International Migration and Development
Gordon H. Hanson
University of California, San Diego and NBER
October 2008

Abstract. In this chapter, I selectively review academic literature on the causes andconsequences of emigration from developing countries. My aim is to identify facts about
international migration that are relevant to those concerned about why labor moves
between countries and how these movements affect sending-country economies.
Empirical work on global labor flows is still in an early state. As is often the case, theliterature provides incomplete answers to some of the most urgent questions.
Nevertheless, recent work yields a number of robust results and is helpful for identifying
where future research should be directed.

I thank Roberto Zagha and Ravi Kanbur for helpful comments.

1
1. Introduction
A decade ago, trade and investment liberalization dominated the global economic
policy agenda. The WTO had recently been created, the US, Mexico and Canada were
implementing NAFTA, and much of Southeast Asia and South America were near the
peak of an economic boom that was driven in part by greater openness to inflows offoreign capital. In bilateral and multilateral discussions of economic integration, global
migration was often missing from the docket entirely.
Today, international labor flows are seen as an integral part of the process ofglobalization. Between 1990 and 2005, the number of individuals residing outside of
their country of birth grew from 154 million to 190 million, reaching a level equivalent to
3% of the world population (United Nations, 2006). In many developing countries,
emigration rates have increased dramatically. Between 1990 and 2000, the fraction of theadult population living in OECD countries rose from 30% to 35% in Jamaica, 14% to
20% in El Salvador, 8% to 13% in the Dominican Republic, 8% to 12% in Mexico, 7% to
12% in Haiti, 4% to 8% in Honduras, and 2% to 6% in Ecuador. 1
The growth in labor flows from low-income to high-income countries has notbeen greeted with universal enthusiasm, either by policy makers or academics. In theory,international migration increases economic efficiency by shifting labor from lowproductivity to high-productivity environments. As workers move from Central America
to the US, North Africa to Europe, or Southeast Asia to Australia, the global labor supply
shifts from labor-abundant to labor-scarce economies, compressing international
differences in factor prices and raising global GDP. Migrants enjoy large income gains(Rosenzweig, 2007), family members at home share in these gains through remittances
1

See Docquier and Marfouk (2006). Adults are those 25 years and older.

2(Ozden and Schiff, 2006; Fajnzylber and Humberto Lopez, 2007), and non-migrating
workers in the sending country enjoy higher wages thanks to a drop in local labor supply(Aydemir and Borjas, 2007). What is not to like?
One source of dissension is that international migration redistributes income
within and between countries. It thus comes as no shock that inflows of foreign labor
provoke political conflict and have become a frequent topic of debate in labor-importing
countries. More surprising, perhaps, is that economists are often among those criticizing
migration. In the literature, one finds two broad complaints. In low-income sending
countries, the concern has long been that the wrong individuals leave (e.g., Bhagwati and
Hamada, 1974). In most of the developing world, the more skilled have the highest
propensity to emigrate . If there are positive spillovers associated with accumulating
human capital (Lucas, 1988) or education is public and financed through taxes (Bhagwati
and Rodriquez, 1975), then the emigration of skilled labor can undermine economic
development (Benhabib and Jovanovic, 2006). Possible corrections include taxing theemigration of skilled labor (McHale, 2007) or having receiving countries admit moreunskilled workers from the developing world (Pritchett, 2006).
In high-income receiving countries, the complaint is that the wrong individuals
are arriving (Borjas, 1999a). In the US and Europe, the average immigrant has much lessschooling than the average native worker (Boeri, Hanson, and McCormick, 2002). If
immigrants have low income relative to natives, increased labor inflows may exacerbate
distortions created by social-insurance programs or means-tested entitlement programs(Borjas and Hilton, 1996; Wellisch and Walz, 1998), fueling political opposition to
immigration (Hanson, Scheve, and Slaughter, 2007). Most rich receiving countries

3
tightly restrict immigrant admissions, in contrast to their pro-liberalization stances on
trade and investment (Hatton and Williamson, 2005).
To be sure, the claims made by both the emigration pessimists and theimmigration pessimists are controversial. On brain drain, recent literature countersearlier arguments by suggesting that opportunities for emigration may increase the
incentive to acquire human capital by enough to create a brain gain (Stark, Helmenstein,
and Prskawetz, 1997; Stark and Wang, 2002). In receiving countries, especially the US,
some economists see the consequences of immigration for native workers as benign oreven positive (Card, 2005; Cortes, 2005; Ottaviano and Peri, 2006).
Another factor complicating international coordination on global labor flows isthat control over international migration is largely in the hands of receiving countries.
Labor flows between rich and poor nations tend to be unidirectional, from the latter to theformer. In 2005, just 12 higher income nations were host to 51% of the global stock ofinternational migrants (United Nations, 2006). 2 The US, alone, is home to 20% of the
global migrant stock, but sends few migrants to developing countries. Because highincome countries are able to set global migration policy unilaterally, they have little
incentive to address sending-country concerns.
The disconnect between sending and receiving-country perspectives oninternational migration raises important policy questions. Is emigration a viable strategy
for developing countries to raise living standards? Are there environments where
emigration may be particularly helpful or harmful? What facts do economists need toknow in order to be sensible policy advice to sending and receiving countries?

2

These 12 were the US, Russia, Germany, France, Canada, the UK, Spain, Australia, Hong, Kong, Israel,
Italy, and Japan.

4
In this chapter, I selectively review academic literature on the causes andconsequences of emigration from developing countries. My aim is to identify facts about
international migration that are relevant to those concerned about why labor moves
between countries and how these movements affect sending-country economies. 3
Empirical work on global labor flows is still in an early state. As is often the case, theliterature provides incomplete answers to some of the most urgent questions.
Nevertheless, recent work yields a number of robust results and is helpful for identifying
where future research should be directed.
In section 2, I begin by describing current trends in international migration.
Developing countries that are small, densely populated, and middle income tend to havethe highest emigration rates. In section 3, I move on to discuss the relationship between
skill and migration. In nearly all countries, the more skilled are those most likely toemigrate. The positive selection of emigrants is consistent with international differencesin labor productivity – rather than international differences in inequality – being the
primary determinant of which types of workers leave. Emigrants sort themselves acrossdestination countries according to the reward to skill, in a manner consistent with income
maximization. In section 4, I discuss the contribution of migrant networks to lowering
migration costs, which for many countries appear to be substantial.
In section 5, I examine research on the impact of emigration on sending
countries. 4 In the few cases that have been studied, labor outflows appear to help raise3

While there are labor flows between low and middle-income countries, data constraints require me tofocus on flows into high income countries. There appear to be sizable flows from the former Soviet
Republics to Russia; Bangladesh to India; Egypt, India, Pakistan, and the Philippines to the Gulf States;
Afghanistan to Iran; Iraq to Syria; other Southern African states to South Africa; Indonesia to Malaysia;
Malaysia to Singapore; Guatemala to Mexico; and Nicaragua to Costa Rica (Ratha and Shaw, 2007).
4
Literature on impacts on receiving countries is much more developed. See Borjas (1999b, 2007).

5
sending-country wages, while having little impact on fiscal accounts. Though there has
been recent progress in the literature, the question of whether opportunities for
emigration produce brain drain versus brain grain remains unresolved. We still will do
not know how opportunities for emigration affect the stock of human capital in sending
countries. Recently, migrant remittances have grown rapidly, helping households smoothconsumption in response to income shocks. There is some evidence that labor outflows
promote trade, technology diffusion, and political openness, though the econometricidentification of these impacts is not problem free.
By way of conclusion, in section 6, I summarize what appear to be the more
empirically robust findings (or nonfindings) in the literature.

6
2. Patterns of International Migration
International migration appears to be on the rise. Only recently have crosscountry data on emigrant stocks have become available. As a result, research oninternational migration is still emerging. In this section, I discuss data sources on thestock of international migrants and then move on to examine emigration rates in sending
countries, the distribution of migrants across receiving countries, the correlates ofbilateral migration flows, and the emigration of skilled labor.

2.1 Data and Recent Trends
There have been several recent attempts to measure international migration. The
OECD (2003) lists the foreign-born population 15 years and older in 2000 by source
country and education level (primary – 0 to 8 years, secondary – 9 to 12 years, tertiary –13 plus years, unknown) for each OECD country. Docquier and Marfouk (2006) extend
the OECD data by constructing more complete estimates of the stocks of internationalmigrants. They use the population censuses for 30 OECD countries in 1990 and 2000 to
obtain the count of adult immigrants (25 years and older) by source country and level of
education (also, primary, secondary, or tertiary schooling). They combine these counts
with the size of adult populations and the fraction of adult populations with different
levels of schooling from Barro and Lee (2000) to obtain emigration rates by educationlevel and source country, yielding 174 source countries in 1990 and 192 in 2000. While
the set of source countries is comprehensive, the coverage of destination countries
excludes those counties not in the OECD as of 2000.

7

Table 1: Share of OECD Immigrants by Sending Region, 2000
Share of Immigrants byOECD Receiving Region
Low Income
Sending Region
Mex., Cen. Am., Caribe
Southeast Asia
Eastern Europe
Middle East
South Asia
North Africa
South America
Cen., So. Africa
Former Soviet Union
Pacific Islands
Total

All
OECD
0.202
0.102
0.099
0.063
0.052
0.044
0.041
0.036
0.029
0.004
0.672

North
America
0.374
0.137
0.049
0.032
0.052
0.009
0.050
0.021
0.023
0.003
0.750

High Income
Sending Region
Western Europe
Asia, Oceania
North America
Total

0.244
0.055
0.029
0.328

0.152
0.062
0.037
0.251

Change inOECD ShareEurope
0.025
0.039
0.161
0.113
0.055
0.098
0.031
0.061
0.042
0.001
0.626

Asia,
Oceania
0.002
0.160
0.116
0.029
0.036
0.018
0.035
0.021
0.010
0.027
0.454

1990 to 2000
0.053
0.016
0.042
0.001
0.011
-0.0060.010
0.007-0.002
0.000
0.132

0.336
0.018
0.020
0.374

0.368
0.156
0.023
0.547

-0.111-0.010-0.011-0.132

Notes: This table shows data for 2000 on the share of different sending regions in theadult immigrant population of the entire OECD and of three OECD subregions. High
Income North America includes Canada and the U.S. and High Income Asia and Oceania
includes Australia, Hong Kong, Japan, Korea, New Zealand, Singapore, and Taiwan.

Low-income countries are an increasingly important source of migrants to highincome countries. Table 1 shows the share of the immigrant population in OECD
countries by sending-country region. 5 In 2000, 67% of immigrants in the OECD werefrom a developing country, up from 54% in 1990. This gain came almost entirely at the5

Tables and figures are based on calculations using raw data from Docquier and Marfouk (2006).

8
expense of Western Europe, whose share of OECD immigrants fell from 36% to 24%.
Among developing sending regions, Mexico, Central America, and the Caribbean is themost important, accounting for 20% of OECD immigrants in 2000, up from 15% in 1990.
Half of this region’s migrants come from Mexico, which in 2000 was the source of 11%
of OECD immigrants, making it by far and away the world’s largest supplier ofinternational migrants. 6 The next most important developing source countries for OECDimmigrants are Turkey (3.5% of OECD immigrants); China, India, and the Philippines(each with 3%); Vietnam, Korea, Poland, Morocco, and Cuba (each with 2%); andUkraine, Serbia, Jamaica and El Salvador (each with 1%).
There is a tendency for different destination regions to draw more heavily on
migrants from particular source countries. Mexico, Central America, and the Caribbean
are the largest source region for North America, but send few migrants to other parts of
the world; Eastern Europe is the most important developing source region for OECD
Europe; and Southeast Asia is the most important developing source region for Australia
and Oceania. Unsurprisingly, geographic distance plays an important role in migration.
The growing importance of lower-income countries in the supply of internationalmigrants has contributed to an overall increase in labor flows into rich countries. Table 2
shows the share of the population that is foreign born in select OECD members. The sizeof the immigrant population varies across destinations, reflecting differences in both their
attractiveness and openness to international migrants. Aside from Luxembourg, thecountries with the largest immigrant presence in 2004 are Australia (24%), Switzerland(24%), New Zealand (19%), and Canada (18%). Next in line are the large economies of

6

As recently as 1990, the UK was the largest source country for immigrants in the OECD.

9
Germany (13%), the US (13%), France (10%), and the UK (10%), with the US hosting
40% of immigrants living in OECD countries.

Table 2: Share of Foreign-Born Population in Total Population

Australia
Austria
Belgium
CanadaCzech Republic
Denmark
FinlandFrance (a)Germany (b)Greece (c)Hungary
Ireland (d)Italy (c)Luxembourg
MexicoNetherlands
New Zealand (d)NorwayPolandPortugal
Slovak Republic (c)Spain (c)SwedenSwitzerland
TurkeyUnited Kingdom
United States

1995
23.0
9.7
16.6
4.8
2.0
11.5
2.8
6.9
30.9
0.4
9.1
16.2
5.5
5.4

10.5
21.4
6.9
9.3

2000
23.0
10.5
10.3
17.4
4.2
5.8
2.6
10.0
12.5
10.3
2.9
8.7
2.5
33.2
0.5
10.1
17.2
6.8
5.1
2.5
5.3
11.3
21.9
1.9
7.9
11.0

Change
1995-2004
0.6

2002
23.2
10.8
11.1
17.7
4.6
6.2
2.8

2004
23.6
13.0
18.0
4.9
6.3
3.2

1.6
1.2

12.8

12.9

1.4

3.0
10.0

3.2
11.0

0.4
4.0

32.9

33.1

2.2

10.6
18.4
7.3
1.6
6.7

10.6
18.8
7.8

1.6
2.6
2.3

6.7
3.9

1.3

11.8
22.8

12.2
23.5

1.7
2.2

8.6
12.3

9.3
12.8

2.3
3.5

1.4

Notes: (a) 2000 value is from 1999; (b) 2004 value is from 2003; (c) 2000 value is from2001; (d) 1995 value is from 1996. Source: International Migration Outlook, OECD, 2006.

10
There is strong evidence that a rising share of labor inflows in rich countries are
made up by illegal entrants, with data for the US being the most extensive. Passel (2006)
estimates that in 2005 illegal immigrants accounted for 35% of the US foreign-born
population, up from 28% in 2000 and 19% in 1996. Of the 2005 population of illegal
immigrants, 56% were from Mexico, implying that 60% of the population of Mexican
immigrants in the US was unauthorized (Hanson, 2006).
There is substantial variation across countries in the propensity to emigrate. As of
2000, there were 22 developing nations with 10% or more of their adult population
having migrated to the OECD, and 16 developing countries with emigration rates above
5%. At the other extreme, 52 developing countries had emigration rates below 1%.
While emigration is rising over time, there is strong persistence in which countries sendmore people abroad, as seen in Figure 1, which plots emigration rates in 1990 and against
those in 2000. The countries with the largest increase in emigration rates over 1990 to2000 include neighbors of the US (the Caribbean, Central America, Mexico) and former
east bloc countries (Albania, Bulgaria). The countries experiencing the largest decreasein emigration rates were Ireland, Lebanon, Panama, and Greece. Interestingly, war-torncountries do not show up as having particularly high emigration rates to the OECDoverall or large increases in emigration rates over the 1990s.

.5

11

Suriname.4

GuyanaEmigration rate, 2000
.2
.3

Jamaica
Barbados

Fiji
El Salvador

Malta
Trinidad andTobagoCape
Verde
Ireland0.1

CyprusPortugal
Lebanon
Dominican
RepublicNew
Zealand
MexicoBahamas,The
Haiti Mauritius
Cuba
Lao PDR
Hong
Kong
Iceland
Nicaragua
GuatemalaGreeceAlbaniaHonduras
Luxembourg
Bulgaria
MoroccoFinlandUK
EcuadorAustria
PanamaTurkeyTunisia
Netherlands
ItalyChina
Philippines
Macao,
Hungary
Equatorial
GuineaAlgeria
PolandDenmark
IsraelCambodia
CanadaComoros
Switzerland
Romania
Czech
Republic
NorwayLiberia
Belgium
TaiwanGermany
Vietnam
Brunei
Somalia
Gambia,
The and Gaza
Singapore
Costa
Rica
Congo,
Rep.
West
Bank
Peru
JordanSri
Lanka
AngolaSenegalKorea
Iraq
Colombia
SwedenUruguay
Spain
Guinea-Bissau
Sierra
Leone
Ghana
Chile
FranceIran
SyrianArab
Republic
KuwaitBolivia
Australia
Kenya
Malaysia
Afghanistan
Zimbabwe
South
AfricaBahrainTogo
Mauritania
Venezuela,RB
UgandaPakistanArgentina
SolomonIslands
Papua
New
GuineaMali
Libya
MozambiqueZambiaGabon
Egypt
Cameroon
Paraguay
Cote
d'Ivoire
ThailandDjibouti
RussiaNigeria
Congo,
Dem.
Rep.
Tanzania
Qatar
Madagascar
Japan
GuineaEthiopia
BrazilUS
Mongolia
Botswana
Benin
Bangladesh
RwandaIndia
CentralAfricanRepublic
BurundiSudan
Nepal
Namibia
Malawi
MyanmarIndonesia
UnitedArab
Emirates
Saudi
ArabiaChina
Burkina
Faso
Oman
LesothoChad
Swaziland
Niger
Bhutan

0

.1

.2

.3

.4

.5

Emigration rate, 1990

Figure 1: Persistence in Emigration Rates

Income is an obvious driver of emigration. In Figure 3, it appears the relation
between emigration rates and income is non-monotonic. There is a level of per capita
GDP of around $3000 (in 2000 PPP-adjusted terms) below which emigration rates arevery low. Above this threshold, emigration is strongly decreasing in average income.
This non-monotonicity is consistent with recent literature on the relation between
international migration and income (Clark, Hatton, and Williamson, 2007), which
reinforces the idea that credit constraints impede emigration from very poor counties.

.5

12

.4

GuyanaJamaica

Emigration rate.2
.3

Barbados
Malta
Cape VerdeTrinidad and Tobago

0.1

IrelandFiji
El Salvador
Macedonia
CyprusBosnia and Herzegovina
Portugal
LebanonCroatia
Dominican
Republic New Zealand
MexicoBahamas, The
Haiti
Mauritius
Slovakia
Lao PDR
Kong
Iceland
Nicaragua
GuatemalaGreeceHong
Albania
Honduras
Luxembourg
Bulgaria
MoroccoFinlandUK
Slovenia
EcuadorPanamaEstonia
Austria
TurkeyLithuania
Tunisia
Italy
Netherlands
Philippines
Algeria
Equatorial
GuineaHungary
Macao,
China
PolandDenmark
IsraelCambodia
CanadaComoros
Switzerland
Republic
Romania
NorwayBelgium
LatviaGermany
VietnamArmenia
Gambia,The Sri
Singapore
Costa RicaCzech
Congo, Rep.
JordanPeru
AngolaLanka
KoreaSpain
SenegalColombia
SwedenUruguay
Eritrea
Belarus
Guinea-Bissau
Ghana
Sierra Leone
Ukraine
Chile
FranceIran
SyrianArab
Republic
KuwaitBolivia
Australia
Kenya
Malaysia
Zimbabwe
South
AfricaBahrain
Moldova
Togo
Mauritania
Argentina
Pakistan
UgandaVenezuela,
RB
Solomon
Papua
Islands
New
GuineaMali
Mozambique
Egypt
Gabon
ZambiaCameroon
Paraguay
Azerbaijan
Coted'Ivoire
Djibouti
ThailandCongo,
Dem.
Rep.
Nigeria
RussiaGeorgia
Tanzania
Ethiopia
BrazilGuineaMadagascar
Benin
Bangladesh
BotswanaOman
Kazakhstan
Mongolia
US
BurundiCentralAfricanRepublic
IndiaIndonesia
RwandaMalawi
Nepal
Namibia
Sudan
Kyrgyzstan
Burkina
Faso
China
Uzbekistan
Saudi Arabia Japan
Tajikistan
Chad
LesothoTurkmenistan
Niger
Swaziland

6

7

8

9

10

11

Log per capita GDP (PPP)

Figure 2: Emigration Rates and Per Capita GDP, 2000

2.2 Brain Drain
Much of the literature on international migration focuses on the movement ofskilled labor, whose departure may drain poor economies of scarce supplies of humancapital. Figure 3 shows the emigration rate for adults with a tertiary education against theemigration rate for all adults. In 2000, there were 41 developing countries withemigration rates for the tertiary educated above 20%.
Brain drain is a concern where there are distortions in the decision to acquire
human capital. Absent distortions, moving labor from a low-productivity to a highproductivity economy unambiguously raises global income (Benhabib and Jovanovic,
2007). However, if there are positive externalities associated with learning (e.g., Lucas,

131988), then the social product of human capital exceeds its private product and theexodus of skilled labor from a country may have adverse consequences for its economic
development (Bhagwati and Hamada, 1974). Another negative impact of brain drain isthat many individuals have their education subsidized by the state, meaning theiremigration would deprive their origin country of tax contributions to offset the cost ofEmigration rate for tertiary educated.2 .3 .4 .5 .6 .7 .8 .9

1

their schooling (McHale, 2007).

GuyanaJamaica

Haiti

Trinidad and Tobago

Cape Verde
Gambia, The
Barbados
FijiThe
Bahamas,Malta
Mauritius
Sierra Leone
Suriname

Ghana
Liberia
Mozambique

0.1

LebanonKenya
Uganda Lao PDR
EritreaAngolaSomalia
CyprusEl Salvador
Sri Lanka
Nicaragua
IrelandMacedonia
Hong
Kong
Papua
New
GuineaCuba
Vietnam
RwandaHonduras
Guatemala
Croatia
Bosniaand Herzegovina
Guinea-Bissau
Afghanistan
Congo,
Rep.
Dominican
Republic
ComorosNew
Zealand
Iceland
Portugal
Togo
Malawi
Cambodia
Senegal
Cameroon
MoroccoZambiaUK
Slovakia
PanamaBrunei
MexicoEast
TimorSingapore
Mali
Iran
Macao,
China
Gabon
PolandPhilippines
SerbiaCongo,Dem.
Rep.
Austria
Hungary
Zimbabwe
TaiwanEquatorial
GuineaTunisia
Pakistan
Tanzania
GreeceRomania
Mauritania
Slovenia
Estonia
Benin
GuineaMalaysia
Iraq
Djibouti
Nigeria
Czech
Republic
Colombia
Italy
Ethiopia
Netherlands
Ecuador
Algeria
Switzerland
Albania
Armenia
LatviaLithuania
BurundiUruguay
Luxembourg
IsraelDenmarkSouth
AfricaMadagascar
Finland
West
Bank
and
Gaza
Jordan
Costa
Rica
KuwaitCentralAfrican
Sudan
Bulgaria
SolomonIslands
NorwayNiger
Chile
SyrianArab
Republic
TurkeyPeru
Bolivia
Cote
d'Ivoire Republic
Korea
Nepal
Germany
Belgium
Bahrain
Egypt
CanadaSwedenSpain
India
Bangladesh
Myanmar
LesothoParaguay
China
Botswana
UkraineVenezuela,RB
Namibia
Moldova
FranceBelarusAustralia
Qatar
Burkina
FasoEmirates
Argentina
ThailandChad
Libya
BrazilIndonesia
Azerbaijan
Georgia
RussiaKazakhstan
Japan
UnitedArab
Mongolia
Saudi
ArabiaUzbekistan
Kyrgyzstan
Oman
BhutanUS
Swaziland
Tajikistan
Turkmenistan

0

.1

.2

.3

.4

.5

.6

.7

.8

.9

1

Emigration rate for all adults

Figure 3: Emigration Rates for the More Educated, 2000

Recent literature explores the possibility that the opportunity for emigration may
actually increase the supply of human capital in a country, creating a brain gain (Stark
and Wang, 2002). With high incomes for skilled labor in rich countries and uncertainty
over who will succeed in emigrating, the option of moving abroad induces individuals toaccumulate enough additional human capital to compensate for the loss in skill to labor14
outflows (Beine, Docquier and Marfouk, 2001). 7 For this argument to go through, theprobability of emigrating must be large enough to affect the expected return to investingin skill. It must also be true that many people believe they have a non-trivial chance ofmoving abroad. If, for most people, the expected probability of emigrating is small, thebrain-gain logic collapses. One environment where this might occur is countries in which
the distribution of wealth is highly unequal, such that few individuals can afford the up
front costs of either acquiring human capital (which may involve both direct costs forschooling and indirect costs in terms of time out of the labor force) or moving abroad(which may involve direct costs to acquire a visa and indirect time costs).
Only a handful of empirical papers examine the relationship between emigrationand human-capital accumulation. For a cross-section of countries, Beine, Docquier, andRapoport (2006b) report a positive correlation between emigration to rich countries(measured by the fraction of the tertiary educated population living in OECD countries in1990) and the increase in the stock of human capital (measured as the 1990 to 2000
change in the fraction of adults who have tertiary education). While this finding is
consistent with emigration increasing the incentive to acquire education, the cross-section
correlation between emigration and schooling is not well suited for causal inference about
the impact of brain drain on educational attainment. Education and migration decisions
are likely to be jointly determined, making each endogenous to the other. Valid
instruments for migration are very difficult to find. Despite four decades of research, we
still do not know how the opportunity to emigrate affects the supply of human capital insending countries, leaving the debate about brain drain unresolved.

7

See Docquier and Rapoport (2007) for a survey of the theoretical literature on brain drain.

15
Finally, it is worth considering how emigration rates for the highly educated havechanged in recent decades. Figure 4 plots emigration rates for the tertiary educatedacross countries in 1990 and 2000. The countries with the largest increase in emigration
rates for the highly educated are primarily countries that have experienced civil conflict,such as Afghanistan, Angola, Congo, Haiti, Mozambique, Rwanda, Sierra Leone, andSomalia. Thus, while civil conflict does not provoke a general flight to OECD countries(see Figure 1), it does appear to provoke the flight of the more skilled. It has long beenrecognized that the induced emigration of skilled labor may be an important cost of civil
war. Figure 4 is consistent with this perception, though careful research quantifying

Emigration rate for high educated, 2000
.1 .2 .3 .4 .5 .6 .7 .8 .9

1

these costs is difficult to find in the literature.

GuyanaHaiti Jamaica
Trinidad and Tobago

Cape VerdeBarbados
Fiji
Bahamas,The
Malta
Mauritius

Sierra Leone

Gambia, The

Suriname

LiberiaGhana
Mozambique

LebanonKenya
UgandaCyprusEl
Salvador
Sri
Lanka
Ireland
Nicaragua
Hong
Kong New Guinea
Cuba
Papua
Vietnam
RwandaHonduras
GuatemalaAfghanistan
Guinea-Bissau
Congo,New
Rep.
Dominican
Republic
Comoros
Zealand
Iceland
Portugal
Togo
Malawi
Cambodia
Senegal
Cameroon
MoroccoZambiaUK
PanamaBrunei
MexicoSingapore
Mali
IranChina
Macao,
Gabon
PolandPhilippines
Congo,
Dem. Rep.
Austria
Hungary
Zimbabwe
Equatorial
GuineaTaiwanPakistan
Tunisia
Tanzania
GreeceMauritania
Romania
Benin
GuineaIraq
Malaysia
Djibouti
Nigeria
Colombia
Czech
Republic
Ethiopia
Italy
Ecuador
Netherlands
Algeria
Albania
Switzerland
BurundiLuxembourg
Uruguay
IsraelDenmark
Finland
Madagascar
South
AfricaCosta
Rica
Jordan
West
Bank
and
Gaza
KuwaitCentralAfricanRepublic
Sudan
Bulgaria
NorwayChile
Niger
SyrianArab Republic Solomon Islands
Bolivia
Peru
TurkeyCote
d'Ivoire
Korea
Nepal
Germany
BahrainBelgium
CanadaEgypt
Bangladesh
India
Spain
SwedenLesotho
MyanmarParaguay
China
Botswana
FranceNamibia
Venezuela,RB
Australia
Argentina
Burkina
Faso
Qatar
Chad
Thailand
Libya
BrazilIndonesia
RussiaJapan
Mongolia
UnitedArab
Emirates
Saudi
ArabiaBhutanOman
Swaziland
US
Lao PDR

0

Angola0.1

Somalia

.2

.3

.4

.5

.6

.7

.8

.9

Emigration rate for high educated, 1990

Figure 4: Persistence in the Emigration of the Highly Educated1

16
3. Selection into Migration
Who migrates from poor to rich countries is the subject of a growing empiricalliterature. The high propensity of the highly educated to migrate abroad is seen clearly in
Figure 5, which plots the share of emigrants with tertiary education against the share of
the general population with tertiary education in 2000. Nearly all points lie above the 45degree line, indicating that in the large majority of countries emigrants are positively
selected in terms of schooling. That is, with the exception of a few countries (e.g.,
Canada, Turkey, the US), the more highly educated are over-represented among.8

emigrants relative to their presence in the population as a whole

Share of more educated among migrants
.2
.4
.6

TaiwanQatar
KuwaitUnitedArab Emirates
Philippines
Saudi Arabia Japan
Oman
South
AfricaHong
Kong
Mongolia
IndiaMalaysia
Venezuela,
RB
Uzbekistan
Brunei
Iran Egypt
Liberia
PanamaIsraelSingapore
Swaziland
MyanmarJordanMacao,
China
Zimbabwe
West
Bank
andAustralia
Gaza
Papua New Guinea
Libya
Korea
Gabon Bahamas,
Kyrgyzstan
Benin
The
Sudan
Georgia
Tanzania
LatviaRussiaNamibia
BurundiTrinidadandTobagoBolivia
Bahrain
SierraLeone
Azerbaijan
Tajikistan
LesothoCameroon
Niger
Kazakhstan
Ethiopia
ZambiaRwandaUK
Argentina
ChinaSolomon Islands
Chad
Chile
Nepal
Armenia
Peru Sweden
Indonesia
UgandaMoldovaParaguay
Costa
RicaNew Zealand
Kenya
Thailand
LebanonSyrianArab
Republic
GuyanaGhana
Madagascar
Brazil Fiji Jamaica
Switzerland
Barbados
MalawiAfghanistan
Colombia
Netherlands
Uruguay
Turkmenistan
Denmark
CentralAfricanRepublic
EritreaCongo,
Rep.
NorwayEstonia
Togo
Vietnam
PolandSri
Lanka
Germany
Hungary
Belgium
Haiti
Pakistan
FranceIraq
IcelandCuba
Nicaragua
Djibouti
CyprusCongo,
Dem. Rep.
Bangladesh
Austria
BotswanaCzech
UkraineRepublic
Romania
Ireland
Cote d'Ivoire
Burkina
Faso
BhutanBelarus Luxembourg
Lithuania
Mauritius
Somalia
EcuadorFinland
Dominican
Republic
Guinea
LaoTimor
PDR Slovenia
Cambodia
East
Honduras
Malta Greece
Mauritania Spain
SerbiaGambia,
TheCroatia
Slovakia
Macedonia
GuatemalaEl
Salvador
Albania
Suriname
Mozambique
Bosnia andHerzegovina
AngolaSenegalItaly
Bulgaria
Cape
Verde
Tunisia
MexicoGuinea-Bissau
Algeria
Comoros
MoroccoEquatorial
GuineaPortugal
Mali
TurkeyNigeria

Canada

0

US

0

.6
.2
.4
Share of more educated among population.
Figure 5: Selection of Emigrants in Terms of Education, 2000

.8

17Interestingly, positive selection of emigrants is at odds with much recent
empirical literature on international migration. In an influential line of work, Borjas(1987, 1991) uses the Roy (1951) model to show how migration costs and internationalvariation in the premium for skill affect the incentive to migrate. In countries with low
average wages and high wage inequality, as appears to be the case in much of thedeveloping world, there is negative selection of emigrants. Those with the greatest
incentive to relocate to rich countries (which tend to have high average wages and low
wage inequality) are individuals with below-average skill levels in their home countries.
Much of the recent empirical research on Borjas’ negative-selection hypothesis
examines labor movements either from Mexico to the US or Puerto Rico to the US
mainland. Puerto Rican outmigrants tend to have low education levels relative to nonmigrants (Ramos, 1992; Borjas, 2006), consistent with migrants being negatively selected
in terms of skill. Mexican emigrants, however, appear to be drawn more from the middleof the country’s schooling distribution, consistent instead with intermediate selection.
Feliciano (2001), Chiquiar and Hanson (2005), Orrenius and Zavodny (2005), McKenzie
and Rapoport (2006), and Cuecuecha (2005), and find that emigrants from Mexico aredrawn from the middle of the wage or schooling distribution, while Ibarraran and
Lubotsky (2005) and Fernandez-Huertas (2006) find that Mexican emigrants are drawn
from the lower middle of the wage or schooling distribution.
Based on Figure 5, Mexico and Puerto Rico (and Turkey) would appear to be
exceptional cases. Positive selection of emigrants is a nearly universal phenomenon.
Despite strong evidence that emigrants are positively selected in terms of schooling, there
is confusion in the literature over the relationship between income inequality and the

18
incentive to emigrate. An empirical approach made popular by Borjas (1987) is toexplain bilateral migration using sending-country per capita GDP and income inequality(e.g., as measured by the GINI coefficient) relative to the receiving country (e.g., Clark etal., 2007; Mayda, 2005). A positive parameter estimate on the GINI coefficient is seen as
an indicator that migrants are negatively selected in terms of skill. However, thisapproach characterizes selection into migration only under restrictive conditions.
To characterize the relationship between income inequality and migration, it isuseful to develop a simple model of the migration decision. Let the wage for individual ifrom sending country s in receiving country r be
(1)

(

)

W=
isr exp µ r + δ r z i ,where μr is the return to raw labor in r, δr is the return to an additional year of schoolinglevel in r, and zi is an individual i’s years of schooling. Let the cost of migrating fromcountry s to country r be given by,(2)

Cisr= fsr + εisr ,where fsr is a fixed monetary cost common to all individuals that migrate from s to r andεisr is an idiosyncratic migration cost term that has mean zero and an extreme value
distribution. Finally, let the utility associated with migrating from country s to country r
be a linear function of wages and migration costs, such that(3)

U=
Wisr − Cisr .
isrwhere utility from not migrating equals the sending-country wage. If individuals makethe migration decision in order to maximize utility, then, given the error is extreme value,

19
the model is a logit. Consider the log odds of an individual with a college education
migrating from s to r, which, given the logit structure, can be written as,
(4)

ln

c
Esr

Esc

(

)

= Wrc − Wsc − fsr ,cis the share of the college educated in s that migrate to r, Esc is the share ofwhere Esr

college educated that remain in s, and Whc is the wage to college educated labor incountry for h=r,s. Equation (6) expresses the logic of the Roy model, in which income
maximization is the motivation for migration. More individuals will move from countrys to country r the larger is the wage differential between the two countries and the smaller
are fixed migration costs. Grogger and Hanson (2007) show that this setup can begeneralized to allow for migration costs specific to skill and correlation in idiosyncratic
migration costs across receiving countries.
To use this model to evaluate migrant selection in terms of skill, I follow Grogger
and Hanson (2007) and compare the log odds of emigrating for those with a college
education (c) relative to those with a primary education (p), which from (4) is given by,
(7)

ln

c
Esr

p
Esr

Es

Esp

− ln
c

(

) (

)

= Wrc − Wsc − Whp − Wsp ,where fixed migration costs are differenced out of the expression. If the net gain toemigrating for the college educated exceeds that for the primary educated, the expressionin (7) would be positive and emigrants from h would be positively selected in terms ofeducation. Using (1), this would require that(8)eµr −µs >e δs z c − 1eδr z c − 1,

20
where zc indicates years of schooling for a college educated worker and the return toprimary educated labor is normalized to equal μ. Under the convenient approximationthat exp(x)-1=x for small x, we can rewrite equation (8) as,
(9)

Wrp
Wsp

>

δs 8
.
δr

On the left of (9) is the ratio of wages paid to raw labor (proxied here by the wage forprimary educated labor) in the receiving relative to sending country, which can bethought of as the ratio of raw labor productivity in the two countries; on the right of (9) is
the ratio of the Mincerian return to schooling (the log wage gain from an additional yearof schooling) in the sending relative to the receiving country.
Equation (9) says that emigrants from sending country s will be positively
selected in terms of schooling as long as the gain in the productivity of raw labor frommoving abroad more than compensates educated workers for the loss in the return toschooling. 9 One can think of the ratio of the return to schooling on the right of (9) as
capturing wage inequality, since, all else equal, higher returns to schooling in country s
will imply greater wage inequality. Apparent in (9) is that higher wage inequality in acountry by no means guarantees more negative selection of emigrants. Other factors
come into play, namely labor productivity. Differences in labor productivity matter for
selection because more skilled workers have more productivity equivalent units of laborto supply than unskilled workers. All else equal, higher labor productivity increases the
incentive to emigrate more for the more skilled. One way to explain positive selection of

8

See Grogger and Hanson (2007) for more details on this derivation.
A similar implication is present in Rosenzweig (2007), who derives a Roy model of migration withmoving costs that include components that are fixed in monetary units and time-equivalent units.

921
emigrants in Figure 6 is that international differences in labor productivity are large
relative to international differences in the Mincerian return to schooling.
To interpret the condition in (9), note that when comparing poor sending countries
to rich receiving countries, it is usually the case that the raw wage is higher in thereceiver while the return to schooling is higher in the sender. Suppose that in Nigeria
someone with a primary education would earn $1,000 a year and someone with a college
education would earn $5,000 a year, while the comparable sums in the US are $20,000
and $40,000. Clearly, the implied return to schooling in Nigeria (log return to schoolingof 0.16) is higher than in the US (log return to schooling of 0.07). Yet, the higher
productivity of raw labor in the US (US/Nigerian raw wage ratio is 20) more thancompensates, making the net gain from emigrating from Nigeria greater for moreeducated workers. Thus, when there are large differences in raw labor productivity
between countries, emigrants will tend to be positively selected in terms of skill.
Negative selection of workers by skill will obtain either where differences in laborproductivity across countries are small or migration costs are increasing in skill. The
latter feature is adopted by Borjas (1987), who assumes that migration costs are fixed inunits of time (such that more skilled workers pay more to migrate). As a result, in his
model (at least in its most simplified form—see Borjas (1991) for more elaborate models
with negative and positive selection) the pattern of migrant selection is determined
entirely by the relative return to skill across countries. However, once one introduces
large productivity differences between countries or migration costs that are fixed inmonetary units, the pattern of selection is indeterminant. Selection may be positive or
negative, depending on relative labor productivity, relative returns to skill, and skill-22
specific migration costs. Even in the simple model of migration I develop here,
migration selection in terms of skill is not robust. While this may seem obvious once one
inspects the theory, it is perhaps a result that is underappreciated in the literature.
Credit constraints in sending countries could make migration costs decreasing inskill, which would strengthen pressure for positive selection. Suppose, for instance, that
education and migration are subject to a fixed monetary cost and credit-market
imperfections make wealthier individuals subject to lower borrowing costs (e.g., Banerjee
and Newman, 1993; Rapoport, 2002). Then, the wealthier will be more likely to become
educated and more likely to migrate abroad (Assuncao and Carvalho, 2007). For Mexico,
McKenzie and Rapoport (2007) find an inverted U-shaped relationship between
migration and wealth, consistent with low-wealth individuals being too poor to afford
migration and high-wealth individuals having an incentive not to leave.
Rosenzweig (2007) examines migrant selectivity with data from the New
Immigrant Survey. The NIS reports the wage an individual earned in his last job before
coming to the US, which Rosenzweig uses to estimate the marginal product of labor bysource country. A country’s overall emigration rate to the US is decreasing in themarginal product of labor, suggesting countries with higher labor productivity send fewer
migrants to the US. Rosenzweig estimates that raising a country’s marginal product oflabor by 10% relative to the US would reduce the number of emigrants obtaining US
employment-based visas by 8%. The average schooling of emigrants to the US is
increasing in the marginal product of labor, indicating that in countries with higher laborproductivity it is the more educated migrants who are most likely to leave. 10

10In related work, Rosenzweig (2006) finds that the numbers of students who come to the US for higher
education and who stay in the US after completing their education are each decreasing in the marginal

23
Any analysis of migrant selection based on observed characteristics leaves open
the question of how migrants are selected on unobservables. McKenzie, Gibson,
Stillman (2006) examine this issue using data on Tonga, in which individuals may apply
to a lottery to obtain a visa to move to New Zealand. Comparing visa applicants who lostthe lottery (meaning they stayed in Tonga) with non-applicants, they find that thosedesiring to migrate have higher earnings, controlling for observed characteristics,
suggesting prospective migrants from Tonga are positively selected in terms ofunobserved skill. McKenzie, Gibson, Stillman find that failing to account for selectionon unobservables leads to substantial overstatement of the gains to migration.
What does the simple model of income maximization in (7) imply about howemigrants sort themselves across destination countries? Rewrite the expression as,
(10)

ln

c
Esr

p
Esr
−
ln
=
Esc
Esp

(W − W ) + α + η
c
r

p
r

s

sr

where αs is a country fixed effect that absorbs sending country wages and ηsr is adisturbance term capturing measurement error in migration flows. Equation (10) is aregression specification which predicts that more skilled workers will flow in greaternumbers to receiving countries that have larger rewards to skill, expressed here by thelevel difference in wages between high and low educated labor. Grogger and Hanson(2007) develop a fixed-effects specification similar to (10) and, using data from Beine,
Docquier and Rapoport (2006a), find that the bilateral flow of more-educated migrants(relative to less-educated migrants) is increasing in the destination-country earnings gap
between high-income and low-income workers.

product of labor in the source country, suggesting that low rewards to skill in a country induce students
seeking university training to pursue their schooling abroad.

24
Their results can account for the observed pattern of emigrant sorting acrossdestinations, seen in Table 3. The US is by far and away the largest destination country
for international migrants, with Canada being the second largest. In 2000, 53% of theforeign-born population in OECD countries resided in North America, while 36% resided
in the European Union and 10% resided in Asia and Oceania. The draw of the US andCanada is strongest for the more educated. While North American attracts only 38% ofemigrants with primary education it attracts 66% of emigrants with tertiary education. In
Europe, the shares are flipped, as it attracts 22% of emigrants with tertiary schooling and
53% of emigrants with primary schooling.

Table 3: Share of OECD Immigrants by Receiving Region and Education, 2000

Destination Region

Education GroupAll Primary Secondary Tertiary

North America

0.514 0.352

0.540

0.655

Europe0.384 0.560

0.349

0.236

Australia & Oceania

0.102 0.088

0.111

0.109

0.355

0.292

0.353

All OECD

Notes: This table shows the share of OECD immigrants by receiving region andeducation group in 2000. Source: Grogger and Hanson (2007).

The pattern of emigrant sorting in Table 3 is consistent with observed differencesin the reward to skill. Among OECD destinations, the level difference in income
between high-skill and low-skill labor is largest in the US, with Canada having the25fourth-largest difference (and the UK and Australia coming in at numbers two and three).
Continental Europe, on the other hand, has a relatively low income gap between high andlow-skill labor, consistent with relatively low income inequality. The consequence of
these income differences appears to be that North America and Australia attract a moreskilled mix of immigrants, while Continental Europe attracts a less-skilled mix.

26
4. Networks and Migration Costs
Although the evidence in Table 2 points to growth in international migration, theglobal stock of emigrants remains small, at around 3% of the world population. This is
surprising, given that the gains to international migration appear to be huge. Hanson(2006) reports that in 2000 the average hourly wage for a male with nine years ofeducation was $2.40 in Mexico and $8.70 for recent Mexican immigrants in the US (inPPP-adjusted prices). At the average labor supply for US adult male workers of 35 hours
per week, this would amount to an annual income gain of $12,000.
One way to reconcile large and persistent cross-country income differences withsmall global labor movements is that receiving countries are successful in restricting
labor inflows. While long queues for immigration visas in receiving countries indicate
legal admission restrictions bind, rising levels of illegal immigration suggest that borders
are porous. Further, observed costs of illegal entry are small in comparison to estimatedincome gains. In a sample of high migration-communities in Mexico during 2002 to 2004,
Cornelius (2005) finds the average price paid by migrants to be smuggled across the US
border was $1700, or one-seventh the apparent income gain.
Another explanation for small global labor flows is the existence of large
unobserved migration costs associated with credit constraints in financing migration,
uncertainty over economic opportunities abroad, the psychic cost of leaving home, orother factors. There is considerable academic interest in the role of migration networks
in lowering such costs. Survey evidence suggests transnational migration networks
provide prospective migrants with information about economic conditions in destination
countries, support in managing the immigration process, and help in obtaining housing

27
and finding a job (Massey et al., 1994; Massey and Espinosa, 1997). Much of theresearch on migration networks focuses on Mexico. On the process of crossing theborder, Orrenius and Zavodny (2005) report that among young males in Mexico theprobability of migrating to the US is higher for individuals whose fathers or siblings haveemigrated. Gathmann (2004) documents that migrants with family members in the US areless likely to hire the services of a professional smuggler, and, among those that do, likely to
pay lower prices. And McKenzie and Rapoport (2005) find that average schooling is lower
among migrants from communities in Mexico with a stronger US presence. These results
are each consistent with networks lowering migration costs.
While we still know little about the magnitude of migration costs, research on
networks suggests that migrant flows are sensitive to changes in these costs. Other evidence
on the sensitivity of migration to migration costs comes from illegal crossings at the
Mexico-US border. For illegal migration, the intensity of border enforcement is an
important determinant of entry costs, which take the form of fees paid to smugglers.
Cornelius (2005) reports that smuggler prices to enter the U.S. illegally increased by 37%
between 1996-1998 and 2002-2004, which spans the period during which the United States
stepped up border enforcement efforts in response to the terrorist attacks of 9/11.
Gathmann (2004) examines the consequences of expanded border enforcement
for migration. She identifies the correlates of smuggler prices paid by migrants from
Mexico to the US and estimates the impact of smuggler prices on migrant demand for
smuggler services. The price a migrant pays to a smuggler is higher in years when borderenforcement is higher, but the elasticity of smuggler prices with respect to enforcement issmall, in the range of 0.2 to 0.5. During the sample period, a one-standard-deviation

28
increase in enforcement would have lead to an increase in smuggler prices of less than$40. The demand for smuggler services and the probability of choosing to migrate to the
US are both responsive to changes in coyote prices. However, given the small
enforcement elasticity of coyote prices, the increase in US border enforcement over 1986
to 1998 (during which real spending on border enforcement increased by four times)
would have reduced the average migration probability in Mexico by only 10%.
In many destination countries, migrants reinforce networks by forming hometown associations that help members of their home communities make the transition toliving in a new location. By creating links between the destination country and a specificcommunity in the source country, these associations may lower migration costs forindividuals linked by kinship or birthplace to migrants living abroad. Of 218 home-town
associations formed by Mexican immigrants enumerated in a 2002 survey in California,
87% were associated with one of the nine central and western states in Mexico that havedominated migration to the US since the early 20th century (Cano, 2004), indicating that
migrant networks in Mexico are organized along regional lines.
Regional variation in migration networks creates regional variation in migration
dynamics. McKenzie and Rapoport (2007) show that in Mexican communities withhistorically weak migration networks moderately more wealthy individuals are more
likely to migrate, though very high wealth individuals are not. Migrants are thus drawn
from the middle of the wealth distribution, meaning that migration increases inequality.
In communities with strong migration networks, however, lower wealth individuals canafford to migrate, such that in these locations migration lowers inequality.

29
5. Impact of Emigration on Sending Countries
Emigration changes a country’s supply of labor, skill mix, and exposure to theglobal economy. These effects may have important consequences for a sending country’s
aggregate output, structure of wages, fiscal accounts, and trade and investment flows,
among other outcomes. In this section, I discuss recent empirical research on the impactof emigration on developing economies.

5.1 Labor Markets and Fiscal Accounts
Most research on the labor-market impacts of emigration focuses on Mexico.
Mishra (2007), applying the regression framework in Borjas (2003), examines thecorrelation between emigration to the US and decadal changes in wages for cohorts in
Mexico defined by their years of schooling and labor-market experience. She estimatesthat over the period 1970-2000 the elasticity of wages with respect to emigration in
Mexico is 0.4, implying a 10 percent reduction in labor supply due to emigration would
raise wages by 4 percent. Using a similar approach, Aydemir and Borjas (2007) estimate
a wage elasticity for emigration in Mexico of 0.6. Wage elasticities of this magnitude
suggest emigration has had a substantial impact on Mexico’s wage structure. Based onher estimation results and that fact that between 1970 and 2000 13% of Mexico’s labor
force emigrated to the US, Mishra (2007) calculates that emigration has raised average
wages in the country by 8%. Upward wage pressure has been strongest for young adults
with above-average education levels (those with 9 to 15 years of schooling), who in the
1990s were those most likely to emigrate (Chiquiar and Hanson, 2005).
In response to changes in labor supply associated with emigration, one might
expect the supply of capital in Mexico to adjust, with the country becoming less attractive

30
to inward foreign direct investment. Alternatively, higher wages could erode Mexico’s
comparative advantage in labor-intensive industries, reducing the net exports of labor
services embodied in goods as a consequence of emigration induced Dutch disease.
Either change would tend to offset the effects of emigration on wages in the country.
Since the estimation approaches in Mishra (2007) and Aydemir and Borjas (2007) are
reduced form, they capture the wage impact of emigration, net of these and otheradjustments. Their results suggest that any response of capital accumulation or trade toemigration is too slow or too small to undo the wage consequences of labor outflows, atleast over ten-year time intervals. Such a finding is not all that surprising. Factor-pricedifferences between the United States and Mexico create an incentive for trade in goods,
north-to-south flows of capital, and south-to-north flows of labor. Despite dramatic
reductions in barriers to trade and investment between the two countries during the lasttwo decades, US-Mexico wage differences remain large. Since trade and investment are
insufficient to equalize factor prices within North America, theory would predict that
migration from Mexico to the US would affect wages in both countries, consistent withthe evidence.
The Mexican emigration experience differs from other countries in terms of theabsence of positive selection, the high fraction of those leaving who enter the destination
country as illegal migrants, and the sheer scale of the exodus. The positive selection ofemigrants in most source countries raises the prospect of important fiscal impacts frominternational migration. In countries with progressive income taxes, the loss of skilled
emigrants could adversely affect public budgets through a loss of future tax contributions.

31
These lost contributions are in part the returns to public investments in the education ofemigrating workers, which, after emigration, accrue to destination countries.
While there is a large body of theoretical literature on the taxation of skilled
emigration (e.g., Bhagwati and Hamada, 1974; Bhagwati and Wilson, 1989; Docquierand Rapoport, 2007), empirical research on the subject is sparse. One recent contribution
is Desai, Kapur, and McHale (2003), who examine the fiscal effects of brain drain from
India. In 2000, individuals with tertiary education made up 61% of Indian emigrants but
just 5% of India’s total population. Between 1990 and 2000, the emigration rate for thetertiary educated rose from 2.8% to 4.3%, compared to an increase of just 0.3% to 0.4%
for the population as a whole. Desai et al. examine Indian emigration to the US, which in2000 was host to 65% of India’s skilled emigrants (and 49% of all Indian emigrants).
They begin by producing a counterfactual income series that gives emigrants the income
they would have earned in India based on their observed characteristics and the returns tothese characteristics in India (using a Mincer wage regression). On the tax side, theycalculate income tax losses by running the counterfactual income series through theIndian income tax schedule and indirect tax losses using estimates of indirect tax
payments per unit of gross national income. On the spending side, they calculate
expenditure savings by identifying categories for which savings would exist – which aremost categories except interest payments and national defense – and then estimating
savings per individual. The results suggest Indian emigration to the US cost India net tax
contributions of 0.24% of GDP in 2000, which are partially offset by the tax take on
remittances (coming off of the sales tax revenue generated by the extra spending
remittances make possible) of 0.1% of GDP. For India, is appears that the tax

32
consequences of skilled emigration are modest. For small countries with very highemigration rates, the tax consequences would obviously be larger.
The research discussed so far address the static consequences of emigration for an
economy, ignoring dynamic considerations that may arise if skilled emigration raises the
incentive of unskilled workers to acquire human capital. In theory, feedback effects from
emigration to human-capital accumulation may change a country’s rate of economic
growth. Mountford (1997) shows that in the presence of human-capital externalities anemigration-induced increase in the incentive to acquire skill can help an economy escapea poverty trap, characterized by low investment in education and low growth, and moveto an equilibrium with high investment and high growth. Yet, it is entirely possible for
feedback effects to work in the opposite direction. Miyagiwa (1991) develops a model inwhich, because of human capital spillovers, the migration of skilled labor from a lowwage, skill-scare economy to a high-wage, skill-abundant economy reinforces the
incentive for brain drain, depleting the low-wage country of skilled labor. In Wong andYip (1999), the negative effects of brain drain on the stock of human capital reduce thelabor-exporting country’s growth rate.
Given that plausible theoretical models offer very different predictions for the
long-run consequences of skilled emigration, the effect of brain drain on an economy isultimately an empirical question. As mentioned in section 2, the literature on howemigration affects the incentive to acquire skill has yet to produce conclusive results,
making it impossible to say whether the consequences of brain drain for growth are likely
to be positive or negative. Case-study evidence is similarly inconclusive. In China,
India, and Taiwan, the migration of skilled labor to Silicon Valley in the US – where33
Indian and Chinese immigrants account for one third of the engineering labor force – hasbeen followed by increased trade with and investment from the US, helping foster thecreation of local high-technology industries (Saxenian, 2002). The recent rise ineducational attainment in China, India, and Taiwan may be partly a result of the lure ofworking in the US and the domestic expansion of sectors intensive in the use of skilled
technicians. 11 In Africa, however, the exodus of skilled professionals, many of whom
work in health care, may adversely affect living standards. Clemens (2006) reports thatin 25 out of 53 African nations at least 40% of native-born individuals practicing as
physicians were living and working abroad as of 2000. He finds a weak negative
correlation between child mortality and the share of the stock physicians (or nurses) thathas emigrated. Schiff (2006) offers further evidence that suggest pessimism about theprospects for a beneficial brain drain.

5.2 Remittances and Return Migration
In a static setting, were the only effect of international migration to move laborfrom one country to another, welfare in the sending country would decline (Hamilton and
Whalley, 1984). While the average incomes of migrants and destination-country natives
would rise, average income in the sending country would fall. Migrants, however, often
remit a portion of their income to family members at home, possibly reversing theincome loss in the sending country associated with the depletion of labor. In the last
several years, there has been substantial academic and policy interest in the consequencesof remittances for economic activity in sending countries.

11

Between 1990 and 2000, the share of the adult resident population (i.e., net of brain drain) with a tertiary
education rose from 2.0% to 2.7% in China, 4.1% to 4.8% in India, and 12.2% to 19.1% in Taiwan.

34
Table 4 shows workers’ remittances received from abroad as a share of GDP bygeographic region. Remittances have increased markedly in East Asia and the Pacific,
Latin America and the Caribbean, South Asia, and Sub-Saharan Africa. As of 2004,
remittances exceeded official development assistance in all regions except Sub-SaharanAfrica and were greater than 65% of foreign direct investment inflows in all regions
except Europe and Central Asia. Among the smaller countries of Central America, theCaribbean, and the South Pacific, remittances account for a large share of national
income, ranging from 10% to 17% of GDP in the Dominican Republic, Guatemala, El
Salvador, Honduras, Jamaica, and Nicaragua, and representing an astounding 53% of
GDP in Haiti (Acosta, Fajnzylber, and Lopez, 2007).

Table 4: Workers' Remittances and Compensation of Employees, % of GDP
Region

1992

1996

2000

2002

2004

2005

East Asia & Pacific

0.56

0.71

1.00

1.47

1.48

1.50

1.02

1.42

1.27

1.28

1.44

Europe & Central Asia
Latin America & Caribbean

0.70

0.79

1.04

1.67

2.06

1.98

Middle East & North Africa

8.31

3.69

3.07

3.76

4.31

4.13

South Asia

1.76

2.42

2.85

3.72

3.57

3.53

Sub-Saharan Africa

0.76

1.04

1.49

1.67

1.60

1.57

Source: World Development Indicators.

Reported remittances reflect those captured by the balance of payments, which
Freund and Spatafora (2007) suggest may understate actual remittances. Formal

35
remittance channels include banks and money transfer operators (e.g., Western Union)
for which service fees average 11% of the value of remittances. Informal remittances,which are moved by couriers, relatives, or migrants themselves, tend to have lower fees,
but presumably higher risk. Formal remittances are negatively correlated with service
charges, with a 10% increase in fees being associated with a 1.5% reduction in transfers.
Fees are lower in economies that are dollarized and more developed financially (asmeasured by the ratio of bank deposits to GDP).
Theoretical literature on migration models remittances as the outcome of a
dynamic contract between migrants and their families (e.g., Lucas and Stark, 1985). A
family helps finance migration costs for one of its members in return for a share of future
income gains associated with having moved to a higher wage location. Remittances are
the return on investments the family has made in the migrant. The prediction is that
remittances would rise following an increase in emigration and decline as existing
emigrants age and pay off debts to their families.
Having migrants abroad may also provide insurance for a family. To the extent
income shocks are imperfectly correlated across countries, migration helps families
smooth consumption over time by keeping remittances high when sending-country
income is low relative to the destination country and low when sending-country income
is relatively high (Rosenzweig and Stark, 1989). Yang (2007) examines changes inremittances to households in the Philippines before and after the Asian financial crisis,
which he uses as a natural experiment to examine the impact of remittances on householdbehavior. As of 1997, 6% of Philippine households had a member that had migrated
abroad. Some had gone to countries in the Middle East, whose currencies appreciated36
sharply against the Philippine peso in 1997-1998, while others had gone to countries in
East Asia, whose currencies appreciated less sharply or even depreciated. Consistent
with consumption smoothing, remittances increased more for households whose migrants
resided in countries that experienced stronger currency appreciation against the peso.
Since income shocks associated with movements in exchange rates are largely transitory
in nature, the response of remittances reveals the extent to which migrants sharetransitory income gains with family members at home. Yang finds that a 10%
depreciation of the Philippine peso is associated with a 6% increase in remittances.
Contrary to Yang’s results, remittances appear to be unresponsive to changes ingovernment transfers. In Mexico (Teruel and Davis, 2000) and Honduras and Nicaragua(Olinto, 2007) remittances are uncorrelated with changes in rural household receipts fromconditional cash transfer programs, which were introduced into communities on arandomized basis, permitting the experimental analysis of their impact on householdbehavior. Were remittances a vehicle for consumption smoothing among rural
households, one would expect them to decline for a sending-country household,following an exogenous increase in government income support.
There is some evidence that increases in remittances are associated with increased
expenditure on education and health. Cox Edwards and Ureta (2003) find that in El
Salvador households that receive remittances are more likely to allow children to stay inschool, with the effect being stronger in rural areas. Why should remittances becorrelated with school attendance? One possibility is that remittances allow creditconstrained households to increase investments in productive activities that capitalmarket imperfections prevent them from financing through borrowing. However, an37equally plausible explanation is that households that receive remittances are less credit
constrained to begin with and so more likely to invest in education, in which case thecorrelation between remittances and educational investments may be the byproduct of
their correlation with some omitted variable, such as unobserved wealth.
To identify the impact of remittances on education, Yang (2007) examineschanges in household expenditure and labor supply in the Philippines before and after the
Asian financial crisis. Households with migrants in countries experiencing stronger
currency appreciation vis-à-vis the peso had larger increases in spending on childeducation, spending on durable goods (televisions and motor vehicles), children’s school
attendance, and entrepreneurial investments. In these households, the labor supply of 1017 year old children fell by more, particularly for boys. In Mexico, Woodruff and
Zenteno (2007) also find a positive correlation between migration and sending-country
business formation. For a sample of small-scale enterprises, capital investment andcapital-output ratios are higher in firms where the owner was born in a state with higher
rates of migration to the United States. Woodruff and Zenteno instrument for current
state migration rates using proximity to the railroads along which Mexico’s initialmigration networks became established (Durand, Massey and Zenteno, 2001). Theirresults are consistent with two different mechanisms for business formation: remittances
relax credit constraints on the creation of small enterprises, or return migrants – who may
have accumulated valuable work experience in the United States – are more likely tolaunch new businesses upon returning to Mexico.
Remittances indicate migrants maintain contacts with family members at home.
They may do so in part because they anticipate returning home in the future, in which

38case return migration may depend on their foreign earning opportunities. Yang (2006)
finds that an exchange rate shock that raises the peso-value of foreign earnings reduces
the likelihood a Philippine emigrant returns home, with 10% real appreciation being
associated with a one-year return rate that is 1.4% lower.
One potential negative consequence of remittances is an increase in demand for
non-traded goods and services, driving up their prices and contributing to real exchange
rate appreciation. Concerns about Dutch disease would be particularly acute for small
countries experiencing large labor outflows, including many economies in Central
America, the Caribbean and the Pacific. Research into the consequences of emigration
for real exchange rates is still in an early stage.

5.3 Information and the Flow of Ideas
The positive correlation between bilateral trade and migration has beeninterpreted as evidence of a “diaspora externality,” in which previous waves of migration
create cross-national networks that facilitate exchange. Gould (1994) finds that bilateral
trade involving the US is larger with countries that have larger immigrant populations in
the US. Head and Reis (1998) find that a 10% increase in Canada’s immigrant
population from a particular country is associated with a 1% increase in bilateral
Canadian exports and a 3% increase in bilateral Canadian imports, with more recent
immigration having a stronger correlation with trade. It is difficult to draw causal
inferences from these results, since immigration may be correlated with unobserved
factors that also affect trade, such as the trading partners’ cultural similarity or bilateral
economic policies (e.g., preferential trade policies or investment treaties that raise thereturn to both migration and trade).

39
Pushing the analysis a step further, Rauch and Trindade (2002) focus specificallyon networks associated with overseas Chinese populations. Successive waves of
emigration from southeastern China have created communities of ethnic Chinese
throughout Southeast Asia, as well as in South Asia and on the east cost of Africa. Rauchand Trindade find that bilateral trade is positively correlated with the interaction between
the two countries’ Chinese populations (expressed as shares of the national population),
similar to the findings in Gould and Head and Ries. More interestingly, the correlation
between Chinese populations and trade is stronger for differentiated products than it is forhomogenous goods. To the extent differentiated products are more subject toinformational problems in exchange (Rauch, 1999), these are the goods one would expect
to be most sensitive to the presence of business networks.
Still unclear is whether greater trade is the natural outcome of increased migration
or a reflection of the types of individuals who select into migration. If more skilled andmore able individuals are more likely to migrate abroad and more likely to exploit
opportunities for commercial exchange, then the correlation between trade and migration
may be a byproduct of migrant self-selection. Subsequent policies to liberalize
immigration in destination countries would not necessarily increase trade with sending
countries, unless they allowed for the admission of individuals with a propensity toengage in trade. Head and Ries (1998) find that immigrants admitted as refugees or on
the basis of family ties with Canadian residents have a smaller effect on trade than
immigrants admitted under a point system that values labor-market skills.
More controversial is the impact of emigration on political outcomes in sending
countries. When individuals live and work in another country they are exposed to new40
political ideologies and alternative systems of government. This exposure may be mostimportant for students who go abroad to obtain a university degree, as they are at an
impressionable age and often travel on visas that require them to return to home after
completing their studies. Spilimbergo (2006) suggests there is an association between acountry’s democratic tendencies and the political systems of the countries under which its
students did their university training. He finds a positive correlation between the
democracy index in for a sending country and the average democracy index in the
countries in which a country’s emigrant students have studied. Unknown is whether thepolitical system of a sending country influences the types of countries in which its
students choose to study. Kim (1998), for instance, finds that the bilateral flow of foreign
students is larger between countries that share a common religion.

6. Final Discussion
Over the last decade and a half, migration flows from low and middle-income
countries to high-income countries have been increasing. The phenomenon is justbeginning to be understood, as cross-country data on international migration have only
recently become available. Another factor hindering research is that migration is jointly
determined with many other outcomes, complicating causal inference on the impact of
migration on economic development. With these concerns in mind, I summarize what
appear to be the more robust findings (or nonfindings) in the literature.

1. Bilateral migration flows are negatively affected by migration costs, as captured bygeographic or linguistic distance between countries, the absence of migration networks,
or the stringency of border enforcement against illegal entry. That migration is41
negatively correlated with migration costs is not surprising. What is surprising is that
migration flows are so small in relation to observed migration costs, suggesting thatunobserved costs – broadly defined – must be substantial.

2. Emigration rates are highest for developing countries at middle income levels and withhigher population densities. The inverse U relationship between average income andmigration is suggestive of credit constraints that prevent individuals with very low
incomes from being able to finance migration through borrowing.

3. In most developing countries, it is the more educated who have the highest likelihoodof emigrating. In the large majority of sending countries, emigrants are positively
selected in terms of observable skill. In theory, positive selection would result from large
international differences in labor productivity, small international differences in the
return to skill, and/or migration costs that do not increase in skill too strongly. In
Australia, Europe and North America, high labor productivity attracts more-educatedimmigrants from low-income countries, despite the fact that many of these individuals
could earn a higher annual percentage return on their schooling at home.

4. Emigrants sort themselves across destinations according to income-earning
possibilities, with the countries that have the highest incomes for skilled labor attracting
the most educated mix of immigrants. The ability of a country to attract more-skilled
emigrants appears to depend on its reward to skill relative to other destinations. Thus,
Australia, Canada, the UK, and the US, in which high-skilled workers enjoy relatively
high earnings, attract a more skilled mix of emigrants than continental Europe.

42
5. Empirically, the impact of opportunities for skilled emigration on the stock of humancapital in a country is unknown. In the last decade, a new theoretical literature has
emerged which takes a more sanguine view of brain drain. While the idea that skilled
emigration raises the incentive to acquire skill in a country is plausible, the literature is
missing well identified econometric estimates of how human capital accumulation andeconomic growth respond to labor outflows. We still do not know whether opportunities
for skilled emigration create brain drain or brain gain.

6. There is some evidence that emigration puts upward pressure on wages in sending
countries. In the short run, economic theory suggests the exodus of labor from a country
would raise wages. Evidence from Mexico indicates that emigration has increased wages
for the skill groups and regions with the highest emigration rates. The preponderance ofrelatively highly educated individuals among emigrants suggests labor outflows may
have adverse consequences on sending countries’ public finances. However, in the caseof India the fiscal effects of skilled emigration appear to be small. If emigration affects
the wage structure, one would expect that it also affects housing prices, but research on
how labor outflows impact real estate values is scant.

7. Migrant remittances tend to positively correlated with household consumption andinvestments in education and entrepreneurial activities in sending countries. For
households sending migrants abroad, remittances may largely replace income lost due tolower labor supply on the national labor market. Remittances appear to decline with amigrant’s time abroad, suggesting the sending country income boost from emigration
may not be long lived.

43

Despite recent advances in the theoretical and empirical analysis of internationalmigration, there is still a great deal that we do not know about global labor movements.
Much of the individual level data on international migration covers Mexico and/or theUS, which are the subject of a large literature. As the largest sending country andreceiving country, there is still more to learn about the Mexico-US context. Yet, thehighest payoff to research is likely to be in the many under-studied parts of the world.
Since 1990, Central and Eastern Europe have become major sending regions; the Gulf
States, Russia, and Spain have become an important receiving regions; and emigrationfrom China, India, Indonesia, Pakistan, and the Philippines have accelerated, to name but
a few of the recent developments in global labor flows.
Among the many questions about international migration that deserve further
study, I would emphasize the following:•

We know little about the magnitude of international migration costs. What is therelative importance of uncertainty, credit constraints, and destination-country
admission policies in keeping the poor from migrating to rich economies?

•

While there is evidence that migration networks play an important role in
reducing moving costs, the dynamics of networks are poorly understood. Are
there diminishing returns in the impact of network size on migration costs? Ordoes the existence of networks imply that spatial opportunities for emigration will
only become more unequal over time?

•

We still know little empirically about the factors that determine who leaves
different countries. What are the contributions of international differences inlabor productivity, returns to schooling, and migration costs to migrant selection?

44
How does the exodus of skilled labor affect the wage structure, housing values,
and the real exchange rate?
•

Given the importance of human capital in economic development, how skilled
emigration affects a country’s relative supply of skill is a question of first-orderpolicy importance. How do how changes in education, tax, or other policies indeveloping countries affect skilled emigration, the domestic supply of skill, orremittances from skilled emigrants?

•

Sending and receiving countries are still far from having equal factor prices, inwhich case we might expect to see trade, migration, and FDI to happen
concurrently, even reinforcing one another. How does migration interact withinternational trade and foreign direct investment?

•

The inflow of remittances has been a welcome financial boon for many laborexporting countries. Do remittances in fact help deepen domestic financial
markets, as households use banks or other intermediaries to manage lumpy
income receipts from abroad?

Within the development policy community, there are calls for rich countries toopen their economies more widely to labor inflows from poor countries (e.g., Prichett,2006). Completely open borders are off the table politically. Were the developed world
to propose an increase in immigration quotas, should developing countries take the offer?
The answer would depend on how destination countries structured the additional labor
inflows. An increase in immigration quotas that targeted workers with higher levels ofskill could raise global income, even as it hurt the less-skilled majority in source
countries (Benhabib and Jovanovic, 2007). Quotas targeted to less-skilled workers could45
raise global welfare. The adoption of such a policy would surely face opposition inreceiving countries over concerns about fiscal and labor-market consequences. Getting
sending and receiving countries to coordinate on international migration is a difficult
task. A helpful first step would be to determine if the interests of the two groups ofcountries are really as far apart as they seem.

46
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