metadata: authors: 0: author: Farchy, Emily
count: World
docna: 0: docna: The impact of EU accession on human capital
            formation : can migration fuel a brain gain ?
docty: Policy Research Working Paper
subtopic: Human Migrations & Resettlements,Tertiary Education,Educational Sciences,International Trade and Trade Rules,Transport Services
teratopic: Macroeconomics and Economic Growth,Social Protections and Labor,Health, Nutrition and Population,Finance and Financial Sector Development,Education
abstracts: cdata!: Can a brain drain be good for
            development? Many studies have established the theoretical
            possibility of such a brain gain. Yet it is only recently
            that the relaxation of data constraints has allowed for
            sound empirical assessments. In utilizing the dramatic
            policy change that accompanied European Union accession as a
            natural experiment, this paper is able to assuage fears of
            reverse causality between migration and human capital
            formation. The results highlight a significant impact of
            European Union accession on human capital formation
            indicating that the prospect of migration can indeed fuel
            skill formation even in the context of middle-income
            economies. And, if accompanied by policies to promote return
            migration, as well as a functioning credit market to enable
            private investment, international labor mobility could
            represent a powerful tool for growth.
repnme: repnme: The impact of EU accession on human capital
            formation : can migration fuel a brain gain ?
display_title: The impact of EU accession on human
            capital formation : can migration fuel a brain gain
listing_relative_url: /research/2009/02/10282570/impact-eu-accession-human-capital-formation-can-migration-fuel-brain-gain
topicv3: Tertiary Education m1326349 482,Transport m1326661 135,Trade m1326924 698,Education m1327048 470,Human Capital Flight m1504170 2845
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Protections and Labor,Health, Nutrition and Population,Finance and Financial Sector Development,Education
no_of_pages: 29
model_extractions: WPS4845 Policy ReseaRch WoRking PaPeR 4845 The Impact of EU Accession on Human Capital Formation Can Migration Fuel a Brain Gain?

_Emily Farchy_ The World Bank Development Economics Policy Review Unit February 2009
Policy ReseaRch WoRking PaPeR 4845

# **Abstract**

Can a brain drain be good for development? Many studies have established the theoretical possibility of such a brain gain. Yet it is only recently that the relaxation of data constraints has allowed for sound empirical assessments. In utilizing the dramatic policy change that accompanied European Union accession as a natural experiment, this paper is able to assuage fears of reverse causality between migration and human capital formation.

The results highlight a significant impact of European Union accession on human capital formation indicating that the prospect of migration can indeed fuel skill formation even in the context of middle-income economies. And, if accompanied by policies to promote return migration, as well as a functioning credit market to enable private investment, international labor mobility could represent a powerful tool for growth.

This paper is a product of the Policy Review Unit, Development Economics Department. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at efarchy@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The Impact of EU Accession on Human Capital Formation: Can Migration Fuel a Brain Gain?<sup>_∗_</sup> Emily Farchy

# **1 Introduction**

This paper sets out to quantify the benefits, in terms of human capital formation, that result from accession to a regional trade block. The motivation behind the analysis that follows is to provide an empirical test of recent arguments (Beine et al 2001, Mountford 1997, Stark,Helmenstein, and Prskawetz 1998) that posit the existence of a brain gain—an increase in stocks of human capital—following labor market deregulation.

Migration is generally thought to have a negative impact on human capital stocks, as educated individuals leave their country in order to take advantage of higher wages abroad.<sup>1</sup> This phenomenon has become known as the brain drain. In contrast the counter-intuitive notion of the brain gain rests on the assumption of the endogeneity of the human capital investment decision. It supposes that the increase in returns to education, prompted by the opportunity to migrate,

> _∗_ The author is grateful to Maurice Schiff for his kind comments and advice. To Professor Robert Skidelsky and Professor Alisdair Smith, for the broad brushstrokes and finer details they respectively provided. And, of course, to Professor Robin Burgess and Patrick Blanchenay.

> The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> 1Another mechanism through which migration opportunities increase the return to investment in skills is analyzed by David Wildasin, who avers that the increased specialization that follows human capital investment exposes skilled workers to region-specific earnings risks, risks that can be mitigated by international mobility of skilled labor.

1
will encourage more individuals to invest in education. A net brain gain results if the positive effect raises human capital stocks above the levels expected in the absence of migration.

In order to assess the likelihood of a net brain gain, it is first necessary to question the magnitude, indeed the existence, of the gross effect of migration on human capital formation. If this gross effect can be quantified it should enable a more informed consideration of the impact of labor market liberalization; of the trade-off between human capital formation and capital flight - the brain gain, and the brain drain.

To examine the magnitude of the brain gain this paper utilizes the natural experiment provided by EU accession. Upon joining the EU, the citizens of the accession country become subject to Article 39 of the EC Treaty—the free movement of persons. If brain gain models are to be given credence there should exist a positive relationship between the increased opportunities for migration prompted by this policy change and enrollment in tertiary education. To test this relationship the analysis of this paper rests on two strategies: firstly a simple difference-in-difference estimation, utilizing the disparate timings of accession negotiations with the Czech Republic and Slovakia, and secondly, a panel regression of 13 countries that have joined the EU. Taken together the results of these two strategies provide evidence in support of the endogeneity of the human capital investment decision, and thus the potential for a net brain gain.

What follows is organized into two sections. The first presents a review of the related literature; the second discusses the analytical methods and main results of the estimated impact of EU accession on human capital formation.

# **2 Economic Theory and Literature Review**

The phenomenon of the brain drain is not new (Bhagwati 1970, Grubel and Scott 1966). It has long been recognized that the outflow of a country’s most 2
skilled and educated workers can have profound implications on the economy they leave behind, in terms of equity and efficiency, in the short and the longterm.

However, recent developments, both theoretical and empirical, have prompted renewed interest in the area of skill migration. From a theoretical perspective, models that endogenize growth (Romer 1986) have highlighted profound potential long-term implications of the brain drain, while models that endogenize demand (Krugman 1991) highlight how the reducing transportation costs involved in trade encourage agglomeration and with it the pull of skill segregation. Combined with the empirical observation of the acceleration of skilled labor from developing countries—recent labor force survey data show that highly skilled migrants accounted for around 38%<sup>2</sup> of EU migration inflow—there is little doubt as to why the magnitude of the brain drain has once more become an urgent question.

# **2.1 The Movement of Skilled Labor: The Brain Drain**

In 1966 when Grubel and Scott first analyzed the brain drain in the context of perfectly competitive markets, they found it had no welfare implications. Wages were equated with the marginal productivity of labor, thus a migrant “removes both his contribution to national output and the income that gives him claim to this share, so that other incomes remain unchanged.” (Grubel and Scott 1966) Yet this reasoning ignores the positive externalities associated with human capital. If one assumes that the skills of workers are complements such that the productivity of a worker depends positively on the productivity of her co-workers, the reduction in opportunities to work with skilled agents following an outflow of skilled labor reduces the welfare of the remaining population. This can be illustrated following a simplified version of Kremer’s 1993 O’Ring model.

Take for example an economy in which output is produced via two tasks, for

> 2Highly skilled migrants are here defined according to the International Standard of Classification on Occupation categories 1-3 (includes managers, professionals and associate professionals) Quoted in Auriol and Sexton 2001

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example research and policy implementation. Let _si_ denote the skill level of a worker involved in task _i_ where _i ∈{_ 1 _,_ 2 _}_ . Thus Furthermore, suppose that the skill of a worker involved in the production of task _i_ may take either of two possible levels: This may be thought of, for example, as reflecting the level of education of the worker, where In order to ascertain which workers, from task 1 and task 2 will choose to work (‘match’) together in equilibrium, we note that a stable match will be one where it is not possible for an individual to rematch and improve her productivity.<sup>3</sup> Assume the wage of a worker involved in task 1 is set according to And skills are assumed to be complements, such that: Given this complementarity, in this context: That is, switching to an educated partner from an uneducated partner will

> 3We assume utility is defined narrowly in terms of wages, and thus productivity.

4
always increase the productivity of an educated worker. Thus an uneducated worker will never be able to profitably incentivize an educated worker to match with him.

The result in a competitive market is a unique stable match involving positive assortative matching, such that skilled and unskilled workers are segregated. If the initial match is non-assortative, then assortative matching makes highskilled workers strictly better-off and low-skilled workers strictly worse-off. In this manner a brain drain can increase the incentives for future generations to migrate creating a poverty trap. The impact on equity is all the more pronounced when one considers the likelihood that education is publicly-subsidized such that the migration of highly-skilled workers imposes a fiscal loss on the remaining inhabitants of the origin country (Bhagwati and Hamada 1974). This can be particularly inequitable if, given the mobile nature of the skilled workers, taxes fall predominantly on the uneducated (Venables, 2005).

Developments in economic geography (Krugman 1991) have highlighted how, as transportation costs fall, in response to policy changes or technological advances, this tendency for skill segregation is likely to increase. The idea behind the simplest economic geography models is that industrial location is demand dependant. Furthermore, demand differences are themselves likely to be endogenous<sup>4</sup> , such that an initial industrial expansion attracts additional labor, which in turn increases demand for goods and services, attracting further labor, and fuelling a continuous feedback cycle. The limit of this cumulative cycle is provided by agricultural production which cannot relocate to industrial hubs and so generates decentralized demand (via agricultural labor) for industrial goods and services. The degree of industrial concentration will be dependent on the relative importance of economies of scale and transport costs.<sup>5</sup> When transportation costs are high, production is constrained to locate near demand

> 4Either because of mobility of workers (Krugman 1991) or because of mobility of firms demanding intermediate goods (Venables 1996).

> 5Krugman (1998) lists other such forces such as; thick labor markets and pure external economies on the centripetal side and land rents and pure external diseconomies on the centrifugal side. Krugman simplifies modelling however, by focusing only on economies of scale and transport costs.

5
and, provided the latter begins relatively dispersed across the two countries agglomeration will never get underway and skilled labor will earn approximately the same real wage irrespective of location. As trade costs fall economies of scale will become relatively more important in the location decision. Industry will cluster in one country, and skilled workers will follow in order to take advantage of their higher marginal productivity. In this manner, models of economic geography illustrate how, in the presence of endogenous demand, initial inequalities can be exacerbated by labor mobility (Krugman and Venables 1995).

Accession to the EU, and the associated policy changes, represents a significant reduction in transport costs as tariffs reduce and the movement of factors becomes less costly. Thus one might expect skilled labor from accession countries to flow out, upon accession, in order to agglomerate with the existing skilled labor in Central Europe. In short an increase in the brain drain. Indeed, in her analysis of industrial location in the EU, Amiti (1998) finds that industries most subject to scale economies are highly concentrated and located in central EU countries.

# **2.2 The Creation of Skilled Labor: The Brain Gain.**

It has long been accepted in the migration literature that if human capital is characterized by network externalities, the levels of investment in education can be characterized by multiple equilibria, where migration and the relocation of skilled labor can perpetuate a bad equilibrium. However, accepting the presence of positive externalities in education, the negative prognosis presented above— that migration will perpetuate a low-education equilibrium—depends entirely on the assumption of a negative relation between migration and domestic human capital stocks.

More recent research has used dynamic models that endogenize the human capital investment decision to question this negative relation, purporting conversely that migration may encourage a brain gain. The hypothesis is that the possibility to migrate and achieve higher wages abroad, modifies the human 6
capital formation decision calculus, raising the expected private return to education. Thus the indirect effect of the migration is to raise the proportion of the population willing to invest in education, concomitantly raising the level of human capital in the economy towards the social optimum level without the use of taxes or subsidies (Stark and Wang 2002).

The relative magnitude of these two effects of migration; the relocation versus creation of skilled labor—or the net brain effect—will have profound implications for the development of the origin country. If one admits for the possibility of network externalities a positive net brain effect may push an economy from a low equilibrium out of a poverty trap and onto a virtuous cycle toward a high education equilibrium<sup>6</sup> , higher productivity levels, and, given the assumption of positive externalities resulting from education, stronger productivity growth.<sup>7</sup> Recent literature has modelled the possibility of the brain gain; under assumptions of, heterogeneous labor (Mountford 1997), and under assumptions of imperfect information and return migration (Stark et al 1997). The model presented below is adapted from Stark et al (1998). Throughout the analysis that follows, education is assumed to be internationally transferable and a necessary but not sufficient condition for migration.<sup>8</sup> Represent the income choices of a two period lived agent as follows: Where we assume:

>  Each agent is endowed with 1 unit of labor per period.

> 6See Mountford 1997 for an analysis of this ‘big push’ in the context of heterogeneous ability

> 7If the average human capital level increases, raising p, this will raise initial stocks of p the following period. This in turn will increase the return for young agents considering investing in human capital, which will, in turn cause another rise in _p_ . This is potentially a perpetual process of endogenous growth.

> 8This is an extreme and disputed assumption, (see Borjas (1990) for a model of negative self-selection of migrants) yet it nevertheless highlights a trend observable in empirical surveys (see Dustmann and Liebig and Sousa-Poza (2004)) and has been justified on the grounds that skilled migrants generally face smaller costs due to informational advantages and secured jobs.

7
-  _πi ∈_ [0 _,_ 1] is the amount of period 1 labor each individual devotes to investment in human capital;

-  _π_ is the average level of education in the population st _π_ = Σ _πi_

-  Wages _w_ ( _π_ ) are a positive function of _π_ . This represents the positive externalities of education.<sup>9</sup>

-  Costs of education are represented as the opportunity cost of forgone labor earnings

-  _φ_ ( _πi_ ) is the amount of productive human capital available in second period measured in efficiency units of labor. Thus _φ_ ( _πi_ ) represents the skill premium. Where _φ_<sup>_′_</sup> ( _πi_ ) _>_ 0 and _φ_<sup>_′′_</sup> ( _πi_ ) _<_ 0

-  Workers consume their entire wages both in period one and in period two.

-  Lifetime utility can be represented as:

where _U ′_ ( _y_ ) _>_ 0 and _U ′′_ ( _y_ ) _<_ 0 and _ρ_ represents the discount factor.

From the above assumptions it can be seen that whilst income in period one is decreasing function of _πi_ , period two income is an increasing in _πi_ . Furthermore, income in both periods is positively effected by the average skill level in the economy A representative agent therefore chooses _π ∈_ [0 _,_ 1] to maximise:

> 9The positive externalities of education, beyond the strictly private gains anticipated by those who invest, have been characterized in a variety of ways. Vidal (1998) models these benefits by assuming an intergenerational transfer whereby the higher human capital level of one generation, the more effective is the human capital of the next generation. Under this premise skilled migration makes future human capital acquisition cheaper in the destination country and dearer in the origin country. Mountford (1997) posits a production externality such that the current MPL depends positively on the share of the population who had education in the previous period. Beine Docquier and Rapaport (2001a) make Mountford’s hypothesis explicit by allowing the average skill of one generation to pass directly to the next, who can then build on it.

8
Where the ex-ante uncertainty involved in making education decisions contingent on migration prospects is captured by p, the probability of migration<sup>10</sup> (0 _≤ p ≤_ 1). Sources of uncertainty can include: emigration policies set by source countries, immigration authorities in destination countries, and the time lag between the two decisions; education and migration.

If the agent successfully migrates overseas she can expect to achieve wage: where wage is a positive function of the aggregate human capital stocks in ’domestic’ or ’foreign’. Thus, since we assume _π_<sup>_f_</sup> _> π_<sup>_d_</sup> we have that _w_<sup>_f_</sup> _> w_<sup>_d_</sup> First order conditions for maximization imply: Given that _u′_ ( _y_ ) _>_ 0, and _w_<sup>_d_</sup> is an increasing function of _π_<sup>_d_</sup> , we can see that the nominator (resp. denominator) is an increasing (decreasing) function of _π_<sup>_d_</sup> . Thus it must be that _∂ψ_ ( _πi, p_ ) _/∂π_<sup>_d_</sup> _>_ 0.

Implicit differentiation of (12) yields: or rewritten: Given the assumptions on the concavity of the skill premium, _φ′′_ ( _πi_ ) _<_ 0, we need only show that andat _∂ψ_ ( _πi, p_ ) _/∂π_<sup>_d_</sup> _>_ 0, and since _φ′′_ ( _πi_ ) _<_ 0, it is clear that all we require

> 10Note that the case of an economy closed to migration is incorporated into equation for as the case when p=0

9
in order that _dπi/dp >_ 0 (a positive brain gain) is that _∂φ_ ( _πi, p_ ) _/∂p >_ 0.

Thus theory would predict that openness to migration should lead to an increase in domestic human capital formation if, and only if: Note that this condition is met by any utility function _u_ ( _x_ ) that maintains _u′_ ( _x_ )+ _xu′′_ ( _x_ ) _>_ 0 for all x,<sup>11</sup> Or alternatively, by any utility function exhibiting relative risk aversion smaller than one.

The fundamental relationship emerging from this theory is a positive link between migration opportunities and the proportion of young individuals who decide to invest in education. Some workers migrate and with them goes a higher level of human capital than had they migrated without factoring in the possibility of migration—a brain drain. But, counterbalancing this, other workers stay in the origin country, and with them stays more human capital than would have done in the absence of the migration possibility—a brain gain.<sup>12</sup> Yet, some have claimed that this brain gain literature is excessively optimistic and likely to remain little more than a theoretical possibility. Schiff (2005) disputes assertions that there will always be a positive level of migration such that next period human capital stocks increase in the origin economy, believing that risk aversion, heterogeneous labor, and the benefits accruing from unskilled migration all combine to ensure that the net brain gain is likely to be negative. Despite extensive theoretical contention, empirical analyses have to date been severely limited by data issues.

> 11If _u′_ ( _x_ ) + _xu′′_ ( _x_ ) _>_ 0 then _xu′_ ( _x_ ) is a strictly increasing function. Hence for _x_ 2 _> x_ 1, we have that _x_ 2 _u′_ ( _x_ 2) _> x_ 1 _u′_ ( _x_ 1). Setting _x_ 2 = _φ_ ( _πi_ ) _, w_<sup>_f_</sup> and _x_ 1 = _φ_ ( _πi_ ) _, w_<sup>_d_</sup> , the inequality, as stated above, holds.

> 12This model does not incorporate the possibility that human capital formation is subsidized by the origin country government.

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# **2.3 Empirical Literature**

Until recently there has been no harmonized migration data on the skill levels of international migrants disaggregated by country.<sup>13</sup> Many origin countries do not collect any such qualitative data, and the data collected by destination countries display much heterogeneity particularly with regards to the educational attainments of international migrants. As such much of the quantitative assessment of this much-debated phenomenon has been either anecdotal or cross sectional.

Anecdotal evidence of a potential brain gain has been strong. Kangasniemi et al (2004) have found that 40% of migrant doctors working in the UK were ’influenced’ to train in medicine by the prospect of migration. Whilst Lucas (2004) notes that despite low domestic returns on skills 72% of all students enrolled in higher education in the Philippines were enrolled in private institutions. Lucas takes this, along with the finding that the choice of field of study responds to shifts in international demand, as evidence that the domestic skill premium is unlikely to be the primary driver for the decision to invest privately in education.

Beine et al (2001a) attempted a cross-sectional empirical test of the brain drain and found that migration appeared to boost human capital formation in poor countries, and further, that the stock of human capital positively influenced growth. However the analysis was severely constrained by data problems such as the need to use gross migration rates as a proxy variable for data on the brain drain. A second attempt (Beine et al (2003)) found results in support of their earlier work other than the finding that the marginal effect of migration on human capital does not differ with the wealth of the origin country. This work, however, was reliant on a dataset compiled by Carrington and Detragiache (1998), that is itself subject to problems.<sup>14</sup>

> 13A recent World Bank sponsored study by Docquier and Marfouk has made tentative steps toward filling this void by providing emigration rates by education attainment for all countries in 1990 and again in 2000

> 14The US census data, on which it was based, does not record where the education took place; furthermore it includes foreign students, such that graduate students may account for

11
Similarly, and also relying on the Carrington and Detragiache dataset, Faini (2002) compares gross enrollment rates across 51 countries to measures of the extent of high skilled migration to the OECD from these countries, and finds little evidence that a greater rate of emigration of the highly skilled induces greater domestic enrollments in higher education.<sup>15</sup> As with all cross country regressions, the robustness of these studies are severely hampered by the potential for omitted variable bias resulting from cross country heterogeneity. Most recently, Beine, Defoort and Docquier (2007) have gone some way to addressing this problem. And, using a panel of 6 observations per country, have found that the impact of skilled migration on education is contingent on the country level of development, that is, it is only ambiguously perceptible in lower income countries while it is unambiguously imperceptible in middle and higher income countries. There are, however, remaining concerns.

Beine et al (2007) highlight the potential endogeneity of the migration rates of skilled workers with respect to the change in the human capital level. Indeed, stylized facts and empirical findings (Docquier et al 2006) suggest that ceteris paribus and increase in natives’ average level of schooling reduces the skilled emigration rate. This may result from: quota restrictions on the number of educated immigrants accepted, such that the higher the proportion of educated adults, the lower the probability that each of them will be able to leave the country - this is the option discussed in Beine et al (2007), alternatively endogeneity may result from complementarities in skilled labor such that an increase in the educated workforce encourages others to stay, conversely and finally more skilled graduates may reduce the wage premium pushing more to migrate.

Beine et al (2007) correct for this endogeneity using lagged migration rates as an instrumental variable for current migration rates. The assumption behind a substantial proportion of individuals with tertiary education; lastly, the data does not cover the former Soviet Union nor Eastern Europe. As a result of these problems the assumptions (such as the generalisation of the US Census data to apply across the rest of the OECD) made by Carrington and Detragiache cause concerns that significant amounts of data manipulation do little to assuage.

> 15Faini does find evidence of induced increases in secondary education but believes this indicates would be migrants choose to pursue their higher education abroad

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the use of lagged migration rates as an instrumental variable is that increased enrollment in time _t_ cannot influence the migration rates at time _t−_ 1. However, it is possible to imagine that rational agents will be able to foresee changes in human capital the following year, for example, in an economic downturn, it may be expected that, as the job market shrinks, the opportunity cost of higher education is reduced and hence more people are likely to enroll the following year. If this is the case then the lagged migration rates, employed by Beine et al (2007), may not entirely eradicate the endogeneity problems.

Since a failure to account for some potential reverse causality is likely to result in biased estimates this paper utilizes the natural experiment of EU accession and the exogenous impact this has on the parameter _p_ (the probability of successful migration). Furthermore, to avoid the data issues that have stymied much of the previous research, this paper has an altogether narrower focus: does an increase in migration opportunity lead to an increase in investment in human capital? The analysis that follows abstracts from measurements of the brain drain—the relocation of skilled workers—focusing instead on the incentivization effect of migration. In order to do this I look at the impact of an exogenous increase in p (the probability that intended migration will succeed) on levels of human capital in the origin country.

# **3 Analytical Methods and Main Results**

Accession to the EU provides a natural experiment to test theories of migration fuelled increases in human capital. This is because, as a member of the EU, a countries citizens benefit from the free movement of persons<sup>16</sup> (Article 39 of the EC Treaty). In terms of the brain gain model outlined in section 1.4, such a policy change represents a significant increase in the parameter p (the possibility of successful migration)<sup>17</sup> . And, whilst the available data limits analysis of

> 16The relevant rights are complemented by a system for the co-ordination of social security schemes and by a system to ensure the mutual recognition of diplomas.

> 17Note that freedom of movement does not increase p to the level that _p_ = 1. If it were to do so there would be no possibility of a brain gain because all those who invest in education

13
migration flows disaggregated by education level,<sup>18</sup> the magnitude of the change in enrollment that follows this increase in p should give some indication as to the feasibility of the brain gain hypothesis that migration may increase the stock of human capital.

To capture investment in education I use the Gross enrollment Ratio; the number of pupils enrolled in tertiary education, regardless of age, expressed as a percentage of the population in the theoretical<sup>19</sup> age group for tertiary education. The GER is calculated according to the following formula: Where:

-  _GER_<sup>_t_</sup> is the Gross enrollment Ratio at time t,

-  _E_<sup>_t_</sup> is the enrollment at time t,<sup>20</sup>

-  _Pa_<sup>_t_isthepopulationinage-groupawhichcorrespondstotertiaryeduca-</sup> tion in school-year _t_<sup>21</sup> .

It should be noted at this point that, if it is the case that enrollment in tertiary education increases with accession to the EU, this is unlikely to be solely a result of students wishing to use their new rights to migrate. Accession to the EU will almost certainly also effect domestic employment opportunities in order to migrate will do so. The time lag and uncertainty about the future ensure that whilst migration is a right it remains that _p <_ 1. This is because there are many other factors involved in the translation of intention to migrate into reality, for example changing personal/economic circumstances may have a substantial impact given the extended time period required to gain a tertiary qualification. Indeed, micro-level surveys on intentions to migrate consistently exceed actual migration figures. Note further that the indiscriminate nature of the freedom of movement of people implies that of the two reasons a potential migrant may wish to invest in education; firstly as a vehicle for exit, and secondly to benefit from the increased wage advantage abroad, only the second is relevent here. Thus the findings are likely to represent a lower bound on the estimated incentive effect of increased migrational opportunities.

> 18As mentioned above the recent dataset by Docquier and Marfouk has begun to relax this constraint.

> 19For the tertiary level, the population used is the five-year age group following on from the secondary school leaving age.

> 20Many thanks to Lucy Mei Hong (UIS UNESCO) for obtaining the historical data not publicly available on the internet.

- 21See `http://esa.unorg/unpp/forUNDPdataonline` .

14
through increased Foreign Direct Investment (henceforth FDI), as well as increasing trade flows - thus increasing returns to investment in human capital may also result from an improved domestic climate. Nevertheless, the impact of increased FDI and trade flows will likely have been felt for several years preceding the accession date. Indeed research into the effect on FDI of EU accession (Bevan et al 2001) finds that as far back 1994 when the EU announced its commitment to enlargement, front-runner candidate countries in Eastern Europe experienced significant increases in inflows of FDI. Barriers to trade were also reduced in anticipation of accession. Thus, if a positive effect on education is identified, the role of the labor market mobility resulting from accession, in increasing enrollment in tertiary education, should not be undermined.

The relative importance, in the decision to invest in education, of FDI flows and the migration opportunity, can be examined through the lens of economic geography models as outlined above. The degree to which accession encourages dispersion across Europe (and hence inward flows of FDI to accession countries) or agglomeration in Central Europe (and with it outward flows of skilled labor from accession countries) is dependent on the relative importance of exogenous parameters such as economies of scale and transport costs. And, although transport costs are likely to be less signicant in high skilled sectors, the impact of increased FDI and trade flows represents a possible source of upward bias on the results. Thus, in the panel regression that follows, I control for FDI levels as well as exports.

In order to assess the impact of European accession on enrollment in tertiary education this paper employs two strategies. Firstly, in section 3.1, with a simple difference-in-difference approach which, along the lines of a simplified propensity score matching methodology, exploits the division of Czechoslovakia in 1993. This enables the use of Slovakia as a counterfactual against which to assess the impact of the EU negotiations that began with the Czech Republic in 1997. Secondly, in an attempt to support these results, section 3.2 examines the impact of EU accession on tertiary enrollment across a panel of 13 countries 15
that have joined the EU.<sup>22</sup>

# **3.1 Czech Republic/Slovakia Case Study**

## **3.1.1 Method**

The goal of this section is to assess the impact that EU accession negotiations had on the GER of the Czech Republic. To do this I follow Abadie’s methodology using a difference-in-difference estimation but rather than create a synthetic counterfactual, I utilise the disparate timing of the opening of EU accession negotiations with the Czech Republic (in November 1997) and Slovakia (in January 2000) to use Slovakia as a Czech counterfactual. If the brain gain hypothesis is correct, and agents incorporate information relating to the probability of a successful migration into their investment decision, I expect to see a rise in the disparity between Czech and Slovak GER following the 1997 announcement of negotiations with the Czech Republic and a fall following the 2000 announcement of negotiations with Slovakia.

I have chosen these countries for two reasons. The first is obvious; until 1993 they were just one country and whilst there were economic differences<sup>23</sup> they were at least subject to the same policy environment and political shocks. The second motivation behind this choice is the micro level survey data (IOM 1998) suggesting these are the two countries with the highest potential migration.

Ultimately the Czech Republic and Slovakia joined the EU at the same time, and so this example can only be utilized to look at the effect of accession negotiations on enrollment. This creates a number of problems with the timing.

> 22Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia, Greece, Portugal, and Spain.

> 23Prior to their separation on January 1st 1993 the Czech and Slovak republics operated under similar institutions and policies and by the time of their separation were considered to have progressed more rapidly toward a market system than their East European neighbours (with the exception of East Germany). However differences do remain: the Czech Republic privatized faster, offered less generous social security benefits, and had a higher proportion of smaller firms offering more opportunities for entrepreneurial activities. In addition the Czech Republic’s long border with Germany enabled many workers to commute beyond its borders. And, whilst the supply of more educated workers, following the fall of communism, increased in both the Czech Republic and Slovakia; labor demand did not. The Slovakian economy experienced higher unemployment than the Czech Republic.

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<!-- Start of picture text --> 30<br>GA Few Years<br>25 @ Rest of Life<br>20<br>15<br>10<br>5<br>0<br>Czech Republic Hungary Poland Slovakia Slovenia<br><!-- End of picture text -->
<!-- Start of picture text --> 0.6<br>05 — Czech Rep<br>— 7 Slovakia<br>“<br>0.4 “7<br>_ a<br>0.3 wee<br>02 aan<br>0.1<br>0<br>EPP IPP EEE EE EE SEES S<br><!-- End of picture text -->
||Pre-Treatment: Talks<br>with Cz|Post Treatment|Diference|
|---|---|---|---|
|Slovakia|0.222346|0.261852|0.03951|
|Czech Republic|0.240419|0.264245|0.02383|
|Diference|0.018073|0.00239|-0.01568|

Table 1: Difference-in-Difference of GER at time _t_ (Treatment 1: The Opening of Accession Negotiations with the Czech Republic)

||Pre-Treatment:|Post Treatment|Diference|
|---|---|---|---|
||Talks with Slovakia|||
|Slovakia|0.261852|0.303742|0.03951|
|Czech Republic|0.264245|0.308823|0.04458|
|Diference|0.00239|0.00508|-0.00269|

Table 2: Difference-in-Difference of GER at time _t_ (Treatment 2: The Opening of Accession Negotiations with the Slovakia) Slovakia increases by nearly 280% of its pre-treatment difference (Table 3).

||Pre-Treatment:|Post Treatment|Diference|
|---|---|---|---|
||Talks with Cz (t+3)|||
|Slovakia|0.287256|0.323255|0.035999|
|Czech Republic|0.294225|0.349701|0.055476|
|Diference|0.006969|0.026446|0.019477|

Table 3: Difference-in-Difference of GER at time _t_ (Treatment 1: The Opening of Accession Negotiations with the Czech Republic (t+3)) Similarly it can be seen from Table 4 that in the last observations the Slovak GER begins to rise at a greater rate than the Czech GER thereby beginning to close the difference. This finding supports the hypothesis that the widening difference-in-difference was a result of the disparate timings of the opening of accession negotiations.

However, whilst the lack of immediate impact can be justified by considering risk-averse agents, two concerns remain. Firstly, it is important to bear in mind that Slovakia does not represent a perfect counterfactual to the Czech Republic. From 1993 the two countries separated and became subject to separate policies 19
<!-- Start of picture text --> 0.6<br>05 — Czech Rep<br>— — Slovakia<br>ol<br>0.4 oo<br>_ a<br>03 aan<br>02 aan<br>0.1<br>0<br>ES PPP EEE PS SF FF re rd<br><!-- End of picture text -->
a cross-country regression using panel data comprising of 13 countries that have completed accession.

# **3.2 Cross-Country Panel Regression**

The cross-country panel consists of the AC-10<sup>25</sup> as well as Spain, Portugal, and Greece, who joined the EU in the 1980s. This earlier Southern EU expansion is included whilst the incorporation of Austria, Finland, and Sweden in 1995 is omitted, because in the 1995 expansion, there was no significant GDP per capita gap between the accession countries and the EU member states. Furthermore free movement of people was already in place in these countries as they were members of the European Economic Area.

## **3.2.1 Method**

Having ensured, via a Dickey-Fuller unit root test, that the assumption of nonstationary appears valid,<sup>26</sup> I can be confident that OLS results will not suffer from the spurious regression problem. However, initial inspection of a GER time plot suggests that a linear trend may not be appropriate thus, in order to stabilize the variance I use a natural logarithmic transformation of the GER.The empirical approach is therefore to a run panel data regression on 13 countries that have joined the EU of the following form: Where ln _GERct_ represents the natural logarithm of the Gross Enrollment Ratio of country _c_ in time _t_ , _accct_ is a dummy that switches on when country c has completed accession, _δDt_ is the time fixed effect, _γDc_ is the country fixed effect and _ε_ is an error term. Fixed effects at the country level control for the usual array of cross-country differences in history, geography, culture,

> 25Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia.

> 26using the Dickey-Fuller unit root test approach given by Holden and Perman in B Bhaskara Rao Conintegration for the Applied Economist 1994

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policy and economic structure that have been constant over the sample period. Whilst the year dummy controls for general trends and shocks affecting tertiary enrollment across countries. _β_ is the coefficient of interest measuring the effect of EU accession on the change in tertiary GER.

# **3.2.2 Results**

||(OLS)|(OLS)|(Clustered)|(Clustered)|
|---|---|---|---|---|
|VARIABLES|LnGER|LnGER|LnGER|LnGER|
|Accession|0.207***|0.291***|0.207*|0.178|
||(0.0542)|(0.0597)|(0.0992)|(0.105)|
|FDI||-0.00612||-0.00219|
|||(0.00568)||(0.00305)|
|Exports||-0.00381***||0.000954|
|||(0.00128)||(0.00374)|
|Year Fixed Efects|yes|yes|yes|yes|
|Country Fixed Efects|yes|yes|yes|yes|
|Observations|222|174|222|174|
|Number of country|15|15|15|15|
|_R_<sup>2</sup>|0.888|.|0.888|0.885|

Standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Estimate of impact of accession talks on ln _GER_ . The data are for the 13 countries over the period 1970–2004. Czechoslovakia split into Czech Republic and Slovakia in January 1993, and, after this date, they are included as separate observations. The panel therefore consists of a total of 442 possible observations with deviations accounted for by missing data.

The results in table 5 suggest that accession to the EU does indeed have an impact on the levels of enrollment in tertiary education. Indeed, ceteris paribus, it can be expected that on average the GER will be approximately 22
20 percent higher following EU accession. Column 2 presents the OLS results controlling for FDI and Exports, column 3 preesnts the results clustered at the country level to assuage concerns of serial correlation. 27 Clustering does indeed substantially raise the standard errors indicating that there may be serial correlation among countries; nevertheless the results remain highly significant at the 6 percent level. Finally column 4 presents the results clustered at the country level and additionally controlling for FDI and exports, in this final regression the results are less significant. Nevertheless, they remain significant at the 12 percent level and the robustness of the coefficient to these various specifications is encouraging.

The impact of EU accession on enrollment in tertiary education identified above, lends support to the assumption of the endogeneity of human capital formation and hence the hypothesis that increased migration opportunities encourage greater levels of human capital formation that may, given the right parameters, offset or even overpower the brain drain that results from the relocation of skilled labor. Such a substantial increase in enrollment suggests that even in the presence of large outflows of human capital, there is the potential for a positive net effect.

Further work may wish to redo the above analyses after some time has passed. This would allow a number of refinements. Firstly, it would enable a comparison of the post accession trends of the Czech Republic and Slovakia therefore providing a check on the validity of the difference in difference approach. Secondly, the pass of time would provide more data to analyse the impact of the Eastern expansion of 2004, it would also increase the sample by enabling the inclusion of the most recent expansion of 2007 to include Romania and Bulgaria. Lastly, given data continuing to a later date, it may be interesting to look more closely at the possibility of lagged effects.

> 27Bertrand, Duflo, and Mullainathan (2004) present an analysis of the dangers in ignoring serial correlation in panel data, using Monte Carlo data and randomly generated placebo laws to illustrate frequency of inaccurate conventional standard errors. Using standard differencein-difference standard errors they find a statistically significant effect in 45% of the placebo interventions.

23
Another avenue for further work would be an analysis of the effects of differing levels of public funding for education on the above results. This would enable an analysis more focused on the welfare implications of labor market liberalization, and as such may identify more policy prescriptions. Clearly a positive net effect on human capital stocks is not enough to conclude that the economic impact of labor market liberalization is positive if the gains from increased participation in tertiary education are funded entirely from the public purse. The positive implications of the brain gain rely on an increase in the private investment in human capital.

Empirical analyses that attempt to quantify the net brain effect continue to be severely hampered by data deficiencies. Nevertheless incremental steps such as those presented in this paper, can help to clarify what has for a long time been, and will likely continue to be, a highly contentious area.

# **Conclusion**

The brain gain literature relies for its positive prognosis on the assumption that human capital is endogenous. This paper has shown that this assumption is well founded. It has examined the relationship between migration opportunities and the private decision to invest in higher education, looking first at the impact of the announcement to open negotiations, through a difference-in-difference comparison between the Czech Republic and Slovakia, and secondly at the impact of accession on a cross-country panel of 13 current EU members. Taken together these results represent evidence that an increase in the opportunity to migrate, heralded by accession to a free-trade area such as the EU, has a significant effect, both economically and statistically, on the proportion of the population that choose to invest in higher education, and hence the skill level of the country. While it is important to remember that this result cannot be entirely attributed to the increased migration opportunities, as increased FDI and trade flows will also raise the domestic return to education, micro-level survey data regarding 24
intentions to migrate indicate that migration to achieve a higher wage abroad plays a significant role.

The example of the brain gain analyzed here represents a very narrow focus on the gross impact of migration opportunities on human capital formation. Clearly the net brain effect will depend also on outward flows of skilled labor. However, if recent policy trends towards a focus on increasing temporary and return migration are successful, the gross effect of migration on human capital formation, identified in this paper, could enable migration to be utilized as a powerful tool for development.

# **Bibliography**

Auriol and Sexton (2001). “Human Resources in Science and Technology: Measurement Issues and International Mobility”, International Mobility of the Highly Skilled,OECD Abadie (2003) “The Economic Costs of Conflict: A Case Study of the Basque Country” _American Economic Review_ , 93(1) p113-132.

Beine, Docquier and Rapoport, (2001) “Brain Drain and Economic Growth: Theory and Evidence” _Journal of Development Economics_ 64 p275-289 Beine, Docquier and Rapoport, (2003) “Brain Drain and LDC’s Growth: Winners and Losers” IZA Working Paper Beine, Defoort and Docquier, (2007) “A Panel Data Analysis of the Brain Drain”, Discussion Papers 2007024, Universite catholique de Louvain, Departement des Sciences Economiques.

Bertrand, Duflo, and Mullainathan (2004) “How Much Should we Trust Differences-in-Differences Estimates?” _Quarterly Journal of Economics_ p249275 Bevan, Estrin and Grabbe (2001) “The Impact of EU accession Prospects on FDI Inflows to Central and Eastern Europe”, ESRC Policy Paper Bhagwati and Hamada (1974) “The Brain Drain, International Integration of Markets for Professionals and Unemployment” _Journal of Development Economics_ . 1(1) p19-42.

Borjas (1990) “Self-Selection and the Earnings of Immigrants: Reply”, _American Economic Review_ . 80(1): p305-308.

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Carrington and Detragiache (1998) “How Bid is the Brain Drain?” IMF Working Paper Chase (1998) “Markets for Communist Human Capital: Returns to Education and Experience in the Czech Republic and Slovakia” _Industrial and labor Relations Review_ , 1(3) p401-423 Docquier, Lohest and Marfouk(2006) “Brain Drain in Developing Countries”World Bank Economic Review, forthcoming.

Dustmann (2003) “The Impact of EU Enlargement on Migration Flows” Home office Report.

Grubel and Scott (1966) “The International Flow of Human Capital” _American Economic Review_ p268-274 International Organisation for Migration (IOM) (1998) “Migration in Central Eastern Europe“. Technical Cooperation Centre for Europe and Central Asia, Geneva.

Kangasniemi, Winters and Commander(2004) “Is the Medical Brain Drain Beneficial? Evidence from Overseas Doctors in the UK”, Mimeo, CNEM, London Business School.

Kremer (1993) “The O-Ring Theory of Economic Development” _Quarterly Journal of Economics_ p551-575 Krugman (1991) “Increasing Returns and Economic Geography” _Journal of Political Economy_ 99(3) p484-499 Krugman and Venables (1995) “Globalization and the Inequality of Nations” _Quarterly Journal of Economics_ Kuhn and McAusland, (2006) “The International Migration of Knowledge Workers: When is Brain Drain Beneficial?,” NBER Working Papers 12761 Kwok and Leland (1982) “An Economic Model of the Brain Drain” _American Economic Review_ p91-100 Mountford (1997) “Can a Brain Drain be Good for Growth in the Source Economy?” _Journal of Development Economics_ 53(2), p287-303 Romer (1986) “Increasing Returns and Long-Run Growth” _Journal of Political Economy_ p1002-37 Schiff, M (2005) “Brain Gain: Claims about its Size and Impact on Welfare and Growth are Greatly Exaggerated’ _World Bank Policy Research Working Paper_ 3708.

Stark, Helmenstein, and Prskawetz (1997) “A Brain Gain with a brain drain” _Economics Letters_ 55, p227-234 26
Stark, Helmenstein, and Prskawetz (1998) “Human Capital Depletion, Human Capital Formation, and Migration: a Blessing or a curse?” _Economics Letters_ 60 p363-367 Stark and Wang (2002)“Inducing human capital formation: migration as a substitute for subsidies.” _Journal of Public Economics_ 86 p29-46 Venables (1996) “Equilibrium Locations of Vertically Linked Industries” _International Economic Review_ 37(2) 341-359 Venables (2005) “Regional Disparities in Regional Blocs: Theory and Policy” Paper prepared for the IADB Vidal (1998) “The Effect of Emigration on Human Capital Formation” _Journal of Population Economics_ .

Wildasin (2000) “Labor-Market Integration, Investment in Risky Human Capital, and Fiscal Competition” _American Economic Review_ 90(2) p73-95

# **Online Resources**

UNDP online data: `http://esa.un.org/unpp/` UNESCO UIS online data: `http://www.uis.unesco.org/` World Bank Development indicators: `http: //publications.worldbank.org/WDI/` 27