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IZA DP No. 3388
Measuring Skilled Emigration Rates:
The Case of Small States
Frédéric Docquier
Maurice Schiff
DISCUSSION PAPER SERIES
Forschungsinstitut
zur Zukunft der Arbeit
Institute for the Study
of Labor
March 2008
Measuring Skilled Emigration Rates:
The Case of Small States
Frédéric Docquier
FNRS, IRES, Catholic University of Louvain
and IZA
Maurice Schiff
World Bank
and IZA
Discussion Paper No. 3388
March 2008
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IZA Discussion Paper No. 3388
March 2008
ABSTRACT
Measuring Skilled Emigration Rates: The Case of Small States
*
Recent changes in information and communication technologies (ICT) have contributed to a
dramatic increase in the integration and interdependence of countries, markets and people.
This paper focuses on an increasingly important aspect of globalization, the international
movement of people, with emphasis on the mobility of skilled people. This issue is of great
concern for the many small states that experience huge brain drain levels.
JEL Classification: F22
Keywords: migration, skilled, brain drain, small states, evidence
Corresponding author:
Maurice Schiff
DECRG (MC3-303)
World Bank
1818 H Street NW
Washington, DC 20433
USA
E-mail:
mschiff@worldbank.org
*
This paper was presented at the World Bank Conference on “Small States, Growth Challenges and
Development Solutions”, Research Program on Economic Growth and Integration of Small States in
the World Economy, PREMED, World Bank, Washington, December 7-8, 2006. We thank David
McKenzie and Edgardo Favaro for their comments. The views expressed here are those of the authors
and do not necessarily reflect those of the World Bank, its Executive Directors or the governments
they represent.
INTRODUCTION
The 1960s and 1970s saw great interest in international migration issues. Interest in the topic
abated in the following years. However, with growth in migration
1
and remittance flows
accelerating in recent years, interest has reappeared by source and host country analysts and
policy makers, as well as in academia and multilateral, regional and bilateral development
institutions (Ozden and Schiff 2006).
The main question addressed in this paper is the relationship between skilled migration rates
and country size. This section presents some stylized facts about the relationship between
country size and various aspects of openness (imports, exports, trade, FDI and brain drain),
examines various explanations for that relationship as well as a number of issues that are
specific to the brain drain.
First, the degree of openness of a country tends to be negatively related to its population size.
Simple bivariate regressions show that the semi-elasticity of import/GDP to population size is
7.15, of export/GDP is 3.72, of trade/GDP is 5.43 (simple average), of FDI/GDP is 0.64 and
of the brain drain is 5.26. A similar value is obtained for the general emigration rate. These
figures indicate that the brain drain is highly sensitive to country size. Its semi-elasticity is
greater than that of exports and FDI, smaller than that of imports, and about the same as that
of overall trade.
Second, there are various reasons for the negative relation between skilled migration rates and
country size, with small countries showing higher rates, particularly the developing ones:
• Production in small states tends to be highly specialized in a limited number of
sectors. Hence, consumers and producers are, respectively, much more dependent on
trade for desired consumer goods and intermediate inputs. Thus, demand for a variety
of skills is very limited;
• The demand for skilled jobs in small developing countries is likely to be even more
limited because of these countries’ specialization in the production of commodities
(e.g., sugar) which are typically less skill intensive;
• Small countries also tend to be more unstable economically. The high degree of
specialization implies a greater vulnerability to fluctuations of the world economy.
Second, the fact that production in many of the poorer small developing economies
1
For instance, South-North migration has increased by 30% from 1990 to 2000 while that of skilled labor has
increased by 70%.
1
tends to be concentrated in commodities makes them even more vulnerable to external
economic shocks (because of low demand elasticity and thus high fluctuations in
price) as well as to weather shocks.
Third, the brain drain raises a number of major issues that are specific to it.
1. While trade imbalances put in motion mechanisms to restore equilibrium between
exports and imports – including exchange rate movements, such mechanisms do not
necessarily exist in the case of the movement of skilled labor. Due to technological
and institutional differences, migration need not reduce the wage gap between source
and host countries. On the contrary, human capital externalities (Lucas 1988)
associated with skilled labor migration might even raise the skilled wage gap between
source and host countries.
2. Human capital is typically considered to be an important element of the engine of
growth. If skilled emigration is not compensated by skilled immigration – an unlikely
outcome for most developing source countries -- or by stronger human capital
accumulation, source countries may gradually lose their capacity to develop.
3. Though the new literature on the brain drain suggests that skilled emigration may
induces positive feedback effects for sending countries (including remittances,
increased trade, transfer of knowledge and behavioral modes
2
), these tend to be
dominated by the direct effect of the brain drain on the stock of human capital (see
Beine et al, 2006).
1. SMALL STATES
There are many possible ways of defining small states. One can use various criteria
(population, GDP, territory size), as well as various thresholds and base years. Unsurprisingly,
these criteria are strongly correlated
3
as cross-country differences are well preserved over
time. Here we use the population size in 2000
There is no special significance in the selection of a particular population threshold to define
small states. Indeed, the Commonwealth, in its work on small states, uses a threshold of 1.5
2
See Fargues (2006) on the impact of international migration on fertility in the sending countries.
3
In 2000, the correlation rate between population and GDP amounted to 78%, and 84% between population and
size.
2
million people, but it also includes larger member countries (Jamaica, Lesotho, Namibia and
Papua New Guinea) because they share many of the same characteristics of smallness. The
World Bank Task Force on small states uses the same threshold as a convenient yardstick for
classifying all small states, and only consider sovereign states.
Using the standard of a population below 1.5 million people in 2000, 45 developing countries
are small (see Alphabetical list of small states by population, population rank & GNP per
capita), accounting for nearly one third of the total number of developing countries. They are
home to 20 million people, less than 0.4 percent of the total population of developing
countries. They range in size from “micro-states” like Cook Islands, Nauru, Niue, Palau, St.
Kitts and Nevis, and Tuvalu (with fewer than 50,000 people each) to Botswana, Gabon, The
Gambia, Guinea-Bissau, Mauritius, and Trinidad and Tobago (with more than 1 million
people each). The per capita GNP in these countries also ranges widely, from less than $400
in several African countries (Comoros, The Gambia, Guinea-Bissau, and Sao Tome and
Principe) to just $700-1,300 in such countries as Cape Verde, Guyana, Kiribati, Maldives,
Solomon Islands, and Tuvalu; to more than $9,000 (The Bahamas, Brunei, Cyprus, Malta, and
Qatar). There are small states in every geographic region, but most countries fall into three
main groups: twelve states are in the Caribbean region, fourteen in East Asia and Pacific, and
twelve in Africa. Of the remaining seven countries, two are in South Asia, two in the Middle
East, and three in Europe.
2. A NEW DATABASE ON EMIGRATION RATES BY EDUCATIONAL
ATTAINMENT
This section describes the methodology and data sources used to compute emigration stocks
and rates by educational attainment and by origin country in 1990 and 2000. In what follows,
the term ''country'' usually designates independent states whilst ''dependent territory'' refers to
other entities attached to a particular independent state. Our 2000 data set distinguishes 192
independent territories (Vatican and the 191 UN member states, including East Timor which
became independent in 2002) and 39 dependent territories.
Stocks are provided for both types of territories while rates are only provided for independent
countries as well as three dependent territories which are treated as countries (Hong Kong,
Macao and Taiwan) and one occupied territory (Palestine). Since most Korean migrants to the
USA did not accurately report their origin, we cannot distinguish between North and South
3
Korea (estimates are provided for Korea as a whole). We distinguish 174 countries in 1990,
before the secession of the Soviet block, ex-Yugoslavia, ex-Czechoslovakia, the independence
of Eritrea and East-Timor, and the German and Yemen reunifications
4
.
For economic and statistical reasons, working on stocks is more attractive than working on
flows. Stock variables are more appropriate to analyze the endogeneity and the dynamics of
migration movements (the equilibrium values are often expressed in terms of stocks).
Regarding statistics, it has long been recognized that migration flow data are less reliable than
stock data, due to the impossibility of evaluating emigration and return migration movements.
We count as migrants all working-aged (25 and over) foreign-born individuals living in an
OECD country
5
. Skilled migrants are those with at least tertiary educational attainment
wherever they completed their schooling. Our methodology proceeds in two steps. We first
compute emigration stocks by educational attainment from all countries of the world. Then,
we evaluate these numbers in percentage of the total labor force born in the sending country
(including the migrants themselves). This definition deserves two main comments.
First, the set of receiving countries is restricted to OECD nations. Compared to existing works
(such as Trends in International Migration - see OECD, 2002), our database provides many
insights about the structure of South-North and North-North migration. Generally speaking,
the skill level of immigrants in non-OECD countries is expected to be very low, except in a
few countries such as South Africa (1.3 million immigrants in 2000), the six member states of
the Gulf Cooperation Council (9.6 million immigrants in Saudi Arabia, United Arab Emirates,
Kuwait, Bahrain, Oman and Qatar), some Eastern Asian countries (4 million immigrants in
Hong-Kong and Singapore only). According to their census and survey data, about 17.5
percent of adult immigrants are tertiary educated in these countries (17 percent in Bahrain,
17.2 percent in Saudi Arabia, 14 percent in Kuwait, 18.7 percent in South Africa).
Considering that children constitute 25 percent of the immigration stock, we estimate the
number of educated workers at 1.9 million in these countries. The number of educated
immigrants in the rest of the world lies between 1 and 4 million (if the average proportion of
educated immigrants among adults lies between 2.5 and 10 percent). This implies that
focusing on OECD countries, we should capture a large fraction of the world-wide educated
migration (about 90 percent). Nevertheless, we are aware that by disregarding non-OECD
4
Note that we report 1990 estimates for a couple of countries which became independent after January 1, 1990
(Namibia, Marshall Islands, Micronesia, Palau).
5
Our working-aged concept includes retirees.
4
immigration countries, we probably underestimate the brain drain for a dozen of developing
countries (such as Egypt, Sudan, Jordan, Yemen, Pakistan or Bangladesh in the neighborhood
of the Gulf states, Swaziland, Namibia, Zimbabwe and other countries which send emigrants
to South Africa, etc.). Incorporating data collected from selected non-OECD countries could
refine the data set.
Second, we have no systematic information on the age of entry. It is therefore impossible to
distinguish between immigrants who were educated at the time of their arrival and those who
acquired education after they settled in the receiving country; for example, Mexican-born
individuals who arrived in the US at age 5 or 10 and graduated from US high-education
institutions are counted as highly-skilled immigrants. Hence, our definition of the brain drain
is partly determined by data availability. Existing data do not allow us to systematically
eliminate foreign-born individuals who arrived with completed schooling or after a given age
threshold. In the US, the proportion of foreign born individuals who arrived before age 10
represents 10 percent of the immigration stock (16 percent for those who arrived before age
16). This average proportion amounts to 13 percent among skilled immigrants (20.4 for age
16).
Important differences are observed across countries. The share is important for high income
and Central American countries (about 20 percent). It is quite low for Asian and African
countries (about 9 percent). Having no systematic data for the other receiving countries, we
cannot control for familial immigration. Our data base includes these individuals who arrived
at young age. Our choice is also motivated by another reason. It is impossible to quantify the
share of these young immigrants who were partly educated in their birth country and/or who
arrived with foreign fellowships. Young immigrants who spent part of their primary or
secondary schooling in the origin country, or who got foreign schooling fellowships induced a
fiscal loss for their origin country.
2.1. Emigration stocks
It is well documented that statistics provided by origin countries do not provide a realistic
picture of emigration. When available, they are incomplete and imprecise
6
. Whilst detailed
immigration data are not easy to collect on an homogeneous basis, information on emigration
6
Bhorat et al. (2002) compare South African emigration data to immigration numbers collected in five important
receiving countries (Australia, Canada, New Zealand, UK and USA). They show that the emigration sum was
approximately 3 times larger than South African official statistics.
5
can only be captured by aggregating consistent immigration data collected in receiving
countries. Information about the origin and skill of natives and immigrants is available from
national population censuses and registers. More specifically, country i's census usually
identifies individuals on the basis of age, country of birth j, and skill level s. Our method
consists in collecting census or register data from a large set of receiving countries, with the
highest level of detail on birth countries and (at least) three levels of educational attainment:
s=h for high-skilled, s=m for medium-skilled, s=l for low-skilled and s=u for the unknowns.
Let
denote the stock of working-aged individuals born in j, of skill s, living in country i
at time t.
ji
st
M
,
,
Low-skilled workers are those with primary education (or with 0 to 8 years of schooling
completed); medium-skilled workers are those with secondary education (9 to 12 years of
schooling); high-skilled workers are those with tertiary education (13 years and above). The
unknowns are either due to the fact that some immigrants did not declare their educational
attainment or to the absence of data on education in some receiving countries. Educational
categories are built on the basis of country specific information and are compatible with
human capital indicators available for all sending countries. A mapping between the country
educational classification is sometimes required to harmonize the data
7
.
By focusing on census and register data, our methodology does not capture illegal
immigration for which systematic statistics by education level and country of origin are not
available. According to the U.S. Immigration and Naturalization Services, the illegal
population residing in the United States amounted to 3.5 million in January 1990 and 7.0
million in January 2000. It is even possible to identify the main countries of origin (in 2000,
68.7 percent were from Mexico, 2.7 from El Salvador, 2.1 from Guatemala, 2.0 from
Colombia and Honduras, etc.)
8
. However, there is no accurate data about the educational
structure of these illegal migrants. For the other member states of the OECD, data on illegal
immigration are less reliable or do not exist. By disregarding illegal migrants, we probably
overestimate the average level of education of the immigrant population (it can be reasonably
assumed that most illegal immigrants are uneducated). Nevertheless, this limit should not
significantly distort our estimates of the migration rate of highly-skilled workers.
9
7
For example, Australian data mix information about the highest degree and the number of years of schooling.
8
See http://uscis.gov/graphics/shared/aboutus/statistics/III Report 1211.pdf.
9
Note that the problem may not be that important for estimation of brain drain determinants because results do
not depend on the extent or share of illegal migrants but rather on their cross-country difference. Hence,
6
As far as possible, we turn our attention to the homogeneity and the comparability of the data.
This induces a couple of methodological choices:
To allow comparisons between 1990 and 2000, we consider the same 30 receiving
countries in 1990 and 2000. Consequently, Czechoslovakia, Hungary, Korea, Poland,
Mexico and Turkey are considered as receiving countries in 1990 despite the fact that they
were not members of the OECD. item Migration is defined on the basis of the country of
birth rather than citizenship. Whilst citizenship characterizes the foreign population, the
concept of foreign-born better captures the decision to emigrate
10
. Usually, the number of
foreign-born is much higher than the number of foreign citizens (twice as large in
countries such as Hungary, the Netherlands, and Sweden)
11
. Another reason is that the
concept of country of birth is time invariant (contrary to citizenship which changes with
naturalization) and independent of the changes in policies regarding naturalization. The
OECD statistics report that 14.4 million of foreign born individuals were naturalized
between 1991 and 2000. Countries with a particularly high number of acquisitions of
citizenship are the US (5.6 million), Germany (2.2 million), Canada (1.6 million), and
Australia and France (1.1 million). Despite the fact that they are partially reported in
traditional statistics (OECD, 2002), the number of foreign-born can be obtained for a large
majority of OECD countries. In a limited number of cases, the national census only gives
immigrants' citizenship (Germany, Italy, Greece, Japan and Korea). As it will appear in
Table 2, 88.3 percent of working-aged immigrants can be characterized in term of country
of birth in 2000 (11.7 percent in term of citizenship). Contrary to common belief, data
availability is not significantly different in 1990, even among European states. We obtain
information about country of birth for 88.0 percent of working-aged immigrants in 1990
(12.0 in term of citizenship).
It is worth noting that the concept of foreign born is not fully homogeneous across OECD
countries. As in many OECD countries, our main criterion relies on both country of birth
and citizenship at birth: we define foreign born as an individual born abroad with foreign
i
m
j
m
ji,
estimation is unaffected if the shares are constant across countries or if the difference between the share
in
country i and
in country j is equal to a random (white noise) variable,
∀
, i.e., for
iAi
mm
ε
+=
A
i
, with
m
is the average share,
ε
is an “iid” random (white noise) variable.
10
In some receiving countries such as Germany, immigrants' children (i.e. the second generation) usually keep
their foreign citizenship.
11
By contrast, in other OECD countries with a restricted access to nationality (such as Japan, Korea, and
Switzerland), the foreign population is important (about 20 percent in Switzerland).
7
citizenship at birth. For example, the U.S Census Bureau considers as natives persons
born in the US (as well as in Puerto Rico or U.S. Island areas), or born abroad from a U.S.
citizen parent
12
. Other residents are considered foreign born. France and Denmark use a
imilar concept. Statistics Netherlands defines first-generation immigrants as persons who
are born abroad and have at least one parent who is also born abroad (Alders M., 2001).
However, in a couple of countries (Australia, New Zealand, and Belgium), the " foreign
born" concept used by the Statistics Institute essentially means " overseas-born" , i.e. an
individual simply born abroad. Whilst it is impossible to use a fully comparable concept
of immigration, we have tried to maximize the homogeneity of our data sources. It is
worth noting that our definition clearly excludes the second generation of immigrants. A
couple of countries offer a more detailed picture of immigration, distinguishing the
foreign born and those with foreign background (basically immigrants' descendants born
locally from one of two foreign-born parents)
13
.
As discussed above, emigration rates are provided for 195 territories in 2000 (191 UN
member states, Vatican, Palestine, Hong Kong, Taiwan, Macao minus one Korean
country). The world configuration has changed between 1990 and 2000. Czechoslovakia
seceded into the Czech Republic and the Slovak Republic, the ex-USSR seceded into 15
countries (7 on the European continent and 8 on the Asian continent), Ex-Yugoslavia
seceded into 5 countries, Eritrea and East Timor emerged as a independent countries in
1993 and 2002. On the contrary, Germany and Yemen were unified. Consequently, we
distinguish 174 countries in 1990 (the ex-USSR replaces 15 countries, ex-Yugoslavia
replaces 5 countries, ex-Czechoslovakia replaces 2 countries). For homogeneity reasons,
we aggregated East and West Germany as well as the Democratic Republic and the
Republic Yemen in 1990. In 1990, the ex-USSR totally belongs to the European area
14
.
A related issue concerns the dependent territories. Each dependent territory is linked to a
nation. Individuals born in these territories have the unrestricted right to move to and to
live in the nation. We naturally consider them as natives of the sovereign nation. Once the
concept of foreign born is chosen, it means that they should not be considered as
immigrants if they move to the sovereign state (internal migration). They should only be
considered as immigrants if they move to another independent state (external migration).
12
See Malone et al. (2003) for more details.
13
Data by foreign background are provided in the Netherlands, France and Scandinavian countries. See Alders
(2001) for the Netherlands or Ostby (2002) for Norway.
8
This criterion is especially important for U.S. dependent territories (such as Puerto Rico
and the US Island Areas such as Guam, etc.), UK overseas territories (Bermuda, Anguilla,
etc.), French dependent territories (such as Guadalupe, Reunion, etc.), Denmark (Greeland
and Faroe Islands, etc.) or around Australia and New-Zealand (Cook Islands, Niue,
Tokelau, etc.).
For example, in accordance with the US Census Bureau definition, we consider that one
million of Puerto Ricans living in the United States are U.S. natives but not immigrants.
This considerably reduces the total stock of Puerto Rican emigrants. We have computed
on the same basis the emigration stock for the other dependent territories, except for
Taiwan, Hong Kong and Macao which are assimilated to independent countries. Then,
given the small numbers obtained, we have eliminated Northern Mariana Islands and
Western Sahara (a disputed rather than dependent territory) and have summed up Jersey
and Guernsey (forming Channel Islands). Stock data for 33 dependent territories are
provided in Table A.3.
As the second step of our analysis consists in comparing the numbers of emigrants and
residents by educational attainment, we have to consider homogeneous groups. Working
on the working-aged population (aged 25 and over) maximizes the comparability of the
immigration population with data on educational attainment in source countries. It also
excludes a large number of students who temporarily emigrate to complete their
education. We cannot control for graduate students aged 25 and over completing their
schooling
15
. As it will appear in Table 1 the age group is slightly different in a limited
number of countries.
Building an aggregate measure of emigration per educational attainment requires a rule for
sharing the unknown values. At the OECD level, the number of migrants whose educational
attainment is not described amounts to 1.287 million, i.e. 2.2 percent of the total stock. Two
reasonable rules could be considered: either unknown values can be distributed in the same
way as the known values or they can be assimilated as unskilled. We combine both rules
depending on the information available in the receiving country. For receiving countries
14
Note that aggregating appropriated stock data would allow computation of emigration rates for ex-Yugoslavia,
the ex-USSR and ex-Czechoslovakia in 2000.
15
Carrington and Detragiache (1998) used data from the Institute of International Education to estimate the
number of graduate students completing their schooling in the United States. We consider that some of these
students aged 25 and over receive grants and can be considered as workers (researchers).
9
where information about immigrants' education is available, we assimilate the unknowns to
unskilled workers
16
.
For example, Australian immigrants who did not mention their educational attainment are
considered unskilled. In receiving countries where no information about skill is available, we
transpose the skill distribution observed in the rest of the OECD area or in the neighboring
region. For example, if we have no information about the skill structure of immigrants to
Iceland, Algerian emigrants to Iceland are assumed to be distributed in the same way as
Algerian emigrants to the rest of the Scandinavian countries. The assumptions will be
discussed below.
Formally, the stocks of emigrants of skill s from country j at time t (
) are obtained as
follows:
j
st
M
•
,
(1)
⎪
⎪
⎪
⎪
⎪
⎪
⎩
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎧
Ψ−+Ψ+=
Ψ+=
Ψ+=
∑∑∑
∑∑
∑
∑∑
∑∑
∑
∑∑
∑∑
∑
•
•
•
ii
i
t
ji
ut
i
is
ji
st
i
ji
mt
i
t
ji
ut
ji
lt
j
lt
ii
is
ji
st
i
ji
mt
i
t
ji
ut
ji
mt
j
mt
ii
is
ji
st
i
ji
ht
i
t
ji
ut
ji
ht
j
ht
M
M
M
MMM
M
M
MMM
M
M
MMM
)1.(..
..
..
,
,
,
,
,
,
,
,
,
,,
,
,
,
,
,
,
,
,,
,
,
,
,
,
,
,
,,
where
is a (time and country dependent) binary variable equal to one if there is no data on
the immigrants' skill in country i, and equal to zero otherwise. Table 1 describes the data
sources.
i
t
Ψ
16
Country specific data by occupation reveal that the occupational structure of those with unknown education is
very similar to the structure of low-skilled workers (and strongly different from that of high-skilled workers).
See Debuisson et al. (2004) on Belgian data.
10
Table 1. Data sources
1990 (+)
2000 (+)
Country - Age group Origin Education Origin Education
Australia (25+) Census (#) Census (#) Census (#) Census (#)
Austria (25+) Census Census Census Census
Belgium (25+) Census Census Improved EC
(**)
LFS
Canada (25+) Census (#) Census (#) Census (#) Census (#)
Czech Rep (25+) Census (#) - Census (#) Census (#)
Denmark (25+) Register Register Register Register
Finland (25+) Register Register Register Register
France (25+) Census (#) Census (#) Census (#) Census (#)
Germany (25-65) Microcensuz*
(Cit)
Microcensuz*
(Cit)
Microcensuz*
(Cit)
Microcensuz*
(Cit)
Greece (25+) EC (Cit) LFS (Cit.) EC (Cit) LFS (Cit.)
Hungary (All;25+) EC (Cit) - Census Census
Iceland (All) Register - Register -
Ireland (25+) Census Census Census Census
Italy (25+) EC (Cit) - Census (Cit) Census (Cit)
Japan (All/25+) Register (Cit) - Census (Cit) -
Korea (All) Register (Cit) - Register (Cit) -
Luxemburg (25+) Census (#) Census (#) Census (#) Census (#)
Mexico (25+) Ipums (+) 10% Ipums (+) 10% Ipums (+) 10.6% Ipums (+) 10.6%
Netherland (All) Census* Census* Census* Census*
New Zealand (15+) Census Census Census Census
Norway (25+) Register Register Register Register
Poland (13+) Census (#) - Census (#) Census (#)
Portugal (25+) Census LFS Census LFS
Slovak Rep (25+) See Czech Rep See Czech Rep Census (#) Census (#)
Spain (25+) Census Census Census Census
Sweden (25+) Census Census Census Census
Switzerland (18+) Census (#) Census (#) Census (#) Census (#)
Turkey (15+) Census (#) Census (#) Census (#) Census (#)
United Kingdom
(15+)
Census* Census* Census* Census*
United States (25+) Ipums (+) 5% Ipums(+) 5% Census 100%* Census 100%*
Notes: EC = European Council (register data); LFS = Labor Force Survey; (*) = limited level of detail.
(**) European Council data corrected by the country specific "foreign born/foreign citizen" ratio in Census 1991.
(+) Year around 1990 and 2000 (for example, the Australian censuses refer to 1991 and 2001)
(#) Data available in Release 1.0.
(+) See Ruggles et al. (2004) on the US and Sobek et al. (2002) on the Mexican sample.
In 2000, we use census, microcensus and register data for 29 countries. European Council
data are used in the case of Greece. Information on the country of birth are available for the
large majority of countries, representing 88.3 percent of the OECD immigration stock.
11
Information on citizenship are used for the other countries (Germany, Italy, Greece, Japan,
and Korea). The educational structure can be obtained in 24 countries and can be estimated in
3 additional countries on the basis of the European Labor Force Survey (Belgium, Greece,
and Portugal). As will appear in Table 2, data built on the Labor Force Survey only represent
2 percent of the OECD migration stock in 2000 (0.7 percent in 1990). In the 3 remaining
countries, the educational structure is extrapolated on the basis of the Scandinavian countries
(for Iceland) or the rest of the OECD (for Japan and Korea). In 1990, European Council data
must be used for Hungary and Italy. These data are based on the concept of citizenship.
Compared to 2000, educational attainment is not available in Italy, the Czech Republic and
Hungary. The Italian educational structure is based on the rest of the EU15. For the other two
countries, we use proportions computed from the rest of Europe. On the contrary, the Belgian
1991 Census is available and provides complete data by country of birth and educational
attainment.
2.2. Emigration rates
In the spirit of Carrington and Detragiache (1998) and Adams (2003), our second step consists
in comparing the emigration stocks to the total number of people born in the source country
and belonging to the same educational category. Calculating the brain drain as a proportion of
the total educated labor force is a better strategy to evaluate the pressure imposed on the local
labor market. It is indeed obvious that the pressure exerted by 1,037,000 Indian skilled
emigrants (4.3% of the educated total labor force) is less important than the pressure exerted
by 16,000 skilled emigrants from Grenada (85% of the educated labor force).
Denote
as the stock of individuals aged 25+, of skill s, living in country j, at time t, we
define the emigration rates by
j
st
N
,
(2)
j
st
j
st
j
st
j
st
MN
M
m
,,
,
,
+
=
12
In particular, provides information about the intensity of the brain drain in the source
country j. It measures the fraction of skilled agents born in country j and living in (other)
OECD countries
j
ht
m
,
17
.
This step requires using data on the size and the skill structure of the working-aged population
in the countries of origin. Population data by age are provided by the United Nations
18
. We
focus on the population aged 25 and more. Data are missing for a couple of countries but can
be estimated using the CIA world factbook
19
. Population data are split across educational
groups using international human capital indicators. Several sources based on attainment
and/or enrollment variables can be found in the literature. These data sets suffer from two
important limitations. First, data sets published in the nineties reveal a number of suspicious
features and inconsistencies
20
. Second, given the variety of educational systems around the
world, they are subject to serious comparability problems.
Three major competing data sets are available: Barro and Lee (2000), Cohen and Soto (2001)
and De La Fuente and Domenech (2002). The first two sets depict the educational structure in
both developed and developing countries. The latter only focuses on 21 OECD countries.
Statistical comparisons between these sets reveal that the highest signal/noise ratio is obtained
in De La Fuente and Domenech. These tests are conducted in OECD countries. Regarding
developing countries, Cohen and Soto's set outperforms Barro and Lee's set in growth
regressions. However, Cohen and Soto's data for Africa clearly underestimate official
statistics. According to the South African 1996 census, the share of educated individuals
amounts to 7.2 percent. Cohen and Soto report 3 percent (Barro and Lee report 6.9 percent).
The Kenyan 1999 census gives 2 percent whilst Cohen and Soto report 0.9 percent (1.2 for
Barro and Lee).
Generally speaking, the Cohen and Soto data set predicts extremely low levels of human
capital for African countries
21
(the share of tertiary educated is lower than 1 percent in a large
number of African countries) and a couple of other non-OECD countries
22
. The Barro and Lee
17
For some countries, immigrants often travel back and forth between their new and old countries (e.g. Mexico).
They are likely to be counted as still being residents in their home country. For that reason, Carrington and
Detragiache (1998) provide an upper bound (m=M/N) and a lower bound (m=M/(N+M)). Since the upper bound
is not interpretable for a large number of countries (higher than one), we only report the lower bound.
18
See http://esa.un.org/unpp.
19
See http://www.cia.gov/cia/publications/factbook.
20
This partly explains why human capital did not prove to be significant or distort the " good sign" in growth
regressions.
21
For this reason, Cohen and Soto (2001) exclude African countries from their growth regressions.
22
In Cyprus, the 2001 census gives 22%, compared with 4.6% in Cohen and Soto (17.1% in Barro and Lee).
13
estimates seem closer to the African official statistics. As the brain drain is particularly
important in African countries, Barro and Lee indicators are preferable. Consequently, data
for
are taken from De La Fuente and Domenech (2002) for OECD countries and from
Barro and Lee (2000) for non-OECD countries. For countries where Barro and Lee measures
are missing (about 70 countries in 2000), we transpose the skill sharing of the neighboring
country with the closest human development index regarding education. This method gives
good approximations of the brain drain rate, broadly consistent with anecdotal evidence.
j
st
N
,
2.3. Changes between 1990 and 2000
The number of skilled migrants has drastically increased in recent decades. This is partly
explained by the many “quality-selective” policies that were introduced in OECD countries in
the 1980s and 1990s. The stock of high-skilled immigrants residing in the OECD increased by
70% between 1990 and 2000, while that of unskilled immigrants increased by only 30%
during the same period (Docquier and Marfouk, 2006). However, this rapid increase in the
stock of skilled migrants does not imply that the rate of skilled migration increased at a
similar rate because the last decade was also characterized by a sharp rise in the educational
attainment in sending countries. Consequently, as shown in the Docquier-Marfouk's database,
the brain drain only experienced a minor change between 1990 and 2000 (from 5.0 to 5.4
percent).
3. RESULTS FOR SMALL STATES
Table 2 presents information on skilled and average emigration rates, and on the schooling
gap (defined below), for 1990, the period 1990-2000, and for 2000, with focus on small states.
The data are provided for small states as a whole, and grouped according to population size,
region and income, and for small developing island states. The emigration rates are also
provided for other country groupings, including those with somewhat larger population, the
world, and for high-income countries and developing countries as a whole.
Table 2 presents a number of interesting findings. We start with those associated with the
skilled emigration rate (first column).
14
4.1. Skilled Emigration Rate
First, the skilled emigration rate or brain drain shows a dramatic difference in the extent of the
brain drain -- or skilled migration rate -- for small states relative to that for developing
countries as a whole. In 1990, the small states’ brain drain was equal to 50% and that for
developing countries as a whole was 7.8% or less than 16% of the former. Similarly, the small
states’ brain drain in 1990-2000 was 36.1% and that for developing countries as a whole was
7.0% or less than 20% of the former. Finally, the small states’ brain drain was equal to 43.2%
in 2000 and that for developing countries as a whole was 7.4% or less than 18% of the former.
Second, the brain drain for small states is even larger when compared with the world as a
whole or with high-income countries. The brain drain in high-income countries was 3.8% or
7.6% of that of the small states in 1990, and 3.5% or 8.1% of the small states’ brain drain in
2000. As for the world average, the corresponding numbers are 5.2% in 1990 and 5.3% in
2000, or 10.4% and 12.3% of the small states’ brain drain, respectively.
Third, though the small states’ brain drain has been extremely large in recent decades (with
the 1990 figure reflecting preceding brain drain episodes), it has been declining. The decline
from 50% in 1990 to 43.2% in 2000 amounts to a 13.6% reduction in a decade. This is a
major change, considering that these figures relate to stocks.
Fourth, Table 2 also presents a disaggregation of the small states into three groups according
to population size P (in millions). These are referred to here as Group 1 (P < .5), Group 2 (.5 <
P < 1), and Group 3 (1 < P < 1.5). The brain drain in Groups 1 and 2 was 46% in 1990 and
69% in Group 3. The latter was thus 50% larger than the former two. The opposite holds in
2000, with the brain drain in Group 3 (40.9%) lower than that in Group 1 (41.7%) and in
Group 2 (47.2%). The 40% reduction in the brain drain of Group 3 from 68.9% in 1990 to
40.9% in 2000 is due to a 60% decline in the period 1990-2000 (from 68.9% to 28.3%). Thus,
the decrease in small states’ brain drain from 50% to 43.2% was essentially due the decline in
Group 3.
23
Fifth, Table 2 presents two other groups according to population, referred to here as Group 4
with 1.5 < P < 3, and Group 5 with 3 < P < 4. The brain drain in Group 4 was half of that in
small states in 1990 (25% versus 50%) and about half in 2000 (20.9% versus 43.2%), while
that in Group 5 was 41.4% of that in small states in 1990 (20.7% versus 50%) and 42.8% in
23
Group 2 experienced a 3% brain drain increase from 1990 to 2000 (from 45.8 to 47.2%) and Group 1
experienced a 9% decrease (from 46 to 41.7%).
15
2000. In other words, the size of the brain drain exhibited a negative relationship with respect
to population size. It was greater for small states as a whole, smaller by about half in Group 4,
and again smaller (by close to 60%) in Group 5.
Sixth, the brain drain for small island developing states was similar to that for small states as a
whole. It is 10% smaller that the latter in 1990 (45% versus 50%) and 2% smaller in 2000
(42.4% versus 43.2%).
Seventh, Table 2 also presents a disaggregation of small states by region and income. The
brain drain in 1990 was largest in Latin America and the Caribbean (75.4%) and in East Asia
and the Pacific (74.2%), smaller in Sub-Saharan Africa (43.3%) and smallest in high-income
countries (24.9%). The figures are similar in 2000, except for East Asia and the Pacific where
the brain drain declined dramatically, from 74.2% in 1990 to 50.8% in 2000 or by over 30%,
due to a huge reduction (by 56%) in the brain drain in 1990-2000 compared to 1990.
4.2.
Schooling Gap
Table 2 also provides information on the “Schooling gap,” which we now define. First, in
order to simplify notation, equation (2) is reproduced here as equation (2’) where the time
subscript t and the country superscript j have been deleted. Then, the skilled emigration rate or
brain drain (
) is given by
h
m
(2’)
hh
h
h
MN
M
m
+
=
,
where
is the stock of skilled migrants aged 25+, and is the stock of skilled
individuals aged 25+ living in their country of birth.
h
M
h
N
The average migration rate is
(3)
M
N
M
m
+
=
,
where M is the stock of migrants aged 25+, and N is the population of individuals aged 25+
living in their country of birth. The schooling gap SG is defined as the share of the skilled in
the migrant population divided by the share of the skilled in the total population, i.e.:
16
(4)
=SG
m
m
NM
M
MN
M
MN
MN
M
M
h
hh
hhhh
=
++
=
⎟
⎠
⎞
⎜
⎝
⎛
+
+
.
Thus, the schooling gap – shown in the third column of Table 3 for each period -- can also be
interpreted as the skilled migration rate
divided by the average migration rate m, i.e., as
/m. Below are a few results from Table 2.
h
m
h
m
First, the schooling gap for small states as a whole was 3.34 in 1990. It was much higher in
Group 3, the largest of the small states groups (1 < P < 1.5), with a value of 7.65 or some
130% greater than that for small states as a whole. The schooling gap was about the same for
Group 2 as for small states as a whole (3.21 versus 3.34 or 4% less), and was 2.27 for Group 1
or 32% smaller than that for small states as a whole. Thus, the schooling gap within the small
states groups was inversely related to the population size, and the same held for 2000.
Second, the schooling gap for small states was very close to the world average both in 1990
and 2000. It was greater than for high income countries (by some 165% in 1990 and 120% in
2000) and was smaller than for developing countries as a whole (by over 20% in 1990 and by
over 30% in 2000).
Third, the small states schooling gap fell from 3.34 in 1990 to 2.81 in 2000 or by some 16%,
about the same percentage decline as that for the brain drain, the reason being that the average
emigration rate remained about the same (changing by only 2%, from 15.0 to 15.3%). It also
fell in Groups 1, 2 and 3, with that of Group 3 exhibiting a dramatic decline from 7.65 in 1990
to 4.20 in 2000 or by 45%. The latter was the main cause that the schooling gap declined for
small states as a whole.
Fourth, the 1990 schooling gap was extremely high in Sub-Saharan Africa at 8.31, was high
for East Asia and the Pacific at 4.38, and was much lower for Latin America and the
Caribbean at 2.52, with the lowest for high-income countries at 2.07. These schooling gaps
were, respectively, 150% greater, over 30% greater, 25% smaller and 38% smaller than that
for small states as a whole. The schooling gaps fell for all three developing regions, with their
order unchanged, except for the high income group where it increased some. Importantly, it
fell significantly in Sub-Saharan Africa, from 8.31 to 6.95 or by 16.4%, slightly more than the
15.8% decrease for small states as a whole.
Fifth, the decline in the schooling gap was about twice as large for developing countries as a
whole as for the small states, with a reduction from 7.18 in 1990 to 4.92 in 2000 or by 31.5%,
17
compared to a reduction of 15.8% for the small states. The greater reduction for the former
was certainly not due to the reduction in the brain drain which was in fact greater for small
states (13.6% versus 4.4%). Rather, it was due to a significant increase in the average
emigration rate from 1.1 to 1.5% or by 36%, compared to a 2% increase for small states.
Sixth, the schooling gap for high-income countries remained constant between 1990 and 2000
at about 1.26 and fell for the world as a whole from 3.316 to 2.993 or by 8.8%. The world
average schooling gap was equal to that for small states in 1990 and some 6% greater in 2000.
Seventh, note that the reduction in the schooling gap should be considered a benefit for source
countries in the sense that the difference between the skill intensity of emigration
and
that of the population as a whole
MM
h
/
)/()( MNMN
hh
+
+
decreases (see equation 4). The reason
is that the brain drain should be less harmful for source countries because of their relatively
greater supply, either because of a reduction in the share of skilled labor in migration or
because of an increase in the share of skilled labor in the population (including the migrants
themselves). This was the case for all country groupings except for high-income small states
and high-income countries as a whole.
Eighth, skilled immigrants are defined as foreign-born workers with university or post-
secondary training living abroad. This definition does not distinguish between education
acquired in the home or in the host country. Rosenzweig (2005) shows on the basis of US
survey data that migration of children represent an important fraction of total migration for
certain countries. On average, 18 percent of permanent resident aliens immigrated to the US
before eighteen and over twenty 25 immigrated before twenty. Among these, some are highly-
skilled today, having most likely acquired a higher education after coming to the US.
18
Table 2. Emigration rates by country group
2000 1990 2000/1990
Nb
Skilled
emigration
rate
Average
emigration
rate
Schooling
gap
Skilled
emigration
rate
Average
emigration
rate
Schooling
gap
Skilled
emigration
rate
Average
emigration
rate
Schooling
gap
Small States (pop < 1.5
million)
46 43.2% 15.3% 2.812 50.0% 15.0% 3.339 36.1% 16.2% 2.228
by population size
Population from 0 to 0.5 million
32 41.7% 21.0% 1.984 46.0% 20.2% 2.274 35.9% 24.1% 1.491
Population from 0.5 to 1 million
8 47.2% 15.7% 3.007 45.8% 14.3% 3.213 49.3% 19.0% 2.591
Population from 1 to 1.5 million
6 40.9% 9.8% 4.198 68.9% 9.0% 7.646 28.3% 10.8% 2.617
by region / income
East Asia and Pacific
12 50.8% 17.0% 2.986 74.2% 16.9% 4.381 32.6% 17.2% 1.900
Latin America and Caribbean
10 74.9% 35.0% 2.143 75.4% 30.0% 2.515 74.3% 51.4% 1.446
Sub-Saharan Africa
10 41.7% 6.0% 6.947 43.3% 5.2% 8.307 39.6% 8.5% 4.649
High-income countries
12 23.0% 10.7% 2.144 24.9% 12.0% 2.073 19.4% 5.3% 3.675
Other Groups of Interest
Small Islands Developing States
37 42.4% 13.8% 3.073 45.0% 11.8% 3.808 39.4% 20.0% 1.965
Population from 1.5 to 3 million
15 20.9% 7.1% 2.960 25.0% 5.7% 4.366 18.5% 8.7% 2.125
Population from 3 to 4 million
13
18.5% 10.0% 1.849 20.7% 11.1% 1.874 16.7% 8.8% 1.904
World average
192 5.3% 1.8% 2.993 5.2% 1.6% 3.316 5.4% 2.4% 2.309
Total high-income countries
41 3.5% 2.8% 1.264 3.8% 3.0% 1.258 2.9% 1.2% 2.529
Total developing countries
151 7.4% 1.5% 4.916 7.8% 1.1% 7.182 7.0% 2.5% 2.831
Skilled emigration rates and average emigration rates are defined by equation (2).
Schooling gap = Skilled emigration rate / Average emigration rate
Source : Docquier and Marfouk (2006)
19
4. ALTERNATIVE MEASURES CONTROLLING FOR AGE OF
ENTRY
The previous data on international skilled migration define skilled immigrants as foreign-born
workers with university or post-secondary training. However, this definition does not account
for whether education has been acquired in the home or in the host country and thus leads to a
potential over-estimation of the intensity of the brain drain as well as to possible spurious
cross-country variation in skilled emigration rates.
As shown by Rosenzweig (2005) on the basis of US survey data, children migration can
represent an important fraction of total immigration for certain countries as over 18 percent of
permanent resident aliens immigrated to the US before age 18, and over 25 percent
immigrated before age 20. Among those who arrived before age 18 or 20, some are highly-
skilled today, having most likely acquired education once in the US. Should we include them
as part of the ''brain drain''?
As explained, existing brain drain data sets are built according to a broad definition in that
they include all foreign-born workers with tertiary schooling; for example, Mexican-born
individuals who arrived in the US at age 5 or 10 and then graduated from US high-education
institutions later on are counted as highly-skilled Mexican immigrants. This can be seen as
providing an upper bound to brain drain estimates.
In contrast, it has been suggested that only people with home-country higher education should
be considered as skilled immigrants (Rosenzweig 2005). This must be considered as a lower-
bound measure of the brain drain. Indeed, except for those arrived at very young age, most of
the immigrants who then acquired host country tertiary education arrived with some level of
home country pre-tertiary schooling. In addition, some of them would still have engaged in
higher education in the home country in the absence of emigration prospects.
In this section, we use immigrants' age of entry as a proxy for where education has been
acquired. Data on age of entry are available from a subset of receiving countries which
together represent more than three-quarters of total skilled immigration to the OECD. Using
these data and a simple gravity model, we estimate the age-of-entry structure of skilled
immigration to the other OECD countries. This allows us to propose alternative measures of
the brain drain by defining skilled immigrants as those who left their home country after age
12, 18 or 22, and to do so for both 1990 and 2000. These corrected skilled emigration rates,
which can be seen as intermediate bounds to the brain drain estimates, are by construction
20
lower than those computed without age-of-entry restrictions by Docquier and Marfouk
(2006), which we take as our upper-bound brain drain measure.
4.1. Methdology
To estimate the structure of immigration by age of entry, we collect census and register data
in a sample of countries where such information is available: the US 1990 and 2000 censuses,
the Canadian 1991 and 2001 censuses, the French 1999 census, the Australian 1991 and 2001
censuses, the New-Zealand 1991 and 2001 censuses, the Danish 2000 register, the Greek
2001 census and the Belgian 1991 census. Together, the countries sampled represent 77
percent of total skilled immigration to the OECD area. The sample is representative of the
OECD in that it includes countries with different demographic sizes, regional locations,
development levels and immigration policy and tradition.
We thus have bilateral information on immigrants' origin, age, education level and age of
entry from 12 host countries' censuses for 192 sending countries. These 2304 observations
allow us to compute the proportion of immigrants arrived before ages 12, 18 and 22 in the
total stocks of immigrants aged 25+ estimated by Docquier and Marfouk (2006). Eliminating
zeros and a few suspicious observations, we end up with 1580 observations for each age
threshold.
Table 3 gives descriptive statistics on the estimated proportions of adult immigrants who
arrived before age J (J = 12, 18 and 22). The average shares vary across receiving countries
(not shown). On the whole, the average shares are 85.7%, 78.2% and 69.1% for immigrants
arrived before age 12, 18 or 22. They are usually higher for Belgium, Denmark and Greece.
The lowest shares are observed in Australia, New Zealand and the United States. Canada and
France are not far from the average distribution.
Obviously, an approach based on Census data is not perfect. As explained by Rosenzweig
(2005, p. 9), “information on entry year ... is based on answers to an ambiguous question - in
the US Census the question is “When did you first come to stay?” ” Immigrants might answer
this question by providing the date when they received a permanent immigrant visa, not the
date when they first came to the US, at which time they might not have intended to or been
able to stay. Only surveys based on a comprehensive migration history would provide precise
data about the location in which schooling was acquired.
21
However, the Census is the only representative source of data available in many countries. In
addition, extrapolating the entry age structure from surveys (such as NIS – 4\% of immigrants
- or NSIP - a sample of 150,000 persons out of more than 25,000,000 adult immigrants - for
the US) can be misleading. The number of observations can be very small for countries with
few emigrants; this is typically the case of small countries which, on the other hand, are
precisely the ones most affected by the brain drain in relative terms.
4.2. Results for small states
Table 3 presents the results for the brain drain from small states for all those who migrated,
irrespective of their age (as in Table 2), for those who migrated before age 12, 18 and 22, and
also the ratio of the latter to all those who migrated (22+/0+). Focusing on that ratio, we see
for 2000 that it is equal to 70.1% for small states as a whole, with little variation across the
three population groups (Groups 1 to 3), and about the same as that for developing as a whole.
It is about 8% smaller for high-income countries and 2% smaller for the world average.
The ratio declined from 1990 to 2000 for small states as a whole (from 74.0 to 70.1%) as well
as for all small states groups (with the major decline for the largest group and for East Asia
and Pacific), implying that a larger share of migrants obtained their degrees at home. On the
other hand, the ratio increased for all other country groups.
Table 4 shows the brain drain by small state for the various age-of-entry groups. The
correlation between the first group (BD 0+) and the last group (BD 22+), with identical
ranking of the top six countries (Guyana, Grenada, Saint Vincent and the Grenadines,
Trinidad and Tobago, Saint Kitts and Nevis, and Samoa), with 9 of the top 10 countries for
BD 22+ also in the top 10 for BD 0+, and with the same top 20 countries in both groups.
22
Table 3. Adjusted brain drain by country group (1990 and 2000)
2000 1990
Nb
BD 0+
BD 12+
BD 18+
BD 22+
Ratio
22+ / 0+
BD 0+
BD 12+
BD 18+
BD 22+
Ratio
22+ / 0+
Small States (pop < 1.5 million)
46 43.2% 39.4% 35.4% 30.2% 70.1% 50.0% 46.3% 42.3% 37.0% 74.0%
by population size
Population from 0 to 0.5 million
32 41.7% 38.0% 34.0% 29.1% 69.8% 46.0% 42.5% 38.8% 33.9% 73.8%
Population from 0.5 to 1 million
8 47.2% 43.0% 38.6% 32.8% 69.4% 45.8% 42.0% 38.0% 32.5% 70.9%
Population from 1 to 1.5 million
6 40.9% 37.6% 34.0% 29.3% 71.6% 68.9% 65.6% 61.8% 56.4% 81.8%
by region / income
East Asia and Pacific
12 50.9% 45.1% 39.9% 34.5% 67.8% 74.2% 69.6% 64.5% 60.0% 80.8%
Latin America and Caribbean
10 74.9% 72.2% 68.2% 62.4% 83.3% 75.4% 73.0% 69.5% 63.9% 84.8%
Sub-Saharan Africa
10 41.7% 38.0% 35.6% 32.1% 76.8% 43.3% 39.2% 36.8% 34.1% 78.6%
High-income countries
12 23.0% 19.9% 17.9% 14.9% 64.6% 24.9% 21.9% 20.1% 17.0% 68.5%
Other Groups of Interest
Small Islands Developing States
37 42.4% 38.0% 33.1% 28.3% 66.6% 45.0% 40.4% 35.5% 30.8% 68.4%
Population from 1.5 to 3 million
15 20.9% 18.4% 15.6% 13.2% 63.4% 25.0% 21.9% 18.2% 15.2% 61.0%
Population from 3 to 4 million
13
18.5% 16.7% 15.6% 13.8% 74.7% 20.7% 18.5% 17.3% 15.3% 73.8%
World average
192 5.3% 4.6% 4.1% 3.6% 68.6% 5.2% 4.4% 4.0% 3.5% 66.9%
Total high-income countries
41 3.5% 2.9% 2.6% 2.3% 64.7% 3.8% 3.1% 2.8% 2.4% 64.1%
Total developing countries
151 7.4% 6.6% 5.9% 5.2% 71.1% 7.8% 6.9% 6.2% 5.4% 69.8%
Source: Beine, Docquier and Rapoport (2006)
23
Table 4. Brain drain in small states (year 2000)
Country BD 0+ BD 12+ BD 18+ BD 22+ Population
Tuvalu 27.3% 26.1% 25.5% 23.8% 11468
Nauru 34.5% 28.0% 23.4% 19.8% 12809
Palau 26.1% 24.1% 22.3% 18.5% 20016
San Marino 17.1% 16.4% 15.9% 14.9% 28503
Saint Kitts and Nevis 78.5% 76.3% 72.0% 65.3% 38836
Marshall Islands 39.4% 39.4% 39.3% 39.2% 57738
Antigua and Barbuda 66.8% 63.4% 57.8% 49.6% 68320
Dominica 64.2% 61.2% 57.4% 51.2% 69278
Andorra 6.9% 5.8% 5.4% 4.6% 69865
Seychelles 55.8% 53.3% 51.0% 47.5% 80832
Grenada 85.1% 83.7% 81.2% 76.9% 89357
Kiribati 23.1% 22.0% 21.2% 20.7% 100798
Tonga 75.2% 70.4% 65.1% 58.8% 101000
Micronesia, Federated States 37.8% 37.4% 36.9% 34.8% 107000
Saint Vincent & Grenadines 84.5% 83.0% 79.8% 75.1% 119000
Saint Lucia 71.1% 68.2% 64.8% 59.2% 147000
Sao Tome and Principe 22.0% 21.5% 21.2% 20.0% 149000
Samoa 76.4% 71.7% 66.4% 60.9% 174000
Vanuatu 8.2% 6.7% 5.8% 4.7% 195000
Belize 65.5% 61.5% 54.8% 47.0% 238000
Barbados 63.5% 59.7% 53.8% 47.5% 266000
Iceland 19.6% 18.3% 17.4% 15.8% 282000
Maldives 1.2% 1.0% 0.9% 0.8% 290000
Bahamas 61.3% 53.7% 47.7% 42.3% 300000
Brunei 15.6% 13.3% 11.5% 9.7% 335000
Malta 57.6% 53.3% 49.7% 44.1% 389000
Suriname 47.9% 44.6% 42.6% 36.7% 425000
Luxembourg 8.0% 7.1% 6.7% 5.8% 436000
Solomon Islands 6.4% 5.0% 4.1% 3.5% 437000
Cape Verde 67.4% 62.9% 59.4% 55.5% 438000
Macao 14.4% 13.3% 12.5% 11.4% 449000
Equatorial Guinea 12.9% 12.0% 11.5% 10.2% 457000
Qatar 2.5% 2.3% 2.1% 1.9% 582000
Djibouti 11.0% 9.2% 8.3% 7.5% 666000
Bahrain 4.9% 4.3% 3.9% 3.5% 677000
East Timor 15.5% 11.7% 9.5% 7.9% 701000
Comoros 21.9% 19.5% 17.3% 13.1% 706000
Guyana 89.0% 87.7% 85.4% 81.9% 761000
Cyprus 33.2% 28.6% 26.3% 21.3% 783000
Fiji 62.2% 56.4% 50.9% 44.5% 813000
Mauritius 56.1% 52.2% 49.4% 45.1% 1186000
Gabon 14.7% 11.6% 10.0% 8.4% 1258000
Trinidad and Tobago 79.3% 76.6% 73.0% 67.5% 1289000
Gambia 63.2% 62.5% 62.1% 60.4% 1312000
Estonia 11.5% 10.8% 10.3% 9.4% 1366000
Guinea-Bissau 24.4% 21.7% 20.5% 18.7% 1367000
24
5. BILATERAL DATA
Table 5 presents for the small states the number and share of skilled migrants by host region.
None of the results are really surprising.
¾ First, 66% or two thirds of skilled migrants from small states go to the US + Canada,
followed by the EU15 with 23% and Australia + New Zealand (10%).
¾ Second, the first destination in 2000 of East Asia and Pacific skilled migrants was
Australia + New Zealand (56%) followed by USA + Canada (41%), that of Latin
America and Caribbean was USA + Canada (84%) and the EU15 (16%), that of Sub-
Saharan Africa is the EU15 (57%), followed by USA + Canada (27%) and Australia +
New Zealand (15%), and that of high-income countries is 43% for both the EU15 and
USA + Canada, and 12% for Australia + New Zealand. Thus, the region whose skilled
migration is the most concentrated across the host regions is Latin America and the
Caribbean, followed by Sub-Saharan Africa, East Asia and the Pacific, and by high-
income countries.
¾ Third, as for evolution, the distribution across host regions has been fairly stable, with
minimal changes in their shares of skilled immigrants from the various source regions
between 1990 and 2000. This may attest to the strength of existing migrant networks
in determining new migrants’ destination. In other words, history matters – whether
the network was formed because of small distance, large income differentials, past
colonial ties, or other.
25
Table 5. Small states brain drain by destination
Skilled emigrants in 2000
To:
DESTINATION
SOURCE
OCDE USA +
Canada
EU15 Australia +
N. Zealand
Others
East Asia and Pacific 76307 31234 1916 43053 104
41% 3% 56% 0%
Latin America and Caribbean 375822 315227 58780 1448 366
84% 16% 0% 0%
Sub-Saharan Africa 44493 12206 25269 6554 464
27% 57% 15% 1%
High-income countries 113555 49201 48641 13837 1876
43% 43% 12% 2%
Total 634100 417289 147626 66107 3079
66% 23% 10% 0%
Skilled emigrants arrived after age 22 in 2000
To:
DESTINATION
SOURCE
OCDE USA +
Canada
EU15 Australia +
N. Zealand
Others
East Asia and Pacific 38851 17152 1391 20210 77
44% 4% 52% 0%
Latin America and Caribbean 208565 171624 35910 712 208
82% 17% 0% 0%
Sub-Saharan Africa 29313 8412 17423 3096 170
29% 59% 11% 1%
High-income countries 66301 28013 29038 7896 925
42% 44% 12% 1%
Total 362082 232161 94476 33073 1532
64% 26% 9% 0%
Skilled emigrants in 1990
To:
DESTINATION
SOURCE
OCDE USA +
Canada
EU15 Australia +
N. Zealand
Others
East Asia and Pacific 48797 15718 736 32329 41
32% 2% 66% 0%
Latin America and Caribbean 217056 178872 36608 1200 275
82% 17% 1% 0%
Sub-Saharan Africa 26587 6763 11173 8393 86
25% 42% 32% 0%
High-income countries 81315 33642 32198 14472 452
41% 40% 18% 1%
Total 373885 234995 80748 56488 803
63% 22% 15% 0%
26
6. CONCLUSION
This paper presented evidence on the brain drain, focusing on small states. We found that
small states i) had three out of seven skilled individuals (43%) living outside their country of
origin in 2000; ii) had a brain drain level that amounts to over 5 times the brain drain in
developing countries as a whole, 12 times that in high-income countries as a whole, and 8
times that of the world, iii) that it declined between 1990 and 2000, iv) that their schooling
gap was much smaller than that for developing countries as a whole and similar to that for the
world as a whole and, and v) that it declined for all small state groups and other country
groups, except for small high-income states and for high-income countries as a whole.
When correcting for the age of entry, we found that in small states in 2000, skilled emigrants
arriving in the host country after the age of 22 (and who presumably obtained their university
education in their country of origin) amounted to about 70% of all skilled migrants (including
those who obtained their university education abroad). This ratio was similar for all
developing countries as well as for the world average, and it declined between 1990 and 2000
in all small states groups while increasing in all other country groups.
We also found that the distribution of migrants across host regions has been fairly stable, with
minimal changes in their shares of skilled immigrants from the various source regions
between 1990 and 2000. We hypothesized that this reflected the strength of existing migrant
networks in determining new migrants’ destination, with past migration patterns strongly
affecting subsequent migration decisions. This is because the networks themselves have a
strong impact and because a number of variables found to affect the incentive to migrate to a
certain region – compared to other regions or staying home — are invariant with respect to
time, including distance and colonial past, while another variable, namely income differential,
did not change significantly over the period.
The negative trend in the brain drain is unlikely to be sufficient to stop the hemorrhage in the
poorer small states anytime in the near or foreseeable future, and implementing some policies
to slow it down might be in order. However, few policies that would succeed in reducing the
brain drain seems have been found, and some might even be counterproductive.
24
A strategy
24
For instance, some African countries have given degrees in certain fields that are not recognized
internationally in order to retain more professionals in the country. However, this policy is likely to have a
smaller effect than, if not opposite to, the intended one. The reason is that the policy is likely to reduce the total
27
from which source countries would benefit is by establishing programs, in cooperation with
host countries, to provide fellowships to study abroad, on the condition that the recipients
return home after graduation for a specified period of time before having the option to
emigrate again.
25
Countries lacking the resources to do so would have to obtain financing
from foreign sources (e.g., the host countries involved). Some students might decide not to
return and the program’s success would depend on agreements between source and host
countries to prevent such occurrences, such as the host country committing not to renew the
visa of those refusing to return after completing their studies or internship.
Another strategy that should help convince some graduates to remain home, and some
migrants and students abroad to return home would be to improve conditions for skilled labor
in the public sector. This would also improve public services by raising the quality of the staff
and reducing the extent of absenteeism. Such a policy could be conducted together with the
previous one and might also require external support.
Finally, source countries would benefit if skilled migrants’ hiring contracts abroad could be
made temporary, possibly stipulating that these migrants would be able to return after a
specified period of time in their home country.
number of students in those fields, and to raise the number of those who study and remain abroad because they
tend to be more successful in the host countries than those who migrate after completing their study at home.
25
Such circular migration would also benefit host countries because it would reduce the extent to which migrants
reneged on their commitment to leave after graduation (Schiff 2007).
28
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29
