Brussels Economic Review
Cahiers Economiques de Bruxelles
Special Issue
Special Issue on Skilled Migration
Edited by : Michel BEINE and Frédéric DOCQUIER
Vol. 47 - n°1
Spring 2004
Editions du DULBEA asbl
Département d’Economie Appliquée
de l’Université Libre de Bruxelles
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
Sommaire - Content
Which attitude should we adopt towards international
skilled migration?
5
Michel Beine and Frédéric Docquier
The economic impact of immigration for the host countries
9
Xavier Chojnicki
The brain drain: A review of theory and facts
29
Simon Commander, Mari Kangasniemi and L. Alan Winters
Selective immigration policy in Australia, Canada,
and the United States
45
Heather Antecol, Deborah A. Cobb-Clark and Stephen J. Trejo
The demand for high-skilled workers and immigration policy
57
Thomas K. Bauer and Astrid Kunze
The impact of temporary migration on human capital
accumulation and economic development
77
Manon Domingues Dos Santos and Fabien Postel-Vinay
Who is afraid of the brain drain? Human capital flight
and growth in developing countries
Hillel Rapoport
2
89
Brain drain and Remittances: Implications for the source country
103
Dilek Cinar and Frédéric Docquier
Temporary migration and self-employment: Evidence from Tunisia
119
Alice Mesnard
Immigration and aging in the Belgian regions
139
Marc Debuisson, Frédéric Docquier, Abdul Noury and Madeleine Nantcho
Brain drain, brain gain and brain exchange: The role of MNEs
in a small open economy
159
Michele Cincera
3
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
WHICH ATTITUDE SHOULD WE ADOPT TOWARDS
INTERNATIONAL SKILLED MIGRATION?
BY MICHEL BEINE
(CADRE UNIVERSITY OF LILLE 2 AND DULBEA,
FREE UNIVERSITY OF BRUSSELS)
AND FREDERIC DOCQUIER
(CADRE UNIVERSITY OF LILLE 2, IWEPS
REGIONAL GOVERNMENT OF WALLONIA AND IZA-BONN)
1. MOTIVATIONS
The international migration of skilled workers (the so-called brain drain) has attracted
a considerable attention in the recent years. The reason is that, despite empirical controversies, there is a strong consensus that deficiency in human capital is a major cause
of inequality between countries. Given recent developments of immigration policies
conducted in receiving countries and the booming demand for highly skilled workers,
available evidence supports the view that the migration of the educated has intensified
over the 1990s. Ranking point systems in Australia and Canada lead to a strong selection of the potential immigrants. The increasing number of H1-B visas in the USA turns
out to raise the proportion of economic migrants. Publications of labor shortage occupation lists (UK, Ireland) and adaptations of recruitment policies towards high-potential
workers (Germany, France, Norway, Korea) have obviously altered the composition of
international migration flows. By the next decades, the size of brain drain is unlikely to
fall given the expansion of the high technology sector and the dark demographic
prospects faced by most industrialized nations.
Today, industrial countries such as Canada, the UK or Germany are worrying about the
magnitude of the emigration flows of skills. However, it is mainly for less developed countries that the detrimental consequences of brain drain have been stressed in the literature.
Can brain drain be considered as a major cause of low development? Which are the countries affected? What are the policy responses, both from an internationalist and a nationalist point of view. There is no clear and straightforward answer to these questions.
By reducing the number of educated remaining in the country, brain drain unambiguously generates a short-run loss for sending countries. The earlier literature on brain
drain essentially focused on this ex-post effect and investigated all its consequences for
5
WHICH ATTITUDE SHOULD WE ADOPT TOWARDS INTERNATIONAL SKILLED MIGRATION?
remaining residents. On the contrary, the “new economics of brain drain” emphasizes
the impact of migration flows and migration prospects on the ex-ante stock of human
capital (before migration is netted out)1. Taking account of some indirect economic
effects, one can reasonably consider that past migration flows or migration prospects
have positive effects on human capital accumulation. The potential channels potentially at work are return migration, remittances and/or the impact of migration prospects on
the expected return on education. In the long-run, the global impact of brain drain balances its ex-ante beneficial effects and the ex-post detrimental effects.
The major difficulty lies in the building of consistent and comparable evaluations of the
ex-ante effect. Whilst the ex-post impact can be roughly approximated, the ex-ante
requires econometric studies based on highly reliable statistics. Today, despite on-going
works, there are no sufficiently reliable database measuring brain drain on a large set of
countries and for different years. The only existing source has been provided by
Carrington and Detragiache (1998). They rely on a set of assumptions to estimate the
rate of emigration of tertiary educated workers from 61 developing countries in 1990.
The strongest assumption is that they transpose the skill structure of US immigrants on
the total OECD immigration stock. For example, immigrants from South Africa to the
UK are assumed to be distributed across educational categories in the same way as
immigrants from South Africa to the US. This assumption is obviously relevant for a
number of countries (Latin America, the Caribbean, selected Asian nations) but is highly misleading for countries with a low migration rate to the USA (Africa, most Asian
countries, Oceania or Europe).
Despite of this, tentative empirical tests based on Carrington and Detragiache’s data
reveal that the case for the beneficial brain drain hypothesis is potentially strong2. In
countries where brain drain is limited (say less that 20 percent of the educated are leaving) and where the education system is deficient (less than 5 percent of the population
opt for higher education), brain drain hardly appears as the cause of low development.
On the contrary, it could even (moderately) stimulate human capital accumulation. In
other countries, brain drain is likely to slow down productivity and economic growth.
Given the quality of the data, we believe that future research should focus on building
more consistent and reliable estimates of brain drain by educational categories and by
occupations. We argue that providing robust and consistent estimates of the ex-ante
effects is a sine qua non condition to capture the efficiency-equity tradeoff behind the
brain drain and to implement adequate policies. Indeed, disregarding or mismeasuring
the ex-ante effects could generate inappropriate responses. This is obviously the case if
additional restrictions on skilled migration would lower human capital investments to its
minimum. In such a case, fighting brain drain could make the world distribution of
income even more unequal.
1
2
See Stark (2003).
See Beine et al. (2003).
6
MICHEL BEINE AND FREDERIC DOCQUIER
2. STRUCTURE OF THE SPECIAL ISSUE
In this context, the purpose of this special issue is offer an up-to-date survey of the
major contributions regarding the international migration of skilled workers. Our panel
of studies provides important insights on the recent policy decisions toward immigration, on the composition of migration flows and on the economic consequences for both
sending and receiving countries.
The first two papers depict the literature on the economic consequences of skilled
migration. Xavier Chojnicki examines the impact on receiving countries, focusing on
the labor market and on public finance. He discusses the role of skilled migration in the
debate on aging and welfare reforms. Simon Commander, Mari Kangasmieni and Alan
Winters present the consequences for sending countries. After reviewing earlier and
recent models, they summarize the conclusions of econometric studies based on UK
individual survey data for health workers and software specialists.
The next two contributions provide highly instructive information on the evolution and
the consequences of selective policies in industrialized countries. Heather Antecol,
Deborah Cobb-Clark and Stephen Trejo compare selective immigration policies in
Australia, Canada and the USA over the 20th century. Then, they review the immigration outcomes in regard of policy changes. Point tests systems implemented in Canada
and Australia have obviously altered the skill levels of immigrants. However, they conclude that factors other than immigration policy are also important (social, historical or
geographic explanations). Thomas Bauer and Astrid Kunze describe the German policy
initiatives on temporary immigration of high-skilled workers. Using an international
employer survey, they argue that the temporary green cards system partly satisfies the
demand of firms for foreign specialists. They therefore point the need for a more comprehensive policy involving permanent visas.
The third part of this issue is devoted to the presentation of original contributions to
the new literature of brain drain. Manon Dos Santos and Fabien Postel-Vinay build a
model in which temporary migration can be seen as a potential source of growth for
the emigrant’s country, since it allows migrants to acquire knowledge and skills
abroad. From the source country point of view, they derive the optimal mix of permanent and temporary visas. Hillel Rapoport provides existing evidence on brain
drain and presents the incentive mechanism. He argues that migration prospects
increase the expected return to education in poor countries and foster domestic
enrollment in education. When this “brain effect” dominates the observed emigration
(or “drain”) effect, a brain drain with a brain gain is obtained. Dilek Cinar and
Frédéric Docquier model the long-run impact of skilled migration when emigrants
remit a part of the income earned abroad. As remittances make liquidity constraints
less binding, a long-run gain can also be obtained. However, they argue that such a
brain gain emerges under some restrictive conditions. Alice Mesnard empirically
demonstrates, in the case of Tunisian workers, that temporary migration has contributed to the economic development of Tunisia via two main channels, remittances
7
WHICH ATTITUDE SHOULD WE ADOPT TOWARDS INTERNATIONAL SKILLED MIGRATION?
and return migration with repatriated savings. She convincingly shows that temporary migration allows workers to overcome credit constraints for investments into
small business projects.
The last two contributions deal with the Belgian particular case. The paper by Marc
Debuisson, Frédéric Docquier, Abdul Noury and Madeleine Nantcho provides a
description of the structure of foreign population in Belgium. It analyses the assimilation of immigrants on the local labor markets and evaluates the regional need for migration in the face of demographic changes. Finally, Michele Cincera illustrates the strong
linkages between human capital mobility and technology. Using worldwide patent statistics, he measures the net foreign investment in the area of R&D and discusses their
effect on the demand for skilled workers in Belgium. The preliminary evidence suggests
that R&D investments in Belgium might have reduced the importance of brain drain:
They could furthermore generate a brain gain as new qualified personnel from the
headquarters of multinational firms are attracted in the country as well as brain
exchange for the host country.
REFERENCES
Beine M., F. Docquier and H. Rapoport, 2003. “Brain drain and LDCs’ growth: winners and losers”, IZA discussion paper, n. 819.
Carrington W.J. and E. Detragiache, 1998. “How big is the brain drain?”, IMF Staff
papers.
Stark O., 2003. “Rethinking the brain drain”, World Development 32(1), 15-22.
8
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST
COUNTRIES
XAVIER CHOJNICKI*
(MEDEE, UNIVERSITY OF LILLE 1)
ABSTRACT:
In this paper, we will investigate the economic consequences of immigration for the host countries.
Recently, the debate has been centered on the role of immigration in the process of aging. A priori, the
immigration of workers is likely to affect the economic situation of the host country in multiple ways, both
positive and negative. Most studies focused on the labor market reveal a weak net gain of immigration
whose distribution depends on the skill structure of immigrants and domestic labor force. Empirical studies show that past immigration had only a weak impact on native wages and unemployment rate. The net
effects on welfare benefits are not clear and are related to the composition of migrant flows. Studies analyzing the relations between the labor force migrations and the dynamics of growth of the concerned areas
put forward different mechanisms according to whether one uses exogenous or endogenous growth models. However, whatever the theoretical framework considered, the immigrants’ skills will be the determinant variable.
JEL CLASSIFICATION: F22, J31, J61.
KEYWORDS: International Migration, Geographic Labor Mobility, Immigrant Workers.
* I am grateful to M. Beine, F. Docquier, H. Jayet, J. Hellier, L. Ragot and S. Jimerson for useful comments.
9
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
INTRODUCTION
For many reasons, international migrations have always been a subject of concern, both
for the countries of origin and reception. Recently, the debate has been centered on the
role of immigration in the process of aging (United Nations, 2000). Indeed, acting on
immigration rather than on the fertility rate in order to attenuate demographic trends has
the advantage of having immediate effects. However, the extent of migratory flows to
implement largely depends on the demographic objectives. UN simulations reveal that
a stabilization of the dependency ratio until 2050 imply migratory flows of an unrealistic size. Hence, Europe should annually accommodate 12,7 million immigrants, either
on the whole 700 million from here to 2050 (for an initial population of 372 million
inhabitants) and the US 10,8 million, or nearly 600 million in 55 years.
Unrealistic as they may be, these projections lead us nonetheless to some interesting
conclusions. On the one hand, it confirms that massive immigration cannot alone constitute a solution to aging in the long run. Indeed, as time goes by, the fertility behavior
of immigrants is aligned with that of the natives. On the other hand, as recently recalled
by the European Commission, immigration can be used in order to alleviate sectoral
labor shortages or to hire highly skilled foreign workforce. Therefore, this debate on
replacement migrations arrives at the same time as that of selective migrant policies.
Several countries, such as the US, Australia or Canada, have already set up selection
programs aiming at increasing the proportion of skilled foreign workers. These selective policies allow these countries to face possible labor shortages in some sectors, such
as information technology1 and to create a flexible labor pool. In the context of skilled
labor shortages where recruitment difficulties can develop in just a few years as a result
of aging, many countries have to consider outlining a new migratory policy.
A priori, the immigration of workers is likely to affect the economic situation of the host
country in multiple ways, both positive and negative. Any serious evaluation must take
all the implemented mechanisms into account and evaluate their relative importance. In
this article, our aim is to outline the economic effects of migratory flows from the host
country point of view. We will successively present the recent trends in international
migrations, the consequences of immigration on the labor market and on government
budgets, and finally the long-term economic implications.
1. TRENDS IN INTERNATIONAL MIGRATION
Despite difficulties in comparing international data, there are both a number of characteristics common to the majority of OECD countries and notable changes in the size and
composition of migratory flows. Historically, the US have always been an immigration
country since they are the largest net recipients of immigrants (850 000 aliens entered
1
OECD (2002) estimated that roughly 850 000 technicians missed in the US and nearly 2 millions in Europe in 2001.
10
XAVIER CHOJNICKI
in 2000). Europe has experienced net flows of migration for four decades. This is particularly the case of Germany (as well as France, Switzerland and the UK), which
receives nearly four times more immigrants than the majority of other European countries. In Japan, immigration has traditionally been negligible, even if the relaxation of
restrictions aiming at temporary migrations allowed 346 000 arrivals in 2000.2 Since the
early 1980s, net migrations have constituted the main population growth factor for the
European Union taken as a whole as well as for the US.
Reflecting the increase in immigration over the last two decades, the stock of foreigners in OECD countries grew by over 13 million between 1988 and 1998, reaching
approximately 57 million people, i.e. 7 % of the total OECD population (OECD, 2001).
On the whole, more than half of the migrants are accommodated by a limited group of
rich countries (Table 1). North America is in first place with more than 30 million immigrants. Western Europe – The European Union and Switzerland - constitutes the second
of these poles. More than 20 million aliens are established there, of which two thirds
come from non EU countries. Finally, Australia accommodates 4,5 million immigrants.
In Europe, the share of foreigners in the total population is relatively weaker (approximately 5 % in 2000) in comparison with the much more important proportions in some
countries (reaching almost 20 % in Australia and Canada and 10 % in the US).
TABLE 1. FOREIGN OR FOREIGN BORN (a) POPULATION IN SELECTED OECD COUNTRIES
IN 2000
Germany
Australia
Belgium
Canada
France
Italy
Japan
UK
Switzerland
United-States
Inflows of
foreigners
(Thousands)
Stock of
foreigners
(Thousands)
Share of the
total population (%)
Foreign
workers
(Thousands)
Share of the
working population (%)
673,9
92,3
68,6
227,2
95,2
271,5
345,8
288,8
87,4
849,8
7 297
4 517
862
4 971
3 263
1 388
1 686
2 342
1 384
28 400
8,9
23,6
8,4
17,4
5,6
2,4
1,3
4,0
19,3
10,4
3 429
2 365
366
2 839
1 571
246
155
1 220
717
17 384
8,8
24,5
8,4
19,2
6,1
1,1
0,2
4,2
18,3
12,4
a) Data for the US, Canada and Australia refer to foreign-born population.
Source: Trends in International Migrations, OECD.
2
For more precisions, see the different editions of the OECD annual report, Trends in International Migrations.
11
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
The migration motivations of this foreign population have considerable importance in
our debate. Even if they vary significantly from one country to another, family reunification prevails in the flows of entries of almost all OECD countries. In developed countries, they generally represent nearly half of the new entries, even reaching 3/4 of the
new arrivals in the US and in France. Recently, the number of asylum seekers has also
increased, reaching relatively large proportions in some countries. Thus, the extrapolation of past trends would leave little room for a selective immigration policy.
The characteristics of the foreign population differ significantly from those of the
nationals and explain the growing interest taken in replacement migrations or in selective immigration policies. First of all, the age structure of this population, even if it
tends more and more to approach that of the natives, is often slightly younger. For
example, the median age of a new immigrant is 30 whereas that of the OECD total population is 36. Then, the fertility rates of immigrant women are generally relatively higher. Foreign births contribute to the natural population increase and slow aging. However,
this phenomenon primarily depends on the persistence of migratory flows. Indeed, a
prolonged stop in immigration results in appreciably reducing these positive effects in
the long term, insofar as the fertility rate of foreign women tends to align itself to that
of natives. Finally, the immigrant population is often characterized by a lower skill level
than that of the natives. Indeed, in a great number of OECD countries, more than half
of the adult foreign population has only a lower secondary level of diploma (Table 2).
TABLE
2. EDUCATIONAL LEVEL OF FOREIGN AND NATIONAL ADULT POPULATION IN 2000
Lower secondary
Unites-States
Germany
France
Italia
UK
Canada
Switzerland
Foreigners
30,1
48,5
66,7
55,0
30,1
22,2
33,6
Nationals
9,3
15,1
34,9
55,8
18,8
23,1
10,5
Upper secondary
Foreigners
24,7
36,1
19,6
32,1
29,1
54,9
42,6
Nationals
33,7
60,4
42,3
34,4
53,3
60,3
64,4
Third level
Foreigners
45,2
15,4
13,7
13,0
40,8
22,9
23,8
Nationals
57,1
24,5
22,7
9,8
27,9
16,6
25,1
Source: Trends in International Migrations, OECD.
Hence, past tendencies clearly show an immigration with different socio-economic
characteristics than those of natives. Let us now focus on the economic consequences
of immigration for the host country.
12
XAVIER CHOJNICKI
2. IMMIGRATION AND LABOR MARKET
2.1. THEORETICAL DEVELOPMENTS
The theoretical analysis of the labor market does not lead to a clear answer to the impact
of immigration on natives’ wages and unemployment. In standard models, the impact of
immigration on the labor market is analyzed as a shock on a factor of production, i.e.
labor supply or even low skilled labor supply. However, the effects are actually multiple: on total population, on final demand, on capital per worker, on employment and
unemployment, and on income distribution.
The most analyzed outcome is the direct effect on labor supply. Since Borjas (1995), it
is well known that an entry of foreign labor not accompanied by physical capital reduces
the equilibrium wage rate and involves a redistribution process. While increasing the
work supply from N to L , immigration induces a fall in the marginal product of labor
and in wages from w0 to w1 (Figure 1). Then, national income increases going from
ABNO to ACLO. Immigrants grant a share equivalent to w1M. The return on other inputs
increases and is now equivalent to Aw1C. This increase can be divided into two parts:
w0BDw1 is an income transfer from native workers and to the benefit of other factors of
production; BCD is the net contribution of immigrants to the natives’ income, entirely
collected by factors of production other than work. As such, immigrants only capture a
part of the wealth they contribute to creating. As a result, the natives then receive an
"immigration surplus".
FIGURE 1. THE IMMIGRATION SURPLUS (COMPETITIVE MARKET)
Borjas (1995) has estimated that the immigration surplus in the US was only on the
order of 0,1 % of GDP. Even if the value of the surplus is low, immigration has a substantial economic impact. “The relatively small size of the immigration surplus –particularly when compared to the very large wealth transfers caused by immigration–
probably explains why the debate over immigration policy has usually focused on the
13
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
potential harmful labor market impacts rather than on the overall increase of native
income. In other words, the debate stresses the distributional issues (the transfer of
wealth away from workers) rather than the efficiency gains (the positive immigration
surplus).” (Borjas, 1995)
The consequences of a change in the labor supply structure are related to the degree
of complementarity or substitutability between immigrant labor and other categories
of labor (and even other factors of production). Until now, we have considered that the
migrant workers were perfectly substitutable with the domestic workers. However,
many observations tend to show that this substitutability is imperfect and that migrant
workers with the same observable characteristics have lower wages than natives
(Borjas, 1994). Hence, it is essential to consider the existence of several categories of
workers, either by identifying the factors of production of which immigration modifies the total supply (Borjas, 1995) or by considering migrant workers as a specific
factor of production (Grossman, 1982; Greenwood and Hunt, 1995; Greenwood, Hunt
and Kohli, 1996). Such studies lead to the well-known result summarized by
Friedberg and Hunt (1995): “In a closed economy model, immigrants will lower the
price of factors with which they are perfect substitutes, have an ambiguous effect on
the price of factors with which they are imperfect substitutes and raise the price of
factors with which they are complements.”
Exclusively focused on labor markets, these studies disregard important channels whose
presence is likely to modify the results and their interpretation. A change of perspective
is then necessary. According to Altonji and Card (1991), the use of a partial equilibrium model can be erroneous. At the same time, migrations shift the labor supply and,
through the demand for goods and services, the labor demand. As immigrants raise the
scale of the economy, the marginal product of capital and labor increases. This additional effect can enlarge the size of the immigration surplus in a substantial way. The
final consequences of a simultaneous increase in the labor supply and demand, induced
by a higher goods and services demand, strongly depend on the overall level of returns
in the economy. Beyond certain thresholds, it seems reasonable to consider that immigration increases the congestion and that the returns to scale are decreasing in the presence of non-reproducible factors.
International mobility of goods and capital can modify the incidence of the immigration effects. Consequently, the analysis of the goods market channel must be
undertaken at the same time as the relations between immigration and foreign trade
(Borjas, Freeman and Katz, 1997). According to the Heckscher-Ohlin theorem, the
mobility of goods and factors induces a convergence of the factor price between the
different regions considered. Most of the argumentation rests on a possible substitutability between imports and domestic production, whose supply can be reinforced
by immigration. For some goods, the nationals can satisfy their demand by importing these goods from low-cost labor countries or they can "import" workers and produce there.
14
XAVIER CHOJNICKI
If trade can be a substitute to factor mobility, particularly to the migration of workers,
the mechanisms at work are complex and cannot be reduced to a one-to-one relation.
Several theoretical contributions3 have established that while deviating, even marginally, from the standard framework, free trade did not necessarily involve the equalization
of the factor prices. Trade and migration could then appear as complementary. For
example, the existence of technological differences between countries (Markusen,
1983) and of specific production factors (Jones, 1971) questions the idea of a substitutability between immigration and foreign trade, which is confirmed by the empirical
study of Collins, O' Rourke and Williamson (1997).
Furthermore, it seems inappropriate in the long term to assume the stability of natives
stock of production factors. Indeed, the change in the labor supply induced by the arrival
of immigrants is also the consequence of indirect effects through the reaction of the
indigenous population. On the one hand, the fall of wages induced by immigration leads
native workers to review the amount offered on the labor market derived from choices
between work and leisure. On the other hand, migrations can also influence the qualitative
aspects of the labor supply, particularly the skill choices. Indeed, the domestic population
can react to the modification of relative wages through training, thereby decreasing the
manpower of unskilled workers to increase the skilled worker supply (Chiswick, 1989).
Finally, taking into account natives’ migratory movements seems to be crucial. Native
migration may attenuate the local impact of immigration. By migrating away from areas
of relatively large immigrant concentration, or not migrating to such areas, natives avoid
the potentially adverse impacts that may be forthcoming through the production structure channel. At the same time, these migratory movements may not be sufficient to produce noticeably significant effects at the macro level. Thus, internal migrations could
distort the estimation of the immigration consequences on the labor market.
2.2. EMPIRICAL STUDIES
Although no clear relation between immigration and unemployment emerges (Figure 2),
migratory flows remain perceived as tending to increase the unemployment rate of
natives and decreasing their earnings. Measuring the effects of immigration on the labor
market gives rise to a vast literature. It is difficult to estimate the size and the nature of
these effects since they depend on the volume of immigration, on the composition of the
successive waves and on the migrants’ assimilation. However, there is a consensus on
the effects of the immigrants’ arrival on the host labor market.
3
See Schiff (2000) for a detailed presentation of these works.
15
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
FIGURE 2. IMMIGRATION AND UNEMPLOYMENT RATE IN OECD COUNTRIES IN 2000
12
Ita
Unemployment rate (%)
10
Fin
Fra
Ger
8
Bel
6
4
Por
2
Can
Austra
Swe
Jap Uk
Ire
Dk
Nor
Austri
Usa
Swi
Lux
Net
0
0
5
10
15
20
25
30
35
40
Foreigns share in the total population (%)
Source: OECD.
Empirical studies based on US data don’t reveal any clearly negative effects on native
wages and employment opportunities (Table 3). On average, these studies conclude that
native wages are slightly lower in areas with a strong rate of immigration. Therefore, the
elasticity of the native wage with respect to the number of immigrants generally lies
between –0,01 % and –0,02 %. It means that a rise of 10 % of immigrants in a given
geographical area would result in a fall in the native wage of about 0,2 % in this area.
However, the immigrant skill level determines the size of this effect, through the complementarity (or the substitutability) between immigrant and national workers.
TABLE 3. ELASTICITY OF NATIVE WAGES WITH RESPECT TO THE NUMBER
OF IMMIGRANTS
Study
Impact on
Dependant variable
Elasticity estimate
Altonji and Card
Less skilled natives
(1991, p. 220)
Bean, Lowell and Taylor Native Mexican men
(1988, p. 44)
Black men
Weekly wages
+0,018
Annual earnings
Annual earnings
-0,005 to +0,05
-0,003 to +0,06
Borjas
(1990, p. 87)
White native men
Black native men
Annual earnings
Annual earnings
-0,01
-0,02
Grossman
(1982, p. 600)
LaLonde and Topel
(1991, p. 186)
All natives
Factor share of native
workers
Annual earnings
Annual earnings
-0,02
Young Black natives
Young Hispanic natives
-0,059
-0,009
Source: Borjas (1994).
Borjas, Freeman and Katz (1992) put forward the considerable decline in the earnings and
employment prospects of unskilled workers in the US. They estimate that immigration and
16
XAVIER CHOJNICKI
foreign trade accounted for 3 to 5 points of the 9 % fall in unskilled wages between 1980
and 1988. According to them, the increase in the trade deficit in the 1980’s (representing
an unskilled implicit supply) and the increase in immigration have raised the unskilled
labor supply by approximately 30 %. This shock on the work supply would explain 30 to
50 % of the increase in inequalities in the US between 1980 and 1988. Similar results were
obtained in a more recent update (Borjas, Freeman and Katz, 1997) like in a similar study
undertaken by Jaeger (1996).
Borjas (1999) has advanced a more fundamental criticism on the empirical approach of
these studies. Most attempts to estimate the impact of immigration on wage rates use a
spatial correlation approach. However, when the mobility costs remain reasonable, the
economic theory suggests that any factor generating interregional differences in welfare
led to migrations from the weakest welfare areas towards the highest. Therefore, Filer
(1992) and Card (1997) show that natives seem to leave the areas where immigration
significantly increases. This would spread out the immigration repercussions over the
entire territory and would prevent the seizing of effects by an interregional comparison.
Moreover, the reactions of the domestic population concentrate primarily on the
unskilled, who are the closest substitutes for new immigrants.
Few studies focus on the impact of immigration on the native employment opportunities.
Table 4 summarizes the representative results in the literature. The bulk of the work again
relates to the US labor market. Estimates such as those of Simon, Moore and Sullivan
(1993) and Winegarden and Khor (1991) reveal a weak positive impact of immigration on
the US unemployment rate. Nevertheless, these results cannot be directly transposed to the
European case. Indeed, the labor markets in Europe are distinguished from the US market
for three reasons: slower adjustment to economic differences, unemployment hysteresis
and stronger imperfections. European studies of the immigration impact on labor market
are fewer but lead to the same conclusions as work on US data.
TABLE
4. ELASTICITY OF NATIVE EMPLOYMENT WITH RESPECT TO THE NUMBER
OF IMMIGRANTS
Study
Impact on
Dependant variable
Elasticity estimate
Altonji and Card
(1991, p. 220)
Borjas (1990, p. 92)
Less skilled natives
White native men
Black native men
Employment-population ratio
Weeks worked
Labor force participation rate
Labor force participation rate
-0,038
-0,062
-0,01
+0,04
Muller and Espenshade
(1985, p. 100)
Black natives
Unemployment rate
-0,01
Simon, Moore and Sullivan
(1993)
Natives
Unemployment rate
+0,001
Winegarden and Khor
(1991, p. 109)
Young White natives
Young Black natives
Unemployment rate
Unemployment rate
+0,01
-0,003
Source: Borjas (1994).
17
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
Although Winkelman and Zimmermann (1993) found that immigration contributed
slightly to increasing unemployment in Germany in the 1970s, Muhleisen and
Zimmermann (1994) did not find any effect in the 1980s. In terms of wage effect, it is
also necessary to distinguish between skilled and unskilled domestic labor force. For
example, DeNew and Zimmermann (1994) demonstrated that immigration appeared to
have depressed the wages of unskilled German workers but had an opposite effect on
those of the skilled workers. They suggested that a 1% increase in the number of immigrants would result in a fall of 4,1 % of the average wages, a fall of 5,9 % of the
unskilled wages and an increase in 3,5 % of the skilled one. Thus, the total effect seems
to be more significant than in the US case.
Over the period 1974-1994, Gross (1999) studies the impact of immigrant inflows on
the French labor market distinguishing between short and long-term consequences. This
study proposes a negative relationship between long-term unemployment and immigration, suggesting a compensation of the employment occupied by immigrants by the
increased demand they create. Alternatively, in the short term, an increase in the number of immigrants temporarily raises unemployment. In addition, the estimates of Jayet
and al. (2002) over the period 1990-1997 hardly reveal any negative effect on native
employment opportunities as well as on wage levels.
A comprehensive study of Gang and Rivera-Batiz (1994) on both the US and European
labor markets aims to isolate the specific skill characteristics of the immigrant and the
domestic labor force. Amongst other results, they suggest that a 1 % increase in the labor
force related to Turkish immigration would reduce the average wages of a Dutch worker
by 0,09 % whereas German workers would only experience a 0,01 % fall. A 1 % rise in
Asian immigrants would reduce average UK wages by 0,08 % and French wages by 0,1
% while North-African inflow would reduce French wages by 0,07 %. As recently pointed out by Borjas (1999), the national origin mix of the immigrant flows is the main factor
accounting for the skill differences across the population of the source countries.
Other well-known studies analyzed the adjustments following "natural" migratory
shocks. Card (1990) observed the impact of the massive exodus of Cubans towards
Miami in the 1980s, Hunt (1992) the return to France of the "pieds-noirs" of Algeria and
more recently, Angrist and Krueger (2003), the migrations following the wars in Bosnia
and Kosovo. Despite the importance of these migratory shocks, these studies showed
tiny effects on labor markets since adjustments were partially facilitated by internal
migrations of natives and firms mobility.
The conclusions of these studies are convergent: the immigration impact on wages and
employment is minimal. It is suggested that the immigrants are usually complementary
rather than substitutable to the indigenous labor force. Therefore, the negative consequences
of immigration will initially be endured by unskilled indigenous workers, especially if the
two groups tend to be concentrated in the same sector. These conclusions are all the more
robust as they are based on a large variety of data and methodological approaches.
18
XAVIER CHOJNICKI
3. IMMIGRATION AND GOVERNMENT BUDGETS
Another part of the debate focuses on the impact on government budgets. The comparison between the benefits drawn by immigrants from the public system (welfare expenditures, education, health, retirement) and the contribution they bring is not only important from the point of view of the public finance. It can also be a criterion for policy
makers to encourage or, conversely, discourage immigration.
In the US, a vast literature attempts to explain the differences in behavior between
immigrants and natives in the use of social programs. Blau (1984) showed that immigrant households had roughly the same probability as native households to receive public assistance in 1976. Nevertheless, with similar socio-economic characteristics, immigrants received lower benefits than nationals. However, a recent study of Gustman and
Steinmeier (2000) demonstrates that the likelihood for an immigrant to receive social
welfare payments increased between the beginning of the 1970s and the late 1990s, in
line with the declining skills of recent immigrants. Borjas (1994) finally displays the
existence of an adaptation period resulting in an increase in the welfare participation
rate for a specific immigrant wave.
The most direct way to evaluate the consequences on net welfare benefits is to compare
immigrants’ taxes and transfers for a particular fiscal year. Most applied studies have
again been carried out on US data. A first wave studied the effects at a local level (see
Rothman and Espenshade (1992) and Vernez and McCarthy (1996) for a survey of this
literature). Despite contrasting results according to the time-period, the geographical
area and the method employed, these studies suggest that immigration represents a net
load for the budgets of immigration states, whereas the balance is rather positive at the
federal level. However, these studies are not necessarily representative at the national
level because of the concentration of immigrants in some geographical areas.
In the early 1990s, the works of Huddle (1993), Passel (1994) and Borjas (1994) calculated the overall net surplus for a particular year. Huddle claims that immigration represents an annual net cost of $43 billion. Passel criticizes these conclusions, which overestimate the real immigration costs, and ends at a fiscal surplus of roughly $30 billion.
In view of these quite different conclusions, Borjas (1994) conducted his own estimates
in order to show the great sensitivity to the key parameters. Initially, he shows that the
difference between immigrants’ taxes and benefits represents a net surplus of $61,6 billion. However, all taxes are only compared with means-tested entitlement programs,
which largely distorts the calculations. Taking this argument into account, immigrants
represent a fiscal burden of $16,2 billion for social programs.
Hence, these studies cannot precisely evaluate the sign and the extent of migrants’ net
contribution to the welfare system. Indeed, their static nature cannot take of the future
taxes and benefits generate by immigrants into account. Simon’s (1984) approach is single insofar as the calculated balance is quasi-longitudinal. The costs of successive immigration cohorts are measured so as to evaluate the configuration of taxes paid and benefits
19
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
received by immigrant households throughout their life. The author shows that all immigrant cohorts that arrived in the US after 1950 are net contributors. But this attempt
obviously questions whether the benefits associated with successive cohorts can be
regarded as a life cycle estimate of the tax position of migrants. Hence, this study largely over-estimates the benefits related to recent immigration waves by including neither
the costs associated with immigrants’ children nor the changes in age and skill profiles
since the 1970s. Therefore, the only meaningful calculation is longitudinal. For example, one knows that immigrant incomes grow with time whereas benefits received
decrease; that a part will claim its old-age pension later like the natives, that another part
will return to its country of origin. Finally, these studies are not appropriate for evaluating the impact of a migratory policy change.
Using a partial equilibrium model, Lee and Miller (1997) projected the long-term fiscal impact of immigration in the US. Using CPS (Current Population Survey) data, they
initially built the age profiles of taxes and benefits of various immigrant generations in
1994. The benefits profiles of natives and immigrants appear quite similar but immigrants pay considerably lower taxes at each age. Then, they project the long-term impact
and demonstrate that an immigrant has a positive average fiscal impact of $80 000
(Table 5). The positive fiscal impact is strongest when immigrants are 10 to 30 years old
and highly depends on their skills (especially for the first immigrant generations).5
TABLE 5. AVERAGE LONG TERM FISCAL IMPACT OF AN IMMIGRANT BY EDUCATION
LEVEL IN THE US
Education level of immigrant
Group
< High School
Immigrants only
-89 000
Descendants
+ 76 000
Immigrants and descendants - 13 000
High School
> High school
Overall
- 31 000
+ 82 000
+ 51 000
+ 105 000
+ 93 000
+ 198 000
- 3 000
+ 83 000
+ 80 000
1996 dollars
Source: Lee and Miller (1997).
Other recent studies, based on generational accounting methodology, consider the
impact of changes in immigration policy on the average fiscal burden of different age
cohorts. The results differ somewhat depending on whether they are carried out in the
US or in Europe. Auerbach and Oreopoulos (1999) show that the fiscal impact of US
immigration is small. Whether there is a gain or a loss relies on the extent to which the
existing fiscal imbalance will be borne by future generations. Moreover, the extent of
expenditure unrelated to population size will largely determine the fiscal impact of
immigration. Finally, a change in immigration policy that alters the composition rather
5
Similar results were obtained in a recent update (Lee and Miller, 2000) taking account of higher projected rates
of productivity, recent tax reform and last demographic projections.
20
XAVIER CHOJNICKI
than the level of migratory flows can potentially reduce the fiscal burden bequeathed to
future generations. Conversely, Bonin and al. (2000) for Germany and Collado and al.
(2003) for Spain, lead to a positive and significant effect of immigration on the
intertemporal budget constraint, which can be substantially strengthened by a selective
immigration policy. These apparently contradictory results rely on the much more dramatic nature of population aging in Europe compared to that in the US.
Contrary to previous partial equilibrium studies, Storesletten (2000) calibrates a general equilibrium overlapping generations model. Agents in the model economy differ in
age, skill and legal status (natives, legal and illegal immigrants). Immigrants are also
distinguished from natives by a higher fertility rate and return migrations are introduced. The author explores whether a selective immigration policy could be used to balance the US budgets in a context of population aging. The net discounted gain to the
government of admitting one additional representative immigrant is a mere $7 400. But
this figure masks strong disparities: the net contribution of a highly skilled immigrant
is $96 000 whereas a medium and a low skilled immigrant represent a respective fiscal
burden of $36 000 and $2 000. The optimal immigration policy able to satisfy the government long-term budget constraint with unchanged fiscal policy would be to increase
the flows of high and medium skilled middle age migrants. This assumes an increase in
the number of annual entries from 0,44 % to 0,62 % (that is to say 1,6 million annual
entries) restricting them to 40-44 year-old high skilled immigrants (Table 6). Hence, if
the age and skill composition of the new immigrants is similar to that of the current one,
an increase in migratory flows could not help to balance the budget in the long run.
TABLE 6. ANNUAL IMMIGRATION (% OF POPULATION) REQUIRED TO BALANCE
THE GOVERNMENT BUDGET WITH FISCAL POLICY UNCHANGED
Age of new immigrants
Skill level
20-34
25-29
30-34
35-39
40-44
45-49
50-54
High-skilled
Medium-skilled
Low-skilled
1,89
-*
-
0,84
3,13
-
0,66
2,01
-
0,62
1,79
-
0,62
2,13
-
0,77
3,86
-
2,01
-
* No positive number large enough to balance the budget in the long run.
Source: Storesletten (2000).
4. IMMIGRATION AND ECONOMIC GROWTH
Most studies presented up to now were conducted over a short time span. However,
immigration is also likely to modify the labor/capital ratio and the technological
choices in the long run. All in all, beyond the labor market adjustments, immigration
influences the growth and the organization of the production system. Although the
recent theoretical works have progressed in explaining the links between immigration
21
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
and economic growth, few empirical studies have been conducted. Moreover, the
effects can be different according to whether the force driving growth is endogenous
or exogenous.
Solow’s model constitutes the starting point to study the links between immigration and
growth. Widening the model to migrations implies a certain degree of mobility of work
and human capital (but the economy remains closed with respect to foreign goods and
assets). In such a model, the determinant variable will be the immigrant skills and therefore the human capital quantity they bring. Along the balanced growth path, the per
capita income is an increasing function of the capital stock per efficient unit of work.
Consequently, when the migratory flows are composed of relatively low skill labor, they
intuitively imply5 a reduction of the per capita capital and of the per capita income of
the host country. Hence, migrations induce a convergence in the living standards across
countries when, as predicted by the market forces, they are carried out from the poorest
countries towards the richest. Thus, migrations have an expansionist impact for the host
country if the migrants are relatively more skilled than the natives and a recessionnist
impact in the opposite case. Table 5 summarizes the results in a modified Solow model.
TABLE 7. IMMIGRATION EFFECTS IN EXOGENOUS GROWTH MODEL IF IMMIGRANTS ARE
LESS SKILLED THAN NATIVES
Immigrants’ human capital
Net immigration rate
Saving rate
Standard capital requirement
Growth rate
Speed of
convergence
Steady state
output level
Current output
level
+
+
-
+
+
+
+
+
-
+
=
=
Source: Dolado, Goria and Ichino (1993).
Obviously, this kind of model has some drawbacks. First of all, the flows are determined
by an ad hoc migration function instead of an optimizing choice of households. Then, the
capital mobility is restricted to the human capital brought by the migrants. Braun (1993)
proposed various extensions postulating variable degrees of capital mobility and a migratory function rising from optimizing decisions. Consequently, if we consider two countries
of different development levels, people and capital will move towards the economy with the
best technology. In order to prevent only one area from remaining populated in the long run,
Braun introduced the concept of a natural resource subject to a congestion effect. However,
the results are still similar except for the speed of convergence across economies that now
relies on the degree of congestion of the fixed factor and on the sensitivity of the migration
rate to the remuneration gap between countries.
5
See Barro and Sala-I-Martin (1995) for a more detailed presentation.
22
XAVIER CHOJNICKI
Few studies have tried to empirically validate these results. The answers brought by
empirical studies are sensitive to the period considered. Barro and Sala-I-Martin (1995)
estimated the effect of migration on convergence for the US, Japan, Germany, Italy,
France, Spain and the UK. When the migration rate is excluded from the list of explanatory variables, the results obtained are close to the usual one. When the net migration
rate is included in the regressions, contrary to expectations, the estimate of ␤ with an
OLS specification does not decrease when the net rate of migration is held constant. The
results are probably influenced by the endogeneity of the net migration rate. Then, the
authors try to isolate the exogenous shifts in migration by using the technique of instrumental variables. Consequently, the net migration rate is explained by 3 explanatory
variables: the log of per capita income, the population density (reflecting a possible congestion effect) and the average temperature (representing a pure amenity). The difference between the convergence speed estimated while excluding and including the
migration rate is weak. Hence, the uncertainty of the results indicates that migration
plays only a minor role in convergence.
Conversely, the studies covering the period 1850-1914 demonstrate the dominating role
of migrations in the convergence process (Taylor and Williamson, 1994; Williamson,
1995). Migrations account for a very large share of the convergence in GDP per worker and real wages. Therefore, the empirical validation of the exogenous growth model
results seems limited and contradictory depending on the period considered. This mitigated impact on convergence supposes that migration also induces divergent phenomena not taken into account by the exogenous growth models.
The literature on labor migration and endogenous growth is mostly focused on the problem of brain drain. Consequently, the main purpose is to study the consequences of the
migrations of skilled workers from poor countries to rich countries. The endogenous
growth theories highlight some interdependencies (a possible source of divergence)
between the quantitative and the qualitative characteristics of the migratory flows and the
technological development. Several works6, Miyagiwa (1991), Mountford (1994) and
Haque and Kim (1995), take up the general framework of the Lucas model, including
migrations. They assume the existence of two countries producing a homogeneous good
through human capital, which is the only production factor. These studies show that the
impact of immigration on the growth rate of host countries is rather ambiguous. It depends
on the migrants’ and natives’ relative level of knowledge as well as the extent of the migratory flows. Indeed, when the flows are relatively important and the immigrants’ human
capital is relatively weak, immigration has a negative impact on the long-term growth rate
of the host country. Only an entry of highly skilled labor would have a positive impact on
the long-term dynamics of the host country. In that case, immigration would be a potential source of divergence between the host and the source countries.
Robertson (2002) confirms this negative impact of low skilled immigration. He modifies the growth model of Lucas in order to integrate unskilled labor as a separate factor.
6
See Domingues Dos Santos (1997) for a more detailed discussion.
23
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
He shows that an unanticipated rise in the stock of unskilled workers leads the economy on a transitional growth path with a slow growth of human capital relative to the balanced path. Indeed, in response to this exogenous rise of unskilled workers, the economy temporarily reduces the level of investment in human capital and increases goods
production. Intuitively, the desire for current consumption outweighs the loss of future
consumption from a lower growth rate of human capital.
Lundborg and Segerstrom (2002) used the framework of a quality ladders growth model
like that of Grossman and Helpman. They consider two structurally different countries.
The two areas are distinguished by the R&D capabilities of their workers. In equilibrium, all "High Tech" production takes place in the North. Then, the authors simulate the
effect of a migration of southern workers towards the North equivalent to a 5 % rise of
the North’s population. This policy increases the growth rate of per capita GNP in both
the North and the South but results in a reduction of the real wages of the northern
workers. Northern firms respond by allocating more resources to R&D activities,
improving the probability of innovation. However, this higher rate of market turnover
tends to reduce firms’ expected discounted profit. On the whole, immigration reduces
the discounted welfare of the northern population. But the growth rate only relies on the
R&D activity that firms carry out. Indeed, this model does not take the externalities
related to the human capital brought by immigrants into account (and therefore the
importance for the host country to follow a selective immigration policy).
CONCLUSION
The purpose of this article is to evaluate the main economic effects of immigration. Most
studies focused on the labor market propose a weak net gain of immigration whose distribution is related to the immigrants’ skills and to how those skills compare with the skills of
natives. Empirical studies show that past immigration only had a weak impact on natives’
wages and unemployment rate. The net effects on welfare transfers are unclear and strongly depend on the composition of the migration flows. Nevertheless, we have seen that a
selective policy on age and skills could represent an alternative instrument to the traditional economic policies with regards to aging. The studies analyzing the relations between
migration and the dynamics of growth of the receiving and sending areas are of two types.
Firstly, when the dynamic of growth is treated as exogenous, unskilled migratory flows
speed up the convergence of wages and per capita GDP between the source and the host
countries. Secondly, the endogenous growth theories highlight some interdependencies,
maybe sources of divergence, between the quantitative and the qualitative characteristics of
the migratory flows and the technological evolution.
To conclude, the skill composition of the immigrant population determines the social
and economic consequences of immigration for the country. Thus, the positive effects
stemming from future immigration mainly depend on the possibility of following a
selective policy, as well as on the age and on the skill level of immigrants.
24
XAVIER CHOJNICKI
REFERENCES
Altonji J.G. and D. Card, 1991. “ The Effects of Immigration on the Labor Market
Outcomes of Less-skilled Natives”, In J. Abowd and R. Freeman, eds., 201-234.
Angrist J. and A. Krueger, 2003. “Protective or Counter-Productive? Labor Market
Institutions and the Effect of Immigration on EU Natives”, The Economic Journal,
113, 488, 302-331.
Auerbach A. and P. Oreopoulos, 1999. “Analysing the Fiscal Impact of US
Immigration”, American Economic Review, 89, 2, 176-180.
Barro R. and X. Sala-I-Martin, 1995. “ Economic Growth”, McGraw-Hill.
Bean F.D., B.L. Lowell and J. Taylor, 1988. “ Undocumented Mexican Immigrants
and the Earnings of Other Workers in the United States”, Demography, 25, 1, 35-52.
Blau F.D., 1984. “ The Use of Transfer Payments by Immigrants”, Industrial and
Labor RelationsReview, 37, 2, 222-239.
Bonin H., B. Raffelhüschen and J. Walliser, 2000. “ Can Immigration Alleviate the
Demographic Burden”, FinanzArchiv, 57, 1, 1-21.
Borjas G.J., 1990. “ Friends or Strangers : The Impact of Immigrants on the U.S.
Economy”, New-York : Basic Books.
Borjas G.J., 1994. “The Economics of Immigration”, Journal of Economic
Literature, 32, 1667-1717.
Borjas G.J., 1995. “The Economic Benefits from Immigration”, Journal of
Economic Perspectives, 9, 2, 3-22.
Borjas G. J., R.B. Freeman and L.F. Katz, 1992. “On the Labor Market Effects of
Immigration and Trade”, in Immigration and the Workforce: Economic
Consequences for the United States and Source Areas, G. J. Borjas et R. Freeman,
eds., 213-244.
Borjas G.J., R.B. Freeman and L.F. Katz, 1997. “How much do Immigration
and Trade Affect Labor Market Outcomes”, Brooking Papers in Economic Activity,
1, 1-85.
Borjas G.J., 1999. “Heaven’s door: Immigration Policy and the American Economy”,
Princeton University Press, Princeton/Oxford.
Braun J., 1993. “Essays on Economic Growth and Migration”, Ph.D dissertation,
Harvard University.
Card D., 1990. “The Impact of the Mariel Boatlift on the Miami Labor Market »,
Industrial and Labor Relations Review, 43, 2, 245-247.
Card D., 1997. “Immigrant Inflows, Native Outflows, and the Local Labor Market
Impacts of Higher Immigration”, NBER Working Paper, 5927.
Chiswick C.U., 1989. “The Impact of Immigration on the Human Capital of
Natives”, Journal of Labor Economics, 7, 4, 464-486.
Collado D., I.I. Iturbe-Ormaetxe and G. Valera, 2003. “Quantifying the Impact of
Immigration in the Spanish Welfare State”, IVEI Working Paper.
Collins W. J., K.H. O’Rourke and J.G. Williamson, 1997. “Were Trade and Factor
Mobility Substitute in History?”, NBER Working Paper, n° 6059.
25
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
DeNew J.P. and K.F. Zimmermann, 1994. “Native Wage Impacts of Foreign Labor:
A Random Effects Panel Analysis”, Journal of Population Economics, 7, 2, 177-192.
Dolado J.J., A. Goria and A. Ichino, 1993. “Immigration, Human Capital and
Growth in the Host Country: Evidence from Pooled Country Data”, CEPR Discussion
Paper, 875.
Domingues Dos Santos M., 1997. “Migration, Chômage et Croissance”, Ph.D dissertation, University of Paris I.
Filer R.W., 1992. “The Effect of Immigrant Arrivals on Migratory Patterns of Native
Workers”, in Immigration and the Workforce: Economic Consequences for the United
States and Source Areas, G. J. Borjas and R. Freeman, eds., 245-269.
Friedberg R.M. and J. Hunt, 1995. “The Impact of Immigrants on Host Country
Wages, Employment and Growth”, Journal of Economic Perspectives, 9, 2, 23-44.
Gang I. and F. Rivera-Batiz, 1994. “Labor Market Effects of Immigration in the
United-States and Europe: Substitution Vs. Complementarity”, Journal of Population
Economics, 7, 2, 157-175.
Greenwood M.J. and G.L. Hunt, 1995. “Economic Effects of Immigrants on Native
and Foreign-born Workers: Complementarity, Substituability and other Channels of
Influence”, Southern Economic Journal, 61, 1076-1097.
Greenwood M.J., G.L. Hunt and U. Kohli, 1996. “The Short-run and Long-run
Factor-market Consequences of Immigration to the United States”, Journal of
Regional Science, 36, 43-66.
Gross D.M., 1999. “Three Million Foreigners, Three Million Unemployed?
Immigration and the French Labor Market”, IMF Working Paper, WP/99/124.
Grossmann J.B., 1982. “The substituability of natives and immigrants in production”, Review of Economics and Statistics, 64, 4, 596-603.
Gustman A. and T. Steinmeier, 2000. “Social Security Benefits of Immigrants and
US Born” in Issues in the Economics of Immigration, G. Borjas, eds., The University
of Chicago Press.
Haque N. and S. Kim, 1995. “Human Capital Flight: Impact of Migration on Income
and Growth”, IMF Staff Papers, 42, 3, 577-607.
Huddle D., 1993. “The Costs of Immigration”, Carrying Capacity Network,
Washington, D.C.
Hunt J., 1992. “The impact of the 1962 repatriates from Algeria on the French Labor
Market”, Industrial and Labor Relation Review, 45, 556-572.
Jaeger D.A., 1996. “Skill Differences and the Effect of Immigrants on the Wages of
Natives”, US Department of Labor Working Paper 273.
Jayet H., L. Ragot and D. Rajaonarison, 2001. “L’immigration : quels effets
économiques?”, Revue d’Economie Politique, 111, 4, 565-596.
Jones R.W., 1971. “A Three-factor Model in Theory, Trade and History”, in Trade,
Balance of Payment and Growth, J. N. Bhagwati and al., eds., Amsterdam, NorthHolland.
LaLonde R.J. and R.H. Topel, 1991. “Labor Market Adjustments to Increased
Immigration”, in Immigration, Trade and the Labor Market, J. Abowd et R. Freeman,
eds., 167-199.
26
XAVIER CHOJNICKI
Lee R. and T. Miller, 1997. “The Future Fiscal Impacts of Current Immigrants”, in
The New Americans, J. Smith et B. Edmonston, eds., Washington, DC : National
Academy Press, 297-362.
Lee R. and T. Miller, 2000. “Immigration, Social Security and Broader Fiscal
Impacts”, American Economic Review, 90, 2, 350-354.
Lundborg P. and P.S. Segerstrom, 2002. “The Growth and Welfare Effects of
International Mass Migration”, Journal of International Economics, 56, 177-204.
Markusen J.R., 1983. “Factor Movements and Commodity Trade as Complements”,
Journal of International Economics, 13, 341-356.
Miyagiwa K., 1991. “Scale Economies in Education and the Brain Drain Problem”,
International Economic Review, 32, 743-759.
Mountford A., 1994. “Can Brain Drain be Good for Growth?”, CEPR Discussion
Paper, n° 9508.
Muhleisen M. and K.F. Zimmermann, 1994. “A Panel Analysis of Job Changes
and Unemployment”, European Economic Review, 38, 793-801.
Muller T. and T.J. Espenshade, 1985. The fourth wave, Washington, DC : Urban
Institute Press.
United Nations, 2000. “Replacement Migrations: Is it a Solution to Declining and
Aging Population?”, Population Division, Department of Economics and Social
Affairs, New York.
OECD, “Trends in International Migrations”, Various editions, OECD editions,
Paris.
OECD, 2001. “Trends in Immigration and Economic Consequences”, Economics
Departement Working Papers N° 284.
OECD, 2002. “International movement of the highly skilled”, OECD Editions,
Paris.
Passel J.S., 1994. “Immigrants and Taxes : A Reappraisal of Huddle’s ‘The Cost of
Immigrants’”, Washington DC, The Urban Institute, PRIP-UI-29.
Robertson P.E., 2002. “Demographic Shocks and Human Capital Accumulation in
the Uzawa-Lucas Model”, Economics Letters, 74, 151-156.
Rothman E. and T.J. Espenshade, 1992. “Fiscal Impacts of Immigration to the
United-States”, Population Index, 58, 3, 381-415.
Schiff M., 2000. “Migration Nord-Sud et Commerce : Une revue de littérature”,
Revue d’économie du Développement, 3, 3-54.
Simon J., 1984 “Immigrants, Taxes, and Welfare in the United States”, Population
and Development Review, 10, 1, 55-70.
Simon J., S. Moore and R. Sullivan, 1993. “The Effect of Immigration on Agregate
Native Unemployment : An Across-city Estimation”, Journal of Labor Resources,
14, 3, 299-316.
Storesletten K., 2000. “Sustaining Fiscal Policy through Immigration”, Journal of
Political Economy, 108, 2, 300-323.
Taylor A. and J. Williamson, 1994. “Convergence in the Age of Mass Migration”,
NBER Working Paper Series, n° 4711.
27
THE ECONOMIC IMPACT OF IMMIGRATION FOR THE HOST COUNTRIES
Vernez G. and K.F. McCarthy, 1996. “The Costs of Immigration to Taxpayers :
Analytical and Policy Issues”, Center for Research on Immigration Policy, Rand,
Santa Monica.
Williamson J., 1995. “The Evolution of Global Labor Markets since 1830 :
Background Evidence and Hypotheses”, Explorations in Economic History, 32,
141-196.
Winegarden C.R. and L.B. Khor, 1991. “Undocumented Immigration and
Unemployment of U.S. Youth and Minority Workers: Econometric Evidence”,
Review of Economics and Statistics, 73, 1, 105-112.
Winkelmann R. and K.F. Zimmermann, 1993. “Aging, Migration and Labor
Mobility”, in Labor Markets in an Aging Europe, Johnson P. et Zimmermann K. F.,
eds., 255-283, Cambridge University Press, Cambridge.
28
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
THE BRAIN DRAIN : A REVIEW OF THEORY AND FACTS
SIMON COMMANDER (LBS & EBRD), MARI KANGASNIEMI (LSE)
AND L. ALAN WINTERS (UNIVERSITY OF SUSSEX)
ABSTRACT:
Skilled migration has increased in recent years, often stimulated by the explicit use of targeted visa programmes by developed countries. This paper examines the available analytical and empirical literature on
the brain drain to try and understand better whether skilled migration from developing countries must
always be harmful to the country of origin. We show that early generation models – mostly dating to the
1970s – found that such migration would be harmful, mostly though the impact on wages and employment, as well as through fiscal costs. A more recent literature has argued that a beneficial brain drain can
arise if migration has educational externatilities. As human capital rises, growth will also be positively
affected. However, we show that if screening is applied such benefits may disappear or become smaller.
Recent empirical work on the health and software sectors provides some contrasting evidence.
JEL CLASSIFICATION: F22, I21, J61.
KEYWORDS: skilled migration, educational externalities, growth.
29
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
INTRODUCTION
The migration of skilled personnel has attracted considerable attention in recent years
as the developed countries have increasingly and explicitly targeted the recruitment of
talented individuals from developing countries. Perhaps the most well known example
has been the use by the USA of H1-B visas in the 1990s to import skilled workers –
mostly from India – for the booming high technology sector. Other countries have also
pursued similar cherry-picking immigration policies. This in turn has opened up debate
about the economic and ethical consequences of such strategies. In particular, the view
that skilled migration must necessarily be detrimental to developing countries – by definition relatively less well endowed in skills than the developed countries – has gained
wide currency, at least in the popular press.
While an earlier literature and policy analysis – dating back to the 1970s – did generally support the view that skilled migration was bad for the sending or developing country, more recent analytical and empirical findings permit a rather more nuanced and
potentially different view of the consequences of skilled migration. In particular, it has
been argued that skilled migration can be beneficial if the possibility of migration in
turn leads to individuals acquiring more skills or education. That acquisition will raise
the human capital stock of the sending country and could contribute positively to
growth and economic performance. Yet – in common with the earlier literature –
attempts at empirical validation have been, as yet, very limited, and the evidence concerning the consequences of skilled migration for developing countries remains not
only limited but also largely inconclusive.
This paper provides an overview of the literature on the brain drain but it also adds findings from some recent empirical work that attempts to address some of the main issues
indicated above. It is organised as follows. Section 1 concentrates on reviewing an earlier generation of models and their key findings. Section 2 then turns to a more recent
class of models that can generate ‘beneficial’ brain drains and the empirical work that
this research has prompted. Section 3 then briefly touches upon some of the associated
effects of skilled migration, such as remittances, networks and the duration of migration. Section 4 concludes.
1. EARLY MODELS OF BRAIN DRAIN
The welfare implications of brain drain in earlier generation static models crucially
depended on the assumptions made about wage setting. Once distortions, such as a gap
between social and private marginal product and/or a public subsidy for education, were
introduced, a welfare loss for those who do not emigrate could result. Bhagwati and
Hamada (1974) – the seminal paper of this era - worked in general equilibrium and
introduced distortions in the wage setting and in the financing of education. The model
– which was subsequently widely employed - can be boiled down to a fairly simple set
of blocs.
30
SIMON COMMANDER, MARI KANGASNIEMI AND L. ALAN WINTERS
The economy produces two outputs with skilled and unskilled labour. The two types
of labour are exclusively allocated to their respective sectors. The real wage for
skilled workers is determined by unions and includes an element of international emulation whereby skilled wages are partly related to skilled wages abroad. Minimum
unskilled wages are fixed by association with the skilled wage or ‘leap frogging’: a
rise in the skilled wage leading to an increase in the unskilled wage. In addition, the
supply side reflects the incentive for education to be acquired so long as the expected wage for educated (skilled) labour exceeds the uneducated (unskilled) wage. A
fixed educational cost is introduced. Unemployment enters the initial equilibrium.
There is also an exogenous flow of educated emigrants. In this model the international
integration of the skilled labour market can affect both sectors’ wages through emulation and leap-frogging, as well as expected wages through the actual foreign wage
and the probability of emigration. The latter will affect education decisions, and education in turn carries a fixed cost.
With respect to unemployment, emigration may act directly to lower skilled unemployment, but it also exerts two other effects. First, it can raise the expected wage by lowering unemployment (and hence may have a supply side effect) and this can be amplified if the emigration wage enters the expected wage. The net result depends on the elasticity of demand for skilled labour which determines whether the skilled labour wage
bill increases or not. If the elasticity is lower than unity, an x% increase in skilled wages
will increase the wage bill and thus be associated with a less than x% fall in employment. Thus the expected wage will have increased and the supply of skilled workers will
tend to rise as a result. To the extent that the acquisition of skills through education is
subsidised, this will similarly raise the cost to the sending country.
Second, if the skilled wage increases because of emigration, this may also spill over into
other sectors and hence have an impact on unemployment in those other sectors. Wage
leap-frogging – letting unskilled wages follow skilled wages – would simply tend to
extend unemployment to the unskilled and amplify the welfare reducing consequences
of skilled labour migration. With respect to national income, a rise in the skilled wage
tends to reduce national income because of the decline in the employment of skilled
labour without any offsetting effect from the unskilled sector (in the case of no associated effect on unskilled wages), while the cost of education will also tend to increase.
However, with the assumption of wage ‘leap-frogging’, the implications for national
income are not so clear cut. Further, to the extent that emigration raises the wage of the
emigrant, this implies that emigrants were receiving less than their marginal product.
This surplus – as measured over the group – would be lost to the sending country in the
event of emigration. The size of the loss depends on the extent to which such workers
are replaceable.
Hamada and Bhagwati (1975) extended the model by introducing a number of refinements to labour markets in the sending countries. For example, if emigration induced a
ladder effect with remaining skilled workers now better matched to skilled, rather than
31
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
unskilled, jobs thereby reducing unskilled unemployment – a variant of Harris-Todaro
– then the effects of emigration could indeed be positive. By contrast, while emigration
of skilled workers – such as doctors - might reduce labour market slack, it could also
reduce the flow of doctors from urban to rural areas and limit any positive diffusion
effect. To the extent that the external labour market is more efficient at screening workers, the result would be the loss of the most efficient to the sending country1.
These early generation models treat the demand side for emigrants as exogenous and
have a range of assumptions regarding education costs. At their heart, lies the specification of the sending country’s labour market: under wage rigidity, emigration tends to
lower sending country employment with the distribution over sectors being contingent
on relative wage setting and ex ante employment levels.
What were the empirical foundations for such models? With regard to wage differentials, the few extant (and generally biased) estimates of wage differentials across countries signal substantial wage gaps for most categories of skilled workers. Indeed, other
evidence confirms that skilled workers systematically earn less – adjusted for purchasing power - in developing than in developed countries. A recent study of new immigrants to the USA, for example, finds that the average immigrant realized major earnings gains over their last job abroad. For men this increase was 68 percent and 62 percent for women. New immigrants who came primarily for work reasons experienced by
far the largest increases in earnings2.
In terms of the impact of migration on labour markets in the sending countries, evidence
has remained even more limited. Arora et al (2001) and Kumar (2000) have found that
one of the major problems perceived by Indian ICT firms is a shortage of skilled labour.
The late 1990s boom in the Indian software sector was clearly associated with increased
demand for engineers and there is evidence of this forcing up skilled wages. But even
here, the consequences may not have been that lasting or necessarily that widespread as
work reported in Commander et al (2004) indicates.
There is more information concerning lost educational investment. In most developing
countries at least some part of the cost of education is borne by the government, partly
because the social return from education is higher than the private one. In recent times,
there has been an increase in the provision of private educational services in many
developing countries where the cost is largely, if not exclusively, borne privately.
However, even when this is the case, any additional social returns to education, as well
as public investment in primary and secondary education, are lost when an individual
emigrates.
2
1
See also Arrow (1973) and Spence (1974).
Jasso, Massey, Rosenzweig and Smith (2000); Jasso, Rosenzweig and Smith (2000).
32
SIMON COMMANDER, MARI KANGASNIEMI AND L. ALAN WINTERS
Estimating the exact cost of education is very difficult and the result depends on the
approach that is taken in allocating fixed costs across outputs. There are some available
cost estimates. For example, the total cost of a medical degree in India has been estimated to be eight times annual GDP per capita (Jayaram 1995), and for an engineering
degree four times annual GDP per capita (Salim 1996). World Bank/UNESCO data
show that average government expenditure per student on tertiary education varies a lot,
but mostly lies in the range of 1000-3000 (international) dollars. In both China and
India the expenditure is around 2000 dollars per student.
Yet simply assuming that the education costs in developing countries are largely publicly financed misses some important innovations in educational services supply and
financing that has occurred in the 1990s. These may in turn have been positively
influenced by the emigration of the skilled. For example, in India private institutions
have begun training specialists for the software industry. According to Arora et al
(2001) while the supply of engineering graduates from the main public educational
institutions is relatively inelastic in the short run, privately the trained supply of software professionals has increased substantially, dampening the wage effect of the
demand side changes.
In China there is also a number of private institutions. It has been estimated that there has
been a strong expansion of private education since the 1980’s. According to the official
figures in 1998 there were 1274 private tertiary institutions, the majority of which prepare students for national exams rather than confer degrees. However, an estimated 4
million students study in private tertiary institutions, not recognised by the Ministry of
Education. (Dahlman and Aubert 2001.)
Of course, such innovations have had little or no impact in sectors where certification and
regulation have been tighter, as, for example, with healthcare and teaching. Indeed, it is
still broadly correct to assume that the bulk of doctors, nurses and teachers in developing countries receive substantial public subsidy to their training. Although the question
of new methods of financing higher education has been raised strongly, in most developing countries students’ own contributions to the costs of higher education are still
small (Johnstone et al, 1998; Tilak 1996 and Jayaram 1995).
This early literature on the brain drain lacked any significant empirical component.
There was no attempt at disaggregation beyond skilled-unskilled categories. Sectoral
differences were ignored and there was no attempt to take the analysis to the level of
the firm. Finally, there was little attention to heterogeneity between sending countries.
The literature also arguably over-emphasised the dichotomy between those who emigrate and those who stay. Modern communications technology has had radical implications for the ways in which work can be done across space. For example, the recent
growth in software activity has been striking for its high network content, linking
firms and individuals in developing and developed countries without necessarily
inducing migration or inducing only temporary mobility. Return migration can also
33
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
be a significant source of positive effects. For example, Dos Santos and Postel-Vinay
(2003) show that it is rational for some migrants to return having enhanced their
human capital and that this may be associated with narrowing the technological gap
between developed and developing countries.
Finally, it is also worth mentioning that positive consequences of a brain drain for the
sending country could arise from changes in the terms of trade as the sending economy’s output falls along with the decrease in its endowments. For example, Winters et al
(2002) find these to be quite significant in a CGE model of migration. Davis and
Weinstein (2002) point out that if a country has a Hicks-neutral technical advantage,
there will be incentives for all factors to migrate towards it. If such migration left relative factor abundance unchanged, incumbent factors from that country would lose as
their own physical marginal productivity would remain unchanged while the prices of
their output fell.
2. ENDOGENOUS GROWTH AND THE ‘BENEFICIAL BRAIN-DRAIN’
A more recent literature has evolved following a decade and more of liberalisation. This
literature has located the brain-drain in explicitly dynamic models and has, on the
whole, come up with significantly more optimistic results than the earlier work. The
central proposition is that if the possibility of emigration encourages more skill-creation
than skill-loss, sending (or home) countries might increase their stocks of skills as
opportunities to move or work abroad open up. If, in addition, this accumulation of
skills has beneficial effects beyond the strictly private gains anticipated by those who
acquire the skills, the whole economy can benefit. Examples of such benefits include
enhanced intergenerational transmission of skills and education (Vidal, 1998) and
spillovers between skilled workers (Mountford, 1997).
There are two critical features of these models. The first is the nature of the social
benefit resulting from higher skills, for which several approaches are evident. In the
simplest form Stark, Helmenstein and Prskawetz (1997, 1998) and Stark and Wang
(2002) merely assume that increasing the average skill level of the sending economy
is desirable. Mountford (1997) postulates a production externality whereby the productivity of current labour depends positively on the share of the population who had
education in the previous period. Beine, Docquier and Rapaport (2001a) formalise
this by allowing the average skill of one generation to pass directly to the next, who
can then build on it by taking education. In all these cases, emigration has a negative
direct effect by draining skilled labour out of the sending economy - a ‘drain’ effect
- but a potentially beneficial effect in encouraging human capital formation - a
‘brain’ effect.
Vidal (1998) assumes an intergenerational transfer whereby the higher the human capital level of one generation, the more effective is the human capital formation of the next
generation. This too would seem to be a force for divergence, for skilled emigration
34
SIMON COMMANDER, MARI KANGASNIEMI AND L. ALAN WINTERS
would appear to make future human capital acquisition cheaper in the receiving country
and dearer in the home country. But, in fact, Vidal prevents this by assuming that, for the
purposes of the spillover, migrants' human capital remains at home. This makes no sense
for permanent migration - the traditional and main concern of the brain-drain literature but it may be plausible for temporary migration, an area of more recent interest – see
Winters et al (2002).
The second critical issue for the beneficial brain-drain is the mechanism that generates
an increased incentive to acquire education but leaves some skilled workers back at
home. All the current literature starts with wages for given levels of skills/ability being
higher abroad than at home. From there, the predominant approach – Mountford (1997),
Stark, Helmenstein and Prskawetz (1998), Vidal (1998), Beine, Docquier and Rapaport
(2001a) and Stark and Wang (2002) – has been to assume that there is uncertainty about
the ability to migrate, so that of N who acquire education only πN (π < 1) actually emigrate. If π were unity, a permanent brain-drain could not be beneficial as all the incremental education would be lost. A further critical assumption is that the probability of
migration is fixed and exogenously given for any individual educated would-be migrant.
This implicitly arises because foreign firms cannot screen migrants to distinguish the
able from the less able and it is this market failure that makes it possible for the braindrain to be beneficial.
We can illustrate the importance of this assumption, using a highly simplified model
which nonetheless captures Mountford's (1997) insight. Following Beine, Docquier
and Rapaport (2001a), assume that ability is uniformly distributed between Amin and
Amax and that education yields private net returns that increase with ability, as in the
line XX’ in Figure 1. With a given private cost of education, indicated by the horizontal line, people with ability between A* and Amax find it profitable to take education. At point A* private cost of education equals expected returns. Now, allow for
the possibility of migration for educated people. If an individual can migrate, her private returns increase to YY’. With a probability of migration 0 < π < 1, the expected
returns to education lie between the domestic and emigration rates of return - say
along ZZ’, and individuals between A** and Amax will take education. Of these, however, a proportion, π, will emigrate leaving the domestic economy with (1 - π) (Amax
- A**) educated people, which may or may not exceed (Amax - A*). Adding social
returns to education is conceptually simple, because they have no immediate effect on
private decisions. For simplicity, let social benefits be proportional to the stock of
educated remaining at home, i.e. ␦ (Amax - A*) with no migration, and ␦ (1 - π) (Amax
- A**) with migration.
35
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
FIGURE 1. THE BENEFICIAL BRAIN DRAIN AND SCREENING
Y ’
Z ’
X ’
A min
A**
A*
A M
A max
The possibility of migration raises expected welfare for anyone who takes education.
Hence there is an increase in aggregate private income, although, of course, some individuals who do not manage to emigrate will regret their education decisions ex post. The
uneducated see no direct change in private returns and welfare and consequently gross
private income rises when migration is permitted. What happens to aggregate welfare,
of course, also depends on the social benefits of education.
Fundamental to this story is that every educated individual has probability π of emigrating - hence all of them experience the increase in expected returns, so that in our
linear example line ZZ’ lies uniformly above XX’. But now suppose that the country of
immigration can screen migrants perfectly for ability. They admit immigrants but only
from the top echelons, so that if, say, they want M people from our target country, they
get the top M lying between AM and Amax in Figure 1. If this is known, the incentives
for individuals with ability below AM are unchanged. The private returns to education
follow the thick line XX”Y”Y. (Amax - A*) are educated, of whom (AM - A*) remain.
The increment to total private income is larger than if the migrants had been randomly
selected, because the same number of migrants makes gains but no-one makes education decisions that they regret ex post. However, there is a loss of social welfare of ␦M,
as M educated people are lost and the social welfare was proportion ␦ of the number of
educated individuals.
36
SIMON COMMANDER, MARI KANGASNIEMI AND L. ALAN WINTERS
Clearly perfect screening is implausible, but even with imperfect screening all that would
happen is that the vertical section of XX”Y”Y would become sloped. But for so long as
it meets XX” above A*, offering migration would affect no-one's education decisions.
Thus, a necessary criterion for a beneficial brain drain to apply is that the marginal
person in education has a positive probability of emigrating3.
The importance of effective screening is also evident in Stark, Helmenstein and
Prskawetz (1997) who distinguish between education and innate ability. For them, the
increased incentive to acquire education among less able workers is that, while foreign
firms can recognise educational qualifications they cannot, at first, distinguish high
from low ability workers. As a result, for a period they offer all migrants with a given
level of education the same wage (the mean level averaged over ability for that level of
education), with the consequence that less able workers are ‘over-paid’. Over time foreign firms may discern workers' true ability and offer 'appropriate' wages, at which time
the benefits of emigration erode and, at least with finite probability, the workers return
home. Even if they have acquired no skills or networks abroad, they are better educated
than they would have been in the absence of migration. In this case it is the imperfections in screening that create the incentives to acquire education.
A possible development of the screening model is that the sending or home country has
some unexploited capacity for education, in the sense that the returns to education are
primarily determined by the demand for skilled workers rather than the ability of the
population. In this case even a perfectly screened emigration would generate net benefits. Suppose that as the workers between AM and Amax migrated, they left openings for
newly educated workers to take jobs with precisely the same returns. The net effect on
the home economy would be to have the same number of educated workers as without
migration and hence the same spillovers, but M fewer uneducated workers. This would
raise average incomes slightly (and average skill-levels). In addition, the migrants
would record positive private gains.
Empirical findings
An important step forward in the literature on the beneficial brain drain is due to
Beine, Docquier and Rapaport (2001a, b) who test the model empirically using crosssectional data. They suggest that the probability of emigration does appear to boost
human capital formation and that the stock of human capital does appear to influence
growth positively4.
3
Of course, actual decisions about education are taken with respect to subjective probabilities of migration not ex
post observed probabilities. Thus, if individuals are overly optimistic about their prospects, marginal candidates
may believe they face improved expected returns even when they do not.
4
This latter finding is, of course, rather different from the results of much of the empirical growth literature, see
Pritchett (2001).
37
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
They also decompose the effects of migration into a ‘brain’ effect - human capital accumulation - and a ‘drain’ effect - losses due to actual emigration. They identify several
countries which would benefit from a decline in the stock of skilled emigration (i.e.
reducing the outflow and receiving some nationals back). These countries typically have
high rates of emigration coupled with relatively ineffective education and training systems. Some would even benefit from a complete ban on skilled migration. Interestingly,
however, the loss of growth due to emigration appears to be rather small, of the order of
0.05% p.a. The obverse of these results is that countries would typically gain from higher emigration if they currently have low rates of emigration and low levels of human
capital. (That is, where the costs of further emigration are relatively low and the benefits in terms of incentives relatively high.) There are limited numbers of countries in this
class, but they include the larger developing countries, such as Brazil, China and India.
Cases of Health and Software
In Commander et al (2002) we review various data as they pertain to the beneficial
brain-drain hypothesis. We illustrate the increasing rate of skilled migration over the
1990’s, resulting in quite large cumulative outflows in some cases. There is evidence of
such increased migration being accompanied by increased take up of education – especially in technical areas (like ICT) where migration occurs - and often at private
expense. We also find, however, prima facie evidence of strong screening mechanisms,
which raises the possibility that the increased education is being substantially drained
away. Further, we argue that there are likely to be important sector-specific effects at
work. As such, our work focusses on two distinct sectors, medicine and software.
In the case of medicine, our evidence is not generally supportive of a beneficial braindrain through increased through increased incentives to obtain education5. Using a small
telephone and postal survey of overseas doctors working in the UK to look at both the
issue of screening and the influence of migration possibilities on educational decisions,
we find that while there are clear grounds for supposing that screening is implemented,
there is little evidence to suggest that migration possibilities have played any significant
role in driving educational decisions6. With respect to screening, evidence both with
regard to the institutions in the sending countries in which they had trained, as well as
information regarding subsequent – post-migration – ability to find a job, clearly support
the view that screening in the case of migrant doctors is actively applied. Turning to the
influence of migration on education decisions, survey responses do not support the view
that migration has exerted a systematic, positive effect on education decisions. Nor does
there appear to be any association between migration having an influence on education
and the individual characteristics of migrants. The negative effect will, if anything, be
5
6
See Kangasniemi et al (2003).
The survey comprised 137 responses. Thus, not only is the survey size small but there are other shortcomings. We
are, for example, not able to compare migrant doctors with their peers who did not move. Nevertheless, the survey provides some useful insights.
38
SIMON COMMANDER, MARI KANGASNIEMI AND L. ALAN WINTERS
amplified by the fact that most of the doctors in the sample had received free or highly
subsidised education, thereby entailing a clear fiscal cost. In part offsetting these features,
doctors – like most migrants – do generally send remittances back to their home countries.
Under some plausible assumptions, we argue that the net benefit of migration may have
been negative in the case of doctors. However, it should be taken into account that a fairly large proportion – arround half – of doctors from low income countries indiacted that
they intended to return home. Further roughly three quarters of doctors from low income
countries also believed that they were easy to replace. Indeed, 19% of Indian doctors in
the sample had actually experienced a unemployment spell prior to migrating. These
responses suggest migration does not necessarily run alongside skill shortages at home.
The software case provides a rather more nuanced example and one that appears generally more supportive to the beneficial brain drain argument, although in a number of
ways that go beyond the effect of migration on education decisions7. Drawing on a firm
level survey of 225 firms in India and an additional 98 software firms in the USA, we
find that there is strong evidence of screening aimed at ensuring that the upper end of
the talent distribution gets poached by US firms. Screening occurs through a variety of
mechanisms including repeated contact with a migrant’s prior employer but – crucially
– screening has also gone alongside relatively large cross border movement of Indian
software workers. Although, it appears that part of the top talent in the sector has indeed
moved out of India, this has been accompanied by substantial temporary migration of
skilled workers. Indeed, the share of skilled workers with some foreign work experience
is strongly and positively correlated with the current and lagged incidence of skilled
migration in the Indian firms in the sample. This suggests the presence of network
effects. Further, the data provide no evidence of any significant negative impact of
migration on performance in the Indian firms. The survey also provides evidence that
migrants send remittances, engage in return investment as well as firms benefiting from
enhanced commercial and other links with firms in developed countries. Putting these
factors together suggests that despite the high skill content of software migration, the
net consequences have been positive for the sending country, India. Moreover, the survey also provides some additional support for the view that the industry’s growth in
India has been accompanied by a strong educational response, not least through the
entry of new private providers targeting the provision of sector specific skills. While
this is not the same as relating migration directly to education decisions, it seems reasonable to suppose that migratory flows have played a positive role in raising educational enrolments and supply. In short, the software example – unlike that of the doctors
– provides a more positive view of the consequences of migration, as well as highlighting the different types of migration.
7
See Commander et al (2004).
39
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
3. REMITTANCES, DIASPORAS AND RETURN FLOWS
It is has long been recognised that any adverse consequences of skilled emigration might be
partly or wholly offset by remittances, the creation of diasporas and return migration. The
software case dealt with above provides an instance of why these factors may be important.
However, to look at these questions more systematically is less easy, given data limitations.
Concerning remittances, aside from considerable imprecision in the aggregate numbers,
it is not possible to separate out the volume of remittances coming from migrants of different skill groups8. Such information as is available confirms that remittances vary
systematically with respect to income, conditions in the sending country, planned duration of stay and household attributes9. It is likely that remittances from highly skilled
migrants follow a very different pattern from those of low skilled migrants.
As to return migration, a positive channel would occur when migrants return with experience, financial resources, links to networks and skills from a stay abroad that are then productively deployed at home. There is some evidence that return migrants tend to choose
self-employment or entrepreneurial activity not least because their savings diminish credit constraints. For example, Dustmann and Kirchkamp (2001) have studied returning
Turkish migrants and their choice of activity and migration duration as a simultaneous
decision. They find that most returnees choose self-employment or non-employment, and
that highly educated individuals are more likely to be active after return. Ilahi (1999) finds
that the level of savings is positively correlated with the choice of self-employment on
return, while McCormick and Wahba (2001) use survey data to show that duration of stay
overseas along with savings increases the probability of becoming an entrepreneur for literate return migrants, which would suggest that skills obtained overseas have are useful
on return. Positive effects from return migration obviously also depend in part on a variety of factors, including government policy in the sending or home country (see Castles
(2000); Dustmann (1996), or concern for the offspring’s future, Dustmann (2001).
Another important aspect of return migration is the possibility that it is a result of
screening of the migrants. Borjas and Bratsberg (1996) have studied the out-migration
decisions of foreign-born people in the USA, and conclude that return migration accentuates the type of selection that generated the immigrant flow. In other words, if emigrants represent the high end of the skill distribution in the source country, the returnees
are the least skilled of the emigrants. Cohen and Haberfeld (2001) also find that Israeli
immigrants returning from the United States are likely to be negatively selected from
those Israelis who emigrated in the first place. Reagan and Olsen (2000) on the other
hand do not find any skill bias in return migration in their study on the National
Longitudinal Survey, when skill is measured with Armed Forces Qualifying Test.
8
Remittances are discussed in detail and existing research reviewed in Puri and Ritzema (2000). The World Bank
(2001) offers some recent data and discussion.
9
For example, Straubhaar (1986) for a study of remittances to Turkey.
40
SIMON COMMANDER, MARI KANGASNIEMI AND L. ALAN WINTERS
Other related research suggests that aspects that do not require return migration of
skilled individuals, can be of major importance. Such channels for beneficial effects are
exports, business and network links related to diaspora populations. There is evidence
that such diaspora can have very beneficial effect on exports – for example, (Rauch
1999, Rauch and Trinidade, 2000). Similarly, foreign direct investment and venture
capital – particularly in the recent period - have often been related to ethnic networks.
An example of this is the Hsinchu Science park in Taipei, where a large fraction of companies have been started by returnees from the United States (Luo and Wang 2001).
There is evidence – already alluded to - of these types of network effects being quite
powerful in the Indian software industry.
CONCLUSION
The brain drain and its consequences for developing countries continues to attract discussion and debate. This paper has reviewed the ways in which economists have thought
about skilled migration over the last forty years. While early generation models were
mostly static and focussed on the labour market consequences of migration, they also
placed emphasis on the fiscal implications of migrants having had their education provided for by public funds. Depending on the precise structure of the model, both such
financing costs and labour market distortions could generate a negative effect of skilled
migration. Interestingly, however, this literature was largely devoid of empirical content
and validation. Proposals for the use of tax instruments to limit migration or, at the least,
ensure that the benefits were not appropriated completely by the migrant and developed
country, similarly found little, if any, application in practice.
The revival of discussion of the brain drain – mostly in the latter half of the 1990s –
was prompted in part by the explicit use of visa and other programmes to encourage
skilled workers to move to developed countries. However, the linking of migration to
endogenous growth theory – through changes in education incentives and their implications for human capital formation – also permitted new insights and suggested that
skilled migration need not necessarily be adverse for the sending country. However, this
literature has also suffered from having limited empirical support or content.
A number of recent attempts to implement empirical work in this area are reported in the
paper. The findings – probably not surprisingly – are far from conclusive. They do however strongly suggest that both sector and country size are likely to matter in determining
whether skilled migration has had positive or negative consequences for the sending country. At risk of simplification, smaller countries are likely to be hit harder than large ones
by skilled migration. In terms of sector, the cases of health (doctors) and software give
rather different results. In the first, it is hard to sustain the view that education incentives
are strong enough to offset other effects. In the software instance, this is not the case, not
least because the nature of the migration that is occurring has itself been changing. The
challenge remains to give greater empirical content to this discussion.
41
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
REFERENCES
Arora, A., V. Arunachalam, J. Asundi and R. Fernandes, 2001. “The Indian software
services industry”, Research Policy, 30, pp.1267-1287.
Arrow, Kenneth J. 1973. “Higher Education as a Filter”, Journal of Public Economics,
2(3), pp. 193-216.
Beine M., F. Docquier and H. Rapoport, 2001a. “Brain drain and economic growth: theory and evidence”, Journal of Development Economics, 64(1) pp. 275-89.
Beine M., F. Docquier and H. Rapoport, 2001b. “Brain drain and growth in LDCs:
winners and losers”, Paper presented at European Economic Association Congress,
August.
Bhagwati J.N., Hamada Koichi, 1974. “The Brain Drain, International Integration of
Markets for Professionals and Unemployment: A Theoretical Analysis”, Journal of
Development Economics 1(1), pp.19-42.
Borjas G. J. & B. Bratsberg, 1996. “Who leaves? The outmigration of the foreign-born”,
Review of Economics and Statistics 78(1), pp. 165-76.
Bratsberg B., 1995. “The incidence of non-return among foreign students in the United
States”, Economics of Education Review 14(4), pp. 373-84.
Castles S., 2000. “The Impacts of Emigration on Countries of Origin”, In Yusuf, Shahid,
Wu, Weiping, and Evenett, Simon (eds.): “Local Dynamics in an Era of Globalisation”,
The World Bank.
Carrington W.J. and E. Detragiache, 1998. “How Big is the Brain Drain?”, IMF
Working paper no. 201.
Cohen Y. and Haberfeld Yitchak, 2001. “Self-Selection and Return Migration: IsraeliBorn Jews Returning Home from the United States During the 1980s”, Population Studies
55(1), pp. 79-91.
Commander S., M. Kangasniemi and L.A. Winters, 2004. “Who gains from skilled
migration? Evidence from the software industry”, London Business School, January.
Dahlman C. J. and J.E. Aubert, 2001. “China and the Knowledge Economy. Seizing the
21st Century”, WBI Development Studies, World Bank Institute.
Dos Santos M. and F. Postel-Vinay, 2003. “Migration as a Source of Growth: The
Pespective of a Developing Country”, Journal of Population Economics, 16(1) 161.
Dustmann Ch., 2001. “Children and Return Migration”, University College, London,
mimeo.
Dustmann Ch. And O. Kirchkamp, 2001. “The Optimal Migration Duration and
Activity Choice after Re-migration”, IZA Discussion Paper No. 266.
Hamada K. and J.N. Bhagwati, 1975. “Domestic Distortions, Imperfect Information and
the Brain Drain”, Journal of Development Economics, 2(3), pp. 265-79.
“Health information of India, 1998”. India, Central Bureau of Health Intelligence.
“Health information of India, 1996”. India, Central Bureau of Health Intelligence.
Ilahi N., 1999. “Return migration and occupational change”, Review of Development
Economics, 3(2), pp. 170-86.
Immigration and Naturalization Service, U.S. Department of Justice, 1999.
“Statistical Yearbook of the Immigration and Naturalization Service”.
Immigration and Naturalization Service, U.S. Department of Justice, 1998.
“Statistical Yearbook of the Immigration and Naturalization Service”.
42
SIMON COMMANDER, MARI KANGASNIEMI AND L. ALAN WINTERS
Immigration and Naturalization Service, U.S. Department of Justice, 1996.
“Statistical Yearbook of the Immigration and Naturalization Service”.
Jasso G., M.R. Rosenzweig and J.P. Smith, 2000. “The Changing Skill of New
Immigrants to the United States: Recent Trends and Their Determinants”, Issues in the
economics of immigration, pp. 185-255, National Bureau of Economic Research
Conference Report. Chicago and London: University of Chicago Press.
Jasso G., D. S. Massey, M.R. Rosenzweig and J.P. Smith, 2000. “The New Immigrant
Survey Pilot Study: Overview and New Findings About U.S. Legal Immigrants at
Admission,” Demography, 37, pp. 127-138.
Jayaram N., 1995. “Political Economy of Medical Education in India”, Higher Education
Policy, 8(2).
Johnson H.G., 1967. “Some economic aspects of the brain drain”, Pakistani
Development Review, 7, pp. 379-411.
Johnstone D.B., A. Arora and W. Experton, 1998. “The Financing and Management of
Higher Education: A Status Report on Worldwide Reforms”, World Bank.
Kangasniemi M., L.A. Winters and S. Commander, 2003. “Is the Medical Brain Drain
Beneficial? Evidence from Overseas Doctors in the UK”, Centre for Economic
Performance Discussion Paper, London School of Economics.
Kumar Nagesh, 2000. “Developing Countries in International Division of Labour in
Software and Service Industries: Lessons from Indian Experience”. Background paper
prepared for International Labour Organisation (2001): World Employment Report,
Geneva.
Luo Y.L. and W.J. Wang, 2001. “High-Skill Migration and Chinese Taipei’s Industrial
Development”, In OECD (2001b).
McCormick B. and J. Wahba, 2001. “Overseas work experience, savings and entrepreneurship amongst return migrants to LDCs”, Scottish Journal of Political Economy, 48(2),
pp. 164-78.
Mountford A, 1997. “Can a brain drain be good for growth in the source economy?”,
Journal of Development Economics, 53(2), pp. 287-303.
OECD, 2001. “Trends in International Migration”, OECD, Paris.
OECD, 2001b. “International Mobility of Highly Skilled”, OECD, Paris.
Pritchett L., 2001. “Where has all the education gone?”, World Bank Economic Review,
15(3), pp. 367-391.
Puri S. and T. Ritzema, 2000. “Migrant Worker Remittances, Micro-finance and the
Informal Economy: Prospects and Issues”, International Labour Office, Social Finance
Unit, Working Paper 21.
Rauch J.E., 1999. “Networks vs. markets in international trade”, Journal of International
Economics, 48(1), pp. 7-35.
Rauch J.E. and V. Trindade, 1999. “Ethnic Chinese Networks in International Trade”,
National Bureau of Economic Research Working Paper 7189.
Reagan P.B. and R.J. Olsen, 2000. “You can go home again: evidence from longitudinal
data”, Demography, 37(3), pp. 339-50.
Salim A.A., 1996. “Institutional cost of higher education – A case study of Kerala”,
Manpower Journal, Vol. 32(1), pp. 1-14.
Spence A.M, 1974. “Competitive and Optimal Responses to Signals: An Analysis of
Efficiency and Distribution”, Journal of Economic Theory, 7(3), pp.296-332.
43
THE BRAIN DRAIN: A REVIEW OF THEORY AND FACTS
Stark O., C. Helmenstein and A. Prskawetz, 1997. “A brain gain with a brain drain”,
Economics Letters, 55(3), 227-34.
Stark O., C. Helmenstein and A. Prskawetz, 1998. “Human capital depletion, human
capital formation, and migration: a blessing or a curse?”, Economics Letters, v60, n3
(September), pp. 363-67.
Stark O. and Y. Wang, 2002. “Inducing Human Formation: migration as a substitute for
subsidies”, Journal of Public Economics, 86(1), 29-46.
Straubhaar T., 1986. “The Determinants of Workers’ Remittances: The Case of Turkey”,
Weltwirtschaftliches Archiv, 122(4), pp. 728-40.
Tilak J.B.G., 1997. “The dilemma of reforms in financing higher education in India”,
Higher Education Policy, 10(1), pp. 7-21.
Vidal J.P., 1998. “The effect of emigration on human capital formation”, Journal of
Population Economics, 11(4), pp. 589-600.
Winters L.A., T.L. Walmsley, Z.K. Wang and R. Grynberg, 2002. “Negotiating the
Liberalisation of the Temporary Movement of Natural Persons”, Commonwealth
Secretariat, London.
World Bank, 2001. “World Development Indicators”, World Bank, Washington DC.
44
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
SELECTIVE IMMIGRATION POLICY IN AUSTRALIA,
CANADA AND THE UNITED STATES
HEATHER ANTECOL* (CLAREMON MCKENNA COLLEGE),
DEBORAH A. COBB-CLARK** (THE AUSTRALIAN NATIONAL UNIVERSITY)
AND STEPHEN J. TREJO*** (UNIVERSITY OF TEXAS)
ABSTRACT:
We compare the selective immigration policies in Australia, Canada and the United States over the twentieth century and as they exist today. We then review existing information about the link between selective
immigration policy and immigration outcomes in the three countries. The literature reviewed suggests that
there does seem to be potential for selective immigration policy to affect immigrant outcomes by altering
the skill levels of immigrants. Still, it is clear that other forces are at work as well. Historical accidents,
social forces, and simple geography may all have a hand in shaping traditional migration patterns, while
labor market conditions—in particular the relative return to skill—are likely to be as important as policy
in producing migration incentives.
JEL CLASSIFICATION: F22, J24, J61, J68.
KEYWORDS: skilled migration, immigration policy.
*
Department of Economics, Claremont Mc Kenna College, Claremont, CA 91711, [email protected]
** Social Policy Research, Evaluation and Analysis Centre and Economics Program, Research School of Social
Sciences, the Australian National University, Canberra, ACT 0200 Australia. [email protected]
*** Department of Economics, University of Texas at Austin, University Station C3100, Austin TX 78712-0301.
[email protected]
45
SELECTIVE IMMIGRATION POLICY IN AUSTRALIA, CANADA AND THE UNITED STATES
INTRODUCTION
Through the years Australia, Canada, and the United States have been the destinations
for large numbers of immigrants. While the magnitude of the immigration flow and the
characteristics of immigrants themselves have varied between these three nations and
over time, it is hard to deny that immigration has had a major hand in shaping the identity of each. The similarities (and dissimilarities) in the experiences of these countries
have provided researchers with excellent opportunities to analyze the role of selective
immigration policy itself in influencing immigration outcomes. This has been an important area of policy-related research as immigration is the one component of population
and labor market growth that comes most directly under the control of policy makers.
This paper will begin by briefly comparing selective immigration policies in Australia,
Canada, and the United States over the twentieth century. Although temporary migration has
become an increasingly important demographic and labor market phenomenon, our focus
will be on the selection of permanent residents. Emphasis will be placed on comparing the
implications of various regulations and policies for the skill composition of the immigrant
stream. We are specifically interested in comparing the selective immigration programs of
these three countries as they exist today. Finally, we review existing information about the
link between selective immigration policy and immigration outcomes in the three countries.
1. SELECTIVE IMMIGRATION POLICY THROUGH THE YEARS
Prior to the twentieth century both Canada and the United States essentially operated an
‘open door’ immigration policy.1 Needing people to push back the frontier, populate the
country and defend the borders, Canada and the United States stood ready to receive new
immigrants and, overcrowded, Europe stood ready to send them. Other than a few policies
to deny entry to the sick, destitute or criminal, immigration was essentially unrestricted.
The primary exception to this was the Chinese Exclusion Act of 1882 that limited the
entry of immigrants from that country to the United States and made national origin an
explicit condition of entry for the first time (Vialet, 1989).2 Similar legislation was passed
in Canada in 1885 (Green, 1995). Enormous increases in the numbers of immigrants in
the early part of the twentieth century resulted in both Canada and the United States passing legislation that established a process for regulating immigration (specifically limiting
immigration through the use of quotas in the case of the United States) and expanded the
use of national origin as a selection criterion. Canada made a distinction between “preferred” (Britain, the United States, and northwest Europe) and “nonpreferred” countries
(Green, 1995) while the United States distributed visas based on the national origin of the
foreign-born population enumerated in the 1920 U.S. Census (Cobb-Clark, 1990).3
1
Much of the historical overview of immigration policy in this section is based on Vialet (1989) and Cobb-Clark
(1990) for the United States, Green (1995) and Green and Green (1995; 1999) for Canada, and Lack and
Templeton (1995) for Australia.
2
See Chiswick (1986) for a review of U.S. immigration policy with respect to Asia.
3
Although this policy had been intended to maintain the ethnic balance, it failed to do so mainly because the countries with the largest quotas left them largely unused (Cobb-Clark, 1990).
46
HEATHER ANTECOL, DEBORAH A. COBB-CLARK AND STEPHEN J. TREJO
Australia’s immigration policy evolved in much the same way as in the United States
and Canada, although as expected given her relative youth, at somewhat later dates.
Restrictions on Chinese migration following violence in the gold fields in the 1850’s
were the origins of the “White Australia” policy (Miller, 1999). Between Federation in
1901 and World War II (WWII), Australia’s focus was directed mainly towards retaining a British identity through British immigration. Australia lost a higher percentage of
her young men during WWII than any other participating nation (Parcell, et al., 1994),
and this combined with a feeling of geographic isolation from Britain gave rise in 1945
to a mass immigration campaign launched with the slogan “Populate or Perish” (Lack
and Templeton, p. xiii, 1995). While British settlers who continued to be preferred
would receive passage assistance, it was also recognized that Britain alone was unlikely to meet the demand for new immigrants. Thus, the Government set out to expand the
immigrant base to include those from other European countries. There was little scope,
however, for Asian immigration.
National origin continued to play an important role in the immigration policies of all
three countries until the 1960s when the discriminatory nature of the national origins
philosophy was called into question. Canada began turning away from national origin
and towards individual characteristics as selection criteria in 1962 (Green, 1995; Green
and Green, 1999), the United States followed in 1965 (Briggs, 1984), and Australia’s
“White Australia Policy” ended in 1973 (CAAIP, 1987).
These changes in selection policies presented an important policy challenge for
Australia, Canada, and the United States because whereas the assessment of national
origin had been straightforward, the assessment of individual characteristics was not.
Each country struggled to find the appropriate balance between the desire to first,
reunite families, second, increase the skill base of the population, and third, meet
humanitarian responsibilities through the acceptance of refugees. The U.S. system
gave more weight to the reunification of families, with relatively few visas (approximately 20 percent) reserved for immigrants selected on the basis of their labor market skills. Australia and Canada placed relatively more weight on encouraging skilled
migration with Canada first introducing a points test for judging the admissibility of
skilled immigrants in 1967 (Green, 1995, Green and Green, 1995; 1999) and
Australia following in the late 1970s (Birrell, 1990). Though the intervening years
saw many changes in the specifics of each country’s immigration program, the basic
framework adopted by each country as selection on the basis of national origin ended
remains today.
2. SELECTIVE IMMIGRATION POLICY TODAY
Australia’s immigration program is modeled on Canada’s and, with minor exceptions,
the policies of the two countries are broadly the same (Clarke, 1994). Both Australia and
Canada separate nonhumanitarian immigration into two components: one based on
close family relationships with citizens or permanent residents and the other based on
47
SELECTIVE IMMIGRATION POLICY IN AUSTRALIA, CANADA AND THE UNITED STATES
an individual’s potential contribution to the labor market.4 In between are the SkilledAustralian Linked migration program in Australia and the assisted relative class in
Canada that assess individuals on both skills and more distant family relationships.5
Skilled migration also consists of independent migrants without family relationships
who are points tested and migrants intending to establish businesses in either Australia
or Canada who must meet certain investment requirements.6
Each year Australia’s Minister for Immigration establishes numerical planned intake
levels7. Caps are set separately for each major category (i.e., family and skill) and applicants passing the points test are issued a visa so long as the relevant cap has not been
reached. Once the cap has been reached, qualified applicants are placed in a queue to
await the availability of a visa. Adjustments may be made at any time to the planned intake
level or to the pass mark of the points tests to control the number of visas granted.8 Since
1996-1997, the family stream has also been subject to planning levels (though not a
points test) (Miller, 1999). The Canadian system operates in a similar fashion, with the
federal government also setting a targeted level of immigration. Although this target is
reviewed annually, it is meant to be maintained at the predetermined level for five years.
In the 1990’s the Canadian government began to take a more long-term view of the benefits of immigration and consequently moved to maintain large inflows of immigrants
despite high domestic unemployment (Green and Green, 1999). Finally, the Canadian
system treats assisted relatives and independent migrants as the residual giving preference to family class migrants and refugees (Green and Green, 1995; Green, 1999).9
The points tests are the primary mechanism for regulating the level and influencing the
characteristics of skilled immigrants in Australia and Canada.10 It is difficult to construct a historical overview of the specifics of the points system because regulations
changed from year to year. Not only did the overall pass mark, and the specific points
awarded to a particular characteristic, say “employability” or occupation, change over
4
Under the 1973 Trans-Tasman Travel Arrangement, New Zealand citizens are allowed to enter Australia to visit,
live, and work without the need for a visa.
5
Until 1989 the Concessional Family Migration (the predecessor to the Skilled-Australian Linked program) and
Independent Migrant classes were combined and fell under the skilled immigration category in Australia (Parcell,
et al., 1994).
6
In both Australia and Canada the points tests in the Skilled-Australian Linked program and assisted relative class
differ from the tests applied in the Independent category. In particular, individuals are given additional points for
family relationships. Pass marks also differ (ADILGEA, 1991; Green and Green, 1995; 1999).
7
In practice it appears that labor market considerations, specifically the unemployment rate, play an important role
in the settling of these targets.
8
Individuals’ who fail to achieve the requisite pass mark, but who do achieve a lower “pool” mark remain active in
the pool of visa applicants for 12 months in case the pass mark is subsequently lowered (Miller, 1999).
9
This has the obvious implication that skilled immigrants make up a small proportion of the overall flow of immigrants in years when the demand for immigration visas from family members and refugees is high.
10
See Miller (1999) for the details of the Australian point system. Details of the Canadian point system can be
found on the web site for Citizenship and Immigration Canada (www.cic.gc.ca) or see Green and Green, (1995)
and (1999).
48
HEATHER ANTECOL, DEBORAH A. COBB-CLARK AND STEPHEN J. TREJO
time, but the way in which officials were meant to evaluate that characteristic also varied. For example, whereas Australia’s points test in 1988/1989 awarded points separately for employability (including English), skills, and education; the 1989/1990 points test
awarded points for employment skills (including education) and English (ADILGEA,
1991).11 In general, however, both Canada’s and Australia’s points tests take into account
an individual’s, age, education, occupation (or intended occupation), and language ability (AADILGEA, 1991; Green, 1995; Green and Green, 1995; 1999).12 Changes to the
Australian points test in the late 1990’s resulted in additional points being awarded if the
applicant’s spouse also meets the minimum age, skill and English language requirements
or if the applicant holds an Australian degree. At the same time, the Australian government established minimum age, skill, and English language criteria which skill-based
migrants to Australia must meet in addition to passing the points test .13
The U.S. immigration program as it emerged from the national origins system appears
quite different. Immigration levels are established by Congress through amendments to
the immigration statute. Thus, the United States, unlike Australia and Canada, does not
attempt to make the level of immigration responsive to stages in the business cycle.
Prior to 1990, the United States did not separate immigrants into distinct family and
skilled immigration programs as was the case in Canada and Australia, but instead
established a system of six hierarchical preference categories. Preferences three and six
were reserved for individuals with “exceptional ability” or whose skills were in short
supply. Remaining preferences were reserved for various family members.14 The
Immigration Act of 1990, however, in addition to increasing overall immigration, established a three-track preference system for family-sponsored, employment-based, and
diversity immigrants (Vialet and Eig, 1990).15
Table 1 shows the proportion of Australian, Canadian, and U.S. immigrants by broad
class admission and region of origin. In 2002 family-based immigrants made up a similar share of the overall immigration flow in both Australia and Canada, although a
much higher proportion of Canadian immigrants entered under a skilled category (58.7
percent) than was true in Australia (40.5 percent). This latter difference results because
11
Before 1989, points testing was based on policy guidelines in Australia. In 1989, changes in migration legislation introduced a legal basis for the points test (ADILGEA, 1991).
12
Throughout most of the 1980s, Australia operated an Occupational Shares System (OSS). This was an eligibility
category for a limited number of people in trades and professions whose skills were—based on an annual industry survey—difficult to fill locally in the short to medium run (Parcell, et al, 1994).
13
Specifically, all applicants must be under the age of 45, be proficient in English at the vocational level, and meet
the Australian requirements for (and have recent experience in) an occupation set out on a skilled occupations
list. See Cobb-Clark, 2003 for a review of Australian policy changes over the 1990s.
14
The Nonpreference category applies to anyone not eligible for one of the first six preference categories. However,
due to a large backlog in visa applications no one was admitted to the United States in this category between the
late 1970s and 1990 (Cobb-Clark, 1990).
15
It is important to note that like Australia and Canada, the United States also permits some individuals, in particular immediate relatives (spouses, minor children, and parents of adult) of U.S. citizens, to enter without limitation (Vialet, 1989).
49
SELECTIVE IMMIGRATION POLICY IN AUSTRALIA, CANADA AND THE UNITED STATES
of the large numbers of New Zealand citizens entering Australia under the 1973 TransTasman Agreement.16 The picture is quite different in the United States with almost two
in three immigrants (63.3 percent) in 2002 entering the country as either immediate relatives of U.S. citizens or as a family-sponsored migrant. Only 16.4 percent of immigrants entering the United States do so as employment-based immigrants.17
TABLE 1. AUSTRALIAN, CANADIAN AND U.S. LEGAL IMMIGRATIONS,
BY REGION OF ORIGIN AND BROAD CLASS OF ADMISSION
All
Regions
Asia &
Pacific
Europe
Latin
Americaa
Africa &
Middle
East
North
America
Australia (2001-2002)
Family
Skilledb
Humanitarian
NonProgram Migrationc
26.3%
40.5%
7.8%
25.4%
23.3%
38.8%
1.5%
36.4%
33.2%
41.5%
14.7%
10.6%
59.1%
27.6%
4.1%
9.2%
23.6%
47.3%
22.8%
6.3%
54.3%
31.9%
0.2%
13.6%
Total Number of Immigrants
100.0%
88,900
100.0%
53,522
100.0%
17,411
100.0%
900
100.0%
15,311
100.0%
1,730
Canada (2002)
Family
Skilled
Humanitarian
Other
28.5%
58.7%
11.0%
1.9%
48.4%
36.0%
13.2%
2.4%
38.5%
47.7%
13.1%
0.7%
55.9%
24.7%
19.0%
0.4%
23.9%
45.2%
30.7%
0.3%
83.2%
15.7%
0.8%
0.3%
Total Number of Immigrants
100.0%
229091
100.0%
118899
100.0%
38841
100.0%
19417
100.0%
46113
100.0%
5288
United States (2002)
Immediate Relativesd
Family-Sponsored
Employment-based
Humanitarian
Diversity
Other
45.7%
17.6%
16.4%
11.9%
4.0%
4.4%
39.8%
20.6%
33.3%
4.1%
1.8%
0.4%
32.0%
3.3%
15.3%
36.1%
9.7%
3.6%
56.2%
23.7%
5.2%
6.1%
0.4%
8.4%
38.7%
8.2%
12.2%
22.3%
18.3%
0.3%
45.2%
3.8%
48.7%
0.1%
0.4%
1.7%
Total Number of Immigrants
100.0%
1,063,732
100.0%
307,626
100.0%
174,209
100.0%
459,354
100.0%
100,299
100.0%
19,589
Sources are as follows: Australia (DIMIA, 2002, Table 1.3); Canada (CIC, 2003); United States (USINS, 2002,
Tables 7 and 8).
a Includes Mexico, Central America, South America and the Caribbean.
b Includes Skilled-Australian Linked immigrants.
c Includes individuals for whom no visa is required, in particular New Zealand citizens and others.
d Includes only immediate relatives of U.S. citizens.
16
New Zealand citizens accounted for 15,468 of the 21,458 (72.1 percent) non-program migrants entering
Australia in 2001 – 2002.
17
Note that for all countries the number of skilled individuals is actually smaller because the numbers reflect
accompanying family members as well as principle applicants.
50
HEATHER ANTECOL, DEBORAH A. COBB-CLARK AND STEPHEN J. TREJO
There are also interesting differences in the distribution of immigrants across these
entry categories by region of origin. Relative to the overall immigration stream, individuals from Asia and Oceania mainly enter the United States on the basis of their
employment credentials, but are more likely to enter Canada on the basis of family connections. At the same time, European immigration appears to be skill based in Canada
and Australia, while a disproportionate number of Europeans enter the United States as
refugees. Latin American immigrants enter the United States predominately as family
members, but tend to enter Australia and Canada as refugees. Immigrants from Africa
and the Middle East are most likely to enter Australia or Canada as skilled immigrants,
whereas the United States accepts a relatively large number of African and Middle
Eastern refugees and family reunification migrants. Finally, it is perhaps not surprising
that relatively large numbers of both family- and skilled-based immigrants move
between the United States and Canada.
3. THE EFFECT OF SELECTIVE IMMIGRATION POLICY ON IMMIGRANT OUTCOMES
The patterns highlighted in Table 1 resulted from changes to immigration programs in
Australia, Canada, and the United States over the 1990s that placed a greater emphasis
on productivity-related characteristics in the immigrant selection process.18 These policy changes stemmed primarily from the belief that skill-based immigrants do better in
some sense than immigrants admitted on the basis of their family relationships—a
belief which researchers have begun to examine. Interestingly, Lowell (1996) suggests
several reasons why the superior performance of immigrants selected primarily for their
skills may not be a foregone conclusion. He points to the similarity in the jobs held by
family- and skill-based immigrants, the high skills of many family migrants, the support provided by sponsoring family members, and the inability to use skills to completely predict labor market success as potential reasons for believing that the difference
in the outcomes for the two types of migrants may be smaller than commonly believed.
Cross-National Studies:
Some researchers have used the similarities in the Canadian, U.S., and occasionally
Australian labor markets and the dissimilarities in their immigration policies to gain
insight into the role of the selection process in immigrant outcomes. For example,
Duleep and Regets (1992) analyze 1980 U.S. and 1981 Canadian Census data to compare immigrants in the two countries. They conclude that immigrants to Canada are
younger and more fluent, but that there is no consistent difference in education.19
Furthermore, the differences in characteristics generated by the Canadian point system
do not appear to translate into a consistent earnings advantage for Canadian immigrants
relative to native-born workers of the same age. In contrast, Borjas (1993) uses data
18
19
See Vialet and Eng (1990), Green and Green (1999) and Cobb-Clark (2003).
The authors note, however, that the questions regarding language ability vary greatly between the two censuses.
51
SELECTIVE IMMIGRATION POLICY IN AUSTRALIA, CANADA AND THE UNITED STATES
from two censuses for each country to compare the experiences of immigrants. As a
result, he is able to focus on the effects of structural changes in policy, in particular the
introduction of the point system in Canada, on immigrant characteristics. He concludes
that the point system “attracted” more educated immigrants, because it altered the
national origin mix of Canadian immigration not because the expected wages or skills
of any particular national origin group were higher in Canada.
Antecol, Cobb-Clark, and Trejo (2003a) and (2003b) re-examine this issue using
Australian, Canadian, and U.S. data. Like Borjas (1993) they find that much of the difference in the skills of immigrants across these countries lies in the large numbers of
relatively unskilled individuals from Mexico, Central and South America in the U.S.
immigration stream. Interestingly, the cross-national patterns are similar for men and
women even though women are much less likely than men to gain immigrant status
through assessment of their labor-market skills. Thus, the authors conclude that factors
other than immigration policy per se—i.e., geographic, historical, and or social explanations—are also important in contributing to the observed cross-national differences
in immigrant skills.20
Longitudinal Evidence:
The difficulty with using the stock of immigrants to assess the impact of policy is that
the skills of the immigrant population are the result of a complex interaction in the
demand for and supply of immigrants (Chiswick, 1987; Cobb-Clark, 1993; Cobb-Clark
and Connolly, 1997). While immigration policy (specifically, regulations regarding
immigrant selection or efforts to reduce illegal migration) may be thought of as the
demand for immigrants, other historical, social or economic forces (for example, wars,
relative economic conditions, or the geographic location of ones relatives) determine the
supply of potential immigrants. The observed skills of the immigrant stock at a point
in time are determined by demand, supply, and selective remigration. Previous analyses of immigrant stocks often ignore the supply or remigration effects, attributing differences between immigrant populations in different countries to differences in demand
(or policy).21
An alternative methodology uses time series data on immigrant flows to gauge the
impact of policy changes. Green and Green (1995) construct a series of quarterly
data on the intended occupations of Canadian immigrants. Their use of entry data
20
Chiswick (1987) also uses Census data for Australia, Canada, and the United States to analyze changes over time
in the source countries of immigrants and in immigrant skills. He concludes that in general immigrants from
newer source countries do less well than immigrants from more traditional sources. See Chiswick (1986) for a
more detailed analysis for the United States.
21
To some extent, Duleep and Regets (1992; 1996) deal with this problem by explicitly incorporating demand
measures, i.e., the proportion of the cohort who enter the United States under an occupational preference category, into the analysis. They find that groups admitted primarily on the basis of family relationships have lower
earnings than groups admitted on the basis of their skills, but have higher earnings growth.
52
HEATHER ANTECOL, DEBORAH A. COBB-CLARK AND STEPHEN J. TREJO
avoids the selective remigration problem encountered in the previous studies. The
authors conclude that the introduction of the Canadian points test in 1967 had a large
and direct effect on occupational distribution of the immigrant flow.22 Green (1995)
follows the same basic methodology as in Green and Green (1995), but compares
both Canada and the United States. He agrees with Borjas (1993) that the Canadian
points test effectively allowed Canada to block the entry of unskilled immigrants.
His interpretation, unlike that of Borjas, is that with the exception of migration from
Latin America, Canada and the United States draw mainly from the same source
countries, but the composition of immigrant skills in the two countries has been very
different.
Studies of Individual Migrants:
Finally, there have been a limited number of studies that make use of individual data
to evaluate the impact of policy on immigrant outcomes. Jasso and Rosenzweig
(1995) use individual-level U.S. data on a sample of immigrants who received legal
permanent residence status in 1977 and had chosen to naturalize by 1990. Although
the data do not contain wage information, the authors are able to compare the occupational attainment at entry and naturalization for two groups of immigrants: those
entering as spouses of U.S. citizens and those entering under third or sixth preference.
They suggest that the occupational distribution for the third and sixth preference
immigrants is more skilled at entry, but over time the skills between the two groups
become more similar. This occurs both because of downward mobility among
employment immigrants and upward mobility among marital immigrants.
Other researchers have matched U.S. Social Security earnings information to a sample of aliens registered in the 1980 Alien Address Registration Program for whom
visa status is known (Sorensen, et al., 1992). Overall the authors conclude that
employment-based immigrant have higher earnings and are more likely to be working as professionals or managers. Still, in many other ways family-based and
employment-based immigrants appear similar. The two groups have similar labor
market attachments, naturalize at the same rate, and tend to make locational decisions based on the same factors.
Australian individual-level survey data point to large differences in the labor market
outcomes of individuals in different visa categories, though these differentials largely appear to reflect the underlying characteristics of immigrants themselves rather
than immigrant categories per se (Miller, 1999; Cobb-Clark, 2000). This is especially true for established—as opposed to recent—immigrants. While the observable
characteristics of individuals within visa categories do seem to be correlated, there
22
In related work, Wright and Maxim (1993) find that increases in the proportion of a cohort entering Canada as
independent migrants is related to increases in relative entry wages. They find similar (though smaller in magnitude) effects for the proportion of a cohort holding family reunification visas.
53
SELECTIVE IMMIGRATION POLICY IN AUSTRALIA, CANADA AND THE UNITED STATES
is little unobserved heterogeneity associated with visa category. To the extent that
migration programs operate by selecting individuals on the basis of readily observable characteristics, this is perhaps not surprising.
Finally, Cobb-Clark (2003) considers the relative capacity of immigration policy to
facilitate the migrant settlement process. She compares two cohorts—entering
Australia five years apart—with dramatically different labor market outcomes. The
results indicate that while changes in selective immigration policy may have led to
increased human capital endowments, as much as half of the fall in unemployment
rates among women and one third the decline among men appears to have occurred
as the result of changes in the labor market returns to demographic and human capital characteristics.
CONCLUSION
As major immigrant receiving nations, Australia, Canada and the United States have
provided researchers with many opportunities to assess the extent to which selective
immigration policies influence the migration process. The literature reviewed above
suggests that there does seem to be potential for selective immigration policy to affect
immigrant outcomes by altering the skill levels of immigrants. Still, it is clear that other
forces are at work as well. Historical accidents, social forces, and simple geography may
all have a hand in shaping traditional migration patterns, while labor market conditions—in particular the relative return to skill—are likely to be as important as policy
in producing migration incentives. Furthermore, immigration policy cannot be made in
a vacuum as evidence suggests that demand for visas to one country may be affected by
the immigration policy of another (Cobb-Clark and Connolly, 1997).
54
HEATHER ANTECOL, DEBORAH A. COBB-CLARK AND STEPHEN J. TREJO
REFERENCES
Antecol H., D.A. Cobb-Clark and S.J. Trejo, 2003a. “Immigration Policy and the
Skills of Immigrants to Australia, Canada, and the United States”, Journal of Human
Resources, 38(1), Winter 2003, pp. 192 – 21.
--------, 2003b. “The Skills of Female Immigrants to Australia, Canada, and the United
States”, Host Societies and the Reception of Immigrants, Jeffrey G. Reitz (ed.), San
Diego: Center for Comparative Immigration Studies, forthcoming.
Australian Department of Immigration, Local Government and Ethnic Affairs
(ADILGEA), 1991. Efficiency Audit, Department of Immigration, Local Government
and Ethnic Affairs, Audit Report No. 11, 1991 - 1992. Canberra: Australian Government
Printing Service.
Birrell R., 1990. The Chains that Bind: Family Reunion Migration to Australia in the
1980s. Canberra: Australian Government Printing Service.
Borjas G.J., 1993. “Immigration Policy, National Origin, and Immigrant Skills: A
Comparison of Canada and the United States” in Small Differences that Matter: Labor
Markets and Income Maintenance in Canada and the United States. David Card and
Richard Freeman, editors. Chicago: University of Chicago Press: 21 - 44.
Briggs Vernon. M. Jr., 1984. Immigration Policy and the American Labor Force.
Baltimore: The Johns Hopkins University Press.
Chiswick B.R., 1986. “Is the New Immigration Less Skilled than the Old?” Journal of
Labor Economics, 4(2): 169 - 192.
-------- 1987. “Immigration Policy, Source Countries, and Immigrant Skills: Australia,
Canada, and the United States” in The Economics of Immigration. Proceedings of
Conference held at the Australian National University. Canberra: Australian
Government Printing Service.
Citizenship and Immigration Canada (CIC), 2003. Facts and Figures: Immigration
Overview 2002, Ottawa: Citizenship and Immigration Canada.
Clarke H., 1994. The Rationale for Forward Planning and Stability in the Migration
Program. Canberra: Australian Government Printing Service.
Cobb-Clark D.A., 1990. Immigrant Selectivity: The Roles of Household Structure and
U.S. Immigration Policy. PhD dissertation, Economics Department, University of
Michigan. Ann Arbor: University of Michigan.
-------- 1993. “Immigrant Selectivity and Wages: The Evidence for Women.” The
American Economic Review 83(4): 986 - 993.
-----, 2000. “Do Selection Criteria Make a Difference? Visa Category and the Labor
Market Status of Immigrants to Australia. The Economic Record, 76(232), March.
--------- 2003. “Public Policy and the Labor Market Adjustment of New Immigrants to
Australia”, Journal of Population Economics, forthcoming.
Cobb-Clark D.A. and M.D. Connolly, 1997. “The Worldwide Market for Skilled
Immigrants: Can Australia Compete?”, International Migration Review, Fall 1997,
31(3), pp. 670-690.
Committee to Advise on Australia’s Immigration Policies (CAAIP), 1987.
Understanding Immigration. Canberra: Australian Government Printing Service.
55
SELECTIVE IMMIGRATION POLICY IN AUSTRALIA, CANADA AND THE UNITED STATES
Department of Immigration and Multicultural and Idigenous Affairs (DIMIA),
2002. Immigration Update: 2001 – 2002, September 2002. Canberra: Australian
Government Printing Service.
Duleep H.O. and M.C. Regets, 1992. “Some Evidence on the Effects of Admissions
Criteria on Immigrant Assimilation” in Immigration, Language and Ethnic Issues:
Canada and the United States. Barry R. Chiswick, editor. Washington: American
Enterprise Institute: 410 - 439.
Green A.G., 1995. “A Comparison of Canadian and US Immigration Policy in the
Twentieth Century” in Diminishing Returns: The Economics of Canada's Recent
Immigration Policy. D. J. DeVortez, editor. Toronto and Vancouver: C.D. Howe Institute
and The Laurier Institution.
Green D.A., 1999. "Immigrant Occupational Attainment: Assimilation and Mobility
over Time". Journal of Labor Economics, 17(1): 49 - 79.
-------- 1996. “Admission Criteria and Immigrant Earnings Profiles.” International
Migration Review 30(2): 571 - 590.
Green A.G. and D.A. Green, 1995. “Canadian Immigration Policy: The Effectiveness
of the Point System and Other Instruments.” Canadian Journal of Economics 28(4b):
1006 - 1041.
-----, 1999. “The Economics Goals of Canada’s Immigration Policy: Past and Present”,
Canadian Public Policy, Vol. XXV, No. 4, pp. 425 – 451.
Jasso G. and M.R. Rosenzweig, 1995. “Do Immigrants Screened for Skills Do Better
than Family Reunification Immigrants?” International Migration Review 29(1): 85 - 111.
Lack J. and J. Templeton, 1995. Bold Experiment: A Documentary History of
Australian Immigration Since 1945. Melbourne: Oxford University Press.
Lowell B.L., 1996. “Skill and Family-Based Immigration: Principles and Labor
Markets” in Immigrants and Immigration Policy: Individual Skills, Family Ties, and
Group Identities Harriet Orcutt Duleep and P. Wunnava, editors. Greenwich: JAI Press.
Miller P.W., 1999. “Immigration Policy and Immigrant Quality: The Australian Point
System”, American Economic Review, Vol. 89(2), May, pp. 192 – 197.
Parcell W., L. Sparkes and L.S. Williams, 1994. A Brief Historical Outline of Skill
Migration in Australia, 1980 - 93. Canberra: Australian Government Printing Service.
Sorensen E., F.D. Bean, L. Ku, and W. Zimmermann, 1992. Immigrant Categories
and The U.S. Job Market: Do They Make a Difference? Washington: The Urban
Institute Press.
U.S. Immigration and Naturalization Service (USINS), 2003. 2002 Yearbook of
Immigration Statistics. Washington: U.S. Government Printing Office.
Vialet J.C., 1989. Immigration: Numerical Limits and the Preference System.
Washington: Congressional research Service, Library of Congress.
Vialet J.C. and L.M. Eig, 1990. Immigration Act of 1990 (P.L. 101-649). Washington:
Congressional Research Service, Library of Congress.
Wright R.E. and P.S. Maxim, 1993. “Immigration Policy and Immigrant Quality:
Empirical Evidence from Canada”, Journal of Population Economics, 6, pp. 337 – 352.
56
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
THE DEMAND FOR HIGH-SKILLED WORKERS
AND IMMIGRATION POLICY
THOMAS K. BAUER* (RWI ESSEN, UNIVERSITY OF BOCHUM, IZA, BONN,
AND CEPR, LONDON) AND ASTRID KUNZE** (NORWEGIAN SCHOOL OF
ECONOMICS AND BUSINESS ADMINISTRATION, BERGEN, AND IZA, BONN)
ABSTRACT:
This paper provides a descriptive analysis of the demand for high-skilled workers using a new firm data
set, the IZA International Employer Survey 2000. Our results suggest that while workers from EU-countries are mainly complements to domestic high-skilled workers, workers from non-EU countries are hired
because of a shortage of high-skilled labour. The paper, furthermore, provides a short description of recent
German policy initiatives regarding the temporary immigration of high-skilled labour. In view of our
descriptive results these temporary immigration policies seem, however, to satisfy only partly the demand
of firms interested in recruiting foreign high-skilled workers. A more comprehensive immigration policy
covering also the permanent immigration of high-skilled workers appears to be necessary.
JEL CLASSIFICATION: C42, F22, J24, J68.
KEYWORDS: skilled migration, immigration policy.
* Corresponding authors, Prof. Dr. Thomas K. Bauer, University of Bochum, Department of Economics,
Universitätsstraße 150, D-44780 Bochum, Germany. Tel.: +49-234-32-28341, Fax.: +49-234-32-14273, E-mail:
[email protected]
** Astrid Kunze, Phd, Norwegian School of Economics and Business Administration, Department of Economics,
Helleveien 30, N-5045 Bergen, Norway. Tel.: +47-55-959-754, E-mail: [email protected]
57
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
INTRODUCTION
In the last decade, an increasing demand for high skilled workers could be observed in
most developed countries. This development has been associated with the development
of increasingly integrated labour markets and the appearance of skill-biased technological change which is often ascribed to the acceleration of technological developments in
the information and communication technology (ICT) and an increasing reorganization
of workplaces. The observed increase in the demand for high-skilled labour challenges
national education systems to produce a sufficiently large number of high skilled and
labour markets to absorb high skilled efficiently. Even though the supply of high-skilled
workers also strongly increased in the last decade, many countries experienced rising
relative wages for skilled labour, indicating that the increasing supply of skilled workers was not sufficient to meet the increasing demand for this type of labour.
In the last few years, employers in developed economies, in particular in the so-called
New Economy, complained about a shortage of skilled workers, leading many countries
to take initiatives to admit more skilled foreign workers. Countries with existing immigration policies, such as the U.S., Canada, or Australia, increased their quotas for high
skilled immigrants. Other countries, especially in Europe, introduced new immigration
possibilities directed exclusively towards high skilled immigrants. Overall, these policy
initiatives suggest an increasing competition of developed countries for high skilled
immigrants (see, among others ROTHGANG und SCHMIDT, 2003).
Empirical evidence that documents the amount of international migration of high
skilled is rather scarce. We are only aware of three studies collecting firm level data on
high-skilled workers: LOWELL (1999) for the U.S., LIST (1996) for Germany and an
EU Report (1992). A caveat of these studies is the low response rate and small sample
size. The EU Report, for example, uses data on 286 firms in the 12 EU member countries.1 The report highlights Germany and France as the countries, and the engineering
and chemical sector the sectors with the highest recruitment rates of university graduates in the EU. According to this report, large organisations are more likely to recruit
graduates across national boundaries and the bulk of international recruitment is into
commercial functions, technical positions, production and information technology (IT).
The internationalisation of business is the most important reason given by firms for
recruitment of foreign graduates.
In this study we present evidence on the demand for high skilled workers using a new
firm data set, the IZA International Employer Survey 2000 (IZA IES). Covering four
countries, Germany, France, the Netherlands, and the UK, the survey focuses on the five
economic sectors – chemical, manufacturing, IT, research and development, and
1
In order to target firms recruiting graduates from other member states, a data base was created using the latest
directories of recruits of graduates, where they existed, or by liaising with individuals or organizations, i.e. one
consultant within each country. The goal of the sampling procedure was to have one observation per 1 million of
adult population. If possible, which was mostly not the case, selection should be proportional to sector size.
58
THOMAS K. BAUER AND ASTRID KUNZE
finance – that are most important for the employment of high skilled workers. Hence,
the data is not representative on a country level, however, arguably representative within sectors. In addition to country, sector and employment characteristics, the data provides a wealth of information on firm characteristics and why firms participate in global labour markets, which makes it particular interesting for our study.
In the following section we provide a descriptive analysis of the demand for high-skilled
of the firms covered by the IZA IES. Concentrating on the German sub-sample of this
data set, we describe which firms recruit high skilled foreigners, the reasons why they
recruit foreign workers, as well as the qualification profile of these foreign workers. The
aim of this analysis is to get a more detailed picture on two hypotheses regarding the
determinants of the demand for foreign high skilled. Do firms recruit internationally
mainly because they want to gain from knowledge spillover, i.e. they want to obtain
knowledge on key technologies that are not nationally available yet or knowledge of foreign markets? In this case the foreign high-skilled workers are complements to native
workers. Or do firms recruit internationally because of a domestic lack of skilled labour,
in which case the foreign high-skilled are substitutes to native high-skilled? The answers
to these hypotheses have important policy implications. In the first case, a more permanent immigration policy is necessary that makes the country more attractive for highskilled workers from abroad. In the second case, a temporary immigration policy focusing on a particular type of workers may be sufficient to reduce the temporary shortage
of labour. The main task for policy in this case is the precise identification of a labour
shortage, possibly well in advance (see WINKELMANN, 2002, and ZIMMERMANN
ET AL., 2002).
Based on the results of this descriptive analysis, Section 2 provides a short description
of recent German policy initiatives regarding the immigration of high skilled labour and
discuss whether these policy initiatives are effective in meeting the demands of the
firms. The last Section gives a short summary of the findings.
1. THE DEMAND FOR HIGH-SKILLED WORKERS: EVIDENCE FROM
AN INTERNATIONAL EMPLOYER SURVEY
In this section we present descriptive statistics on the demand for foreign high-skilled
workers in West-Germany using data from the IZA IES.2 This survey has been conducted within four neighbouring European Countries: West Germany, France, the U.K.,
and the Netherlands. In order to ensure a sufficiently large number of firms employing
high-skilled foreign workers, the sampling strategy used to collect the data targeted only
firms with more than 100 employees. Additionally, the survey focused on the five most
important economic sectors for the employment of high-skilled workers: chemical,
manufacturing, information technology (IT), research and development (R&D), and
2
For more details see WINKELMANN et al. (2001), WINKELMANN (2002), and KUNZE and WARD (2002).
59
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
finance.3 The data was collected through a telephone interview with the individual
responsible for the recruitment of high-skilled workers. In the survey, ‘high-skilled’ has
been defined as ‘holding a university degree’ and ‘foreign high-skilled’ as ‘workers with
a university degree, who obtained their qualifications abroad and who are foreign citizens’. Workers that are not foreign are labelled ‘domestic’.4 Where the respondent was
in charge of recruitment for more than one country, he/she was asked to restrict answers
to the domestic firm only, in order to exclude foreign based units of multinationals.
Accordingly, the firm size in the survey refers to domestic units only. The total sample
of the survey contains 850 firms. Dropping firms for which there is missing information on the main variables reduces the sample to 527 firms, of which 234 firms are
located in Germany, 99 in France, 76 in United Kingdom, and 118 in the Netherlands.
In the following, we show the main results for the demand of high skilled and foreign
skilled focusing our discussion on the Germany sub-sample.
1.1. THE DEMAND FOR HIGH SKILLED FOREIGNERS IN GERMANY
Table 1 shows some basic descriptive statistics of the IZA IES by country. Within the
five sectors covered by the data set, 36.3 percent of the German firms employ some foreign workers. With an average size of 902 employees, these firms are quite large. 23.6
percent of the employed workers within these firms are high-skilled and 3.33 percent of
the high-skilled are foreign. Note that the figures for Germany are quite similar to those
for France and the United Kingdom. Nevertheless, while firms in the Netherlands have
hired fewer high-skilled, the fraction of foreigners among the high-skilled is higher than
in the other three countries.
Comparing firms with foreign high-skilled to those without foreign high-skilled workers shows that the skill structure between these groups differs. While German firms with
foreign high-skilled workers have on average 33.8 percent high-skilled workers among
their employees, the share of high-skilled workers in firms without foreigners is only
17.7 percent. Although the corresponding percentages vary slightly across the four
countries the general findings are similar.
Breaking down figures further by country and sector shows that among the five sectors
covered by the survey, IT and R&D are the sectors with highest shares of high skilled
workers, followed by financial services (see Table 2). With 8 to 10 percent, the highest
share of foreigners among the high skilled is observed in the R&D-sector. In financial
services, foreign high skilled seem to be the exception, and for the remaining sectors the
fractions vary between 2 and 7.5 percent.
3
These sectors were identified as particularly important for the recruitment of high-skilled workers through the use
of a pre-test.
4
Hence, those with domestic citizenship and higher education from abroad or foreign citizenship and domestic
higher education are included in the group of domestic high-skilled workers.
60
THOMAS K. BAUER AND ASTRID KUNZE
TABLE 1. SUMMARY STATISTICS, BY COUNTRY
Country
All firms
Germany
France
United
Kingdom
Netherlands
Number of firms
Number of firms with foreign workers
Mean size
234
85
902
99
33
528
76
26
831
118
31
745
(High-skilled/Employment)*100
23.59
(1.53)
0.010
(0.0018)
3.33
(0.56)
37.79
(2.87)
0.015
(0.0053)
3.35
(0.82)
29.36
(2.97)
0.006
(0.002)
3.68
(1.35)
17.78
(1.91)
0.011
(0.011)
4.58
(1.28)
33.84
(2.87)
9.16
(1.32)
44.81
(5.35)
10.0
(2.03)
33.84
(5.86)
10.7
(3.62)
31.3
(3.44)
17.4
(4.14)
17.7
(1.59)
34.2
(3.32)
27.0
(3.31)
12.9
(2.06)
(Foreign High-skilled /Employment)*100
(Foreign High-skilled / High-skilled) *100
Firms with foreign workers
(High-skilled /Employment)*100
(Foreign High-skilled / High-skilled)*100
Firms without foreign workers
(High-skilled /Employment)*100
Note: Results reported using the IZA International Employer Sample 2000. Standard errors in parentheses.
TABLE 2. PERCENTAGE OF FOREIGN HIGH-SKILLED WORKERS BY SECTORS
Country
Germany
France
United Kingdom
Netherlands
4.83
1.93
1.58
4.54
10.88
2.19
3.09
1.56
2.60
10.68
4.14
3.56
.28
3.41
8.84
10.33
7.30
1.05
4.49
9.58
Sector
Chemical
Manufacturing
Financial
IT
R&D
Note: Source: International Employer Survey 2000. Reported percentages are the ratio of the number of
foreign high-skilled workers divided by the number of high-skilled workers.
61
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
TABLE 3. SUMMARY STATISTICS FOR FIRMS WITHOUT FOREIGN HIGH-SKILLED
AND WITH FOREIGN HIGH-SKILLED WORKERS, PERCENTAGES
Factor
Firms with
Firms with fordomestic work- eign workers
ers only
with foreign
degree
Firms employing foreign
workers mainly
from the EU
Firms employing foreign
workers mainly
from the non
EU
Language problems
Socio cultural differences e.g
different mentality of habits
Acceptance by superiors
Acceptance by subordinates
Acceptance by customers
Difficulties in evaluating foreign
worker experience
Lack of awareness of foreign education
systems, grades and qualifications
High recruitment costs
Is it difficult to obtain a work permit
non EU workers
No applicants
No need – vacancies filled with
domestic workers
10.17
47.45
44.44
41.30
5.96
0.25
1.74
3.97
53.57
7.14
12.76
11.22
54.72
9.43
13.2 1
13.21
56.52
6.52
10.87
10.87
4.96
21.94
22.64
28.26
5.71
5.71
26.02
19.39
27.36
16.98
28.36
26.09
60.53
38.91
65.96
-
60.61
-
68.52
-
22.08
-
-
-
Note: Results reported using the German subsample from IZA International Employer Sample 2000.
234 observations. 149 without and 85 with foreign high-skilled workers.
TABLE 4. REASONS FOR HIRING FOREIGN HIGH-SKILLED WORKERS, PERCENTAGES
Most common field of
domestic employees
Most common field of
foreign employees
Engineering
Maths and natural science
IT
Law
Economics
Medicine
Other
38.32
12.15
14.95
1.87
21.5
2.8
8.41
38.68
15.09
23.58
0
13.21
2.83
6.6
Total
100
100
Note: Results reported using German subsample from IZA International Employer Survey 2000.
Proportion of firms responding that they agree (strongly agree) that a factor was a consideration in
the decision making process for hiring foreign employees with a university degree. Response from
firms hiring foreign workers.
62
THOMAS K. BAUER AND ASTRID KUNZE
TABLE 5. PROBLEMS WITH RECRUITING FOREIGN WORKERS, PERCENTAGES
Variable
Firms without for- Firms with foreign t-test
eign high-skilled high-skilled
Multinational firm
Share of foreign business
Foreign owned
Foreign language important
Experience abroad important
Chemical Industry
Manufacturing
Financial Servces
Data Processing
Research and Development Sector
15.9
33.4
34.6
67.3
26.7
17.0
38.3
24.1
13.9
6.5
35.4
45.9
46.8
78.3
33.1
24.6
29.1
13.7
17.1
15.4
3.72
3.6
2.7
2.6
1.5
2.0
2.0
2.8
1.0
3.3
Note: Results reported using the German subsample from IZA International Employer Sample 2000.
Proportion of firms responding that a factor was potentially problematic when recruiting foreign
employees with a university degree.
TABLE 6. SUBJECTS OF STUDY OF HIGH-SKILLED WORKERS
Factor
‘We hire foreign employees because’
Agree
Strongly agree
Overall they are the best candidates
There is a lack of good domestic applicants
They know foreign markets
They speak foreign languages
They speak English
The type of knowledge required for these jobs is
not produced by the domestic education system
Their skills better fit our work tastes
49.07
55.45
64.86
71.17
56.13
9.26
10.91
36.04
47.75
26.42
27.93
51.35
4.5
15.32
Note: Results reported using the German subsample from IZA International Employer Sample 2000
and only firms with foreign high-skilled in Germany.
63
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
1.2. WHICH FIRMS RECRUIT FOREIGN HIGH-SKILLED WORKERS ?
What distinguishes firms who actually hire foreign workers from other firms? In Table
3 we look at more detailed summary statistics comparing firms with and without foreign high-skilled workers. Simple t-test statistics on the differences between these two
types of firms confirm significant differences. It appears that those firms that are more
internationally orientated are also more likely to employ foreign high-skilled workers.
More specifically, we find that they are more likely to be part of a multinational company, have a higher export share and are more likely to be foreign owned. Furthermore,
they value the knowledge of foreign language by applicants and experience abroad more
highly. Moreover, the distribution across sectors is different and, which is not shown
here, they are more likely to be large firms. Firms without foreign high-skilled are more
likely to be found in manufacturing and financial services.
In addition to the overall strategy of a firm, distinguishing features may result from differences in the personnel or recruitment strategy. The IZA survey includes three interesting questions referring to these strategies. They were all asked only to firms with foreign high-skilled workers. The first question asked whether firms never search internationally for applicants. The other questions asked whether they sometimes or never pay
for moving costs and costs for language courses. 35 percent of the firms agreed that
they never search internationally, 21 percent said that they never reimburse moving cost
and 27 percent pay never language courses. Hence, a considerable part of firms with
foreign high-skilled workers has not made a particular effort to recruit those. One can
only speculate how come that they have had applicants from abroad at all. The studies
by WINKELMANN et al. (2001) and KUNZE and WARD (2002) have shown, that
demand analyses conditional on active search does not alter the results.
1.3. WHAT IS THE QUALIFICATION PROFILE OF THE FOREIGN HIGH-SKILLED WORKERS ?
So what are the reasons why firms recruit and not recruit foreign high-skilled workers?
The IZA survey includes a list of reasons to recruit firms answered in three categories:
agree strongly, agree partly, and agree not at all. In Table 4 we present the results for
Germany.
Particularly high agreement rates are found for all questions stressing international
competence. These include the knowledge of foreign markets and the knowledge of languages, and speaking English. Particularly high disagreement rates are found for all
questions stressing the comparison with German applicants. These results suggest that
firms recruit foreign high-skilled workers mainly because they have some knowledge
that is not available nationally, i.e. the foreign workers are complements to the natives.
Asking all firms in the sample about reasons for not recruiting from abroad one gets
quite different responses dependent on whether the firms have hired foreign high skilled
(see Table 5). While firms with no foreigners agree that getting a working permit causes large difficulties, firms that have direct experience with foreigners add that there are
64
THOMAS K. BAUER AND ASTRID KUNZE
much more specific difficulties, such as language problems, socio-cultural differences and the lack of knowledge about the foreign education system. Especially the
latter suggests that firms may face difficulties in judging the qualification of foreign
applicants. Splitting the sample into firms with mainly employees from the EU and
non-EU countries, results remain virtually unchanged. This result may be restricted
due to the fact that the question in this firm survey concerning the country of origin
of the foreign workers is asked in a too general way in order to perform more detailed
analyses.
Consistent with the latter finding, the IZA IES shows that in fact the qualification
profile of foreign and domestic applicants is not very different with respect to field
of study. In Table 6 the distribution within firms with foreign high-skilled workers
across fields is shown.
Among domestic high-skilled the most important field is engineering (38 percent)
followed by economics (22 percent). IT is third. The ranking and distribution among
foreigners is surprisingly quite similar to the one among domestic high skilled. The
main difference is that IT is second and economics third in the ranking. The latter
result may be biased due the fact that we pool hires from the EU and the non-EU
countries. Indeed, when one distinguishes these two groups one finds that while
engineering is still the most important field among foreigners from EU countries, IT
is the most important one among foreigners from non-EU countries. More specifically, looking at the countries of origin, firms recruit most often IT-workers from
East European countries.
Information on the qualification or work experience of workers is provided by broad
measures of the field of work and the position. Six fields of work are distinguished:
research and development, IT technology, manufacture, marketing, administration
and others. Again the distribution for the two groups of workers, which are not
reported here, are quite similar and suggest that domestic and foreign workers are
substitutes. Workers are most likely to work in the R&D departments of the firms,
followed by marketing and IT. Distinguishing again between hires from EU countries
and non-EU countries suggests that, however, EU nationals are more likely to be in
the marketing section. This may indicate that their foreign experience or language
proficiency are particularly valuable to the firms. For non-EU nationals we still find
that they are most likely to work in R&D, hence, are perhaps hired because of their
particular qualification. Furthermore, the survey suggests that firms use foreign
high-skilled in positions as specialists and as managers in the medium level.
65
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
2. IMMIGRATION POLICY TOWARDS HIGH-SKILLED LABOUR: THE GERMAN
EXAMPLE
2.1. INTERNATIONAL COMPETITION FOR HIGH-SKILLED WORKERS
In the last decade, increasing flows of high-skilled migrants could be observed.5 This
increasing mobility of high-skilled labour has been associated with the development of
increasingly integrated labour and product markets, an increasing appearance of skillbiased technological change in developed economies which is often ascribed to the acceleration of technological developments in the information and communication technologies
(ICT) and the re-structuring of the organization of work. Increasing complaints of firms,
especially in the so-called New Economy, about a reputed shortage of adequately skilled
workers led many developed countries to take new but modest initiatives to admit more
skilled labour migrants (ROTHGANG and SCHMIDT, 2003). At least for European countries, these new initiatives mark an outstanding change in immigration policy, given the
‘zero-immigration’ policy they followed since the first oil-price shock in the early 1970s.
In Western Europe, these new initiatives focus on a selective policy based on higher
skills relevant for some specific industries, such as the information technology and
health industries (OECD, 2002; IOM, 2003). This skill-based entry system in fact is currently the main manner in which non-EU citizens can come to live and work in the EU.
All these initiatives have in common, that they reduced existing restrictions for employers to hire high-skilled foreign workers. Nevertheless, almost all of them require either
that the employers provide evidence that no appropriate native worker can be found or
restrict the facilitation of hiring foreign workers to specific industries. Furthermore, the
conditions under which the foreign workers are employed must be identical to those of
the native worker with respect to payment and general working conditions.
In the UK, for example, there was some reduction in the skills requirements for highly
educated workers, such as little after-graduation labour market experience being required,
to enable employers to gain access to a wider range of work permits. Currently, work permits can be applied for electronically in order to reduce transaction costs. Furthermore, if
a foreign worker were to change employers in the same field, the worker would not be
required to apply for a new work permit. In January 2002, France established a system to
induce high-skilled workers from outside the EU to live and work in France. The French
Labour Ministry handled the approval procedure and, if successful for the foreign applicant, the employer’s application was approved by the Labour Ministry and Ministry of the
Interior promptly. Also several countries outside Europe entered the apparent global competition for high-skilled labor. The U.S. increased the number of H1B-visas (temporary
visas for high skilled workers) issued every year several times, and Australia and Canada
increased the number of immigration quotas issued through their point systems.6
5
See BAUER, HAISKEN-DENEW, and SCHMIDT (2003) for a brief survey of recent developments in international migration.
6
See BAUER, LOFSTRÖM and ZIMMERMANN (2000) for a brief description of the immigration policy in
Australia and Canada.
66
THOMAS K. BAUER AND ASTRID KUNZE
In the following, we provide a more detailed description of the German Green Card initiative for IT-specialists from the summer 2000 for at least two reasons. First, this initiative could be seen as being representative for similar initiatives in other European
countries. Second, the introduction of the Green Card started a heated debate on the
German immigration policy, leading to the establishment of an immigration commission that aimed to produce a report with recommendations on a more coherent and comprehensive German immigration law. A short survey of the main recommendations and
the development of a German immigration law will also be described in this section.
2.2. THE GERMAN “GREEN CARD” INITIATIVE
Reacting to increasing complaints from firms in the ITC industry that they are unable to fill
vacancies and that this shortage of appropriately skilled workers will harm innovations and
the competitiveness of the German industry, chancellor Schröder announced in February
2000 that a so-called Green Card for foreign IT-specialists will be introduced.7 In August
2001, the Green Card came into force, giving German IT-firms the opportunity to hire up
to 20,000 non-EU IT-specialist for a maximum of five years.8 This quota stayed far behind
the 75,000 IT job vacancies announced by the industry.
In order to hire a foreign IT-specialist, the German IT firm had to apply for a work permit
at the employment office. The employment office then verified within a week whether (i)
no unemployed skilled German or an EU specialist could meet the requirements of the firm,
(ii) the person a firm wanted to hire is qualified for the position, and (iii) the employer is
offering the foreign specialist the same working conditions and wage as a comparably qualified German specialist would receive. In order to assess the qualification of the foreign specialist, it was required that foreign IT-specialist has a degree from a university or polytechnic in the field of information and communication technology or the employer needed to
confirm that he is willing to pay an annual salary of at least Euro 51,000. The Green Card
also applied to foreigners graduating from German universities and polytechnics, who had
to leave the country after their graduation before the Green Card came into force.9
7
The German Green Card should not be confounded with the Green Card issued in the United States. As will be
described in more detail, the former allows the immigration of high-skilled workers on a temporary basis whereas the Green Card in the US addresses permanent migrants. The German Green Card is rather more similar to the
H1-B visa in the US, which represent temporary visas for high skilled workers.
8
IT-specialist are defined as specialist in software development, the development of circuits and IT systems, multimedia development and programming, and IT consulting, as well as system specialists, Internet specialists and
network specialists (WERNER, 2002).
9
See WERNER (2002) for a more detailed description of the regulations and procedures of the Green Card initiative.
67
68
in %
(2)
2.96
5.08
7.19
3.54
6.79
12.98
24.98
1.42
3.00
2.64
29.43
100.00
Total
(1)
418
719
1,017
500
961
1,836
3,533
201
424
373
4,162
14,144
Number of Work Permits
92.22
77.48
84.05
87.70
87.85
92.33
98.01
82.06
84.76
91.40
94.69
80.14
(3)
Male
7.78
22.52
15.95
12.30
12.15
7.67
1.99
17.94
15.24
8.60
5.31
19.86
(4)
Female
Gender Composition
(in %)
34.91
82.04
73.38
84.76
90.96
94.62
81.59
84.28
94.20
92.80
95.94
84.45
(5)
65.09
17.96
26.62
15.24
9.04
5.38
18.41
15.72
5.80
7.20
4.06
15.55
(6)
Immigrated Foreign
from a for- graduate of
eign country a German
University
Origin (in %)
94.10
77.21
82.84
83.50
91.83
74.89
89.55
86.79
92.43
84.80
83.04
90.19
(7)
University
degree
(10)
(9)
68.18
56.47
60.67
63.60
67.33
65.85
62.24
63.68
52.12
46.38
51.11
58.89
(8)
9.81
13.21
7.57
15.20
16.96
8.17
25.11
10.45
5.90
22.79
17.16
16.50
16.98
17.43
16.00
17.03
14.54
21.60
10.45
14.33
18.19
15.40
15.09
10.77
101 to 500
30.90
36.19
32.89
24.07
19.61
16.16
25.87
29.21
21.14
21.00
17.59
21.05
(11)
> 500
Firm Size (in %)
Certificate of ≤100
annual salary
of at least
51,000 Euro
Qualification (in %)
18
23
103
277
39
20
7
15
20
13
10
9
(12)
Rejected
Applications
Source: Bundesanstalt für Arbeit, Nürnberg: Statistik der zugesicherten/abgelehnten Arbeitserlaubnisse nach der IT-ArGV, BA IIIb3; own calculations.
Bulgaria
Jugoslavia, Kroatia
Bosnia-H., Slovenia,
Macedonia, Montenegro
Rumania
Hungary
Czech and Slovak Republic
Russia, Weissrussland, Ukraine,
Baltic States
India
Pakistan
North Africa (Algeria,
Marokko, Tunesia)
South America
Other countries /regions
Total
Country of Origin
TABLE 7. WORK PERMISSIONS ASSURED TO FOREIGN IT-SPECIALIST BY SELECTED CHARACTERISTICS AND
COUNTRY OF ORIGIN, APRIL 2003
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
THOMAS K. BAUER AND ASTRID KUNZE
FIGURE 1. WORK PERMISSION ASSURED TO FOREIGN IT-SPECIALISTS, AUGUST 2000 –
APRIL 2003
1,400
1,200
1,000
800
600
400
200
0
Source: Bundesanstalt für Arbeit, Nürnberg: Statistik der zugesicherten/abgelehnten
Arbeitserlaubnisse nach der IT-ArGV, BA IIIb3; own calculations.
FIGURE 2. WORK PERMISSION ASSURED TO FOREIGN IT-SPECIALISTS BY REGION,
AUGUST 2000 – APRIL 2003
30
25
Percent
20
15
10
5
Saxony
Thuringia
Saxony-Anhalt
Brandenburg
Berlin
Bavaria
Baden-Württemberg
Saarland
Rhineland-Palatinate
Hessen
North Rhine-Westphalia
Bremen
Lower Saxony
Mecklenburg-Western
Pomerania
Hamburg
Schleswig-Holstein
0
Source: Bundesanstalt für Arbeit, Nürnberg: Statistik der zugesicherten/abgelehnten
Arbeitserlaubnisse nach der IT-ArGV, BA IIIb3; own calculations.
69
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
During the validity of the Green Card, the foreign IT-specialist is allowed to change to
another IT job in another firm. Becoming self-employed is only possible under certain
circumstances. The spouses of the IT-specialists with a Green Card are able to obtain a
working permission in Germany after a waiting period of one year. Originally, the deadline to apply for a working permission under the Green Card initiative for the first time
was on July 31, 2003. This deadline, however, has been extended by the German government to the end of 2004, when a new immigration law is expected to regulate work
and residency permits for high-skilled, non-European Union workers seeking employment in the country.
Figure 1 shows the number of work permits assured to IT-specialist under the Green
Card initiative every month from August 2000 until April 2003. Note that this number
could be higher than the actual number of IT-specialists immigrated, because, among
other reasons, firms could revise their demand for IT-specialists between the assurance
of the working permit and the time the work permit is actually granted or because several firms could apply for the assurance of the same foreign IT-specialist (SCHREYER,
2003). Throughout the period, the number of assured work permits shows a downward
trend, with peaks occurring every other quarter. In the first year of the initiative, 680
work permits have been granted on average every month. A sharp drop of the number
of work permits could be observed in September 2001. Thereafter, the downward trend
levels out to about 200 work permits per month. From the introduction of the Green
Card in August 2000 until the end of April 2003, 14,144 Green Cards have been assured
to IT specialists from outside the EU (see Table 7).
Figure 1 and Table 7 shows that the total quota of 20,000 green cards has not been used
up by the German IT-industry and - given the current average number of 200 work permits per month - will also not be reached until the end of 2004. This seems rather surprising, given the estimated shortage of 75,000 IT-specialist announced by the industry
in 2000 and the fact that that only about 6,000 German IT-specialists graduate every
year from German universities. Several reasons may be responsible for this discrepancy. First, since the mid of year 2001 the new economy experienced a crisis, which
reduced the demand for IT-specialists. Even though there are no administrative statistics
available, surveys among Green Card-holders suggest that about 7% of them become
unemployed while staying in Germany (SCHREYER, 2003). Furthermore, the sharp
drop of Green Cards assured in September 2001 indicates that the events of September
11, 2001 had also an impact on the demand for foreign specialists.
Table 7 also reports some statistics on the characteristics of the IT-specialist who
obtained a German Green Card. Almost 88% of them are male, and about 15% had
graduated from a German university of polytechnic. Slightly more than 16% received
the work permit as a result of an agreement concerning an annual salary of at least
51,000 Euro and almost 60% of the Green Card holders are employed in firms with
fewer than 100 employees. In the discussion around the introduction of the Green Card,
the media and most politicians expected that the Green Card will be used mainly for
70
THOMAS K. BAUER AND ASTRID KUNZE
IT-specialist from India. Even though India is the single most important country for
Green Card holders, their share is far behind the initial expectations. This could be
explained with the preferences of Indians to migrate either to the United States or the
UK. In both countries, English is spoken, and both have a large Indian community. In
addition, the United States offers better opportunities to become self-employed and to
settle on a permanent basis. According to Table 7, more than one third of all Green Card
holders come from Central or Eastern European countries, which again could be
explained by a rather good migration network with Germany. Finally, Table 7 shows that
only 1.6% of all applications for a Green Card have been rejected.
Figure 2 shows the number of assured work permits to foreign IT-specialists are regionally very concentrated. Almost 93% of all work permits have been assured to firms located
in West Germany, and the federal states Bavaria, Baden-Württemberg, Hessian, and North
Rhine-Westphalia account for almost 84% of all Green Cards. Even these numbers, however, deceive the true regional concentration of the Green Card-holders, because half of
them are located in either Munich, Frankfurt, or the region of Bonn and Cologne.
2.3. THE NEW GERMAN IMMIGRATION LAW
The introduction of a “Green Card” for IT-specialists in Germany started a heated
debate on the German immigration policy. This debate resulted in the establishment of
an immigration commission, called the Süßmuth-Commission after the chairwomen
Rita Süßmuth, whose mission was to produce a report with recommendations on a more
coherent and comprehensive German immigration law. The commission published its
final report in July 2001 (INDEPENDENT COMMISSION ON MIGRATION TO
GERMANY, 2001). It proposed that Germany should officially acknowledge itself as
an immigration country. One of the main arguments of the commission for the need of
increased immigration to Germany was the apparent demographic problems and the
ageing of the German population. The major recommendations of the commission were
to introduce a coherent flexible migration policy that allows both the immigration of
temporary and permanent labor migrants, to introduce measures to foster the integration of immigrants, measures to speed up the German asylum procedure while recognizing Germany’s obligations arising from the Geneva Refugee Convention and the
European Human Rights Convention, and measures to combat illegal immigration.
Concerning labor migration, the Süßmuth-Commission differentiated six groups of
migrants: (i) qualified permanent immigrants, (ii) students, (iii) trainees within the
German apprenticeship system, (iv) temporary workers to cover labour shortages, (v)
executives and key members of staff of firms, scientists, and academics, and (vi) startup entrepreneurs. Qualified permanent immigrants are proposed to be selected following to a nationwide point system similar to the one use by Canada and Australia.10
Applicants must score a minimum number of points. Of the applicants who have scored
10
See ZIMMERMANN et al. (2002 for a detailed description of the Canadian and Australian immigration system.
71
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
more than this minimum number of points, those who have scored the highest number
of points should be chosen. The crucial selection criteria for which points are rewarded
should indicate an applicants’ ability to integrate into the labor market and the society
well. As main indicators the commission mentions a person’s age, qualification and the
ability to speak German. The commission further suggested setting an initial quota of
20,000 permanent immigrants including their family members, which could be changed
later on according to the demographic development in Germany.
In addition to the permanent immigration of qualified workers, the commission suggested to allow also the temporary immigration of workers in order to react in a flexible way to short-term shortages in the labour market under a system of strict quotas and
limits on the length of time. Two different methods of identifying labour shortages
should be tested in an initial phase.11 According to the first method, labour shortages
should be determined using statistical diagnoses. As ZIMMERMANN et al. (2002)
show, however, this method is subject to potentially large errors and not able to identify labour shortages in a reliable way. According to the second method, a fee paid by the
employers should identify labour shortages and guarantee that domestic applicants will
continue to be attractive to the labour market. It could be questioned, however, that a fee
to employers could really meet these goals, mainly because the fee will reflect the actual value of hiring a foreigner through that system only by chance. Because of these problems scientists rather suggest to auction temporary immigration visa to domestic firms
(see ZIMMERMANN et al., 2002).
For executives and managers of multinational firms, key staff of firms, scientists and
academics as well as start-up entrepreneurs the commission recommended to make
access to the German labour market much easier than for all other groups and to offer
them the best possible residence conditions. Executives, for example, are only required
to prove that they earn twice as much as the income threshold for statutory health insurance12 in order to obtain full access to the labour market. In addition, start-up entrepreneurs with a sound business idea should be given quick entrance to Germany. Selection
of these entrepreneurs should be based on certified business plans, which are reviewed
by authorities - such as local chamber of industry and commerce, banks, or industrial
development corporations - located in the region where the applicant wants to settle. In
addition to having an equity or loan commitment to ensure that the business idea can be
implemented, the entrepreneur should not be older than 45, must certify that they are of
good health, have a good reputation and can cover their subsistence for an initial period. Finally, the commission suggested to implement a program that encourages young
foreigners to either study at a German University or to undergo training in the German
dual training system. For the latter they suggested an immigration quota of 10,000 visas.
11
12
A detailed discussion of how to identify labor shortages is given by ZIMMERMANN et al. (2002).
Currently, this threshold is an annual income of 46.350 Euro.
72
THOMAS K. BAUER AND ASTRID KUNZE
The report by the commission formed the basis for a new German immigration act.13
Concerning the immigration of workers, this Immigration Act followed most of recommendations by the Süßmuth-Commission.14 Even though one of the main goals of the
law is to select immigrants more according to the needs of the labour market and to
increase the share of skilled migrants, the Ministry of the Interior stresses that the point
system to select migrants will only be available to a very limited number of immigrants
in the beginning and will not be expanded before 2010. The Immigration Act passed
both chambers of the parliament, but was nullified by the Federal Constitutional Court
in December 2002 due to a procedural error during voting in the second chamber (the
Bundesrat) on March 22, 2002.15 Without changing the content of the Immigration Act,
the government once again submitted the draft bill for adoption in January 2003 and
passed the first chamber (the German Bundestag) on 8 May 2003. In June 2003, the
German Bundesrat rejected the Immigration Act. A mediation committee will now
examine the bill.16
CONCLUSION
Using a newly available data set of German firms within five potentially high skilled
labour intensive sectors, the IZA International Employer Survey 2000, this paper
provides a descriptive analysis of the demand for high-skilled foreign labour. The
analysis has shown that on average 3.3 percent of all high skilled workers are foreigners. It seems that foreigners and domestic high skilled are quite similar with
respect to field of study, yet an important difference is the international experience
and knowledge of languages of the foreigners that are highly valued by the firms. A
comparison of the German figures with outcomes for France, the UK and the
Netherlands has shown that sector differences are more important than country differences. Nevertheless, the size of the country and the labour market may be important as the case of the Netherlands suggests. This is the country with lowest shares
13
See http://www.bmi.bund.de/dokumente/Pressemitteilung/ix_59920.htm for more information.
Concerning family reunification and asylum, the law envisages further restrictions on the possibility to immigrate. With regard to family reunification, the new law plans to give only children under the age of 12 (currently 16) a legal claim to enter the country in order to ensure that the children of immigrants integrate into German
society as soon as possible. Note that this restriction does not hold for children of refugees and foreigners who
have been granted a settlement permit as highly qualified persons or within the framework of the selection procedure. The children of these groups of migrants will have a legal claim to enter the country until the age of 18.
According to the new law, family members entering the country after their families will have the same possibilities of accessing the labour market as the persons they are joining. The current law allows most family members
to access the labour market only after a one-year waiting period. Finally, the new law includes many new regulations aiming at making the current asylum procedure more efficient and restricting the possibilities to claim asylum as well as the access to social security.
15
Six of the federal states led by the Christian Democratic Union party (CDU) had opposed passing the law in
March 2002 and took their complaint to the highest court. The two representatives from the state of Brandenburg,
which is governed by a coalition between the Social Democrats (SPD) and the CDU, had been unable to deliver
a unanimous vote. The German constitution however prescribes a uniform casting of votes of each state.
16
The task of the Mediation Committee is to find a compromise whenever there are differences of opinion between
the Bundestag and Bundesrat on a piece of legislation.
14
73
THE DEMAND FOR HIGH-SKILLED WORKERS AND IMMIGRATION POLICY
of high skilled within sectors and highest fractions of foreigners among those. This
may be explained by the fact that the Netherlands is a small, very internationally orientated country.
One of the most important questions for policy concerning the immigration of skilled is
whether domestic and foreign workers complement or substitute each other. The
descriptive analysis does not provide an unequivocal answer to this question, since we
find some support for both hypotheses. Even though the results point towards a complementary relationship between foreign and domestic high-skilled, the concentration of
foreign high-skilled from non-EU countries in IT-related subjects and functions suggests that the employment of these workers may be driven by a shortage of skilled
labour in this area (Winkelmann, 2002). In addition, because those firms who hire foreigners tend to have a lot of high-skilled in their work force overall, the above results
support the interpretation of a lack or scarcity of high-skilled workers in the short run
at fixed prices in the domestic labour market (see Winkelmann, et al., 2001). The IZA
IES further shows that the majority of firms who have hired foreign high skilled have
paid for moving cost and language courses. This could be interpreted as the payment of
efficiency wages to foreigners in order to extract more effort from the employed high
skilled (EPSTEIN et al., 2002).
Furthermore, we give a detailed description of the German Green Card initiative that
started in 2001 and gives German firms the opportunity to hire IT-specialists from nonEU countries on a temporary basis. This initiative is surely effective in reducing part of
the shortage of skilled IT specialist which has been announced by employers in the New
Economy and partly confirmed by our descriptive analysis. However, our descriptive
analysis also indicates, that such a temporary immigration policy satisfies the demand
of firms interested in recruiting foreign high-skilled workers only partly. The analysis
of the IZA International Employer Survey 2000 has shown that German firms hire to a
large extent foreign workers that are complements to domestic high-skilled, i.e. they
recruit foreign high-skilled mainly because of their knowledge of foreign markets and
languages and because of the transfer of new technological skills that are yet not available domestically. An immigration policy that satisfies these types of demand must
make Germany more attractive for foreign high-skilled workers in the long term. This
includes the reduction of institutional barriers to international mobility not only for
high-skilled workers but also for their family members. In addition, smooth and rapid
integration should be promoted. Despite some weaknesses, the proposed new immigration law for Germany, which has been described in more detail in section 3 of this paper,
is a first step towards reaching this goal. However, the law still awaits its ratification. In
view of the importance of globalized product and labour markets and rapid technological progress in modern economies, a fast adoption of this law appears to be necessary
for Germany not to fall behind in the global competition for high-skilled labour.
74
THOMAS K. BAUER AND ASTRID KUNZE
REFERENCES
Bauer T., J.P. Haisken-DeNew and C.M. Schmidt, 2003. “International Labour
Migration, Economic Growth and Labour Markets: The Dynamics of
Interrelationships,” mimeo., University of Bochum.
Bauer T., M. Lofström and K.F. Zimmermann, 2000. “Immigration Policy,
Assimilation of Immigrants and Natives’ Sentiments towards Immigrants: Evidence
from 12 OECD-countries”, Swedish Economic Policy Review, 7(2), 11-53.
Epstein G.S., A. Kunze and M. Ward, 2002. “High Skilled Migration and the Exertion
of Effort by the Local Population,” IZA Discussion Paper No. 540, IZA: Bonn.
Independent Commission on Migration to Germany, 2001. Structuring Immigration,
Fostering Integration. http://www.bmi.bund.de/Annex/en_14625/Download.pdf
IOM, 2003. World Migration 2003 – Managing Migration: Challenges and Responses
for People on the Move. Geneva: International Organization for Migration (IOM).
Kunze A. and M. Ward, 2001. “Firms’ Prepardness for the Global Labor Market:
Evidence from a Survey of Large Firms Employing Highly Skilled Workers,” mimeo.
OECD, 2002. International Mobility of the High-skilled. Organisation for Economic
Co-Operation and Development: Paris.
Rothgang M. and C.M. Schmidt, 2003. “The New Economy, the Impact of
Immigration, and the Brain Drain,” in D.C. Jones (ed.), New Economy Handbook.
Amsterdam, New York and Tokyo: Elsevier Science.
Schreyer F., 2003. “Von der Green Card zur Red Card?” IAB Kurzbericht No. 7.
Nürnberg: Institut zur Arbeitsmarkt und Berufsforschung (IAB).
Werner H., 2002. “The Current ‘Green Card’ Initiative for Foreign IT Specialists in
Germany,” in OECD (ed.): International Mobility of the High-skilled. OECD: Paris,
321-326.
Winkelmann R., 2002. “Why Do Firms Recruit Internationally? Results from the IZA
International Employer Survey 2000,” Schmollers Jahrbuch, 122, 155-178.
Winkelmann R., A. Kunze, L. Locher and M. Ward, 2001. “Die Nachfrage nach
internationalen hochqualifizierten Beschäftigten. Gutachten im Auftrag des
Bundesministeriums für Bildung und Forschung,” IZA Report No. 4, IZA: Bonn.
Zimmermann K.F., T. Bauer, H. Bonin, R. Fahr and H. Hinte, 2002. Arbeitskräftebedarf bei hoher Arbeitslosigkeit. Heidelberg: Springer-Verlag.
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VOL. 47 - N°1 SPRING 2004
THE IMPACT OF TEMPORARY MIGRATION ON HUMAN CAPITAL
ACCUMULATION AND ECONOMIC DEVELOPMENT
MANON DOMINGUES DOS SANTOS*(UNIVERSITE DE MARNE LA VALLEE AND
CREST-INSEE) AND FABIEN POSTEL-VINAY** (INRA-PARIS JOURDAN,
CREST-INSEE AND CEPR)
ABSTRACT:
We study the long-run growth impact on the emigrants' country of origin of a change in immigration policy implemented by the host country. The policy change takes the form of an increase in the ratio of temporary to permanent visas issued. This policy change has two counteracting effects on the source country:
first, it discourages human capital accumulation (which is harmful for development), and second, it facilitates the diffusion of knowledge (which encourages growth). We are able to analyze the determinants of
an “optimal” (i.e. growth-maximizing) share of temporary visas.
JEL CLASSIFICATION: F22, J24, J68.
KEYWORDS: skilled migration, immigration policy, human capital, growth.
* [email protected]
** [email protected]
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THE IMPACT OF TEMPORARY MIGRATION ON HUMAN CAPITAL ACCUMULATION
AND ECONOMIC DEVELOPMENT
INTRODUCTION
As OECD (1998) points out, many of the traditional “host” countries have recently redirected their immigration policy toward stricter conditions of admission for candidate
immigrants. The general trend is an increase in the share of temporary visas issued (relative to permanent visas). From the host country's viewpoint, this shift in migration
quotas---another aspect of which is to favor the immigration of skilled workers---is
clearly aimed at facilitating the economy's response to aggregate fluctuations in economic activity. What is less clear, and will be the issue addressed in this paper, is the
impact of this shift on the source countries.
Specifically, we show that exogenously raising the proportion of returnees among
migrants has a generally ambiguous impact on long-run growth in the source country in
a model where the engine of growth is knowledge accumulation. This ambiguous overall impact is the sum of two counteracting forces: a reduction in the educational effort
put forth by locals in the source country, and an increase in knowledge diffusion. On
one hand, assuming that individual skills are complementary to the economy's overall
level of technological development, natives of developing countries are induced to
invest more into education, the higher the probability that they can combine their skills
with the more productive technology available abroad. Thus, a higher probability of
only getting a temporary visa (as opposed to a permanent one) reduces the returns to
education from the source countries' natives' point of view, which in turn reduces the
aggregate level of educational effort undertaken in the developing economies and has
an adverse effect on growth. On the other hand, assuming that returning emigrants contribute to knowledge diffusion, their higher number also has a positive impact on knowledge accumulation in their country of origin through this particular channel.
Many existing contributions already explore the causes and consequences of return
migration. A very brief overview follows.
First, return migration can either be chosen or it can be constrained. For instance, some
candidate immigrants only manage to receive a temporary visa (where they would have
preferred a permanent one), and are therefore obliged to return to their country of origin against their will. Recent trends in the migration policies implemented in many traditional “host” countries clearly tend to amplify this phenomenon. On the other hand,
some emigrants freely choose to return to their country of origin for a variety of reasons. For instance, they may have made their initial decision to emigrate based on erroneous information (Borjas and Bratsberg 1996). Return migration may also have been
planned as part of an optimal life-cycle relocation sequence (Borjas and Bratsberg 1996,
Djajic and Milbourne 1988, Stark et al., 1997).
Second, the perspective of being able (or even forced) to return to one's country of origin after a stay abroad is likely to influence some of the typical migrant's economic
choices. For example, migrants who expect to return to their country of origin in the
future tend to participate more than the locals in the labor market (Dustmann 1996).
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MANON DOMINGUES DOS SANTOS AND FABIEN POSTEL-VINAY
However, contributions analyzing the impact of migration opportunities on the behavior of potential migrants before they leave their country of origin only address the issue
of incentives to acquire education. What those contributions show is that being able to
emigrate raises the expected returns to education and induce workers to train themselves
more (Mountford, 1997). Subsequent work by Beine et al. (2001) brings some empirical support to this latter idea. One limitation of those contributions, though, is that they
all consider that migration can only be permanent, and are completely silent on the
impact of return migration.
Finally, the return of emigrants can be seen as a potential source of growth for the emigrants' home country, to the extent that they contribute to the diffusion of the more
advanced skills that they have acquired during their stay abroad (Domingues Dos Santos
and Postel-Vinay, 2003). Limited empirical evidence exists to support this idea, including Co, Gang and Yun (2000) who show that Hungarian migrants enjoy a wage premium when returning home, and Barrett and O'Connell (2000) who reach similar conclusions in their study of Irish migrants. However, all migration decisions are made freely
in the model of Domingues Dos Santos and Postel-Vinay (2003). In other words, they
completely shut down any possible constraint imposed by binding migration quotas, or
any sort of migration policy. The aim of this contribution is precisely to incorporate constraining migration policy into a simple model of return migration.
This is organized as follows: the next section presents the economic framework. Section 2
looks at the long-run equilibrium with a particular focus on the long-run consequences
for economic groth in the source country of the migration policy implemented in the
host country. Section 3 concludes.
1. THE MODEL
We consider a dynamic two-country model – the foreign country, labelled by A and the
home country, labelled by B – each country being populated by overlapping generations
of two-period lived consumers.
1.1. TECHNOLOGY
Both countries produce one homogeneous consumption good thanks to a continuum of
competitive firms with one worker each. Firms can freely enter or exit the market, so
that any agent can start a firm and work in any period. Production requires two inputs:
A certain amount of efficient labor, , and a country-specific, publicly available stock
of knowledge. The stock of knowledge available at date t in country A ( B ) is denoted
by at ( bt ). Per period output is simply the product of both inputs, so that using units
of labor in period t returns
units of good in country A and
units
of good in country B.
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THE IMPACT OF TEMPORARY MIGRATION ON HUMAN CAPITAL ACCUMULATION
AND ECONOMIC DEVELOPMENT
The first key assumption that we make is that the foreign country is technologically
more advanced than the home country. To model this, we adopt the extreme assumption
that, if both countries stay in autarchy, the technology in country B stagnates at some
initial level b, whereas the technology in country A grows at some positive rate g:
at+1/at=1+g. Since we only want to focus on the consequences of this assumption, we
shall keep g an exogenous constant: The developing country has no engine of growth of
its own whereas the developed country benefits from an exogenous source of technological progress.1
1.2. PREFERENCES
Generations are of fixed, unit mass in each country. Upon being born, natives of the
home country are endowed with one unit of labor, =1. All agents have identical preferences over consumption, independently of their country of origin, given by:
U(c1, c2)=c1+c2
(1)
where c1 and c2 respectively denote consumption in youth and old age. For simplicity,
we assume that agents don't discount the future and only care about total consumption
over their life cycle.
At the beginning of their life, natives of country B face an educational choice which
takes the form of choosing the fraction ␪⑀[0, 1] of their youth that they will spend at
school. The cost of education is an opportunity cost (they will only spend a fraction (1-␪)
of their youth working and earning an income), while the reward to education is
enhanced productivity. Specifically, an agent having spent ␪ at school ends up with
units of efficient labor to supply per unit time. The parameter h thus loosely measures
(1+h␪) the efficiency of the schooling system in country B.
After their training period, workers are given the possibility to migrate to the more
advanced country A with probability m. Moreover, conditional on getting a migrant's
visa, the visa is permanent with conditional probability p/m and temporary with conditional probability r/m=(m-p)/m. Temporary visas only allow migrants to stay in country
during their youth, while permanent visas allow them to stay until they die. Temporary
visa holders thus return to their home country, B, in their old age.
Given negligible migration costs (which we shall assume), natives of country B always
want to migrate to country A if offered the opportunity to do so provided that labor productivity is higher in country A than in country B. Throughout the paper, we shall
1
The way growth and migration dynamics would be modified under the assumption that both countries have an
endogenous source of growth (based on human capital accumulation for instance) is left for later exploration. The
interplay between endogenous growth, migration and knowledge diffusion is the issue in Domingues Dos Santos
(1999), in a model where knowledge diffusion occurs through sheer technological imitation.
80
MANON DOMINGUES DOS SANTOS AND FABIEN POSTEL-VINAY
assume that country A always grows faster than country B, i.e. that g>(bt+1-bt)/bt for all
t. This will imply some restrictions on the parameters, to which we will return in the
sequel.
Under those assumptions, a native of country B born in period t who did not get any visa
(i.e. who has lost the m-lottery) enjoys utility:
US(c1, c2;␪)=(1+h␪)[(1-␪)bt+bt+1],
(2)
while a worker who receives a permanent visa (which happens with probability p=m-r)
benefits from the higher stock of knowledge available in country A and enjoys:
UP(c1, c2;␪)=(1+h␪)[(1-␪)at+at+1]
(3)
Finally, a worker who receives a temporary visa benefits from the stock of knowledge
at in her/his first period of life, while s/he has to return to the less productive country in
her/his home country in her/his old age. Here we further assume that migration entails
the following additional benefit: migrants learn from working in a technologically more
advanced environment, and increase their labor endowment which will be effective in
the following period. The way migrants acquire knowledge in the host country is not
explicitly formalized: migrants benefit from a positive `learning-by-doing' type of
externality. More specifically, we suppose that the amount by which their labor endowment is increased in each period –in other words, how much they can learn in each period– is an increasing function of their initial level of education ␪. Formally, we simply
assume that the temporary migrants' second period labor endowment equals 1+(h+␣)␪,
instead of 1+h␪ for non-migrants. As a result, temporary migrants reach a level of wellbeing given by:
UT(c1, c2;␪)=(1+h␪)[(1-␪)at+bt+1]+␪␣bt+1
(4)
Following the set of assumptions that we made, the level of training initially chosen by
natives of country B at the beginning of their life solves:
␪*=arg max {(1-m)[US(c1, c2;␪)+(m-r)UP(c1, c2;␪)+rUT(c1, c2;␪)},
subject to ␪ ⑀[0,1].
(5)
Given our functional forms, an interior solution must solve the following first-order
condition:
h[(1-m)[(1- ␪ )b t +b t+1 ]+(m-r)[(1- ␪ )a t +a t+1 ]+r[(1- ␪ )a t +b t+1 ]]+r ␣ b t+1 =(1-h ␪ )
[(1-m)bt+mat]
(6)
Clearly, an interior solution is not always guaranteed. Moreover, equation (6) can be
simplified in various ways depending on the migration policy that is being implemented
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THE IMPACT OF TEMPORARY MIGRATION ON HUMAN CAPITAL ACCUMULATION
AND ECONOMIC DEVELOPMENT
(the parameters m and r). In the following section we will carry out an exhaustive analysis of all possible situations. Before we do this, however, we must define the law of
motion of bt, the stock of knowledge in country B.
1.3. KNOWLEDGE ACCUMULATION
Here we simply assume that bt+1 (the stock of knowledge available in country B at date
t+1) equals the output per old worker residing in country B. This assumption has two
parts. First, saying that next period productivity is proportional to output per worker in
this period is akin to a standard “learning-by-doing” hypothesis and probably needs no
further comment. The second part of the assumption is that only old workers “count” in
knowledge accumulation. This can be loosely justified by saying that only experienced
workers effectively diffuse knowledge to their fellow workers. Here we will only say
that we make this assumption for analytical simplicity.2
Formally, we thus have:
(7)
where the last (approximate) equality stems from the fact that m and r, which are shares
of migrants in a generation, are typically small numbers. One thus sees that productivity growth in country B has two sources: one is the direct effect on mean productivity
of the initial education that workers choose to take ( h␪ ), and the other is the diffusion
of knowledge due to temporary migrants returning from coutnry A in their old age.
2. EQUILIBRIUM CONFIGURATIONS
We now go through all the possible long-run equilibrium situations, the occurrence of
which depends on migration policy parameters m and r. We start with the simplest possible case, which is autarky.
2.1. AUTARKY (m = 0)
Absent any migratory flows (m=0), the only source of knowledge accumulation in country B is education. In this case it is easy to show, using (6) and (7), that the equilibrium
values of ␪* and gB are as follows:
(8)
4
Assuming that young workers also contribute to knowledge accumulation is a straightforward extension of this
setting. It only leads to analytical complications without changing the main results.
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MANON DOMINGUES DOS SANTOS AND FABIEN POSTEL-VINAY
The growth rate of country B thus only depends on the productivity of the educational
system, as measured by h. If it is low (h≤1/2), then the gains in terms of productivity
(which are only proportional to the low technological stock bt in the absence of any
opportunity to migrate) are not enough to compensate for the opportunity cost of eduction. In this case, country B stagnates and its natives do not acquire any training.
As the efficiency of training rises (h>1/2), it becomes worthwhile for young workers to
get some education, which increases their productivity and guarantees a positive growth
rate for the economy B, through sheer learning by doing. Note in this case that our
assumption that country B always lags behind country A amounts to assuming that g>h,
which is the maximum rate of growth attainable by country B.
2.2. POSITIVE MIGRATORY FLOWS (m > 0)
From the moment when some natives of country B are allowed to migrate (be it only
temporarily) to country A, then the productivity level of country A, at, enters the typical
agent's arbitrage equation (6). Focusing on the long-run, and given the assumption that
country A always grows faster than country B, one can then simplify (6) if m>0 by notic.
Specifically, (6) simplifies
ing that bt becomes negligible compared to at as
into:
(9)
This formula gives the long-run equilibrium educational choice of country B natives,
provided that it is an interior solution (i.e. it has to lie between 0 and 1).
This equilibrium value (and its consequences on the growth rate of economy B) again
depend on the particular migration policy that is being implemented. In this paper we
will be interested in the effects of r given a value of m, i.e. we want to analyze the impact
of changing the proportion of temporary visas given a fixed total number of entry visas, m.
Also, for expositional clarity, we start with the simple case where r=0, i.e. where all
migration is permanent.
2.2.1. PERMANENT VISAS ONLY: r = 0
In this case, keeping in mind that ␪ cannot be outside of [0, 1], equations (7) and (9)
imply the following equilibrium pattern:
(10)
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THE IMPACT OF TEMPORARY MIGRATION ON HUMAN CAPITAL ACCUMULATION
AND ECONOMIC DEVELOPMENT
The minimum value of h ensuring a positive growth rate for economy B now becomes
1/(2+g), which is less than 1/2, the corresponding threshold in autarky. Otherwise stated, allowing some people to migrate makes it more likely that the source economy
exhibits sustained growth in the long-run. In particular, whenever 1/(2+g)<h≤1/2, country B stagnated in autarky and grows under positive probability of migration. The reason is clearly that a positive probability of migration increases the private returns to education by a considerable amount —really an infinite amount, in the long-run. Natives of
country B thus get more training. Some of the benefits of this increased educational
effort go to country A, as a share m of country B natives migrate and stay abroad forever. But since a share (1-m) of each generation is forced to stay in their home country,
their educational effort contributes to increasing the productivity in country B. Our
model thus reproduces a stylized version of the mechanism originally pointed out by
Mountford (1997).
2.2.2. PERMANENT AND TEMPORARY VISAS (r > 0)
We now turn to the situation on which this paper is focused, i.e. the case where temporary visas are issued, together with permanent ones. In order to stick to one single, “realistic” case, we do this under the following restriction on the parameters:
Assumption 1. Country B stagnates in the “autarky” regime and sustains positive longrun growth with
in the “permanent visas only” regime, i.e.
Clearly, this assumption is not necessary for the upcoming analysis. We only adopt it in
order not to have to distinguish between several degenerate sub-cases5. The lower bound
h≥ 1/(2+g) ensures that
(and thus
) are positive. The restriction h≤1/2 ensures
that
: country B stagnates in autarky. Finally, the condition h≤1/g ensures that
, i.e. country B natives spend at least some of their youth working (as opposed
to getting educated). Note in passing that the bounds thus imposed on h are not as tight
as it might first seem: since the time unit here is half the aldult life of a native of country B (say, somewhere in the vicinity of 20 years), g is likely to be a fairly large number. For instance, assuming that country A grows at 1.5 percent per annum, then
1+g=1.01520~ 1.35, implying g~0.35. As a by-product of this quick look at reasonable
orders of magnitude, one sees that the upper bound h≤1/g is not likely to place any additional restriction on top of h≤1/2.
Formally, the analysis doesn't differ from the r=0 case: again using equations (7) and
(9), we get:
5
A complete analysis is available upon request to the authors.
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MANON DOMINGUES DOS SANTOS AND FABIEN POSTEL-VINAY
(11)
Given our interest in r as a policy parameter, it may be more convenient to redefine the
threshold between the 2 regimes in (11) explicitly in terms of r. To this end, let us
define:
(12)
It is straightforward to check that Assumption 1 ensures that r is greater than 0 smaller
than m.6 With this notation, (11) rewrites as:
(13)
The first thing we can notice about (13) is that increasing the share of temporary visas,
r/m, always discourages training in the source country B: formally, is an unambiguously decreasing function of r. Under Assumption 1, is always positive—i.e. natives
of country B always undertake at least some training when all visas are permanent.7 As
r is increased, the equilibrium amount of time spent at school
decreases and even
hits 0 before r reaches its maximum value of m, i.e. before one reaches the situation
where only temporary visas are issued.
The reason why an increase in the share of temporary visas discourages education is
clear enough: a lower chance of getting a permanent visa means a lower chance of being
able to “combine” one's personal labor input (1+h␪) with a more productive technology
(at+1 vs. bt+1) in the following period. The expected returns to training are therefore
lower, the higher the probability of getting a temporary visa only.
6
7
In fact, it is smaller than gm/(1+g).
It is also strictly less than 1, which implies that
or solution for whenever 0<r<r.
is also strictly less than 1 for any r>0. We thus have an interi-
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THE IMPACT OF TEMPORARY MIGRATION ON HUMAN CAPITAL ACCUMULATION
AND ECONOMIC DEVELOPMENT
2.2.3. THE OPTIMAL SHARE OF TEMPORARY VISAS
Does all that mean that introducing temporary visas (as opposed to permanent ones) is
necessarily harmful for the source country's growth rate? Not necessarily, at least not
under our assumption that return migration comes with the benefit of knowledge diffusion. On one hand, increasing r discourages training which lowers the average productivity of country B natives and tends to slow down growth in country B. But on the other
hand, increasing r fosters knowledge diffusion: as more temporary migrants return to
their home country in their old age, more productive skills are brought back from country A to country B.
Those two counteracting effects can be translated formally: (13) states that
is the product of the equilibrium educational effort,
, which
decreases with r, by the overall “social returns” to educational efforts, (h+ra), which
increases with r. It is thus possible that
be maximized at some strictly positive value
of the share of temporary visas, r/m. Clearly, this will be the case if
is positive
at r=0.8 Specifically, turning to (13) and denoting the optimal —i.e. growth-maximizing for country B —share of temporary visas by (r/m)*, one has:
(14)
We can now examine the determinants of the optimal share of temporary visas.
Whenever (r/m)*>0 (we shall return to the case (r/m)*=0 at the end of this discussion),
equation (14) tells us that (r/m)* increases with a, m and g, and reacts ambiguously to
changes in h. That (r/m)* increases with a is unsurprising: when each returning migrant
comes back with more productive skills, it is interesting (from the viewpoint of country
B) to have more of them come back. Likewise, (r/m)* increases with m because what
matters for knowledge diffusion is the absolute number (not the share) of returning
migrants. Thus, when there are more migrants to start with (a higher m), a marginal
increase in the share of temporary visas brings back a greater absolute number of
migrants and therefore has a larger positive impact on knowledge diffusion. The fact
that (r/m)* increases with g may sound less intuitive. This is an indirect effect that flows
through educational choices. Faster growth in the host country A increases the expected returns to education and therefore induces country B natives to increase their educational investment
. As a result of higher training efforts, returning migrants become
more valuable to the source country B because of the complementarity between private
training and public knowledge in production. Formally, looking at (7), one sees that a
ceteris paribus higher ␪* reinforces the positive impact on gB of raising r. Finally, the
ambiguous response of (r/m)* to an increase in h is more tricky to analyze. A first, positive effect of h on (r/m)* is similar to the effect of g on (r/m)*: since a higher value
of h means higher returns to training, it encourages training and thus implies a higher
8
Note in passing that the optimal value of r can be zero, but surely has to be in [0, r] as
1 and
for all r≥r.
86
under Assumption
MANON DOMINGUES DOS SANTOS AND FABIEN POSTEL-VINAY
equilibrium value of ␪*. As in the case of a rise in g, this tends to increase (r/m)*. But
on the other hand, raising (r/m) tends to discourage training. And since a higher h also
implies a higher direct effect of ␪* on the growth rate, a higher h makes it ceteris paribus
more costly for growth to discourage education by raising the share of temporary visas.
This latter effect pleads for a lower value of (r/m)* when h increases. Overall, those two
counteracting forces add up to an ambiguous response of the optimal share (r/m)* to an
increase in h.9
To conclude this discussion, we should re-emphasize the fact that it is only substantial
in the parameter configurations such that (r/m)* is indeed positive. No looking at (14),
one sees that (r/m)* is in fact very likely to equal zero, since again m is likely to be a
very small number. So, unless ␣ (the efficiency of knowledge diffusion) is really large,
the negative term in (14) probably dominates. Of course, one may not want to take this
particular conjecture too seriously, given how stylized our model is. Much of it may
depend on the specific functional forms that we have chosen (mostly for the sake of
tractability). And after all, we don't really know how important the somewhat abstract
phenomenon of “knowledge diffusion” can be in reality...
CONCLUDING REMARKS
Do emigration countries benefit or suffer from the increase in the share of tempory
visas ? Here our goal was to highlight some aspects of the nexus between migration and
long-run growth. More precisely, our contribution focuses on the impact of the propensity to return on human capital accumulation. We show that the intensification of return
migration has an ambiguous effect on the human capital accumulation process: on the
one hand, it discourages training, whereas on the other hand, it fosters knowledge diffusion.We notably show how the result of this trade off depends on the total share of
migrants, the efficiency of the schooling system, the growth rate of the receiving country and the efficiency of knowledge diffusion.
However, our model does not account for the impact of the propensity to return on
another crucial source of economic development: the accumulation of physical capital.
The propensity to return is likely to have at least two effects on this second engine of
growth. Firstly, returning emigrants invest in their home country thanks to the savings
they made abroad (Ilahi, 1999 ; Mc Cormick and Wahba, 2001). Second, temporary
emigrants remit a higher part of their income than permanent ones while abroad (Lucas
et Stark, 1985 ; Hoddinott, 1994). Hence; taking into account the impact of the propensity to return on physical capital accumulation is likely to reinforce the expansionary
effect of return migration on the source country. This assertion has to be confirmed.
9
Yet, formally, one can easily see that ∂(r/m)*/∂h has the sign of 1/[4(1+g)2]-1/[2m␣]. Since m is likely to be a
small number, it would take a large value of ␣ for (r/m)* to react positively to an increase in h. That is, (r/m)* is
likely to decrease with h.
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AND ECONOMIC DEVELOPMENT
REFERENCES
Barrett A. and P. O'Connell, 2000. “Is there a wage premium for returning Irish
migrants?”, IZA Discussion Papers No 135.
Barro R. and X. Sala-I-Martin, 1995. Economic Growth. McGraw-Hill.
Beine M., F. Docquier and H. Rapoport, 2001. “Brain drain and economic growth:
theory and evidence”, Journal of Development Economics, 64(1), 275-289.
Borjas G. and B. Bratsberg, 1996. “Who leaves? The outmigration of the foreign
born”, Review of Economics and Statistics, 78(1), 165-176.
Co C., I. Gang and M. Yun, 2000. “Returns to returning: who went abroad and what
does it matter?”, Journal of Population Economics, 13(1), 57-79.
Djajic S. and R. Milbourne, 1988. “A general equilibrium model of guest-worker
migration”, Journal of international Economics, 25, 335-351.
Domingues Dos Santos M. and F. Postel-Vinay, 2003. “Migration as a source of
growth: The perspective of a developing country”, Journal of Population Economics,
16, 161-175.
Dustmann C., 1996. “Return migration: the European experience”, Economic Policy,
215-250.
Hoddinott J., 1994. “A model of migration and remittances applied to western Kenya”,
Oxford Economic Papers, 46, 459-476.
Ilahi N., 1999. “Return migration and occupational change”, Review of Development
Economics, 3, 170-186.
Lucas R. and O. Stark, 1985. “Motivations to remit: evidence from Bostwana”,
Journal of Political Economy, 93, 901-918.
McCormick B. and J. Wahba, 2000. “Overseas work experience, savings and entrepreneurship amongs return migrants to LDCs”, Scottish Journal of Political Economy,
48, 105-133.
Mountford A., 1997. “Can a brain drain be good for growth in the source economy”,
Journal of Development Economics, 53, 287-303.
OECD, 1999. Trends in International Migration, OECD.
Stark O., C. Helmenstein and A. Prskawetz, 1997. “A brain drain with a brain gain”
Economics Letters, 55, 227-234.
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BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
WHO IS AFRAID OF THE BRAIN DRAIN?
HUMAN CAPITAL FLIGHT AND GROWTH IN DEVELOPING
COUNTRIES*
HILLEL RAPOPORT
(DEPARTMENT OF ECONOMICS, BAR-ILAN UNIVERSITY, CADRE, UNIVERSITY
OF LILLE II, AND STANFORD CENTER FOR INTERNATIONAL DEVELOPMENT
(SCID), STANFORD UNIVERSITY)
ABSTRACT:
This paper presents a non-technical review of the recent theoretical and empirical literature on the
growth effects of the brain drain in developing countries. It focuses on the idea that migration prospects
may foster human capital formation at home even after emigration is netted out. Channels through
which highly-skilled migrants continue to impact on their home country's economy are also reviewed,
remittances, return migration, and the role of migrants' networks in promoting bilateral trade and
knowledge diffusion.
JEL CLASSIFICATION: F22, J24, J68.
KEYWORDS: skilled migration, immigration policy, human capital, growth.
* This paper is the extended version of a policy brief written for SIEPR, the Stanford Institute for Economic Policy
Research, in April 2002 (Rapoport, 2002). It draws on joint work with Michel Beine and Frederic Docquier
(Beine et al., 2001 and 2003) and Ravi Kanbur (Kanbur and Rapoport, 2004). Correspondance: Hillel Rapoport,
Department of Economics, Bar-Ilan University, 52900 Ramat Gan, Israel. Email: [email protected].
89
WHO IS AFRAID OF THE BRAIN DRAIN?
HUMAN CAPITAL FLIGHT AND GROWTH IN DEVELOPING COUNTRIES
INTRODUCTION
The term “'brain drain” was first popularized in the 1950s with reference to the immigration to the US of first-rank scientists from countries such as the U.K., Canada, or the
former Soviet Union; it is now used in a more general sense to designate the international transfer of human capital (people with tertiary education) from developing to
developed countries. During the 1970s, it was taken for granted that the emigration of
highly-skilled people was detrimental to the origin countries. Many prestigious academic economists (notably Jagdish Bhagwati) were part of this consensus and delivered
more or less the following message: i) the brain drain is basically a negative externality imposed on those left behind in developing countries; it amounts to a zero-sum game,
with the rich countries getting richer and the poor countries getting poorer; and, iii) at a
policy level, the international community should implement a mechanism whereby
international transfers could compensate the origin countries for the losses incurred as
a result of the brain drain, for example in the form of an income "tax on brains" (later
coined "Bhagwati Tax") to be redistributed internationally.1 This view is perfectly illustrated in the following citation:
“In contrast to the case of foreign investment, where the gain from the international
factor movement is divided by the two countries, the developed country gains now at
the cost of those left behind in the less-developed country. The emigrants similarly
are seen to gain at the sacrifice of those left behind'' (Hamada, 1977, p. 20)
During the last two decades, there has been a tremendous increase in the magnitude of the
brain drain. However, as I explain below, it may well be that some developing countries,
if not the majority of them, have experienced a social gain from this brain drain. The main
reason for this is that migration prospects increase the expected return to education in poor
countries and, hence, foster domestic enrollment in education. When this incentive (or
"brain") effect dominates the observed emigration (or "drain") effect, the origin country
may in fact end up with more human capital than its erstwhile no-migration human capital stock. I first summarize in Section 1 the data on the magnitude of the brain drain, and
then consider in Section 2 the possible positive feedbacks for the origin country, showing
that these are unlikely to compensate for potential losses. The central idea of the new brain
drain theoretical literature, namely, that migration prospects may foster human capital formation in developing countries even after actual emigration is netted out, is exposed in
Section 3. Section 4 summarizes the results from recent empirical studies who found supportive evidence for the beneficial brain drain hypothesis. The last Section concludes.
1. HOW BIG IS THE BRAIN DRAIN ?
Although the numbers may be disputable, it is clear that the brain drain has increased
dramatically since the 1970s. Indeed, nearly thirty years ago, the United Nations estimated
1
90
See the special issue of the Journal of Public Economics edited by Bhagwati on “Income taxation in the presence of international personal mobility”, August 1982.
HILLEL RAPOPORT
the total number of highly-skilled South-North migrants for 1961-72 at only 300,000
(UNCTAD, 1975); less than a generation later, in 1990, the U.S. Census revealed that
there were more than 2.5 million highly educated immigrants from developing countries
residing in the U.S. alone, excluding people under the age of 25 (that is, without counting most foreign students). Country studies commissioned by the International Labor
Organization also showed that nearly 40% of Philippines' emigrants are college educated, and, more surprisingly, that Mexico in 1990 was the world's third largest exporter of
college-educated migrants (Lowell and Findlay, 2001).
Until recently, there were no comparative data on the magnitude of the brain drain. Such
data are now available thanks to William Carrington and Enrica Detragiache from the
International Monetary Fund, who used US 1990 Census data and other OECD data to
construct estimates of emigration rates at three educational levels (primary, secondary
and tertiary) for about 50 developing countries (Carrington and Detragiache, 1998 –
henceforth CD). The CD estimates, however, suffer from four main shortcomings. First,
it is assumed for each country that the skill composition of its emigration to non-US
OECD countries is identical to that of its emigration to the US; consequently, the CD
estimates are reliable only for countries for which the US is the main migration destination. Second, at the time Carrington and Detragiache conducted their study, the EU
immigration data did not allow for a full decomposition of the immigrants' origin-mix;
more precisely, most EU countries used to publish statistics indicating the country of
origin only for the top 5 or 10 sending countries. For small countries not captured in
these statistics, the figures reported in the CD data set are therefore clearly biased: the
total number of emigrants is under-estimated, and one is (mis)led to conclude that 100%
of those who immigrated to countries belonging to the OECD immigrated to the US; as
acknowledged by Carrington and Detragiache, this may approximate the reality for
Latin America, but is clearly erroneous, for example, in the case of Africa. Third, the
CD data set excludes South-South migration, which may be significant in some cases
(e.g., migration to the Gulf States from Arab and Islamic countries).
The CD data set is an important step towards building a fully-harmonized data set on
migration rates by education levels. However, it must be used with caution because the
reliability of the CD estimates for a given country depends on whether the US immigration data gives a good quantitative and qualitative approximation of overall migration outflows from that country. In an effort to reflect the limitations of the CD data,
Table 1 thus splits the Carrington and Detragiache estimates into three groups of countries: it is only for group A, composed mainly of Latin American countries, that the CD
estimates may be considered reliable.
Finally, due to the definition of immigrants as foreign-born individuals, children arriving with their parents and who acquired higher education in the host country later on are
counted as highly-skilled immigrants; this is a source of over-estimation of the brain
drain, which is potentially important for some countries.
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WHO IS AFRAID OF THE BRAIN DRAIN?
HUMAN CAPITAL FLIGHT AND GROWTH IN DEVELOPING COUNTRIES
TABLE 1. MIGRATION RATE OF SKILLED WORKERS PER COUNTRY OF ORIGIN
Code
Guy
Jam
Tat
Sal
Gha
Pan
Nic
Hon
Kor
Dom
Gua
Mex
Phi
CR
Pak
Chl
Col
Egy
Bol
Ecu
Uru
Per
Chn
Arg
Ind
Ven
Par
Indo
Tha
Bra
Gam
SL
Fi
Ug
Ken
Moz
Mau
Zam
Zim
Cam
Syr
Les
Png
Rwa
Malw
Sud
CAR
Tog
Mali
Con
Ben
Tun
Alg
Sen
Tur
SrL
Mal
92
Country
Brain drain
Migration rate
(in %)
(in %)
PART A: Limited sample with highly reliable countries (30 countries)
Guyana
77,5
14,5
Jamaica
77,4
20,3
Trinitad-Tobago
57,8
9,5
El Salvador
26,1
11,3
Ghana
25,7
0,4
Panama
19,6
6,7
Nicaragua
18,8
4,7
Honduras
15,7
3
South Korea
14,9
4,2
Dominican Rep.
14,7
6,5
Guatemala
13,5
3,4
Mexico
10,3
7,7
Philippines
9
3,1
Costa Rica
7,1
2,4
Pakistan
6,7
0,3
Chile
6
1,1
Colombia
5,8
1,1
Egypt
5
0,5
Bolivia
4,2
0,7
Ecuador
3,8
1,9
Uruguay
3,8
1,1
Peru
3,4
1
China
3
0,1
Argentina
2,7
0,6
India
2,6
0,2
Venezuela
2,1
0,4
Paraguay
2
0,2
Indonesia
1,5
na
Thailand
1,5
0,2
Brazil
1,4
0,2
Part B: Small countries with missing non-US immigration data (21 countries)
Gambia
61,4
0,2
Sierra Leone
24,3
0,3
Fiji
21,3
3,6
Uganda
15,5
0,1
Kenya
10
0,1
Mozambique
8,6
na
Mauritius
7,2
0,2
Zambia
5
0,1
Zimbabwe
4,7
0,1
Cameroon
3,2
na
Syria
3
0,7
Lesotho
2,9
na
Papua-NG
2,2
na
Rwanda
2,2
na
Malawi
2
na
Sudan
1,8
na
Central African Rep.
1,7
na
Togo
1,3
na
Mali
0,9
na
Congo
0,5
na
Benin
0,4
na
Part C: Countries with a share of US emigrants lower than 30% (8 countries)
Tunisia
63,3
8,6
Algeria
55
6,3
Senegal
47,7
2,4
Turkey
46,2
8,5
Sri Lanka
23,6
0,8
Malaysia
22,7
1,2
US Immigrants
(in volume)
US Immigrants
(in % of OECD)
61936
159913
65810
263625
12544
68583
61168
54346
377940
187871
127346
2743638
728454
28784
52717
36252
162739
53261
18772
89336
15716
86323
404579
64080
304030
22634
4313
32172
53118
53904
100,0
61,0
100,0
100,0
53,3
100,0
100,0
100,0
36,0
96,7
100,0
100,0
71,6
100,0
35,2
54,3
96,9
50,6
100,0
100,0
100,0
87,1
51,5
72,3
44,1
77,4
100,0
90,5
87,6
44,0
747
4155
11420
5060
8372
920
1100
1613
3161
1694
27504
160
480
200
381
2496
160
460
220
200
180
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
100,0
100,0
100,0
100,0
100,0
2816
3904
1370
43605
8751
15261
1,1
0,6
2,0
1,9
14,1
18,2
HILLEL RAPOPORT
Table 1 reveals that the brain drain is a general phenomenon, at work for all types
of developing countries (large and small), from all regions of the developing world:
from the CD figures, it comes that the total cumulative loss of “brains” by region may
be set at approximately 15% (of the remaining stock of people with tertiary education)
for Central America, 6% for Africa, 3% for South America, and 5% for Asia.
Since 1990, the chief causes of the brain drain have gained in strength and it is therefore likely that the trends described above have been confirmed. Indeed, selective
immigration policies first introduced in Australia and Canada in the 1980s have
spread to other OECD countries: the Immigration Act of 1990 as well as the substantial relaxation of the quota for highly-skilled professionals (H1-B visas) in the US certainly constitute the most influential change in immigration policy over the last
decade; in addition, a growing number of EU countries (notably France, Germany and
the UK) have recently introduced similar programs aiming at attracting a qualified
workforce (OECD, 2002). In the current context of globalization, such selective
immigration policies can only reinforce the natural tendency for human capital to
agglomerate where it is already abundant.
What are the consequences for sending countries? To the same extent that immigration
of a skilled labor force is seen as beneficial to receiving countries, it would seem that
depriving developing countries from one of their most scarce resources can only affect
their growth prospects negatively. In fact, such a pessimistic view may be mitigated in
two ways: first, there could be positive feedbacks for the source country in terms of
remittances or technology transfers; second, one has to correctly qualify the no-migration scenario and wonder about the right counterfactual when it comes to evaluating the
growth effects of the brain drain.
2. WHAT FEEDBACK EFFECTS ?
Obviously, the brain drain may induce positive feedback effects as emigrants continue to affect the economy of their origin countries. Such possible feedbacks include
migrants' remittances, return migration after additional skills have been acquired
abroad, and the creation of networks that facilitate trade, capital flows and knowledge diffusion.2
In the case of migrants' transfers, we know from the remittances literature that the two
main motivations to remit are altruism, on the one hand, and exchange, on the other
hand. It is well-known that altruism is primarily directed towards one's immediate family,
and then decreases in intensity with social distance. By contrast, in principle, no such
proximity is required in the case of exchange; the exchange-based theory of remittances
2
See Rapoport and Docquier (2003) for a review of the remittances literature, and Domingues Dos Santos and
Postel Vinay (2003) on return migration and knowledge diffusion.
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WHO IS AFRAID OF THE BRAIN DRAIN?
HUMAN CAPITAL FLIGHT AND GROWTH IN DEVELOPING COUNTRIES
posits that remittances simply buy various types of services such as taking care of the
migrant’s assets (e.g., land, cattle) or relatives (children, elderly parents) at home. Such
transfers are typically observed in case of a temporary migration and signal the
migrants’ intention to return. Hence, someone moving with his or her immediate family on a permanent basis is less likely to remit (or is likely to remit less) than someone
moving alone on a temporary basis. And indeed, we know from household surveys that
despite their higher earnings potential, educated migrants tend to remit relatively less
than their unskilled compatriots, precisely because they migrate on a more permanent
basis (with family). This is confirmed at an aggregate level by Faini (2002), who shows
that migrants’ remittances decrease with the proportion of skilled individuals among
emigrants and concludes that “this result suggests that the negative impact of the brain
drain cannot be counterbalanced by higher remittances”.
As to return migration, we also know that in general, return migration is not significant among the highly educated unless sustained growth preceded return. For
example, less than a fifth of Taiwanese PhDs who graduated from US universities in
the 1970s in the fields of Science and Engineering returned to Taiwan (Kwok and
Leland, 1982) or Korea, a proportion that rose to about one half to two-thirds in
the course of the 1990s, after two decades of impressive growth in these countries.
Interestingly, the figures for Chinese and Indian PhDs graduating from US universities in the same fields during the period 1990-99 are fairly identical to what
they were for Taiwan or Korea 20 years ago (stay rates of 87% and 82%, respectively) (OECD, 2002). In the case of India, Saxeenian (2001) shows that despite the
quick rise of the Indian software industry, only a fraction of Indian engineers in
Bangalore are returnees. Hence, there seems to be no room for optimism on this
front either, return skilled migration appearing more as a consequence than has a
trigger of growth.
Another channel whereby the brain drain may positively affect the source country is
through the creation of business and trade networks; such a “Diaspora externality” has
long been recognized in the sociological literature3 and, more recently, by economists in
the field of international trade. In many instances indeed, and contrarily to what one
would expect in a standard trade-theoretic framework, trade and migration appear to be
complements rather than substitutes (e.g., Gould, 1994). Interestingly, such a complementarity has been shown to prevail mostly for trade in heterogeneous goods, where ethnic networks help overcoming information problems linked to the very nature of the
goods exchanged (Rauch and Casella, 2002, Rauch and Trindade, 2003). How is the
relationship of substitutability or complementary between trade and migration impacted by the skill composition of migration, however, remains unclear. In the same vein,
whether FDI and migration are substitutes (as one would expect) or complements
remains an unanswered question. On the whole, the evidence on the exact role played
3
Gaillard and Gaillard (1997), and Lowell and Findlay (2001), review this literature.
94
HILLEL RAPOPORT
by the highly educated in the creation of trade, business and scientific networks has so
far been too anecdotal and limited to undermine the strongly negative view of the brain
drain that has prevailed until recently.
3. AN OPTIMAL BRAIN DRAIN ? THEORY
Modern theories of endogenous growth have considerably renewed the analysis of the
relations between education, migration and growth. Unsurprisingly, the first models to
address the issue of the brain drain in an endogenous growth framework all emphasized
its negative effects (e.g., Miyagiwa, 1991, Haque and Kim, 1995). At the same time,
however, a series of studies have tried to promote the simple idea that one should also
look at how a given stock of human capital is built up. In particular, it is likely that in
the presence of huge inter-country wage differentials, as is the case between developing
and developed countries, the prospect for migration deeply modifies the incentive structure faced by developing countries’ residents when making their education decisions.
The idea that education investments are impacted by migration prospects is not new,
however, and may be traced back in the brain drain literature at least to Bhagwati and
Hamada (1974) and McCullock and Yellen (1977). The novelty in the more recent literature lays primarily in the introduction of uncertainty into the migration process, creating the possibility of a gain for the source country. The conditions required for this possibility to materialize have been the subject of a number of theoretical contributions
(Mountford, 1997, Stark et al., 1998, Vidal, 1998, Docquier and Rapoport, 1999, Beine
et al., 2001). Using a slightly different perspective, Stark et al. (1997) elaborated on the
possibility of a brain gain associated with a brain drain in a context of imperfect information with return migration. McCormick and Wahba (2000) also obtained the result
that more highly-skilled migration may benefit to those left behind, but in a trade-theoretic model where migration, remittances and domestic labor-market outcomes are
jointly determined and multiple equilibria arise, with the high-migration equilibrium
pareto-dominating the low-migration equilibrium. Finally, holding wage differentials
constant but allowing for differences in the variability of the rate of return to human
capital, Katz and Rapoport (2001, 2003) argued that migration imparts education with
an option value and showed that increased variability may well increase the expected
(post migration) stock of human capital at origin.
The basic mechanisms at work in these models are best illustrated through a numerical
example. Assume, for example, that the expected annual wage premium for someone
with tertiary education is $5,000 in the home country and $30,000 in the U.S.; then,
even a relatively small probability of immigration to the US, of, say, 20%, has a huge
effect on the expected return to human capital (in this numerical example, it is exactly
doubled assuming a zero emigration probability for an unskilled individual) and, therefore, is likely to foster domestic enrollment in education significantly even after emigration is netted out.
95
WHO IS AFRAID OF THE BRAIN DRAIN?
HUMAN CAPITAL FLIGHT AND GROWTH IN DEVELOPING COUNTRIES
For this to occur it is essential to assume that education is a necessary but insufficient
condition for migration. In other words, the education decision made during one's youth
is made under uncertainty regarding future migration, with educated agents facing a
probability p to be allowed to migrate and a probability (1-p) to stay home once adults.
Such uncertainty may be due to personal or external factors, the most obvious justification for the context of uncertainty being that international mobility is restricted by
immigration authorities at destination, and is so with some arbitrariness. To account for
this, assume that the probability of migration depends solely on the achievement of a
given educational requirement, which is observable, and not on individuals' ability,
which are not (i.e., migrants are assumed to be randomly selected among those who satisfy some kind of prerequisite with informational content regarding their ability - in our
case, education). This is clearly a simplification, however; in reality, immigration
authorities may be combining education with other selection devices such as tests of IQ
or host-country language fluency. Would IQ be a perfect signal of ability and the only
criterion retained, migration could only be detrimental to human capital formation at
home (since the ability distribution is assumed to be given). Still, and to the extent that
IQ or other tests are imperfect signals of ability, introducing them into these models
would not affect the quality of the results. Indeed, their main effect would be to introduce a probability, q(a), q'>0, that an educated applicant would receive an entry visa. In
this setting, migration prospects still increase the expected return to education (even for
individuals with relatively low success probability), with the expected impact on human
capital formation at home depending on the steepness of the success profile at different
ability levels.
Figure 1 provides a simple diagrammatic interpretation of the essence of the results
from these models. Assume that individual ability is uniformly distributed on the space
[a, a] with people above a certain ability threshold choosing to invest in education and
people below that threshold choosing not to invest in education. Assume also that
human capital is the sole engine of growth, with the rate of growth depending on the
proportion of educated in the country. In increasing the expected return to education,
the effect of migration prospects is then to move the critical ability to the left. Figure 1
shows very clearly the two effects of migration on human capital formation (i.e., that
skilled emigrants are drawn out of a larger pool of educated people when the economy
is opened to migration). Denoting by aF and aE the ability of the individual who is indifferent as to whether to invest in education in the closed and the open economy, respectively, one can see from Figure 1 that the proportion of educated among the remaining
population (the proportion B/A+B) may well be higher in the latter case:4
4
Note that for diagrammatic convenience, we represented the partition of the population between groups A (the
uneducated), B (the remaining educated) and C (the educated emigrants) as if the migrants were self-selected
instead of being randomly selected among the educated.
96
HILLEL RAPOPORT
FIGURE
1. THE PARTITION OF THE POPULATION INTO DIFFERENT SUB-GROUPS
a
a
aF
A
B
a
a
aE
A
B
C
4. WHO LOSES, WHO WINS, AND HOW MUCH ? EVIDENCE
To the best of our knowledge, the first study to attempt at estimating the growth effects
of the brain drain using cross-country comparisons is our joint work with Michel Beine
and Frederic Docquier (Beine et al., 2001); in a cross-section of 37 developing countries, we found that migration prospects have a positive and significant impact on
human capital formation at origin, especially for countries with low initial GDP per
capita levels. This was a first but imperfect try because, at the time the study was written (in 1998), we had no comparative data on international migration by education levels and therefore used gross migration rates as a proxy measure for the brain drain.
Thanks to Carrington and Detragiache (1998), such comparative data became available
later and in a subsequent study, we used the CD estimates on emigration rates for the
highest (tertiary) education level as our brain drain measure; again, we found a positive
and highly significant effect of migration prospects on human capital formation, this
time in a cross-section of 50 developing countries (Beine et al., 2003).
We also computed country specific effects, with the following results. First, countries
that experienced a positive growth effect (the ‘winners’) generally combined low levels
of human capital and low migration rates, whereas the ‘losers’ were typically characterized by high migration rates and/or high enrollment rates in higher education (this is
quite intuitive, since in this case most migrants are picked up from a stock of people that
would have engaged in education even without contemplating emigration). Second, we
showed that except for extreme cases such as Guyana and Jamaica, the growth effects
of the brain drain were relatively limited: around plus or minus a maximum of 0.20% in
terms of annual GDP per capita growth; this is not negligible, however, in a dynamic
perspective. Finally, it was also striking that while there were more losers than winners,
the latter included the largest countries in terms of demographic size (China, India,
Indonesia, Brazil) and represented more than 80% of the total population of the sample.
Although it is a simplification, our results are suggestive of an inverse U-shaped relationship between migration and growth: too much migration is detrimental, but too little is sub-optimal. Interestingly, the within-country result predicted by the theory outlined above (i.e., that some migration should be good as long as it is not excessive) is
97
WHO IS AFRAID OF THE BRAIN DRAIN?
HUMAN CAPITAL FLIGHT AND GROWTH IN DEVELOPING COUNTRIES
what comes out at the cross-country level apparent on Figure 2. The X-axis gives the
Carrington-Detragiache migration rates for the highly educated and the Y-axis gives the
net growth effect of the brain drain as computed by Beine et al. (2003). The variability
across countries at given migration rates is due to the impact of other right hand side
variables, and the curve itself is adjusted using a second-order polynomial.
FIGURE 2. BRAIN DRAIN AND LDC'S GROWTH
Net effect on annual GDP growth rate (in %)
0,04
Pakistan
Indonesia
Guatemala
Honduras
China
0,02
India
Paraguay
Colombia
Brazil
0,00
Argentina Bolivia
Thailand
0
2
4
6
Egypt
Venezuela
Chile
Peru Uruguay
Ecuador
8
10
Mexico 12
14
16
Costa Rica
Dominican Rep
-0,02
18
20
Nicaragua
Philippines
South Korea
-0,04
Emigration rate of tertiary educated workers (in %)
CONCLUSION
The main conclusion to draw from the above analysis is that for any given developing
country, the optimal migration rate of its highly educated population is likely to be positive. Whether the current rate is greater or lower than this optimum is an empirical
question that must be addressed country by country. This implies that countries that
would impose restrictions on the international mobility of their educated residents,
arguing for example that emigrants' human capital has been largely publicly financed,
could in fact decrease the long-run level of their human capital stock. This also suggests
that rich countries should not necessarily see themselves as free riding on poor countries’ educational efforts. The difficulty is then to design quality-selective immigration
policies that would address the differentiated effects of the brain drain across origin
countries without distorting too much the whole immigration system; this could be
achieved, at least partly, by designing specific incentives to return migration to those
countries most negatively affected by the brain drain, and promote international cooperation aiming at more brain circulation.
98
HILLEL RAPOPORT
On a final note, it may be appropriate to emphasize that we are well aware of the fact
that our empirical findings need to be confirmed before we may seriously challenge the
conventional view on the brain drain. As explained above, our results are based on
cross-section regressions, meaning that we neglect the dynamics of migration rates as
well as the dynamics of education levels and that, due to the absence of a time series
dimension, it is impossible to control for individual-country effects in the regression
estimates. Given the strong heterogeneity of the sample (in terms of countries' sizes,
levels of development, etc.), such country-fixed effects are likely to play some role in
the value of the estimates. The underlying drawbacks of the methodology used so far
therefore call for the collection of additional data: improving the CarringtonDetragiache observations for the year 1990 and combining them with new data points
for each country of the sample would make it possible not only to extend the time frame
of our research but also to address some of its methodological limitations.
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REFERENCES
Beine M., F. Docquier and H. Rapoport, 2001. “Brain Drain and Economic Growth:
Theory and Evidence”, Journal of Development Economics, 64(1), 275-89.
Beine M., F. Docquier and H. Rapoport, 2003. Brain Drain and LDCs’ Growth:
Winners and Losers, IZA Discussion Papers No 819, July.
Bhagwati J.N. and K. Hamada, 1974. “The brain drain, international integration of
markets for professionals and unemployment”, Journal of Development Economics, 1(1),
19-42.
Carrington W.J. and E. Detragiache, 1998. “How Big is the Brain Drain?”, IMF
Working Paper No 98.
Docquier F. and H. Rapoport, 1999. “Fuite des cerveaux et formation de capital
humain”, Economie Internationale, 79, 63-71.
Domingues Dos Santos M. and F. Postel-Vinay, 2003. “Migration as a source of
growth: The perspective of a developing country”, Journal of Population Economics,
16(1), 161-75.
Faini R., 2002. “Dévelopement, commerce international et migrations”, Revue
d’Economie du Développement, 2, 85-116.
Gaillard J. and A.M. Gaillard, 1997. “The international migration of brains: Exodus
or circulation?”, Science, Technology and Society, 2(2).
Hamada K., 1977. “Taxing the brain drain: A global point of view”, in Jagdish N.
Bhagwati, ed.: The New International Order, Cambridge, Mass.: M.I.T. Press.
Haque N.U. and S.-J. Kim, 1995. “'Human capital flight': impact of migration on
income and growth”, IMF Staff Papers, 42(3), 577-607.
Kanbur R. and H. Rapoport, 2004. “Migration selectivity and the evolution of spatial inequality”, Journal of Economic Geography, forthcoming.
Katz E and H. Rapoport, 2001. Macroeconomic instability, migration and the option
value of education, CREDPR Working Paper No 121, Stanford University, October.
Katz E. and H. Rapoport, 2003. “On human capital formation with exit options”,
Journal of Populations Economics, forthcoming.
Kwok V. and H. Leland, 1982. “An economic model of the brain drain”, American
Economic Review, 72(1), 91-100.
Lowell L.B. and A.M. Findlay, 2001. “Migration of highly-skilled persons from eveloping countries: impact and policy responses”, Geneva: International Labour Office,
Draft Synthesis Report.
McCormick B. and J. Wahba, 2000. “Overseas unemployment and remittances to a
dual economy”, Economic Journal, 110, 509-34.
McCullock R. and J.T. Yellen, 1977. “Factor mobility, regional development and the
distribution of income”, Journal of Political Economy, 85(1), 79-96.
Miyagiwa K., 1991. “Scale economies in education and the brain drain problem”,
International Economic Review, 32(3), 743-59.
Mountford A., 1997. “Can a brain drain be good for growth in the source economy?”,
Journal of Development Economics, 53(2), 287-303.
OECD, 2002. International mobility of the highly-skilled, OECD Policy Brief, Paris, July.
100
HILLEL RAPOPORT
Rapoport H., 2002. Who is afraid of the brain drain? Human capital flight and growth
in developing countries. SIEPR Policy Brief, Stanford University, April.
Rapoport H. and F. Docquier, 2003. The economics of migrants' remittances; in L.-A.
Gerard-Varet, S.-C. Kolm and J. Mercier Ythier, eds.: Handbook of the Economics of
Reciprocity, Giving and Altruism, Amsterdam: North-Holland, forthcoming.
Saxeenian A., 2001. Bangalore, the Silicon Valley of India?, CREDPR Working Paper
No 91, Stanford University.
Stark O., C. Helmenstein and A. Prskawetz, 1997. “A brain gain with a brain drain”,
Economics Letters, 55, 227-34.
Stark O., C. Helmenstein and A. Prskawetz, 1998. Human capital depletion, human
capital formation, and migration: A blessing or a 'curse'?, Economics Letters, 60(3),
363-7.
UNCTAD, 1975. The reverse transfer of technology: Its dimensions, economic effects,
and policy implications, New York: United Nations Conference on Trade and
Development.
Vidal J.-P., 1998. “The effect of emigration on human capital formation”, Journal of
Population Economics, 11(4), 589-600.
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BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE
SOURCE COUNTRY*
DILEK CINAR (CADRE, UNIVERSITY OF LILLE) AND FREDERIC DOCQUIER
(CADRE, IZA BONN, IWEPS)
ABSTRACT:
In this paper, we model a developing economy in which individual decisions about education and migration are constrained by capital market imperfections (liquidity constraints). We examine the joint impact
of brain drain and international remittances on human capital accumulation in the emigration country. We
derive the condition under which the emigration of the most talented workers stimulates the economy-wide
average stock of human capital in the sending country (compared to the closed economy benchmark). Such
a BBD outcome (beneficial brain drain) is obtained (i) when the return to education is high compared to
the costs of education and migration and (ii) when remittances received by each young are important.
Unlike recent papers in that literature, the BBD cannot be obtained if emigration rates are small.
JEL CLASSIFICATION: F22, J24, J61, J68.
KEYWORDS: skilled migration, immigration policy, human capital, growth.
* We thank Michele Cincera, Hillel Rapoport, Abdeslam Marfouk and Mouna Aguir for useful comments. The
usual disclaimer applies.
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BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
INTRODUCTION
This paper mixes two strands of the literature on international migration, i.e. the economics of international remittances and the economics of the brain drain. Our purpose is
to analyze the effects of increasingly “quality-selective” immigration policies on human
capital in the source countries when talented emigrants remit a part of their earnings.
Clearly, the joint impact of brain drain and international remittances is ambiguous. On
the one hand, international remittances increase ceteris paribus the welfare of remaining
residents. On the other hand, the brain drain resulting from the quality selection is usually seen as a detrimental phenomenon for the sending country. We combine these two
interrelated facts and examine their global impact on human capital formation.
Regarding international transfers, it is well documented that workers’ remittances often
make a significant contribution to GNP and are a major source of income in many
developing countries. For labor-exporting countries such as Egypt, Pakistan, Turkey,
Caribbean or Pacific countries, it is not uncommon to observe flows of remittances that
equal about half the value of their exports or 10% of their GDP. These remittances may
have a short-run macroeconomic impact through their effects on price or exchange rate
levels (see Djajic, 1986). The long run implications of remittances are also likely to be
significant. They impinge on households’ decisions in terms of labor supply, investment, education, migration, occupational choice, fertility with potentially important
aggregated effects1. This is especially the case in poor countries where capital market
imperfections (liquidity constraints) reduce investment possibilities in low-income
classes. Since dollars are fungible and education has a relatively high income-elasticity,
one would expect remittances to have significant positive effects on the educational
attainments of children from households with emigrants. Few studies have looked for and found - clear evidence on this potential link between remittances and education.
Hanson and Woodruff (2002) use the 2000 Mexican Census and show that children in
households with a migrant member complete significantly more years of schooling,
with an estimated increase that ranges from 0.7 to 1.6 years of schooling. Interestingly,
the gain is the highest for the categories of children traditionally at risk of being dropped
from school, i.e. girls and older children (13 to 15 year-olds). In their study on El
Salvador, Cox Edwards and Ureta (2003) show that remittances significantly contribute
to lower the hazard of leaving school.
Regarding brain drain, there is a fair amount of evidence suggesting that the brain drain
is now much more extensive than, say, 25 years ago. Since 1984, Australia's immigration policy has officially privileged skilled workers, with the candidates selected
according to their prospective ''contribution to the Australian economy''. The Canadian
immigration policy follows along similar lines, resulting in an increasing share of highly educated people among the immigrants selected. In the US, the Immigration Act of
1
See Rapoport and Docquier (2003) for a survey.
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DILEK CINAR AND FREDERIC DOCQUIER
1990 established the selection of highly skilled workers through a system favoring candidates with academic degrees and/or specific professional skills. Immigration policies
in EU countries are less clear and still oriented towards traditional targets such as asylum seeking and family reunion. However, there is some evidence suggesting that
European countries (such as Germany) are also leaning towards becoming qualityselective2.
The classical literature on the economic impact of brain drain emphasizes that the welfare of those left behind would fall if migrants' contribution to the economy is greater
than their marginal product (this is obviously the case when the social return to education exceeds its private return)3. However, recent studies suggest that migration
prospects can boost human capital accumulation4, or that some gain can be associated
with imperfect information and return migration5.
This paper belongs to the literature on the economic impact of brain drain for the source
countries. We show that brain drain can boost the stock of human capital per capita
when the resulting remittances are sufficiently high. To demonstrate this result, we build
a simple theoretical supply-side model examining the enhancing effect of remittances
on human capital formation.
Along the lines suggested by Perotti (1993), we consider that liquidity constraints
impede educational investment and migration within the low and medium income classes. Since education is a prerequisite for migration, emigrants belongs to the most talented class (those who can afford paying for both education and migration costs). This
assumption seems particularly realistic for developing countries. Such a brain drain has
a negative direct impact on the stock of human capital per capita. Skilled emigrants then
remit a part of the migration gains. There are evidence reveal that remittances increase
with remitters' income. In the UK, Kangasmieni et al (2004) show that 45 percent of
doctors send remittances to their home country (on average, these transfers amount to
16 percent of their income in the UK). In our framework, remittances enable some liquidity constrained agents to pay for education costs.
Our results reveal that the global effect of brain drain can be positive under some specific conditions. The return to education must be high, compared to the costs of education and migration, remittances received by each young must be high. Hence, unlike
most recent papers on the beneficial effect of brain drain, a positive impact can be
obtained if the number of skilled emigrants is sufficiently large.
2
See the contribution of Bauer and Kunze in this special issue.
See Haque and Kim (1995) or Grubel and Scott (1996).
4
See Mountford (1997), Vidal (1998), Beine et al (2001).
5
See Stark et al. (1997).
3
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BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
The remainder of this paper is organized as follows. Section 1 presents our assumptions.
The closed economy solution is briefly described in Section 2. The open economy equilibrium is characterized in Section 3. Then, Section 4 provides the conditions under
which a beneficial brain drain is obtained. Finally, the last Section concludes.
1. ASSUMPTIONS
Our model depicts a small developing economy with overlapping generations of households. Individuals live for two periods, youth and adulthood. At the beginning of their
life, they decide whether to invest in education or not, by maximizing their life-cycle
income. Education is modeled as a “take it or leave it” decision.
We introduce heterogeneity by considering that the inherited level of human capital of
the young (denoted by yi) is exogenously distributed on the domain [0,y]. Low skill and
high skill workers are perfect substitutes on the local labor market6. The wage rate per
efficiency unit of human capital is constant and normalized to unity. Hence, yi also
measures individual's income.
For the sake of simplicity, we consider a stationary uniform density function:
(1)
If he opts for education, each young agent faces a fixed cost ye and expects to get a rate
of return to education, R. The cost of educational service is fully borne privately. This
hypothesis clearly holds in most developing countries. As in Perotti (1993), the return
to education is higher than its cost for all individuals: R>ye (see assumption A1 below).
However, liquidity constraints impede human capital investment within low-income
classes. Individuals whose income is lower than the education cost cannot borrow to pay
for human capital formation.
At the end of the first period of life, educated agents have the possibility to emigrate to
a richer country at an exogenous migration cost ym. As in Mountford (1999), Vidal
(1999) and Beine et al. (2001), a central assumption of our model is that education is a
necessary prerequisite for migration (emigrants are partly out-selected). However, we
assume that education is also a sufficient condition. By migrating, educated agents sell
their human capital at a higher price: they get a higher rate of return than in the domestic country (R*>R). The return gap exceeds migration cost: migration always increases
lifetime income (see assumption A2 below). However, liquidity constraints are impeding migration within middle income classes: agents cannot borrow to pay for migration
costs (emigrants are partly self-selected).
Consequently, the effective migrants belong to the upper-income class of the population
(those who can afford paying for education and migration costs). Our model thus reproduces the brain drain phenomenon.
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DILEK CINAR AND FREDERIC DOCQUIER
Our purpose is to examine the effect of brain drain on the average level of human capital of remaining members. We do not formalize the externalities associated to human
capital but consider them as a crucial by-product of our analysis. At this stage, brain
drain is unambiguously detrimental for human capital accumulation. However, if we
consider that migrants remit a constant fraction of their foreign wage, these remittances
enable some liquidity-constrained agents to pay for the education cost in the source
country: a higher share of the population has an access to human capital formation. This
beneficial effect must then be compared to the detrimental effect sketched above.
For altruistic reasons that we do not explicitly formalize here, we consider that migrants
remit a fraction ␪ of the return gap on human capital (R*-R ) to their origin country. This
assumption is made for analytical convenience. Similar results would be obtained by
assuming that migrants remit a constant fraction of their income. The source country is
small and cannot influence the size of the return gap.
Young agents from generation t (young at time t) receive an amount of altruistic transfers ␶t from the previous generation of emigrants. We do not deal with intergenerational
mobility in the ability scale and with endogenous differences in the amount transferred.
For simplicity, we assume that each young receives an identical amount of remittances 7.
The decision to educate for agent i is denoted by a discrete variable eit (eit=1 denotes
investing in education and eit=0 denotes no access to education). Similarly, the decision
to migrate is denoted by a discrete variable mit (mit=1 denotes opting for migration and
mit=0 denotes not to migrate). Individuals choose eit and mit so as to maximize lifetime
income W(eit ,mit ) subject to a non-negativity constraint on saving, i.e.
[
Max Wit (eit , mit ) ≡ y i + τ t − eit y e − mit y m
eit , mit
[
]
+ (1 − mit )(y i + eit R )+ mit ( y i + eit R ) − eit mit θ (R − R )
*
*
]
(2)
subject to: yi+␶t- eit ye-mitym≥0
Our set of assumptions can be written as follows:
A1: For each agent i, the rate of return on education exceeds the education cost:
Wit (1,0)>Wit(0,0). Formally, this requires R>ye.
A2: For each educated agent i, migration always induces a gain in lifetime income:
Wit (1,1)>Wit(1,0). Formally, this requires (R*-R) (1-␪)>ym.
6
7
Introducing imperfect substitution would not change the nature of our effects.
Galor and Zeira (1993) examine the inequality effects of the transmission of wealth within families.
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BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
A3: Even without remittances, a strictly positive share of the population would have an
access to education and migration : ye<ye+ym<y.
A4: The product ␪(R*-R) is lower than y. Given (2), this conditions ensures that the second-period income is non negative for the highest ability emigrants. As it will appear
below, this condition also implies that the long run level of per capita amount of remittances is finite.
Given the amount of altruistic transfers per member ␶t at time t, we have at most three
groups in our economy. Agents with income yi<ye-␶t cannot afford paying for education
cost. Agents with income ye-␶t≤yi≤ye+ym-␶t can afford paying for education but cannot
pay for migration costs. Finally, agents with income yi≥ye+ym-␶t get educated and emigrate. Of course, the amount of remittances can be such that one group totally disappears. For example, each young has an access to education when ye-␶t<0.
Assumption A3 ensures that a positive share of the population can pay for education and
migration costs even without remittances. However, the size of remittances determines
the skill structure of the population as well as the number of emigrants. Three types of
equilibrium can be obtained:
- in an open economy such that ␶t<ye, a positive share of the population has no access
to education. Such an equilibrium is labeled as a type-A equilibrium;
- if ye<␶t<ye+ym, all agents opt for education but a positive share of the population has
no access to migration. Such an equilibrium is labeled as a type-B equilibrium;
- finally, if ␶t>ye+ym, all agents opt for education and migration. Such a trivial equilibrium is labeled as a type-C equilibrium.
Hence, the structure of the population is fully determined by the distribution of human
capital at birth relatively to two critical levels (ye-␶t and ye+ym-␶t). Obviously, an
increase in the level of remittances displaces both critical levels to the left.
2. THE CLOSED ECONOMY BENCHMARK EQUILIBRIUM
Assume that the domestic government is able to prevent any form of emigration. It follows that mit=0 for all i and t. Hence, there is no altruistic remittances (␶t=0). At each
period of time, the share of educated agents is given by (y-ye)/(y).
The average stock of human capital of adults is given by
(3)
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DILEK CINAR AND FREDERIC DOCQUIER
It obviously comes out that the average stock of human capital decreases with the cost
of education and increases with R, the domestic rate of return to education. This closed
economy result will be used as a benchmark for examining the effect of brain drain on
human capital formation.
3. THE SMALL OPEN ECONOMY EQUILIBRIUM
In an economy opened to migration, the high skilled agents export their human capital
abroad. In return, they send altruistic remittances that displace the critical levels of ability to the left. This improves the access to education and migration for the next cohort.
The total impact of brain drain balances these effects.
Basically, the average human capital stock in an open economy is given by
(4)
The average stock at time t+1 clearly depends on the decision of migration taken by the
members of the previous generation (the number of adult emigrants at time t determines
the amount of remittances, ␶t).
In (4), the first term under brackets is the stock of human capital of the uneducated; the
second term is the stock of human capital of the educated remaining in the source country. These two terms are multiplied by a fraction capturing the total proportion of agents
remaining in the domestic country. Hence, (4) measures the average stock of human
capital among remaining residents.
A type-A equilibrium emerges when ␶t<ye. In that case, a positive share of the population has no access to education and the first term between brackets is positive (max [0,
ye-␶t]=ye-␶t>0). When ye<␶t<ye+ym, a type-B equilibrium emerges and the first term
between brackets disappears (max [0, ye-␶t]=0).
Developing the integral in (4), we express the stock of human capital per head as a function of remittances:
(5)
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BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
The general function Yop,t+1 is determined by
or by
according to the sign of
␶t-ye. Obviously, when ␶t-ye=0,
and
coincide. Hence, the analysis of the small
open economy solution requires a complete description of the dynamics of altruistic
remittances.
Clearly, the current level of altruistic remittances is related to the number of emigrants
within the previous generation. Since liquidity constraints are restricting migration
opportunities, the past flow of emigration is itself related to the past amount of remittances. The small open economy problem is intertemporal: the amount remitted at time
t depends on the amount remitted at time t-1.
Remember that each young receives an identical amount of transfers (there is no specific wealth transmission rule). It follows that the aggregate amount of remittances is
equally shared among the young:
(6)
This dynamic equation ␶t=␾(␶t-1) fully describes the time path of altruistic transfers and,
given (5), the time path of the average human capital stock per adult. Let us now focus
on the steady state equilibrium of our small open economy. The following result is
obtained:
Proposition 1. The steady state level of altruistic transfers is given by
τ ss =
(
θ (R * − R ) y − y e − y m
y − θ (R − R )
*
)
.
Using assumption A4, this long run solution is strictly positive and globally stable.
Proof: Using ␶t=␶t-1=␶ in (6) clearly gives ␶SS. The stability property of this steady state
can be studied by examining the derivative ␾'.
∂τ t
θ ( R * − R)
One obtains 0 < φ ' =
=
< 1.
∂τ t −1
y
Given A4, the steady state is globally stable.
Figure 1 illustrates how changes in ␪(R*-R), determining both φ(0) =
θ (R *− R)
y − y e− y m
y
(
)
and the slope of the dynamic locus, affect the type of equilibrium. In each case, the
unique intersection wit the 45° line corresponds to a steady state.
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DILEK CINAR AND FREDERIC DOCQUIER
FIGURE 1. ALTRUISM AND THE STEADY STATE AMOUNT OF TRANSFERS –
DYNAMIC REPRESENTATION
45°
␶t
C
B
A
␶ASS
ye
␶BSS
ye+ym ␶CSS
␶t-1
• For small values of ␪(R*-R), the steady state is depicted by point A. The long-run
amount of remittances ( ␶ASS ) is lower than : a positive share of the population has no
access to education (type-A equilibrium).
• For intermediate values of ␪(R*-R), the steady state is depicted by point B. The longrun amount of remittances ( ␶BSS ) lies between ye and ye+ym: all agents have an access
to education and a positive fraction of the population is staying put (type-B equilibrium).
• For high values of ␪(R*-R), the steady state is depicted by point C. The long-run amount
of remittances ( ␶CSS ) is above ye+ym: all agents educate and emigrate so that the population size tends to zero (type-C equilibrium).
Let us now determine the type of equilibrium as a function of the parameters of our model:
Corollary 1. A type-A solution emerges when ␪(R*-R)<
A type-B solution emerges when
yy e
y − ym
yye
y − ym
.
<␪(R*-R)<ye+ym .
A type-C solution emerges when ␪(R*-R)>ye+ym.
Proof. The condition for a type-A equilibrium is ␶SS<ye . Conditions for a type-B equilibrium are ␶SS>ye together with ␶SS<ye+ym. A condition for a type-C equilibrium is
ye+ym<␶SS<y.
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BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
As illustrated on figure 2, the steady state level of altruistic transfers is a convex
function of the product ␪(R*-R). The type-C critical value ␶SS<ye+ym is reached when
␪(R*-R)=ye+ym: the function intersects with the 45° line at this point.
FIGURE 2. ALTRUISM AND THE STEADY STATE AMOUNT OF TRANSFERS –
THE LONG RUN SOLUTION
␶SS
Type A
Type B
Type C
45°
ye+ym
ye
yye
y−y
ye+ym
y
␪(R*-R)
m
Finally, given assumption A3, the migration process takes off at period 1. Starting from
a closed economy at time 0 (␶0=0 ), we have ␶1=␾(0)>0.
4. BRAIN DRAIN AND HUMAN CAPITAL FORMATION
To examine the global impact of brain drain on human capital accumulation in the
source country, we compare the closed economy level of human capital per capita (Ycl)
to the open economy level (Yop,t). We focus on the long-run impact of brain drain by
considering the small open economy solution expressed at the steady state (time indexes are dropped).
Note that the small open economy result exclusively depends on the steady state value
of altruistic transfers. If the amount remitted is too high, all agents emigrate in the long
run. We do not consider such trivial type-C solutions and focus on type-A and type-B
equilibria.
Our analysis is made in two steps. First we investigate the existence of an interval of
altruistic transfers on which a beneficial brain drain can be observed (at least in the long
run). Then, we examine if some altruistic rate can generate such a long run equilibrium.
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DILEK CINAR AND FREDERIC DOCQUIER
The type-A case
The type-A case emerges when the steady state level of altruistic transfers is such that
some agents have no access to education (␶SS<ye). Brain drain is then beneficial for
human capital accumulation when Yop(1) is higher than Ycl, that is when
(7)
This condition can be rewritten as
(7’)
These two functions can be represented in terms of ␶SS.
The
-locus is a linear decreasing function such that
and
The
,
.
-locus is a decreasing and convex function such that
,
and
.
The following result emerges:
Lemma 1. Given assumption A3, a beneficial brain drain cannot be observed when the
amount of altruistic transfers is too low.
Proof. Under A3 (ye<ye+ym<y ), we have
The
-locus thus starts below
.(0)<
<(0).
-locus.
This result stands in contrast with the theoretical model of Beine et al. (2001). They
argue that, by increasing the expected return on education, migration prospects have a
positive impact on human capital formation. In this framework, a beneficial brain drain
can be obtained in countries where the migration rate of the highly educated is rather
low. In our model, a beneficial brain drain can only be obtained when the amount
received by each remaining resident is sufficiently high, i.e. when the proportion of emigrants is not too small.
It should be noted that, as the level of remittances approaches the education cost, the
locus can become lower or higher than the
-locus. One can easily shows that
-
(ye)>␸cl(ye) when 2Rye>y(y-ym), labeled as condition C1. This condition will be used
below.
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BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
The type-B case
The type-B case emerges when the steady state level of remittances is such that all
agents become educated (␶SS>ye). Brain drain is then beneficial for human capital formation when Y op(1) is higher than Ycl, that is
(y
(
+ y m − τ ss) + 2 R(y e + y m − τ ss ) y + 2 R y − y e
>
2(y e + y m − τ ss )
2y
This may be rewritten as
2
2
e
[
)
(8)
]
( 2)
ϕ op
(τ ss )≡ y y e+ ( y m − τ ss) + 2 R(y e + y m − τ ss ) > ϕ cl (τ ss )
2
(8’)
These two functions can be represented in terms of ␶SS. The ␸cl-locus is identical to that
obtained in the type-A case. The ϕ op(2)-locus is also a decreasing and convex function such
that
and
.
(2)
(1)
Graphically, the ϕ op -locus intersects with the ϕ op -locus when the amount remitted is
equal to the education cost. Then it decreases and intersects with the ␸cl-locus when
␶SS=ye+ym. Does the ϕ op(2) -locus approach the ␸cl-locus from above of from below? To
answer this question, one has to compare the derivatives at ␶SS=ye+ym. Given ϕ op(2) is a
convex and decreasing function, it approaches ␸cl from above when its derivative is
smaller, i.e. when 2Rye>y, labeled as condition C2. This condition will be used below.
Global result
Let us now combine type-A and type-B equilibria. Three possible cases can be distinguished according to inequality C1 and inequality C2.
(1)
• In case (a), we consider that C1 does not hold. This implies ϕ op (ye)< ␸cl(ye), i.e.
2Rye<y(y-ym). It follows that C2 does not hold too, i.e. 2Rye<y. The ␸op-locus is
always below the ␸cl -locus and brain drain is always detrimental for human capital
accumulation.
• In case (b), we consider that C1 holds (2Rye>y(y-ym)) but C2 does not hold (2Rye<y).
The ␸op-locus is above the ␸cl-locus for intermediate values of remittances, i.e. for
intermediate migration rates. The possibility of beneficial brain drain exists if the
emigration rate is not too small and not too large.
• In case (c), we consider that both C1 and C2 hold (2Rye>y(y-ym) and 2Rye>y), the
␸op-locus is above the ␸cl-locus for sufficiently high values of remittances. The possibility of beneficial brain drain exists if the emigration rate is sufficiently high.
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DILEK CINAR AND FREDERIC DOCQUIER
These three cases are depicted on figure 3 where the beneficial brain drain corresponds to the bold segment on the X-axis.
It is worth noticing that these conditions do not depend on the altruistic rate (␪) and
on the gap in the rate of return on education (R*-R). Nevertheless, remember that the
product ␪(R*-R) determines the steady state level of remittances. If a beneficial brain
drain segment exists, it will then generally be possible to find a product ␪(R*-R) such
that the long-run level of remittances belongs to that interval.
Proposition 2. A necessary condition to obtain a segment of beneficial brain drain is
R ye
ym
that 2 × ×
.
> 1−
y y
y
If this condition holds, two configurations are distinguished:
ym
R ye
if 1 −
< 2× ×
< 1 brain drain is beneficial for intermediate levels of remittances.
y
y y
yye
;
The altruistic factor ␪(R*-R) must be close from
y−y
m
e
if 1 − y < 1 < 2 × R × y brain drain is beneficial for high levels of remittances.
y
y y
e
The altruistic factor ␪(R*-R) must be close from or higher than y y .
y − ym
Proof. The first condition is obtained by developing ␸op (ye)>␸cl (ye) (C1).
The second condition combines C1 and C2. According to corollary 1, we have ␶SS=ye when
yye
␪(R*-R)=
.
y − ym
For illustrative purpose, consider an economy where the cost of education represents
25% of income and where the cost of migration (including transport, visa, search costs,
housing and the monetary value of psychic costs) amounts to 50% of income for the
highest ability individual (ye/y=2.5 and ym/y=.5). According to proposition 2, if the rate
of return to education (R/y) is lower than 1, there is no possibility of beneficial brain
drain. If the return to education is between 1 and 2, a beneficial brain drain is obtained
when the amount remittances is not too small and not too high. For higher rates of
return, a beneficial brain drain is obtained when the amount of remittances is not too
small. For the two latter cases, the maximal impact on human capital is obtained when
the amount of remittances is just equal to the cost of education.
More generally, a beneficial brain drain interval is obtained when the return to education is high, compared to the costs of education and migration. If such an interval exists,
brain drain effectively increases human capital formation. Remittances received by each
young are such that an important part of the population gets the access to education.
115
BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
FIGURE 3. ON THE POSSIBILITY OF A BENEFICIAL BRAIN DRAIN
Case (a): Detrimental brain drain
(1)
␸op
␸cl
(2)
␸op
ye
ye+ym
␶SS
Case (b): Beneficial brain drain for intermediate migration rates
(1)
␸op
␸cl
(2)
␸op
BBD
ye
ye+ym
␶SS
ye+ym
␶SS
Case (c): Beneficial brain drain for high migration rates
(1)
␸op
(2)
␸op
␸cl
BBD
ye
116
DILEK CINAR AND FREDERIC DOCQUIER
CONCLUSION
In this paper, we examine the consequences of the migration of skilled workers on
human capital accumulation in the source country. Our model relies on two major
assumptions: (i) liquidity constraints impede human capital investment in low-income
classes and (ii) migrants altruistically remit a part of their earnings into the source country. In the long-run, brain drain involves two opposite effects on human capital formation:
- the most educated (those who can afford paying for both education and migration
costs) are leaving the source country, reducing the average level of human capital for
those staying put (traditional effect);
- international remittances enable some liquidity constrained agents to pay for education costs, raising the proportion of agents opting for education (better access to
schooling).
A beneficial brain drain can be obtained when the better access to schooling dominates
the traditional effect: the migration of skilled workers associated to altruistic transfers
can then be beneficial for human capital accumulation.
We thus explore the theoretical conditions under which such an outcome appears. It is
shown that a beneficial brain drain is obtained under some restricted conditions. More
precisely, it requires that the level of altruistic transfers per capita must be such that a
large share of population gets the access to education. Such a condition does not hold
in developing countries. Hence, despite the fact that remittances is a major source of
income for remaining residents, they should not be large enough to stimulate the economy-wide average level of education. Once negative spillover effects are considered
(intergenerational and intragenerational externalities associated to human capital), the
net impact on remaining residents' welfare is ambiguous.
117
BRAIN DRAIN AND REMITTANCES: IMPLICATIONS FOR THE SOURCE COUNTRY
REFERENCES
Beine M., F. Docquier and H. Rapoport, 2001. “Brain drain and Economic development: theory and evidence”, Journal of Development Economics, 64, 275-289.
Cox Edwards A. and M. Ureta, 2003. “International migration, remittances and
schooling: Evidence from El Salvador”, Journal of Development Economics, forthcoming.
Djajic S., 1986. “International migration, remittances and welfare in a dependent
economy”, Journal of Development Economics, 21, 229-34.
Galor O. and J. Zeira, 1993. “Income distribution and macroeconomics”, Review of
Economic Studies, 60, 35-52.
Grubel H.G. and A. Scott, 1996. “The international flow of human capital”, American
Economic Review, 56, 268-74.
Hanson G.H. and C. Woodruff, 2002. “Emigration and educational attainment in
Mexico”, Mimeo., University of California at San Diego.
Haque N.U. and S.-J. Kim, 1995. “Human capital flight: Impact of migration on
income and growth”, IMF Staff Papers, 42(3), 577-607.
Kangasmieni M., L.A. Winters and S. Commander, 2004. “Is the medical brain drain
beneficial? Evidence from overseas doctors in the UK”, Mimeo, CNEM, London
Business School.
Mountford A., 1997. “Can a brain drain be good for growth in the source economy?”,
Journal of Development Economics, 53(2), 287-303.
Perotti R., 1993. “Political equilibrium, income distribution and growth”, Review of
Economic Studies, 60, 755-776.
Rapoport H. and F. Docquier, 2004. “The economics of migrants' remittances”, in
L.A. Gerard-Varet, S.C. Kolm and J. Mercier Ythier (eds), Handbook of the Economics
of Reciprocity, Giving and Altruism, Amsterdam: North-Holland, forthcoming.
Stark O., C. Helmenstein and A. Prskawetz, 1997. “A brain gain with a brain drain”,
Economic Letters, 55, 227-234.
Vidal J.-P., 1998. “The effect of emigration on human capital formation”, Journal of
Population Economics, 11(4), 589-600.
118
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
TEMPORARY MIGRATION AND SELF-EMPLOYMENT :
EVIDENCE FROM TUNISIA
ALICE MESNARD
(INSTITUTE OF FISCAL STUDIES, LONDON)
ABSTRACT:
Based on statistics from the Central bank of Tunisia and on a survey describing Tunisian workers who have
returned from migration, this paper shows that temporary migration has potentially important consequences for sending countries like Tunisia. The effects operate through at least two channels. On one hand,
transfers sent by migrants to their origin country represent a sizeable source of foreign currency and
income. On the other, savings repatriated upon return under different types of goods allow poor workers
to overcome credit constraints for investment into small projects.
JEL CLASSIFICATION: E22, F22, H81.
KEYWORDS: international migration, investment, credit constraints.
119
TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
INTRODUCTION
It has long been recognised that the effects on the countries of migrant workers of remittances sent home by them depend crucially on whether they are used for consumption
or investment. In the 1970s, most socioeconomic studies outlined the strong negative
effects of remittances used for conspicuous consumption (e.g. expensive houses) with
limited dynamic effects (see for example Rempel and Lodbell (1978). Remittances may
also increase relative deprivation of non migrants or discourage labour-supply effort for
recipients, thus increasing dependency and postponing rural development (see Durand
et al (1996) for a critical review of these arguments). At the same time, a few studies
following Griffin (1976) and Stark (1978, 1991) started challenging this view, by stressing the positive effects of remittances on development. They showed that remittances
contribute also to finance investments in production, in particular in poor rural areas
characterised by very limited access to credit markets and that they may provide coinsurance to household members, hence permitting poor households to invest into risky
projects.
Recently, capital market failures have been emphasized extensively as an aid to understanding barriers to development. Because of limited commitment or moral hasard problems, poor workers do not have free access to credit when they want to invest, implying
long run effects on economic growth1. This gave rise to several empirical papers, showing that liquidity constraints are important in explaining occupational choices of workers. A flourishing literature emphasized the positive effect of individual wealth on entrepreneurship in developed countries (see for examples Evans and Jovanovic (1989), Evans
and Leighton (1989), Holtz-Eakin, Jouflaian and Rosen (1994), Magnac and Robin
(1996), Lindh and Ohlsson (1996), Blanchflower and Oswald (1998)). More recently,
empirical evidence on developing countries has started to accumulate, with a special
focus on return migrants. For example, Ilahi (1999) for Pakistan, Mesnard (1999, 2003)
for Tunisia, Mc Cormick and Wahba (2001) for Egypt, show that savings repatriated by
migrants are used for investment into small businesses.
Under these conditions, we understand quite easily that temporary migration may be a
way out of a development trap for a poor, liquidity constrained economy, as developed
by Mesnard (2001). If workers from a poor economy have the choice to migrate into
high wages countries, a new equilibrium on the labour market may follow from large
return migration flows. This happens if a proportion of workers who would not have
invested without migration overcome their liquidity constraints and invest in their home
country with their savings accumulated abroad.
In practice, both migration flows and transfers sent by migrants are difficult to observe.
Apart from obvious reasons linked to the illegality of a large part of migration and the
importance of the informal economy that is very difficult to measure through official
1
See, for examples, Banerjee and Newman (1993) and Aghion and Bolton (1997).
120
ALICE MESNARD
statistics, there are also problems in gathering information both in the countries of origin and destination in order to have a complete picture of migration. Nevertheless, several sources of statistics exist on these flows and already a few attempts have been made
to study empirically the effects of migration for the countries of origin of the migrant
workers2. This paper contributes to the empirical knowledge of capital flows linked to
labour migration, by quantifying the importance of these flows for a developing country like Tunisia and stressing their significant role in increasing self-employment.
Studying migration flows of Tunisian workers over the period 1974-1986 is of particular interest, since many of them have chosen to return to Tunisia after having worked
abroad, given the particular historical background outlined in Section 1. Section 2
describes the characteristics and activities of these workers, using an original data set
belonging to the Arabic League3. Section 3 investigates whether savings accumulated
abroad by temporary migrants allow them to overcome liquidity constraints and start up
projects in Tunisia after return.
1. IMPORTANCE OF MIGRATION FLOWS AND FINANCIAL TRANSFERS FROM
MIGRANTS TO TUNISIA
1.1. HISTORICAL BACKGROUND
After the second world war, the chaotic history of international migration of Tunisian
workers results in a heterogenous population of migrants who have returned to Tunisia
before 1986, the date of the TSAO survey. Two periods may be broadly distinguished in
this process, before and after 1974.
Before 1974, outmigration flows towards European countries increased continuously.
Indeed bad economic conditions in Tunisia generate rising unemployment problems, at the
same time as European countries have high labour demands in sectors with low levels of
qualification. In order to control these flows, several agreements were signed by the
Tunisian government, firstly with France in 1963, then with Germany in 1965, with
Belgium in 1969, and other countries like Hungary and Holland. In 1967 the Tunisian government created an agency called “Office de l'Emploi et de la Formation Professionnelle”
that organised the direct recruitment of unskilled Tunisian workers for industry and building sectors in European countries. Implicitly, these agreements expected that individuals
will migrate temporarily to work abroad and eventually return to Tunisia to live with their
families. During the same period, outmigration started to expand towards Libya, very
often illegally, due to good prospects linked to the exploitation of new oilfields.
2
For example Woodruff and Zenteno (2001) study the effects of remittances on the creation of microenterprises in
the urban areas of Mexico combining the population Census, the data of the Bank of England on remittances and
a national survey on microenterprises.
3
I am indebted to R.Ben Jelili, H. Mzali, and the OTTE (Office des Travailleurs Tunisiens à l'Etranger) who provided the data and help in using them.
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TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
1974 was a turning point in the evolution of Tunisian migration for two main reasons.
Firstly, most of European countries closed their borders and started to encourage workers to return home. For example, RFA was officially closed to new migrants in 1973 and
France restricted immigration to family members joining already settled migrants, while
encouraging workers to return to their home country. As a consequence, temporary
migration of single workers was transformed into a permanent migration of family settlement. Moreover, in most host countries, migrants had to face severe problems of
unemployment. Secondly, in the same period, political problems between Libya and
Tunisia led to the breakdown of the migration expansion towards Libya. A chaotic period developed after 1974, characterised by more irregular out-and return migration
between Tunisia and traditional host countries and by a new political orientation of
Tunisian migration towards the Gulf countries. In particular after 1983, when Tunisian
workers were massively expelled from Libya, many of them migrated towards other
Arabic countries but also towards new European countries (like Spain, Italy, Greece,
etc...) where illegal migration continued to rise.
1.2. EVOLUTION OF TUNISIAN MIGRATION FLOWS
It is difficult to estimate precisely the number of migrants because many of them
migrate either illegally or temporarily and the legal situation of individuals leaving
Tunisia for different purposes can change over time. Official sources of information
come mainly from the National Institute of Statistics, based on the reports from the
police at the border, as well as from the consular services in foreign countries. To
complete this information, a survey was conducted in 1986 by the Tunisian Settled
Abroad Office (TSAO) in the Ministry of Foreign Affairs in collaboration with the
Arabic League. This survey enquires about living conditions of a representative sample of workers living in rural and urban areas, with particular focus on an over-sampled group of individuals who have worked abroad between 1974 and 1986 and, subsequently, have returned to Tunisia4. Based on this survey, Zaiem (1992) estimates that
around 316,000 Tunisian workers have worked in a foreign country between 1974 and
1986. This includes 214,000 migrants who have already returned to settle in Tunisia,
16,000 migrants still living abroad but who were temporarily visiting Tunisia at the
date of survey, and 86,000 workers who are still abroad at the date of survey. Thus,
according to this source, around one third of the workers who have migrated abroad
between 1974 and 1987 were still living abroad. Note, however, that these statistics
do not take into any individuals accompanying Tunisian workers like spouses and
children. Adding them, Zaiem estimates the total number to be between 535,000 and
570,000 individuals5. Furthermore, these estimates reported by households surveyed
4
This very rich survey was initially designed in order to understand better the reasons why Tunisian workers wanted to migrate and their difficulties of insertion that they had to face upon return, as well as economic consequences of migration for Tunisia.
5
These estimates are quite close to estimates by consular services, who find that around 512,000 Tunisians have
left Tunisia before 1989, whereas the police at the border estimates that 320,000 workers have left Tunisia to work
abroad before 1986.
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ALICE MESNARD
in Tunisia only take into account migrants who are still linked to their country of origin and may underestimate migrants who are living with their family abroad.
Therefore this survey is better designed to give more accurate information on migrants
who have returned to Tunisia at the date of survey. Based on Zaiem's results, Table 1
describes the evolution of return migration flows over the period 1974-1986. Starting at
the beginning of the seventies, with an average of 3600 workers per year between 1970
and 1975, the movement has strongly accelerated between 1979 and 1984 (around 6850
workers per year on average) before slowing down.
TABLE 1. EVOLUTION OF RETURN MIGRANTS FLOWS PER COUNTRY OF LAST MIGRATION
<1974
Total
1119
from Libya 669
from Europe 450
1981
Total
10005
from Libya 5635
from Europe 3595
1974
2741
1951
790
1982
14245
9488
3872
1975
6535
5206
1329
1983
26142
16847
8126
1976
8056
5099
2760
1977
6827
5076
1751
1984
37322
28960
5797
1978
9837
8038
1691
1985
34898
27612
6322
1979
12665
10419
1878
1986
20315
14633
3922
1980
15474
11881
3593
1987
4352
1246
2041
Over the period, three types of return migrants may be distinguished: those who
returned after European countries borders were closed in 1973, those who returned after
having been expelled from Libya, (in particular in 1983,1984 and 1985) and those who
returned from other Arabic countries for economic and social reasons.
1.3. IMPORTANCE OF CAPITAL FLOWS LINKED TO MIGRATION
Another important feature linked to Tunisian migration is the increasing volume of
transfers sent by migrants. The main source of information comes from the Central
Bank of Tunisia that estimates, among resources of the balance of payments (BP), transfers from Tunisian workers living abroad with their family. These funds are either transferred directly by the migrants6 or by official agencies in host countries that collect
social contributions for pensions, family allocations and health insurance from workers
and employers. Representing one of the main sources of foreign currency for Tunisia,
these transfers are playing a very important economic role, in particular during a period characterised by increasing debt and shrinking resources from oil exploitation. Table
2 from Zaiem (1992) reports the evolution of these transfers (T) in millions of current
dinars and compares them to the current resources of payment balance (BP), to the
growth national product (GNP), to the debt service (DS), to the resources from tourism
(RT) and to oil exportations (OE).
6
By bank (for 2/3 of them), by mail, or rapatriated by the migrants themselves during visits or upon return.
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TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
TABLE 2. EVOLUTION OF TRANSFERS IN MILLIONS OF CURRENT DINARS (A)
OR IN PERCENT
1960-1970
1970-1980
1980-1990
TOTAL
Ta
75,4
829,7
3080,8
3985,9
T/BP
5,4
9,5
10,5
10,1
T/GNP
1,3
3,7
4,6
4,2
T/DS
32
87
48
52
T/RT
42,7
54,1
58,6
57,2
T/OE
68
40
74,4
61,4
Over the period 1960-1990 these transfers represent on average around 4% of GNP,
more than half of the debt service, and 10% of the current resources of the balance of
payment, being the third most important resource after resources from oil exploitation
and resources from tourism. Note that these statistics from the Central Bank underestimate strongly the total amount of transfers from Tunisian migrants. Indeed strong regulations limit the convertibility of foreign currency to Tunisian dinars. To overcome
these barriers, an informal compensation system has been set up by workers. During
visits in their family, many migrants bring back goods bought in foreign countries (like
equipment for agriculture, cars, furniture, electro appliances, etc.) that are eventually
exchanged against Tunisian dinars, with typically big mark-ups. This became very popular after 1981, when the currency from Libya was no longer convertible in Tunisia.
The saving efforts made by migrants abroad to transfer money back home appear very
substantial over this period. Computing the ratio of total transfers estimated by the
Central Bank to the total population of Tunisians working abroad, Zaiem reports the
evolution of average transfer per worker in the following Table:
TABLE 3. AVERAGE TRANSFERS PER MIGRANT (IN CONSTANT DINARS IN 1990)
1977
371
1978
420
1979
469
1980
539
1982
796
1983
746
1984
703
1985
715
1987
1179
1989
952
1990
937
Transfers per worker (estimated in constant dinars in 1990) have tripled between 1977
and 1987. These transfers respond strongly to economic and political backgrounds in
host countries and in Tunisia, as shown by big downturns during 1982-1984 and after
1987. Over 1987-1990, the yearly mean amount transferred per worker reached 1000
Tunisian dinars, representing over 80% of GNP per capita.
2. WHO ARE THE MIGRANTS WHO HAVE RETURNED TO TUNISIA ?
Already established as considerable in the previous section, transfers from migration and
migration flows of return migrants may have very different consequences on development,
depending on what migrants do after return and how transfers are used in their origin country. In the following, we will describe findings from the TSAO survey providing rich information at individual level on workers' migration history and labour market outcomes.
124
ALICE MESNARD
2.1. SELECTION OF THE SAMPLE
From the survey, two samples of workers living in rural and urban areas can be distinguished. One sample consists of a group of workers living in Tunisia and having migrated to work in a foreign country at least once since 1974 (hereafter, the migrants). The
other sample consists of workers who have never migrated in the past and will be used
as a control group (hereafter, the non migrants). In view of having more homogeneous
samples of workers, in our final samples we kept only male workers, aged between 20
and 60 in 1986: 1168 workers who have returned from migrating and 944 workers who
have never migrated7.
The double selection through migration and return explains a few differences between
the two groups as shown in Table (). Migrants are on average older than non migrants
(37.3 versus 35.9 years old), having spent on average 4.1 years abroad and 4.2 years in
Tunisia after return before being surveyed. They are also more often married (81%) than
non migrants (59%). This difference observed between the two groups may be
explained by life-cycle reasons and the fact that 22% of migrants have returned for family motives, in particular to get married in Tunisia (see the Appendix for the list of other
motives). Moreover, return-migrants have larger households with 1.3 more dependents
on average than non migrants.
Interestingly, in the survey workers report the legal or illegal status of their migration. 64% of them left Tunisia with a tourist visa and 31% with a work visa, whereas
5% migrated illegally. Also 85 % of these workers lived abroad without any family,
while 63 % were married before migrating. Only 2.6 % of them left Tunisia with their
wife and children, 7.8% migrated with other relatives or siblings, and 4.6% had some
of their children joining them abroad during migration. A simple explanation is that
these migrants were planning to return to Tunisia. Indeed, we have to bear in mind
that these statistics do not represent the whole set of migrants and we have no information on workers who were still living abroad at the date of survey, possibly with
their family.
2.2. HUMAN CAPITAL
It is also questionable whether temporary migration has led to a brain drain process in
Tunisia8. Indeed, migration models based on human capital accumulation predict that
highly (lowly) educated individuals may gain more (less) from migration than lowly
(highly) educated workers depending on the returns to the skills differential between the
two economies (Borjas, 1987). For example, applying this selection model twice,
7
Surveyed return migrants in the initial sample are predominantly male since most of women having migrated
between 1974 and 1986 were following their husband. The women (numbered 12) who had migrated to work are
dropped out of the sample of return migrants.
8
See the recent controversy on effects of outmigration for human capital accumulation in source countries, Haque
and Kim (1995), Stark, Helmenstein and Prskawetz (1997), Vidal (1998), Beine, Docquier and Rapoport (2001).
125
TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
Ramos (1991) shows that return-migration reinforces this auto-selection mechanism9. In
our sample, we observe that return migrants have significant lower education levels than
non migrants. 84% (73%) of migrants (non migrants) have less than a primary school
level. 36% (32%) have no schooling and 48% (41%) have a primary education level.
Only 4% (7%) of migrants (non migrants) have a short secondary degree and 12%
(20%) have a higher education level. Again these statistics should be interpreted with
care since workers are selected through migration and return and, in contrast to Ramos,
we have no information on workers staying abroad.
Nevertheless, there is very little evidence of human capital accumulation through
migration. Less than 20% of migrants report to have acquired new skills in the foreign
country10 and, for those who have, less than 8% of them report to use these skills in their
job after return. Also note that 35% of migrants claim to have a job similar to the job
they had before migrating. Workers also describe how working experience abroad has
affected the job they have after return. For 15% of them quality and efficiency on the
job have improved. For 15% of them, speed in work after migration is higher than before
migration, and 7% (respectively 6%) of them claim that organization (respectively management) of work has improved, and only 3% claim to have a better control of tools and
machine or to have improved creativity in working. Hence, migration experience seems
to have improved the rationalization of work more than having brought any particular
technical skills or engineering knowledge. Of course since all these statistics are based
on self-reported information, they could be biased in which case we would need better
information to give a more conclusive answer.11
This stands in contrast to the traditional literature on migration, which often considers
migration as a way to acquire human capital as, for example, in the case of students'
migration. This may not be too surprising since the migrants with very low school levels
correspond to the flows of workers who were massively hired by firms in European and,
later on, Arabic countries, as a response to labour shortages of unskilled labour force.
2.3. SAVINGS ACCUMULATED ABROAD
The survey gives interesting information on the amount of savings brought back to Tunisia
at return and on transfers made during migration. However the amounts of transfers
reported by migrants themselves suffer from too many missing answers (only 83 answers
were given). This can be explained by strong social norms existing in Tunisia that make
the reporting of how much one earns or transfers to one's family frowned upon.
9
In particular he observes that the highest skilled among the low skilled Puerto Rican immigrants in the United
States return to Puerto Rico.
10
Skills were acquired on the job for 83% of them, through special training for 13.5% of them, and through other
methods for the rest of the respondents.
11
Unfortunately we do not have better measures of human capital accumulation during migration. Although migration duration could be considered as a proxy for human capital accumulation abroad, this variable is potentially
endogenous for different reasons, which would be very difficult to disentangle (see, for example, Mesnard, 2004).
126
ALICE MESNARD
TABLE 4. SAVINGS ACCUMULATED ABROAD AND TRANSFERS
(IN DINARS IN 1986 / 1 DINAR IN 1986 = 1.6 US DOLLAR)
(nb of obs.)
savings
transfers
all
587(1024)
6260(83)
France
928(186)
16186(10)
Libya
380(901)
1208(64)
Arabic country Europe
625(50)
1608(36)
20299(6)
47406(3)
Therefore we used another variable that adds up all types of savings that migrants report
to have brought back from migration. In contrast, this variable is much more frequently reported by migrants. Strikingly, workers returning from European countries have
accumulated on average 2.5 as much savings as migrants from Arabic countries. Table
5 shows that savings are mainly used to acquire houses, building fields or real estate
(42.2% of total savings).
TABLE 5. SAVINGS SPENDING BY RETURN MIGRANTS PER COUNTRY OF LAST MIGRATION
use of savings
(%total savings)
all
France
Libya
other
Arabic
other
European
monetary savings
gold
building fields
real estate
furniture
electric-housing appliances
land
cattle
equipment for agriculture
industrial equipment
transport means
shops
other
12.7
4.8
3.8
38.4
12.3
8.8
3.7
3.4
1.6
1
6
1.7
1.5
9.2
5.5
2.4
35.5
11.7
7.2
6.1
2.9
1.9
1
11.5
3.1
1.8
12.9
4.6
4.3
40.6
12.2
8.6
3.5
3.5
1.7
0.8
4.5
1.4
1.5
23.5
7.1
3.6
23.1
15.7
11.5
1
3.1
6
2.1
8.6
0
0
10
4.4
0.6
22.6
14.8
18.1
1.9
5
0
5.8
12.7
4.3
2
In addition, Table 5 shows that workers coming back from France have spent relatively
more of their savings to buy land (6.1%), transport means (11.5%) or shops (3.1%) and
less to buy building fields (2.4%) and real estate (35.5%) compared to those coming
back from Libya (who have spent, respectively for these items, 3.5%, 4.5%,1.4%, 4.3%
and 40.6% of their total savings).
These statistics, however, must not be over-interpreted. It is indeed difficult to distinguish savings that are effectively invested into projects after return from savings
used for private consumption. Indeed savings brought back in kind as, for example,
pieces of furniture, electric housing-appliances or cars, have been very often
exchanged to obtain local currency, given the complicated legal restrictions on
importations and convertibility of foreign currency in Tunisia12. Therefore, in the
12
A non resident Tunisian worker can only bring back a limited amount of goods and foreign currency per year.
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TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
remaining of the paper, we will use the total amount of savings accumulated abroad,
either in kind or monetary, as a proxy for individual wealth at the date of return.
3. WHAT DO THEY DO AFTER RETURN ?
Comparing activities of migrants to non migrants is not easy since the questions used
in the survey are different for the two samples. While workers who have returned from
migration are asked about their activities after return and about the last activity they
had before migrating, workers who have never migrated are asked about their last
activity and their activity in 197413. Studying how temporary migration affects activities chosen by workers would require at least to have homogenous spells for the two
groups, which is not the case in our data. However, the following description of the
activities chosen by the two groups of workers suggests interesting features linked to
temporary migration.
3.1. IN WHICH SECTOR DO THEY WORK ?
Do migrants work after return in the same sectors as before migration? Table 6 shows
that, on average, migrants are less likely to work in the building sector after return than
before they left, and are significantly more likely to work in the trade and transport sectors. Table 7 shows that these changes correspond to a general trend in economic activity of Tunisia. However, we cannot push too far our comparison between the two samples, since the period of analysis varies for migrants and, in most of the case, is much
shorter than for non migrants (they migrated, on average, 8.3 years before the survey
and returned 4.2 years before the survey). This might also explain why migrants are
much more frequently employed in the building sector before migrating than non
migrants, and less employed in the agriculture sector.
Interestingly the proportion of migrants working in the trade sector is twice as high after
return compared to before migrating. This may not be surprising since 70% of workers
in this sector are self-employed, as compared to 25% of workers in other sectors.
Moreover, although the proportion of non migrants working in industry increased
between 1974 and 1986, return migrants were still less often employed in this sector, as
compared to before they migrated.
13
Therefore all non migrants report having a job, whereas, in the initial sample, a few migrants report to be unemployed or retired but we chose to drop them out of the sample, for comparison.
128
ALICE MESNARD
TABLE 6. REPARTITION OF MIGRANTS PER SECTOR
Sector of activity
before migration
at the date of survey
Agriculture
Industry
Mines
building sector
Trade
Transport
total
30,7
11
1
37,2
4,4
15,7
100
29,5
10,5
1,4
30
9,8
18,8
100
TABLE 7. REPARTITION OF NON-MIGRANTS PER SECTOR
Sector of activity
in1974
last activity before
the survey
agriculture
industry
mines
buiding industry
trade
transport
total
35
12,2
2,5
15,4
8,5
26,4
100
30,5
15,4
2,6
13
9,3
29,2
100
3.2. WHICH TYPE OF WORK DO MIGRANTS CHOOSE UPON RETURN ?
Return migrants seem to have chosen more often to work in sectors characterised by a
large number of small enterprises like trade and transport. The survey gives further details
on the projects realised after return : 37% of these projects are in agriculture, 27% in trade,
18% in transport, 9% in industry and 9% in building sector. Also, types of projects differ
across sectors : 86% of projects in agriculture are of family type, versus 9% of projects in
other activities, which are dominated by individual enterprises. Whatever their type, most
of these enterprises are small, employing less than 5 (10) employees for 92% (98%) of
them. Unfortunately we cannot observe how these informal projects, as being defined by
their small size (OECD, 1992), have developed over time and we have no further details
on their realisation apart from their financing. Indeed, workers mainly use their own capital for investment after return: 87% of projects are realised with savings accumulated during migration and only 13% of migrants receive complementary funds from special programs. But none of the self-employed return-migrants rely on bank credit14. Furthermore,
14
Nevertheless we cannot rule out that migrants have access to other funds to invest after returning, e.g., informal
credit sources but we have no information on transfers or borrowing relationships between the migrant and other
family members after return and the only proxy given on transfers during migration is of bad quality, as previously described.
129
TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
when surveyed about the main obstacles workers had to face in starting up their projects,
they explicitly mentioned their difficulties in getting access to credit markets.
We may then ask whether temporary migration has increased self-employment in
Tunisia. Although the proportion of self-employed workers among return migrants
(26.3%) is not significantly different from the proportion of self-employed workers
among non migrants (23.8%), self-employment has increased among return migrants,
since only 15.6% of them were self-employed before migrating.15 This increase could
be due to an age effect. However, comparing self-employment rates before migration
and after return for individuals in same age cells, the differences remain important.
Hence we would like to understand which factors determine the decision to start up a
business after return for workers who were not self-employed before migration. After
selecting these workers, we compare workers who started up a project at return to
salaried workers. As shown in Table 8, only a couple of characteristics appear significantly different between these two groups.
TABLE 8. CHARACTERISTICS OF RETURN MIGRANTS
(1)
(2)
Took up self-employ- Did not take up
ment on return
self-employment
(n=210)
(n=840)
(n=1050)
Age in 1986
Age at return
no education (%)
Primary school (%)
Short secondary school (%)
Long secondary school (%)
Number of dependents
Married (%)
Born in area of Tunis (%)
Born in Center East (%)
Born in Center West (%)
Born in Southern East (%)
Born in Southern West (%)
Born in Northern East (%)
Born in Northern West (%)
Migration duration (months)
France (%)
Libya (%)
Europe (%)
Arabic (%)
38.92
34.87
36.4
46.4
4.3
12.9
5.0
81.4
5.3
23.3
2 21
15.7
10
7.6
17.1
75.4
27
66
4
3
37.18
32.93
33.9
49.5
4.6
12
4.64
80.3
5
19.5
23.4
21.6
10.5
6.4
13.6
52.63
15
78
3
4
Savings accumulated abroad
(1 dinar in 1986 = $US1.6)
1086
(1539.13)
Return migrants not selfemployed before migration
(10.92)
(10.60)
(2.94)
(69.4)
36.75*
32.44*
33.3
50.2
4.7
11.8
4.55
80
4.9
18.6
24
23.1*
10.6
6.1
12.7
46.64*
13 *
80 *
2
5
442.35*
(951.77)
(11.07)
(10.66)
(2.93)
(49.72)
Full sample
(11.07)
(10.69)
(2.93)
(55.6)
580.52
(1134.70)
* significantly different from the mean in column (1), t-test. Standard errors in parenthesis.
15
For comparison, 26.8% of non migrants were self-employed in 1974, which is not significantly different from the
proportion in 1986.
130
ALICE MESNARD
Strikingly, workers who are self-employed after return, have accumulated much larger amounts of savings during migration (more than twice as much). Even after controlling for other individual characteristics, the amount of savings repatriated by
those who enter self-employment is significantly higher than that brought back by
salaried return-migrants. Also, workers who start a business after return have stayed
abroad, on average, for 6.3 years, whereas salaried workers returned after 3.9 years
spent abroad. Finally, migrants who invest into projects after return come more often
from European countries and less frequently from Arabic countries than salaried
return migrants. All these descriptives suggest a story where migrants choose their
migration duration, migration destination and effort of saving abroad according to
the occupation they intend to have after return, as developed in Mesnard (2004). It
appears likely that credit constrained workers migrated to high wages countries until
they accumulated enough savings in order to invest in their origin country. However,
we cannot push too far the interpretation of these correlations, since, very likely,
workers with different abilities have chosen different destination countries, different
occupations and different migration durations, and these heterogeneous abilities cannot be observed. Therefore, in the following section, we propose an econometric test
of whether savings accumulated abroad determine occupational choice at return,
once controlling for potential endogeneity problems and other determinants of selfemployment.
3.3. DOES SAVINGS ACCUMULATED ABROAD INCREASE SELF-EMPLOYMENT AT RETURN ?
Following Mesnard and Ravallion (2001), we use savings accumulated abroad as a
proxy for workers' wealth at the date of return and test to which extent this variable
increases the probability to start up projects at return for workers who were not selfemployed before migration. As compared to the traditional literature on liquidity
constraints and self-employment, an obvious advantage is that our savings variable
is predetermined at the date of occupational choice. Hence, from this viewpoint, it
is less likely to be endogenous than any variable capturing individual wealth at the
date of survey16. A second advantage is that we built this variable by adding up all
types of goods repatriated at return, and thus obtained much fewer missing answers,
compared to using any self-reported measure of individual wealth or income in
Tunisia.
Yet, being predetermined does not guarantee exogeneity of the savings variable.
Indeed there are several potential sources of endogeneity that could cloud the savings effect, if not properly tackled empirically. In particular, temporary migrants
may be selected on their wealth level and abilities to accumulate wealth abroad, if
migration is a way to overcome liquidity constraints in the origin country. Hence, we
replicated for our selected sample of return migrants the test for exogeneity of savings developed in Mesnard (2004) and could not reject that savings are statistically
16
This is also the reason why we could not perform similar regressions for the sample of non migrants for whom
we only have a proxy of their savings at the date of survey.
131
TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
exogenous17. Hence we can straightforwardly discuss the effect of savings accumulated abroad on the probability to start up a project at return.
The survey provides us with information on a number of factors, which are likely to
affect the occupation chosen at return. Among control variables, we entered variables on education levels and age, which are likely to affect the returns of self versus wage employment, as well as variables on family composition (marital status and
family size), which might operate through several channels (for example, through
providing migrants with cheap labour, or better access to informal sources of credit, or offering different job opportunities in family type enterprises.) Even though
proximity to markets is likely to play an important role in determining occupation at
return, we could not enter variables charaterising the area where migrants live at the
date of survey, since they are likely to be endogenous. Instead, we entered the area
of birth. Similarly, we could not control for important factors, like the country of
destination, migration duration or wages abroad, since all these variables are likely
to be endogenous in a setting where migrants determine their future occupation
simultaneously with all migration outcomes.
Results presented in Table 9 show that, apart from the amount of savings accumulated abroad, few factors play a role in explaining business start-ups at return.
Married respondents are less likely to be self-employed at return18 and individuals
leaving in the Center-East of Tunisia are more likely to start up small projects, probably due to the particular dynamism of the whole area around Sousse in trade and
tourism activities. Our main result is that savings at return increases significantly
the probability to start up a project, but at a decreasing rate. To estimate the magnitude of this effect, we simulated the increase in the probability of being self-employment that would follow an increase of savings of one standard deviation for an individual having the mean characteristics of the sample. The estimated subsequent
effect of 27.25% would more than double the observed percentage of self-employment among return migrants.
17
Table 9 shows that the coefficients associated to the residuals of the two instrumental regressions for savings and
savings squared are individually not significant. They are also not jointly significant. For more details on the two
step instrumental variable test a la Rivers and Vuong (1988), and a discussion of our instruments and results, see
Mesnard (2004).
18
This is difficult to interpret, however, since several effects are captured by this variable.
132
ALICE MESNARD
TABLE 9. PROBIT
FOR PROBABILITY OF STARTING A BUSINESS AFTER RETURN
dF/dx
z-stat.
savings
savings squared
age at return
no education
short secondary school
long secondary school
married
number of dependents
born in Center East
born in Center West
born in Northern East
born in Northern West
born in South East
born in area of Tunis
0.0004**
-7.04e-08**
0.0022
0.0225
-0.070
-0.040
-0.11*
0.0093
0.1291*
-0.0049
0.0180
0.0317
-0.05
-0.0328
7.13
-4.40
1.40
0.62
-1.17
-0.97
-2.25
1.60
2.3
-0.10
0.27
0.56
-1.02
-0.47
Log likelihood
observed frequency
predicted frequency at mean var.
number of observations
-396.85272
0.206
0.179
887
Exogeneity test:
residuals of
• Savings
• Savings squared
Log likelihood :
coefficients:
z-stat :
0.005
-2.5e-06
-395.35165
0.96
-1.02
Notes: *significant at 5% level; ** significant at 1% level.
1% richest individuals dropped out of the sample.
dF/dx is equal to the infenitisimal change in each continuous independent variable.
For dummy variables it is equal to the discrete change in probability when the dummy variable
changes from 0 to 1.
coefficients associated to the residuals of the two instrumented regression (for savings and savings squared) imbedded into the main regression.
133
TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
CONCLUSION
Based on statistics from the Central bank of Tunisia and on a survey describing
Tunisian workers who have returned from migration, this paper shows that temporary migration has potentially important consequences for sending countries like
Tunisia, that are playing through the flows of physical capital linked to labour
migration. Even though we found very little evidence of human capital accumulation in Tunisia through temporary migration, as could be explained by the particularity of these migration flows responding mainly to labour shortages of unskilled
labour in receiving countries, and even though the effects from selective migration
are very difficult to assess given the limited data we have, the paper concludes that
temporary migration has contributed to economic development of Tunisia through at
least two channels.
On one hand, transfers sent by migrants to their origin country represent a sizeable
source of foreign currency and income for developing countries. This may be crucial for highly indebted countries and has often been recognised through policy
measures aimed at attracting remittances19. On the other hand, savings repatriated
upon return under different types of goods allow poor workers to overcome credit
constraints for investment into small projects.
19
For more details on these measures, see Mesnard (1999).
134
ALICE MESNARD
REFERENCES
Banerjee A.V. and A.F. Newman, 1993. “Occupational Choice and the Process of
Development”, Journal of Political Economy, 101, 274-98.
Beine M., F. Docquier and H. Rapoport, 2001. “Brain drain and economic growth:
theory and evidence”, Journal of Development Economics, 64(1), 275-89.
Blanchflower D.G. and A.J. Oswald, 1998. “What Makes an Entrepreneur? Evidence
on Inheritance and Capital Constraints”, Journal of Labor Economics 16(1), 26-60.
Borjas G.J., 1987. “Self-selection and the earnings of immigrants”, American
Economic Review, 77, 531-53.
Durand J., W. Kandel, E.A. Parrado and D.S. Massey, 1996. International migration
and development in Mexican communities, Demography, 33(2), 249-64.
Evans D. and B. Jovanovic, 1989. “An estimated model of entrepreneurial choice
under liquidity constraints”. Journal of Political Economy, 97, 808-27.
Evans D.S. and L.S. Leighton, 1989. “Some Empirical Aspects of Entrepreneurship”,
American Economic Review, 79, 519-35.
Galor O. and J. Zeira, 1993. “Income distribution and macroeconomics”, Review of
Economic Studies, 60, 35-62.
Griffin K., 1976. On the emigration of the peasantry, World Development, 4(5),
353-61.
Haque N.U. and S. Kim, 1995. “Human capital flight: impact of migration on income
and growth”IMF Staff Papers, 42(3), 577-607.
Holtz-Eakin D., D. Joulfaian and H.S. Rosen, 1994. “Entrepreneurial decisions and
liquidity constraints”, RAND Journal of Economics, 25, 334-47.
Ilahi N., 1999. “Return Migration and Occupational change”, Review of Development
Economics, 3, 170-86.
Magnac T. and J.M. Robin, 1996. “Occupational choice and liquidity constraints”,
Ricerche Economiche, 50, 105-133.
McCornick B. and J. Wahba, 2000. Overseas work experience, savings and entrepreneurship amongst return migrants to LDCs, Scottish Journal of Political Economy,
Special Conference Issue, 48, 164-78.
Mesnard A., 1999. “Migration internationale, accumulation d'épargne et retour des travailleurs” PhD. dissertation, Ecole des Hautes Etudes en Sciences Sociales, Paris, 380p.
Mesnard A., 2001. “Temporary migration and intergenerational mobility”, Louvain
Economic Review, 67, 59-88.
Mesnard A. and M. Ravallion, 2001. “Wealth distribution and self-employment in a
developing country”, CEPR Discussion Paper DP3026.
Mesnard A., 2004. Temporary migration and Capital Market Imperfections, Oxford
Economic Papers, 56, 1-21.
OECD, 1992. Secteur informel en Tunisie : cas reglementaire et pratique courante,
Documents techniques, 80.
Ramos F.A., 1991. “Out-migration and return migration of Puerto-Ricans” in Abowd
and Freeman Immigration, trade and the labor market University of Chicago Press.
Rempel H. and R. Lobdell, 1978. “The role of urban-rural remittances in rural development”, Journal of Development Studies, 14, 324-41.
135
TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
Rivers D. and Q.H. Vuong, 1988. “Limited information estimators and exogeneity tests
for simultaneous probit models”, Journal of Econometrics, 39, 347-86.
Stark O., 1978. “Economic-demographic interaction in agricultural development: the
case of rural-to-urban migration”, Rome: UN Food and Agriculture Organization.
Stark O., 1991. The migration of labor, Oxford and Cambridge, MA: Basil Blackwell.
Stark O., C. Helmenstein and A. Prskawetz, 1997. “A brain gain with a brain drain”,
Economics Letters, 55, 227-234.
Vidal J.P., 1998. “The effect of emigration on human capital formation”, Journal of
Population Economics, 11, 589-600.
Zaiem, 1993. “Rapport sur l'enquête OTTE”, mimeo, Office des Travailleurs Tunisiens
à l'Etranger, Tunis.
Woodruff C. and R. Zenteno, 2002. “Remittances and Microenterprises in Mexico”
UCSD working paper.
136
ALICE MESNARD
APPENDIX. REASONS FOR RETURNING
The main reason to return was given as family motives, reported by 22% of the surveyed
workers. Other frequently reported motive is the legal situation of the migrant abroad,
either because migrants were not able to normalise their legal situation or because their
tourist visa or job contract expired. Other frequently cited motives involve working conditions abroad (eg, the end of a job contract, unemployment problems, insufficient
income abroad), or related to working conditions in origin country (eg, realisation of a
project, job offer, end of a leave for absence, retirement). Interestingly very few respondents mention the special policy schemes aimed at encouraging return migration that
were offered after 1974 by host countries like France or Germany to migrants, conditionally on their returning to Tunisia (on these measures, see Mesnard, (1999)).
TABLE A.1. REASONS FOR RETURNING
sufficient amount of savings
end of job contract
unemployment
illegal situation
retirement
illness
insufficient income abroad
difficulties to transfer savings
end of a touristic period
racial discrimination
special policy schemes
realisation of a project
job offer in Tunisia
family reasons
homesickness
end of leave for absence
all
Arabic countries
Europ. countries
5.62
8.56
3.93
11.42
1.16
4.19
2.41
7.23
4.91
0.89
0.45
3.21
0.89
21.86
7.14
1.16
5.70
8.16
3.58
11.40
1.45
4.47
2.57
6.70
5.03
0.89
0.56
3.24
0.89
22.12
7.15
1.34
5.33
10.22
5.33
11.56
0
3.11
1.78
9.33
4.44
0.85
0
3.11
0.89
20.89
7.11
0.44
Reasons related to accumulation of savings also appear important since 5.62% of workers report to have returned to Tunisia once they had accumulated enough savings, and
7.23% of them because they had difficulties to transfer money through banks.
The motives of migrants coming back from European or Arabic countries are slightly
different in emphasis. Unemployment problems, non renewal of job contracts or difficulties to transfer money explain more frequently the decision to return from European
countries than from Arabic countries. On the other hand, migrants in Arabic countries
have more often returned because of insufficient income than migrants in European
countries. Indeed workers migrating to different destination countries correspond to different waves of migrants and different working conditions abroad.
137
TEMPORARY MIGRATION AND SELF-EMPLOYMENT : EVIDENCE FROM TUNISIA
TABLE A.2. SAMPLE CHARACTERISTICS
Characteristics
Migrants
mean
age at survey date
37.3
no education(%)
36
Primary school level(%)
48
short secondary school level(%) 4
long sec.sch. level or more(%) 12
Number of dependents
4.9
Married (%)
81
age at return
32.8
migrated to:
France (%)
16
Libya (%)
77
other Arab countries(%)
3
other European countr.(%)
4
duration since return
4.17
migration duration
4.1
self-employment(%)
26.3
born in area of Tunis(%)
5
born in Center East(%)
21
born in Center West(%)
24
born in Northern East(%)
6
born in Northern West(%)
14
born in South East(%)
20
born in South West(%)
10
accumulated savings
586*
income
5693
migrated before 1974(%)
20.8
1168
s.d.
10.2
3
Non-migrants 944
s.d.
35.9
12.8
32
41
7
20
3.6
3.2
59
9.7
3.37
4.7
1111
6908
23.8
9
20
19
8
15
19
10
510**
172
940
269
* For return migrants, savings are accumulated during migration and this variable measures the
stock of savings brought back at return.
** For non-migrants savings variable measures the stock of savings at the date of survey.
138
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRING 2004
IMMIGRATION AND AGING IN THE BELGIAN REGIONS*
MARC DEBUISSON (IWEPS, REGION WALLONNE), FREDERIC DOCQUIER
(CADRE, IWEPS, UNIVERSITY OF LILLE 2, IZA BONN), ABDUL NOURY
(ECARES AND DULBEA) AND MADELEINE NANTCHO (UNIVERSITY OF
LIEGE)
ABSTRACT:
In this note, we first depict the structure of the foreign population (When did they come? From where?
What about their skills?) and discuss its assimilation on the domestic labor market. Then we evaluate the
demand for skilled immigration in the Belgian regions raised by domestic population changes. We demonstrate that replacement immigration is a sustainable policy in Flanders but not in Wallonia and Brussels,
where it would jeopardize demographic stability. Using a projection methodology that takes into account
the changes in the demand and supply of labor, we then show that an additional flow ranging from 500 to
9,000 skilled immigrants would be necessary to stabilize the Flemish dependency ratio.
JEL CLASSIFICATION: F22, J11, J61, J62.
KEYWORDS: skilled migration, immigration policy, replacement, aging.
*
We thank Cecilly Defoort, Jean Houard and Nathalie Tousignant for their comments. The usual disclaimer applies.
139
IMMIGRATION AND AGING IN THE BELGIAN REGIONS
INTRODUCTION
Over the last years, economists and policymakers have discussed the opportunity to
define a new labor-market oriented policy of immigration. Such a policy would be especially useful in the face of massive demand and supply shocks on the labor market (such
as changes in the age structure of the population). Immigration increasingly appears as
a potential solution against aging.
In their report on replacement migration, the United Nations (2000) demonstrate that
keeping the dependency ratio constant over the period 2000-2050 requires multiplying
European annual immigration flows by 50 (by 15 in the United States). Here, by taking
into account both demographic and economic dimensions of the issue, we provide an
alternative methodology to evaluate immigration needs. Our methodology is applied to
the Belgian regions.
Compared to the United Nations (2000), the following elements are incorporated in our
analysis:
• first of all, analyzing replacement immigration requires considering the human capital characteristics of natives and immigrants. As shown in several studies1, the immigrants' level of education determines their contribution to the national economy. If non
selected immigration is obviously justified from humanitarian and political points of
view, we argue that replacement migration only makes sense if new immigrants produce more than they consume, i.e. are selected according to their skills;
• secondly, replacement immigration should not be seen as an exclusive way of balancing the adverse effects of aging on dependency. In addition to necessary social
reforms2 , we argue that a consistent measure of immigration needs should take into
account the potential rise in natives’ participation rates on the labor market (especially female’ participation rates) as well as the evolution of their employability;
• thirdly, dealing with the relative skills of immigrants requires a projection of the
demand for skilled and unskilled workers. In countries such as the USA or UK, the
demand for skilled labor has hugely increased over the last decades. A skill biased
technical change has pushed the return to education upwards. In European continental countries, the skill premium has remained constant or has slightly decreased. This
can be due to differences in labor market institutions or to differences in technical
biases themselves. It is relevant to evaluate immigration needs in regards of the potential changes in technology;
1
2
See Auerbach and Oreopoulos (2000), Bonin et alii (2000) or Storesletten (2000).
Which are not explicitly modeled here.
140
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
• finally, replacement immigration should not jeopardize the demographic stability of
receiving regions, i.e. it should not generate an explosive growth of working age population. Demographic constraints must be introduced.
The paper is organized as follows. Section 1 describes the features of the Belgian immigration with a particular emphasis on the regional characteristics of immigrants. Current
Belgian immigrants are less skilled and less “employable” than natives. Hence, a pure
expansion of current flows would not reduce the burden of aging. Section 2 presents the
flows of skilled immigrants required to stabilize the dependency ratio under several scenarios. We show that an additional flow ranging from 500 to 9,000 entrants would be sustainable. Finally, section 3 provides a discussion of the outstanding issues determining
the stakes for sending countries, for natives and for the immigrants themselves.
1. IMMIGRATION AND IMMIGRANTS IN THE BELGIAN REGIONS
Let us first mention how an immigrant is defined in our analysis. There are several ways
one can define an immigrant (place of birth, nationality or both). In the remainder of
this paper, we use nationality or citizenship, which is an often-used criterion in Europe
to define an immigrant. As a result, foreigners are considered as immigrants.
Since 1945, Belgium has been a country of immigration. In the late nineties, foreigners
represented about 9% of the total population. This section describes the evolution and
the structure of immigration flows as well as the way immigrants become integrated into
the regional labor markets.
1.1.THE SIZE OF BELGIAN IMMIGRATION
As depicted on Figure 1, the annual number of immigrants ranged from a maximum of
85,000 entries in the early sixties to a minimum of 20,000 entries in 1980. Over the 50
last years, the average immigration flow has amounted to 50,000 persons per year.
Given that Belgium is a small country with large borders proportionally to its size, most
of the foreigners come from bordering countries. Nationals from the other EU member
states are also largely represented3.
In the past, an important part of these flows were oriented toward the labor market.
After 1918, foreign manpower was recruited from Eastern Europe and Italy. These
immigrants worked in the building sector, in the textile industry and in the coal mining industry4. The 1930s economic depression and the resulting restrictions to foreign
immigration reduced the size of immigration between 1930 and 1939. After World War
3
On figure 1, it obviously appears that the dynamics of total immigration is ruled by the number of European immigrants. However, the proportion of Europeans in the total flow has declined from about 100% in the fifties to 70%
in the late nineties.
4
It was an economic immigration but also a political one, an antifascist one (Morelli, 1992).
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IMMIGRATION AND AGING IN THE BELGIAN REGIONS
II, immigration started again, but the flows were collectively ruled by international
agreements. The labor demand was strong in the coal industry and in the national
reconstruction. In 1948, annual flows reached a maximum of 84,000 people (mainly
Italians). In 1956, the disaster at “Bois du Casier” caused many deaths among Italian
miners. This dramatic event led to stop Italian immigration, as Italy asked for better
security conditions for its nationals. Consequently, agreements were signed with Spain
and Greece. Between 1958 and 1961, trade unions pleaded to reduce immigration
flows. However, new agreements were negotiated with Morocco and Turkey in 1964.
The newcomers mainly originated from rural regions with low skills (see Lewin,
1997).
Immigration flows were strongly linked to the economic context in Belgium.
Governments tend to rule much more severely immigration during depression periods.
This explains why immigration stopped in 1974. Due to the oil crisis, the foreign population structure progressively changed. Between 1974 and 1980, the number of newcomers declined. Family reunion became an important motive. Since the late 1980s,
the number of asylum seekers and refugees has increased.
FIGURE 1. BELGIAN IMMIGRATION FLOWS BY COUNTRY OF ORIGIN 1955-1995
90 000
80 000
70 000
60 000
50 000
40 000
30 000
20 000
10 000
0
1955
1959
1963
Total
1966
1969
Europe
1972
1975
Africa & Asia
1978
1981
1984
1987
1990
1993
America and Oceania
Source: INS.
Over the last two decades, the average flow has amounted to 50,000. Today, about 70
percent of the Belgian immigrants come from other European countries. The share of
North Americans reaches 8%. The share of immigrants coming from less developed
countries (mainly from North Africa) amounts to 22%. The Moroccan colony is largely
represented.
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MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
1.2. REGIONAL DISPARITIES
Between WW2 and the 1960s, the Walloon industrial basins (mining, iron and steel
industries) were privileged sites to host immigrants. During the years 1962-1966, economic prosperity increased the need for foreign workers in large urban areas (Brussels,
Antwerp, Ghent). Source countries were very heterogeneous5.
As the Walloon industrial basins drastically declined in the 1970s, immigrant flows
originated from Italy, Spain, Turkey and Maghreb were reoriented to large cities (especially Brussels).
As shown in Figure 2, Brussels accounts for about 35% of Belgian immigrants (whilst
its population share amounts to 12%). Wallonia and Flanders respectively draw 24 and
41% of the national flows. Due to history, network effects and language differences, the
origin of immigrants differs across regions. In Brussels, immigrants mainly come from
Portugal. In Flanders, they come from the Netherlands, United Kingdom, Turkey, and
the United States. In Wallonia, they come from Germany and Italy. Note that immigrants from France and Morocco are well represented in all Belgian regions.
FIGURE 2. IMMIGRATION FLOWS BY REGION OF DESTINATION 1994-2001
70 000
60 000
50 000
40 000
30 000
20 000
10 000
0
1994
1995
1996
1997
1998
1999
2000
2001
Belgium
Wallonia
Flanders
Brussels
Source: INS.
5
See Grimmeau (1984) and Martens (1976).
143
IMMIGRATION AND AGING IN THE BELGIAN REGIONS
1.3. THE SKILL STRUCTURE OF IMMIGRANTS
Since 1974, there has been five ways to enter the Belgian territory:
• the free mobility of people within the European Union generates an annual flow of
about 28,000 individuals;
• a few thousands of work permits are delivered to foreign workers. They are usually
delivered to skilled workers on a temporary basis: for instance, about 7,467 work permits were delivered in 2000 (including 4,606 renewals);
• foreign students are also allowed to immigrate (the Belgian law on students' immigration became more restrictive in the 1980s);
• each year, family reunification allows several thousands of immigrants to enter the
country;
• asylum seekers and refugees constitute a large group of immigrants: in 2001, more
than 24,000 requests were registered by the National authority.
As most European countries, Belgium has no economic policy of immigration6. Hence,
immigrants are usually less skilled than Belgian citizens. Two statistical sources can be
used to evaluate the relative skills of immigrants by region:
• the last available Belgian Population Census (BPC) gives interesting information
about the population structure in 1991. With these data, it became possible to analyze
the nationality at birth7;
• the annual Labor Force Survey (LFS) gives more recent information extrapolated on
the basis of a large sample of individuals.
Distinguishing three levels of education (more than secondary school, secondary school
and less than secondary school), Tables 1 and 2 give the relative skills of immigrants
from these two datasets.
Table 1 shows that, in 1991, immigrants were less skilled than natives. At the national
level, the proportion of low skilled nationals was 60%. These numbers can be considered
as pessimistic since all “unknown skills” are assimilated to a low level of education8.
The proportions of low skilled European and extra-European immigrants were respectively 70% and about 77% (for migration after 1981) (Service Public Fédéral Emploi,
2003). It is worth noticing that foreigners are always more skilled as they were born in
Belgium. There is no significant regional difference in aggregate numbers. However,
high skilled EU foreigners are less numerous in Wallonia than in the two other regions
whilst the number of non-EU foreigners is higher.
6
Except for a small proportion of work permits.
It should be noted that the nationality legislation was deeply modified in 1984 and 1991 (Debuisson, 1992).
8
See Docquier and Debuisson (2002) for a discussion of this assumption.
7
144
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
Based on the LFS, Table 2 depicts the situation almost ten years after. To obtain significant
information about immigrants, we aggregate 6 annual waves of data from 1996 to 2002.
Hence, our average LFS dataset broadly refers to the year 1999. Regarding nationals, one
obtains more optimistic results than with the BPS dataset. The discrepancy between BPS
and LFS can be explained (i) by more optimistic assumptions about the treatment of the “no
answers”, (ii) by differences in the survey questions and (iii) by high education investments
among young cohorts. The share of low skilled workers falls to 40% among citizens. Over
the period 1992-2002, there is a constant decrease of 2% per year in the share of unskilled.
TABLE 1. SKILL STRUCTURE OF POPULATION AGED 25-64 - CENSUS 1991
Belgium
Citizenship at birth
Nationals
European Union
non immigration
After 1980
1974-1980
Before 1974
Other countries
non immigration
After 1980
1974-1980
Before 1974
Total
Wallonia
Citizenship at birth
Nationals
European Union
non immigration
After 1980
1974-1980
Before 1974
Other countries
non immigration
After 1980
1974-1980
Before 1974
Total
Flanders
Citizenship at birth
Nationals
European Union
non immigration
After 1980
1974-1980
Before 1974
Other countries
non immigration
After 1980
1974-1980
Before 1974
Total
Brussels
Citizenship at birth
Nationals
European Union
non immigration
After 1980
1974-1980
Before 1974
Other countries
non immigration
After 1980
1974-1980
Before 1974
Total
In percent of the
Low
Medium
87.4
90.8
1.6
2.2
1.6
1.2
0.8
0.6
4.6
2.7
0.4
0.4
1.2
0.6
1.0
0.5
1.5
0.8
100
100
In percent of the
Low
Medium
81.4
84.5
3.5
5.2
2.0
1.6
1.2
1.0
8.3
5.0
0.7
0.9
0.9
0.6
0.7
0.4
1.3
0.9
100
100
In percent of the
Low
Medium
93.9
95.8
0.5
0.6
0.9
0.8
0.4
0.4
1.8
1.2
0.1
0.1
0.7
0.4
0.6
0.3
0.9
0.4
100
100
In percent of the
Low
Medium
68.2
75.8
1.8
3.2
3.9
3.0
1.5
1.3
8.2
6.1
0.6
1.0
4.9
3.1
4.3
2.3
6.6
4.2
100
100
population
High
94.0
1.4
0.9
0.4
1.4
0.3
0.6
0.4
0.7
100
population
High
91.6
3.0
0.8
0.4
2.0
0.6
0.6
0.4
0.7
100
population
High
97.0
0.4
0.7
0.3
0.7
0.1
0.3
0.2
0.3
100
population
High
84.3
2.4
2.4
0.8
3.0
0.8
2.0
1.7
2.6
100
Total
89.3
1.7
1.4
0.7
3.6
0.4
1.0
0.8
1.2
100
Total
83.7
3.7
1.7
1.0
6.6
0.7
0.8
0.6
1.1
100
Total
94.9
0.5
0.9
0.4
1.5
0.1
0.6
0.5
0.7
100
Total
72.8
2.1
3.4
1.4
6.8
0.7
4.0
3.4
5.3
100
In
Low
60.6
59.0
70.3
70.8
77.9
61.1
75.6
78.4
76.7
61.9
In
Low
61.8
59.3
74.3
73.6
80.0
61.8
72.7
74.0
73.3
62.6
In
Low
60.2
62.2
65.2
66.8
74.2
66.0
76.2
81.6
79.4
60.8
In
Low
58.7
52.0
71.3
70.8
76.0
53.1
77.0
78.2
77.0
61.5
percent of the
Medium
20.8
26.7
18.2
19.7
15.5
24.0
13.6
12.3
13.9
20.5
percent of the
Medium
20.4
27.8
18.3
20.1
15.1
24.8
15.1
14.7
16.1
20.8
percent of the
Medium
21.5
23.7
20.6
21.1
16.8
21.9
13.4
12.0
13.2
21.2
percent of the
Medium
17.3
24.7
14.3
16.4
14.9
23.0
12.7
11.3
12.9
16.0
group
High
18.6
14.3
11.5
9.5
6.6
15.0
10.8
9.2
9.4
17.7
group
High
17.8
12.9
7.4
6.3
4.9
13.3
12.2
11.4
10.6
16.6
group
High
18.3
14.1
14.2
12.0
8.9
12.0
10.4
6.4
7.4
17.9
group
High
24.0
23.4
14.4
12.8
9.1
23.8
10.2
10.5
10.1
22.5
Note: The percentage distributions were scaled on the 1991 “Census Monography on Education”
(Mainguet, 1998).
Source : Population Census, 1991, INS and Point d’appui démographie VUB.
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IMMIGRATION AND AGING IN THE BELGIAN REGIONS
TABLE 2. SKILL STRUCTURE OF THE POPULATION AGED 25-64 - LFS
(AVERAGE 1996-2002)
Belgium - All regions
Citizenship
National
European Union
including recent immigration (a)
Other
including recent immigration (a)
Total
Wallonia
Citizenship
Nationals
Other EU
Other
Total
Flanders
Citizenship
Nationals
Other EU
Other
Total
Brussels
Citizenship
Nationals
Other EU
Other
Total
In percent of the population
Low
Medium
High
87.7
92.7
93.5
7.6
5.7
5.0
0.8
1.0
1.8
4.7
1.6
1.4
1.1
0.6
0.8
100
100
100
In percent of the population
Low
Medium
High
84.0
89.5
94.4
13.5
9.4
4.5
2.6
1.1
1.1
100
100
100
In percent of the population
Low
Medium
High
94.0
96.2
96.0
3.0
2.9
3.2
3.1
0.9
0.8
100
100
100
In percent of the population
Low
Medium
High
60.2
76.5
79.5
15.7
13.3
15.1
24.1
10.1
5.5
100
100
100
Total
90.8
6.3
1.1
2.9
0.9
100
Total
88.3
10.0
1.8
100
Total
95.2
3.0
1.8
100
Total
71.1
14.9
14.1
100
In percent of the group
Low
Medium
High
40.6
32.3
27.1
50.5
28.5
21.0
29.6
28.3
42.1
68.7
18.1
13.2
54.1
22.9
23.0
In percent of the group
Low
Medium
High
42.1
31.1
26.8
59.8
28.8
11.4
64.5
20.0
15.5
In percent of the group
Low
Medium
High
40.6
33.4
26.0
40.3
32.0
27.7
71.8
16.4
11.8
In percent of the group
Low
Medium
High
33.7
27.9
38.5
42.0
23.2
34.8
68.0
18.7
13.4
Note: Recent immigrants = less than 11 years of residence.
Source: Labor Force Survey.
As in the 1991 BPC, foreigners are less skilled than nationals, especially immigrants
from non-European countries. In all regions, the skill of non-European foreigners is
lower than the skill of natives (the gap with nationals is very important in Flanders).
However, European foreigners are less skilled than natives in Wallonia. Just as in the
BPC, there is a difference in the skill composition of immigrants in Wallonia, compared
to the other regions.
Given the small number of observations, information about the skill structure of immigrants cannot be crossed with information about the year of entry at the regional level.
Nevertheless, it is possible to evaluate the structure of recent immigration flows at the
national level, which shows that European immigrants with less than 11 years of residence have a positive impact on the average level of education within the Nation. Recent
immigrants from the rest of the world are more educated than those of the previous
waves but less than nationals.
1.4. IMMIGRANTS AND THE LABOR MARKET
Applying econometric tools to LFS data allows us to illustrate how immigrants perform
and assimilate on the regional labor markets. Our analysis is based on the LSF dataset
146
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
over the period 1992-20019, which delivers information about individual labor market
status (inactive, unemployed or employed) and characteristics (such as education, sex,
age, region of residence, years of residence, country of birth and citizenship). Regarding
citizenship, we distinguish between nationals from EU and North America and nationals from the rest of the world. As a result, the variable “IMMIGRANT” is defined to be
someone who is not a national from Belgium, EU or North America.
To analyze discrimination and assimilation we estimate a very simple model specified
as follows:
y ijt = α + β ' xijt + γ ' z ijt + µ j + τ t + ε ijt
*
 y ijt = 1 if

 y ijt = 0
y * ijt > 0
otherwise
(1)
where yijt* is the underlying response variable measuring the employability of individual i in region j at period t; yijt is a dummy variable indicating whether the individual is
employed, xijt denotes personal characteristics, and ⑀ijt is the error term. Our main variable of interest is zijt, which indicates whether the individual is an immigrant. As is clear
from equation (1), the model contains time dummies (year dummies), which control for
the effect of common shocks that occur during a given period to a given region.
Assuming that the error term has a logit distribution and denoting pijt= Pr(yijt=1) the
probability that individual i in region j at period t is employed, we end up with the following equation to be estimated:
p ijt
log e (
) = α + β ' xijt + γ ' z ijt + µ j + τ t
1 − p ijt
(2)
We use an error component logit model10 to perform two tests. First we test whether
there is any evidence of discrimination against immigrants in Belgium. To that end, we
estimate our model with “IMMIGRANT” as the main variable of interest. If the coefficient associated to this variable is statistically significant then one can conclude
that there is some empirical evidence of discrimination against immigrant in Belgium.
Note however that our data do not allow us to discriminate between statistical discrimination and pure discrimination. Second we analyze the question of immigrant assimilation in Belgium. That is, we analyze the extent to which the probability that an immigrant is employed increases as a function of the number of years of residence in
9
10
Descriptive statistics are provided in Table A1 in the appendix.
To be precise, the procedure just described applies to panel data. Strictly speaking, this methodology cannot be
applied in our case because the data at hand are not a panel or even rotating panel. In particular, one cannot use
the standard panel data econometric tools to control for unobserved heterogeneity or measuring dynamics, which
is one of the main attractions of panel data. Instead of standard panel or rotating panel we have repeated crosssectional observations. Despite the different nature of these data, one can use the panel data framework in order
to control for the effect of location (i.e. region, country) and time (business cycle).
147
IMMIGRATION AND AGING IN THE BELGIAN REGIONS
Belgium. A closely related question is the extent to which immigrant assimilation is
affected by immigrant qualification. The results are reported in Table 3. The first three
columns use full sample and are devoted to analyze discrimination. The last three
columns use immigrant sample and focus on immigrant assimilation.
TABLE 3. RESULTS OF THE LOGIT REGRESSIONS
Variables
AGE
AGE Squared
SEX (Female)
YEARRES1 (1 to 4)
YEARRES2 (5 to 10)
YEARRES3 (11+)
Discrimination Analysis (full sample)
Wallonia
Flanders
Brussels
0.665
0.634
0.476
(8.75)**
(9.88)**
(6.01)**
-0.031
-0.034
-0.024
(5.45)**
(7.02)**
(4.11)**
-0.611
-0.69
-0.663
(8.17)**
(10.37)**
(8.64)**
-0.227
-0.377
-0.467
-1.58
(2.41)*
(3.37)**
-0.522
-0.436
-0.544
(3.97)**
(2.85)**
(4.11)**
-0.324
-0.364
-0.454
(4.28)**
(4.43)**
(5.14)**
YEARRES1* Educ H
YEARRES2 *Educ H
YEARRES3 *Educ H
EDUC M (Medium)
EDUC H (High)
IMMIGRANT
EU
NORTH AMERICA
CONSTANT
Year Dummies
Observations
Wald Chi2
Pseudo R-squared
0.617
(7.25)**
1.505
(16.19)**
-0.96
(8.58)**
0.773
(7.13)**
0.84
-1.79
-0.455
-1.68
Yes
12838
1007.81
0.11
0.65
(8.74)**
1.475
(17.40)**
-1.327
(11.70)**
1.048
(9.42)**
1.781
(2.43)*
-0.186
-0.83
Yes
15115
1348.92
0.12
0.614
(7.03)**
1.428
(15.04)**
-1.022
(10.49)**
1.036
(10.59)**
0.773
(1.96)*
1.183
(4.58)**
Yes
11148
1328.33
0.12
Assimilation Analysis (immigrant sample)
Wallonia
Flanders
Brussels
0.384
0.146
0.486
(2.26)*
-0.78
(3.25)**
-0.02
-0.013
-0.033
-1.51
-0.86
(2.69)**
-0.297
-0.559
-0.255
(2.04)*
(3.34)**
(2.08)*
0.263
-0.084
-0.033
-1.03
-0.31
-0.16
0.071
0.081
0.267
-0.3
-0.29
-1.34
0.1
0.337
0.218
-0.52
-1.47
-1.35
-0.988
-0.189
0.233
-1.89
-0.32
-0.5
-0.409
0.735
-0.537
-0.83
-1.19
-1.15
-0.265
-0.103
-0.036
-0.58
-0.17
-0.08
0.148
0.446
0.611
-0.97
(2.59)**
(4.51)**
1.165
0.704
1.004
(3.09)**
-1.62
(2.93)**
-0.337
-0.67
Yes
1591
68.07
0.04
0.383
-0.68
Yes
1296
56.02
0.05
-0.797
-1.78
Yes
2063
91.3
0.05
Dependent Variable is BEING EMPLOYED. YEARRES is Years of residence; EDUC is level of Education.
Robust z statistics in parentheses. * significant at 5%; ** significant at 1%
As far as discrimination is concerned, several important findings need to be emphasized. First, not surprisingly, the link between the probability of being employed and age
is non-linear.
Age and age squared are significant with respectively positive and negative signs, meaning that the probability of an individual’s employment increases with age but less and
less so when he or she gets older. Second, gender is significant with a negative effect, a
clear indication that being a woman means a lower probability of being employed.
Third, as expected, education variables are highly significant with positive signs.
148
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
Finally, and more importantly being an immigrant is significant and has a negative sign
capturing the fact that there is some evidence for discrimination against immigrants. As
already mentioned it is important to note that here the lack of relevant data prevents us
from being more precise on the nature of discrimination. In particular, we cannot discriminate between statistical and pure discriminations. The results also show that an EU
national or a national from North America has a higher probability of being employed.
That is, they face a positive discrimination. This finding is not surprising given that
these ‘immigrants’ are employed either by the EU institutions like the European
Commission or by multinational firms. Overall, the results are highly similar across all
regions of Belgium.
After concluding that some discrimination exists against immigrants in Belgium we
focus our attention on the important question of assimilation. To do that, we consider
the sample containing only immigrants and examine whether years of residence in
Belgium reduce the level of discrimination. The results regarding assimilation are
reported in the last third columns of Table 3. As far as age, sex and education is concerned, the results are essentially similar to those obtained with the full sample. The
most striking finding that emerges from the analysis is that there is no evidence for
assimilation. That is, the probability that an immigrant is employed does not increase
with the years of residence. This result remains true even when looking at the behavior
of high skilled immigrants. Controlling for the skill or education level, the years of residence do not have any explanatory power. Several explanations can be provided regarding this finding. First, the variables measuring the years of residence are potentially
endogenous. Second, other important relevant factors such as type of education are not
available in this database. As a result, care must be taken to interpret our econometric
analysis on immigrant discrimination and assimilation.
2. EVALUATING REPLACEMENT IMMIGRATION NEEDS
In this section, we evaluate the need for replacement immigration in the Belgian
regions. We define the maximal immigration stock resulting from a simple demographic constraint. Then we compute the number of new immigrants required to stabilize the
dependency ratio.
2.1. PROJECTION METHODOLOGY
The demographic constraint. Demographers usually emphasize the dangers of resorting to replacement immigration policies. The reason is simple: in many industrialized
countries, a policy of mass immigration is likely to generate a dynamically explosive
path of population size. Such an issue is discussed by Blanchet (2002), who distinguishes two opposite patterns of aging:
• under scenario A, changes in the number of retirees are small while, on the contrary,
the working age population falls. Aging is essentially due to the drop in fertility rates.
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IMMIGRATION AND AGING IN THE BELGIAN REGIONS
In that case, stabilizing the working age population through immigration does not
jeopardize the dynamics of the population;
• under scenario B, changes in the working age population are relatively small but, on
the contrary, the number of retirees strongly increases. Aging is essentially due to the
displacement of large baby boom cohorts in the age pyramid and to the rise in life
expectancy. In that case, a replacement immigration policy requires the working age
population to grow at the same pace as the old-age population. The size of immigration flows can be very important. Such a policy is likely to generate an unsustainable
growth of the population size: if the working age group must be doubled by 2030, it
will have to be quadruplicated by 2070.
As it will appear below, a replacement immigration policy is demographically sustainable in Flanders. On the contrary, resorting to mass immigration would jeopardize the
demographic stability in Brussels and in Wallonia11.
Aging and dependency. To capture the impact of aging on the economy, we use various
measures of the dependency ratio (i.e. the ratio of total population to the working aged).
We start from demographic forecasts and introduce economic features in the analysis.
At the numerator, individuals are weighted by consumption needs depending on their
age. At the denominator, individuals are weighted by their employability and by their
productivity at work. Hence, our analysis incorporates assumptions about the evolution
of participation rates, skill levels and unemployment rates. Basically, our dependency
ratio at time t can be written as
∑ ∑
=
∑ ∑ ∑
100
∆t
a =1
g = m, f
∑
100
a =1
g = m, f
s =l ,i , h
s = l ,i , h
N α
g ,s
a ,t
N ag,,ts c a
g ,s
a ,t
(1 − u
(3)
g ,s
t
s
t
)w
where
measures the number of individuals of age a, gender g (males or females),
skill level s (low skills, intermediate skills or high skills) at year t;
is their labor participation rate;
and
are the unemployment rate and the marginal productivity of
labor; ca is a parameter measuring the relative consumption needs at age a.
Given official population forecasts (INS, 2001), equation (3) contains the key trends
determining the burden of aging and the benefits of a selective immigration. These
trends concern participation rates, unemployment rates and the marginal productivity of
labor. In this paper, we use a simple mechanical approach based on a set of scenarios.
11
It is worth noticing that, in the rest of the world, scenario B is dominant. In large immigration countries such as
Australia, Canada and the US, the working age population keeps increasing between 2000 and 2050. Scenario A
applies in some European countries and in Japan.
150
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
- as for participation rates per gender and per age12, we assume that the Belgian
regions will catch up the EU-15 maximal rates between 2000 and 2005, in part (25,
50 or 75% of the gap) or in totality (full convergence). The convergence process is
linear;
- as for unemployment rates, we use the LFS data to compute the rates by gender and
skill levels for each region in 2000 as well as the minimal unemployment rates
obtained at the European level. Then we consider two alternative scenarios. The status
quo scenario considers that the regional unemployment rates remain constant over
time. The optimistic scenario considers a progressive convergence towards the minimal European rates at the horizon 2050. The convergence process is linear;
- as for the population structure per skill level, we use the LFS data to compute the share
of low, intermediate and high skilled individuals in each region by the year 2000.
Then, for age groups above 25 (those who have completed their education), we extrapolate these cohort shares on the basis of the observations for 2000. For future cohorts
of younger cohorts (aged 15-29), we use the average shares observed over the recent
period 1990-2000 and keep them constant over time. Since young cohorts are more
educated than older cohorts, our assumption induces a progressive rise in the educational attainment of the labor force;
- the marginal productivity of labor is projected using a production function that distinguishes low and high skilled workers (medium skilled and low skilled are aggregated)
and allows for skill biased technical change, Yt={[(1-t)Lt] + [tHt]}1/. In this equation, L and H are the stock of skilled and unskilled workers (determined by participation rates and unemployment rates), captures a skill biased technical change and determines the elasticity of substitution between low skilled and high skilled workers13.
The marginal productivity of workers is given by the partial derivatives. The wage
ratio is clearly depending on technical change and the stocks of skilled and unskilled
workers. In our simulation, we calibrate in such a way that the wage ratio corresponds to 1.5 in 2000. Then we consider two alternative scenarios. A scenario of constant technology keeps as constant. A scenario of skill bias considers an exogenous
impulse in raising the wage ratio by 25% between 2000 and 2050.
- in each scenario, the parameter of consumption need is taken from the US study of
Cutler et al. (1990), i.e. 0.7 for individuals aged 0-24, 1.0 for those aged 25-64 and 1.3
for those aged 65 and more.
How many skilled immigrants? Clearly, the impact of a new immigrant on the ratio
depends on his characteristics (in terms of gender, skill level and age), on the labor market situation (captured by the unemployment rate and the marginal productivity of
12
13
Data for 2000 are taken for the Labor Force Survey. See appendix.
According to the empirical literature, the elasticity of substitution lies between 1 and 2 (L and H are gross substitutes). We opt for 1.5.
151
IMMIGRATION AND AGING IN THE BELGIAN REGIONS
labor) and on the current state of dependency. We assume that new skilled immigrants
are not discriminated against. That is, we assume that on the job market they perform
exactly as well as natives. The impact of immigration on dependency is given by
∂∆ t
=
∂N ag,,ts
c a − α ag,,ts (1 − u tg , s )wtg , s ∆ t
∑ ∑
100
a =1
g =m, f
∑
(4)
N ag,,tsα ag,,ts (1 − u tg , s ) wtg , s
s = l ,i ,h
A new immigrant makes the ratio decreasing if and only if his own consumption/wage
ratio (ca / α ag,,ts (1-u tg ,s ) wtg ,s ) is lower than the aggregate dependency ratio ( ⌬t). For each
scenario, we estimate the number of skilled immigrants required to stabilize the dependency ratio. To simplify, we consider that new immigrants are high skilled males aged 35.
2.2. RESULTS
The simulated dependency ratios are given in Table 4. At the national level, the most
optimistic scenario is the one with full convergence of participation rates, decreasing
unemployment rate and no skill bias. In that case, the dependency ratio falls by 8%. In
the most pessimistic case (constant unemployment rate, slow convergence in participation rates and skill biased technical change), the rise in dependency reaches 20%.
TABLE 4. SIMULATED DEPENDENCY RATIO UNDER VARIOUS ASSUMPTIONS
Participation rates (25% convergence)
No skill bias
Constant unemployment rate
Decreasing unemployment rate
Skill bias
Constant unemployment rate
Decreasing unemployment rate
Participation rates (50% convergence)
No skill bias
Constant unemployment rate
Decreasing unemployment rate
Skill bias
Constant unemployment rate
Decreasing unemployment rate
Participation rates (75% convergence)
No skill bias
Constant unemployment rate
Decreasing unemployment rate
Skill bias
Constant unemployment rate
Decreasing unemployment rate
Participation rates (full convergence)
No skill bias
Constant unemployment rate
Decreasing unemployment rate
Skill bias
Constant unemployment rate
Decreasing unemployment rate
Source: Authors' calculations.
152
Wallonia
Flanders
Brussels
Belgium
2000 2030 2050 2000 2030 2050 2000 2030 2050 2000 2030 2050
1.000 1.073 1.097 1.000 1.147 1.183 1.000 0.982 1.007 1.000 1.117 1.148
1.000 0.994 0.970 1.000 1.129 1.153 1.000 0.897 0.871 1.000 1.090 1.102
1.000 1.090
1.000 1.012
Wallonia
2000 2030
1.000 1.038
1.000 0.961
1.141 1.000 1.165
1.017 1.000 1.148
Flanders
2050 2000 2030
1.039 1.000 1.113
0.918 1.000 1.096
1.231 1.000 1.016
1.201 1.000 0.931
Brussels
2050 2000 2030
1.129 1.000 0.955
1.100 1.000 0.872
1.083 1.000 1.142
0.944 1.000 1.115
Belgium
2050 2000 2030
0.963 1.000 1.083
0.832 1.000 1.057
1.207
1.161
1.000 1.055
1.000 0.979
Wallonia
2000 2030
1.000 0.938
1.000 0.868
1.080 1.000 1.131
0.962 1.000 1.114
Flanders
2050 2000 2030
0.976 1.000 1.016
0.862 1.000 0.999
1.173 1.000 0.989
1.145 1.000 0.905
Brussels
2050 2000 2030
1.066 1.000 0.880
1.039 1.000 0.804
1.034 1.000 1.107
0.902 1.000 1.081
Belgium
2050 2000 2030
0.917 1.000 0.985
0.793 1.000 0.960
1.147
1.104
1.000 0.953
1.000 0.884
Wallonia
2000 2030
1.000 0.953
1.000 0.882
1.014 1.000 1.031
0.902 1.000 1.015
Flanders
2050 2000 2030
0.908 1.000 1.025
0.801 1.000 1.009
1.107 1.000 0.910
1.080 1.000 0.834
Brussels
2050 2000 2030
0.993 1.000 0.896
0.968 1.000 0.818
0.985 1.000 1.006
0.859 1.000 0.982
Belgium
2050 2000 2030
0.869 1.000 0.998
0.751 1.000 0.973
1.081
1.040
2050
1.092
1.048
2050
1.030
0.988
2050
0.960
0.920
1.000 0.968 0.943 1.000 1.041 1.031 1.000 0.927 0.933 1.000 1.019 1.007
1.000 0.898 0.838 1.000 1.025 1.005 1.000 0.849 0.813 1.000 0.994 0.968
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
Changes in technology have a small impact on the results. Given our production function, technical changes exert an ambiguous effect on the aggregate productivity. Skill
biased technical changes boost (resp. reduce) economic performance when the share of
high skilled workers is high (resp. low). In all Belgian regions, a skill bias technical
progress increases economic dependency.
The evolution of the labor market (demand and supply) is a key determinant for the burden of aging. This is especially the case in Wallonia and Brussels, where unemployment
rates are high and participation rates are low.
TABLE 5. REPLACEMENT IMMIGRATION IN THE BELGIAN REGIONS
2000
Wallonia
Demographic constraint
Part. rate 25% - Constant u.r. - No bias
Effect of a decreasing u.r.
Effect of a skill bias
Part. rate 75% - Constant u.r. - No bias
Part. rate 100% - Decreasing u.r. - No bias
Flanders
Demographic constraint
Part. rate 25% - Constant u.r. - No bias
Effect of a decreasing u.r.
Effect of a skill bias
Part. rate 75% - Constant u.r. - No bias
Part. rate 100% - Decreasing u.r. - No bias
Brussels
Demographic constraint
Part. rate 25% - Constant u.r. - No bias
Effect of a decreasing u.r.
Effect of a skill bias
Part. rate 75% - Constant u.r. - No bias
Part. rate 100% - Decreasing u.r. - No bias
Belgium
Maximal immigration need
(Part. rate 25% - Const u.r. - Skill bias)
Minimal immigration need
(Part. rate 100% - Decr. u.r. - No bias)
2010
2020
2030
2040
2050
0
0
0
0
0
0
0
597
- 597
3 983
0
0
0
53 183
- 53 183
9 833
0
0
0
126 609
- 98 336
13 368
0
0
9 387
158 528
- 137 719
21 515
2 250
0
0
166 591
- 166 591
34 584
20 347
0
0
0
0
0
0
0
0
41 438
- 8 600
8 382
0
0
26 913
219 224
- 26 563
14 992
0
16 219
243 665
447 463
- 45 334
10 892
125 212
137 057
378 175
536 843
- 63 601
17 743
262 979
132 084
435 602
542 228
- 80 593
37 734
294 870
22 919
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
244
0
0
0
3 311
- 3 311
18 495
0
0
0
14 587
- 14 587
27 531
0
0
0
18 441
- 18 441
38 353
0
0
0
0
26 913
243 665
387 562
435 602
0
0
16 219
137 057
132 084
22 919
Source: Authors' calculations.
At the regional levels, it is worth noticing that the demographic constraint is strongly
binding in Wallonia and in Brussels. In these two regions, increasing immigration flows
would jeopardize demographic stability. Despite this constraint, the immigration need
would be low, ranging from zero to 160,000 new immigrants in Wallonia, ranging from
zero to 18,000 new immigrants in Brussels.
153
IMMIGRATION AND AGING IN THE BELGIAN REGIONS
The demand for skilled immigration is larger in Flanders, ranging from 23,000 to
542,000 individuals according to the set of assumptions. In annual flows, these numbers
correspond to the entry of 500 to 10,000 new skilled immigrants. The evolution of
unemployment and technology explains a small proportion of this range. Changes in
participation rates are very important.
The last part of the table gives the range of replacement (skilled) immigration needed
for Belgium as a whole. Taking into account the demographic constraint, the total number of new immigrants goes from 23,000 to 435,000 in 2050. Over 50 years, this roughly represents an additional flow of 500 to 9,000 selected immigrants per year. This number must be compared to the current unselected flow of 55,000 individuals, including
about 11,000 high skilled workers (see table 2). The additional flows would only concern the Flemish region where the current annual flows amount to 25,000 immigrants
(including about 6,000 skilled workers).
3. DISCUSSION
Immigration is usually seen as a partial solution to reduce the burden of aging. In this
note, we evaluate the demand for skilled immigration in the Belgian regions. We
develop a simple projection methodology that takes into account the dynamics of
population, the changes in the demand and supply of labor and the technological
progress.
We show that replacement immigration is a sustainable policy in Flanders, but not in
Wallonia and Brussels, where it would jeopardize demographic stability. Compared to
the official projections, an additional flow ranging from 500 to 9,000 skilled immigrants
would be necessary to stabilize the economic dependency ratio. Of course, such a
change in selected immigration would increase, through family reunification, the number of unselected candidates.
Despite the simplicity of our analysis, we would like to emphasize that replacement
immigration still raises many outstanding issues:
• how to reduce labor market discrimination against foreign workers? Table 3 reveals
that the probability to be employed is lower for immigrants than nationals, even after
controlling for individual characteristics. Resorting to immigration is sustainable if
assimilation and integration raise no problem;
• what would be the short-run costs of an increased immigration? Can immigration
reduce the wage of natives or, in the presence of labor market rigidities, can it increase
the equilibrium unemployment rate in the receiving region? There is a large literature
on this issue (see Borjas, 1995), with controversial results;
• have the European regions the capacity to select immigrants? This question is especially relevant given the perspectives of enlargement of the European Union to Eastern
154
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
countries with lower income per capita. The importance of welfare programs is also
likely to generate self-selection within migration networks;
• is there a sufficient supply of skilled labor at the world level? If large immigration
countries such as Canada, Australia and the United States resort to replacement immigration, a shortage of skilled workers is likely to be observed14;
• what would be the consequence for emigration countries? If several industrialized
countries resort to replacement migration, this will increase the brain drain flows from
the South to the North. This can be detrimental for international inequality (see
Commander et al. for a survey).
We argue that these questions should be clearly addressed before implementing any
replacement policy. International cooperation between sending and receiving countries
is also necessary to share the gain between the parties concerned.
14
A quick glance at population forecasts reveals that mass immigration would jeopardize the demographic stability
in these three immigration countries. Interestingly, replacement immigration seems mainly sustainable in some
European countries and Japan.
155
IMMIGRATION AND AGING IN THE BELGIAN REGIONS
REFERENCES
Auerbach A.J. and P. Oreopoulos, 2000. “The fiscal impact of US immigration: A
generational accounting perspective”, in: J. Poterba (ed.), Tax policy and the economy,
Vol. 14, MIT Press: Cambridge.
Blanchet D., 2002. “Immigration et avenir démographique”, in: Commissariat général
du Plan, Immigration, marché du travail, intégration, La Documentation française:
Paris.
Bonin H., B. Raffelhüschen and K.J. Walliser, 2000. “Can immigration alleviate the
demographic burden?”, FinanzArchiv, 57(1).
Borjas G.J., 1995. “The economic benefits from immigration”, Journal of Economic
Perspectives, 9(2), 3-22.
Debuisson M. and M. Poulain, 1992. “Des étrangers, des immigrés, combien sont-ils
en Belgique?”, Migrations et Espaces 2, Academia: Louvain-la-Neuve.
Docquier F. and M. Debuisson, 2002. Marché du travail et immigration sélective.
Bilan et perspectives en Belgique, Capital humain et marché du travail : perspectives
régionales et européennes, Commission 1 Discriminations et inadéquations de l’offre et
de la demande sur le marché du travail, p.103-126. (Quinzième Congrès des économistes belges de langue française, Namur)
Grimmeau J.P., 1984. “Soixante ans d'immigration étrangère en Belgique”, in L'Année
Sociale, 214-221.
Institut national de statistique (INS). Démographie mathématique. Perspectives de
population 2000-2050 par arrondissement. Bruxelles, Ministère des affaires
économiques, 2001. 358 p.
Martens A., 1976. Les immigrés. Flux et reflux d'une main-d'oeuvre d'appoint, Presse
universitaire de Louvain.
Morelli A., 1992. Histoire des étrangers et de l’immigration en Belgique, de la préhistoire à nos jours, EVO/CBAI
Service public fédéral Emploi, Travail et Concertation sociale, 2003. “L'immigration
en Belgique. Effectifs, mouvements et marché du travail”, Rapport 2001.
Mainguet C. and M. Demeuse, 1998. “Scolarisation, niveau d'instruction et insertion
professionnelle”, Monographie 9, INS.
Lewin R., 1997. Balises pour l’avant 1974, dans La Belgique et ses immigrés, Les
politiques manquées, De Boeck Université.
Storesletten K., 2000. “Sustaining fiscal policy through immigration”, Journal of
Political Economy, 108(2), 300-323.
United Nations, 2000. Replacement migration, UN report.
156
MARC DEBUISSON, FREDERIC DOCQUIER, ABDUL NOURY AND MADELEINE NANTCHO
APPENDIX. DATA FROM THE EUROPEAN LABOR FORCE SURVEY
TABLE A1. DESCRIPTIVE STATISTICS LFS (NUMBER OF OBS. = 39 295)
Variables
BEING EMPLOYED
AGE
AGE Squared
SEX (Female)
EDUC L (Low)
EDUC M (Medium)
EDUC H (High)
YEARRES1 (1 to 4)
YEARRES2 (5 to 10)
YEARRES3 (11+)
Belgian Nationals
EU Nationals
North American Nationals
IMMIGRANT
Est. Mean
0.9171
7.0726
60.7260
0.5111
0.4270
0.2396
0.1543
0.0094
0.0101
0.0601
0.9161
0.0506
0.0008
0.0312
Std. Err.
0.0016
0.0370
0.5642
0.0073
0.0068
0.0046
0.0033
0.0002
0.0002
0.0011
0.0016
0.0010
0.0000
0.0009
[95% Conf.interval]
0.9140
0.9202
7.0001
7.1450
59.6200
61.8320
0.4968
0.5255
0.4136
0.4403
0.2306
0.2485
0.1478
0.1608
0.0090
0.0098
0.0096
0.0105
0.0580
0.0622
0.9130
0.9192
0.0487
0.0526
0.0007
0.0009
0.0294
0.0330
Source: Authors' calculations.
TABLE A2. PARTICIPATION RATES AND UNEMPLOYMENT RATES IN THE BELGIAN REGIONS
Participation rate profile per gender and per region (in %)
Males
Wallonia
Flanders
Brussels
Min EU
Mean EU
Max EU
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
Females
9.6%
58.3%
92.0%
93.6%
93.9%
91.5%
89.8%
78.0%
49.6%
18.4%
Wallonia
11.6%
66.0%
94.7%
96.3%
95.9%
94.4%
92.1%
83.2%
53.5%
17.2%
Flanders
7.7%
49.0%
84.9%
91.8%
91.7%
91.6%
87.6%
81.2%
57.8%
26.5%
Brussels
9.7%
60.8%
81.4%
91.2%
91.8%
91.2%
88.2%
80.2%
51.8%
11.1%
Min EU
31.0%
71.2%
90.0%
94.8%
95.1%
94.4%
92.5%
86.8%
68.2%
34.4%
Mean EU
65.3%
84.2%
93.7%
97.2%
97.3%
96.8%
94.7%
90.7%
84.5%
56.4%
Max EU
5.0%
50.0%
79.9%
76.8%
73.6%
70.5%
61.3%
46.0%
26.1%
6.1%
8.0%
57.8%
86.6%
82.9%
79.2%
72.5%
61.7%
43.0%
21.7%
5.0%
4.4%
42.2%
76.0%
76.0%
74.2%
73.2%
68.4%
60.4%
38.7%
9.7%
6.4%
48.8%
61.1%
62.9%
60.3%
56.9%
49.0%
38.0%
20.2%
5.5%
27.2%
62.6%
76.2%
74.4%
73.7%
73.1%
68.5%
58.9%
41.2%
17.8%
63.1%
77.0%
82.8%
84.1%
87.0%
89.1%
89.1%
87.1%
78.4%
48.9%
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
Unemployment rate per gender, per skill level and per region (in %)
Males
Wallonia
Flanders
Brussels
Min EU
Mean EU
Max EU
Low skilled
Medium skilled
High skilled
Females
22.0%
9.9%
4.0%
Wallonia
7.7%
4.4%
2.9%
Flanders
26.8%
17.6%
9.0%
Brussels
4.7%
2.4%
2.1%
Min EU
10.2%
7.0%
4.0%
Mean EU
19.5%
13.4%
9.2%
Max EU
Low skilled
Medium skilled
High skilled
39.2%
21.2%
6.1%
15.8%
8.1%
3.5%
36.3%
20.0%
7.5%
6.8%
4.2%
2.4%
13.1%
10.5%
5.8%
27.2%
26.3%
18.9%
Source: Labor Force Survey - Authors' calculations.
157
BRUSSELS ECONOMIC REVIEW - CAHIERS ECONOMIQUES DE BRUXELLES
VOL. 47 - N°1 SPRINGER 2004
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE :
THE ROLE OF MNES IN A SMALL OPEN ECONOMY*
MICHELE CINCERA
(UNIVERSITE LIBRE DE BRUXELLES, DULBEA-CERT AND CEPR**)
ABSTRACT:
Based on European and US patent statistics, this paper is an empirical analysis of R&D activities carried
out by foreign MNEs in Belgium over the last two decades. The paper investigates the role of demand-pull
and technology-push determinants of the MNE’s decision to delocalise its R&D in a host country as well
as the impact of these activities on any brain drain of Belgian R&D personnel. The results suggest that
MNEs invest in R&D in Belgium mainly in order to gain access to the local science base. The presence of
these companies positively affects the demand for highly skilled workers and hence reduces the importance of brain drain.
JEL CLASSIFICATION: F23, O31, O32, O34.
KEYWORDS: Brain drain, R&D, US and EPO patents, MNEs, Belgian economy.
* The author is grateful to the guest editors of this special issue as well as to Lydia Greunz for comments and helpful discussions on earlier drafts. Helpful suggestions were also received from seminar participants at INNO-tec,
University of Munich and the AEA Conference on “Innovation and Intellectual Property: Economic and
Managerial Perspectives”, Singapore July 15-16, 2004. This Paper is produced as part of a CEPR research network on ‘Product Markets, Financial Markets and the Pace of Innovation in Europe’, funded by the European
Commission under the Research Training Network Programme (Contract No: HPRN-CT-2000-00061). The
usual disclaim applies.
** DULBEA CP140, 50 av. F.D. Roosevelt, B-1050 Brussels. Email: [email protected]
159
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
INTRODUCTION
Over the recent years, European policy makers have been more and more concerned
about emigration flows for qualified scientists beyond Europe's borders. This socalled scientific ‘brain drain’ is on the rise and could represent a threat to Europe's
knowledge-based economy. A recent report of the European Commission (2003) gives
evidence that the brain drain of people born in the EU is increasing. For instance,
about 75% of EU-born US doctorate recipients who graduated between 1991 and
2000 had no specific plans to return to the EU, and more and more are choosing to
stay in the US. The most important reasons keeping EU-born scientists and engineers
abroad relate to the quality of work. Better prospects and projects, and easier access
to leading technologies were most often cited as reasons behind plans to work abroad.
Another factor for explaining emigration flows of highly skilled workers is that production factors used in the production process, which include besides traditional
inputs, human and knowledge capital, are increasingly mobile across national borders. These factors play an important role in economic growth and international competition for these inputs has increased their cross-border mobility. It is therefore
important to have a better understanding of the main determinants that affect the
direction and the magnitude of these flows of inputs as well as their economic impact
for both the origin and destination countries. In the economic literature on multinational enterprises (MNEs), forces such as scale economies, trade and transaction
costs, as well as factor abundance are often mentioned to explain the location and
investment decisions of workers, firms and in particular MNEs.
The purpose of this paper is to shed some light on one aspect of this international
mobility of factors by examining the interactions between the emigration of highly
skilled workers and the presence of subsidiaries of foreign MNEs in a small open
economy like Belgium. Most empirical evidence indicates that inward Foreign Direct
Investment (FDI) in R&D has a positive impact on the demand of highly skilled workers in the host country. As a result, high levels of inward FDI can be expected to
diminish the importance of brain drain, i.e. the net emigration rate of highly educated people. In that case, we can talk about a reduced brain drain. Furthermore, MNEs’
investment decisions bring to the host economy new qualified personnel from the
headquarters. In that case we can talk about a ‘brain gain’. Finally, ‘brain exchange’
between MNEs affiliates and local firms can arise through a variety of direct and indirect channels such as for instance knowledge spillovers, patent licensing, formal R&D
collaborative agreements or informal contacts between scientists and engineers and
training of the R&D personnel hired in the host country. A second objective of the
paper is to assess the main determinants, i.e. market driven and technology-push factors, that affect the delocalisation of MNEs’ R&D activities in a host economy. On the
one hand, the core activity of MNEs’ foreign subsidiaries may consist in adapting
products and processes developed in the first place at the headquarters to the need of
local markets. On the other hand, a well trained and educated workforce may not only
retain domestic firms but also attract foreign MNEs, which in turn invest in physical
capital, R&D and training activities. These questions are investigated by means of
160
MICHELE CINCERA
descriptive statistics and indicators based on patent statistics from the two main patent
offices in the world, the European Patent Office (EPO) and its US homologue
(USPTO).
The plan of the paper is as follows. Section 1 reviews the main impacts of MNEs’ R&D
activities in host countries as well as the main determinants that affect their investment
and location decisions. Section 2 presents the data set and derives the main hypothesis
of the paper. The main empirical findings are reported in Section 3. Some concluding
remarks and policy implications are discussed in the last section.
1. R&D ACTIVITIES OF MNES
FDI in the area of R&D is an increasing phenomenon that has already been subjected to
various research. As far as Belgium is concerned, MNEs largely dominate the Belgian
innovation system and a first question that is worth examining is what are the impacts of
this high internationalisation of Science and Technology activities for the local economy.
1.1. IMPACTS OF MNES R&D ACTIVITIES
In a survey, Blomström and Kokko (1998) examine the effects of knowledge spillovers
generated by the R&D activities of MNEs’ subsidiaries. From the host country’s perspective, these externalities not only influence the R&D of domestic firms operating in
the same MNE’s industry but also the R&D of firms located in other industry sectors.
According to the studies surveyed, these effects have in general a positive impact on
domestic R&D. However, they systematically vary across countries and industries and
increase with the local capability and the level of competition1. On the other hand the
effects of MNEs’ R&D activities on the home country are more difficult to identify. As
far as the Belgian economy is concerned, there have been only a few studies examining
the impact of international spillovers in the local economy. Veugelers and Vanden Houte
(1990), in an analysis based on Belgian R&D firms, find that the higher the presence of
multinationals in an industry, the weaker is the innovative efforts of domestic firms in
the same industry. Cincera (2003) reports a similar result though the variable of interest
if not the level of R&D effort but the output of this activity as measured by the number
of patent applications. Fecher (1990) estimates a positive impact of domestic R&D
spillovers on Belgian firms’ productivity performance, while no effect of international
spillovers is found. More recently, Veugelers and Cassiman (1999), find that MNEs are
more likely to transfer technology to the Belgian economy. However the main conclusion of the study is that it is not so much the international character of the firm, but
rather its access to the international technology market that is important for generating
external knowledge transfers to the local economy.
1
As emphasised by Jaffe (1986: p. 984), “From a purely technological point of view, R&D spillovers constitute an
unambiguous positive externality. Unfortunately, we can only observe various economic manifestations of the
firm’s R&D success. For this reason, the positive technologically externality is potentially confounded with a negative effect of other’s research due to competition”.
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BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
MNEs activities can also affect the labour market in host countries, in particular the
demand for and the supply of highly skilled workers (Slaughter, 2002). According to the
author, on the demand side, inward FDI stimulates the demand for more skilled workers in host countries through several channels. Demand for highly skilled workers may
increase when (direct) technology transfer from the MNE to subsidiaries take place. But
even more indirect mechanisms such as knowledge spillovers, market driven technology flows or investment in capital related to technology innovations may increase the
demand for highly skilled workers. On the supply side, MNEs can facilitate investments
in human capital via short-term firm level activities such as training or via long-term
country level activities that collectively contribute to the overall macro environment in
which fiscal policy can support education policy.
1.2. DETERMINANTS OF MNES R&D ACTIVITIES
As regards the degree of internationalisation of R&D, technology production has usually been centralised in the host country of MNEs. The reduction of the costs related to
communications and control, economies of scale in R&D and a better coordination
between central and peripheral research labs are often mentioned in the literature to
explain this situation (Terpstra, 1985)2. However, during the past decade, the involvement of MNEs in overseas R&D has increased significantly. Companies all over the
world are investing more and more in overseas R&D as a tool to increase their competitive advantages and to exploit their resources in order to create higher quality products3.
MNEs have accelerated the pace of their direct investments in overseas R&D, and have
established or acquired multiple R&D laboratories abroad and are increasingly integrating these laboratories into global R&D networks4.
According to Granstrand et al. (1992), the reasons for the ongoing process of increased
decentralisation and internationalisation of R&D activities can be explained by three
main categories of factors: demand-side, supply-side and environmental or institutional
related factors. The demand-side factors include a greater adaptation of products and
technologies to the needs of local markets, a higher proximity to customers, an increase
of competitiveness through the transfer of technology and the pressures of subsidiaries
to enhance their status within a corporation. Among the main supply-side factors, the
monitoring of technology developed abroad and the hiring of a foreign and barely
mobile highly skilled labour can be mentioned. Finally, the environmental factors
include the legislation on intellectual property, the provision of R&D incentives by the
2
As pointed out by Cantwell and Santagelo (1999), non-codified technological activities that necessitate highly
tacit capabilities will in general require a higher proximity.
3
Angel and Savage (1996) and Belderbos (2001) among others, analyse the determinants of the localisation of
Japanese R&D labs abroad; Cantwell and Harding (1998) measure the R&D internationalisation of German firms;
Dunning and Narula (1995) and Florida (1997) examine the R&D activities of foreign firms in the US and Pearce
and Papanastasiou (1999) in the UK.
4
Research joint ventures, firm’s acquisitions and the establishment of greenfield units are the three main ways to
access a foreign market.
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MICHELE CINCERA
domestic government, such as tax advantages and R&D subsidies, and governmental
pressures to improve the subsidiary’s capabilities beyond the simple assembly of proven
products to innovative activities.
Belderbos (2001) identifies two different motives for overseas R&D activities. The first
motive, which consists in the exploitation of the firm’s technology abroad, means that
companies adapt their products and processes to suit the local market and manufacturing processes and to fulfil local standards or manufacturing conditions. The second
motive is the sourcing of foreign technology, which explains the founding of basic R&D
for the world market. In this case, firms attempt to gain access to specific expertise in
the local science base and hire foreign skilled engineers and researchers5. New established subsidiaries generally focus on the design and the development of products to
meet local markets needs in exploiting the mother company’s existing technologies,
while R&D activities of acquired subsidiaries are more concerned with applied research
and scanning of local technologies.
2. DATA AND HYPOTHESES
Among the main indicators of Science and Technology activities available to economists, patent statistics have probably been the most extensively used6. However, like
other technological indicators, patent statistics have their own weaknesses. The same
weight given to patents by simply counting them is an important drawback of this indicator. In fact, the pure technical content as well as the intrinsic economic value of a
patent may vary widely among patents. Moreover, not all inventions are patented, nor
all are patentable, and other existing methods in appropriating the outcomes of R&D
activities may be preferred7. The propensity to patent may change substantially over
time, across countries and among technological sectors. For example, it is generally
recognised that the propensity to patent is important in sectors such as machinery or
chemicals but very weak in aerospace and in software since in the latter industries,
inventions can be more easily imitated
2.1. DATA
The European Patent Office (EPO) and its US homologue (USPTO) are the main
sources of information in this study. All patents with at least one Belgian inventor have
been extracted from the ESPACE-BULLETIN database for European patents and from
5
The notions of Home Base Augmenting (HBA) and Home Base Exploiting (HBE) are often used to characterise
these motives. For Kuemmerle (1999), HBA sites are more likely to be located near universities or public research
and technology organisations. HBA units have increasingly been used as part of the MNE’s strategy to build up
and exploit S&T know-how located beyond the boundaries of the group while the activities of HBE sites are more
aimed at transferring the knowledge developed within the group.
6
For the relevance of patent statistics as an indicator of Science and Technology activities, see for instance Bound
et al. (1984), Basberg (1987), Glisman and Horn (1988), Griliches (1990) or Archibuggi and Pianta (1992).
7
Industrial secrecy or lead time are two well-known examples.
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BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
the dataset published by Hall et al. (2001) on the NBER website for US patents8. Table
1 lists the main variables available for each patent document which are subsequently
used in the descriptive analysis.
TABLE 1. LIST OF VARIABLES FOR PATENT DATA
Variables
EPO
USPTO
Application year
Name of the applicant
Country of residence of the applicant
Applicant is part of a foreign group
Name of the inventor(s)
Country of residence of the inventor(s)
Technological sector
Number of claims
Number of citations received
Share of self-citations made
with respect to total number of citations
x
x
x
x
x
x
x
x
x
USPC
x
x
x
x
x
IPC
Notes: IPC = International Patents Classification; USPC = US Patent Classification.
A main difference between these two databases is that European patents refer to patent
applications while for the US, the patents are the ones that are granted9. Another difference is that information on patent citations is only used for US patents. The year in
which the patent has been applied rather than granted is considered for both data
sources. According to Jaffe (1986) and Tong and Frame (1994), patents classified by
date of application are preferable because they reflect the moment when a firm realises
an innovation and because of the existence of long lags between the filing of a patent
application and a patent grant10.
Three categories of patent applicants can be distinguished according to the criterion
of whether the patent owner is a Belgian firm, a Belgian subsidiary of a foreign
MNE or a foreign company11. The latter category represents patents involving at least
one inventor residing in Belgium but which were applied by non Belgian firms. This
can happen when the output of the R&D performed by the subsidiary is directly
patented by the multinational in its home country. Several factors can explain this
8
www.nber.org/patents. See also http://www.bl.uk/services/information/patents/spec.html#des for more information on the contents of a patent specification.
9
The share of patents granted as a percentage of filed applications was 67% for European patents and 68% for US
ones over the period 1995-1999 (Quillen and Webster, 2001).
10
On average, according to the EPO, it takes just over three years between the filing of the patent application and
the patent grant.
11
Information gathered by the Belgian central balance sheet office contains the composition of the shareholders.
When more than 50% of shareholders are from abroad, the firm is considered as a subsidiary of a foreign group.
164
MICHELE CINCERA
strategy. First, the IP department of a large firm with important patenting activities
is generally located at the headquarters of the MNE and not in its foreign subsidiaries. Second, contrary to other countries like the US or the UK (Bertin and
Wyatt, 1988), the Belgian patent law does not request a first filing in Belgium if an
invention has been generated on the domestic territory. Third, the geographic distance between the MNE’s home base and the host country can be another reason
explaining a lower patenting propensity. Maskus (1998) for instance, finds that the
number of patents filed by US subsidiaries in host countries positively depends on
the strength of intellectual property rights’ protection of the latter as well as on the
geographic distance to the US.
2.2. HYPOTHESES
The objectives of the paper are twofold. First, it aims at investigating the main determinants of the delocalisation of MNEs’ R&D investments. Second, it seeks to assess the
impact of MNEs’ foreign subsidiaries R&D activities on the local labour market for
highly skilled workers. To that end six hypotheses are formulated.
H1: Home-Base Augmenting (HBA) R&D activities are more important in technological sectors in which Belgium holds scientific comparative advantages;
H2: Patents resulting from HBA activities have a higher technological and economic
value;
H3: Patents resulting from Home-Base Exploiting (HBE) R&D activities have a lower
technological and economic value;
H4: Brain drain is negatively correlated with the importance of MNEs’ R&D activities
in the local innovation system;
H5: MNEs’ R&D delocalisation increases the demand for local researchers (brain gain);
H6: MNEs’ R&D delocalisation stimulates the exchange of ideas and knowledge
between local and foreign researchers and inventors (brain exchange).
Hypotheses 1-3 are concerned with the first objective, hypotheses 4-6 with the second.
As regards Hypothesis 1, if the main reasons for MNEs to delocalise are the access to
the local science base, and to benefit from the availability of a highly educated labour
force in order to augment its own knowledge base, then we can expect a positive correlation between the scientific fields where the host economy holds scientific relative
comparative advantages and R&D (and as result patents) activities carried out by the
MNEs subsidiaries in the host country. In order to test this hypothesis, the Scientific
Revealed Comparative Advantage (SRCA) index has been constructed on the basis of
the number of scientific publications contained in the ISI-web of science database. A
second indicator based on citations has been considered as well. The number of citations
per scientific publication can be used as a proxy for its quality and importance.
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BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
Therefore, if scientific publications in a given scientific field are more cited on average
in a country or a region as compared to a reference group, the relative strength of the
region’s scientific base can be expected to be high.
As previously discussed, MNEs will invest in HBA R&D activities in order to increase
the group’s knowledge base as a result of potential spillovers arising from local productive R&D organisations such as universities, publicly funded research institutes and innovative competitors, or to make effective use of the general strong local technological and
research infrastructures. On the other hand, MNEs will engage in HBE R&D activities
abroad to further exploit their own research capabilities in a foreign environment. These
activities typically concern the development and adaptation of existing technologies to
the local market conditions such as consumer tastes, environmental legislation or standards. Given the different nature of these two types of research activities, the technological and economic value of HBA R&D output as measured by patenting can be expected to be higher as compared to HBE ones (Hypotheses 2 and 3). Therefore the value of
patents related to HBA R&D should be higher when compared to the one generated by
HBE R&D. Several indicators have been suggested in the literature to assess the value of
a patent12. For instance, the claims provide a definition of what the patent protects. The
scope of protection will be higher, the higher the number of claims and several studies
have found a significant correlation between the number of claims and the patent value
(Lanjouw and Schankerman, 1999). As for scientific publications, the number of citations by subsequent patents is another well known indicator for assessing the value of
patent (Hall et al., 2000)13. Citations that come from patents assigned to a same firm or
MNE refer to previous patented inventions of that firm. These so-called self-citations are
therefore more likely to be linked with home-based exploiting R&D activities aimed at
improving and adapting existing protected inventions.
As far as the impact of MNEs’ R&D activities on the labour market in the host country
is concerned, three effects are investigated. The first effect refers to the idea that the
higher the presence of foreign R&D MNEs in a host country the less important is the
brain drain or the emigration of highly skilled workers from that country. In order to test
this assumption (Hypothesis 4), the degree of internationalisation of R&D activities, as
measured by the share of patents with at least one Belgian inventor and applied by
Belgian subsidiaries of foreign MNEs and foreign firms in the host country’s total count
of patents is compared to the rate of emigration of highly educated persons. Hypothesis
5 examines whether FDI in R&D are associated with a ‘brain gain’, i.e. an increase of
the demand for local researchers by the foreign MNEs. This hypothesis can be tested by
comparing the number of new inventors in patent documents applied by foreign subsidiaries and domestic firms. Finally, Hypothesis 6 tests whether the MNE’s R&D delo-
12
See Harhoff et al. (2003) for a recent review of studies on various indicators used to estimate the economic value
of patents.
13
The authors find a positive correlation between the firm market value and the stock of citation-weighted patents.
166
MICHELE CINCERA
calisation stimulates the exchange of ideas and knowledge between local and foreign
researchers and inventors. This ‘brain exchange’ can be assessed by identifying the
inventors’ residence country documented in co-invented patents.
3. EMPIRICAL FINDINGS
3.1. THE HIGH CONCENTRATION OF THE BELGIAN TECHNOLOGICAL BASE
A major feature of the Belgian technological landscape is the high concentration of
innovation activities among a few large firms. Figure 1 sheds some light on the patenting activities of the top 50 Belgian firms over the last two decades. As can be observed,
this activity is quite concentrated. Indeed, in terms of European patents, the two firms
with the highest number of patent applications hold 15.6% and 6.4% respectively of the
total number of patents applied for by Belgian applicants between 1980 and 2000. In
terms of US patents, these shares are even higher (24.4% and 10.3% respectively). The
cumulated share of US patents of the top 50 Belgian firms is about 78% against 61%
for European patents suggesting that mainly the largest firms have patenting activities
outside the European market.
FIGURE 1. CUMULATED DISTRIBUTION OF THE NUMBER OF PATENT APPLICATIONS
OF THE TOP 50 BELGIAN FIRMS (EPO AND USPTO, 1980-2000)
90
cumulated share (%)
80
EPO
USPTO
70
60
50
40
30
20
10
0
1
2
3
4
5
6-10
11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50
number of fims
Sources: EPO and USPTO databases; own calculations.
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BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
TABLE 2. THE TOP 20 BELGIAN FIRMS IN TERMS OF EUROPEAN AND US PATENT
APPLICATIONS, 1980-2000
Rank
EPO
C%
USPTO
C%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Agfa-Gevaert
Solvay
Janssen Pharmaceutica
Fina Research
Bekaert
Alcatel/Bell Telephone
IMEC
Ford New Holland
Picanol
Raychem
Smithkline Biologicals
Centre de Recherches Metallurgiques
Innogenetics
Heraeus Electro-Nite International
ACEC
Esselte
UCB
Sofitech
Xeikon
Michel Van de Wiele
15.6
22.0
25.4
27.7
29.8
31.6
33.4
35.2
37.0
38.6
40.0
41.3
42.3
43.3
44.2
45.1
45.9
46.7
47.5
48.2
Agfa-Gevaert
Solvay
Janssen Pharmaceutica
Bekaert
Fina Research
Picanol
Glaverbel
Raychem
Staar
Centre de Recherches Metallurgiques
UCB
IMEC
Plant Genetic Systems
Michel Van de Wiele
Dow Corning
Esselte
Metallurgie Hoboken-Overpelt
Fabrique National Herstal
Texaco Belgium
Innogenetics
24.4
34.7
42.2
44.9
47.6
50.1
52.4
54.6
56.4
58.0
59.7
60.9
61.9
62.9
63.8
64.7
65.6
66.5
67.2
67.9
Note: C% = cumulative share; the companies in italics are in only one of the top 20 rankings.
Sources: EPO and USPTO databases; own calculations.
Table 2 gives the list of the 20 largest companies in terms of patents. As can be seen,
three companies (Agfa-Gevaert, Solvay and Janssen Pharmaceutica) concentrate
25.4% and 42.2% of the patent applications at the EPO and the USPTO respectively. Globally, Belgian patent activity is highly dependent on a few companies.
Another specificity of Belgian patenting activities is that a significant number of
these companies are subsidiaries of foreign MNEs. This is particularly the case for
Agfa-Gevaert, Janssen Pharmaceutica, and Alcatel-Bell, which account for more
than 20% of all Belgian applications at the EPO.
3.2. THE HIGH INTERNATIONALISATION OF THE BELGIAN TECHNOLOGICAL BASE
The share of foreign companies and subsidiaries of foreign MNEs in national innovative activities as measured by patents with at least one Belgian inventor represents
more than 80% of the total number of patents at the end of the nineties. This share
is by far the largest among the industrialised countries (Patel and Pavitt, 1991) and,
as can been seen in Figure 2, it has steadily increased over the past two decades. In
the eighties, the share was about 60%, which suggests that since a long time there
have been strong linkages between MNEs and the Belgian science and technology
base. Because of the relative small size of the country and the ensuing need for a
168
MICHELE CINCERA
high degree of specialisation, the internationalisation of the Belgian technology
base is indisputable. Another feature that emerges from Figure 2 is the higher importance of foreign companies as compared to Belgian subsidiaries of foreign MNEs in
terms of patent applications. The share of the former represents about 70% of the
total number of patents applied by these two categories of applicants. This indicates
that patents are mostly applied in the headquarters of the local subsidiaries’ mother
companies.
Figure 3 shows the geographic origin of foreign companies and subsidiaries of foreign MNEs that applied for patents involving at least one Belgian inventor over the
period 1983-1999. As a whole, for both European and American patents, two countries namely Germany and the US, largely dominate the picture. Belgium’s main
trade partners and neighbours, France, The Netherlands and the United Kingdom,
also appear to be important. All in all, these five countries represent 87.0% for
European patents and 92.8% for US patents of the total number of patents with
Belgian inventors applied for by foreign applicants (Belgian subsidiaries of foreign
MNEs and foreign firms).
On the basis of the technological class of each patent, it is possible to examine the
main technological fields in which foreign applicants are most present, as well as
their relative importance as compared to the Belgian applicants14. The main technological fields in which foreign applicants are the most active are reported in Tables
3, 4 and 515. In terms of European patents (Table 3), chemistry (42.8%) is by far the
most important technological class in terms of patents applied for by foreign companies. Electrical materials and equipment and technologies related to material processing in textiles and paper (6.4% each) are the other major technological fields. In
terms of US patents, subsidiaries of foreign companies (Table 4) and foreign companies (Table 5) appear to be again specialised in the chemical and pharmaceutical
sectors (54.2%).
14
Unfortunately, technological classes according to which European and US patents are classified are not directly
comparable. European patents are classified according to the International Patent Classification. US patents are
classified according to IPC and according to the US patent classification (USPC). Only the latter is available in
the database of Hall et al.
15
Full results are reported in Table A1 and A2 in the appendix.
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BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
FIGURE 2. PATENTS WITH BELGIAN INVENTORS, SHARE OF FOREIGN
APPLICANTS, 1983-1999
100,0
EPO-FOR
USPTO-FOR
USPTO-FOR+SUBS
90,0
80,0
70,0
Share in %
60,0
50,0
40,0
30,0
20,0
10,0
0,0
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Note: EPO-FOR and USPTO-FOR refer to foreign applicants and USPTO-FOR+SUBS includes Belgian subsidiaries of foreign MNEs in addition to foreign applicants.
Sources: EPO and Hall et al. (2001) databases; own calculations.
FIGURE 3. PATENTS WITH BELGIAN INVENTORS, ORIGIN OF FOREIGN
APPLICANTS, 1983-1999
EPO-FOR
USPTO-SUBS
USPTO-FOR
others
GB
NL
FR
US
DE
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
Share in %
Note: EPO-FOR and USPTO-FOR refer to foreign applicants and USPTO-SUBS to Belgian subsidiaries of
foreign MNEs.
Sources: EPO and Hall et al. (2001) databases; own calculations.
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MICHELE CINCERA
TABLE 3. PATENTS WITH BELGIAN INVENTORS BY TECHNOLOGY CLASS,
EPO APPLICATIONS BY FOREIGN COMPANIES, 1983-1999
Technology sector
% tot col
% tot row
Chemical and petrol industry, basic materials chemistry
Macromolecular chemistry, polymers
Organic fine chemistry
Electrical machinery and apparatus, electrical energy
Materials processing, textiles, paper
Handling, printing
Telecommunications
Biotechnology
Pharmaceuticals, cosmetics
Materials, metallurgy
14.9
13.3
6.7
6.4
6.4
5.8
5.5
4.3
3.6
3.5
74.0
62.2
45.0
55.4
34.3
33.5
56.9
43.5
44.7
36.8
Total
100.0
40.0
Notes: % tot col = % of patents by technological class with respect to total number of patents;
% tot row = % of patents applied by foreign firms in a given technological class with respect
to total number of patents applied in that class.
Sources: EPO database; own calculations.
TABLE 4. PATENTS WITH BELGIAN INVENTORS BY TECHNOLOGY CLASS, USPTO
APPLICATIONS BY BELGIAN SUBSIDIARIES OF FOREIGN MNES, 1983-1999
Technology sector
% tot col
% tot row
19 Miscellaneous-chemical
31 Drugs
69 Miscellaneous-Others
14 Organic Compounds
54 Optics
44 Nuclear & X-rays
51 Materials Processing. & Handling
15 Resins
21 Communications
23 Computer Peripherals
40.0
14.2
8.6
6.2
4.9
3.7
3.1
3.0
2.3
2.2
36.0
42.6
24.2
24.5
60.3
49.7
11.6
7.0
15.5
79.2
Total
100.0
21.2
Notes: % tot col = % of patents by technological class with respect to total number of patents;
% tot row = % of patents applied by MNEs’ subsidiaries in a given technological class
with respect to total number of patents applied in that class.
Sources: Hall et al. (2001) database; own calculations.
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BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
TABLE 5. PATENTS WITH BELGIAN INVENTORS BY TECHNOLOGY CLASS, USPTO
APPLICATIONS BY FOREIGN COMPANIES, 1983-1999
Technology sector
% tot col
% tot row
19 Miscellaneous-chemical
15 Resins
51 Materials Processing & Handling
69 Miscellaneous-Others
21 Communications
31 Drugs
14 Organic Compounds
41 Electrical Devices
33 Biotechnology
61 Agriculture, Husbandry, Food
21.1
12.5
5.5
5.4
5.1
4.9
4.1
3.4
3.3
3.2
40.8
62.3
44.7
32.4
72.8
31.8
34.4
75.0
54.2
68.0
Total
100.0
45.5
Notes: % tot col = % of patents by technological class with respect to total number of patents;
% tot row = % of patents applied by foreign firms in a given technological class
with respect to total number of patents applied in that class.
Sources: Hall et al. (2001) database; own calculations.
3.3. MARKET DRIVEN VS. TECHNOLOGY-PUSH FACTORS
The high dependence of the Belgian innovation system with respect to foreign MNEs
could be an important reason for its lower propensity to patent16. Subsidiaries can be
specialised in the adaptation to the Belgian market of products and services developed
and patented in the first place in the research labs of the multinational. These subsidiaries could also be involved in HBA research activities, the local availability of a
highly qualified workforce and an appealing knowledge base being the main reasons for
their presence in the foreign country. In the first case, one can expect a lower propensity to patent for a given amount of R&D since the original invention is already protected. Then, in both cases the output of R&D performed by the subsidiary can be directly
patented by the multinational in its home country and not in Belgium. Finally, the geographic distance between the MNE’s home base and the host country can be another reason explaining a lower patenting propensity.
16
As shown in Capron and Cincera (2000), the R&D productivity index as measured by the ratio of patents on
R&D expenditures was 95 for Belgium in 1995 against 100 for the EU average.
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MICHELE CINCERA
Table 6 analyses the SRCA index of Belgium as regards scientific publications across
scientific fields:


 n ij 
SRCA ij = 

 n ij 
 i

∑






∑n
∑n
j
i, j



ij 


ij
where nij is the number of publications of the jth country in the ith scientific field.
Three reference groups are considered: the world, the OECD and the EU-15. Table 6
also reports for each scientific field the difference of the average number of citations
to scientific papers between Belgium and the three reference groups. With respect to
the OECD reference group, Belgium appears to hold strong comparative advantages
in scientific fields closely related to agriculture (agricultural sciences, plant and animal science), bio-chemistry (immunology, microbiology, pharmacology and toxicology) and clinical medicine. Though, there is no direct correspondence between the
patent technological classification and the one for scientific publications, the scientific areas where Belgium appears to be better positioned could explain the relative
high importance of (both EPO an USPTO) patents applied by foreign subsidiaries and
foreign firms in related technological areas such as drugs, organic fine chemistry or
biotechnology. The main reasons for the delocalisation of R&D activities in that case
may be the benefits associated in accessing the local scientific base and know-how
available in these technological fields. However, this hypothesis does not appear to
hold for the patents applied in electrical devices, material processing and handling,
communications and computers, as Belgium’s scientific position in material and computer sciences, mathematics and engineering appears to be relatively less favourable.
However, for the last three scientific fields, the average number of citations per publication is significantly higher in Belgium as compared to the OECD reference group.
An alternative way to examine this question is to look at the relative value of patents
applied for by foreign subsidiaries and foreign firms as compared to domestic ones.
Patents associated with research activities aimed at increasing the knowledge base of the
foreign group can be expected to be of higher value as compared to the ones related to
the development and adaptation of existing technologies to the needs of the local economy. As previously discussed, patents characterised by an above than average number of
claims, a high frequency of citations received and a low frequency of self-citations, can
be expected to be of higher value. Therefore, these patents should more reflect research
activities aimed at increasing the knowledge base of the mother company. Table 7 summarises these three indicators for the US patents with at least one Belgian inventor
applied for by Belgian firms, foreign subsidiaries and foreign firms. With regards to the
average number of self-citations, we observe that the patents of foreign firms and subsidiaries have systematically more self-citations. This can be explained by the fact
that the average size of the patent’s portfolio of the foreign companies is much more
173
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
important as compared to the domestic firms17. As a result, the probability of being selfcited is much higher. With respect to the domestic firms, this indicator has however a
much higher value for the foreign patents assigned to organic compounds, drugs and
biotechnology. Conversely, the value of this indicator is relatively lower for patents in
electrical devices and material processing and handling. According to the average number of claims and the average number of citations received, foreign firms and subsidiaries appear to better perform in four technological sectors, namely chemicals, communications, electrical devices and optics. Except for chemicals, the number of selfcitations is also relatively lower. Consequently, the patents assigned to these technological classes should have a higher economic value and as such may reflect the outcomes
of R&D activities of the HBA type. On the other hand, patents assigned to organic compounds and biotechnology have on average a lower number of claims and are more selfcited. Therefore, these patents can be expected to have a lower value and may be more
related to R&D activities aimed at adapting or improving existing inventions carried out
in the mother company’s research labs. For the other technology classes, it is more difficult to identify the type of R&D carried out by the foreign firms and subsidiaries as
no clear-cut patterns emerge from the values taken by the three indicators.
On the whole, the indicators reported in Table 7 give a somewhat different picture than
the conclusions based on the scientific comparative advantages of Belgium. Patents
related to biotechnology, organic compounds and fine chemistry have a relative lower
technical and economic value but corresponds to scientific fields where Belgium is
comparatively better positioned, i.e. the importance of scientific activities in terms of
publications is relatively more important as compared to the OECD average. Foreign
firms could therefore be interested in investing in HBE R&D activities to benefit from
the availability of a highly qualified local workforce. At the other end, patents classified
in electrical devices, communications and computers appear to have a relative higher
economic value. While Belgium does not hold particular scientific comparative advantages in the corresponding scientific fields, their performance in terms of citations is
well above the average score observed at the OECD level. Therefore, the local expertise and scientific excellence could be one of the main driving force explaining the
MNEs’ decision to invest in R&D in the foreign economy.
17
The average total number of patents (irrespective of the country of residence of the inventor) applied (at the
USPTO) by Belgian firms is 14.6 against 1459.1 for foreign companies and subsidiaries (with at least one patent
involving at least one Belgian inventor).
174
MICHELE CINCERA
TABLE 6. SCIENTIFIC REVEALED COMPARATIVE ADVANTAGES BASED
ON SCIENTIFIC PUBLICATIONS AND CITATIONS PER PAPER (1993-2003)a
SRCA
Agricultural Sciences
Biology & Biochemistry
Chemistry
Clinical Medicine
Computer Science
Economics & Business
Engineering
Environment/Ecology
Geosciences
Immunology
Materials Science
Mathematics
Microbiology
Molecular Biology & Genetics
Multidisciplinary
Neuroscience & Behavior
Pharmacology & Toxicology
Physics
Plant & Animal Science
Psychiatry/Psychology
Social Sciences, General
Space Science
Citations per paper
Worldb
OECDc
UE15
Worldb
OECDc
UE15
1.02
1.08
0.93
1.24
0.98
1.07
0.89
0.99
0.62
1.22
0.77
0.95
1.41
1.02
0.43
0.83
1.25
0.92
1.24
0.70
0.44
0.76
1.10
1.01
1.08
1.12
0.97
0.95
0.97
0.97
0.63
1.07
0.92
1.03
1.35
0.91
0.64
0.74
1.22
1.05
1.27
0.60
0.39
0.74
1.03
1.07
0.96
1.08
0.97
1.22
1.03
1.01
0.62
1.09
0.88
0.96
1.24
0.99
0.78
0.79
1.20
0.93
1.29
0.83
0.64
0.64
64
76
58
50
43
41
74
22
19
53
36
63
69
18
-27
33
39
-4
43
29
-2
7
8
5
-4
15
13
20
23
-6
-2
21
-6
28
27
-8
-14
-1
-2
-9
-2
3
-5
-22
9
14
1
16
20
31
25
-4
1
30
0
31
30
-3
-3
3
3
-6
-1
2
-3
-17
Notes: a) difference of average number of citations to scientific papers between Belgium and the
three reference groups; b) 152 countries; c) Australia, Austria, Belgium, Canada, Denmark,
Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, Luxembourg, Netherlands,
Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, USA.
Source: ISI web of science, own calculations.
175
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
TABLE 7. PATENTS WITH BELGIAN INVENTORS : AVERAGE NUMBER OF CLAIMS,
AVERAGE NUMBER OF CITATIONS RECEIVED AND NUMBER OF SELF-CITATIONS
CLAIMS
14
15
19
21
23
31
33
41
44
51
54
61
69
SELFCIT
Diff
-1.6
0.2
1.5
1.2
0.1
1.2
-6.3
0.8
-0.8
-0.3
2.0
-0.7
-1.0
1
2.1
4.4
3.5
2.7
3.2
3.9
1.7
1.7
2.3
4.1
2.4
3.5
3.0
2
2.7
3.5
5.7
5.0
5.8
3.5
1.8
3.2
4.4
4.3
4.7
3.4
4.6
Diff
0.5
-0.9
2.2
2.4
2.5
-0.4
0.2
1.5
2.1
0.2
2.3
-0.1
1.6
1
0.08
0.09
0.13
0.01
0.06
0.10
0.02
0.05
0.06
0.06
0.06
0.05
0.08
11.9 12.6 0.7
3.5
4.3
0.8
0.08 0.18 0.10
1
Organic Compounds
9.7
Resins
13.4
Miscellaneous-chemical
11.3
Communications
12.4
Computer Peripherals
11.2
Drugs
11.3
Biotechnology
19.7
Electrical Devices
10.8
Nuclear & X-rays
12.7
Materials Processing. & Handling 12.3
Optics
13.3
Agriculture, Husbandry, Food
13.4
Miscellaneous-Others
13.5
Total
CITREC
2
8.1
13.6
12.8
13.6
11.3
12.5
13.4
11.6
11.9
12.0
15.3
12.7
12.5
2
0.26
0.18
0.24
0.09
0.14
0.40
0.28
0.08
0.11
0.09
0.13
0.12
0.14
Diff
0.17
0.09
0.11
0.08
0.08
0.30
0.26
0.03
0.06
0.03
0.07
0.07
0.06
Notes: claims = average number of claims; citrec = average number of citations received; selfcit =
average number of citations made; 1 = domestic applicants; 2 = patent applied for by
foreign subsidiaries and firms; diff. = difference between 1 and 2.
Sources: Hall et al. (2001) database; own calculations.
3.4. MNES R&D ACTIVITIES AND BRAIN DRAIN
Another main objective of this paper is to shed some light on the importance of MNEs
R&D activities and the emigration of highly qualified workforce. As previously discussed, the higher the presence of foreign R&D subsidiaries in a host country, the higher the demand for domestic researchers and therefore the lower the importance of emigration or brain drain. The recent dataset constructed by Docquier and Marfouk (2004)
gathers information regarding immigration and emigration rates of highly educated
workers for about 150 countries18. This harmonised data set is based on country population censuses for two periods: 1990 and 2000. Table 8 indicates that Belgium is one
of the most internationalised countries in the world in terms of patents with domestic
inventors applied by foreign companies. Only two countries, Luxembourg and Portugal
exhibit higher scores. However, the market shares of these countries in terms of patenting activities are marginal. For both periods, the emigration rate in Belgium is about half
the performance obtained at the EU level (59.1 and 67.4 in 1990 and 2000 respectively), while the degree of internationalisation as measured by the presence of foreign
firms in patenting activities is about three times larger in Belgium as compared to the
EU (351.9 and 280.7 for the periods 1987-89 and 1997-99 respectively).
18
The emigration rates is defined as the emigration stock by educational attainment as a proportion of the labour
force born in the sending country.
176
MICHELE CINCERA
TABLE 8. EMIGRATION RATE OF POPULATION WITH TERTIARY EDUCTION
(1990 AND 2000) AND EPO PATENTS (1987-89/1997-99) WITH DOMESTIC
INVENTORS APPLIED BY FOREIGN APPLICANTS; EU-15=100
Emigration rate – tertiary education
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United Kingdom
UE-15
1990
159.5
59.1
65.3
61.2
45.5
143.7
159.2
320.8
88.8
99.2
87.0
148.3
31.2
44.6
142.8
100.0
2000
125.6
67.4
77.3
89.6
45.0
110.5
167.7
404.7
87.1
94.7
94.2
181.9
30.2
50.4
173.5
100.0
Share of foreign applicants
1987-1989
215.4
351.9
185.8
81.8
86.7
60.1
316.5
280.2
75.1
448.1
120.9
472.8
179.2
111.9
166.3
100.0
1997-1999
191.4
280.7
124.0
52.9
96.4
62.3
165.3
219.6
90.3
393.2
104.3
316.9
168.2
93.4
194.6
100.0
Sources: Docquier and Marfouk (2004) and EPO database, own calculations.
Table 9 reports the results of a fixed effects panel data regression based on the relationship between emigration rates and the importance of foreign companies in national
R&D activities as measured by EPO patent applications19. The negative coefficient associated with the importance of foreign R&D activities in the host country is statistically
significant at the 10% level. This finding suggests that higher degrees of R&D internationalisation are associated with lower rates of emigration of highly educated workers
and as a result the importance of brain drain is smaller.
As discussed in Section 2, the presence of foreign MNEs in the host country positively
affects the labour market by increasing the demand for local researchers. Figure 4 shows
the number of new Belgian inventors in all domestic and foreign patents for the period
1983-199920. It follows that for the foreign subsidiaries and firms, this number is of the
same order of magnitude as for Belgian companies. In other words, if the foreign firms
would not have invested in Belgium, the number of new inventors would have been half
of the current number. It can also be noted, that the share of Belgian new inventors in
foreign applications has grown more rapidly compared to the share in domestic ones.
19
20
The Hausman test statistic leads one to reject the random-effect model.
By ‘new’ inventors, we mean inventors that appear for the first time in the patent document. They are identified
on the basis of their last and first names and city of residence.
177
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
The term Belgian inventor refers to the country of residence of the inventor and not to
its citizenship. It is unfortunately not possible to identify the nationality of these Belgian
inventors, but it can be assumed that a non-negligible share of them are researchers of
the MNEs’ mother company that moved to Belgium when the subsidiary was established. Therefore, this additional availability of ‘imported’ human-capital produces a
‘brain gain’ for the host country.
TABLE 9. RELATIONSHIP BETWEEN EMIGRATION RATE OF PEOPLE WITH TERTIARY
EDUCTION AND INTERNATIONALISATION OF R&D ACTIVITIES (SHARE OF
FOREIGN APPLICANTS IN PATENTS WITH AT LEAST ONE DOMESTIC INVENTOR)
Estimated coefficient
Constant
% of foreign firms
in domestic patents
# of obs.
F-test
Hausman test
R2
150.21 (20.41)
-0.1840 (0.1057)
30
33.67 [0.0000]
4.89 [0.0271]
0.0723
Notes: standard error in brackets; P-value in square brackets; F-test for fixed effects
(H0: ␣1 =…= ␣15 = 0); Hausman test (H0: ␤random effects - ␤fixed effects ~ 0)
FIGURE 4. NUMBER OF ‘DIFFERENT’ INVENTORS IN US PATENTS APPLIED BY BELGIAN
AND FOREIGN FIRMS (1983-1999)
300
DOM
FOR
250
200
150
100
50
0
1983
1984
1985
1986
1987 1988
1989
1990
1991
1992
1993
Notes: DOM = domestic applications; FOR = foreign applications.
Sources: Hall et al. (2001) database; own calculations.
178
1994
1995
1996
1997
1998
1999
MICHELE CINCERA
TABLE 10. SHARE OF CO-INVENTORS BY COUNTRY OF RESIDENCE AND BY TYPE
OF APPLICANTS (BELGIAN FIRM, FOREIGN SUBSIDIARY AND FOREIGN FIRM),
USPTO, 1983-1999
Belgium
USA
Germany
France
The Netherlands
United Kingdom
Belgian firms
Belgian subsidiaries
of foreign firms
Foreign firms
94.0
0.8
1.9
1.2
0.7
0.2
91.5
1.9
0.6
2.1
0.3
1.0
62.1
13.7
8.3
3.8
4.2
2.6
Sources: Hall et al. (2001) database; own calculations.
CONCLUSION
Based on European and US patent statistics, this paper attempts to identify the main determinants explaining the decision of MNEs to delocalise their R&D in a small open economy. The impact of these activities on the local labour market for highly skilled workers
is examined as well. Regarding the first question, the scientific fields where Belgium
holds comparative advantages with respect to the OECD, i.e. agriculture, bio-chemistry
and clinical medicine, appear to be positively correlated with the technological classes in
which the number of patents applied for by foreign subsidiaries and firms are relatively
the most important. It could therefore be concluded that the main motive for R&D MNEs
to invest in Belgium is to gain access to specific knowledge resources which are abundant
in the local economy. The indicators based on the patent scope, the number of received
citations and the number of self-citations reveal a relatively low value of the patents
applied by the foreign subsidiaries and assigned to these technological classes, which suggests that the main objective of the MNEs’ R&D units operating in these sectors may be
the transfer and adaptation of existing knowledge to the host country. At the other end, the
sourcing of foreign technologies and competencies within the local S&T base appear to
be the main driving force of foreign firms and subsidiaries’ R&D activities (as measured
by patents) in electrical devices, communications and computers sectors. In terms of comparative advantages, Belgium is not particularly well positioned in the scientific fields
corresponding to these technological sectors. Yet, the importance and quality of the output of these scientific fields as measured by citations is relatively higher as compared to
the OECD reference group. Furthermore, the patents assigned to these technological
classes and applied by the foreign firms and MNEs’ subsidiaries appear to have a relative
higher economic value. As regards the effects of MNEs on the demand for local R&D personnel, the results suggest a reduced brain drain (negative correlation between the rate of
emigration of highly educated people and the level of internationalisation of R&D activities), a positive ‘brain gain’ (higher number of new inventors in patents applied by foreign
subsidiaries and MNEs as compared to domestic firms) and an important ‘brain
exchange’ (higher number of foreign inventors in co-invented patents applied by foreign
subsidiaries and firms) in the host country.
179
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
The results of this study lead to several important policy implications although one has
to be cautious in drawing any firm conclusions at this stage of the research.
Firstly, MNE’s R&D activities abroad indisputably generate positive spillovers in the
host country through a positive demand of highly qualified people in the host country.
As a result, a strengthening of policies designed to attract FDI in research and innovation activities is highly desirable. Among these policies, we can mention financial
incentives such as R&D tax concession and subsidies, the improvement of the local
infrastructure and quality of the workforce or measures directed at decreasing the
importance of administrative burdens and easing the starting of new businesses.
Secondly, S&T collaborations are another important source of spillovers brought by foreign R&D subsidiaries in the local economy. Such formal and informal agreements
between scientists from different companies and research organisations represent an efficient mean by which partners can exchange ideas, acquire new technological capabilities
and improve their innovative performances. Technology policies aimed at promoting collaborative agreements should therefore be encouraged and further strengthened.
Thirdly, the development by multinationals of external networks of relationships with local
counterparts can also be a source of knowledge spillovers from the subsidiary to the parent
company, foreign affiliates gaining access to external knowledge sources and application
abilities in the host country. This ‘repatriation’ of local research results and the exploitation
of their commercial outcomes in the MNE’s home country may represent a serious loss of
income from the point of view of the host country. It is therefore important to correctly
assess the trade-off between the gains of FDI-induced knowledge spillovers and the benefits of research activities that spill out outside the domestic borders. With that respect, policies aimed at better internalising the fruits of foreign affiliates R&D and at anchoring their
economic exploitation in the domestic economy deserve a particular attention.
Given these preliminary results, further analysis and data collections would be helpful
in order to better identify and support the policy implications implied by the high degree
of internationalisation of R&D investment in Belgium. Among the main questions to be
addressed in future research, we can mention:
What forces determine the location decisions of MNEs R&D activities? What are the
benefits of MNEs’ R&D activities in the host/home countries? What are the reasons of
R&D clusters in economic hubs (role of public research organisations and universities
as key drivers)? What kind of cost-effective policy instruments can be implemented to
attract foreign and to retain domestic MNEs R&D activities (R&D direct and indirect
support, education policies)? What policies are likely to attract and retain highly skilled
workers (language training, citizenship policies)?
As far as the last question is concerned, two recent initiatives at the EU level are worth
being mentioned (European Commission 2003): the launch of the development of the
“European Researcher’s Charter” and the outline of a “Code of conduct for the recruitment of researchers”. The first initiative consists of a framework for the career management of human R&D resources, based on voluntary regulation and the second is
based on best practises to improve recruitment methods.
180
MICHELE CINCERA
REFERENCES
Angel D.P. and L.A. Savage, 1996. “Global localization? Japanese research and development laboratories in the USA”, Environment and Planning, 28(5), 819-833.
Arrow K., 1962. “Economic welfare and the allocation of resources for invention”, in
R. Nelson (ed.), The Rate and Direction of Inventive Activity, Princeton, 609-626.
Belderbos R., 2001. “Overseas innovations by Japanese firms: An analysis of patent
and subsidiary data”, Research Policy, 30(2), 313-332.
Bertin G. and S. Wyatt, 1988. Multinationals and Industrial Property: The Control of
the World’s Technology, Hemel Hempstead, UK: Harvester Wheatsheaf.
Blomström M. and A. Kokko, 1998. “Multinational corporations and spillovers”,
Journal of Economic Surveys, 12(2), 247-277.
Bound J., C. Cummins, Z. Griliches, B.H. Hall and A.B. Jaffe, 1984. “Who does
R&D and who patents?”, in Z. Griliches (ed.), R&D, Patents and Productivity, Chicago:
University of Chicago.
Cantwell J. and R. Harding, 1998. “The internationalisation of German companies’
R&D”, National Institute Economic Review, 0(163), 99-125.
Cantwell J. and G.D. Santangelo, 1999. “The frontier of international technology networks: Sourcing abroad the most highly tacit capabilities”, Information Economics and
Policy, 11(1), 101-123.
Cincera M., 2003. “The determinants of patenting activities: Some Evidence from
Belgian manufacturing firms”, mimeo.
Cohen W.M., 1995. “Empirical studies of innovative activities”, in P. Stoneman (ed.),
Handbook of the Economics of Innovation and Technological Change, Oxford, UK:
Blackwell Publishers Ltd, 182-264.
Docquier F. and A. Marfouk, 2004. “Measuring the International Mobility of Skilled
Workers (1990–2000): Release 1.0”, Worldbank Policy Research Working Papers, No.
3381.
Dunning J.H. and R. Narula, 1995. “The R&D activities of foreign firms in the United
States”, International Studies of Management and Organisation, (25), 39-73.
European Commission, 2003. “The brain drain - emigration flows for qualified
scientists”.
Fecher F., 1990. “Effets directs et indirects de la R&D sur la productivité: une analyse
de l’industrie manufacturière belge’, Cahiers Economiques de Bruxelles, 128, 459-482.
Florida R., 1997. “The globalization of R&D: Results of a survey of foreign-affiliated
R&D laboratories in the USA”, Research Policy, 26(1), 85-103.
Geroski P., 1995. “Markets for Technology: Knowledge, Innovation and
Appropriability”, in P. Stoneman (ed.), Handbook of the Economics of Innovation and
Technological Change, Blackwell publishers.
Granstrand O., 1999. The Economics and Management of Intellectual Property,
Towards Intellectual Capitalism, Edward Edgar Publishing Ltd.
Granstrand O., L. Hakanson and S. Sjolander, 1992. Technology Management and
International Business, Wiley, Chichester.
Hall B.H., A. Jaffe and M. Trajtenberg, 2000. “Market value and patent citations: A
first look”, NBER Working Paper 7741.
181
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
Hall B.H., A. Jaffee and M. Trajtenberg, 2001. “The NBER patent citations data file:
Lessons, insights and methodological tools”, NBER Working Paper #8498.
Harhoff D., M. Scherer and K. Vopel, 2003. “Citations, family size, opposition and the
value of patent rights”, Research Policy, 32(8), 1343-1363.
Jaffe A., 1986. “Technological opportunity and spillovers of R&D”, American
Economic Review, 76, 984-1001.
Kuemmerle W., 1999. “Foreign direct investment in industrial research in the pharmaceutical and electronics industries - Results from a survey of multinational firms”,
Research Policy, 28(2-3), 179-93.
Lanjouw J. and M. Schankerman, 1999. “The quality of ideas: Measuring innovation
with multiple indicators”, NBER Working Paper 7345.
Maskus K., 1998. “The international regulation of intellectual property”,
Weltwirtschaftliches Archiv, 123(2), 186-208.
Patel P. and K. Pavitt, 1991. “Large firms in the production of the world’s technology:
An important case of non-globalization”, Journal of International Business Studies, 22,
1-20.
Pearce R. and M. Papanastassiou, 1999. “Overseas R&D and the strategic evolution
of MNEs: evidence from laboratories in the UK”, Research Policy, 28(1), 23-41.
Quillen C. and O. Webster, 2001. “Continuing patent applications and performance of
the US patent office”, Federal Circuit Bar Journal, 1, 1-21.
Slaughter M., 2002. “Does inward foreign direct investment contribute to skill upgrading in developing countries?”, CEPA Working Paper 2002-08.
Terpstra V., 1985. “International product policy: the role of foreign R&D”, in H.V.
Wortzel and L.V. Wortzel, (Eds.), Strategic management of multinational corporations:
The essentials, New York: Wiley.
Veugelers R. and B. Cassiman, 1999. “Importance of international linkages for local
know-how flows: Some econometric evidence from Belgium”, CEPR Discussion Paper #
DP2337.
Veugelers R. and P. Vanden Houte, 1990. “Domestic R&D in the presence of multinational enterprises”, International Journal of Industrial Organization, 8, 1-15.
182
MICHELE CINCERA
APPENDIX
TABLE A1. EPO PATENTS WITH BELGIAN INVENTORS BY TECHNOLOGY CLASS,
1983-1999
EPO-BE
Technology sector
Agricultural and food processing,
machinery and apparatus
Agriculture, food chemistry
Analysis, measurement, technology
Audio-visual technology
Biotechnology
Chemical and petrol industry,
basic materials chemistry
Chemical engineering
Civil engineering, building, mining
Consumer goods and equipment
Electrical machinery and apparatus,
electrical energy
Engines, pumpes, turbines
Environmental technology
Handling, printing
Information technology
Machine tools
Macromolecular chemistry, polymers
Materials processing, textiles, paper
Materials, mettalurgy
Mechanical elements
Medical technology
Nuclear engineering
Optics
Organic fine chemistry
Pharmaceuticals, cosöetics
Semiconductors
Space technology, weapons
Surface technology, coating
Telecommunications
Thermal processes and apparatus
Transport
Total
EPO-FOR
# pat
% col
% row
# pat
% col
% row
Total
244
83
271
77
247
3.6
1.2
4.0
1.1
3.7
83.8
50.3
68.6
72.0
56.5
47
82
124
30
190
1.1
1.8
2.8
0.7
4.3
16.2
49.7
31.4
28.0
43.5
291
165
395
107
437
234
119
348
305
3.5
1.8
5.2
4.5
26.0
60.1
76.5
72.8
665
79
107
114
14.9
1.8
2.4
2.6
74.0
39.9
23.5
27.2
899
198
455
419
232
60
67
517
92
124
360
545
271
125
132
55
874
367
197
73
46
178
186
108
167
3.5
0.9
1.0
7.7
1.4
1.8
5.4
8.1
4.0
1.9
2.0
0.8
13.0
5.5
2.9
1.1
0.7
2.7
2.8
1.6
2.5
44.6
72.3
64.4
66.5
64.8
69.3
37.8
65.7
63.2
66.5
51.0
74.3
93.2
55.0
55.3
76.0
88.5
62.5
43.1
76.1
56.0
288
23
37
261
50
55
592
285
158
63
127
19
64
300
159
23
6
107
246
34
131
6.4
0.5
0.8
5.8
1.1
1.2
13.3
6.4
3.5
1.4
2.8
0.4
1.4
6.7
3.6
0.5
0.1
2.4
5.5
0.8
2.9
55.4
27.7
35.6
33.5
35.2
30.7
62.2
34.3
36.8
33.5
49.0
25.7
6.8
45.0
44.7
24.0
11.5
37.5
56.9
23.9
44.0
520
83
104
778
142
179
952
830
429
188
259
74
938
667
356
96
52
285
432
142
298
6704
100.0
60.0
4466
100.0
40.0
11170
Notes: % tot col = % of patents by technological class with respect to total number of patents;
% tot row = % of patents applied by foreign firms in a given technological class
with respect to total number of patents applied in that class.
Sources: EPO database; own calculations.
183
BRAIN DRAIN, BRAIN GAIN AND BRAIN EXCHANGE : THE ROLE OF MNES IN A SMALL OPEN ECONOMY
TABLE A2. USPTO PATENTS WITH BELGIAN INVENTORS BY TECHNOLOGY CLASS,
1983-1999
USPTO-BE
USPTO-SUBS
USPTO-FOR
Technology sector
# pat
% col % row # pat
% col % row # pat
% col % row Total
Agriculture. Food. Textiles
Coating
Gas
Organic Compounds
Resins
Miscellaneous-chemical
Communications
Computer Hardware & Software
Computer Peripherals
Information Storage
Drugs
Surgery & Medical Instruments
Biotechnology
Miscellaneous-Drug&Med
Electrical Devices
Electrical Lighting
Measuring & Testing
Nuclear & X-rays
Power Systems
Semiconductor Devices
Miscellaneous-Elec.
Materials Processing & Handling
Metal Working
Motors. Engines & Parts
Optics
Transportation
Miscellaneous-Mechanical
Agriculture. Husbandry. Food
Amusement Devices
Apparel & Textile
Earth Working & Wells
Furniture. House Fixtures
Heating
Pipes & Joints
Receptacles
Miscellaneous-Others
37
61
12
201
256
500
34
16
4
67
165
26
109
10
23
14
70
28
28
10
43
223
181
28
34
37
78
62
12
213
41
22
36
9
44
299
1.2
2.0
0.4
6.6
8.4
16.5
1.1
0.5
0.1
2.2
5.4
0.9
3.6
0.3
0.8
0.5
2.3
0.9
0.9
0.3
1.4
7.4
6.0
0.9
1.1
1.2
2.6
2.0
0.4
7.0
1.4
0.7
1.2
0.3
1.5
9.9
4
20
29
167
0.8
1.6
0.2
6.2
3.0
40.0
2.3
0.9
2.2
0.0
14.2
0.0
0.3
0.0
1.2
0.1
0.6
3.7
0.4
0.2
0.8
3.1
0.2
0.4
4.9
0.3
1.0
0.0
0.0
0.1
0.1
0.0
0.2
1.0
1.5
8.6
69
60
39
168
519
877
211
89
7
48
205
94
136
16
141
81
59
45
49
11
57
228
93
70
28
90
91
132
8
26
25
8
25
16
103
223
1.7
1.4
0.9
4.1
12.5
21.1
5.1
2.1
0.2
1.2
4.9
2.3
3.3
0.4
3.4
2.0
1.4
1.1
1.2
0.3
1.4
5.5
2.2
1.7
0.7
2.2
2.2
3.2
0.2
0.6
0.6
0.2
0.6
0.4
2.5
5.4
56.6
39.7
70.9
34.4
62.3
40.8
72.8
73.0
13.2
41.7
31.8
78.3
54.2
61.5
75.0
84.4
42.1
31.0
58.3
45.8
49.1
44.7
33.6
66.7
17.9
68.2
48.1
68.0
40.0
10.8
37.3
26.7
38.5
35.6
58.5
32.4
122
151
55
489
833
2150
290
122
53
115
645
120
251
26
188
96
140
145
84
24
116
510
277
105
156
132
189
194
20
241
67
30
65
45
176
689
Total
3033
100.0 33.3
1931
100.0 21.2
4147
100.0 45.5
9111
30.3
40.4
21.8
41.1
30.7
23.3
11.7
13.1
7.5
58.3
25.6
21.7
43.4
38.5
12.2
14.6
50.0
19.3
33.3
41.7
37.1
43.7
65.3
26.7
21.8
28.0
41.3
32.0
60.0
88.4
61.2
73.3
55.4
20.0
25.0
43.4
16
30
4
120
58
773
45
17
42
275
6
24
1
11
72
7
3
16
59
3
7
94
5
20
2
1
13.1
19.9
7.3
24.5
7.0
36.0
15.5
13.9
79.2
0.0
42.6
0.0
2.4
0.0
12.8
1.0
7.9
49.7
8.3
12.5
13.8
11.6
1.1
6.7
60.3
3.8
10.6
0.0
0.0
0.8
1.5
0.0
6.2
44.4
16.5
24.2
Notes: % tot col = % of patents by technological class with respect to total number of patents;
% tot row = % of patents applied by MNEs’ subsidiaries in a given technological class
with respect to total number of patents applied in that class.
Sources: Hall et al. (2001) database; own calculations.
184
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