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Labour Economics
journalhomepage:www.elsevier.com/locate/labeco
Immigration and the Tower of Babel: Using language barriers to identify individual labor market effects of immigration ☆
Maria Forthun Hoen
∗The Ragnar Frisch Centre for Economic Research, Oslo, and the University of Oslo, Department of Economics, Norway
a r t i c le i n f o
JEL classification:
F22 J24 J31 Keywords:
Immigration Earnings Employment Language requirements
a b s t r a ct
Thispaperintroducesanovelapproachtoestimatingimmigrationimpactsonnatives’labormarketoutcomes.
Differentiallanguagerequirementsacrossoccupationsserveasanarguablyexogenoussourceofvariationduring thelargeandsuddenimmigrationsurgetoNorwayaftertheenlargementsoftheEuropeanlabormarketin 2004and2007.Migrantinflowintooccupationsisinstrumentedwithoccupations’requiredlevelof(Norwegian) languageskills.Administrativeregisterdataallowforarichsetofindividual-leveloutcomes.Comparingworkers inoccupationswithdifferentlanguagerequirements,Ifindthataonepercentagepointincreaseintheshare ofEasternEuropeanworkersreducednativeworkers’laborearningsby0.75percent.Ifurtherfindadverse employmenteffectsandevidenceofskill-upgrading,butlargelynootherformofworkermobilityamongtreated individuals.Inparticular,youngwoŕkerswerehitinthewagedimensionandoldworkersintheemployment dimension.Theresultsarehighlyrobust.
1. Introduction
Whathappenstothelabor-marketcareersofnativeworkersaftera suddeninflowofmigrantsintotheiroccupations?Thequestionisdiffi- culttoanswerempiricallybecauselabor-demandchangesaffectmigra- tionflows,causingasimultaneityproblem.Inthispaper,Iprovidean answerbasedonindividual-leveladministrativeregisterdataforNor- wayandnovelexogenous immigrationvariationat theoccupational level.Thevariationarisesfromoccupation-specificrequirementsforlan- guageskillscombinedwiththeenlargementofthecommonEuropean labormarket.
TheexpansionsoftheEuropeanUnion(EU)eastwardin2004and 2007with,in total,12newcountries(EU121) ledtoanimmigration surge intoNorway. Labor immigrationfromthe newmember states increasedstronglyandcontributedtomorethanaquarterof thenet increasein theNorwegianworkforce between 2005and2011.Prior totheEUaccession,EU12immigrationhadbeenlimitedtoseasonal workersandspecialistswithworkpermitsviatheirNorwegianemploy-
☆ThisworkwassupportedbytheNorwegianMinistryofLaborandSocialAffairs(project”EffectsofLaborMigration”)andtheNorwegianResearchCouncil(grant numbers227072and236992).DatamadeavailablebyStatisticsNorwaywereessentialforthisresearch.Dataonabilityscoreswereobtainedbyconsentfromthe NorwegianArmedForces,whoarenotresponsibleforanyofthefindingsandconclusionsreportedinthepaper.IthanktheGuestEditorFransescoFasaniandtwo anonymousrefereesfortheirhighlyvaluablefeedback.IalsothankKnutRøed,AndreasMoxnes,OddbjørnRaaum,BerntBratsberg,MetteFoged,GiovanniPeri,Vasil Yasenov,JanStuhler,DavidCard,JesseRothstein,PatrickKlein,andparticipantsatseveralseminarsandconferencesfortheirhelpfulcommentsandsuggestions.
∗Correspondingauthor.
E-mailaddress:[email protected]
1 Bulgaria,Cyprus,theCzechRepublic,Estonia,Hungary,Latvia,Lithuania,Malta,Poland,Romania,SlovakiaandSlovenia.
2 U.S.DepartmentofLabor,www.onetcenter.org.
ers.AsEUcitizens,however,theywerefreetoenteremploymentin Norway.
ThearrivingEU12migrantssortedintolesslanguage-intensiveoccu- pationsbecauseoflimitedNorwegianlanguageskills.Norwegianislin- guisticallydistantfromEasternEuropeanlanguagesandhardlyspoken ortaughtoutsideNorway.Consequently,nativesandimmigrantswith otherwiseidenticalformalqualificationsbecameemployedindifferent occupations.Theleftpanelof Fig.1illustratesthehandicapofEU12 migrants.Theverticalaxismeasuresthepercentagepointchangeinoc- cupations’shareofEU12workersfrom2005to2011,andthehorizontal axisranksalloccupationsbytherequiredlevelofNorwegianlanguage skills.Imeasurelanguagerequirementswithastandardizedindexbased onO∗NETdataadaptedtoNorwegianoccupations.2Eachcirclerepre- sentsanoccupation,weightedby(total)2005employment.Occupations withabove-averagelanguagerequirementsreceivedsubstantiallyless EU12immigrationthandidoccupationswithbelow-average.
Fig.1illustratesthatoccupation-specificlanguagerequirementsare well suitedto instrumentforEU12 immigrationintotheNorwegian
https://doi.org/10.1016/j.labeco.2020.101834
Received14September2018;Receivedinrevisedform12March2020;Accepted25March2020 Availableonline4April2020
0927-5371/© 2020TheAuthor.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense.(http://creativecommons.org/licenses/by/4.0/)
Fig.1. Immigrationintooccupationsandlanguagerequirements.Note.They-axismeasuresthepercentagepointchangeinoccupations’shareofEU12immigrants (leftpanel)andrecentScandinavianimmigrants(rightpanel)from2005to2011.Thex-axesmeasureoccupation-specificrequirementsforlanguageskills,givenby astandardizedindexretrievedfromO∗NETdata.Eachcircleisanoccupation,weightedbyitssizein2005.Thesolidlineisalinearfit.Scandinavianimmigrants arenewlyarrivedSwedesandDanestoexcludethosewithalreadyacquiredNorwegianlanguageskills.
labormarket,butlesssoforimmigrationfromcountrieswithlinguisti- callyproximatemajoritylanguages,forinstance,Scandinavia.TheScan- dinavianlanguages—Swedish,Danish,andNorwegian—aresosimilar thatSwedesandDanesinprinciplecan entermost Norwegianoccu- pations.AsvisibleintherightpanelofFig.1,Scandinavianmigrants enteredoccupationsalongthecompletelanguage-skilldistribution.3In parallel,languagerequirementsarelesssuitedtoinstrumentforimmi- grationtocountrieswithaworldlanguageasthemajoritylanguage, wheresuchrequirementsgenerallyformnobarriers.
IexploittherelationshipintheleftpanelofFig.1toestimatethe causaleffectofthehistoricallylargelaborimmigrationsurgetoNorway onnatives’occupation-specificoutcomes—thatis,laborearnings,sev- eralemploymentoutcomes,andvariousmobilityforms.Iinstrumentfor thepossiblyendogenousinflowofEU12workersintooccupationswith thelanguagerequirementindex.Thebaselinemodelgivestheeffectof EU12immigrationinto(unprotected)natives’initialoccupation(fixed in2005)onthechangeintheircumulativelaborearningsfrombefore toaftertheonsetoftheimmigrationsurge.
Theresultsshowthataonepercentagepointincreaseinanoccupa- tion’sEU12sharereducedtheearningsofnativesinitiallyemployedin thatoccupationby0.75percentrelativetolanguage-protectednatives.
Theestimateincorporatesallmedium-runchangesinlabor-marketac- tivitythatpossiblyaffectlaborearnings,includingoccupationalmobil- ity.Ifindevidenceofstrongadverseimpactsonwagesandunemploy- mentinsurancereceiptforyoungworkers,andonworkhours,full-time employment,anddisabilityprogramparticipationforallworkers,and inparticularforoldworkers.Surprisingly,Ifindnoeffectonmobility acrossareas,industries,sectors,orfirmsforworkersexposedtoEU12 immigration.However,theprobabilityofre-educatingandenteringoc- cupationswithhigherlanguagerequirementsincreased,indicatingskill- upgrading.
Tointerprettheestimatedearningseffectcausally,Imustassume parallelearningstrendsinabsenceofmigration—thatis,thattheearn- ings of workers in occupations with varying language requirements wouldhavedevelopedequallywithoutincreasedinflowofEU12work- ers,conditionalonthecontrolvariables.Section5givescredibilityto theassumptionbothgraphicallyandwithnumerousrobustnesschecks, andshows thatEU12immigrationcannotpredict previous earnings.
Further,estimatingonsamplestrataandaddingandalteringvarious
3 Since1954,laborhasmovedfreelywithintheNordic(herein,Scandinavian) countries.
individual-andoccupation-levelcontrols,suchasintelligence,indus- try,region,andrequirementsforseveralcognitiveabilitymeasures,do notchangethemainconclusion.Neitherdoestheinclusionoffixedef- fectsfornineaggregatedoccupationgroups,whichalterstheidentifica- tionlevelfromacrossalloccupationstoacrossoccupationswithineach group. Allchecksprovethattheestimatedimmigrationcoefficientis highlyrobust.
Thispaperintroduceslanguagerequirementsasanovelinstrument fortheendogenousallocationofimmigrantsattheoccupationallevel.
Ratherthanestimatingimpactsontheskillcontentofnatives’occupa- tions,asinFogedandPeri(2016),PeriandSparber(2009),andPeriand Sparber(2011),Iusethespecific(language)skillcontentofeachinitial occupationtopredictmigrantinflowandtherebytheheterogeneousim- migrationexposureofnativeworkerswithunequallanguageprotection.
Theidentifiedeffectistherelativeeffectonnativesemployedinoccupa- tionswithdifferentlanguagerequirements,andtherebywithdifferent immigrantexposure,andnotthetotaleffecton(all)natives.Iestimate theearningsdifferentialsresultingfromincreasesinthesupplyoflow- languageworkers.
Icannotidentifytheunderlyingtheoreticalmechanisms,whichmay includehighsubstitutabilitybetweennativesandimmigrantswithinoc- cupationsandcomplementaritiesacrossoccupations.Increaseddemand forhigh-languageworkersorforlanguageskillsperseinresponsetoin- creasedlow-languagelaborsupplywouldamplifytheeffect.Opposite, natives’selectiveentryintomorelanguage-intensiveoccupationswould reducethewagesinthoseoccupations,dampeningthe(relative)effect.
Thetotaleffectislikelytobelessnegativeduetocross-occupational complementarities.
TworelatedNorwegianstudiesbyBratsbergandRaaum(2012)and Finseraasetal.(2019)exploitdifferentiallicensingrequirementsamong occupations in theconstruction sector. Bothestimate almost identi- cal wageeffects as myearnings estimate. To my knowledge,other occupation-levelstudiesestimatetotalwageeffectsandthereforeare not directly comparable. The comparability is also low due to the setting-specific nature of immigrationimpacts and becausedifferent estimation strategies identify different parameters (Dustmann et al., 2016).Nevertheless,myresultsalignwiththenegativeeffectsonman- uallaborersinOrreniusandZavodny(2007);thelargenegativeeffects onserviceoccupationsinSteinhardt(2011);andthenegativeeffectsat thebottom,theinsignificantatthemiddle,andthepositiveatthetop ofthecommunicative-to-manualtaskintensitydistributioninBollinger andSharpe(2019).BecauseIestimaterelativeeffects,myresultsare
Fig.2. ImmigrationtoNorwayfromEU12andallothercountries,1993–2015.Left:annualgrossimmigration.Right:immigrantemploymentshares.Notes.Counts includefirst-timemovesonly.TheworkingpopulationincludesallresidentsofNorwayaged18–70yearswithannuallaborearningsabove1.5G.
consistentwiththe(insignificant)largepositivetotalearningseffectsin Friedberg(2001),aswell.4
Thepresentestimationstrategyfallsintothebroader”skill-cellap- proach,” inwhichworkersaredividedintoexperience-educationcells.
Commonly,skill-cellstudiesestimatenegative(short-run)wageeffects on (low-skill)natives relativetomore experiencednatives (Aydemir andBorjas,2011;Borjas,2003;Llull,2018).Myresultsalignwiththese studies,aswell,althoughtheidentifiedeffectisrelativetoworkersin occupationswithdifferentlanguagerequirementsratherthandifferent experiencelevelswithineducationcells.
Exploitingoccupation-level immigrationvariationhasseveral ad- vantages,includingreducedbiasresultingfrommisplacementofimmi- grantsintoskillcells(”downgradingbias”)andfromlowsubstitutability ofimmigrantsandnativeswithinskillcells.Thesubstitutabilityislikely higherwithinoccupationsthanskillcells(Card,2001).5Further,when comparingrepeatedcross-sectionsovertime—asinbothskill-cellstud- iesand”spatialstudies”—mobileworkersmaycausebiasbyaltering thegroupcompositions.Iavoidpotentialattenuationbiasarisingfrom suchendogenousmobilitybyfollowingindividualsandkeeping their occupationfixed.6
Therestofthepaperisorganizedasfollows.Section2describesthe immigrationsurge;Section3explains theidentificationstrategyand dataused;Section4presentstheestimationsetupandbaselineresults;
Section5testsforvalidityandrobustness;Section6examinespossible mechanismsbehindtheestimatedearningsimpact;andSection7con- cludes.
2. EasternEuropeanmigrationtoNorway
Iexploit thelargeandsuddenimmigrationsurge toNorwayafter theeastwardEUenlargementsasa”naturalexperiment” inestimating immigrationimpactsonthereceivinglabormarket.BecauseNorwayis notaformalEUmember,thepolicydecisiontoenlargetheEUwasun- relatedtoNorwegianeconomicconditions.Norwayisneverthelesspart
4 BorjasandMonras(2017)’sfindingscontradictFriedberg’s(2001)results withsignificantnegativeearningseffectson(high-skill)Israelis,butaredisputed byClemensandHunt(2017).
5 Morerecentstudieshaverelaxedtheassumptionofperfectsubstitutability withincells(e.g.,Manacordaetal.,2012;OttavianoandPeri,2008;Periand Sparber,2009).
6 Iignorepossiblefactormobilityacrossoccupations.BecauseIexamineonly 6years,thismobilityisprobablylimited.Further,industryandareafixedeffects reflecttime-invariantdifferencesinexistingcapitalstock.
ofthecommonEuropeanlabormarket(EuropeanEconomicArea,EEA).
Economicupturnsandincreasinglabordemand,combinedwithacom- pressedwagestructureandhighrelativecompensationtolow-skilled labor,attractedmany newEUcitizensintoNorway.Infearofsocial dumpingandwelfaretourism,in2004manyexistingEEAcountriesim- plementedtransitionalrestrictionsonfreemovementofpeoplefromthe newmemberstates.Norwayrequiredcontractedfull-timeemployment withconditionsaccordingtoNorwegianrulesandstandardsinorderto immigrate.Afterterminationoftherestrictionsin2009,EU12citizens werefreetomigratewithintheEEA.PriortotheEUenlargement,dif- ficultiesobtainingworkpermitshadgreatlylimitedlaborimmigration toNorwayforotherthanspecialists.
Fig.2’sleftpaneldepictsthegrossinflowofmigrantstoNorway between1993and2015separatelyforEU12(lightgray)andallother origins(darkgray).EU12immigrationrosefromnearlynone(roughly 1000 per year) pre-enlargement tomore than25,000 in 2011—and graduallydecreasedthereafter.Thefinancialcrisiscausedasmalldip in2009.Overtheperiodwiththelargestimmigrationincrease(2005–
2011),EU12workersaccountedformorethanathirdofthetotalim- migrationtoNorwayandincreasedtheworkingpopulationbynearly3 percent.TheEU12employmentsharerosefrom0.4to3.0percent—and upto5.0percentin2015(Fig.2,rightpanel).However,thedegreeof migrantcompetitionfacedbynativeswashighlyunequalduetohet- erogeneous flowsintoareas,industries, and—most importantin this study—occupations.
Basically,allEU12immigrantswerelabormigrantsandtherefore entereddirectlyintoanoccupation.Theywere,however,confinedto occupationswithoutlanguagebarriers.ThelargestEU12-receivingoc- cupationsbetween2005and2011hadwell-below-averagelanguagere- quirements.Thetopfivewerecleaners,carpenters,constructionwork- ers,cabinetmakers,andfish-processingmachineoperators.Thetopre- ceivingsectorswereconstruction,service,manufacturing,agriculture, andforestry.
The Norwegian language skills of EU12 immigrants are widely knowntobepoor.NorwegianishardlyspokenortaughtoutsideNor- wayandislinguisticallydistantfromEasternEuropeanlanguages.Fur- ther,unlikehumanitarianimmigrants,EEAlabormigrantsarenoten- titledtostate-financedlanguagetrainingorintegrationprograms.Only 21 percent of employers withEastern European laborers offersome formoflanguagetraining(FribergandTyldum,2007).Ingeneral,im- migrants’languageskillsarecorrelatedwiththeirageatimmigration, education,andperceivedaffiliationinthehostcountry(Vrålstadand Wiggen,2017),allofwhichfurtherreducethelikelihoodofsuchskills amongEU12workers.
ThetypicalEU12immigranttoNorwayis a30-year-oldman(70 percentmen)fromPolandwithouthighereducationoranyparticular languageskill(DølvikandEldring,2006).7Theselectioninthetypeof PolishimmigrantstoNorwayislikelylinkedtolanguagebarriersinthe Norwegianlabor market(Friberg,2013).8Ina2010surveyofPolish workersintheOsloregion,asmanyas70percentofmalerespondents, includingthosewithseveral yearsof residency,hadno orvery lim- itedNorwegianlanguageskills(FribergandEldring,2011).Neverthe- less,thecrucialpointforthepresentidentificationstrategyisthatEU12 workersdidnotspeakNorwegianuponarrivalandthereforeenteredoc- cupationswithoutlanguagebarriers.Section4detailsthispoint.Simi- larly,ChiswickandTaengnoi(2007)findthatintheUnitedStates,im- migrantswithlinguisticallydistantmothertonguesareemployedmore ofteninoccupationswithlowEnglish-languagerequirements.
3. Data
Thisstudyisbasedonhigh-quality,individual-leveladministrative registerdataofallresidentsofNorway.Thepanelstructureofthedata allowsmetofollowindividualsovertime.ThemainsourceistheReg- isterofEmployersandEmployees,withinformationoncashpayments, duration,industry,andmunicipalityofallemploymentspellseachyear.
Since2003,italsoincludesoccupationandcontractedworkhours.I keepeachworker’smainoccupation,definedasthebestpaid(full-or part-time)occupationortheuniquefull-timeoccupationatendofthe baseyear(2005).
DataonannuallaborearningscomefromtheNorwegianTaxRegis- ter.Earningsincludeincomefromemploymentandself-employment, taxable in-kindearnings,andsickness- andparental-leave benefits.I censornegativewageearningsto0andtopincometo10millionNOK annually.Ideflateearningswiththeoverallwagegrowth,approximated bythegrowthintheSocialSecuritysystem’sbasicamount (G),such thatmonetaryvariablesaremeasuredin2005value.9Employmentis definedasannuallaborearningsabove1.5G.
ImmigrantanddemographicdataaredrawnfromtheCentralPopu- lationRegister.”Immigrants” arepersonsbornabroadbytwoforeign- bornparents.Occupations’immigrantsharesarebasedontheuniverse ofresidentsaged18to70yearswithlaborearningsabove1.5G,ex- cludingtemporaryandseasonalmigrants.
Dataonnatives’highestcompletededucationfollowtheNorwegian StandardClassificationofEducationwithsix-digitcodesforeachedu- cationalattainment(BarrabésandØstli,2015).Iusethree-digitcodes, capturingboth level andmainfields.Industry data follow two-digit codesoftheStandardIndustrialClassification of2007.Iusealterna- tivespecificationsintherobustnessanalyses.Firms’sectorsaregiven by33codesandlocalizationby46commutingzones,followingBhuller (2009).
TableA.1,Column1,describestheestimationsample,consistingof 772,310nativeresidentsaged23to62yearsbetween2005and2011.
Toensurecomparabilityofworkersacrossoccupationsandoftheirearn- ingsdevelopmentsintheoutcomeperiod,Ilimitthesampletofull-time employeeswithannualearningsabove1.5Ginthe4yearspriortothe immigrationsurge(2002–2005)andnotineducationin2005.Forthis group,theoutcomevariablescanbeconstructedconsistently.Allout- comesmeasurechangesfrombeforetoaftertheonsetofthesurge—that is,cumulativeoutcomesovertheperiod2006to2011relativeto2002 to2005,exceptweeklyworkhoursandhourlywages,whichareinstead
7 Educationdataforimmigrantsarebasedonsurveydataandavailableforless than40percentoftheEU12immigrantswhoarrivedbetween2005and2011 andwere23to62yearsold.Ofthosewithdata,64percenthavesecondary educationasthehighestcompletedlevel.
8 BabaandDahl-Jørgensen(2013)furtherdiscusstheroleoflanguageand languagepoliciesinthemigrationfromPolandtoNorway.
9 OneGwasequalto60,699Norwegiankroner(NOK),roughlyUSD7,500in 2005.
relativeto2003to2005duetopoordataqualityin2002.Immigration exposure isdefined asthegrowthin occupations’EU12employment sharesfrom2005to2011.10IexcludetheArmedForcesduetolackof O∗NETdataandoccupationswithlessthan100workersbecauseofthe potentiallyvolatileimmigrant-sharemeasure.Thisleaves 318of349 occupations.11
TheO∗NETdatabaseprovidesdataonworkerandoccupationalchar- acteristics,fromwhichIconstructanindexthatmeasuresoccupations’
languagerequirements.12Theindexisastandardizedaverageoverfour
”workerrequirements”:speakingskills,writingskills,knowledgeofEn- glish,and(theinverseof)foreignlanguageknowledge.Thelattertwo account for majority languagerequirements(i.e., Englishin the U.S., andNorwegianinNorway)ratherthangeneralcommunicationskills.
Requirementsforforeignlanguagesenterinverselybecausetheymay correlatenegativelywithmajoritylanguagerequirements,andhence positivelywiththeinflowofEU12workers.Thus,theywouldweaken thecorrelationbetweenlanguagerequirementsandEU12immigration intoNorwegianoccupations.13Iaverageoverthefourrequirementsand standardizetomean0andstandarddeviationof1.14
TomapthelanguageindextoNorwegianoccupations,Imanually construct amapping betweenthe occupationalclassificationsystems inNorway(”StandardyrkesklassifiseringNOSC521,” inStatisticsNor- way,1998)andtheU.S.StandardOccupationalClassificationSystemas ofthe2000CensusofPopulationandHousing.15,16Ithoroughlyinspect thattheresultinglanguageskillrankingofNorwegianoccupationsis sensible.However,animperfectrankingwouldweakenonlythecorrela- tionbetweenlanguageandimmigration—reducingthefirststage—but notinvalidatetheidentificationstrategy.Section4showsthatthefirst stageisindeedstrong.
Unfortunately, dataon workers’occupationsarescarceinthebe- ginningoftheestimationperiod.TheEmployerandEmployeeRegis- terincludesinformationonoccupationsforaselectionofworkersfrom 2003.Thequantityandqualityoftheoccupationdatagraduallyincrease overtime,asmoreemployersregistertheiremployees’occupations(cor- rectly).By2010,basicallyallworkershaveoccupationdatarecorded.In 2005,occupationisavailablefor66percentofworkers.17Iincreasethat shareto85percentbyaddingoccupationdataforworkersincludedin theWageStatisticsSurveyfromStatisticsNorwayandbyextrapolating
10TheshareisdefinedasthenumberofEU12workersdividedbythetotal numberofworkersinanoccupationinyeart.Thismeasuremayleadtoabias ifnativesselectivelychangeoccupationandtherebyaltertheimmigrantshare foraconstantnumberofimmigrants(CardandPeri,2016).Nevertheless,dueto improvementsinoccupationdataovertheperiod,Istillusetheannualnumber ofworkersinthedenominatorratherthanfixingitin2005.Myassessment isthatthemorecorrectimmigrant-sharemeasuresurpassesthepossiblebias.
Furthermore,itminimizespossibleattenuationbias(AydemirandBorjas,2011).
Resultwiththedenominatorfixedin2005indicatesrobustnesstothechoice (TableA.6,PanelD).
11Examplesofexcludedoccupationsincludehunters;pawnbrokers;handicraft workers;fashionmodels;andhistorians,archaeologists,andphilosophers.
12IuseVersion9.0from2005.
13Thefirststage,aswellasthepointestimate,isindeedreducedwithoutfor- eignlanguagerequirementsintheindex(TableA.6,PanelB).
14O∗NETincludesinformationonboththerequiredlevelandtheimportance ofeachrequirement.Irescaleeachrequirementleveltotheimportancescale (1–5)andmultiplythemtogetherbeforeaveragingoverthefourrequirements.
15https://www.bls.gov/soc/2000/socguide.htm
16ThetechnicalreportHoen(2016)providesthecompletemappinganddetails ontheO∗NETdataused.
17Betteroccupation data, whichincrease thesample size andreducethe measurementerrorintheimmigrantshares,drivethedecisiontouse2005 ratherthan2004(whentheEUexpanded)asthebaseyear.Thechoicerepre- sentsacompromisebetweendataqualityandnatives’endogenousoccupation choices.Nativesmayhavestartedadjustingtoincreasedimmigrationin2005, butFig.2showslittleimmigrationthefirstyear.Thus,theendogeneityissuein 2005isprobablysmall.
occupationbackoneyearforworkersemployedwithinthesamefirm fortwosuccessiveyears.
TableA.1comparestheestimationsampletothesampleofworkers whowouldhavebeenincludediftheyhadoccupationdatain2005.By far,thelargestdifferenceisinthepublicsectorshare—91percentofthe
”lost” workersareinthatsector.Becausepublicsectorworkersusually earnless,arebettereducated,andaremostly(70percent)women,the samplesdifferintheseaspects,aswell,butareotherwisecomparable.
Initially,largeprivatelyownedfirmswererequiredtoregistertheir workers’occupations,whereasmostsmallfirmsandpublicsectorem- ployerswereexempted.Alloccupationsover-representedinthebegin- ningofthedataperiodarehenceintheprivatesector.Theyhavemainly below-averagelanguagerequirements.Manypublicemployersinstead reported”positions” butgraduallychangedtoreportingoccupations.18 Thus,publicsector occupations—in particularwithin healthanded- ucation—with well-above-averagelanguage requirements,areunder- represented.Thisreducesthe(control)groupwithlimitedimmigration exposure,butis,however,unlikelytobiastheresultsbymismeasur- ingtheEU12sharesduetothelimitednumberofEU12immigrantsin thepublicsector.Ataminimum,theestimaterepresentsalowerbound (fewerpublicsectorworkersin2005attenuatestheincreaseintheEU12 sharesinlanguage-intensiveoccupations).Moreover,theresultsarero- busttoexcludingpublicsectoremployees,aswellasestimatingwithin onlythelargest(10percent)privatefirmsandoccupationswithbelow- medianlanguagerequirements(TableA.5).
The estimation sample may not represent the Norwegian work- forceperfectlyduetoscarceoccupationdata.However,becauseIes- timateindividual-level impactsrather thanoccupation averages, the non-representativenessshouldnotbiastheresults.Ifixworkers’occupa- tionsandfollowchangesintheirlabor-marketcareersthatresultfrom changesintheinitialoccupation’sEU12employmentshare.Aslongas theimprovedregistrationofworkers’occupationsdonotdiffersystem- aticallybetweennativesandimmigrants,theimmigrantshareswillnot bebiased.Suchasystematicdifferenceisunlikelybecausefirmshadto registerallemployees’occupations,regardlessofmigrantstatus.Fur- ther,theresultsareinsensitivetoexchangingthechangesintheEU12 sharesfrom2005to2011withthelevelsin2011,whenthesharesare measuredperfectly(TableA.6,PanelC).Inall,limitedoccupationdata areunlikelytobiastheresultsbutmayreducetheexternalvalidity.
4. Identificationandestimation
Estimatingtheimpactofimmigrationonnatives’earningsbysimply comparingworkersinoccupationswithdifferentmigrantinflowswould yieldabiasedestimate.Arguably,immigrantsselectintoboomingoccu- pations,upwardbiasingtheestimate.Ontheotherhand,theymaybe confinedtodecliningoccupationsdueto,forinstance,discrimination, andthereby downwardbiastheestimate.Tocircumventtheseselec- tionissues,Iexploitoccupation-specificlanguagerequirementsasiden- tifyingvariation.Thevariationisarguablyexogenoustolabor-demand changes,conditionalonthecontrolvariables,asitiscausedbyoccupa- tions’distinctivefeatures.
IinstrumentforthepossiblyendogenousinflowofEU12migrants into natives’ (initial) occupations by occupations’ language require- ments.Thebaselineoutcomeisthechangeincumulativelabor earn- ingsfroma4-yearpre-immigrationperiodtoa6-yearoutcomeperiod.
Itincludesallfactorsthatpossiblyaffect totalearnings(e.g.,wages, workhours,unemployment,self-employment,occupationalmobility).
Thelongoutcomeperiodcaptures sluggishresponsestoimmigration andaccountsforthegradualnatureoftheimmigrationsurgeandthe uncertaintimingofimmigrationimpacts.
18 ”Positions” arelinkedtowagepaymentsandtenureratherthantasksand workcontent,andthereforenotdirectlyrelatabletooccupations.Toincreasethe publicsectorshare,Ineverthelesstranslatepositionsintooccupationswhenever possible.(SeeHoen(2016)forfurtherdetailsonthedataissues.)
Tobeavalidinstrument,languagerequirementsmustsatisfytwo properties.First,theymustpredicttheinflowofEU12workersintooc- cupations—thatis,thefirststageintheIVestimationmustbestrong.I verifythisgraphicallyandintheestimation.Second,languagerequire- mentscanhavenoindependenteffectonchangesinnatives’earnings, andnovariablecorrelatedwithbothlanguageandearningscanbeomit- tedfromtheestimation.Althoughthelattercannotbetesteddirectly, Fig.3providesgraphicalevidenceandSection5presentsnumerousro- bustnesschecks.
Thebaselineestimationmodelisasfollows.Eq.(1)isthefirst-stage regressionoftheimmigrationvariableontheinstrumentandthevector of controlvariablesandEq.(2)isthesecondstageregressionof the outcomevariableonthepredictedimmigrantsharefromthefirststage andthesamesetofcontrols.19
Δ𝐸𝑈12𝑗,05−11=𝛾0+𝛾1𝑙𝑎𝑛𝑔𝑗+𝑋𝑖,′05𝛾2+𝛾3̃𝑌𝑖,02−05+𝛾4𝑠𝑜𝑐𝑗+𝜉𝑖𝑗 (1) 𝑦𝑖𝑗=𝛽0+𝛽1𝐼𝑉Δ̂𝐸𝑈12𝑗,05−11+𝑋𝑖,′05𝛽2+𝛽3̃𝑌𝑖,02−05+𝛽4𝑠𝑜𝑐𝑗+𝜖𝑖𝑗 (2)
Δ𝐸𝑈12𝑗,05−11isthechangeinoccupationj’sEU12employmentshare fromthebaseyear(2005)totheimmigrationpeak(2011),andlangjis thestandardizedlanguageindexexplainedinSection3.Theearnings measure,𝑦𝑖𝑗=𝑙𝑛(∑2011
2006𝑌𝑖𝑗𝑡)−𝑙𝑛(∑2005
2002𝑌𝑖𝑗𝑡),is thelogarithmofcumu- lativeannual(real)laborearningsfrom2006to2011forworkeriin occupationj,minusthesamelaggedfouryears(2002–2005).Itisspeci- fiedintermsofchangestoaccountfordifferencesinoccupation-specific labordemandandindividuals’earningspotential.Theparameterofin- terest,𝛽𝐼𝑉1 ,givestheearningseffectofincreasedEU12immigrationinto occupations,measuredrelativetonativesprotectedbylanguagebarri- ers.
Ifixnatives’occupationsin2005toavoidbiascausedbyendoge- nousoccupationshifts.Thelefthandsidefollowsworkers’annualla- borearnings, regardless oflabor market activity,whereas theimmi- grationmeasure capturesinflowofEU12workersintotheinitialoc- cupation.Xi,05isavectorofcontrolvariablespossiblycorrelatedwith theerrorterm. Theyincludefixedeffectsforsex,birthyear,highest educationcompleted,industry,commutingzone,andyearsoftenure, and a linearcontrol for labor-marketexperience, all fixed in 2005.
̃𝑌𝑖,02−05=(𝑌𝑖,05−𝑌𝑖,02)∕𝑌𝑖,02isearningsgrowthfrom2002to2005.Fi- nally,socj is occupations’requirementsforsocialskills.20 Such skills mightcauseomittedvariablebiasiftheywereincreasinglyrewarded inthelabormarketduringtheestimationperiod(Deming,2017;Edin etal., 2017)andpositively correlatedwithlanguagerequirements.I clusterstandarderrorsattheoccupationallevel,andcheckforsensitiv- itywithhigherlevelclustering.
Table1,Column1showsthemainresult.Aonepercentagepoint higherEU12shareledto0.75percentlowerearningsintheperiodfrom 2006to2011relativeto2002to2005fornatives initiallyemployed in occupationswithoutlanguagebarriers.Thefirststage(Column2) is significantatanyconventional levelandhasarobust F-statisticof 33.Onestandarddeviationhigherlanguageindexreducesanoccupa- tion’sEU12shareby3.4percentagepoints.Column3showstheordi- naryleastsquares(OLS)results froman(uninstrumented)estimation withtheEU12sharedirectlyinserted intoEq.(2). Aonepercentage pointhighershareisassociatedwith0.36percentlowerearnings.The upward-biasedOLSestimaterevealsthatEU12workerssortedintooc- cupationswithincreasingdemand.
Toillustratetheeconomicmagnitudeoftheeffect,theoverallreal wagein Norwaygrew around17percent from2006to2011.21 The EU12sharegrewonaverage3.8percentagepointsmoreinoccupations
19TheestimationwasconductedwithCorreia(2016)’sStatamodule.
20Astandardizedaverageoftherequiredlevelofsocialperceptiveness,coor- dination,persuasion,andnegotiationfromO∗NET,followingDeming(2017).
21Wageperstandardperson-year,deflatedbytheconsumerpriceindex.Num- bersfromStatisticsNorway’sStatBank,Table311464.
Table1
Mainestimationresults.
(1) (2) (3)
Growth in total labor earnings IV First stage OLS from 2002–2005 to 2006–2011 b/se b/se b/se EU12 share − 0.745 ∗∗∗ − 0.359 ∗∗∗
(0.156) (0.048)
Language − 0.034 ∗∗∗
(0.006) Occupations’ social skills YES YES YES Earnings growth 2002–2005 YES YES YES Education fixed effects YES YES YES
Industry fixed effects YES YES YES
Region fixed effects YES YES YES
Previous labor market history YES YES YES
Demographics YES YES YES
R 2 0.070 0.071
F -statistic 33.06
R 2adj 0.488
N 772,310 772,310 772,310
Estimationofbaselinemodel(Eqs.(1)and(2).Outcomeisthelogarithm oftotallaborearningsin2006–2011minusthesamein2002–2005.
”EU12share” isthechangeinoccupations’shareofEU12workersfrom 2005to2011.Theshareisinstrumentedwithoccupations’languagere- quirementsincolumn2.Workers’occupationsandallcontrolsarefixed in2005.Controlsincludethree-digiteducation,two-digitindustry,com- mutingzone,birthyear,yearsoftenure,andsexfixedeffects,aswell aslabor-marketexperience,earningsgrowthfrom2002to2005and occupations’social-skillrequirements.Baselinesampleincludesnative residentsborn1949–1982whowereemployedfull-time,hadannualla- borearningsabove1.5Gin2002–2005,andwerenotineducationin 2005.Standarderrorsclusteredattheoccupationallevelaregivenin parentheses.ReportedF-statisticisKleibergen-PaaprkWaldF-statistic.
∗p<0.05,∗∗p<0.01,∗∗∗p<0.001
withbelow-medianlanguagerequirementsthanabove.Theaveragerel- ativeearningslossbetweenworkerswithandwithoutlanguageprotec- tionwasthus nearly3percent,orroughlyone-sixthofthereal-wage growth(althoughtheearningsestimatealsoincludesemploymentef- fects).Similarly,BratsbergandRaaum(2012)findidenticalpointes- timatesandFinseraasetal.(2019)find1to2percentreducedwage
growthforworkersunprotectedbyoccupationallicensingrequirements intheNorwegianconstructionsector.
TheIntroductionraisedcaveatsofcomparingestimatesacrossstud- ies.Withthecaveatsinmind,myestimateisneverthelessclosetothe immigrationcoefficientsinBorjas(2003)’sseminalstudy:-0.60onlog weeklyearningsand-0.92onlogannualearnings.AydemirandBor- jas (2011)findsomewhatweakerwageeffects(around -0.5forboth Canada andthe U.S.). Llull(2018) reports wageestimates of more thandoublethesizeofminebutOLSestimatesthatarecloser.Finally, Card(2001)estimatesarelativewageeffectonlow-skillednativesof -0.15percent.Thus,myresultseemstoalignwiththeliterature.
5. Robustness
5.1. Parallelearningstrends
Thecrucialassumptionforacausalinterpretationoftheestimated earningseffectisthattheearningstrendsinoccupationswithvarying languagerequirementswouldhavebeenequalin theabsenceof im- migration,conditionalonthecontrolvariables.Ifthemodeldoesnot controlforallfactorsthatpossiblycorrelatewithbothearningsdevel- opmentsandlanguagerequirements,theestimateisbiased.Severaltests ofthisparallelearnings-trendassumptionfollow.
First,Ishowgraphicallythattheearningsdevelopmentsinquartiles ofthelanguage-requirementdistributionweresimilarpre-immigration.
Inapaneldatasetup,seeEq.(3)below,Iregresslogannual(real)earn- ingsofindividuali’s2005-occupationjonyearfixedeffects(Tt)inter- actedwiththelanguagequartile(Qj,05)ofi’soccupation.Iincludeall baselinecontrols(Xit)exceptearningsgrowth,aswellasfixedeffectsfor year,quartile,andyear-by-groupfornineaggregatedoccupationgroups (Jj,05).Theninegroupsaredefinedbythefirstdigitoftheoccupation codesandroughlyrepresentskilllevels(StatisticsNorway,1998).
𝑦𝑖𝑗𝑡=𝛼+𝛼1𝑇𝑡×𝑄𝑗,05+𝑋𝑖𝑡′𝛼2+𝑇𝑡+𝑄𝑗,05+𝑇𝑡×𝐽𝑗,05+𝜖𝑖𝑗𝑡 (3) Fig.3plotsthecoefficientsonthequartile-by-yearinteractions(𝛼1) together with 95 percent confidence intervals. They measure aver- age(residual)earningsineach languagequartilerelativetothelow- estquartile in thebase year2002. Ikeepworkers’ occupationfixed in 2005.Fig.A.1showstheexactsamefigurebutwithtime-varying
−.050.05.1
2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
2.quartile 3.quartile 4.quartile
Fig. 3. Average (residual) earnings in lan- guagequartiles,relativetothelowestquartile.
Note.Thefigureshowsthe𝛼1-coefficientsfrom Eq.(3)onquartile-by-yearfixedeffectstogether with95percentconfidence intervals.Theco- efficients are relative tothe lowestlanguage quartile in 2002. To minimize compositional changesinthequartilesovertime,Ilimitthe sample eachyeartofull-timeemployeeswith annualearningsabove1.5G,notineducation, andofage23–62years.Ifixworkers’occupa- tionandthelanguagequartilesin2005,butlet allcontrolsvaryovertime.Thelargestoccupa- tionsineachquartileare1)carpenters,cleaners, andplumbers;2)shopsalespersons,clericaloffi- cers,andstockclerks;3)nursingassistants,sales representatives,andchildcareworkers;and4) teachers,chiefexecutives,andsecretaries.
Table2
Previousearningsasoutcome.
(1) (2) (3)
Log Log Growth
b/se b/se b/se
EU12 share − 0.928 − 0.922 − 0.401 ∗ (0.643) (0.641) (0.169) Earnings growth 02–05 No Control Outcome
R 2 0.525 0.525 0.061
N 772,310 772,310 772,310
Estimationofbaselinemodelwithpreviousearningsasout- comevariable.Outcomein(1)and(2)islogoftotalearnings over2002–2005.Earningsgrowthfrom2002to2005isin- cludedascontrolin(2)andasoutcomevariablein(3).∗ p<0.05,∗∗p<0.01,∗∗∗p<0.001
occupations.Reassuringly,earningsdifferinsignificantlyacross quar- tilespre-immigration,lendingsupporttotheparallelearnings-trendas- sumption.Furthermore,earningsinthetopthreequartilesgrowsignif- icantlymoretowardstheendoftheperiod.Asexpected,thetrendsdo notdivergeimmediatelyafter2005duetothegradualnatureofboth theimmigrationsurgeanditsimpact.However,Fig.3excludestheun- employmentcomponentoftheearningsdeclinebecausetheunderlying sampleincludesonlyemployedindividuals(seeFig.3notes).
Second,I checkwhetherEU12 immigrationcan explainearnings backwardin time,whichwouldreducethecredibilityoftheparallel trendassumption.Iestimatethebaselinemodelwithearningspriorto theimmigrationsurgeasthedependent variable,keepingeverything elseequalexceptfrom(re-)movingtheearningsgrowthcontrolvari- able.TheoutcomesinTable2,Columns1and2arethelogarithmof cumulativeearningsfrom2002to2005(equaltothedenominatorofthe baselineoutcomevariable).22Column 2controlsforearningsgrowth from2002to2005(thebaselineearningsgrowthcontrolvariable),as well.This growthvariableistheoutcomein Column3—thatis,itis movedfromtherighttotheleftsideofthebaselinemodel.Theonly (weakly)significant estimateisin Column3,butbothitsmagnitude andsignificancearemuchsmallerthanthebaseline.TableA.6,PanelD repeatsthesamespecificationsforthemainoutcomeperiod,showing thattheresultisrobusttoalternativespecificationsandthatthemodels ofthe”placebotests” inTable2arenotchosentopassthetest.
Third,Isplitthesamplealongcharacteristicsthatmaybe associ- atedwithdifferentiallabor-demandtrends.Morethanone-thirdofthe EU12migrantssettledintheOsloregion(calledEast).Thethreelargest immigrantcommutingzones,Oslo,Bergen,andStavanger,togetherre- ceived56percentofthenetEU12immigrationbetween2005and2011.
Ithereforedropthe3and10largestEU12-commutingzones,respec- tively,and,asshowninTableA.5,estimateseparatelywithinNorway’s fivemajorregions(Bhuller,2009).Theestimatedimmigrationimpact issimilaracrossareasbutsomewhatlargerinthedensestEU12-migrant region.Theestimateissimilaracrosssectors,aswell,althoughnotsta- tisticallysignificantinthepublicsector(TableA.5).Theconstruction sectorestimateisslightlysmallerthanBratsbergandRaaum(2012)’s wageestimate—possiblybecauseofthedifferentperiods(i.e.,theyend whenIstart).
Finally, I add and alter potentially omitted control variables (TableA.6,PanelA).Iaddfixedeffectsforthenineaggregatedoccupa- tiongroups.Thelevelofidentifyingvariationchangesfromacrossalloc- cupationstoacrossoccupationswithineachgroup.Thebaselineestimate survivescontrollingforthegroups,althoughthesignificanceandfirst stagediminish.Ifurtherallowforregion-specificlabor-demandchanges withinindustriesandincludemoredetailedcontrolsforindustryand
22 Cumulativeearningsarenotrelativetopreviousearnings,asinthebaseline model,becausetheywouldyieldahighlyselectedsample,namelythoseem- ployedfor7yearspriortotheimmigrationsurge.Theestimatethenwouldbe lesscomparableacrosstimeandmodels.
educationthaninthebaseline,aswellasforyearsofcompletededu- cation.Theestimatedimmigrationeffectishighlyrobusttoalternative controls.TableA.6,PanelEshowsrobustnesstosuccessivelyexcluding eachfixedeffectofthebaselinemodel.Theimmigrationcoefficientis unalteredwhenIdropindustrybutdecreaseswhenIdropcommuting zone,revealingimmigrants’ endogenousgeographicalallocation.The exercisefurtherrevealseducationasanimportantcontrolvariable,asit likelypositivelycorrelateswithbothlanguagerequirementsandearn- ings(atleastinlevels).Inthesamespirit,Icontrolforintelligencein thesubsamplewithabilityscoresfrommilitaryexaminations—withno changeintheimmigrationcoefficient(PanelA).
MotivatedbyDeming(2017),whofindsthatthelabormarkethas increasinglyremuneratedsocialskillsincombinationwithhighcogni- tiveabilities,Iaddseveralcognitive-abilitymeasures(Table3).Each columnincludesacognitivemeasure,basedonO∗NETdata,separately andinteractedwithsocialskills,inadditiontothebaselinecontrols.In Column5,Idropfourverbalabilitiesfromthecognitivecompositebe- causetheycloselyresemblethebaselinelanguageindex.23 TheEU12 coefficientisstableacrossallspecificationsbut,interestingly,nointer- actionandonlyoneseparatecoefficient(reasoning)issignificant.
5.2. Heterogeneity
Theestimatedearningseffectrepresentsanaverageoveralloccu- pations,butthetrueeffectmaybeheterogeneous.Inparticular,insti- tutionalsettings,suchaslicensingrequirements,unionarrangements, andminimumwages,cancausedifferentiallabor-supplyelasticitiesand wagerigiditiesacrossoccupations.Thepresenceofheterogeneityfur- thercomplicatesinterpretationoftheresults(Dustmannetal.,2016).
Systematicdifferences amongoccupationswithunequallanguagere- quirementscoulddrivetheestimateupordown.However,bothmin- imumwagesandlicensingarepresentatalllanguage-skilllevels.For instance,both cleanersandsalespersonshave minimumwages;both plumbersanddoctorsneedlicenses.Minimumwagesarenevertheless morecommoninlow-languageoccupationsandthereforelikelytoim- posedownwardwagerigidityatthebottomofthelanguage-skilldistri- bution,reducingtheearningseffect.
Toinvestigatetheroleofminimumwages,Idropoccupationsthey typically protect. Norway has no general minimum wage, but cer- tain sectors have minimum wages through collective agreements.24 Table A.5showsthatthepointestimateis indeedlargerwithout the mostcommonoccupationsinthesesectors.
Labor-supplyelasticitiesarelikelylessheterogeneouswithingroups ofsimilarworkers.Ithereforesplitthesamplealongseveralcharacteris- tics(TableA.5)andshowthattheestimateisinsignificantlydifferentfor maleandfemaleworkers.Igroupworkersbyeducationlevelin2005:
no/primary education, high school (12 years),or college/university degree.The effectincreaseswith educationlevel, althoughinsignifi- cantamongtop-skilledworkers,whowereeitherlittleexposedtomi- grantcompetitionorabletocounteracttheeffect,asinPeriandSpar- ber(2011).Workerswithhigh-schooleducation(morethanhalfofthe sample)inoccupationswithoutlanguagebarriersaremostaffectedrel- ativetootherworkerswithhigh-schooleducations.Thepointestimates forlow-andmedium-educationlevels,aswellastheinsignificantfor thetop,arereassuringlyclosetoBratsbergandRaaum(2012).
23PeriandSparber(2009)andOttavianoetal.(2013),amongothers, use indeedthesefourabilitiesasalanguagemeasure.TableA.6showsthatthe resultisrobusttoemployinganinstrumentbasedonthefourabilities,aswell asadichotomousinstrumentforbelow-medianlanguagerequirements.
24Generallyapplicablecollectiveagreementsareagreementsconcerningpay andworkingconditions thatapply toeveryone ina specificsector,regard- lessofunionization.Sectors/occupationswithminimumwagesareconstruc- tion;maritimeconstruction;agricultureandhorticulture;cleaningworkers;fish- processingenterprises;electricians;freighttransportbyroad;passengertrans- portbytourbus;andhotel,restaurant,andcatering.
Table3
Additionalcontrolsforoccupationalrequirements.
(1) (2) (3) (4) (5)
b/se b/se b/se b/se b/se
EU12 share − 0.709 ∗∗∗ − 0.660 ∗∗∗ − 0.552 ∗∗ − 0.695 ∗∗∗ − 0.738 ∗∗∗ (0.16) (0.18) (0.18) (0.18) (0.17)
Social 0.007 ∗ 0.002 0.001 0.005 0.006
(0.00) (0.01) (0.00) (0.00) (0.00)
Non-routine 0.003
(0.00) Social × non-routine 0.003 (0.00)
Abstract 0.008
(0.01)
Social × abstract 0.000
(0.00)
Reasoning 0.012 ∗∗
(0.00)
Social × reasoning 0.002
(0.00)
Cognitive 0.005
(0.00)
Social × cognitive 0.002
(0.00)
Cognitive wo. verbal 0.003
(0.00)
Social × cognitive wo. verbal 0.002
(0.00)
R 2 0.071 0.071 0.071 0.071 0.070
N 772,310 772,310 772,310 772,310 772,310
Eachcolumnisaseparateestimationofthebaselinemodelwithadditionalcontrolvariablesasfollows:
(1)”non-routineabilities” fromDeming(2017),capturingmathematicalcompetence;(2)”abstract tasks” (non-routinecognitive)fromAcemogluandAutor(2011);(3)4reasoningabilities,capturing non-routineabilitiesfromO∗NET’scontentmodel;and(4)acompositeofthe21”cognitiveabilities” fromO∗NET.(5)equals(4)withoutfourverbalabilities.∗p<0.05,∗∗p<0.01,∗∗∗p<0.001.
FollowingOrreniusandZavodny(2007),Igroupoccupationsbyskill levelandfindasignificantnegativeeffectamongmanuallaborersand noeffectamongprofessionals(TableA.5).Thepointestimateformanual occupationsisidenticaltoOrreniusandZavodny(2007)—althoughthey estimatetotaleffectsandIrelativeeffectsamongmanuallaborerswith unequallanguageprotection.Thedifferentnatureoftheestimatesmay explainwhyIfindasubstantialeffectamongserviceworkersandthey none.Steinhardt(2011),however,estimatesnegativetotalwageeffects inserviceoccupations.
AsdiscussedintheIntroduction,cross-elasticitiesoflabordemand acrosscells mayamplifytheestimate (Dustmannetal.,2016).With alarge numberofcells,many potentialcross-elasticitiesareatplay.
Therefore,inTableA.2,Iaggregateoccupationsbythefirsttwodigits (Columns1and2)andthreedigits(Columns4and5)ofthefour-digit occupationcodes,respectivelyproviding30and107groups.Columns2 and5areweightedbythesizeofthefour-digitoccupationswithineach group.Theestimatesfortwo-digitoccupationsarenegativebutinsignif- icant—asexpected,withonly30groups.Theestimatesforthree-digit occupations,bothweightedandunweighted,aresimilartothebase- line.Columns3and6showrobustnessofthebaselinemodeltohigher levelclustering—althougharguably,30two-digitoccupationsaretoo fewuponwhichtocluster.
Inall,thechecksinthissectionconfirmthattheadverseearnings impactofEU12immigrationisrobusttoalternativespecificationsand samplestrata.Theylendcredencetotheparallelearnings-trendassump- tion,suggestingthattheestimationidentifiesthecausaleffectofimmi- gration.Furthertestsareavailableuponrequest.
6. Mechanisms
Toexamine possiblemechanismsbehindtheearningseffect, Ies- timateasetofemploymentandadjustmentresponseoutcomesinthe
following2SLSestimation.
Δ𝐸𝑈12𝑗,05−11 =𝛾0+𝛾1𝑙𝑎𝑛𝑔𝑗+𝑋′𝑖,05𝛾2+𝛾3̃𝑌𝑖,02−05+𝛾4𝑠𝑜𝑐𝑗
+𝛾5𝑌𝑖,02−05+𝜉𝑖𝑗 (4)
𝑞𝑖𝑗 =𝛽0+𝛽1𝐼𝑉Δ̂𝐸𝑈12𝑗,05−11+𝑋′𝑖,05𝛽2+𝛽3̃𝑌𝑖,02−05+𝛽4𝑠𝑜𝑐𝑗
+𝛾5𝑌𝑖,02−05+𝜖𝑖𝑗 (5)
qijmeasureschangesfrombefore(2002–2005)toafter(2006–2011) theonsetoftheimmigrationsurgeinthefollowinglabormarketout- comes: contractedweekly workhours,(approximated)hourlywages, full-timeemployment,(un)employment,anddisabilityprogrampartic- ipation,aswellaschangeofemployer,commutingzone,industry,sec- tor,education,andoccupation.25Iincludeallcontrolvariablesfromthe baselinemodelplusthelogarithmofcumulativeearningsfrom2002to 2005(thedenominatorintheearningsoutcomevariable).Inthewage estimation, thelatter is substitutedwith wagegrowthfrom2003 to 2005.26
6.1. Employment
Table4revealsadverseeffectsinallemploymentdimensionsonna- tiveslessprotectedbylanguagebarriersrelativetomoreprotectedna- tives.AonepercentagepointincreaseintheEU12sharereducescon- tracted weeklyworkhours(Column1)by0.12percent (meanof 37 hours)andwages(conditionalonemployment)by0.42percent(Col- umn2).27 Thewageeffectequalsroughlyhalfoftheearningseffect.
25Hoursandwagesaremeasuredrelativeto2003to2005duetopoordata qualityin2002.
26Resultsaresimilarwithearningsgrowthasthecontrol.
27Thecoefficientonhourlywagesshouldbeinterpretedwithcautionbecause themeasureisaroughapproximationbasedoncontractedweeklyworkhours, duration,andannualcashpaymentsofthemainemploymentspell.Further, contractedandactualhoursworkedarenotnecessarilyidentical.
Table4
Employmentoutcomes.
(1) (2) (3) (4) (5) (6)
Weekly hours Hourly wages Full-time employment Employment Unemployment insurance Disability insurance
b/se b/se b/se b/se b/se b/se
EU12 share − 0.118 ∗ − 0.424 ∗∗ − 0.967 ∗∗∗ − 0.471 ∗∗∗ 0.446 ∗∗ 0.563 ∗∗∗
(0.058) (0.147) (0.257) (0.123) (0.167) (0.084)
R 2 0.028 0.029 0.053 0.043 0.114 0.164
N 757,815 747,537 772,310 772,310 772,310 772,310
EachcolumnisaseparateestimationofEqs.(4)and(5)withoutcomevariablesasfollows:changesinlogofaveragecontracted weeklyworkhours(1)andhourlywages(2)inthemainjobfrom2003–2005to2006–2011,theprobabilityoffull-timeemployment (3)andearningsabovetheemploymentthreshold(4)eachyearin2006–2011,andtheprobabilityofreceivingunemployment(5)and disability(6)insurancein2006–2011,controllingforreceiptin2002–2005.Baselinesample.Samplesin(1)and(2)aresomewhat reducedduetolimiteddataonworkhours.∗p<0.05,∗∗p<0.01,∗∗∗p<0.001
Table5
Employmentoutcomes,byagegroups.
(1) (2) (3)
Young Middle aged Old
b/se b/se b/se
Earnings
EU12 share − 0.972 ∗∗∗ − 0.569 ∗∗∗ − 0.701 ∗∗∗ (0.166) (0.145) (0.206) Hourly wages
EU12 share − 0.635 ∗∗∗ − 0.249 − 0.187 (0.187) (0.133) (0.154) Weekly hours
EU12 share − 0.100 ∗ − 0.124 ∗ − 0.166 ∗ (0.042) (0.063) (0.072) Full-time employment
EU12 share − 0.798 ∗∗∗ − 0.991 ∗∗∗ − 1.278 ∗∗∗ (0.231) (0.247) (0.320) Employment
EU12 share − 0.385 ∗∗∗ − 0.340 ∗∗ − 0.714 ∗∗∗ (0.112) (0.104) (0.194) Unemployment insurance
EU12 share 0.533 ∗∗∗ 0.451 ∗∗ 0.320 (0.147) (0.169) (0.199) Disability insurance
EU12 share 0.455 ∗∗∗ 0.457 ∗∗∗ 0.722 ∗∗∗ (0.070) (0.079) (0.129) N (earnings outcome) 252,114 271,572 248,600 EachcellisaseparateestimationofEqs.(4)and(5)forthreeage groupsofworkersin2005:(1)23–36years,(2)37–46years,and (3)47–53years.SeeTable4notesfordefinitionoftheoutcomes.
Baselinesample.∗p<0.05,∗∗p<0.01,∗∗∗p<0.001
Correspondingly,theprobabilitiesoffull-timeemployment(Column3) andlaborearningsabovetheemploymentthreshold(Column4)arere- ducedby0.97(meanof80percent)and0.47percentagepoints(mean of 92 percent). Theemployment effect is somewhatsmallerthanin Dustmannetal.(2017).
Finally, the probabilities of receiving unemployment insurance (Table4,Column5)anddisabilityinsurance(Column6)foratleast3 monthswithinayeararerespectively0.45and0.56percentagepoints higherforeachpercentagepointincreaseintheEU12share.Multiplying thembytheaveragedifferenceintheincreaseinEU12share(3.8per- centagepoints),yieldsrespectively1.7and2.1percentagepointshigher unemploymentanddisabilityratesamongoccupationswithbelow-than above-medianlanguagerequirements.Forcomparison,theaverageun- employmentrateovertheperiod2006to2011was3.4percentandthe disabilityratewas8.0percentinthe(full)estimationsample.Similarly, BratsbergandRaaum(2012)estimatesubstantialemploymenteffects fromimmigrationtotheNorwegianconstructionsector,andBalsviket al.(2015)fromincreasedimportcompetitionfromChina.
Table5displaystheheterogeneityoftheemploymenteffectsacross agegroups.Notsurprisingly,youngworkers(Column1)arestrongly affectedrelativetotheir(language-protected)peersintheearningsand
wagedimensions.Thisalignswiththeliterature,whereinimmigrants commonlyarefoundtocompetemorewithyoungandlow-skillednative workers(e.g.,Borjas,2003;Card,2001).Thewageimpactsareinsignif- icantamongbothmiddle-agedandolderworkers.Theformer(Column 2)aregenerallytheleastaffected,whereasthelatter(Column3)are stronglyaffectedinallemploymentdimensionsexceptunemployment insurancereceipt.Thisisexpectedbecauseolderworkerstendtoenter earlyretirementor(permanent)disability,ratherthanunemployment, asaconsequenceofthedesignoftheNorwegianSocialSecuritySystem.
Dustmannetal.(2017)alsofindstrongwageeffectsforyoungworkers andstrongemploymenteffectsforoldworkers.
6.2. Mobility
Fromstandardeconomictheory,weexpectthatnativescompeting withimmigrantsrespondbymovinggeographicallyorchangingtoless exposed jobs,for instanceby upgradingskills.Surprisingly, theesti- matedeffectsonnativemobilityalongmostdimensionsareinsignifi- cant.However,Ifindthatlow-languagenativesmoreoftenre-educate andchangetooccupationswithhigherlanguagerequirements.Table6 shows that the(unconditional) probabilities of changing commuting zone, firm,industry,sector, andoccupationdifferinsignificantly be- tweenworkerswithunequalimmigrantexposure.Alloutcomevariables equal1forindividualswhochangestateatleastonceduringtheout- comeperiodand0otherwise(includingforworkerswholeaveemploy- ment).Icontrolforthesamevariablelagged.
Themobilityresultscaptureoutflowsandshiftsofworkingnatives, butnotinflowsorchangesinoccupationalemployment,whicharepo- tentialimportantdriversoftotalimmigrationeffects(e.g.,Autorand Dorn,2009;Dustmannetal.,2017).Becauseoflimitedoccupationdata (seeSection3)andbecausetheestimationsampleconsistsofonlyini- tiallyemployednatives,changesininflowsoremploymentstockscan- notbedetected.Improvedregistrationofworkers’occupationsoverthe periodcannotbeseparatedfromactualgrowthinoccupations.
TheinsignificantoccupationalmobilityresultsinTable6,Column 5mustbe interpretedwithcaution duetoscarceoccupationdata.28 Icannotproperlycontrolformobilityratespriortotheimmigration surge whichshould be includedifthey systematicallydifferedalong thelanguage-requirement distributionforreasons unrelatedtoimmi- gration.Ineverthelessincludearoughcontrolfortheshareofworkers whochanged(non-missing)occupationbetween2003and2005,based onthe(non-representative)subsetofworkerswithoccupationdatain 2003and2004.TableA.3demonstratesthattheimmigrationcoefficient decreasesinbothmagnitudeandsignificanceasthecontrolforprevi- ousmobilityimproves(butitneverreachesperfect).Asevidentfromthe negativecoefficient,previousmobilitywaslowerinoccupationswith- outlanguagebarriers(conditionalonthecontrols).
28However,BratsbergandRaaum(2012)alsofindnoeffectonjobchanges.