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PolicyResearchWorkingPaper10928

JoblessDevelopment

FranziskaOhnsorge

RichardRogerson

ZoeLeiyuXie

WORLDBANKGROUP

SouthAsiaRegion

OfficeoftheChiefEconomistSeptember2024

PolicyResearchWorkingPaper10928

Abstract

AnalysesofGDPpercapitadifferencesacrosscountriesfocusalmostexclusivelyondifferencesinproductivity.Thispapershowsthattherearealsolargedifferencesinmedi-um-rundynamicsintheemployment-to-populationratio.ThepaperfindsageneraltendencyforproductivitygrowthtobenegativelycorrelatedwithchangesintheemploymenttopopulationratioforalargesampleofEMDEs—aphe-nomenondescribedusingthetermjoblessdevelopmentinthispaper.Thepaperalsoshowsthattherearelargedifferencesinthesteadystatelevelsoftheemploymenttopopulationratiosthatcountriesareconvergingto.Therearealsocountriesthatexperiencesubstantialincreasesintheir

employment-to-populationratioduringthedevelopmentprocess.Usingatwo-stageprocedure,thepaperstudiesthisissueinalargesampleofEMDEs.Inthefirststage,thepaperestimatesdifferencesinsteady-stateemploymentratiosacrosscountries.Inthesecondstage,itdocumentswhichinstitutionalandpolicyfactorsarecorrelatedwithsteady-stateemploymentratios.Thepaperfindsparticularlylargedifferencesacrosscountriesinsteady-stateemploy-mentratiosforwomen.Fewerlegalprotectionsofwomen’srightsareassociatedwithlowersteady-stateemploymentratiosforwomen,withoutanoffsettingpositiveeffectformen.

ThispaperisaproductoftheOfficeoftheChiefEconomist,SouthAsiaRegion.ItispartofalargereffortbytheWorldBanktoprovideopenaccesstoitsresearchandmakeacontributiontodevelopmentpolicydiscussionsaroundtheworld.PolicyResearchWorkingPapersarealsopostedontheWebat

/prwp

.Theauthorsmaybecontactedatlxie@.

ThePolicyResearchWorkingPaperSeriesdisseminatesthefindingsofworkinprogresstoencouragetheexchangeofideasaboutdevelopmentissues.Anobjectiveoftheseriesistogetthefindingsoutquickly,evenifthepresentationsarelessthanfullypolished.Thepaperscarrythenamesoftheauthorsandshouldbecitedaccordingly.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheInternationalBankforReconstructionandDevelopment/WorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentstheyrepresent.

ProducedbytheResearchSupportTeam

JoblessDevelopment*

FranziskaOhnsorgea,b,RichardRogersonc,d,andZoeLeiyuXiea

aWorldBank,WashingtonDC,USA

bCEPR,London,UK;CAMA,Canberra,Australia

cPrincetonSchoolofPublicandInternationalAffairsdNationalBureauofEconomicResearch

JEL-codes:J11;J16;J21;F66;O41;O47

Keywords:employment;emerginganddevelopingcountries;structuraltransformation;femalelaborforceparticipation.

*WewouldliketothankNinaArnhold;MargaretArnold;NajyBenhassine;XimenaDelCarpio;PatriciaFernandes;IsisGaddis;JonJellema;NandiniKrishnan;RobinMearns;GauravNayyar;AnnaO’Donnell;NethraPalaniswamy;LokendraPhadera;MartinRaiser;andJavierSanchez-Reazafortheirhelpfulcomments.Thefindings,interpretationsandconclusionsexpressedinthispaperarethoseoftheauthorsandshouldnotbeattributedtotheWorldBank,itsExecutiveDirectors,orthecountriestheyrepresent.

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1.Introduction

ChangesinGDPpercapitaarethemostcommonmetricusedtotrackacountry’soveralldevelopment.BecauseGDPpercapitaistheproductofGDPperworkerandtheemployment-to-populationratio,changesinGDPpercapitareflectchangesinbothproductivityandtheemployment-to-populationratio.Inparticular,changesintheemployment-to-populationratiooverthemediumruncaneitherdampenoramplifytheeffectsofproductivitygrowth.

Wecointhetermjoblessdevelopmenttodescribecountriesthatexperiencedecreasesintheiremployment-to-populationratioduringaperiodofproductivitygrowth.Whilealargeliteraturehasstudiedthedynamicsofproductivitygrowthforemergingmarketanddevelopingeconomies(EMDEs),thedynamicsoftheemployment-to-populationratioforthesecountrieshavereceivedverylittleattention.Inthispaperweaddressthisgapintheliteraturebystudyingemployment-to-populationratiodynamicsamongEMDEsinthepost-1990period.

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Onereasonforthelackofattentiondevotedtolabormarketoutcomesinthecontextofmacroeconomicdevelopmentisthefactthattheemployment-to-populationratioislargelyuncorrelatedwiththelevelofdevelopmentinabroadcross-sectionofcountries.Thishasledresearcherstoabstractfromconsideringitasanimportantfactor.Butthisnear-zerocorrelationdoesnotimplythatdifferencesinlabormarketoutcomesaresmall;onthecontrary,thesedifferencesarelargeinmanycasesandouranalysisfocusesonthesedifferences.

TheexamplesofSouthKoreaandIndiaservetomotivateouranalysis.BothSouthKoreaandIndiahaveexperiencedsustainedperiodsofrapidgrowthasmeasuredbyincreasesinGDPpercapita.InSouthKoreathisperiodcovered1965-1987,whenGDPpercapitaincreasedby170logpoints.InIndia,itcovers1990-2018whenGDPpercapitaincreasedby130logpoints.Inbothcountries,periodsofrapidgrowthinGDPpercapitawerealsoperiodsofrapidgrowthinproductivity---GDPperworkerroseby140logpointsinbothcountries.

However,SouthKorea’sandIndia’sdevelopmentdiffersinoneimportantrespect.Figure1displaysthetimeseriesofthelogratioofemploymenttotheworkingagepopulation(hereafterEWAP)forSouthKoreaandIndiasince1960.WhereasSouthKorea’speriodofrapidgrowthcoincidedwithalargeincreaseinEWAP,India’speriodofrapidgrowthwasassociatedwithalargedecreaseinEWAP.Thisdifferenceisquantitativelyimportant:EWAPwas24logpointshigherinIndiathanSouthKoreain1960,butasof2019itis28logpointslower—areversalof52logpoints.Ifonetakes2percentasastandardvaluefor“normal”annualgrowthinrealGDPpercapita,thisreversalamountsto26yearsofgrowth.

1Severalpreviousstudieshaveestimatedthecorrelatesofemploymentgrowthinlargecross-sectionsofcountries(Crivelli,Furceri,andToujas-Bernaté2012;Kapsos2005).Seethemeta-analysisinWorldBank(2024).

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OurperspectiveonEWAPdynamicsinEMDEsismotivatedbytheliteratureonstructuralchange.AkeydynamicamongEMDEsisthemovementofeconomicactivityoutofagricultureandintonon-agriculture.Labormarketsinthesetwosectorshaveverydifferentoperatingfeatures:agriculturallabormarketsaredominatedbyself-employmentinlow-densityruralareas,whereasnon-agriculturallabormarketsaredominatedbyformalorinformalemploymentinhigher-densityurbanareas.Additionally,manypolicyandinstitutionaldifferencesarelikelytohaveverydifferenteffectsonlabormarketsinthesetwosectors.Forthesereasons,theprocessofstructuralchangewillplausiblygiverisetodynamicsinaggregatelabormarketoutcomes,asactivityswitchesfromlargelyself-employmentinruralareastoformalorinformalemploymentinurbanareas.Long-runlabormarketoutcomeswillthusreflectthesteadystateoutcomesinthenon-agriculturalsector.

OurmethodologyforstudyingEWAPdynamicsdrawsheavilyonthegrowthliteraturethatstudiesconvergencepatternsincross-countrypaneldatasets.Specifically,followingtheliteratureonconditionalconvergence,weincludecountryfixedeffectsinourconvergenceregressionstoallowforthepossibilitythateachcountryisconvergingtoitsownsteady-statelevelofEWAP.Whenimplementingthisprocedure,wealsocontrolfortwotime-varyingdrivingforces:productivityandpopulation.

OuranalysisofEWAPdynamicsproceedsintwosteps.ThefirststeprunsconvergenceregressionsforEWAP.Aby-productofthisfirststeparecountry-specificsteady-statevaluesfortheEWAP.Inthesecondstep,weexaminethecorrelationbetweenthesesteady-statelevelsandvariousindicators.

Ourfirststepdeliversthreekeyresults.First,whiletherearemanycountrieswithverysimilarsteady-stateEWAPlevels,therearemanycountrieswithsteady-stateEWAPlevelsthatdisplaylargedeviationsfromthemean.Second,higherpopulationgrowthisassociatedwithsignificantlylowerEWAP.Third,higherproductivitygrowthisalsoassociatedwithsignificantlylowerEWAP.ThisthirdfindingsuggeststhatEMDEsmayfaceatradeoffbetweenemploymentandproductivity.Thiswouldbethecaseifthesecountriestendtohavemanyindividualsemployedinlow-productivityactivities,aseliminatingthesejobswouldincreaseproductivitygrowthviacompositioneffects.

OursecondstepidentifiesseveralfactorsthatdisplaysignificantcorrelationswithlongrunlevelsofEWAP.Theseincludegreateropennesstointernationaltrade;moreefficientlabor,land,andproductmarkets;largerfirmsize;andbettereducationoutcomes.Weemphasizethattheseresultsonlyreflectcorrelationsandsodonotnecessarilyimplycausation.Butwethinkthesecorrelationsareinformativeasafirststepinthinkingaboutthepotentialeffectsofvariouspolicies.

OurbenchmarkresultsarefortheaggregatelevelofEWAP.WealsorepeatouranalysisforEWAPlevelsbygender.Wefindthatdifferencesinsteady-stateEWAPlevelsforwomenaremuchmoresubstantialthandifferencesformen.

Inthispaper,wedeliberatelyfocusontheaggregatequantityofemployment,assumingthatemploymentisapolicygoalinitsownright.Individualsvaluejobsfortheearnings,aswellasfortheircontributionstoself-esteemandhappinessandjobsinfluencelivingstandardsandsocialcohesion(WorldBank2013).Switchingjobs,especiallyintonon-agriculture,isoneofthemostcommonlyusedformsofclimateadaptationbyhouseholds.Thequalityofemploymentintermsofitsvariouscharacteristics—laborproductivity,laborincomeshares,wagerates,orcontractualarrangements—isnolesscriticalforthedevelopmentprocessbutiswell-coveredintheliteratureandbeyondthescopeofthispaper.

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Ourpaperisrelatedtotwoliteratures.Severalpapershaveestimatedthecorrelatesofemploymentgrowthinlargecross-sectionsofcountries(Crivelli,Furceri,andToujas-Bernaté2012;Kapsos2005).WorldBank(2024)summarizesestimatesoftheelasticityofemploymentwithrespecttooutputgrowthfromthisbodyofliterature.Relativetothisliterature,wehavetwokeycontributions.First,akeydistinguishingfeatureofouranalysisisthatweexaminechangesinemploymentratios,ratherthanemploymentgrowth.ThisiscriticalbecausemanyEMDEshaverapidlygrowingworking-agepopulations,anditisdifficulttointerpretagivenlevelofemploymentgrowthwithoutcontrollingforpopulationgrowth.Second,ouranalysisfocusesonlongrunoutcomes,whereastheabovementionedpapersoftenfocusonshortruneffects.

AsecondstrandofliteratureusestheWorldBank’sEnterpriseSurveystostudyfirm-levelemployment,ofteninspecificcountries.(See,forexample,AgaandFrancis2015;Ayyagari,Demirgüç-Kunt,andMaksimovic2011;Khan2023).Relativetothisliterature,ourkeycontributionistousetheseSurveystolinklongrunaggregateemploymentoutcomestofirm-levelconstraints.Oursisthefirstanalysisoftheextenttowhichgovernmentregulationsonlabor,land,finance,andtradehelporhindertheabsorptionofagrowingworking-agepopulationintoemploymentinEMDEs.

Theremainderofthepaperisorganizedasfollows.Section2documentsthedataandmethodology.Section3summarizespatternsinsteady-stateEWAPsandtheeffectofproductivityandpopulationgrowth.Section4showsthecorrelatesofthesesteady-stateEWAPs.Section5concludesanddiscussesthepolicyimplications.

2.Methodologyanddata

2.1Conceptualframework

Economy-wideemploymentcanbethoughtofastheequilibriumoutcomeoflaborsupplybyhouseholdsthatneedtoearnincometoconsumeandlabordemandbyfirmsthatneedlaborasaninputintoproduction.Thenaturalstartingpointforthinkingaboutdynamicsinaggregateproductivityandemploymentistheone-sectorgrowthmodel.Onelimitationofthisframeworkforunderstandingdynamicsindevelopingeconomiesisthatitabstractsfromtheprocessofstructuraltransformation,whichisanotherkeydynamicprocessthatpotentiallyimpactsbothlaborsupplyandlabordemand.Herrendorf,Rogerson,andValentinyi(2014)modelastylizedeconomywiththreesectors—agriculture,industry,andservices—thattogetherproduceaggregateoutputY.Forourpurposesitissufficienttoconsideraneconomywithtwosectors:agriculture(a)andnon-agriculture(n).EachsectorjusesaCobb-DouglasfunctiontechnologytoproduceoutputYj(soldatthepricepj)usinglaborLjandotherinputsKj(includingintermediategoods)withtechnologyAj:

Yj=AjKjαLj1-α

Aggregateoutput(andincome)isthengivenby:

Y=paYa+pnYn

Thisframeworkshouldbeseenasasimplebenchmark.Onecangeneralizeitalongseveraldimensions:toallowfordifferencesinfactorintensityacrosssectors,richerpatternsofsubstitutionbetweenfactors,non-neutralformsoftechnicalchange,andmultipletypesoflabor.

Asinaone-sectormodel,increasesinoverallproductivityinthisframeworkwillraisewagesandincome,andaffectoveralldemandandsupplyoflabor.Butthisframeworkalsofeaturesadditional

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channels.Inparticular,changesinrelativeproductivityacrosssectorswillinducechangesinrelativepricesacrosssectorsandinfluencerelativedemandacrosssectors.Changesinoverallproductivitythatinducechangesinincome,mayalsoinfluencerelativedemandacrosssectorsifincomeeffectsdifferacrosssectors.

Akeyimplicationofthesemodelsisthattheprocessofdevelopmentisassociatedwithasecularreallocationoflaboroutoftheagriculturesectorintothenon-agriculturalsectors,drivenbytheforcesjustmentioned.Itfollowsthatthelong-runlabormarketequilibriumthataneconomyconvergestowardreflectsoutcomesinthenon-agriculturalsector.

Importantly,andfromapracticalperspective,thenatureoflaborsupplyanddemandmayvaryacrosssectors.Individualslivinginruralareasmayhavedifferentlevelsoflaborsupplyacrosssectors,andfirmsindifferentsectorsmaydemanddifferenttypesofworkers.Ifthisisthecase,thenthedynamicsofstructuraltransformationwillpotentiallyinfluencethedynamicsofequilibriuminthelabormarket.

Itisalsoplausibletothinkthattheeffectofvariousinstitutionsandregulationshavedifferentialimpactonboththedemandandsupplyoflaboracrosssectors.Regulationsthatdisproportionatelyaffectlargeestablishmentswilllikelyhaveverydifferenteffectsinagriculturalversusnon-agriculture.Policiesthatincreasethecostofcapitaloraccesstocreditwillhavedifferentialimpactacrosssectorsifsectorsdifferintheimportanceofcapitalorcredit.Theimplicationofthisobservationisthatthedynamicsoflabormarketequilibriummayvaryacrosscountrieswithdifferentlabormarketinstitutions.Moreover,theoverallimpactofaparticularinstitutionorregulationmaydifferalongthedevelopmentpath:institutionsthatnegativelyaffectlabordemandinthenon-agriculturalsectorwillhaveasmalleraggregateimpactinaneconomyinwhichalmosteveryoneworksinagriculture.Alargebodyofresearchshowstheeffectonlabormarketequilibriumofsuchfactorsaslabormarketpolicies,institutions(societalnorms,bothformal,suchaslaws,andinformal,suchastraditions),andregulationsonlabormarketequilibrium(DuvalandLoungani2019;McKenzie2017;NickellandLayard1999).

Theaboveframeworkmodelsproductionatthesectorallevel.Itislikelyrelevanttoalsostudyproductionatamoregranularlevel,astheoverallleveloflabordemandwithinagivensectormayalsobeinfluencedbypoliciesthatdistortthedemandforlaboracrossindividualfirms.(HsiehandKlenow2009;2014).Thatis,theproductivitytermsinthesectoralproductionfunctionsmaythemselvesbefunctionsoftheinstitutionalandregulatoryenvironment.

ThisconceptualframeworkhasmuchincommonwiththatusedtostudythedynamicsofGDPpercapitaacrosscountries(BarroandSala-i-Martin1992;Kremer,Willis,andYou2022;Patel,Sandefur,andSubramanian2021).Liketheframeworkusedhere,thisliteratureviewseachcountryashavingitsownsteady-statelevel,dictatedbycountry-specificfactorsthatreflectpoliciesandinstitutionsandallowforadynamicprocessofconvergence.

2.2Methodology

TodiscerntherelationshipbetweenEWAPsandtheircorrelates,whileremainingagnosticaboutcausality,weconductatwo-stageexercise.Inthefirststage,weestimatefixed-effectspanelregressionsofthechangesinEWAPsonlaggedlevels,controllingforotherfactors,torecoverthelong-runsteady-stateEWAPforeachcountry.Inthesecondstage,weestimatelinearregressionsofthesesteady-stateEWAPsonpolicyvariablesthathavebeenshowntocorrelatewithemploymentgenerationintheliterature.Theanalysisisconductedatdifferentlevelsof

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aggregation:forthewholeeconomy,formenandwomenseparately,foragricultureandnon-agricultureseparatelyandforwomeninnon-agriculture.

2.2.1Firststage:Panelregression

Weestimateapanelregressionoftheyear-over-yearchangesinEWAPs.Themainpurposeofthisanalysisistoidentifythesteady-stateEWAPthateachcountryisconvergingtowards.Forthispurpose,wecontrolforlaborproductivitygrowthandworking-agepopulationgrowth,timeandcountryfixedeffects.

Thebaselinepanelregressionisasfollows:

EWAPc,t-EWAPc,t-1=α+β1∆Prodc,t+β2∆WAPc,t+β3EWAPc,t-1+dt+dc+εc,t(1)

wherethedependentvariableisthechangeincountryc’sEWAP(inpercentagepoints)betweentheyearst-1andt;∆Prodc,tiscountryc’soveralllaborproductivitygrowth(inpercent)fromt-1tot;∆WAPc,tiscountryc’sworking-agepopulationgrowth(inpercent)overthesameperiod;EWAPc,t-1iscountryc’sEWAP(inpercent)inyeart-1;yeardummies(dt)controlforcommonshocksovertime,suchasglobalrecessions;andcountryfixedeffects(dc)capturecountrycharacteristicsthatdonotchangeovertime.

Asnotedpreviously,weestimate(1)formeasuresofEWAPatdifferentlevelsofaggregation,consideringbothsectorandgender.Intheanalysisbygender,gender-specificworking-agepopulationgrowthisused.Intheanalysisbysector,twosectorsareconsidered:agricultureandnon-agriculture.Thelinearanalysisheredoesnotseparateindustryandservicesbecausetheliteraturehasdocumentedanonlinearrelationshipforindustry,acomplexitythatgoesbeyondthescopeoftheanalysishere(Herrendorf,Rogerson,andValentinyi2014;Rodrik2016;Timmer,deVries,anddeVries2015).

2.2.2Firststage:Countryfixedeffects

Asnotedearlier,ourspecificationsharesmuchincommonwiththeliteratureonconvergencepropertiesforGDPpercapita.

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Withoutcountryfixedeffects,thecoefficientβ3onlaggedEWAPcapturestheextentofunconditionalconvergence,thatis,thesteady-stateEWAPtowhichallcountriesconverge.Whencountryfixedeffectsareincludedasin(1),β3togetherwiththecountryfixedeffectsdccapturethepresenceandspeedofconditionalconvergence,whereeachcountryisallowedtoconvergetoadifferentsteady-stateEWAP.ThisparallelsthediscussionofconvergenceinGDPpercapitainBarroandSala-i-Martin(1992)andDurlauf,Johnson,andTemple(2005).

Intheliteratureonconvergenceinoutputperworker,thegrowthofoutputperworkerinacountryisrelatedtoitsdistancefromthesteady-statelevelofoutputperworker.Ifallcountrieshavethesamesteadystate,thenthis“unconditionalconvergence”canbedescribedbythefollowingexpression:

logyit+1-logyit=a+b(logyit-logy*),

2See,forexample,BarroandSala-i-Martin(1992),Kremer,Willis,andYou(2022),andPatel,Sandefur,andSubramanian(2021).

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whereyitiscountryi’soutputperworker,andy*isacommonsteadystatelevelforallcountries.Sincey*isaconstant,b×logy*canbecombinedintotheconstantterm.Thismodificationmotivatesrunningthefollowingregression:

logyit+1-logyit=c+b×logyit.

Inthe“conditionalconvergence”literature(forexample,BarroandSala-i-Martin1992;2003;Mankiw,Romer,andWeil1992),eachcountryihasitsownsteadystatevalueyi*.Theequationthatcapturestherelationshipbetweengrowthandsteadystatebecomes:

logyit+1-logyit=a+b(logyit-logyi*).

Thevalueb×logyi*isnowacountry-specificconstant,whichmotivatesthefixedeffectsregression:

logyit+1-logyit=ci+b×logyit,

wherethevariationinsteady-statelevelsacrosscountriesisembeddedinthefixedeffectsci.Thesefixedeffectsimplydifferencesingrowthratesconditionaloncurrentlevels,buttheyalsocapturedifferencesinthesteady-stateoutputperworkertowhicheachcountryisconverging.

Returningtoourregressionspecification(1),wecanapplythesameinterpretation.Specifically,thecountryfixedeffectsdcdividedbythecoefficientonthelaggedEWAPβ3arethedeviationofeachcountryc’ssteady-stateEWAPfromthesampleaverage,aftercontrollingforlaborproductivityandworking-agepopulationgrowth.

2.2.3Secondstage:Cross-countryregression

Inthesecondstage,weinvestigatehowthesecountryfixedeffectsarecorrelatedwithfeaturesoftheeconomicenvironmentandpoliciesthathavebeenassociatedwithhigheremploymentintheliterature.Specifically,weestimatethefollowingcross-countryregression:

dc=γXc+ηc(2)

whereXcisapolicyvariablethattheliteraturehasfoundtobecorrelatedwithfasteremploymentgrowth,andhencecouldbecorrelatedwithhighersteady-stateEWAPsinoursetup.Theregressionusestheaverageofeachpolicyvariableover2000–2019,capturingthelong-termaverageofthesevariables.Becausemanyofthepolicyvariablesarecorrelatedwitheachother,theregressionisrunseparatelyforeachvariable.Ourgoalwiththisexerciseissimplytodeterminewhichvariablesdisplaystatisticallysignificantcorrelationswithsteadystateemployment.Webelievethisinformationisausefulinputintofutureworkthatseekstoisolatethecausalfactorsbehindthedifferencesinsteady-stateemploymentlevels.

Thesepolicyvariablesserveasproxiesforfactorsrelatedtolabordemand(suchastrade,accesstofinance,andpoliciesthatdirectlyaffectfirms)andlaborsupply(suchaseducationandgender-biasedpolicies).Effectively,thissecond-stageregressionestimatesthelong-runcorrelatesofsteadystateEWAPs.

Alargebodyofresearchstudiestheeffectonlabormarketequilibriumofsuchfactorsaslabormarketpolicies,institutions(societalnorms,bothformal,suchaslaws,andinformal,suchastraditions),andregulationsonlabormarketequilibrium(DuvalandLoungani2019;McKenzie2017;NickellandLayard1999).Animportantdistinctionbetweenthesestudiesandourprocedureisthatthesestudiesfocusoncontemporaneousrelationshipsbetweenlabormarketpoliciesandlabormarketoutcomes,implicitlyassumingthatcurrentoutcomesreflectthesteady-stateeffectsoflabormarketpolicies.Ourapproachallowsfortherealitythattheearlystagesofthe

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developmentprocessinvolvealargereallocationofactivityfromtheagriculturalsectortothenon-agriculturalsector,andthatthenatureoflaborsupplyanddemandmayvaryacrosssectors.Individualslivinginruralareasmayhavedifferentlevelsoflaborsupplyacrosssectors,andfirmsindifferentsectorsmaydemanddifferenttypesofworkers.Ifthisisthecase,thenthedynamicsofstructuraltransformationwillpotentiallyinfluencethedynamicsofequilibriuminthelabormarket.

Itisalsoplausibletothinkthattheeffectofvariousinstitutionsandregulationshavedifferentialimpactsonboththedemandandsupplyoflaboracrosssectors.Regulationsthatdisproportionatelyaffectlargeestablishmentswilllikelyhaveverydifferenteffectsinagriculturalversusnon-agriculture.Policiesthatincreasethecostofcapitaloraccesstocreditwillhavedifferentialimpactacrosssectorsifsectorsdifferintheimportanceofcapitalorcredit.Theimplicationofthisobservationisthatthedynamicsoflabormarketequilibriummayvaryacrosscountrieswithdifferentlabormarketinstitutions.Moreover,theoverallimpactofaparticularinstitutionorregulationmaydifferalongthedevelopmentpath:institutionsthatnegativelyaffectlabordemandinthenon-agriculturalsectorwillhaveasmalleraggregateimpactinaneconomyinwhichalmosteveryoneworksinagriculture.

2.3Data

Thedatasetincludes160countriesover1960–2019.However,thebaselinesamplefocuseson103EMDEsthatarenotsmallstatesfor2000–19,aperiodinwhichthereisgooddatacoverageformostEMDEsandwhichexcludestheoutlieryearsduringtheCOVID-19pandemic.

ThemaindatasourcesincludetheWorldBank’sWorldDevelopmentIndicators(WDI)database,theInternationalLabourOrganization(ILO)’sILOSTATdatabase,andthePennWorldTables.

DataforemploymentcomesfromthePennWorldTables,andincludesbothformalandinformal(includingsubsistence)formsofwork.

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ThedataforbaselinerealoutputarefromtheWorldBank’sGlobalEconomicProspectsdatabase,supplementedwithdatafromWDIforearlieryears,splicedbysectorusingWDI’ssectoralgrossvalue-addeddata.Productivityiscalculatedastheratioofrealoutputtothenumberofworkers.

Totalandworking-agepopulationcomefromWDI.Theworking-a