Download Migration, remittances and the brain drain

Migration, Remittances and the Brain Drain:
Causes and Linkages1
Yoko Niimi and Çağlar Özden2
DECRG
World Bank
April 2007
Abstract
The paper examines the determinants of remittance flows at the cross-country level. It considers,
among other things, the significance of migration level, the education level of migrants and
financial sector development in determining the amount of remittances received. The paper
accounts for the potential endogeneity bias, or possible reverse causality between remittances and
these key variables through instrumental variable estimation. Both the stock of migrants abroad
and their education levels are found to be the main drivers of remittances. The results show a
positive correlation between remittance flows and the level of migration, but surprisingly,
remittances are negatively correlated with the education level of migrants.
Key Words: migration, remittances, brain drain
JEL Codes: F22, F24, J61, O15, O16
1
We would like to thank Maurice Schiff, L. Alan Winters, Pablo Fajnzylber, Caroline Freund, Humberto
Lopez, Richard H. Adams and Michel Beine for comments and Ileana Cristina Neagu for her help with the
data. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors.
They do not necessarily represent the views of the World Bank, its executive directors, or the countries they
represent.
2
World Bank. Mailing address: DECRG, World Bank Mailstop MC3-303. 1818 H Street NW Washington,
DC 20433. E-mail: [email protected], [email protected]
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1. Introduction
The recent years have witnessed a dramatic increase in migrant remittances to developing
countries. Officially recorded remittances, measured as the sum of workers’ remittances,
compensation of employees, and migrant transfers, are estimated to have increased from US$58
billion in 1995 to US$167 billion in 2005. This growth rate has outpaced that of private capital
flows and official development assistance (ODA) over the last decade, making remittances the
second largest source of external funding for developing countries after foreign direct investment
(World Bank, 2005).
There are several factors that are said to explain for the recent increase in remittance flows. They
include growth in the number of migrants and their income, lower costs and wider networks in
the global financial services industry, and government policies that improve financial market
access, which promote the use of official channels (World Bank, 2005). Whatever the reasons
behind this surge, the growing importance of remittances as a source of foreign exchange and
their contribution to economic development have certainly attracted an increasing attention from
policy-makers and academics. The existing empirical research on the determinants of remittances
has, however, been rather limited and tended to be at the microeconomic level based on
household surveys. Although there have been a number of studies that examine the response of
remittance flows to macroeconomic factors (e.g. El-Sakka and McNabb, 1999; Aggarwal et al.,
2005; Freund and Spatafora, 2005), the work in this area is less established.
The main aim of this paper is improving our current understanding of how remittance flows are
determined at the cross-country level. The main variables we consider are (1) the level of
migration, (2) the education level of migrants and (3) the financial sector development of the
home countries. One of the crucial issues in the migration literature is that migrants are not
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selected from a random sample of the underlying native population of the home country. As a
result, the desire to remit strongly influences migration levels and many other characteristics of
migrants, such as their education levels. Thus the potential endogeneity biases surrounding the
migration, remittances, brain drain and financial development linkages need to be properly taken
into account in any empirical analysis. This is one of the main goals in the paper.
Among all of the determinants of remittances, particular attention is paid to the effect of
migrants’ education levels. With economic research providing increased evidence on the
importance of human capital in the development process, the brain drain has become a very
important area of concern. It is often argued that the negative effects of brain drain are offset by
the fact that educated migrants typically earn more and thus remit more (e.g. Ratha, 2003). Some
existing studies do not, however, support this view (e.g. Faini, 2006). It is an important question
to interrogate further given that immigration policies of many destination countries are
increasingly favoring relatively educated migrants from developed countries. Thus our paper
provides interesting, and relatively negative, evidence to the debate on the impact of brain drain
on economic development.
The rest of the paper is structured as follows. The next section reviews the existing work on the
determinants of remittances, identifies some of their limitations and describes how the present
paper can contribute to the current literature. Section 3 provides a brief description of the data
employed for the empirical analysis and specifies the estimation models. Section 4 presents the
estimation results. The final section summarizes the findings.
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2. Literature on Determinants of Remittances
The existing literature on the determinants of remittances is largely based on microeconomic
analyses. Lucas and Stark (1985), for instance, is a pioneering work on possible motives of
migrant workers to remit home, based on the data drawn from the National Migration Study of
Botswana. They consider several motivations to remit, ranging from pure altruism to pure selfinterest and find that tempered altruism or enlightened self-interest seems to explain the
remittance behavior in their data. Tempered altruism/enlightened self-interest views remittance
flows as part of a self-enforcing contractual arrangement between migrant and family, which are
of mutual benefit. The migrant adheres to the arrangement as long as it is in his or her interest to
do so, which may be either altruistic or more self-seeking (Lucas and Stark, 1985). Much of the
existing work based on the micro data follows this approach (e.g. Aggarwal and Horowitz, 2002;
Ilahi and Jafarey, 1998).
A macro-level analysis of remittances is, on the other hand, less established. Aggregate
remittance flows are likely to reflect the underlying microeconomic considerations that determine
individual decisions to remit (El-Sakka and McNaab, 1999). As a result, some of the
macroeconomic variables that have been examined include those concerning the economic
conditions of home country as well as investment variables such as interest rate differentials
between home and host countries (e.g. El-Sakka and McNabb, 1999; Higgins et al., 2004;
Straubhaar, 1986; Swamy, 1981). Nonetheless, there is little consensus on the key
macroeconomic determinants of remittance flows. Straubhaar (1986), for instance, finds that
remittance flows from Germany to Turkey are not affected by exchange rate variations or by
changes in the real rate of return on investment. On the other hand, using the data for Egypt, ElSakka and McNabb (1999) find that both exchange rate and interest rate differentials are
important in attracting remittances through official channels.
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Although country-specific work such as those noted here is useful in its own right, this limits us
from generalizing the findings. There have been a number of studies that examine the
macroeconomic determinants of remittances using cross-country or panel data sets in recent years.
In a very innovative paper on informal remittance flows to developing countries, Freund and
Spatafora (2005) estimate their determinants in order to predict their volume. They consider such
variables as the cost of sending remittances through formal channels, the presence of exchangerate and other economic distortions, migration levels, and other country characteristics. They find
that the stock of migrants in OECD countries is the primary determinant of remittances based on
a panel data analysis. In addition, their results show the importance of financial services industry
since that transaction costs are systematically related to concentration in the banking sector, lack
of financial depth, and exchange rate volatility.
Similarly, Buch and Kuckulenz (2004) investigate the determinants of remittances as well as of
private and official capital flows using panel data. They find that traditional variables such as
economic growth, the level of economic development, and proxies for the rate of return on
financial assets do not have a clear impact on remittance flows. They argue that this may be
because workers’ remittances share features of private and official capital flows, which are driven
by different macroeconomic factors. These similarities are caused by the fact that remittances are
to a large extent market-driven, but also influenced by social considerations of migrants.
Yang (2005), on the other hand, examines the impact of hurricanes on various types of
international financial flows to developing countries based on a panel data set. His results show
that greater hurricane exposure leads to large increases in foreign aid. For other types of
international financial flows, he finds the varying impact of hurricanes according to the income
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level of the country. In the poorer half of the sample, for instance, hurricane exposure is found to
generate a substantial increase in migrants’ remittance flows.
These studies have certainly provided us with a broader view of how remittance flows are
determined. However, there is sill a great scope for improving our current understanding of the
issue. One of the factors that deserve more attention is the impact of the education level of
migrant workers on remittances at the cross-country level. If a significant proportion of migrants
are highly educated, they are likely to earn higher income and thus remit more. Yet it can also be
argued that educated migrants tend to come from better-off families who rely less on remittances
for their livelihood. It is therefore not clear as to which force dominates (Niimi and Ozden, 2006).
In his analysis of the skill level of migrants on remittance flows based on a cross-country panel
data set, Faini (2006) finds no evidence that skilled migrants remit more. He argues that this is
not only because skilled migrants tend to come from relatively wealthy families, but also because
they have a lower propensity to remit home, presumably reflecting the fact that they are keener
(and more able) to bring their close family members in the destination country (Faini, 2006). His
findings indeed question the common belief that the negative effects of brain drain are offset by
the fact that educated migrants typically earn more and thus remit more.
Faini’s findings certainly have an important policy implication given that immigration policies
are increasingly favoring relatively educated migrants in most developed destination countries.
Nonetheless, his study is not without any caveat. Most importantly, there is a potential
endogeneity problem between remittance flows and the education level of migrants. Some of the
existing studies, for example, show the positive impact of remittances on school attainment in
home countries (e.g. Cox-Edwards and Ureta, 2003; Duryea et al., 2005). This suggests a possible
reverse causality between remittances and migrants’ education level. It is therefore important to
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account for the possible endogeneity bias in the analysis which is one of our main goals in this
paper.
Another important determinant we should examine is financial sector development of home
countries. While several studies have examined investment or portfolio variables as noted above,
there is little work that assesses the effect of financial development on remittance flows at the
macroeconomic level with several exceptions. Based on a panel data analysis, Aggarwal et al.
(2005) show the positive effects of remittances on the development of financial sector measured
as the ratio of bank deposit or credit to GDP. They, however, also recognize the possibility that
better financial development leads to larger measured remittances either because financial
development enables remittance flows or a larger percentage of remittances is measured when
those remittances flow through formal channels. Additionally, financial development might lower
the transaction costs, which in turn increase remittances. This potential reverse causality is
addressed through instrumental variable (IV) and Arellano-Bond Generalized Method of
Moments (GMM) estimations. It would thus be interesting to test the significance of financial
sector development as a determinant of remittances. Freund and Spatafora (2005) do include the
financial development variable in their panel regression of the determinants of remittances and
find its positive and significant effect on remittance flows. However, they do not control for any
reverse causality between financial sector development and remittance levels.
In sum, this paper aims to contribute to the existing literature by examining the impact on
remittance flows of, among other things, the following three factors: (1) the level of migration;
(2) the education level of migrants; and (3) financial sector development of home countries. The
endogeneity issues regarding these three variables discussed earlier will be taken into account in
our analysis through the IV estimation.
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3. Econometric Specification and Data
Table 1 summarizes the mean values of some of the key variables by regions in order to provide
an overview of their variation across regions. The ratio of measured remittances to GDP is found
to be the highest in the Middle Eastern and North African countries, followed by the Sub-Saharan
countries. On the other hand, in terms of its ratio of migrants to the population, Western European
countries have the highest level, mainly due to intra-European labor flows, and they are followed
by the American and the Middle Eastern & North African countries. As for the education level of
migrants, it is interesting to observe that more than half of the migrants from both the SubSaharan Africa and South Asia have tertiary education. These figures can be compared with the
ratio of people with tertiary education to the total population in home countries. The comparison
illustrates that migrants are significantly more highly educated than the rest of the population
across the world. However, the difference is substantial, particularly for the Sub-Saharan Africa
and South Asia. This seems to underline the brain drain issue, which is a growing concern in
many developing countries (see e.g. Ozden and Schiff, 2006).
[Table 1 around here]
The table has shown some important variations in remittance flows as well as in other factors
including the number of migrants and their education level across the regions. These variations
would certainly be even greater if we were to examine across the countries. The main aim of our
empirical analysis is to examine what are the key determinants of these observed variations in
remittance flows.
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3.1 Econometric Specification
In order to empirically examine the significance of various factors as the determinants of
remittance flows, the following equation will be estimated:
(1)
REM i     i1 MIGi   i2 FDi   i3GDPi   i4 GDPgrowthi   i5 Edu i   i
where i refers to home country i. For our dependent variable, REMi, we will try three different
variables, namely the ratio of remittances to GDP, the logarithm of GDP, and the logarithm of
remittances in per-capita terms. MIGi is the level of migration from country i. We express this
either as the logarithm of migrants abroad or as the ratio to population size of home country i. FDi
is financial sector development variable of country i, which is the ratio of bank deposit to GDP.
As for GDPi, both the logarithm of GDP and of GDP per capita are employed. GDPgrowthi is the
growth rate of GDP of country i and Edui is the ratio of migrants with tertiary education to the
total number of migrants. αi and βi are parameters to be estimated and εi is an error term.
Table 2 summarizes all the variables employed for the estimation. We start by estimating
equation (1) by Ordinary Least Squares (OLS) by ignoring any possible endogeneity problems.
We will then estimate it through the IV estimation, first only instrumenting the migration
variables, then instrumenting the migration and education variables, and finally the migration,
education and financial development variables. The rest of the section describes the variables and
discusses the endogeneity issues and our choice of instruments for the IV estimation.
[Table 2 around here]
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Stock of migrants abroad
The level of migration is likely to be the most important determinant of the size of remittances.
This is expressed in two different specifications - the logarithm of the number of migrants abroad
and it’s the ratio of migrants to population in home countries. The key issue that needs to be
properly addressed is the endogeneity bias since the desire to send remittances is among the main
reasons behind the migration decision of most people. We control for the endogeneity bias
through the IV estimation. The labor market participation rate, the population density, the
percentage of urban population, the dependency ratio (i.e., the ratio of dependents to working-age
population), the distance between the origin and destination countries expressed in terms of the
great-circle distance, an English language dummy variable that is unity if English is spoken in
home country, and the cost of obtaining a national passport as a share of GDP per capita are all
employed as identifying instruments for the level of migration.
The qualifying requirement for being an appropriate instrument is that they should be correlated
with the regressor for which it is to serve as an instrument, but uncorrelated with the dependent
variable. In other words, they should be correlated with the number of migrants abroad, but not
with remittance flows. The first four variables represent the demographic/labor market conditions
of home countries whereas the latter three variables indicate the costs of migrating to destination
countries.
The labor market participation rate and urbanization rate (assuming there are more working
opportunities in urban areas) are likely to indicate the availability of work in home countries,
which will be negatively correlated with the number of migrants leaving their home country. On
the other hand, population density in home countries is often thought to be a push factor for
migration. As for the dependency ratio, its lower figure informs us a higher percentage of
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working-age population. Hence this variable will be negatively correlated with the level of
migration. These four variables are thought to represent the demographic trends of home
countries and we believe that remittance flows are unlikely to influence these long-term trends.
As far as the costs of migrating are concerned, the longer the distance between the home and
destination countries and the higher the passport cost, the greater the cost of migrating and thus
the lower the number of migrants abroad. In addition, if English is commonly spoken in home
country, this reduces the migration cost and, as a result, generates a greater outflow of migrants.
The available empirical evidence shows the importance of distance in determining the level of
migration (e.g. Mayda, 2006) Moreover, McKenzie (2005) exploits the observed variation in the
cost of obtaining a national passport across the world and finds high passport costs are associated
with lower levels of outward migration. It should be reasonable to assume these variables are not
correlated with remittances and we therefore use them to instrument for the stock of migrants.
Given that there is no theoretical guidance as to which variables should be used as the identifying
variables, it cannot be denied that our verification of the set of identifying variables is a crude
way of determining their validity. This applies to the other sets of instruments described below
and should be taken into account when discussing the results.
Education level of migrants
Our education variable is the ratio of migrants with tertiary education to the total number of
migrants. The expected sign of its effect is unclear as there are some countervailing forces. On the
one hand, a higher level of education among migrants enables migrants to earn more and thus
remit more. On the other hand, it implies that these migrants are likely to come from better-off
households relative to the rest of the population and thus there is less need to send money to their
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families at home. In addition, it is easier for educated migrants to obtain legal residency in the
destination country and bring their immediate families over with them, which would decrease the
incentives to remit money back home (Niimi and Ozden, 2006). As discussed earlier, we have to
control for the possible endogeneity bias between remittances and migrants’ education level. The
instruments used in our IV estimation are the logarithm of public spending on education and per
student tertiary education expenditure. They are employed in turn. These variables are expected
to positively affect the education level of migrants, but we can reasonably assume that remittance
flows are unlikely to affect the size of these public expenditures.
Financial Sector Development
The more developed and efficient the financial sector of home countries is, the higher will be
amount of remittances sent by migrants and the higher will be share sent through formal channels.
A developed financial market lowers the transaction costs, increases the accessibility of recipients
to the money sent and provides investment opportunities for the savings of migrants. Hence we
will assess whether financial sector development has any positive impact on remittance flows
using the ratio of the bank deposit to GDP. Recall, however, that the recent work by Aggarwal et
al. (2005) shows that the level of remittances also has a positive impact on the development of the
financial sector of home countries. We therefore control for the possible endogeneity bias by
instrumenting the financial development variable by inflation rate and a dummy variable for
controlling for countries with British legal origin (i.e., whether their legal system is based on
Common Law) as they are suggested in Aggarwal et al. (2005) as the determinants of financial
sector development. It has been shown that there is a significant and economically important
negative relationship between inflation and both banking sector development and equity market
activity (Boyd et al., 2001). Further, given that the existing literature provides enough evidence
for the importance of legal institutions for financial sector development (e.g., Beck et al., 2003;
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La Porta et al., 1997, 1998), a dummy for countries with British legal origin (i.e., it is unity if the
country’s legal system is based on Common Law) is also employed as an instrument. Although
the latter is unlikely to be correlated with remittance flows, we may not be able to entirely rule
out the possibility of correlation between remittances and inflation. This should be taken into
account when interpreting the results.
Economic conditions of home countries
As a proxy for the general state of the development and size of home countries’ economy, we will
also include the logarithm of GDP per capita (ppp adjusted) and of total GDP. The former
measures the level of economic development of home countries and the expected sign of the
coefficient would be negative because migrants from wealthier countries are likely to have less
incentive to remit. As for the logarithm of GDP, which captures the size of home countries’
economy, we would expect the sign to depend on how we express our dependent variables, i.e.,
remittance variables. For example, if the dependent variable is the natural logarithm of
remittances, then this should increase with the size of the country since larger countries, such as
China or India, receive more in absolute amount. If the dependent variable is expressed as a share
of GDP, we would expect this to decrease with the country size. There is much evidence that
argues smaller and isolated small economies are less likely to sustain many economic activities,
which induces their citizens to migrate for better opportunities. This would in turn increase
remittance flows to these economies. In addition to the above variables, the rate of economic
growth of home countries is included in the estimation equation. Its expected impact on
remittance flows is not very clear. It can be argued that the growing economy reduces the
incentives to migrate and hence implies less remittance to those countries. It is, however, equally
possible that the high economic growth rate might induce migrants to invest more at home and
thus increase remittance inflows to home countries.
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3.2 Data
The data used for our analysis covers 85 countries, for which we have observations for all the
variables employed in the estimation. Although it would be ideal to have a panel data set, we will
concentrate our analysis on a single point in time, 2000, given that there are no time-series data
on the number of migrants abroad and their education level. Aggregate data on remittances come
from the IMF’s Balance of Payment statistics, which are the sum of the three items, namely
workers’ remittances, compensation of employees, and migrant transfers. The number of migrants
in OECD countries and the ratio of those with tertiary education among migrants come from
Docquier and Marfouk (2006). The cost of acquiring a national passport as a percentage of GDP
per capita is obtained from McKenzie (2005). Our financial sector development variable, namely
the ratio of bank deposit to GDP, is from the IMS’s International Financial Statistics. The rest of
the variables mostly come from the World Development Indicators. Appendix I provides a
description of the variables and their sources in more details.
4. Estimation Results
4.1 OLS Estimation
Our estimation starts with OLS whereby we ignore the endogeneity issues discussed in the
previous section. We estimate three regressions using a different definition of the dependent
variable, namely the ratio of remittances to GDP, the logarithm of remittances per capita, and the
logarithm of remittances. Each variable represents a different dimension of the economic
importance of remittances. Remittance to GDP ratio represents overall size of the remittances
relative to the economy. Remittances per capita capture the importance at the individual level.
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These are related but distinct measures of remittances. For example, for two countries with
similar remittances/GDP ratio, a poorer country will have a higher remittances per capita level.
We use migrants to population ratio as the main explanatory variable in the specifications that
employ these two remittance variables. On the other hand, we simply use logarithm of migrants
as the explanatory variable in the specification with logarithm of remittances.
The first three columns of Table 3 report the results. The level of migration is positively
correlated with the amount of remittances sent home as we would expect, though it is significant
only in two specifications (columns 2 and 3). For instance, one percent increase in the number of
migrants increases remittance flows by about 0.4 percent (column 3). However, given that the
increase is less than one percent (i.e., the coefficient is less than the unity), an increase in the level
of migration actually decreases remittances sent by each migrant! This may be partly explained
by family reunification effect. It would be interesting to examine such an effect, but unfortunately
our data do not allow us to do so.
[Table 3 around here]
As far as the education level of migrants is concerned, its effect on remittances is found to be
negative, though significant only in one specification (column 2). The negative sign of the
coefficients implies that relatively educated migrants are likely to remit a smaller amount of
remittances to their home countries. These results are consistent with Faini (2006). This could be
due to the fact that educated migrants come from relatively better-off families and there is less
need to send money home to support the livelihood of their family members. We could also argue
that migrants with higher level of education are more capable of bringing their close family
members to their destination countries, and thus there is less need to send money home.
The impact of the financial sector development of home countries is positive, but not significant
in any of the specifications. The effect of the size of the domestic economy and the level of
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economic development of home countries varies depending on the definition of the dependent
variable. While smaller countries receive a relatively small amount of remittances, they receive
more as a percentage of GDP, which is intuitive. The smaller domestic economy presumably has
relatively limited opportunities for economic activities, and migrants thus have to remit more to
support their families at home. Moreover, migrants from poorer countries are found to remit a
greater amount of money home in terms of the absolute amount or a share of GDP. However,
poorer countries seem to receive less remittances in per-capita terms.
As for the economic condition of home countries, the economic growth does not seem to have
any effect on remittance flows. This is consistent with some of the existing literature (e.g. Buch
and Kuckulenz, 2004). This is likely to be due to the fact that the two countervailing forces
described above – high economic growth may reduce the incentives to migrate and hence lead to
less remittances, but it can also induce migrants to invest more at home – cancel out.
4.2 IV Estimation (1)
We move on to the IV estimation that enables us to control for the possible endogeneity problems.
We first instrument only for the migration variables (IV (1)). The results are reported in Table 4.
In order to check the validity of our set of instruments, the Hansen J-statistics for the
overidentification test are reported at the bottom of Table 4. The test results support the validity
of our instruments, i.e., they are uncorrelated with the error term and excluded instruments are
correctly excluded from the estimated equation. Furthermore, our instruments are found to
explain a good deal of variation in the variables that they are instrumenting. The validity of our
instruments is supported by our results also for the other specifications where we instrument not
only for the migration level, but also for the education and financial development variables.
[Table 4 around here]
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The most striking difference in the results between the OLS and IV estimation is found in the
coefficients on the migration variables. When we control for the endogeneity of the migration
variables, the coefficients become statistically significant in all three specifications and
significantly larger in the absolute size. Based on this set of results, for example, one percent
increase in the number of migrants abroad will increase remittances by about 0.7 percent (column
3 of Table 4) in comparison with 0.4 percent based on the OLS results (column 3 of Table 3).
This difference endorses our claim for the importance of controlling for the endogeneity bias. The
rest of the results are relatively similar to those from the OLS estimation. Both the education and
financial development variables are not statistically significant even though we control for the
endogeneity of the migration variables.
It should briefly be noted that most of the coefficients on our instruments have the expected sign.
The key determinant of the stock of migrants seems to be whether English is commonly spoken in
the home country. If English is widely spoken in a particular country, the people from the country
are more likely to migrate, presumably because the ability to speak English eases the language
barrier when migrating to a foreign country.
4.3 IV Estimation (2)
We now instrument for both the migration and the education variables. In addition to the
instruments employed for the stock of migrants, variables for public expenditures on education
are also included as our instruments. These expenditure variables are found to be positively
correlated with the ratio of migrants with tertiary education as we would expect. Among the other
instruments, the urbanization rate, the distance between home and destination countries and the
language variable have the significant coefficients. It is interesting to observe the positive
correlation between the education level of migrants and the distance variable while we observe
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the negative correlation between the overall stock of migrants and the distance. These findings
suggest that educated migrants migrate to a country that is relatively far from their home country,
presumably because they are more capable of affording the cost of migrating to longer-distant
countries.
Table 5 reports the second-stage regression results for IV (2). The negative effect of the education
level of migrants is found to be statistically significant in all three specifications once we control
for the endogeneity of both the migration and education variables. In his analysis of the
determinants of remittances, Faini (2006) also finds a negative correlation between remittances
and the education level of migrants. However, his coefficient on his education variable is not
statistically significant even when he accounts for the endogeneity bias in the total migration level.
Since the coefficients only became significant when we controlled for both the migration and
education variables in our analysis, the statistically insignificant effects of migrants’ education
level found in Faini’s estimation might be explained by the endogeneity bias. According to our
estimation results, a one-percent increase in the number of migrants will lead to a decrease of
about 0.3 percent in remittance flows in terms of its ratio to GDP (column 1 of Table 5). Our
results therefore do not seem to support the view that the negative impact of brain drain on home
countries is mitigated by the fact that educated migrants remit more to their families.
[Table 5 around here]
As far as the migration variables are concerned, we find the positive and statistically significant
correlation between remittance flows and migration level, though the absolute size of their effects
are smaller than those estimated in IV (1). The coefficient on the financial development variable
has the expected sign, but again not statistically significant. The significance and sign of the other
coefficients are generally consistent with the previous two specifications (i.e., with OLS and IV
(1)).
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4.4 IV Estimation (3)
Finally, we instrument for the migration, education and financial development variables. As our
explanatory variables for the financial development variable, we included inflation rate and a
dummy for the British legal origin in the first-stage regression as in Aggarwal et. al. (2005).
Despite the fact that we control for the endogeneity of the financial development variable, its
coefficient is not statistically significant in any specification (Table 6). Hence although we find
the positive impact of the financial sector development of home countries on the amount of
remittances sent home, its significance is not supported by our analysis. This certainly merits
further investigation, particularly on the choice of other possible instruments.
[Table 6 around here]
In sum, the stock of migrants abroad, the education level of migrants and the level of economic
development of home countries are found to be the key determinants of international remittance
flows. The greater the number of migrants abroad, the larger the amount of remittances sent home
as we would expect. In addition, we have also found that less educated migrants are more likely
to remit home, perhaps due to the fact that they tend to come from less well-off families and thus
need to send money to support the livelihood of their family members, or because they are not
capable of bringing their immediate family members to the destination country. Furthermore, our
findings suggest that poorer countries are likely to receive relatively more remittances in terms of
the absolute total amount and its ratio to GDP, though they are found to receive less in per-capita
terms.
5. Conclusion
The paper has examined empirically the significance of some of the key factors in determining
remittance flows based on the cross-country data. The results show that the level of migration and
19
the education level of migrants are the main drivers of remittances. The size of domestic economy
and the level of economic development of home countries are also found to play a role in
determining the flow of remittances. Financial sector development seems to have a positive
impact on remittances, but its significance is found to be less clear. Our findings also illustrate the
importance of accounting for the potential endogeneity of the stock and education levels of
migrants in the estimation.
Some important policy implications can be drawn from these findings. Firstly, the negative
correlation between remittance flows and migrants’ education level suggests that less educated
migrants who are more likely to come from less well-off families are remitting more to their
families at home. Moreover, poorer countries are found to be receiving relatively more
remittances. These findings seem to suggest some possible positive impact of remittances on the
welfare of recipient families and of home countries, though a more detailed microeconomic
analysis is obviously required to make any definitive conclusion. However, the common belief
that the negative effect of brain drain is mitigated by remittances sent by those educated migrants
is not supported by our results.
Further analysis is obviously required to examine the robustness of the findings presented in this
paper. Despite the limited availability of data, a panel data analysis certainly merits future work.
Moreover, our empirical estimation was conducted based on the officially measured remittance
data. We are therefore not able to generalize our findings on the determinants of remittance flows
to the case of remittances transferred through informal channels. Because of their particular
relevance to the economic development of developing countries, more empirical work on
informal remittance flows is certainly required. This in turn indicates the need for further efforts
in collecting data on migration and remittances.
20
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22
List of Tables:
Table 1: Summary Statistics (mean values) by Regions (sample size: 85 countries)
Rem/GDP
(%)
Americas
Western Europe
Eastern and Central Europe
Middle East and North Africa
Sub-Saharan Africa
South Asia
East Asia and Pacific
1.44
0.48
0.88
3.38
2.95
2.47
0.58
Mig/Pop
(%)
Ratio of migrants
with tertiary edu.
(%)
2.41
3.60
1.55
2.09
0.39
0.21
0.36
37.4
34.3
45.0
30.3
55.4
55.3
49.9
Ratio of
population with
tertiary edu. in
home countries
(%)
11.9
18.6
17.4
9.4
3.5
4.4
6.6
Bank
deposit/GDP (%)
25.1
74.9
25.2
53.2
22.2
39.6
102.0
GDP per capita,
ppp
(2000 constant,
international $)
7,275
24,550
7,798
5,396
2,282
2,203
5,827
1.45
0.92
48.2
8.0
64.1
6,382
Sample average
Note: Figures are weighted by the population. They are based on the mean values for the period between 1998-2002 except for the number of migrants abroad,
the ratio of migrants to population, and the ratio of migrants with tertiary education to total migrants, which are the figures for the year 2000.
23
Table 2: List of Dependent and Explanatory Variables
Mean
Dependent variables
Ratio of remittances to GDP (%)
Log of remittances
Log of remittances per capita
Independent variables
Log of migrants abroad
Ratio of migrants abroad to population size
Ratio of bank deposit to GDP
Log of GDP
Log of GDP per capita, ppp
GDP growth rate (%)
Ratio of migrants with tertiary education to total number of
migrants (%)
S.D.
2.839
19.489
3.263
4.601
1.985
1.586
12.188
4.902
0.475
24.171
8.746
3.475
1.620
7.587
0.299
2.229
1.085
2.221
39.225
13.972
Instrumental variables
Labor market participation rate (%)
68.850
7.698
Population density (people per sq km)
140.548
191.139
Urbanization (%)
55.868
22.979
Dependency ratio
0.629
0.163
Log of distance (km)
1.171
0.950
Dummy for English language
0.318
Passport cost (% of GDP per capita)
3.003
5.698
Log of public spending on education, ppp
21.717
2.042
Per student tertiary education expenditure, ppp
4,324
3,525
Inflation rate (%)
8.109
13.723
Dummy for British legal origin
0.329
Note: All the variables are the mean values for the period between 1998-2002 except for the logarithm of
migrants abroad, the ratio of migrants to population, and the ratio of migrants with tertiary education which
are the figures for the year 2000, and for public spending on education, the logarithm of public spending on
education, school enrollment and per student tertiary education expenditure that are the mean values for the
period between 1990-2000. They are unweighted means.
24
Table 3: Regression Results: OLS
Rem/GDP
Migrants/population
0.134
[0.133]
OLS
Log of Rem
per capita
0.066***
[0.020]
Log of migrants
Ratio of migratns with tertiary edu.
Bank deposit/GDP
Log of GDP
Log of GDP per capita
GDP growth
Constant
-0.040
[0.027]
0.925
[1.373]
-0.416
[0.506]
-1.216***
[0.438]
-0.261
[0.336]
24.896**
[10.813]
Log of Rem
-0.022**
[0.010]
0.366
[0.861]
0.493***
[0.192]
-0.051
[0.078]
-0.503
[1.387]
0.390**
[0.179]
-0.012
[0.012]
0.655
[0.670]
0.556***
[0.117]
-0.521**
[0.215]
-0.058
[0.074]
6.206***
[1.904]
85
85
85
Observations
0.268
0.363
0.628
R2
5.13 (0.00)
9.73 (0.00)
18.34 (0.00)
F-statistic (p-value)
Note: Figures in parentheses are robust standard errors.
***significant at 1% level, **significant at 5% level, and *significant at 10% level.
25
Table 4: Regression Results: IV (1)
Instrumenting only for the migration variable
Rem/GDP
Migrants/population
0.337*
[0.176]
IV (1)
Log of Rem
per capita
0.112**
[0.045]
Log of migrants
Ratio of migratns with tertiary edu.
Bank deposit/GDP
Log of GDP
Log of GDP per capita
GDP growth
Constant
-0.027
[0.034]
-0.482
[1.367]
0.051
[0.280]
-1.724***
[0.432]
-0.205
[0.310]
17.045***
[6.584]
Log of Rem
-0.016
[0.011]
0.132
[0.856]
0.493***
[0.182]
-0.047
[0.076]
-0.861
[1.366]
0.710***
[0.230]
2.97E-04
[0.013]
0.486
[0.634]
0.362**
[0.180]
-0.429**
[0.220]
-0.062
[0.078]
5.810***
[1.952]
85
85
85
Observations
0.209
0.326
0.601
R2
4.69 (0.00)
9.39 (0.00)
19.46 (0.00)
F-statistic (p-value)
8.57 (0.20)
8.92 (0.18)
6.39 (0.38)
Overidentification χ2 (p-value)
Note: Figures in parentheses are robust standard errors.
***significant at 1% level, **significant at 5% level, and *significant at 10% level.
26
Table 5: Regression Results: IV (2)
Instrumenting for the migration and education variables
Rem/GDP
Migrants/population
0.282**
[0.123]
IV (2)
Log of Rem
per capita
0.086***
[0.033]
Log of migrants
Ratio of migratns with tertiary edu.
Bank deposit/GDP
Log of GDP
Log of GDP per capita
GDP growth
Constant
-0.098**
[0.044]
0.465
[1.258]
0.053
[0.393]
-1.948***
[0.499]
-0.295
[0.337]
21.875**
[9.154]
Log of Rem
-0.043**
[0.018]
0.515
[0.812]
0.410**
[0.180]
-0.081
[0.079]
1.005
[1.526]
0.444**
[0.209]
-0.031*
[0.018]
0.804
[0.636]
0.564***
[0.138]
-0.618***
[0.211]
-0.082
[0.077]
6.984***
[2.024]
85
85
85
Observations
0.195
0.313
0.607
R2
3.75 (0.00)
8.77 (0.00)
17.85 (0.00)
F-statistic (p-value)
7.37 (0.29)
8.75 (0.19)
6.40 (0.38)
Overidentification χ2 (p-value)
Note: Figures in parentheses are robust standard errors.
***significant at 1% level, **significant at 5% level, and *significant at 10% level.
27
Table 6: Regression Results: IV (3)
Instrumenting for the migration, education and financial development variables
Rem/GDP
Migrants/population
0.233*
[0.125]
IV (3)
Log of Rem
per capita
0.088***
[0.033]
-0.092**
[0.042]
1.779
[3.233]
-0.098
[0.449]
-1.930***
[0.552]
-0.308
[0.349]
24.808**
[11.659]
-0.042**
[0.018]
0.333
[1.172]
0.441**
[0.222]
-0.079
[0.081]
0.760
[1.938]
0.429**
[0.201]
-0.033*
[0.020]
1.565
[1.193]
0.561***
[0.146]
-0.738***
[0.258]
-0.090
[0.086]
8.040***
[2.698]
85
0.220
3.76 (0.00)
8.06 (0.33)
85
0.315
8.09 (0.00)
8.74 (0.27)
85
0.597
18.35 (0.00)
6.61 (0.47)
Log of migrants
Ratio of migratns with tertiary edu.
Bank deposit/GDP
Log of GDP
Log of GDP per capita
GDP growth
Constant
Observations
R2
F-statistic (p-value)
Overidentification χ2 (p-value)
Log of Rem
28
Appendix: Variable Definitions and Sources
Variable name
Description
Source
Remittances to GDP
Ratio of remittances to GDP (%), where remittances
are defined as the sum of “workers’ remittances”,
“compensation of employees” , and “migrants’
transfers” (see Appendix A in Freund and Spatafora
(2005)).
Log of remittances (constant 2000 US$), which are
calculated by multiplying the ratio of remittances to
GDP by GDP figures.
Balance of
Payments Statistics
(IMF)
Log of remittances
Log of remittances per
capita
Log of remittances per capita (constant 2000 US$).
Log of migrants abroad
Log of total number of migrants in OECD countries.
Ratio of migrants to
population
Ratio of total number of migrants in OECD countries
to the population size of home countries (%).
Ratio of migrants with
tertiary education to the
total number of migrants
Bank deposit to GDP
Ratio of migrants with tertiary education to the total
number of migrants (%).
Log of GDP
Bank deposit to GDP calculated as: {(0.5)*[F(t)/P_e(t)
+ F(t-1)/P_e(t-1)]}/[GDP(t)/P_a(t)] where F is demand
and time and saving deposits, and the rest as defined as
above.
Log of GDP (constant 2000 US$).
Log of GDP per capita
Log of GDP per capita, ppp adjusted (constant 2000
international $).
GDP growth
GDP growth (annual %).
Labor market
participation rate
Labor market participation rate (%) – percentage of
population aged 15-64 who are in labor market.
Population density
Population density (people per sq km).
Urbanization
Urban population (%) – percentage of population living
29
Remittances:
Balance of
Payments Statistics
(IMF)
GDP: World
Development
Indicators
Remittances:
Balance of
Payments Statistics
(IMF)
Population: World
Development
Indicators
Docquier and
Marfouk (2006)
Migrants: Docquier
and Marfouk
(2006),
Population: World
Development
Indicators
Docquier and
Marfouk (2006)
International
Financial Statistics
(IMF)
World
Development
Indicators
World
Development
Indicators
World
Development
Indicators
World
Development
Indicators
World
Development
Indicators
World
in the urban sector.
Dependency ratio
Age dependency ratio – ratio of dependents to
working-age (15-64) population.
Log of distance
Log of distance between destination and home
countries calculated using the formula for the greatcircle distance (km). We calculated for each region as
follows:
USA, Canada, EU, Australia, New Zealand:
Zero distance
Eastern and Central Europe, Middle East, Africa:
Average distance to EU countries weighted by
the number of migrants
Central America, Mexico, Caribbean, South America:
Distance to USA
South Asia, East Asia and Pacific:
Average distance to USA/Canada and EU
countries weighted by the number of migrants.
English language (1 indicates countries where English
is commonly spoken).
English language dummy
Passport cost to GDP per
capita
Inflation
Passport cost normalized by the country’s GDP per
capita expressed in US dollars, inflation adjusted (%):
(passport cost/ GDP current dollars per
capita)*100/((1+inflation90/100)(1+inflation91/100)…
.((1+inflation2004/100))
Inflation, consumer prices (annual %).
British legal origin
British legal origin (1 indicates countries with
Common Law origin).
Log of public spending on
education
Log of public spending on education, ppp adjusted
(constant 2000 international $).
Per student tertiary
education expenditure
Per student tertiary education expenditure, ppp adjusted
(constant 2000 international $).
30
Development
Indicators
World
Development
Indicators
Authors’
calculations based
on data from CIA
World Factbook
Docquier (2006)
and CIA World
Factbook
Passport cost:
McKenzie (2005)
GDP: World
Development
Indicators
World
Development
Indicators
La Porta, López de
Silanes, Shleifer
and Vishuny
(1998)
World
Development
Indicators
World
Development
Indicators