Migration, Human Capital Formation (Brain Drain)

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Migration, Human Capital Formation, and Growth:

An Empirical Investigation

CORRADO DI MARIA and EMILIYA A. LAZAROVA *

University of Birmingham, Edgbaston, UK

Summary. — We study the effect of skilled emigration on human capital formation and growth in a sample of developing countries. Wefind that the migration rate exerts statistically significant effects on both the level and the composition of human capital. We are able totrace the impact of these changes on the growth rate of sending countries via regression analysis and simulations. Our results show thatwhile there are both winners and losers, almost 70% of the population in our sample suffers lower growth as a consequence of skilledmigration. Moreover, the losses are concentrated in countries with low levels of technological sophistication. 2011 Elsevier Ltd. All rights reserved.

Key words — education, brain drain, migration, human capital, economic growth, Asia, Africa, South America

1. INTRODUCTION

Over the last decades, an increasing number of developedcountries have put in place different mechanisms to encouragethe immigration of only the most talented, skilled individualsfrom developing countries. 1

As a consequence of such arrangements, the world has wit-nessed a dramatic modification in the composition of the poolof migrants moving from developing to developed countries.Over the last two decades, the share of highly skilled migrantsin the total number of migrants has increased dramatically.Docquier and Marfouk (2006), for example, estimate that dur-ing 1990–2000 the number of foreign-born workers with ter-

tiary schooling living in OECD member countries increasedby 63.7%, while for unskilled migrants the increase was only14.4% over the same period. Such accelerating brain drain isarguably one of the most striking features of globalization.

Whether the flow of skilled migrants from developing todeveloped countries is a curse or a blessing for sending coun-tries has been a contentious issue among economists for sev-eral decades. 2 One recent strand of literature, started by thepioneering work of Oded Stark and co-authors (Stark, Hel-menstein, & Prskatwetz, 1997, 1998), recognizes that the pos-sibility of migration raises the returns to education and leadsto an increase in the level of human capital that may ultimatelyprove beneficial for sending countries—a Beneficial BrainDrain or Brain Gain (BG). This influential literature has

shaped the direction of much of the recent debate on skilledmigration (Beine, Docquier, & Rapoport, 2001; Beine et al.,2008; Dustmann, Fadlon, & Weiss, 2011; Mountford, 1997;Stark & Wang, 2002; Vidal, 1998, to name but a few), andhas spawned an empirical literature aimed at testing its theo-retical predictions. The earliest contribution in this respect isthe paper by Beine et al. (2001). The authors, however, usegross migration rates to proxy for the migration rate of skilledworkers. As a consequence, their findings in support of the BGhypothesis need to be taken with caution. Beine, Docquier,and Rapoport (2003) use the data on immigration rates to-ward the US by level of education published by Carringtonand Detragiache (1998), and also find empirical support forthe existence of a BG in a cross-section of 50 developing coun-tries. Their regressions, however, show that migration has a

negative growth effect in most developing countries. In the

revised version of the previous paper, Beine et al. (2008) usethe recent data set by Docquier and Marfouk (2006) to testfor the existence of “incentive effects” in human capital accu-mulation, that is, the positive effect of migration probabilitieson human capital accumulation. The authors conclude thatthese effects are indeed positive and go on to perform counter-factual simulations to compare the ex-post level of human cap-ital in sending countries, when skilled workers do not benefitfrom a higher migration rate. In this instance their conclusionsare not clear-cut, as more than half the countries in their sam-ple suffer from brain drain, rather than benefit from a braingain. In very recent additions to this literature, Beine, Defoort,and Docquier (2011a) provide further evidence in favor of the

beneficial brain drain hypothesis using a new panel data setthat allows them to explicitly address issues of endogeneity,while Batista, Lacuesta, and Vicente (2012) test the “braingain” hypothesis for Cape Verde, using specially collecteddata. Both papers provide additional evidence in favor of the existence of substantial incentive effects.

The fact that migration possibilities exert a positive incen-tive effect conforms to economic intuition and represents animportant result. The literature mentioned above, however,neglects another, more subtle aspect of the brain gain, in thatit abstracts from the possibility that migration might changenot only the level , but also the composition of human capital.Indeed, elsewhere in the literature, a number of authors haveemphasized how the possibility of migration encourages

would-be migrants to concentrate on disciplines that are asso-ciated with higher probabilities of migration, especially inhealth care and ICT (Clemens, 2007; Commander, Chanda,Kangasniemi, & Winters, 2008; Connell, Zurn, Stilwell,Awases, & Braichet, 2007; Gibson & McKenzie, 2011;Kangasniemi, Winters, & Commander, 2007; Lorenzo,

* We are grateful to seminar participants at the Queen’s University of

Belfast, the University of Stirling, the National University of Ireland,

Maynooth, as well as conference participants at the European Economic

Association 2010 meeting in Glasgow. We greatly benefited from the co-

mments of four anonymous referees and of the Journal Editor. We would

also like to thank Hillel Rapoport for making the data set for the paper

Beine et al. (2008) available to us for comparison purposes. Final revision

accepted: September 27, 2011.

World Development Vol. 40, No. 5, pp. 938–955, 2012 2011 Elsevier Ltd. All rights reserved

0305-750X/$ - see front matter

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Galvez-Tan, & Javier, 2007). 3 To date, few theoretical contri-butions have suggested a connection between the possibility of migration and the type of skills the agents choose to acquire.Mariani (2007) discusses the allocation of talent in a rent-seek-ing framework à la Murphy, Shleifer, and Vishny (1991). Heconcludes that if skills traditionally associated with rent-seek-ing, such as legal ones, are less conducive to emigration, an in-

crease in the probability to migrate might encourage a shifttoward skills connected to entrepreneurship, like engineering,ultimately benefiting growth. While Mariani (2007) discussesan alternative channel leading to a beneficial brain drain, hedoes not allow for the level effect discussed above. In a contri-bution more focussed on technological change, Di Maria andStryszowski (2009) argue that the possibility of migration,while potentially leading to an increase in the level of humancapital, produces the wrong type of skill composition andslows down the process of economic development in sourcecountries. Within a framework inspired by the literature on“appropriate institutions” (Gerschenkron, 1962), they observethat certain skills are relatively more valuable, and hence morerewarded, in countries closer to the technological frontier.This is due to the fact that in technologically advanced coun-

tries productivity advances are due to innovation, while in lessdeveloped ones imitation plays a major role. By allowing forthe endogenous accumulation of skills on the part of workers,who base their decision on the relative rewards such skills en-tail, they show that the possibility of migration distorts theoptimal formation of human capital, and hinders economicgrowth. As this effect is stronger the less developed the sendingcountry, Di Maria and Stryszowski conclude that this pro-vides a potential explanation for the co-existence of winnersand losers among sending countries emphasized by Beineet al. (2008). From their theoretical analysis, then, it emergesthat neglecting composition effects might generate misleadingresults.

In this paper we build on the theoretical insights of Di Mar-

ia and Stryszowski (2009) and assess empirically the effect of migration on the formation of human capital—both on its le-vel and composition—and on economic growth in a sample of developing countries. Using a data set covering 130 developingcountries for 1990 and 2000, we find evidence that the possibil-ity of migration does indeed affect both the level of humancapital, and the type of skills accumulated in sending coun-tries. Furthermore, consistent with the prediction of Di Mariaand Stryszowski (2009), our results show that both effects de-pend on the level of technological sophistication of the sendingcountry. 4 We also provide evidence that the pace of techno-logical development (and hence the growth rate of the econ-omy) is affected by the composition rather than the level of human capital. We conclude that migration, by affecting theprocess of skills accumulation has potentially detrimental im-

pacts on growth. To illustrate the substantive implications of our findings, we conclude our analysis by simulating the effectsof changes in the skilled migration rate on the composition of human capital and on productivity growth. Our simulationsallow us to conclude that there are indeed winners and losersamong developing countries. In our data set roughly one thirdof countries suffer as a result of increased migration; these los-ers, however, account for over 70% of the total population inthe sample (over 2.7 billion people), and are characterized by alower level of technological development.

Besides being closely related to the literature on the braindrain, our work is also related to the literature studying howthe composition of human capital affects growth. While theirmain contribution focuses on the allocation of talent betweenentrepreneurship and rent-seeking, the empirical evidence

found in Murphy et al. (1991) provides an early example of linking growth to the skill composition of the work force. Iyi-gun and Owen (1999) build on these findings and show thatdifferent ratios of entrepreneurs to professionals are optimalin different phases of economic development. The present pa-per is closer in spirit to the work of Vandenbussche, Aghion,and Meghir (2006), who explicitly emphasize the interaction

between skill composition and distance from the technologicalfrontier in determining a country’s growth performance. Theirfocus is, however, quite different from ours, given that theydiscuss secondary vs. tertiary education, do not deal withinternational migration, and concentrate their empirical effortson OECD countries.

In the next section we describe the conceptual frameworkunderlying our empirical analysis. The data used in the empir-ical investigation are described in Section 3, and the results of the analysis are discussed in Section 4. The substantive effectsof these results for the developing countries in the sample arethe object of Section 5. Finally, Section 6 concludes the paperwith a summary of the results, and some implications formigration policy.

2. MIGRATION, HUMAN CAPITAL ACCUMULATIONAND GROWTH

Before we proceed to our empirical investigation, we outlinethe theoretical framework that underpins that analysis. Inwhat follows we sketch a model of endogenous growth, whereheterogenous labor inputs are used to produce a final goodand to generate technological improvements. The model isan extension of the framework in Di Maria and Stryszowski(2009). Its structure is presented in Figure 1.

The structure of production is the familiar one of nonscaleSchumpeterian models, where the final good (Y ) is producedusing a continuum of mass M of intermediate products (xi )

and the labor input of lower-skilled workers L, according to

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:

Y t ¼ Lt = M ð Þ1a

Z M 0

A1ait xait di: ð1Þ

where Ait represents the productivity of intermediate i attime t.

If, following Young (1998), we assume that the number of intermediates per worker converges to a constant, the produc-tion structure above can be shown to be consistent with sus-tained growth without scale effects.

Producers of intermediate goods maximize their profits ineach period by choosing optimally the levels of production,and the pace of productivity change. Productivity improvesas the result of both bona-fide innovation and imitation activ-ities (Benhabib & Spiegel, 1994; Vandenbussche et al., 2006).The former pushes the world technological frontier outwards,whereas the latter consists in copying technologies from theworld technological frontier. Following Di Maria andStryszowski (2009), we assume that both activities requiretwo types of highly-skilled labor inputs, that we refer to astechnically skilled (T ) and generalists (G ), so that productivitychanges according to:

Ait Ait 1 ¼ Ait 1T /nit G

1/nit þ ð At 1 Ait ÞT

rmit G

1rmit ; ð2Þ

where the subscripts n and m identify skilled labor inputs em-ployed in innovation and imitation activities, respectively, and A represents the frontier’s productivity level. In what follows,we further assume that workers endowed with general skillsare relatively more productive when employed in creative

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endeavors (innovation) rather than in imitative activities (e.g.,reverse engineering), that is, we let r > /. 6

Given that countries further away from the technologicalfrontier have a larger “advantage of backwardness” (Gers-chenkron, 1962), entrepreneurs in less developed countriesspecialize in imitation; both imitation and innovation takeplace at intermediate levels of the technological development;and finally only innovation occurs once the technological fron-tier has been reached. On the one hand, this implies that thegrowth rate of output declines over time as development pro-gresses; on the other, since general skills become more produc-

tive closer to the frontier, the relative wages of generalists tendto increase as countries develop.Successive generations (each of mass 1) of heterogeneous

workers are born, accumulate skills, work, and die within eachperiod. Each worker is born with a different level of talent,which determines the relative opportunity cost (in terms of working time) of acquiring skills. Thus, comparing the relativecosts and returns to accumulating skills, workers divide them-selves into lower-skilled, generalists, and technologists. Order-ing workers in terms of talent, we can represent theendogenous split of workers in the three groups as in Figure 2.

Any increase in the relative wages of skilled workers in-creases the share of skilled workers in the economy (reducesL). Moreover, if the relative rewards accruing to G skills in-crease, so does the share of G -skilled workers.

Given the absence of spillovers, the maximizing behavior of agents in the decentralized equilibrium described above maxi-mizes GDP growth, and guarantees that each country con-verges to the frontier of technology over time. 7

Allowing for the possibility of migration changes the resultsof the model by distorting the workers’ skills accumulationdecisions. Since this paper focuses on skilled migration, we

simplify the model by assuming that only skilled workers mi-grate, while lower-skilled workers are internationally immo-bile.

When skilled workers are able to emigrate with some posi-tive (exogenous) probability, their expected wages change.Letting x j,t indicate the expected wage for a worker of type j at time t, we have:

x j;t ¼ ð1 r jÞx H j;t þ r jx

F j;t ; ð3Þ

where the H and F superscripts identify domestic and foreignwages, respectively, while r j is the probability with which a

worker of type j expects to secure a job abroad. 8 Clearly, emi-gration is only from less to more developed countries, since itis driven by productivity differences. Thus, Eq. (3) implies thatthe expected returns to skills increase with the probability of migration, which means that also the share of skilled workersincreases with it. This is what we call the level effect of migra-tion. In terms of Figure 2, L shrinks as G + T increases. Fur-thermore, the wage gap shrinks with a source country’sapproach to the technological frontier, thus, the level effectdiminishes with technological development.

In addition to this conventional effect, there is a more subtlemechanism at work in this framework. As migration is partic-ularly appealing—and empirically most relevant 9 —from lessdeveloped countries to destination ones at (or close to) the

technological frontier, migrants move from countries whereimitation activities are still commonplace to countries whereproductivity can be improved only via innovation. This sug-gests that the relative productivity, and hence the relativewage, of G -skilled workers is higher in destination countriesthan in source ones. Thus G -skills benefit from a larger“migration premium” relative to T -skills. When migration ispossible and workers consider foreign jobs when deciding onskills accumulation, G -skills become relatively more attractiveand more workers elect to acquire such skills. We refer to thisas the relative productivity effect.

Eqn. (3), however, clarifies that the relative attractiveness of different skill types does not only depend on labor remunera-tion in the destination country, but also on the different migra-tion probabilities of workers of different type. Indeed, one of

the stylized facts on brain drain is that technically skilled

Unskilled workers Skilled workers

L G T

Figure 2. The skill composition of the workforce.

UN SKILLE D WORKE RS SKILLE D WORKE RS

TE C H NICA L (T ) AND

GE NERAL (G)

IM ITATIO N (M) IN NOVATIO N (N)

TE C H N OLOGY (A )

PRO D UC TIO N (Y)

Figure 1. The structure of the model.

940 WORLD DEVELOPMENT

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workers tend to be more successful in emigrating. Thus, if rT issufficiently larger than rG , the possibility of migration mighthave the opposite effect from the one previously discussed,and lead to an increase in the share of technically skilled work-ers. We call this effect the relative probability effect.

Since both the relative productivity and relative probabilityeffects have implications for the share of technically skilled

workers, we refer to them collectively as the composition effect.Given that the two effects operate in opposite directions, thesign of the composition effect is ambiguous. All else equal,the relative migration premium of a G -skilled worker is higherthe further from the frontier is his/her country of origin. Thus,one would conclude that based on the theoretical model dis-cussed above, the relative productivity effect dominates furtheraway from the frontier (and the overall composition effect isnegative), while the relative probability effect dominates closerto the frontier (implying a positive composition effect). 10

Finally, it is clear that migration-induced changes in the le-vel and composition of the workforce induced by the probabil-ity of migration also affect the growth path of sourcecountries. Since we have argued above the growth rate is max-imized in the market equilibrium without migration, we must

conclude that the probability of migration may reduce the rateof growth in sending countries.

From this brief discussion we can take away the followingtestable implications: (i) the probability of migration affectspositively the proportion of skilled workers in the labor force(level effect), (ii) the level effect is smaller the closer the countryis to the technological frontier, (iii) the probability of migra-tion affects the proportion of technically skilled individualsin the labor force (composition effect), (iv) the sign of the com-position effect is expected to be negative for countries laggingfar from the frontier, and positive as the frontier is ap-proached. Finally, our theoretical discussion points to the factthat the growth rate of a country should depend on both theshare of skilled workers (level of human capital) and the distri-

bution of skill types (composition of human capital). Thus, togauge whether the distortionary effects of migration enhanceor hamper technological development (and growth), oneshould investigate the impact of both the level and the compo-sition of human capital on growth. In the rest of the paper, weprovide empirical tests of the above hypotheses.

3. DATA DESCRIPTION

The data set needed to pursue our empirical strategy is con-structed from five different sources. Docquier and Marfouk’s(2006) data set provides data on the level of human capitalby educational attainment for 194 countries in 1990 and

2000.

11

Given that skilled migration introduces a wedge be-tween the educational attainment of natives and the amountof skilled workers available on the domestic labor market,we need to define the level of human capital before and aftermigration. The ex ante measure, H a, is defined as the ratioof working-age nationals with tertiary education (i.e., work-ing-age residents with tertiary education plus the working-age stock of emigrants with tertiary education) to total work-ing-age nationals (that is the sum of working-age residents andworking-age emigrants). The corresponding ex-post variable,H p, is instead defined as the proportion of working-age resi-dents with tertiary education divided by the total number of working-age residents. For our estimations we also use an-other of the series provided by Docquier and Marfouk(2006), namely the stock of working-age emigrants from a gi-

ven source country i to OECD countries with secondary edu-

cation (MS sec,i ) to instrument for the rate of skilled migrationof that country.

The Docquier and Marfouk’s (2006) data set also containsemigration rates to OECD countries by educational level for194 source countries. These series, however, are constructedbased on census data from destination countries and theymay overestimate the extent of skilled migration, as they fail

to control for where the skills have been acquired. Fortunatelyan alternative data source is provided by Beine, Docquier, andRapoport (2007) who use additional survey data from a sub-set of OECD countries on the age of entry, in order to controlfor where tertiary education was acquired. According to theauthors, the data on age of entry is available for 77% of skilledimmigrants to the OECD. Therefore, the data provide a reli-able indicator of the age-of-entry structure of immigrationto the OECD. 12 Focussing attention only on OECD countriesis clearly a limitation of these data, however, since about 90%of all high-skilled emigration is toward the OECD, the emigra-tion rates provided by Beine et al. (2007) are a good proxy forthe overall high-skilled emigration rate. 13 In what follows, rhis the emigration rate of working-age natives with tertiary edu-cation to OECD countries, for individuals who first reached

their destination after the age of 22. The data set containsinformation for 168 source countries in 1990, and 192 in 2000.

We exclude from our analysis 29 countries which are consid-ered “immigration-receiving”. 14 We further remove from thesample 26 former socialist countries, since human capital for-mation in these countries in the early 1990’s was severely af-fected by the transition from a centrally-planned to amarket-driven economy. 15 Overall, our sample comprises130 countries the majority of which are either low (34) or low-er middle income (47) countries. 34 countries in our sample areupper middle income countries, while the remaining 14 arehigh income nonOECD countries. 16

The descriptive statistics of the main variables, H a, H p, andrh are presented in Table 1. There is a sufficient variation in all

of the variables, in particular the data set presents a widerange of migration rates. At one end of the range, the countrywith the lowest emigration rate of tertiary educated workers inthe sample is Swaziland. Other countries with emigration ratesbelow 0.005 are the United Arab Emirates, Oman, and Mon-golia. At the other extreme, the countries with the largestskilled emigration rates are Samoa (93% in 1990), Guyana, Pa-lau, and Tonga, all above 80%. Among these, all but Guyanaare island states and they all have relatively small populations.The case of Guyana is particularly striking since it is one of the

Table 1. Descriptive statistics

Variable Obs. Mean Std. Dev. Min. Max.

rh 262 0.19 0.22 0.00 0.94H a 262 0.08 0.06 0.00 0.28

H p 262 0.06 0.05 0.00 0.22S &T 129 0.37 0.16 0.00 0.76

PROXIM 164 0.60 0.11 0.37 0.84 g TFP,5 yr 154 0.0044 0.0209 0.0773 0.0478

DENS 387 231.02 1342.58 1.34 16776.24EDU 256 4.36 2.37 0.42 15.35

REMIT 302 4.04 7.17 0.00 64.87PHONE 299 197.18 272.60 1.27 1849.56

ROAD 216 36.31 28.02 0.80 100.00GDP pc 216 2534.341 4969.159 129.8224 46605.66

MS sec 262 48915.92 167590.30 14.00 2408250.00

POP 387 3.51E+07 1.40E+08 15122 1.30E+09


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countries with the highest ex-ante rate of human capital accu-mulation (0.21 in 2000), but the post emigration share of skilled workers in the total is just 4% in 2000. Mongolia, onthe other hand, thanks to the low migration rate emerges asone of the countries with higher ex-post proportion of tertiaryeducated labor force. Several African states like Malawi,Mozambique, Niger, Rwanda, and Uganda are among the

countries with the lowest level of human capital.Data on the composition of human capital are taken fromthe UNESCO Education Statistics. To proxy for the shareof technically skilled workers we focus on the proportion of students enrolled in science and technology out of total enroll-ment in tertiary education, our S &T variable. Given therestructuring of the International Standard Classification of Education (ISCED) after 1997, data are only available for1970, 1980, 1985, 1990, 1995, 1996, and 1997. Due to the greatnumber of missing values in the series, however, we use valuesin 1985 and 1980 to represent the values in 1990 when theseare missing. To match the composition of human capital datawith the series on the level of human capital and emigrationrates, we take the most recent values (1997) to representS &T in 2000. Where the 1997 data are missing, we use the

1996 or 1995 values as available. In this way we are able toconstruct data on S &T for 83 countries in 1990, and 46 coun-tries in 2000. The average proportion of tertiary students en-rolled in science and technology specialities is 37% in oursample and there is a sufficient variation among countries, asshown in Table 1. Countries with negligible shares of studentsenrolled in any science and technology specialities in highereducation are Brunei, Djibuti, Lao, and Seychelles. At theother end of the spectrum are those countries for whichS &T is above 70% such as Algeria, Angola, Dominica (attainsthe maximum in the sample in 1990), El Salvador, Jamaica,and Trinidad and Tobago.

To control for the degree of technological sophistication insending countries, we construct an indicator of proximity to

the world technological frontier, PROXIM , as the ratioof the total factor productivity (TFP ) of country i to that of the US. Thus, a proximity index close to 1 indicates that acountry is close to the technological frontier, whereas techno-logical laggards are characterized by an index close to 0. Asstandard in the literature, we calculate TFP as the log of out-put per worker, minus the log of capital per worker times thecapital’s share. Finding accurate estimates of the capital sharefor developing countries is not easy (Gollin, 2001). Followingthe recent practice in the literature (e.g., Caselli, 2005; Caselli& Coleman, 2006) we take the labor shares estimates providedby Bernanke and Gürkaynak (2002) in Table 10, page 42. 17

Given the limited coverage of developing countries, we canonly collect labor share data for 29 countries in our sample.For the countries for which data is not available we proceed

as Bernanke and Gürkaynak (2002) and take the labor shareto be 0.65 (i.e., the capital share is 0.35). 18 To construct thecapital stock series we follow Vandenbussche et al. (2006),and use a perpetual inventory method with a 6% depreciationrate. As capital investment, we take gross capital formation inconstant 2000 US dollar, I i,t, from the World DevelopmentIndicators (World Bank, 2009). Thus, the initial level of capi-tal for country i , K i ,0 is given by:

K i;0 ¼ I i;1

g i þ 0:06;

where I i ,1 is the earliest available data on gross capital forma-tion for country i ; and g i is the growth rate of GDP of countryi in the period from the earliest till the latest date of available

data on gross capital formation.19

As shown in Table 1, the average proximity index in thesample is approximately 0.6 which we would expect as we havea sample of developing countries. The variation in the sample,however, is not very large. As a consequence, in some of ourestimations, we encounter issues of imperfect multicollinearity.Two major issues that may arise in relation to imperfect mutl-icollinearity are (a) the parameter estimates may be sensitive to

changes in sample composition and (b) the standard errors onthe individual coefficients may be very high. Thus, we reportthe results of robustness checks with respect to the numberof observations and we provide the results from joint hypoth-esis tests throughout the empirical investigation. The countriesin the sample closest to the technological frontier with an in-dex above 0.8 are the Bahamas, Brunei (it attains the highestvalue in the sample in 1990), Costa Rica, Kuwait, Saudi Ara-bia, United Arab Emirates. Not surprisingly these are all highincome countries, with the exception of Costa Rica (uppermiddle income). Among the countries lagging furthest fromthe frontier, we have Ecuador (the lowest value in the samplein 2000) and Malawi both of which have a proximity index be-low 0.4.

Based on the TFP series, we also construct the dependent

variable used in the growth analysis. For each country, wecompute 5-year averages of TFP growth to smooth out anycyclical movement in the data. The summary statistics for thisvariable can also be found in Table 1. The country with thelowest average TFP growth in the sample is Jordan and thecountry with the highest average TFP growth is Gabon.

The remaining control variables in the regression analysiscome from the World Development Indicators (World Bank,2009). We use population density (people per squared kilome-ter), DENS , total public spending on education as a percent-age of GDP , EDU , mobile and fixed-line telephonesubscribers per employee, PHONE , percentage of paved roadsin the country ROAD, remittances as a percentage of GDP ,REMIT , and initial value for GDP per capita GDP pc. In addi-

tion, as an instrument for the skilled migration rate, we use to-tal population, POP . The descriptive statistics of thesevariables are also shown in Table 1. The mean values and stan-dard deviations testify once more of the diversity of countriespresent in the sample.

4. EMPIRICAL RESULTS

In this section we put the theoretical implications describedin Section 2 to the test. Due to data availability, most of theanalysis will be performed using cross-sectional samples witha relatively small number of observations. As the data provideus with only a snapshot of events, we cannot make conclusive

inferences regarding causality. The small sample sizes, further-more, may imply that some of our results are sensitive to sam-ple composition. The analysis involves estimating threeequations. The first two refer to the impact of migration onthe formation of human capital, and account for both its leveland composition. The third equation links human capital tothe growth performance of developing countries, specificallyallowing for the role played by each country’s level of techno-logical sophistication. As these effects are contemporaneous,one could argue that there are common shocks affecting allthree regressions, calling for a system estimation approach.The difficulty of conducting our analysis within a system esti-mation approach arises from data availability. In fact, thereare only 16 countries for which our three general equationscan be estimated simultaneously. 20 Instead, we choose to fo-

cus on each component separately and investigate possible


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endogeneity issues in depth. We start off with an investigationof the level effect.

(a) Migration and the level of human capital

In studying the incentive effects of migration on human cap-ital accumulation, the relevant definition of human capital in-cludes not only the residents in the sending country, but alsoskilled natives working abroad. For this reason, we use theex-ante, measure of human capital, H a, defined in Section 3.Our empirical specification extends that of Beine et al.(2008), to control for the level of technological developmentof the source country:

D logð H a;0090Þ ¼ a0 þ a1 logðr h;90Þ þ a2 logð PROXIM 90Þ

þ a3 logð PROXIM 90Þ logðr h;90Þ þ a4

logð H a;90Þ þ a5 DENS 90 þ a6

logð EDU 90Þ þ a7 REMIT 90 þ a8SSA

þ a9 LAT þ e: ð4Þ

The dependent variable, Dlog(H a,00–90) = log(H a,00)

log(H a,90), is the growth rate of the ex-ante stock of humancapital during 1990–2000. We use the stock of human capitalat the beginning of the period, H a,90, to control for the possibil-ity of convergence across countries in the proportion of tertiaryeducated workers. The migration rate of tertiary educated indi-viduals in 1990, rh,90, captures the incentive effects discussed inSection 2. To control for potential nonlinear impacts of migra-tion at different levels of economic development of the sourcecountry, we include log(PROXIM 90) and the interaction termlog(PROXIM 90) log(Rh,90) in some of our regressions, wherePROXIM 90 is the proximity of the country to the world tech-nological frontier. Population density in 1990, DENS 90, is usedas a proxy for the cost of acquiring education, since one wouldexpect that the higher the population density, the smaller the

average distance from schools, the lower the cost of education.Additionally, we introduce public spending on education in1990, EDU 90, to better proxy for the supply (both in terms of quantity and quality) of education. 21 REMIT is workers’remittances in 1990, a control for return migration, and allevi-ated credit constraints on human capital investment. Finally,SSA and LAT are regional dummies identifying Sub-SaharanAfrican and Latin American countries, respectively, as definedby the World Bank. Note that due to the lag structure of thespecification, and data availability, (4) specifies a cross-sec-tional regression.

Since the accumulation of human capital and the migrationrate may be simultaneously determined—a higher level of hu-man capital may induce higher migration rate due to a reduc-tion in the skill premium on the local labor market compared

to foreign ones, for example—one possible source of concernwhen estimating (4) is the possible endogeneity of the migra-tion rate. Notice, however, that we calculate both the migra-tion rate and the change in the skilled labor force usingimmigrants stock, rather than flows, and that the first differ-ence in skilled labor stock is calculated over a period of 10 years. As a consequence, endogeneity might not be such arelevant issue in this model. Nevertheless, we believe it isimportant to address potential endogeneity issues using aninstrumental variable approach. Of the various instrumentssuggested in the literature (e.g., Durlauf, Johnson, & Temple,2005) such as the country’s population size, the initial stock of immigrants, life expectancy at birth, various indices of socialunrest and racial tensions, and the per capita GDP , Beineet al. (2008) advocate the use of only the first two, either be-

cause the others are very highly correlated with the initial levelof human capital or because of the insufficient number of observations. In our search for valid instruments, we also usedthe CEPII data set that contains measures of geographical andcultural distances between pairs of countries, including thephysical distance of a source country to each one of the sixmajor destination countries: Australia, Canada, France, Ger-

many, United Kingdom, United States; information on thecolonial history of a source country; and whether a sourcecountry has English, French, or German as one of its officiallanguages. Arguably, all of these variables could be consideredas valid instruments: they are relevant as they affect the cost of migration like travel costs, cultural proximity, and languagebarriers; and they should be exogenous as they should not af-fect the individual decision to acquire tertiary education. Ouranalysis, however, shows that none of these variables passesthe (Stock & Yogo, 2005) test for weak instruments. More-over, when they are included together with our instruments,they weaken the significance of the test statistics. This is whywe rely as instruments for the migration rate of tertiary edu-cated individuals on the stock of immigrants of the samenationality with secondary education (MS sec) and population

size of the source country (POP ). We believe the stock of immigrants to be a valid instrument for the migration rate be-cause (i) a higher stock of immigrants, that is, a larger dias-pora, reduces the cost of emigration, 22 but (ii) the stock of immigrants with secondary education should not directly af-fect an individual’s decision to acquire tertiary education. Fur-thermore, we use the stock of immigrants with secondaryeducation without correction for age of entry. These individu-als have not acquired tertiary education in the destinationcountry post-migration, and thus arguably have even less re-lated to the choice to acquire tertiary education at home. Pop-ulation size should not be per se a factor in the human capitalequation either, if one accepts that the absence of scale effectsis a realistic feature of the underlying human capital accumu-

lation model. On the other hand, a larger population might re-duce the chances to emigrate, since restrictions on immigrationby destination countries do not fully reflect the size of the poolof would-be migrants. In what follows, to test whether theinstruments are relevant, we use the critical values tabulatedby Stock and Yogo (2005), while instrument exogeneity istested using a J -test. Finally, we test whether the migrationrate is in fact an exogenous regressor in the model employingWooldridge’s (1995) robust score test.

We use as a benchmark the model studied by Beine et al.(2008), Model I in Table 2. The first two columns presentthe results from the OLS and IV estimations, respectively. 23

The estimate of the effect of the migration rate on skill forma-tion that we obtain are comparable in magnitude to the resultsin Beine et al. (2008): a 1% increase in the migration rate of

high-skilled workers increases the growth rate of the share of high-skilled workers by about 0.05% points in both specifica-tions. We also find evidence of convergence in human capitallevels among countries in the sample, given that the coefficientfor the initial level of human capital has a statistically signifi-cant negative value. According to our estimates, neither publicspending on education (EDU ), nor population density(DENS ), nor workers’ remittances (REM IT ) have a statisti-cally significant effect in either regression. 24

The same holds for the Latin America dummy (LAT ). Infact, a test on the joint significance of these variables indicatesthat they can be excluded from the model as a group ( p-valueof 0.6167 in the OLS, and 0.5687 in the IV regression).

A comparison of the OLS and IV results reveals that the twoestimates are quite similar: all regressors have comparable size


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of coefficients and the same level of significance under bothOLS and IV. The instruments pass the test of relevance witha test statistics of 346, which is much above the critical valueof 10. The test on instrument exogeneity supports our viewthat the instruments are exogenous as we do not reject the nullhypothesis at 56%. Moreover, our analysis suggests thatmigration rate is not endogenous as we cannot reject the nullthat it is exogenous.

Our next specification, Model II in Table 2, extends thebenchmark model to include a measure of the degree of tech-nological development of the sending country. Based on ourtheoretical discussion in Section 2, we would expect an in-crease in the portion of tertiary educated labor force as acountry approaches the technological frontier. This hypothesisis supported by our regression results as demonstrated by boththe OLS and IV estimations of the model. In the OLS regres-sion, the effect of the migration rate on the proportion of ter-tiary educated natives is very similar in magnitude to the onefound in the previous model. The IV estimate of the same ef-fect is almost twice as high, 0.08%, which may indicate adownward bias in the OLS. Also in this case, as instrumentsfor the migration rate we use the stock of immigrants with sec-ondary education and the country’s population size. In addi-tion, we also instrument for the proximity to the frontier inorder to account for possible reverse causality. As an instru-ment we use the 10-year lag of that variable, that is, PROX-IM 80. As an indication of the instruments’ relevance we use

the Kleibergen and Paap (2006) rank Wald statistics and we

can reject the null hypothesis of weak instruments based onthe Stock and Yogo’s (2005) critical values at the 10% maxi-mal IV size. The test for instruments exogeneity also indicatesthat they are valid instruments. Finally, the test on the endo-geneity of the regressors indicates that both the migration rateand the proximity to the frontier may be treated as exogenous.

The inclusion of the proximity to the frontier as a regressor,and the change in the sample’s composition altered the statis-tical significance of some of the other explanatory variables.The coefficient of population density (DENS ), which is insig-nificant in the previous regressions, becomes significant atthe 5% and 1% in the OLS and IV estimations, respectively.The sign of the coefficient, however, is negative. This is con-trary to what one would expect assuming that this variablecaptures accessibility to higher education. The negative sign,instead, is consistent with a Malthusian interpretation of pop-ulation density as a proxy for social conflict and environmen-tal pressure (see Malthus, 1798, and more recently Urdal,2005). In addition, the effect of remittances becomes positivewhich complies with the intuition that higher remittances playa role in relieving credit constraints, allowing more individualsto pursue higher education. This variable is only statisticallysignificant in the OLS regression, and only at 10%.

To see whether the probability of migration affects the levelof human capital differently at different stages of development,we include an interaction term between these two variables inour last specification, Model III in Table 2. 25 None of the

three main variables of interest—migration rate, proximity

Table 2. Level of human capital—dependent variable Dlog H a,0090

Model I Model II Model III

OLS IV OLS IV OLS

log(rh,90) 0.0482 0.0500 0.0481 0.0802 0.0246(0.0187)** (0.0206)** (0.0202)** (0.0267)*** (0.0580)

log(PROXIM 90) 0.2824 0.2870 0.1663

(0.1429)

*

(0.1425)

**

(0.3510)log(rh,90) log(PROXIM 90) 0.1588(0.1169)

log(H a,90) 0.2456 0.2454 0.1896 0.1574 0.1738(0.0596)*** (0.0563)*** (0.0312)*** (0.0350)** (0.0314)***

DENS 90 0.0002 0.0002 0.0006 0.0009 0.0006(0.0002) (0.0002) (0.0002)** (0.0002)*** (0.0002)***

log(EDU 90) 0.0587 0.0584 0.0261 0.0748 0.0230(0.0415) (0.0394) (0.0629) (0.0681) (0.0658)

REMIT 90 0.0007 0.0008 0.0272 0.0257 0.0274(0.0019) (0.0018) (0.0161)* (0.0151) (0.0162)*

SSA 0.3434 0.3432 0.2164 0.2127 0.2270(0.1269)*** (0.1191)*** (0.0716)*** (0.0785)*** (0.0709)***

LAT 0.0122 0.0119 0.0210 0.1178 0.0407(0.0460) (0.0439) (0.0669) (0.0692)* (0.0632)

Const. 0.1883 0.1825 0.1674 0.5050 0.0241(0.1489) (0.1381) (0.1753) (0.2318)** (0.2194)

No. of Obs. 82 82 55 45 55

R2 0.4946 0.5843 0.5947Weak 346.615a 94.382b

J -test (0.2896) (0.2154)Endogeneityc (0.7661) (0.1555)H 0: a1 = a3 = 0 (0.0255)

**

Notes: Heteroskedasticity robust standard errors are reported in parenthesis.* Indicate significance at 10% levels, respectively.** Indicate significance at 5% levels, respectively.*** Indicate significance at 1% levels, respectively.a Robust F -statistic reported.b K–P Wald statistic.c p-Value of the robust score stats reported.


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to the technological frontier, or their interaction—is shown tohave a statistically significant effect. This may be explained bythe high correlation between the interaction term and the logof migration rate, 0.7463, and that between the interactionterm and the log of proximity to the technological frontier,0.6004. 26 Indeed, a test for the joint significance of all threecoefficients, indicates that they are significant at 1% ( p-value of

0.0059). A joint hypothesis test on the significance of the (non-linear) effect of migration (i.e., H 0: a1 = a3 = 0) indicates thatthis effect is significant at 5%.

Based on the signs of the estimated coefficients for themigration rate and the interaction term, we find some supportfor the theory described in Section 2: the positive effect of migration on human capital accumulation is stronger the fur-ther the countries are from the technological frontier. 27 Thiseffect fades as the frontier is approached, and eventually van-ishes once the frontier is reached. 28

It is worth noting that, compared to the OLS estimation of Model II, the coefficient estimates for the other variables arerobust to the inclusion of the interaction term.

(b) Migration and the composition of human capital

The possibility that certain skills may be more demanded— and more rewarded—in destination countries relatively to thehome market, adds another layer of complexity to the analysisof human capital formation under migration. Our aim of thissection is to put to the test the theoretical prediction that thepossibility for migration distorts the composition of humancapital in the source country, and that this distortionary effectdepends on its distance from the technological frontier.

Ideally, we would like to use data on migration rates of ter-tiary educated natives by field of study. Such data are unfor-tunately unavailable, and we can only use migration rates byeducational level. We thus have to assume that all tertiary edu-cated workers face the same migration rate. 29 This impliesthat the ex-ante and ex-post skill composition of human capi-tal is the same, and simplifies our notation as we do not needto introduce subscripts to distinguish between gross and netvariables. Furthermore, due to lack of data we use the propor-tion of enrollment in tertiary education with scientific and tech-nical major, S &T , as a proxy for the proportion of science andtechnology graduates in the stock of skilled workers. Whilethis can be seen as a limitation for our growth regressions be-low, enrollment is indeed the best point of analysis to identifythe incentive effects of migration on the composition of humancapital, since enrollment reacts to work prospects much fasterthan the stock of university graduates.

We model the empirical relation linking the composition of human capital (before migration) to the migration rate of skilled workers as

S &T t ¼ b0 þ b1r h;t þ b2 PROXIM t þ b3 PROXIM t r h;t

þ b4Qt þ b5 EDU t þ b6 D2000 þ b7SSA þ b8 LAT

þ tt ; ð5Þ

where the variables S &T t, rt, PROXIM t, EDU t, SSA, andLAT have all been defined above and are measured at timet 2 {1990, 2000}. The public expenditure on education is in-cluded in this model to control for potential distortionary ef-fect of government involvement in education, thepresumption being that higher public expenditure in educationis associated with more centrally planned education sectors,and higher distortions. Qt here indicates one of different prox-ies for the domestic demand for technically skilled individuals.We use two alternative variables to check the robustness of

our results: phones per employee and the percentage of pavedroads. 30 The additional variable D2000 is a dummy variablefor year 2000.

We start off by estimating a baseline specification in whichwe abstract from possible nonlinear effects of migration, thatis, we assume b3 = 0 in both Models I and II of Table 3. Mod-els I and II differ for the proxy used to control for domestic

demand for technical skills. As in Section 4(a), we present bothOLS and IV results for each specification. In the IV estima-tions we correct for the possible endogeneity of the migrationrate and the proximity to the technological frontier for reasonssimilar to the one discussed in the context of human capitalaccumulation above. We use the same set of instruments asin the IV estimates in Section 4(a): the stock of immigrantswith secondary education, the population size, and the 10-yearlag of the proximity to the technological frontier. When we usephones per employee as our proxy, the performance of theinstruments is the most satisfactory, both in terms of their rel-evance and exogeneity. When the percentage of paved roads isused as a proxy, the instruments pass the test for relevance at15% Maximal IV Size 31 and pass the test for exogeneity at the10% level but not at the 5%. 32 Further investigation of the

endogeneity of migration rate and proximity to the frontierindicates that they can be treated as exogenous. The effect of skilled migration on the proportion of students enrolled in sci-ence and technology specialties appears robust in the two spec-ifications: a percentage point increase in the skilled migrationrate, leads to an increase in the proportion of higher educationstudents enrolled in science and technology degrees of about0.19% points for all specifications in the first four columnsof Table 3. All other coefficients, except for the one of proxim-ity, are rather stable across OLS and IV estimates. For thecoefficient on proximity, the OLS and IV regressions producedifferent signs when phones per employee is used to proxy forthe demand of technicians. 33 Going back to our theory, onewould expect to see a negative sign on this variable as gener-

alists become relatively more productive, the higher the degreeof technological sophistication. The demand proxies have thetheoretically predicted positive sign, while the negative sign of the coefficient for public expenditure in education is ratherpuzzling. This may indicate that a tertiary education sectorcharacterized by a high involvement of the government facesa cap on the enrollment in technical degrees.

Next, we investigate the evidence on relative probability andrelative productivity effects identified in Section 2. In the pres-ence of such effects, we would expect to find a statistically sig-nificant estimate for b3. Indeed, we find evidence that theinteraction term is statistically different from zero in bothModels III and IV in Table 3. Furthermore, the results indi-cate that for countries further away from the technologicalfrontier the effect of skilled migration on the proportion of stu-

dents enrolled in technical degrees is negative, while for thosecloser to the frontier the effect is positive. The estimatedthreshold values for the reversal in sign are 0.5022 and0.5336 in Models III and IV, respectively. 34 In both casesthe threshold is statistically smaller than 1 ( p-values for H 0:b1 = b3 equal 0.0000 and 0.0012 in Models III and IV,respectively).35 Thus, in countries with relatively low levelsof technological sophistication the possibility of migration re-duces the enrollment in science and technology specialties,compared to a situation in which no emigration is allowed.The opposite occurs in relatively more developed countries.The result complies with our intuition: the relative productiv-ity effect, which predicts a negative effect of migration on theproportion of science in technologies, dominates the relativeprobability effect, which predicts a positive effect of migration


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on S &T , for countries lagging further behind in terms of tech-nological development. It is easy to imagine that science andtechnology skills, that are more prone to obsolescence thanother types of skills, may be deemed less marketable if ac-quired in countries that lag far away from the frontier. As aconsequence, students in the least developed countries couldbe more inclined to acquire other skills, studying arts andhumanities, for example, in view of migration. 36 At the sametime, emigration is generally believed to be easier for scienceand technology graduates (provided of course that their skillsare current), explaining our results for the relatively moredeveloped countries in our sample. 37

Proximity to the technological frontier is shown to also af-

fect the composition of enrollment in tertiary education di-rectly in both Model III and IV. Indeed, the p-value of thetest for the joint significance of b2 and b3 is 0.0019 in ModelIV. The direction of the overall effect depends on the migra-tion rate: for countries with a migration rate higher than a cer-tain threshold of migration, approaching the frontier has apositive effect on the share of enrollment in science and tech-nology degrees; for countries with a migration rate belowthe threshold, proximity to the frontier has the opposite effect.The value of the estimated threshold is not very robust acrossspecifications: it is estimated at 35% according to Model IIIand 14% according to Model IV, this latter value, moreover,is found not to be statistically different from 0 at 10%. Despitethe empirical uncertainty as refers to the value of the thresh-old, these results are consistent with our theoretical frame-

work. In particular, they conform with an interpretation

focussed on two mechanisms. First, as countries move closerto the frontier, workers’ incentives to acquire technical skillsmay decrease if complementarities across skills become morepronounced, and a broader variety of skills are demanded. Ithas been suggested that this is indeed what happens whencountries complete the transition from being mere imitatorsof foreign technologies, a phase in which a high share of tech-nically inclined graduates is preferable, to being properly inno-vative, when a wider range of skills become necessary(Feinstein, 2006; Yusuf, 2007). This view implies that the shareof technically skilled labor force should decline as proximityincreases. Second, as migration becomes easier, a larger pro-portion of workers actively seek jobs abroad. Since most des-

tination countries favor technically skilled immigrants,investing in S &T skills equips potential migrants with compe-tencies that are in high demand overseas (provided that thecountry is advanced enough). This latter mechanism is moreprominent in high emigration countries. The interplay betweenthese two mechanisms explains our results above: at low levelsof migration the former effect dominates, while for higher lev-els of migration it is the latter effect that has the stronger im-pact.

(c) Human capital and growth

The last step in our empirical endeavor is to gauge the sig-nificance that changes in human capital formation due tomigration may have on economic growth. To this end, we

study the effect of both the level and composition of human

Table 3. Composition of human capital—dependent variable: S&T t

Model I Model II Model III Model IV

OLS IV OLS IV OLS OLS

rh,t 0.2060 0.1924 0.1874 0.2016 0.8262 1.1402(0.0933)** (0.1113)* (0.0980)* (0.1130)* (0.4501)* (0.3838)***

PROXIM t 0.4159 0.2802 0.0032 0.0440 0.6191 0.3017

(0.2202)

*

(0.2167) (0.2033) (0.1995) (0.2226)

***

(0.2070)rh,t PROXIM t 1.6452 (0.6721)** 2.1367 (0.5793)***

log(ROADt) 0.0418 0.0271 0.0262(0.0275) (0.0081) (0.0289)

log(PHONE t) 0.0339 0.0282 0.0381(0.0204) (0.0257) (0.0202)*

EDU t 0.0215 0.0271 0.0256 0.0292 0.0229 0.0274(0.0084)** (0.0081)*** (0.0082)*** (0.0075)*** (0.0084)*** (0.0074)***

D2000 0.0051 0.0045 0.0644 0.0400 0.0003 0.0749(0.0341) (0.0328) (0.0445) (0.0492) (0.0329) (0.0419)*

SSA 0.0232 0.0357 0.0592 0.0536 0.0424 0.0596(0.0467) (0.0435) (0.0379) (0.0397) (0.0492) (0.0368)

LAT 0.1192 0.0796 0.0014 0.0027 0.0877 0.0120(0.0464)** (0.0464)* (0.0409) (0.0386) (0.0546) (0.0398)

Const. 0.5244 0.5408 0.3890 0.3889 0.7256 0.5718

(0.1252)

***

(0.1219) (0.1123)

***

(0.1192) (0.1496)

***

(0.1205)

***

No. of Obs. 68 60 82 73 68 82R2 0.3293 0.2952 0.3734 0.3740Weaka 13.293 15.506J -testb (0.0851)* (0.4791)Endogeneityb (0.9463) (0.8882)

Notes: Heteroskedasticity robust standard errors are reported in parenthesis.* Indicate significance at 10% levels, respectively.** Indicate significance at 5% levels, respectively.*** Indicate significance at 1% levels, respectively.a Shea’s partial R2 reported for each endogeneous variable.b p-Value of the robust score stats reported.


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capital on the growth rate of the country’s TFP . Here we buildupon the empirical growth model of Vandenbussche et al.(2006) who study the effect of human capital on growth as afunction of the country’s technological development. Whilethe aim of Vandenbussche et al. (2006) is rather different fromours, their model is well suited to study the hypothesis that dif-ferent skill compositions of human capital are better for

growth at different stages of development.The empirical relation between the level and composition of human capital and TFP growth is given by the following equa-tion:

g TFP ;5 yr ;t ¼ c0 þ c1 logð H p ;t Þ þ c2 PROXIM t logð H p ;t Þ þ c3

log S &T t 10 þ c4 PROXIM t logðS &T t 10Þ

þ c5 PROXIM t þ c6GDP pc;t 10 þ c7SSA þ c8 LAT

þ mt : ð6Þ

The dependent variable, g TF P ,5 yr,t is the average annual growthrate of TFP over 5 years. 38 H p,t is the level of human capitalafter migration. S &T , the proportion of enrollment in tertiaryeducation with technical and science specialty, is used here to

proxy for the proportion of the stock of workers in the labormarket with the same characteristics. Changes in enrollmentonly gradually manifest themselves as corresponding changesin the stock of human capital, for this reason in this equationwe use a 10-year-lag for this variable. To capture possible non-linear effects of the level of human capital and its skill compo-sition, we include interaction terms of both variables with theindex of technological development, PROXIM . The initial va-lue of GDP per capita, GDP pc,t10, is included to gauge theexistence of catching-up effects, and therefore we expect it tohave a negative coefficient. Finally, SSA and LAT are thecountry group dummies described above. Given the lag struc-ture of the model, almost all specifications are based on cross-sectional data. The only exception is the specification in which

the effect of S &T is assumed to be zero.

The set of relevant regression results are presented in Table 4.From our point of view the main insight is that the effect of thecomposition of human capital is statistically significant in allspecifications. Models III–V allow for possible nonlinear effectsof the proportion of workers with technical education.Although the individual coefficients of S &T and the interactionterm are not statistically significant in any of these specifica-

tions, joint significance tests indicate that the null hypothesiscan be rejected at 5% in Model III and at 10% in Models IVand V. This is easily explained by the high correlation (0.8501)between S &T and its interaction term in the sample. All theseregressions indicate that a higher proportion of workers withtechnical skills are beneficial for TFP growth, but that this eff ectis decreasing with the proximity to the technological frontier.39

These results confirm our theoretical priors derived by the workof Di Maria and Stryszowski (2009).

While we find statistically significant effect of the type of composition of the labor force on TFP growth, we fail to iden-tify any statistically significant effect of the proportion of ter-tiary educated workers in any of the regression specifications.When we include an interaction term between the level of hu-man capital and the proximity to the frontier—in Models II

and III—our estimates show that the effect of the proportionof tertiary educated people on TFP growth is weaker, thehigher the degree of technological sophistication. This effectgoes counter the evidence provided by Vandenbussche et al.(2006) for OECD countries.

For completeness, the second column of Model I reports theresults of the IV estimation of the basic regression model with-out nonlinear effects in which we control for the endogeneityof the proximity to the technological frontier. We follow theestimation strategy of Vandenbussche et al. (2006) and weuse a lagged value of proximity to instrument for PROXIM 00.In particular, we use PROXIM 80. We cannot test for the exo-geneity of this instrument as the system is exactly identified.We argue, however, that this is an acceptable assumption, gi-

ven the length of the lag. The instrument is highly relevant, the

Table 4. Total factor productivity growth—dependent variable: g TFP ,5 yr,t

Model I Model II Model III Model IV Model V

OLS IV OLS OLS OLS OLS

log(H p,00) 0.0027 0.0015 0.0916 0.0560 0.0050(0.0516) (0.249) (0.1650) (0.1814) (0.0538)

log(H p,00) PROXIM 00 0.1823 (0.2654) 0.1002 (0.3047)log(S &T 90) 0.0138 0.0161 0.0339 0.0347 0.0343

(0.058)** (0.0062)** (0.0262) (0.0262) (0.0258)log(S &T 90) PROXIM 00 0.0365 (0.0443) 0.0355 (0.0433) 0.1047 (0.0435)PROXIM 00 0.0568 0.0424 0.0715 0.0201 0.0306 0.0188

(0.0223)** (0.0249)* (0.0564) (0.0443) (0.0757) (0.0552)

GDP pc,t10 0.0074 0.0058 0.0064 0.0074 0.0075 0.0073

(0.0024)*** (0.0038) (0.0022)*** (0.0022)*** (0.0024)*** (0.0024)***

SSA 0.0066 0.0058 0.0002 0.0066 0.0070 0.0063(0.0072) (0.0072) (0.0061) (0.0066) (0.0079) (0.0051)

LAT 0.0005 0.0016 0.0005 0.0010 0.0012 0.0012(0.0050) (0.0059) (0.0047) (0.0044) (0.0052) (0.0051)

Const. 0.0275 0.0288 0.0018 0.0493 0.0434 0.0496(0.0152)* (0.0226) (0.0265) (0.0354) (0.0459) (0.0352)

No. of Obs. 56 50 71 56 56 56

R2 0.2768 0.1276 0.2850 0.2864 0.2851

H 0: c1 = c2 = 0 (0.7263) (0.9473)H 0: c3 = c4 = 0 (0.0453)

** (0.0699)* (0.0524)*

Notes: Heteroskedasticity robust standard errors are reported in parenthesis.* Indicate significance at 10% levels, respectively.** Indicate significance at 5% levels, respectively.***

Indicate significance at 1% levels, respectively.




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F -statistic is 216.91, which is much higher than 10, the criticalvalue for the Stock and Yogo’s (2005) weak instrument test.The robust Wald test for the endogeneity of proximity indi-cates that we can reject the null hypothesis that PROXIM 00is exogenous in this model at 5% but we cannot reject it at10% ( p-value 0.0634). Given the remarkable stability of thecoefficients on the level and composition of human capital,

and the indicator of proximity to the technological frontier be-tween the OLS and IV estimates, however, we believe that theOLS estimates are sufficiently reliable.

Finally, we want to remark that the coefficient of the initialvalue of GDP per capita has the statistically significant nega-tive sign, as expected. The regional dummy variables, on theother hand, are not significant in any of the specifications.

5. MIGRATION, HUMAN CAPITAL AND GROWTH:A DISCUSSION

So far we have provided empirical evidence that the possibil-ity to emigrate affects the process of human capital formationby changing the incentives to accumulate human capital and

to acquire certain types of skills. We have found that bothof these distortionary effects are statistically significant andcomply with the underlying theoretical predictions. To gaugethe economic significance of these effects, we have also inves-tigated the role of human capital in the productivity growthof a country. In this sample of developing countries, we findrobust and statistically significant effects of the compositionof tertiary education labor force on growth, but no such effectfor the proportion of tertiary educated labor force. This is why

in our subsequent discussion we focus solely on the composi-tion of human capital. Our aim is to derive a better under-standing of the substantive effects of the possibility of emigration on growth operating through this channel. To thisend, we adopt a simulation-based approach, as discussed byKing, Tomz, and Wittenberg (2000) and employ the statisticalsoftware CLARIFY developed by these authors.

Our first step is to study the effect of the migration rate onthe proportion of individuals enrolling in a science and tech-nology degree for the set of 82 observations for which wecould estimate Model IV of Table 3. We simulate an increasein the rate of skilled migration in each country in the sampleby 40% (e.g., a country with an initial migration rate of 0.19would face an increase to 0.19 1.4 = 0.27.) while keepingthe level of technological development, the percentage of pub-lic expenditure on education, and the number of phones peremployee at their actual levels. 40 Our statistical exercise usesstochastic simulations techniques to simulate the differencein the share of workers with S &T major, by drawing 1000 setsof simulated parameters for each country, from the samplingdistribution of the parameters’ estimates. The confidence inter-val for each observation is obtained by ordering the simulated

values and considering the 5th and 95th percentile. The resultsof these simulations are presented in Table 5. 41

Clearly, an increase in the emigration rate by 40% representsa small change in percentage point terms for countries withlow emigration rates—it is less than 1% point for Brazil, forexample—while for countries with high emigration rates, thisis a large change in absolute terms—it is equivalent to 19%points for Barbados, for example. Table 5 illustrates that onaverage, a country like Argentina could have seen its share

Table 5. The effect of an increase of the migration rate by 40% on S&T

Country Year Drh DS &T 90% conf. Interval

Trinidad and Tobago 2000 0.2699 0.1434 0.0828 0.2052Trinidad and Tobago 1990 0.2646 0.1419 0.0818 0.2029Barbados 2000 0.1900 0.0872 0.0505 0.1238Sri Lanka 1990 0.0924 0.0297 0.0155 0.0427Panama 1990 0.0498 0.0284 0.0164 0.0407Mauritius 1990 0.2172 0.0251 0.0058 0.0573Mauritius 2000 0.1805 0.0243 0.0010 0.0502Malaysia 1990 0.0642 0.0235 0.0129 0.0336Domenican Republic 2000 0.0512 0.0154 0.0077 0.0225Iran 1990 0.0763 0.0148 0.0046 0.0255Honduras 1990 0.0560 0.0115 0.0040 0.0192Costa Rica 1990 0.0189 0.0112 0.0064 0.0160Turkey 1990 0.0278 0.0111 0.0063 0.0159Egypt 1990 0.0182 0.0096 0.0055 0.0137El Salvador 2000 0.0730 0.0089 0.0015 0.0195Tunisia 1990 0.0518 0.0087 0.0016 0.0161

El Salvador 1990 0.0836 0.0087 0.0033 0.0215Iran 2000 0.0416 0.0081 0.0025 0.0139Colombia 1990 0.0253 0.0076 0.0038 0.0110Colombia 2000 0.0296 0.0072 0.0031 0.0113Tunisia 2000 0.0339 0.0065 0.0019 0.0112Jordan 1990 0.0210 0.0057 0.0027 0.0086Nicaragua 2000 0.0776 0.0057 0.0059 0.0181Uruguay 2000 0.0214 0.0054 0.0024 0.0084Nicaragua 1990 0.0800 0.0052 0.0070 0.0181Argentina 1990 0.0108 0.0049 0.0028 0.0069Uruguay 1990 0.0184 0.0047 0.0021 0.0073Chile 1990 0.0191 0.0047 0.0020 0.0073Chile 2000 0.0170 0.0046 0.0021 0.0069Kenya 1990 0.1520 0.0041 0.0207 0.0308




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of students enrolled in science and technology in 1990 increaseby 0.49% points, had it had a skilled emigration rate of 3.78%rather than the actual 2.7%. Out of our 82 observation, 26indicate an average decrease in S &T , however, only one forone of them, that of Ecuador, this effect is significantly nega-tive at the 95% confidence level. This is not surprising as Ecua-dor is one of the countries lagging furthest from the frontier(PROXIM of 0.38 in 1990), and our theory predicts that in thiscase the relative productivity effect should dominate. Overall,

we can identify 36 observations for which the impact of the

change of the emigration rate is statistically significant at the90% confidence level. The largest impact in magnitude, morethan 14% point, is observed in Trinidad and Tobago. Trinidadand Tobago, unlike Ecuador, is a country close to the techno-logical frontier (PROXIM of 0.78 in both 1990 and 2000). Infact, there are two countries for which the effect of the increasein the migration rate is much higher than for the rest, Trinidadand Tobago and Barbados. Both of these countries have highproximity and relatively high skilled migration, though they

are not the countries with this higher migration rate. Guyana,

Table 5 (continued )

Country Year Drh DS &T 90% conf. Interval

Mexico 1990 0.0217 0.0037 0.0007 0.0068Cameroon 1990 0.0357 0.0032 0.0020 0.0088Namibia 2000 0.0109 0.0030 0.0015 0.0045Algeria 1990 0.0188 0.0028 0.0001 0.0055Yemen, Republic 2000 0.0195 0.0028 0.0000 0.0056Morocco 1990 0.0683 0.0025 0.0085 0.0143Djibouti 2000 0.0298 0.0025 0.0018 0.0073Brazil 1990 0.0051 0.0020 0.0011 0.0028United Arab Emirates 2000 0.0028 0.0017 0.0010 0.0025Zimbabwe 1990 0.0205 0.0015 0.0015 0.0048Saudi Arabia 2000 0.0023 0.0013 0.0008 0.0019Sudan 1990 0.0168 0.0013 0.0011 0.0040Cote d’Ivoire 1990 0.0083 0.0012 0.0001 0.0024Venezuela 1990 0.0105 0.0012 0.0003 0.0028Zimbabwe 2000 0.0357 0.0011 0.0046 0.0074Thailand 2000 0.0068 0.0011 0.0001 0.0021Indonesia 1990 0.0112 0.0007 0.0011 0.0025Philippines 1990 0.0386 0.0006 0.0058 0.0075Senegal 1990 0.0380 0.0004 0.0060 0.0073Benin 1990 0.0176 0.0003 0.0026 0.0035Swaziland 2000 0.0016 0.0003 0.0000 0.0005India 2000 0.0137 0.0001 0.0022 0.0026Swaziland 1990 0.0006 0.0001 0.0000 0.0002Philippines 2000 0.0413 1.32E05 0.0071 0.0076Peru 1990 0.0165 1.91E05 0.0029 0.0030Benin 2000 0.0306 3.26E05 0.0033 0.0056India 1990 0.0090 0.0002 0.0018 0.0015Bangladesh 1990 0.0070 0.0003 0.0016 0.0011Burkina Faso 1990 0.0039 0.0006 0.0016 0.0004China 1990 0.0099 0.0008 0.0028 0.0014Botswana 1990 0.0064 0.0009 0.0024 0.0007Madagascar 1990 0.0138 0.0009 0.0036 0.0021Lesotho 2000 0.0158 0.0012 0.0044 0.0022Mali 1990 0.0220 0.0015 0.0057 0.0033

Paraguay 1990 0.0096 0.0015 0.0038 0.0009Madagascar 2000 0.0176 0.0018 0.0055 0.0022Guinea 2000 0.0393 0.0031 0.0109 0.0056Lesotho 1990 0.0394 0.0031 0.0109 0.0056Togo 1990 0.0302 0.0032 0.0096 0.0038Vietnam 1990 0.0589 0.0033 0.0145 0.0090Guinea 1990 0.0406 0.0038 0.0121 0.0054Ecuador 1990 0.0128 0.0041 0.0082 0.0000Guyanaa 1990 0.1428 0.0046 0.0307 0.0230Ethiopia 1990 0.0240 0.0051 0.0114 0.0015Togo 2000 0.0601 0.0070 0.0200 0.0072Ethiopia 2000 0.0290 0.0070 0.0151 0.0012Uganda 2000 0.1228 0.0103 0.0350 0.0170Mozambique 1990 0.0978 0.0145 0.0370 0.0099Erithrea 2000 0.1116 0.0157 0.0410 0.0118

Malawi 1990 0.0644 0.0188 0.0384 0.0010Ghana 1990 0.1354 0.0195 0.0505 0.0140Uganda 1990 0.1574 0.0204 0.0553 0.0177

a The skilled migration rate used in the simulations was capped at 100%, as a 40% increase would imply a migration rate in excess of 100%.



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the country with the highest skilled migration rate in our sam-ple, would face on average a negative effect on its S &T ratio asa result of a further increase in migration: the reason for that isthat it has a relatively low degree of proximity (0.52). Figure 3plots the simulations results against each country’s index of proximity to the frontier and shows the reversal of the compo-sition effect that occurs as the relative probability effect comesto dominate the relative productivity effect.

Having obtained central predictions for the counterfactuallevel of S &T , we are able to take our thought experiment

one step further and ask what would be the impact of suchmigration-induced changes on the TFP growth for each coun-try. Our second set of simulations is based on the estimation of the parsimonious version of Eq. (6) presented in Table 4.3 asModel III. Using a similar methodology as before, we simulatethe impact of changes in S &T on TFP growth for each of the56 countries for which we are able to obtain a counterfactualvalue of S &T for 1990, and compute the relative confidence

intervals. Keeping the value of all other variables in thegrowth equation at their actual values, we change the valueof ln(S &T ) to ln(S &T + DS &T ) where DS &T is the reportedchange in S &T presented in Table 5. In this way we simulatechanges in growth rates for 50 countries, as presented in Ta-ble 6.

Just like Beine et al. (2007), we find that sending countriesmay end up winning or losing from an increase in skilledmigration. Out of the 50 countries in Table 6, 32 experiencea positive growth impact, for only 12 of them, however, the

change is significantly positive at the 95% confidence level.Mauritius, which has the highest average positive effect failsthe significance test at the 90% confidence level. Among thecountries in our data set, Kenya seems to be the one thathas the most to gain from an increase in the skilled migrationrate. The remaining 18 countries experience a negative effect,with a statistically significant impact for 16 of them. Figure 4plots changes in TFP growth rates against our index of

Table 6. The effect of an increase of the migration rate by 40% on TFP growth

Country DS &T DTFP g rowth 90% conf. Interval

Mauritius 0.02510 0.00121 0.00016 0.00261Malaysia 0.02350 0.00035 0.00117 0.00173Trinidad and Tobago 0.14190 0.00338

0.00546 0.00544

El Salvador 0.00870 0.00027 0.00002 0.00056Sri Lanka 0.02970 0.00023 0.00080 0.00118Honduras 0.01150 0.00023 0.00005 0.00052Kenya 0.00410 0.00022 0.00005 0.00041Iran 0.90870 0.00022 0.00011 0.00054Tunisia 0.00870 0.00018 0.00009 0.00044Colombia 0.00760 0.00017 0.00016 0.00048Jordan 0.005670 0.00013 0.00013 0.00038Nicaragua 0.00520 0.00013 0.00001 0.00026Uruguay 0.00470 0.00010 0.00010 0.00027Zimbabwe 0.00150 0.00010 0.00002 0.00017Cameroon 0.00320 0.00008 0.00001 0.00016Mexico 0.00370 0.00008 0.00002 0.00017

Figure 3. Changes in S&T following a 40% increase in the rate of skilled migration vs. proximity to the frontier.



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technological sophistication (PROXIM ). The diagram illus-trates that the winners are found among the countries closer

to the technological frontier, while the losers among countrieslagging further from it.

Table 6 (continued )

Country DS &T DTFP g rowth 90% conf. Interval

Morocco 0.00250 0.00008 0.00002 0.00014Turkey 0.01110 0.00007 0.00029 0.00040Chile 0.00470 0.00007 0.00008 0.00021Sudan 0.00130 0.00007 0.000003 0.00014Panama 0.02840 0.00005 0.00279 0.00258Algeria 0.00280 0.00005 2.01E06 0.00010Cote d’Ivoire 0.00120 0.00004 0.00001 0.00008Venezuela 0.00120 0.00004 2.71E06 0.00007Egypt 0.00960 0.00003 0.00114 0.00108Argentina 0.00490 0.00003 0.00026 0.00029Brazil 0.00200 0.00003 0.00007 0.00013Philippines 0.00060 0.00003 0.00001 0.00005Indonesia 0.00070 0.00002 3.76E06 0.00004Benin 0.00030 0.00002 4.22E06 0.00004Senegal 0.00040 0.00001 2.15E06 0.00002Swaziland 0.00010 2.73E06 7.05E07 6.15E06Peru 1.02E10 4.07E12 0.00000 0.00000Costa Rica 0.01120 6.07E06 0.00121 0.00107Bangladesh 0.00030 8.83E06 0.00002 1.84E06India 0.00020 9.97E06 0.00002 2.03E06China 0.00080 0.00001 0.00003 2.31E06Burkina Faso 0.00060 0.00002 0.00004 4.19E06Madagascar 0.00090 0.00003 0.00005 6.26E06Botswana 0.00090 0.00007 0.00012 0.00001Mali 0.00150 0.00010 0.00018 0.00002Paraguay 0.00150 0.00010 0.00019 0.00001Guinea 1.30221 0.00011 0.00020 0.00002Togo 0.00320 0.00012 0.00021 0.00003Ecuador 0.00410 0.00022 0.00043 3.35E6Lesotho 0.00310 0.00022 0.00040 0.00005Guyana 0.00970 0.00026 0.00050 0.00003Mozambique 0.01450 0.00045 0.00080 0.00009Ghana 0.01950 0.00076 0.00136 0.00017Malawi 0.01880 0.00291 0.00534 0.00032

Figure 4. Changes in TFP growth following a 40% increase in the rate of skilled migration vs. proximity to the frontier.



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According to our simulations, the losers group containsamong others China, India, and Bangladesh. Overall, the los-ers account for over 69% of the total population in our sam-ple, and 2.6 billion people in total (in 2000). In terms of welfare, our results have even stronger implications. Not onlyis almost 70% of the population affected by losses, but also, gi-ven the decreasing marginal utility of income, losses among

the poorest countries should be weighted more than gains tomore developed ones.

6. CONCLUSION

By bridging two strands of literature, that on the economicconsequences of brain drain, and the growth one focussing onthe role of human capital formation, we provide several newinsights on the relation between migration, human capital for-mation and growth. First, we find support for the existence of an incentive effect on the level of (ex-ante) human capital accu-mulation. Second, we present evidence that the possibility of migration also affects the types of skills that agents chooseto acquire. This underscores that the level effect exists along-

side a composition effect. Third, in line with our theoreticalpriors, we show that both these effects depend on the levelof technological development of the sending country (its prox-imity to the frontier). Differences in wages and the degree of marketability of migrants’ skills depend on the level of techno-logical development, thus the effect of migration needs to bediscussed taking explicitly into account the technological gapof each sending country. Fourth, our simulations show that

more than one third of the countries in our data set, represent-ing almost 70% of the total population, suffer as a result of anincrease in skilled migration. The losers are found among therelative less developed countries, implying that, overall, thewelfare costs of skilled migration may be large.

As is the case for any empirical endeavor, our results arehighly influenced by the quality of the data used. In this re-

spect, data on educational attainment by field of study leavemuch to be desired, especially when focussing on nonOECDcountries as we do. The need to invest resources in the gener-ation of better data to help empirical research cannot be over-stated in this field. Despite this caveat, our analysis providesclear empirical support to the claim made by developing coun-tries that recent immigration policies of OECD countries mayhave dire consequences for the migrants’ countries of origin.While selecting the most talented individuals from developingcountries has a clear economic rationale for destination coun-tries, our work focuses on its implications for sending coun-tries: by changing both the level and the composition of human capital, an increase in the possibility of migration for(certain types of) skilled workers reduces the growth rate of TFP in many source countries. We draw two conclusions from

our work. First, we stress the need for better quality data tosupport additional research efforts in this area, to test therobustness of our findings, and to better inform the policy pro-cess. Second, based on our findings, the need for a more con-certed approach to migration policy among developed anddeveloping countries emerges very starkly from our analysis.

NOTES

1. A recent review of the debate, including a survey of existing andproposed policies and of their consequences, is offered by ILO (2006).

2. Commander, Kangasniemi, and Winters (2004) present an excellentreview of this literature. See also the discussions in Beine, Docquier, andRapoport (2008) and Di Maria and Stryszowski (2009).

3. Anectodal evidence in this respect is abundant. Lorenzo et al. (2007),for example, report that in the Philippines the number of nursing collegesrose from 170 in 1999 to 460 in 2005, and that most of these new collegeshave curricula specifically tailored to foreign health systems. Kangasniemiet al. (2007) survey Indian doctors working in the UK and find that 30% of them acknowledge that migration prospects influenced their educationplans and effort. Finally, Commander et al. (2008) find evidence of migration-induced skills accumulation in their survey of the Indian ICTsector. The International Organization for Migration summarizes thisevidence stating that “ prospects of working abroad have increased the

expected return to additional years of education, and led many people toinvest in more schooling, especially in occupations in high demand overseas”

(IOM, 2003).

4. Beine et al. (2011a) also find that level effects are stronger for poorercountries, but do not consider composition effects, nor the overall impactof migration on growth.

5. For our purposes, higher skilled

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Migration, Human Capital Formation (Brain Drain)

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