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The Impact of EU Accession on Human Capital Formation Appunti scolastici Premium

Dispense per il corso di Differenziali Economici e Migrazioni della Prof.ssa Paola Giacomello e del Dott. Paolo Sellari. Trattasi dell'articolo di Emily Farchy dal titolo "The Impact of EU Accession on Human Capital Formation: Can Migration Fuel a Brain Gain" all'interno del quale l'autrice espone la sua teoria sull'effetto che la... Vedi di più

Esame di Differenziali Economici e Migrazioni docente Prof. P. Giacomello

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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 long-

term.

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 poten-

tial long-term implications of the brain drain, while models that endogenize de-

mand (Krugman 1991) highlight how the reducing transportation costs involved

in trade encourage agglomeration and with it the pull of skill segregation. Com-

bined with the empirical observation of the acceleration of skilled labor from

developing countries—recent labor force survey data show that highly skilled

2

migrants accounted for around 38% 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

2 Highly skilled migrants are here defined according to the International Standard of Clas-

sification on Occupation categories 1-3 (includes managers, professionals and associate pro-

fessionals) Quoted in Auriol and Sexton 2001 3

example research and policy implementation. Let s denote the skill level of a

i

∈ {1,

worker involved in task i where i 2}. Thus

y = f (s , s ) (1)

1 2

Furthermore, suppose that the skill of a worker involved in the production

of task i may take either of two possible levels:

∈ {e }

s , u (2)

i i i

This may be thought of, for example, as reflecting the level of education of

the worker, where e>u> 0 (3)

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

3

productivity.

Assume the wage of a worker involved in task 1 is set according to

∂f (s , s )

1 2

M P L : w = (4)

1 ∂s

1

And skills are assumed to be complements, such that:

2

∂w ∂ f (s , s )

1 1 2

= > 0 (5)

∂s ∂s ∂s

2 1 2

Given this complementarity, in this context:

∂f (s , u)

∂f (s , e)

1 1

> (6)

∂s ∂s

1 1

That is, switching to an educated partner from an uneducated partner will

3 We 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 posi-

tive assortative matching, such that skilled and unskilled workers are segregated.

If the initial match is non-assortative, then assortative matching makes high-

skilled 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 pro-

nounced when one considers the likelihood that education is publicly-subsidized

such that the migration of highly-skilled workers imposes a fiscal loss on the re-

maining 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 ad-

vances, 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 en-

4

dogenous , 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

5

on the relative importance of economies of scale and transport costs. When

transportation costs are high, production is constrained to locate near demand

4 Either because of mobility of workers (Krugman 1991) or because of mobility of firms

demanding intermediate goods (Venables 1996).

5 Krugman (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 cen-

trifugal 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 ag-

glomeration 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 geog-

raphy 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 signif-

icant reduction in transport costs as tariffs reduce and the movement of factors

becomes less costly. Thus one might expect skilled labor from accession coun-

tries 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 con-

versely 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 edu-

cation. 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 ver-

sus creation of skilled labor—or the net brain effect—will have profound implica-

tions 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

6

education equilibrium , higher productivity levels, and, given the assumption of

7

positive externalities resulting from education, stronger productivity growth.

Recent literature has modelled the possibility of the brain gain; under as-

sumptions of, heterogeneous labor (Mountford 1997), and under assumptions of

imperfect information and return migration (Stark et al 1997). The model pre-

sented below is adapted from Stark et al (1998). Throughout the analysis that

follows, education is assumed to be internationally transferable and a necessary

8

but not sufficient condition for migration.

Represent the income choices of a two period lived agent as follows:

− ·

y = (1 π ) w(π) (7)

1i i

·

y = φ(π ) w(π) (8)

2i i

Where we assume:

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

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

ability

7 If 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.

8 This 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

ˆ ∈

π [0, 1] is the amount of period 1 labor each individual devotes to

i

investment in human capital;

ˆ π is the average level of education in the population st π = Σπ

i

ˆ Wages w(π) are a positive function of π. This represents the positive

9

externalities of education.

ˆ Costs of education are represented as the opportunity cost of forgone labor

earnings

ˆ φ(π ) is the amount of productive human capital available in second pe-

i

riod measured in efficiency units of labor. Thus φ(π ) represents the skill

i

0 00

premium. Where φ (π ) > 0 and φ (π ) < 0

i i

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

ˆ Lifetime utility can be represented as:

U (y) = u(y ) + ρu(y ) (9)

1 2

0(y) 00(y)

where U > 0 and U < 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 π , period two income is an increasing in π . Furthermore,

i i

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:

d f d

− · · · · − · ·

u[(1 π ) w ] + ρ [p u(φ(π ) w ) + (1 p) u(φ(π ) w )] (10)

i i i

9 The 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 ben-

efits 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 con-

10

tingent on migration prospects is captured by p, the probability of migration

≤ ≤

(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:

f f

w = w(π ) (11)

where wage is a positive function of the aggregate human capital stocks in

f d f d

’domestic’ or ’foreign’. Thus, since we assume π > π we have thatw > w

First order conditions for maximization imply:

d

− ·

1 u0[(1 π ) w ]

i ≡

·

φ0(π ) = ψ(π , p) (12)

i i

f

w

ρ f d

− ·

· · · + (1 p)u0(φ(π ) w )

p u0(φ(π ) w ) i

i d

w

d d

Given that u0(y) > 0, and w is an increasing function of π , we can see that

d

the nominator (resp. denominator) is an increasing (decreasing) function of π .

d

Thus it must be that ∂ψ(π , p)/∂π > 0.

i

Implicit differentiation of (12) yields:

∂ψ(π , p) ∂ψ(π , p) dπ dπ

i i i

i

· ·

+ = φ00(π ) (13)

i

∂p ∂π dp dp

i

or rewritten:

∂ψ(π , p) ∂ψ(π , p) dπ

i i i

− ·

= φ00(π ) (14)

i

∂p ∂π dp

i

Given the assumptions on the concavity of the skill premium, φ00(π ) < 0,

i

we need only show that

d

andat ∂ψ(π , p)/∂π > 0, and since φ00(π ) < 0, it is clear that all we require

i i

10 Note that the case of an economy closed to migration is incorporated into equation for as

the case when p=0 9

in order that dπ /dp > 0 (a positive brain gain) is that ∂ϕ(π , p)/∂p > 0.

i i

d d f d d

− · · −

u (1 π ) w u0 φ(π ), w w /w u0 φ(π ), w

∂ϕ(π, p) i i i

= (15)

f f d d

· − −

∂p ρ [pu0 [φ(π ), w ) (w /w ) (1 p)u0 (φ(π ), w )]

i i

Thus theory would predict that openness to migration should lead to an

increase in domestic human capital formation if, and only if:

f f d d

· ·

w w

u0 φ(π ), w > u0 φ(π ), w (16)

i i

Note that this condition is met by any utility function u(x) that maintains

11

u0(x)+xu00(x) > 0 for all x, 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 work-

ers stay in the origin country, and with them stays more human capital than

12

would have done in the absence of the migration possibility—a brain gain.

Yet, some have claimed that this brain gain literature is excessively opti-

mistic 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, be-

lieving 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.

11 If u0(x) + xu00(x) > 0 then xu0(x) is a strictly increasing function. Hence for x > x , we

2 1

f d

have that x u0(x ) > x u0(x ). Setting x = φ(π ), w and x = φ(π ), w , the inequality, as

2 2 1 1 2 1

i i

stated above, holds.

12 This model does not incorporate the possibility that human capital formation is subsidized

by the origin country government. 10

2.3 Empirical Literature

Until recently there has been no harmonized migration data on the skill levels

13

of international migrants disaggregated by country. 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 educa-

tional 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 institu-

tions. 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

14

(1998), that is itself subject to problems.

13 A 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

14 The 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

15

greater domestic enrollments in higher education.

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 endo-

geneity 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.

15 Faini does find evidence of induced increases in secondary education but believes this

indicates would be migrants choose to pursue their higher education abroad

12

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 ac-

cession 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 incen-

tivization 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

16

countries citizens benefit from the free movement of persons (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

17 . And, whilst the available data limits analysis of

of successful migration)

16 The 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.

17 Note 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 18

migration flows disaggregated by education level, 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

19

as a percentage of the population in the theoretical age group for tertiary

education. The GER is calculated according to the following formula:

t

E

t

GER = (17)

t ·

(P ) 100

a

Where:

ˆ t

GER is the Gross enrollment Ratio at time t,

ˆ t 20

E is the enrollment at time t,

ˆ t

P is the population in age-group a which corresponds to tertiary educa-

a 21

tion in school-year t .

It should be noted at this point that, if it is the case that enrollment in ter-

tiary 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.

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

constraint.

19 For the tertiary level, the population used is the five-year age group following on from the

secondary school leaving age.

20 Many thanks to Lucy Mei Hong (UIS UNESCO) for obtaining the historical data not

publicly available on the internet.

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

14

through increased Foreign Direct Investment (henceforth FDI), as well as in-

creasing 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 preced-

ing 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 com-

mitment 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 trans-

port 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


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DESCRIZIONE DISPENSA

Dispense per il corso di Differenziali Economici e Migrazioni della Prof.ssa Paola Giacomello e del Dott. Paolo Sellari. Trattasi dell'articolo di Emily Farchy dal titolo "The Impact of EU Accession on Human Capital Formation: Can Migration Fuel a Brain Gain" all'interno del quale l'autrice espone la sua teoria sull'effetto che la partecipazione al mercato comunitario genera sulla formazione ed accumulazione di capitale umano negli stati membri.


DETTAGLI
Corso di laurea: Corso di laurea magistrale in analisi economica delle istituzioni internazionali
SSD:
A.A.: 2011-2012

I contenuti di questa pagina costituiscono rielaborazioni personali del Publisher Atreyu di informazioni apprese con la frequenza delle lezioni di Differenziali Economici e Migrazioni e studio autonomo di eventuali libri di riferimento in preparazione dell'esame finale o della tesi. Non devono intendersi come materiale ufficiale dell'università La Sapienza - Uniroma1 o del prof Giacomello Paola.

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