Migration, Human Capital Formation

8/17/2019 Migration, Human Capital Formation

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eral decades. 2 One recent strand of literature, started by thepioneering work of Oded Stark and co-authors (Stark, Hel-menstein, & rskatwet!, "##$, "##%&, recogni!es that the pos-sibility of migration raises the returns to education and leadsti'e e ectff conforms to economic intuition and represents animportant result. he literature mentioned abo'e, howe'er,neglects another, more subtle aspect of the brain gain, in that itabstracts from the possibility that migration might change

to an increase in the le'el of human capital that may ultimately not only the le'el, but also the composition of human capital.pro'e bene cialfi for sending countries)a *ene cialfi *rain +ndeed, elsewhere in the literature, a number of authors ha'erain or *rain ain (*. his in uentialfl literature has emphasi!ed how the possibility of migration encouragesshaped the direction of much of the recent debate on skilled would-be migrants to concentrate on disciplines that are asso-migration (*eine, ocuier, & /apoport, 200"1 *eine et al., ciated with higher probabilities of migration, especially in

200%1 ustmann, adlon, & 3eiss, 20""1 4ountford, "##$1 health care and +5 (5lemens, 200$1 5ommander, 5handa,Stark & 3ang, 20021 6idal, "##%, to name but a few&, and 7angasniemi, & 3inters, 200%1 5onnell, 8urn, Stilwell,has spawned an empirical literature aimed at testing its theo- 9wases, & *raichet, 200$1 ibson & 4c7en!ie, 20""1retical predictions. he earliest contribution in this respect is 7angasniemi, 3inters, & 5ommander, 200$1 :oren!o,the paper by *eine et al. (200"&. he authors, howe'er, usegross migration rates to pro;y for the migration rate of skilledworkers. 9s a conseuence, their ndingsfi in support of the *

s ?ni'ersity of

hypothesis need to be taken with caution. *eine, ocuier, *elfast, the ?ni'ersity of Stirling, the @ational ?ni'ersity of +reland,and /apoport (200A& use the data on immigration rates to- 4aynooth, as well as conference participants at the Buropean Bconomicward the ?S by le'el of education published by 5arrington 9ssociation 20"0 meeting in lasgow. 3e greatly bene tedfi from the co-and etragiache ("##%&, and also find empirical support for mments of four anonymous referees and of the Cournal Bditor. 3e wouldthe e;istence of a * in a cross-section of D0 de'eloping coun- also like to thank Hillel /apoport for making the data set for the papertries. heir regressions, howe'er, show that migration has a *eine et al. (200%& a'ailable to us for comparison purposes. inal re'isionnegati'e growth e ectff in most de'eloping countries. +n the acceptedE September 2$, 20"".

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www.else'i er.comFlocat eFworldde'

doi:10.1016/j.worlddev.2011.11.011

3orld e'elopment 6ol. G0, @o. D, pp. #A%#DD, 20"2 20"" Blse'ier :td. 9ll rights reser'ed

0A0D-$D0IFJ - see front matter

4igration, Human 5apital ormation, and rowthE

9n Bmpirical +n'estigation

5O//9O + 49/+9 and B4+:+K9 9. :989/O69 <

?ni'ersity of *irmingham, Bdgbaston, ?7

Summary. — 3e study the e ectff of skilled emigration on human capital formation and growth in a sample of de'eloping countries. 3e ndfithat the migration rate e;erts statistically signi cantfi e ectsff on both the le'el and the composition of human capital. 3e are able to trace theimpact of these changes on the growth rate of sending countries 'ia regression analysis and simulations. Our results show that while there areboth winners and losers, almost $0L of the population in our sample su ersff lower growth as a conseuence of skilled migration. 4oreo'er,the losses are concentrated in countries with low le'els of technological sophistication. 20"" Blse'ier :td. 9ll rights reser'ed.

7ey words ) education, *rain drain, migration, human capital, economic growth, 9sia, 9frica, South 9merica

". +@(/O?5(+O@

O'er the last decades, an increasing number of de'elopedcountries ha'e put in place di erent mechanismsff to encouragethe immigration of only the most talented, skilled indi'idualsfrom de'eloping countries. "9s a conseuence of such arrangements, the world has wit-nessed a dramatic modi cationfi in the composition of the poolof migrants mo'ing from de'eloping to de'eloped countries.O'er the last two decades, the share of highly skilled migrants inthe total number of migrants has increased dramatically.ocuier and 4arfouk (200M&, for e;ample, estimate that dur-ing "##02000 the number of foreign-born workers with ter-tiary schooling li'ing in OB5 member countries increasedby MA.$L, while for unskilled migrants the increase was only"G.GL o'er the same period. Such accelerating *rain drain isarguably one of the most striking features of globali!ation.

3hether the owfl of skilled migrants from de'eloping tode'eloped countries is a curse or a blessing for sending coun-tries has been a contentious issue among economists for se'-

re'ised 'ersion of the pre'ious paper, *eine et al. (200%& use therecent data set by ocuier and 4arfouk (200M& to test for thee;istence of “incenti'e e ectsff ” in human capital accumulation,that is, the positi'e e ect

ff of migration probabilities on humancapital accumulation. he authors conclude that these e ectsff are indeed positi'e and go on to perform counter- factualsimulations to compare the e;-post le'el of human capital insending countries, when skilled workers do not bene tfi from ahigher migration rate. +n this instance their conclusions are notclear-cut, as more than half the countries in their sample su erff from *rain drain, rather than bene tfi from a brain gain. +n 'eryrecent additions to this literature, *eine, efoort, and ocuier(20""a& pro'ide further e'idence in fa'or of the bene cialfi *raindrain hypothesis using a new panel data set that allows themto e;plicitly address issues of endogeneity, while *atista,:acuesta, and 6icente (20"2& test the “brain gain” hypothesisfor 5ape 6erde, using specially collected data. *oth paperspro'ide additional e'idence in fa'or of the e;istence of substantial incenti'e e ects.ff he fact that migration possibilities e;ert a positi'e incen-

http://www.elsevier.com/locate/worlddevhttp://www.elsevier.com/locate/worlddevhttp://www.elsevier.com/locate/worlddevhttp://www.elsevier.com/locate/worlddevhttp://dx.doi.org/10.1016/j.worlddev.2011.11.011http://dx.doi.org/10.1016/j.worlddev.2011.11.011http://www.elsevier.com/locate/worlddev


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igure ". he structure of the model.

endea'ors (inno'ation& rather than in imitati'e acti'ities (e.g.,re'erse engineering&, that is, we let r P / . Mi'en that countries further away from the technologicalfrontier ha'e a larger “ad'antage of backwardness” (ers-chenkron, "#M2&, entrepreneurs in less de'eloped countriesspeciali!e in imitation1 both imitation and inno'ation takeplace at intermediate le'els of the technological de'elopment1and nallyfi only inno'ation occurs once the technological fron-tier has been reached. On the one hand, this implies that thegrowth rate of output declines o'er time as de'elopment pro-gresses1 on the other, since general skills become more producti'ecloser to the frontier, the relati'e wages of generalists tend toincrease as countries de'elop.Successi'e generations (each of mass "& of heterogeneousworkers are born, accumulate skills, work, and die within eachperiod. Bach worker is born with a di erentff le'el of talent,

which determines the relati'e opportunity cost (in terms of working time& of acuiring skills. hus, comparing the relati'ecosts and returns to accumulating skills, workers di'ide them-sel'es into lower-skilled, generalists, and technologists. Order- ingworkers in terms of talent, we can represent theendogenous split of workers in the three groups as in igure 2.9ny increase in the relati'e wages of skilled workers in-creases the share of skilled workers in the economy (reduces :&.4oreo'er, if the relati'e rewards accruing to skills increase,so does the share of -skilled workers.i'en the absence of spillo'ers, the ma;imi!ing beha'ior of agents in the decentrali!ed euilibrium described abo'e ma;i-

mi!es growth, and guarantees that each country con-'erges to the frontier of technology o'er time. $

9llowing for the possibility of migration changes the results of the model by distorting the workers> skills accumulationdecisions. Since this paper focuses on skilled migration, we

*&9:illed wor:er9 Silled wor:er9

L G T

igure 2. he skill composition of the workforce.

simplify the model by assuming that only skilled workers mi-grate, while lower-skilled workers are internationally immo-bile.3hen skilled workers are able to emigrate with some posi-ti'e (e;ogenous& probability, their e;pected wages change.:etting $ O,t indicate the e;pected wage for a worker of type attime t, we ha'eE

$ j)t ¼ " r j #$4

( r j $;

)

A# j)t j)t

where the H and superscripts identify domestic and foreignwages, respecti'ely, while r is the probability with which aworker of type e;pects to secure a ob abroad. % 5learly, emi-gration is only from less to more de'eloped countries, since it isdri'en by producti'ity di erences.ff hus, B. (A& implies that thee;pected returns to skills increase with the probability of migration, which means that also the share of skilled workersincreases with it. his is what we call the le'el e ectff of migra-

tion. +nterms

ofigure 2,

: shrinks as Q increases.

ur-thermore, the wage gap shrinks with a source country>sapproach to the technological frontier, thus, the le'el e ectff diminishes with technological de'elopment.+n addition to this con'entional e ect,ff there is a more subtlemechanism at work in this framework. 9s migration is partic-ularly appealing)and empirically most rele'ant #)from lessde'eloped countries to destination ones at (or close to thetechnological frontier, migrants mo'e from countries whereimitation acti'ities are still commonplace to countries whereproducti'ity can be impro'ed only 'ia inno'ation. his sug-gests that the relati'e producti'ity, and hence the relati'ewage, of -skilled workers is higher in destination countriesthan in source ones. hus -skills bene tfi from a larger“migration premium” relati'e to -skills. 3hen migration ispossible and workers consider foreign obs when deciding onskills accumulation, -skills become relati'ely more attracti'eand more workers elect to acuire such skills. 3e refer to this asthe relati'e producti'ity e ectff .Bn. (A&, howe'er, clari esfi that the relati'e attracti'eness of di erentff skill types does not only depend on labor remunera-tion in the destination country, but also on the di erentff migra-tion probabilities of workers of di erentff type. +ndeed, one of thestyli!ed facts on *rain drain is that technically skilled


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workers tend to be more successful in emigrating. hus, if r issu cientlyffi larger than r-, the possibility of migration mightha'e the opposite e ectff from the one pre'iously discussed,and lead to an increase in the share of technically skilled work-ers. 3e call this e ectff the relati'e probability e ectff .Since both the relati'e producti'ity and relati'e probabilitye ectsff ha'e implications for the share of technically skilledworkers, we refer to them collecti'ely as the composition e ectff .i'en that the two e ectsff operate in opposite directions, thesign of the composition e ectff is ambiguous. 9ll else eual,

the relati'e migration premium of a -skilled worker is higherthe further from the frontier is hisFher country of origin. hus,one would conclude that based on the theoretical model dis-cussed abo'e, the relati'e producti'ity e ectff dominates furtheraway from the frontier (and the o'erall composition e ectff isnegati'e&, while the relati'e probability e ectff dominates closerto the frontier (implying a positi'e composition e ect&.ff "0inally, it is clear that migration-induced changes in the le-'el and composition of the workforce induced by the probabil-ity of migration also a ectff the growth path of sourcecountries. Since we ha'e argued abo'e the growth rate is ma;-imi!ed in the market euilibrium without migration, we mustconclude that the probability of migration may reduce the rateof growth in sending countries.

rom this brief discussion we can take away the followingtestable implicationsE (i& the probability of migration a ectsff positi'ely the proportion of skilled workers in the labor force(le'el e ect&,ff (ii& the le'el e ectff is smaller the closer the countryis to the technological frontier, (iii& the probability of migra-tion a ectsff the proportion of technically skilled indi'idualsin the labor force (composition e ect&,ff (i'& the sign of the com-position e ectff is e;pected to be negati'e for countries laggingfar from the frontier, and positi'e as the frontier is ap-

proached. inally, our theoretical discussion points to the factthat the growth rate of a country should depend on both theshare of skilled workers (le'el of human capital& and the distri-bution of skill types (composition of human capital&. hus, togauge whether the distortionary e ectsff of migration enhanceor hamper technological de'elopment (and growth&, oneshould in'estigate the impact of both the le'el and the compo-sition of human capital on growth. +n the rest of the paper, wepro'ide empirical tests of the abo'e hypotheses.

A. 99 BS5/+(+O@

he data set needed to pursue our empirical strategy is con-structed from 'efi di erentff sources. ocuier and 4arfouk>s(200M& data set pro'ides data on the le'el of human capital byeducational attainment for "#G countries in "##0 and2000. "" i'en that skilled migration introduces a wedge be-tween the educational attainment of nati'es and the amount of skilled workers a'ailable on the domestic labor market, weneed to de nefi the le'el of human capital before and aftermigration. he e; ante measure, Ha, is de nedfi as the ratioof working-age nationals with tertiary education (i.e., working-age residents with tertiary education plus the working- agestock of emigrants with tertiary education& to total working-age nationals (that is the sum of working-age residents andworking-age emigrants&. he corresponding e;-post 'ariable, Hp,is instead de nedfi as the proportion of working-age resi- dentswith tertiary education di'ided by the total number of working-age residents. or our estimations we also use another of the series pro'ided by ocuier and 4arfouk (200M&,namely the stock of working-age emigrants from a gi- 'en sourcecountry i to OB5 countries with secondary edu-

cation (4Ssec,i& to instrument for the rate of skilled migration of that country. he ocuier and 4arfouk>s (200M& data set also containsemigration rates to OB5 countries by educational le'el for

"#G source countries. hese series, howe'er, are constructedbased on census data from destination countries and theymay o'erestimate the e;tent of skilled migration, as they fail tocontrol for where the skills ha'e been acuired. ortunately analternati'e data source is pro'ided by *eine, ocuier, and/apoport (200$& who use additional sur'ey data from a sub- setof OB5 countries on the age of entry, in order to control forwhere tertiary education was acuired. 9ccording to theauthors, the data on age of entry is a'ailable for $$L of skilledimmigrants to the OB5. herefore, the data pro'ide a reli-able indicator of the age-of-entry structure of immigration tothe OB5. "2 ocussing attention only on OB5 countries isclearly a limitation of these data, howe'er, since about #0L of allhigh-skilled emigration is toward the OB5, the emigrationrates pro'ided by *eine et al. (200$& are a good pro;y for theo'erall high-skilled emigration rate. "A +n what follows, rh is theemigration rate of working-age nati'es with tertiary education toOB5 countries, for indi'iduals who rst

fi reached theirdestination after the age of 22. he data set containsinformation for "M% source countries in "##0, and "#2 in 2000.3e e;clude from our analysis 2# countries which are considered“immigration-recei'ing”. "G 3e further remo'e from the sample2M former socialist countries, since human capital formation inthese countries in the early "##0>s was se'erely af- fected bythe transition from a centrally-planned to a market-dri'eneconomy. "D O'erall, our sample comprises"A0 countries the maority of which are either low (AG& or low- ermiddle income (G$& countries. AG countries in our sample areupper middle income countries, while the remaining "G arehigh income nonOB5 countries. "M he descripti'e statistics of the main 'ariables, Ha, Hp, and rhare presented in able ". here is a su cientffi 'ariation in all of the 'ariables, in particular the data set presents a wide rangeof migration rates. 9t one end of the range, the country with thelowest emigration rate of tertiary educated workers in the sampleis Swa!iland. Other countries with emigration rates below 0.00Dare the ?nited 9rab Bmirates, Oman, and 4on- golia. 9t theother e;treme, the countries with the largest skilled emigrationrates are Samoa (#AL in "##0&, uyana, a- lau, and onga, allabo'e %0L. 9mong these, all but uyana are island states andthey all ha'e relati'ely small populations. he case of uyana isparticularly striking since it is one of the

able ". escripti'e statistics

6ariable Obs. 4ean Std. e'. 4in. 4a;.

rh 2M2 0."# 0.22 0.00 0.#G

Ha 2M2 0.0% 0.0M 0.00 0.2%Hp 2M2 0.0M 0.0D 0.00 0.22S& "2# 0.A$ 0."M 0.00 0.$M/OI+4 "MG 0.M0 0."" 0.A$ 0.%G

g (2,Dyr "DG 0.00GG 0.020# 0.0$$A 0.0G$%B@S A%$ 2A".02 "AG2.D% ".AG "M$$M.2GB? 2DM G.AM 2.A$ 0.G2 "D.AD/B4+ A02 G.0G $."$ 0.00 MG.%$

HO@B 2## "#$."% 2$2.M0 ".2$ "%G#.DM/O9 2"M AM.A" 2%.02 0.%0 "00.00

pc 2"M 2DAG.AG" G#M#."D# "2#.%22G GMM0D.MM4Ssec 2M2 G%#"D.#2 "M$D#0.A0 "G.00 2G0%2D0.00

O A%$ A.D"BQ0$ ".G0BQ0% "D"22 ".A0BQ0#


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<

countries with the highest e;-ante rate of human capital accu-mulation (0.2" in 2000&, but the post emigration share of skilled workers in the total is ust GL in 2000. 4ongolia, on theother hand, thanks to the low migration rate emerges as one of the countries with higher e;-post proportion of tertiary educatedlabor force. Se'eral 9frican states like 4alawi,4o!ambiue, @iger, /wanda, and ?ganda are among thecountries with the lowest le'el of human capital.ata on the composition of human capital are taken fromthe ?@BS5O Bducation Statistics. o pro;y for the share

of 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& 'ariable. i'en therestructuring of the +nternational Standard 5lassi cationfi of Bducation (+S5B after "##$, data are only a'ailable for"#$0, "#%0, "#%D, "##0, "##D, "##M, and "##$. ue to the greatnumber of missing 'alues in the series, howe'er, we use 'alues in"#%D and "#%0 to represent the 'alues in "##0 when theseare missing. o match the composition of human capital datawith the series on the le'el of human capital and emigrationrates, we take the most recent 'alues ("##$& to represent S&in 2000. 3here the "##$ data are missing, we use the"##M or "##D 'alues as a'ailable. +n this way we are able toconstruct data on S& for %A countries in "##0, and GM coun-tries in 2000. he a'erage proportion of tertiary students en-rolled in science and technology specialities is A$L in oursample and there is a su cientffi 'ariation among countries, asshown in able ". 5ountries with negligible shares of studentsenrolled in any science and technology specialities in highereducation are *runei, ibuti, :ao, and Seychelles. 9t theother end of the spectrum are those countries for whichS& is abo'e $0L such as 9lgeria, 9ngola, ominica (attains thema;imum in the sample in "##0&, Bl Sal'ador, Camaica, and rinidad and obago.

o control for the degree of technological sophistication insending countries, we construct an indicator of pro;imity tothe world technological frontier, /OI+4, as the ratio of the total factor producti'ity ( (2& of country i to that of the?S. hus, a pro;imity inde; close to " indicates that a countryis close to the technological frontier, whereas techno- logicallaggards are characteri!ed by an inde; close to 0. 9s standardin the literature, we calculate as the log of output perworker, minus the log of capital per worker times the capital>sshare. inding accurate estimates of the capital share forde'eloping countries is not easy (ollin, 200"&. ollowing therecent practice in the literature (e.g., 5aselli, 200D1 5aselli& 5oleman, 200M& we take the labor shares estimates pro'idedby *ernanke and uR rkaynak (2002& in able "0, page G2. "$

i'en the limited co'erage of de'eloping countries, we canonly collect labor share data for 2# countries in our sample.or the countries for which data is not a'ailable we proceedas *ernanke and uR rkaynak (2002& and take the labor shareto be 0.MD (i.e., the capital share is 0.AD&. "% o construct the

capital stock series we follow 6andenbussche et al. (200M&,and use a perpetual in'entory method with a ML depreciationrate. 9s capital in'estment, we take gross capital formation inconstant 2000 ?S dollar, +i,t, from the 3orld e'elopment+ndicators (3orld *ank, 200#&. hus, the initial le'el of capi-tal for country i, 7i,0 is gi'en byE

- i)"

9s shown in able ", the a'erage pro;imity inde; in thesample is appro;imately 0.M which we would e;pect as we ha'e asample of de'eloping countries. he 'ariation in the sample,howe'er, is not 'ery large. 9s a conseuence, in some of ourestimations, we encounter issues of imperfect multicollinearity. wo maor issues that may arise in relation to imperfect mutl-icollinearity are (a& the parameter estimates may be sensiti'e tochanges in sample composition and (b& the standard errors onthe indi'idual coe cientsffi may be 'ery high. hus, we report theresults of robustness checks with respect to the number of obser'ations and we pro'ide the results from oint hypoth- esistests throughout the empirical in'estigation. he countries in thesample closest to the technological frontier with an inde;abo'e 0.% are the *ahamas, *runei (it attains the highest 'alue inthe sample in "##0&, 5osta /ica, 7uwait, Saudi 9ra- bia, ?nited9rab Bmirates. @ot surprisingly these are all high incomecountries, with the e;ception of 5osta /ica (upper middleincome&. 9mong the countries lagging furthest from the frontier,we ha'e Bcuador (the lowest 'alue in the sample in 2000& and4alawi both of which ha'e a pro;imity inde; below 0.G.*ased on the series, we also construct the dependent'ariable used in the growth analysis. or each country, we

compute D-year a'erages of growth to smooth out anycyclical mo'ement in the data. he summary statistics for this'ariable can also be found in able ". he country with thelowest a'erage growth in the sample is Cordan and thecountry with the highest a'erage growth is abon. he remaining control 'ariables in the regression analysiscome from the 3orld e'elopment +ndicators (3orld *ank,

200#&. 3e use population density (people per suared kilome-ter&, B@S, total public spending on education as a percent-age of , B?, mobile and ;ed-linefi telephonesubscribers per employee, HO@B, percentage of pa'ed roadsin the country /O9, remittances as a percentage of ,/B4+( , and initial 'alue for per capita pc. +n addi-tion, as an instrument for the skilled migration rate, we use to- talpopulation, O. he descripti'e statistics of these 'ariablesare also shown in able ". he mean 'alues and stan- dardde'iations testify once more of the di'ersity of countries presentin the sample.

G. B4+/+59: /BS?:(S

+n this section we put the theoretical implications described inSection 2 to the test. ue to data a'ailability, most of theanalysis will be performed using cross-sectional samples with arelati'ely small number of obser'ations. 9s the data pro'ide uswith only a snapshot of e'ents, we cannot make conclusi'einferences regarding causality. he small sample si!es, further-more, may imply that some of our results are sensiti'e to sam- plecomposition. he analysis in'ol'es estimating threeeuations. he rstfi two refer to the impact of migration on theformation of human capital, and account for both its le'el andcomposition. he third euation links human capital to thegrowth performance of de'eloping countries, speci callyfiallowing for the role played by each country>s le'el of techno-logical sophistication. 9s these e ectsff are contemporaneous,one could argue that there are common shocks a ectingff all

, i)0 ¼i (

)0:0M three regressions, calling for a system estimation approach. hedi cultyffi of conducting our analysis within a system esti-

where +i," is the earliest a'ailable data on gross capital forma-tion for country i1 and gi is the growth rate of of countryi in the period from the earliest till the latest date of a'ailabledata on gross capital formation. "#

mation approach arises from data a'ailability. +n fact, there areonly "M countries for which our three general euations can beestimated simultaneously. 20 +nstead, we choose to focus oneach component separately and in'estigate possible


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endogeneity issues in depth. 3e start off with an in'estigation of the le'el e ect.ff (a& 4igration and the le'el of human capital

+n studying the incenti'e e ectsff of migration on human cap-ital accumulation, the rele'ant de nitionfi of human capital in-cludes not only the residents in the sending country, but alsoskilled nati'es working abroad. or this reason, we use the e;-ante, measure of human capital, Ha, de nedfi in Section A. Ourempirical speci cationfi e;tends that of *eine et al. (200%&,to control for the le'el of technological de'elopment of thesource countryE/ log4 a)00 #0# ¼ a0 ( a" logr =)#0# ( a2 log821>-M #0

#

( aA log821>-M #0# logr =)#0 # ( aG

log4 a)#0# ( aD/.+S#0 ( aM

log./* #0# ( a$2.M-% #0 ( a%SSA

( a#!A% ( e: G#

he dependent 'ariable, /log(Ha,00#0& log(Ha,00&log(Ha,#0, is the growth rate of the e;-ante stock of humancapital during "##02000. 3e use the stock of human capital atthe beginning of the period, Ha,#0, to control for the possibility of con'ergence across countries in the proportion of tertiaryeducated workers. he migration rate of tertiary educated indi-'iduals in "##0, rh,#0, captures the incenti'e e ectsff discussed inSection 2. o control for potential nonlinear impacts of migra-tion at di erentff le'els of economic de'elopment of the sourcecountry, we include log(/OI+4#0& and the interaction termlog(/OI+4#0& log(/h,#0 in some of our regressions, where/OI+4#0 is the pro;imity of the country to the world tech-nological frontier. opulation density in "##0, B@S#0, is used asa pro;y for the cost of acuiring education, since one woulde;pect that the higher the population density, the smaller thea'erage distance from schools, the lower the cost of education.9dditionally, we introduce public spending on education in

"##0, B?#0, to better pro;y for the supply (both in terms of uantity and uality of education. 2"

/B4+ is workers>remittances in "##0, a control for return migration, and alle'i-ated credit constraints on human capital in'estment. inally,SS9 and :9 are regional dummies identifying Sub-Saharan9frican and :atin 9merican countries, respecti'ely, as de nedfi bythe 3orld *ank. @ote that due to the lag structure of thespeci cation,fi and data a'ailability, (G& speci esfi a cross-sec-tional regression.Since the accumulation of human capital and the migrationrate may be simultaneously determined)a higher le'el 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 e;ample)one possible source of concernwhen estimating (G& is the possible endogeneity of the migra-tion rate. @otice, howe'er, that we calculate both the migra-tion rate and the change in the skilled labor force usingimmigrants stock, rather than ows,fl and that the rstfi di er-ff encein skilled labor stock is calculated o'er a period of "0 years. 9s a conseuence, endogeneity might not be such arele'ant issue in this model. @e'ertheless, we belie'e it isimportant to address potential endogeneity issues using aninstrumental 'ariable approach. Of the 'arious instrumentssuggested in the literature (e.g., urlauf, Cohnson, & emple,200D& such as the country>s population si!e, the initial stock of immigrants, life e;pectancy at birth, 'arious indices of socialunrest and racial tensions, and the per capita , *eineet al. (200%& ad'ocate the use of only the rstfi two, either be-

cause the others are 'ery highly correlated with the initial le'el of human capital or because of the insu cient numberffi of obser'ations. +n our search for 'alid instruments, we also used the5B++ 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 si;maor destination countriesE 9ustralia, 5anada, rance, er-many, ?nited 7ingdom, ?nited States1 information on thecolonial history of a source country1 and whether a sourcecountry has Bnglish, rench, or erman as one of its o cialffilanguages. 9rguably, all of these 'ariables could be considered as'alid instrumentsE they are rele'ant as they a ectff the cost of migration like tra'el costs, cultural pro;imity, and languagebarriers1 and they should be e;ogenous as they should not af-fect the indi'idual decision to acuire tertiary education. Ouranalysis, howe'er, shows that none of these 'ariables passes the(Stock & Kogo, 200D& test for weak instruments. 4ore- o'er,when they are included together with our instruments, theyweaken the signi cancefi of the test statistics. his is why we relyas instruments for the migration rate of tertiary edu- catedindi'iduals on the stock of immigrants of the samenationality with secondary education (4Ssec& and populationsi!e of the source country (O&. 3e belie'e the stock of immigrants to be a 'alid 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 indi'idual>s decision to acuire tertiary education. ur-thermore, we use the stock of immigrants with secondaryeducation without correction for age of entry. hese indi'idu-als ha'e not acuired tertiary education in the destinationcountry post-migration, and thus arguably ha'e e'en less re-lated to the choice to acuire tertiary education at home. op-ulation si!e should not be per se a factor in the human capitaleuation either, if one accepts that the absence of scale e ectsff isa 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 immigration bydestination countries do not fully re ectfl the si!e of the pool of would-be migrants. +n what follows, to test whether theinstruments are rele'ant, we use the critical 'alues tabulated byStock and Kogo (200D&, while instrument e;ogeneity is testedusing a C-test. inally, we test whether the migration rate is infact an e;ogenous regressor in the model employing3ooldridge>s ("##D& robust score test.

3e use as a benchmark the model studied by *eine et al.(200%&, 4odel + in able 2. he rstfi two columns presentthe results from the O:S and +6 estimations, respecti'ely. 2A he estimate of the e ectff of the migration rate on skill forma-tion that we obtain are comparable in magnitude to the resultsin *eine et al. (200%&E a "L increase in the migration rate of high-skilled workers increases the growth rate of the share of high-skilled workers by about 0.0DL points in both speci ca-fitions. 3e also ndfi e'idence of con'ergence in human capitalle'els among countries in the sample, gi'en that the coe cientffi forthe initial le'el of human capital has a statistically signi -fi cantnegati'e 'alue. 9ccording to our estimates, neither publicspending on education (B?&, nor population density(B@S&, nor workers> remittances (/B4+( & ha'e a statisti-cally signi cantfi e ectff in either regression. 2G he same holds for the :atin 9merica dummy (:9( &. +n fact,a test on the oint signi cancefi of these 'ariables indicates thatthey can be e;cluded from the model as a group (p-'alue of 0.M"M$ in the O:S, and 0.DM%$ in the +6 regression&.

9 comparison of the O:S and +6 results re'eals that the twoestimates are uite similarE all regressors ha'e comparable si!e


7/18

able 2. :e'el of human capital)de p endent 'ariable / log H a,00 #0

4odel + 4odel ++ 4odel +++

O:S +6 O:S +6 O:S

log(rh,#0& 0.0G%2 0.0D00 0.0G%" 0.0%02 0.02GM (0.0"%$&


8/18

to the technological frontier, or their interaction)is shown toha'e a statistically signi cantfi e ect.ff his may be e;plained bythe high correlation between the interaction term and the log of migration rate, 0.$GMA, and that between the interaction termand the log of pro;imity to the technological frontier, 0.M00G. 2M +ndeed, a test for the oint signi cancefi of all threecoe cients,ffi indicates that they are signi cantfi at "L (p-'alue of 0.00D#&. 9 oint hypothesis test on the signi cancefi of the (non-linear& e ectff of migration (i.e., H0E a" aA 0& indicates that thise ectff is signi cantfi at DL.

*ased on the signs of the estimated coe cientsffi for themigration rate and the interaction term, we ndfi some support forthe theory described in Section 2E the positi'e e ectff of migration on human capital accumulation is stronger the fur-ther the countries are from the technological frontier. 2$ hise ectff fades as the frontier is approached, and e'entually 'an-ishes once the frontier is reached. 2%+t is worth noting that, compared to the O:S estimation of

4odel ++, the coe cientffi estimates for the other 'ariables arerobust to the inclusion of the interaction term.

(b& 4igration and the composition of human capital

he possibility that certain skills may be more demanded)and more rewarded)in destination countries relati'ely to thehome market, adds another layer of comple;ity to the analysis ofhuman 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 e ectff depends on its distance from the technological frontier. +deally,we would like to use data on migration rates of tertiaryeducated nati'es by eldfi of study. Such data are unfor- tunatelyuna'ailable, and we can only use migration rates by educationalle'el. 3e thus ha'e to assume that all tertiary educated workersface the same migration rate. 2# his implies that the e;-anteand e;-post skill composition of human capital is the same,and simpli esfi our notation as we do not need to introducesubscripts to distinguish between gross and net 'ariables.urthermore, due to lack of data we use the proportion ofenrollment in tertiary education with scienti cfi and tech- nicalmaor, S&( , as a pro;y for the proportion of science andtechnology graduates in the stock of skilled workers. 3hilethis can be seen as a limitation for our growth regressions be-low, enrollment is indeed the best point of analysis to identifythe incenti'e e ectsff of migration on the composition of humancapital, since enrollment reacts to work prospects much fasterthan the stock of uni'ersity graduates.3e model the empirical relation linking the composition of human capital (before migration& to the migration rate of skilled workers as

S?% t ¼ @0 ( @"r =)t ( @2821>-M t ( @A821>-M tr =)t

( @Gt ( @D./* t ( @M/2000 ( @$SSA (

@%!A%

our resultsE phones per employee and the percentage of pa'edroads. A0 he additional 'ariable 2000 is a dummy 'ariablefor year 2000.3e start off by estimating a baseline speci cationfi in whichwe abstract from possible nonlinear e ectsff of migration, that is,we assume @A 0 in both 4odels + and ++ of able A. 4od- els +and ++ di erff for the pro;y used to control for domestic demandfor technical skills. 9s in Section G(a&, we present both O:S and+6 results for each speci cation.fi +n the +6 estimations wecorrect for the possible endogeneity of the migration

rate and the pro;imity to the technological frontier for reasonssimilar to the one discussed in the conte;t of human capitalaccumulation abo'e. 3e use the same set of instruments as inthe +6 estimates in Section G(a&E the stock of immigrants withsecondary education, the population si!e, and the "0-year lag of the pro;imity to the technological frontier. 3hen we use phonesper employee as our pro;y, the performance of the instrumentsis the most satisfactory, both in terms of their rel- e'ance ande;ogeneity. 3hen the percentage of pa'ed roads is used as apro;y, the instruments pass the test for rele'ance at"DL 4a;imal +6 Si!e A" and pass the test for e;ogeneity at the"0L le'el but not at the DL. A2 urther in'estigation of theendogeneity of migration rate and pro;imity to the frontierindicates that they can be treated as e;ogenous. he e ectff of skilled migration on the proportion of students enrolled in sci-ence and technology specialties appears robust in the two spec-i cationsEfi 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."#L points for all speci cationsfi in the rstfi four columnsof able A. 9ll other coe cients,ffi e;cept for the one of pro;im-ity, are rather stable across O:S and +6 estimates. or thecoe cientffi on pro;imity, the O:S and +6 regressions producedi erentff signs when phones per employee is used to pro;y forthe demand of technicians. AA oing back to our theory, one

would e;pect to see a negati'e sign on this 'ariable as gener-alists become relati'ely more producti'e, the higher the degreeof technological sophistication. he demand pro;ies ha'e thetheoretically predicted positi'e sign, while the negati'e sign of the coe cientffi for public e;penditure in education is ratherpu!!ling. his may indicate that a tertiary education sectorcharacteri!ed by a high in'ol'ement of the go'ernment facesa cap on the enrollment in technical degrees.

@e;t, we in'estigate the e'idence on relati'e probability andrelati'e producti'ity e ectsff identi edfi in Section 2. +n the pres-ence of such e ects,ff we would e;pect to ndfi a statistically sig-ni cantfi estimate for @A. +ndeed, we ndfi e'idence that theinteraction term is statistically di erentff from !ero in both4odels +++ and +6 in able A. urthermore, the results indi-cate that for countries further away from the technologicalfrontier the e ectff of skilled migration on the proportion of stu-dents enrolled in technical degrees is negati'e, while for thosecloser to the frontier the e ectff is positi'e. he estimatedthreshold 'alues for the re'ersal in sign are 0.D022 and

AG

( tt ) D#0.DAAM in 4odels +++ and +6, respecti'ely. +n both cases

where the 'ariables S& ( t, rt, /OI+4t, B?t, SS9 , and :9ha'e all been de nedfi abo'e and are measured at time t 2T"##0, 2000U. he public e;penditure on education is in-cluded in this model to control for potential distortionary ef-fect of go'ernment in'ol'ement in education, thepresumption being that higher public e;penditure in education isassociated with more centrally planned education sectors, andhigher distortions. =t here indicates one of di erentff pro;- ies forthe domestic demand for technically skilled indi'iduals. 3e usetwo alternati'e 'ariables to check the robustness of

the threshold is statistically smaller than " (p-'alues for H0E @" @A eual 0.0000 and 0.00"2 in 4odels +++ and +6,respecti'ely&. AD hus, in countries with relati'ely low le'elsof 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.

he opposite occurs in relati'ely more de'eloped countries. he result complies with our intuitionE the relati'e producti'-ity e ect,ff which predicts a negati'e e ectff of migration on the

proportion of science in technologies, dominates the relati'eprobability e ect,ff which predicts a positi'e e ectff of migration


9/18

able A. 5omposition of human capital)dependent 'ariableE S& t

4odel + 4odel ++ 4odel +++ 4odel +6

O:S +6 O:S +6 O:S O:S

rh,t 0.20M0 0."#2G 0."%$G 0.20"M 0.%2M2 "."G02 (0.0#AA& incenti'es to acuire technical skills maydecrease if complementarities across skills become morepronounced, and a broader 'ariety of skills are demanded. +thas been suggested that this is indeed what happens whencountries complete the transition from being mere imitators of foreign technologies, a phase in which a high share of tech-nically inclined graduates is preferable, to being properly inno-'ati'e, when a wider range of skills become necessary(einstein, 200M1 Kusuf, 200$&. his 'iew implies that the share of technically skilled labor force should decline as pro;imityincreases. Second, as migration becomes easier, a larger pro-portion of workers acti'ely seek obs abroad. Since most des-tination countries fa'or technically skilled immigrants,in'esting in S& skills euips potential migrants with compe-tencies that are in high demand o'erseas (pro'ided that thecountry is ad'anced enough&. his latter mechanism is moreprominent in high emigration countries. he interplay betweenthese two mechanisms e;plains our results abo'eE at low le'els of migration the former e ectff dominates, while for higher le'- els of migration it is the latter e ectff that has the stronger impact.(c& Human capital and growth

he last step in our empirical endea'or is to gauge the sig-ni cancefi that changes in human capital formation due tomigration may ha'e on economic growth. o this end, westudy the e ectff of both the le'el and composition of human


10/18

capital on the growth rate of the country>s (2. Here we buildupon the empirical growth model of 6andenbussche et al.(200M& who study the e ectff of human capital on growth as afunction of the country>s technological de'elopment. 3hilethe aim of 6andenbussche et al. (200M& is rather di erentff fromours, their model is well suited to study the hypothesis that dif-ferent skill compositions of human capital are better forgrowth at di erentff stages of de'elopment. he empirical relation between the le'el and composition ofhuman capital and growth is gi'en by the following eua-

tionE

-M t logS?% t "0#

( BD821>-M t ( BM'/8 CB)t "0 ( B$SSA ( B%!A%

( mt : M#

he dependent 'ariable, g (2,Dyr,t is the a'erage annual growthrate of o'er D years. A% Hp,t is the le'el of human capitalafter migration. S& ( , the proportion of enrollment in tertiaryeducation with technical and science specialty, is used here topro;y for the proportion of the stock of workers in the labormarket with the same characteristics. 5hanges in enrollmentonly gradually manifest themsel'es as corresponding changes inthe stock of human capital, for this reason in this euation weuse a "0-year-lag for this 'ariable. o capture possible non- lineare ectsff of the le'el of human capital and its skill composition,we include interaction terms of both 'ariables with the inde; of technological de'elopment, /OI+4. he initial 'a- lue of per capita, pc,t "0, is included to gauge the e;istenceof catching-up e ects,ff and therefore we e;pect it to ha'e anegati'e coe cient.ffi inally, SS9 and :9 are the countrygroup dummies described abo'e. i'en the lag structure of the model, almost all speci cationsfi are based on cross- sectionaldata. he only e;ception is the speci cationfi in which the e ectff of S& is assumed to be !ero.

is decreasing with the pro;imity to the technological frontier. hese results con rmfi our theoretical priors deri'ed by the workof i 4aria and Strys!owski (200#&.

3hile we ndfi statistically signi cantfi e ectff of the type of composition of the labor force on growth, we fail to iden-tify any statistically signi cantfi e ectff of the proportion of ter-tiary educated workers in any of the regression speci cations.fi3hen we include an interaction term between the le'el of hu-man capital and the pro;imity to the frontier)in 4odels ++and +++)our estimates show that the e ectff of the proportionof tertiary educated people on growth is weaker, thehigher the degree of technological sophistication. his e ectff goes counter the e'idence pro'ided by 6andenbussche et al.(200M& for OB5 countries.

or completeness, the second column of 4odel + reports theresults of the +6 estimation of the basic regression model with-out nonlinear e ectsff in which we control for the endogeneityof the pro;imity to the technological frontier. 3e follow theestimation strategy of 6andenbussche et al. (200M& and weuse a lagged 'alue of pro;imity to instrument for /OI+400.+n particular, we use /OI+4%0. 3e cannot test for the e;o-geneity of this instrument as the system is e;actly identi ed.fi3e argue, howe'er, that this is an acceptable assumption, gi-'en the length of the lag. he instrument is highly rele'ant, the

able G. otal factor producti'ity growth)dependent 'ariableE g (2,Dyr,t

4odel + 4odel ++ 4odel +++ 4odel +6 4odel 6

O:S +6 O:S O:S O:S O:Slog(Hp,00& 0.002$ 0.00"D 0.0#"M 0.0DM0 0.00D0 (0.0D"M&(0.2G#& (0."MD0& (0."%"G& (0.0DA%&log(Hp,00& /OI+400 0."%2A (0.2MDG& 0."002 (0.A0G$&log(S& ( #0& 0.0"A% 0.0"M" 0.0AA# 0.0AG$ 0.0AGA

(0.0D%&


11/18

-statistic is 2"M.#", which is much higher than "0, the critical'alue for the Stock and Kogo>s (200D& weak instrument test. he robust 3ald test for the endogeneity of pro;imity indi-cates that we can reect the null hypothesis that /OI+400 ise;ogenous in this model at DL but we cannot reect it at"0L (p-'alue 0.0MAG&. i'en the remarkable stability of thecoe cientsffi on the le'el and composition of human capital,and the indicator of pro;imity to the technological frontier be-tween the O:S and +6 estimates, howe'er, we belie'e that the O:Sestimates are su cientlyffi reliable.

inally, we want to remark that the coe cientffi of the initial'alue of per capita has the statistically signi cantfi nega-ti'e sign, as e;pected. he regional dummy 'ariables, on theother hand, are not signi cantfi in any of the speci cations.fi

D. 4+/9+O@, H?49@ 59+9: 9@ /O3(HE 9+S5?SS+O@So far we ha'e pro'ided empirical e'idence that the possibil- ityto emigrate a ectsff the process of human capital formation bychanging the incenti'es to accumulate human capital and toacuire certain types of skills. 3e ha'e found that both of these distortionary e ects areff statistically signi cantfi andcomply with the underlying theoretical predictions. o gauge theeconomic signi cancefi of these e ects,ff we ha'e also in'es- tigatedthe role of human capital in the producti'ity growth of acountry. +n this sample of de'eloping countries, we ndfi robustand statistically signi cantfi e ectsff of the composition of tertiaryeducation labor force on growth, but no such e ectff for theproportion of tertiary educated labor force. his is why

in our subseuent discussion we focus solely on the composi-tion of human capital. Our aim is to deri'e a better under-standing of the substanti'e e ectsff of the possibility of emigration on growth operating through this channel. o thisend, we adopt a simulation-based approach, as discussed by7ing, om!, and 3ittenberg (2000& and employ the statisticalsoftware 5:9/+2K de'eloped by these authors.Our rstfi step is to study the e ectff of the migration rate onthe proportion of indi'iduals enrolling in a science and tech-nology degree for the set of %2 obser'ations for which we

could estimate 4odel +6 of able A. 3e simulate an increase inthe rate of skilled migration in each country in the sample byG0L (e.g., a country with an initial migration rate of 0."# wouldface an increase to 0."# ".G 0.2$.& while keeping the le'elof technological de'elopment, the percentage of pub- lice;penditure on education, and the number of phones peremployee at their actual le'els. G0 Our statistical e;ercise usesstochastic simulations techniues to simulate the di erenceff inthe share of workers with S& maor, by drawing "000 sets of simulated parameters for each country, from the samplingdistribution of the parameters> estimates. he con dencefi inter-'al for each obser'ation is obtained by ordering the simulated'alues and considering the Dth and #Dth percentile. he results of these simulations are presented in able D. G"5learly, an increase in the emigration rate by G0L represents asmall change in percentage point terms for countries withlow emigration rates)it is less than "L point for *ra!il, fore;ample)while for countries with high emigration rates, this isa large change in absolute terms)it is eui'alent to "#L pointsfor *arbados, for e;ample. able D illustrates that on a'erage, acountry like 9rgentina could ha'e seen its share

able D. he e ectff of an increase of the migration rate by G0L on S&

5ountry Kear /rh S& #0L conf. +nter'al

rinidad and obago 2000 0.2M## 0."GAG 0.0%2% 0.20D2 rinidad and obago "##0 0.2MGM 0."G"# 0.0%"% 0.202#*arbados 2000 0."#00 0.0%$2 0.0D0D 0."2A%Sri :anka "##0 0.0#2G 0.02#$ 0.0"DD 0.0G2$anama "##0 0.0G#% 0.02%G 0.0"MG 0.0G0$4auritius "##0 0.2"$2 0.02D" 0.00D% 0.0D$A

4auritius 2000 0."%0D 0.02GA

0.00"0 0.0D024alaysia "##0 0.0MG2 0.02AD 0.0"2# 0.0AAMomenican /epublic 2000 0.0D"2 0.0"DG 0.00$$ 0.022D+ran "##0 0.0$MA 0.0"G% 0.00GM 0.02DDHonduras "##0 0.0DM0 0.0""D 0.00G0 0.0"#25osta /ica "##0 0.0"%# 0.0""2 0.00MG 0.0"M0 urkey "##0 0.02$% 0.0""" 0.00MA 0.0"D#Bgypt "##0 0.0"%2 0.00#M 0.00DD 0.0"A$Bl Sal'ador 2000 0.0$A0 0.00%# 0.00"D 0.0"#D unisia "##0 0.0D"% 0.00%$ 0.00"M 0.0"M"Bl Sal'ador "##0 0.0%AM 0.00%$ 0.00AA 0.02"D+ran 2000 0.0G"M 0.00%" 0.002D 0.0"A#5olombia "##0 0.02DA 0.00$M 0.00A% 0.0""05olombia 2000 0.02#M 0.00$2 0.00A" 0.0""A unisia 2000 0.0AA# 0.00MD 0.00"# 0.0""2

Cordan "##0 0.02"0 0.00D$ 0.002$ 0.00%M@icaragua 2000 0.0$$M 0.00D$

0.00D# 0.0"%"

?ruguay 2000 0.02"G 0.00DG 0.002G 0.00%G@icaragua "##0 0.0%00 0.00D2 0.00$0 0.0"%"9rgentina "##0 0.0"0% 0.00G# 0.002% 0.00M#?ruguay "##0 0.0"%G 0.00G$ 0.002" 0.00$A5hile "##0 0.0"#" 0.00G$ 0.0020 0.00$A5hile 2000 0.0"$0 0.00GM 0.002" 0.00M#7enya "##0 0."D20 0.00G" 0.020$ 0.0A0%


12/18

5ountry Kear /rh S& #0L conf. +nter'al

4e;ico "##0 0.02"$ 0.00A$ 0.000$ 0.00M%5ameroon "##0 0.0AD$ 0.00A2 0.0020 0.00%%@amibia 2000 0.0"0# 0.00A0 0.00"D 0.00GD9lgeria "##0 0.0"%% 0.002% 0.000" 0.00DD

Kemen, /epublic 2000 0.0"#D 0.002% 0.0000 0.00DM4orocco "##0 0.0M%A 0.002D

0.00%D 0.0"GA

ibouti 2000 0.02#% 0.002D 0.00"% 0.00$A*ra!il "##0 0.00D" 0.0020 0.00"" 0.002%?nited 9rab Bmirates 2000 0.002% 0.00"$ 0.00"0 0.002D8imbabwe "##0 0.020D 0.00"D 0.00"D 0.00G%Saudi 9rabia 2000 0.002A 0.00"A 0.000% 0.00"#Sudan "##0 0.0"M% 0.00"A 0.00"" 0.00G05ote d>+'oire "##0 0.00%A 0.00"2 0.000" 0.002G6ene!uela "##0 0.0"0D 0.00"2 0.000A 0.002%8imbabwe 2000 0.0AD$ 0.00"" 0.00GM 0.00$G hailand 2000 0.00M% 0.00"" 0.000" 0.002"+ndonesia "##0 0.0""2 0.000$ 0.00"" 0.002Dhilippines "##0 0.0A%M 0.000M 0.00D% 0.00$DSenegal "##0 0.0A%0 0.000G 0.00M0 0.00$A

*enin "##0 0.0"$M 0.000A

0.002M 0.00ADSwa!iland 2000 0.00"M 0.000A 0.0000 0.000D+ndia 2000 0.0"A$ 0.000" 0.0022 0.002MSwa!iland "##0 0.000M 0.000" 0.0000 0.0002hilippines 2000 0.0G"A ".A2B 0D 0.00$" 0.00$Meru "##0 0.0"MD ".#"B 0D 0.002# 0.00A0*enin 2000 0.0A0M A.2MB 0D 0.00AA 0.00DM+ndia "##0 0.00#0 0.0002 0.00"% 0.00"D*angladesh "##0 0.00$0 0.000A 0.00"M 0.00""*urkina aso "##0 0.00A# 0.000M 0.00"M 0.000G5hina "##0 0.00## 0.000% 0.002% 0.00"G*otswana "##0 0.00MG 0.000# 0.002G 0.000$4adagascar "##0 0.0"A% 0.000# 0.00AM 0.002":esotho 2000 0.0"D% 0.00"2 0.00GG 0.00224ali "##0 0.0220 0.00"D 0.00D$ 0.00AA

araguay "##0 0.00#M

0.00"D

0.00A% 0.000#4adagascar 2000 0.0"$M

0.00"%

0.00DD 0.0022

uinea 2000 0.0A#A 0.00A" 0.0"0# 0.00DM:esotho "##0 0.0A#G 0.00A" 0.0"0# 0.00DM ogo "##0 0.0A02 0.00A2 0.00#M 0.00A%6ietnam "##0 0.0D%# 0.00AA 0.0"GD 0.00#0uinea "##0 0.0G0M 0.00A% 0.0"2" 0.00DGBcuador "##0 0.0"2% 0.00G" 0.00%2 0.0000uyanaa "##0 0."G2% 0.00GM 0.0A0$ 0.02A0Bthiopia "##0 0.02G0 0.00D" 0.0""G 0.00"D ogo 2000 0.0M0" 0.00$0 0.0200 0.00$2Bthiopia 2000 0.02#0 0.00$0 0.0"D" 0.00"2?ganda 2000 0."22% 0.0"0A 0.0AD0 0.0"$04o!ambiue "##0 0.0#$% 0.0"GD 0.0A$0 0.00##Brithrea 2000 0."""M 0.0"D$ 0.0G"0 0.0""%

4alawi "##0 0.0MGG

0.0"%%

0.0A%G 0.00"0hana "##0 0."ADG

0.0"#D

0.0D0D 0.0"G0

able D (continued&

?ganda "##0 0."D$G 0.020G 0.0DDA 0.0"$$ a he skilled migration rate used in the simulations was capped at "00L, as a G0L increase would imply a migration rate in e;cess of "00L.

of students enrolled in science and technology in "##0 increaseby 0.G#L points, had it had a skilled emigration rate of A.$%Lrather than the actual 2.$L. Out of our %2 obser'ation, 2Mindicate an a'erage decrease in S&( , howe'er, only one forone of them, that of Bcuador, this e ectff is signi cantlyfi nega-ti'e at the #DL con dencefi le'el. his is not surprising as Bcua-dor is one of the countries lagging furthest from the frontier(/OI+4 of 0.A% in "##0&, and our theory predicts that in thiscase the relati'e producti'ity e ectff should dominate. O'erall,we can identify AM obser'ations for which the impact of the

change of the emigration rate is statistically signi cantfi at the#0L con dencefi le'el. he largest impact in magnitude, morethan "GL point, is obser'ed in rinidad and obago. rinidadand obago, unlike Bcuador, is a country close to the techno-logical frontier (/OI+4 of 0.$% in both "##0 and 2000&. +nfact, there are two countries for which the e ectff of the increasein the migration rate is much higher than for the rest, rinidadand obago and *arbados. *oth of these countries ha'e highpro;imity and relati'ely high skilled migration, though theyare not the countries with this higher migration rate. uyana,


13/18

igure A. 5hanges in S& following a G0L increase in the rate of skilled migration 's. pro;imity to the frontier.

the country with the highest skilled migration rate in our sam-ple, would face on a'erage a negati'e e ectff on its S& ratio as aresult of a further increase in migrationE the reason for that is thatit has a relati'ely low degree of pro;imity (0.D2&. igure A plotsthe simulations results against each country>s inde; of pro;imity to the frontier and shows the re'ersal of the compo-sition e ectff that occurs as the relati'e probability e ectff comes todominate the relati'e producti'ity e ect.ff Ha'ing obtained central predictions for the counterfactualle'el of S& ( , we are able to take our thought e;perimentone step further and ask what would be the impact of suchmigration-induced changes on the growth for each coun-try. Our second set of simulations is based on the estimation of the parsimonious 'ersion of B. (M& presented in able G.A as4odel +++. ?sing a similar methodology as before, we simulatethe impact of changes in S& on growth for each of theDM countries for which we are able to obtain a counterfactual'alue of S& for "##0, and compute the relati'e con dencefi

inter'als. 7eeping the 'alue of all other 'ariables in thegrowth euation at their actual 'alues, we change the 'alue of ln(S&( & to ln(S& Q /S&( & where S& is the reported changein S& presented in able D. +n this way we simulate changes ingrowth rates for D0 countries, as presented in a- ble M. Cust like *eine et al. (200$&, we ndfi that sending countriesmay end up winning or losing from an increase in skilledmigration. Out of the D0 countries in able M, A2 e;perience

a positi'e growth impact, for only "2 of them, howe'er, thechange is signi cantlyfi positi'e at the #DL con dencefi le'el.4auritius, which has the highest a'erage positi'e e ectff fails thesigni cancefi test at the #0L con dencefi le'el. 9mong thecountries in our data set, 7enya seems to be the one thathas the most to gain from an increase in the skilled migrationrate. he remaining "% countries e;perience a negati'e e ect,ff with a statistically signi cantfi impact for "M of them. igure Gplots changes in growth rates against our inde; of

able M. he e ectff of an increase of the migration rate by G0L on growth

5ountry S& g rowth #0L conf. +nter'al

4auritius 0.02D"0 0.00"2" 0.000"M 0.002M"

4alaysia 0.02AD0 0.000AD 0.00""$ 0.00"$A rinidad and obago 0."G"#0 0.00AA% 0.00DGM 0.00DGGBl Sal'ador 0.00%$0 0.0002$ 0.00002 0.000DMSri :anka 0.02#$0 0.0002A 0.000%0 0.00""%Honduras 0.0""D0 0.0002A 0.0000D 0.000D27enya 0.00G"0 0.00022 0.0000D 0.000G"+ran 0.#0%$0 0.00022 0.000"" 0.000DG

unisia 0.00%$0 0.000"%

0.0000# 0.000GG5olombia 0.00$M0 0.000"$

0.000"M 0.000G%

Cordan 0.00DM$0 0.000"A 0.000"A 0.000A%@icaragua 0.00D20 0.000"A 0.0000" 0.0002M?ruguay 0.00G$0 0.000"0 0.000"0 0.0002$8imbabwe 0.00"D0 0.000"0 0.00002 0.000"$5ameroon 0.00A20 0.0000% 0.0000" 0.000"M4e;ico 0.00A$0 0.0000% 0.00002 0.000"$


14/18

4orocco 0.002D0 0.0000% 0.00002 0.000"G urkey 0.0"""0 0.0000$ 0.0002# 0.000G05hile 0.00G$0 0.0000$ 0.0000% 0.0002"Sudan 0.00"A0 0.0000$ 0.00000A 0.000"G

anama 0.02%G0 0.0000D

0.002$# 0.002D%9lgeria 0.002%0 0.0000D 2.0"B 0M 0.000"05ote d>+'oire 0.00"20 0.0000G 0.0000" 0.0000%6ene!uela 0.00"20 0.0000G 2.$"B 0M 0.0000$Bgypt 0.00#M0 0.0000A 0.00""G 0.00"0%9rgentina 0.00G#0 0.0000A 0.0002M 0.0002#*ra!il 0.00200 0.0000A 0.0000$ 0.000"Ahilippines 0.000M0 0.0000A 0.0000" 0.0000D+ndonesia 0.000$0 0.00002 A.$MB 0M 0.0000G*enin 0.000A0 0.00002 G.22B 0M 0.0000GSenegal 0.000G0 0.0000" 2."DB 0M 0.00002Swa!iland 0.000"0 2.$AB 0M $.0DB 0$ M."DB 0Meru ".02B "0 G.0$B "2 0.00000 0.000005osta /ica 0.0""20 M.0$B 0M 0.00"2" 0.00"0$*angladesh 0.000A0 %.%AB 0M 0.00002 ".%GB 0M

+ndia

0.00020

#.#$B 0M

0.00002

2.0AB 0M5hina

0.000%0

0.0000"

0.0000A

2.A"B 0M*urkina aso 0.000M0 0.00002 0.0000G G."#B 0M4adagascar 0.000#0 0.0000A 0.0000D M.2MB 0M*otswana 0.000#0 0.0000$ 0.000"2 0.0000"4ali 0.00"D0 0.000"0 0.000"% 0.00002araguay 0.00"D0 0.000"0 0.000"# 0.0000"uinea ".A022" 0.000"" 0.00020 0.00002 ogo 0.00A20 0.000"2 0.0002" 0.0000ABcuador 0.00G"0 0.00022 0.000GA A.ADB M:esotho 0.00A"0 0.00022 0.000G0 0.0000Duyana 0.00#$0 0.0002M 0.000D0 0.0000A4o!ambiue 0.0"GD0 0.000GD 0.000%0 0.0000#hana 0.0"#D0 0.000$M 0.00"AM 0.000"$

able M (continued&

5ountry S& g rowth #0L conf. +nter'al

4alawi 0.0"%%0 0.002#" 0.00DAG 0.000A2

technological sophistication (/OI+4&. he diagram illus-trates that the winners are found among the countries closer to the technological frontier, while the losers among countrieslagging further from it.

igure G. 5hanges in growth following a G0L increase in the rate of skilled migration 's. pro;imity to the frontier.


15/18

9ccording to our simulations, the losers group containsamong others 5hina, +ndia, and *angladesh. O'erall, the los- ersaccount for o'er M#L of the total population in our sample,and 2.M billion people in total (in 2000&. +n terms of welfare,our results ha'e e'en stronger implications. @ot only is almost$0L of the population a ectedff by losses, but also, gi- 'en thedecreasing marginal utility of income, losses among the poorestcountries should be weighted more than gains to morede'eloped ones.

M. 5O@5:?S+O@

*y bridging two strands of literature, that on the economicconseuences of *rain drain, and the growth one focussing onthe role of human capital formation, we pro'ide se'eral newinsights on the relation between migration, human capital for-mation and growth. irst, we ndfi support for the e;istence of anincenti'e e ectff on the le'el of (e;-ante& human capital accu-mulation. Second, we present e'idence that the possibility of migration also a ectsff the types of skills that agents choose toacuire. his underscores that the le'el e ectff e;ists along- sidea composition e ect.ff hird, in line with our theoretical priors,we show that both these e ectsff depend on the le'el of technological de'elopment of the sending country (its pro;-imity to the frontier&. i erencesff in wages and the degree of marketability of migrants> skills depend on the le'el of techno-logical de'elopment, thus the e ectff of migration needs to bediscussed taking e;plicitly into account the technological gapof each sending country. ourth, our simulations show that

more than one third of the countries in our data set,represent- ingalmost $0L of the total population, su erff as a result of anincrease in skilled migration. he losers are found among therelati'e less de'eloped countries, implying that, o'erall, thewelfare costs of skilled migration may be large.9s is the case for any empirical endea'or, our results arehighly in uencedfl by the uality of the data used. +n this re-spect, data on educational attainment by eldfi of study lea'emuch to be desired, especially when focussing on nonOB5countries as we do. he need to in'est resources in the gener-

ation of better data to help empirical research cannot be o'er-stated in this eld.fi espite this ca'eat, our analysis pro'idesclear empirical support to the claim made by de'eloping coun-tries that recent immigration policies of OB5 countries mayha'e dire conseuences for the migrants> countries of origin.3hile selecting the most talented indi'iduals from de'elopingcountries has a clear economic rationale for destination coun-tries, our work focuses on its implications for sending coun-triesE by changing both the le'el and the composition of human capital, an increase in the possibility of migration for(certain types of skilled workers reduces the growth rate of in many source countries. 3e draw two conclusions fromour work. irst, we stress the need for better uality data tosupport additional research e ortsff in this area, to test therobustness of our ndings,fi and to better inform the policy pro-cess. Second, based on our ndings,fi the need for a more con-certed approach to migration policy among de'eloped andde'eloping countries emerges 'ery starkly from our analysis.

@O(BS

". 9 recent re'iew of the debate, including a sur'ey of e;isting andproposed policies and of their conseuences, is o eredff by +:O (200M&.

2. 5ommander, 7angasniemi, and 3inters (200G& present an e;cellentre'iew of this literature. See also the discussions in *eine, ocuier, and/apoport (200%& and i 4aria and Strys!owski (200#&.

A. 9nectodal e'idence in this respect is abundant. :oren!o et al. (200$&, fore;ample, report that in the hilippines the number of nursing colleges rosefrom "$0 in "### to GM0 in 200D, and that most of these new colleges ha'ecurricula speci callyfi tailored to foreign health systems. 7angasniemi et al.(200$& sur'ey +ndian doctors working in the ?7 and ndfi that A0L of themacknowledge that migration prospects in uencfl ed their education plansand e ort.ff inally, 5ommander et al. (200%& ndfi e'idence of migration-induced skills accumulation in their sur'ey of the +ndian +5( sector. he+nternational Organi!ation for 4igration summari!es this e'idencestating that “prospects of working abroad ha'e increased the e;pectedreturn to additional years of education, and led many people to in'est inmore schooling, especially in occupations in high demand o'erseas” (+O4,200A&.

G. *eine et al. (20""a& also ndfi that le'el e ectsff are stronger for poorercountries, but do not consider composition e ects,ff nor the o'erall impact of migration on growth.

D. or our purposes, higher skilled workers are those with post-secondary education, thus “lower-skilled” workers are all the rest.

M. @ote that this does not imply that generalists are more producti'e inabsolute terms than technologists in either imitation or inno'ation, butsimply that comple; tasks like the management and smooth running of ad'anced research 'entures reuire more than ust technical skills.

$. i 4aria and Strys!owski (200#& pro'ide formal proof of this result in aclosely related framework.

%. his modeling choice re ectsfl the idea of the “small door” proposed byStark et al. ("##%&E while the incenti'es to accumulate human capitalimpro'e for all workers due to the possibility of migration, only a smallfraction of them e'entually lea'es, resulting in a more skilled, albeitsomewhat smaller, workforce. his might generate welfare gains rather thanlosses, a phenomenon to which these authors refer as “brain gain.”

#. 4igrants mo'ing from less de'eloped to OB5 countries account for%DL of the total according to the calculations of ocuier and 4arfouk

(200M&.

"0. 5learly, this assumes a sign re'ersal in the composition e ect,ff howe'er, such an e ectff might not occur for speci cfi parameters combi-nations.

"". Here we use a measure of educational attainment)the share of working age nati'es who ha'e completed tertiary education)as a pro;y forhuman capital. his is only one of many possible 'ariables (imper- fectly&measuring the uantity of human capital. 9!ariadis and ra!en ("##0& useadult literacy rates, 4ankiw, /omer, and 3eil ("##2& resort to schoolenrollment ratios, while *arro and :ee ("##A& de'elop interna- tionallycomparable data on a'erage years of schooling for a large sample of countriesand years. 9n interesting parallel literature, associated with the seminalwork of Bric Hanushek and co-authors (see Hanushek & 3oessmann,200%, for a re'iew&, has emerged in recent years. hey suggest to complementsuch measures of attainment with measures of achie'e- ment, or educationaluality, such as the results of the OB5 +S9 international tests. heirresults show that after controlling for cogniti'e skills, educational attainmenteither becomes statistically insigni cant,fi or


16/18

is shown to ha'e 'ery little additional e ectff on economic growth in a panel of de'eloped countries. Since our focus here is on de'eloping economies,howe'er, and gi'en the dearth of comparable data on achie'ement forsuch countries, we are forced to abstract from considering cogniti'e skills,and to concentrate on the a'ailable attainment data.

"2. 3e refer the interested reader to the original source for a compre-hensi'e description of the data and for discussions of the data collectionand interpolation techniues. he authors also pro'ide a fair discussion of the strengths and pitfalls of their data.

"A. See both ocuier and 4arfouk (200M& and *eine et al. (200%& formore detailed discussions on this issue.

"G. hese traditional immigration countries areE 9ustralia, 9ustria,*elgium, 5anada, 5yprus, enmark, inland, rance, ermany, reece,Hong 7ong, +celand, +reland, +srael, +taly, Capan, the /epublic of 7orea,:u;embourg, 4alta, the @etherlands, @ew 8ealand, @orway, ortugal,Singapore, Spain, Sweden, Swit!erland, the ?nited 7ingdom, and the?nited States. Our choice mirrors a similar strategy pursued by +'le's and de4elo (200%&.

"D. he 2M countries to be dropped from the sample areE 9lbania,9rmenia, 9!erbaian, *elarus, *osnia and Her!ego'ina, *ulgaria, 5roa- tia,5!ech /epublic, Bstonia, eorgia, Hungary, 7a!akhstan, 7yrgy!- stan,:at'ia, :ithuania, 4acedonia, 4oldo'a, oland, /omania, /ussia, Slo'akia,Slo'enia, aikistan, urkmenistan, ?kraine, and ?!bekistan.

"M. his classi catifi on is based on the current 3orld *ank ta;onomy, notthe historical situation. he 3orld *ank di'ides economies into groupsaccording to their 200# @+ per capita, calculated using the 3orld *ank9tlas method. he groups areE low income, ?SJ##D or less1 lower middleincome, ?SJ##M?SJA,#GD1 upper middle income, ?SJA,#GM?SJ"2,"#D1and high income, ?SJ"2,"#M or more.

"$. ollowing 5aselli (200D&, we take the 'alues from the “9ctualOS?B” column whene'er a'ailable, failing that, we choose column“+mputed OS?B,” and nallyfi the “:” column when neither of thepre'ious two is a'ailable. he interested reader is referred to ollin (200"&and *ernanke and uR rkaynak (2002& for e;tensi'e discussions on theunderlying issues, the methodology of calculating labor shares and dataa'ailability for de'eloped and de'eloping countries.

"%. @otice that this assumption is common in the literature gi'en the lack of appropriate data on labor shares. opel ("###&, 5aselli (200D& and6andenbussche et al. (200M& all make similar assumptions.

"#. Our series on gross capital formation starts in "#M0. ue to missingobser'ations, howe'er, the earliest a'ailable data for some countries can beas late as the "#%0>s.

20. @otice, howe'er, that when estimating the three euations as asystem of seemingly unrelated euation, we cannot reect the nullhypothesis that three models are indeed unrelated. ?sing the *reuschagan test for independent euations we obtain a p-'alue of 0.#%"A.4oreo'er, $GL of the countries used in the o'erall analysis are employed inat least two of the models and GAL are employed in the analysis of the le'el,composition, and growth models. 3e ha'e also computed the summarystatistics of the main 'ariables in our analysis using the samples employed inthe le'el, composition, and growth models, respecti'ely. hese statisticsare remarkably similar across samples. he tables with these descripti'estatistics are a'ailable from the authors upon reuest.

2". *eine et al. (200%& do not include this 'ariable because of its highcorrelation with the initial le'el of human capital in their sample. +n oursample, howe'er, the pairwise correlation between the log of initial human

capital and the log of the public e;penditure on education as a percentage of is fairly small (0.0M#A&, and we include both 'ariables.

22. *eine, ocuier, and OR !den (20""b& ndfi that a larger diasporaincreases the migration rates for both skilled and unskilled migrants.

2A. hroughout the paper, in the interest of bre'ity, we do not present therstfi stage regressions of the +6 procedures. he results are a'ailable fromthe authors upon reuest.

2G. he coe cieffi nt estimates on B?, B@S, and /B4+ are not robust tochanges in speci cationfi as e'ident from the estimates of 4odels ++ and +++ in able 2. hese di erenc

ff es are also dri'en by di erenc

ff es in sample si!eacross models. 3hen we re-estimate 4odel + with the same samples used in4odel ++, we ndfi that (a& B@S is signi cantfi at the "0L and "L le'el in theO:S and +6 estimates, respecti'ely1 (b& the coe cieffi nt on /B4+ ispositi'e and signi cafi nt at "0L in both the O:S and +6 estimates1 and(c& the coe cientffi on B? is negati'e and not statistically signi cafi nt inboth the O:S and +6 estimates. here are no signi cafi nt changes in thesi!e and sign of the other coe cieffi nts. hese additional estimations area'ailable from the authors upon reuest.

2D. 9s neither the migration rate nor pro;imity to the technologicalfrontier are found to be endogenous in the pre'ious speci cations,fi we donot conduct an +6 analysis here.

2M. he correlation between the log of the migration rate and the log of pro;imity in this sample is 0.00%#.

2$. @otice that since /OI+4 is a 'ariable ranging between 0 and ", itsnatural logarithm, ln(/OI+4&, is negati'e.

2%. 3e cannot reect the null hypothesis that a" aA at "0LE the p-'alue euals 0.2#AG. hese results con rmfi the ndingsfi of *eine et al.(20""a& who control for potential nonlinear e ectsff by introducing adummy for the richFpoor countries in their data set. *eine, ocuier, and/apoport (20"0& follow a similar strategy but ndfi no e'idence of nonlinear e ects.ff

2#. @ote that the main conclusions of i 4aria and Strys!owski (200#&, alsosketched in the e;tended framework in Section 2)that the distor- tionale ectff of migration possibilities slows down growth in sending countries)does not hinge upon the migration rates being di ereff nt across skill types. +tis, howe'er, true that the distortions are strongest when the migrationprobabilities di erff for di ereff nt types of skills. 9s a conseuence, ouranalysis might underestimate the distortionary e ectff of migration.

A0. 9s a robustness check, we re-estimate 4odels ++6 presented in able A using a sample of countries for which both pro;ies for thedomestic demand for technically skilled workers are a'ailable. +n theseregressions we use MD obser'ations for the O:S and D% obser'ations for the+6 estimations. he results are by and large robust with respect to samplesi!e with two notable e;ceptions discussed below. hese regressions area'ailable from the authors upon reuest.

A". he critical 'alue for the "0L ma;imal +6 si!e is "A.GA which is onlyslightly higher than the obtained 7leibergenaap 3ald -statistics of "A.2#A.

A2. +t should be noted that the C-test produced a p-'alue of 0.22GM when weused only the D% obser'ations for which data on both log(/O9t& andlog(HO@Bt& are a'ailable, that is, 2 obser'ations less.

AA. Such sign change is not obser'ed in the +6 when we restrict ours tocountries for which both log(/O9t& and log(HO@Bt& are a'ailable.


17/18

AG. o calculate the thresholds, compute the marginal e ectff of a change inrh,t on S& from (D&, obtainingE oS& ( tForh,t @" Q @A /OI+4t. hesign of this e ectff is positi'e when /OI+4t e;ceeds @"F @A, that is) usingthe coe cientffi estimates from 4odel +++, for e;ample)when /OI+4tP 0.D022.

AD. +n our sample there are twenty countries with a pro;imity indicatorbelow 0.D022 in "##0 or 2000. 9mong these countries we ndfi *urkinaaso, the 5entral 9frican /epublic, Bthiopia, uinea, 4adagascar,4alawi, and ogo.

AM. 9lternati'ely, as suggested by one of the referees, it might simply be thecase that for 'ery poor countries, it is too di cultffi or e;pensi'e to o er S&ff degrees. his is, howe'er, not the case for countries in our data set gi'en thepositi'e 'alue of S& for all of them.

A$. 9bella (200M& discusses policies implemented by se'eral OB5countries in'ol'ed in the on-going international “competition for globaltalent.” oint systems, temporary admissions under skill-based categories (e.g.,H-"* 'isas&, and facilitation of family migration for speci cfi categoriesof workers ha'e all been used to fa'or workers in the eldfi of science andtechnology, markedly health professionals, geneticists and + specialists.

A%. 9s customary in the literature, we use D-year-period a'erages for thegrowth rate, reducing the risk of capturing business cycle e ects.ff

A#. 3e could not ndfi any statistical e'idence that the e ectff of theproportion of workers with technical skills on growth becomesnegati'e close enough to the frontier. he threshold for the change in sign (DBA "DBG # ¼ 0:#%20 in 4odel +++, 0.#G#$ in 4odel +6, and 0.#%20 for4odel 6. @one of these estimates are statistically di erentff from " with p-'alues 0.#$G%, 0.#22M, and 0.#DAD for the tests in 4odels +++, +6, and 6,respecti'ely.

G0. 3e chose to increase the emigration rate by G0L as the a'erage rate of increase in the emigration rate during "##02000 for the countries in oursample was A%L.

G". or a detailed discussion of this method, together with illustrati'ee;amples, please ref er to 7ing et al. (2000&. he 5larify documentation,a'ailable on-line at www.sta n ford.eduFV tom!Fsoftwa r eFclarify.pd f , pro- 'idesadditional details on the method and its applications.

/B2B/B@5BS

9bella, 4. (200M&. lobal competition for skilled workers and conse-uences. +n 5. 7uptsch, & B. 2. ang (Bds.&, 5ompeting for globaltalent. ene'aE +nternational :abour O ce.ffi9!ariadis, 5., & ra!en, 9. ("##0&. hreshold e;ternalities in eco-nomic de'elopment. he =uarterly Cournal of Bconomics, "0D(2&,

D0"D2M.*arro, /. C., & :ee, C. ("##A&. +nternational comparisons of educational

attainment. Cournal of 4onetary Bconomics, A2(A&, AMAA#G.*atista, 5., :acuesta, 9., & 6icente, . 5. (20"2&. esting the Wbrain gain>

hypothesisE 4icro e'idence from cape 'erde. Cournal of e'elopmentBconomics, #$("&, A2GD.

*eine, 4., efoort, 5., & ocuier, . (20""a&. 9 panel data analysis of the brain gain. 3orld e'elopment, A#(G&, D2ADA2.*eine, 4., ocuier, ., & OR !den, 5. (20""b&. iasporas. Cournal of

e'elopment Bconomics, #D("&, A0G".*eine, 4., ocuier, ., & /apoport, H. (200"&. *rain drain and

economic growthE heory and e'idence. Cournal of e'elopmentBconomics, MG, 2$D2%#.

*eine, 4., ocuier, ., & /apoport, H. (200$&. 4easuring internationalskilled migrationE 9 new database controlling for age of entry. he3orld *ank Bconomic /e'iew, 2"(2&, 2G#2DG.

*eine, 4., ocuier, ., & /apoport, H. (200%&. *rain drain and humancapital formation in de'eloping countriesE 3inners and losers.Bconomic Cournal, ""%, MA"MD2.

*eine, 4., ocuier, ., & /apoport, H. (200A&. *rain drain and :5>sgrowthE 3inners and losers. +89 working paper, %M#.

*eine, 4., ocuier, ., & /apoport, H. (20"0&. On the robustness of brain gain estimates. 9nnales d>Bconomie et de Statistiues, #$F#%,

"GA"MD.*enhabib, C., & Spiegel, 4. 4. ("##G&. he role of human capital in

economic de'elopment e'idence from aggregate cross-country data. Cournal of 4onetary Bconomics, AG(2&, "GA"$A.

*ernanke, *., & uR rkaynak, /. (2002&. +s growth e;ogenousX aking4ankiw, /omer, and 3eil seriously 200". @*B/ 4acroeconomics9nnual, "M, ""$2.

5arrington, 3. C., & etragiache, B. ("##%&. How big is the *rain drainX+4 working papers, #%F"02.

5aselli, . (200D&. 9ccounting for cross-country income di erences.ff +n .9ghion, & S. urlauf (Bds.&. Handbook of economic growth (6ol. ",pp. M$#$G"&. 9msterdamE @orth-Holland (chap. "0&.

5aselli, ., & 5oleman, C. (200M&. he world technology frontier. he9merican Bconomic /e'iew, #M(A&, G##D22.

5lemens, 4. (200$&. o 'isas killX Health e ectsff of 9frican healthprofessional emigration. 5enter for global de'elopment working paper,""G.

5ommander, S., 5handa, /., 7angasniemi, 4., & 3inters, :. 9. (200%&.4ust skilled migration be a *rain drainX B'idence from the +ndiansoftware industry. he 3orld Bconomy, A"(2&, "%$2"".5ommander, S., 7angasniemi, 4., & 3inters, :. 9. (200G&. he braindrainE 5urse or boonX 9 sur'ey of the literature. +n /. B. *aldwin, &:. 9. 3inters (Bds.&, 5hallenges to globali!ationE analy!ing theeconomics. 5hicago, +:E 5hicago ?ni'ersity ress.5onnell, C., 8urn, ., Stilwell, *., 9wases, 4., & *raichet, C.-4. (200$&.

Sub-Saharan 9fricaE *eyond the health worker migration crisisX.Social Science & 4edicine, MG(#&, "%$M"%#".

i 4aria, 5., & Strys!owski, . (200#&. 4igration, human capitalaccumulation and economic de'elopment. Cournal of e'elopmentBconomics, #0(2&, A0MA"A.

ocuier, ., & 4arfouk, 9. (200M&. +nternational migration by educa-tional attainment, "##02000. +n 5. O!den, & 4. Schiff (Bds.,+nternational 4igration, /emittances and the *rain rain(pp. "D""##&. @ew KorkE algra'e 4acmillan.urlauf, S. @., Cohnson, . 9., & emple, C. /. (200D&. rowtheconometrics. +n . 9ghion, & S. @. urlauf (Bds.&. Handbook of economic growth (6ol. ", pp. DDDM$$&. 9msterdamE Blse'ier (chap. %&.ustmann, 5., adlon, +., & 3eiss, K. (20""&. /eturn migration, humancapital accumulation and the *rain drain. Cournal of e'elopmentBconomics, #D("&, D%M$.

einstein, C. S. (200M&. he nature of creati'e de'elopment. Stanford, 59EStanford ?ni'ersity ress.

erschenkron, 9. ("#M2&. Bconomic backwardness in historical perspecti'eE9 book of essays. 5ambridge, 49E *elknap ress of Har'ard?ni'ersity ress.

ibson, C., & 4c7en!ie, . (20""&. he microeconomic determinants of emigration and return migration of the best and brightestE B'idencefrom the aci c.fi Cournal of e'elopment Bconomics, #D("&, "%2#.

ollin, . (200"&. etting income shares right. Cournal of oliticalBconomy, ""0(2&, GD%G$G.

Hanushek, B. 9., & 3oessmann, :. (200%&. he role of cogniti'e skills ineconomic de'elopment. Cournal of Bconomic :iterature, GM(A&,M0$MM%.+:O (200M&. +n 5. 7uptsch, & B. 2. ang (Bds.&, 5ompeting for globaltalent. ene'aE +nternational :abour O ce.ffi

+O4 (200A&. 3orld migration 200AE 4anaging migration)5hallenges andresponses for people on the mo'e. ene'aE +nternational Organi!ationfor 4igration.

+'le's, 9., & de 4elo, C. (200%&. +, the *rain drain and tradeE 5hannelsand e'idence. 5B/ iscussion aper, @o. $002.+yigun, 4. ., & Owen, 9. :. ("###&. Bntrepreneurs, professionals, andgrowth. Cournal of Bconomic rowth, G(2&, 2"22A2.

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7angasniemi, 4., 3inters, :. 9., & 5ommander, S. (200$&. +s the medical*rain drain bene cialXfi B'idence from o'erseas doctors in the ?7.Social Science & 4edicine, MD(D&, #"D#2A.7ing, ., om!, 4., & 3ittenberg, C. (2000&. 4aking the most of statistical analysesE +mpro'ing interpretation and presentation. 9mer-ican Cournal of olitical Science, GG(2&, AG$AM".7leibergen, ., & aap, /. (200M&. enerali!ed reduced rank tests using thesingular 'alue decomposition. Cournal of Bconometrics, "AA,

#$"2M.:oren!o, 4., al'e!-an, C., & Ca'ier, :. (200$&. @urse migration from a

source country perspecti'eE hilippine country case study. HealthSer'ices /esearch, G2, "G0M"G"%.

4althus, . 9. ("$#%&. 9n essay on the principle of population ("#%Mth ed.&.

:ondonE 3. ickering.4ankiw, @. ., /omer, ., & 3eil, . @. ("##2&. 9 contribution to theempirics of economic growth. he =uarterly Cournal of Bconomics,"0$(2&, G0$GA$.

4ariani, . (200$&. 4igration as an antidote to rent-seekingX. Cournal of e'elopment Bconomics, %G(2&, M0#MA0.

4ountford, 9. ("##$&. 5an a brain drain be good for growth in the sourceeconomyX. Cournal of e'elopment Bconomics, DA(2&, 2%$A0A.

4urphy, 7. 4., Shleifer, 9., & 6ishny, /. 3. ("##"&. he allocation of talentE +mplications for growth. =uarterly Cournal of Bconomics,"0M(2&, D0ADA0.

Stark, O., Helmenstein, 5., & rskatwet!, 9. ("##$&. 9 brain gain with a*rain drain. Bconomic :etters, DD, 22$2AG.

Stark, O., Helmenstein, 5., & rskatwet!, 9. ("##%&. Human capitalformation, human capital depletion, and migrationE a blessing or a“curse”X. Bconomic :etters, M0, AMAAM$.

Stark, O., & 3ang, K. (2002&. +nducing human capital formationE4igration as a substitute for subsidies. Cournal of ublic Bconomics,%M, 2#GM.

Stock, C., & Kogo, 4. (200D&. 3eak instruments in linear +6 regression. +n. 9ndrews, & C. Stock (Bds., +denti cationfi and inference ineconometric modelsE Bssays in honor of honas C. /othenberg (chap.D&. @ew Kork, @KE 5ambridge ?ni'ersity ress.

opel, /. ("###&. :abor markets and economic growth. +n O. 9shenfelter,& . 5ard (Bds.. Handbook of labor economics (6ol. A5,

pp. 2#GA2#%G&. 9msterdamE Blse'ier (chap. GG&.?rdal, H. (200D&. eople 's. 4althusE opulation pressure, en'ironmentaldegradation, and armed con ictfl re'ised. Cournal of eace /esearch,G2(G&, G"$GAG.

6andenbussche, C., 9ghion, ., & 4eghir, 5. (200M&. rowth, distance tofrontier and composition of human capital. Cournal of Bconomicrowth, ""(2&, #$"2$.

6idal, C.-. ("##%&. he e ectff of emigration on human capital formation. Cournal of opulation Bconomics, ""(G&, D%#M00.

3ooldridge, C. 4. ("##D&. Score diagnostics for linear models estimated bytwo stage least suares. +n . S. 4addala, . 5. *. hillips, & ( . @.Srini'asan (Bds.&, 9d'ances in econometrics and uantitati'e economicsEBssays in honor of rofessor 5. /. /ao (pp. MM%$&. O;fordE *lackwell.

3orld *ank (200#&. 3orld de'elopment indicators. 3ashington, 5E he3orld *ank.

Koung, 9. ("##%&. rowth without scale e ects.ff Cournal of oliticalBconomy, "0M("&, G"MA.

Kusuf, S. (200$&. rom creati'ity to inno'ation.

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Migration, Human Capital Formation