Skills

EnterpriseNoteNo.43

EducatedWorkersandManagersintheEU-27*

MohammadAmin

misBriefhighlightsissuesrelatedtotheeducationandskilllevelofworkersandtopmanagersin?rmsin27EuropeanUnioncountries(theEU-27),usingtheWorldBankEnterpriseSurveys(WBES).meexerciseisanimportantsteptowardunderstandingtheuseofskilledandadequately

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PublicDisclosureAuthorized

educatedworkersandtopmanagersbya?rmanditslikelyefects.meBriefidenti?esseveralfactorsattheNUTS2regionleveland?rmlevelthatarecorrelatedwiththedi般culty?rmsfaceinobtainingadequatelyeducatedworkersaswellastheskilllevelandeducationleveloftheworkersandtopmanagers.Somewhatsurprisingly,incomeperinhabitantintheNUTS2regionsisnotastrongpredictoroftheuseofskilledandeducatedworkersandtopmanagersor?rms’reporteddi般cultyin?ndingadequatelyeducatedworkers.Several?rmperformancemeasures—suchaslaborproductivity,employmentgrowth,exporting,researchanddevelopment(R&D),andmanagementquality—arefoundtobecorrelatedwiththeuseofskilledandeducatedworkersandtopmanagers.Someofthesecorrelationsdifersharplybetweenlowandhighlevelsoftheoutcomevariables.mereisevidencethattrainingprovidedtoworkersbythe?rmsisassociatedwithlessdispersionoflaborproductivitybetween?rms,andgreateruseofskilledworkersisassociatedwithlessdispersionofwageratesacross?rms.Overall,theBrief?ndsthatstartingatlow-incomelevelsinEUregions,policyfocusneedstoshiftmoretowardensuringtheavailabilityofadequatelyeducatedworkersthanonreducingotherobstaclesastheeconomydevelops.misshiftingofpolicyfocuscanstabilizeaftertheeconomyissu般cientlydeveloped.

Possiblecausesofaninadequatelyeducatedworkforceattheregionlevelandfirmlevel

Morethananyotherconstraint,?rmsintheEU–27countries1rankinadequatelyeducatedworkersastheirtopobstacle(seethe?rstBriefinthisseries).ForatypicalareaintheEuropeanUnionwithbetweenabout800,000and3millioninhabitants(NUTS2regions),227percentof?rmsreport“aninadequatelyeducatedworkforce”asthetopobstacletotheiroperations.Inmorethanhalfofthe186NUTS2groupingsanalyzedintheseries,thisobstacleisthemostfrequentlycited.Such?rmsaboundamong?rmsofdiferentsizes,sectors,incomegroups,andages(?gure1).However,theirproportionissigni?cantlyhigheramongmediumandlarge?rmscomparedtosmall?rms;manufacturing?rmscomparedtoservicessector?rms;mostdevelopedNUTS2regionsfollowedbytransitionregionsandthentheleastdevelopedregions;andolder?rms(morethan10years)comparedtoyounger?rms.mus,thesegroupsof?rmsmaybetargetedbypolicymakersonaprioritybasis.

merearesharpdiferencesbetweenNUTS2regionswithinacountryintheincidenceof?rmscitinginadequatelyeducatedworkforceasthetopobstacle(?gure2).mus,itisimportanttoalsoconsiderregionalorNUTS2–levelfactorstounderstandtheproblemofinadequatelyeducatedworkersfacedby?rms.

Figure2.meshareof?rmsthatreportinadequatelyeducatedworkforceasthetopobstaclevariessubstantiallybetweenNUTS2regions

Understandingregionalcharacteristicsthatarecorrelatedwiththelikelihoodof?rmsreportinginadequatelyeducatedworkforceasthetopobstacleisagoodstartingpointforidentifyingpossiblecausesofaninadequatelyeducatedworkforceanditslikelyefects,thetypeofpoliciesrequiredtoaddresstheproblem,andwhichtypesofpoliciesshouldbetargeted.

Economicdevelopment.memostnaturaldeterminantoftheavailabilityofadequatelyeducatedworkersisthelevelofeconomicdevelopment(seeLangeetal.2018).Macro–levelstudieshaveshownthatrichercountrieshaveamuchhigher

PublicDJanisclosu2re2Authorized,2025

*A擾liations:WorldBank,DevelopmentEconomics,EnterpriseAnalysis.Forcorrespondence:mamin@.

Acknowledgments:仍isBriefisapartofaseriesfocusingonissuesofregionaldisparitiesandgrowthopportunitiesintheEU-27area.仍eseriesisaproductoftheWorldBank’sEnterpriseAnalysisteam(DECEA)andhasbene?ttedfromgeneroussupportfromtheEUDGREGIOdirectorate.仍eteamwouldalsoliketothankNormanV.LoayzaandJorgeRodriguezMezaforcommentsandguidingthepublicationprocess.NancyMorrisonprovidedexcellenteditorialassistance.

Objectiveanddisclaimer:仍e?ndingsinthisseriesofBriefsdonotnecessarilyrepresenttheviewsoftheWorldBankGroup,itsExecutiveDirectors,orthegovernments

theyrepresent.AllBriefsintheseriescanbeaccessedvia:

/en/research/brief/global-indicators-briefs-series

.

ENTERPRISESURVEYS

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EnterpriseNoteNo.43

Figure1InadequatelyeducatedworkersisthebiggestobstacleforseveraltypesoffirmsinEU-27countries

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

Note:EU-27=the27membercountriesoftheEuropeanUnion(EU)intheeuroarea.

levelofhumancapitalthanthepoorercountries(seeLange,Wodon,andCarey2018).IstheproblemofinadequatelyeducatedworkerslessseverecomparedtootherobstaclesinthemoredevelopedNUTS2regions?DopolicymakersneedtofocuslessoneducationofworkersandmoreonotherobstaclesasincomeleveloftheNUTS2regionsrises?

medatarevealthatmore?rmsrankinadequatelyeducatedworkersasthetopobstacleasincomeperinhabitantincreases(?gure3).However,thisincreasetapersofandbecomesinsigni?cantaboveacertainthresholdlevelofincome.merearetwoimplicationsforpolicy.First,comparedtopoorerNUTS2regions,richerregionsneedtofocusmoreonensuring

Figure2

Theshareof

substantially

firmsthatreportinadequatelyeducatedworkforceasthetopobstaclevariesbetweenNUTS2regions

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

Note:NUTS2regionshavebetweenabout800,000and3millioninhabitants.NUTS=NomenclatureofTerritorialUnitsforStatistics.

EnterpriseNoteNo.43

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Figure3usdis,onceoftenasthetopobstacleastheincomelevelof

Incomeperinhabitant(logs,2019)

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

Note:NUTS2regionshavebetweenabout800,000and3millioninhabitants.NUTS=NomenclatureofTerritorialUnitsforStatistics.

theavailability,relativetodemand,ofadequatelyeducatedworkersthanonotherobstacles.Second,startingatlow-incomelevels,policyfocusneedstoshiftmoretowardensuringtheavailability,relativetodemand,ofadequatelyeducatedworkersthanonreducingotherobstaclesastheeconomydevelops.misshiftingofpolicyfocuscanstabilizeaftertheeconomyissu伍cientlydeveloped.

Othermeasures.Severalothermeasuresareavailabletocapturetheuseandavailabilityofskilledandadequatelyeducatedworkersandtopmanagers.Somearebasedon?rms’perceptionsandothersareobjectivemeasures.meanalysisthatfollowsfocuseson15indicatorsoftheuseofskilledandeducatedworkersandtopmanagersand?rms’reporteddi伍cultyin?ndingthemwhenaveragedattheNUTS2level.meseindicatorspotentiallycaptureboththedemandandsupplyofskilledandeducatedworkersandmanagers.meanalysisexaminestheirrelationshipwithincomeperinhabitantasof2019.meresultsaremixed,

First,asexpected,higherincomeisassociatedwithasigni?cantlyhighershareofskilledworkersamongproductionworkersinthemanufacturingsectors,andalowershareofsemi-skilledandlow-skilledworkers(?gure4).

Second,threeotherindicatorsshowsigni?cantlybetterskillsavailabilityinthericherNUTS2regions.meseindicatorsaretheproportionof?rmsthatprovidetrainingtotheirworkers;theproportionof?rmsthatfacedi伍cultyin?ndingworkerswithforeignlanguageskills;andtheproportionof?rmsthatfacedi伍cultyin?ndingworkerswithtechnicalskills(otherthanininformationtechnology,IT),vocationalskills,orjob-speci?cskills.

mird,threemoreindicatorsshowthattheproportionof?rmsthatfacedi伍culty?ndingworkerswithnaturalsciences,

mathematics,andengineeringskillsissigni?cantlyhigherinthericherNUTS2regions.

Fourth,theremainingeightindicatorsshownosigni?cantcorrelationwiththeincomelevel.meseindicatorsaretheproportionof?rmsthatfacedi伍cultyin?ndingworkerswithappropriateinterpersonalandcommunicationskills,problemsolvingorcriticalthinkingskills,managerialandleadershipskills,computerorgeneralITskills;theproportionof?rmsthatreportinadequatelyeducatedworkersasthetopobstacle;thepercentageofworkerswithasecondaryeducationina?rmonaverage;thepercentageofworkerswithauniversitydegreeina?rmonaverage;andthepercentageof?rmswiththetopmanagerhavingabachelor’sorhigherdegree.

Foracoupleofvariables,incomemattersatsu伍cientlyhighlevelsbutnototherwise.matis,aboveacertainlevelofincome,butnotbelow,higherincomeisassociatedwithasigni?cantlyhigherproportionofworkerswithauniversityeducation(?gure5)andasigni?cantlyhigherproportionof?rmswiththetopmanagerhavingabachelor’sorhigherdegree.Summingup,whileeconomicdevelopmentmaysomewhatimprovetheavailabilityofskilledandeducatedworkersandtopmanagersrelativetodemand,itisunlikelytosolvetheproblemofinadequatelyskilledandeducatedworkersandtopmanagersinamajorwayorcompletely.

Differentialsintraining,education,andskillsandtheireffects

Training

FirmsinEU-27countriesoftenprovidetrainingtotheirworkers.InatypicalNUTS2region,43percentofthe?rmsprovidesuchtraining.Asmentioned,theproportionof?rms

EnterpriseNoteNo.43

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Percentoffirms

60

50

40

30

20

10

0

Figure4FirmsinricherNUTS2regionsemploymoreskilledworkersandaremorelikelytoprovidetraining

4949

35

29

Skilledproductionworkers

(%)

%offirmsthatprovidetraining

LeastdevelopedLTransitionandMostdevelopedeastdeveloped

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

Note:NUTS2regionshavebetweenabout800,000and3millioninhabitants.NUTS=NomenclatureofTerritorialUnitsforStatistics.

thatprovidetrainingincreaseswiththeincomeleveloftheNUTS2regions.However,thisrelationshipislargelydrivenbyNUTS2regionsatthelowendoftheincomedistribution.Aboveacriticallevelofincome,increasesinincomeshownofurtherincreaseintheproportionof?rmsthatprovidetraining.meprovisionoftrainingmaybeespeciallyattractiveforlarge?rmsduetothe?xedcostsinvolved(seeFrazis,

Gittlemann,andJoyce2000).Afewstudieshavealsoshownthatyounger?rmsaremorelikelytotrainworkers.IntheEU–27countries,thereisnosigni?cantrelationshipbetweenthelikelihoodthata?rmprovidestrainingandtheageofthe?rm.However,trainingissigni?cantlymorecommonamonglarge?rmsthansmallandmediumenterprises(SMEs).About41percentofSMEscomparedto70percentoflarge?rms

Figure5

Theshareofworkerswithauniversitydegreedecreaseswithhigherincomelevelatinitiallevelsofincomebutincreasesathigherlevelsofincome

Incomeperinhabitant(logs)

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

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providetraining.AttheNUTS2and?rm-level,trainingismorelikelyamong?rmsthatreportthatinadequatelyeducatedworkersisamoresevereobstacle(on0–4scale)fortheiroperations.mus,itseemsthattrainingisinpartaimedatresolvingtheshortageofskilledworkers.

Oneconcernwithtrainingisthatitmaybeameresubstituteforeducationacquiredoutsidethe?rm.misis“traindrain.”Iftrue,itimpliesthatagreateravailabilityofhighereducatedworkersoutsidethe?rmmayleadtolesstrainingprovidedby?rms.Asaresult,trainingby?rmsmaynotincreasethetotalstockofhumancapitalinthecountry.Bycontrast,ifhighereducationandtrainingarecomplements—aswouldbethecaseifnewlyhiredgraduatesalsoreceivedadditional,on-the-jobtraining—theoverallstockofhumancapitalwillincreaseduetotraining.InthecaseoftheEU-27countries,attheNUTS2level,thereisnoevidenceof“traindrain”foreitheruniversity-educatedorsecondary-educatedworkers.Infact,thereisasigni?cantpositiverelationshipbetweentrainingandtheshareofuniversity-educatedworkersina?rm,suggestingthattraininganduniversityeducationarecomplements(?gure6).

Trainingseemstoalterthedistributionoflaborproductivityacross?rms.Averagelaborproductivityishigherbyabout48percentfor?rmsthatprovidetrainingcomparedto?rmsthatdonot.mediferenceishighlysigni?cant.Whatismore,thereisevidencethattrainingleadstoamuchalargerimprovementinlaborproductivityofthelessproductive?rmsthanthemoreproductive?rms.3mus,thereisthepossibilitythattrainingmayallowthelessproductive?rmstocatchupwiththemoreproductive?rms(box1).

Educationandskilllevels

OnaverageacrossthetenEU-27countriesforwhichdataareavailable,aboutoneintenworkersintheEU-27hasauniversitydegree.medistributionofuniversity-educatedworkersacross?rmsisskewed.Only30percentof?rmsemployuniversity-educatedworkersatall.Large?rms,exporters,andforeign-owned?rmsaremorelikelytoemployuniversity-educatedworkersand,inturn,alsoemployhigherproportionsofuniversity-educatedworkers(table1,columns1and2).FirmsthatspendonR&Dalsohaveproportionatelymoreuniversity-educatedworkers,butthisrelationshipismainlybecause?rmsthatspendonR&Dhappentobelarge?rms,whichtendtoemploymoreuniversity-educatedworkers.Akeyconcernforpolicymakersiswhyonlyone-quarterofSMEsemployuniversity-educatedworkersandtheirshareaverageslessthan9percentofallworkers.DoSMEs?ndtheseworkerstoocostlyoraretheylackinginthekindsofskillsusefultoSMEs?

Most?rmsintheEU-27countriesuseskilledproductionworkers.Nearly80percentof?rmsemployskilledproductionworkers,andtheaverageshareofskilledworkersamongallproductionworkersina?rmis44percent.Incontrasttouniversity-educatedworkers,theshareofskilledworkersamongproductionworkersdeclinessigni?cantlywith?rmsize(table1,column3),whiletheshareofthesemi-skilledandlow-skilledworkersincreases.Skilledworkersandsecondary-educatedworkersseemtocomplementeachother.matis,theshareofsecondary-educatedworkersissigni?cantlyhigherfor?rmsthathaveahigherproportionofskilledworkers(table1,column4)andalowerproportionofsemi-and

Figure6FirmsacrossNUTS2regionsseemtoprovidetrainingtouniversity-educatedworkers

%offirmsthatoffertraining

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

Note:NUTS2regionshavebetweenabout800,000and3millioninhabitants.NUTS=NomenclatureofTerritorialUnitsforStatistics.

EnterpriseNoteNo.43

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Box1:Canlessproductivefirmscatchupwiththemoreproductivefirmsthroughtraining?

mepossibilityof“catchup”canbetestedusingthemethodologyofCombesetal.(2012).mismethodologytestsfordiferencesinthedistributionofavariablebetweentwogroups.mecomparisonissummarizedinthreekeyparameters—shift,dilation,andtruncation.meseparametersrefertohowmuchthe?rstdistributionneedstobealteredtobestapproximatetheseconddistribution.meparametersare(1)arightwardshiftofthe?rstdistribution(Shift);(2)whatconstantfactoreachoftheobservationsinthe?rstdistributionneedtobedividedbytomatchtheseconddistribution(Dilation);and(3)whatshareoftheobservationsinthe?rstdistributionneedtobeexcludedfromitslefttail(Truncation).Intuitively,theShiftparametercapturesthediferenceinthemeanvalueoflaborproductivity,Dilationcapturesifonedistributionismorehomogenousthantheother.TruncationreAectspossibleselectionefectswhereby?rmswithverylowvaluesofthevariableunderconsiderationaremorelikelytosurviveinonegroupthantheother.

TableB1.1providestheestimatesofthethreeparametersforthedistributionsoflaborproductivityof?rmsthatprovidetrainingversusthosethatdonot(column1)andforthebottomhalfversusthetophalfoftheNUTS2regionsintermsofthepercentageof?rmsthatprovidetraining(column2).mestatisticalsigni?canceshownisforthefollowingnullhypothesis:Shift=0,Dilation=1,Truncation=0,whichbasicallybenchmarksthecasethatthedistributionsarethesame.

Considercolumn1?rst.Asmaybeexpected,Shift>0,implyingthatlaborproductivityishigherfor?rmsthatprovidetraining.meDilationfactorislessthan1andstatisticallysigni?cantlyso(atthe1percentlevel).misimpliesthatthedistributionoflaborproductivityismorehomogeneousamong?rmsthatprovidetrainingthanthosethatdonot.Inotherwords,laborproductivityismoredispersedandheterogenousamong?rmsthatdonotprovidetraining.FigureB1.1illustratesthepointgraphically.meresultsarequalitativelysimilarwhencomparingthedistributionoflaborproductivityinthebottomhalfversustophalfoftheNUTS2regionsintermsofthepercentageof?rmsthatprovidetraining(column2).

Tosummarize,trainingprovidedby?rmstotheirworkersbene?tstherelativelylessproductive?rmsmoreandtherebynarrowsthedispersionoflaborproductivity.Asaresult,trainingallowsthelessproductive?rmstocatchupwiththemoreproductive?rms.miscanhaveimportantefectsonthepossible(mis)allocationofresources,withconsequentefectsontheoverallproductivityoftheregionsandcountries(seeHsiehandKlenow2009;HeiseandPorzio2022).

TableB1.1.Howtrainingafectsthedistributionof(logof)

laborproductivityof?rmsandNUTS2regions

(1)

(2)

Shift

1.973***

(0.249)

2.668***

(0.290)

Dilation

0.866***

(0.021)

0.817***

(0.024)

Truncation

-0.002

(0.006)

.0004

(0.010)

R-squared

0.986

0.984

Observations

17,236

17,292

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

Note:NUTS2regionshavebetweenabout800,000and3millioninhabitants.

ForDilation,thesigni?cancelevelisforthedeviationfrom1.Bootstrappedstandarderrorswith500replicationsshowninparentheses.NUTS=NomenclatureofTerritorialUnitsforStatistics.

***p<0.01

FigureB1.1.Distributionoflaborproductivityismorehomogenousamong?rmsthatprovidetraining

5

10

Laborproductivity(logs)

Firmprovidestraining

Firmdoesnotprovidetraining

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

EnterpriseNoteNo.43

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Table1Relationshipbetweentheuseofskilledandhighlyeducatedworkersandjobsgrowth

Dependentvariable:

Shareof

university-

educated

workers(%)

Firmemploys

university-

educated

workersY:1N:0

(Marginaleffects)

Shareof

skilledamong

production

workers

(%)

Shareof

secondary-

educated

workers

(%)

Employmentgrowthrate,(%,annual)

(1)(2)(3)(4)(5)(6)

ExporterY:1N:0

Foreign

ownershipY:1N:0

Numberofworkers

(logs)

Shareofskilled

amongproductionworkers(%)

Shareofsemi-

6.787***

(1.673)

10.351***

(2.588)

1.607**

(0.748)

skilledamongproduction

workers(%)

Shareofuniversity

educatedworkers(%)Multiestablishment?rmY:1N:0

Ageof?rm

(logs,years)

Numberofworkers

3?scalyearsago(logs)Industrydummies

(ISIC,2digit)Constant

NumberofobservationsR-squared

1.556

(1.877)

-0.438

(0.669)

Yes

4.005*

(2.181)

3,915

0.306

0.085***

(0.025)0.072**

(0.029)

0.166***

(0.012)

0.011(0.030)0.003(0.012)

Yes

3,913

0.170(2.174)

0.148

(2.978)

-6.865***(0.886)

1.715(2.829)2.447*(1.318)

Yes

56.437***

(4.453)

9,233

0.082

-3.132*(1.845)

-0.769(2.661)

-0.163

(0.815)0.074**(0.029)

-1.052(3.070)

-0.088(1.182)

Yes

65.030***

(4.637)

8,344

0.048

1.810**

(0.802)2.438**

(1.169)

-0.026**(0.012)

-0.020*(0.011)

2.078**

(0.834)

-2.352***(0.455)

-1.972***

(0.327)

Yes

15.779***

(2.056)

8,873

0.076

0.981(1.037)

1.254(1.888)

0.024(0.038)3.607**(1.690)

-3.212***(0.775)

-2.517***

(0.452)

Yes

17.678***

(2.770)

3,779

0.127

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

Note:Huber-WhiterobuststandarderrorsclusteredonNUTS2levelinbrackets.Logit(marginaleffects)estimationincolumn2andordinaryleastsquares(OLS)inalltheothercolumns.NUTS2regionshavebetweenabout800,000and3millioninhabitants.NUTS=NomenclatureofTerritorialUnitsforStatistics.

***p<0.01,**p<0.05,*p<0.1

low-skilledworkers.meshareofskilledproductionworkersisalsolowerfor?rmsthatusemanualproductionprocesses.However,thisrelationshipbecomesweakandstatisticallyinsigni?cantafteraccountingfor?rmsize.

Employmentgrowth

Oneconcernisthatgreateruseofskilledworkersislaborsavingandthereforeitmayhinderjobsgrowth.Skilledlaborisoftenaccompaniedbygreateruseofcomputers,robots,andotherlabor-savingtechnologies.Italsoembodiesgreaterhumancapitalthanlow-orunskilledworkers,whichmayreducetheneedforadditionalworkers.However,itisalsopossiblethatskilledworkersmayboost?rmproductivityand

growth,whichmayleadtomorejobsoverall.

meempiricalevidenceontheissueismixedingeneral(see,forexample,BalsmeierandMartin2019;Jungetal.2017)andintheEU-27countries,inparticular.First,controllingforconvergenceortheinitiallevelofemploymentatthe?rm,thegrowthrateofemploymentoverthelastthree?scalyearssigni?cantlydeclinesastheshareofskilledandsemi-skilledworkersrises(table1,column5).misresultisdrivenbydiferencesbetween?rmswithinNUTS2regionsratherthanacrossregions.mus,macro-levelstudiesthatexplorediferencesacrossregionsbutnotwithinregionsmaynotdetectalowergrowthrateofemploymentassociatedwithgreateruseofskilledandsemi-skilledworkers.Second,thereisno

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signi?cantrelationshipbetweenthegrowthrateofemploymentandtheshareofworkersthathaveauniversitydegree(table1,column6)orsecondaryeducation.Overall,theevidenceontherelationshipbetweentheuseofskilledandhighlyeducatedworkersandjobsgrowthintheEU–27countriesisinconclusive.

Laborproductivity

Ahighershareofuniversity–andsecondary–educatedworkersisassociatedwithhigherlaborproductivity.However,thisrelationshipisweakandstatisticallyinsigni?cantatlowerquantilesoflaborproductivity,andlargeandsigni?cantathigherquantilesinEU–27countries.Forinstance,aonestandarddeviationincreaseintheshareofuniversity–educatedworkersisassociatedwithanincreaseinlaborproductivityby2percentoftheinitiallevel(insigni?cantatthe10percentlevel)atthe20thpercentileoflaborproductivityandby24.6percent(signi?cantatthe1percentlevel)atthe80thpercentile.mus,itisthemoreproductive?rmsthattakeadvantageofmoreeducatedworkers,whilethelessproductive?rmsarecompletelydeprivedofanybene?ts.Assumingthattherearesubstantialgainstobereapedbyimprovingproductivityatthelowend,policymakersshouldtrytoincreaseeducatedworkers,usefulnessoruseotherpolicytoolsforthelessproductive?rms.

AttheNUTS2regionslevel,ahighershareofuniversity–educatedworkersispositivelycorrelatedwithlaborproductivityatrelativelylowlevelsofincome(belowthemedian),butthereisnosigni?cantcorrelationbetweenthetwoathighlevelsofincome.mus,poorerregionsbene?tmorefromanincreaseinuniversity–educatedworkersthanthericher

regions.miscouldbebecauseofdiminishingreturnstoeducation,giventhatpoorerregionstypicallyhavefeweruniversity–educatedworkers.Anotherreasoncouldbemoreimitationandinnovationpossibilitiesinthepoorerregionsthatcomplementuniversity–educatedworkers.

Atthe?rmlevel,laborproductivityishigherfor?rmsthathaveahigherproportionofskilledworkersamongproductionworkers,butthisrelationshipisnotstatisticallysigni?cant.However,acrossNUTS2regions,thereisastrongandsigni?cantpositiverelationshipbetweenthetwo.Likewise,highersharesofsemi–skilledandlow–skilledworkersacrossNTUS2regionsissigni?cantlyandnegativelycorrelatedwithlaborproductivity.mereissharpdiferenceintheserelationshipsatlowversushighlevelsoflaborproductivity.matis,attheNUTS2levelandthe?rmlevel,therelationshipbetweenlaborproductivityandtheshareofskilledworkersispositiveandsigni?cantatlowerquant