技術(shù)與企業(yè)水平生產(chǎn)力 Techies and Firm Level Productivity

NBERWORKINGPAPERSERIES

TECHIESANDFIRMLEVELPRODUCTIVITY

JamesHarrigan

AriellReshef

FaridToubal

WorkingPaper31341

/papers/w31341

NATIONALBUREAUOFECONOMICRESEARCH

1050MassachusettsAvenue

Cambridge,MA02138

June2023

WethankDaronAcemoglu,MaryAmiti,ZsofiaBarany,EricBartelsman,FloraBellone,AndrewBernard,TiborBesedes,EstherAnnB?ler,DavinChor,ChiaraCriscuolo,JanDeLoecker,AlonEizenberg,GeorgGraetz,GiammarioImpullitti,BeataJavorcik,GuyMichaels,Jean-MarcRobin,andJohnVanReenenforhelpfulcomments.ThepaperbenefitedfromcommentsfromseminarparticipantsatAarhusUniversity,AmericanUniversity,AMSE,BarIlanUniversity,BenGurionUniversity,CREST,DresdenUniversity,GroningenUniversity,FranceStratégie,HebrewUniversity,UniversityofLeMans,LSE,NottinghamUniversity,NewYorkUniversity,SAIS,andtheParisTradeSeminar,aswellasworkshopparticipantsattheCEPR,DEGIT,ENEF,ERWIT,IFN(Stockholm),SETC,andTRISTAN(Dresden).ThisresearchwassupportedbytheBankardFundforPoliticalEconomyattheUniversityofVirginiaandbytheAgenceNationaldelaRechercheundergrantnumbersANR-16-CE60-0001-01andANR-10-EQPX-17(Investissementsd’Avenir).TheviewsexpressedhereinarethoseoftheauthorsanddonotnecessarilyreflecttheviewsoftheNationalBureauofEconomicResearch.

NBERworkingpapersarecirculatedfordiscussionandcommentpurposes.Theyhavenotbeenpeer-reviewedorbeensubjecttothereviewbytheNBERBoardofDirectorsthataccompaniesofficialNBERpublications.

?2023byJamesHarrigan,AriellReshef,andFaridToubal.Allrightsreserved.Shortsectionsoftext,nottoexceedtwoparagraphs,maybequotedwithoutexplicitpermissionprovidedthatfullcredit,including?notice,isgiventothesource.

TechiesandFirmLevelProductivity

JamesHarrigan,AriellReshef,andFaridToubal

NBERWorkingPaperNo.31341

June2023

JELNo.D2,D24,O3,O33

ABSTRACT

Westudytheimpactoftechies—engineersandothertechnicallytrainedworkers—onfirm-levelproductivity.WefirstreportnewfactsontheroleoftechiesinthefirmbyusingFrenchadministrativedataanduniquesurveys.TechiesareSTEM-skillintensiveandareassociatedwithinnovation,aswellaswithtechnologyadoption,management,anddiffusionwithinfirms.Usingstructuraleconometricmethods,weestimatethecausaleffectoftechiesonfirm-levelHicks-neutralproductivityinbothmanufacturingandnon-manufacturingindustries.Wefindthattechiesraisefirm-levelproductivity,andthiseffectgoesbeyondtheemploymentofR&Dworkers,extendingtoICTandothertechies.Innon-manufacturingfirms,theimpactoftechiesonproductivityoperatesmostlythroughICTandothertechies,notR&Dworkers.Engineershaveagreatereffectonproductivitythantechnicians.

JamesHarrigan

DepartmentofEconomics

UniversityofVirginia

P.O.Box400182

Charlottesville,VA22904-4182

andNBER

james.harrigan@

AriellReshef

UniversitéParis1-PanthéonSorbonneMaisondesSciencesEconomiques

106-112Boulevarddel'Hopital

75647ParisCedex13

France

andCNRS,ParisSchoolofEconomics,CEPIIariell.reshef@psemail.eu

FaridToubal

UniversityofParis-Dauphine-PSL

PlaceduMaréchaldeLattredeTassigny75775ParisCedex16

France

andCREST,CEPII

farid.toubal@dauphine.psl.eu

1

1Introduction

Engineersandothertechnicallytrainedworkers(techies)havelongbeenrecognizedasfun-damentalindrivingproductivitygrowth.Forexample,engineersareattheheartofmodernendogenousgrowththeory,ashighlightedby

Romer

(1990)

.Theimportanceoftechiesforproductivitygrowthhasalsobeenemphasizedintheeconomichistoryliterature

.1

Inthispaper,westudytheroleoftechiesinenhancing?rm-levelproductivitygrowth.Weshowthattechiesraise?rm-levelproductivityandthatthise?ectextendsbeyondtechieswhodoresearchanddevelopment(R&D).Techiesthatworkwithinformationandcommunicationtechnology(ICT)andothertechnicaltasksequallya?ect?rmlevelproductivitygrowth.Wealsoshowthattheire?ectisimportantnotonlyinmanufacturingbutalsointhenon-

manufacturingsector.

Westartbyprovidingacomprehensivedescriptionoftechiesbasedonpreciseadministra-tiveandsurveydatasetsfromtheFrenchnationalstatisticalinstitute,INSEE.WeidentifytechieworkersbyusingthecomprehensiveFrenchoccupationalclassi?cation(

INSEE,

2003

).TechiejobsaredistinguishedfromotheroccupationsbyINSEEbecausetheyarerelatedtotheinstallation,management,maintenance,andsupportofICT,productandprocessdesign,longer-termR&Dactivities,andothertechnology-relatedtasks.WeshowthattechiesarealsodistinguishedfromotherworkersbytheirSTEM(science,technology,engineering,andmath,includingcomputerscience)diplomas,skills,andexperience.Wealsoshowthatthey

adopt,manage,anddi?usetechnologywithin?rms.

Techiesarenothomogeneous,andweclassifythembasedontheirspecializationsinR&D,

1

Kellyetal.

(2014)and

BenZeevetal.

(2017)highlighttheimportanceoftheBritishapprenticesys

-temduringtheBritishIndustrialRevolutioninsupplyingthebasicskillsneededfortechnologyadoption.Similarly,

Kellyetal.

(2023)showthattheBritishIndustrialRevolutionstartedinareaswheretechnically

trainedmechanicswereabundant,and

Hanlon

(2022)showshowtheemergenceof“professional”engineers

underpinnedtheIndustrialRevolution.

MaloneyandValenciaCaicedo

(2017)constructadatasetofengineer

intensityfortheAmericasandU.S.countiesaround1880andshowthatthisintensityhelpspredictincometoday.

2

ICTorothertechnology-relatedoccupations.Thesedistinctionsareimportantbecausetheyallowustodistinguishtheimpactofthesethreedi?erenttypesoftechieson?rm-levelproductivity.Importantly,R&Dtechiesaremuchmorecommoninmanufacturingthantheyareinnon-manufacturing,whilethereverseistrueforICTtechies.Therefore,limitingthefocustoR&Dtechiesalonedoesnotprovideanaccuratepictureoftheoverallin?uence

oftechiesacrossindustries.

AlargeliteraturehasstudiedtheroleofR&Dexpenditureinshaping?rm,industryandnationaloutcomes.Our?rm-levelanalysisusesthewagebillofR&DworkersinsteadoftotalR&Dexpenditures,whichisnotalimitationfortworeasons.First,mostofR&DexpenditureinFranceisonwages,andbyalargemargin,comparedtootherR&D-relatedexpenditures.Consistentwiththis,R&DwagesarehighlycorrelatedwithtotalR&Dexpendituresatthe?rmlevel.Second,non-wageR&Dexpendituresareincludedinourmeasureofthe?rms’

purchasedinputsandcapital.

Therightwaytomeasure?rm-levelproductivitydi?erencesiscontentious,butthereisbroadconsensusthatthesedi?erencesareverylarge.Thereismuchlessconsensusabout,toechothetitleofthein?uentialsurveyby

Syverson

(2011),whatdeterminesproductivity

di?erences.Asnotedby

DeLoeckerandSyverson

(2021),onlyafewpapershavetriedto

answerthisquestioninastructuralway,whichrequiresamethodologythatpermitsbothconsistentestimationof?rm-levelproductivityanditscausaldeterminants.Ourpaperaddstothisliteratureintwodimensions:wearethe?rsttojointlystudytheimpactofR&D,ICT,andothertechieson?rm-levelproductivity,andalsothe?rsttostudy?rmsinnon-manufacturinginadditiontomanufacturing.Thisbroadenedfocusallowsustopaintamore

completepictureoftheoverallin?uenceoftechiesonproductivitygrowth.

Ouranalysisofthesurveyandadministrativedataarecomplementaryonetotheother.Thethreesurveysthatweanalyze(oneattheindividuallevelandtwoatthe?rm-level)allowustostudythequali?cationsandtasksoftechies,andhowtechiesarecorrelatedwith?rm-

levelinnovatione?ortandoutcomes.Thislendscredencetothestructuralanalysis,which

3

isbasedonadministrativedata.Weusetheadministrativedatatoconstructa?rm-levelunbalancedpanelofmanufacturingandnon-manufacturing?rmsfrom2011to2019.Thepanelincludesdataon?rms’inputs(capital,laborbydetailedoccupation,andexpenditureonmaterials)andrevenue,aswellasanindicatorforexporting.Weusethepaneltoestimatestructuralmodelsof?rm-levelHicks-neutraltotalfactorproductivity(TFP)andthecausale?ectoftechiesandexportingonproductivity.Weusetworecentstructuralproductionfunctionestimators,dueto

Griecoetal.

(2016)(hereafter,GLZ)andto

Gandhietal.

(2020)

(hereafter,GNR),whichhavedi?erentadvantagesanddisadvantagesforourapplication

thatwediscussbelow.

Oureconometricstrategyisbasedontwoassumptions.First,techiesa?ectHicks-neutralTFPwithalag.Second,techiesa?ectoutputonlythroughtheirimpactonfuturepro-ductivity,andnotthroughanycontemporaneouscontributiontofactorservicesthata?ectcurrentoutput.Thisisanalogoustothewayeconomistsusuallythinkaboutcurrentinvest-mentspending,whichdoesn’tincreasecurrentoutputbutincreasesfutureoutputthroughitsimpactonfuturecapitalstock.ThisisalsohoweconomistsusuallythinkaboutR&Dexpenditure,a?ectingonlyfutureoutcomes.Weusea?exiblespeci?cationofthe?rm’sproductivityprocess,whichpermitsustomakecausalstatementsaboutthee?ectsof?rms’

employmentofR&D,ICTandothertechies,aswellasexportstatus.

We?ndthat?rmsthatemploytechieshavesubstantiallyhigherfutureproductivitythanthosewhodonot.Thepresenceoftechiesleadsto4or5percenthigherproduc-tivityayearlater,withalongrune?ectofover45percentinbothmanufacturingandnon-manufacturing?rms.Ouranalysiscon?rmstheimportanceofR&DtechiesforTFPgrowthinmanufacturing,asin

DoraszelskiandJaumandreu

(2013)

.Inaddition,we?ndthatthepositiveimpactoftechiesonproductivityisnotlimitedtoR&D.ICTandothertechieworkersalsopositivelyimpactproductivityinmanufacturingandnon-manufacturingindustries.Interestingly,R&Dtechiesdonotsigni?cantlycontributetotheproductivity

growthofnon-manufacturing?rms.Inaddition,we?ndthatbothengineersandtechnicians

4

increase?rmproductivityinbothmanufacturingandnon-manufacturingindustries,with

engineershavingabiggerimpactthantechnicians.

TFPisde?nedasrealoutputperunitofrealinputs.However,ourdatareportsrevenueratherthanrealoutput,andexpendituresonmaterialsratherthanquantities—atypicalfeatureof?rm-leveldatasetsandofproductivitystudies.Weaddressthesechallengesby

applyingtheestimatorof

Griecoetal.

(2016),whichwasdevelopedforsuchdatasets

.

TheGLZestimatorrestsonthreemainassumptions.First,itassumesthatall?rmsinanindustryhavethesameconstantelasticityofsubstitution(CES)productionfunction.Second,itrestrictsreturnstoscaletobeconstant.Third,itassumesthatbothmaterialsandlaborinputsfullyand?exiblyadjustinresponsetocurrentproductivityshocks.Weexaminethesensitivityofourresultsbyextendingthemethodologyof

Gandhietal.

(2020)

tooursetting,whererealoutputisnotobserved.

UnlikeGLZ,GNRimposesnofunctionalformrestrictionsontheproductionfunctionanddoesnotrequireconstantreturnstoscale.Furthermore,GNR’s?exibilityinaccommodatinglaborasa“dynamic”(predeterminedinperiodt)inputisparticularlyattractivegiventhelabormarketinstitutionsinFrance.WeemploytwovariationsofGNR:oneinwhichbothlaborandmaterialsare“static”inputs,similartoGLZ,andanother,inwhichlaborisdynamicanddoesnotrespondtocurrentproductivityshocks.However,ourapplicationofGNRcomeswithtwodrawbacks:itassumesthatrealmaterialsinputquantitiesareknownwhiletheyarenot,andwecanonlyidentifytheimpactoftechiesonproductivityuptoanunknownparameter.Despitethedi?erencesbetweentheestimators,ourestimatesoftheimpactoftechiesonproductivityusingtheGNRmethodologyarequalitativelysimilarto

thoseweobtainusingGLZ.

Ourassumptionthattechiesdon’ta?ectthecurrentoutputbutdoa?ectfuturepro-ductivityiskeytoourresearchdesign.Weexaminethevalidityofthisassumptionbyconsideringthesimplenullhypothesisthattechiesarenodi?erentthanotherworkersand

rejectthisnullinfavorofthealternativethatourbaselineassumptionisabetter?ttothe

5

data.Wealsoshowthatourinferencesaboutthee?ectoftechiesarerobusttoanonlinearadjustmentprocessandtoare-classi?cationofOther(notR&DnorICT)techiesasregular

labor.

Relatedresearch.Asmallliteratureexaminestheimpactoftechies’impactonoutput,employmentstructure,andproductivityatthe?rmlevel.Themotivationforthisliteratureisstatedsuccinctlyby

TambeandHitt

(2014):“thetechnicalknow-howrequiredtoimplement

newITinnovationsisprimarilyembodiedwithintheITworkforce”.Similarly,

Demingand

Noray

(2018)showthat,intheirwords,“STEMjobsaretheleadingedgeoftechnology

di?usioninthelabormarket”.Whiletheliteratureon?rm-levelimpactsofinvestmentinITandinR&Disvast,itrarelystudiestheimportanceofthosekeyworkerswhoinstall,

manageanddi?useITandothertechnologieswithinthe?rm.

Alackof?rm-occupation-leveldatainmostadministrativeandsurveydatasetshashampered?rm-levelresearchonthisproposition.Anexceptionis

Harriganetal.

(2021),

whichusesdetailedoccupationaldata(includingdataontechies)fortheentireFrenchprivatesectorfrom1994to2007.

Harriganetal.

(2021)showthatemploymentgrowthishigherin

French?rmswithmoretechiesandalsothatmoretechiesleadtowithin-?rmskillupgrading.

Lichtenberg

(1995)and

BrynjolfssonandHitt

(1996)?ndthatITlaborhasapositiveoutput

elasticity,aresultcon?rmedonlaterdataby

TambeandHitt

(2012)

.UsingaremarkabledatasetthattracksthemovementofITworkersacross?rms,

TambeandHitt

(2014)?nd

whattheyinterpretasevidenceforknowledgespilloversacross?rmsthroughthechanneloftechiemobility.Noneofthesepapersstructurallyestimatetheimpactoftechiesonproductivity,nordotheystudythedi?erenttasksthattechiesperform(e.g.,ITversus

R&D).

Werelyonrecentadvancesinthemethodologyofestimating?rm-levelproductivityanditsdeterminants.Thisliteraturewasinitiatedby

OlleyandPakes

(1996)(OP)byestimat

-

ingproductionfunctionsandassociated?rm-speci?c,time-varyingHicks-neutraltotalfactor

6

productivitydi?erences.Otherkeymethodologicalpapersinthisliteratureinclude

Levin-

sohnandPetrin

(2003)(LP)and

Ackerbergetal.

(2015)(ACF),asetoftechniqueswhich

wewillrefertoasOP/LP/ACF.Thecommonthreadthatrunsthroughthesepapersisthattheyapplythe“controlfunction”approachforidentifyingtheproductionfunction.Count-lesspapershaveappliedtheOP/LP/ACFmethodologytoestimateTFP,butthestudyof

thedeterminantsof?rm-levelTFPisremarkablysparse.

Twopioneeringpapersthatstudythedeterminantsof?rm-levelTFPare

DeLoecker

(2013)(exporting)and

DoraszelskiandJaumandreu

(2013)(expenditureonR&D).Wedis

-cussthesepapersbelow,asourmethodologyreliesontheirinsights.Themethodologyof

DoraszelskiandJaumandreu

(2013)requiresobservingrealinputsandoutputs,aspeci?c

functionalformfortheproductionfunction,andassumptionsonlabor?exibility.Asdis-cussedabove,ourapplicationsofGLZandGNRaddresstheselimitationsinoursetting,in

di?erentways.

Twoseriousconcernshaverecentlybeenraisedforthecontrolfunctionapproach.First,

Gandhietal.

(2020)identifyaweakinstrumentsproblem.Second,

Ackerbergetal.

(2021)

showthatthecontrolfunctionapproachsu?ersfroma“weakmoments”problem,wheretheGMMobjectivefunctionadmitsmultiplesolutionswithequalvalueoftheproblem.TheseproblemsarenotpresentintheGLZandGNRestimators,whichfurthermotivatesusto

applythem,ratherthantheOP/LP/ACFapproach.

Therestofthepaperisorganizedasfollows.InSection

2

weprovideadetailedaccountofthesourcesandconstructionofourdatasets.InSection

3

wepresentacomprehensiveanalysisoftheroleoftechies,highlightingtheirtechnicalexpertiseandtheircrucialroleinadopting,mediating,anddi?usingtechnologyatthe?rmlevel.Section

4

outlinesthetheo-reticalbasisfortheinclusionoftechiesinourproductivitymodelandhowtheycanimpactproductivity.InSection

5

wedescribeourmethodology,comparingtherelativeadvantagesoftheGLZandGNRestimators.Therewealsoprovideacomprehensivediscussionofthe

econometricchallengesandthestepstakentoaddressthem.InSection

6

wepresentthe

7

mainresultsofouranalysisandperformvarioussensitivitycheckstotesttherobustnessofour?ndings.WeconcludeinSection

7

withasummaryofourkeyresultsandadiscussion

oftheirimplicationsforpolicymakers.

2Data

Weconstructapaneldataseton?rmsintheFrenchprivatesectorbetween2011and2019bymergingthreecon?dential,administrative?rm-leveldatasets

.2

Wecomplementthisinfor-mationwithsurveydatatocharacterizetechiesanddescribetheirrolesin?rms.Matching?rmsacrossthesedatasetsisstraightforwardbecause?rmsareidenti?edbythesameiden-ti?cationnumber(SIREN)ineachofthethreedatasets.Wehighlightkeyfeaturesofthe

datahereandrelegateotherdetailstoAppendix

A.

2.1Thecompositionoflaborwithin?rms

OurdataonemploymentisfromtheDADS.3

All?rmswithemployeesarerequiredtoreportwages,hourspaid,occupation,andthe2-digitsectorofactivityofthe?rm.Theestimation

sampleincludes?rmsin17industriesinbothmanufacturingandnon-manufacturingsectors

.4

TheDADSreportsdetailed4-digitoccupationalcodes,almost500intotal,classi?edusingtheFrenchPCSclassi?cation.Theseoccupationalcodesarede?nedandexplainedingreatdetailin

INSEE

(2003),andweusethesede?nitionstoselectthe584-digitoccupations

thatweclassifyastechies.AswewillshowinSection

3,workersintheseoccupationsdi?er

fromotherworkersintheireducationandtrainingaswellasinthetaskstheyperform.Theirworkiscloselyrelatedtotheinstallation,management,maintenance,andsupportof

ICT,productandprocessdesign,longer-termR&Dactivities,andothertasksrelatedto

22011isthe?rstyearforwhichourdataareavailableand2019isthelastpre-pandemicyear.3DéclarationAnnuelledeDonnéesSociales

4Onesector(cokeandre?nedpetroleum)isdroppedbecauseithastinysharesoftotalhoursworked,andonesector(Transportationandstorage)isdroppedbecausetheestimationoftheproductionfunctionusingGLZfailedtoconverge.Wealsodropthecomputersandelectronicssectorbecauseofitsveryhighintensityintechieworkers.

8

technology.Inshort,theemploymentoftechiesisadirectmeasureof?rms’investmentin

technology.

Foranalyticalpurposes,wegroupthetechieoccupationsintwoalternativeways.The?rstsimplyclassi?esthembytheir2-digitcategories,technicalmanagersandengineers(PCS38)andtechnicians(PCS47).Thesecondgroupingcomprisesthreecategoriesde?nedbyus:R&Dtechies,ICTtechies,andOthertechies,seeTable

A1.

Thesethreecategoriesare

basedonthede?nitionsanddescriptionsofthe4-digitcategoriesin

INSEE

(2003)

.

Thedocumentationin

INSEE

(2003)makesitclearthattechiesperformdi?erenttasks

thanworkersinotheroccupations.Forexample,technicalmanagersandengineers(PCS38)aredistinguishedfromothermanagers(PCS37)bythefactthatfortheformer,“thescienti?cortechnicalaspecttakesprecedenceovertheadministrativeorcommercialaspect”,whereasforthelatter“theadministrativeorcommercialaspectprevails”.Similardistinctionsaremadebetweentechniciansandotheroccupations

.5

Beyondwhatissuggestedbytheiroccupationaltitles(reportedinTable

A1),theINSEEdocumentationalsomakesclearthat

techiesperformtasksthatsupportproductionbutarenotproductionorfabricationtasksperse.Thisgroundsourassumptionthattheroleoftechiesistoincreaseproductivityrather

thantocontributetocurrentoutputlikeothertypesofworkers.

Ourclassi?cationoftechiesintoR&DandICTtechiesisunambiguousandisbasedonareadingoftheoccupationalde?nitionsreportedinTable

A1

(

INSEE,

2003

).Forexample,

alltheoccupationsclassi?edasR&Dtechieshavethephrase“researchanddevelopment”in

theirjobdescriptions,whilethoseclassi?edasICTtechiesallhavethephrases“Informationtechnology”,“computerscience”and/or“telecommunications”intheirjobdescriptions.Acloselookatthedetailed

INSEE

(2003)descriptionsoftheOthertechiescategoryyields

twoobservations.First,thisgroupexhibitsheterogeneityintheircompositioncomprisingengineers,technicalexecutives,andtechniciansinvolvedintheadoptionanddissemination

oftechnologiesnotrelatedtoR&DorICTandnewproductionmethodswithintheir?rms.

5pages191,221and343of

INSEE

(2003),

9

Acaseinpointaretheengineersandmanagersofproductionmethod(PCS387c),whoareresponsibleforadaptingandoptimizingmanufacturingmethodsintheprivatesector.Secondly,despitebeingnotablydi?erentfromproductionandfabricationactivities,theyoptimizetheproductivityofworkersinthose?elds.Inourbaselineresultsbelow,weincludeOthertechiesalongwithR&DandICTtechiesasdriversofproductivity,butwealsoreportresultsthattreatOthertechiesasordinaryworkerswhocontributetocurrentoutputratherthanimproveproductivitywithalag.Ourresultsarenotsensitivetothisreallocationof

Othertechies.

Hoursworkedinnon-techieoccupationsareassumedtocontributedirectlytocurrent

output,asisstandardinthestructuralproductivityestimationliterature.

2.2Balancesheetsandexporting

FirmbalancesheetinformationcomesfromtheFAREdatasetfor2011–2019

.6

Thesourceofinformationis?rms’taxdeclarations.Weusetheinformationontotalrevenues,materialexpenditures,andthenecessaryseriestoconstructeach?rm’scapitalstock.Appendix

A

describesthesourcedataandexplainshowweconstruct?rm-levelcapitalstocks.

FrenchCustomsprovidedataontheexportsof?rmslocatedinFrance.Weusethis

informationtogenerateanindicatorofexportstatusforeach?rm-year.

2.3Surveydata

Anovelcontributionofourpaperisourfocusontechiesandtheirimpacton?rmlevelproductivity.Wefocusontechiesbecauseoftheircentralroleinplanning,installing,andmaintaininginformationandcomputertechnology(ICT),inResearchandDevelopment(R&D)andothertechnologies,andintrainingandassistingotherworkersintheuseoftechnology.Wecomplementthestructuralestimationoftechies’impactonproductivityby

collectinginformationfromthreesurveydatasources,whichprovideadditionalinformation

6FichierApprochédesRésultatsésane

10

ontechiesthatallowsustocharacterizebettertheirroleinthe?rm.

First,weprovideinformationoneducationinSTEM?elds(Science,Technology,Engi-neering,andMath,includingComputerScience)andSTEMtrainingoftechieworkersfromtheTrainingandProfessionalQuali?cation(TPQ)surveyin2015.Thesurveycollectsdataonthespecializationofthehighestdegreeobtainedbytheindividualandwhetherandwhich

trainingafterthehighestdegrees/hereceived.

Second,wecollectdataon?rms’expendituresonR&D(bothinternalandexternal)fromtheAnnualSurveyontheMeansdedicatedtoResearchandDeve