The rise of robots and the fall of routine jobs

University of Groningen

The rise of robots and the fall of routine jobs

de Vries, Gaaitzen J.; Gentile, Elisabetta; Miroudot, Sebastien; Wacker, Konstantin M.

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Labour Economics

DOI:

10.1016/j.labeco.2020.101885

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de Vries, G. J., Gentile, E., Miroudot, S., & Wacker, K. M. (2020). The rise of robots and the fall of routine

jobs. Labour Economics, 66, [101885]. https://doi.org/10.1016/j.labeco.2020.101885

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ContentslistsavailableatScienceDirect

Labour

Economics

journalhomepage:www.elsevier.com/locate/labeco

The

rise

of

robots

and

the

fall

of

routine

jobs

Gaaitzen

J.

de

Vries

_,

_Elisabetta

_Gentile

_,

_Sébastien

_Miroudot

_,

_Konstantin

_M.

_Wacker

b Asian Development Bank, Philippines

c Organisation for Economic Co-operation and Development (OECD), Trade and Agriculture Directorate, France

a r t i c l e

i n f o

a b s t r a c t

Thispaperexaminestheimpactofindustrialrobotsonjobs.Wecombinedataonrobotadoptionandoccupations byindustryinthirty-sevencountriesfortheperiodfrom2005to2015.Weexploitdifferencesacrossindustries intechnicalfeasibility– definedastheindustry’sshareoftasksreplaceablebyrobots– toidentifytheimpactof robotusageonemployment.Thedataallowustodifferentiateeffectsbytheroutine-intensityofemployment. Wefindthatariseinrobotadoptionrelatessignificantlytoafallintheemploymentshareofroutinemanual task-intensivejobs.Thisrelationisobservedinhigh-incomecountries,butnotinemergingmarketandtransition economies.

1. Introduction

Rapid improvements in robot capabilities have fuelled concerns abouttheimplicationsof robotadoptionforjobs.Whilethecreation ofautonomousrobotswithflexible3Dmovementcontinuestobea ma-jorchallengetoengineers,rapidprogressisbeing made.Robotscan nowperformavarietyoftasks,suchassealing,assembling,and han-dlingtools.Asrobotcapabilitiescontinuetoexpandandunitpricesfall, firmsareintensifying investmentinrobots(FreyandOsborne, 2017;

GraetzandMichaels,2018;AcemogluandRestrepo,2020).Whatisthe impactofrobotadoptiononlabourdemand?Dorobotssubstitutefor taskspreviouslyperformedbyworkers?

Themaincontributionofthispaperistoempiricallystudythe im-pactofindustrialrobotsontheoccupationalstructureoftheworkforce acrossindustriesinasetofhigh-incomeaswellasEmergingMarketand TransitionEconomies(EMTEs).Wecombinealargeanddetailed occu-pationsdatabasewithdataonindustrialrobotdeliveriesfromthe Inter-nationalFederationofRobotics.Thedatabaseonoccupational employ-mentfromReijndersanddeVries(2018)allowsustoexaminetheshare ofemploymentinoccupationswithahighcontentofroutinetasks– i.e. tasksthatcanbeperformedbyfollowingawell-definedsetof proce-dures.Wedelineateoccupationsalongtwodimensionsofthe character-isticsoftasksperformed,namely‘analytic’versus‘manual’,and‘routine’ versus‘non-routine’.1_{Wethusdistinguishfourkeyoccupational}

group-∗_{Correspondingauthor.}

E-mailaddress:g.j.de.vries@rug.nl(G.J.deVries).

1 _{Thedistinctionbetweenmanualandanalyticoccupationsisbasedon} differ-encesintheextentofmentalversusphysicalactivity.

ings,namelyroutinemanual,routineanalytic,non-routinemanual,and non-routineanalytictask-intensiveoccupations(asinAutoretal.2003;

ReijndersanddeVries2018;Cortesetal.2020).WefollowGraetzand Michaels(2018)inconstructingmeasuresofrobotadoptionby country-industrypairsandrelatethesetochangesinoccupationalemployment shares.Oursamplecovers19industriesfor37countriesatvarying lev-elsofdevelopmentfrom2005to2015,andincludesmajorusersof in-dustrial robots,such asthePeoples Republicof China(PRC),Japan, SouthKorea,Germany,andtheUnitedStates.Ourmainfindingisthat country-industry pairsthatsawamorerapidincreaseinrobot adop-tionexperiencedlargerreductionsintheemploymentshareofroutine manualjobs.

Our approachis motivated bythefollowing economic considera-tions.Firmsproduceavarietyofproductsusingacontinuumoftasks (AcemogluandAutor,2011),andtheseproductsdifferinthenumber oftasksthatcanbeperformedbyrobots(GraetzandMichaels,2018). Forexample,theshareofreplaceabletasksbyrobotsdiffersbetween apparelandautomotiveandappearslargerinthelatter.2_{Thisgivesrise} todifferencesacrossindustriesinthetechnicalfeasibilityofrobots substi-tutingtaskspreviouslyperformedbyhumans.Advancesinmachine ca-pabilitiesexpandthesetoftaskscarriedoutbymachines(Acemogluand Restrepo,2018).Firmswilladoptrobotsifitistechnicallyfeasibleand theprofit gainsexceed thecostsof purchasingandinstallingrobots. Givenhigherwagesinadvancedcountries,thetechnicalconstraintsto

2_{Seee.g.theEconomist,24August2017,“Sewingclothesstillneedshuman} hands.Butforhowmuchlonger?”

https://doi.org/10.1016/j.labeco.2020.101885

Received31January2020;Receivedinrevisedform22June2020;Accepted10July2020 Availableonline11July2020

robotsreplacingtasksaremorelikelytobindforfirmsinthese coun-tries.Hence,improvementsinrobotcapabilitieswouldresultinalarger employmentresponseinadvancedcountriescomparedtodeveloping countries.

Weusethese economicinsightsinouranalysis.Inparticular,the technicalfeasibilityofadoptingrobotsguidesourinstrumentalvariables (IV)strategytoidentifythecausalrelationbetweenrobotsandlabour demand.Economicfeasibilitymotivatesourdistinctionoftheimpact ofrobotadoptionbetween advancedanddevelopingcountries.Using two-stageleastsquares(2SLS)estimation,wefindthatrobotadoption lowerstheemploymentshareofroutinemanualoccupations.This rela-tionisobservedinhigh-incomecountries,butnotinemergingmarket andtransitioneconomies.

Thispaperrelatestorecentstudiesthatexaminetheimpactofrobot adoptiononsocio-economicoutcomes.GraetzandMichaels(2018)find thatrobotadoptioncontributedtoanincreaseinproductivitygrowth across industries in high-income countriesbetween 1993 and 2007. Their findings suggest that robot adoption did not reduce employ-ment, which is corroborated in this paper. This is also observed by

Dauthetal.(2019),butnotbyAcemogluandRestrepo(2020),who ex-aminegeographicvariationinrobotadoptionacrosstheUnitedStates andfindthatrobotsarelabourreplacing.Dauthetal.(2019)use de-tailedlinkedemployer-employeedataforGermany toshow that dis-placementeffectsarecancelledoutbyreallocationeffects,suchthatin theaggregatenoemploymenteffectsfromrobotadoptionareobserved. Dataavailabilitydidnotallow GraetzandMichaels(2018)to exam-inetheimpactofrobotsonworkersthatperformdifferenttasks.Yet,

Autor(2015)emphasizesthatworkerswithroutinetask-intensive occu-pationsaremostlikelytobeaffectedbyautomation.Thispaperaimsto contributetoourunderstandingoftheimpactofrobotsonsuch occu-pationalshifts.

Theremainderofthispaperisorganizedasfollows. Section2 re-viewsthekeytheoreticalmechanismsbetweenautomationandlabour demand.Section3describesthemethodology andinstrumental vari-ables.Section4documentspatternsintheoccupationalstructureofthe workforceandrobotadoption.Section5empiricallystudiestheimpact ofrobotadoptiononthetaskcontentoflabourdemand.Section6 con-cludes.

2. Theoreticalframework

Thissectionstartswithadiscussionofrobotadoptioninthecontext ofatraditionalcapital-labourmodel.Inthismodel,technologyis factor-augmenting:itincreasestheefficiencyofoneoftheproductionfactors employed(AcemogluandAutor,2011).Themodelputsthefocuson thecomplementarityandsubstitutabilitybetweenrobotsandtasks per-formedbyworkers.Wethendescriberecentmodellingeffortsthat em-phasizetheabilityofmachinestoreplaceworkersinawideningrange oftasks(AcemogluandRestrepo,2018).Thesemodelshelptoclarify mechanismsbywhichrobotsmayimpactlabourdemandandmotivate ourempiricalanalysis.

Themodelswedescribeanalysetheimpactofautomation. Automa-tionreferstocomputer-assistedmachines,robotics,andartificial intel-ligence(AcemogluandRestrepo,2018).Thus,robotsareasubsetof automation.Robotsaredrivenbyalgorithms,whichhavebecome in-creasinglycomplex.Theycannowoperatewithoutrequiringanyoneto explicitlyprogramthemechanismsofthetasksperformed.Yet,notall algorithmsdriveaphysicalmachine.Infact,manyalgorithmsare em-bodiedindevicesorapplications.Oncethesealgorithmsaredesigned, theycanbeusedformanytasksanywhereandatanytime.Forrobots, thealgorithmsareembodiedinthemachines.Expandingtherangeof tasksperformedbyrobotsthusrequiresinvestinginrobots,i.e.robots arerival(MartensandTolan,2018).Thiscontraststoalgorithms,which arenon-rivalinnature.Robotsaremorefrequentlystudiedin empiri-calworkbecauseoftheavailabilityofstatisticsontheiruse.However,

giventhepropertiesofrobotics,studiesthatuserobotdatacaptureonly partoftheimpactofautomationonlabour.

In the traditional model, automation enhances the productiv-ity of workers by complementing the tasks they perform (see e.g.

Autoretal.1998; Feenstra,2008; VanReenen2011).Yet,for work-erswhoperformtasksthatcanbesubstitutedbyautomation, increas-ingavailabilityofmachineswilllowertheirlabourdemand.Scholars havearguedthatnewtechnologiestendtosubstitute foroccupations thatareintensiveinroutinetasks,suchasassemblers,andcomplement non-routinetask-intensiveoccupations,suchasmanagersand techni-calscientists(Autoretal.2003;VanReenen2011;Goosetal.2014;

Dauthetal.2019).Thisisbecauseforroutinetasks,suchasmonitoring, measuring,controlling,andcalculating,therearewell-specified proce-dureswhich allowthetask tobe automated.Yet,knowingtherules thatgoverntaskproceduresisnotatrivialrequirement.Formany non-routine tasks, suchasthoserequiringcreativity andproblem-solving skills,automationisdifficultandrathercomplementstheperformance ofthesetasksdonebyhumans.Inlinewiththisreasoning,ananalysis forWesternEuropeancountriesbyGoosetal.(2014)findsthatrecent technologicalprogresshasbeenreplacingworkersdoingroutinetasks. Thisisreferredtoas“routine-biasedtechnologicalchange” (RBTC).3

Predictionsinthetraditionalmodelarestraightforward.Firmsadopt robotsifitiseconomicallyfeasibletodoso,whichisthecasewhen prof-itsexceedpurchasingandinstallationcosts.Therefore,substitutionof robotsforroutinetasksismorelikelyincountrieswithhigherwage lev-els,andthereafallinthefixedcostsortherentalpricewillresultinan increaseinrobotadoption(GraetzandMichaels,2018).

RecentmodellingeffortsbyAcemogluandRestrepo(2018)adda distinctive featureof automation:thetechnical abilityof machinesto replaceworkersinawideningrangeoftasks.Theysplittheproduction processintotasksdonebyworkersandmachines.Advancesinmachine capabilitiesexpandthesetoftaskscarriedoutbymachinesandreplace labour,thusloweringlabourdemand.

However,roboticautomationtechnologiesalsoresultinthecreation ofnewtasksthatcannotbedonebymachines,suchasprogramming, design, andmaintenanceofhigh-techequipment (Acemogluand Re-strepo,2019).This‘re-instatementeffect’increaseslabourdemand.The combinationoftasksdisplacedbyrobotsandthere-instatementofnew tasksdeterminethereallocationoftasksbetweenworkersandmachines. ComplementaritybetweenmanandmachineintheAcemogluand Restrepo(2018)modeloriginatesfromtwoindirecteffectsthatcome ontopofcomplementarityeffectsinthetraditionalmodel(Martensand Tolan, 2018). The first is a price-productivity effect whereby robot adoptionlowerspricesofproducedgoods,leadingtheindustryto ex-pandsalesandincreaseitsdemandforlabour.Thesecondisa scale-productivityeffectwherebyloweraggregategoods’pricesenablethe (local)economytoexpandandthusalsoincreaselabourdemand.The overall impact of robotization on labour demand then depends on whetherthedisplacementorthecomplementaryeffectsdominate.So far,empiricalevidenceontheaggregateemploymenteffectsfrom robo-tizationareinconclusive.4

In line with Acemoglu and Restrepo (2018), Graetz and Michaels (2018) model the production process as a continuum of tasks. Yet, Graetz and Michaels (2018) assume that products differ

3_{Autoretal.(2003)examinetheimpactofcomputerizationonlabour} de-mandinU.S.industriesfrom1960-1998.Theyfindapositiverelationbetween thedemandfornon-routinetasksandcomputerizingindustries.Ross(2017)and

DeLaRicaetal.(2020)studytheimpactofRBTConthewagepremiumforjob tasks.

4_{AcemogluandRestrepo(2020)findthatrobotadoptionlowerslabour}

de-mand in US locallabour markets. Dauthet al.(2019) argue in an

analy-sisforGermanythatworkersdisplacedbyrobotsreallocatetoservicesand there is no decline in aggregate employment. In a cross-country analysis,

Ghodsietal.(2020)findthatrobotadoptiondoesnotsignificantlyaffect ag-gregateemployment,althoughtheimpactvariesattheindustrylevel.

intheshare oftasksthat canbe carriedout bymachines.Garments provide aclearexample:sewinggarmentsis acomplex processthat requireshumanintuitionanddexterity,whichisdifficulttoprogram. Incontrast,ithasproven easiertoprogramrobots toperform tasks inautomobileassemblylines.5 _{Automationofcarassembly lineshas} helpedtoreduce error rates andenhancesthecontrol ofrepeatable tasks.Thetechnicalfeasibilityofmachinestakingovertasksthusdiffers byindustry.

Inthis expandedmodel,theimprovementofmachinecapabilities maydrive automation.6 _{That is,ifrobot adoption is constrained by} theproductionnatureofcertainindustries,therentalpriceof robots doesnotmatter.Rather,itisanexpansioninmachinecapabilitiesthat willdriveautomation.Giventhatlabourcostsarehigherinadvanced economies,therelaxingoftechnologicalconstraintsbyexpandingrobot capabilitieswillleadtohighereconomicincentivesforrobotizationin advancedcountriesandhencestrongeremploymentresponses.

Thetraditionalandexpandedmodelcapturethekeyeconomic mech-anismsdrivingrobotadoptionandtheiremploymenteffects.ThePRCis aninterestingcasetoillustratehowadditionalfactorsdriverobot adop-tion.WagelevelsinChinaarebelowhigh-incomecountries,butitis theworld’slargestadopterofindustrialrobots(Chengetal.2019).This seemscounterintuitivetothemodellingofrobotadoption.Yet,robotuse inChinadoescoincidewithrisingwagesandaslowdowninthegrowth ofitsworking-agepopulation.Besideslabourcosts,concernsover prod-uctqualityandproductionexpansionarefoundtoinfluencedecisions byfirmsinadoptingrobots(Chengetal.2019).Inaddition,the Chi-nesegovernmenthasinitiatedvariousprogramsandprovidessubsidies thatencouragethedevelopmentoftheroboticsindustry(Yang,2017;

Lin,2018).

Robotsmayalsoreversethetrendtorelocatefabricationactivities fromadvanced towardslow-wagecountries.Inaninteresting contri-bution,Faber(2018)pointsoutthatadvancesinroboticswillreduce productioncosts,nomatterwheretheproductisproduced.That,he ar-gues,willincreasetheattractivenessofproducingdomesticallyrelative tooffshoring.Ineffect,workersinexportsectorsofdevelopingcountries canbedisplacedbytheadoptionofrobots,eitheronshoreoroffshore. Essentially,foreignrobotsactasaformofcompetitionontheexport market.UsingamethodologicalapproachsimilartoAcemogluand Re-strepo(2020),Faber(2018)findsthatUSrobotadoptionlowerslabour demandinMexicanexport-producingsectors.7

Thesemodelsinformtheempiricalanalysisinourpaper.Thenext sections describe the methodology and data to examine the aggre-gate(cross-country)implicationsofrobotization.Weviewthis analy-sisasacomplementaryapproachtothewithin-countrycomparisonsin

AcemogluandRestrepo(2020),Dauthetal.(2019),andFaber(2018).

3. Methodology

Toexaminetherelationbetweenrobotadoptionandchangesinthe structureoftheworkforce,weestimateregressionssimilartothosein

GraetzandMichaels(2018)thattaketheform

ΔLci=βΔRobotadoptionci+𝐗′ciγ +δc+εci, (1)

5 _{Clearly,sometextileproductioncannowalsobenearlyfullyautomated;an} exampleistheAdidas’Speedfactory‘(Faber,2018).Yet,relativelyspeaking, theshareoftasksthatrobotscanperformvariesacrossindustries.

6 _{Wethankananonymousrefereeforpointingthisout.}

7 _{Ifrobotsresultinreshoringofafactory,thiswillaffectallworkersatthe} exportingplant inthedevelopingcountry.Faber(2018)findsthatMexican workersincommutingzonesmostaffectedbyU.S.robotsarelow-educated ma-chineoperatorsandtechniciansinmanufacturingandhigh-educatedworkersin managerialandprofessionaloccupations.UsingtheWorldInput-OutputTables,

Krenzetal.(2018)findevidenceforapositiverelationbetweenreshoringand thedegreeofautomation.

where∆Lciisthechangeintheemploymentoutcomeofinterestin

in-dustry iof countryc.8 _{∆Robot adoption}

ciis thechange oftherobot

stockrelativetolabourinputineachcountry-industrypair.9_Most spec-ificationsincludecontrolvariableswhicharechangesin:investmentto valueaddedratios,and(thenaturallogarithmof)valueadded.Wealso examine resultscontrollingfortheadoptionofinformationand com-municationtechnologies(discussedbelow).𝛿crepresentscountryfixed

effects,whichinafirst-differenceequationareequivalentto country-specifictimetrendsinalevels’equation.Regressionsareestimatedin long-runchangesbetween2005and2015becauseweareinterestedin longer-termtrends.Theregressionsweightindustriesusingtheir2005 employmentshareswithineachcountry.Thisensuresthatestimates re-flecttheimportanceofindustrieswithincountries,butwegiveequal weighttocountriesintheanalysis(ase.g.inGraetzandMichaels,2018). Weuseheteroscedasticity-robuststandarderrorsthataretwo-way clus-teredbycountryandindustry.10_{Thisisaconservativeapproachbecause} theresultingstandarderrorsaretypicallylargercomparedtoone-way clusteringbycountryorindustry.

3.1. Endogeneityconcernsand2SLSestimation

Estimating(1)usingOLSraisesseveralconcernsaboutendogeneity. First,onemightworryaboutreversecausalityandomittedvariablebias. Forinstance,industriesthatexperienceafastergrowthinproduct de-mandmayinvestmoreinrobots.Especiallyifthelabourmarketistight, apositivedemandshockismorelikelytoresultininvestmentinrobots ratherthananexpansionofemployment(Faber,2018).11_Thisisacase ofreversecausality,becauseloweremploymentgrowthresultsinhigher robotadoption.Also,relevantvariablesmightbeomittedfromthe re-gressionanalysis.Forinstance,Harriganetal.(2016)findthatadoption ofnewtechnologiesismediatedbytechnicallyqualifiedworkers. Sec-ond,onemayworryaboutattenuationbiasof𝛽 in(1)dueto measure-menterrorinthevariablerobotadoption.Clearly,theavailabledata onrobotadoption,discussedinSection4.1,isimperfect,asitdoesnot informonthequalityandothercharacteristicsofrobotsinstalled. In addition,weestimateregressionspecificationsinchanges,whichmay worsenthesignal-to-noiseratiocomparedtoregressionsofvariablesin levels.Duetomeasurementerror,thevariablerobotadoptioncouldbe correlatedwiththeerrorterm𝜀ciandOLSestimationof𝛽 wouldbe

bi-aseddownwards.Finally,industriesthatadoptrobotsmaydifferfrom otherindustriesinnon-randomways,whichwouldalsobiasthe coeffi-cientifnotappropriatelycontrolledfor.Hence,thedirectionofbiasin

𝛽 isnotclearapriori,althoughthepreviousliteraturesuggeststhata downwardbiasinOLSismorelikely(e.g.GraetzandMichaels,2018). In an attempt to address these endogeneity concerns, we use two industry-specific instruments introduced by Graetz and

8_{Theemploymentoutcomeofinterestiseithertheaverageannual} percent-agegrowthrateinemploymentbycountry-industrypair,whichisestimatedas ((ln(EMPci,2015/EMPci,2005))/10)∗100,oritisthechangeinthetask-specific employmentsharebycountry-industrypair,measuredasthesharein2015 mi-nusthesharein2005.

9_{Robotadoptionisdefinedasthenumberofrobotsinstalledperthousand} personsemployed.WefollowGraetzandMichaels(2018)andusethepercentile rankofthechangeinrobotadoptionasourmainexplanatoryvariable.Thisis furtherelaborateduponinSection4.1.

10_{WeimplementStata’s‘ivreg2’commandforOLSand2SLSregressions.} Two-wayclusteredstandarderrorsarerobust toarbitraryheteroscedasticity and intra-groupcorrelationwithineachofthetwo(non-nested)categories “coun-try” and“industry” (Cameronetal.2012).Thisallowsforrobustinference, forexample,iferrorsarecorrelatedwithincountries(e.g.duetounobserved country-specificpolicies)andhaveseparatecorrelationstructureswithin indus-tries(e.g.duetotechnologyshocks).

11_{InhisanalysisoftheMexicanlabourmarket,Faber(2018)pointsoutthata} positivedemandshockduetotheNorthAmericanFreeTradeAgreementmay haveputupwardpressureonindustriesorlocallabourmarketstoadoptrobots iftheyhadlessroomtoexpandemployment.

Michaels(2018)andestimate (1)using 2SLS.12 _{Thefirstinstrument} measurestheshareofeachindustry’s labourinputthatisreplaceable byrobots.Thisinstrumentisconstructedusinginformationonthetasks performedbyrobots(IFR,2012).Asdiscussedabove,theextentof robo-tizationforeachtaskcouldbeendogenoustoindustryconditions. There-fore,GraetzandMichaels(2018)useinformationonUSoccupationsin eachindustryfromthe1980census,whichdatesbackbeforetheriseof robots.Occupationsaredefinedas‘replaceable’if(partof)theirtasks couldhavebeenreplacedbyrobotsin2012.Theythencomputethe fractionofhoursworkedineachindustryin1980thatwasperformed byoccupationsthatsubsequentlybecamemorepronetoreplacement byrobots.Thisinstrumentisnotwithoutlimitations:itisbasedondata fromtheUSandlaboursharesmightthereforebedifferentifconstructed usingdatafromothercountries.13

Thesecondinstrumentismotivatedbyrapidimprovementsinthe abilityofroboticarmstoperform‘reachingandhandling’tasks.It mea-surestheprevalenceofoccupationsineachindustrythatrequire reach-ingandhandlingtaskscomparedtootherphysicaldemandsin1980,prior torobotadoption.Roboticarmsareasalientcharacteristicofrobots, andmuchtechnologicaladvancesarelinkedtothedevelopmentofthese roboticarms(GraetzandMichaels,2018).Itisthereforemorelikelythat roboticarmsareatechnologicalcharacteristicofrobots,lessdrivenby thedemandside(duetoindustries’taskrequirements),whichcould re-flectreversecausality.Thisinstrumentisconstructedusingtheextent towhichoccupationsineachUSindustryrequirereachingandhandling taskscomparedtootherphysicaltasksin1980.14_{Similarlimitationsas} tothefirstinstrumentapplyhere,butonemayarguethatthis instru-mentislesslikelytoviolatetheexclusionrestriction.

Clearly,neitherinstrumentcanguaranteetoresolveall endogene-ityconcerns.Bothinstrumentsreflectvariationacrossindustriesinthe shareoftasksthatarepotentiallyreplaceablebyrobots,whichmay cor-relatewithotherchangesovertime.Nevertheless,theinstrumentsare helpfultocontrastOLSwith2SLSresults.

4. Dataanddescriptiveanalysis

WefirstdescribethedataonrobotsandoccupationsinSection4.1. DescriptivestatisticsarepresentedinSection4.2.

4.1. Occupationsandrobots

Wecombinetwodatasetswithinformationonoccupationsandrobot purchases.Thefirstdatasetwithoccupationalemploymentby country-industryoriginatesfromReijndersanddeVries(2018)andwasupdated byBuckleyetal.(2020).Thedataisconstructedusingdetailedsurvey andcensusdatafromstatisticalofficesfortheperiodfrom2000to2015. Thesourcesusedinconstructingthisdatasetcloselyalignwiththose fromotherstudies.15_{Thedatasetprovidesemploymentforthirteen} oc-cupationalgroupingsbycountry-industrypairs.Itcovers40countries, namelythe27membersoftheEuropeanUnion(perJanuary2007), Aus-tralia,Brazil,Canada,India,Indonesia,Japan,Mexico,thePRC,Russia, SouthKorea,ChineseTaipei, TurkeyandtheUnitedStates.Foreach

12 _{Theinstrumentsarecomputedfor2-digitindustriesintheISICrevision3} classification,whichmatcheswiththeindustryinformationonrobotstocksand occupationalemploymentsharespresentedinSection4.1.Notethatthe instru-mentsdonotvaryacrosscountriesbutonlyacrossindustries.

13 _{Alsonotethereplacementvaluesareanupperboundbecauseoccupations} areconsideredtobereplaceableevenifonlypartoftheirworkcanbereplaced byrobots.

14 _{InformationonthetaskcontentofoccupationsistakenfromtheDictionary} ofOccupationalTitles.

15 _{Forexample,fortheU.S.,thesourcesarethe2000Censusandtheannual} AmericanCommunitySurveys.ThesesourcesarealsousedinAutor(2015). DataforEuropeancountriesarefromtheharmonizedindividuallevelEuropean UnionLabourForceSurveys,whicharealsousedinGoosetal.(2014).

ofthesecountries,occupationalemploymentsharesby35ISICrevision 3.1industriesthatcovertheoveralleconomyaredistinguished.They include14 two-digitmanufacturingindustries(suchastextile manu-facturingandelectronicsmanufacturing),aswellasagriculture, min-ing,construction,utilities,finance,businessservices,personalservices, tradeandtransportservices,andpublicservicesindustries.Thedataset thushasdimensionsof13occupationalgroupings×35industries×40 countries×16years.Occupationdataisintrinsicallynotexactly compa-rableacrosscountries,andinpracticewillalsovaryduetodifferences inthetypeofsourcesandnationaldatacollectionpractices. Intertempo-ralchangeswithincountry-industriesarelikelymoreconsistentbecause

ReijndersanddeVries(2018)usedatafromthesamenationalsource for eachcountry. Ourempiricalanalysisexploits thiswithin-country variation.

Weexaminetheimpactofrobotadoptionontasks,whichwe dis-tinguish intoroutine versus non-routine andmanual versus analytic tasks.Ourmeasurementstrategyistoinfertheimpactofrobot adop-tionontasksfromdataontheoccupationalstructureoftheworkforce. Thedistinction betweenoccupationswithdifferent taskintensitiesis basedontheso-calledRoutineTaskIntensity(RTI)indexdevelopedby

Autoretal.(2003)andmappedintotheInternationalStandard Classifi-cationofOccupations(ISCO88)byGoosetal.(2014).Table1provides theallocationofoccupationalgroupingstotasks.

The second database includes deliveries of industrial robots by country-industryfromtheInternationalFederationofRobotics(IFR).16 TheIFRprovidescountrydataonthenumberofindustrialrobots de-liveredfrom 1993onwards. Yet coveragevaries andthebreakdown ofrobotinvestmentbycountry-industryisonlyconsistentlyavailable formostcountriesafter2004.Inaddition,robotinvestmentsincreased rapidlyduringthe2000s.Wethereforebuildthedatasetusing informa-tionforallavailableyearsbutfocusontheperiodfrom2005to2015 intheempiricalanalysis.17

Weusetheperpetualinventorymethodtobuildrobotstocks, assum-ingadepreciationrateof10%asinGraetzandMichaels(2018).18_We thendefine‘robotdensification’orsimply‘robotadoption’astherobot stock perthousandpersonsemployed.Weexamine changesin robot adoptionovertime.Thedistributionofchangesinrobotadoptionfor thecountry-industriesincludedinouranalysishasmostlyeitherzeroor smallpositivevalues,withalongrighttail.Analysingrawchangesin robotdensityisthereforenotrecommendableandweusethepercentile ofchangesinrobotadoption(basedontheemployment-weighted dis-tributionofchanges)asinGraetzandMichaels(2018).19

16_{Purchasesofservicesrobotsareonlyavailableforrecentyearsandfew} coun-tries,whichlimitsstudyingtheimpactontaskdemandofrobotadoptionin servicessectors.

17_{Programcodetoreplicatetheanalysisisavailablefromtheauthorsupon} request.

18_{The perpetual inventory method to build robot stocks is: RS}

ci,t = (1-d)∗_RS

ci,t-1+RDci,t ,whereRSistherobotstockofindustryiincountrycat timet;RDarerobotdeliveries,anddisthedepreciationrate.Ourmainresults arerobusttobuildingtherobotstockusinga5anda15percentdepreciation rate.

19_{We follow Graetz and Michaels (2018) and calculate}

within-country employment-weighted distributions of changes in robot

adoption between 2005 and 2015. We use the Stata code

that Graetz and Michaels (2018) made available at https:// dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5JWBXU. Specifically,wedenoterobotadoptionbyRA_ci,t=RS_ci,t/EMP_ci,t,i.e.therobot stockperthousandpersonsemployedinindustryiofcountryc.Wedenotewsc theweightedchangeinrobotadoptionofcountryc,whichisthesummationof changesinrobotadoptionbyindustryiweightedbytheiremploymentshares. Thechangeinrobotadoptionnetoftheweightedchangeinrobotadoption isΔRAci= (RAci,t -RAci,t-1)-wsc.Wethencalculate thepercentilerankof thechangeinrobotadoption(ΔRAci)andusethisvariableintheregression analysis.Theuseof percentilesiscommon intheeconomicsliterature and helpfulwhenthedataisskewed,seeforexampleAutoretal.(2003).

Table1

Mappingoccupationstotasks.

Routine Non-routine

Manual Production workers (71-74, 81-82, 93) Agricultural

workers (61-62, 92) Others (01, 999)

Support-services workers (51, 910, 912-916) Drivers (83)

Analytic Administrative workers (41-42) Legislators (11) Managers (12-13) Engineers (21, 31) Health professionals

(22, 32) Teaching professionals (23, 33) Other professionals (24, 34) Sales workers (52, 911)

Notes:MappingofthirteenoccupationsfromReijndersanddeVries(2018)tofourdifferentgroupsbasedonAutoretal.(2003)andGoosetal.(2014).Numbers inbracketsrefertoInternationalStandardClassificationofOccupationscodes(ISCO88).

Table2

Descriptivestatistics.

Obs. Mean SD p5 p95

Dependent variables

Employment growth (average annual, in %) 700 -0.78 3.41 -6.0 3.9

Δ Routine employment share 700 -0.04 0.10 -0.2 0.1

Δ Routine manual employment share 700 -0.04 0.12 -0.2 0.1 Δ Routine analytic employment share 700 -0.00 0.05 -0.1 0.1 Δ Non-routine manual employment share 700 -0.00 0.06 -0.1 0.1 Δ Non-routine analytic employment share 700 0.04 0.10 -0.1 0.2

Independent variables

Percentile of changes in robot adoption 700 0.50 0.29 0.0 1.0

Robot adoption, 2005 700 2.23 10.17 0.0 10.5

Robot adoption, 2015 700 4.98 22.54 0.0 21.1

Δ Investment to value added ratio 700 0.02 0.69 -0.2 0.2 Δ (natural logarithm of) value added 700 0.21 0.60 -0.7 1.1 Percentile of changes in information technology adoption 277 0.51 0.29 0.0 1.0 Percentile of changes in communication technology adoption 277 0.50 0.30 0.0 1.0

IV: Reaching and handling tasks 700 0.45 0.05 0.3 0.5

IV: Replaceable tasks 700 0.25 0.12 0.0 0.4

Notes:A‘Δ’infrontofavariablereferstothechangebetween2005and2015.Forvariabledescriptions, seeSection4.1.Inthecolumns,‘obs’referstothenumberofobservations,SDthestandarddeviation,p5 the5thpercentile,andp95the95thpercentile.

Wematchthedataonrobot adoptionwithoccupational employ-ment.20_{Thenineteensectorsthatarematchedare14manufacturing} in-dustries,agriculture,mining,utilities,construction,and‘educationand R&D’.The(unweighted)averageemploymentshareofthesesectorsin thetotaleconomyacrossthesampledcountriesis46%and39%in2000 and2015,respectively.Thesharevariesacrosslevelsofdevelopment. Itisabout aquarteroftheworkforcein advancedcountriessuchas Denmark,theNetherlands,andtheUnitedStatesthroughoutthe sam-pleperiod.Itisover50%oftotalpersonsemployedinindustrializers suchasthePRC,Turkey,andPoland.

Inmostregressionspecifications,wecontrolforchangesinthe in-vestmenttovalueaddedratios,and(thenaturallogarithmof)value added.21 _{Although robotsare a visible andmuch discussedform of}

20 _{Aftermatchingthedatasets, wehavedatafor37countriesand19} sec-tors,withmissingdataforafewcountry-industrypairs.High-income coun-triesincludethe‘old’EU15countries,westernoffshoots,andhigh-incomeEast Asiancountries,namelyAustralia,Austria,Belgium,Canada,Germany, Den-mark,Spain,Finland,France,theUnitedKingdom,Greece,Ireland,Italy,Japan, SouthKorea,Malta,theNetherlands,Portugal,Sweden,ChineseTaipei,andthe UnitedStates.EMTEsaretheothers,namelyBrazil,thePRC,CzechRepublic, Estonia,Hungary,Indonesia,India,Lithuania,Latvia,Mexico,Poland,Romania, Russia,Slovakia,Slovenia,andTurkey.

21 _{ThisdataisobtainedfromtheWIOD2016release(Timmeretal.2015).} Thefirstcontrolvariable,investmenttovalueaddedratiosmaybesubjectto concernsaboutmulti-collinearityasrobotsarepartofphysicalcapital invest-ment.Weexploredtheshareofrobotinvestmentinoverallinvestmentbyusing turnover-basedpricesofrobotsfortheUSprovidedinIFR(2012).Thenumber ofrobottimestheirunitpricegivesaroughapproximationofnominal invest-ment.Ourestimatessuggestthattheshareofrobotinvestmentintotal invest-mentissmall,typicallynotexceeding1percent.Thefirstdifferencesofour dataforrobotadoptionandinvestmenttovalueaddedratiosareonlyloosely correlated,withacorrelationcoefficientof-0.06.

automation, computersandotherdigitaltechnologiesimpact jobsas well.InformationandCommunicationTechnologies(ICTs)havebeen foundtobe skill-biased,raisingtheproductivityof high-skilled work-ers and lowering demand for low-skilled workers (Feenstra 2008;

Michaelsetal.2014).Incontrast,robotsarepartofrecentinnovations andconsideredroutine-biased,astheysubstituteforworkersperforming routine-manualtasks(Goosetal.2014).Theseroutinetasksareoften performedbyworkerswithamiddlinglevelofeducation,suchas fab-ricationjobs involvingrepetitiveproductiontasks(Autor,2015).We therefore expectadirecteffectofrobot adoptiononthedemandfor routine-manualtask-intensiveoccupationsindependentofICT invest-ment.

TocontrolforICTadoption,weusedatafromtheEUKLEMSRelease 2019forgrossfixedcapitalformationincomputingandcommunication equipment(Stehreretal.2019).TheseICTinvestmentsareexpressedas ashareintotalinvestment.ChangesintheICTinvestmentshareare in-cludedintheanalysis,alsointheformofthepercentileofchangesinICT adoption(basedontheemployment-weighteddistributionofchanges).

4.2. Descriptiveanalysis

Table 2showsdescriptivestatistics ofourkeydependentand ex-planatoryvariables.Thetoprowsshowchangesinemploymentshares for occupations by task intensity. Onaverage,the routine (manual) employmentsharedeclinedby4percentagepointsbetween2005and 2015.Thistrendisobservedin35outof37countries,butthedecline intheroutinesharediffersacrosscountriesandindustries.Thiscanbe seeninAppendixFigs.1and2,whichdepictthechangesinemployment sharesforourfouroccupationalgroupingsbycountryandindustry, re-spectively.Thedeclineinroutinemanualoccupationsis mirroredby theriseofnon-routineanalyticjobs,whichincreasedby4percentage

Fig.1. Robotsandtheroutineemploymentshare.

Notes:Observationsarecountry-industrycells.Thesizeofeachcirclecorrespondstoanindustry’s2005within-countryemploymentshare.Verticalaxisdisplays thechangeintheroutineemploymentsharebetween2005and2015.Horizontalaxisofpanel(a)showsthepercentileofchangesinrobotadoption(basedonthe employment-weighteddistributionofchanges),seeSection4.1.Panel(b)changesinrobotadoption(basedontheemployment-weighteddistributionofchanges). Fittedregressionlinesareshown.Coefficients(standarderrors)ofthelinearfitarerespectively-0.00033(0.00010)and-0.0013(0.0004).Sources:seeSection4.1.

pointsonaverage.22_{Thecomparabilityoftheshiftsinroutinemanual} andnon-routineanalyticoccupationsacrossoursampleofhigh-income countriesandEMTEsmakesitlikelythatacommonsetofforces con-tributestoshareddevelopmentsinlabourmarkets.Theprimesuspect isautomation(Autor, 2015).Atthesametime,variationin country-specificexperiencesunderscoresthatnocommoncausewillexplainthe fulldiversityoflabourmarketdevelopmentsacrosstheseeconomies.

Theaveragerobotstockperthousandpersonsemployedmorethan doubledfrom2.23in2005to4.98in2015.Thestandarddeviationof robotizationrevealssubstantialvariationinrobotizationacross coun-triesandindustries.Mostofthisvariationstemsfromcross-industry dif-ferenceswithincountriesasopposedtovariationbetweencountries.23 Morerobotswereinstalledinallcountries,withthenumberofrobots perthousandpersonsemployedsurginginGermany,Japan,andSouth Korea(seeAppendixFig.A3).24_{Highrobotdensityisobservedin} ma-chinery,electronics,andautomotive(seeAppendixFig.A4).For indus-triesthatproducechemicalsandmetalproductswealsoobservean in-creaseinrobotdensity,albeitstartingfromlowlevels.

AppendixFig.A5showsthenumber ofrobots per1,000 persons employedbyindustryinthePRCandGermanyfor2015.Thisfigure helpsclarifythelowerlevelofrobotsperthousandpersonsemployedin China.Forexample,in2015,thenumberofrobotsinstalledinChina’s automotiveindustrywasabout50,000,whichcomparestoaslightly lowernumberofaround48,500robotsinthatindustryforGermany. Yet,in2015thenumberofpersonsemployedinautomotiveisabout 6.8millioninChinacomparedto965thousandinGermany,soa fac-tor7differenceinthesizeoftheworkforceinthatindustry.Hencethe

22 _{Changesinthesharesofroutineanalyticandnon-routinemanualjobsare} typicallysmallerandweobservesubstantialvariationacrosscountries(see Ap-pendixFig.A1).

23 _{Thestandarddeviationoftherobotstockperthousandemployedbetween} countriesis8.06in2015.Incomparison,thestandarddeviationofrobot adop-tionwithincountriesis21.06in2015.Thosearecalculated,respectively,as thestandarddeviationsofcountrymeans𝑥𝑐andoftheirdeviations𝑥𝑐𝑖−𝑥𝑐+𝑥, wherexindicatesrobotadoptionand𝑥isitsglobalaverage.

24 _{ForJapan,reporteddeliveriesandstocksofrobotschangedovertimedue} toa reclassificationof machinesasrobots(GraetzandMichaels, 2018).In

Section5.2weshowthatthemainresultsarerobusttodroppingJapanfrom thesample.

numberofrobotsinstalledperthousandpersonsemployedisabout7in Chinacomparedto50inGermany.

Table2alsoprovidesdescriptivestatisticsfortheinstrumentsand controlvariables.Theinstrumentsreplaceabletasksandreachingand han-dling tasksarepositivelycorrelated,butdifferent.25 _{Forexample,the} highestshareofreplaceabletasksisobservedinautomotiveandmetal manufacturing,whereastheextentofreachingandhandlingstasksis highestintextileandfoodmanufacturing.

Fig.1plotsthechangeintheroutineemploymentshareagainst mea-suresofincreasedrobotuse.Insub-figure(a),weplotthepercentileof thechangeinrobotdensitynetofcountrytrendsonthehorizontalaxis, aswellasthefittedregressionline.Theslopeisnegativeandstatistically significant.Thedistributionofdatapointsaroundthefittedlinesuggest thattherelationshipbetweentheroutineshareandthepercentileof robotdensificationiswellapproximatedbyalinearfunctionalform.In subfigure(b),weinsteadplotchangesinrobotdensityonthehorizontal axis(againnetofcountrytrends),togetherwiththefittedline.Herea linearfunctionalform(thoughalsonegativeandsignificantat conven-tionallevels)seemsmuchlessadequate,andtheestimatedslopeappears sensitivetoseveraloutlyingobservationsnearthetopofthe distribu-tionofrobotdensification.Thus,followingGraetzandMichaels(2018), intheregressionanalysiswewillusethepercentileofchangesinrobot densification.

Panel(a)ofFig.2showsadescriptiverelationbetweenrobot adop-tionandindustryaveragechangesintheroutineemploymentshare be-tween2005and2015(seeTableA1fortheindustrydescriptions).We observea(slightly)strongerreductionintheroutineshareforindustries thatinvestedmoreinrobots.Sectorssuchaspaperandutilities experi-encedadeclineintheshareofroutinejobswithonlyarelativelysmall increaseinrobotization.Inmanufacturingindustriessuchasmachinery, electronics,andautomotive,weobserveadecreaseintheshareof rou-tinejobs.Theseindustriesarealsoamongtheoneswiththestrongest increaseinrobotadoption.Panels(b)and(c)suggestbothinstruments aregoodpredictors, asindustrieswitha highershareof replaceable tasksorthosemoreintensiveinreachingandhandlingtaskshave

in-25_{NotetheinstrumentsaremeasuredbyindustrybasedondatafortheUS(see} Section4.1)andmatchedtothecountry-industrypairs.

Fig.2. Cross-industryvariationinIVsandchangesintheroutineemployment share.

Notes:Onthehorizontalaxisisthe(unweighted)averagepercentileofchanges inrobotadoptionbyindustry.Inpanel(a),theverticalaxisshowsthe

indus-try(unweighted)average changein theroutineemploymentshare between

2005and2015.Thecoefficient(standarderror)ofthelinearfitinpanel(a) is-0.013(0.007).Theverticalaxisofpanels(b)and(c)showthevaluesfor theinstruments,coefficients(standarderrors)ofthelinearfitarerespectively 0.59(0.11)and0.20(0.07).Sources:seeSection4.1.

stalledmorerobotscomparedtoothers.Thenextsectionformallytests theserelationships.

5. Econometricresults

We present our main results from OLS and 2SLS regressions in

Section 5.1. We find thatrobot adoption relatestoa decline in the employmentshareofoccupationswithahighcontentofmanual rou-tinetasks.InSection5.2wepresentseveralextensionsandrobustness checks.Wefirstdocumentthatresultsappearneitherdrivenbyspecific sectorsorcountriesnorspuriousindustrytrends.Wethenexploit het-erogeneityintaskintensityacross(blue-collar)productionworkersand findthatrobot adoptionrelatestodecliningdemandforoccupations thataremoreintensivein routinetasks.Finally,weexplorewhether globaldevelopmentsinrobotizationimpactlabourdemandinEMTEs.

5.1. MainOLSand2SLSresults

Ourmainregressionresults aresummarizedinTable3,withOLS results inpanelA and2SLSresults inpanelB.Westarttheanalysis byregressingtheaverageannualpercentagegrowthofemploymenton robotadoption.Countryfixedeffectsareincluded;thus,coefficientsare identifiedfromvariationacrossindustries.Weuseaconservative two-wayclusteringofstandarderrorsatthecountryandindustrylevel. Col-umn1ofTable3indicatesthatrobotadoptionisnegativelycorrelated withtheaveragegrowthrateofemploymentbetween2005and2015. However,thisrelationshipisnotstatisticallydifferentfromzero.It sug-gestsrobotadoptionisnotlabourreplacing,whichwasalsoobserved byGraetzandMichaels(2018).Ourfindingindicatesthisresultholds inalargercountrysample.

Incolumn(2)ofTable3,weexaminetherelationbetweenrobot adoptionandtheshareofroutinejobs.Wefindthatincreasedrobot usecontributestoadeclineintheroutineemploymentshare.Toassess theeconomicmagnitude,considerthedifferencebetweenanindustry withamediantrendinrobotadoptionandanindustrywithnorobot adoption,whichequals0.5x-0.047=-0.02intheOLSregression.This differenceamountstoabout59%oftheaveragechangeintheroutine employmentshare(whichis-0.04,seeTable2).Whilethisindicatesa sizeableimpactofrobotsonoccupationalshifts,theR-squaredof2% incolumn(2)wherecountryfixedeffectsarepartialledout,indicates thatmanyotherfactorsthanrobotadoptionaffectchangesintheshare ofroutinejobs.Thecoefficientmorethandoublesinthe2SLS regres-sion,whereweusetheshareof replaceabletasksinindustriesasan instrument(panel B,column2).Theinstrumentispositivelyand sta-tisticallysignificantlycorrelatedwithrobotadoptioninthefirststage, whichisreportedincolumn(4)ofpanelB.Identificationisstrong,with theCragg-DonaldWaldFstatistic(268.53,assumingi.i.d.errors)and theKleibergen-PaapF-statistic(23.42)surpassingthe10%criticalvalue (16.38).Under-identificationisrejectedatthe5%levelofstatistical sig-nificance.Theconsiderableincreaseintheestimatedsecondstage co-efficientforrobotadoption,whencomparedtoOLSresults,mayreflect measurementerrorinourmainexplanatoryvariable:anincreaseinthe noise-to-signalratioinrobotadoptionwillbiasOLSestimatestowards zero.Moreover,theincreaseinthecoefficientin2SLSestimatesmay re-flectthatourinstrumentforrobotadoptiononlyvariesacrossindustries andthatglobalindustrytrendsimpactchangesinroutineemployment shares(seeSection5.2below).Using‘reachingandhandling’tasksas aninstrumentgivessimilarresults,althoughmorepronetoweak iden-tificationconcerns(seeAppendixTableA2).

Anadvantageofourdatasetisthebroadcountrycoverage,including variousemergingmarketand(post-)transitioneconomies.Incolumn (3)ofTable3,wedifferentiatetherelationbetweenrobotadoptionand routinesharesacrosshigh-incomecountriesandEMTEs.26_Wedosoby

26_{GiventhenumberofrobotsinstalledinthePRC,itmightbelessappropriate} toclassifyitasanEMTE.Tocheckforrobustnessofreportedresults,weomitted

Table3

Baselineregressionresultsofemploymentgrowthandchangeinroutineemploymentshare.

Panel A: OLS

(1) (2) (3) (4)

ΔEmployment ΔRoutineemployment share ΔRoutineemployment share Percentile of changes in robot adoption Percentile of changes in robot

adoption -0.354 -0.047

∗∗∗ _-0.055 ∗∗∗

(0.73) (0.02) (0.02)

Percentile of changes in robot adoption x dummy EMTE

0.040 ∗∗∗

(0.02)

R2 _{0.001 0.025 0.028}

Observations 700 700 700

Number of countries 37 37 37 37

Panel B: 2SLS (IV: Replaceable tasks)

Percentile of changes in robot adoption

-2.714 -0.120 ∗∗ _-0.156 ∗∗

(3.03) (0.05) (0.06)

Percentile of changes in robot

adoption x dummy EMTE 0.136

∗∗

(0.06)

Replaceable tasks 0.892 ∗∗∗

(0.18)

Cragg-Donald Wald F statistic 268.53

Kleibergen-Paap F-statistic 23.42

Kleibergen-Paap under identification test ( p -value)

0.013

R2 _{-0.052 -0.027 -0.053}

Observations 700 700 700 700

Number of countries 37 37 37 37

Notes:Robuststandarderrorsinparentheses.Multi-wayclusteringbycountryandindustry.Thedependentvariableemploymentgrowthincolumn(1)isthe averageannualpercentagegrowthinemploymentfortheperiodfrom2005to2015.Thedependentvariableincolumns(2)-(3)isthechangeintheroutine employmentsharebetween2005and2015.Column(4)reportsthefirststagefor2SLSestimation.Theshareofreplaceabletasksinanindustryisusedasan instrumentforrobotadoption.Regressionsincludethechangeintheinvestmenttovalueaddedratioandthechangein(thelogof)valueaddedbetween2005 and2015ascontrolvariables.CountryfixedeffectsareincludedinallregressionsandpartialledoutinthereportedR2_.

∗_p<0.1. ∗∗∗_p<0.01 ∗∗_p<0.05

interactingadummyvariableforEMTEswithrobotadoption.27_The re-lationshipbetweenrobotadoptionanddecliningroutinesharesappears tomainlyoccurinhigh-incomecountries:forboth,theOLSand2SLS regressions,thenegativeoverallcoefficientestimateforrobotadoption incolumn(3)isalmostequalinsizetothepositiveinteractionterm withtheEMTEdummy,indicatingthattheeffectofrobotadoptionis essentiallynullifiedinthosecountries.28_{Sincetechnicalconstraintsto} robotsreplacingtasksaremorelikely tobindforfirmsinhigh-wage advancedcountries,improvementsinrobotcapabilitiesmightaccount forthelargeremploymentresponseinadvancedcountriescomparedto EMTEs.

Additionally,ourdatasetallowsustofurtherdisaggregateroutine and non-routine employment shares into manual and analytic task-intensiveoccupations.ResultsarereportedinTable4,againwithOLS resultsinpanelAand2SLSresultsinpanelB.29_{Wefindthatthe} neg-ativerelationbetweenrobotadoptionandroutineemploymentshares isexclusivelydrivenbymanualroutinejobs:theestimatesincolumn

Chinafromthesampleandre-classifieditasanon-EMTE.Thisdidnotalterthe results(availableuponrequest).

27 _{Inthereported2SLSregressions,weonlyinstrumentrobotadoptionbutnot} theinteraction.Weadditionallyestimated2SLSregressionswiththe interac-tioninstrumented,whichrequiredinteractionofourinstrumentwithanEMTE dummyinthefirststage.Results,whichareavailableuponrequest,were quan-titativelyandqualitativelysimilartothosereported,butmorepronetoweak identificationconcerns.

28 _{OLSand2SLSestimatesof𝛽 arenotstatisticallysignificantlydifferentfrom} zerowhenestimatingequation(1)forEMTEsonly.Resultsareavailableupon request.

29 _{Notethatfirststageresultsforthe2SLScasearethesameasinTable3.}

(1)ofTable4essentiallymimicthoseofcolumn(2)inTable3,while norelationshipcanbefoundbetweenrobotadoptionandanalytic rou-tineemploymentshares(Table4,column2).Itthusappearsrobotsare bettersuitedtosubstituteforroutine-manualtasksduetotheability of robotstomanipulateobjects.Conversely,theshareofnon-routine analytic occupationspositivelyrelatestorobot adoption(column4). Thisisconsistentwiththeintuitionthatnon-routineanalytictasksare complementedbyrobotsinproduction(Autor,2015).Norelevant rela-tionshipisobservedbetweenrobotadoptionandchangesinthemanual non-routineemploymentshare(column3).

5.2. Robustnessandextensions

We performed several robustness checks. These are summarized in Section5.2.1.TheotherSectionsfocusonaspectsconsidered rele-vant tobetterunderstandtherelationbetween robotizationand rou-tine employmentsharesandtomotivatefutureresearchinthisarea.

Section5.2.2examinestherelationbetweenrobotadoptionacross pro-ductionoccupationsthatdifferintaskintensity.Section5.2.3examines whethertheresultsaredrivenbylonger-termindustrytrends.Finally,

Section5.2.4explorestheroleofglobalindustrytrendsinrobot adop-tionfordrivingcountry-industrychangesinemploymentshares.

5.2.1. Robustnessandheterogeneity

WefirstexamineregressionresultswhenaddingICTinvestmentto theanalysis.Thisisbecausecomputersseemparticularlysuitedto sub-stitute foranalytictasksandthedevelopmentof computerand com-municationequipmentisnotindependentofrobotadoption,suchthat omittingICTmaybiasthecoefficientforrobotadoption.Including vari-ablesforcomputerandcommunicationinvestmentleadstoa

consider-Table4

Robotadoptionandchangesinemploymentsharesbytasktype.

Panel A: OLS (1) (2) (3) (4) ΔRoutinemanual employment share ΔRoutineanalytic employment share ΔNon-routinemanual employment share ΔNon-routineanalytic employment share Percentile of changes in robot

adoption

-0.049 ∗∗∗ _{0.002 -0.008 0.055} ∗∗∗

(0.02) (0.00) (0.01) (0.02)

Δ Investment to value added ratio 0.003 ∗∗∗ _{0.001 -0.001 -0.003} ∗∗∗

(0.00) (0.00) (0.00) (0.00)

Δ (natural logarithm of) value added 0.005 0.002 0.004 -0.009 (0.01) (0.00) (0.00) (0.01) R2 _{0.024 0.003 0.007 0.031} Observations 700 700 700 700 Number of countries 37 37 37 37

Panel B: 2SLS (IV: Replaceable tasks) Percentile of changes in robot

adoption

-0.119 ∗∗ _{-0.003 -0.032 0.152} ∗∗∗

(0.05) (0.01) (0.02) (0.05)

Δ Investment to value added ratio 0.004 ∗∗∗ _{0.001 -0.001 -0.004} ∗∗∗

(0.00) (0.00) (0.00) (0.00)

Δ (natural logarithm of) value added 0.012 0.003 0.006 -0.019 ∗∗ (0.01) (0.00) (0.01) (0.01) R2 _{-0.020 0.001 -0.021 -0.059} Observations 700 700 700 700 Number of countries 37 37 37 37

Notes:Robuststandarderrorsinparentheses.Multi-wayclusteringbycountryandindustry.Thedependentvariableisthechangeintherespectiveemployment sharebetween2005and2015.Theshareofreplaceabletasksinanindustryisusedasaninstrumentforrobotadoption.Countryfixedeffectsareincludedinall regressionsandpartialledoutinthereportedR2_.

∗_p<0.1. ∗∗∗_p<0.01 ∗∗_p<0.05

abledeclineinthesampleto277observationsbecausetheEUKLEMS datasetdoesnotreportICTinvestmentbyindustryformanyEMTEs. Theestimatedcoefficientfortherelationbetweenrobotadoptionand routineemploymentsharesissmallerbutremainsnegativeand statisti-callysignificantintheOLSandIVregressions(seecolumn1ofAppendix

TableA3).30

Toavoidresultsbeingdrivenbycertaincountries,weinspectthe pat-ternofOLSresiduals(depictedinAppendixFig.A6).Furthermore,we lookatthedistributionofcountry-specificparameterestimates,which weobtainbyinteractingrobotadoptionwithamatrixofcountrydummy variablesinourmainOLSspecification(seeAppendixFig.A7).Thereis aclusterofhighfittedvaluesforIreland(AppendixFig.A6,panelA)and tworesidualsfromRomaniaandSwedenobtainarelativelyhigh lever-ageandarepotentialoutliers(AppendixFig.A6,panelB).Moreover,the country-specificestimationcoefficientsinAppendixFig.A7suggest co-efficientestimatesforIreland,Lithuania,andLatviadeviatefromother countries.Wehenceexcludethese5countriesaswellasPortugal,which sawsomewhatdifferentemploymentdynamicsthantherestofour sam-ple,accordingtoourdescriptiveanalysis(cf.AppendixFig.A1).Results arereportedincolumn(2)ofAppendixTableA3.Droppingthese coun-triesdoesnotqualitativelyaffectourmainresult.31

Similarly,wealsocomputeindustry-specificcoefficientsforthe re-lationshipbetweenrobot adoptionandtheshareof routinejobs. Ap-pendixFig.A8suggeststhattheelectricity,gas,andwatersupplysector

30 _{Moreover,thechangeintheparameterestimateappearstooriginatefroma} samplecompositioneffectandnotfromomittedICTvariables:re-estimatingthe baselinemodelwiththe277observationsforwhichICTdataisavailable pro-ducesthesamecoefficientforrobotadoptionasinthepresenceofICTvariables: -0.033∗∗∗_.

31 _{Wealsoexcludedseveralofthosecountries/countrygroupsseparately,with} equallyrobustresults.ThisalsoappliestoexcludingJapanfromtheanalysis, whichwasdroppedfromthesamplebyGraetzandMichaels(2018).

couldbeanoutlierthatpotentiallydrivestheoverallresult,together withtheeducationandR&Dsector,whichsawdifferentroutine employ-menttrendsaccordingtoourdescriptiveanalysis.Wethusre-estimate ourbaselineregressionsandsequentiallyomitthesesectors.Columns (3)and(4)ofAppendixTableA3suggestourresultsarenotdrivenby thesesectors,althoughomittingtheeducationandR&Dsectorin2SLS estimationpushesstatisticalsignificanceoftherobotadoption parame-terslightlybeyondthecritical10%level(forthenullhypothesisofno relationship).Tocheckwhethercountriesthataccountforthemajority ofrobotsinstalledaredrivingourestimates,wealsoexcludedJapan, SouthKorea,Germany,thePRCandtheUSfromourestimates,leaving thebaselineestimateforrobotizationunaffected.Forthesamerationale, wealsoexcludedthehighrobot-adoptingautomotiveandelectronic in-dustries(columns(5)and(6)ofAppendixTableA3respectively).All parameterestimatesforrobotadoptionwherenegativeandstatistically differentfrom0andt-testsdonotallowrejectingthenullhypothesis ofequalityoftheseparameterestimateswiththebaselineresult(atthe 10%levelofstatisticalsignificance).

Wealsoinvestigatedwhetherasamplesplitatthemedian(0.5)of thepercentilechangeinrobotadoptionaffectsourresults.Theresults indicatethattheparameterestimateforthesloweradopters(<0.5)are considerablyhigherbutestimatedwithlowprecision,sothattheyare not statisticallydifferent from0.Neitheroftheestimated OLSorIV parametersforthesamplesplitarestatisticallyspeakingdifferentfrom thosein thebaselineresultof column(2)intable3, inlinewithan approximatelylinearrelationshipsuggestedbypanel(a)inFig.1.32

32_{Wealsoexaminedresultswhenclusteringstandarderrorsatthecountry} levelandnotclusteringatall.Thealternativetreatmentofstandarderrorsdoes notaffectthestatisticalsignificanceoftherelationbetweenrobotadoptionand theshareofroutinejobsintheOLSregressionsandthecoefficient(𝛽)isdifferent fromzeroatthe1%levelofstatisticalsignificanceinthe2SLSregressions.

Table5

Robotadoptionandchangesintheemploymentshareofproductionworkers.

Panel A: OLS

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

No weight RII weight (global average) RII weight (U.S.) RII weight (Germany) RTI weight Percentile of changes in robot adoption -0.031 ∗ _-0.066 ∗∗∗ _-0.065 ∗∗∗ _-0.058 ∗∗∗ _-0.103

(0.02) (0.02) (0.02) (0.02) (0.08)

R2 _{0.016 0.036 0.054 0.035 0.019}

Observations 450 450 450 450 450

Panel B: 2SLS (IV: Replaceable tasks)

Percentile of changes in robot adoption -0.083 ∗ _-0.122 ∗∗ _-0.143 ∗∗∗ _-0.113 ∗∗ _-0.318 ∗

(0.04) (0.06) (0.05) (0.06) (0.19)

R2 _{-0.018 0.013 -0.021 0.006 -0.033}

Observations 450 450 450 450 450

Notes:Robuststandarderrorsinparentheses.Multi-wayclusteringbycountryandindustry.Dependentvariableisthechangeintheemploymentshareofproduction workersbetween2005and2015,withweightsindicatedinthecolumnheader.InPanelB,theshareofreplaceabletasksinanindustryisusedasaninstrument forrobotadoption.CountryfixedeffectsareincludedinallregressionsandpartialledoutinthereportedR2_.

∗∗∗_p<0.01 ∗∗_p<0.05 ∗ _p<0.1.

5.2.2. Robotadoptionandproductionworkers

InTable1,productionworkersarecategorizedashavingahigh con-tentofroutine-manualtasks.Yet,productionworkersaretypically la-belledblue-collarworkers.Hence, therelation betweenrobotsanda decliningemploymentshareofroutinemanualjobscouldreflecta sub-stitutionofrobotsforbluecollarproductionworkers,insteadofa sub-stitutionforroutinetasks.

Itishardtoruleoutsuchanalternativeinterpretation.Yet,for24 countriesinoursampleweareabletodistinguishseven2-digitISCO occupationsthattogethercomprisetheoccupationalgroupinglabelled ‘productionworkers’(cf.Table1).33_{Theroutinetask-intensityforeach} ofthese2-digitoccupationsisprovidedbyAutoretal.(2003)and, us-inganalternativeapproach,byMarcolinetal.(2019).Weusetheseto createaweightedaverageofthechangeintheemploymentshareof productionworkers.Theweightsweusearetheroutineintensityindex (RII)fromMarcolinetal.(2019)andtheroutinetaskintensity(RTI) gaugedbyAutoretal.(2003).Thetask-intensitybyoccupationis re-portedinAppendixTableA4.Clearly,thesevenoccupationslabelled productionworkersareheterogeneousinthecontentofroutinetasks.

ThefirstcolumnofTable5regressesthechangeintheemployment shareofproductionworkersonrobotadoption.Resultsindicatea signif-icantnegativerelationbetweenrobotadoptionandchangesintheshare of(routinemanualtask-intensive)productionjobs.Subsequentcolumns examinethesamerelation,butherechangesintheshareofproduction jobsarecalculatedasaroutinetask-intensityweightedaveragechange. Occupationsthathaveahighercontentofroutinetasksreceiveagreater weightinthisapproach.34

Weightingbyroutineintensitystrengthensthenegativeassociation betweenrobotizationandchangesintheshareofproductionjobs:the resultingparameterestimatesincolumns(2)-(5)arelargercompared tocolumn(1).Thisresultisobservedifweuseasweightstheglobal averageroutineintensity(RII)reportedbyMarcolinetal.(2019),see column(2),ortheRIIfortheUSorGermany(columns(3)and(4), re-spectively).ItisalsoobservedifweweightoccupationsusingtheRTI

33 _{ThesevenISCO2-digitoccupationsthatcanbedistinguishedareISCO88} codes71,72,73,74,81,82,and93.Thecountriesforwhichweareableto makethissplitareAustria,Belgium,CzechRepublic,Denmark,Estonia, Fin-land,France,Germany,Greece,Hungary,Ireland,Italy,Lithuania,Latvia,Malta, theNetherlands,Poland,Portugal,Romania,Slovakia,Slovenia,Spain,Sweden, Turkey,andtheUnitedKingdom.

34 _{Thetask-intensitymeasuresarePearson-transformed,i.e.centredat0with} astandarddeviationof1.Weadded+1tothemeasure.Hence,anoccupation withmeanroutineintensitygetsaweightof1,abelow-averageroutineintensity occupationalowerweight,andanabove-averageroutineintensityoccupation aweightabove1(seeAppendixTableA4).

fromAutoretal.(2003),seecolumn(5),althoughtheparameteris es-timatedwithlessstatisticalprecisionintheOLSand2SLSregressions. Overall,theseresultsprovideadditionalevidencethatrobotadoption is relatedtoadeclineintheshareofoccupationsthathaveahigher contentofroutinetasks.

5.2.3. Controllingforlong-termindustrytrends

Aremainingconcernisthattherecouldbealong-rundeclineinthe shareofroutinetasksdonebyworkers,whichismorepronouncedin industriesinvestingmoreinrobotsyetnotdrivenbyrobotizationper se.Acommonwaytoexaminethisconcernistoregressemployment outcomes fromapre-periodontheperiodduringwhichrobots were adopted.

Ideally,wethusrelatepre-periodemploymentoutcomesonthe cur-rentriseofrobots.However,weareconstrainedbycross-country occu-pationsdatawhichareavailablefrom2000onwards.By2000,robots werealreadybeinginstalled(GraetzandMichaels,2018).Still, descrip-tivestatisticsinTable2forthenumberofrobotsperthousandpersons employedin2005and2015suggesttheybecameubiquitousfromthe mid-2000sonwards.

Incolumn(1)ofTable6wethereforeregressthechangeinthe rou-tineemploymentsharebetween2000and2005onourpost-2005 mea-sureofrobotadoption.Weindeedfindarelationship,althoughthe coef-ficientissmallerandlesspreciselyestimatedcomparedtoourbaseline results(cf.column(2)ofTable3).35_{Pre-trendcorrelationisanecessary} conditionforunobservedsectorheterogeneity,butitisnotasufficient conditiontorenderidentificationinvalid.Thisispartlybecausethe pre-trenddoesnotpre-datetheriseofrobots.Yet,tocontrolforlonger-term industrytrends,weprovidetwoadditionalestimationapproaches: ex-plicitlyaccountingforpre-trendsbyincludingthechangeintheroutine employmentsharebetween2000and2005asalaggeddependent vari-ableandincludingindustryfixedeffects.

Columns(2)and(3)ofTable6addpre-trendstotheregressionson changesintheroutineemploymentshareandtheroutinemanual em-ploymentshare,respectively(cf.column(2)ofTable3andcolumn(1) ofTable4).Weobserveapositiveautocorrelationinemployment dy-namics.Yet,robotadoptionaddsadditionalinformationbeyondthose pre-trendsasthecoefficientremainsstatisticallysignificant. The

esti-35_{Notethatthepre-trendsinemploymentsharechangescovera5year} pe-riod.Estimatedcoefficientsandstandarderrorsthushavetobeapproximately multipliedbyafactor2tomakethemcomparablewithourmainresultsforthe 10yearperiodfrom2005to2015.Whenthepre-trendsareincludedaslagged dependentvariables(columns2and3ofTable6),theyaccordinglyhavetobe dividedby2.

Table6

Accountingforlong-termindustrytrends.

Panel A: OLS (1) (2) (3) (4) (5) ΔRoutineemployment share2000-2005 ΔRoutineemployment share ΔRoutinemanual employment share ΔRoutineemployment share ΔRoutinemanual employment share Percentile of changes in robot adoption -0.020 ∗∗ _-0.044 ∗∗∗ _-0.046 ∗∗∗ _-0.016 ∗∗∗ _-0.026 ∗∗∗ (0.01) (0.01) (0.01) (0.00) (0.01) Change in dependent variable, 2000-2005 0.174 ∗ _0.147 ∗ (0.10) (0.08)

Industry Fixed Effects No No No Yes Yes

R2 _{0.014 0.035 0.030 0.007 0.007} Observations 700 700 700 700 700 Panel B: 2SLS (IV: Replaceable tasks) Percentile of changes in robot adoption -0.053 ∗∗ _-0.113 ∗∗ _-0.114 ∗∗ (0.02) (0.05) (0.05) Change in dependent variable, 2000-2005 0.133 0.109 (0.09) (0.08)

Industry Fixed Effects No No No

R2 _{-0.018 -0.012 -0.010}

Observations 700 700 700

Notes:Robuststandarderrorsinparentheses.Multi-wayclusteringbycountryandindustry.Thedependentvariableisthechangeintherespectiveemployment shareovertherespectiveperiod.Theshareofreplaceabletasksinanindustryisusedasaninstrumentforrobotadoption.Regressionsincludethechangeinthe investmenttovalueaddedratioandthechangein(thelogof)valueaddedbetween2005and2015ascontrolvariables.Countryfixedeffectsareincludedinall regressionsandpartialledoutinthereportedR2_.

∗∗∗_p<0.01 ∗∗_p<0.05 ∗_p<0.1.

matedcoefficientiscomparabletothebaselineresults.Perhapsthemost convincingevidencethatthenegativerelationshipbetweenroutine em-ploymentsharesandrobotadoptionisnotexclusivelydrivenby spuri-ousindustrydynamicscanbefoundincolumns(4)and(5)ofTable6, whereweaddindustryfixedeffectstoourOLSregressions.36_Thisis arestrictivemodelthat assumesindustry-specific timetrendsin lev-elsandthusnotonlyaccountsforheterogeneousindustryemployment trendsbutalsoremovesaconsiderabledegreeofvariationinthedata thatmayberelevantforidentification.Yet,thenegativeassociation be-tweenrobotizationandroutineemploymenttrendsisstillobservedand statisticallysignificant.

5.2.4. Globaldevelopmentsinrobotadoption

AsdiscussedinSection2,advancesinthetechnicalabilityofrobots mightrelatetothe“reshoring” ofjobstoadvancedcountries.For ex-ample,Faber(2018)observesadecreaseinlabourdemandinMexico associatedwithrobotadoptionintheUnitedStates.Weexplorethis re-lationinacross-countrycontextusingtwomeasuresofrobotadoption thatvaryacrossindustriesbutnotacrosscountries.First,wetakeglobal averages,definedasthecross-countrymeanofthepercentilechangein robotadoptionbyindustry.Thisreflectstheideathatinan intercon-nectedworldthoseindustrieswithhigherrobotadoptionwillseefaster declinesinroutineemploymentsharesregardlessofthelocationof pro-duction.Second,weuserobotadoptionofU.S.industriestorepresent globalindustrytrends.

ResultsarereportedinTable7.Incolumns(1)and(2)theglobal av-eragesofindustry-specificrobotadoptionisused.Theregressions sug-gestastatisticallysignificantandnegativerelationbetweenchangesin theroutineemploymentshareandglobaltrendsinrobotadoption.37 In-terestingly,thepositiveinteractionbetweenrobotadoptionandEMTEs

36 _{Wecannotestimatethemodelwithindustryfixedeffectsusing2SLSbecause} theinstrumentonlyvariesacrossindustries.

37 _{Usingmeasuresofrobotadoptionthatvaryacrossindustriesbutnotacross} countries,wealsodonotfindastatisticalsignificantassociationbetweenrobot

shownincolumn(2)nolongermakesupforthenegativeoverallrobot adoptionparameter: thehypothesis thatthesumofbothparameters addsupto0canberejectedatthe5%levelofstatisticalsignificance. Thissuggeststhatglobaldevelopmentsinrobotadoptionimpactlabour marketsinEMTEs.Note,however,thisisnotobservedifweuserobot adoptionin U.S.industriestocharacterizeglobaltrends(seecolumn (4)).38 _{Nevertheless,these exploratoryregressionsprovide suggestive} evidenceforthepotentialrelevanceofglobalproductionnetworksand associatedjobreshoringpatternsduetoautomation,whichremainsan interestingareaforfurtherresearch.

6. Concludingremarks

Westudytherelationbetweenindustrialrobotsandoccupational shiftsbytaskcontent.Usingapanelof19industriesin37high-income andEMTEsfrom2005-2015,wefindthatincreaseduseofrobotsis as-sociatedwithpositivechangesintheemploymentshareofnon-routine analyticjobsandnegativechangesintheshareofroutinemanualjobs. Thepatternsthatwedocumentarerobusttoinstrumentalvariable es-timationandtheinclusionofvariouscontrolvariables,buttheydiffer acrosslevelsofeconomicdevelopment:weobserveasignificant rela-tionforhigh-incomecountries,butnotinEMTEs.Finally,wedonot findasignificantrelationbetweenindustrialrobotadoptionand aggre-gateemploymentgrowth.Thissuggeststhatindustrialrobotsdidnot replacejobs,buttheydidimpacttaskdemandandthushaddisruptive effectsonemployment.

Our analysis covered industrial robots, but much of the recent roboticdevelopmentshavebeentakingplaceinservices,suchasthe emergenceofmedicalrobots,logisticshandlingrobots,anddeliveryby

adoptionandtheaverageannualpercentagegrowthinemploymentin specifi-cationswithandwithouttheinteractionwithadummyforEMTEs.

38_{ItisalsonotobservedifweuserobotadoptioninGermanindustriesto} characterizeglobaltrends.