in purchasing power parity differential with Germany was about 42 percent) from both the restrictive immigration policies adopted in Northern Europe and changes in
their own governments accompanied by positive growth expectations. Such changes created strong incentives for existing migrants to
return home, and for others to postpone emigration.6
The history of Irish emigration is very similar to what has been
described above. The Irish have long been the United Kingdom’s
“unsung gastarbeiters” (Ford 1994, p. 67). The long-run decline in
Irish migration can be accounted for by the growth of Irish income
and living standards relative to those in Britain. Irish industrial earnings rose from 70 percent of those in Britain in 1950 to 90 percent in
1990 (Ó Gráda and Walsh 1994, pp. 130–1).
Boxes 3.2 and 3.3 present two of the most recent and interesting
experiences of emigration among the EU member countries, and they
represent the opposite ends of the skill spectrum, the highly skilled
Irish and the lower-skilled Portuguese. They also represent two different patterns of emigration, though both demonstrate that migration became temporary when the home countries grew and decreased
the per capita income differential with host countries.
The above discussion supports this chapter’s theory that wage or
income differentials of 30 to 40 percent are probably a necessary but
Determinants of Migration
BOX 3.2
Irish Migration Dynamics
Irish emigration declined steadily until the beginning of the 1970s: the net migration rate was
negative 12.7 per thousand over the period 1871–81 and declined to negative 6.3 per thousand
over the period 1936–46; it increased for the last time to negative 14.0 per thousand during
1951–61, reached negative 4.0 per thousand in 1961–71, and became positive in the subsequent
decade. In addition to this reversal of Ireland’s net migration balance, the composition of Irish
emigration changed in favor of higher skilled and educated workers.
In the late 1960s and the 1970s, the average education level increased in Ireland, and in the 1980s
the workers that emigrated to the United Kingdom (44 percent), to the other EU countries (14 percent) and to the United States (14 percent, with 27 percent to the rest of the world) were better
educated. As Ó Gráda and Walsh (1994) show, the proportion of emigrants among the Irish with
education at the tertiary level and above was between 18 and 30 percent, while those with secondary level educations composed less than 10 percent. This was not only due to an increase in
average education in Ireland, but also resulted from a more selective emigration strategy. Migration among the lower educated may have yielded returns too low to make it worthwhile, while it
was still rewarding for the higher-educated as a general career strategy (Barrett 1999; Breen
1984). Thus, on the one hand, welfare discouraged emigration by the poor, while on the other,
high taxes encouraged emigration by the better educated (Callan and Sutherland 1997).
As a result of trade liberalization during the 1990s and the attractiveness of foreign direct investment, the Irish economy underwent rapid growth, which induced many high-skilled emigrants to return (mainly from non-UK destinations, where the cost of migration was probably
higher because of differences in culture and language). Owing to their experience abroad, return
migrants were able to earn on average 10 percent more than similarly educated natives who had
not moved (Barrett and O’Connell 2000). Furthermore, thanks to its rapid economic growth, Ireland became a country of immigration that attracted high-skilled EU workers and that sought to
attract high-skilled ECA workers as well.
Source: World Bank staff.
not sufficient condition to determine the end of emigration. The decision to migrate depends jointly on the income differential and on
other economic and noneconomic variables. However, any such discussion should be careful not to lump all migrants together; what discourages one group of migrants may encourage another. For instance,
unskilled migration may be replaced by skilled, and permanent
migration by temporary.
As discussed below, much of the explanation for the slowing of
emigration in the mid- to late 1990s and the conversion of many
89
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Migration and Remittances: Eastern Europe and the Former Soviet Union
BOX 3.3
Portuguese Migration Dynamics
The case of Portugal provides a good contrast to that of Ireland (box 3.2). Portugal has a long history of emigration, and its overseas territories have served as migrants’ main destinations in past
centuries (Bagahna 2003). Even after World War II, the main emigrant destination was Brazil.
This picture changed during the 1960s. In line with encouraging the industrialization of the Lisbon area, the government decided that emigration had a positive impact on the labor market and
contributed to the country’s progress and development. Emigration took off when the country
started to grow, drawing parallels to Italy; that country’s emigration reached its peak with its industrialization at the beginning of the 1900s. While the Portuguese were the last of the Southern EU populations to emigrate, they followed the pattern set by Italian and Spanish workers:
first to France, then to Germany and Switzerland. In contrast to the Irish, however, the average
human capital of Portuguese workers was rather low, and emigrants left the country for lowskilled jobs abroad. Little by little, the Portuguese economy grew and emigration declined.
The relationship between Portugal and Germany, a major destination country for Portuguese emigrants, demonstrates the role of interacting supply and demand in the decision to migrate.
When Portugal joined the EU in 1986, the GNP per capita in purchasing power parity of Germany
was double that of Portugal. As a result, Germany experienced positive net immigration from
Portugal. After 1993, when free mobility by Portuguese workers began (and the GNP differential
was still high at about 40 percent), there was an increase amounting to 27,000 persons and only
5,000 employees.
However, the need for Portuguese labor in Germany had not disappeared. Permanent employment emigration was replaced by contracted temporary emigration or Werkvertragsarbeiter.
These workers were employed by Portuguese companies operating in Germany and therefore
did not show up in any emigration statistics. The demand for this type of worker declined when
the German government obliged foreign companies to pay German wages and social security
contributions.
Source: World Bank staff.
Central European countries from net emigration to net immigration
status partly reflects expectations about the improvement in the
quality of life in many countries in the region. However, it is first
important to recognize the role of the EU in migration trends in
Southern Europe, because accession of the ECA countries (or those
proximate to the region) will likely affect ECA migration patterns in
the future.
Determinants of Migration
European Union Accession and Migration Trends
EU participation has also certainly played a major role in European
migration, but probably a role different from the one expected. First,
new member countries in the period before entry into the EU were
required to implement a series of reforms that increased and favored
the expansion of goods production. Italian development was export
led, because domestic demand was too low to absorb the new production (low consumption, high savings, and the like). Similar patterns were displayed by other countries—both Ireland and Portugal
also experienced export-led development—even if such development
took place later on in the 1990s. Second, transfers from the Structural
Fund that countries received after entry were an additional source of
growth that increased domestic demand for labor, and that also
helped indirectly by increasing the ability of these countries to attract
foreign investments, which in turn increased the domestic demand
for labor. Finally, factors in addition to strictly economic components
help predict future migration trends. Expectations of future growth
may be as important as current job availability in the decision to
migrate, and membership in the EU has had an important effect upon
potential migrant expectations
While growth prospects have traditionally been associated with
increased migration, Burda (1993) points out that the freedom to
move can reduce near-term immigration, because migrants are free
to put off the move until later. Whereas a potential migrant would
have to have taken whatever opportunities luck presented preunification, the postunification migrant can delay moving for as long as he
or she wishes. If the quality of life at home shows signs of improvement, the potential migrant may decide to wait and see.
Despite the role EU membership may play in growth opportunities
and migration incentives, it is important not to overemphasize its
role. The emigration rates in Southern European countries had
already started declining at the beginning of the 1970s, and have
never regained those previous dynamics, even after EU membership.
The country most at risk for large-scale emigration was Portugal. As
described in box 3.3, when Portugal joined the EU in 1986, Germany’s per capita GNP at purchasing power parity was double Portugal’s. After 1993, when free mobility by Portuguese workers began,
temporary contracts replaced permanent immigration, and even the
former slowed substantially with the Portuguese economic boom of
the 1990s.
This is an important finding for the ECA emigration countries and
in particular for the new EU members. Joining the EU has already
91
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Migration and Remittances: Eastern Europe and the Former Soviet Union
favored economic growth or expected growth for these countries,
and both direct investment from the Social Fund and the indirect
attraction of foreign investment will further enhance growth
prospects. These factors, as well as higher expectations of a better
quality of life at home and the reduced cost of postponing the emigration decision, will discourage emigration. Furthermore, entrance
into the EU may further support the prediction of declining migrant
outflows, because temporary movements may increase in comparison to permanent ones. Such a trend toward temporary movement is
already taking place between Germany and Poland, for example.
Simulating the Determinants of Migration
One of the themes of this chapter is that spreads in per capita income
cannot alone explain contemporary migration flows in the ECA. In
addition to evidence from history and statistical estimation, an economic model was employed to further understand the role that
expected quality of life at home can play in driving migration or
restricting migratory flows in the face of constant income differentials
among countries. Such a model provides an opportunity to test the
reaction of migrants to changes in the quality of life.
The model is an extension of GTAP, a comparative-static, multiregional computable general equilibrium model developed by the
Global Trade Analysis Project (see appendix 3.2 for further information on the model). Versions of this model have been used previously
to look at questions relating to the impact of international migration.7
An extension of the GTAP model is used to examine the impact of
an improvement in the general “quality of life” in ECA countries on
migration flows into the EU-15 countries. The index (known as the
Country Policy and Institutional Assessment or CPIA) is a World Bank
index that takes a variety of a country’s attributes into account,
including macroeconomic policy, financial sector policy, trade, social
equity, business investment environment, environmental policy, and
political accountability.8
In this analysis, the CPIA index is treated as an exogenous factor
that represents changes in overall quality of life. The impact of two
simulations of improvement in the quality of life in the ECA
migration-sending countries are illustrated in figure 3.6 for three
groups of countries: (a) western ECA, (b) former Soviet Union, and
(c) Turkey. The figure indicates the impact of increasing the quality of
life on gross migration flows into the EU-15 for the western ECA
countries by 10 percent and for FSU countries and Turkey by 3 per-
93
Determinants of Migration
FIGURE 3.6
percentage decrease in total migration
Percentage Decrease in Total Migration Flows into the EU Owing to
Improvements in Quality of Life
1.2
1.0
0.8
0.6
0.4
0.2
0
Western ECA
FSU
Turkey
quality of life improves 10% in western ECA and 3% in Turkey and FSU
quality of life improves to EU-15 levels in western ECA and 15% in Turkey and FSU
Source: World Bank simulations. For more information on the simulations, see appendix 3.2.
Note: FSU = Former Soviet Union.
cent, and the impact on flows if quality of life in western ECA was
identical to that of the EU-15 while Turkey and the FSU countries
realized a 15 percent improvement.
The results indicate that migration from western ECA would fall by
just over 0.4 percent with the 10 percent improvement and over 1
percent if quality of life is equalized. Flows from Turkey and the FSU
also fall with an improvement of 15 percent in the quality of life
index. Outflows from these two fell by about 0.63 percent.
Looking at the other side of the issue, figure 3.7 presents the results
of our simulation on flows from the EU-15 countries into western
ECA, Turkey, and the FSU. As before, we see the impact of improving
the quality of life index in western ECA by 10 percent and to EU-15
levels, and in Turkey and the FSU by 3 percent and 15 percent. The
simulations find that migration outflows do increase as quality of life
improves in the ECA countries. In the case of the larger shock, the
improvement of western ECA’s quality of life to EU-15 levels increases
migration from the EU-15 by 1 percent and into Turkey and the FSU
by about 0.5 and 0.6 percent, respectively. This may very well reflect
return or indeed circular migration flows from natives of these ECA
countries.
Though the magnitudes of change in migration flows found with
these simulations are not enormous, the results show that improvements in quality of life do have the potential to shift the direction of
94
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 3.7
percentage change in gross migration outflows
Percentage Increase in Migration Outflows from EU-15 to ECA
Countries Owing to Improvement in Quality of Life in ECA
1.2
1.0
0.8
0.6
0.4
0.2
0
Western ECA
Turkey
FSU
quality of life improves 10% in Western ECA and 3% in Turkey and FSU
quality of life improves to EU-15 levels in Western ECA and 15% in Turkey and FSU
Source: World Bank simulations. For more information on the simulations, see appendix 3.2.
Note: FSU = Former Soviet Union.
migration patterns in the region in such a way that improvements in
economic, political, and social policies can slow outflows and perhaps
encourage return flows.
Taken as a whole, the results of our simulations and the history of
migration in Southern Europe and Ireland provide qualified support
to the hypothesis that the quality of life in migration-sending countries matters as a determinant of migration, even in the presence of
constant income differentials. Moreover, the results suggest that these
policies are even capable of creating incentives for circular migration
or return migration. As is discussed in chapter 3, encouraging circular
migration may represent a positive step toward enhancing the returns
of migration to sending and receiving countries and migrants themselves. As further simulation results in chapter 4 will indicate, these
effects are magnified when immigration policies encourage temporary or circular migration.
Endnotes
1. For summaries of the migration literature, see Lucas (2005); Bauer and
Zimmerman (1999).
2. Full details on the econometric estimations of the determinants of migration in ECA are presented in appendix 3.1.
Determinants of Migration
3. For instance, Ireland and the Southern EU countries have long histories
of international emigration (first overseas, later in Europe). This is highly
different from present ECA migration, with the exception of Poland,
which has only recently seen international migration on a large scale.
4. This section draws heavily from Venturini (2004).
5. The rapid growth rate produced a reduction of 1,900,000 persons active
in agriculture, and 800,000 emigrants (INE).
6. By 1974 the underemployment in agriculture in Greece was reduced;
between 1963 and 1973 GNP growth was about 6 percent. It is thus not
very clear whether the increase in the unemployment rate in Germany
or the reduction of the unemployment rate in Greece reduced the emigration rate.
7. World Bank 2006.
8. For more information, see information on the CPIA index at
www.worldbank.org.
95
CHAPTER 4
International Regulatory
Framework
International labor migration within Eastern Europe and the former
Soviet Union (FSU) and between this region and Western Europe
occurs within two regimes:
• For the migration of skilled workers, the General Agreement on Trade
in Services (GATS) under the auspices of the World Trade Organization (WTO) has emerged as a vehicle for the multilateral relaxation of
restrictions on temporary transborder labor movements.
• A set of bilateral labor agreements facilitates most legal labor
migration.
The WTO provisions currently focus on extending freedom of passage to a limited subset of international migrants in multinational
firms. Thus, the provisions and any proposed revisions to them have
little consequence for unskilled migrants at present. Most legal
unskilled migration is governed by a series of bilateral agreements on
labor activity and the social protection of citizens working outside
their countries.
The diverse range of bilateral policies makes it difficult to generalize
about the impact of their specific provisions. If, however, one judges
the impact of these agreements by looking at actual unskilled migratory flows—in particular, the very large levels of undocumented
97
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Migration and Remittances: Eastern Europe and the Former Soviet Union
migration—it would seem that these policies often do not provide sufficient incentives for or facilitate legal migration by unskilled workers.
This chapter has two key parts. The main portion of the chapter
reviews the existing framework for international labor migration in
Eastern Europe and the FSU. It documents the bilateral migration
agreements among ECA countries and between them and the EU-15
countries.
The second part of this chapter proposes the outlines of an alternative regime for organizing international migration. Drawing upon the
information presented throughout this report and the results of simulations, this section proposes that bilateral migration agreements could
be modified to encourage legal migration by unskilled workers. Though
some countries may want to encourage more permanent migration, in
instances where this is not preferred, circular migration may allow for
the effective matching of supply and demand for international labor
without necessarily creating higher rates of permanent migration. The
alternative framework presented here provides the contours of incentives designed to encourage such circular migration flows.
Surveys with ECA migrants conducted for this report suggest that
the shift to a circular pattern of labor migration is likely a closer match
with the preferences of many migrants to spend short periods
abroad—building human and financial capital—and then return
home. Moreover, circular or temporary migration may have the
advantage of limiting “brain or brawn drain” from the migrants’
home country. Temporary migration also has the advantage of reducing cultural friction in the migration receiving country.
As the UN (2005) report on migration highlights, migration
involves a complex organization of political, economic, and social
forces. This complexity requires that policy prescriptions be highly
qualified. The exact policies needed will certainly vary by country,
whether on the sending or receiving side of the equation. This chapter suggests the rough outlines of the sorts of international cooperation that might increase the returns from migration for sending and
receiving countries and migrants and their families.
Given the uncertainty of policy, the best way forward may be a stepwise “learning by doing” approach that takes the form of pilot, temporary managed-migration schemes among willing pairs of countries.
Current Regime
This section provides an overview of the existing policies for facilitating international labor movements from Eastern Europe and the FSU.
International Regulatory Framework
The section begins with a brief overview of the WTO provisions on
labor migration. It then discusses the various bilateral agreements
that have been made directly between migrant sending and receiving
countries in this region.
Multilateral Arrangements and Their Limitations
The major multilateral policy effort to address international legal
migration flows is the Mode 4 framework of the GATS. Mode 4 tackles the provision of services by allowing cross-border movements of
certain citizens of the WTO countries. Its introduction generated initial optimism that eventually the broader liberalization of labor markets could be negotiated. A commitment to deepen the coverage of
Mode 4, however, has not yet emerged. Even though services represent over 70 percent of the GDP of developed economies, only very
small portions of international migrants qualify as “service providers”
by WTO standards.
Unlike trade liberalization in products and other services, providing for the free movement of labor generates a number of negative
externalities: the values, rights, responsibilities, and risks that the
migrants bring to the receiving society and economy may create various forms of conflict.1 As a result, GATS protections are only
extended to “natural persons” who intend to relocate temporarily or
provide a service abroad. Moreover, even if GATS were to progress, a
large portion of ECA labor migrants would not be covered by its provisions because the framework only addresses skilled labor.
Bilateral Agreements
Given the weaknesses in multilateral agreements for cross-border
migration movements, a collection of bilateral labor agreements have
been negotiated between the migration-sending and -receiving countries that facilitate a great deal of the legal transborder labor flows in
the region. It is difficult to generalize about the impact of these agreements, because they vary dramatically in type and scope across countries. Bilateral agreements facilitate short- to medium-term migration
across countries for the purposes of seasonal employment, specific
project-related employment, apprenticeships or trainee-ships, and
other purposes. As with migration flows more generally, bilateral
agreements have a strong, bi-axial regional orientation (table 4.1).
The majority of the agreements involving the Central and Eastern
European countries (CEECs) are with Western Europe or other
CEECs (82 percent). In contrast, a large majority (64 percent) of CIS
bilateral agreements create labor flow links with other CIS members.
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Migration and Remittances: Eastern Europe and the Former Soviet Union
TABLE 4.1
Regional Composition of Bilateral Agreements
(percentage)
Country group
CEECs
CIS
CEECs
CIS
EU-15
21
31
18
64
61
4
Source: World Bank Staff estimates.
Note: Cells indicate the percentage of agreements signed between the subregions in the rows and columns. Percentages
may not sum to 100% due to rounding.
Agreements Between the EU-15 and CEECs
The number of bilateral agreements within Europe is very large and has
increased rapidly during the 1990s: of the 92 agreements in existence,
some 75 percent were signed after 1989. There are several reasons for
this, the most important ones being the collapse of the Soviet Union
and the disintegration of the former Yugoslavia. It should be stressed,
however, that many bilateral agreements were signed to manage the
large ethnically motivated and conflict-related migration streams during the first half of the 1990s. Because the second half of that decade
saw a return to more “normal” migration volumes (see chapter 1), it is
not clear to what extent existing agreements are still operational.
The need for bilateral agreements between the countries of Western and Eastern Europe will expire as the latter obtain membership in
the EU’s single labor market. The Accession Treaty of 2003 set out
that there will be a transitional period for the free movement of workers allowing the EU-15 to postpone the opening of their labor markets for up to seven years. The so-called 2+3+2 regulation divides the
transitional period into three phases. During the first phase, the EU15 can apply national rules on access to their labor markets for the
first two years after enlargement. The diverse national measures have
resulted in several legally different migration regimes. Since the
accession of the EU-8 countries to the EU in May 2004, only eight
countries have fully opened their labor markets to the new member
states: Ireland, Sweden, and the United Kingdom never had restrictions on workers from the EU-8. Greece, Finland, Spain, and Portugal lifted restrictions in May 2006. Italy ended the transitional
arrangements in July 2006, while France, Belgium, and Luxembourg
softened their restrictions on workers from the EU-8. Poland, Slovenia, and Hungary apply reciprocal restrictions to nationals from the
EU-15 member states applying restrictions. All new member states
have opened their labor markets to EU-8 workers.
In May 2006, the second phase of the transitional period started,
which allowed EU-15 member states to continue national measures
101
International Regulatory Framework
for up to another three years. At the end of this period (2009) all
member states will be invited to open their labor markets entirely.
Only if countries can show serious disturbances in the labor market,
or a threat of such disturbances, will they be allowed to resort to a
safeguard clause for a maximum period of two years. From 2011, all
member states will have to comply with European Commission rules
regulating the free movement of labor.2 Thus, in the short-run, bilateral migration agreements may remain relevant for some countries in
western ECA.
Germany is by far the most important country in terms of the number of agreements, perhaps because it is the largest destination for
CEEC migrants. Over half of all existing bilateral agreements have
been signed by Germany; all CEECs have agreements with Germany
except Serbia and Montenegro, which has no bilateral migration
agreement with any EU country (table 4.2). Out of the 15 EU countries, 14 have bilateral agreements with one or more CEEC (the only
exception is Denmark). On average, each EU country has signed
between two and three bilateral agreements with countries in Central
and Eastern Europe.
On the CEEC side, there is a substantial variation in the number of
agreements, ranging from 15 for Poland and 12 for Hungary to 7 for
Bulgaria and Romania. A number of intra-CEEC agreements exist,
but only for a few countries, notably Poland, the Czech Republic, and
the Slovak Republic. These are mainly cross-border arrangements.
Most CEECs do not have any intra-CEEC agreements.
The CEEC countries have very few bilateral agreements with
Organisation for Economic Co-operation and Development (OECD)
countries outside the EU. Only three non-EU countries in the OECD
have bilateral agreements with CEEC countries—Canada, Finland,
and Switzerland. Moreover, these agreements have mainly been
TABLE 4.2
Geographical Distribution of Bilateral Migration Agreements between CEEC and EU-15
Germany
Poland
Hungary
Czech Rep.
Slovak Rep.
Bulgaria
Romania
Turkey
Serbia and Montenegro
Other CEECs
Source: Compiled from OECD (2003).
6
4
5
5
3
3
2
0
15
Luxembourg
Austria
France
Other EU-15
1
1
1
1
1
1
0
0
0
0
2
2
0
0
0
1
0
0
2
1
1
1
0
0
1
0
0
6
4
0
1
3
3
3
0
—
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Migration and Remittances: Eastern Europe and the Former Soviet Union
signed by the CEEC countries with relatively high per capita income,
specifically the Czech Republic, Hungary, Poland, and the Slovak
Republic.
Table 4.3 shows that the most common types of agreements are
guest worker schemes and trainee arrangements. Together, they
account for over half of all agreements. Seasonal-worker agreements
and project-type agreements together account for another third.
It is useful to distinguish between agreements that target unskilled
labor and those aimed at skilled labor. Typically, seasonal arrangements and cross-border agreements do not require migrants to possess specific skills; the same appears often to be true for guest worker
agreements. Project-type and trainee agreements, however, often
explicitly state required skills or experience that migrants must
demonstrate (see Hárs 2003 for an account of Hungary’s agreements
with the EU-15). Table 4.3 suggests that agreements requiring skilled
labor are mainly between the EU-15 and CEECs with relatively high
per capita income, while seasonal and guest worker agreements are
mainly between the EU-15 and the relatively poorer CEECs. Consequently, geography and CEEC income are important variables for
explaining the number and the nature of bilateral agreements in the
region.
The motives for migration-sending countries in the CEEC to sign
bilateral agreements are at least fourfold. First, it is a way to reduce the
amount of surplus labor in these countries by reducing unemployment. Second, remittances are sometimes (as detailed in chapter 2) a
TABLE 4.3
Bilateral Migration Agreements between the EU and CEECs by Country and Type
Country
Seasonal
Projects
Guest
Trainee
Cross-B
Others
Total
Austria
France
Germany
Spain
Other
Total
0
2
8
2
4
16
0
0
13
0
2
15
1
1
13
2
4
21
2
6
3
2
14
27
2
0
1
0
1
4
0
0
7
0
2
9
5
9
45
6
27
92
Czech Rep.
Hungary
Poland
Slovak Rep.
Bulgaria
Romania
Turkey
Other
Total
1
1
3
1
3
2
0
5
16
1
1
1
1
2
1
1
7
15
1
1
3
1
1
2
6
6
21
4
5
6
4
1
2
1
4
27
1
1
1
0
0
0
0
1
4
1
1
1
1
0
2
0
3
9
9
10
15
8
7
9
8
26
92
Source: Compiled from OECD (2003).
International Regulatory Framework
very large share of total income in the economy and may provide
funds for savings and investment. Remittances costs or security may
be higher when they are sent through formal, legal channels. Third,
temporary employment in relatively wealthier countries may increase
skills that can be used productively when the migrant returns home.
Finally, and arguably the most important in comparison with costs of
undocumented migration, a bilateral agreement may help migrants to
enjoy reasonable working conditions and to get access to the social
safety net in the receiving country. This would increase their human
capital and make them more valuable on return.
The available migration data suggest that labor migration into the
EU-15 from the CEECs is employed in sectors or activities where it
does not compete with local labor. Thus, for instance, Germany
received over 200,000 seasonal workers in the late 1990s while only
33,000 workers were employed as contract workers (OECD 2001,
tables 2.4–2.5), and according to Garnier (2001), the number of Polish seasonal workers received in Germany is approximately eight
times as large as the number of workers received under all other categories. It is important to point out that the skills or education of
migrants do not necessarily provide an indication of the positions in
which they will work in the recipient countries. Frequently, highly
skilled migrants take jobs with low skill requirements and thus create
“brain waste” (Garnier 2001).
Agreements Within the CIS
The intra-CIS agreements differ from the agreements directed at
Europe by not focusing on quotas while concentrating on legal status
and social protection. Also, agreements directed at Europe have more
of a “migration creating” role, whereas agreements within the CIS
seem to be a reaction to existing migration flows.
As a result, the current regulatory framework of legal migration
flows in the CIS is characterized by a series of regional and bilateral
agreements on labor activity and social protection of citizens working
outside of their countries. This situation is the result of the disintegration of the Soviet Union, which obliged the newly independent states
to pragmatically defend their citizens’ interests. The main regional
agreement is the “Agreement on cooperation in the field of labor
migration and the social protection of migrant workers,” accepted in
1994 by all CIS states. This agreement, however, did not come to force
because it was to be implemented through bilateral agreements, which
were never signed (IOM 2002). In the field of undocumented migration, the cornerstone of regional cooperation is the 1998 Agreement
on cooperation in Combating Illegal Migration (IOM 2002, 2005b).
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Migration and Remittances: Eastern Europe and the Former Soviet Union
The Russian Federation has concluded the most bilateral agreements (with 9 out of the 11 CIS member states). Belarus has concluded the next largest number of bilateral agreements, with six other
CIS countries. Kazakhstan and Ukraine have concluded four each.
Kazakhstan, the main receiving country in Central Asia, has no agreements with its Central Asian neighbors except for an agreement with
the Kyrgyz Republic on the labor activities and the social protection
of labor migrants working in the agricultural sector in the border
areas.
Along with the intergovernmental agreements, interagency agreements are a form of international cooperation that has emerged more
recently. Since 2002, the Russian Ministry of Internal Affairs has concluded such agreements in the migration sphere with the counterpart
agencies in the Kyrgyz Republic, Tajikistan, and Ukraine.
The majority of CIS labor migrants do not profit from the protection
provided for in these agreements or from any other legal protection,
however, because they work under an undocumented status. In both
Russia and Kazakhstan, the largest recipient countries in the region,
the estimated number of irregular migrants is several times higher
than the number of official migrants. For example, according to IOM
(2005a), Tajikistan had 16,800 legal migrant workers in Russia in
2002, while the actual number of undocumented labor migrants was
estimated at more than 600,000. Uzbekistan had 16,100 legal labor
migrants, while the labor emigration from Uzbekistan is estimated at
between 600,000 and 700,000. Similarly, the number of foreign
“licensed” workers employed in Kazakhstan was 11,800 in 2002 while
IOM estimates the number of irregular immigrants to be 20 to 50 times
higher. According to official estimates, from 220,000 to 300,000
migrant workers are employed now in the country while experts and
official statistical analysis suggest up to 500,000 (table 4.4).
TABLE 4.4
Number of Registered Foreigners and Estimated Number of Aliens
Living Irregularly in Some CIS Countries, 2000
Country
Belarus
Kazakhstan
Russian Federation
Tajikistan
Ukraine
Uzbekistan
Foreigners
94,570
81,133a
58,200b
—
456,300
—
Source: IOM 2002.
Note: — = not available.
a. Foreigners who settled in Kazakhstan for a period longer than six months.
b. Non-ECA aliens who were granted a residence permit at year end.
Irregular migrants
50,000–150,000
200,000
1,300,000–1,500,000
20,000
1,600,000
30,000
International Regulatory Framework
This large number of undocumented labor migrants reflects that
there is a demand for labor that can be satisfied neither from the resident labor force nor from the existing legal quotas. Also, movement
is facilitated by the low transportation costs (generally less than $300)
and the ability of most CIS citizens (with the exception of Georgians
in Russia and Turkmen in general) to travel to Kazakhstan or Russia
without a visa. Moreover, a survey showed that about one of five
Tajik migrants traveled and worked in Russia without passport or official document (Bokozada 2005).
At the same time, irregular status arises because migrants are
required to have work and residency permits (with the exception of
citizens of Belarus in Russia). Indeed, except for visa-free travel,
migrants from CIS countries have no advantages over migrants from
other countries in either Kazakhstan and Russia. This means that
they also have to apply for work permits within the general quota
established by the government. These quotas for legal immigration
are allocated to each region of the receiving country, and are established on a yearly basis. In Russia, this yearly quota is on average set
at 0.3 percent of the active population, in Kazakhstan it was 0.14 percent and 0.21 percent of the active population in 2003 and 2004
respectively (IOM 2005a). However, excessive bureaucracy and the
small overall quotas result in most migrants never applying for work
permits.
The resulting outcome is suboptimal. It leaves millions of workers
without any legal protection not only from employers, but also from
government agencies. Moreover, the situation causes considerable
losses in terms of tax revenues to the government.
Costs of the Current Regime
The bilateral-agreement frameworks may fail to meet their stated
objectives in many instances. To the degree that the objective of these
agreements is to facilitate legal international migration, they often do
not appear to be successful, as indicated by the high levels of undocumented migration in the region (chapter 1). Large amounts of irregular migration can impose significant social, economic, and national
security costs on receiving and sending countries (see box 4.1). Moreover, undocumented migrants are more likely to be subject to abuse.3
Furthermore, as the previous section highlighted, the agreements
are often not able to facilitate large amounts of legal, unskilled migration. The high bureaucratic costs of applying for many of these programs and insufficient quotas provide incentives for migrants to
pursue other channels through which to migrate—especially undocumented options.
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Migration and Remittances: Eastern Europe and the Former Soviet Union
BOX 4.1
Possible Costs and Externalities of Illegal Immigration
1. With the exception of sales tax, the income earned by illegal immigrants is not taxable. This
represents forgone fiscal revenue.
2. Illegal migrants offer an unfair competitive advantage to firms that employ them over firms
that do not.
3. Irregular migrants are not covered by a minimum wage or national and industry wage agreements. They are therefore more likely to undercut the wages of the low skilled.
4. Whether entry is legal or illegal may affect the quality of migrants, even if the legal migration scheme does not select on the basis of skill. Skilled workers or professionals are much
more likely to enter if there is a legal channel, even if their qualifications are not a condition
of entry.
5. Employers may decide not to abide by health and safety regulations, leading to the potential for migrant death and injury. Police and health services may be called upon to rescue or
treat the injured, to investigate the reasons for death, or to bury the dead.
6. Illegal migrants are not screened for diseases and viruses upon arrival, and have little access
to health services during their stay. At the same time, they risk having been exposed to illnesses on their journey, especially if they have been smuggled or trafficked. This has the potential to generate large public health externalities because diseases can spread to the native
population. Particularly important examples include tuberculosis, which seems to be
reemerging in parts of Europe, and HIV, as many trafficked women become involved in the
sex industry. By way of illustration, in 2002–03, those apprehended on the Slovak-Ukraine
border were found to be suffering from respiratory tract infections, tuberculosis, and scabies.
7. Forced to live underground, and with little access to legitimate employment, migrants are
more likely to be exposed to the world of crime.
8. Stigmatization of illegal migrants can undermine social cohesion if it spreads to cover those
who entered legally.
9. Illegal migrants may be encouraged to stay longer than they might desire and to remain
even when unemployed because of the risks of detection and associated costs of entering
and leaving.
10. Trafficking is a subject that is far too large to be addressed in this report; however, the trade
in many ways exacerbates the previous costs, as well as being a source of organized crime
(for further information on some of the social and human costs of trafficking, see chapter 3).
Source: World Bank staff.
International Regulatory Framework
Finally, most agreements do not contain mechanisms to encourage
circular or repeated migration. If it is costly for potential migrants to
apply for a space on a temporary migration program, they may well
have an incentive to remain abroad—even if through illegal channels
by overstaying their visas—for longer periods than they prefer. As
will be discussed below, surveys with migrants conducted for this
report found that most migrants would prefer to spend shorter periods abroad, then return home. Agreements that facilitate this temporary migration while opening up the option to migrate abroad at a
later stage with relatively low transactions costs might represent an
improvement over the current system.
Despite these weaknesses, bilateral agreements have some advantages
relative to the most-favored-nation approach used in trade negotiations,
and particularly are useful for policy makers in receiving countries who
are seeking to balance labor-market demand with the potential externalities of migration.4 As discussed before, migration generates a number of
social and political externalities not found in the cross-border movement
of products and other services (box 4.2). Such agreements can limit
adverse selection by choosing particular groups of migrants and may provide a framework to send home migrants who impose too high a cost on
social benefits or are socially disruptive. Most important, however, they
provide a legitimate way for nations to legally and safely supply business
with the labor it demands. As a result, an alternative framework that
improves upon the existing bilateral structure may represent a good
direction forward for improving policy.
A Proposal for an Alternative Framework
This section details the broad contours of an alternative framework
that could be employed by migration-sending and -receiving countries to facilitate the legal migration of unskilled labor. Given the complexity of migration, general policy prescriptions must be qualified.
Further study and perhaps policy experimentation are required to
better understand how to improve upon the limitations of the existing framework, as identified earlier in this chapter.
What follows is a collection of observations, derived from the information presented throughout this report and from economic modeling, on the sorts of elements that seem to be missing from existing
international migration policies, but that could increase the payoffs to
migration for sending and receiving countries and migrants and their
families. Future policy experiments and analytical studies could keep
these considerations in mind when moving forward.
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Migration and Remittances: Eastern Europe and the Former Soviet Union
BOX 4.2
Social Externalities Generated by Migration
This box provides a summary of some of the social externalities arising from migration.
• First, migrants from other cultures bring different values, which some sections of the native
population may resent.
• Second, unlike imported goods, migrants are people who hold a package of political, social,
and moral rights and obligations. While a migrant may be welcome for the labor he or she can
provide, the implications of allowing a human being to enter a country go beyond purely economic functions. For example, migrants may make demands for family reunification or treatment that is different from that given to residents, for example, religious holidays, food,
dress, and safety regulations. While these considerations need not in themselves be negative, they are externalities not present with the decision to import a good.
• Third, while buying an imported product is a one-time decision, bringing in a migrant could result in future labor market commitments, including the renewing of visas, paying of taxes,
and provision of relevant training. Furthermore, even in conditions of temporary labor market
employment, if the labor market tightens it may not be possible to send the migrant home.
• Finally, a migrant generates implicit or explicit claims for social protection that, depending on
the taxation regime and the effectiveness of tax administration, may result in a net fiscal cost.
Source: World Bank staff.
The findings of this report suggest that the international governance of migration could be more coherent and requires improved
capacity at the national level and closer coordination between states.
Any framework to replace the existing one could recognize, organize,
and facilitate unskilled labor migration, while acting on both demand
and supply to limit undocumented migration. The outcome could be
an improvement in the protection given to temporary workers, while
still offering migration-receiving countries needed labor.
Given variations in national attributes and preferences, such a temporary framework could take a variety of different forms and be
organized bilaterally, regionally, or internationally. Yet, there are a
number of common elements that such policies might include:
• Recognize that the labor market, like any other market, needs to
balance supply as well as demand. The framework could explicitly
target measures at the supply of low-skilled labor as well as at the
demand for such labor.
International Regulatory Framework
• The new regime could channel migrant labor to sectors or subsectors with little native labor to ensure that migrants are complements to and not substitutes for domestic labor.
• On the demand side, receiving countries need policies that limit
the employment of undocumented migrants by offering employers
the means to hire legally the workers they need. To promote development and coordinate with the preferences of many ECA
migrants to go abroad temporarily, an alternative regime could
emphasize circular migration. World Bank surveys for this report
found that the majority of migrants would prefer to spend shorter
times abroad and then return home (see figure 4.1).
• To ensure that employment under the new regime is temporary
and not permanent, incentives could be designed to encourage
return home when not employed. For example, unemployment
and pension benefits could both be portable and only payable in
the country of origin.
• Policies should respect the rights of migrants to be treated with dignity while abroad, including clear and transparent rules regarding
remuneration, work conditions, or dismissal procedures. Moreover, migrants’ rights to appeal to receiving-country authorities to
adjudicate disputes and protect themselves from crime could be
communicated and enforced.
Bilateral migration agreements that include some or all of these
features could have a number of advantages over many existing
policies:
• Agreements could stimulate circular migration, allowing employers in receiving countries to obtain affordable nontraded services
while respecting the law, and reduce incentives for potential
migrants to use illegal means of entry.
• Such an approach seems commensurate with migrants’ preferences to spend shorter periods abroad and the need for receiving
countries to obtain labor services but not necessarily absorb a permanent population of migrants.
• Moreover, in the sending country, increased circular migration,
encouraged by the lowering of transportation costs, could reduce
many of the negative social effects that result from the separation
of families during long-term migration5 and reduce the incidence
and degree of “brain drain” from migration-sending countries in
ECA.6
109
110
Migration and Remittances: Eastern Europe and the Former Soviet Union
FIGURE 4.1
Migrants’ Preferences for Short- versus Long-Term Migration
80
70
60
percent
50
40
30
20
10
0
Bosnia and
Herzegovina
Romania
Georgia
Bulgaria
Kyrgyz Rep.
Tajikistan
leave temporarily and return fairly soon
leave temporarily without plan to return
leave for a long time and return
leave permanently
Source: World Bank surveys with returned migrants.
For undocumented migrants, a regime with these features—with
incentives for legal migration—could strengthen the rights that
migrants receive in the receiving country and allow them to obtain
social protection benefits that are out of reach today. Undocumented
migrants have no access to adjudicative processes when abroad and
hence have no legal recourse to oppose abuse. By drying up the
incentives and opportunities for undocumented hiring, legal protections for large stocks of foreign workers could be expanded.
To make the system credible and useful, it may be necessary to
increase enforcement against undocumented hiring. The GTAP model
described in chapter 3 was used to examine the impact of an increase
in the penalty for hiring undocumented labor, combined with an
increase in the probability of being caught hiring undocumented
labor, which serves as a proxy for better enforcement of such rules.7
111
International Regulatory Framework
The results suggest (figure 4.2) that undocumented labor becomes
more expensive under these circumstances, thereupon the demand
for undocumented migrant workers decreases and interregional labor
movements slow down.
The framework proposed in this section is not without its flaws.
Nevertheless, its strength is that it allows for the recalibration of
incentives for undocumented labor. The benefits of moving to a
regime of legal migration for all interested parties cannot be
overemphasized.
FIGURE 4.2
Percentage Decrease in Illegal Migration into the EU Owing to
Increase in Penalty for Hiring Illegally
FSU
Czech Rep.
other Eastern Europe
Slovak Rep.
Poland
Hungary
Turkey
Croatia
0
1
2
3
4
5
6
7
8
9
10
percentage decrease in undocumented migration
Source: World Bank simulations.
Note: Results are based on an increase in the penalty for hiring undocumented labor by 80 percent from current levels and
the probability of being caught hiring undocumented labor at 20 percent, that is, effective enforcement. Other Eastern Europe is Bulgaria, Romania, Estonia, Lithuania, Latvia, and Slovenia.
112
Migration and Remittances: Eastern Europe and the Former Soviet Union
Endnotes
1. See appendix 4.1 for a more detailed discussion of the integration of
migrants in the receiving country.
2. See appendix 4.2 for a more complete discussion of the EU’s transitional
arrangements for incorporating new CEEC member states into the single
labor market.
3. See appendix 4.3 for further information on undocumented migration
and some of the risks that it poses to migration-sending and -receiving
countries and migrants themselves.
4. Most-favored-nation status is given by one country to another in matters
of international trade. This status ensures that the receiving country will
receive identical trade access and terms that any third country would
receive.
5. For further information on the impact of longer-term migration on communities left behind, see appendix 4.4.
6. To date, there is not a good understanding of the prevalence and impact
of brain drain in the ECA region. For a summary of the existing state of
knowledge, see appendix 4.5.
7. See appendix 3.2 for a discussion of the model.
APPENDIX 1.1
Survey Methodology
For the World Bank’s ECA migration report, returned migrants were
surveyed in six countries: Bosnia and Herzegovina, Bulgaria, Georgia,
Kyrgyz Republic, Romania, and Tajikistan. The survey instrument
was a comprehensive, 77-item questionnaire that addressed a full
range of the returned migrants’ experiences before, during, and after
migration. Questions covered the financial, social, family, and personal aspects of migrants’ experiences both during and after migration. The full questionnaire and survey results will be available on the
ECA Web site, www.worldbank.org/ECA.
The survey was designed to provide an impressionistic, rather than
representative, picture of returned migrants’ experiences. For the
purposes of this survey, a “returning migrant” was defined as anyone
who has been abroad for more than three months with the purpose
of employment, and has hound him/herself in their home country
during the survey. The survey also provides some information on the
number of migrants who have returned permanently as opposed to
those who have expressed desire to migrate again.
Though the same survey instrument was utilized in each of the six
countries, local teams relied on slightly different methodologies to
select the sample of returned migrants to interview. In most cases,
this involved some form of “network” or “snowball” method in which
113
114
Migration and Remittances: Eastern Europe and the Former Soviet Union
returned migrants were identified through references from other
returned migrants or affiliated formal and informal institutions. The
preference for this methodology stemmed from the fact that no systematic view, including prior studies on migrant flows and experience
or the possibility to use household surveys, were found to support a
more comprehensive methodology. In some cases national censuses
have allowed for some blueprint on this selection.
Though in most case, efforts were taken to ensure that a national
sample is taken and various regions of the six countries were sampled, the extent to which the survey is representative of the universe
of returned migrants in these countries cannot be measured. The survey generated relatively large sample sizes—about 1,200 returned
migrants in each country—yet the results must be interpreted with
caution.
APPENDIX 1.2
Migration Statistics
APPENDIX TABLE 1.2.1
Population Change in the ECA States, 1989–2004
(beginning-of-year; thousands)
Total population (1)
Russian Federation
Ukraine
Belarus
Moldova
Latvia
Lithuania
Estonia
Armenia
Azerbaijan
Georgia
Kazakhstan
Kyrgyz Republic
Tajikistan
Turkmenistan
Uzbekistan
Poland
Czech Republic
Slovak Republic
1989
2004
147,400
51,707
10,152
4,338
2,667
3,675
1,566
3,449
7,021
5,401
16,465
4,254
5,109
3,518
19,882
37,885
10,360
5,264
144,534
47,442
9,849
4,247
2,319
3,446
1,351
3,212
8,266
4,544
14,951
5,037
6,640
5,158
25,707
38,191
10,211
5,380
Total
⫺2,866
⫺4,265
⫺303
⫺91
⫺347
⫺229
⫺215
⫺236
1,245
⫺857
-1,513
783
1,531
1,640
5,825
306
⫺149
116
Absolute change
Natural
increase
Migration
⫺8,635
⫺3,482
⫺332
147
⫺149
6
⫺62
399
1,476
242
1,892
1,174
2,302
1,269
7,125
973
⫺168
168
5,769
⫺782
29
⫺238
⫺199
⫺235
⫺153
⫺635
⫺232
⫺1,099
-3,406
⫺390
⫺771
371
⫺1,300
⫺667
19
⫺53
Total
Percent change
Natural
increase
⫺1.9
⫺8.2
⫺3
⫺2.1
⫺13
⫺6.2
⫺13.7
⫺6.9
17.7
⫺15.9
⫺9.2
18.4
30
46.6
29.3
0.8
⫺1.4
2.2
⫺5.9
⫺6.7
⫺3.3
3.4
⫺5.6
0.2
⫺4
11.6
21
4.5
11.5
27.6
45.1
36.1
35.8
2.6
⫺1.6
3.2
Migration
3.9
⫺1.5
0.3
⫺5.5
⫺7.4
⫺6.4
⫺9.8
⫺18.4
⫺3.3
⫺20.4
⫺20.7
⫺9.2
⫺15.1
10.6
⫺6.5
⫺1.8
0.2
⫺1
(Table continues on the following page.)
115
116
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 1.2.1 (continued)
Population Change in the ECA States, 1989–2004
(beginning-of-year; thousands)
Total population (1)
Hungary
Albania
Bulgaria
Romania
Slovenia
Croatia
FYR Macedonia
Bosnia and
Herzegovina
Serbia and
Montenegro
1989
2004
Total
10,589
3,182
8,987
23,112
1,996
4,495
1,881
10,117
3,103
7,801
21,713
1,996
4,442
2,030
⫺472
⫺80
⫺1,185
⫺1,399
0
⫺54
149
4,435
3,785
⫺651
10,445
10,662
217
Sources: UNICEF TransMONEE Database and national statistical offices.
Note: .. = negligible.
Absolute change
Natural
increase
Migration
⫺498
681
⫺497
⫺154
5
⫺35
225
26
⫺760
⫺688
⫺1,245
⫺5
⫺18
⫺76
..
398
..
⫺182
Total
Percent change
Natural
increase
⫺4.5
⫺2.5
⫺13.2
⫺6.1
0
⫺1.2
7.9
⫺4.7
21.4
⫺5.5
⫺0.7
0.2
⫺0.8
12
0.2
⫺23.9
⫺7.7
⫺5.4
⫺0.2
⫺0.4
⫺4
⫺14.7
..
..
2.1
3.8
⫺1.7
Migration
117
Appendix 1.1: Migration Statistics
APPENDIX TABLE 1.2.2
Population by Place of Birth in the FSU, 1989
Place of
permanent
residence
Russian Federation
Ukraine
Belarus
Uzbekistan
Kazakhstan
Georgia
Azerbaijan
Lithuania
Moldava
Latvia
Kyrgyz Republic
Tajikistan
Armenia
Turkmenistan
Estonia
Persons born in
Russian
Federation
135,549,786
5,211,922
786,672
915,978
2,450,213
191,274
161,999
173,938
248,674
384,423
348,471
234,030
53,766
175,788
300,430
Place of
permanent
residence
Moldava
Russian Federation
Ukraine
Belarus
Uzbekistan
Kazakhstan
Georgia
Azerbaijan
Lithuania
Moldova
Latvia
Kyrgyz Republic
Tajikistan
Armenia
Turkmenistan
Estonia
228,795
186,983
7,502
6,426
27,499
2,243
1,830
1,935
3,739,090
4,212
2,052
1,830
668
2,608
1,635
Ukraine
Belarus
Uzbekistan
Kazakhstan
4,595,811
44,332,132
268,015
199,096
510,702
65,974
31,650
47,453
266,585
93,528
53,652
43,446
13,294
33,182
46,322
1,408,619
419,031
8,883,290
27,169
136,939
9,654
7,840
88,093
15,640
116,621
10,056
7,977
2,297
9,630
25,299
529,814
137,095
14,828
18,108,456
139,495
4,074
6,910
4,608
5,979
5,241
69,560
86,619
2,116
36,860
2,771
1,825.035
343,730
61,894
202,204
12,714,676
14,685
14,921
14,391
21,091
14,240
125,534
27,788
4,257
16,309
8,072
Georgia
423,040
79,571
14,141
35,511
44,485
5,038,710
24,831
2,235
7,882
3,225
6,597
2,350
60,756
2,736
2,328
Azerbaijan
Lithuania
478,594
84,629
11,153
26,989
40,361
16,573
6,604,318
2,407
3,703
4,827
3,548
4,337
125,123
19,916
2,343
116,115
26,258
17,403
2,577
10,088
954
549
3,299,039
1,041
37,197
784
498
322
947
3,386
Latvia
Kyrgyz
Republic
Tajikistan
Armenia
Turkmenistan
Estonia
Persons born
abroad and
persons not
indicating
birthplace
99,932
20,965
10,496
3,038
5,274
902
606
12,247
1,024
1,974,518
817
890
220
964
6,467
260,914
38,745
4,792
79,663
93,616
1,486
987
1,105
1,846
2,115
3,585,832
14,926
645
3,755
1,187
153,806
36,207
5,305
84,089
21,958
1,529
1,008
1,626
1,379
4,097
11,215
4,649,781
1,534
3,358
904
151,484
36,498
2,912
12,280
10,756
37,742
137,027
895
1,318
1,399
1,701
2,302
2,570,422
4,436
758
140,551
32,406
5,098
52,226
42,141
1,466
7,819
3,668
1,962
1,811
4,059
5,825
1,977
3,204,771
1,056
65,485
10,994
3,246
1,551
2,428
644
243
1,663
606
5,401
353
565
148
376
1,154,585
994,088
454,868
55,059
52,824
213,830
12,931
18,640
19,499
17,540
13,712
33,524
9,439
467,231
7,081
8,119
Persons born in
Sources: EastView Publications and CIS Statistical Committee, USSR Census Results 1989 CD-ROM.
118
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 1.1.3
Population by Nationality, 1989–1991 and 1999–2002
(beginning-of-year; thousands)
Population by nationality
(percent)
(thousands)
1989–1991
1999–2002
1989–1991
1999–2002
CEEC and CIS states
Russian Federation
Russians
Tatars
Ukrainians
Other
Ukraine
Ukrainians
Russians
Other
Belarus
Belarussians
Russians
Other
Moldova
Moldovans
Ukrainians
Russians
Other
Latvia
Latvians
Russians
Other
Lithuania
Lithuanians
Russians
Poles
Other
Estonia
Estonians
Russians
Other
Armenia
Armenians
Russians
Azeris
Other
Azerbaijan
Azeris
Russians
Armenians
Other
Georgia
Georgians
Russians
Other
100.0
81.3
3.7
3.0
12.0
100.0
72.4
22.0
5.7
100.0
77.5
13.2
9.3
100.0
64.4
13.8
13.0
8.8
100.0
52.0
34.0
14.0
100.0
79.6
9.4
7.0
4.0
100.0
61.5
30.3
8.1
100.0
93.3
1.6
2.6
2.6
100.0
82.5
5.6
5.5
6.4
100.0
69.6
6.3
24.1
100.0
79.8
3.8
2.0
14.3
100.0
78.5
17.4
4.1
100.0
81.2
11.4
7.4
100.0
69.8
12.9
11.3
6.0
100.0
57.7
29.6
12.8
100.0
83.4
6.3
6.7
3.5
100.0
67.9
25.6
6.5
100.0
97.9
0.5
..
1.6
100.0
90.6
1.8
1.5
6.1
100.0
83.7
1.6
14.7
147,400
119,866
5,522
4,363
17,649
51,707
37,419
11,356
2,932
10,200
7,905
1,342
953
4,338
2,795
600
562
381
2,667
1,388
906
373
3,675
2,924
344
258
148
1,566
963
475
128
3,449
3,218
54
89
88
7,038
5,805
392
391
450
5,443
3,787
341
1,314
145,164
115,869
5,558
2,943
20,794
47,843
37,542
8,334
1,967
10,045
8,159
1,142
744
4,293
2,997
552
484
260
2,377
1,371
703
303
3,484
2,907
220
235
122
1,370
930
351
89
3,213
3,145
15
..
53
7,953
7,206
142
121
484
4,372
3,661
68
643
Change from 1989–1991 to
1999–2002
(thousands)
(percent)
⫺2,236
⫺3,997
36
⫺1,420
3,145
⫺3,864
123
⫺3,022
⫺965
⫺155
254
⫺200
⫺209
⫺45
202
⫺48
⫺78
⫺121
⫺289
⫺17
⫺202
⫺70
⫺191
⫺17
⫺125
⫺23
⫺26
⫺196
⫺33
⫺124
⫺39
⫺236
⫺73
⫺39
..
⫺35
915
1,401
⫺250
⫺270
34
⫺1,071
⫺126
⫺273
⫺671
⫺1.5
⫺3.3
0.7
⫺32.5
17.8
⫺7.5
0.3
⫺26.6
⫺32.9
⫺1.5
3.2
⫺14.9
⫺21.9
⫺1.0
7.2
⫺8.0
⫺13.9
⫺31.9
⫺10.8
⫺1.2
⫺22.3
⫺18.7
⫺5.2
⫺0.6
⫺36.2
⫺8.9
⫺17.7
⫺12.5
⫺3.4
⫺26.0
⫺30.5
⫺6.8
⫺2.3
⫺72.1
..
⫺40.0
13.0
24.1
⫺63.8
⫺69.0
7.5
⫺19.7
⫺3.3
⫺80.1
⫺51.1
119
Appendix 1.1: Migration Statistics
APPENDIX TABLE 1.1.3 (continued)
Population by Nationality, 1989–1991 and 1999–2002
(beginning-of-year; thousands)
Population by nationality
(percent)
(thousands)
1989–1991
1999–2002
1989–1991
1999–2002
Kazakhstan
Kazakhs
Russians
Germans
Ukrainians
Other
Kyrgyz Republic
Kyrgz
Russians
Uzbeks
Other
ECA states
Tajikistan
Tajiks
Uzbeks
Russians
Other
Turkmenistan
Turkmen
Russians
Uzbeks
Other
Uzbekistan
Uzbeks
Russians
Tadzhiks
Kazakhs
Other
Czech Republic
Czech
Slovak
Other
Slovak Republic
Slovak
Czech
Other
Hungary
Hungarian
German
Croatian
Slovakian
Other
Albania
Albanian
Greek
Macedonian
Other
Change from 1989–1991 to
1999–2002
(thousands)
(percent)
100.0
40.4
38.5
5.9
5.5
9.7
100.0
52.0
21.4
12.8
13.8
100.0
53.4
30.0
2.4
3.7
10.6
100.0
64.9
12.5
13.8
8.8
16,185
6,535
6,228
957
896
1,570
4,290
2,230
917
550
594
14,953
7,985
4,480
353
547
1,588
4,823
3,128
603
665
427
⫺1,232
1,450
⫺1,748
⫺604
⫺349
18
533
898
⫺313
115
⫺167
⫺7.6
22.2
⫺28.1
⫺63.1
⫺38.9
1.1
12.4
40.3
⫺34.2
20.9
⫺28.1
100.0
62.1
23.5
7.6
6.8
100.0
71.8
9.4
9.0
9.8
100.0
71.0
8.3
4.7
4.1
11.9
100.0
94.8
3.1
2.1
100.0
85.6
1.1
13.3
100.0
97.8
0.3
0.1
0.1
1.7
100.0
98.0
1.8
0.1
0.1
100.0
79.9
15.3
1.1
3.7
100.0
77.0
6.8
9.2
7.0
100.0
77.8
5.0
5.0
4.0
8.2
100.0
90.4
1.9
7.7
100.0
85.8
0.8
13.0
100.0
92.7
0.6
0.2
0.2
6.3
–
–
–
–
–
5,109
3,172
1,198
388
350
3,534
2,536
334
317
347
19,905
14,142
1,653
934
808
2,367
10,302
9,771
315
217
5,274
4,519
59
696
5,390
5,269
18
7
6
89
3,182
3,118
59
5
1
6,127
4,898
937
68
224
4,418
3,402
299
407
310
24,231
18,861
1,202
1,204
966
1,997
10,230
9,250
193
787
5,379
4,615
45
720
5,348
4,958
32
8
10
339
–
–
–
–
–
1,019
1,726
⫺261
⫺320
⫺125
884
866
⫺35
90
⫺37
4,326
4,719
⫺451
271
158
⫺370
⫺72
⫺521
⫺122
570
105
96
⫺15
24
⫺42
⫺311
15
1
4
249
–
–
–
–
–
19.9
54.4
⫺21.8
⫺82.5
⫺35.9
25.0
34.1
⫺10.5
28.3
⫺10.6
21.7
33.4
⫺27.3
29.0
19.5
⫺15.6
⫺0.7
⫺5.3
⫺38.6
263.0
2.0
2.1
⫺24.8
3.5
⫺0.8
⫺5.9
83.5
12.0
62.0
278.6
–
–
–
–
–
(Table continues on the following page.)
120
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 1.1.3 (continued)
Population by Nationality, 1989–1991 and 1999–2002
(beginning-of-year; thousands)
Population by nationality
(percent)
(thousands)
1989–1991
1999–2002
1989–1991
1999–2002
Bulgaria
Bulgarian
Turkish
Roma
Other
Romania
Romanians
Hungarians
Roma
Ukrainians
Other
Slovenia
Slovenes
Croats
Serbs
Others
Croatia
Croats
Serbs
Hungarians
Others
FYR Macedonia
Macedonians
Albanians
Turks
Roma
Other
CEE-CIS states
Serbia and Montenegro
Serbia
Serbs
Hungarians
Albanians
Roma
Other
Montenegro
Montenegrians
Serbs
Albanians
Other
Change from 1989–1991 to
1999–2002
(thousands)
(percent)
100.0
85.8
9.7
3.4
1.1
100.0
90.7
7.2
0.7
0.3
1.1
100.0
88.3
2.8
2.5
6.5
100.0
78.1
12.1
0.5
9.3
–
–
–
–
–
–
100.0
83.9
9.4
4.7
2.0
100.0
89.5
6.6
2.5
0.3
1.2
100.0
83.1
1.8
2.0
13.1
100.0
89.6
4.5
0.4
5.4
100.0
64.2
25.2
3.9
2.7
4.1
8,473
7,272
822
288
91
22,810
20,683
1,639
167
64
257
1,913
16
53
47
123
4,784
3,736
582
22
444
–
–
–
–
–
–
7,929
6,655
747
371
156
21,681
19,400
1,432
535
61
253
1,964
1,631
36
39
258
4,437
3,977
202
17
242
2,023
1,298
509
78
54
84
⫺544
⫺617
⫺75
83
65
⫺1,129
⫺1,284
⫺207
369
⫺2
⫺4
51
⫺58
⫺17
⫺8
135
⫺347
241
⫺380
⫺6
⫺202
–
–
–
–
–
–
⫺6.4
⫺8.5
⫺9.2
28.8
71.6
⫺4.9
⫺6.2
⫺12.6
221.1
⫺3.9
⫺1.5
2.6
⫺3.5
⫺32.6
⫺17.8
109.1
⫺7.2
6.4
⫺65.3
⫺25.8
⫺45.5
–
–
–
–
–
–
100.0
65.9
3.5
17.1
1.4
29.1
100.0
61.9
9.3
6.6
22.2
100.0
82.9
3.9
0.8
1.4
11.8
100.0
61.9
9.3
6.6
22.2
9,779
6,447
344
1,674
140
2,848
615
380
57
40
137
7,498
6,216
292
60
105
885
651
403
61
43
144
⫺2,281
⫺231
⫺52
⫺1,614
⫺35
⫺1,963
36
22
3
3
8
⫺23.3
⫺3.6
⫺15
⫺96.4
v25.1
⫺68.9
5.8
5.8
5.3
6.2
5.7
Sources: Data are from censuses conducted in the CEEC and CIS countries between 1989 and 1991 and again between 1999 and 2002. Nationalities shown for each
country are those numerically significant. Actual number of number of nationalities shown for each country differ.
Note: .. = negligible; – = not available.
121
Appendix 1.1: Migration Statistics
APPENDIX TABLE 1.1.4
Income Differentials Between ECA Countries and Western Europe, 2000–02
Per capita GDP PPP (US$)
Percent of that of Western Europe
17,587
14,933
12,863
12,133
11,303
10,253
9,530
8,420
61.8
52.5
45.2
42.6
39.7
36.0
33.5
29.6
9,660
6,700
6,190
6,147
33.9
23.5
21.7
21.6
6,477
—
—
22.8
—
—
EU-8
Slovenia
Czech Republic
Hungary
Slovak Republic
Estonia
Poland
Lithuania
Latvia
EU accession countries
Croatia
Bulgaria
Turkey
Romania
Other Western Balkans
FYR Macedonia
Bosnia and Herzegovina
Serbia and Montenegro
Sources: World Bank, SIMA database, and staff estimates.
Note: — = not available. PPP = purchasing power parity.
APPENDIX TABLE 1.1.5
Income Differentials Between ECA Countries of the CIS and Western Europe and the Russian
Federation, 2000–02
Russian Federation
Kazakhstan
Belarus
Ukraine
Azerbaijan
Armenia
Georgia
Kyrgyz Republic
Uzbekistan
Moldova
Tajikistan
Per capita
GDP PPP (US$)
Percent of that
of Western Europe
Percent of that
of Russian Federation
7,730
5,263
5,160
4,517
2,887
2,757
2,077
1,607
1,603
1,380
900
27.2
18.5
18.1
15.9
10.1
9.7
7.3
5.6
5.6
4.8
3.2
n.a.
68.1
66.8
58.4
37.3
35.7
26.9
20.8
20.7
17.9
11.6
Sources: World Bank, SIMA database, and staff estimates.
Note: n.a. = not applicable.
122
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 1.1.6
Net Migration by Country for the FSU States, 1989–2003
(thousands)
Country
Russia
1989–2003
Ukraine
1990–2000
Belarus
1990–2001
Moldova
1990–1997
Latvia
1990–2003
Total
Russian Federation
Ukraine
Belarus
Moldova
Latvia
Lithuania
Estonia
Armenia
Azerbaijan
Georgia
Kazakhstan
Kyrgyz Republic
Tajikistan
Turkmenistan
Uzbekistan
Total FSU
Germany
Israel
United States
Australia
Canada
Poland
Sweden
Finland
Other
Total non-FSU
3,796.5
—
374.9
-16.1
98.6
118.5
54.0
70.7
224.2
398.5
403.1
1,703.2
326.5
377.0
140.2
779.2
5,052.5
⫺753.7
⫺299.1
⫺132.0
⫺4.3
⫺11.5
⫺2.3
⫺2.0
⫺9.8
⫺41.1
⫺1,255.9
⫺232.4
⫺229.3
—
-4.2
17.8
7.8
3.2
3.6
17.6
26.7
23.3
38.7
6.5
16.7
5.7
82.8
16.9
⫺25.4
⫺157.5
⫺58.5
⫺1.9
⫺3.8
0.5
⫺0.1
⫺0.1
⫺2.4
⫺249.5
110.0
96.2
28.5
—
4.1
25.3
16.2
6.6
4.8
8.1
7.3
35.8
3.4
6.1
3.9
9.7
255.9
⫺6.5
⫺75.6
⫺30.5
⫺0.8
⫺0.9
⫺1.3
—
—
⫺30.3
⫺146.0
⫺142.2
⫺40.8
⫺28.2
⫺2.4
—
0.8
0.2
0.3
0.6
0.7
0.9
1.2
0.1
0.2
0.3
0.4
⫺65.7
⫺10.7
⫺48.1
⫺13.8
—
—
—
—
—
⫺2.3
⫺75.0
⫺146.5
⫺80.0
⫺19.3
⫺23.3
⫺0.8
—
⫺1.6
0.0
0.1
⫺0.5
0.1
⫺0.9
⫺0.1
0.0
0.0
⫺0.2
⫺126.6
⫺6.4
⫺4.7
⫺4.2
0.0
⫺0.6
0.0
0.0
⫺0.1
⫺0.2
⫺19.3
Source: National statistical offices of the FSU countries.
Note: Data in columns show net migration for each FSU state with countries listed in left column, for the time period indicated.
“—” indicates data not available or not applicable. A zero indicates that net migration rounded to less than 100.
Lithuania
1990–2000
⫺57.5
⫺30.8
⫺7.2
⫺12.9
⫺0.1
1.9
—
0.3
0.3
0.1
0.3
0.4
0.0
0.1
0.1
0.0
⫺47.5
⫺1.1
⫺3.5
⫺2.2
—
⫺0.2
⫺0.7
—
—
0.0
⫺9.9
Estonia
1989–98
⫺84.6
⫺55.8
⫺10.5
⫺5.8
⫺0.3
0.0
⫺0.3
—
0.1
0.0
0.4
⫺0.3
0.0
0.1
0.0
0.0
⫺72.5
⫺3.6
⫺2.0
⫺1.6
0.0
⫺0.2
0.1
⫺0.3
⫺4.4
0.2
⫺12.1
123
Appendix 1.1: Migration Statistics
Country
Armenia
1990–2001
Total
⫺60.4
Russian Federation ⫺125.6
Ukraine
3.5
Belarus
⫺3.9
Moldova
⫺0.6
Latvia
—
Lithuania
—
Estonia
—
Armenia
—
Azerbaijan
60.3
Georgia
10.0
Kazakhstan
⫺1.1
Kyrgyz Republic
⫺0.1
Tajikistan
0.4
Turkmenistan
0.9
Uzbekistan
1.9
Total FSU
⫺33.0
Germany
⫺0.1
Israel
⫺1.4
United States
⫺21.0
Australia
—
Canada
—
Poland
—
Sweden
—
Finland
—
Other
⫺4.9
Total non-FSU
⫺32.3
Azerbaijan
1990–2003
Georgia
1990–1992
⫺284.6
⫺252.9
⫺2.3
⫺6.7
⫺0.4
0.1
0.0
⫺0.1
⫺31.0
—
19.7
1.3
1.5
0.4
⫺0.8
16.4
⫺251.6
⫺1.1
⫺25.2
⫺6.3
—
—
—
—
—
⫺0.4
⫺33.0
—
⫺85.2
0.9
⫺3.3
⫺0.4
—
—
—
⫺6.0
⫺13.7
—
⫺1.7
⫺0.1
⫺0.1
⫺0.1
⫺0.1
⫺109.6
—
—
—
—
—
—
—
—
—
—
Kazakhstan
1990–2000
⫺1,581.1
⫺957.6
10.0
⫺21.1
⫺1.1
0.0
⫺0.1
0.0
1.3
0.3
3.0
—
4.6
13.0
24.9
36.3
⫺883.6
⫺808.5
⫺19.9
⫺4.7
—
—
—
—
—
⫺35.5
⫺851.1
Kyrgyz Rep.
1990–1996
⫺392.1
⫺278.8
1.8
⫺2.9
⫺0.1
—
—
—
0.1
⫺2.6
0.2
⫺2.1
—
8.9
0.0
⫺22.3
⫺297.1
⫺93.8
⫺5.9
⫺2.7
—
—
—
—
—
⫺2.9
⫺105.1
Tajikistan
1990–1995
⫺357.1
⫺258.3
⫺3.7
⫺4.7
⫺0.2
—
—
—
⫺0.4
⫺0.3
0.0
⫺11.4
⫺5.7
0.0
⫺7.0
⫺30.8
⫺322.3
⫺19.6
⫺12.5
⫺2.1
—
—
—
—
—
⫺0.7
⫺34.8
Turkmenistan Uzbekistan
1990–1995
1990–1998
⫺52.4
⫺51.2
2.9
⫺1.8
⫺0.2
—
—
—
⫺0.5
0.1
0.3
⫺17.4
0.2
7.0
0.0
9.3
⫺51.1
⫺0.5
⫺0.7
⫺0.1
—
—
—
—
—
⫺0.2
⫺1.3
⫺728.3
⫺542.8
⫺28.1
⫺7.0
⫺0.5
—
—
—
⫺2.8
⫺13.5
0.5
⫺42.5
17.9
30.6
⫺7.8
⫺595.0
⫺15.2
⫺53.2
⫺10.4
—
—
—
—
—
⫺3.2
⫺139.5
APPENDIX 2.1
Remittance Data
APPENDIX TABLE 2.1.1
Remittance Contributions to the Balance of Payments in Selected ECA Countries, 1995 to 2004
(US$ millions)
Country
1995
1996
1997
High migration
Bosnia and Herzegovina
Albania
Slovenia
Armenia
Kazakhstan
Belarus
Georgia
Moldova
Intermediate migration
Estonia
Ukraine
FYR Macedonia
Croatia
Latvia
Kyrgyz Republic
Azerbaijan
Tajikistan
114
—
427
272
65
116
29
—
1
69
1
—
—
544
—
1
3
—
180
—
551
279
84
89
351
—
87
98
2
6
68
668
41
2
—
—
179
—
300
241
136
60
295
284
114
95
2
12
78
617
46
3
—
—
1998
470
2,048
504
228
92
72
315
373
124
94
3
12
63
625
49
2
—
—
1999
2000
2001
418
1,888
407
226
95
64
193
361
112
96
2
18
77
557
49
9
54
—
400
1,595
598
205
87
122
139
274
179
112
3
33
81
641
72
9
57
—
407
1,521
699
200
94
171
149
181
243
150
9
140
73
747
113
11
104
—
2002
438
1,526
734
214
131
205
140
230
323
206
17
207
106
885
138
37
182
79
2003
2004
519
1,745
889
255
168
148
222
239
486
274
40
330
171
1,085
173
78
171
146
393
1,824
—
267
340
167
244
303
—
281
133
411
—
1,222
229
—
—
252
(Table continues on the following page.)
125
126
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 2.1.1 (continued)
Remittance Contributions to the Balance of Payments in Selected ECA Countries, 1995 to 2004
($ millions)
Country
Low migration
Bulgaria
Lithuania
Russian Federation
Hungary
Turkmenistan
Poland
Serbia and Montenegro
Romania
Turkey
Czech Republic
Slovak Republic
1995
630
—
1
2,503
152
—
724
—
9
3,327
191
26
1996
678
42
3
2,771
169
4
774
—
18
3,542
112
21
1997
701
51
3
2,268
213
—
848
—
16
4,197
85
29
1998
823
51
3
1,925
220
—
1,070
—
49
5,356
350
24
1999
2000
2001
667
43
3
1,292
213
—
825
—
96
4,529
318
20
760
58
50
1,275
281
—
1,726
—
96
4,560
297
18
637
71
79
1,403
296
—
1,995
—
116
2,786
257
—
2002
568
72
109
1,359
279
—
1,989
—
143
1,936
335
24
2003
2004
705
67
115
1,453
295
—
2,655
1,397
124
729
500
425
627
103
308
2,668
307
—
2,709
—
—
804
—
—
Sources: IMF Balance of Payment Statistics; UN International Migration Database; Walmsley, Ahmed, and Parsons 2005.
Note: — = not available. Received remittances = received compensation of employee + received worker’s remittances + received migrants’ transfers.
High-migration countries have over 180 migrants per thousand population. Intermediate migration countries have between 120 and 180 migrants per thousand population. Low-migration countries have fewer than 120 migrants per thousand population.
APPENDIX 2.2
Estimations of the Impact of
International Remittances on
Macroeconomic Growth
This appendix presents background information for the econometric
estimation of the impact of international remittances on macroeconomic growth presented in chapter 3 (box 3.1).1 The intention is to
extend the model developed by Chami, Fullenkamp, and Jahjah
(2003), which posits that because remittance transfer takes place
under asymmetric information and uncertainty, remittances are burdened with a moral hazard problem that limits their ability to contribute to positive business and human capital investment in
developing economies, thus leading to negative economic growth.
After briefly outlining their model, we show how, using the same
general empirical methodology but making slight modifications and
adding institution variables, the results could be significantly different
from those obtained by Chami, Fullenkamp, and Jahjah.
Using panel data on workers’ remittances, per capita GDP, gross
capital formation (formerly categorized as gross domestic investment), and net private capital flows (all reported over the period
1970–98), Chami, Fullenkamp, and Jahjah first examine the relationship between worker remittances and per capita GDP growth
using standard population-averaged cross-section estimation. The
estimated equation is based on
∆yi = β0 + β1 y0i + β2 wri + β3 gcfi + β 4 npcfi + ui
127
128
Migration and Remittances: Eastern Europe and the Former Soviet Union
where y is the log of real GDP per capita, y0 is the initial value of y, wr
is the log of worker remittances to GDP ratio, gcf is the log of gross
capital formation to GDP ratio, and npcf is the log of net private capital flows to GDP ratio. They also use an alternative specification using
change in the log of workers’ remittances to GDP ratio as an independent variable:
∆yi = β0 + β1 y0i + β2 ∆wri + β3 gcfi + β 4 npcfi + ui
This specification is problematic because a country would need to
increase remittances year after year to promote growth, which would
end up with a 100 percent share of remittances on GDP in the limit.
Therefore, unlike Chami, Fullenkamp, and Jahjah, we look at the
level, rather than growth, of remittances to GDP.
Furthermore, as mentioned above, we include institutional quality
variables that seem important, based on previous experience. Also,
abstracting for missing observations, our dataset adds five years of
observations to the data considered by the Chami model and covers
the period 1970–2003.
Last, and more important, Chami, Fullenkamp, and Jahjah fail to
address the problems associated with running panel estimations. One
possible problem arising from the panel specifications is that estimated coefficients may be biased if errors are autocorrelated due to
misspecified dynamics. It is very likely that growth is autocorrelated
due to business cycle effects. One solution would be to pool observations from peak to peak of the business cycle or take five- or six-year
averages of the data. The first option is implausible because it would
require previous knowledge of business cycle features for each economy. The second appears to be very arbitrary. Both options also lead
to a large loss of information.
Another, more rigorous, alternative is to model these dynamics by
introducing the lagged rate of growth of per capita income as an independent variable. This, however, leads to some estimation problems
that have to be dealt with by using Dynamic Panel Data (DPD) estimators. In our estimations, we used the annual data and introduced
one lag of the rate of growth of per capita GDP. The estimator used in
most equations is the Anderson and Hsiao (1981) method. This
method estimates the equation in first differences and instrumentalizes the lagged growth of GDPpc by using its lagged level in t – 2. This
estimation method is superior to the popular Arellano and Bond
(1991) generalized method of moments (GMM) estimator for the
typical macroeconomic panel datasets as demonstrated by Judson
and Owen (1999). Nevertheless, the results of using the GMM esti-
Appendix 2.2: Estimations of the Impact of International Remittances on Macroeconomic Growth
mator are also relevant because we do not have specific Monte Carlo
evidence on the appropriateness of each estimator for our panel settings. In both cases we provide a two-step estimator.
Another potential problem that arises is the endogeneity of the
remittances variables. This can arise because it is likely that countries
experiencing less successful economic performance would receive
larger remittances from their émigrés. To deal with this problem, we
have estimated the equations instrumentalizing also the remittances
variable with its first and second lagged level in the transformed (first
difference) equation. This is different from Chami, Fullenkamp, and
Jahjah because we believe their results are heavily biased in the
absence of this instrumental variables estimator.
In all the estimations we have used the logarithm of the remittances
to GDP ratio as the independent variable, as well as the control variables mentioned in the previous estimates. We provide the estimated
coefficients and their standard errors, the p-value of a Wald test of joint
model significance (high p-values indicate joint significance), the pvalue of the Sargan test for instrument validity (high p-values indicate
valid instruments) and p-values of autocorrelation tests of orders 1 and
2. Note that autocorrelation of order 1 is expected due to first differencing even if the original-level errors are not autocorrelated unless
they follow a random walk. Finally, we provide the long-run dynamic
solution for the coefficient on remittances and its standard error,
which is to be interpreted as the impact of remittances on growth in
equilibrium. We use several specifications depending on the control
variables introduced in the regression. We provide, in specifications
(1) to (6), the results from the Anderson-Hsiao (AH) estimator. Specifications (7) to (9) present the results from estimating the model using
a two-step GMM estimator with robust standard errors.
The results of the analysis are indicated in appendix tables 2.2.1
through 2.2.5. The main result of our analysis is that, although no firm
conclusions can be made regarding the effect of remittances on economic growth, models that account for endogeneity concerns indicate
that remittances make a positive, albeit modest, contribution to growth.
The cross-section and panel analysis2 conducted in accordance
with the Chami, Fullenkamp, and Jahjah model, over two separate
periods, 1970–2003 and 1991–2003, show inconclusive results, but
certainly do not find a negative relationship between remittances and
economic growth (appendix tables 2.2.1 through 2.2.4). The robustness of the coefficients on remittances depends on model specifications, but in the instances where results are significant, they show a
positive effect of remittances on growth. The inclusion of institutional
variables also yields inconclusive results, which could be due to the
129
130
Migration and Remittances: Eastern Europe and the Former Soviet Union
severe endogeneity problems associated with both remittance estimations and the use of subjective institutional indexes. The cross-section
analysis conducted as the average over the same two periods leads to
a similar outcome. However, although the panel and cross-section
estimations (appendix tables 2.2.1 and 2.2.2) produce uncertain
results, they do not give any indication that remittances have a negative impact, as suggested by Chami, Fullenkamp, and Jahjah.
Moreover, certain panel and cross-section estimations conducted
with data on workers’ remittances only, as in Chami, Fullenkamp,
and Jahjah, showed a highly robust positive correlation between
increases in remittances and GDP growth if institutional quality is
accounted for. The consensus in the empirical literature, however, is
that data on workers’ remittances alone do not fully reflect the
amount of money remitted by migrants, and thus the results of these
estimations are not reported here.3
The results of the dynamic panel estimations are shown in appendix
table 2.2.5. We present first the estimate of a simple dynamic model
with remittances as the only independent variable and then add different control variables at a time. Specification (9) only includes variables
that appeared to be significant in at least one of the previous equations.
The inclusion of the Transparency International index and the United
Nations Human Development Index (UNHDI) reduce dramatically the
number of observations and countries, although this is also the case for
the rest of the institutional variables. The result is a shorter panel, especially in the time dimension, in which we end up with four to five consecutive time series per country (this is an unbalanced panel). In that
context, the GMM estimator is more reliable than the AH estimator.
The Wald test for the AH estimator when these variables are included
shows clearly that the model is not significant and is grossly misspecified. For this reason, we recommend looking at the results provided in
equations (1) to (3) and (7) to (9).
The main result is that remittances appear to have a positive and statistically significant impact on growth in five out of nine of these specifications. Only in one specification is the impact negative but not
significant (when we do not instrumentalize or use control variables).
The significant long-run coefficients range from 0.001 to 0.022. This
denotes that the estimates cannot be considered very robust. What
seems to be more robust, however, is the fact that, if anything, remittances appear to have a positive effect on growth. The other important
result is that the impact of remittances appears to be more positive when
(a) we control for the potential endogeneity bias in remittances and (b)
we consider remittances in conjunction with institutional variables that,
in general, also appear to be significant and show the expected sign.
131
Appendix 2.2: Estimations of the Impact of International Remittances on Macroeconomic Growth
APPENDIX TABLE 2.2.1
Remittances (as Percentage of GDP) and Economic Growth: Cross-Section Estimation Ordinary
Least Squares (1970–2003)
Dependent variable:
log(GDP per capita growth)
Log(GDP per capita 1970)
Log(remittances/GDP)
(1)
(2)
(3)
(4)
(5) Quadratic
⫺0.003*
(0.002)
0.001
(0.001)
⫺0.014***
(0.002)
-0.000
(0.001)
⫺0.006***
(0.001)
0.000
(0.001)
⫺0.007***
(0.002)
0.001
(0.001)
0.041***
(0.008)
0.000
(0.002)
0.028***
(0.006)
⫺0.003**
(0.002)
0.004***
(0.001)
0.083***
(0.017)
0.037***
(0.005)
⫺0.002
(0.001)
0.039***
(0.007)
⫺0.004
(0.002)
⫺0.007***
(0.002)
0.001
(0.001)
0.000
(0.001)
0.039***
(0.007)
⫺0.003
(0.002)
0.001***
(0.000)
⫺0.111***
(0.023)
62
0.55
0.001***
(0.000)
⫺0.110***
(0.023)
62
0.55
(Log(remittances/GDP))2
Log(GCF/GDP)
Log(NPCF/GDP)
TI Corruption Perception Index
UNHDI
Voice and accountability
⫺0.004
(0.003)
⫺0.001
(0.003)
0.005
(0.005)
0.004
(0.004)
0.016**
(0.006)
⫺0.004
(0.006)
Political stability
Government efficiency
Regulatory quality
Rule of law
Corruption
ICRG Composite Political Risk Indicator
Constant
Observations
R-squared
⫺0.090***
(0.021)
77
0.44
⫺0.044**
(0.022)
69
0.71
⫺0.052***
(0.016)
75
0.72
Note: GCF = gross capital formation; ICRG = International Country Risk Guide; NPCF = net private capital flows; TI = Transparency International; UNHDI = UN Human
Development Index. Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.
132
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 2.2.2
Remittances (as Percentage of GDP) and Economic Growth: Cross-Section Estimation Ordinary
Least Squares (1991–2003)
Dependent variable:
log(GDP per capita growth)
Log(GDP per capita 1970)
Log(remittances/GDP)
(1)
(2)
(3)
(4)
(5) Quadratic
⫺0.001
(0.002)
0.000
(0.001)
⫺0.004
(0.005)
⫺0.000
(0.002)
⫺0.007***
(0.002)
⫺0.000
(0.001)
⫺0.004
(0.003)
0.001
(0.002)
0.027***
(0.009)
0.000
(0.002)
0.024**
(0.010)
0.001
(0.003)
0.006***
(0.002)
0.003
(0.031)
0.027***
(0.008)
0.000
(0.002)
0.022*
(0.011)
0.001
(0.003)
⫺0.004
(0.003)
0.001
(0.002)
⫺0.000
(0.001)
0.022*
(0.011)
0.001
(0.003)
0.001
(0.000)
⫺0.066**
(0.030)
90
0.17
0.001
(0.000)
⫺0.065**
(0.031)
90
0.17
(Log(remittances/GDP))2
Log(GCF/GDP)
Log(NPCF/GDP)
TI Corruption Perception Index
UNHDI
Voice and accountability
⫺0.009**
(0.004)
0.002
(0.004)
0.014
(0.009)
0.012**
(0.006)
⫺0.001
(0.009)
0.005
(0.008)
Political stability
Government efficiency
Regulatory quality
Rule of law
Corruption
ICRG Composite Political Risk Indicator
Constant
Observations
R-squared
⫺0.069***
(0.026)
119
0.13
⫺0.059*
(0.030)
104
0.20
⫺0.021
(0.022)
114
0.40
Note: GCF = gross capital formation; ICRG = International Country Risk Guide; NPCF = net private capital flows; TI = Transparency International; UNHDI = UN Human
Development Index. Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.
133
Appendix 2.2: Estimations of the Impact of International Remittances on Macroeconomic Growth
APPENDIX TABLE 2.2.3
Remittances (as Percentage of GDP) and Economic Growth: Panel Estimation (1970–2003)
Dependent variable:
log(GDP per capita growth)
Growth GDPpc (t – 1)
Log(remittances/GDP)
(1)
(2)
(3)
(4)
(5) Quadratic
(6) Quadratic
0.180***
(0.020)
0.001**
(0.001)
0.299***
(0.057)
⫺0.000
(0.001)
-0.068*
(0.038)
0.003
(0.003)
0.017
(0.028)
0.000
(0.002)
0.030***
(0.003)
0.001
(0.001)
0.029***
(0.007)
0.004*
(0.002)
0.001
(0.002)
⫺0.018
(0.018)
0.065***
(0.009)
⫺0.001
(0.002)
0.045***
(0.006)
⫺0.000
(0.001)
0.097***
(0.020)
0.006***
(0.001)
0.001*
(0.000)
0.036***
(0.004)
0.001
(0.001)
0.018
(0.028)
0.003
(0.002)
0.001**
(0.001)
0.045***
(0.006)
⫺0.000
(0.001)
⫺0.097***
(0.014)
1,913
123
0.08
⫺0.000
(0.000)
⫺0.120***
(0.019)
1,108
91
0.07
(Log(remittances/GDP))2
Log(GCF/GDP)
Log(NPCF/GDP)
TI Corruption Perception Index
UNHDI
Voice and accountability
0.005
(0.009)
0.005
(0.005)
⫺0.011
(0.007)
0.003
(0.006)
⫺0.017
(0.011)
⫺0.006
(0.008)
Political stability
Government efficiency
Regulatory quality
Rule of law
Corruption
ICRG Composite Political Risk Indicator
Constant
Observations
Number of ID
R-squared
⫺0.080***
(0.010)
1,913
123
⫺0.074***
(0.023)
297
80
⫺0.184***
(0.026)
716
114
0.11
⫺0.000
(0.000)
⫺0.117***
(0.019)
1,108
91
0.06
Note: GCF = gross capital formation; ICRG = International Country Risk Guide; NPCF = net private capital flows; TI - Transparency International; UNHDI = UN Human
Development Index. Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.
134
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 2.2.4
Remittances (as Percentage of GDP) and Economic Growth: Panel Estimation (1991–2003)
Dependent variable:
log(GDP per capita growth)
Growth GDPpc (t – 1)
Log(remittances/GDP)
(1)
(2)
(3)
(4)
(5) Quadratic
(6) Quadratic
0.143***
(0.026)
0.001
(0.001)
0.299***
(0.057)
⫺0.000
(0.001)
-0.068*
(0.038)
0.003
(0.003)
-0.027
(0.034)
⫺0.004
(0.002)
0.038***
(0.005)
0.002
(0.001)
0.029***
(0.007)
0.004*
(0.002)
0.001
(0.002)
⫺0.018
(0.018)
0.065***
(0.009)
⫺0.001
(0.002)
0.061***
(0.008)
⫺0.000
(0.002)
0.078***
(0.027)
0.001
(0.002)
⫺0.001
(0.001)
0.056***
(0.007)
0.001
(0.001)
-0.027
(0.034)
⫺0.003
(0.003)
0.000
(0.001)
0.061***
(0.008)
⫺0.000
(0.002)
⫺0.155***
(0.021)
1079
122
0.10
0.000
(0.000)
⫺0.194***
(0.028)
807
91
0.10
(Log(remittances/GDP))2
Log(GCF/GDP)
Log(NPCF/GDP)
TI Corruption Perception Index
UNHDI
Voice and accountability
0.005
(0.009)
0.005
(0.005)
⫺0.011
(0.007)
0.003
(0.006)
⫺0.017
(0.011)
⫺0.006
(0.008)
Political stability
Government efficiency
Regulatory quality
Rule of law
Corruption
ICRG Composite Political Risk Indicator
Constant
Observations
Number of ID
R-squared
⫺0.102***
(0.016)
1079
122
⫺0.074***
(0.023)
297
80
⫺0.184***
(0.026)
716
114
0.11
0.000
(0.000)
⫺0.194***
(0.028)
807
91
0.10
Note: GCF = gross capital formation; ICRG = International Country Risk Guide; NPCF = net private capital flows; TI = Transparency International; UNHDI = UN Human
Development Index. Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.
135
Appendix 2.2: Estimations of the Impact of International Remittances on Macroeconomic Growth
APPENDIX TABLE 2.2.5
Worker Remittances and Growth: Dynamic Panel Estimation (1970–2003)
Dependant variable:
growth of GDP per capita
Endogenous variable:
log (remittances/GDP)
Growth GDPpc (t – 1)
Log(remittances/GDP Growth)
(1)
AH
(2)
AH-IV
(3)
AH-IV
(4)
AH-IV
(5)
AH-IV
(6)
AH-IV
(7)
AH-IV
(8)
GMM
(9)
GMM
0.202^
(0.014)
⫺0.005
(0.010)
0.170^
(0.006)
0.002*
(0.001)
0.132^
(0.005)
0.002
(0.001)
0.082^
(0.004)
0.083^
(0.000)
0.001**
(0.000)
0.070^
(0.000)
⫺0.004^
(0.000)
0.035
(0.071)
0.021**
(0.090)
0.086**
(0.035)
⫺0.001
(0.007)
⫺0.020*
(0.011)
⫺0.455
(0.657)
0.037^
(0.013)
0.012^
(0.002)
0.047^
(0.008)
⫺0.002
(0.002)
0.039
(0.051)
0.010*
(0.006)
0.056^
(0.018)
0.000
(0.001)
0.05^
(0.006)
0.002
(0.002)
0.063^
(0.002)
0.006
(0.006)
⫺0.002
(0.004)
⫺0.016^
(0.006)
0.040^
(0.004)
0.004
(0.005)
0.012^
(0.001)
0.018^
(0.002)
0.005**
(0.002)
⫺0.007^
(0.001)
⫺0.081
(0.197)
0.012
(0.022)
0.124*
(0.075)
⫺0.019
(0.022)
⫺0.039
(0.026)
0.042
(3.037)
0.014
(0.051)
⫺0.007
(0.026)
0.046
(0.045)
⫺0.071
(0.064)
⫺0.001
(0.027)
0.012
(0.016)
0.008
(0.019)
0.011
(0.012)
0.003
(0.007)
1017
89
0.000
0.450
0.000
0.538
0.013^
(0.002)
212
60
0.000
0.757
0.037
0.171
0.010
0.022
Log(GCF/GDP)
Log(NPCF/GDP)
TI Corruption Perception Index
UNHDI
Bureaucracy quality
Corruption
Ethnic tensions
Law and order
Democratic accountability
Government stability
Socioeconomic conditions
Investment profile
Political risk
Observations
Number of ID
Wald
Sargan
AR(1)
AR(2)
Long-run remittances coeff.
2946
155
0.000
0.083
0.000
0.671
⫺0.006
(0.012)
2946
155
0.000
0.251
0.000
0.790
0.003*
(0.002)
2860
152
0.000
0.4290
0.000
0.819
0.002
(0.002)
1790
121
0.000
0.701
0.000
0.992
0.001**
(0.000)
217
65
0.004
0.634
0.005
0.544
0.022**
(0.011)
0.005
0.005**
(0.005)
(0.002)
⫺0.000
(0.005)
⫺0.004
(0.004)
0.007
(0.005)
⫺0.001
(0.003)
0.004**
0.002^
(0.002)
(0.000)
0.002
0.002^
(0.003)
(0.001)
⫺0.000
(0.002)
0.001 ⫺0.001**
(0.001)
(0.000)
1017
89
0.004
0.490
0.000
0.621
0.010*
(0.006)
1710
120
0.000
0.233
0.000
0.374
0.002
0.002
Note: GCF = gross capital formation; ICRG = International Country Risk Guide; NPCF = net private capital flows; TI = Transparency International; UNHDI = UN Human
Development Index. Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.
Specifications (1) to (7) were obtained using the two-step AH estimator and the AH estimator with instruments for the remittances variable. Specifications (8) to (10)
were obtained using the two-step GMM estimator of Arellano and Bond (1991) with robust standard errors.
136
Migration and Remittances: Eastern Europe and the Former Soviet Union
Data Definitions
Workers’ remittances and compensation of employees,
received (US$): Current transfers by migrant workers and wages
and salaries earned by nonresident workers. This new World Development Indicator (WDI) category comprising both workers’ remittances and compensation of employees was introduced in mid-2005.
Data are in current U.S. dollars. Source: Workers’ remittances and
compensation of employees, received (US$): World Bank World
Development Indicators.
GDP per capita (constant 2000 US$): GDP per capita is gross
domestic product divided by midyear population. GDP is the sum of
gross value added by all resident producers in the economy plus any
product taxes and minus any subsidies not included in the value of
the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural
resources. Data are in constant U.S. dollars. Source: GDP per capita
(constant 2000 US$): World Bank national accounts data, and OECD
National Accounts data files.
GDP (current US$): GDP at purchaser’s prices is the sum of gross
value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the
products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural
resources. Data are in current U.S. dollars. Dollar figures for GDP
are converted from domestic currencies using single-year official
exchange rates. For a few countries where the official exchange rate
does not reflect the rate effectively applied to actual foreign
exchange transactions, an alternative conversion factor is used.
Source: World Bank national accounts data, and OECD National
Accounts data files.
Gross capital formation (current US$): Gross capital formation
(formerly gross domestic investment) consists of outlays on additions
to the fixed assets of the economy plus net changes in the level of
inventories. Fixed assets include land improvements (fences, ditches,
drains, and so on); plant, machinery, and equipment purchases; and
the construction of roads, railways, and the like, including schools,
offices, hospitals, private residential dwellings, and commercial and
industrial buildings. Inventories are stocks of goods held by firms to
meet temporary or unexpected fluctuations in production or sales,
Appendix 2.2: Estimations of the Impact of International Remittances on Macroeconomic Growth
and “work in progress.” According to the 1993 system of national
accounts, net acquisitions of valuables are also considered capital formation. Data are in current U.S. dollars. Source: World Bank National
Accounts Data, and OECD National Accounts data files.
Private capital flows, net total (current US$): Net private capital
flows consist of private debt and nondebt flows. Private debt flows
include commercial bank lending, bonds, and other private credits;
nondebt private flows are foreign direct investment and portfolio
equity investment. Data are in current U.S. dollars. Source: World
Bank, Global Development Finance.
Transparency International (TI) Corruption Perception Index
(CPI): The TI Corruption Perceptions Index (CPI) ranks countries in
terms of the degree to which corruption is perceived to exist among
public officials and politicians. It is a composite index, drawing on
corruption-related data in expert surveys carried out by a variety of
reputable institutions. It reflects the views of business people and
analysts from around the world, including experts who are locals in
the countries evaluated. Source: http://www.icgg.org/.
UN Human Development Index: Data are linearly interpolated by
the UN Human Development Report Office. Otherwise, data conform
to those used in Human Development Report 2004. Source: Unofficial
data received as correspondence.
Governance indicators: The Web page http://info.worldbank.org/
governance/kkz2002/tables.asp presents the updated aggregate governance research indicators for almost 200 countries for 1996–2002,
for six dimensions of governance:
• Voice and accountability
• Political stability and absence of violence
• Government effectiveness
• Regulatory quality
• Rule of law
• Control of corruption.
The data and methodology used to construct the indicators are
described in “Governance Matters III: Governance Indicators for
1996–2002” (World Bank Policy Research Working Paper 3106).
137
138
Migration and Remittances: Eastern Europe and the Former Soviet Union
ICRG Political Risk Rating: A means of assessing the political stability of a country on a comparable basis with other countries by
assessing risk points for each of the component factors of government
stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religious tensions, law and order, ethnic tensions, democratic accountability, and
bureaucracy quality. Risk ratings range from a high of 100 (least risk)
to a low of 0 (highest risk), though lowest de facto ratings generally
range in the 30s and 40s. Source: Monthly data were collected from
www.countrydata.com, and yearly averages calculated by the
authors.
Endnotes
1. See Catrinescu et al. (2006) for a more detailed discussion of the estimation of the impact of remittances on growth using these methods.
2. The choice of fixed-effects or random-effects models in each instance
was determined by the results of the Hausman test.
3. These are available on request from the corresponding author.
APPENDIX 3.1
Estimating the Determinants
of Migration in ECA
In this appendix, we construct an econometric model of the determinants of migration in Europe and Central Asia (ECA), and present the
results of a statistical estimation of this model.
The Theoretical Model
By releasing the assumption of full employment, the Harris and
Todaro framework has been generalized to understand that migration
for individual i in the period t from the individual’s home country h
to potential destination country d is best understood as
I (t ) − I h (t )
M hd = ∫ d
I h (t )
(3.1.1)
where
Ih(t)
Id(t)
represents the discounted present value of the expected real
income stream in country h over a potential migrant’s planning horizon, and
is the discounted presented value of the expected real
income stream in country d over a potential migrant’s planning horizon.
139
140
Migration and Remittances: Eastern Europe and the Former Soviet Union
Given some of the weaknesses of this basic model to explain and predict migration, we follow the work of Hatton (1995) and Fertig (2001)
as a starting point toward designing an alternative specification. The
model is based on the concepts of individual utility maximization and
migration as a form of investment in human capital. The probability of
migration depends on the difference between expected utility in destination and home countries, where utility is represented by a monotonic
function of expected income, probability of employment, and cost of
migration, which depends on the current stock of immigrants.
U t = ln(w d )t + γ ln(ed )t − ln(w h )t − η ln(eh )t − z t
(3.1.2)
where wd, wh, ed, eh are income and probability of employment in the
destination and origin countries, respectively, and z is the cost of
migration.
The formation of expectations of future utility streams follows a
geometric series of past values with the most recent utility streams
given greater weight.
U t* = λU t + λ 2U t −1 + λ 3U t −2 + ...,
0 < λ <1
or
(3.1.3)
U = λU t + λU
*
t
*
t −1
The immigration rate (Mt) is assumed to be a function of current
and net present value levels of utility from immigration.
M t = β(U t* + αU t ), α > 1
(3.1.4)
where β stands for the aggregation parameter, and α for the extra
weight given to the current utility.
Extending the basic migration model and following Zoubanov (2004)
to account for a nonlinear relationship between the cost of migration
and the current stock of immigrants, the squared current stock of immigrants (MST) from a given origin country is also incorporated. To account
for quality-of-life considerations, the same adaptive expectations structure is used as above. The European Bank for Reconstruction and Development (EBRD) transition index1 is used to account for the quality of
life in the origin country. As such, the final specification is as follows:
∆ ln(w d / w h )t + γ∆ ln(ed )t − η∆ ln(eh )t − ε1 ∆MSTt
2
− ε 2 ∆MSTt + ∆EBRDt
n(w d / w h )t −1 + γ ln(ed )t −1 − η ln(eh )t −1
ε 0 + ln
+ β(α + λ − λα )
2
+ ε1 MSTt −1 + ε 2 MSTt −1 + EBRDt −1
∆M t = β(α + λ)
−(1 − λ)M t −1
(3.1.5)
Appendix 3.1: Estimating the Determinants of Migration in ECA
141
Empirical Specification and Estimation Results
This model is applied to Austria, Denmark, Germany, the Russian
Federation, Sweden, and the United Kingdom as destination countries. The samples of countries for estimation and the time period covered are presented in appendix table 3.1.1.
The dependent variable is the change in gross migration rates
(inflows from origin to destination country divided by the population
stock of origin country). Real wages wd and wx are approximated by
the per capita income data (in purchasing power parity) of destination and origin countries, respectively. Ignoring labor market participation, the employment rates ed and eh are proxied by 100 percent
minus the unemployment rate in destination and origin countries,
respectively. The model also incorporates distance between the capitals of destination and origin countries2 as a dependent variable, as
well as the EBRD transition index. Appendix table 3.1.2 provides the
summary statistics of the variables in the dataset.
An iterated GLS estimator with assumed heteroscedasticity across
the cross-sectional units and autocorrelation within each cross-sectional unit with a unit-specific coefficient is used. The choice of the
estimator was justified by computing the LR-Test statistic for the
hypothesis of homoscedasticity in the original model, which proved
that heteroscedasticity is indeed present. Appendix table 3.1.3 summarizes the LR-Test results.
The estimations have mixed results in explaining and predicting
migration across the region. Appendix table 3.1.4 summarizes those
results and suggests that wage and employment differentials were statistically significant predictors of migration in the expected directions
only about half the time. In a number of cases, these differentials
APPENDIX TABLE 3.1.1
Countries Employed in Model Investigating the Determinants of Migration
Destination country
Austria
Denmark
Germany
Russia
Sweden
United Kingdom
Origin countries
15 origin countries (Albania, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Poland, Romania, Russia, Slovak Republic, Slovenia, and Ukraine)
16 origin countries (Albania, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Moldova, Poland, Romania, Russia, Slovak Republic, Slovenia, and Ukraine)
16 origin countries (Albania, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Moldova, Poland, Romania, Russia, Slovak Republic, Slovenia, and Ukraine)
12 origin countries (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Latvia, Lithuania, Moldova,
Tajikistan, Ukraine, and Uzbekistan)
16 origin countries (Albania, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Moldova, Poland, Romania, Russia, Slovak Republic, Slovenia, and Ukraine)
4 origin countries (Bulgaria, Hungary, Poland, Romania)
Time frame
1996–2001
1992–2002
1994–2003
1990–2002
1992–2002
1991–2001
142
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 3.1.2
Descriptive Statistics
Country
Russia
Germany
United Kingdom
Austria
Sweden
Denmark
Variable
Migration rate
Log of per capita income ratio
Log of employment rate of destination country
Log of employment rate of origin countries
Stock of migrants
EBRD transition index
Distance
Migration rate
Log of per capita income ratio
Log of employment rate of destination country
Log of employment rate of origin countries
Stock of migrants
EBRD transition index
Distance
Migration rate
Log of per capita income ratio
Log of employment rate of destination country
Log of employment rate of origin countries
Stock of migrants
EBRD transition index
Distance
Migration rate
Log of per capita income ratio
Log of employment rate of destination country
Log of employment rate of origin countries
Stock of migrants
EBRD transition index
Distance
Migration rate
Log of per capita income ratio
Log of employment rate of destination country
Log of employment rate of origin countries
Stock of migrants
EBRD transition index
Distance
Migration rate
Log of per capita income ratio
Log of employment rate of destination country
Log of employment rate of origin countries
Stock of migrants
EBRD transition index
Distance
Number of
observations
Mean
Standard deviation
156
151
144
120
132
156
156
158
160
160
144
128
160
160
44
44
44
44
44
44
44
90
90
90
90
90
90
90
174
176
176
174
160
176
176
173
176
176
174
165
176
176
.0048186
.7880049
4.514289
4.538245
1,047,410
2.147179
1,577.333
.0010321
1.255495
4.51303
4.49148
60,086.52
2.96
960.3813
.0000156
1.140287
4.525252
4.484731
22,196.48
2.871818
1,751.25
.0001618
1.270075
4.565212
4.486468
12,394.56
3.011222
750.3333
.0000268
1.25652
4.524534
4.49469
2,786.981
2.781976
1,215.688
.0000305
1.362795
4.539178
4.49469
701.1152
2.781976
1,126.25
.0039413
.7069455
.0373602
.0604201
1,402,638
.7582831
837.706
.0007154
.6040464
.0071116
.0548698
81,026.13
.5460401
356.8575
7.99e-06
.2874813
.0195157
.0480524
26,543.6
.6664912
306.4516
.0002537
.4556397
.0027299
.0501821
17,443.51
.5368109
455.1169
.0000437
.5791293
.0207685
.0598797
4,383.52
.630339
473.6677
.0000553
.5799466
.0220577
.0598797
1,311.389
.630339
355.4167
143
Appendix 3.1: Estimating the Determinants of Migration in ECA
APPENDIX TABLE 3.1.3
LR-Test Results for Groupwise Heteroscedasticity
Country
LR-Test statistic
Russia, 2(11) = 19.7
Germany, 2(15) = 25.0
United Kingdom, 2(3) = 7.8
Country
LR-Test statistic
Austria, 2(14) = 23.7
Sweden, 2(15) = 25.0
Denmark, 2(15) = 25.0
104.825
79.599
9.340
95.302
448.140
389.607
seemed to produce the opposite of the expected effect. These uneven
results might reflect the poor quality of migration data.
In general, the results for the Russian model are broadly in line with
our hypothesis that the migration rate is positively correlated with
expected income differentials and negatively correlated with the
expectations of improving quality of life at home. The significant negative effect of the stock of migrants seems to reject the commonly referenced “network” effect in the models for Russia, Austria, and
Denmark, suggesting instead the existence of factors such as increased
competition in the labor market of the destination country, anti-immigration policy, racial intolerance, and other factors may make migrant
stock a poor predictor of future migrant flows. As was expected, distance is negatively correlated with the migration rate in all models.
Once the specification developed by Fertig is dropped, the per
capita income ratio and employment rate variables are removed, and
only the EBRD index is left to account for the quality of life (appendix table 3.1.5).
APPENDIX TABLE 3.1.4
Signs of the Coefficients in the Models
Migration to
Russia
Germany
United Kingdom
Austria
Sweden
Denmark
PCI ratio
⫹
0
⫺
0
0
0
Changes
E in d
MST
⫺
⫹
⫺
0
⫹
0
⫺
⫹
0
⫺
0
0
EBRD
PCI ratio
E in d
Lagged levels
MST
⫹
0
0
0
0
0
⫹
0
⫺
⫺
0
⫹
0
⫹
0
⫹
⫹
⫹
⫺
⫺
0
0
⫹
⫺
EBRD
⫺
⫹
⫹
⫹
⫺
⫺
M
D
⫺
⫺
0
⫺
⫺
⫺
⫺
⫺
0
⫺
⫺
⫺
Note: PCI = per capita income; E = employment rate; MST = stock of immigrants; EBRD = EBRD Transition index; M = migration rate; D = distance between destination and origin country; d = Migrants’ destination country. If a variable encourages statistically significant migration from h to d, it receives a “⫹” sign; if negative, a
“⫺” sign; if insignificant, a 0 is assigned.
144
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 3.1.5
Estimation Results: Dependent Variable ∆Mt
Russia model
Coeff
Z-score
Changes
EBRD index
MST
Squared MST
Lagged levels
EBRD index
Migration rate
MST
Squared MST
Distance
Wald chi2
Log likelihood
Coeff
Germany model
Z-score
0.0019455
⫺7.85E-08
8.37E-15
6.64
⫺3.77
4.57
0.000062
2.22E-09
2.49E-14
⫺0.0009978
⫺0.4538256
⫺2.64E-08
2.74E-15
⫺5.38
⫺4.71
⫺10.37
12.32
0.0002729
⫺0.4367562
⫺1.93E-09
8.41E-15
⫺0.0000191
⫺7.65
1.44E-08
466.76
653.3195
0.88
0.71
3.62
5.27
⫺11.19
⫺3.70
4.62
0.14
322.15
869.0873
UK model
Coeff
Z-score
⫺8.46E-06
⫺2.84E-08
2.1E-13
⫺4.06
⫺2.63
2.12
0.0000017
⫺0.0354594
1.62E-09
⫺1.04E-14
1.61
⫺0.26
1.74
⫺1.35
9.45E-08
1.88
75.57
473.441
Estimation Results: Dependent variable ∆Mt
Explanatory
variable
Country-specific
effectsa
Armenia
Azerbaijan
Belarusb
Estonia
Georgia
Kazakhstan
Lithuania
Latvia
Moldova
Tajikistan
Ukraineb
Changes
PCI ratio
Employment destin.
Employment origin
MST
Squared MST
EBRD index
Lagged levels
PCI Ratio
Employment destin.
Employment origin
Migration rate
MST
Squared MST
EBRD index
Inherent dynamics
MST
Distance
Constant
Wald chi2
Log likelihood
Russia model
Coeff
Z-score
Coeff
Z-score
0.0033819
0.0016483
0.0068437
0.0088344
0.0037153
0.0168113
0.0077908
0.0084265
⫺0.0016337
⫺0.0032731
0.0194571
3.64
3.32
5.00
6.64
5.38
5.26
5.37
7.61
⫺1.96
⫺2.56
3.02
⫺0.0053404
⫺0.0035908
⫺1.95
⫺2.72
⫺0.0057415
⫺0.0029113
0.024169
⫺0.0067712
⫺0.005681
⫺0.0125317
⫺0.0052925
0.021917
⫺1.52
⫺1.71
5.61
⫺1.82
⫺1.61
⫺4.34
⫺2.65
3.55
0.0056835
⫺0.0128993
-0.0090613
11.36
⫺8.93
⫺12.61
0.0050619
⫺0.0134857
⫺0.0091548
⫺2.26E-08
1.31E-15
0.0057828
0.0130283
0.015399
⫺0.6209453
9.28
6.89
17.06
⫺35.85
⫺3.04E-09
⫺2.13
⫺0.1334887
16669.77
526.3308
⫺12.07
Coeff
Z-score
⫺0.0162904
⫺0.0120918
⫺0.0194683
⫺0.0259414
⫺0.0148168
0.017718
⫺0.0279602
⫺0.0261036
⫺0.0258678
⫺0.0032557
⫺3.45
⫺3.78
⫺3.76
⫺3.38
⫺3.44
6.32
⫺3.69
⫺3.48
⫺4.08
⫺2.63
9.13
⫺8.84
⫺12.02
⫺1.42
0.82
0.0063701
⫺0.0079046
6.07
⫺1.98
⫺3.66E-08
3.5E-15
0.0008346
⫺2.75
2.95
2.50
0.0052635
0.0112431
0.0144664
⫺0.5837639
⫺1.03E-08
7.49E-16
8.21
5.47
13.56
⫺14.58
⫺3.08
2.02
0.0025046
⫺0.0013986
3.28
⫺0.39
⫺0.4079454
⫺1.09E-08
9.17E-16
⫺0.0009004
⫺4.74
⫺4.27
3.89
⫺2.89
⫺0.0000042
⫺0.1024069
14268.97
530.5909
⫺4.82
⫺6.47
⫺0.0000115
0.0480387
274.79
643.7139
⫺4.07
2.29
a. To prevent multicolinearity, a country dummy for Uzbekistan is not included.
b. In the second and third specifications, respectively, Belarus and Ukraine country dummies were dropped because of colinearity.
145
Appendix 3.1: Estimating the Determinants of Migration in ECA
Austria model
Coeff
Z-score
Coeff
Sweden model
Z-score
Coeff
Denmark model
Z-score
9.57E-06
⫺2.21E-08
5.48E-13
4.37
⫺4.33
4.80
6.04E-07
3.52E-09
⫺6.15E-13
0.58
1.32
⫺2.67
8.01E-07
4.15E-09
⫺8.53E-13
0.90
1.05
⫺0.91
0.0000141
⫺0.556848
5.52E-09
6.14E-14
4.01
⫺5.38
3.67
0.75
7.61E-07
⫺0.7567089
4.81E-09
⫺4.54E-13
1.60
⫺14.24
3.56
⫺3.45
1.38E-06
⫺0.1185468
⫺5.09E-10
4.42E-13
3.10
⫺2.33
⫺0.39
2.08
⫺1.32E-07
⫺3.96
⫺1.31E-07
⫺7.10
1.40E-09
98.61
780.9786
357.77
1600.348
33.44
1689.842
0.77
146
Migration and Remittances: Eastern Europe and the Former Soviet Union
Endnotes
1. The EBRD transition index is a composite index calculated as an arithmetic average of the eight indexes published in the EBRD Transition
Reports. These include an index of price liberalization, index of foreign
exchange and trade liberalization, index of small-scale privatization,
index of large-scale privatization, index of enterprise reform, index of
competition policy, index of banking sector reform, and an index of
reform of nonbanking financial institutions. The measurement scale
ranges from 1 to 4.25 where 1 represents little or no change from a
planned economy and 4.25 represents the standard of a developed market economy.
2. The City Distance Tool (http://www.geobytes.com/CityDistanceTool.htm)
was used to calculate the distance between two cities.
APPENDIX 3.2
Computable General Equilibrium
Model of Migration
The computable general equilibrium (CGE) model used is based on
the Global Trade Analysis Project (GTAP), which is a comparativestatic, multiregional CGE model. To mimic migration, the standard
GTAP structure was modified so that the extended model allows for
bilateral movement of labor. Unlike the standard GTAP model, the
factor labor is now able to cross borders and take part in the production process of foreign firms in different regions similar to production commodities. This migration mechanism generates a
country’s labor in- and outflow endogenously driven by the different regions’ labor demand and supply, and the interregional wage
differentials. Accordingly with the interregional differences in labor
demand and wage level representing the driving forces of migration, this approach to modeling follows the classical migration theory inspired by Adam Smith and the approach of Harris and Todaro
(1970).
In addition to the extensions described above, the model was
adjusted to consider illegal migration. Thus, in addition to (legal)
domestic and foreign unskilled and skilled workers, employers can
hire illegal foreign workers. Illegal workers are assumed to belong to
the group of unskilled employees. A full description of the model and
its calibration follows.
147
148
Migration and Remittances: Eastern Europe and the Former Soviet Union
Description of the Model
GTAP is a comparative-static, multiregional CGE model. It provides
an elaborate representation of the economy including the linkages
between farming, agribusiness, industrial, and service sectors of the
economy. The use of the nonhomothetic constant difference of elasticity (CDE) functional form to handle private household preferences,
the explicit treatment of international trade and transport margins,
and a global banking sector that links global savings and consumption
is innovative in GTAP. Trade is represented by bilateral trade matrixes
based on the Armington (1969) assumption. Further features of the
standard model are perfect competition in all markets as well as
profit- and utility-maximizing behavior of producers and consumers.
Usually policy interventions are represented by price wedges. They
lead to different prices according to different market stages. Price differentiation adjusts through introduction or change of taxes and subsidies, respectively. Quantitative restrictions or quantitatively induced
price adjustments do not exist in the standard version. The framework of the standard GTAP model is well documented in the GTAP
book (Hertel 1997) and available on the Internet (http://www.gtap
.agecon.purdue.edu/).
Previous (Migration) Extensions of the Model
The standard version of the GTAP model allows for the bilateral
exchange of industrial and agricultural products as well as for trade in
services. Thus, these components are not only demanded by domestic firms, private households, and the government but also by foreign
firms, foreign private households, and foreign governments. In contrast, the remaining input factors—capital, natural resources, land,
and labor—are assumed to be regionally fixed. However, when it
comes to the analysis of regional integration processes, this means
that a border opening for production factors, labor for example, cannot be considered simultaneously with a trade-liberalizing event.
Thus, interdependencies between both aspects and resulting economic impacts cannot be observed.
To mimic migration, the standard GTAP structure was modified so
that the extended model allows for bilateral movement of labor.
Unlike the standard GTAP model, the factor labor is now able to cross
borders and take part in the production process of foreign firms in different regions similar to production commodities. This migration
mechanism generates a country’s labor in- and outflow endogenously
149
Appendix 3.2: Computable General Equilibrium Model of Migration
driven by the different regions’ labor demand and supply, and the
interregional wage differentials. Accordingly with the interregional
differences in labor demand and wage level representing the driving
forces of migration, this approach to modeling follows the classical
migration theory inspired by Adam Smith and the approach of Harris
and Todaro (1970).
For the implementation of this new feature, the “nested” production
structure of the standard GTAP framework was expanded by an additional “nest” (appendix figure 3.2.1). This component is responsible for
the split of a country’s total labor force into foreign workers and domestic workers. Thus, in contrast to the standard model, firms now choose
from a pool of workers composed of both nationals and foreigners.
Appendix figure 3.2.1 represents the basic mechanism regulating
the distribution of workers across countries. At the bottom of the circle,
a country’s total labor force (total LF in r) is divided into workers who
decide to stay in their home country (LF in r) and are employed in their
home country’s economy, and workers who decide to emigrate.
At that point, the workers’ decision making is regulated by a CES
(Constant Elasticity of Substitution) function. In accordance with the
Harris-Todaro theory, the driving force of migration flows is the development of the different regions’ wages. Thus, the corresponding
parameters reflect the intensity of the workers’ reactions to the developments of the wage level across regions. Furthermore, the CES function ensures a distinction between the different nationalities of
migrant workers and the resultant different preferences regarding the
choice of a host country (equation 3.2.1).
APPENDIX FIGURE 3.2.1
Extended GTAP Production Structure
X
land,
capital
intermediate goods
total LF in s
total foreign
LF in s
domestic LF in s
LF from
other
regions
total LF from r in s
Economy in r
LF flow from r in s
LF in r
total LF in r
Source: World Bank.
Note: LF = labor force; r = countries; s = countries; X = final product.
LF from r in
other
regions
foreign LF
stock from r in s
150
Migration and Remittances: Eastern Europe and the Former Soviet Union
X i ,r = (α * Yiη,r + (1 − α )* Ziη,r )1/η
(3.2.1)
where
Xi,r
α
Yi,r
Zi,r
η
total labor force in r
share of emigrating labor
emigrating labor
staying labor in r
elasticity of substitution
The reason for such preferences can be found in social factors such
as geographical and cultural nearness, tradition, and the like. This
theory is supplemented by another assumption implying a certain
influence of the development of unemployment in different regions.
It is assumed that migrants compare the unemployment situation in
their home country and potential host country. Accordingly, if the
development in the worker’s home country is more favorable than in
the destination location, the incentive to emigrate declines and vice
versa. With unemployment reflecting a disequilibrium situation, a
CGE model is not capable of representing unemployment in its standard set-up. Thus, the implementation of unemployment is conducted through application of Okun’s law, which states that there
exists an inverse relationship between the development of a country’s
GDP and the country’s unemployment rate. This consideration of
unemployment can only be regarded as an approximation because
other related aspects, such as unemployment benefits, a social security system, and so forth, are not taken into account. With this theoretical background, the migrants who decided to move from r to s (LF
from r in s), together with the community of workers from r already
living in s (foreign LF stock from r in s), form the total pool of workers coming from r “available” in s (total LF from r in s) while the
remaining migrants scatter across the other destinations (LF from r in
other regions). Of course, workers from regions other than r will have
chosen s as their working destination. Thus, summing up all the
immigrants stemming from countries all over the world leads to a
pool of foreign labor (total foreign LF in s).
Together with the domestic workers who decided to stay in s
(domestic LF in s), this represents the total labor force available to producers in s (total LF in s). The remaining production decisions made
are conducted in the “old-fashioned” CGE-GTAP manner. Together
with land and capital, labor flows into the production process and
builds the value-added nest. The last step to the final product (X) is the
combination of value-added and other intermediate commodities.
Appendix 3.2: Computable General Equilibrium Model of Migration
In addition to this main mechanism, further extensions of the
model framework incorporate remittances. Based on figures
obtained from the International Monetary Fund, shares of migrants’
income that are sent back to their home country or spent in the host
country, respectively, are calculated. This enables the consideration
of the interregional redistribution of remittances. Thus, outgoing
money is subtracted from regional and private household income,
while incoming money is added on top of the corresponding
income.
New Extensions to the Model
In addition to the extensions described above, the model was
adjusted to consider illegal migration. Thus, in addition to (legal)
domestic and foreign unskilled and skilled workers, employers can
hire illegal foreign workers. Illegal workers are assumed to belong to
the unskilled-employees group. Thus, according to the data in
appendix table 3.2.1 the ratio between a country’s legal and illegal
migrant stock and inflow refers to the data for immigrating unskilled
labor. In value terms, the percentage share of illegal workers is
slightly less because it is assumed that illegal workers face lower
wages than legal workers.
A payroll tax of 40 percent was implemented on legal skilled and
unskilled employees working in a member state of the EU-15. This
payroll tax applies to every production sector.
Furthermore, workers’ migration behavior now also depends on
the change in a country’s or region’s quality-of-life index. This index
is represented as an exogenous variable and reflects characteristics
of a country such as social equity, structural improvement, and the
like. It is assumed that workers compare the development of the
quality of life in their home country with that in potential destination countries. Similar to the situation concerning the development
of unemployment, a quality-of-life improvement in the home country relative to potential host countries weakens the emigration
motivation. The parameter determining the strength of the qualityof-life index on workers’ migration behavior is adopted from Karemera et al. (2000). That study found a migration elasticity with
respect to the development of a country’s unemployment rate.
Because no migration elasticity considers quality-of-life concerns,
this parameter might be an adequate approximation since a country’s unemployment situation might reflect a certain part of a
region’s quality of life.
151
152
Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 3.2.1
Irregular Migration
(thousands)
Country
North America and Canada
United States
Canada
High-income Europe
Greece
Portugal
Italy
United Kingdom
Spain
Belgium
Germany
Switzerland
Netherlands
France
Ireland
Finland
Total
ECA countries
Poland
Ukraine
Tajikistan
Czech Republic
Slovak Republic
Turkey
Russia
Kazakhstan
Belarus
Kyrgyz Republic
Uzbekistan
Lithuania
Total
number of
nigrants
Estimated number of irregular migrants
Max
Min
34,988
5,826
10,300
200
534
233
1,634
4,029
1,259
879
7,349
1,801
1,576
6,277
310
134
26,015
320
100
500
1,000
280
150
1,000
180
163
400
10
1
4,104
2,088
6,947
330
236
51
1,503
13,259
3,028
1,284
572
1,367
339
600
1,600
60
40
8
200
1,500
300
150
30
30
2
100
112
1,300
220
50
Estimated
year
Average %
of total
migrants
2004
2003
29.44
3.43
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
59.87
42.96
30.59
24.82
22.24
17.06
13.61
9.99
8.72
6.37
3.23
0.75
15.78
2000
2000
2002
2003
1998
2001
2000
2002
2000
1998
2000
1997
28.73
23.03
18.16
16.98
15.69
13.31
11.31
9.91
11.68
5.24
2.19
0.59
Source: Pew Hispanic Center, IOM, ILO, World Bank, Home Office in UK, ISTAT, Jimenez (2003), Centre on Migration, Policy and Society of the University of Oxford,
EU business, Counsil of Europe, Ministry of Labor in Finland, Sadovskaya (2002), Migration Policy Group, Jandl (2003).
Notes: 1. Estimation methods are different for each country. 2. Total number of migrants is at the point in 2000 and is estimated by UN (2003).
Model Design (Regional and Sectoral Aggregation)
The aggregation strategy was dictated by two main requirements: on
the one hand, the selection of countries must allow for capturing relevant labor flows and, on the other hand, to keep calculation effort to
a reasonable scope, the aggregation must not exceed a certain size.
Therefore, all countries representing home regions of most of the
immigrants coming to Germany are treated as single individual countries. Obviously, Germany and Poland are among those single regions
153
Appendix 3.2: Computable General Equilibrium Model of Migration
as well as several other CEECs, Turkey, and the former Sovjet Union.
The remaining countries are put together as aggregated regions,
either in the group representing the rest of the EU-15, or comprising
the rest of the CEECs, respectively (see appendix table 3.2.2)
The 57 industries included in the GTAP database were aggregated
to 11 sectors including 6 agricultural sectors. This aggregation was
predominantly determined by a sector’s relevance in terms of
migrant workers’ employment and by a sector’s labor intensity.
Because Germany’s vegetables and fruits sector, in particular, and
the construction sector account for major shares of seasonal foreign
employees, both industries are represented as disaggregated sectors.
To be able to observe differences regarding impacts on labor-intensive and less labor-intensive sectors, agricultural production is split
up into primary production sectors and processing production sectors. With regard to calculation effort, the same restriction applies as
in regional aggregation. Thus, agricultural production is represented
in the form of the main agricultural production categories, plant
and animal production (see appendix table 3.2.2).
Limitations
In a quantitative analysis it is very difficult to depict any qualitative circumstances. With regard to the migration this becomes particularly
APPENDIX TABLE 3.2.2
Regional and Sectoral Aggregation
Regions
Germany
Rest of the EU-15
Austria, Belgium, Denmark, Finland,
France, Greece, Ireland, Italy,
Luxembourg, Netherlands, Portugal,
United Kingdom, Spain, Sweden
Poland
Czech Republic
Hungary
Slovak Republic
Rest of candidate countries
Bulgaria, Estonia, Latvia,
Lithuania, Romania, Slovenia
Croatia
Former Soviet Union
Turkey
Rest of the world
Source: own illustration.
Abbreviation
D
EU15
PL
CZE
HUN
SVK
CAND6
HRV
FSU
TUR
ROW
Sectors
Plant products (primary)
Paddy rice, wheat, cereal grains,
oilseeds, sugarcane, sugar beet,
Plant products (processed)
Vegetable oils and fats, processed
rice, sugar, other food products
Vegetables and fruits
Animal products (primary)
Cattle, sheep, goats, horses,
raw milk
Animal products (processed)
Meat: cattle, sheep, goats, horses,
meat products, dairy products
Other animal products
Construction
Primary products
Manufactures
Services
Abbreviation
plant
plantproc
vandf
animal
aniproc
oap
constr
prim
mnfcs
svces
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Migration and Remittances: Eastern Europe and the Former Soviet Union
apparent when it comes to the representation of migration restrictions. Those restrictions mostly exist as certain bureaucratic procedures, special requirements a potential immigrant has to fulfill, and
the like. Due to a lack of quantitative estimations of such rules and formalities, migration restrictions are not considered. The same limitation
applies to migration costs. Even though migration costs do represent a
quantitative factor, they are not taken into account in the migration
part of the model because corresponding data are not available.
Furthermore, data availability imposes major problems on modeling opportunities. Data collection on the share of foreign workers in
a country’s labor force, migration flows by home and host country,
and so forth, turned out to be particularly difficult for the CEECs.
Some simulation results may be distorted because of this lack of data.
Another difficult task was the introduction of adequate parameters.
Because labor migration elasticities with respect to international
wage differentials could not be retrieved from the literature for the
analysis at hand, these parameters are based on income migration
elasticities. There are estimations of migration elasticities with
respect to wage differentials on a sectoral or intraregional (for example, rural-urban) basis. But because these are neither specifically
estimated for labor movements nor for international migration they
did not seem appropriate for application to international labor movements. The same shortcoming applies to the quality-of-life index. As
previously described, the parameter for this variable is only an
approximation because parameters referring to the influence of a
country’s quality of life on people’s migration behavior could not be
obtained from the literature.
Further research is also necessary on technological progress and
the resulting development of or advances in labor-saving production
processes, particularly with regard to transition countries. Last, in the
case of Germany especially, it is essential to focus more extensively on
the characteristics of the very complex social security system and its
interactions with migration behavior.
Sensitivity Analyses
To verify the robustness of the results on exogenous parameters and
shocks a sensitivity analysis was conducted.
There are two ways to carry out such sensitivity tests—Monte
Carlo Analysis or Systematic Sensitivity Analysis. Both procedures
treat exogenous variables as continuous random variables (Arndt
1996; Arndt and Pearson 2000). The two procedures differ when it
Appendix 3.2: Computable General Equilibrium Model of Migration
comes to the determination of the expected value. Using Systematic
Sensitivity Analysis, a sample of solution values within the corresponding integral is selected; Monte Carlo Analysis determines the
expected value through a sufficient number of simulations. However,
because of the high number of simulations and repetitions necessary,
the Monte Carlo method is not practicable. Thus, the Systematic Sensitivity Analysis is used more often. A particularly suitable procedure
for the calculation of the integral is the Gaussian Quadrature. The
Systematic Sensitivity Analysis available in GTAP is based on this
approach and offers two different methods developed by Stroud
(1957) and Liu (1997). With these methods, estimates of mean and
deviation of endogenous variables are calculated by specifying a distribution for the corresponding exogenous parameters. Furthermore,
based on this information and the assumptions concerning the distribution of the parameters, a confidence interval can be determined.
Usually the selection of the parameters to be subject to the sensitivity analysis is geared to the conducted experiments. Thus, for this
sensitivity analysis, the parameters to be checked are the ones that
significantly influence the development of migration flows and labor
demand. The corresponding parameters were simultaneously varied
by 50 percent in the course of the Systematic Sensitivity Analysis,
assuming that each value is equally likely (uniform distribution). The
procedure used here is the procedure developed by Stroud (1957).
The sensitivity analysis showed that for all the examined and reported
variables (change in migration flows, welfare, GDP, and so forth), the
standard deviations take quite low values. Accordingly, assuming that
variables are normally distributed, the corresponding confidence intervals are small. Generally, standard deviations and confidence intervals
are larger for those variables to which high shocks are applied. Nevertheless, in most of the cases, the algebraic sign of the results can be classified as reliable at a level of 95 percent. Very few results show only a 68
percent probability. These results are close to zero, so the difference
between a positive and a negative value is marginal.
Data and Calibration
The database used is GTAP database 5, comprising 76 regions and 57
sectors. The base year of the database is 1997. Although a 2001 GTAP
database has been released, it was not available when the database for
the extended model version was prepared, so the 1997 version was
used. For detailed documentation of data collection, calibration, and
so forth of GTAP database version 5, see Dimaranan and McDougall
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Migration and Remittances: Eastern Europe and the Former Soviet Union
(2002) (https://www.gtap.agecon.purdue.edu/databases/archives/
v5/v5_doco.asp). The data necessary for simulating migration were
calibrated to this dataset and represent a benchmark global equilibrium situation; that is, the global data of the GTAP database were not
modified. The majority of the migration-related data are also from
1997. However, because information on foreign workers by home
and host country is difficult to obtain, data from a different year are
sometimes used. The share of foreign workers in a country’s different
production sectors was allocated according to information from
OECD (2001). The classification of migrant workers into skilled and
unskilled is also based on this information. Because substitution elasticities cannot be endogenously obtained through a calibration
process, the substitution elasticities required for the migration-related
functions were obtained from secondary literature. However, the
elasticities that could be retrieved from the literature represent migration elasticities with respect to the wage development in the country
of origin. Elasticities of substitution with regard to migration incentives from wage development in both host and home country at an
international level could not be obtained. Thus, the elasticity mentioned above was used as an approximation. The same applies to the
elasticities of substitution with respect to the development of unemployment and the change of quality of life in home and host countries. To take account of these inaccuracies, the sensitivity analysis
gives information about changes in the results caused by a variation
of parameter values.
APPENDIX 4.1
The Impact of Migrants and
the Receiving Society:
Integration Policies
The term “integration” is widely used today to denote the process
through which a migrant becomes an accepted part of a new society
(Penninx 2005). Integration refers to all the processes, activities, and
initiatives introduced by host societies that help migrants not only to
complete their travel and settle in a host country, but also to find a
place in the country, both in physical and in sociocultural terms. The
integration process involves such diverse activities as finding housing,
jobs, and income; gaining access to educational and health facilities;
and adopting new languages and ways of life.
Integration policies are meant to facilitate migrants’ participation in
host societies by, on the one hand, enabling migrants to live independently and be self-sufficient and, on the other, supporting their active
participation in all aspects of the host society’s life, including the political process (European Commission 2003). Family reunification, citizenship and naturalization, and antidiscrimination legislation are key
elements of traditional approaches to migrant integration, yet these are
more specific to permanent immigrants, rather than to the circular or
temporary migrants central to this study. Thus, the focus of this appendix is more directly on social inclusion policies. This section will briefly
consider the integration processes that apply to temporary migrants,
and how the presence of immigrants more broadly affects the receiving
society—in this case, the European Union (EU).
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Conceptualizing EU Integration Policies
To understand the European integration process, one must first
understand the diverse ways in which integration is conceptualized
in EU countries. This section highlights two such debates: the falling
out of favor of assimilation and migrant conformity, and the variety
of entities into which migrants can integrate.
The concept of integration needs to be understood separately from
“assimilation” and the implied conformity once expected from
migrants. Contemporary democratic societies are complex social
orders with diverse cultures, lifestyles, values, and institutional
processes, which are constantly in flux. In many societies, however,
political pressures to assimilate still persist. In view of the tendency to
collapse integration and one-way assimilation, the concept of integration is often replaced with terms such as “inclusion” and “participation.” Community organizations, in particular, emphasize the
concept of participation, which denotes democratic notions of access,
agency, and change, though it does not directly refer to relationships
between social groups.
Successful integration requires meaningful interaction between
migrants and the receiving society, which means that integration
must be conceived of as a two-way process. The host society must
ensure that the migrant has the opportunity to participate in economic, social, cultural, and civil life. Conversely, migrants are
expected to respect the fundamental norms and values of the host
society and participate actively in the integration process, though
they are not expected to relinquish their own identity (European
Commission 2003).
The speed at which integration occurs varies in different sectors of
society. For example, migrants can be integrated in the labor market
but excluded from participation in civil society and political processes.
Others can be included as citizens and participate in social and cultural interactions, but lack access to education and employment
opportunities. Both cases could be considered integration failures, but
would require different policy responses. Integration can also involve
completely different modes of interaction with the receiving society.
For example, typical indicators of integration include the level to
which migrants establish social networks or find partners among the
majority population. Many others, however, rely on family and kinship networks, or neighbors of the same racial or ethnic background,
to create stability and develop roots in the receiving society. Both
modes can be considered integration successes, and policies that stifle
interaction in any form are likely to be counterproductive.
Appendix 4.1: The Impact of Migrants and the Receiving Society: Integration Policies
The experience of migration changes in complex ways depending
on the individual’s characteristics, including gender, age, racial, ethnic, or religious background. This implies that most policies must
address a complex combination of issues. Some policies, however,
should target specific factors that disadvantage particular groups. For
example, while discrimination on grounds of nationality can lead to
racial discrimination, a policy addressing issues of nationality-related
discrimination will not affect black and minority ethnic citizens
because they are already nationals of the receiving country. At the
same time, while racial discrimination may be a major cause of exclusion for black citizens, Muslims in Europe are subject both to religious
and racial discrimination.
Generally, the process of integration appears particularly challenging when migrants are perceived as physically, culturally, and religiously different from the host society. For Europe and Central Asia
(ECA), this may become more relevant as migrants increasingly move
from the southern Muslim belt to non-Islamic countries. At the same
time, one positive legacy of the Soviet system is that migrants from
ECA Islamic countries may be more attuned to the secular values of
the main receiving countries than are migrants with similar religious
affiliations from other part of the world.
EU Practices and Policies
EU countries practice a variety of integration schemes. In France,
regardless of their ethnic, racial, or religious composition, migrants
are expected to be subject to a set of universal social rights and values
that presumably bind the whole society together. Austria, Denmark,
Germany, and Greece emphasize ethnic ancestry as a basis for membership in society, while countries such as the Netherlands and the
United Kingdom traditionally subscribe to a multicultural model of
membership and promote pragmatic management of relationships
between different ethnic and religious groups (though this has been
changing in recent years). The EU Commission has called on the political leadership of Europe to address inherent social divisions and to
promote acceptance for diversity and difference in the enlarged
union. In the commission’s view, the implementation of integration
policies that promote at once equality and diversity is the route to a
desired social cohesion, based on recognition of the pluralist nature of
European society.
Specific policies to counter the particular disadvantages faced by
various groups will also operate differently in each EU member state.
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While U.K. policy making on diversity and cohesion is characterized
by a discourse on race relations, this resonates differently in Germanic countries, where there are few black citizens and immigration
mainly originated in Southern Europe. In Scandinavia, most migrants
came from Islamic countries, and public attitudes and integration
measures there have centered on religious and cultural differences. A
problem that many member states share, however, is a reluctance to
monitor how different target groups are affected by processes of
exclusion. They often monitor social indicators only in relation to
nationality (plus gender and age), not race, ethnicity, or religion. This
means that there is insufficient information about the social situation
of many migrants and ethnic minorities, including their progress
toward inclusion.
Many reasons underlie the social and political exclusion, economic
deprivation, and disadvantages that migrant populations often face,
particularly undocumented migrants. Hence, integration requires a
range of different tools to address these disadvantages, including legislation, social inclusion policies, and policies to enhance participation
in civil society and democratic decision making. Before turning specifically to social inclusion issues, the following briefly discusses the role
of social networks in organizing migrant experience and providing
the essential safety net and emotional stability to foreign workers.
The Role of Social Networks
Given the current international mobility in ECA (see chapter 2), social
networks play a key role in the flow of information, goods, money,
services, and people. Migrants depend on both local and international
networks for successful outcomes and personal safety (Vertovec
2003). The well-being of migrants abroad largely rests on the availability of work to generate sufficient income, on a clear and secure
legal status, on access to social services and social and health protection, and on their participation in the host society. Integration policies, where available, provide a general structure to the migrant
experience and life, yet the social networks that emerge among
migrants are often what make it livable.
Temporary and undocumented migrants, who often fall outside
formal institutions that assist and organize legal migration flows, rely
on social networks to provide an essential social safety net for
migrants (World Economic and Social Survey 2004). Research has
shown that labor markets in the Russian Federation and Ukraine, for
instance, have been closely linked with sending countries through
Appendix 4.1: The Impact of Migrants and the Receiving Society: Integration Policies
the interpersonal and organizational ties surrounding migrant networks.1 The majority of migrants who decide to move to Kiev have
relatives, family members, or Ukrainian acquaintances in the city
(Kennan Institute 2004). Similarly, among Tajik migrants in Moscow,
the presence of established networks of people dates back to partnership enterprises developed during the Soviet period in Tajikistan and
Russia. In some cases, managers of Tajik plants that ceased production
have used their contacts to help their laid-off workers find employment in partner enterprises in Russia. Tajiks continue to work at the
fuel and energy complex in Tumen because in Soviet times they had
already been employed there as shift workers (IOM 2003).
International standards that provide for the protection of temporary foreign workers’ rights in the destination country, established by
the International Labour Organization, are not widely ratified.
Migrants thus rely on social networks to protect themselves.2 For
instance, research among migrants in Kiev, who came from various
parts of the former Soviet Union, has shown that those foreign workers who lacked legal work permits, and who therefore were unable to
find employment in the formal sector, came to rely exclusively on the
assistance of charitable organizations and family members or other
acquaintances from their homeland to make a living. African
migrants in Kiev, because of their weaker social safety net, found
themselves more often unemployed in comparison to Afghan or Vietnamese migrants, who lived in more closely knit communities and
developed a successful system of mutual assistance (Kennan Institute
2004).
While at present migrant networks provide essential support to
foreign workers from ECA, they also signal the absence of effective
integration programs in host countries that would alleviate the burdens of the migrant experience. Ultimately, this reduces the value of
migrants’ contributions to host societies. Furthermore, migrant networks are not immune to internal conflicts. Among other problems,
they may endanger women and children because, when faced with
scarce resources and information, women and children become more
vulnerable to abuse from other family members.
Other vulnerabilities pertain to the area of employment, because
individuals forced to rely on social networks without alternatives
may also be exploited. Such vulnerabilities may arise from the very
start of the recruitment process, during travel to the host country, and
during the process of finding employment. In ECA, cases proliferate
in which recruitment agencies take advantage of migrant workers’
limited information about working and living conditions in the host
country, misinforming them, charging excessive fees that bear little
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Migration and Remittances: Eastern Europe and the Former Soviet Union
resemblance to the actual costs of recruitment, and even assisting in
smuggling and trafficking (especially of women and children).
The concentration of migrant workers’ networks is often linked
with the emergence of ethnic migrant neighborhoods, which may
lead to the creation of various forms of ghettos. To summarize, some
of the common experiences for host communities arising from the
presence of a large number of foreign workers include (a) the emergence of “immigrant sectors” in the host country’s labor market, (b)
the vulnerability of migrant workers to various forms of exploitation
in recruitment and employment, (c) the tendency of temporary
migration to become longer in duration and bigger in size than initially envisaged, (d) resistance on behalf of the local population to
accept the newcomers, as well as (e) the emergence of undocumented
foreign workers who, together with local employers, circumvent
existing regulations.3
Endnotes
1. By way of example, such patterns and processes of network-conditioned
migration were extensively and comparatively examined in 19 Mexican
communities. See Massey et al. (2004).
2. The problem of protecting temporary foreign workers is a serious one.
On the one hand, the sending country does not have any legal jurisdiction outside its territory. The host country, on the other hand, is often
reluctant to assume full responsibility unless migrant workers are permanent residents or become citizens. Finally, as reflected in the low ratification percentages of the three global legal instruments developed for
the protection of migrant workers, efforts by international organizations
to represent and effectively protect the rights and interests of migrant
workers have so far had only very limited success.
3. For a detailed discussion of temporary foreign workers programs and
their social and economic impact on host societies, see Ruth (2002).
APPENDIX 4.2
Transitional Arrangement for the
Free Movement of Workers from
the New Member States
The transitional arrangements for the free movement of workers from
the new member states (except Cyprus and Malta) following enlargement of the European Union (EU) on May 1, 2004, allow the EU-15
to decide to postpone the opening of their labor markets for a maximum of seven years.1 Transitional periods for the free movement of
labor have already been granted in other enlargement rounds. What
makes the present rules different is that the EU delegated the decision
to adopt transitional arrangements to the individual member states.
This appendix will briefly discuss the nature and impact of these transitional arrangements, and how they are expected to change in
upcoming years.
Since the accession of the EU-8 countries to the EU in May 2004,
only seven countries have fully opened their labor markets to the
new member states: Ireland, Sweden, and the United Kingdom never
had restrictions on workers from the EU-8. Finland, Greece, Portugal,
and Spain lifted restrictions in May 2006. Italy ended the transitional
arrangements in July 2006, while Belgium, France, and Luxembourg
softened their restrictions on workers from the EU-8. Hungary,
Poland, and Slovenia apply reciprocal restrictions to nationals from
the EU-15 member states applying restrictions. All new member
states have opened their labor markets to EU-8 workers.
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Migration and Remittances: Eastern Europe and the Former Soviet Union
In May 2006, the second phase of the transitional period started,
which allowed member states to continue national measures for up
to another three years. At the end of this period (2009), all member
states will be invited to open their labor markets entirely. Only if
countries can show serious disturbances in the labor market, or a
threat of such disturbances, will they be allowed to resort to a safeguard clause for a maximum of two years. From 2011, all member
states will have to comply with European Commission rules regulating the free movement of labor.
Available evidence suggests that transitional arrangements after
EU enlargement resulted in the diversion of migration flows from the
new member states. Figures from the Irish Department of Family and
Social Affairs2 show that Ireland is the most popular destination for
migrants from these countries. During the first year after enlargement, over 85,000 migrants from accession states were allocated
social security numbers in Ireland, with Polish workers composing
almost half the number of newcomers. A report by the U.K. Department of Work and Pensions estimates net flows of approximately
80,000 workers from the eight new member states to the United
Kingdom (Portes and French 2005). This number suggests that
migrant flows from these states are in excess of those predicted by
econometric analyses. Denmark, which opened its labor market in a
similar way to Ireland and the United Kingdom, issued 2,048 work
permits to workers from the CEECs in 2004. In Sweden, the only
country that grants full access to its labor market and welfare system
to EU-8 workers, the number of migrants nearly doubled from 2,097
in 2003 to 3,966 in 2004; however, the total is much lower than predicted. Available evidence from Germany, the traditional destination
country for migrants, suggests that the number of migrants from the
CEECs declined during 2004 to 2005, while the number of residents
from new member states dropped by 13.2 percent. The overall picture
that emerges from the available data indicates a diversion of migration flows from countries that tightly close their borders (Austria and
Germany) to countries with more liberal transitional regimes, particularly English-speaking countries (Ireland and the United Kingdom).
Endnotes
1. According to the transitional arrangements (2+3+2 regulation) the EU15 can apply national rules on access to their labor markets for the first
two years after enlargement. After two years (2006), the European Commission will review the transitional arrangements. Member states that
Appendix 4.2: Transitional Arrangement for the Free Movement of Workers from the New Member States
wish to continue national measures need to notify the European Commission and will be allowed to apply national measures for up to another
three years. At the end of this period (2009), all member states will be
invited to open their labor markets entirely. Only if countries can show
serious disturbances in the labor market or a threat of such disturbances,
will they be allowed to resort to a safeguard clause for a maximum period
of two years. From 2011, all member states will have to comply with the
Community rules regulating the free movement of labor.
2. Data from the Irish Department of Family and Social Affairs, at
http://www.breakingnews.ie/printer.asp?j=117490020&p=yy749x6xx&
n=117490629&x. Retrieved August 18, 2005.
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APPENDIX 4.3
Undocumented Immigration
and Vulnerabilities
The majority of migrant workers find themselves on the low-skilled
side of the occupational spectrum. Deception, discrimination,
exploitation, and often abuse are employment-related situations
commonly and increasingly encountered by poorly skilled and
undocumented migrants. Lacking work permits, migrants may experience difficulty finding the employment they aspire to, and must settle instead for low-paying, hazardous, or demeaning jobs. This
appendix will briefly describe how undocumented status can influence migrants in all aspects of their lives, including the most extreme
example—human trafficking.
Migrants are more susceptible to unemployment and layoffs,
unfair labor practices, lesser pay, and other forms of exclusion. A
study of Organisation for Economic Co-operation and Development
(OECD) countries shows that rates of employment were significantly
lower among migrants than among citizens between 2000 and 2001.
In Denmark and Switzerland, the migrant unemployment rate for
men was over three times the corresponding nonmigrant rate, and
unemployment rates for migrant women were over 20 percent in
Finland, France, and Italy (OECD 2003). Furthermore, undocumented migrants lack access to public housing, schools, health care,
and other social services. Simultaneously, they lose pension funds
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Migration and Remittances: Eastern Europe and the Former Soviet Union
and social security entitlements at home. This makes them vulnerable to various recruiters and recruitment agencies.
Undocumented status can render migrants vulnerable to other
forms of abuse, especially because they lack legal recourse to challenge such abuses. They may be simultaneously invisible to the
guardians of the law and subject to excessive forms of policing. Millions of people undergo mistreatment and are subject to xenophobia
in part because their presence and labor in foreign countries without
papers has been criminalized as “illegal” and subjected to various,
often excessive, forms of policing. The undocumented are often
denied fundamental human rights and many rudimentary social entitlements, which leaves them in an uncertain sociopolitical situation.
Deciding to Become and Stay Undocumented
Given these disadvantages, why might migrants choose a path of undocumented migration? The legal requirements associated with migration
and the enforcement of such provisions constrain and shape migrants’
choices. Reports on the process of obtaining work visas and permits in
the Russian Federation demonstrate that it is a complicated and expensive endeavor, even where migration quotas or bilateral agreements
between countries exist presumably to facilitate the process. Large-scale
corruption accompanies this process at every level (Hill 2004). Even
though the risks and costs associated with undocumented travel may be
high, migrants opt for undocumented entry into host countries when
the chances of obtaining legal migrant status are unlikely.
Furthermore, within the current migration regime it appears that
some migrants prefer to keep their unauthorized status even if the
option of legalizing is open. Those who remain at the fringes of the
law gain certain flexibilities, including the option to change employers or negotiate workload and remuneration. This is an issue that
deserves serious consideration in specific national contexts, because it
contradicts a general truism about undocumented migration being
more costly than legal migration. It appears that under the current
international migration regime, it is sometimes more expensive to
migrants to take part in legal contracts and interactions than to pay
the social price of being undocumented migrants. The preference of
some migrants for staying undocumented suggests the need to construct multilevel migration policies that include all stakeholders
(including employers, migrants, native workers, and the sending
country) in the discussion and, at least to some extent, also in the
determination of policy parameters.
Appendix 4.3: Undocumented Immigration and Vulnerabilities
To foreign nationals from the Commonwealth of Independent
States (CIS) countries and Central Asia, for instance, entry into Russia can occur without a visa. Yet, registration with the passport office
of the local police station is required upon arrival. While failure to
register constitutes an administrative offense and is punishable by a
small fee, migrants tend to ignore this regulation. Legalization is
viewed as time-consuming and bureaucratic: applications can be
rejected and multiple visits to various institutions may be needed,
entailing the payment of bribes to various officials. Even after registering, a quarter of migrants continue to be harassed by police who
openly ask them for bribes. For Tajik migrants to obtain legal status in
Russia, Kazakhstan, or the Kyrgyz Republic, they must either marry
a local citizen, legal or fictitious, or alternatively “buy” a passport at a
cost of $1,000–2,000 (Olimova and Bosc 2003). The murkiness of
today’s migration regime exacerbates these problems, because the
lack of transparency allows civil servants to take the “rule of law” into
their own hands and thus makes migrants vulnerable to their subjective decisions.
Another example confirms the above argument. In Greece, only
50 percent of undocumented migrants applied for residence and work
permits in the first migrant regularization program of 1998. Similarly,
our survey indicates that many migrants used the same documents in
their most recent trip as they had during their first trip. This suggests
that even those who had already lived and worked in Greece did not
change their legal status or fell out of status after a particular time. It
is possible that some migrants purposely did not change their status,
particularly if the costs exceeded the benefits in doing so, and given
the fact that legal migrants were not permitted to be accompanied by
family members. Additionally, such migrants may have lacked necessary information to apply, or feared retribution for exposing their
undocumented status.
While the above examples make an undocumented status appear
slightly less inconvenient, they do not take away from the fact that
illegality exposes migrants to numerous vulnerabilities. Even so, in
these cases, it seems the social cost of becoming legal exceeds the economic inconvenience of illegality.
Migrant Vulnerability at Its Extreme: Human Trafficking
The emergence of the human smuggling and trafficking industries are
perhaps the most worrying consequences of the mismatches between
labor supply and demand and the economic incentives to migrate vis-
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Migration and Remittances: Eastern Europe and the Former Soviet Union
à-vis the legal means for doing so.1 With more than 175,000 persons
trafficked annually, Europe and Central Asia (ECA) is the second
largest source of trafficked persons in the world after Southeast Asia.2
Victims of human trafficking largely come from the Balkans and the
poorer countries of the CIS (in particular Albania, Bulgaria, Lithuania, Moldova, Romania, and Ukraine).
Trafficking is distinguishable from smuggling, although sometimes
the two activities may merge or smugglers may collaborate with traffickers. The smuggling of migrants, while often undertaken in dangerous or degrading conditions, involves migrants who have
consented to the course of action. Trafficking victims, conversely,
have either never consented or, if they initially consented, their consent has been rendered meaningless by the coercive, deceptive, or
abusive actions of the traffickers. Trafficking involves the ongoing
exploitation of the victims to generate illicit profits for the traffickers.
The majority of such victims in ECA are females who are trafficked to
work in the sex industry. However, male victims and adolescents are
also forced to labor in the building industry, agriculture, or smallscale production; are brought into households; or are set to beg in the
streets. In some Balkan countries, such as Albania, Bosnia and Herzegovina, and Serbia and Montenegro, minors of Roma origin in particular were a large percentage, if not the majority, of victims assisted by
the IOM in the region (Surtees 2005).
Most of the trafficking networks operating in Europe are believed
to be Albanian, Russian, or Turkish (Clert and Gomart 2004). While
criminal groups in these countries are known for their drug trafficking, the high profits obtained through human trafficking, as compared with the relatively low risk in running such operations, make
this activity highly attractive.
Trafficking of humans typically starts at the place of origin. Traffickers target those who are interested in finding employment abroad
but are unable to make the journey independently or perceive a high
risk in doing so. Recruitment most often involves the promise of a
high-paying job, marriage to a Western European, or kidnapping.
Most such arrangements are made informally. Interestingly, 60 percent of victims of trafficking in Southeast Europe were contacted
through someone they knew (Laczko and Gramegna 2003). Recruitment through job advertisements and job agencies is less common in
countries where awareness-raising campaigns have already addressed
the use of such techniques (as in Bulgaria). As a result, increasingly,
new recruitment strategies are employed, including female recruiters
who are often victims themselves or former victims, and recruitment
by couples. Trafficking increasingly occurs within a façade of legality,
Appendix 4.3: Undocumented Immigration and Vulnerabilities
where victims are trafficked with legal documents and cross borders
at legal border crossings (Surtees 2005).
The risks and costs involved in human trafficking mean that the
typical victim is someone whose situation at home is relatively poor.
Typically these conditions include poverty, unemployment or underemployment, a difficult or abusive family background, and experience with political instability, violence, or discrimination. As a
consequence, a substantial portion of trafficked victims are young,
female adults, with low education levels.
Trafficking magnifies the disadvantages suffered by undocumented
migrants. By definition, they are exploited, so will not earn as much
as legal or other undocumented migrants. Exposure to a variety of
inhumane living and working conditions is common. These include,
but are not limited to, mental violence, including blackmail, insult,
manipulation, humiliation, and threats; physical violence, including
beating and threats with physical violence; or sexual attack, including
rape. Along with limited sphere of movement, trafficked persons find
themselves highly isolated; they lack the vital social networks available to other undocumented workers and are often under constant
surveillance by traffickers.
Apart from human rights violations, trafficked victims face serious
health risks, such as exposure to sexually transmitted diseases including HIV/AIDS, and other communicable diseases such as tuberculosis
and hepatitis; reproductive health problems such as sexual abuse and
violence, unwanted and unsafe motherhood, and complications associated with teenage pregnancies; physical traumas from severe beatings; and psychological and mental health disorders, including
substance abuse or misuse. Political concern for the public health
implications of human trafficking in ECA was spelled out in the
Budapest Declaration of 2003.3 For those without access to health
care, these cases will go untreated and sufferers will lack access to
necessary information. In migration-receiving countries, the result is
a heightened risk of infection among the native population. The link
between human trafficking and the sex trade means that the prevalence of HIV/AIDS and other sexually transmitted diseases is a particular area of concern. Given that most migrants will, at least
periodically, return to their home countries, these risks apply equally
to source countries.
Those who have fallen victim to human trafficking find it much
harder to return home and would be expected to have less surplus
income to remit back home. More directly, the families of the victims
may have to pay financially, socially, or psychologically for the consequences of their relatives’ abuse. The family may have to meet the
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Migration and Remittances: Eastern Europe and the Former Soviet Union
costs of the necessary medical and psychological care for returned
migrants, some of whom may be unable to work again. Families may
also suffer trauma and guilt or face social stigma. If the migrant
returns with a communicable disease, such as HIV or tuberculosis,
family members risk infection. In cases of death, there will be a permanent loss of potential family income, as well as personal loss.
Endnotes
1. According to the UN Protocol to Prevent, Suppress and Punish Trafficking in Persons, Especially Women and Children, Supplementing the
Convention on Transnational Organized Crime, UN, Palermo 2000, the
“trafficking in persons” is the exploitation of others for (a) prostitution or
other forms of sexual exploitation, (b) forced labor or service, (c) slavery
or practices similar to slavery, (d) servitude, or (e) the removal of organs.
2. http://www.unfpa.org/news/news.cfm?ID=48.
3. Trafficking in human beings and health implications. Seminar on Health
and Migration, June 9–11, 2004. Session II B—Public Health and Trafficking: When Migration Goes Amok.
APPENDIX 4.4
Incentives for Criminality
in Migration
Criminality, defined for the purposes of this report as any transaction
that is illegal or a constituent of the informal economy, is present in
almost all types of migration and in most stages of migration (that is,
in the countries of origin, and in transit and destination countries).
Criminality ranges in its level of severity from bribing the passportissuing agency to obtain a travel passport, to entering into a marriage
with a citizen of the destination country to receive citizenship. Incentives for these types of criminality arise partly from the lack of legal
channels for migration. The most violent and grave forms of criminality are exercised by organized criminal groups that traffic and smuggle
human beings and drugs. An incentive for this type of criminality is
usually enormous profits derived from human and drug trafficking.
Migrant smuggling and human trafficking are often an integral
part of the illegal economy that is connected with other forms of illegal business (Phongpaichit, Piriyarangsan, and Treerat 1998). It has
been reported that human trafficking and drug trafficking routes are
often the same. Estimates indicate that up to 175,000 persons are
trafficked from Central and Eastern Europe and the Commonwealth
of Independent States (CIS) annually (Organization for Security and
Cooperation in Europe 1999).
This appendix reviews some of the incentives for criminality in
migration.
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Economic Disparity
The wage gap between poorer countries in Europe and Central Asia
(ECA) and typical migrant destination countries is enormous. For
example, an average salary in the Kyrgyz Republic is $48 per month.1
Labor migrants working in low-wage jobs in the United States
reported making $1,500–2,000 per month. Men working in the construction industry earn at least twice as much.2 Such a wage difference serves as a powerful incentive to seek jobs abroad. In the absence
of legal channels for migration, people migrate illegally.
Demand for Cheap Labor in Destination Countries
There is substantial demand for inexpensive labor in high-income
and many middle-income economies. Unskilled migrants often work
in jobs that the native population or legal migrants would not take at
the wages being offered. In most cases, such jobs pay below the minimum wage and provide no overtime payment or benefits. The growing demand for cheap labor may result in illegal activities, such as
employment of illegal immigrants or migrants with no proper work
authorization, if there are not sufficient legal channels for matching
the demand with the supply of unskilled labor.
However, some have found that while there is demand for cheap
labor in the Western European countries, greater homogeneity,
smaller territories, and a strict registration system make it more difficult for illegal immigrants to find jobs and live in Europe. Thus, a
larger share of illegal migrants in Europe may be women trafficked
for sexual exploitation (Shelley 2003).
Political Instability or Ethnic Conflicts in Countries of Origin
In some cases, economic disparity is not the main push factor for migration; political instability or ethnic conflicts (or both) force individuals to
flee their home countries. This category of migrants often turns to illegal
migration to escape persecution or conflict. In such cases, migrant smuggling is an overlapping issue between migration and human rights.
Koser (2001), for example, examines asylum seekers as another major
source of human smuggling, often falling between that uncomfortable
dichotomy of “freedom fighter” and “evil smuggler.” He argues that one
should not put too fine a point on the distinction between human smuggling as a migration issue and human trafficking as a human rights issue.
Asylum seekers straddle this distinction in that they are often escaping
Appendix 4.4: Incentives for Criminality in Migration
human rights violations by seeking out smugglers but then also
encounter additional human rights violations along the way.
Restrictive Immigration Regime in Destination Countries
Stricter border control may not be an effective way to combat smuggling of undocumented immigrants. Opponents of restrictive immigration regimes argue that “as more restrictive policies increase the
obstacles to crossing borders, migrants increasingly turn to smugglers
rather than pay the growing costs of unaided attempts that prove
unsuccessful” (Koser 2001, pp. 207–8). Moreover, tougher immigration control will only enrich smugglers and traffickers because fees, and
consequently debts, to be paid by would-be immigrants rise dramatically. As Koslowski (2001, p. 208) puts it, “if potential migrants are willing to pay the additional costs while at the same time stiffer border
controls prompt more migrants to enter into the market, border controls will most likely increase the profits of human smuggling and entice
new entrants into the business.”
Conclusion
Overall, it is likely that migration from poorer ECA countries into
wealthier ones will continue as economies of sending countries deteriorate and the demand for low-wage labor in receiving countries
remains high. Because channels for legal labor migration are limited,
irregular migration is likely to prevail. The consequences of this
migration are serious for the countries concerned, as well as for labor
migrants themselves. The International Organization for Migration
(2001, p. 11) reports, “99 percent of labor migration in the Eurasian
Economic Union formed of Tajikistan, Kyrgyz Republic, Kazakhstan,
the Russian Federation, and Belarus is irregular. Due to their irregular situation, most labor migrants do not benefit from the same protection rights other regular citizens enjoy and are thus more
vulnerable to exploitation by underground employers” (IOM 2001,
p. 11).
Endnotes
1. National Statistics Committee of the Kyrgyz Republic, [http://www.
stat.kg/Eng/Annual/Labor.html#Top1], accessed on August 15, 2005.
2. Interviews with Kyrgyz labor migrants in the United States, December
2005 to January 2006.
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APPENDIX 4.5
Migrants, Their Families,
and Communities “Left Behind”
The impact of migration is felt nowhere as keenly as in the family.
Migration alters not only kin relationships and the size and composition of families, but also affects predominant gender roles and responsibilities, the care of the elderly and children, the education of
children, reproductive patterns, and even patterns of social and political participation and civic engagement of citizens. The consideration
of “family migration” has consistently been neglected in European
scholarship and policy debates. This appendix briefly attempts to fill
this gap by investigating how the absence of family members, as well
as their return, is dealt with by the family and the larger community.
Migrants and Their Families
For some families in Europe and Central Asia (ECA), sending a family member to work abroad is one of the few options available to
avoid poverty or improve quality of life and social status. The fact that
migration is often perceived as a family coping strategy is expressed in
the frequency of survey answers; many migrants desire to “save
money for the education of children” or “buy a house upon return.”
In all of the researched cases, the decision to work abroad is overwhelmingly economic. Yet, at the same time, there is little mention of
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any reasons for migration related to increasing the earning capacity of
migrants (to learn new skills, to acquire a new profession) and thus
improve their economic situations in the long run. This suggests that
migration may not necessarily be part of a consciously defined longterm investment plan but rather a reaction to the pressures to satisfy
everyday needs. In the absence of secure employment alternatives,
strategic employment planning, and more tactical migration management in countries of origin, migrants prioritize improving their immediate economic situation.
The departure of any family member transforms the family structure and its economics, which may have far-reaching effects for the
structure of society as a whole. Many of these implications—including
the country’s fertility rates and number of divorces—are gendered.
Women who are left behind have developed a number of strategies
to cope with the absence of partners. In countries and areas where
recent wars took many male lives, three or four generations of
women may live together as a coping strategy. With the overall
decline in household incomes in ECA and the growing number of
women in poverty as a “push” factor on the one hand, and the
demand for domestic labor abroad as a “pull” factor on the other,
households often resort to financial strategies that stretch across
national divides. The increase in recent decades in the demand for
female labor in the home care services (domestic work, care of children) of Northern European and North American countries has put
new pressure on women to look for employment.
Such efforts have also changed the structure of family care relationships. Caring at a distance involves relying on older children,
grandparents, and relatives; however, such arrangements are contingent on the socioeconomic conditions and other reasons that underlie migration. The current immigration regime in Europe, in
particular, makes it hard for many migrant families to have recourse
to other family members to help with care, because restrictions exist
on the number of family members allowed to join the migrant in the
destination country.
The migration of women has boosted family incomes, but also contributes to reshaping gender relationships as women become more
active as decision makers. Furthermore, there has been little study in
ECA on the impact of the migration of women on children they leave
behind. Children of emigrants tend to receive less supervision; they
lag behind in their education and often do not receive regular medical care. For example, it has been suggested that migration has been
a significant factor in declining school enrollment of children in
Moldova and Bulgaria. Moldova has also seen an increase in the
Appendix 4.5: Migrants, Their Families, and Communities “Left Behind”
number of street children in the larger urban areas. Children abandon their families for various reasons: feeling disconnected, lacking
attention, and even because of hunger and abuse. Specialists in
Moldova fear that among other negative repercussions, inadequate
education (both at school and at home) will have long-term negative
implications for human development in the country. Separation from
parents can disturb the psychological and social development of children and in the long run can contribute to a deterioration of the stock
of human capital in the society.
Returning Home and Reintegration
Return migration has emerged recently in international debates as a
central topic when development opportunities for countries of origin
are discussed. Despite the impact of remittances on consumption and
investment, return migration is seen as essential for human development and positive social change, the circulation of knowledge and
ideas, and the benefits of skills return. There are various factors that
affect the potential of migrant return to improve development. These
include the number and concentration of returnees in a particular
period, the duration of their absence from home countries, the social
class of migrants, their motives for return, the degrees of difference
between countries of destination and origin, the nature of acquired
skills and experiences, the organization of return, and the political relationships between the countries of immigration and return. The developmental impact of return also depends heavily on a healthy business
environment in the country of origin, characterized by a sound legal
framework, an effective banking system, honest public administration,
and a functioning physical and financial infrastructure.
From the individual’s perspective, the return experience may not
be universally positive. Some migrants may find that their country or
families are not as they remembered. For others, changes in the labor
market in their absence, or the weakening of important social networks, reduce the quality of job opportunities. A migrant’s condition
on return will reflect the income, experiences, and skills earned or
gained while abroad. Migrants who have been away for a longer
period are likely to return with more cash and experience, but may
find it more difficult to adjust to their own, perhaps greatly changed,
communities.
Some migrants return after they have accomplished the objectives
they left to pursue; this has a positive impact on their attitude to
return. Furthermore, the more returning migrants respond to posi-
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Migration and Remittances: Eastern Europe and the Former Soviet Union
tive socioeconomic developments that attract them home, the greater
the chance for innovation. Other migrants return home after a relatively short stay abroad because they are disappointed with the actual
conditions of life and work in the destination country. They may not
be able to bear the psychic cost of separation from family and loved
ones or familiar environments, or the difficulties interacting with
people who speak a different language and have a different culture
and ways of doing things. Finally, some migrants return home
because of unforeseen and undesirable changes such as health problems or family crises at home. Those migrants who make a conscious
decision to return and who have planned ahead emerge as most valuable to their home communities in terms of the invested interest and
the human capital they are able to transfer to their country of origin.
Surveys with returned migrants conducted for this report point to
a general improvement in household living standards in Bulgaria,
Bosnia and Herzegovina, and Romania despite the difficulties
encountered by family members when the migrant is away. This
means that migrants have reported they are now better able to
finance their household expenses, buy clothing, pay utility bills, purchase electrical appliances, buy a new car, and even travel abroad.
More crucial, however, is the extent to which the country of origin
is prepared to offer reintegration strategies for returning migrants and
to nurture their newly acquired skills and capital. Options include
making social benefits portable (discussed in chapter 4) and designing
programs that support returning migrants in making informed decisions about the use of their resources. Many ECA migrants have
expressed their desire to start businesses of their own, yet almost all
point to investment constraints and a lack of trust in formal institutions, such as banks, as discouraging factors. Training programs and
access to microcredit facilities are also in high demand. Such programs should make special provisions to target women in particular—research shows that women make the most effective use of
remittances.
APPENDIX 4.6
Brain Drain in
the ECA Region
This appendix provides a brief overview of the quantity and type of
“brain drain” resulting from the migration of skilled workers from
Eastern Europe and the former Soviet Union since transition.
Past Efforts at Estimating the Importance of Brain Drain in
ECA Countries
In an attempt to estimate the importance of brain drain in developing
countries (Carrington and Detragiache 1999), experts from the International Monetary Fund explained that their justification for excluding the former Soviet Union and Eastern European countries from
their study was the lack of reliable data. Four years later, the availability of data has not significantly improved and the exact nature of
brain drain is still not well understood. Studies undertaken during the
last 10 years have come to somewhat contradictory conclusions. The
absence of a generally accepted definition of “highly skilled migration” is also a problem, as is the lack of reliable information on
migrants’ job qualifications, both in the countries of origin and the
destination (with the sole exception of the United States [Straubhaar
and Wolburg 1999]).
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Migration and Remittances: Eastern Europe and the Former Soviet Union
Moreover, estimates of the importance of highly qualified migration tend to depend on the approach adopted. Taking into account
the point of view of the country of origin or of destination may
affect any conclusions regarding the exact nature of the phenomenon. For example, Albanian migrants to the United States have generally been viewed in the United States as highly qualified (Kosta
2004), while they were seen as relatively poorly qualified in Greece
or Italy. From an Albanian point of view, emigrants are perceived as
belonging simultaneously to both unskilled and highly skilled
groups (Galanxhi et al. 2004). Therefore, any conclusions regarding
migration and possible brain drain will necessarily depend on the
country of reference.
Generally speaking, highly skilled migration from the ECA countries flows toward the Western and Northern European countries, as
well as toward Canada and the United States. Migratory flows from
one ECA country to another are not characterized by a large proportion of highly skilled migrants, even though some students regularly
do come to the Russian Federation. The nature of the phenomenon
differs from one country of origin to another, in both the numbers
and proportions of highly skilled emigrants. Both of these measures
are comparatively low in the former Yugoslavia and Albania as compared with Bulgaria, Poland, and Romania.
Previous studies on brain drain have distinguished between student migration, migration of researchers and scientists, and migration
of other highly skilled persons (such as managers, engineers, artists,
athletes and clergy).
Student Migration
About 100,000 foreign students from the ECA region were enrolled
in tertiary education in industrial countries in 1998–99, according to
UNESCO. Among them, 37,000 foreign students from ECA countries
(including Poland, 7,800; Russia, 5,400; Croatia, 4,600; Serbia and
Montenegro, 4,300) were studying in Germany and 21,100 (including Russia, 6,100; Bulgaria, 2,400; Romania, 2,100) in the United
States. However, student statistics in the United States clearly show
that the Russian community was not the largest group of student
migrants, in fact, not even one of the five largest groups. Graduate
students from China, the Republic of Korea, India, and Taiwan
(China) constitute most of the migrant student population in the
United States. In Europe, even though the enlargement of the European Union in 2004 effectively increased student mobility, the
increase in student migration from the ECA was rather small.
Appendix 4.6: Brain Drain in the ECA Region
Migration of Active, Highly Skilled Populations
The proportion of highly qualified persons in each migration flow
varies according to factors such as the type of migration (“political”
emigrants are generally not particularly qualified), the selectivity of
emigration (the socioeconomic structure of the aspiring emigrant
population), the match between the level of the educational system
and the labor market in the country of origin, and the average level
of education in the country of origin. It is important to remember that
enrollment in tertiary education is generally very high in ECA countries. The gross enrollment ratio (appendix table 4.5.1) shows considerable variation in the level of education according to country. Of
course, those countries with a high level of education (such as
Belarus, Bulgaria, Lithuania, Russia, Slovenia, or Ukraine) are more
likely to have highly skilled people among emigrants than countries
with fewer university graduates (such as Armenia, Azerbaijan, or
Albania). However, the impact of brain drain (that is, the problems
caused by the emigration of the highly skilled) may be more evident
in countries with a relatively low proportion of highly skilled persons.
A country’s size also plays a role in highly skilled emigration and
brain drain. A report from the World Bank (2006) recently stated that
countries with more than 30 million inhabitants, such as Russia, were
not massively affected by brain drain. According to this report, the
proportion of emigrants in the former Soviet Union (FSU) should
amount to something between 3 percent and 5 percent of the total
number of persons having completed tertiary education. In recent
years, the high proportion of tertiary-educated persons in Russia and
other Commonwealth of Independent States (CIS) countries has
largely compensated for the emigration of highly qualified persons.
Smaller countries, such as Bulgaria, have been more likely to suffer
from the negative impact of brain drain.
Migration of Scientists
The frequent attempts to estimate migration undertaken by scientists
have often been subject to debate. Russian sources suggest that the
emigration of scientists is not a problem in CIS countries. The Ministry of Education and Research has considered the emigration of
researchers from Russia as “normal.” It is harder for the Russian government to deal with the fact that young researchers prefer to work
in the private sector, where wages are higher.1
Nonetheless, it is no surprise that the youngest and best researchers
have been the most likely to leave the country. Academics often left
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Migration and Remittances: Eastern Europe and the Former Soviet Union
APPENDIX TABLE 4.5.1
Gross Enrollment Ratio, Tertiary Level, by Country, 1998–99 and 2002–03
(regardless of age, as a percentage of the population of official school age for that level)
Men
Country
Albania
Armenia
Azerbaijan
Belarus
Bulgaria
Croatia
Czech Republic
Estonia
Georgia
Kazakhstan
Kyrgyz Republic
Lithuania
Poland
Moldova
Romania
Russian Federation
Serbia and Montenegro
Slovak Republic
Slovenia
Tajikistan
FYR Macedonia
Ukraine
Uzbekistan
Women
1998–99
2002–03
1998–99
2002–03
10.9
20.7
20.0
41.6
34.5
29.6
25.7
42.3
30.4
22.0
29.8
36.1
38.5
25.8
20.4
—
31.1
25.2
45.3
20.3
19.3
44.1
—
11.7
23.3
18.6
51.7
35.9
36.1
34.3
50.1
38.3
38.7
38.5
56.2
49.6
25.7
31.3
59.1
—
31.0
58.4
24.4
23.6
56.5
17.5
17.2
25.2
12.4
55.4
52.9
34.2
26.5
60.0
34.1
25.5
30.9
55.0
53.1
33.1
22.1
—
37.0
27.9
60.7
7.1
24.7
50.5
—
20.9
27.4
14.4
72.1
42.2
42.8
36.8
83.4
37.5
50.7
45.9
87.5
70.6
34.0
38.7
79.3
—.
36.4
79.0
8.3
31.6
67.2
13.9
Source: UNESCO, http://stats.uis.unesco.org/ReportFolders/reportfolders.aspx.
Note: — = Not available.
the country during the 1990s to continue their work abroad. This has
significantly diminished the quality of research, especially in the natural sciences such as mathematics, where such research flourished
during the Cold War. UNESCO’s 1998 World Science Report (UNESCO
1998) estimated that the number of Russian scientists involved in
research and development (R&D) fell from 900,000 to 500,000 from
1991 to 1995. Izvetzia (March 20, 2002) was more cautious, estimating the number of researchers who emigrated after the fall of the Iron
Curtain at 200,000. Armenia saw a similar decrease in scientists
involved in research (from 15,000 to 3,000) and in Ukraine, approximately 15,000 specialists with higher education degrees (not only
scientists) have left the country each year.2 Bulgaria was estimated to
have lost annually 50,000 qualified scientists and skilled workers
(Chobanova 2006) following the collapse of the Warsaw Pact; the
main destinations were the United States, Canada, and Germany.
The cooperative programs in R&D between Western European
countries and FSU member countries set up during the last 10 years
185
Appendix 4.6: Brain Drain in the ECA Region
were an attempt to remedy the pattern of emigration. However, programs favoring research in Central and Western Europe were not able
to stop the decline of the research infrastructure and capabilities of
FSU countries. Even so, according to an international survey carried
out in 10 Eastern European countries, they did have a positive impact,
with the brain drain turning out to be less serious than previously
feared (INCO 1997).
A Polish survey on scientists who emigrated clearly demonstrated
that the opportunity to work with new technologies was not the main
reason behind emigration between 1995 and 1999. Most emigrant
researchers explained that a salary increase was the reason that best
explained their decision (Koszalka and Sobieszczanski 2003). The
recent move on the part of the Russian government to improve the
wages of researchers was an attempt to solve the salary-related emigration problem.3 However, wage differentials between Russia and
industrial countries are still significant. If they remain high,
researcher emigration will likely continue.
Highly Skilled Emigrants in Six Countries of Origin
Surveys undertaken for this project provide an estimate of highly
skilled emigration in six countries (appendix table 4.5.2). The proportion of persons having completed higher education (master of arts
or other degree) among return migrants varied according to sex and
country of origin. Because of the different return rates observed
between highly skilled and low-skilled emigrants, those proportions
imperfectly reflected the exact nature of the phenomenon.
However, these results clearly showed the high level of qualification
among migrants in countries such as Georgia (53 percent of female
APPENDIX TABLE 4.5.2
Proportion of Return Migrants Having Completed Higher Education
(Bachelor or Master’s Degree)
(percent)
Country
Bosnia and Herzegovina
Bulgaria
Georgia
Kyrgyz Republica
Romania
Tajikistanb
Source: World Bank staff.
a. University degree.
b. Master’s degree or higher.
Female
Male
11.0
31.5
52.7
30.3
11.5
28.8
9.5
25.0
37.7
20.0
12.8
17.2
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Migration and Remittances: Eastern Europe and the Former Soviet Union
return migrants held a university degree). FSU countries and Bulgaria
were also characterized by high levels of return migrants who had completed university, while Bosnia and Herzegovina and Romania showed
a low proportion of highly skilled migrants. Perhaps cross-country differences could have been partially explained by the respective education systems and tertiary education enrollment statistics (see appendix
table 4.5.1). Moreover, female return migrants were more frequently
highly qualified than males. Such a result could have been partially
explained by the fact that work opportunities abroad (particularly in
Russia) were probably less numerous for lower-qualified women. It is
also possible that emigration strategies differed according to the education levels of the partners: in a couple with a woman whose qualifications are higher than the man, the gain resulting from female
emigration would tend to be higher than from male emigration.
The Effects of the Brain Drain in Countries of Origin
According to a number of theoretical approaches summarized by,
among others, Straubhaar and Wolburg (1999) and Abu-Rashed and
Slottje (1991), the emigration of skilled labor, contrary to that of
unqualified workers, clearly has a positive impact on the global
income of destination countries. The effect on “donor” countries of
highly skilled migration is not so clear.
It is generally agreed that the international mobility of highly qualified labor is positive. However, in the case of brain drain, which
implies an irreplaceable loss to the stock of highly skilled populations
in the country of origin, the overall impact is hard to estimate. One
important implication of brain drain frequently mentioned in the
case of Africa is that a part of the investment in education in the
country of origin is not replaced once migrants leave. Consequently,
a shortage of skills becomes evident, leading to the impossibility of
ensuring economic growth. However, the aforementioned high level
of enrollment in tertiary education and universities in most ECA
countries may help offset this situation in the future.
Emigration on the part of highly skilled labor also leads to an aging
of the more highly qualified population at home (it is the younger
workers who emigrate), and to a rapid decrease in the development
of sectors such as R&D. This has been observed in Russia following
the departure of top researchers, who not only go abroad but also
move to work in other sectors of the economy.
However, negative effects may occasionally be counterbalanced by a
decrease in unemployment in the country of origin or by an increase in
Appendix 4.6: Brain Drain in the ECA Region
remittances from highly skilled emigrant labor, which can partially or
totally compensate for any losses from emigration. Straubhaar and Wolburg (1999), in fact, argue that brain drain can improve economic efficiency from an international perspective. Therefore, the main issue to be
resolved is how to compensate for certain negative aspects of brain drain
in the countries of origin without diminishing the overall positive effect.
Easing Temporary Migration as an Answer to Brain Drain
Brain drain probably cannot be avoided in ECA countries, but its negative impact on research and industrial development may be attenuated by implementing measures aimed at making it worthwhile for
highly trained professionals to stay home or to come back. Many programs encouraging the return of highly skilled migrants have been
implemented in African countries. In the ECA region, programs promoting R&D in the countries of origin will probably play an important role in the future.
Maintaining the Quality of R&D
Maintaining the quality of R&D in countries of origin is also an important factor when attempting to avoid brain drain. To reach this objective, it is important to replace emigrants with competent locals at the
same rate as they depart. Specialization abroad is a good thing, and job
opportunities for emigrants may exist in both scientific and economic
domains. The simplification of investments and business in the country
of origin is an important factor in using highly skilled labor emigration
to the advantage of the country of origin.
Networking Between Migrants and Nonmigrants
Another aspect frequently mentioned is the need to encourage the
creation of networks between emigrants and their countries of origin,
for instance, by providing information to migrants. Such networks
would allow the dissemination of professional and scientific knowledge and know-how through contacts between emigrants and
researchers who have stayed in the home country. Networking
between migrants and nonmigrants has already become more frequent with the rising development of communications services.
The Role of Remittances
Remittances have a positive impact on highly qualified migration. This
is the case even when surveys show that highly educated persons send
less money than those with lower qualifications (see chapter 3,
“Migrants’ Remittances”). A differential between remittances sent by
highly skilled and other emigrants can be easily explained by factors
187
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Migration and Remittances: Eastern Europe and the Former Soviet Union
such as specific spending behaviors abroad, the kind of migration (individual or family), the financial necessities of the family at home, and
the expectations concerning the duration of migration. Remitted
money can have an immediate impact on economic development in
countries of origin when it is used for investments. However, as mentioned in chapter 3 of this report, only one-fifth of remitted money corresponds to an investment in material capital, and 14 percent to
investment in human capital (education of children). Among the
investments in material capital, the portion of investment in business is
small (about 6 percent of the total amount of remittances). Therefore,
to improve the economic impact of highly skilled migration for the
country of origin, it would also be useful to provide incentives to invest.
Conclusion: The Nature of Migration within ECA Countries
and Between ECA Countries and the Industrial World—
Brain Drain, Brain Gain, or Brain Waste?
Surveys in CIS countries (Tajikistan and Kyrgyz Republic) have clearly
demonstrated that, during their stay in Russia, highly skilled migrants
frequently worked in sectors requiring a low qualifications (such as
agriculture, transportation, or construction). Therefore, emigration
may lead to “brain waste,” that is, a downward adjustment of
migrants’ aspirations to reconcile with the divergent characteristics of
the Russian labor market (appendix figure 4.5.1). Brain waste, a negative effect of migration flows, has also been observed, to a lesser
extent, in Western Europe, in border countries (principally Austria
[Fassmann, Kohlbacher, and Reeger 1995]), and among Russian
migrants to Israel (Hansen 2006). Highly skilled migrants, especially
women, working in domestic sectors or in nonqualified (and seasonal)
work are frequently observed in Western Europe. Swiss data show
that highly qualified migrants from ECA countries, and particularly
from the former Yugoslavia and countries of the FSU, are much more
affected by brain waste (that is, the fact that a job requires less qualification than their skills) than migrants from Western Europe (appendix
figure 4.5.1). Obstacles encountered in the Western labor market (such
as infrequent recognition of diplomas) may increase such brain waste.
Industrial societies are progressively moving toward a tertiary
economy with a high level of added value. Therefore, the demand for
highly qualified immigrants will probably increase. Furthermore, during recent decades, migratory flows have increasingly been composed
of highly skilled migrants. Such highly skilled migration will probably
also increase in the future.
189
Appendix 4.6: Brain Drain in the ECA Region
APPENDIX FIGURE 4.5.1
Proportion of Migrants with Tertiary Education from ECA Countries and from the Main Western
Communities Who Are Active in Work Requiring Low Skills
Macedonia
Bosnia and Herzegovina
Serbia and Montenegro
Albania
Other CIS countries
Russia
Ukraine
Slovak Rep.
Hungary
Poland
Croatia
Bulgaria
Czech Rep.
Austria
France
Italy
Romania
Slovenia
Netherlands
United Kingdom
Germany
Spain
0
5
10
15
20
25
% overqualified workers
ECA countries
Source: Swiss 2000 census.
Even so, labor market segmentation is still evident, leading to a
demand for relatively unqualified immigrants, which can cause brain
waste. Enlargement of the European Union in May 2004 and the
consequent free movement of workers may turn out to be a factor
influencing the ratio between brain waste and brain gain.
In short, highly skilled migration is a reality that cannot be avoided.
The extent to which countries of origin are capable of using it to their
own advantage depends on a variety of factors. The issue for the next
decade will therefore be how highly skilled migration can become a
positive factor in the development of countries of origin, rather than
a negative phenomenon resulting in waste.
Endnotes
1. See a report from the French Senate on Russian education (www.senat
.fr/rap/r04-274/r04-2746.html).
2. State Committee of Statistics of Ukraine.
3. President Putin decided at the beginning of 2006 to increase wages (from
the equivalent of US$800 to up to the equivalent of US$1,000).
30
35
40
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205
Index
Accession Treaty of 2003, 100
aging populations, 54
AH estimator, 130
Albania, 5, 61–62, 72t
Armenia, 71–72, 72t
assimilation, 158
asylum seekers, 41, 174–175.
See also refugees
Austria, 142t, 145t
Azerbaijan, 42
Baltic states, 48, 48f, 82–83
Belarus, 48f
bilateral agreements, 14, 17–19,
97, 99, 104, 107
between EU and CEECs, 102t
costs, 105
failure of, 15
geographical distribution,
101t
illegal migration, 17
legal migration, 98
regional composition, 100t
types, 102
border control policy impacts,
175
Bosnia and Herzegovina, 5,
30–31
brain drain, 109, 181–182, 185,
186–188
brain waste, 103, 188–189
Budapest Declaration of 2003,
171
Caucasus countries, 49
Central and Eastern European
countries (CEECs),
100–103, 101t, 102t
Central Asia, 48f, 49
CES. See Constant Elasticity of
Substitution
CGE. See computable general
equilibrium
children, 178–179
207
208
Migration and Remittances: Eastern Europe and the Former Soviet Union
churning, 34–35
circular migration, 13, 17
encouraging, 98, 107, 109
programs, xiii
quality of life impacts, 94
City Distance Tool, 146n
Commonwealth of Independent
State (CIS) countries, 14,
33f, 103–105, 104t
competition between migrants
and local labor, 103
computable general equilibrium
(CGE) model, 147, 148
design, 151–153
conflict, 174–175
Constant Elasticity of Substitution (CES), 149
consulting companies, 74n
costs, 103, 105
of becoming legal migrant,
169
Country Policy and Institutional
Assessment (CPIA) index,
92
creditworthiness, 8, 62
criminality, 173–175
Czech Republic, 50, 51f
data, 26, 154, 155–156
remittances, 58–60
decision to migrate, 41–42, 43,
75
family impact, 177–178
demand for labor, 15, 52, 76,
174
demographic patterns, 5
Denmark, 142t, 145t
destinations, 3, 34, 35t
Baltic migrants, 48–49
choice, 41–42
Russian migrants, 47
development impacts, 60–62,
179
diaspora migration, 38, 79, xi–xii
discrimination, 159
drivers of migration, 1–2, 9, 11,
77–79, 78t, 82
changing factors, 23, 24f
Eastern Europe and FSU,
79–82, 86
Germany, 37
Ireland and Southern Europe,
86–88
drug trafficking, 173
Dutch disease, 67
dynamic panel estimations, 130
Eastern Europe, 6f, 59f, 79–82,
86
economic growth, 66, 127
remittances impact, 129–130,
131t–135t
education, 89b, 103
brain waste, 189f
gross enrollment ratio, 184t
return migrants, 185t
emigrants, highly skilled,
185–186
emigration, 12f, 92
totals by region, 35t
emigration patterns, 88
employment, 78, 109, 150
estimators, 128–129, 130, 141
ethnic homelands, 79
Eurasian Economic Union,
175
Europe and Central Asia (ECA),
3, 14, 21n, 24f
migrant preferences, 18f
net migration, 33f
western population, 52f
European Bank for Reconstruction and Development
transition index, 83b
European Union (EU), 5, 12,
91–92
209
Index
bilateral agreements, 14,
100–103, 102t
distribution, 101t
illegal migration decrease due
to penalties, 111f
integration policies, 158–160
labor market, 14, 21n, 101
transition, 100, 163–164,
164n
migrants from CEECs, 103
migration flows, 44, 46, 94f
net migration projection, 55
regional and sectoral aggregation, 153t
remittances and GDP, 57
undocumented immigration
estimates, 44, 46
exchange rates, 62
exploitation of migrants, 161–162
family migration, 177–180
female labor, 178
fertility levels, 30
financing, 8, 62
foreign exchange, 7, 8, 61–62
free movement of labor, 91, 97,
99
EU transition, 163–164
FSU. See Soviet Union, former
Gaussian Quadrature, 155
GDP. See gross domestic product
General Agreement on Trade in
Services (GATS), 13, 97, 99
generalized method of moments
(GMM) estimator,
128–129, 130
geographic migration, 3
Georgia, 49, 72t
Germany, 34–35, 37, 101
determinant model estimations, 144t
relationship with Portugal, 90b
statistics, 142t
Global Trade Analysis Project
(GTAP), 92, 147, 148
database 5, 155–156
production structure, 149f
governance indicators, 137
Greece, 95n, 169
gross capital formation, 136–137
gross domestic product (GDP)
per capita, 127–128, 136
disparities, 9–10, 10f, 81, 81f
gross domestic product (GDP),
calculation of, 136
GTAP. See Global Trade Analysis
Project
Harris-Todaro approach, 75–76,
139, 149
health issues, 16b, 106b, 171
heteroscedasticity, 142, 143t
host countries. See receiving
countries
household wealth, 73
human trafficking, 16b, 106b,
169–172, 172n, 173
Hungary, 49, 51f
IBRD. See International Bank for
Reconstruction and Development
ICRG political risk rating, 138
illegal immigration, costs and
externalities, 16b
illegal migration, 17, 103, 147
costs and externalities, 106b
decreases due to penalties,
111f
immigration by region, 35t
immigration policies, 2
incentives. See drivers of migration
income differentials, 86, 87–90,
121t
210
Migration and Remittances: Eastern Europe and the Former Soviet Union
income equalization, 81
income inequality, 67
income levels, widening of, 10,
81, 82
institutions, quality of, 8
integration policies, 157–162
interagency agreements, 104
internal displacement, 4
internal migration, 53
internally displaced persons,
37–38, 38f, 39f, 40–41,
40f
International Bank for Reconstruction and Development
(IBRD) transition index,
146n
international migration, 9, 98
International Organization for
Migration, 44
investment rates, 66
Ireland, 11–12, 88, 89b, 164
migration drivers, 86–88
irregular migrants, 45t, 46. See
also undocumented
migrants
irregular migration, 152t, 175.
See also undocumented
migration
Italy, 12
Kyrgyz Republic, 72t
labor agreements, 13. See also
bilateral agreements; multilateral arrangements
labor migration, 77
legal migration, facilitating, 107
legal status, 43, 44
methodology of study, 19, 20b
Mexico, 21n
migrant determinants, variable
estimations, 144t–145t
migrant rights, 17, 18–19
strengthening, 109, 110
migrants, 27–28, 161. See also
undocumented migrants
Armenian, 49
as service providers, 99
brain waste, 188, 189f
CIS, 104, 105, xii
counting, 19
economic perceptions, 10–11
Greek, 169
illegal, 16b
protection of, 17
return of, 113, 185t
Russian destinations, 47
short- vs. long-term preferences, 18f, 110f
social externalities, 108b
social networks, 160–161
statistical, 3, 24, 28–29, 30
temporary, 157–159
transit, 42, 43
unemployment, 167
migration, 3–6, 9, 10, 11, 73, 77,
98. See also circular migration; undocumented
migration
costs, 88
diaspora, 38, 79, xi–xii
forced, 49
governance of, 17
highly skilled, 183
long-term vs. short-term preferences, 18f
measuring, 33–34
problems, 26–30
optimizing, 19–20
predictions, 77, 140, 141–142
student, 182
temporary, 98, 187
transit, 41–46
migration determinants, 8–11,
92–94
211
Index
estimating, 83b–84b
model, 139–140
countries included, 141t
migration flows, 3–5, 14, 32–37,
35t, 36, 36f, 46
capturing data, 27
diaspora, xi–xii
EU, 44
from EU to ECA, 94f
GDP per capita, 9–10
highly skilled, 182
origins and destinations, 34, 35t
quality of life improvement
impacts, 93, 93f
western ECA, 37f
migration management, 46
migration partners, 46–50, 51f
migration patterns, 5, 24, 86, 94
Poland, 49
predictions, 11, 50–53
FSU, 53–56
migration policies, 2, 13, 17,
44–45, 107–111
migration programs, xiii
migration rate, 84b, 143
migration reform, xiii
models, 77, 147–149
determinant coefficients, 143t
estimating determinants,
83b–84b
migration determinants,
139–140
countries included, 141t
variable estimations,
144t–145t
quality of life impacts, 92
Moldova, 48f, 66, 178–179
monitoring, 160
Monte Carlo Analysis, 155
moral hazard, 127
most-favored nation status, 112n
motivation. See drivers of migration
multilateral arrangements,
99–105, 107
multiplier effects, 66
natural increase, 30–31, 31f
neoclassic approach, 75–76, 79
net migration, 30–31, 31f, 48
by region, 48f
ECA and CIS, 33f
emigration and immigration
countries, 85f
FSU, by country, 122t–123t
rates, 82
Russia, 47f
networking, 187
Okun’s law, 150
origin countries. See sending
countries
pensions, 17, 109
perceptions, 10–11, 77
Poland, 49, 50f
policy, 2, 13, 17, 44–45
development, 15, 107–111
optimizing migration, 19–20
political instability, 174–175
political risk rating, 138
population, 26, 28
aging, 54
by nationality, 118t–120t
declining, 5, 32, 52
distribution of, 9f
foreign born by country,
23–24, 25f
FSU, 53, 54f, 117t
Soviet Union, 29t
Western Europe, 52, 52f
population change, 3–5, 30–32,
115t–116t
Portugal, 90b, 91
postwar emigration, 12f
poverty reduction, 7, 8, 67, 70
212
Migration and Remittances: Eastern Europe and the Former Soviet Union
private capital outflows, 137
push and pull factors, 78t
quality of life, 12–13, 78–79
CGE model, 151
effects on migration, 93, 93f
migration decision, 76
quotas, 45–46, 105
receiving countries, 25f, xii, xiii
common experiences, 162
policies, 2, 17
refugees, 37–38, 39f, 40–41, 40f
regional and sectoral aggregation, 153t
regulatory framework, 17
reintegration, 179–180
remittances, 6–8, 21n, 71–72,
136, 188, xii
amounts, 57
and annual consumption, 72t
as a share of exports, 62, 63f
as a share of household
expenditures, 65f
as portion of GDP, 6f
bilateral agreements, 103
contributions to the balance
of payments, 125t–126t
data, 58–60, 129
distribution of, 8, 9f, 70, 70f
Eastern Europe and FSU, 59f
economic impacts, 63–67
external financing, 6, 57
flows, 59–60, 61t
growth rate, 60f
household wealth, 73
impact on development,
60–62
impact on growth, 68b–69b,
127, 129–130, 131t–135t
including in CGE model, 151
inequality, 71
measuring, 19
poverty reduction and
inequality, 67, 70–73
spending of, 64, 64f
top receiving countries, 58f
replacement migration, 54, 55.
See also circular migration
research and development, 187
restrictive immigration, 175
return migrants, 113, 185t
return migration, 13, 82,
179–180. See also circular
migration
Romania, 50, 51f
Russia, 31–32, 46, 82, 105
censuses, 26, 29
determinant model estimations, 144t
nationality of immigrants, 80,
80f
net migration, 47, 47f, 54–55
and natural increase, 55f
rate, 82
population, 4, 55
statistics, 142t
Schengen Agreement, 56n
scientists, 183–185
sending countries, 25f, 34, 35t,
xii, xiii
bilateral agreements, 18
brain drain effects, 186–188
policies, 2, 13
reducing negative effects, 109
sensitivity analyses, 154–155
service providers, 13
service provision, 99
simulations, 12–13
Slovak Republic, 50
Slovakia, 51f
smuggling of migrants, 170, 173,
174, 175
social externalities, 108b
social inclusion policies, 157
213
Index
social networks, 160–162
Southern Europe, 11–12, 86–88,
91
Soviet Union, 5, 29, 29t, 30
Soviet Union, former (FSU), 39f,
53, 93
migration drivers, 79–82, 86
net migration, 47, 122t–123t
population, 54f, 117t
remittances, 6f, 59f
Spain, 87
statistical migrants, 3, 24, 28–29,
30
Structural Fund, 91
student migration, 182
survey instrument, 113
surveys, 26, 27
U.K. Economic and Social
Research Council, 42
Sweden, 142t, 145t
Systematic Sensitivity Analysis,
155
Tajikistan, 72t
temporary contracts, 91
temporary migrants, 157–159
temporary migration, 98, 187
Transcaucasus states, 48f
transit migration, 41–46
transition index, 83b, 146n
transition of migration system,
24f
transition years, 1, xi–xii
Transparency International Corruption Perceptions Index,
137
travel agencies, 74n
Turkey, 52f, 53, 93
Turkmenistan, 30–31
Ukraine, 48f
undocumented immigrants, costs
of becoming legal, 169
undocumented immigration, 44,
46, 167–172
undocumented migrants, 3, 43,
45t, 46
bilateral agreements, 18–19
choosing to stay undocumented, 168–169
CIS, 104
employment, 109
transit countries, 42
United States, 56n
undocumented migration, 15,
28, 41–46, 152t
costs, 103, 169
ECA growth, 44–45
Eurasian Economic Union,
175
incentives, 1–2
regional cooperation agreement, 103
unemployment, 17
United Kingdom, 142t, 144t
United Kingdom Economic and
Social Research Council
Survey, 42
United Nation Global Commission on International
Migration, 15
United Nations Human Development Index, 137
United States, 28, 182
undocumented migrants, 56n
unskilled labor, 2, 107
wage differentials, 77, 78,
88–90
wage gap, 174
Wald test, 130
women, 178
World Trade Organization
(WTO), 13, 97
Yugoslavia (former), 38f
ECO-AUDIT
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This report is part of a series undertaken by the Europe and Central Asia Region of
the World Bank. The series draws on original data, the World Bank’s operational
experience, and the extensive literature on the Region. Poverty, jobs, trade, migration,
and infrastructure will be among the topics covered.
F
or many net emigration countries in Eastern Europe and the Former Soviet
Union, household income and national output are often strongly tied to the
remittances of migrants living and working abroad, while migration-receiving CIS
and European Union economies rely on migrant labor to help support economic
growth and living standards. However, there is considerable scope for improving
the outcomes and payoffs to migration and migrants’ remittances for both the
sending and receiving countries.
First, reforms to the business investment climate and economic governance
in the countries of origin could reduce the incentives for migration as well as
encourage migrants to return home and to fruitfully employ there their newly
acquired skills and capital. Second, sending and receiving countries could more
closely coordinate migration policies so that the supply for international migrant
labor can meet demand through legal channels that respect the rights of migrants
while also satisfying the political and social sensitivities of the receiving countries.
Migration and Remittances: Eastern Europe and the Former Soviet Union analyzes the
history of international migration flows since the start of the transition and lays
out the basic tenets of policy interventions that can enhance the gains from both
sides of the equation, at the micro as well as at the macro level. Migration and
Remittances is a must read for policy makers, labor and international economists,
and civil society specialists who have an interest in social analysis and policies,
and poverty reduction strategies.
ISBN 0-8213-6233-X