DP

PaperSeries24-E-084

RIETIDiscussion

MacroeconomicImpactofArtificialIntelligenceonProductivity:Anestimatefromasurvey

MORIKAWA,Masayuki

RIETI

ResearchInstituteofEconomy,Trade&Industry,IAA

TheResearchInstituteofEconomy,TradeandIndustry

https://www.rieti.go.jp/en/

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RIETIDiscussionPaperSeries24-E-084

December2024

MacroeconomicImpactofArtificialIntelligenceonProductivity:Anestimate

fromasurvey

*

MasayukiMorikawa(RIETI)

Abstract

BasedonasurveyofJapaneseworkers,thisstudydocumentsthecharacteristicsofworkerswhouseartificialintelligence(AI)intheirjobsandestimatestheeffectsofthisnewgeneral-purposetechnologyonmacroeconomicproductivity.Theresultsindicate,first,8.3%ofworkersusedAIintheirjobsin2024,whichisapproximately1.5timesthanin2023.Second,moreeducatedandhigh-wageworkerstendtouseAI,suggestingthatitsdiffusionmayincreaselabormarketinequality.Third,theuseofAIisestimatedtohaveincreasedlaborproductivityinthemacroeconomyby0.5–0.6%.Fourth,nearly30%ofworkersexpecttouseAIfortheirjobsinthefuture,suggestingthatitsmacroeconomiceffectswillincrease.However,theproductivityeffectofAIforthosewhorecentlystartedusingitisrelativelysmall,suggestingadiminishingproductivityimpactofAI.

Keywords:artificialintelligence,productivity

JELClassification:D24,J24,J31,O33,O47

RIETIDiscussionPapersSeriesaimsatwidelydisseminatingresearchresultsintheformofprofessionalpapers,therebystimulatinglivelydiscussion.Theviewsexpressedinthepapersaresolelythoseoftheauthor(s),anddonotpresentthoseoftheResearchInstituteofEconomy,TradeandIndustry.

*IwouldliketothankSeiichiroInoue,ArataIto,YukiHashimoto,EiichiTomiuraandtheseminarparticipantsatRIETIfortheirvaluablecommentsandsuggestions.ThisresearchissupportedbytheJSPSGrants-in-AidforScientificResearch(23K17548,23K20606).

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MacroeconomicImpactofArtificialIntelligenceonProductivity:Anestimate

fromasurvey

1.Introduction

Thebusinessuseofartificialintelligence(AI),includinggenerativeAI,isspreadingrapidlyandexpectedtosubstantiallyincreasemacroeconomicproductivity.Amongautomationtechnologies,manystudieshavequantifiedtheproductivityimpactofindustrialrobots(e.g.,GraetzandMichaels,2018;Kromannetal.,2020;Cetteetal.,2021;Dauthetal.,2021)becausedataontheiruseareavailablefromtheInternationalFederationofRobotics.DataontheuseofindustrialrobotsinJapanareavailablefromtheJapanRobotAssociation,andstudieshavebeenconductedusingthesedata(e.g.,Dekle,2020;Adachietal.,2024).However,thequantitativeimpactofAIonproductivityisnotyetwellunderstood,mainlybecauseofthelackofstatisticaldataontheuseofAI.

Recently,severalstudieshavereportedresultsfromrandomizedexperimentsonspecifictasks,showingthatAIhasasubstantialpositiveeffectonproductivity(e.g.,Kanazawaetal.,2022;Brynjolfssonetal.,2023;NoyandZhang,2023;Pengetal.,2023).Thesestudiesprovidevaluablecontributions,astheyrevealthecausaleffectsofAIonproductivity.However,themacroeconomiceffectsareimpossibletoinferfromtheseresultsbecausetheanalysiscoversnarrowlydefinedtasks,suchastaxidriving,customersupport,writingtasks,andsoftwareprogramming.

Acemoglu(2024)estimatesthemedium-termeffectofAIonproductivityoverthenext10yearsasthepercentageoftasksaffectedbyAImultipliedbytask-levelcostsavingsbasedonexistingtask-levelstudies.Accordingtohisstudy,themacroeconomicimpactofAIisnon-negligiblebutsmall,withacumulativetotalfactorproductivity(TFP)increaseoflessthan0.7%over10years.However,heexpressesreservations,inthatwhichtaskswillbeautomatedandwhatthecostsavingswillbearelargelyuncertain.

Bicketal.(2024)applysurveydatatoshowgenerativeAIusebyUSworkersandestimatethatgenerativeAIincreaseslaborproductivityby0.125–0.875percentagepoints.However,thisfigureisbasedontheassumptionthatthetask-levelproductivityeffectis25%,whichisthemedianoffiverecentstudies.BasedonaDanishsurveyofworkersin11occupationswithhighexposuretogenerativeAI(e.g.,softwaredevelopersandmarketingprofessionals),HumlumandVestergaard(2024)reportthat32%areusingChatGPT,arepresentativegenerativeAIprogram,

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andthattheyestimatethatChatGPThalvestheirworkinghoursforone-thirdoftheirtasks,onaverage.ThesefindingsindicatethatChatGPTincreasestheaggregate-levellaborproductivityoftheseoccupationsbyapproximately5%.However,thiscannotbegeneralizedtotheentirelabormarketbecausethereportedfiguresareforspecificoccupationswithhighAIexposure.

Againstthisbackground,thisstudyusesasurveyofJapaneseworkersconductedinOctober2024toidentifythenumberandtypeofworkersusingAIintheirjobsandestimatestheimpactofthisnewgeneral-purposetechnologyonmacroeconomicproductivity.Althoughthisstudyemploysanextremelysimpleapproach,itcancapturethemacroeconomiceffectsofAIbecauseitcoverstheentireworkforceratherthanspecificoccupations.AlthoughtheproductivityeffectsarebasedonthesubjectiveevaluationsofworkerswhouseAI,thismethodhastheadvantageofavoidingendogeneityconcerns,unlike,forexample,aproductionfunctionapproach,becausethesurveyasksAIuserstomakecomparisonswithsituationsinwhichAIisnotused.Furthermore,becausethisstudyusestwo-yearpaneldata,wecancompareworkerswhobeganusingAIinthepastyearwiththosewhohavebeenusingAIlonger.

Themainfindingsaresummarizedasfollows.First,approximately8%ofworkerscurrentlyuseAIintheirjobs,andthisnumberisrapidlyincreasing.Second,moreeducatedandhigh-wageworkerstendtouseAI,whichmayincreaseinequalityinthelabormarket.Third,weestimatethatAIusehasincreasedmacroeconomiclaborproductivityby0.5–0.6%.Fourth,nearly30%oftherespondentsexpecttouseAIintheirjobsinthefuture,suggestingthatthemacroeconomiceffectsofAIwilllikelyincrease.However,theproductivityeffectsofthosewhohaverecentlybegunusingAIarerelativelysmall,suggestingthepossibilitythatAI’simpactonproductivitywillgraduallydiminish.

Theremainderofthispaperisorganizedasfollows.Section2describestheworkersurveyandanalysismethodusedinthisstudy.Section3reportsourresultsonthemacroeconomicimpactofAIandthecharacteristicsofworkerswhouseAIfortheirjobs.Section4summarizesthefindingsanddiscussestheirimplications.

2.Outlineofthesurvey

Thedatawereretrievedfromthe“SurveyofLifeandConsumptionundertheChangingEconomicStructure”designedbytheauthorandadministeredbyRakutenInsight,Inc.inOctober

2024.ThesurveytargetedrespondentsfromasurveyconductedinSeptember2023(Morikawa,2024).The2023surveywasconductedwithworkersaged20yearsandolderfromasamplepool

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ofmorethan2millionregisteredindividuals.Thestudysamplewasselectedsuchthatthegenderandagecompositionmatchedthatofthe2022EmploymentStatusSurvey(MinistryofInternalAffairsandCommunications),whichisanofficialstatisticalsurveyconductedinJapaneveryfiveyears.Thus,thesampleisrepresentativeoftheentireJapaneseworkforce.The2023surveyhad13,150respondents.Ofthese,12,763stillregisteredatthetimeofthe2024surveyweresentquestionnaires,and8,633responded.Datafrom8,269oftheserespondents,excludingthosenotworkingasofthe2024survey,areusedintheanalysis.ThegenderandagecompositionoftherespondentsisshowninappendixTableA1.

Themainsurveyitemsusedinthisstudyassessthefollowing:(1)theuseofAIatwork,(2)percentageoftasksperformedusingAI,and(3)effectofAIuseonworkefficiency.Inaddition,thesurveyincludesinformationontherespondents’gender,age,educationalbackground,industryinwhichtheywork(44categoriesaggregatedinto14categories),occupation(13categoriessuchasmanagerial,professional/technical,clerical,andsalespositions),typeofemployment(10categoriessuchasfull-timeregularemployment,part-timeemployment,andself-employment),weeklyworkinghours(12categories),andwages(18categoriesdenotingannualearningsfromwork),whichareusedintheanalysis.

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ThespecificquestionregardingtheuseofAIatworkis“WewouldliketoaskyouaboutyouruseofArtificialIntelligence(AI)includinggenerativeAI.”Thethreechoicesare:1)“IcurrentlyuseAIatwork,”2)“IdonotcurrentlyuseAIatwork,butIthinkIwillinthefuture,”and3)“IdonotuseAIatworkanddonotthinkIwillinthefuture.”

RespondentswhoselectthefirstresponseareaskedaboutthepercentageoftheirworktasksthatusedAIandtheeffectofAIontheirworkefficiency.ThequestionregardingthepercentageoftasksperformedusingAIis“WhatpercentageofyouroveralltasksisperformedusingAI?”Answersareprovidedasspecificnumbers(%).

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ThequestionabouttheefficiencygainsfromAIis“HowmuchmoreefficientdoyoufeeltheuseofAIinyourtaskscomparedtowithoutusingAI?”Thisquestionisalsoansweredintheformofaspecificnumber(%).Thelowerboundoftheresponseissetat0%(iftherespondentbelievesthatAIusehasnothingtodowithworkefficiency),andtheupperboundissetat100%toavoidextremeresponses,althoughinsomecases,efficiencymaybeasmuchastwiceashighwithAIthanwithout.However,becauseonly

1Theweeklyworkinghoursaredividedinto12categories:lessthan15hours,15–19hours,20–

21hours,22–29hours,30–34hours,35–42hours,43–45hours,46–48hours,49–59hours,60–

64hours,65–74hours,and75hoursormore.Workers’annualincomeisdividedinto18categories:lessthanJPY500thousand,JPY500to990thousand,JPY1,000to1,490thousand,...,JPY15,000to1,749thousand,JPY1,750to1,999thousand,andmorethanJPY20,000thousand.

2Thelowerandupperboundsoftheresponsearesetat1%and100%,respectively.

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asmallnumberofAIusers(1.7%)responded100%,thepossibledownwardbiasarisingfromsettingtheupperboundislimited.

Basedontheanswerstothesequestions,thepercentageofworkerswhouseAIfortheirwork(AI_User),percentageoftasksusingAI(AI_Taskshare),andefficiencygains(AI_Efficiency)aretabulatedbygender,age,education,andotherworkercharacteristics.Theworker-levelproductivityeffect(AI_Productivity)iscalculatedforAIusersasAI_Taskshare*AI_Efficiency.Forexample,ifaworkerusesAIfor30%oftheirtasksandtheefficiencyeffectofAIis20%,theoverallproductivityoftheirworkis6%higherthanwhentheydonotuseAI.MultiplyingthemeanofthisfigurebythepercentageofAIusersindicatesthemacroeconomicproductivityeffect.

However,AIusersmaybemorelikelythannon-userstohavelongerworkinghours,suchasfull-timeemployees,andmaybemorehighlyeducatedandearnhigherwages.Therefore,weperformanaggregationweightedby(1)workinghoursand(2)annualearnings.Whenworkinghoursareusedasweights,macroeconomiceffectsareestimatedonalaborinputbasis,andwhenannualearningsareusedasweights,macroeconomiceffectsareestimatedclosertoavalue-addedbasis.

WhenestimatingtheeffectsofAIuseonproductivity,forexample,anapproachtoestimatingaproductionfunction,selectionbiasresultsinaseriousprobleminwhichfirmsandworkerswithhigherproductivitytendtouseAImorethanthosewithlowerproductivity.Althoughtheestimatedproductivityeffectsinthisstudydependonworkers’subjectiveevaluations,meaningthatmeasurementerrorsareinevitable,ourapproachhastheadvantageofavoidingsuchendogeneitybiasbecausethesurveyasksAIusersaboutefficiencygainsincomparisontosituationsinwhichAIisnotused.

3.Results

3.1.UseofAIandProductivityEffects

Table1summarizestheaggregationresultsofthemainsurveyquestions.Oftherespondents,8.3%useAIatwork(AI_User).Inthe2023surveyconductedayearearlier,thecorrespondingfigurewas5.8%(5.3%whenlimitingthesampletothosewhoalsorespondedtothe2024survey).Therefore,thenumberofworkerswhouseAIatworkisapproximately1.5timeshigherthanitwasinthepreviousyear(Morikawa,2024).

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Thepercentageofrespondentswhochoose“Ido

3Thethreeresponsechoicesinthe2023surveyare(1)usingAIatwork,(2)usingAIbutnotat

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notcurrentlyuseAIatwork,butIthinkIwillinthefuture,”is27.8%,suggestingthattheuseofAIinworkwillcontinuetoincrease.

Table1.UseofAIanditseffect(%)

Mean

Std.Dev.

N

A.AI_User

8.32

-

8,269

B.AI_Taskshare

15.1

16.8

688

C.AI_Efficiency

25.9

22.9

688

Note:RowsBandCshowthefiguresfortherespondentswhouseAIintheirwork.

ThemeanpercentageoftasksthatuseAI(AI_Taskshare)amongthosewhouseAIfortheirjobsis15.1%,althoughtheindividualdifferencesarelarge,withastandarddeviationof16.8%.Therefore,evenwhenAIisusedforwork,thepercentageoftasksthatdonotuseAIismuchhigherthanthatoftasksthatdo.

ThesubjectiveeffectofAIuseonworkefficiency(AI_Efficiency)alsovarieswidelyamongindividualworkers;however,themeanvalueis25.9%(standarddeviationis22.9%).

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AswedonothaveinformationonthecostofAI(capitalinput),thesefiguresshouldbeinterpretedastheeffectsonlaborproductivity,notTFP.AlthoughtheirstudyislimitedtotheeffectsofgenerativeAI,Bicketal.(2024)assumea25%productivityeffectofgenerativeAIusewhenconductingtheiranalysis.Thefigureinourstudyisnotsimplycomparablewiththeirsbutisalmostidentical.ApositivecorrelationisobservedbetweenAI_EfficiencyandAI_Taskshare,withthosewhoevaluatetheefficiencygainsofAIreportingahigherpercentageoftasksusingAI.Quantitatively,a1percentagepointincreaseinAI_Efficiencyisassociatedwitha0.3percentagepointincreaseinAI_Taskshare.

ThemeanproductivityeffectofAIattheworkerlevel(AI_Productivity)is5.6%,meaningthatworkerswhouseAIfortheirjobsare5.6%moreproductivethanthosewhodonot.Themacroeconomicproductivityeffect,calculatedbymultiplyingthisfigurebythepercentageofAIusers,is+0.46%(column(1)ofTable2).However,thisfigureisbasedonthenumberofworkersanddoesnotconsiderthedifferencesinworkinghoursandwages.Whentheproductivityeffectiscalculatedusingworkinghoursastheweight,itis+0.50%(column(2)ofTable2),andwhenannualearningsareusedastheweight,itis+0.58%(column(3)ofthe

table).5

Thisdifference

work,and(3)notusingAI.

4Thesamequestionwasaskedinthe2023survey,andthemeanwas21.8%(21.0%forapanelsample).Thus,thefigurefor2024issomewhathigher.

5Basedontheresultsbyindustry,aweightedaverageusingtheratioofthenumberofworkersbyindustryintheEmploymentStatusSurveyof2022yieldsslightlylowerproductivityeffects:unweighted0.42%,weightedbyworkinghours0.46%,andweightedbyannualearnings0.50%.

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mainlyreflectsthatthosewithlongerworkinghoursandthosewithhigherannualearningsaremorelikelytouseAI(extensivemargin),whereasthedifferencesintheintensivemargin,proportionoftasksusingAI(AI_Taskshare),andproductivityeffectofAIuse(AI_Efficiency)arerelativelysmall.Intermsoftheeffectonvalueaddedatthemacrolevel,annualearningsareappropriatetouseastheweight.Therefore,atthispoint,ourpreferredestimateisa0.5–0.6%boosttolaborproductivityatthemacrolevelcomparedtothecasewithoutAI.

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Table2presentstheresultsbyindustry,education,andannualearnings,whicharediscussedlaterinthispaper.

Table2.MacroeconomicproductivityeffectsofAI(%)

(2)Hours(3)Annualearnings

(1)Unweighted

weightedweighted

Allindustries

0.46

0.50

0.58

Construction

0.30

0.33

0.31

Manufacturing(machinary)

0.66

0.70

0.82

Manufacturing(others)

0.36

0.37

0.49

Utilities

1.41

0.88

0.88

Informationandcommunications

1.54

1.76

1.60

Transportation

0.57

0.69

0.70

Wholesale

0.14

0.16

0.18

Retail

0.55

0.62

0.62

Bankingandfinance

0.83

0.90

0.94

Services

0.30

0.32

0.40

Education

0.30

0.25

0.38

Healthcare

0.30

0.26

0.41

Publicservices

0.31

0.30

0.28

Otherindustries

0.22

0.28

0.31

Highschoolorless

0.21

0.35

0.39

Vocationalschool

0.28

0.43

0.46

Juniorcollege

0.18

0.26

0.34

University

0.38

0.58

0.58

Graduateschool

0.70

0.86

1.02

Lessthan5,000thousand

0.25

0.39

0.37

5,000-9,999thousand

0.45

0.64

0.63

10millionorhigher

0.56

0.72

0.75

Note:Productivityeffectsincolumn(1)arecalculatedasAI_User*AI_Taskshare*AI_Efficiency.Figuresincolumns(2)and(3)arecalculatedusingworkinghoursandannualearningsasweights,respectively.

Therefore,respondentstooursurveytendtoworkinindustrieswithhighlevelsofAIuse.

6Thelaborproductivityeffectestimatedhereisapproximately0.3%whenconvertedtoTFPusingthelaborsharefigure(0.535)usedbyAcemoglu(2024).

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The2024surveywasconductedwithrespondentswhoalsoparticipatedinthe2023survey,andthe2023surveyalsoaskedaboutAIuseatwork.Therefore,thosewhorecentlystartedusingAIduringthepastyearcanbedisaggregatedfromthosewhohavecontinuedtouseAI.Table3summarizesthecomparisonbetweenthesetwocategoriesofAIusers.BoththepercentageoftasksusingAI(AI_Taskshare)andtheeffectonworkefficiency(AI_Efficiency)aresignificantlylowerforthosewhorecentlystartedusingAIthanforthosewhohaveuseditcontinuously.Thus,theeffectofAIuseonoverallworkproductivitydifferssignificantly(AI_Productivity).AI_ProductivityforcontinuousandnewAIusersis7.9%and4.4%,respectively.ThisresultsuggeststhatthediffusionofAIstartedwithjobsforwhichitseffectislargeandgraduallyspreadtojobsforwhichitseffectissmall.

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Ifthesetrendscontinue,theadditionalcontributionofAItomacroeconomicproductivitymaydiminishgraduallyasthenumberofAIusersincreases.

Table3.ComparisonofnewandcontinuousAIusers(%)

NewAIusers

ContinuousAIusers

Diff.

AI_Taskshare

13.73

17.78

-4.05

***

AI_Efficiency

23.60

30.16

-6.56

***

AI_Productivity

4.37

7.85

-3.48

***

N

451

237

Note:***:p<0.01.NewAIusersarethosewhostartedusingAIinthepastyear.

InadditiontothosewhocurrentlyuseAIintheirjobs,27.8%oftherespondentsansweredthattheyexpecttostartusingAIinthefuture.AssumingthatthepercentageofworkthatusesAIandtheeffectonworkefficiencyassociatedwithAIusearethesameasthoseofcurrentAIusers,theadditionalmacroeconomicproductivityeffectofthesepotentialuserswouldbeapproximatelyfourtimesgreater,orapproximately+2%,comparedtothecasewithoutAI.

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However,asnotedabove,thepercentageoftasksthatuseAIandtheeffectofAIonworkefficiencymaybecomesmallercomparedtotasksforwhichAIwasusedearlier.However,iftheratiooftasksthatuseAIincreasesamongworkerswhocontinuouslyuseAIfortheirjobs,theoverallproductivityeffectmayincrease.

7SomeworkersusedAIfortheirjobsatthetimeofthe2023surveybutnotin2024(“quitter”).TheaverageeffectofAIonworkefficiencyfortheseindividualsin2023is18.7%,whichislowerthanthefigureforthosewhocontinuedtouseAIin2024(22.9%),althoughthedifferenceisquantitativelysmall.

8TheestimatedeffectonTFP,consideringthelaborshare,is+1.1%,whichislargerthanthefigurereportedinAcemoglu(2024).

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3.2.CharacteristicsofWorkersWhoUseAIfortheirJobs

Thecalculationresults,disaggregatedbyworkercharacteristics,arereportedinappendixTableA2.WorkerswithhigherratesofAIusearegenerallymale,intheir20sand30s,andhighlyeducated,especiallythosewithadvanceddegrees(seecolumn(1)ofthetable).

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Byindustry,theinformationandcommunications,manufacturing(machinery),andfinance/insuranceindustrieshavehighratesofAIuse.Byoccupation,management,sales,andprofessional/technicaloccupationshavehighratesofAIuse.Bytypeofemployment,companyexecutivesandfull-timeemployeeshavehighratesofAIuse.High-incomeworkers(annualearningsof10millionyenormore)havehighratesofAIuse.

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Column(1)ofTable4showstheresultsofasimpleprobitestimationthatexplainstheuseofAIatworkaccordingtoworkercharacteristics.Annualearningsarelog-transformedusingthemedianofthe18categories.ThelowestcategoryistreatedasJPY250thousandandthehighestcategoryistreatedasJPY22.5million.

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Thecoefficientforfemaleisinsignificant,indicatingnogenderdifferencesinAIusewhencontrollingforindustry,occupation,typeofemployment,andothercontrolvariables.Youngerworkersintheir20sand30s,thosewithhighereducation(collegeandgraduatedegrees),andthosewithhigherannualearningsfromworkhaveahigherprobabilityofusingAI.

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Column(2)ofTable4showstheresultsofthesameestimationusingdatafromthe2023survey.Therelationshipsbetweenage,education,andannualincomeandtheprobabilityofAIusearethesameasthosefoundinthe2024survey.Morikawa(2017),inanearlystudythatanalyzedtherelationshipbetweenAIadoptionandemployeeeducationusingJapanesefirm-levelsurveydata,indicatesacomplementaritybetweenAIandworkereducation.Dracaetal.(2024)demonstratesthecomplementaritybetweenmachinelearning/AIandskills(collegegraduatesandSTEMoccupations)throughananalysisusingUKdata.Ourresultsareconsistentwiththoseof

9Amongtheeducationcategories,“juniorhighschoolandelementaryschoolgraduates”aremergedwith“highschoolgraduates”andlistedas“highschoolgraduatesandbelow.”Graduateschoolissurveyedseparatelyformaster’sanddoctoraldegrees,buttheyaremergedinto“graduateschool.”

10Inthesurvey,annualearningsfromworkareclassifiedinto18categoriesrangingfrom“lessthanJPY500thousandyen”to“morethan20millionyen,”buttoavoidcomplications,annualearningsareaggregatedintothreecategories.

11Theweeklyworkinghoursareusedasacontrolvariablebylog-transformingthemedianofthe12categories.

12Throughadifferentapproach,Eloundouetal.(2024)indicatethatindividualsearninghigherincomeshavegreaterexposuretolargelanguagemodels.

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previousstudies.

Table4.WorkercharacteristicsandtheprobabilityofAIuse

(1)2024FY

dF/dxRobustSE

(2)2023FY

dF/dxRobustSE

Female

0.009

(0.007)

-0.007

(0.004)*

20s

0.062

(0.013)***

0.047

(0.008)***

30s

0.027

(0.009)***

0.026

(0.006)***

50s

-0.013

(0.007)*

-0.009

(0.005)*

60s

-0.020

(0.008)**

-0.010

(0.005)*

70orolder

-0.024

(0.016)

0.017

(0.014)

Vocationalschool

0.008

(0.012)

0.008

(0.007)

Juniorcollege

0.006

(0.012)

-0.005

(0.007)

University

0.034

(0.008)***

0.018

(0.005)***

Graduateschool

0.104

(0.020)***

0.046

(0.011)***

lnearnings

0.026

(0.005)***

0.019

(0.003)***

lnworkinghours

yes

yes

Industry

yes

yes

Occupation

Worktype

yes

yes

yes

yes

Observations

8,200

13,140

PseudoR2

0.1102

0.1120

Notes:Probitestimationswithrobuststandarderrorsareinparentheses.Thefiguresindicatemarginaleffects.***:p<0.01,**:p<0.05,*:p<0.10.Thereferencecategoriesaremale,age40s,andhighschooleducationorless.

ThepercentageoftasksthatuseAIandtheeffectofAIonworkefficiency(columns(2)and(3)ofappendixTableA2)havenoclearrelationshipwithworkercharacteristics.Inmanycases,thepercentageoftasksthatuseAIortheeffectofAIonworkefficiencyishigh,evenincategorieswithlowratesofAIuse.Thus,thedifferencesintheproductivityeffectsofAIbasedonworkercharacteristicsarelimited.However,aweakbutsystematicrelationshipexistsbetweeneducationandannualincomewiththeproductivityeffectsofAI.TheproductivityeffectofAItendstobesmallerforthosewithmoreeducationandhigherearnings.Therefore,althoughless-educatedandlow-wageworkersarenotablylesslikelytouseAIintheirjobs,theproductivityeffectsaresomewhatlargerwhentheydo.

Recentstudiesonspecifictasks(e.g.,Kanazawaetal.,2022;Brynjolfssonetal.,2023;NoyandZhang,2023)haveshownthatAIproductivityeffectsaregreaterforrelativelyless-skilledworkersinthesametask.Theabovefindingsaresimilartothoseinthesepreviousstudies.However,whenordinaryleastsquareestimationisperformedusinggender,age,education,industry,occupation,employmenttype,annualearnings,andhoursworkedperweekas

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explanatoryvariables,thecoefficientsforeducationandannualearningsarestatisticallyinsignificant.Furthermore,thecoefficientsofindustry,occupation,andtypeofworkarelargelyinsignificant.Thus,theproductivityeffectofusingAIforworkisgenerallyunrelatedtoobservableworkercharacteristics.

Table2showsthemacroeconomicproductivityeffectsofAIdisaggregatedbyindustry,education,andannualearnings.Theindustry-levelproductivityeffects(weightedbyannualearnings)arelargefortheinformationandcommunications(1.60%)andfinance/insurance(0.94%)industriesandsmallforthewholesaleandconstructionindustries.ThedifferencesamongindustriesaremostlyduetodifferencesinthepercentageofAIusers(extensivemargin).Forexample,theinformationandcommunicationsindustrydoesnothaveparticularlyhighAI-usingtasksorefficiencyeffectsofAIuse.Byworkercharacteristics,aggregatedproductivityeffectsaregreaterforhighlyeducatedandhigh-incomeworkergroups.

4.Conclusion

UsingdatafromanoriginalsurveyofJapaneseworkers,thisstudyshowsthecharacteristicsofworkerswhouseAIintheirjobsandestimatesthemacroeconomiceffectsofAIonproductivity.Themainresultsareas