Post on 30-Apr-2020
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SKILLED MIGRATION, FDI AND HUMAN CAPITAL INVESTMENT*
Daniele Checchi†
(Università di Milano and IZA)
Gianfranco De Simone
(Università di Milano and Centro Studi Luca d’Agliano)
Riccardo Faini‡
(Università di Roma “Tor Vergata”, CEPR and IZA)
current version: May 2008
Abstract
In a globalised world, where factors of production are increasingly mobile, the process of
domestic accumulation of human capital (HC) can be affected in several ways through
migration and capital inflows. In addition, endowment of skilled labour and foreign direct
investments (FDI) may reinforce each other through possible “complementary effects”.
Our paper aims to advance the existing empirical literature on the relationship between
international factor mobility and domestic accumulation of HC in developing countries.
We provide new evidence on how the presence of foreign firms in the domestic economy
and the emigration of skilled workers impact the domestic school enrolment. We also
investigate whether existing supply of skilled labour is an important determinant of
inward flows of foreign capital, finding no significant effects. The interdependence
between factor mobility and HC accumulation supports some simple back-of-the-envelop
calculations that tend to exclude the presence of a significant ongoing virtuous circle
between HC accumulation and FDI inflows. Less developed countries tend to gain
domestic human capital under foreign capital inflow (job opportunities for skilled
individuals), but they loose domestic human capital through the emigration of highly
educated people both directly and indirectly (“disincentive effect” on further investment
in higher education).
Keywords: Human Capital Investment, Factor Mobility, FDI, Brain Drain/Gain,
Complementarity Effects, Developing Countries
JEL Classification: F22, F23, O15
* This research has benefited from comments received at the Final Conference on “Trade, Industrialization and
Development” (Paris, October 2006), CNR International Economics Working-Group Conference (Torino, February
2007), 9th ETSG Conference (Athens, September 2007), Sus.Div. General Meeting (Athens, September 2007),
“Transnationality of Migrants” Meeting (Riga, October 2007). We would like to thank Giorgio Barba Navaretti,
Massimiliano Bratti, Jaime de Melo and Andrea Gavosto for useful remarks and suggestions. This paper is part of the
project “People and Firms” funded by Fondazione CRT (Torino) and managed by Centro Studi Luca d’Agliano
(Milano). † Corresponding author. E-mail address: daniele.checchi@unimi.it. ‡ Riccardo Faini passed away while we were working at this paper. The paper significantly suffers from his
disappearance.
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1. Introduction
It is commonly believed that accumulation of human capital (HC) and availability of physical
and financial capitals are among the major determinants of economic growth; it is also widely
accepted that the lack of these resources (along with the inability to expand them) are potential
reasons behind the delay of many poor countries in achieving development.
In a globalised world, where factors of production are increasingly mobile, the process of
domestic accumulation of HC might be affected in several ways. In fact, while in principle the
availability of foreign capital in the form of inward foreign direct investments (FDI) and an elastic
supply of skilled (educated) workers may individually enhance growth prospects, they can also
reinforce each other through possible “complementary effects”. The presence of foreign investors in
the home economy can provide incentives to invest in education for both people and governments:
people may want to attain higher level of education in order to access better job opportunities
offered by foreign firms, and governments may want to support the accumulation of HC in order to
benefit from possible spillovers of FDI (technology and knowledge transfer). In addition, a good
HC endowment makes the investment climate more attractive for foreign investors, offering an
educated workforce which is also likely to be associated to socio-political stability.
Ideally, a virtuous circle of HC and FDI can be attained whenever «host countries experience
continuous inflow of FDI over time by increasingly attracting higher value-added MNEs, while at
the same time upgrading the skill contents of pre-existing MNEs and domestic enterprises»
[Miyamoto (2003), p.9]. Symmetrically, a Pareto inferior equilibrium is also possible: inadequate
supply of skills discourages FDI and the lack of FDI depresses the demand for skills.
But factor mobility does not concern financial and physical capitals only. Domestic workforce is
also mobile, and when international migration is considered, the domestic accumulation of HC
needs further qualification. Even if migration flows have grown less than trade and FDI flows over
the last decades [see Sapir (2000), Faini (2006)], the ongoing “brain drain”, enhanced by selective
immigration policies1 in developed economies, is one of the suspects among the forces negatively
affecting the economic performance of developing countries. According to an established view,
skilled migration causes the flee of the most talented and entrepreneurial individuals from the
countries of origin, and severely hampers its growth prospects. Thus the outflow of educated
workers is expected to negatively impact onto the domestic stock of cumulated HC.
1 In response to the growing shortage of skilled workers, most receiving countries have tried to shift the focus of their
immigration policy, favouring the recruitment of highly skilled workers. This new twist in the policy stance toward
immigration has become a source of considerable concern in traditionally sending countries, which fear the loss of their
most skilled and entrepreneurial workers.
3
In sharp contrast with this expectation, a recent but rapidly developing literature emphasizes a
possible positive effect of skilled migration on the origin country. The brain drain becomes, in this
view, a “brain gain”. Among others, three different channels can be distinguished for a beneficial
brain drain to operate: a) skilled migrants raise economic welfare at home thanks to a relatively
large flow of remittances2; b) selective immigration policies in host countries may raise the
attractiveness of migration for high skilled individuals, which in turn raises private returns to
education (due to reduced supply) and induces additional investment in education at home; c)
skilled migration may favour growth-enhancing technology transfer, trade and foreign direct
investments between the source and the host country (network effects).
Points b) and c) provide further qualifications about possible complementarities between HC
and FDI in the wake of international migration. Mountford (1997) was the first to suggest the
possibility that migration prospects create incentives to invest more in education: since not all of
those who invest in education can (or will choose to) migrate, the post-migration level of human
capital can increase. Similar results were found by Stark et al. (1998). Stark et al. (1997) add to this
literature by showing that the possibility of a brain gain might stem from the imperfect information
of destination country’s employers on the skills of the migrants and the impact of return migration.
The wage adjustment taking place once the true ability of immigrants is revealed to foreign
employers may induce a subset of individuals to return home. Under certain conditions the post-
return average level of human capital is higher than that of a closed economy. The literature on this
issue is rapidly growing, but the empirical evidence is mixed. In a cross-country regression with 50
developing countries, Beine et al. (2001), using data from Carrington and Detragiache (1998), find a
positive effect of skilled migration on human capital investment in the source country and a positive
relation between growth and the proportion of highly educated individuals at home. Applying a
different empirical approach to the same dataset, Faini (2002) found that the rate of migration
among educated individuals was weakly and negatively correlated with tertiary enrolment at home.
Using a new dataset on migration stocks and rates by country of origin and educational attainment,
developed by Docquier and Marfouk (2005), Mariani (2004) estimates a cross-country growth
regression on a large number of developing countries and finds that the relation between brain drain
and growth is non linear and high skilled migration affects positively the growth rate only if a large
proportion of individuals at home is enrolled in (or have completed) at most the secondary school;
according to the author, this result indicates that larger countries are more likely to enjoy positive
2 The underlining argument proceeds as follows: skilled migrants typically earn relatively more and, ceteris paribus,
will therefore save more and remit to relatives remaining inland. However, skilled migrants are also likely to spend a
longer span of time abroad and also are more likely to reunite with their close family in the host country. Both factors
should be associated with a relatively smaller rather than larger flow of remittances from skilled migrants. Faini (2006)
provides evidence supporting this counter-argument.
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feedbacks from high skilled migrations. Thus, if the focus is on the accumulation of human capital,
the role of skilled migration cannot be neglected and it still represents an unsettled empirical issue.
For what concerns implications of point c), namely technology transfer through networking, it is
worth noticing that since developing countries typically lack resources to develop new technologies
on their own, what matters for growth is their ability to appropriate and adopt advanced
technologies developed elsewhere. The literature on technology diffusion/transfer has focused on
trade and foreign direct investments as the two main channels in this respect, provided that the host
country is endowed with a sufficient level of competences to make this absorption viable. Migrants
may personally be involved in trading and investing in their home country, thus boosting trade and
foreign capital inflows, thanks to their inside knowledge or their social ties. Network effects with
people still living in their country of origin can also be exploited by their foreign employers to enter
their home market (Lucas, 2004).
Our paper aims to address empirically some of these open questions guided by theoretical
considerations at the basis of a simple conceptual framework (see Appendix B). In Section 2 we
provide further evidence on the relationships between international factor mobility (FDI and
migration) and domestic accumulation of HC in developing countries. In Section 3 we explore
potential complementarities between FDI and HC by investigating whether existing supply of
skilled labour is a significant determinant of inward flows of foreign capital. The interdependence
between factor mobility and HC accumulation supports some back-of-the-envelop calculations on
the impact of migration on domestic HC accumulation in Section 4. Section 5 concludes.
2. Do migration and inward FDI impact enrolments?
We start by focussing on the consequences of factor mobility onto educational choices in
developing countries. A simple equation (which corresponds to equation (5) in Appendix B) relates
enrolment rate j
ite in educational level j ( j = secondary, tertiary) in country i and year t to the
presence of foreign firms (proxied by the cumulated stock of FDI ) in the domestic economy and to
migration trends of educated workers ( MIG )
ittitiit
T
ik
kti
j
i
j
it CMIGFDIe ε+τ+⋅δ+⋅β+
⋅β+µ= ∑
=− 2,1 log (1)
where itC is a set of country specific factors affecting educational choices (control variables), jiµ is
a country fixed effect, tτ is a specific time effect and itε is an error term.
5
On the basis of our theoretical considerations (see Appendix B), one would expect the
presence of foreign firms providing incentives to enrol in higher education programs ( 01 >β ) . As
far as the migration of skilled workers is concerned, a negative impact on domestic enrolment
( 02 <β ) can be taken as evidence of the discouraging effects of “brain drain”, whereas a positive
impact ( 02 >β ) can be taken as evidence of “brain gain”. Relevant control variables for this
specification are related to the stage of development of the economy (presence of liquidity
constraint / endemic poverty), to the quality of the educational system and to other supply side
factors3.
2.1 Dataset and variables definition
Our dependent variables are extracted by data on educational enrolment on quinquennial base
collected by Barro and Lee (2000) integrated by data on emigration rates by educational level
collected in Doquier and Marfouk (2005). The intersection of these two datasets containing non
missing information in at least one of the two points in time (1990 and 2000) is non empty for 147
developing countries. When we consider a balanced panel version of this sample of countries, their
number reduces significantly.
We expand this dataset with information on the existing stock of foreign direct investment
(referred to the two relevant years or in their proximity) from UNCTAD, with quality of the
education indicators from World Bank4, and additional control variables
5 related to the level of
country development. We have considered alternative measures of education quality, including
public spending on education as percentage of GDP, the pupil-teacher ratio at primary school (the
corresponding measure for secondary school is only available for recent years) and the repetition
rate at primary school, but the only variable showing statistical significance in some regression is
the pupil/teacher ratio at primary level, that we have retained in the text. As a complement to this
indicator we employ the population density in order to capture per-capita availability of school
resources.6 In previous version of the paper, we have also considered the inclusion of urban/rural
3 Theoretical considerations would suggest to add a variable accounting for remittances among regressors. This would
capture a potential poverty relief feedback effect of migration. But an improvement in financial conditions of a family
could enhance both investment in education and further migration (i.e. migration cost becoming affordable, family
reunion, etc.). Hence, the impact of such a variable is not univocal from a conceptual point of view. Unfortunately, data
coverage of series on international remittances is not complete for many countries in our sample. Furthermore, since not
all financial flows related to migration follow official channels, data on remittances are not entirely reliable and they
could represent an inappropriate proxy for what is called “diaspora externality”. For these reasons we decided to
exclude remittances from our regressions. 4 EdStat on-line service provided by the World Bank.
5 Control variables are drawn from the World Bank’s World Development Indicators database. 6 In principle one would expect highly concentrated population decreasing the cost of providing schooling services
(positive correlation with enrolments), unless the country is characterized by saturation effects and/or lack of school
resources leading to possible supply-side constraints to education (negative correlation with enrolments).
6
population shares (to further account for the supply side of educational resources), the fuel exports
share in GDP (to proxy possible disincentive effects on educational investment) as well as measures
of income inequality (to capture potential liquidity constraints in educational choices), but given the
weak statistical significance and the sample reduction forced by these regressors, at the end we have
decided to leave all of them out of the present analysis7. Thus in addition to GDP per capita we have
considered two other indicators of life conditions and possible poverty constraints in the country of
origin: credit to the private sector (from Beck et al. 2000) and life expectancy at birth. The choice
for the latter variable is suggested by two considerations: first, a short life prospect is usually highly
correlated with endemic poverty, and second, it might be also correlated with educational decisions
since the perspective of a longer life increases the number of years over which the returns from
investment in knowledge can be collected8. The private credit by (deposit money) banks over GDP
accounts for financial market imperfections that render liquidity constraints more stringent for poor
families.
2.2 Results
We have selected gross enrolment rates by educational level (secondary and tertiary – primary
enrolment is compulsory almost everywhere, and attendance rates in recent decades tend to reach
100%) over almost two decades (1985-2000); but available data on migration rates by skill levels
restrict us to two points in time only (1990, 2000). Taking into account missing information on
regressors, in its largest version we have 196 observations covering 113 countries for secondary
enrolment, and 182 observations for 109 countries in case of tertiary education. Given its structure,
our dataset consists of an unbalanced sample, with a very limited temporal dimension which
consequently does not offer large variation over time. This implies that when we try to account for
unobserved individual heterogeneity at the country level by estimating a specific fixed effect
parameter iµ , we might end up capturing too much of it, with the estimated individual intercept
washing out part of the effects that are supposed to be explained by the regressors.9 Thus we have
chosen a less demanding specification in which we include macro-regional controls instead of
7 We experimented with data on inequality of income distribution drawn from World Bank (Deininger and Squire)
dataset as well as from UNU-Wider dataset, but in both cases the sample size was almost halved and the variable was
always non significant. Checchi (2003) finds a significant negative correlation between Gini index and secondary
educational attainment, in a larger dataset of low-middle income countries. 8 See Castelló-Climent and Doménech (2008) on the role played by life expectancy onto educational investments.
Complementary considerations on mortality rates are in Grossman (2005). 9 A way to partially relax this assumption and to allow our independent variables to be correlated with the individual
random effect ( iµ ) would be to proceed with the estimator proposed by Hausman and Taylor. We have followed this
strategy in Checchi-DeSimone-Faini (2007) finding similar results.
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specific country effects.10
This approach is consistent with a peculiar aspect of factor mobility in the
globalising world: Miyamoto (2003) shows that FDI inflows to developing country exhibit highly
differentiation by sector at the regional level. Differently from other regions, the share of inward
investment in the natural resource sector in Africa is high and constant over time. On the contrary,
the Latin American and the Caribbean regions show a large drop in the manufacturing sector with a
corresponding increase in the services sector. The Asian region and the Central Eastern European
Countries exhibit a large and stable share of investment in the manufacturing sector. Since different
types of investment require different types of workers (in terms of skilled/unskilled composition),
they also provide different incentives to invest in education. Thus a specification with regional fixed
effects should enable us to capture possible differentiated effects of inward FDI.
Descriptive statistics are reported in table A1 in the Appendix A.
In Table 1 we report our estimates for secondary enrolment, while Table 2 contains the
corresponding estimates for tertiary enrolment; tables A2 and A3 in the Appendix A repeat the same
analysis by cross sections in single years (1990, 2000). The first two columns of both tables reports
OLS correlations with regional controls, while columns 3 and 4 deal with the potential endogeneity
of migration rates by means of an IV estimator.
We start with secondary enrolment in Table 1. Secondary educational attainment is associated
with the stage of development of a country, possibly reflecting the availability of resources to
families which are necessary to undertake educational investments. While correlations with GDP
per capita and life expectancy at birth are highly significant, point estimates for the credit to the
private sector bear the expected sign, but they are weakly significant. Previous enrolment in primary
education is an important determinant of current enrolment in secondary schools, consistently with
the idea of schooling being a vertically integrated process. The additional control provided by the
pupil/teacher ratio at primary level as a proxy the (average) quality of education received is
negative and statistically significant; similarly the negative sign associated with the (log of)
population density would confirm the lack of school resources and possible supply-side constraints,
but it is statistically insignificant.
As far as factor mobility is concerned we observe that the migration rate of people with
tertiary educational attainment exhibits positive correlation with secondary school enrolments under
all specifications, but the impact is statistically significant only when we do not tackle the
simultaneity issue.11
When we consider an instrumental variable estimator the coefficient on
10
Regional controls are: East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle
East and North Africa, South Asia and Sub-Saharan Africa. See Checchi-De Simone-Faini (2007) for estimates with
country fixed effects and an extended discussion of their implications. 11
Ideally, when considering the impact of migration trends onto enrolment at secondary school one would include
among regressors not only the migration rate at the corresponding educational level, but also at the tertiary level. In fact,
8
migration rate at tertiary level decreases in size and looses statistical significance, suggesting an
upward bias for OLS estimates. In our first-stage migration equation we included the following
instruments:
i) the (log of) stocks of national migrants in major destination areas (US and EU)12
. One may
reasonably argue that the stock of previous migration may attract additional migrants, without
necessarily affect educational choices at home if not indirectly by the perspective of easier
migration and reunion;
ii) an interaction between the (log of) distance from major destination areas (US and EU) and
the domestic population. Empirical literature on migration has indicated the cost of migrating
(proxied by distance from the destination) as one of the major restraints to the movement of
workers. Domestic population accounts for different effects. Small countries tend to be more
prone to migration; furthermore, with immigration systems in developed economies becoming
increasingly selective and/or based on quotas by country (like the US one), the higher the
domestic population and supply of skilled workers is, the lower the probability to migrate13
.
Thus both distance and domestic population are supposed to be negatively correlated with the
migration rate. Taking their interaction allows us to use the distance variable, which is time-
invariant on a panel dimension, and reduces the risk of population not being orthogonal with
respect to our dependent variable.14
Given the evidence of potential endogeneity of tertiary migration with respect to secondary
enrolment, we are inclined to consider skilled migration as having no impact on schooling decisions
at secondary level, just like the presence of foreign investors in the domestic economy (measured by
the log of inward FDI stock) which is never significant in these estimation. Even interacting this
variable with regional controls (in column 4) does not allow us to find evidence of possible regional
patterns. We then conclude that, regardless of the kind of inward foreign investment, the
accumulation of secondary level human capital seems not affected by the presence of foreign firms.
emigration of graduate workers could affect the decision to invest in HC also at the previous level. The high correlation
between the migration rates at secondary and tertiary level (0.74) poses serious collinearity problems on such
specification. So we restrict ourselves to one migration rate only. For the sake of comparability we present here results
obtained by employing the “rate of migration at tertiary level” as independent variable for both (secondary and tertiary)
enrolment rates. We have run regressions including an average of migration rates at secondary and tertiary level; results
are quantitatively and qualitatively consistent with those reported here. 12
See Javorcik et al. (2006) and Beine et al. (2006). 13 See Beine et al. (2006) 14
We have tested for both possible weakness of our instruments and over identifying restrictions. Instruments proved to
be strongly correlated with tertiary migration in the first-stage (partial r-squared = 0.51) and the associated Cragg-
Donald statistics is such to reject the hypothesis of weakness (F=27.15). Sargan statistic of joint over identification of
all instruments point to the direction of a correct choice of instrument at 5% degree of confidence.
9
Table 1 – Gross enrolment rate – Secondary Education (1990-2000) – Unbalanced Panel
1 2 3 4
OLS+RC OLS+RC IV-OLS+RC IV-OLS+RC Log GDP per capita 0.082 0.058 0.063 0.057
[4.70]*** [3.16]*** [3.68]*** [3.24]***
Log of Life Expectancy at Birth, years 0.218 0.231 0.215 0.188
[2.13]** [2.10]** [2.04]** [1.75]*
Private Credit by Deposit Money Banks / GDP 0.125 0.115 0.1 0.118
[1.74]* [1.57] [1.52] [1.74]*
Log inward FDI stock -0.005 -0.002 -0.006
[0.69] [0.23] [1.10]
Migration rate tertiary educated 0.11 0.127 0.019 0.066
[1.81]* [1.91]* [0.25] [0.73]
Enrolment rate primary 5 years before 0.233 0.243 0.24 0.261
[5.28]*** [5.17]*** [4.44]*** [4.57]***
Log pupil/teacher primary -0.131 -0.159 -0.163
[2.85]*** [3.46]*** [3.57]***
Log Population density (people per sq. km) -0.011 -0.006 -0.008
[1.05] [0.65] [0.85]
Log inward FDI stock × East Asia and Pacific -0.007
[0.71]
Log inward FDI stock × Eur. and Central Asia -0.007
[0.93]
Log inward FDI stock × Latin Am. and Carib. 0.012
[0.82]
Log inward FDI stock × Mid. East and N. Africa -0.032
[0.99]
Log inward FDI stock × South Asia 0.004
[0.17]
Log inward FDI stock × Sub-Saharan Africa -0.011
[0.92]
Observations 196 175 173 173
R² 0.8 0.81 0.81 0.82
Robust t statistics in brackets - * significant at 10%; ** significant at 5%; *** significant at 1%
Regional controls and year dummy included
IV for FE: log of stock of own migrants in US and in EU, log of (population*distance from EU), log of
(population*distance from US)
We now consider tertiary enrolment, as reported in Table 2. Most of stage-of-development
controls do not work in this case: credit to the private sector and life expectancy are not statistically
significant here, while there is a negligible negative impact of GDP per capita. This may not be
surprising, since people attending university in developing countries are typically self-selected from
the upper tail of the income distribution, and they are relatively unaffected by what happen in the
lower tail.15
As in the previous stage of education, we find a positive contribution of enrolment rates
at secondary level, while the negative and significant sign on pupil/teacher ratio suggests supply-
side constraints.
15 One additional control that has been introduced in the previous literature is the share of fuel and raw materials in total
exports. The rational is that whenever a country is abundant in natural resource, its population has fewer incentives to
get educated. We tried to include this indicator in our regressions for both secondary and tertiary enrolment, finding a
limited negative impact, as expected. Once again, the presence of missing data has suggested to stick to our current
specification that excludes this variable.
10
Table 2 – Gross enrolment rate – Tertiary Education (1990-2000) – Unbalanced Panel
1 2 3 4
OLS+RC OLS+RC IV-OLS+RC IV-OLS+RC Log GDP per capita -0.01 -0.021 -0.02 -0.017
[0.94] [1.96]* [2.15]** [1.80]*
Log of Life Expectancy at Birth, years 0.045 0.051 0.05 0.025
[1.03] [1.05] [0.89] [0.45]
Private Credit by Deposit Money Banks / GDP 0.037 0.054 0.055 0.046
[0.80] [1.18] [1.56] [1.31]
Log inward FDI stock 0.009 0.011 0.012
[2.77]*** [3.74]*** [3.79]***
Migration rate tertiary educated -0.199 -0.185 -0.149 -0.151
[6.68]*** [6.24]*** [2.92]*** [2.67]***
Enrolment rate secondary 5 years before 0.241 0.202 0.2 0.225
[6.26]*** [5.62]*** [5.37]*** [6.08]***
Log pupil/teacher primary -0.061 -0.058 -0.051
[2.39]** [2.22]** [2.01]**
Log Population density (people per sq. km) -0.002 -0.004 -0.004
[0.49] [0.77] [0.75]
Log inward FDI stock × East Asia and Pacific 0.012
[2.13]**
Log inward FDI stock × Eur. and Central Asia 0.02
[4.62]***
Log inward FDI stock × Latin Am. and Carib. 0.014
[1.81]*
Log inward FDI stock × Mid. East and N. Africa 0.018
[1.05]
Log inward FDI stock × South Asia 0.003
[0.26]
Log inward FDI stock × Sub-Saharan Africa -0.006
[0.95]
Observations 182 163 163 163
R² 0.76 0.77 0.77 0.79
Robust t statistics in brackets - * significant at 10%; ** significant at 5%; *** significant at 1%
Regional controls and Year dummy included
IV for FE: log of stock of own migrants in US and in EU, log of (population*distance from EU), log of
(population*distance from US)
When considering factor mobility, we find that migration rate at tertiary level has a negative
and statistically significant correlation with enrolment under all specifications (column 1-4); the
same effect declines just slightly when we take care of potential endogeneity.16
In this case we also
find that the presence of foreign firms in the domestic market (proxied by the stock of inward FDI)
exerts a significant positive impact17
. We interpret it as evidence that inward FDI creates job
opportunities for skilled workers, thus providing an incentive to enrol in a higher education
program. Looking at compositional effects (column 4) we observe that it is mostly driven by
16 Instruments explain up to 46% of migration rate of tertiary educated workers in the first-stage and the associated
Cragg-Donald statistics is such to reject the hypothesis of weakness (F=30.64). Sargan statistic of joint over
identification of all instruments point to the direction of a correct choice of instrument at all degrees of confidence. 17
The impact of capital inflows onto higher educational attainment has been studied by Hegger et al. (2005), finding
positive correlation in the Barro-Lee dataset.
11
formerly planned economies18
and East Asian countries, which typically receive inward investments
in the manufacturing sector, and by Latin American countries, which experienced significant inflow
of foreign capital in the service sector due to privatisation. As expected, investments in the primary
sector (exploitation of natural resources) do not provide incentives to accumulate human capital in
developing countries.
Overall, migration rate of skilled workers seems to discourage enrolments (that per se would
imply a discouraging effect of the brain drain), but the presence of foreign firms on domestic
market provides offsetting positive incentives to higher education enrolment. Thus, we would be in
the presence of a peculiar form of brain waste. Natives would be attracted into tertiary education by
existing job opportunity created by foreign firms in the local economy (stock of inward FDI), but
the outflow of tertiary graduates through migration would dampen this incentive.19
Evaluated at
sample averages,20
the opening to international factor flows would imply an increase in tertiary
enrolment, since the impact of the inflow of foreign capital would dominate the corresponding
negative impact created by the outflow of skilled workers. But the migration of skilled worker also
directly reduces the existing stock of tertiary educated workers, the overall effect being negative, as
it is discussed in section 4.
Our results are in line with those obtained by Groizard and Llull (2006) but we model the
stock-flow relationship in a more consistent way. In fact, they study the impact of skilled migration
on the cumulated stock of human capital in the country, which almost by construction yields a
negative impact (since there is a one-to-one correspondence between a migration of a graduate
worker and a (marginal) decline in the average years of education in the working population of the
source country). On the contrary, if there are disincentive effects of migration, these should work
through the accumulation of new human capital, namely the enrolment (and, if available,
completion) rates, as we have done in our regressions. In addition, they neglect other factors that
may affect the educational attainment in the country, out of the initial level, while we have provided
a richer analysis of the process.
18 Bulgaria, Hungary, Poland and Romania in our sample. 19
Data on migration rates provided by Doquier and Marfouk (2004) define skilled migrants by education level without
distinguishing whether they acquired their education in the home or the host country. This might suggest that
coefficients on the migration rate of tertiary educated migrants obtained in our estimates could be biased. As an
additional robustness check we replicated our two sets of regressions (i.e. secondary and tertiary enrolments) employing
a migration rate of skilled workers which accounts only for foreign-born adults arrived in the destination country after
age 22. The assumption is that immigrants' age of entry is a good proxy for where they acquired their education (see
Beine et al. (2007)). Results obtained are not dissimilar from those shown here. This it is not surprising, since the
correlation rate of the two series exceeds 90% in our sample 20
The relevant elasticities are rather different at sample averages: by considering an estimated coefficient between -0.13
and -0.20, the migration elasticity lies in an interval comprised between 0.18 and 0.28, while the elasticity of the inward
FDI stock (using an estimated coefficient of 0.01) is equal to 0.42.
12
Thus our overall conclusion of this section casts doubt on the presumed beneficial effect of
factor mobility onto domestic accumulation of HC: international flows of capital and labour
generate conflicting incentives to the accumulation of HC in developing countries.. On one side
there is evidence that skilled labour migration plays a disincentive effect on enrolment decision at
the corresponding level of education. On the other side there is evidence that inward FDI modify
the relative incentives to acquire tertiary education (possibly through the adjustment of relative
returns to educational attainment).
3. Are FDI attracted by the availability of human capital?
It has been argued that foreign firms determine the choice of location looking at the
availability of human capital. Thus, along with other possible determinants, relative endowment of
human capital should affect the attractiveness of certain locations. Related questions concern the
type of human capital (education and skills) that foreign investors are seeking for, and whether
different types of firms seek different sets of skills.
Our second equation aims to model the dynamics of physical capital accumulation through
domestic inflow of foreign capitals. A linear version of equation (6) in Appendix B describes the
determinants of FDI inflows, including the domestic endowment of human capital
ittitij
it
T
ik
ktiiit ZHCFDIFDI λ+τ+⋅φ+⋅θ+
⋅θ+γ= ∑
=− 2,1 log (2)
where the cumulated sum of past FDI proxies the current stock of foreign capitals, Z is a set of
country specific factors affecting investment decision choices (control variables), iγ is a country
(area) fixed effect, tτ is a time fixed effect and itλ is an error term.
In order to fully account for possible feedback effects due to factor mobility, it would be
desirable to include in this specification the impact of return migration on the inflows of FDI.
Unfortunately comparable cross-country series on return migration rates are not available. Including
alternative measures of the stock of national migrants living abroad in the investing countries is a
method adopted in recent contributions to account for possible network effects21
. Ideally, this
approach would rely on a strict bilateral setting, otherwise it would be impossible to ascertain
whether largest flows of FDI to the domestic economy actually come from countries hosting larger
share of own migrants. Unfortunately, data on FDI flows available for developing countries are
rarely collected on a bilateral basis. In order to maintain the cross-country dimension of our analysis
21
See Docquier and Lodigiani (2006) and Javorcik et al. (2006).
13
we are therefore forced to employ data on total inflows and stocks of FDI regardless of the country
of origin. Data on stock of immigrants by country of origin in developed economies are provided by
Defoort (2006) on a large panel (1975-2000) for 6 major OECD destinations only: Australia,
Canada, France, Germany, United Kingdom, US. Considering that outward flows of migrants from
developing countries are mostly directed toward these few destinations22
which are also among the
major foreign direct investors at the world level23
, we can include in our specification the stock of
own migrants in these countries in the previous period to account for possible network effects in a
supposedly pseudo-bilateral setting.
3.1 Dataset and variables definition
We have created a second dataset integrating series of average net inflows of FDI computed
on a five-year basis (1990-1994, 1995-1999, 2000-2004) from the UNCTAD database, with series
of alternative proxies for human capital stocks obtained merging data from Barro and Lee (2000)
and Cohen and Soto (2007)24
, so to cover the highest possible number of countries in our sample.
Taking the average of FDI flows allows us to smooth the intrinsic volatility of capital flows aimed
to buy existing activities and allow for the non-instantaneous setting-up of newly started business.
We have also added some standard controls introduced in the literature on the determinants of
foreign direct investment (market-seeking, efficiency-seeking):
i) (log of) inward stock of FDI, to take into account the effects of reinvested profits and scale
economies;
ii) (log of) GDP per capita, to proxy the stage of development;
iii) (log of) population, to capture “market size” effects;
iv) price inflation, measured by consumer price index annual percent changes, averaged over 5-year
intervals (1988-1992, 1993-1997, 1998-2002), to account for economic stability;
v) to account for political stability and other determinants of institutional quality we relied on six
different indicators collected by Kaufmann et al. (2004): Voice and Accountability, Political
Stability, Government Effectiveness, Regulatory Quality, Rule of law, Control of Corruption.
Since all these measures (which are obtained by aggregating different opinion surveys
worldwide) are highly correlated among them, we summarise them by extracting a common
22
Defoort (2006) shows that the listed 6 major destinations collect up to 77% of world immigration. 23
Canada, France, Germany, United Kingdom, and US ranked among the top ten investors at the world level (Outward
FDI stocks) in the 1990-2002 period. Australia ranked 14th
. See UNCTAD (2003). 24
The overall series for HC measures are built by integrating the Cohen and Soto (2007) (C-S) data with information in
Barro-Lee (2000) (B-L) for missing countries (retaining the C-S observations in case of overlap). C-S collect data on
educational attainments in total population on a 10-year basis, while data in BL are available with 5-years intervals.
Thus, we built the series for 1995 for countries in C-S by projecting the 1990 observations using the growth rates
obtained from B-L.
14
factor from the series using factor analysis (principal component method). The first common
factor obtained, which is used in our analysis, summarizes up to the 78% of original series
variations25
;
vi) we also include trade openness (proxied by the (Import + Export) share in GDP) to consider the
exposure to globalisation forces in a country. Following our previous work (Faini 2004), we
were to include telephone mainlines (per 1000 people), to account for the endowment of
infrastructures at country level, but this variable restricts the sample significantly without ever
being significant; as a consequence we have left it out of our best models.
This set of control variables includes what the current empirical literature recognizes as
major determinants. Nevertheless, the focus of our analysis is the identification of a potential role of
human capital endowment in attracting FDI. Given the fact that we try to capture possible fixed-
effect (group/country specific) with appropriate estimation techniques, the possible risk of omitted
variables does not seem to be a major impediment. As far as our measure of the stock of domestic
human capital is concerned, we have considered alternative measures, either based on the average
years of education in the population or on the distribution of the educational attainment in the same
population. We have selected the second alternative, because it allows us to distinguish between
different levels of skill (associated to different level of educational attainments).
Descriptive statistics of this dataset are reported in Table A4. Since in this equation we do
not rely on migration-related information, our dataset is not anymore restricted to two points in
time: when considering the unbalanced version we have 243 observations from 85 countries,
whereas the balanced panel is composed of 180 observations for 60 countries, referred to 1990,
1995 and 2000. There is only a partial overlap with the dataset used in the previous section (63
countries when considering both balanced versions), because some countries26
report information
on migration, but do not give account of FDI inflows, while some other countries27
attract funds
from abroad, but do not seem to be sending migrants out of the country.
25
Data collected in Kaufmann et al. (2004) go back to mid-1990s only. We use the first available observation for 1990
and the proper one for 2000. An average of the two is assumed to be the corresponding value for 1995. 26 Countries included in the enrolment model of section 2 and not in the FDI model of section 3 are Albania, Armenia,
Bahamas, Belize, Cambodia, Cape Verde, Chad, Croatia, Czech Republic, Estonia, Ethiopia, Georgia, Guinea-Bissau,
Kazakhstan, Kyrgyzstan, Laos, Latvia, Libya, Lithuania, Macedonia, Moldova, Mongolia, Namibia, Oman, Romania,
Rwanda, Samoa, Slovakia, Slovenia, Vanuatu, Vietnam, Yemen, (average GDP per capita in 2000 equivalent to 2161
US dollars). 27
Countries included in the FDI model of section 3 and not in the enrolment model of section 2 are Afghanistan,
Barbados, China, Cuba, Ethiopia, Fiji, Liberia, Myanmar, Rwanda (average GDP per capita in 2000 equivalent to 1034
US dollars).
15
3.2 Results
Our results for the unbalanced panel using alternative measure for HC (secondary and
tertiary attainment) are reported in Table 3. Results for the balanced panel are in Tables A5 in the
Appendix A. Here we start with the two key variables in our conceptual framework under OLS
estimator and regional controls (column 1 and 4); then we add regressors accounting for motives of
FDI (columns 2 and 5), “market-seeking” and/or “efficiency-seeking”; finally, we include all other
controls regarding the business environment and economic and political stability plus the stock of
own migrants in major OECD destinations to account for possible network effects (columns 3 and
6). In column 7 we replicate the regression of the largest specification jointly considering the
secondary and the tertiary attainment in the population.
An elastic supply of HC seems not to attract foreign investors: neither the availability of
secondary educated workers nor that of tertiary educated workers are significant determinants of the
inward flows of FDI in our sample. A substantial part of inflows is due to reinvested profits or
expansion of existing investments (as witnessed by the positive correlation with existing FDI
stock). The level of development, as proxied by GDP per capita, seems to make the location more
attractive for foreign investors. Given the positive and significant correlation with the log of
population (which proxies for the size of the market) one might think that “market-seeking” motives
for FDI are still relevant in inducing foreign investment in the country.
The openness to trade is positively associated with the inward flows of FDI but its impact is
rather limited. Economic instability (here proxied by the average inflation rate) bears the correct
sign but it is never significant, while political stability seems to have a significant positive impact
on the location attractiveness. There is some collinearity between the GDP per capita and the
government factor (correlation in the unbalanced sample is equal to 0.65). Since more developed
countries experience more stable governments (the executives are more effective, the rule of law is
more frequently enforced, the level of corruption is lower, the regulatory quality is more valuable),
the effect of GDP per capita that we measure once we introduce this factor (from column 4 onward)
is basically the net effect of the stage of development. Finally, we find no evidence of possible
network effects. Having large communities of own migrants in the 6 major OECD destinations does
not seem to give any advantage at the aggregate level in terms of incoming FDI.
As argued above, when analysing the relationship between existing supply of HC and the
inflows of capital from abroad, it is crucial to discuss the nature of foreign investment. If FDI is
aimed to the exploitation of natural resources, the local availability of educated labour force could
be less relevant than in the case of investments in manufacturing or services. This has been already
16
highlighted in the previous section, where we found a differential effect in the impact of inward FDI
on educational decisions, depending on the level of education.
Table 3 – 5-years Average Inflows of FDI – 1990-1995-2000 – Unbalanced Sample
1 2 3 4 5 6 7
OLS+RC OLS+RC OLS+RC OLS+RC OLS+RC OLS+RC OLS+RC
population with secondary attained 0.314 0.773 0.366 0.329
[0.53] [1.20] [0.54] [0.48]
population with tertiary attained 1.387 1.003 0.627 0.466
[0.73] [0.56] [0.32] [0.24]
log stock of inward FDI 0.91 0.573 0.481 0.91 0.588 0.49 0.482
[16.29]*** [7.49]*** [5.43]*** [16.44]*** [8.13]*** [5.85]*** [5.39]***
log gdp per capita 0.611 0.536 0.632 0.535 0.531
[5.29]*** [3.74]*** [5.49]*** [3.54]*** [3.54]***
log population 0.412 0.628 0.39 0.608 0.621
[5.20]*** [5.80]*** [5.33]*** [5.93]*** [5.47]***
Factor extracted from political variables 0.289 0.299 0.289
[2.26]** [2.44]** [2.25]**
Trade (% GDP) 0.005 0.005 0.005
[2.27]** [2.29]** [2.23]**
Inflation, consumer prices (annual %) -0.017 -0.016 -0.017
[0.92] [0.91] [0.93]
Stock of own migrants in 6 OECD at (t-1) -0.008 0.004 -0.004
[0.16] [0.07] [0.07]
Observations 243 231 206 243 231 206 206
R-squared 0.77 0.80 0.82 0.77 0.80 0.82 0.82
Number of Countries 85 81 77 85 81 77 77
Robust t statistics in brackets - * significant at 10%; ** significant at 5%; *** significant at 1%
Year dummies and RC (regional controls) included and not reported
4. Policy implications
Is there any evidence of a virtuous circle of human capital formation and increased inflow of
FDI? What are the implications of our estimates? In order to adapt our estimates to our theoretical
framework (see Appendix B), we need to clarify the relationship between human capital and
enrolment rates. If we approximate the total human capital stock H by the average years of
education in the population, it is defined as TlSlPlH tsp ⋅+⋅+⋅= where tsp lll ,, are respectively
the school length of primary, secondary and tertiary education, while TSP ,, are the corresponding
population shares. Taking tsp lll ,, as fixed, we have that TlSlPlH tsp&&&& ⋅+⋅+⋅= . If we consider a
stationary population with a fixed life length λ , then λ/1 is the relative size of each age cohort, and
λ/il is the reference population for educational level i .28
The share of population with a given
educational attainment increases whenever the corresponding enrolment rate is greater than the
existing share. For example the variation of the population share with secondary education can be
28
One could argue that life expectancy varies with the educational attainment. But then changes in educational
attainment would translate in changes in total population, making the analytics unmanageable.
17
described by the following expression λ
−≅
SenrolmentS S& .
29 Thus the variation of human capital
stock is given by
( ) ( ) ( )Tenrolmentl
Senrolmentl
Penrolmentl
H TT
SS
PP −⋅
λ+−⋅
λ+−⋅
λ=& (3)
Thus equation (3) implies that the overall effect of FDI on the accumulation of human
capital is given by K
enrolmentl
K
enrolmentl
K
enrolmentl
K
H TTSSPP
∂
∂⋅
λ+
∂
∂⋅
λ+
∂
∂⋅
λ=
∂
∂ &
. If we neglect
the impact of FDI on primary enrolment, where it is statistically insignificant in any specification,
and we take the estimate reported in third column of Tables 1 and 2, we obtain that
( ) ( ) ( ) 001.001.0006.060
5 *** +=+−⋅≅∂
∂⋅
λ+
∂
∂⋅
λ=
∂
∂
K
enrolmentl
K
enrolmentl
K
H TTSS&
which is positive
but very small. Since K is measured in logs, it implies that doubling the stock of FDI ( 1+=∆K )
would (dynamically) increase the human capital stock by 0.001 years of education in the
population, while changing the skill composition in the labour force in favour of tertiary educated
workers.
If we take the migration decision as exogenous, the Jacobian corresponding to system (8) in
Appendix B is therefore given by30
29
This expression can be derived as follows. If we define totPOP as the total population and secPOP the population
with secondary educational attainment, then totPOPPOPS /sec= as well as sec
sec
sec
sec
POP
POP
POP
POP
POP
POP
S
S
tot
tot&&&&
=−= under the
assumption of constant population. Given the fact that secsecsec deadgraduate −=POP & , that graduates are assumed to be
a constant fraction α of (current) enrolment
λ⋅⋅α=⋅⋅α=⋅α= totPOP
secsecsecsec rateenrolment population referencerateenrolment enrolledgraduate
and that the outflow of secondary educated population is a constant fraction of the existing stock λ
= secsecdead
POP,
then ( )SSPOP
POP
POP
POP
S
S−⋅α
λ=⇔
λ−
⋅λ
⋅⋅α== sec
sec
totsec
sec
sec rateenrolment 11rateenrolment
&&&
which corresponds
to what shown in equation (3) for 1=α .
30
The figures reported in the second row of the Jacobian (4) are obtained by OLS regression of average FDI flow onto
FDI stock and average years of education in the population, which replaces the population shares with different
educational attainment (primary, secondary and tertiary). In the unbalanced sample it yields
[ ] [ ]controls regionallog619.0127.0
67.763.1++⋅+⋅= POPKHK&
Results are however unstable depending on whether GDP per capita is included/excluded and the data source for years
of education. Finally, the coefficient of HH ∂∂ /& is derived under the assumption of identical duration for each
18
×
−=
K
H
K
H
619.0127.0
001.008.0
&
&
(4)
which is saddle-path stable.
Going to the debate over brain gain/drain, let us consider an exogenous increase in migration
of skilled (tertiary educated) workers, in the order of 100%. At sample mean of the balanced panel,
this implies a passage from 0.145 to 0.290. Looking at Tables 1 and 2 (column 4) this entails an
increase in secondary enrolment of 0.9 (corresponding to an impact of +0.066×+0.145, though non
statistically different from zero) accompanied by a reduction in tertiary enrolment of 2.1
(corresponding to an impact of –0.151**×+0.145). If we are available to assume that the average
school length at secondary and tertiary level is approximately 5 years, we obtain a reduction in the
average years of education of 0.06 (which becomes 0.10 if we consider only statistically significant
effects), approximately one twentieth (one tenth respectively) of a year of schooling in the
population. We now know from previous results, that this produces a reduction in capital inflow:
since our dependent variable in the estimation of Table 3 is the log of the ratio between capital
inflow and GDP, a variation of –0.037 (corresponding to +0.627×–0.06, though not statistically
significant) implies a significant drop of capital inflows, in the order of one fifth of pre-existing
flows (equal to 0.18 at sample mean of the balanced panel). In the long run, this reduction
cumulates in lower stock, yielding lower enrolment and lower human capital.
educational level ( 5=== TSP lll ) and an identical life expectancy for any educational attainment, equal to 60 years
( 08.060
5−=− ) (sample average is 61.43).
19
Figure 1
EastAsia Pacific
European/central Asia
Lat.America Carribbean
mid.East North Africa
south Asia
subSaharan Africa
24
68
ave
rag
e y
ea
rs o
f e
du
ca
tio
n
4 5 6 7 8log inflow FDI
Change over 1990-2000
When we graph the region position according to these state variables (human capital stock
H , proxied by the average years of education in the population, and physical capital stock, K ,
proxied by the log of foreign investment) we observe that regions tend to align along a downward
sloping ray, and they shift rightward over the decade at different speeds (see figure 1). Qualitative
analysis of the dynamic properties of system described by equation (4) indicates that the stable
branch of the saddle path will exhibit a negative slope comprised between 85.2217.0
619.0−≅− and −∞ ,
while the unstable branch can be either positively or negatively sloped, but with a slope lower than
01.008.0
001.0+≅ . Thus, according to our model, most of the countries in the regions would be
positioned in the proximity of the stable path, leading to a convergence in long run to given values
in both H and K . In this case both human capital and physical capital move together, exhibiting a
negative correlation (given the negative slope of the stable branch of the saddle-path).
20
5. Concluding remarks
Two main results are obtained in this paper, which can be summarized as follows:
1. we do not find strong evidence of the existence of a virtuous cycle between human capital
accumulation and foreign direct investment. In our estimates, FDI does not affect secondary
enrolment while favouring tertiary enrolment, and the overall effect is weakly positive. On
the other side, in our data FDI are weakly affected by the existing endowment of human
capital, mostly depending on the proxy adopted for HC.. If the underlying dynamics of the
dynamical system in ( )KH , space is analysed by dynamical system analysis, we would
identify a saddle-path stable system, which implies the existence of a unique combination of
stocks of human capital and foreign capital leading to a stable equilibrium, all other
combinations leading to unstable trajectories;
2. in addition to direct reduction of domestic human capital, we find evidence of a sort of brain
drain through skilled (tertiary educated) worker migration. We interpret this result as a
disincentive effect: when the domestic population observes that a large share of university
graduate migrating abroad, it takes this as evidence of lack of adequate local job
opportunities, and reduces the corresponding investment in higher education.
On both grounds, less developed countries are not necessarily benefited by factor mobility: they
gain domestic human capital under foreign capital inflow and but they loose domestic human
capital through skilled worker migration. Unfortunately we do not possess data on the type of FDI
involved in this analysis. Looking at their geographical distribution, we suspect that our story
involves natural resource exploitation (like mining and oil extraction) rather than Greenfield
investment. In such a case the local endowment of human capital is less relevant, as well as the
incentive to further human capital accumulation.
21
APPENDIX A – Additional tables
Table A1 – Descriptive statistics (1990-2000)
Variable Obs Mean
Std.
Dev. Min Max Obs Mean
Std.
Dev. Min Max
unbalanced panel balanced panel
gross enrolment rate secondary 283 0.55 0.30 0.05 1.15 120 0.48 0.28 0.05 0.05
gross enrolment rate tertiary 255 0.15 0.14 0.00 0.59 120 0.11 0.11 0.00 0.51
Enrolment rate primary 5 years before 254 0.91 0.25 0.10 1.47 120 0.91 0.22 0.24 0.47
Enrolment rate secondary 5 years before 254 0.49 0.30 0.03 1.19 120 0.43 0.26 0.03 0.01
log GDP per capita 277 6.95 1.18 4.45 9.69 120 6.79 0.23 0.45 0.38
log inward stock of FDI 283 6.15 2.65 -4.60 12.17 120 6.96 0.45 0.60 0.54
Log of Life Expectancy at Birth, years 299 4.12 0.19 -0.57 12.88 120 4.07 0.20 0.43 0.35
Private Credit by Deposit Money Banks / GDP 250 0.23 0.19 0.00 1.04 120 0.23 0.19 0.00 0.04
log pupil/teacher primary 249 7.94 0.41 6.97 8.95 120 8.06 0.39 0.96 0.84
Migration rate secondary educ 281 0.08 0.14 0.00 0.70 120 0.04 0.06 0.00 0.30
Migration rate tertiary educ 281 0.21 0.23 0.00 0.92 120 0.15 0.16 0.00 0.84
log Population density (people per sq. km) 295 3.87 1.31 0.30 6.90 120 3.83 0.29 0.29 0.85
Table A2 – Gross enrolment rate – Secondary Education – Cross Section 1990 / 2000
1990 1990 1990 2000 2000 2000 OLS+RC OLS+RC IV-OLS+RC OLS+RC OLS+RC IV-OLS+RC Log GDP per capita 0.095 0.07 0.072 0.078 0.052 0.057
[3.21]*** [2.32]** [2.67]*** [3.59]*** [2.60]** [2.83]***
Log of Life Expectancy at Birth,
years 0.225 0.316 0.296 0.142 0.087 0.113
[1.49] [2.10]** [1.96]** [0.97] [0.63] [0.75]
Private Credit by Deposit Money
Banks / GDP 0.035 0.03 0.02 0.149 0.122 0.112
[0.32] [0.27] [0.17] [1.54] [1.32] [1.46]
Log inward FDI stock -0.014 -0.017 -0.019 0.004 0.014 0.006
[1.45] [1.87]* [2.73]*** [0.40] [1.48] [0.69]
Migration rate tertiary educated 0.109 0.118 0.043 0.134 0.177 0.032
[1.11] [1.06] [0.40] [1.75]* [2.34]** [0.29]
Enrolment rate primary 5 years
before 0.218 0.249 0.235 0.278 0.245 0.252
[3.26]*** [3.45]*** [3.05]*** [4.64]*** [3.75]*** [3.61]***
Log pupil/teacher primary -0.055 -0.081 -0.182 -0.197
[0.89] [1.27] [3.00]*** [3.29]***
Log Population density (people per
sq. km) 0.006 0.007 -0.019 -0.011
[0.36] [0.60] [1.54] [0.97]
Observations 87 78 77 109 97 96
R² 0.74 0.75 0.76 0.83 0.86 0.85
Robust t statistics in brackets - * significant at 10%; ** significant at 5%; *** significant at 1%
Regional controls included.
IV for FE: log of stock of own migrants in US and in EU, log of (population*distance from EU), log of
(population*distance from US)
22
Table A3 – Gross enrolment rate – Tertiary Education – Cross Section 1990 / 2000
1990 1990 1990 2000 2000 2000
OLS+RC OLS+RC IV-OLS+RC OLS+RC OLS+RC IV-OLS+RC
Log GDP per capita -0.004 -0.008 -0.007 -0.013 -0.026 -0.026
[0.42] [0.86] [0.74] [0.81] [1.52] [1.82]*
Log of Life Expectancy at Birth,
years -0.027 -0.024 -0.015 0.102 0.1 0.117
[0.68] [0.57] [0.27] [1.35] [1.20] [1.12]
Private Credit by Deposit Money
Banks / GDP 0.036 0.045 0.038 0.051 0.079 0.075
[0.76] [0.95] [0.95] [0.71] [1.04] [1.48]
Log inward FDI stock 0.007 0.006 0.007 0.003 0.007 0.006
[2.89]*** [2.62]** [2.56]** [0.57] [1.38] [1.03]
Migration rate tertiary educated -0.212 -0.211 -0.175 -0.181 -0.153 -0.191
[7.08]*** [6.68]*** [4.13]*** [3.54]*** [2.87]*** [2.10]**
Enrolment rate primary 5 years
before 0.185 0.166 0.16 0.31 0.254 0.254
[4.09]*** [3.84]*** [4.73]*** [4.88]*** [3.94]*** [3.77]***
Log pupil/teacher primary -0.028 -0.025 -0.064 -0.064
[1.27] [1.08] [1.47] [1.46]
Log Population density (people per
sq. km) 0.005 0.003 -0.005 -0.003
[1.33] [0.76] [0.74] [0.40]
Observations 83 75 75 99 88 88
R² 0.77 0.76 0.75 0.77 0.79 0.79
Robust t statistics in brackets - * significant at 10%; ** significant at 5%; *** significant at 1%
Regional controls included.
IV for FE: log of stock of own migrants in US and in EU, log of (population*distance from EU), log of
(population*distance from US)
Table A4 – Descriptive statistics – 1990-1995-2000
Variable Obs Mean
Std.
Dev. Min Max Obs Mean Std. Dev. Min Max
unbalanced panel balanced panel
log of inflow over GDP - average over 3 years 439 4.49 2.38 -6.21 10.84 180 5.44 2.23 0.67 10.84
population share with secondary 259 0.19 0.14 0.01 0.79 180 0.20 0.15 0.01 0.79
population share with tertiary completed 259 0.05 0.05 0.01 0.32 180 0.06 0.05 0.01 0.32
log gdp per capita 418 6.92 1.18 4.03 9.69 180 6.87 1.20 4.45 9.38
log stock of inward FDI 428 6.17 2.52 -4.61 12.17 180 7.42 1.95 1.79 12.17
factor extracted from political variables 362 0.00 1.00 -2.57 3.06 180 0.16 0.89 2.45 3.06
Trade (% GDP) 411 80.96 40.34 3.15 228.88 180 69.08 38.38 14.41 228.88
Inflation, consumer prices (annual %) 373 0.88 4.44 -0.03 53.99 180 1.17 5.98 0.03 53.99
log population 448 15.38 2.01 10.62 20.96 180 16.50 1.48 13.11 20.96
23
Table A5 – 5-years Average Inflows of FDI – 1990-2000 – Balanced Panel –
1 2 3 4 5 6 7
OLS+RC OLS+RC OLS+RC OLS+RC OLS+RC OLS+RC OLS+RC
population with secondary attained 0.645 1.206 0.748 0.764
[1.03] [1.86]* [1.11] [1.13]
population with tertiary attained 1.173 0.653 0.233 -0.182
[0.58] [0.35] [0.12] [0.09]
log stock of inward FDI 0.839 0.411 0.416 0.85 0.445 0.436 0.415
[12.9]*** [4.63]*** [4.55]*** [13.7]*** [5.27]*** [5.05]*** [4.48]***
log gdp per capita 0.775 0.535 0.819 0.544 0.537
[5.54]*** [3.33]*** [5.73]*** [3.23]*** [3.23]***
log population 0.572 0.65 0.53 0.622 0.653
[6.21]*** [5.90]*** [6.29]*** [5.94]*** [5.65]***
factor extracted from political variables 0.331 0.357 0.331
[2.31]** [2.64]*** [2.30]**
Trade (% GDP) 0.005 0.005 0.005
[1.63] [1.78]* [1.67]*
Inflation, consumer prices (annual %) -0.018 -0.016 -0.018
[0.97] [0.93] [0.96]
Stock of own migrants in 6 OECD at (t-1) 0.022 0.035 0.02
[0.37] [0.56] [0.32]
Observations 180 180 180 180 180 180 180
R-squared 0.77 0.82 0.83 0.77 0.82 0.83 0.83
Number of Countries 60 60 60 60 60 60 60
Robust t statistics in brackets - * significant at 10%; ** significant at 5%; *** significant at 1%
Year dummies included – RC (regional controls) included in OLS .
Countries included: Algeria, Argentina, Bahrain, Bangladesh, Bolivia, Brazil, Bulgaria, Burkina Faso, Chile, China, Colombia,
Congo, Dem. Rep. of the, Congo, Rep. of the, Costa Rica, Ecuador, Egypt, El Salvador, Ethiopia, Gambia, Ghana, Guatemala, Haiti,
Honduras, Hungary, India, Jamaica, Jordan, Madagascar, Malawi, Malaysia, Mali, Mauritius, Mexico, Morocco, Nepal, Nicaragua,
Niger, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Senegal, Sierra Leone, Sri Lanka, Syria,
Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, Uganda, Uruguay, Venezuela, Zambia, Zimbabwe.
24
APPENDIX B – Theoretical considerations
We are interested in analysing the long run consequences of factor mobility on human
capital investment in developing countries, when feedbacks from capitals and workers mobility
onto educational choices of the population are taken into account. While in the empirical analysis
we will distinguish between different types of educational attainment (as proxy for different levels
of skills in the workforce), here let us define M as the migration rate (defined as the fraction of
nationals leaving the domestic country, which is assumed to take the role of “less developed”
economy), H as the domestic human capital stock and K as the domestic physical capital stock.
While in principle an economy could be either exporter or importer of both workers and
capitals, developing countries are typically net exporter of workers and net importer of (foreign)
capital in the form of foreign direct investment. In addition, the low level of domestic production
and/or the high level of domestic absorption make it rather difficult to obtain domestic
accumulation of physical capital. For this reason we assume that immigration of foreign workers
and domestic investment are set to zero.
Domestic human capital can be augmented through (domestic) school attendance and
decreased through migration of educated workers (the so-called “brain drain”). Furthermore, brain
drain can discourage further investment in education if those who are still in school take the
emigration of skilled individuals as a signal of lack of opportunities for educated workers. However,
some recent literature has drawn attention on the potential existence of a sort of “brain gain”
originated by the increased returns on education due to the increasingly selective immigration
policies in developed countries. Better educated people have higher chances to succeed in
emigrating. This provides an incentive to acquire education in the country of origin yielding an
overall positive balance onto domestic human capital accumulation, under the hypothesis that not
all perspective migrants will succeed in their purpose.
Since both possibility are equally likely, and we are agnostic on this issue, we leave the data
speak. Therefore our first equation is given by
( ) MXMKeH e −=±+
,,& (5)
where dtdHH /=& (the Newtonian derivative), ( )eXMKe ,, summarises school enrolment (with a
supposedly positive impact of foreign investment K in the domestic economy, an ambiguous effect
of migration M and country specific factors eX affecting educational choices – like income
inequality, poverty, school resources and so on). Equation (5) indicates that domestic human capital
stock is increased by school attendance and decreased by migration of educated workers (even if in
the long run the incentive created by migration may rise enrolment and therefore its long run stock).
25
The sign of 0/ >dMde is taken as indicator of the occurrence of “brain gain” (incentive effect),
whereas 0/ <dMde is interpreted as evidence of the possible discouraging effect due to the
ongoing “brain drain”.
Our second equation aims to model the dynamics of physical capital accumulation through
domestic inflow of foreign capitals. We know from the literature that FDI tend to be attracted by the
existence of local favourable conditions31
(like infrastructure, political stability) as well as by the
local availability of skilled labour [Lucas (1990), Zhang and Markusen (1999)], which is positively
correlated with the educational attainment in the population. In addition, we also consider the
possibility of economies of scale and/or of technology/knowledge linkages: both make a new
investment more likely in countries where other investments have already taken place. We also
consider the possibility of a decline in the relative profitability of domestic investment (due to
decreasing marginal productivity, exhaustion of raw materials, shortage of adequate skill): in such a
case, the impact of current stock on new investment would be obviously negative. Thus our second
equation takes the form
( )hXHKkK ,,
+±=& (6)
where hX indicates country specific factors affecting FDI inflows (i.e. infrastructures, degree of
openness, country size, political stability, etc.).
Finally, our third equation takes into account the determinants of outward migration. Here
again the literature is quite substantial. We do consider two aspects: the first one is the impact of the
availability of skilled jobs on the decision to migrate, which is correlated with the prevailing
technology in the country; if the technological progress is embodied in the newly invested physical
capital, then migration should report a negative correlation with foreign direct investment. The
second aspect is the internal competition for skilled jobs, since the greater is the unemployment in
the educated labour force, the longer will be the unemployment spell, and the more likely becomes
the migration. Our assumptions are then summarised in the following
( )mXHKmM ,,
±−= (7)
where mX include the identifying restrictions for this equation, like language facilities, distance,
former colony status, and so on.
Equations (5)-(6)-(7) describe a dynamical system in 2R . In facts, by replacing equation (7)
into (5) we obtain the following system
31
See Blonigen (2005) for a comprehensive review of the literature on FDI determinants. Faini (2004) provides
evidence of a positive effect of domestic human capital stock (proxied by average years of education in the population)
as well as domestic infrastructure (proxied by telephone lines) onto FDI.
26
( )( )
=
=
±+
±±
h
em
XKHkK
XXKHhH
,,
,,,
&
&
(8)
In the case of “brain gain” the Jacobian associated to the system (8) takes the form
±+
−±, while it
exhibits the following signs
±+
+m when negative effects of the “brain drain” prevail. The system
incorporates a feed-back mechanism that contributes to its stabilisation. In facts, when capital stock
increases, it tends to reduce (skilled) workers migration, thus favouring domestic accumulation of
human capital (through the reduced outflow of skilled migrants as well as through an incentive
effect on enrolled students to proceed further on in education). In its turn, an increase in human
capital stock makes additional inflows of new capitals more likely. In both cases, global stability
can be assessed only when the sign and the size of the elements on the Jacobian main diagonal are
known.
27
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