【国际货币基金组织IMF】2025欧洲人工智能与生产力研究报告

ArtificialIntelligenceandProductivityinEurope

FlorianMisch,BenPark,CarloPizzinelliandGalenSher

WP/25/67

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**WethankSimonBunel,EraDabla-Norris,RomainDuvalandDavidKollforexcellentcomments.MahikaGandhiprovidedexcellentresearchassistance.

©2025InternationalMonetaryFundWP/25/67

IMFWorkingPaper

EuropeanDepartment

ArtificialIntelligenceandProductivityinEurope

PreparedbyFlorianMisch*,BenPark,CarloPizzinelliandGalenSher**

AuthorizedfordistributionbyStephanDanningerandKristinaKostial

Month2025

IMFWorkingPapersdescriberesearchinprogressbytheauthor(s)andarepublishedtoelicit

commentsandtoencouragedebate.TheviewsexpressedinIMFWorkingPapersarethoseofthe

author(s)anddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,orIMFmanagement.

ABSTRACT:ThediscussiononArtificialIntelligence(AI)oftencentersarounditsimpactonproductivity,butmacroeconomicevidenceforEuroperemainsscarce.UsingtheAcemoglu(2024)approachwesimulatethemedium-termimpactofAIadoptionontotalfactorproductivityfor31Europeancountries.Wecompilemanyscenariosbypoolingevidenceonwhichtaskswillbeautomatableinthenearterm,usingreduced-form

regressionstopredictAIadoptionacrossEurope,andconsideringrelevantregulationthatrestrictsAIuse

heterogeneouslyacrosstasks,occupationsandsectors.Wefindthatthemedium-termproductivitygainsforEuropeasawholearelikelytobemodest,ataround1percentcumulativelyoverfiveyears.While

economciallystillmoderate,thesegainsarestilllargerthanestimatesbyAcemoglu(2024)fortheUS.Theyvarywidelyacrossscenariosandcountriesandaresustantiallylargerincountrieswithhigherincomes.

Furthermore,weshowthatnationalandEUregulationsaroundoccupation-levelrequirements,AIsafety,anddataprivacycombinedcouldreduceEurope’sproductivitygainsbyover30percentifAIexposurewere50

percentlowerintasks,occupationsandsectorsaffectedbyregulation.

RECOMMENDEDCITATION:[Misch,F.,Park,B.,Pizzinelli,C.andSher,G.(2024).ArtificialIntelligenceandProductivityinEurope.IMFWorkingPaperWP/25/67.

JELClassificationNumbers:

E24,J24,O30,O47

Keywords:

ArtificialIntelligence;Productivity;Technology;Regulation

Author’sE-MailAddress:

FMisch@;

HPark2@;

CPizzinelli@;

GSher@

WORKINGPAPERS

ArtificialIntelligenceandProductivityinEurope

PreparedbyFlorianMisch

1,

BenPark,CarloPizzinelliandGalenShe

r2

1Correspondingauthor.

2MahikaGandhiprovidedexcellentresearchassistance.

IMFWORKINGPAPERSArtificialIntelligenceandProductivityinEurope

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Contents

1.Introduction 3

2.StylizedFacts 6

3.Methodology 6

4.Results 8

4.1VariationinMedium-termProductivityGains 8

4.2PreferredScenario 12

4.3TheRoleofRegulation 15

5.ConclusionsandPolicyImplications 16

Appendix1:SampleandCountry-SpecificData 18

Appendix2:AIExposureofTasksandOccupations 19

Appendix3:AIAdoptionRate 22

Appendix4:LaborCostSavingsfromAI 28

Appendix5:NationalOccupationRegulation 29

Appendix6:EUAIAct 30

Appendix7:DataPrivacyLaws 31

References 32

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1.Introduction

Artificialintelligence(AI)isoftenseenasageneral-purposetechnologythathasthepotentialtotransformtheeconomyandspurbroad-basedeconomicgrowth,akintothearrivalofelectricityandpersonalcomputers.

Muchofthedebateonitslikelyimpactthusfocusesonitseffectonproductivity.InEurope,thisquestionis

especiallytopicalgivenlacklusterproductivitygrowthoverrecentdecades,whichresultedinalarge

productivitygapvis-à-vistheUS(IMF,2024).Moreover,thereisawidespreadviewthattheregionisfallingbehindtheUSandChinainAIdevelopmentandadoption,notleastbecauseofitsmorestringentregulatoryenvironment(e.g.,TheEconomist,2023).Theobjectiveofthispaperistoprovideestimatesofthesizeof

effectsofAIontotalfactorproductivity(TFP)acrossEuropeancountriesoverthemediumtermandexamineanyimpedingeffectsofregulationinEurope.

MicroeconomicstudiessuggestlargeproductivitygainsfromdifferenttypesofAIforspecificoccupations.

Theseestimates,whichinmostcasesarebasedonrandomizedtrialswherethetreatmentgroupisgiven

accesstoAItools,rangefrom14%forlow-skilledtaxidriverstoover50%forsoftwareengineers;see

Appendix4forasummary.Firm-levelstudiesontheeffectsofadoptionofAItechnologiesotherthan

generativeAIfindsmallerbutstillsignificantproductivitygains(seeComunaleandManera,2024,and

Filippuccietal.,2024aforsurveys).AseparatestrandoftheliteraturelooksattheimpactofAIonemployment.Cazzanigaetal.(2024),forinstance,notethatalargeshareofjobsgloballyislikelytobeaffectedbyAI,

particularlyinadvancedeconomies,andthatAIwillsubstituteratherthancomplementhumanlaborinmanyjobs.UsingdatafromtheUS,Bonfigliolietal.(2025)andHuang(2024)showthathigherAIadoptionis

associatedwithfallsintheemployment-to-populationratios.AseparatestrandoftheliteratureexaminesmacroeconomicpoliciestobroadenthegainsfromAI;seeBrolloetal.(2024).

Theextenttowhichthesemicro-levelproductivitygainsandpossibleemploymenteffectsareassociatedwithaggregateproductivitygainsandgrowth,however,remainsunclear.Studiesexaminingthemedium-term

macroeconomiceffectofAIshowasubstantiallywiderrangeofestimates.McKinsey(2023)andGoldman

Sachs(Hatziusetal.,2023)envisioncumulativeGDPgainsofabove35percentforadvancedeconomiesand7percentgloballyovera10-yearperiod,respectively.Commissiondel’IntelligenceArtificielle(2024)infers

potentialgrowthimpactsfromAIofupto1.3annuallybydrawingparallelstotheeffectsofelectricityand

InformationandCommunicationtechnologies.Similarly,IMF(2024)andCazzanigaetal.(2024)estimate

annualgrowthimpactsofupto0.8percentagepointsbasedonlaborreallocationandchangesinthecapitalsharecomparabletothoseobservedforautomationinthepast.

Bycontrast,Acemoglu(2024)doesnottakeintoaccountanypotentiallonger-termtransformationaleffectsofAIandthereforeestimatesmuchmoremodestTFPgainsoflessthan0.7percentcumulativelyover10yearswhichhereferstoas‘mediumterm’.Heusesarigorousframeworkthatquantifiesthegainsbottom-upusingmeasuresoftheAIexposureofindividualtasksforeachoccupation.AghionandBunel(2024)showthatusingalternativeassumptionswithinAcemoglu’s(2024)frameworkcan10-foldtheestimatedproductivitygainsfor

theUS.

EvidenceconsideringEurope-specificfactorsandcross-countryheterogeneitywithintheregionremains

relativelyscarce.Bergeaud(2024)simulatesproductivitygainsfromAIfortheeuroareausingtheAcemoglu(2024)framework,combiningsomeoftheoriginalpaper’sparametervalueswithownassumptionsand

estimatestogenerateafewalternativescenarios.Hefindscumulativeproductivitygainsof2.9percentforthe

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euroareainhiscentralscenario,whilehiscountry-specificresultsrangefromaround1.5inIrelandto3.3in

Belgium.Filippuccietal.(2024b)extendtheAcemoglu(2024)frameworkbymoreexplicitlymodellingsectoralspilloversandpresenttheproductivitygainsforallG7countrieswhileassumingdifferentadoptionrates.TheestimatedgainsarealsosignificantlyhigherthantheAcemoglu(2024)resultsformostcountriesand

scenarios.

Ourstudyisbroaderinscopeintermsofcountrycoverage,numberofscenariosandconsiderationofthe

effectsofregulation,andituseseconometrically-groundedparameterestimates.Weinvestigatecross-countryvariationfor31EuropeancountriesbothintermsofthemagnitudeandtheuncertaintyoftheproductivitygainsmoresystematicallybyallowingtheratesofAIadoptiontovaryacrosscountriesaccordingtotheireconomic

characteristics.Tothisend,wealsouseAcemoglu’s(2024)frameworktoestimatethemedium-term

productivitygainsfromAIinEurope(whichweinterprettobe5years,giventhemodelcharacteristicsandourassumptions)

.1

First,wequantifytheuncertaintyaroundtheimpactofAIonTFP.Ratherthanmakingourownassumptions,wecombineacomprehensivesetoftheavailableestimatesofAIexposureofindividualtasks,delivering44

scenarios.Foreachscenario,wecalibrateestimatesofAIadoptionbySvanbergetal.(2024),asusedin

Acemoglu(2024),tospecificcountriesandsectorsbasedonregressionevidenceofthedriversofAIadoptioninEurope.WagelevelsturnouttobethemaindriverofAIadoption,ratherthancapitalcosts,industry

concentration,digitalization,orhumancapital.Thisallowsustotakeintoaccounthowdifferencesinwage

levelsandothersectoralandcountry-levelfactorsaffectAIadoptionandshapecross-countryvariationoftheproductivitygains.

Second,weexaminetheroleofregulationinreducingproductivitygainsofAIwhichisoneareaofpolicythatisoftendiscussedwhenitcomestoAI;seeforexampleBradford(2024)forabroaderdiscussion.Survey

evidencefromGermanysuggeststhatwhilefirmsseemanybarrierstoAIadoption,regulationisseenasmostimportant(Wintergerst,2024).Tothisend,weconsiderregulationwhichcouldplausiblyhavelargeeffectson

AIadoption:licensingandtrainingrequirementsforspecificoccupationsatthenationallevel,dataprivacylaws,andtheEUAIAct

.2

Wethenidentifythetasks,occupationsandsectorswherethisregulationcouldundermineAIadoptionandassumethatregulationhalvesAIcapabilities,strikingareasonablemiddlegroundbetween

assumingthatregulationcompletelypreventsAIuseandthatregulationhasnoimpact.

Weshowthatinourpreferredscenario,whichisbasedonwhatwethinkarethemostplausibleassumptionsonAIoccupationalexposureandadoptionratesinEuropeancountries,theEurope-wideeffectsaremodestataround1.1percentcumulativelyoverthemediumterm,whichexceedsAcemoglu’sestimatesfortheUSby

almost60percent.ThesedifferencesaredrivenalmostentirelybymoreoptimisticassumptionsofAI

capabilitieswhichwethinkarejustifiedinlightofarecentrefereedpublicationwhichweexplainbelow.

However,thereissignificantheterogeneityacrosscountries.EstimatedTFPgainsinhigher-incomecountriestendtobemuchlargerthanthoseinlower-incomeeconomiesinlinewithfindingsbyCeruttietal.(2025),due

1IncontrasttoAcemoglu(2024)whoconsidersa10-yearperiod,weassumethatthesimulatedproductivitygainsrefertoa5-yearhorizonfortworeasons.First,theframeworkbyAcemoglu(2024)doesnotcaptureanylarge-scaletransformationaleffects

whichwethinkcouldindeedariseoverperiodsexceeding5years.Second,weassumethatAIcapabilitiesandtheAIadoptionrate(i.e.,theproportionoftasksforwhichitwillbeprofitabletouseAI)willremainconstantovertime.Over10years,botharemorelikelytochange.

2TheEUAIActisakeyAIsafetylawwhichcapsthecapacityofAIsystemsandincreasesthecostofusingAIinadefinedlistofhigh-risksystems.

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toboththeirlargershareofvalueaddedinindustriesthathavegreaterAIexposure(e.g.,financialservices)andtheirhigherwagelevels—whichprovidegreaterincentivesforlabor-savingorlabor-augmentingAI

adoption.

Moreover,inhigherincome-countries,therearemuchlargerupsiderisksfromAI.Forexample,inourpreferredscenario,thegainsinLuxembourgcouldbe2percentcumulatively,almosttwicetheEuropeanaverage,and

morethan4timeslargerthanthoseinRomania.ThisisduetothecompositionoftheLuxembourgish

economy,withmorevalueaddedinsectorslikefinancialserviceswithhigherAIexposure,anddueto

Luxembourg’shigherwages,whichgiveemployersthereagreaterincentivetoadoptAI.Inaddition,

productivitygainsinLuxembourgcouldbemorethantwiceashighifAIturnsouttobemorecapablethaninthe‘preferred’scenario,pointingtolargerupsiderisksaswell.

Wealsofindthatthecombinedadverseeffectsofnationaloccupation-levelregulation,theEUAIActanddataprivacylawsonproductivitycouldbesignificant,withtheformertwohavingthelargesteffects.Occupational

restrictionsareoftenoverlookedindebatesonAIbutcouldsubstantiallyreducetheproductivitygainsfromAI.Bycontrast,dataprivacylawswidelyaffectsomeindustrieswithhighAIexposure,suchastheITandfinancialservicessectors,buttheirproductivity-inhibitingeffectissomewhatsmaller.

Ourresultscaninformongoingpolicydebates.Ontheonehand,theysuggestthatoverthemediumterm,AIisnotasilverbullettosignificantlyboostsluggishproductivitygrowthinEurope,ortoclosetheproductivitygaptotheUnitedStates.Moreover,AIcouldslowtherateofincomeconvergenceamongEuropeancountries

becausethegainsfromAItendtobelargerinmoreadvancedeconomies.Ontheotherhand,ourresultshelpinformthepotentialtrade-offsbetweenthebenefitsofregulationsincludingrelatedtoprivacyandsafety,andmaximizingtheproductivitygainsfromAI,whilenotingthatourstudyisnotacomprehensivecost-benefit

analysisoftheseregulations.

Thispaperisorganizedasfollows.Section2presentsstylizedfactsonthediffusionandadoptionofAIto

motivatetheanalysisandthefocusonmedium-termeffects.Section3providesasummaryoftheAcemoglu

(2024)modelanddiscusseshowwecalibratesomeofitskeyparameterstotheEuropeancontext,consideringarangeofalternativescenarios.Section4presentstheresultsandtheanalysisofregulations.Section5

concludes.

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2.StylizedFacts

Figure1.SpreadofPastInnovations

(Yearstoreach100millionusers/units)

100

M

M

M

M

M

M

M

M

M

M

M

0135791113151719212325···80YearstoReach100MillionUsers/Unit

NumberofUsers

90

80

70

60

50

40

30

20

10

0

GenAI

TV

Telephone

InternetCableTV

ComputersCellphone

Source:CHATdatabase;IMFstaffcomputation.

Note:TheGenAIandInternetlinereflectstheusercount.ThefiguresforComputers,TV,andCellphonerepresentthenumberofunits.CableTVandTelephoneindicatethenumberof

connections.

ThefastspeedofdiffusionofgenerativeAI(genAI),asubsetofthebroadsetoftechnologiesthatfall

underthedefinitionofAI,overthelasttwoyears

explainsinparttherecentinterestinandpublic

debatesontheeffectsofAImorebroadly.

Comparedtopastinnovations,ittookonlyafew

monthsforgenAItoreach100millionusers

(measuredbytheuserbaseofOpenAI’sChatGPTwhichwasthefirstwidelyavailableandaccessiblegenAIapplication).Bycontrast,ittookyears(and

sometimesdecades)forothergeneral-purpose

technologiestoreachthesamenumberofusers.

Eventhoughthesedifferencesmaybemainly

drivenbytheverylowaccesscostofgenAI

technologiesthroughsmartphonesandpersonal

computers,thishistoricallyunprecedentedspeedofadoptionpointstothepotentialforAItobe

applicabletoawiderangeoftasks.However,aswediscussbelow,thesheernumberofindividualusersneitherimpliesthatAIisbeingemployedtoawide

rangeoftasksnorisitsynonymousforbroaderAIadoptionbyfirms.

3.Methodology

TosimulatetheeffectsofAIonproductivity,thispaperusesthemodelfromAcemoglu(2024),whichinturnisbasedonAcemogluandRestrepo(2018,2019,2022).Inthemodel,theproductionofauniquefinalgood

requiresafixedsetoftaskstobeperformed,andinturn,thesetaskscanbeproducedwitheithercapitalor

labor.Thisframeworkserveswelltoexaminethemedium-termeffectsofAI,interpretedasthegrowthinoutputandTFPoriginatingfromsmallchangesinproductivityandinthemixoflaborandcapitalinputs,butwithout

fundamentallong-runchangesinthestructureoftheeconomy(i.e.,itssectoralcompositionandtasks).

Acemoglu(2024)includestwochannelsforAI-basedproductivitygains:automationandtaskcomplementarity.Theformerentailsasubstitutionofworkersintheperformanceofindividualtaskswithinanoccupation,

decreasingtheoverallneedforhumanlabor.Thelatterreferstopartialautomationoftaskssothatthemarginallaborproductivityincreasesincomplementarytasksperformedbyhumans.

Acemoglu(2024)thenappliesHulten’stheorem(Hulten,1978),whichshowshowmicro-level,non-

transformationalproductivityimprovementsinindividualoccupationstranslateintomacrochangesand

aggregatedproductivitygrowthincompetitiveeconomieswithconstantreturnstoscale.SinceAcemoglu’s

focusisonsmallchangesintechnologyandthecompetitiveequilibriumisefficient,theimpactofall

reallocationsoffactorsacrosstasksandindirecteffectsviapricesareofsecondaryorderandthereforesmallenoughtobeignoredincomputingtheproductivityandGDPgainsduetoAI.Thisalsoimpliesthatthemodel

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isnotsuitableforestimatinganypotentiallylargetransformationalandlonger-termeffects,suchasthecreationofnewindustriesoranaccelerationintherateofscientificdiscoveries.

EstimatingtheproductivitygainsofAIadoptionintheAcemoglu(2024)modelamountstocalibratingthreekeyparameters.ThefirstrequiredinputconsistsofameasureoftheexposuretoAIofdifferentoccupations,

essentiallyreflectingassumptionsaboutAIcapabilitiesandthepotentialscopeofAIapplications.Importantly,forthepurposeofestimatingproductivitygains,exposurecanrefertobothautomationandtask

complementarities

.3

Usingaversionofthetask-basedoccupationalexposuremeasureconstructedby

Eloundouetal.(2024),Acemoglu(2024)calculatesthatthewagebill-weightedshareofexposedtasksinthe

U.S.economyis19.9percent,meaningthatAIhasthecapabilityofperformingaround20percentoftasksin

theU.S.economy(whenwagebillweightsareusedtoproxytherelativeimportanceoftasksandoccupations).

ThesecondparameterofinterestistheAIadoptionrate,i.e.,theshareofAI-exposedtaskswherethebenefitsofusingAIexceedthecosts,thusmakingAIadoptionprofitable.Intuitively,theremaybetasksthatAIcan

performbutwhereitsapplicationmaybetoocostlyrelativetothepriceoflaborinthesamejob.Hence,therawexposuremeasureofanoccupationshouldbecombinedwithanestimateoftheeconomicfeasibilityof

adoption.Acemoglu(2024)drawsfromthecostingestimatesofSvanbergetal.(2024)toassumethatthebenefitsofusingAIexceedthecostsfor23percentofAI-exposedtasks.

Finally,translatingtheapplicationofAIintoproductivitygainsrequiresanestimateofthesavingsintermsof

laborcoststhatAIprovideswhenproducingaunitofoutput.Acemoglu(2024)usesanaverageofthree

microeconomicproductivityestimatestocalibratetheassumptionthattasksautomatedbyAIreducelabor

costsby27%.Theselaborcostsavingstranslateinto15%total(laborandcapital)costsavings,giventhat

laborcostsaccountfor53percentofoutput.Multiplyingthesethreekeyparameters(19.9%,23%,and15%),

Acemoglu(2024)estimatesa0.71%cumulativemedium-termincreaseintotalfactorproductivityfortheUS.IncontrasttoAcemoglu(2024)whoconsidersthemediumtermtobe10years,weassumethatthecumulative

medium-termgainsariseover5years.

Weapplythesamemethodologyto31Europeancountries(seeAppendix1forlistofcountries).Foreach

country,weobtainthefirstkeyparameter—theshareofAI-exposedtasksinindustry-specificvalueadded—byweightingoccupation-levelexposuresbyeachoccupation’swagebillwithinanindustry(seeAppendix2fortheAIexposureestimatesfromtheliteratureweuse).Toobtainthesecondkeyparameter—theAIadoptionrate—weestimatethehistoricalrelationshipbetweenAIadoptionandcountries’andsectors’economic

characteristics(e.g.,laborandcapitalcosts)inEurope.Thisgivestheshareoftasksineachcountry-industry

pairforwhichitisprofitabletoapplyAI(seeAppendix3).Wecalibratethethirdkeyparameter(costsavings)inthesamewayasAcemoglu(2024).Finally,wecalculateproductivitygainastheproductofthesethreekey

parameters(seeAppendix1fordetailsondatausedforweightingandlaborshares).

3Thedistinctionbetweenautomationandcomplementarity,however,wouldberelevantforexaminingtheimpactofadoptiononemploymentandlaborproductivity,whichweleaveforfutureresearch.

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4.Results

4.1VariationinMedium-termProductivityGains

Wepresenttheresultsinseveralstages.First,wecompareproductivitygainsinEuropetotheestimatesby

Acemoglu(2024)fortheUS.Second,weshowhowalternativeassumptionsofAIcapabilitiesandAIadoptionaffecttheproductivitygainsacrossthecountriesinoursample.Finally,wecombineallassumptionstoarrivetoaplausiblerangeofestimatesforeachcountry.

First,weapplythesamemethodologyasinAcemoglu(2024)toEuropeinordertocompareourestimateswiththeonesforAcemoglu(2024)fortheUS.Inparticular,wecalculatetheproductivitygainsforEuropean

countriesandEuropeasawhole,usingthesamemeasureofoccupation-levelAIexposure,AIadoptionrate,

andlaborcostsavingsfromAIasintheoriginalAcemoglu(2024)paper.Thisparametrization,henceforth

referredtoasthe‘Acemoglu(2024)baseline’,allowsustoexaminehowdifferencesinthesectoralcompositionandwagestructuretranslateintodifferencesintheproductivitygainsfromAIbetweentheUSandEuropean

countries,giventhattherearenootherdifferencesbetweenAcemoglu’sresultandourresultsforEurope.

Figure2showsthat,basedondifferencesinthesectoralcomposition,theaverageproductivitygainsinEuropearesomewhatlargerthanintheUS,butnotbymuch:theyamounttocloseto0.8percentinEurope,

cumulativelyoverthemediumterm,comparedtoaround0.7inAcemoglu(2024)fortheUS.However,thereissubstantialcross-countryvariation,rangingfromaround0.5percentinRomaniatocloseto1percentin

Luxembourg.Broadly,higher-incomecountrieshavelargergains,aresultdrivenbythehigherprevalenceofwhite-collarservicesincludingforinstancefinancialservices,whichtendtobemoreexposedtoAI.

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Figure2.ProductivitygainsinEuropeandtheUScompared

Sources:Eurostat,EULFS,EUSILC,andIMFstaffcalculations.

Note:TheblackandredlinesrepresenttheaverageTFPgainforthe31EuropeancountriesinoursampleandfortheUSasestimatedbyAcemoglu(2024),respectively.

Second,wequantifytheuncertaintyaroundtheAcemoglu(2024)baselineresultspresentedinFigure2usingacomprehensivesetofalternativescenariosaboutAIcapabilities(AItask-levelexposuremeasures)

.4

WethusrepeattheexerciseconsideringvariousalternativeestimatesonAIexposure.Appendix2providesalistofAI

exposureestimatesandbriefdescriptionsoftheirmainmethodologicaldifferences.Forinstance,some

computeexposureforspecifictasksandthenconsidereachoccupationasabundleoftasks(Eloundouetal.,2024,Gmyreketal.,2023,Webb,2019),whileothersfocusontheoverlapbetweenAIapplicationandhumanskills(Feltenetal.,2021).Beyondthemechanicalmeaningoftestingthesensitivityoftheresultstothese

assumptions,exploringalternativemeasuresofexposurethusalsoreflectstheconsiderableuncertaintyaroundhowindividualtasksandoccupationswillbeaffectedbyAI.Figure3showsthatwhilemoreconservative

assumptionsmute