WORKING PAPER SERIES

Temporary versus Permanent Migration

WP 9 (2017)

What attracts highly skilled migration to Europe?

HÉCTOR CEBOLLA-BOADO AND MARÍA MIYAR-BUSTO

TEMPER EU ProjectGrant Agreement: 613468

Website: www.temperproject.euTwitter: @temper2014

Facebook: www.facebook.com/temperproject.euEmail: info@temperproject.eu

WhatattractshighlyskilledmigrationtoEurope?HéctorCebolla-BoadandMaríaMiyar-Busto

Abstract: This paper analyzes the potential of a number of pull factors (unrelated to

immigrationpolicies)inattractinghighlyskilledmigrantsin18Europeancountries.Todoso

we built a unique dataset combining information on the flows by level of skills from the

EuropeanLaborForceSurvey(Eurostat)witha large listofproxiesofpull factorsobtained

from different OECD databases. Specifically, using country fixed effects we predict the

absolutenumberofmigrantswithtertiaryeducationcredentialsarrivingovertime(between

1999and2013).Thelistofpullfactorswhoseeffectswestudycoversdifferentdimensions

of returns to education and the welfare configurations of the selected countries. Our

analysis reveals thatwages are, by and large, themost important factor attracting skilled

migrationflows.Otherindicatorsoffactorssuchastherateofunemploymentorthedegree

towhichtheeconomyisinnovativearemuchlessrelevant.Thewelfaremagnethypothesis

isalsoconfirmed.SocialexpenditureattractsmoreskilledmigrantsTherearealsobasesto

arguethat fiscalpressureshrinksthe flowof themostwantedmigrants,particularlywhen

theydonotnecessarilyhavetheintentionofstayinginthelongterm.

Keywords:Immigration,highskilledmigration,pullfactors,returnstoeducation,welfare

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INDEX

1.Introduction.....................................................................................................2

2.Whatattractshighlyskilledmigrants?...........................................................2

3.Datasetandvariables......................................................................................8

4.Method..........................................................................................................11

5.Results............................................................................................................12

6.Summaryoffindings......................................................................................18

References.........................................................................................................22

Appendix............................................................................................................27

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1.Introduction

Across emigration countries it is the most educated that are more likely to engage in

internationalmigration (Daoetal.2016).Thisexplainswhy,sincethestartof thecentury,

highly skilled migration represents an increasingly large component of global migration

streams(WidmaierandDumont2011).Recentestimatessuggestthatthenumberoftertiary

educatedmigrants in the OECD increased by 70% in the last decade to reach 30% of all

migrants in theOECD(UN-DESA2013)and thatnations compete fiercely toattract them.

According tomigrationexperts in themediumtermEuropewillneedasmanyhighskilled

migrants as it has now, if not more (Kahanec and Zimmermann 2011). The increasing

relevanceoftheICTsectorincontemporaryeconomiesalsoseemstoincreasethedemand

for highly skill workers (Michaels, Natraj, and Van Reenen 2013). The most educated

migrantsarealsothemostwantedtypeofmigrationbynationalpublicopinionworldwide

(HelblingandKriesi2014).Inthecontextofthiscompetition,manydevelopedcountriestake

explicitactionstoclearaccessforhighlyskilledmigrants(HSM)intotheirterritory.Yet,the

international evidence on themost relevant factors that effectively help to attract skilled

migrantsistosomeextentinconclusive.Muchhasbeensaidabouttheroleofimmigration

policies (Chaloff and Lemaitre 2009; Papademetriou et al. 2008, Papademetriou and

Sumption2013),withasupplydriven(points-based)systembeingmoreeffectivethanthe

alternatives(Czaikaetal.2015).Inhisanalysisof14OECDcountriesfrom1980to2005,Peri

(2009) concludes that even though on average these advanced economies passed an

averageoftworeformsreducingtheaccessof immigrantstobenefitsavailabletocitizens,

they also passed about 2.5 laws on skilled migration. Meanwhile, there is an extensive

literaturedepictingHSMasincome-maximizers.Asaconsequence,returnstoeducationand

the cost of migration are seen as determinant pull factors in the sorting of HSM across

countries.

Canada, theUS,New Zealand andAustralia togetherwith theUK, appear to be themost

successfulplayersintheglobalracefortalent(Kerretal.2016:31).Inthispaper,welookat

thenon-immigrationpolicyrelateddeterminantsofhighlyskilledmigrationtoaselectionof

Europeancountries.WiththeexceptionoftheUK,Europeancountriesappeartolagbehind

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otheradvancedeconomiesinattractingHSMandindevelopingefficienttoolsforattracting

themosttalentedmigrants(Cebolla-Boadoetal.2016).Ourpaperprovidesevidenceonthe

sorting of HSM across European countries, a region that is under-represented in our

literatureofreference.Wealsogobeyondthetraditionaldescriptionofreturnstoeducation

relatedtopullfactors,whichdominatetheliteratureonHSM,andbringinfactorslinkedto

thewelfareconfigurationofdestinationcountries(pubicspendingandtaxation),whichare

predominantlydescribedasdriversofmidandlowskilledaswellaswelfaremigration.Our

contributionisalsoempiricalsincewehavebuiltamacroleveldatasetmerginginformation

from the European Labor Force Survey (Eurostat) on the percentage of immigrants with

highereducationarrivingtoEuropeancountriespercountryandyearwithEurostatdataon

the corresponding total number of inflows to build the dependent variable. It includes a

wide range of country level characteristics that may work as pull factors from the OECD

databases.

Thepaperisorganizedasfollows.Wefirstreviewtheliteratureanalyzingtheroleofreturns

toeducationandwelfareinattractingmigrationandHSM,fromwhichweproduceanumber

of theoretical expectations. We then present our dataset and the methods, before

proceeding to the presentation of our empirical results. A final concluding section

summarizesthemultipleresultsanddevelopstheimplicationsofourresearch.

2.Whatattractshighlyskilledmigrants?

Thenumberandeducationalcompositionsofmigrantsarrivingtodifferentcountriesdiffer

widelybyspaceandtime(GroggerandHanson2011;FredericDocquierandMarfouk2004)

and the challenges that migration represents for destination countries obviously differ

depending on the skill composition of the flow (Nathan 2014). HSM, a flow leaded by

entrepreneurial individuals (Zucker and Darby 2007; Benson 2010), is supposed to have

largely positive effects on destination countries (Regets 2001), decreasing inequality

(AydemirandBorjas2007)andloweringlevelsofsocialspending(GiuliettiandWahba2012).

Highlyskilledmigrationalsobooststhelevelsofinnovationinreceivingeconomies(Aghion

etal.2012)whileexpandinghighvalueknowledgeintensiveproductivesectors(Nathanand

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Lee, 2013) and exports (Docquier and Rapoport 2008; Peri et al. 2009) as well as

preparedness for international investment (PandyaandLeblang2012).Thearrivalofmore

skilled migrants also promotes ties with foreign research institutions, improves

technological exportations and expands the higher education system (Borjas and Doran

2012).ResearchhasalsoidentifiednegativeconsequencesassociatedtoHSMinorigin(Boeri

2012) and destination, whereby the reduction of wages that it could create may

disincentivizetheeducationalinvestmentofnatives(KerrandKerr2011;BorjasandDoran

2012).

In the light of thesemassive benefits, the clarification of pull factors for HSM remains a

dynamic field of enquiry. At the risk of oversimplifying complex traditions, there are two

broadstreamsofelaborationonpullfactors:thereturnstohumancapitalandtheeffectof

welfaresystems.

Ithasbeensuggestedthatdifferencesinreturnstoeducationexplainmostoftheearnings

divergencebetweenmigrantsandautochthonousworkers(LamandLiu2002a;LamandLiu

2002b). The idea has been widely accepted since the seminal model developed by Roy

(1951),whichsuggeststhatthedirectionandsizeof theselectionofmigrantsdependson

theeducational returnsobtained in sendingand receiving countries.Borjas (1987) further

developedthismodelsuggesting that negativeselectionofmigrantshappens inpoorand

unequalcountriesandpositiveselectionwhenthedistributionofincomeismoredispersed

in thedestination rather than the origin country (a finding also confirmed inPareyet al.

2015;StolzandBaten2012).

It is a well-known regularity that international migration decisions respond to earnings

differences (Bertoli et al. 2013, Stolz andBaten2012) and especially to differences in the

returns to education (Gould and Moav 2016), even if highly skilled migrants dot not

necessarilygreatlyimprovetheirsalariesaftermigrationrelativetotheirinitialbenchmarks

(Kaczmarczyk and Tyrowicz 2015). Accordingly, there is consistent evidence regarding

positive correlationbetween income inequality and selectivity (Brücker andDefoort 2006;

Aydemir 2013). Still, there is some evidence that the most talented workers are not

5

necessarily thosemigrating to destinations with the largest wage inequalities (Gould and

Moav2016).

Togetherwith salaries (Giulietti 2014a), other type of returns to education also favor the

arrivalofhighlyskilledflows(BelotandHatton2012),particularlyemploymentopportunities

(Cadena and Kovak 2016) and recognition of educational credentials (Czaika et al. 2015).

Finally, recent research has also studied the potential of innovative economies to attract

talentwhentherearestable institutionalsettings, favorabletechnicalenvironments (Mihi-

Ramirezetal.2016),tradeopennessandfastergrowthofinformationandcommunication

technologies(Michaelsetal.2013).

Whilemostresearchonpull factorsattractinghighskilledmigrationhaveconcentratedon

returns to education, migrants of different profiles are sensitive to provision of public

welfareandfiscalpressure.Thisintheliteratureistheso-calledwelfaremagnethypothesis

(Borjas1999),althoughthisfindinghasalsobeencontested(LevineandZimmerman1999;

Giulietti2014b).Acommoncriticismofthisliteraturefromourperspectiveisthatwelfareis

mostlyconceptualizedasamagnetforgeneralmigrants(Barrettetal.2013),orformidand

low skilled ones.However, the impact ofwelfare states and fiscal regimes onhigh skilled

migrationismuchlesswell-known.AccordingtoBorjas(1989)welfarestimulatesthearrival

ofboth thehighlyand lower skilled. Yet, this findinghasbeenchallengedbyGiulietti and

Wahba’s (2012)analysisofOECDcountries from1990to2001,whichproves thatwelfare

disincentivizes the arrival of HSM. This is coherent with the general idea that the use of

welfare services ismore common amongstmigrants with poor outcomes in integration

(Pellizzari2013;Riphahn,Sander,andWunder2013). Insomewaylinkingboththereturns

toeducationandthewelfaremagnetarguments,certainresearchhasexploredthenegative

influence of fiscal pressure and the arrival of HSM (Razin and Wahba 2015), and, very

importantly,themosttalented(Akcigit,Baslandze,andStantcheva2016)

Restrictingtheanalysisofpullfactorsforhighlyskilledmigrantstopurelyeconomicfactors

appears too inaccurate. Research has shown the importance of factors such as distance

between origin and destination (Belot and Hatton 2012; Brücker and Defoort 2006) and

6

social networks (McKenzie and Rapoport 2007; Munshi 2003), as well as of other non-

economic factors such as the general social climate (Hendriks and Bartram 2016). Along

theselines,ithasalsobeendocumentedthathighlyskilledemigrantsperceivemigrationper

seasafruitfulpersonalexperience(TriandafyllidouandGropas2014)

Expectations

Inspired by the literature reviewed above we here present a set of hypotheses that

summarizetwomainargumentsusingdifferentempiricalindicators.Thefirstblockrefersto

returnstoeducation.Highlyskilledmigrantsmayoptforcountrieswheretheycanmaximize

therewardtheyobtainfromtheirformaleducationintermsofwages(salaries,overalllevels

of inequality andprices), employment stability (unemployment) andother factors such as

the level of innovation of economies (as measured by number of patents). Secondly we

speculatethatwelfareregimescanalsoworkaspowerfulmagnetsforskilledmigrants(we

hereusedifferentaspectsofpublicspending,taxesandabroadermeasureofoverallsocial

wellbeing).

ReturnstoEducation

Wagesarethemostobviousproxyofreturnstohumancapitalinthelabormarket,together

withtheevolutionofprices.

H1:Weexpectcountrieswithhigherwagestoattractmorehighlyskilledmigrants.

H2:Weexpectcountrieswithlowinflationtobemoreattractivetohighlyskilledmigrants.

Education appears to be a shelter against unemployment, although this may happen

differentlyacrosscountries.Theriskofunemploymentisanotherobviousfactorexplaining

howmigrants are sorted across countries. The duration of unemployment is yet another

consideration that can shape the flow of the most skilled international workers towards

differentdestinations.Weestimate that long-termunemploymentmaybemoredeterring

givenitsscarringeffect(Arulampalam2001).

H3.a:Weexpectcountrieswithlowunemploymenttobemoreattractivetoskilledmigrants.

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H3.b: Short-termunemploymentmaybe less discouraging for highly skilledmigrants than

long-termunemploymentsinceitmayonlyreflecthighlevelsofoccupationalrotationthat

mayeventuallyimprovethematchingbetweenskillsandoccupations.

According to the large traditionofempirical and theoretical research income inequality in

destinationcountriesisassociatedwithmorepositivelyselectedmigrationflows.

H4:Weexpectmoreunequalcountriestoattractmorehighlyskilledmigration.

Afinalexpectationisthatbetterskilledworkersmayalsoenjoyhigherlevelsofprofessional

andpersonaldevelopment inmore innovativeeconomicsettingswheremorepatentsand

brilliantcolleaguesmayconcentrate(Kerretal.2016).

H5:Moreinnovativeeconomies(asmeasuredbythenumberofpatents)attractmorehighly

skilledmigrants.

TheWelfareMagnet

Overall levelsofpublic spending canmakemigratoryplans less costly in the long runand

certain destinationsmore attractive than others. This idea fits the description of welfare

states asmagnets formigration. Yet, the compositionofpublic spending canalsoprovide

importanthints.Spendingonhealthcaremay turnout tobemoreefficacious inattracting

skilledmigrants than remedial investments targetingdeprived segmentsof thepopulation

such as housing or active labor market policies. Investments targeting the elderly are

probablyonlyrelevantinattractingmigrantswiththeintentionofstayinglongterm.

H6:Weexpectsocialexpendituretoincreaseattractiveness.

H7:Publicspendingtargetingdeprivedpopulationssuchashousingmayshrinktheflowof

skilledmigrantswhileotherfactorssuchasspendingonhealthcaremayincreasetheflow.

Fiscal pressure is then an essential aspect since better skilledmigrantsmay enjoy better

wagesthanunskilledworkersand,thus,bereluctanttointenseprogramsofredistribution.

8

H8:Weexpectfiscalpressuretodecreasetheattractivenessofdestinationsforhighlyskilled

migrants.

Finally, more diffuse aspects proxying general levels of social wellbeing may also have a

determinant role in redirecting skilled workers to different destinations. This is a finding

alreadysupportedbyasignificantbulkofqualitativeresearch.

H9:Moreblurredmeasuresofthequalityoflifesuchaslifeexpectancyincreasethearrival

ofhighlyskilledmigrants.

3.Datasetandvariables

While until recently the literature concentrated on explaining differences in the stocks of

skilledmigrants acrossdestination countriesover time (Dumontand Lemaître2004;Belot

andHatton2012),dataonflowsisonlyrecentlybecomingmorecommon,althoughitisstill

largely insufficient (Kerr et al. 2016). For the analytic objectives of thisworking paperwe

haveconstructedadatasetcombiningdifferent resources. Information fromtheEuropean

Labor Force Survey (ELFS), the best available source of harmonized international data for

building comparable estimations of the educational composition of flows to European

countries,was used to calculate the yearly flow ofmigrantswith tertiary education from

1999to2013.

Flowswerecalculatedapplyingthreesimultaneousrestrictionstothecountry/yearsample:

(a) foreigners who could join the active population upon their arrival in the destination

country (ageofarrival<63years); (b) foreignerswhoseageuponarrivalallowed themto

havecompletedtertiaryeducation(>25years);(c)foreignerswhoatthetimeofthesurvey

had been residents in the destination country for a short period of time1. Because our

database provides repeated cross-sectional information, it allows us to observe people

declaringtheirarrival inagivenyear indifferentsurveys (country/yearsamples).Weused

1 Unfortunately, the ELFS only includes information on year of arrival for nationals with a migrant background for a limited number of countries and years. Nevertheless, we argue that this is not a problem when working with recently arrived migrants whose time of residence did not allow them to naturalize.

9

two alternatives to define the time span. On the one hand (1), we selected foreign

respondentswhohadbeeninthedestinationcountryfor lessthanoneyear.Ontheother

hand(2)wechoseforeignerswithfiveyearsofresidence.Usingthesetwoapproaches,we

calculatedtheindividualyearofarrivaltoeachdestinationcountry.Eachbringsanumberof

advantagesanddisadvantages.

(1) the selection of respondents having spent less than one year of residence in their

destination, reduces the returnbias that is inherent to immigration researchusing

crosssectionaldata(Borjas1987).Thereisadisadvantagehere,thenumberswhen

using this approach are smaller than when the flows are calculated using the

subsamplesofrespondentshavingresidedfortwoyearsormore.Thisisduetothe

factthattheLFShasdifficultiessurveyingrecentlyarrivedmigrants.Weprovethisin

FigureA.1intheAppendix,whichshowshowthenumberofrespondentsdeclaredto

havearrivedinagivenyearincreasesaswerelaxtherequirementofhavingresided

lessthanoneyear2.

(2) Calculating the flows on the basis of respondents having been in the destination

country for fiveyears,ouranalysis loses short-term residentswho left the country

during the previous years. The advantage of this approach is that we are able to

calculateyearlyflowsusinglargernumbersofrespondents.

The replicationofouranalysisusingbothdependentvariablesaffordsus thepossibilityof

revealing the impact of pull factors in attracting foreigners whose migration plans have

differenttimehorizons3.

2 For the sake of simplicity, we illustrate this for a single year of arrival (2004) and three selected countries (Austria, France and Sweden). 3 In any case, sample size restrictions forced us to drop countries where the number of migrants meeting our analytic requirements [(a), (b) and (c)] was below 75. This excluded from our analysis countries with low migration rates or small sample sizes in the ELFS such as Bulgaria, Iceland, Croatia, the Slovak Republic, Romania, Hungary, Slovenia, Poland, Lithuania, Cyprus, Malta and Estonia. The Appendix (Table A.1) shows a table with the number of skilled migrants in the ELFS national samples used to build the information of flow composition. Table A.2 shows the availability of country-year information.

10

As a final step in the calculation of our dependent variables,we calculate the number of

HSM arriving to each country, by multiplying the percentage of migrants with tertiary

educationinouragesofinterest[obtainedfrom(1)and(2)]bythetotalnumberofmigrants

arrived each year according to Eurostat. Note that when we use information on

characteristicsofmigrants thatstill live in thecountry5yearsafterarrival,wereduce the

period of analysis from 1999 to 2008 (see the second panel in Figure 1). The Appendix

includestwoplotsforscrutinyofthedistributionofthesevariablesacrosscountries(Figures

A.2.1andA.2.2).

Our database complements the information on the yearly flows to each country with

measurementsofpullfactorsobtainedfromdifferentOECDsurveys.Theseincludeabattery

of variables that allow us to proxy the role of returns to education and welfare as pull

factors. They include (1) information on the national employment situation: the

unemployment rate for workers with tertiary education4; and the overall composition of

unemployment by duration5. (2) Indicators of wages including real minimumwages

6 and

average annual wages7 and prices

8. (3) A proxy of the degree of innovation of national

4 Unemployment rates among workers with tertiary education register unemployment rates for people without work but actively seeking employment and currently available to start work. This indicator measures the percentage of unemployed in the population aged 25-64 (Source: Education at a glance: Educational attainment and labour-force status; see here). 5 Incidence of unemployment by duration was obtained from the Labor Force Statistics (see here). Specifically, rates of unemployment experiences lasting less than 1 month, from 1 to 3 months, from 3 to 6 months, from 6 months to 1 year and more than 1 year were coded for each country 6 Real hourly and annual minimum wages are statutory minimum wages converted into a common hourly and annual pay period for the 25 countries for which they are available. The resulting estimates are deflated by national Consumer Price Indices (CPI). For more information see here. 7 Average annual wages per full-time equivalent dependent employee are obtained by dividing the national accounts based total wage bill by the average number of employees in the total economy, which is then multiplied by the ratio of average usual weekly hours per full-time employee to average usually weekly hours for all employees. Average annual wages in 2013 are calculated in constant process at 2013 USD PPPs and constant process at 2013 USD exchange rates. 8 Consumer Price Indices (CPIs) measure average changes in the prices of consumer goods and services purchased by households. In most instances, CPIs are compiled in accordance with international statistical guidelines and recommendations. However, national practices may depart from these guidelines, and these departures may impact on international comparability between countries. Information on the evolution of prices was obtained from the Monthly Monetary and Financial Statistics (see here: Selected variables were Relative consumer price index 2010=100). Competitiveness-weighted relative consumer prices and unit labor costs for the overall economy are in dollar terms. Competitiveness weights take into account the structure of competition in both export and import markets of the goods sector of 49 countries. An increase in the index indicates a real effective appreciation and a corresponding deterioration of the competitive position.

11

economies (number of patents)9. (4) An indicator of how unequally income is nationally

distributed10. (5) Fiscal pressure

11. (6) Social expenditure as a percentage of GDP

12. And

finally,(7)asyntheticindicatorofthequalityoflifeindifferentcountrieswasalsoselected

tobepartofourdataset:femalelifeexpectancyatbirthinyears13.

4.Method

Thestructureofourdatasetallowsustomodelvariationontwolevels:countryandtime.In

thispaper,weareinterestedinstudyingtheimpactofvariablesrelatedtopullfactorsthat

mayvaryovertimeandcountriestoseehowtheycorrelatewiththearrivalofmigrantswith

tertiaryeducation.

Inthispaper,weoptedfortimeseriescountrylevelfixedeffectsregressionanalysis.Fixed

effectsallowustocontrolforanyobservableorunobservablepredictoratthecountrylevel

that does not vary over time (including, for instance country size). In this way, we can

concentrate on within-country action, in other words, the country level characteristics

subjecttochangeovertime.Thespecificationofourmodelsis:

Yit=β1Xit+αi+uit

9 To proxy the degree of innovation in the participating economies we also gathered data from the OECD International Cooperation in Patents dataset (see dataset). The selected information includes patent applications filed under the International Patent System and patent (Patent Co-operation Treaty; PCT) applications to the European Patent Office (EPO). Patents are a key measure of innovation output, as patent indicators reflect the inventive performance of countries, regions, technologies, firms, etc. They are also used to track the level of diffusion of knowledge across technology areas, countries, sectors, firms, etc. and the level of internationalization of innovative activities. Patent indicators can serve to measure the output of R&D, its productivity and structure and the development of a specific technology/industry. Among the few available indicators of technology output, patent indicators are probably the most frequently used. The relationship between patents as an intermediate output resulting from R&D inputs has been investigated extensively. Patents are often interpreted as an output indicator; however, they could also be viewed as an input indicator, as inventors use patents as a source of information. 10 Inequality measure by the GINI index comes from the Income Distribution and Poverty (see here) showing the distribution of disposable income, post taxes and transfers. 11 Information on taxes come from the Taxing Wages Comparative Dataset (see dataset: One earner married couple at 100 of average earnings, 2 children; Single worker at 100% of average earnings with no child; and childless single at 167% of average earnings). 12 Variables on social expenditure are obtained from the Social Expenditure –Aggregated Dataset (see dataset: active labor market programs as percentage of gross domestic product, unemployment, family, public health, public housing, old age [including pensions] and an overall measure of social expenditure as a percentage of GDP). 13 Information obtained from the OECD Health Status Dataset (see dataset).

12

Where Yit is the measure of dependent variable in country i (i=1....n) and year t

(i=1999....2013). αi unknown intercept for each country. β1 refers to the coefficient of a

givenindependentvariableanduitisanerrorterm.

In order to respect the logic of time explanations, in our analyses the effects of all

independentvariablesareestimatedwitha lagofoneyear (morethanoneyeardoesnot

changeour resultsbutshrinksoursamplesize)14.Finally,allourmodelsarecontrolled for

yearlyincreaseincountryGDP.

5.Results

Over time therehasbeen remarkable stability in the flowofhighly skilledworkers toour

selected European countries. Figure 1 includes two panels which show the temporal

evolutionoftheflowofhighlyskilledmigrantstoourdestinationsandhelpsusunderstand

thestructureofourdataset.

Thefigureisdividedintotwopanelsplottingthenumberofimmigrantsarrivingtoeachof

our destination countries. The first of them uses the criteria of having resided in the

destinationcountryforlessthanoneyear.Thesecondselectsmigrantshavingresidedfora

periodoffiveyears(whichexplainstheexclusionofyears2009-2014fromtheanalysiswhen

using this dependent variable). Accordingly, Germany, theUK and Spain appear for some

yearstobethecountriescontributingthelargestnumberofarrivals.

14 Results are available upon request.

13

Figure1.DescriptionoftheflowofHSMtoselectedEuropeancountries

Source:ELFSandEurostatstatistics.Ourcalculation

Theimpactofpullfactorsonthenumbersofskilledmigrantsamongnewcomers

Wenowanalyzetheimpactofourpullfactorsselectedmeasuresontheabsolutenumberof

foreignersarrivingwithtertiaryeducation.Recallthatbecauseoftheunder-identificationof

respondents of foreign nationality who lived for a short time in their countries of

immigration in the ELFS we opt for duplicating our analysis with two proxies of the

registeredyearlyflows.Todoso,weselectrespondentswhoseresidenceineachcountryis

less thanone year and those forwhom it is equal to five years. This duplicationnot only

worksasarobustnesscheckonourresults,butalsoallowsustoanalyzeiftheimportanceof

our pull factors is differentwhen predicting the joint arrivals of potential short and long-

termforeignresidents(yearsofresidence<1)oronlylongtermones(yearsofresidence=

5).Anydifferenceintheestimateofourproxiesforpullfactorscouldindicatethatagiven

characteristic of destination countries might be more important in attracting skilled

migrationwithdifferentintentionsofstaying.

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NL

FR

CY

EI

LTDK

CH

PT

ES

GR

SE

LX

DE

AT

IT

FI

020

000

4000

060

000

8000

010

0000

1200

0014

0000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Country year Year average

Yrs=5

14

Table 1 summarizes the results of the analyses conducted with both versions of our

dependentvariable.Ingeneral,theresultsareconsistentregardlessofwhetherweusedthe

versionofflowsthatcapturesallarrivalsorthatwhichonlyregistersimmigrantswhostayed

inthelongrun(atleastfiveyears).Returnstoeducationappearassignificantpullfactorsof

skilledmigration. Both proxies of the average wage at constant 2013 prices are positive,

significantlyassociatedwithskilledflowsandappeartobeasimilarsize(H1).Theestimate

correspondingtotheminimumwageisalsopositive,afindingalsoseeninGiulietti(2014a),

thoughitisonlysignificantforlongtermmigrants.Theunemploymentrateofworkerswith

tertiary education shrinks the number of graduate migrants (H3a). The duration of

unemployment (which reflects the percentage of unemployed workers by duration of

unemployment) is also a significant factor to consider. According to our prediction (H3b),

while short-termunemploymentdoesnot shrink the flowofeducatedmigrants (it iseven

associatedwithanintensificationofthiskindofflow),long-termunemploymentrepresents

a significantdisincentive to thearrivalof themost skilledmigrants.Our results contradict

previousresearchshowingapositiverelationshipbetweenincomeinequalityandHSM.Our

proxyofthedegreetowhicheconomiesareinnovativeappearstobeanirrelevantpredictor

ofthekindofflowsweareanalyzing.

Table1.Summaryoffindings.Pullfactorsonpercentageofskilledmigrants

Returnsto

education

Unit Estimate Welfare

magnet

Unit Estimate

Realmin.

wage

USD/100 Yrs=0 -0.27 Activelabor

market

policies

%GDP Yrs=0 -8493.4***

Yrs=5 11.2*** Yrs=5 -3248.3

Av.wage

2013PPP

NCU/100 Yrs=0 0.32* Pensions %GDP Yrs=0 -1122.0*

Yrs=5 2.36** Yrs=5 -2069.2

Av.wage

2013USD

USD/100 Yrs=0 0.27** Health %GDP Yrs=0 436.8

Yrs=5 1.66** Yrs=5 13429.5***

15

Patents

(PCT)

Number Yrs=0 0.23 Housing %GDP Yrs=0 -4492.8

Yrs=5 3.65 Yrs=5 -39460.4

Unemp.:

tertiary

% Yrs=0 -739.8** Socialexp.

(overall)

%GDP Yrs=0 -1367.9

Yrs=5 -

4176.8**

Yrs=5 36926.5***

Lessthan1

month

%maleu Yrs=0 86.9 Single100%

earnings,

childless

% Yrs=0 -1068.3***

Yrs=5 803.7* Yrs=5 50.0

From1to3

months

%maleu Yrs=0 200.7* Single167%

earnings,

childless

% Yrs=0 -896.7***

Yrs=5 621.0 Yrs=5 444.7

From3to6

months

%maleu Yrs=0 234.4 Femalelife

expectancy

Yrs. Yrs=0 497.6

Yrs=5 -113.1 Yrs=5 7626.1***

From6

monthsto1

yr

%maleu Yrs=0 23.0

Yrs=5 -339.2

Morethan

1year

%maleu Yrs=0 -160.4**

Yrs=5 -777.5**

GINI Scale0-1 Yrs=0 13359.2

Yrs=5 -

287723.2

Legend:NCU:NationalCurrencyUnits.USD:UnitedStatesDollars.

Source: Estimates obtained from time series regressionmodelswith country fixed effects

(modelsshownintablesA.3toA.7intheAppendix).AllmodelscontrolforGDPgrowth(1-

yearlag)

16

The argument of welfare structures operating as pull factors is also confirmed in our

analysis.Generalsocialexpenditureisassociatedwithintensificationofthearrivalofhighly

skilled migrants (H6). However, this is only the case for our second dependent variable,

whichweinterpretas longtermmigrantsbeingmoreattractedbysettings inwhichpublic

spendingishigher.However,notallareasofpublicspendinghaveapositivepullingeffect.

Confirming our expectations, spending on healthcare increases the flow, while public

spending on pensions and active labormarket policies are negative determinants ofHSM

(H7). Incontrast, fiscalpressuremaydeterthearrivalofskilledmigrants(H8),butweonly

seethiseffectfortheflowsthatincludeshortandlong-termstayers.Inotherwords,taxes

may deter the arrival of HSM mostly when their time horizon is not long term. This

statement is confirmed for both proxies of fiscal pressure we used (the case of a single

worker whose salary is the mean average earning, and one who is making 167% of the

averagewage).Finally,andinterestingly,ourhypothesisregardingmoreblurredaspectsof

social wellbeing being significant magnets for HSM is also accepted (H9). Female life

expectancyispositivelyassociatedwiththearrivalofHSMinbothcases,butonlysignificant

inthesecondofthem.

This summaryofour resultsdoesnotprovideuswitha clear impressionofwhichare the

most powerful pull factors for highly skilled migration. To be able to make comparative

statements about their strength in attracting the most skilled migrants, we need to

standardize our independent variables. Figures 2 and 3 analyze their impactmeasured in

standarddeviations,sothatwecandistinguishwheremostoftheactivityis.

17

Figure2.Summaryoffindingsfortheeffectofpullfactorsonthenumberofarrivalsofhighly skilledmigrants. Standardized estimates (dependent variable build using lessthan1yearofresidence)

Source: Estimates obtained from time series regression models with country fixed

effects(modelsshownintablesA.3toA.7intheAppendix).

Wages,andaboveall, fiscalpressureare themost influential.Our results showthat these

factorsare themost relevantpull factors inattractinghighlyskilledmigrants regardlessof

whethertheyaretobeshortorlong-termresidents.Theimpactofothersignificantfactors

inouranalysissuchasincludingtheunemploymentrateofworkerswithtertiaryeducation

appearstobemuchsmaller.

Overall,ourresultsarestableifwemeasuretheflowsusingonlytheinformationprovided

by long-term stayers. In that case, the strongest effects are those of wages and public

spending,butnotfiscalpressure.

Wages: 2013 constant prices at 2013 PPPs

Wages: 2013 constant prices at 2013 USD

Wages: Real Min. wage constant prices at 2013 USD

Prices: Relative consumer price index

Unemployment rate: tertiary education

Unemp: Less than 1 month

Unemp: From 1 to 3 months

Unemp: From 3 to 6 months

Unemp: From 6 months to 1 year

Unemp: More than 1 year

GINI

Patents: PCT

Social Exp: Active labor market prog.

Social Exp:Old age

Social Exp: Health

Social Exp: Housing

Social Exp: Social expenditure

Taxes: Single 100% av.earnings, no child

Taxes: Single 167% av.earnings, no child

Female life expectancy

-14000-12000-10000 -8000 -6000 -4000 -2000 0 2000 4000 6000 8000

18

Figure3.Summaryoffindingsfortheeffectofpullfactorsonthenumberofarrivalsofhighly skilledmigrants. Standardized estimates (dependent variable build using lessthan5yearsofresidence)

Source: Estimates obtained from time series regression models with country fixed

effects(modelsshownintablesA.3toA.7intheAppendix).

6.Summaryoffindings

This paper explores the impactof differentpull factors in attracting skilledmigration to a

numberofEuropeancountries.Wecontributetothe literatureby lookingata large listof

pullfactorsthatwesystematizeintwoblocksoftheoreticalargumentsrelatedtoreturnsto

educationandpublicwelfareconfigurations indestinationcountries.Wealsoelaborateda

comparisonofthestrengthofdifferentindicatorsofpullfactors.

In this paper,we looked at the evolution of numbers of skilledmigrants heading to each

country.Byusingfixedeffectsmodels,weneutralizedbetweencountrydeterminantssuch

ascountrysize,andconcentratedonwithincountryvariation.Becauseofinherentproblems

inourdataforthemeasurementofflows,weusedtwoversionsofourdependentvariable.

Wages: 2013 constant prices at 2013 PPPs

Wages: 2013 constant prices at 2013 USD

Wages: Real Min. wage constant prices at 2013 USD

Prices: Relative consumer price index

Unemployment rate: tertiary education

Unemp: Less than 1 month

Unemp: From 1 to 3 months

Unemp: From 3 to 6 months

Unemp: From 6 months to 1 year

Unemp: More than 1 year

GINI

Patents: PCT

Social Exp: Active labor market prog.

Social Exp:Old age

Social Exp: Health

Social Exp: Housing

Social Exp: Social expenditure

Taxes: Single 100% av.earnings, no child

Taxes: Single 167% av.earnings, no child

Female life expectancy

-40000 -20000 0 20000 40000 60000 80000

19

Oneofthemselectedmigrantswhosetimeofresidenceinthecountriesofdestinationwas

less than one year. The ELFS is less efficient in detecting newcomers than more settled

migrants.Forthisreason,wealsousedthesampleofmigrantswithresidenceperiodsofat

least 5 years to predict the impact of conditions in their host countries upon arrival. The

combinationofbothproxiesof thenumberofarrivalsallowsustocomparethe impactof

pullfactorsinattractingmigrantswithmoreandlessstablemigratoryplans.

Ourevidence indicatesthatcomparedtoother factorswages,oneofourkeymeasuresof

returnstoeducation,arethemostimportantpullfactorattractingHSM.Theargumentthat

welfaresystemsworkasmagnets isalsoconfirmedinourpaper,althoughitdoessomore

ambiguously.Whilepublicspendingiseffectiveinattractingeducatedforeigners,whostay

longer, lower levels of fiscal pressure could be more attractive to short-term foreign

residents.

Beyond these general comparative arguments,which refer to the relative strengthof pull

factors,ourpaperprovidesmoredetailedconclusions(adetailedsummaryofall results in

Table2).

Table 2. Summary of hypotheses, indicators and empirical results (signs and statistical

significance).

Mainargument Nyrs0 Nyrs5

Returns to

education

Wages Realminimumwage N P

AveragewagePPPs P P

AveragewageUSD P P

Prices Relativeconsumerprices P P

Unemployment

rate

N N

DurationofU. Lessthanamonth P P

1to3months P P

20

3to6months P P

6monthsto1year P N

Morethanayear N N

Patents PCT P P

Inequality GINI P N

Welfare

magnets

Taxes Singleat167%of

averageearnings,no

child

N P

Singleat100%of

averageearnings,no

child

N P

Socialexp. Activelabormarket

programs

N N

Oldage N N

Health P P

Housing N N

Socialexpenditure

(overall)

N P

Lifestyle Lifestyle Femalelifeexpectancy P P

Source:Authors’elaboration.

Higher salaries increase the number of HSM arriving to a given country. The evolution of

prices, by contrast, seems to be a non-relevant predictor. The risk of being unemployed

amongworkerswith tertiary education also pushes down the number of skilledmigrants

arriving to a given destination. Clearly, long-term (over 1 year) unemployment has a

deterrent effect for the kind of migration we here study. Other measures of returns to

educationsuchas thedistributionof income(GINI)or thedegreetowhicheconomiesare

more innovative are generally positively associated with the arrival of the most skilled

migrants,althoughinouranalysestherearenosignificanteffects.

21

Theideathatpublicprovisionofwelfareshouldbeconceptualizedasamagnetformigrants

alsoappliestothehighlyskilled.Inouranalysis,itdoessoinadifferentwaydependingon

whetherweusedthevariablethatcoversarrivalsofbothshortandlong-termstayers,orif

weonlylookedatthearrivalsoflongtermones.Fiscalpressureappearstodisincentivethe

arrivalofHSMonlyinthefirstcase,whilepublicspendingisasignificantpullfactorwhenwe

lookatthesecond.Weinterpretthisasproofthatthereisinconsistencyintermsofwhich

welfareconfigurationisthemostattractivetothebest-educatedmigrantsasitdependson

whethertheirintentionisashort-termstayoriftheyaimtobecomelong-termresidents.

Finally,wewouldliketocommentontheprincipallimitationsoftheseanalyses.Theexisting

internationaldatasetsmakeitverycomplicatedtocompareflowsacrosscountriesandover

time.We here propose away of resolving this difficulty using data from the ELFS. Aswe

show here LFS, which we used to calculate the percentage of highly skilled workers,

underestimates thenumbersof recentlyarrived foreigners.Ontheotherhand,calculating

flows from long-term residents skews the results, discounting outflows of short-term

stayers. Even though we tried to circumvent these difficulties, none of our dependent

variables fullydoesso.Thismayexplain thedifference in the resultswe foundonspecific

pullfactors,andmostimportantlyourproxiesofthewelfaremagnetargument.

22

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AppendixTableA.1.Migrants25-65,withtertiaryeducationandlessthan1yearofresidenceinselectedELFScountriesfrom1999to2013.Country N

FIFinland 78

CYCyprus 139

DKDenmark 168

GRGreece 184

LULuxembourg 188

PTPortugal 261

NONorway 282

ITItaly 345

SESweden 347

NLNetherlands 376

ATAustria 593

IEIreland 630

FRFrance 1016

BEBelgium 1669

CHSwitzerland 1670

ESSpain 1782

UKUnitedKingdom 2957

DEGermany 8964

Source:ELFS.Ourcalculations.

28

TableA.2.Summaryoftheavailabilityofcountryyearinformationonthearrivalsofmigrantswithtertiaryeducationinourdataset

Source:Authors’elaboration.

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

AT x x x x x x x x x x x x x x x

BE x x x x x x x x x x x x x x

DK x x x x x x x x x x x x x x

FI x x x x x x x x x x x x x x

FR x x x x x x x x x x x x x x

DE x x x x x x x x x x x x x x x

GR x x x x x x x x x x x x x x x

EIx x x x x x x x x x x x x x x

UK x x x x x x x x x x x x x x x

CY x x x x x x x x x x x x x x

IT x x x x x x x x x x

LX x x x x x x x x x x x x x x x

NL x x x x x x x x x x x x x x x

NO x x x x x x x x x x x x x x x

PT x x x x x x x x x x x x x x

ES x x x x x x x x x x x x x x x

CH x x x x x x x x x x x x x

SE x x x x x x x x x x x x x x

29

FigureA.1.Sampleofforeignersarrivedin2007bytimeofresidenceinthreeselectedcountries.

Source:ELFS.Ourcalculations.

30

FigureA.2.1Distributionofarrivalsofmigrantswithtertiaryeducation(aged25-65)inselectedELFScountriesfrom1999to2013(yearsofresidence=0)

Source:ELFS,ourcalculations.

010

0002

0000

3000

0400

000

1000

0200

0030

0004

0000

010

0002

0000

3000

0400

000

1000

0200

0030

0004

0000

2000 2005 2010 2015 2000 2005 2010 2015

2000 2005 2010 2015 2000 2005 2010 2015 2000 2005 2010 2015

AT BE DK FI FR

DE GR EI IT LX

NL NO PT ES CH

SE UK CY

Abso

lute

flow

of H

SM (y

ears

of r

esid

ence

=0)

31

FigureA.2.2Distributionofarrivalsofmigrantswithtertiaryeducation(aged25-65)inselectedELFScountriesfrom1999to2013(yearsofresidence=5)

Source:ELFS.Ourcalculations.

050

000

1000

0015

0000

050

000

1000

0015

0000

050

000

1000

0015

0000

050

000

1000

0015

0000

2000 2005 2010 2015 2000 2005 2010 2015

2000 2005 2010 2015 2000 2005 2010 2015 2000 2005 2010 2015

AT BE DK FI FR

DE GR EI IT LX

NL NO PT ES CH

SE UK CY

Abso

lute

flow

of H

SM (y

ears

of r

esid

ence

=5)

32

TableA.3Returnstoeducation(1)salariesandprices (1) (2) (3) (4) (5) (6) (7) (8) Yrs=0 Yrs=0 Yrs=0 Yrs=5 Yrs=5 Yrs=5 Yrs=0 Yrs=5RealMin.wage -0.27 11.2*** (0.47) (1.62) Averagewage2013PPP

0.32* 2.36**

(0.14) (0.70) Averagewage2013USD

0.27** 1.66**

(0.10) (0.52) Patents(PCT) 0.23 3.65 (0.54) (2.14)IncreaseinGDP 328.4 347.7** 353.2** 803.6 1061.1 995.2 233.3* 684.9 (168.6) (120.9) (118.2) (951.8) (857.6) (860.4) (110.5) (890.8)Constant 12854.3 -3781.3 -4380.6 -178000.4*** -63895.1* -52633.1* 8876.6*** 21820.4*** (9053.9) (5984.8) (5350.2) (30514.9) (28246.3) (26257.9) (1521.0) (5886.9)N 128 216 216 80 135 135 216 135Sigma(u) 7867.2 8251.8 8517.2 85244.7 37711.2 44276.6 8098.4 25665.0Sigma(e) 4800.9 4418.4 4398.7 11667.2 13607.2 13668.5 4471.5 14078.4F 3.48 4.73 5.66 23.8 6.14 5.57 2.29 1.92Allindependentvariablesare1yearlagged.Standarderrorsinparentheses*p<0.05,**p<0.01,***p<0.001Source: Authors’ elaboration

33

TableA.4Returnstoeducation(2)unemploymentrates (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Yrs=0 Yrs=0 Yrs=0 Yrs=0 Yrs=0 Yrs=5 Yrs=5 Yrs=5 Yrs=5 Yrs=5Lessthan1month 86.9 803.7* (83.3) (366.1) From1to3months

200.7* 621.0

(87.6) (364.7) From3to6months

234.4 -113.1

(133.5) (625.7) From6monthsto1year

23.0 -339.2

(118.1) (546.5) Morethan1year -160.4** -777.5** (54.0) (285.7)IncreaseinGDP 249.3* 309.4** 301.1* 256.5* 355.4** 1274.3 1487.5 884.6 912.2 2386.7* (113.4) (114.9) (116.2) (115.8) (116.5) (952.6) (1001.2) (1007.9) (959.9) (1071.2)Constant 8392.8*** 5429.3** 5309.2* 9050.3*** 14655.3*** 19523.8** 16960.7 32850.7** 36563.7*** 52404.7*** (1080.6) (1793.0) (2388.6) (2109.6) (1786.4) (5947.8) (8649.0) (12189.3) (9917.6) (8522.5)N 211 211 211 211 211 130 130 130 130 130Sigma(u) 8542.4 8733.0 8629.3 8558.3 8816.4 37255.1 36388.2 35569.4 35562.4 39117.1Sigma(e) 4499.5 4451.8 4476.4 4511.8 4412.0 14202.8 14321.9 14505.6 14482.6 14046.7F 3.02 5.15 4.04 2.48 6.98 2.91 1.94 0.50 0.67 4.21Allindependentvariablesare1yearlagged.Standarderrorsinparentheses*p<0.05,**p<0.01,***p<0.01Source: Authors’ elaboration

34

TableA.5Returnstoeducation(3)inequalityandpatents (1) (2) (3) (4) Yrs=0 Yrs=5 Yrs=0 Yrs=5GINI 13359.2 -287723.2 (42440.5) (269488.0) Patent(PCT) 0.23 3.65 (0.54) (2.14)Constant 5820.3 120061.6 8876.6*** 21820.4*** (12515.1) (79356.9) (1521.0) (5886.9)IncreaseinGDP

304.1* 2425.3 233.3* 684.9

(132.7) (2261.0) (110.5) (890.8)N 134 71 216 135Sigma(u) 9175.6 42251.6 8098.4 25665.0Sigma(e) 4465.1 17721.0 4471.5 14078.4F 2.76 1.04 2.29 1.92Allindependentvariablesare1yearlagged.Standarderrorsinparentheses*p<0.05,**p<0.01,***p<0.001Source: Authors’ elaboration

35

TableA.6Welfaremagnet(1)publicspendingas%ofGDP (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Yrs=0 Yrs=0 Yrs=0 Yrs=0 Yrs=0 Yrs=5 Yrs=5 Yrs=5 Yrs=5 Yrs=5Activelabormarketpolicies

-8493.4*** -3248.3

(2168.3) (10963.3) Pensions -1122.0* -2069.2 (500.9) (3638.0) Health 436.8 13429.5*** (642.2) (3248.1) Housing -4492.8 -39460.4 (4317.9) (20863.7)

Socialexpenditure(overall)

-1367.9 36926.5***

(1396.0) (8254.3)IncreaseinGDP 196.6 97.0 279.7* 223.8 138.2 1157.5 1155.7 1793.9 1392.4 2794.7** (105.4) (122.3) (135.6) (119.5) (140.8) (1023.1) (1008.2) (941.6) (1091.4) (985.5)Constant 16197.0*** 18454.0*** 6604.6 12448.6*** 12920.2*** 7830.5 21178.1 -114054.9 55967.1 -110936.2 (1761.8) (4043.0) (4173.9) (1906.3) (3577.0) (58897.3) (62146.7) (62073.7) (66221.3) (60133.2)N 200 200 200 176 200 125 125 125 111 125Sigma(u) 9190.7 7759.4 8424.8 8921.8 8521.2 36925.6 36829.9 37007.3 44989.3 56689.8Sigma(e) 4067.3 4178.7 4230.8 4422.2 4225.0 13948.8 13933.2 12940.9 14334.6 12789.0F 9.96 4.68 2.35 2.36 2.60 0.64 0.72 6.41 1.74 7.40Allindependentvariablesare1yearlagged.Standarderrorsinparentheses*p<0.05,**p<0.01,***p<0.001Source: Authors’ elaboration

36

TableA.7Welfaremagnet(2)publicspendingas%ofGDP (1) (2) (3) (4) (5) (6) Yrs=0 Yrs=0 Yrs=5 Yrs=5 Yrs=0 Yrs=5Singleat100%

-1068.3*** 50.0

(251.3) (1319.0) Singleat167%

-896.7*** 444.7

(243.4) (1282.4) Femalelifeexpectancy

497.6 7626.1***

(378.7) (1768.7)IncreaseinGDP

337.4** 321.7** 1276.6 1216.4 311.4* 770.8

(112.0) (113.0) (1019.4) (999.4) (125.6) (832.2)

Constant 52471.8*** 50477.5*** 27632.2 9397.8 -31667.4 -593509.7*** (10108.9) (11123.1) (52587.2) (58406.4) (31340.1) (144756.3)N 206 206 125 125 216 135Sigma(u) 11437.4 9657.7 36380.1 36827.7 8528.6 34589.9Sigma(e) 4316.2 4364.4 13896.4 13888.7 4454.1 13232.8F 12.1 9.74 0.87 0.93 3.08 9.83Allindependentvariablesare1yearlagged.Standarderrorsinparentheses*p<0.05,**p<0.01,***p<0.001Source: Authors’ elaboration