Productivity employment and effects of digital complementarities Knowledge Journal Innovation& of

Journal of Innovation

& Knowledge

https://www.journals.elsevier.com/journal-of-innovation-and-knowledge

Productivity and employment effects of digital complementarities

María Teresa Ballestar

^a,∗

, Ester Cami ˜ na

^b

, Ángel Díaz-Chao

^c

, Joan Torrent-Sellens

^d

aESICBusiness&MarketingSchoolandUniversidadReyJuanCarlos,CaminoValdenigriales,s/n,28223PozuelodeAlarcón,Madrid,Spain

bUniversidadComplutensedeMadridandICAE.Av.Séneca,2,28040Madrid,Spain

cUniversidadReyJuanCarlos,PaseodelosArtilleross/n,28032Vicálvaro,Madrid,Spain

dFacultyofEconomicsandBusiness,OpenUniversityofCataloniaRambladelPoblenou,156,08018Barcelona,Spain

a rt i c l e i nf o

Articlehistory:

Received19September2020 Accepted24October2020 Availableonline27December2020

JEL:

J23 J24 Keywords:

Robotisation

Productivitydigitalisation

a b s t ra c t

Moderneconomicgrowthisnolongerfoundintotalfactorproductivity(TFP)becausetherearegainsfrom technologicalchangethatareneverrecordedinthereturnsfrominnovationorintheNationalAccounts.

Theexistenceofcomplementaritiesamongtechnologiesderivedfromtheuseofrobotics,electronic commerce,orinnovationisdifficulttoassessthroughcountry-levelrecords.Becausetheliteraturehas mainlyfocusedonrobotisationatanaggregateorindustrylevel,researchfocusingonafirmleveland complementaritiesanalysishavebeenlimited.Tofillthegap,inthispaper,weintendtoprovidenewevi- denceregardingtheeffectsofrobotisation,digitisation,andinnovationonproductivityandemployment infirms,byusingalargesampleof5511Spanishmanufacturingfirmsfortheperiod1991–2016.This datacapturesthepayofftothehighratesofinvestmentnecessarytoupgradetheproductiontechnology forfirmsinanewgloballycompetitiveframework.

©2020JournalofInnovation&Knowledge.PublishedbyElsevierEspa ˜na,S.L.U.Thisisanopenaccess articleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction

Theincreaseintheuseofautomatisationhashadadifferen- tialeffectacrossoccupationsandsectorsthathasnotbeenfully explored(OECD,2019).Researchhasfocusedmainlyonrobotisa- tionatanaggregateorindustrylevel,andtheresearchfocusingon firmlevelandcomplementaritiesanalysishasbeenscarce,leav- ingmanyquestionsunanswered(Seamans&Raj,2018).Tofillthe gap,inthispaper,weintendtoprovidenewevidenceregardingthe effectsofrobotisation,digitisation,andinnovationonproductivity andemploymentinfirms.

Thegrowingliteratureontheeffectsoftechnologiessuchas robotisation, artificial intelligence (AI), or big data attempts to explain thetransformation experiencedbyfirms sincethe90’s.

However,noclearaccountofwhyandhowthechangesinpro- ductivityareoccurringandthecomplementaritiesamongsomeof thosetechnologieshavebeenprovided.Explanationsrangefrom themismeasurementofcreativedestruction,suggestingthatnew products offerhigher qualityand utilityto consumers(Aghion, Bergeaud,Boppart,Klenow,&Li,2019),tomiscalculationsofthe priceindexesofe-commercegoods,especiallywhenconsidering

∗ Correspondingauthor.

E-mail addresses: mariateresa.ballestar@esic.edu (M.T. Ballestar), ecaminac@ucm.es(E.Cami ˜na),angel.diaz@urjc.es(Á.Díaz-Chao),jtorrent@uoc.edu (J.Torrent-Sellens).

freebiesinonlinemarkets(Brynjolfsson&Oh,2012;Goolsbee&

Klenow,2018)orthatthestoppageoccursmerelybecauseof a reversalinthegrowthoffirms’productivityaftertheinitialgainsof theinformationandcommunicationstechnologies(ICT)revolution (Syverson,2011).

Thereisagreementamongtheliteraturethatthisproductiv- itygrowthinWesterneconomiesisduetotheinnovationofICT firmsthatstartedattheendofthelastcenturyandpersisteddur- ingtheGreat Recession,whichallowedfortheimprovement of livingstandards(Jorgenson,Ho,&Samuels,2011).Theimpactwas bothdirect,throughtheinnovativefirms,andindirect,throughthe effectsthattechnologicalinnovationhadontraditionalsectors,and theeffectwaslargerwhentherewasadigitaltransformationofthe firm(Kijek&Kijek,2019;Kraus,Palmer,Kailer,Kallinger,&Spitzer, 2019),whichrequiresagooddealofcomplementarities,bothin newandestablishedfirmsinwhatSoriano,Martinez-Climent,and Tur-Porcar(2018))calla‘virtuouscircle’thatimproveswelfareand growthbyimprovingorganisationwithinthefirm.

Theassessmentofthis ‘virtuouscircle’,includingtheimpact ofentrepreneurship,theadoptionof oneoftheseforms ofdig- italtransformation,theuseofindustrialrobotics,isincreasingly importantinthecurrentcontextofthenewindustrialrevolution.

Althoughmanycompaniesareeagertoadoptthesetechnologies asawaytoincreaseproductivity,someconcernshavebeenraised aboutthecostimpactofthetransformation,anditseffectonthe workforceduetotraining and newforms ofworkorganisation (Acemoglu,Makhdoumi,Malekian,&Ozdaglar,2019;Díaz-Chao,

https://doi.org/10.1016/j.jik.2020.10.006

2444-569X/©2020JournalofInnovation&Knowledge.PublishedbyElsevierEspa ˜na,S.L.U.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://

creativecommons.org/licenses/by-nc-nd/4.0/).

Sainz-González,&Torrent-Sellens,2015;Pi ˜neiro-Chousa,López- Cabarcos,Romero-Castro,&Pérez-Pico,2020).

Oneofthemissingpieceswhenevaluatinggrowthistherole ofcomplementarities.AsCarlawandLipsey(2002)posited,new technologiesinvigorategrowthbycreatingtechnologicalcomple- mentarities, whichare notadequately measuredby totalfactor productivity (TFP). Gains from technological change will occur throughindirecteffectsandexternalitiesthatarenoteasilyshown in national statistics.To bridgethat gap,weusedata fromthe EncuestasobreEstrategiasEmpresariales(BusinessStrategySur- vey,ESEE).TheESEEprovidesapaneldatarepresentingSpanish fabricmanufacturingbusinesses.Thisannualsurveyisconducted bytheSpanishMinistryofFinanceandPublicAdministrationand coversabroadperiodfrom1990to2015,includingtheeconomic recession and the subsequentrecovery period (Torrent-Sellens, 2018).

The ESEE contains yearly information for approximately 1800 Spanish firms and covers three main areas: the strate- gic decision-making on prices, costs, markets,and investment;

the value process, which involveshuman capital, organisation, entrepreneurialinnovation,Research&Development(R&D),and ICT;andthemostimportantindicatorsand ratiosfrombalance sheetsandprofitsandlosses.

Withthisdata,andthroughamodelderivedfromVanReenen (1997), Kromann, Skaksen, and Sørensen (2011), and DeCanio (2016),weestablishtheeffectofcomplementaritiesintermsof labourproductivityandfirmperformance.Ourfindingsarerele- vanttounderstandingtheimpactofthetransformationtorobotics andallowforthedevelopmentofentrepreneurialstrategiesthat boosttheefficiencyofcompaniesandprovidethemtoolstoprotect themfromnegativefinancialevents,leadingtoanoptimalsizing oftheirworkforce.

Theremainderofthepaperisstructuredasfollows:Section2 presentsaliteraturereviewofdigitalcomplementarities.InSection 3,wederiveamodelthathelpscapturethoseeffectsfromthedatais profuselydescribeit.Section4presentstheeconometricestimates ofthemodel,whichwediscussindetailinSection5.Finally,Section 6presentsaconcisesetofconclusionsandfurtherdirectionsfor research.

Literaturereview

CarlawandLipsey(2002)describethatcomplementarityexists in‘...insituationinwhichthepastorpresentdecisionsoftheiniti- atingagentswithrespecttotheirowntechnologiesaffectthevalue of thereceivingagents’ existingtechnologiesand/ortheirfirms which tend tofollowproactiveinnovation strategies,especially intermsofprocessandtechnologyadoption,likemarketingnew products,improvingtheirquality,increasingcapacityorinvesting ininnovativeprocessesproducethosecomplementarities’.

Buttotakeadvantageofcomplementarities,firmsneedawider varietyoftechnologyresourcesthatimprovetheirperformance.

Currently,robotics,AI,machinelearning,andbigdataareinsep- arablylinkedwithinthatinnovationintheoveralleconomy,but especiallyintheindustrialsector.Ascompanieshaveevolved,they have focused on thenecessity of offeringbetter productswith higherquality,makingtechnologyimprovetheirperformanceand openingspaceforfurtherdevelopments(Dickson&Hadjimanolis, 1998;Kijel&Kijek,2019).

Theeffectsofthesecomplementaritiesarediverseandnotfully understood. Someof the issues, suchas theeffect on employ- ment,haveattractedsubstantialinterestbecausetheproblemis not simplya matter of thoseworkers who willbereplaced by automation,definedastheactionthatreplaceshumanlabouractiv- itywithworkdonebymachines.Thegoalofautomationtrough

complementaritiesistwofold;toincreasequalityandreduceunit cost(Muro,Maxim,Whiton,&Hathaway,2019).Theimpactwill beobservable: TheOrganisation forEconomicCo-operationand Development(2019)estimatesthat14%ofcurrentjobsmaydisap- pearbecauseofautomationand35%maybeseriouslyaffectedby thesamephenomenon.

Theeffectonthelabourmarketwillbeabsolute,eitherdirectly orindirectly,becauseofthesuccessivedisplacementofhumancap- italexpelledfromitspreviouspoststootherpostswithaneed fortraditionalqualifications.Chui,Manyika,andMiremadi(2016)) estimatethatinpredictablephysicalactivities(e.g.productionlines packing),anddependingonthecomplementarities,substitution couldreach78%,andinthosethatarenotforeseeable(e.g.forestry workorfarmers),thereplacementlevelis25%,andsubstitutionon serviceswilldependontheircharacteristicsandontheirsources ofinnovation(Ballestar,SainzandSoriano,2018;Philipson,2020).

GraetzandMichaels(2018)focusonproductivitybyanalysing datafromthe InternationalFederation ofRobotics (IFR)onthe roleofrobots.Theirestimationspresentnochangeinthenumber ofhoursworkedafterincreasingthedensityofrobots;however, withouta changein theircomposition, there is atechnological biasinfavourofemployeeswithhighandmediumqualifications.

Faber(2018)obtainsasimilarresult:theincreaseinthedensity ofautomationintheUnitedStateshasanegativeeffectontheoff- shoringofproductiontoMexicoandaffectsknowledgeasymmetry.

AcemogluandRestrepo(2020)alsousedatafromIFRandesti- matetheeffectofcompetitionbetweenrobotsandhumanlabour onemploymentandwagesintheUnitedStates.Theyfindanet reductioninemploymentbysubstitutionbetweentherobotsand labour,whichmaybelinkedtodifferencesinusebetweensectors.

Withsimilardata,FreyandOsborne(2017)pointtoareduction inemploymentnotonlyinsectorsthataretraditionallyusersof robots,suchastheautomotivesector,butalsoinothersthusfar sheltered,suchasservices.

DoraszelskiandJaumandreu(2018)usefirmdataandashorter samplethanoursfromthesamedatabaseandfindbiasintech- nologicalchange and that there is indeeda technological bias;

however,contrarytowhatwehavecommentedinprecedingpara- graphsroboticshasapositivesigntowardsemployment.Withthe samedata,Ballestar,Díaz-Chao,Sainz,andTorrent-Sellens(2020) confirmasignificant(5%),increasingfactorinproductivitygains linkedtoagreaterpresenceofqualityhumancapitalinthecom- panybutdonotprovidedetailsonhowthosegainsareachieved.

AlsoconfirmingtheintuitionofSeamansandRaj(2018)that firmdatawillgathermoreinsightsintoautomation,Blanas,Gancia, andLee(2019))advancethisideabyusingtheEUKLEMSdatapanel and,througha relativelysimplemodelofjobdemand,findthat workerswhoperformroutineandlow-skilledtasks,whichnor- mallycorrespondstowomenandyoungadultswillbethemost affectedbytheintroductionofrobots,becauseofgainsinproduc- tivity.

Theseanalysesadvance,butdo notdetail,thecharacteristics ofthischangethatcorrespondstothetrainingofemployees,how companieshavechangedtheirmanagementoftheknowledgeof theirhumancapital,andtheeffectsofthesechangeslinkedtotrain- ing.Toachievethis,wemustdeterminethecomplementaritiesof changeinemploymentandproductivitytodeterminethepolicies oftrainingnecessarytoavoidunemploymentandtofocussupport onthegroupsmostdisadvantaged.

Contrary to Graetz and Michaels (2018) or Acemoglu and Restrepo (2020); Autor and Salomons (2017); Doraszelski and Jaumandreu(2018),andBallestaretal.(2021)havedemonstrated thatproductivitygainsareoneofthemajorbenefitsintheexpan- sion,concurringwiththeresultsthatindustriescanbenefitfrom thegrowthof labour-augmentingproductivitythroughcomple- mentarities,andasemploymentseemstofallwithinanindustry,

asindustry-specific productivityincreases,positive spilloversto somesectorsmorethanoffsetthenegativeeffectsonotherindus- triesorsectors.SeamansandRaj(2018)suggestempiricalwork thatprimarilyusesstatisticsaggregatedbyindustryorcountrybut do investigatehowcomplementarities hideunder,for example, marketstructuresandlaboursubstation.

Thisresearchisthefirsttoestimatesomeofthecomplementar- itiesbyusingfirm-leveldataandwhetherthegainsarelimitless, orreachasaturationpoint, assumingthatswitchingtorobotics causesproductivitytoincreasebutdoesnotincreaseitindefinitely, regardlessofthecomplementarities,whichaffectstheTFPgrowth effects of automationonboth hoursworkedand onthelabour share,whichshouldbereflectedinwages,leadingtoanincreasein humanlabourcosts.

Methodology Modelandhypothesis

By adapting VanReenen (1997); Kromannet al.(2011),and DeCanio(2016),ourbaselinemodelconsidersaperfectlycompet- itivefirmoperatingunderconstantreturnstoscale.Weassumea constantelasticityofsubstitutionproductionfunctionwiththree perfect substitutableinputs(labour, capital,andhumancapital) andwiththreeknowledge-basedtechnologies(robotisation,inno- vation,anddigitisation)oftheform

Q =[(RN)^(−1)/+(IN)^(−1)/+(DN)^(−1)/

+K^(−1)/+H^(−1)/]^/(−1) (1) whereQisfirmoutput,andN,K,andHarefirmlabour,firmcapital, andfirmhumancapital,respectively.R(robotisation),I(innova- tion),andD(digitisation)arelabour-augmentingHarrod-neutral technologies,and␴istheelasticityofsubstitutionbetweenlabour, capital,andhumancapital.

We assume that robotisation, innovation, and digitisation mainlyresultinanincreaseinR,I,andD.AnincreaseinR,I,andD impliesthatthesameamountoflabourservices(RN),(IN),and(DN) requirelessinputfromlabour(N);assumingthat,intotallycom- petitiveenvironments,realwage

W/P

isequaltothemarginal productoflabour,andthefirst-orderconditionforlabourcanbe writtenas

logQ−logN= +log

_W

P

−(−1) logR−(−1) logI−(−1) logD (2)

Inthesamemanner,inperfectcompetitiveenvironments,we canassumethatthemarginalproductofcapitalequalsthecostof capital (C).Therefore,thefirst-orderconditionforcapitalcanbe writtenas

logQ−logK= logC (3)

Finally,andfollowinghumancapitaltheory,wecanassumethat themarginalproductofhumancapitalequalsthecostofemployee educationandtraining (T).Therefore,thefirst-orderconditionfor humancapitalcanbewrittenas

logQ−logH=logT (4)

Combiningthesethreeexpressions,wecanobtainouremploy- mentdemandfunction:

logN= −log

_W

P

+(−1) logR+(−1) logI

+(−1) logD+logK+logC+logH+logT (5)

Alternatively,thelabourproductivityfunctioncanbeexpressed as

logQ−logN= +log

_W

P

−(−1) logR

−(−1) logI−(−1) logD (6)

where the second equation terms −(−1) logR,

−(−1) logI, and−(−1) logD, refer to the TFP based on technological changes (or TFP), and the first equation term log

W/P

referstolabourdeepening.Whentheelasticityofthe substitution betweenlabour, capital, and humancapital is low (␴<1)andforthegivenrealwages,labourproductivityincreases in R, I,and D.Consequently, employment decreasesas long as output (the given level of production) and real wages remain constant.Thedecreaseinemploymentoccursbecausetheincrease inR,I,andDimpliesthatlesslabourisnecessarytoachieveagiven leveloflabourservices(RN),(IN),and(DN)becausethelowdegree ofsubstitutionbetweenlabour,capital,andhumancapitalimplies asmallincreaseintheuseoflabourservices.Bycontrast,when theelasticityof substitution is high(␴>1), labourproductivity decreasesinR,I,andD(forgivenrealwages), andemployment increases(forgivenQ).Thereasonforthisisthatthedecreasein R,I,andDimpliesthatmorelabourisnecessarytoachieveagiven leveloflabourservices(RN),(IN),and(DN),andduetotheshift fromcapitalservicestolabourservices,morelabourisnecessary toachieveagivenleveloflabourservices.

WefollowEq.(5)andobservethatforagivenlevelofcapital andhumancapital,therealwagesandusercostofcapital,human capital,andemploymentincreasesinR,I,andDiftheelasticityof substitutionishigh(␴>1),butdecreasesiftheelasticityofsub- stitutionislow(␴<1).Therefore,theimplicationsofrobotisation, innovation,anddigitisationonemploymentareequaltothecaseof agivenlevelofproduction(Eq.6).Empiricalevidencesupportsthat thevalueof␴isbelow1inthecaseofrobotisation(León-Ledesma, McAdam,&Willman,2010)andabove1inthecaseofinnovation (Harrison,Jaumandreu,Mairesse,&Peters,2014).

Thus,wecanconcludethatforagivencapitalandhumancap- italstockandforagivenoutputstock(i.e.intheshortterm),the impactofrobotisation,innovation,anddigitisationonproductivity oremploymentdependsonthesizeoftheelasticityofthesubsti- tutionbetweenlabour,capital,andhumancapital.However,inthe longterm,outputandcapitalsareendogenous.Thus,itispossi- bletoexpectthatrobotisation,innovation,anddigitisationwould reducethemarginalcostsofproduction,whichcouldencourage investment,productivity,andoutput.Dependingontheelasticity ofdemand,thisimprovementineconomicactivitycouldincrease employmentinthelongterm.Hence,althoughrobotisation,inno- vation,anddigitisationtendtoreduceemploymentintheshort term,thistrendmayreverseinthelongterm(Acemoglu&Restrepo, 2019;Autor&Salomons,2018).Iftheincreaseinoutputissuffi- cientlyhigh,thenetlong-termeffectofrobotisation,innovation, anddigitisationonemploymentcouldbepositive.Inthissense,we proposeHypotheses1and2:

H1. Robotisation,innovation,anddigitisationincreaselabourpro- ductivityin the longterm (ie: non-givencapitals ornon-given outputstock).Thishypothesisonlyrequiresthattheelasticityof substitutionbetweenlabour,capital,andhumancapitalisbelow1.

H2. Robotisation,innovation,anddigitisationincreaseemploy- ment in the long run. This hypothesis suggests significant employmentcreationinthelongterm,whichwouldcompensate fortheshort-termreductioninjobs.

However,asinthefirst digitalwave where aclearlinkwas establishedbetweenintangibleassets,suchashumancapitaland workplaceinnovation,bothwithICTuses(Acosta,Sainz,&Salvador,

2006;Venturini,2015),weexpecttheuseofroboticstolinkwith thetypesoffirmknowledgeflows.Therefore,andextendingour model, we alsoaimto evaluatethecomplementarity effects of robotisation,innovation,anddigitisationonfirmproductivityand employment. Theconditionsof (long-term)flexibilityofoutput, capitals, real wages, and usercosts of capital and human cap- ital are established as in theprevious model. Additionally, we expectthatthecomplementarityeffectofrobotisation,innovation, and digitisationreducesthemarginal costsofproduction, rein- forcesproductivity,andincreasesthedemandofafirm’soutput.In thispositivesituation,thedisplacingeffectonemploymentinthe shorttermwouldbeclearlyacceleratedbytheincreasesinout- putandhumancapitaloverthelongterm(Brynjolfsson,Rock,&

Syverson,2018;Doraszelski&Jaumandreu,2018).Thus,wepro- poseHypotheses4and5:

H3. Thecomplementarityeffectamongrobotisation,innovation, anddigitisationstrengthensproductivityinthelongterm.

H4. Thecomplementarityeffectbetweenrobotisation,innova- tion,digitisation,andhumancapitalincreasesemploymentinthe longterm.

Estimationfunctionsandmethods

Weaimtoestimatetherelationshipbetweenrobotisation,inno- vation,digitisation,labourproductivity,andemploymentbyusing twotypesofmodels.Thefirstmodelestimatestheindividualeffects ofunitlabourcost,robotisation,innovation,anddigitisationonpro- ductivityandemployment(togetherwithcapitalperworkerand humancapital).Thesecondmodelestimatesthecomplementarity effectsofrobotisation,innovation,anddigitisationonproductiv- ity(togetherwiththeindividualeffectofunitlabourcost)andthe complementarityeffects ofrobotisation,innovation,digitisation, andhumancapitalonemployment(togetherwiththeindividual effectsofunitlabourcostandcapitalperworker).

Assuggestedinthemodelandthehypotheses,theproductivity andemploymenteffectsoftechnologydependonthe.Long-term effects areestimatedinloglevels.Firmdifferencesin loglevels reflectthedifferencesintheproductivityoremploymentexplana- toryvariablesoverthelongterm.Thestochasticformofequations ofproductivity(derivedfromEq.6)andemployment(derivedfrom Eq.5)inthelongtermare

q_it−_it= ˇ(w−p)_it+˛R_it+I_it+D_it+se+_si+u_it (7)

it=−ˇ(w−p)_it+kit+hit+˛Rit+Iit+Dit+se+si+εit

(8)

Lowercaselettersdenotelogs.Eqs(7)and(8)refertothemodels ofindividualeffects.seandsiaresectorandsizefirmdummies.

Thesedummiescontrolforunobservedheterogeneityinthemanu- facturingsectorandfirmsize.Rit,Iit,andDitaretheuseofindustrial robots,innovationactivities,anddigitisation,respectively,infirm iinperiodt. (w−p)_itreferstothereallabourunitcost.Capital(k_it) andhumancapital(h_it)arealsocaptured.InEq.(5),theusesofthe costofcapital(logC)andtheusesofthecostofhumancapital(log T)affectlabourdemand.However,wheretherearedifferencesin theusercostofcapitalandhumancapitalacrossmanufacturing sectorsorfirmsize,thesefactorsarecapturedbythefixedeffects.

Thisphenomenonimpliesthattheirdifferencesareconstantover time.Additionally,u_itand␧itarewhitenoiseterms.

TheEqs(9),(10),(11),(12),and(13)refertothemodelsofcom- plementarityeffects.Eqs(9)and(10)depicttheproductivityand employmentfunctionswithtwocomplementarityeffects.Eqs(11) and(12)depictproductivityandemploymentfunctionswiththe

three-complementarityeffects. Eq.(13)depicts an employment functionwithfour-complementarityeffects:

q_it−_it= ˇ(w−p)_it+˛₁RI_it+˛₂RD_it+˛₃ID_it+se+_si+u_it(9)

_it= −ˇ(w−p)_it+k_it+˛₁RI_it+˛₂RD_it+˛₃RH_it+˛₄ID_it +˛5IHit+˛6DHit++se+si+εit (10) qit−it= ˇ(w−p)_it+˛RIDit+se+si+uit (11) _it=−ˇ(w−p)_it+k_it+˛₁RID_it+˛₂RIH_it

+˛₃RDH_it+˛₄IDH_it++se+_si+ε_it (12)

it= −ˇ(w−p)_it+kit+˛RIDHit+se+si+εit (13) Therearetwo-complementarity effects:RI (robotisation and innovation), RD(robotisation and digitisation), RH (robotisation andhumancapital),ID(innovationanddigitisation),IH(innova- tionandhumancapital),andDH(digitisationandhumancapital).

Thethree-complementarityeffectsareRID(robotisation,innova- tion,and digitisation),RIH(robotisation,innovation,andhuman capital),RDH(robotisation,digitisation,andhumancapital),and IDH (innovation, digitisation, and human capital). Finally, the four-complementarityeffectintheemploymentequationisRIDH (robotisation,innovation,digitisation,andhumancapital).

Theprovisionofannualseriesforanextendedperiodallows theestimationofexplanatoryfactorsforthemanufacturingfirms’

long-termproductivityand employment. Thus;thus,we elabo- ratethevariables’andindicators’arithmeticaverageoftheforthe twoestablishedestimationperiods.Thefirstperiodisfrom1991 to2016.Asaresultoftheprogressiveadaptationoftheinforma- tionsourcetothebusinesscontext,weuseasecondestimation periodfrom2000to2016thatincludestheindicatorsrelatedto themanufacturingfirms’digitisationprocess.

The estimation of the hypothesised functions is conducted usingordinaryleastsquares(OLS)withtheintroductionmethod.

OLS regression should be used only if somestandard require- mentsofthedataareachieved,suchasnormality,linearity,and homoscedasticity(Hairetal.,2010).Theskewnessandkurtosis- obtained values in all the estimated models suggest that the variablescanbeassumedtobenormallydistributed(belowthe thresholdof2.58).Multicollinearitydiagnoseshavebeenaddressed bytestingthetoleranceandvarianceinflationfactor(VIF)among theexplanatoryvariables.Becauseallthesevalues havethresh- oldtolerance=0.10andVIF=10.0,multicollinearitymaynotbea concernin ourregression models.Thecorrelationmatricesalso indicatetheabsenceofmulticollinearity.Finally,homoscedasticity isvisuallyexaminedandtestedinplotsofstandardisedresiduals againstthepredictedvalue(Durbin-Watsontest,1.5<DW<2.5).

Informationsource

Theinformationsourceused fortheanalysisistheESEE, an annualsurveyofaround1800Spanishmanufacturingfirmscon- ductedbytheSpanishGovernment’sMinistryofFinanceandPublic Administrationfrom1990to2016.ESSEprovidesdetailedinforma- tiononbusinessesintheareasofstrategicdecision-making(prices, costs,markets,andinvestment)andthevalueprocess(humancap- ital, organisation, innovation,R&D, and ICT use). In addition, it coversthemostimportantindicatorsandratiosfromfirms’balance sheetsandprofitandlossaccounts.Consequently,thispaneldata allowsadetailedstudyofthemicroeconomicsofproductivityand employment,andtheanalysisofchangesinSpanishmanufacturing firmsduringvariousstagesofthebusinesscycle(Torrent-Sellens, 2018).

TheESEEcontainssegmentedinformation formanufacturing firmswithmorethan200workers(largefirms)andfirmswith 10–200workers(SMEs).Asaresultofthedatacollection,theclassi- ficationofSMEsisdifferentthanthatoftheEuropeanCommission (EuropeanCommission,2012).InthecaseoftheESEE,thelimit usedtodefineanSMEis200employees,andtheEuropeanCom- missionusesamaximumsizeof250workers.Thisdifferenceis due tothe samplingprocedureused by theESEE, in which all largemanufacturingfirms(morethan200workers)areincluded inthesample.However,forSMEs(from10to200workers),strati- fied,proportional,andsystematicsamplingisusedbyindustries (nationaleconomic activitytwo-digitclassificationcode,NACE), andthesizeofthefirmisalsoused.Thesamplingexcludesmanu- facturingmicro-firms(i.e.firmswithlessthan10workers).

TheESEEalsoprovidesdetailed informationfor20manufac- turing branches of activity,which can beobserved in detail in AppendixA(dimension:TableA1;andindustries:TableA2)regard- ingthesampleoffirmsused.Theanalysisofthepaneldatasuggests agrowingpresenceofsmallerfirms(from63.3%ofSMEsin1991to 81.0%ofSMEin2016)andanotablereorientationoftheindustrial branchesofactivity.In1991,sixindustrieseachaccountedformore than7%ofthenumberoffirmsinthesample—textileandclothing (11.3%), foodand tobacco(10.4%),metalproducts(7.6%),chem- icalandpharmaceuticals(7.4%),non-metallicmineral(7.2%),and machineryandelectricalequipment(7.1%)—in2016,specialisation hadincreasedsignificantlyandonlythreeindustriesaccountedfor 7%ormoreofthetotalnumberoffirms:foodandclothing(13.5%), metalproducts(12.7%),andchemicalandpharmaceuticals(7.0%).

Variablesandindicators

Thedependentvariablesarelabourproductivityandemploy- mentinmanufacturingfirmsandareapproximatedbyusingthe logarithmofaddedvalueperhourworked(HPT)andthelogarithm ofthetotalstaffemployed(allcontracts)inthefirm(EMPL).

Tocapturetheuseofrobots(R),weuseadichotomousvariable thatis0whenthefirmdoesnotuserobots,and1whenafirmuses robots.Despitetheobviousrestrictionsoftheuseofadichotomous variable,itsincorporationintothepredictivemodelallowsusto assesstheimpactonfirmproductivityandemploymentwhena firmstartstouseindustrialrobotics.

Regardinginnovation,weusetwodichotomousvariablesthat take two values (value 1, innovation generation; value 0, no innovation):productinnovation(PRODI)whenthefirmhaspro- ducedtotallynewproductsorproductswithmodificationsthat aresorelevantthattheymakethemdifferentfromthoseprevi- ouslyproduced;andprocessinnovation(PROCI)whenthefirm hasintroducedsomeimportantmodificationintotheproduction and/ordistributionprocess.

Regarding digitisation, we built an additive indicator that reflects internet-based electronic commerce (D). This indicator takesfourvalues(0–3)andistheresultofthesumofthreedichoto- mous variables (value1,use; value0, nouse):purchasesfrom suppliersovertheinternet(PFSD),salestoendconsumersover theinternet(B2CD),andsalestofirmsovertheinternet(B2BD).

Realwagesareapproximatedbyusinganindicatoroflabour costsperworker(LCW),thecapitalstockofthefirmwasapproxi- matedbyusingthelogarithmoffinancialassetsperworker(KPTW), andthehumancapitalstockofthefirm(HKPTW)ismeasuredby usingthelogarithmofthepercentageofemployeeswithtertiary (university)education(bachelor’sdegreelevelandhigher).

Tocapturetheeffectofsectoral(se)andsize(_si)dummies,we constructfouradditionalvariablesthatcapturethenon-observed heterogeneityinthemodels.Fromtheaveragevaluesofthepro- ductivity,exports,R&Dexpenditure,andproportionofemployees withauniversityeducation,weconstructfourdichotomousvari-

ablesthatassignthevalue1tothemanufacturingsectorswhen thevaluesofproductivity,exports,R&Dspending,anduniversity trainingofemployeesareabovetheaverage,andthevalue0in thealternativecase.Whenweobtainthesedichotomousvariables, wemultiplythembythefirmsizevariable,whichtakesvalue0for firmswith200employeesorlessonaverageinthereferenceperiod, andvalue1inthecaseoffirmswithmorethan200employeeson averageinthereferenceperiod.

Asaresultofthesecombinations,weobtainthefollowingfour variables:(1)LAREF(largeandefficientfirms)identifieslargefirms locatedinmanufacturingsectorswithabove-averageproductivi- ties;(2)LAREXP(largefirmswithexportintensity)identifieslarge firmslocatedinmanufacturingsectorswithabove-averageexport- ingvalues;(3)LARHC(largefirmswithintensityinhumancapital) identifies largefirms located in manufacturingsectors withan above-averagenumberofemployeeswithauniversityeducation;

and(4)SMER&D(SMEswithR&Dintensity)identifiessmalland medium-sizedfirms(SMEs)locatedinmanufacturingsectorswith above-averageR&Dexpenditure.

Allthevariablesandindicatorsexpressedinnominaltermshave beendeflatedbyusingaPaascheindexreferredtothevariation inpricesofintermediateconsumption.Thisindexhasbeenbuilt ontwogroupsofgoods:producergoodsandenergyandservices acquired.Becausewehavenorelativeweightsofproducergoods andenergy,weaddavariationofthesetwocomponentsbyusinga geometricmeanwithfixedweights.Thus,thepriceindexofinter- mediateconsumptiontakesthefollowingform:

PIINTCON(t)= VPGE(t)

VINTCON (t)PIPGE(t)+ VSER(t)

VINTCON (t)PISER(t) (14) where PIINTCON (t) is theprice index of intermediate consump- tion in period t (to be calculated); VPGE(t) is the value of the purchasesconsumedin period t;VINTCON(t) is thevalue ofthe intermediateconsumptioninperiodt;PI_PGE(t) isthepricevari- ationofproducergoodsandenergybetweent-1andtobtained asPIPGE(t)= [(PIPG(t)]^0,95x[(PIE(t)]^0,5,wherePIPGandPIEarethe priceindicesofproducergoodsandenergyprovidedbythefirm;

VSER(t) isthevalueoftheservicesacquiredinperiodt;andPISER

isthepriceindexoftheservicesacquiredintheperiodt-1and t.AppendixB(TableB1)presentsthedescriptivestatisticsofthe variablesandindicatorsusedintheanalysis.

Results

Individualeffectsonproductivityandemploymentestimations Table1presentstheindividualeffectsofestimatingthelong- termtrendofproductivitylevel(worked-hourlyproductivity,HPT).

ConsistentwithEq.7,thefirstcolumn(Model1)analysestheeffects ofLCWonproductivity.Inthesecondcolumn(Model2),theeffects ofknowledge-basedtechnology(robotsandprocessinnovation) areincorporated.Inthethirdcolumn(Model3),sizeandindustry dummiesarealsointegrated.

Theuseofindustrialrobotsandprocessinnovationresultina significantincreaseinlabourproductivity.Theseindividualeffects arerobust(increasesinthechangeofadjustedR² andmodelsp value=0.000)totheinclusionofLCWandsizeandindustrydum- miesasexplanatoryvariables,whichisevidentthroughcolumn analysis.Boththeunitlabourcostandthesizeandindustrydum- mieshavesignificantimpactsontheexpectedsigns.However,the unitlabourcostcoefficienttendstodecreaseasweincorporate morevariablesintotheregression(Models2and3comparedto Model1).Inthesamemanner,theroboticsuseandprocessinno- vationcoefficientstendtodecreasewhen thesizeand industry dummiesareincorporated(Model3comparedtoModel2).

Table1

Labourproductivity(addedvalueperhourworked,HPT),individualexplanatoryfactorsinSpanishmanufacturingfirmsin1991–2016and2000–2016.

1991–2016 2000–2016

HPT(addedvalueperhourworked) Model1 Model2 Model3 Model1 Model2 Model3

(Constant) −3.451*** −3.288*** −3.218*** −3.598*** −3.374*** −3.268***

(0.050) (0.051) (0.054) (0.071) (0.071) (0.076)

Labourcostperworker(LCW) 0.785*** 0.752*** 0.740*** 0.738*** 0.699*** 0.682***

(0.012) (0.012) (0.013) (0.016) (0.016) (0.017)

Useofrobots(R) 0.051*** 0.044*** 0.060*** 0.050***

(0.007) (0.007) (0.007) (0.007)

Processinnovation(PROCI) 0.090*** 0.089*** 0.119*** 0.117***

(0.007) (0.007) (0.008) (0.008)

Largeandefficientindustry(LAREF) 0.052** 0.049**

(0.013) (0.015)

LargeandHCintensiveindustry(LARHC) 0.055*** 0.066***

(0.01) (0.01)

SMEsintensiveinR&Dindustry(SMEsR&D) 0.086*** 0.081***

(0.012) (0.014)

Statistics

N(observations) 5,408 5,408 5,408 4,061 4,061 4,061

AdjustedR² 0.616 0.629 0.633 0.545 0.565 0.57

EstimationSE 0.177 0.174 0.173 0.174 0.17 0.169

ChangeofAdjustedR² 0.616 0.013 0.004 0.545 0.021 0.005

Fvalue 8,689 3,055 1,552 4,860 1,760 896.2

pvalue 0.000 0.000 0.000 0.000 0.000 0.000

Durbin-Watson 1.906 1.881

Note:Realmonetarydataareinloglevels.Regressionanalysis:Ordinaryleastsquareswiththeintroductionmethod.Estimatedcoefficients:Standardisedcoefficients.

Standarderrorsofthenon-standardisedeffectsareinbrackets.

***p<0.01;**p<0.05;*p<0.1.

Table2

EmploymentandindividualexplanatoryfactorsinSpanishmanufacturingfirmsin1991–2016and2000–2016.

1991–2016 2000–2016

Employment Model1 Model2 Model3 Model1 Model2 Model3

(Constant) −3.972*** −3.216*** −1.112*** −5.395*** −4.172*** −1.557***

(0.204) (0.187) (0.147) (0.255) (0.231) (0.194)

Labourcostperworker(LCW) 0.406*** 0.353*** 0.194*** 0.447*** 0.372*** 0.201***

(0.056) (0.051) (0.040) (0.066) (0.060) (0.050)

Capitalperworker(KPTW) 0.109*** 0.059*** 0.066*** 0.100*** 0.042** 0.047***

(0.018) (0.016) (0.012) (0.020) (0.018) (0.014)

Humancapitalperworker(HKPTW) −0.222*** −0.244*** −0.240*** −0.187*** −0.203*** −0.201***

(0.024) (0.022) (0.017) (0.027) (0.025) (0.020)

Useofrobots(R) 0.228*** 0.124*** 0.262*** 0.148***

(0.019) (0.015) (0.019) (0.016)

Processinnovation(PROCI) 0.103*** 0.078*** 0.105*** 0.083***

(0.023) (0.018) (0.024) (0.019)

Productinnovation(PRODI) 0.227*** 0.129*** 0.192*** 0.121***

(0.024) (0.019) (0.027) (0.021)

Digitisation(D) - - 0.058*** 0.048***

(0.013) (0.011)

Largeandefficientindustry(LAREF) 0.414*** 0.391***

(0.023) (0.027)

Largeandexportingindustry(LAREXP) 0.324*** 0.302***

(0.026) (0.03)

SMEsintensiveinR&Dindustry(SMEsR&D) 0.112*** 0.106***

(0.033) (0.037)

Statistics

N(observations) 4,480 4,480 4,480 3,502 3,502 3,502

AdjustedR² 0.199 0.354 0.624 0.213 0.377 0.605

EstimationSE 0.528 0.475 0.361 0.507 0.451 0.359

ChangeofAdjustedR² 0.199 0.155 0.271 0.213 0.165 0.228

Fvalue 371.1 409.3 827.9 316.8 303.9 536.8

pvalue 0.000 0.000 0.000 0.000 0.000 0.000

Durbin-Watson 1.817 1.881

Note:Realmonetarydataareinloglevels.Regressionanalysis:Ordinaryleastsquareswiththeintroductionmethod.Estimatedcoefficients:Standardisedcoefficients.

Standarderrorsofthenon-standardisedeffectsareinbrackets.

***p<0.01;**p<0.05;*p<0.1.

Thereductionintheunitlabourcostcoefficientisrelatedtothe typeofinvestmentandefficiencymodelofthetechnology-based firms.Robotisationandprocessinnovationimpliesasmallereffect ofthelabourcostintheproductivityexplanation,whichalready startssuggestingthatthereexistsalabour-share-displacingeffect.

Inthesamemanner,theintroductionofsizeandindustrydum- miesreflectstheheterogeneityofthefirm.Largefirmsinefficient andhumancapital-intensiveindustriesandSMEsinR&D-intensive industriestendtobemoreefficientbythemselves,reducingthe effectsoflabourcostandtechnologyonproductivity.

Table3

Labourproductivity(addedvalueperhourworked,HPT)andtwo-complementarityexplanatoryfactorsinSpanishmanufacturingfirmsin1991–2016and2000–2016.

1991–2016 2000–2016

2-complementarityfactors Model1 Model2 Model3 Model1 Model2 Model3

(Constant) −3.433*** −3.333*** −3.234*** −3.598*** −3.441*** −3.308***

(0.050) (0.051) (0.054) (0.069) (0.070) (0.075)

Labourcostperworker(LCW) 0.783*** 0.765*** 0.747*** 0.741*** 0.715*** 0.694***

(0.011) (0.012) (0.012) (0.015) (0.016) (0.017)

Robots(R)xProcessInnov.(PROCI) 0.077*** 0.067*** 0.075*** 0.064***

(0.009) (0.010) (0.011) (0.011)

Processinnov.(PROCI)xDigitisation(D) - - 0.049*** 0.046***

(0.008) (0.008)

Largeandefficientindustry(LAREF) 0.048** 0.042*

(0.013) (0.015)

LargeandHCintensiveindustry(LARHC) 0.070*** 0.078***

(0.010) (0.010)

SMEsintensiveinR&Dindustry(SMEsR&D) 0.083*** 0.075***

(0.013) (0.014)

Statistics

N(observations) 5,458 5,458 5,458 4,196 4,196 4,196

AdjustedR² 0.614 0.619 0.624 0.55 0.56 0.565

EstimationSE 0.177 0.176 0.174 0.175 0.173 0.172

ChangeofAdjustedR² 0.614 0.006 0.005 0.55 0.011 0.005

Fvalue 8,673 4,439 1,810 5,119 1,780 908.1

pvalue 0.000 0.000 0.000 0.000 0.000 0.000

Durbin-Watson 1.895 1.872

Note:Realmonetarydataareinloglevels.Regressionanalysis:Ordinaryleastsquareswiththeintroductionmethod.Estimatedcoefficients:Standardisedcoefficients.

Standarderrorsofthenon-standardisedeffectsareinbrackets.

***p<0.01;**p<0.05;*p<0.1.

Table4

Labourproductivity(addedvalueperhourworked,HPT)andthree-complementarityexplanatoryfactorsinSpanishmanufacturingfirmsin2000–2016.

3-complementarityfactors Model1 Model2 Model3

(Constant) −3.571*** −3.509*** −3.326***

(0.069) (0.069) (0.075)

Labourcostperworker(LCW) 0.738*** 0.728*** 0.698***

(0.015) (0.016) (0.017)

Robots(R)xProcessinnov.(PROCI)xDigitisation(D) 0.059*** 0.046***

(0.009) (0.010)

Largeandefficientindustry(LAREF) 0.055*

(0.015)

LargeandHCintensiveindustry(LARHC) 0.084***

(0.010)

SMEsintensiveinR&Dindustry(SMEsR&D) 0.076***

(0.014) Statistics

N(observations) 4,245 4,245 4,245

AdjustedR² 0.544 0.547 0.554

EstimationSE 0.177 0.176 0.174

ChangeofAdjustedR² 0.544 0.004 0.007

Fvalue 5,063 2,565 1,055

pvalue 0.000 0.000 0.000

Durbin-Watson 1.867

Note:Realmonetarydataareinloglevels.Regressionanalysis:Ordinaryleastsquareswiththeintroductionmethod.Estimatedcoefficients:Standardisedcoefficients.

Standarderrorsofthenon-standardisedeffectsareinbrackets.

***p<0.01;**p<0.05;*p<0.1.

Thecomparisonofresultsbetweenthetwoconstructeddata series:1991–2016and2000–2016capturethedifferentialeffects onproductivityfromthe2000s.For simplicity,wefocus onthe modelthatincorporatesalltheexplanatoryvariablesintotheanal- ysis(Model3).Theresultsobtainedconfirmthesignificanteffects fromrobotisationandprocess innovationonproductivity.From 1991to2016,themarginaleffectontheworked-hourlyproduc- tivitylevelofonemorerobotisedfirmis0.044percentagepoints, andanadditionalprocess-innovativefirmboostedworked-hourly productivityby0.089percentagepoints.Theseresultshaveaccel- eratedsince the2000s.From2000to2016,themarginal effect onworked-hourlyproductivitylevelofonemorerobotisedfirmis 0.050percentagepoints,andanadditionalprocess-innovativefirm boostedworked-hourlyproductivityby0.117percentagepoints.In

addition,thereisagreaterlabour-displacingeffect.Anadditional realeurooflabour costperemployee increasedworked-hourly productivityby0.682percentagepointsbetween2000and2016, comparedto0.740percentagepointsin1991–2106.Contraryto whatweexpected,digitisation(useofelectroniccommerce)had nosignificanteffectontheproductivityexplanation(neitherper workernorperhourworked).

Table 2 presents the individual effects from estimating the long-term trend of the employment level. Unlike productivity, industrialemploymenthasclearlyevolveddownwardsduringthe periodsanalysed.Therefore,apositivecoefficientimpliesapos- itive contribution tothe declining trend of employment (i.e. a positivecoefficientimpliesadecreaseinemployment),andaneg- ativecoefficientimpliesanegativecontributiontothedowntrend

Table5

Employmentandtwo-complementarityexplanatoryfactorsinSpanishmanufacturingfirmsin1991–2016and2000–2016.

1991–2016 2000–2016

2-complementarityfactors Model1 Model2 Model3 Model1 Model2 Model3

(Constant) −3.972*** −2.968*** −0.957*** −5.395*** −3.986*** −1.427***

(0.204) (0.188) (0.148) (0.255) (0.233) (0.194)

Labourcostperworker(LCW) 0.406*** 0.350*** 0.193*** 0.447*** 0.374*** 0.202***

(0.056) (0.051) (0.040) (0.066) (0.060) (0.050)

Capitalperworker(KPTW) 0.109*** 0.065*** 0.069*** 0.100*** 0.049** 0.051***

(0.018) (0.016) (0.012) (0.02) (0.018) (0.014)

Robots(R)xHumanCapital(HC) 0.241*** 0.129*** 0.272*** 0.151***

(0.018) (0.014) (0.017) (0.014)

Processinnovation(PROCI)xHumanCapital(HC) 0.112*** 0.084*** 0.111*** 0.089***

(0.021) (0.016) (0.021) (0.017)

Productinnovation(PRODI)xHumanCapital(HC) 0.223*** 0.127*** 0.187*** 0.118***

(0.021) (0.016) (0.023) (0.018)

Digitisation(D)xHumancapital(HC) 0.067*** 0.054***

(0.012) (0.009)

Largeandefficientindustry(LAREF) 0.415*** 0.393***

(0.023) (0.027)

Largeandexportingindustry(LAR EXP) 0.328*** 0.311***

(0.026) (0.03)

SMEsintensiveinR&Dindustry(SMEsR&D) 0.116*** 0.114***

(0.033) (0.037)

Statistics

N(observations) 4,480 4,480 4,480 3,502 3,502 3,502

AdjustedR² 0.199 0.348 0.622 0.213 0.374 0.604

EstimationSE 0.528 0.476 0.362 0.507 0.453 0.36

ChangeofAdjustedR² 0.199 0.15 0.274 0.214 0.16 0.23

Fvalue 371.1 400.2 821.3 316.8 297.9 532.7

pvalue 0.000 0.000 0.000 0.000 0.000 0.000

Durbin-Watson 1.722 1.786

Note:Realmonetarydataareinloglevels.Regressionanalysis:Ordinaryleastsquareswiththeintroductionmethod.Estimatedcoefficients:Standardisedcoefficients.

Standarderrorsofthenon-standardisedeffectsareinbrackets.

***p<0.01;**p<0.05;*p<0.1.

inemployment(i.e.anegativecoefficientimpliesanincrease in employment).ConsistentwithEq.8,thefirstcolumn(Model1) analysestheeffectsofLCW,capitalperworker (financialassets per employee), and human capital per worker (percentage of employeeswithtertiaryeducation)onemployment.In thesec- ondcolumn(Model2),theeffectsofknowledge-basedtechnology (robotisation,innovation—productandprocess—anddigitisation) areincorporated.Inthethirdcolumn(Model3),sizeandindustry dummiesarealsointegrated.

Theuseofindustrialrobots,innovation,anddigitisationsignif- icantlydecreasestheemploymentlevel(positivecontributionto thedowntrendinemployment).Theseindividualeffectsarerobust (increasesinthechangeofadjustedR²andmodelspvalue=0.000) to theinclusionof LCW,capital perworker, humancapital per worker,andsizeandindustrydummiesasexplanatoryvariables.

Boththeunitlabourcostandhumancapitalperworkerhavesignif- icantimpactsandwiththeexpectedsigns:aslabourcostincreases, employmentdecreases;andtertiaryeducationincreases,employ- mentincreases.However,thecapitalperworkerandthedummies donotbehaveasweexpected:asthecapitalperworkerincreases, theemploymentdecreases.Largefirmsinefficientandexporting industries andSMEsinR&D-intensiveindustriestendtobeless labour-intensive.

WecompareModel1withModel2,andrelevantconsiderations areobtained.Intheexplanationoftheemploymentreduction,the coefficientsofLCWandofcapitalperworkerevolvedownwards whenthetechnologicalvariablesareincorporatedintotheanalysis.

Investmentanduseoftechnologydisplacethelabourandcapital returns,whileanincreaseinthehumancapitalindicatesgreater educationalneedsforamoreappropriateuseoftechnology.The comparisonofModels2and3alsoprovidesrelevantconclusions.

Theintroductionofsizeandindustrydummiesreflectsfirmhet- erogeneity.Largefirmsinefficientandexportingindustriesand

SMEsinR&D-intensiveindustriestendtobelesslabour-intensive bythemselves,reducingtheeffectsoflabourcost,humancapital, andtechnologyonemployment.

The comparison of the results obtained for the 1991–2016 and 2000–2016 intervals allows us to evaluate the explana- tory factors of the observed destruction of employment and, more particularly, to analyse whether they have been accen- tuated since the2000s. Regardingthe technologicaldimension (Model 3), the results obtained confirm the significant and negative effects of robotisation, process, and product inno- vation and digitisation on employment. From 1991 to 2016, having one more robotised firm reduced employment levels (increased negative employment trend) by 0.124 percentage points,anadditionalprocess-innovativefirmdecreasedemploy- ment by 0.078 percentage points, and an additional product- innovative firm decreased employment by 0.129 percentage points.

Theseresultshaveacceleratedsincethe2000s.From2000to 2016,havingonemorerobotisedfirmreducedemploymentlevels by0.148percentagepoints,andanadditionalprocess-innovative firmlessened employment by0.083percentage points.Bycon- trast,thecontributionof productinnovationfell slightly (0.121 percentage points). Additionally, and in the 2000–2016 inter- val,digitisation also reduced employment by 0.048 percentage points. There are also greater labour-displacing and human- capital-displacing effects, and a lower capital-displacing effect.

Anadditional realeuro of labour cost per employee decreased employmentby0.201percentagepointsbetween2000and2016, comparedto0.194percentagepointsbetween1991and2106.An additionalemployee withtertiary educationincreased employ- ment by 0.201 percentage points in the 2000–2016 interval, comparedto0.240percentagepointsinthe1991–2016interval.An additionalrealeuroofcapitalperworkerdecreasedemployment

by0.047percentagepointsinthe2000–2016interval,compared to0.066percentagepointsinthe1991–2016interval.

Complementarityeffectsonproductivityandemployment estimations

Thecomplementarityeffectsobtainedfromestimatingthelong- termtrendoftheproductivitylevel(worked-hourlyproductivity, HPT)arepresentedinTable3(two-complementarities,Eq.9)and Table 4(three-complementarities, Eq. 11).Asin theestimation oftheindividualeffects,thefirstcolumn(Model1)analysesthe effectsofLCWonproductivity.Inthesecondcolumn(Model2),the effectsoftechnologycomplementarities(robots,processinnova- tion,anddigitisation)areincorporated.Inthethirdcolumn(Model 3),sizeandindustrydummiesarealsointegrated.

Thecomplementaritiesbetweenindustrialrobotsandprocess innovationandbetweenprocessinnovationanddigitisationresult inasignificantincreaseinlabourproductivity.Thesecomplemen- tarity effectsare robust(increasesin thechangeof adjustedR² andmodels’pvalue=0.000)totheinclusionofLCWandsizeand industrydummiesasexplanatoryvariables.Boththeunitlabour costandthesizeandindustrydummieshavesignificantimpacts andtheexpectedsigns.Asinthecaseofindividualeffects,thetwo regressions’modelcomparisonsuggestsalabour-share-displacing effectandreflectsfirmheterogeneity.Theunitlabourcostcoef- ficientstendtobereducedwhenincorporatingtechnology-based complementaritiesand sizeand industrydummies(fromModel 1 to Models 2 and 3). In thesame manner, thecoefficients of technologicalcomplementaritiesarereducedbyintroducingthe dummieseffect(from Model2toModel 3).In thissense,tech- nologicalcomplementaritiesalsointroducechangesintothefirm efficiencymodels,withlowercontributionsfromthelabourfactor.

Additionally,thelocationofthefirminlarge,efficient,andhuman- capital-intensiveindustriesorinR&D-intensiveSMEsalsoreduces thecontributionofthelabourfactorandtechnologycomplemen- tarities.

Thecomparisonofthecoefficientsobtainedforthetwocon- structeddataintervals(1991–2016and2000–2016)suggeststwo notableresults.First,wecanconfirmthesignificanteffectsoftwo technologicalcomplementarities(betweenrobotisationandpro- cessinnovation,andbetweenprocessinnovationanddigitisation) andthreetechnologicalcomplementarities(betweenrobotisation, processinnovation,anddigitisation)onproductivity.From1991 to2016,havingonemorerobotisedandprocess-innovativefirm increasedworked-hourlyproductivitylevelby0.067percentage points. However,thiscomplementarity hasweakenedsince the 2000s.From2000to2016,havingonemorerobotisedandprocess- innovativefirmincreasedtheworked-hourlyproductivitylevelby 0.064percentagepoints.

Bycontrast,thecomplementaritybetweenprocessinnovation anddigitisationhasalsocontributedtotheadvanceinproductiv- ity.From2000to2016,havingonemoreprocess-innovativeand digitisedfirmincreasedworked-hourlyproductivitylevelby0.046 percentage points.However,theintroduction ofrobotisationto thetwo-complementarityeffectbetweenprocessinnovationand digitisationdidnotimprovethecontributiontotheproductivity advance.Thethree-complementarityeffect(robotisation,process innovationanddigitisation)onproductivitywas0.046percentage pointsinthe2000–2016interval(thesamepercentageasinthe 1991–2016interval).

Thecomparisonbetweentheindividualandcomplementarity effects in theexplanationofproductivitysuggestsweakresults intermsofthecombinationoftechnologicalfactors.Considering two-complementaritiesand the2000–2016interval,theoverall individualeffectofrobotisationandprocessinnovationonproduc- tivityperhourworkedwas0.167percentagepoints,clearlyabove

thecomplementarityeffect(0.064).Likewise,thejointindividual effectofprocessinnovationanddigitisationwas0.166percentage points;again,wellabovethecomplementarityeffect(0.046).Addi- tionally,consideringthree-complementarities,theresultsobtained arestillmoredifferentiated.Theoverallindividualeffectofrobo- tisation, process innovation, and digitisation on worked-hourly productivitywas0.216percentagepointsinthe2000–2016inter- val,andthecomplementarityeffectwasmuchlower(0.046).

The complementarity effects from estimating the long-term trend of employment level are presented in Table 5 (two- complementarities,Eq.10),Table6(three-complementarities,Eq.

12),andTable7(fourcomplementarities,Eq.13).Asfortheindivid- ualeffectsofemploymentestimation,thefirstcolumn(Model1) analysestheeffectsofLCWandcapitalperworkeronemployment.

Inthesecondcolumn(Model2),theeffectsofcombinedtechnology (robotisation,innovation–productandprocess–anddigitisation) andhumancapitalareincorporated.Inthethirdcolumn(Model3), sizeandindustrydummiesarealsointegrated.

Thecomplementarity effects of theuseof industrial robots, innovation,digitisation,andhumancapitalsignificantlydecrease theemploymentlevel(positivecontributiontothedowntrendin employment).Theseindividualeffectsarerobust(increasesinthe changeof adjustedR² andmodelspvalue=0.000)totheinclu- sionofLCW,capitalperworker,andsizeandindustrydummies asexplanatoryvariables.In theexplanationof theemployment reduction,thecoefficientsofLCWandofcapitalperworkerevolve downwardswhen thetechnologicalandhumancapital comple- mentaritiesareincorporatedintotheanalysis(fromModel1to Model 2). Technological and human capital complementarities seemtodisplacelabourandcapitalreturns.Similarly,theintroduc- tionofthesizeandindustrydummiesreflectsfirmheterogeneity.

LargefirmsinefficientandexportingindustriesandSMEsinR&D- intensiveindustriestendtobelesslabour-intensivebythemselves, reducingtheeffectsoflabourcostandtechnologicalandhuman capitalcomplementaritiesonemployment.

Regardingtwo-complementarityeffects (Model3inTable8), theresultsobtainedconfirmthesignificantandnegativeeffects ofrobotisationandhumancapital,processinnovationandhuman capital, productinnovation and humancapital, and digitisation and human capital onemployment. From 1991to 2016, more robotisedandhumancapital-intensive(employees withtertiary education)firmsreducedemploymentlevels(increasednegative employment trend) by 0.129 percentage points, an additional process-innovativeand human capital-intensivefirm decreased employment by 0.084 percentage points, and an additional product-innovativeand humancapital-intensive firm decreased employmentby0.127percentagepoints.

These results have accelerated from 2000 to 2016. Having one more robotised and human capital-intensive firm reduced employmentlevelsby0.151percentagepoints,andanadditional process-innovativeand human capital-intensivefirm decreased employment by 0.089percentage points. By contrast, thecon- tribution of product innovation and human capital decreased (0.118 percentage points). Additionally, and in the 2000–2016 interval, two-complementarity effects between digitisation and human capital also reduced employment by 0.054 percentage points.There wasalso a greater labour-displacing and a lower capital-displacingeffect. An additional real euro of labour cost peremployeedecreasedemploymentby0.202percentagepoints between2000and2016,comparedto0.193percentagepointsin 1991–2016.Anadditionalrealeuroofcapitalperworkerdecreased employmentby0.051percentagepointsinthe2000–2016interval, comparedto0.069percentagepointsinthe1991–2016interval.

The results related to the three-complementarity and four- complementarity effects (Model 3 in Tables 6 and 7) and are in line with those obtained in the two-complementarity anal-