【世界银行】欧盟27国受过教育的工人和管理人员-2025

Skills

EnterpriseNoteNo.43

EducatedWorkersandManagersintheEU-27*

MohammadAmin

misBriefhighlightsissuesrelatedtotheeducationandskilllevelofworkersandtopmanagersinfirmsin27EuropeanUnioncountries(theEU-27),usingtheWorldBankEnterpriseSurveys(WBES).meexerciseisanimportantsteptowardunderstandingtheuseofskilledandadequately

T

PublicDisclosureAuthorized

educatedworkersandtopmanagersbyafirmanditslikelyefects.meBriefidentifiesseveralfactorsattheNUTS2regionlevelandfirmlevelthatarecorrelatedwiththedi般cultyfirmsfaceinobtainingadequatelyeducatedworkersaswellastheskilllevelandeducationleveloftheworkersandtopmanagers.Somewhatsurprisingly,incomeperinhabitantintheNUTS2regionsisnotastrongpredictoroftheuseofskilledandeducatedworkersandtopmanagersorfirms’reporteddi般cultyinfindingadequatelyeducatedworkers.Severalfirmperformancemeasures—suchaslaborproductivity,employmentgrowth,exporting,researchanddevelopment(R&D),andmanagementquality—arefoundtobecorrelatedwiththeuseofskilledandeducatedworkersandtopmanagers.Someofthesecorrelationsdifersharplybetweenlowandhighlevelsoftheoutcomevariables.mereisevidencethattrainingprovidedtoworkersbythefirmsisassociatedwithlessdispersionoflaborproductivitybetweenfirms,andgreateruseofskilledworkersisassociatedwithlessdispersionofwageratesacrossfirms.Overall,theBrieffindsthatstartingatlow-incomelevelsinEUregions,policyfocusneedstoshiftmoretowardensuringtheavailabilityofadequatelyeducatedworkersthanonreducingotherobstaclesastheeconomydevelops.misshiftingofpolicyfocuscanstabilizeaftertheeconomyissu般cientlydeveloped.

Possiblecausesofaninadequatelyeducatedworkforceattheregionlevelandfirmlevel

Morethananyotherconstraint,firmsintheEU–27countries1rankinadequatelyeducatedworkersastheirtopobstacle(seethefirstBriefinthisseries).ForatypicalareaintheEuropeanUnionwithbetweenabout800,000and3millioninhabitants(NUTS2regions),227percentoffirmsreport“aninadequatelyeducatedworkforce”asthetopobstacletotheiroperations.Inmorethanhalfofthe186NUTS2groupingsanalyzedintheseries,thisobstacleisthemostfrequentlycited.Suchfirmsaboundamongfirmsofdiferentsizes,sectors,incomegroups,andages(figure1).However,theirproportionissignificantlyhigheramongmediumandlargefirmscomparedtosmallfirms;manufacturingfirmscomparedtoservicessectorfirms;mostdevelopedNUTS2regionsfollowedbytransitionregionsandthentheleastdevelopedregions;andolderfirms(morethan10years)comparedtoyoungerfirms.mus,thesegroupsoffirmsmaybetargetedbypolicymakersonaprioritybasis.

merearesharpdiferencesbetweenNUTS2regionswithinacountryintheincidenceoffirmscitinginadequatelyeducatedworkforceasthetopobstacle(figure2).mus,itisimportanttoalsoconsiderregionalorNUTS2–levelfactorstounderstandtheproblemofinadequatelyeducatedworkersfacedbyfirms.

Figure2.meshareoffirmsthatreportinadequatelyeducatedworkforceasthetopobstaclevariessubstantiallybetweenNUTS2regions

Understandingregionalcharacteristicsthatarecorrelatedwiththelikelihoodoffirmsreportinginadequatelyeducatedworkforceasthetopobstacleisagoodstartingpointforidentifyingpossiblecausesofaninadequatelyeducatedworkforceanditslikelyefects,thetypeofpoliciesrequiredtoaddresstheproblem,andwhichtypesofpoliciesshouldbetargeted.

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

PublicDJanisclosu2re2Authorized,2025

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

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

Objectiveanddisclaimer:仍efindingsinthisseriesofBriefsdonotnecessarilyrepresenttheviewsoftheWorldBankGroup,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?

medatarevealthatmorefirmsrankinadequatelyeducatedworkersasthetopobstacleasincomeperinhabitantincreases(figure3).However,thisincreasetapersofandbecomesinsignificantaboveacertainthresholdlevelofincome.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.Somearebasedonfirms’perceptionsandothersareobjectivemeasures.meanalysisthatfollowsfocuseson15indicatorsoftheuseofskilledandeducatedworkersandtopmanagersandfirms’reporteddi伍cultyinfindingthemwhenaveragedattheNUTS2level.meseindicatorspotentiallycaptureboththedemandandsupplyofskilledandeducatedworkersandmanagers.meanalysisexaminestheirrelationshipwithincomeperinhabitantasof2019.meresultsaremixed,

First,asexpected,higherincomeisassociatedwithasignificantlyhighershareofskilledworkersamongproductionworkersinthemanufacturingsectors,andalowershareofsemi-skilledandlow-skilledworkers(figure4).

Second,threeotherindicatorsshowsignificantlybetterskillsavailabilityinthericherNUTS2regions.meseindicatorsaretheproportionoffirmsthatprovidetrainingtotheirworkers;theproportionoffirmsthatfacedi伍cultyinfindingworkerswithforeignlanguageskills;andtheproportionoffirmsthatfacedi伍cultyinfindingworkerswithtechnicalskills(otherthanininformationtechnology,IT),vocationalskills,orjob-specificskills.

mird,threemoreindicatorsshowthattheproportionoffirmsthatfacedi伍cultyfindingworkerswithnaturalsciences,

mathematics,andengineeringskillsissignificantlyhigherinthericherNUTS2regions.

Fourth,theremainingeightindicatorsshownosignificantcorrelationwiththeincomelevel.meseindicatorsaretheproportionoffirmsthatfacedi伍cultyinfindingworkerswithappropriateinterpersonalandcommunicationskills,problemsolvingorcriticalthinkingskills,managerialandleadershipskills,computerorgeneralITskills;theproportionoffirmsthatreportinadequatelyeducatedworkersasthetopobstacle;thepercentageofworkerswithasecondaryeducationinafirmonaverage;thepercentageofworkerswithauniversitydegreeinafirmonaverage;andthepercentageoffirmswiththetopmanagerhavingabachelor’sorhigherdegree.

Foracoupleofvariables,incomemattersatsu伍cientlyhighlevelsbutnototherwise.matis,aboveacertainlevelofincome,butnotbelow,higherincomeisassociatedwithasignificantlyhigherproportionofworkerswithauniversityeducation(figure5)andasignificantlyhigherproportionoffirmswiththetopmanagerhavingabachelor’sorhigherdegree.Summingup,whileeconomicdevelopmentmaysomewhatimprovetheavailabilityofskilledandeducatedworkersandtopmanagersrelativetodemand,itisunlikelytosolvetheproblemofinadequatelyskilledandeducatedworkersandtopmanagersinamajorwayorcompletely.

Differentialsintraining,education,andskillsandtheireffects

Training

FirmsinEU-27countriesoftenprovidetrainingtotheirworkers.InatypicalNUTS2region,43percentofthefirmsprovidesuchtraining.Asmentioned,theproportionoffirms

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,increasesinincomeshownofurtherincreaseintheproportionoffirmsthatprovidetraining.meprovisionoftrainingmaybeespeciallyattractiveforlargefirmsduetothefixedcostsinvolved(seeFrazis,

Gittlemann,andJoyce2000).Afewstudieshavealsoshownthatyoungerfirmsaremorelikelytotrainworkers.IntheEU–27countries,thereisnosignificantrelationshipbetweenthelikelihoodthatafirmprovidestrainingandtheageofthefirm.However,trainingissignificantlymorecommonamonglargefirmsthansmallandmediumenterprises(SMEs).About41percentofSMEscomparedto70percentoflargefirms

Figure5

Theshareofworkerswithauniversitydegreedecreaseswithhigherincomelevelatinitiallevelsofincomebutincreasesathigherlevelsofincome

Incomeperinhabitant(logs)

Source:OriginalcalculationsforthisBriefbasedonWorldBankEnterpriseSurveys.

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providetraining.AttheNUTS2andfirm-level,trainingismorelikelyamongfirmsthatreportthatinadequatelyeducatedworkersisamoresevereobstacle(on0–4scale)fortheiroperations.mus,itseemsthattrainingisinpartaimedatresolvingtheshortageofskilledworkers.

Oneconcernwithtrainingisthatitmaybeameresubstituteforeducationacquiredoutsidethefirm.misis“traindrain.”Iftrue,itimpliesthatagreateravailabilityofhighereducatedworkersoutsidethefirmmayleadtolesstrainingprovidedbyfirms.Asaresult,trainingbyfirmsmaynotincreasethetotalstockofhumancapitalinthecountry.Bycontrast,ifhighereducationandtrainingarecomplements—aswouldbethecaseifnewlyhiredgraduatesalsoreceivedadditional,on-the-jobtraining—theoverallstockofhumancapitalwillincreaseduetotraining.InthecaseoftheEU-27countries,attheNUTS2level,thereisnoevidenceof“traindrain”foreitheruniversity-educatedorsecondary-educatedworkers.Infact,thereisasignificantpositiverelationshipbetweentrainingandtheshareofuniversity-educatedworkersinafirm,suggestingthattraininganduniversityeducationarecomplements(figure6).

Trainingseemstoalterthedistributionoflaborproductivityacrossfirms.Averagelaborproductivityishigherbyabout48percentforfirmsthatprovidetrainingcomparedtofirmsthatdonot.mediferenceishighlysignificant.Whatismore,thereisevidencethattrainingleadstoamuchalargerimprovementinlaborproductivityofthelessproductivefirmsthanthemoreproductivefirms.3mus,thereisthepossibilitythattrainingmayallowthelessproductivefirmstocatchupwiththemoreproductivefirms(box1).

Educationandskilllevels

OnaverageacrossthetenEU-27countriesforwhichdataareavailable,aboutoneintenworkersintheEU-27hasauniversitydegree.medistributionofuniversity-educatedworkersacrossfirmsisskewed.Only30percentoffirmsemployuniversity-educatedworkersatall.Largefirms,exporters,andforeign-ownedfirmsaremorelikelytoemployuniversity-educatedworkersand,inturn,alsoemployhigherproportionsofuniversity-educatedworkers(table1,columns1and2).FirmsthatspendonR&Dalsohaveproportionatelymoreuniversity-educatedworkers,butthisrelationshipismainlybecausefirmsthatspendonR&Dhappentobelargefirms,whichtendtoemploymoreuniversity-educatedworkers.Akeyconcernforpolicymakersiswhyonlyone-quarterofSMEsemployuniversity-educatedworkersandtheirshareaverageslessthan9percentofallworkers.DoSMEsfindtheseworkerstoocostlyoraretheylackinginthekindsofskillsusefultoSMEs?

MostfirmsintheEU-27countriesuseskilledproductionworkers.Nearly80percentoffirmsemployskilledproductionworkers,andtheaverageshareofskilledworkersamongallproductionworkersinafirmis44percent.Incontrasttouniversity-educatedworkers,theshareofskilledworkersamongproductionworkersdeclinessignificantlywithfirmsize(table1,column3),whiletheshareofthesemi-skilledandlow-skilledworkersincreases.Skilledworkersandsecondary-educatedworkersseemtocomplementeachother.matis,theshareofsecondary-educatedworkersissignificantlyhigherforfirmsthathaveahigherproportionofskilledworkers(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.meseparametersrefertohowmuchthefirstdistributionneedstobealteredtobestapproximatetheseconddistribution.meparametersare(1)arightwardshiftofthefirstdistribution(Shift);(2)whatconstantfactoreachoftheobservationsinthefirstdistributionneedtobedividedbytomatchtheseconddistribution(Dilation);and(3)whatshareoftheobservationsinthefirstdistributionneedtobeexcludedfromitslefttail(Truncation).Intuitively,theShiftparametercapturesthediferenceinthemeanvalueoflaborproductivity,Dilationcapturesifonedistributionismorehomogenousthantheother.TruncationreAectspossibleselectionefectswherebyfirmswithverylowvaluesofthevariableunderconsiderationaremorelikelytosurviveinonegroupthantheother.

TableB1.1providestheestimatesofthethreeparametersforthedistributionsoflaborproductivityoffirmsthatprovidetrainingversusthosethatdonot(column1)andforthebottomhalfversusthetophalfoftheNUTS2regionsintermsofthepercentageoffirmsthatprovidetraining(column2).mestatisticalsignificanceshownisforthefollowingnullhypothesis:Shift=0,Dilation=1,Truncation=0,whichbasicallybenchmarksthecasethatthedistributionsarethesame.

Considercolumn1first.Asmaybeexpected,Shift>0,implyingthatlaborproductivityishigherforfirmsthatprovidetraining.meDilationfactorislessthan1andstatisticallysignificantlyso(atthe1percentlevel).misimpliesthatthedistributionoflaborproductivityismorehomogeneousamongfirmsthatprovidetrainingthanthosethatdonot.Inotherwords,laborproductivityismoredispersedandheterogenousamongfirmsthatdonotprovidetraining.FigureB1.1illustratesthepointgraphically.meresultsarequalitativelysimilarwhencomparingthedistributionoflaborproductivityinthebottomhalfversustophalfoftheNUTS2regionsintermsofthepercentageoffirmsthatprovidetraining(column2).

Tosummarize,trainingprovidedbyfirmstotheirworkersbenefitstherelativelylessproductivefirmsmoreandtherebynarrowsthedispersionoflaborproductivity.Asaresult,trainingallowsthelessproductivefirmstocatchupwiththemoreproductivefirms.miscanhaveimportantefectsonthepossible(mis)allocationofresources,withconsequentefectsontheoverallproductivityoftheregionsandcountries(seeHsiehandKlenow2009;HeiseandPorzio2022).

TableB1.1.Howtrainingafectsthedistributionof(logof)

laborproductivityoffirmsandNUTS2regions

(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,thesignificancelevelisforthedeviationfrom1.Bootstrappedstandarderrorswith500replicationsshowninparentheses.NUTS=NomenclatureofTerritorialUnitsforStatistics.

***p<0.01

FigureB1.1.Distributionoflaborproductivityismorehomogenousamongfirmsthatprovidetraining

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(%)MultiestablishmentfirmY:1N:0

Ageoffirm

(logs,years)

Numberofworkers

3fiscalyearsago(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.meshareofskilledproductionworkersisalsolowerforfirmsthatusemanualproductionprocesses.However,thisrelationshipbecomesweakandstatisticallyinsignificantafteraccountingforfirmsize.

Employmentgrowth

Oneconcernisthatgreateruseofskilledworkersislaborsavingandthereforeitmayhinderjobsgrowth.Skilledlaborisoftenaccompaniedbygreateruseofcomputers,robots,andotherlabor-savingtechnologies.Italsoembodiesgreaterhumancapitalthanlow-orunskilledworkers,whichmayreducetheneedforadditionalworkers.However,itisalsopossiblethatskilledworkersmayboostfirmproductivityand

growth,whichmayleadtomorejobsoverall.

meempiricalevidenceontheissueismixedingeneral(see,forexample,BalsmeierandMartin2019;Jungetal.2017)andintheEU-27countries,inparticular.First,controllingforconvergenceortheinitiallevelofemploymentatthefirm,thegrowthrateofemploymentoverthelastthreefiscalyearssignificantlydeclinesastheshareofskilledandsemi-skilledworkersrises(table1,column5).misresultisdrivenbydiferencesbetweenfirmswithinNUTS2regionsratherthanacrossregions.mus,macro-levelstudiesthatexplorediferencesacrossregionsbutnotwithinregionsmaynotdetectalowergrowthrateofemploymentassociatedwithgreateruseofskilledandsemi-skilledworkers.Second,thereisno

EnterpriseNoteNo.43

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significantrelationshipbetweenthegrowthrateofemploymentandtheshareofworkersthathaveauniversitydegree(table1,column6)orsecondaryeducation.Overall,theevidenceontherelationshipbetweentheuseofskilledandhighlyeducatedworkersandjobsgrowthintheEU–27countriesisinconclusive.

Laborproductivity

Ahighershareofuniversity–andsecondary–educatedworkersisassociatedwithhigherlaborproductivity.However,thisrelationshipisweakandstatisticallyinsignificantatlowerquantilesoflaborproductivity,andlargeandsignificantathigherquantilesinEU–27countries.Forinstance,aonestandarddeviationincreaseintheshareofuniversity–educatedworkersisassociatedwithanincreaseinlaborproductivityby2percentoftheinitiallevel(insignificantatthe10percentlevel)atthe20thpercentileoflaborproductivityandby24.6percent(significantatthe1percentlevel)atthe80thpercentile.mus,itisthemoreproductivefirmsthattakeadvantageofmoreeducatedworkers,whilethelessproductivefirmsarecompletelydeprivedofanybenefits.Assumingthattherearesubstantialgainstobereapedbyimprovingproductivityatthelowend,policymakersshouldtrytoincreaseeducatedworkers,usefulnessoruseotherpolicytoolsforthelessproductivefirms.

AttheNUTS2regionslevel,ahighershareofuniversity–educatedworkersispositivelycorrelatedwithlaborproductivityatrelativelylowlevelsofincome(belowthemedian),butthereisnosignificantcorrelationbetweenthetwoathighlevelsofincome.mus,poorerregionsbenefitmorefromanincreaseinuniversity–educatedworkersthanthericher

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

Atthefirmlevel,laborproductivityishigherforfirmsthathaveahigherproportionofskilledworkersamongproductionworkers,butthisrelationshipisnotstatisticallysignificant.However,acrossNUTS2regions,thereisastrongandsignificantpositiverelationshipbetweenthetwo.Likewise,highersharesofsemi–skilledandlow–skilledworkersacrossNTUS2regionsissignificantlyandnegativelycorrelatedwithlaborproductivity.mereissharpdiferenceintheserelationshipsatlowversushighlevelsoflaborproductivity.matis,attheNUTS2levelandthefirmlevel,therelationshipbetweenlaborproductivityandtheshareofskilledworkersispositiveandsignificantatlowerquant