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PolicyResearchWorkingPaper10794

MeasuringGreenJobs

ANewDatabaseforLatinAmericaandOtherRegions

HernanWinkler

VincenzoDiMaro

KellyMontoya

SergioOlivieri

EmmanuelVazquez

WORLDBANKGROUP

PovertyandEquityGlobalPracticeJune2024

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PolicyResearchWorkingPaper10794

Abstract

Agrowingbodyofliteratureinvestigatesthelabormarketimplicationsofscalingup“green”policies.Sincemostofthisliteratureisfocusedondevelopedeconomies,littleisknownaboutthelabormarketconsequencesfordevelopingcoun-tries.Thispapercontributestofillingthisgapbyprovidingnewstylizedfactsontheprevalenceofgreenoccupationsandsectorsacrosscountriesatvaryinglevelsofeconomicdevelopment.GreenoccupationsaredefinedusingtheOccupationalInformationNetwork,andgreensectorsarethosewithrelativelylowergreenhousegasemissionsperworker.Thepaperoffersaninitialassessmentofhowtheimplementationofgreenpolicies—aimedatexpandinggreensectorsandstrengtheningtherelativedemandforgreenskills—mayaffectworkersindevelopingeconomies.Itfindsthattheshareofgreenjobsisstronglycorrelated

withthelevelofgrossdomesticproductpercapitaacrosscountries.Whencontrollingforunobservedheterogeneity,a1percentincreaseingrossdomesticproductpercapitaisassociatedwith0.4and4.1percentagepointincreasesinthesharesofnewandemerging,andenhancedskillsgreenjobs,respectively.ThepaperthenfocusesonLatinAmericaandfindsthatonly9percentofworkershaveagreenjobwithrespecttobothoccupationandsector.Thefindingsshowthatwithincountries,workerswithlowlevelsofincomeandeducationaremorelikelytobeemployedinnon-greensectorsandoccupations,andtolacktheskillsforagreenereconomy.Thisevidencesuggeststhatcomple-mentarypoliciesareneededtomitigatethepotentialroleofgreenpoliciesinwideningincomeinequalitybetweenandwithincountries.

ThispaperisaproductofthePovertyandEquityGlobalPractice.ItispartofalargereffortbytheWorldBanktoprovideopenaccesstoitsresearchandmakeacontributiontodevelopmentpolicydiscussionsaroundtheworld.PolicyResearchWorkingPapersarealsopostedontheWebat

/prwp.Theauthorsmaybecontactedathwinkler@

.Averifiedreproducibilitypackageforthispaperisavailableat

,click

here

fordirectaccess.

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

ProducedbytheResearchSupportTeam

MeasuringGreenJobs:

ANewDatabaseforLatinAmericaandOtherRegions

HernanWinkler,VincenzoDiMaro,KellyMontoya,SergioOlivieri,EmmanuelVazquez

*

Keywords:GreenJobs,GreenSectors,ClimateChange,LaborMarkets,StructuralTransformation.JELCodes:Q5,Q52,Q56,J01,J21,

*DiMaro,Montoya,Olivieri,andWinklerarewiththeWorldBankPovertyandEquityGlobalPractice,VazqueziswithCEDLAS(IIE,FCE)-UniversidadNacionaldeLaPlata,Argentina.WearegratefultoGuillermoBeylis,MikiDoan,AlvaroGonzalez,RuthHill,NancyLozano,GhazalaMansuri,CarlosRodriguez-Castelan,TrangVanNguyenandHugoNopoforusefulcommentsanddiscussions.

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1.Introduction

Thereisagrowingbodyofliteratureinvestigatingthelabormarketconsequencesofscalingup“green”policies.

1

However,mostoftheevidenceisfocusedonthelabormarketimplicationsfordevelopedeconomies,andlittleisknownabouttheconsequencesofgreenergrowthonjobsfordevelopingcountries.

2

WhilerichcountriesaccountforadisproportionateshareofGreenhouseGasEmissions(GHGE;seeIPCC,2022),developingeconomiesareexpectedtoexperienceadisproportionateincreaseinGHGEinthefuture(Blacketal.,2022).Inaddition,policiestopromoteagreenergrowthstrategymayinduceaskill-biasedshiftinlabordemand,potentiallycreatingbottlenecksindevelopingcountrieswithlowerstocksandadaptabilityofskills.Thisshiftwouldraiseconcernsaboutthetransitionpathofdisplacedworkerstonewjobsinthegreeneconomy.

Thispapercontributestothisliteraturebydevelopinganewdatasetongreenoccupationsfor120countries.Itdescribesnewstatisticsongreenoccupationsacrosscountriesatdifferentlevelsofeconomicdevelopment.Thisoffersaninitialpictureofhowtheavailability(orscarcity)ofgreenoccupationscanaffecttheimplementationofgreenpoliciesandtheirimpactonthelabormarket.ThepaperthenfocusesonLatinAmericaandCaribbean(LAC)countriestoprovidemoregranularinsightsintotherelationshipbetweengreenoccupations,greensectors,andthesocio-economicprofileofgreenjobholders.

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Greenoccupationsaremeasuredfollowingthe“GreenEconomy”programdevelopedbytheOccupationalInformationNetwork(O*NET).

4

Itconsidersthreetypesofoccupations:newoccupationsthatareemergingdirectlybecauseofgreenpolicies(“newandemerging”),existingoccupationsthatareexpectedtoexperiencechangesinthetaskstheyentail(“enhancedskills”),andexistingoccupationsthatwillnotchangesubstantiallybutthatwillbeinhigherdemand(“increaseddemand”).AmethodologicalcontributionofthispaperistodevelopamappingoftheO*NETclassificationofgreenoccupationsintothe4-digitInternationalStandardClassificationofOccupations(ISCO)-08taxonomy.

Usingdetailedoccupationaltabulations(2-digitISCO-08)fromlaborforcesurveysfor120countriesfrom

2011to2020,

5

thepaperfindsthat,onaverage,21.4percentofemploymentisingreenoccupations,and

1Abroaddefinitionofgreenpoliciesincludesallpoliciestoremovebarrierstogreen,clean,andresilientgrowth(seeAwe,2012).

2Someexceptionsincludethe

2011UNEPILOGreenJobsreport.

Morerecently,seethe

2018ILOGreeningwithJobsreport

and

2021K4DFDCOCreatingGreenJobsinDevelopingCountriesreport.

SeealsoTimilsina(2022)foradiscussionofrelatedissues.

3SeealsoAlfonsoetal.(2022)forarecentanalysisofgreenjobsinLAC.

4O*NETisadatabasedevelopedbytheU.S.DepartmentofLaboronoccupationalinformationfortheU.S.workforce(see

/overview.html)

.

5Dataisavailableforeachyearintheperiod2012-2019forasetof47countries.

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that1.8,10.3,and9.3percentarethesharesofemploymentin“newandemerging,”“enhancedskills”and“increaseddemand”categories,respectively.Thepapertheninvestigatesthelinksbetweengreenjobsandincomebetweencountries,tounderstandifthelabormarketadjustmenttoagreenereconomyisexpectedtobemoredemandinginpoorercountries.Whencomparingcountriesinthelatestyearofavailabledata,theresultsshowthattheshareofgreenjobsis,infact,lowerinpoorercountries,buttherearesubstantialdifferencesacrosstypesofgreenjobs.Theassociationwithincomelevelsisverystrongfor“newandemerging”and“enhancedskills”occupations,butweakerfor“increaseddemand”ones.Afixed-effectsregressionconfirmsthesestrongassociations.

Thesecondpartofthepaperasksthefollowingquestion:ifcountriesweretoimplementpoliciesthatpenalizeGHGE(suchasacarbontax,acarbonborderadjustment,orbanningcertainformsofenergyproduction),whichjobswouldbemoreatrisk?Toanswerthisquestion,thepaperestimatestheoccupationalstructureofsectorsofeconomicactivityaccordingtotheirGHGElevelsperworker.

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Thenitclassifiessectorswithineachcountryasgreenandnon-greenaccordingtowhethertheyareaboveorbelowthesectormedianGHGElevelineachcountry.Itisimportanttoemphasizethatthisdefinitionof“greenness”doesnotincludeotherimportantdimensionssuchasthepotentialfortransformationthroughtheadoptionofgreentechnologies.Combiningthegreenjobandthesectorclassification,fourgroupsofworkersaredefined:(i)Greenoccupationsingreensectors(GOGS);(ii)Greenoccupationsinnon-greensectors(GONS);(iii)Non-greenoccupationsingreensectors(NOGS),and(iv)Non-greenoccupationsinnon-greensectors(NONS).Greensectorswouldbelessvulnerabletoanincreaseinthecostofcarbon.Asaresult,workersingreenoccupationsinsuchsectorswouldfacethelowestriskofdisplacementandhighestre-employmentlikelihoodinagreenereconomyincaseofjobloss.Incontrast,workerswithnon-greenoccupationsinnon-greensectorswouldfacethehighestlabormarketvulnerabilityintermsofbothjob-lossriskandre-employmentopportunities.

UsingdatafromcountriesinLatinAmericaandtheCaribbean(LAC)—becauseofbetteravailabilityofgranularoccupationdataattheISCO082-digitlevel—weanalyzetherelationshipbetweengreenoccupationsandsectors.Thefirstfindingisthattheconcentrationofemploymentinnon-greenoccupationsandnon-greensectors–i.e.themostvulnerablegroupduringagreentransition–is

6ThefocusonGHGEimpliesthatthepaperfocusesonmitigation,ratherthanadaptation,indefininggreensectors.Wenoteherethatthisisnottheonlywaygreensectorscanbedefined.Forinstance,analternativedefinitionofsectorsthatconsidersmoreprominentlytheadaptationanglewoulddefinegreensectorsnotonlybasedonthelevelofemissions,butalsoonthepotentialtocreategreenjobsand/orfostergreentechnologiesinthelongerrun.Examplesofsectorsthatwouldbeaffectedbythisalternativedefinitionincludewatermanagementandwastemanagement.

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significantinseveralLACcountries.Around90percentofworkersineachcountrywithavailabledatahaveajobineitheranon-greensectororanon-greenoccupation.Non-greensectorsaccountforalargeshareofjobs,from37percentinArgentinato59percentinGuatemala,Bolivia,andHonduras.Mostworkerswithgreenoccupationsareinanon-greensector(about60percentoftheminmostcountries),whichsuggeststhathigh-emissionsectorscouldfindsomeoftheneededskillsforthetransitionalreadyinthelaborforcetheyoccupy.Itisimportanttohighlighttheveryhighconcentrationofnon-greenoccupationsinahigh-emissionsectorsuchasagriculture—about90percentormoreinmostLACcountries.

Thedynamicsofgreenoccupationsandsectorsalsoofferimportantinsights.Theshareofgreenoccupationshasbeenremarkablyconstantoverthelastdecade(i.e.between2010and2019)inLAC.Theshareofnon-greenjobsinnon-greensectorsandoccupationsfellslightlyfrom35to34percentduringthatperiod.Inaddition,weimplementadecompositionofchangesinGHGEperworkerinbetween-andwithin-sectorcomponents.GHGEperworkerincreasedbetween2005and2018insevenlargeLACcountries,mostlydrivenbyanincreaseinGHGEperworkerwithinsectors.Incontrast,thereallocationofworkersfromrelativelyhighGHGEsectors—i.e.agricultureandindustry—torelativelylowGHGEsectors—e.g.utilities,transport,andInformationandCommunicationTechnologies(ICT)—contributedtopartiallymitigatetheincreasedrivenbythewithin-sectorchanges.Thisdecompositionillustratessomeoftheimplicationsoftheprocessofstructuraltransformationonthegreeneconomy,wherebyincreasingenergyintensitytendstomaketheeconomylessgreen,butthereallocationofworkersawayfromagricultureandmanufacturingtowardsservicestendsto“green”theeconomy(assumingeverythingelseremainsthesame).

Thepaperalsoinvestigatestheprofileofgreenjobholders.Overall,greenoccupationsaremoreprevalentformalesandresidentsofurbanareas.Totalemployment(includinggreenandnon-greenoccupations)ingreensectorsishigherforfemalesandmoreeducatedworkers.Greenoccupationalsharesdonotvarymuchbyinformalitystatusorsizeoftheemployerfirm.Still,alargeshareofinformalworkersisconcentratedinthenon-greenoccupationandsectorgroup,whichcanbeconsideredthemostvulnerableduringatransition.Likewise,poorerworkers(thoseinthelowestincomequintiles)areconcentratedinthismostvulnerablegroup.Thisisconsistentwiththefactthatgreenjobsarelinkedtohigherwages.

Therestofthispaperisstructuredasfollows.Section2discussesthedefinitionofgreenjobs,themethodologytogeneratethecategoriesofgreenjobsandsectorsconsideredinthispaperanddescribesthedata.Section3presentscross-countrypatternsofgreenjobs,patternsofgreenoccupationsand

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sectors,andtheprofileofgreenjobholdersinLAC.Section4discussessomecaveatsofthestudyandpresentssomerobustnesschecks.Finally,section5drawssomeconclusions.

2.MethodologyandData

Greenoccupations

Therearedifferentdefinitionsofgreenjobs.ThispaperusestheOccupationalInformationNetwork(O*NET)

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classificationofoccupationsdevelopedbyDierdorffetal.(2009,2011)todefinegreenandnon-greenjobs.

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Dierdorffetal.(2009)arguethatthefirststeptoclassifyingjobsasgreenornon-greenistodefinethegreeneconomy,whichtheyconsiderinvolvingeconomicactivitiesrelatedtoreducingtheuseoffossilfuels,decreasingpollutionandGHGE,improvingenergyefficiency,increasingrecyclingandadoptingrenewableformsofenergy.Consideringthis,the“greening”ofoccupationstakesplacewhenthegrowthofthegreeneconomyincreasesthedemandforexistingoccupations,shapestheworkandworkerrequirementsneededforoccupationalperformance,orgeneratesuniqueworkandworkerrequirements.

TheclassificationisbasedontheUS2010StandardOccupationalClassification(SOC)System.Itprovidesalistof1,110occupations,204ofwhichareidentifiedasgreenanddividedintothreemutuallyexclusivecategories:GreenIncreasedDemand(64occupations),GreenEnhancedSkills(62occupations),andGreenNewandEmerging(78occupations).Table1showsthedefinitionofeachgroupofoccupationswithexamplesofjobsfallingintoeachcategory.Therestofthelistedoccupations(906)areconsiderednon-greenandrepresentjobsthatarenotaffecteddirectlybythegreeningoftheeconomy.

7SeealsotheAppendixformoredetailedinformationonthedatasourcesusedinthispaper.

8Incontrast,ILO(2018)definesgreenjobsbasedonwhether“theyreducetheconsumptionofenergyandrawmaterials,limitGHGE,minimizewasteandpollution,protect,andrestoreecosystemsandenableenterprisesandcommunitiestoadapttoclimatechange.Inaddition,greenjobshavetobedecent”.Decentjobsaredefinedas“opportunitiesforwomenandmentoobtaindecentandproductiveworkinconditionsoffreedom,equity,securityandhumandignity”(ILO,1999).However,thisdefinitioncombinesseveraldimensionssuchasthetasksinvolvedinthejob,the“greenness”oftheeconomicsectororfirm,aswellascharacteristicsofthejobnotclearlylinkedtothegreeneconomy(forexample,socialsecuritybenefitsorwagelevels).ThispaperdoesnotfollowILO’sapproachbecauseitsgoalistoinvestigatetheimplicationsofthegreeningoftheeconomy(basedonthe“greenness”ofsectorsofeconomicactivity)onworkersaccordingtotheirgreenskillsortasks.Insteadofusingthe“decency”ofajobtocharacterizeitasgreenornon-green,thispaperexploresthecharacteristicsofjobsrelatedtosuchdimensions(e.g.,wagelevels,informalstatus,etc.)accordingtotheirvulnerabilitytothegreeningoftheeconomy(i.e.,bywhethertheyareinsectorswithahighlevelofemissionsand/orhavethegreenskillsneededintheneweconomy).

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Table1–Greenoccupationcategories

Greenjobcategory

Definition

Examples

IncreasedDemandOccupations

Occupationsthatalreadyexist.Theymayfaceincreaseddemandwithanexpandinggreeneconomy.Whiletheirworkcontextmaychange,thetasksandskillstheyusewillnot.

Carpentersandweldersthatwillberequiredintheconstructionofnewenergy-efficientbuildingsoragriculturalworkersneededformoreorganicfarming.

EnhancedSkillsOccupations

Occupationsthatalreadyexist.Agrowinggreeneconomymayormaynotincreasethedemandfortheseoccupations,butitwouldaffectthetypeoftasksorskillsrequired.

Constructioninspectors,agriculturaltechnicians,andarchitects.Workersinthesejobsmayneedtolearnhowtousenewmaterialsoradheretonewenergy-efficientbuildingcodes.

NewandEmergingOccupations

The“purest”formsofgreenjobs.Theseoccupationsemergebecauseofthegreeneconomycreatingtheneedfornewanduniqueworkandworkerrequirements.Assuch,theseoccupationsemergedmorerecently.

Windenergymanagers,climatechangeanalysts,orwaterresourcespecialists.

Source:OwnelaborationbasedonO*NETdatabaseandDierdorffetal.(2009,2011).

ThispaperusestheInternationalStandardClassificationofOccupations(ISCO)oritsadaptationstoclassifyoccupations,insteadofO*NET.PreviousstudieshavemappedtheO*NET-SOC2010classificationofgreenoccupationsintothelatestISCO-08taxonomytoallowformorestraightforwardcross-nationalcomparisonsofgreenoccupations(Hogarth,2011;SofroniouandAnderson,2021).However,theirmappingsareproducedattheISCOthree-digitlevelatbest.Moreimportantly,theirmethodologiesarebasedonadichotomicdefinitionofgreenjobs,inwhichawholeISCOcategoryisconsideredeithergreenornon-green.ThispapermapstheO*NET-SOCclassificationofgreenoccupationsintothemostdetailedfour-digitISCO-08taxonomyusingaprobabilisticinsteadofadichotomicapproach.Specifically,thepaperusesdataontotalemploymentatthe2-digitlevelISCOfor120countriesfromcirca2001tocirca2020(676country-yearobservations).SinceeachISCOcodemayhaveseveralO*NET-SOCoccupationsassociatedwithit(notallnecessarilygreen),itwouldbemisleadingtoconsiderawholeISCOoccupationasgreenornon-greenbasedonwhetherthenumberofO*NET-SOCassociatedoccupationsareprimarilygreenornon-green.Therefore,insteadofconstructingabinaryclassification,thepaperestimatestheshareofemploymentwithinanISCO-08categorythatcorrespondstoO*NET-SOCoccupationsconsideredgreenandthenconsidersthisshareastheprobabilitythatanoccupationwiththatISCOcodeisgreen.SincetheO*NET-SOCclassificationisbasedontheUSstructureofemployment,employmentsharesare

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basedonU.S.estimates,whichareobtainedfromthe2018OccupationalEmploymentandWageStatistics(OEWS)publishedbytheU.S.BureauofLaborStatistics.

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TheO*NET-SOC2010codesarelinkedtotheircorrespondingfour-digitISCO-08codesusingtheU.S.BureauofLaborStatisticscorrespondencetablebetweenSOC2010CodesandISCO-08categories.Thiscorrespondence,aswellastheemploymentestimatesintheOEWS,isavailableatthesix-digitleveloftheSOC.Atthesametime,theO*NET-SOC2010classificationofgreenoccupationsispresentedwithaneight-digitlevelofdetail.Since12.7percentofthe6-digitSOCoccupationsareassociatedwithmorethanone8-digitSOC,thetotalnumberofworkersina6-digitSOCoccupationwasequallydistributedamongthe8-digitSOCcodesassociatedwithit.

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Asanexample,Table2showshowtheshareofgreenoccupationswasconstructedforaspecificISCOcode,representingtheprobabilitythatanindividualwithanoccupationinthisISCOcodehasagreenjob.

Table2–ExampleofthemethodologyusedtoestimatetheprobabilityforanISCO-08codeto

representagreenoccupation.

ISCO-08Code

O*NET-SOC2010Code

GreenJob?

2010SOCCode

NumberofUSworkers

“Simulated”

NumberofUS

workers

ShareofGreenoccupations

1234

17-2061.01

Yes

17-2061

10

5

5%

=5/(5+5+40+50)

1234

17-2061.02

No

17-2061

5

1234

17-2063.00

No

17-2063

40

40

1234

17-2064.00

No

17-2064

50

50

Source:OwnelaborationbasedonO*NETdatabase(GreenOccupations&O-NET-SOC2010OccupationList),U.S.Bureauof

LaborStatisticscorrespondencebetweenSOC2010CodesandISCO-08categories,and2018OccupationalEmploymentand

WageStatistics.

9Estimatesfor2018areusedsinceitisthemostrecentyearinwhichsurveydatawascollectedusingthe2010SOCclassification.Afterthisyear,estimatesuseahybridofthe2010and2018SOCsystems,whichmakesthelinkwiththeO*NET-SOC2010classificationofgreenjobslessdirect.Whileemploymentestimatesfromthisprogramarenotperfectsincetheyexcludeself-employment,theyaretheonlysourceofpubliclyavailabledatawithinformationatthe6-digitleveloftheSOC.

10Thisassumptionhadtobeusedinothercasesinwhichmoreinformationwasnotavailable:1)forsixSOCoccupationsatsix-digitsforwhichU.S.employmentdatawasonlyavailableatfivedigits(inthesecasesemploymentat5-digitswasequallydistributedamongtheSOCoccupationsatsix-digitscorrespondingtothe5-digitcode);2)toestimateemploymentinsomeofthesixcasesinwhichtheSOCclassificationfromO*NETandOEWSweredifferent:SOC211018inOEWS(SubstanceAbuse,BehavioralDisorder,andMentalHealthCounselors)wasequallydistributedbetweenSOC211011(SubstanceAbuseandBehavioralDisorderCounselors)and211014(MentalHealthCounselors)fromO*NET;SOC512028inOEWS(Electrical,Electronic,andElectromechanicalAssemblers,ExceptCoilWinders,Tapers,andFinishers)wasequallydistributedbetweenSOC512022and

512023fromO*NET(ElectricalandElectronicEquipmentAssemblers+ElectromechanicalEquipmentAssemblers);SOC512098inOEWS(AssemblersandFabricators,AllOther,IncludingTeamAssemblers)wasequallydistributedbetweenSOC512092and

512099fromO*NET(TeamAssemblers+AssemblersandFabricators,AllOther).

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Greensectors

Asmentionedabove,thispaperusesthelevelofGHGEperworkertoclassifysectorsintogreenandnon-

greencategories.TocalculatethelevelofGHGEbysector,thispaperfollowsthemethodologyproposedbyAlcantara(2007),Alcantaraetal.(2010),andBinSuetal.(2013).Thisisbecausetoaccountforthetrue‘greenness”ofasector,weshouldconsiderboththedirectandindirectGHGE.Forexample,whilethefinancialsectormayhavelowlevelsofdirectGHGEperworker,itstotalGHGEmaybesubstantiallyhigheronceweconsideritslinkswith,forexample,theutilitiesortransportationsectors.ThemethodologyappliestheInput-OutputmodelproposedbyLeontief,W.(1986)toavectorofemissionsmultiplier.TheoriginalLeontiefmodeltocalculategrossproductionhasthefollowingstructure:

Y=[I—A]−1f

WhereYcorrespondstoacolumnvectorcontainingthelevelofgrossproductionbyeconomicsector,Iistheidentitymatrixoforderncorrespondingtothen-sectors,Aisannxnmatrixcontainingthetechnologycoefficientsforeachincludedsector,andfisacolumnvectorcorrespondingtothefinaldemandforeacheconomicsector.

Anemissionmultipliermatrixisincorporatedintothepreviousequationtocalculatethelevelofemissionsbysector.Thismatrixisdiagonalandcontainsthenumberofemissionsperproductionunitforeachsector.Thenewequationforcalculatingemissionsbysectorhasthefollowingstructure:

C=C^[I—A]−1f

WhereCcorrespondstoacolumnvectorcontainingthelevelofemissionsbyeconomicsector,andC^isadiagonalmatrixcontainingtheemissionsmultipliersforallsectors.

Thelevelofemissionsperworkerbysectorisestimatedasthelevelofemissionsdividedbythenumberofworkersaged15to64yearsineachsector.Inthissection,threeprimarysourcesofinformationareimplementedtoestimateGHGEperworkerbysector:theSocioeconomicDatabaseforLatinAmericaandtheCaribbean(SEDLAC),

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the2018editionoftheOrganizationforEconomicCo-operationandDevelopment(OECD)country-levelInput-OutputTables(IOTs),

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andtheClimateWatch(CAIT)Historical

11SEDLACisadatabaseofharmonizedsocio-economicstatisticsconstructedfromtheLatinAmericanandCaribbean(LAC)householdsurveys.SEDLACincludesinformationfromover300householdsurveyscarriedoutprimarilyin18LACcountriesforwhichacomparableincomeaggregate(forwelfareanalysis)canbecreated:Argentina,Bolivia,Brazil,Colombia,CostaRica,Chile,DominicanRepublic,Ecuador,ElSalvador,Guatemala,Haiti,Honduras,Mexico,Nicaragua,Panama,Paraguay,Peru,andUruguay.TheSEDLACdatabaseandprojectwerejointlydevelopedandarejointlymaintainedbyCEDLAS(UniversidadNacionaldeLaPlata)andTheWorldBank’sLACTeamforStatisticalDevelopment(LACTSD)inthePovertyandEquityGlobalPractice.

12Availablein

/Index.aspx?DataSetCode=IOTSI4_2018.

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GHGE.

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TheOECDIOTscontaininformationabouthoweachsector’sgrossproductionisdistributedamongalleconomicsectors(intermediatedemand)andthefinaldemand.Thelattercompriseshouseholds’domesticdemand,non-profitinstitutions’domesticdemand,government’sdomesticdemand,capitalformation,changeininventories,exports,andimports.Thesectorcl