Policy Research Working Paper 8135

Global Inequality in a More Educated WorldSyud Amer AhmedMaurizio Bussolo

Marcio CruzDelfin S. Go

Israel Osorio-Rodarte

Development Economics Development Prospects GroupJune 2017

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Produced by the Research Support Team

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy Research Working Paper 8135

This paper is a product of the Development Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sahmed20@worldbank.org, mbussolo@worldbank.org; marciocruz@worldbank.org; dgo@worldbank.org; and iosoriorodarte@worldbank.org.

In developing countries, younger and better-educated cohorts are entering the workforce. This developing world-led education wave is altering the skill composition of the global labor supply, and impacting income distribution, at the national and global levels. This paper analyzes how this education wave reshapes global inequality over the long run using a general-equilibrium macro-micro simulation framework that covers harmonized household surveys

representing almost 90 percent of the world population. The findings under alternative assumptions suggest that global income inequality will likely decrease by 2030. This increasing educated labor force will contribute to the clos-ing of the gap in average incomes between developing and high income countries. The forthcoming education wave would also minimize, mainly for developing countries, potential further increases of within-country inequality.

GlobalInequalityinaMoreEducatedWorld1Syud AmerAhmed,MaurizioBussolo,MarcioCruz,DelfinS.Go,andIsraelOsorio-Rodarte2

JELClassification:D31,J11,J31,E24KEYWORDS:GlobalInequality,Education,Demographictrends,Structuralchange1We thankChristinaCalvo,FranciscoFerreira,AyhanKose,MarylaMaliszewska,HansTimmer,PhilipSchellekens, JosVerbeek,andparticipantsofseminarsorganizedbytheWorldBank’sEquityandPublicPolicyPracticeGroup,DevelopmentProspects Group, Graduate Program in Economics at the Federal University of Parana, Brazil, and the 17th AnnualConferenceonGlobalEconomicAnalysisinDakar,Senegal,fortheirusefulcomments.KyunChang,NathanielRussel,andIvanTorreprovidedvaluableresearchassistance.WealsothankthesupportfromtheKnowledgeforChangePartnershipmulti-donortrustfund.Theviewsexpressedinthispaperaretheauthors’only.2Authors’contacts:sahmed20@worldbank.org,mbussolo@worldbank.org;marciocruz@worldbank.org;dgo@worldbank.org;andiosoriorodarte@worldbank.org.

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1. IntroductionThe global labor market is undergoing a fundamental transformation with importantconsequencesonglobalinequality.Inthedecadesahead,youngerandbetter-educatedcohortswill enter the globalworkforcewhile older, less educated ones leave.With better education,workershavebetterskills(hereafter,skilledworkersinthispaperrefertothosehavingnineormoreyearsofeducation,andthetwoterms,skilledworkersandbetter-educatedworkers,areused synonymously.) The new entrants of better-educated workers will come mainly fromdevelopingcountries(figure1).Thispaperanalyzestheeffectsofthisforthcomingdemographicandeducationtransitiononglobalinequalityusingageneral-equilibriummacro-microsimulationframeworkthatcoversharmonizedhouseholdsurveysforalmost120developingcountries.Ourfindingssuggest thatamoreeducated labor force indevelopingcountrieswillcontributetoareductioninglobalincomeinequalityby2030.Figure1Futuresourcesofeducatedandskilledworking-agepopulation,newentrantsw.r.t2015

Source:Authors’projections.Notes: Skilled isdefinedasworkerswithmore thannineyearsof education.Projections arebasedonUN (2015) andeducationinformationfromharmonizedhouseholdandlaborsurveysfrom117countries.Theimpendingchangeintheskillcompositionoflaboristhelatestwaveofdemographicandeducationtrendsthathavebeenshapingthegloballabormarket.Inthefirstrecentwave,atrulygloballabormarkettookshapealmostallatonceinthe1990s,whenChina,India,andtheformerSovietblocjoinedtheglobaleconomy,increasingthesizeofthelaborpoolfrom1.46billionworkers to2.93billionworkers.This “greatdoubling”ofglobal labor, asFreeman(2008and2007)calledit,camefromnewentrantswhowerelowskilledandlowwage.Theimpactofthisgreatdoublingon global inequality is not immediately clear, as it dependson changes in thedispersionofincomeswithinaswellasbetweencountries.FreemanarguedthatChina,IndiaandtheformerSovietblocwouldnotcontributemuchtotheglobalcapitalstock,andthereforethe

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capital-laborratioatthegloballevelwouldbegreatlyreducedbyabout60%ofwhatitwouldhavebeenwithoutthegreatdoubling.Thisfactorratiowouldcauseanincreaseinthereturnstocapitalvis-à-vis towagesand,sincephysicaland financialassetsaremoreconcentrated thanhumancapital,itwouldtendtopushinequalityup.3Yet,othermechanismsalsoinfluencetheresults,sothat global inequality fell over the firstwave despite the increase in inequalitywithinmanycountries(WorldBank2006and2007,LaknerandMilanovic2016).Thekeyfactsaboutthesecondwaveofupcomingchangesinthegloballabormarketarealsostriking:thistime,awaveofskilledworkersfromdevelopingcountrieswilltakecenterstage.On current trends, based on UN population projections (UN 2015) and present rates ofeducational enrollment (conservatively kept constant into the future), theworldwill see thenumberofskilledworkersrisingfrom1.66billionin2011to2.22billionby2050,anincreaseofabout560millionor33percent. As in thecaseof thegreatdoubling, the roleofdevelopingcountries is crucial. Due to their investments in education and their growing populations,developingcountrieswillcontributealloftheadditionalworkerstotheworldpoolofeducatedworkers(seefigure1).Thenumberofskilledworkersinhigh-incomecountriesisprojectedtodecline,from603millionin2011to601millionin2030and594millionin2050.Notexactlyanothergreatdoubling,butstilladramaticchange.Whilein2011,eachskilledworker in high-income countrieswas sharing the globalmarketwith two skilledworkers indevelopingcountries;by2030thisratiowillbeonetothree.Theincreaseinthesupplyofskilledworkerswill likelydrivedowntheeducationpremiatheseworkersenjoy(otherthingsbeingequal),andunlikethefirstwave,itmayaffectinequalitywithincountriesinabeneficialway.Thiskindofresulthas,forexample,alreadybeenobservedindevelopingcountriesinLatinAmericaandtheCaribbean(CruzandMilet,2017andLopez-CalvaandLustig,2010).Notethat,becauseoftradelinks,wagesofskilledworkersinhigh-incomecountrieswillalsocomeunderpressureevenif their domestic supplywill not be increasing. Signs of this pressure have also been alreadyidentifiedandattributedtotradebyrecentresearch(Autoretal.2016).Animportantfactormay,however,haveacounteractingrole.Asinthepast,technologicalprogressmaybeskill-biasedandthusoffsetthesupply(education)effect.Researchhasshownthat inhigh-incomecountries like theUnitedStates, thepremia forskilledworkersremainedfirmly high even during expansions of the skilled labor supply (Acemoglu 1998 and 2009).Conditionalonwhatwillhappenonthedemandforlabor(i.e.onwhathappenstotechnologicalchange), the possible decrease in skilled and sectoral earnings premia may have profoundconsequences for: i) international trade patterns; ii) global and local economic growth; iii)inequalityacrosscountries;and,asmentionediv)inequalitywithincountries(seeEdwardsandHertel-Fernandez2010;OECD1999;Shimer2001;Freeman2007). 3Indeed,severalauthors(e.g.Atkinson2015,Bourguignon2015,Galbraith2012,Piketty2014,andStiglitz2012)havecalledattentiontotherecentissueofrisingcapitalshareintotalvalueaddedandtotheconcentrationofincomeandwealthatthetopofthedistributioninmanycountries.

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Our analysis focuses on forces of the educational wave that shape future supply anddemandinthelabormarketsandtheirensuingeffectsonglobalincomedistribution.Onthesupplyside,itconsidersdemographicshifts,improvementineducationachievements,andpoliciesthatincreaseaccesstoeducationandenableinter-sectoralmobility.Onthedemandside,itaccountsfortechnologicalchange,sectoralpatternsofgrowth,andtrade.Itthendrawstheeffectsoftheseforcesonglobalinequalityby2030,whichisthetargetyearoftheSustainableDevelopmentGoals(SDGs) as well as the World Bank’s goals of ending extreme poverty and boosting sharedprosperity.4Becauseitusuallytakeslongerthan15yearsforthestockofskilledworkerstoshowsignificantimprovementfromthenewinflowofyoungerandmoreeducatedworkersreachingthelabormarket,thetimehorizonto2030willpresentonlyapartialeffectoftheeducationwave,henceaveryconservativescenario.This paper uses twowell-tested global economicmodels, the LINKAGEglobal generalequilibrium model and the Global Income Distribution Dynamics (GIDD) microsimulationframework.Assuch,theapproachallowsforasystematicquantitativeanalysisoftheglobaleffectsofeducationalattainmentanddemographictrends,whichhithertohavebeenrelativelyneglected.Forexample,Ahmedetal.(2016)looksonlyatthedemographicdividendoftheAfricaregion.Inlookingattheeffectofchangesinskillpremiaandsectormobilityoflaboronincomeinequality,theapproachrelaxestheassumptionofdistribution-neutralgrowthfoundinotherstudies(e.g.Ravallion2013)toanalyzeglobalpoverty.Likethefirstwave,ourresultssuggestthatthenextwaveofeducationanddemographictrendswillalsoreduceglobalinequalitybetweencountries.Moreover,thesecondwavemayamelioratetheoverallincreaseininequalitywithincountries.Indeed,thebaselinescenariosuggestsadecreasetheGiniindexformostcountriesrelativetothecaseofnoeducationwave.5 Theremainderofthispaperisorganizedasfollows:Section2presentsobservedpatternsofglobalinequalityinthelastdecadesandexplainshowtheeducationwavemayimpactinequalitythroughthelabormarkets.Section3presentsthemethodology,particularlytheGIDDmodelingframeworkanddescriptivestatisticsof theunderlyingmicrodata.Section4showssimulationresultsalongwithrobustnesschecks.Thelastsectionconcludeswithpolicyimplications.2. Recentchangesinglobalinequalityandlaborforcecomposition2.1EvolutionofglobalinequalityToanalizetheevolutionofinequalityacrosstheworld,Milanovic(2013)suggeststhreeconceptsof global inequality: 1) inequality across countries based on averages for GDP per capita or

4 ThetopicofthispaperisdirectlyrelatedSDG10(Reduceinequalitywithinandamongcountries).5 These results, particularly within country, face several uncertainties regarding the possibility of disruptive technological changes related to automation and jobs’ polarization (Autor, 2015).

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householdincome;2)inequalityacrosscountriesbasedonpopulation-weightedGDPpercapitaorhouseholdincome;and3)globalinequalityusinghousehold-leveldatafromsurveys.Thefirsttwoconceptsrefertoinequalitybetweencountries.Relativetoconcept1,inequalityasmeasuredbyconcept2reflectstheweightofrelativepopulationsizeinadditiontodiscrepanciesinaverageincomes. Thewithin-countrycomponentofinequalityisnonethelesscritical.It ispossible,forinstance,thatalow-incomecountrycouldcatchuponaveragepercapitaincomeattheexpenseofrisinginequalityamongitsresidents.Thiseffectwillneitherbecapturedbyconcept1orconcept2.Amongthethreeconceptsofglobalinequality,theindividual-basedconcept3istheonlyonethattakesintoaccountinequalitybetweenaswellaswithincountries.Itrequires,however,alargeamountofharmonizedmicrodatatoperformanyattemptforinternationalcomparison.Inthispaper,weanalyzeglobalinequalityusingtheindividual-based(concept3)approach.From1950to2000s,inequalitybetweencountriesusingconcept1increased,whileinequalityusingconcept2decreased.However,duringthelastdecade,bothconceptsshowa dramatic reduction in inequality (Milanovic 2013). The individual-based inequality(concept3)alsoshowedadecreasingtrendbetween2000and2010(seeMilanovic2013;Lakner and Milanovic 2016), but at a slightly smaller reduction when compared to thepatternofconcepts1and2.Sinceconcepts1and2areaboutbetween-countryinequality,theindividual-basedresultsomewhatsuggeststhatwithin-countrycomponentofinequalitymayhaveincreasedinthisperiod.LaknerandMilanovic(2016)showthat(individual-based)globalinequality,asmeasuredby the Gini index, dropped from 72.2 in 1988 to 70.5 in 2008. This recent decline in globalinequalitycanbelargelyexplainedbytheeconomicprogressduringthelastdecadesinlow-andmiddle-incomecountries,particularlybythesustainedgrowthofpopulouscountrieslikeChinaand India.6 Increased tradebenefited low-skilledworkers inChina, Indiaandpartly from theSovietbloc.Demandforthegoodstheyproducedwentup,andsodidtheirwages.Theoppositehappened for low-skilled workers in high-income countries. Also, increased global economicintegration accelerated diffusion and adoption of technology, which in turn supported theeconomic boom in developing countries before the recent Great Recession.7 The resultingeconomicprosperityreducedthegapsinincomepercapitabetweenhigh-incomeanddevelopingcountries,whichwasthemaindriverofthereductioninglobalinequality.Notwithstandingthisrecentreduction,theworldasawholeremainsveryunequal.Infact,iftheworldwereacountryitwouldhaveahighestGiniin2008.Also,globalconvergencehasbeenaccompaniedwithincreasingnationaldivergences.LaknerandMilanovic(2016)estimatesthattheGiniindiceshaveincreasedformanycountriesandregionsfromabout1988to2008:38.2to41.9%formatureeconomies;32.0to42.7%forChina;31.1to33.1%forIndia;and53.5to58.3 6WorldBank(2011)andBussoloetal(2012).7See,forexamples,WorldBank(2011)forthemultipolarityofglobalgrowthandArbacheetal.(2010)forthecaseofAfrica.

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forSub-SaharanAfrica.Anotherstudy,Alvaredoetal.(2013),findsthattheshareofincomeheldbythetop1%intheUSArosedramatically.2.2Theengineoftheeducationwave:EducationanddemographictransitionsbyregionLookingforward,whatdoesanincreaseofskilledlaborsupplydrivenbydevelopingcountriesmean for global inequality? The first step in the analysis of the impact of the forthcomingeducationwaveistoassessthegeographicallydiverseincreasesinthesupplyofskilledworkers.These increases depend on two key factors: the intergenerational education gap – i.e. thedifferenceineducationattainmentsoftheoldcohortsthatareexitingthelabormarketvis-à-vistheyoungenteringcohorts–andtheintergenerationaldemographicgap–i.e.thedifferenceinpopulationsizebetweentheyoungandtheoldcohorts.Largeintergenerationaleducationanddemographicgaps,andthusalargeincreaseinthesupplyofskilledworkers,areobservedforcountries thathaveboosted their investment ineducationand thathavestillhighpopulationgrowth, i.e. a large share of young people. Conversely, aging countries where most of theirpopulationhasalreadyenteredadvancedsecondaryandtertiaryeducationwillnotexperienceincreasesinthesupplyofskilledworkers.Figure2:Educationtransitionbyregiona) ECA,LAC,SSA,andHigh-income b) EAP,MENA,andSouthAsia

Source:AuthorselaborationbasedonBarroandLee(2013)Note:Oldcohortreferstothepopulationbetween60and64yearsold.Youngcohortreferstothepopulationbetween20and24yearsold.

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Figure2summarizestheeducationaltransitionbyregionfrom1960to2010.Itshowstheremarkableanddiversedevelopmentsineducationattainmentacrosstheseregions.Theaverageyearsofschoolingfortheyoungcohorts(20-24yearsold)hasimprovedwhencomparedtotheoldcohorts(60-64yearsold)inalldevelopingregionsexceptEasternEuropeandCentralAsia.Inthe1960s,youngworkersinLatinAmericaandtheCaribbean,MiddleEastandNorthAfrica,SouthAsia,andSub-SaharanAfricahadaboutoneadditionalyearofeducationthantheircontemporaryolderworkers.By2010,thisdifferencehadincreasedtoaboutfourormoreyearsofeducation,ortheequivalentofalmostafullcycleofsecondaryeducation.ThistrendismarginallydifferentforcountriesinEastAsiaandthePacificwhichstartedwithaslightlyhigherintergenerationalgapandprogressedabitmoreslowly.ThelinesinFigure2areregionalaverages,andthereissomevariationacrosscountrieswithineachregion.InLatinAmericanandtheCaribbean(LAC),forexample,Bolivia,startedwithanintergenerationaleducationgapof3.5yearsand,by2005,thegaprosetooverfiveyears.ThistrendisfollowedbyBrazil,Mexico,andChile.Theseincreasesareevidentbothinaverageyearsofschoolingaswellasintertiaryschooling.InBolivia,thedifferenceintheaveragenumberofyearsoftertiaryeducationbetweenyoungandoldwasinitiallyclosetozero,butthedifferencerosetoaboutaquarterofayearby2005.ForBenin,inSub-SaharanAfrica(SSA),thedifferenceintheaverageyearsofschoolingfor25-29-year-oldsascomparedto60-64-year-oldswasalmostnon-existent(0.51yearsvs.0.48years)in1980.By2010,thisdifferenceroseto1.5yearsofschooling.8Indeed,thistrendisfollowedbyothercountriessuchasRwanda,Gambia,andKenya.MostofthegainsinrisingeducationalattainmentinAfrica,however,areinprimaryandsecondaryeducation.Therehasbeenlittleprogressinincreasingtheratesoftertiaryeducationandbeyond(exceptinafewcountries,likeGabon).BecauseChinaandIndiahavebeenmajordriversofrecentdecliningglobalinequalityaswellascontributorstothegloballaborforce,apressingquestioniswhetherfuturegenerationswillbeabletosupplyenoughskilledworkers.InChina,theoverallrateofeducationalattainmentandthesupplyofyoungworkershavestabilized.ThesenumbersarereflectedinFigure2intheperformanceofEastAsiaand thePacific,where theaverageyearsof schooling for theyoungcohortshadimprovedsignificantlyuptothe1980s,withalevelingoffoftheintergenerationalgapafterthat.India’seducationalattainmentlevelshavebeenrising,althoughstartingfromalowerlevelthanChina’s.ThedifferenceinyearsofschoolingbetweenyoungandoldercohortsinIndiawas1.7in1980and3.0in2005–withonlyasmallincreaseintertiaryeducation,whichlooksweakincomparisontoothermiddle-incomecountries.ThisobservationisconsistentwiththelargegapinaverageyearsofschoolingobservedinSouthAsia(SAR).Tworegionsstandoutbecauseoftheirverydifferenthistoricalperformance:thehigh-incomecountriesgroupandEuropeandCentralAsia.Bothregionsstartoffinthe1960swithahigher(vis-à-visdevelopingregions)gapbetweenyoungandoldschoolingachievements,dueto 8Thedirectionandmagnitudeofthesedifferencesarerobusttowidercohortdefinitions.

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theirearlierinvestmentineducation.Thisgapincreasesinthefirsttwoorthreedecades,butthendecreasesand,towardstheendoftheperiod,thegaphasalmostdisappeared.Thistrendshouldnotbeconsideredaslackofprogress.Onthecontrary,onceallyoungpeopleachieveadesired(high)levelofschooling,andthisismaintainedforallnewcohorts,thedifferenceineducationbetweenoldandtheyoungwoulddisappear.However,anintergenerationaleducationgapclosetozeromeansthattheregion-wideaverageeducationwillnotincrease.Somecountries,notablytheRussianFederation,areevenexperiencinganegativeintergenerationalgapineducation:oldercohortstendtohavemoreyearsofeducationthantheyoung.Oncetheoldworkersretire,averageeducationmaygodown.Assumingthatforeacholdworkerexitingthelabormarketanew,youngreplaceshimorher,theaverageeducationlevelofthelaborforcewouldincreaseinlinewiththetrendsshowninfigure2.Incontrast,somehigh-incomeOECDandEastEuropeancountriesareeitherinastableorevenadownwardtrendintermsofeducationalattainment.Therefore,thiseducationwavewillnotbeuniformacrossregions,anddevelopingcountrieswillplaytheleadingroleinthisprocess,especiallythosewithdemographicbonuses.Forthisreason,itiscriticaltoaddeducationbyagecohorts,aswellasimportantcharacteristicssuchasgender,asdemographicdimensionsinlong-termprospectiveeconomicanalysis(seeLutz,Goujon,andDoblhammer-Reiter1998;LutzandGoujon2003).Remarkably, the largestgaponyearsofeducationbetweengenerations iscurrently inregionsthatwillcontributemosttothegrowthofglobalworkingagepopulation,namelyLAC,MiddleEastandNorthAfrica(MENA),SAR,andSSA.Moreover,theaverageyearsofschoolingofyoungcohortsintheseregions,stilllagbehindhigh-incomecountries(HIC),particularlySARandSSA—evenwithoutgettingintoimportant issuesrelatedtoquality.ResultsfromthePISAtest(OECD,2015)suggeststhatthegapregardingeducationindevelopingcountriesisnotonlyrelatedto school attainment butmore importantly to the quality of educationwhen comparing theperformanceofstudentsusingstandardtests. While83%ofstudentsfromhouseholdsinthebottomquintileofincomedistributioninSingaporedemonstratethebasiccompetenciesinmath,thecorrespondingnumbersarebelow20%forstudentsfromhouseholdsinthebottomquintileofincomedistributioninLatinAmericancountries(e.g.Colombia,Brazil,andPeru).Inadditiontotheyoungergenerationbecomingmoreeducatedthantheolderone,themagnitudeofitseffectonthesupplyofskilledworkersisenhancedbyagrowingcohort’ssize(ofthat working-age group). In a similar way to the previous figure, Figure 2 shows theintergenerationaldemographicgap.Thisisexpressedastheratioofthepopulationsizeoftwoagecohorts:theyoung,aged20to24andtheoldaged60to64.Notethattheseratiosvarybothinlevelsandtrends.Foradvancedeconomies,andECAcountriestheratioataround2islowandslightlydecliningintheperiod1960-2010.Thismeansthatnewyoungercohortsarestilllarger

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thanolderones,butsincetheeducationleveloftheyoungisnotmuchhigherthanthatoftheold,averageeducationisnotexpectedtoincrease.9Figure3:Demographictransitionbyregiona) ECA,LAC,SSA,andHigh-income b) EAP,MENA,andSouthAsia

Source:AuthorselaborationbasedonBarroandLee(2013)An interestingregion isLAC,wherecountriesarebeginning theiragingprocessas theintergenerational demographic ratio shows a kind of inverted U in Figure 2, but this iscompensatedbytherapidlyincreasingeducationalgap.AsimilarpatternisobservedforEAP,withadecisivelystrongeragingtrend.Figure2showsthattheotherregions–MENA,Sub-SaharanAfrica, South Asia – are in earlier phases of the demographic transition. Their increasingdemographicratioswillboost the impactof the intergenerationaleducationgapandpushupaverageeducation level strongly.populationwillbe shapedbyayoungerandmoreeducatedcohortcomingfromdevelopingcountries,especiallyregionsthatarelaggingbehindoneconomicgrowthandpercapitaincomelevel.Figures2and3 canbeextended to the futureusingpopulationprojectionsandsomesimpleassumptionsoneducationoffutureyoungcohort.Indeed,goingforward,theproportionofskilledworkerswillincreaseabout43.7%inlow-andmiddle-income(LMI)countriesbetween2015and2050; itwilldeclineapproximately3.3%inhigh-incomecountriesduring thesameperiod.Moreover,theshareofthepopulationfromLMIcountrieswasapproximately82.7%in2015.By2050theywillrepresentabout85.7%oftotalpopulation.10Figure1intheintroductionsummarizeshowtheincreasinginthenumberofworkerswithmorethannineyearsofschoolingwillbepredominantlydrivenbycountriesinLMIregions.Theforthcomingwaveofchangesinthegloballabormarketwill,therefore,comefromthecombinationandgrowingintensityofthedemographicandeducationtransitionsjustdescribed. 9 Ahmedet.al.(2016)providesfurtherdescriptionofdifferenttrendsofdemographicchangeacrosstheword. 10UnitedNations(2015),mediumvariantscenario.

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Astheglobalpopulationagesinthefuture,youngerandbetter-educatedcohortswillentertheworkforce while older, less educated, ones leave. The average schooling of the working agepopulationwill tend to increase, evenwithout any additional efforts to improve educationalattainment rates – a pipeline effect or natural progression as students move up from oneeducationalgradetothenextovertime.Thelargerintergenerationaleducationgapindevelopingcountries,combinedwiththelargesizeandgrowingpoolofyoungercohortsrelativetotheolderonesinthosecountries,formsthekeyandsignificantdynamicsofthesecondwavethatisanalyzedinthispaper.Inthenextsection,wedescribethemethodologytoquantifytheeffectsofthisandotherrelatedfactorsonglobalinequality.3. Methodology3.1TheglobaleconomicmodelsTheglobaldistributionalimpactsoftheeducationwavedependonchangesinpercapitaincomesbetweencountriesandchangesininequalitywithincountries.Tocapturethefull–betweenandwithincountries–distributionalchange,oneneedsaframeworkthatcapturesbothconvergencesat themacro level (country averages) and the evolution of factormarkets at themicro level(dispersion).Thispaperadoptsadynamicglobalmicrosimulationapproachwhichallowstakingintoaccountboththeseaverageanddispersioneffects.Theoriginofdynamicmicrosimulationcanbetracedbacktothe1950sseminalworkofOrcutt(1957)whosecontributionsaimedatovercomingthelimitationsofmodelsavailableatthattime.Thesemodelscouldbeusedtopredict theaggregate impact,butcouldnotdescribethedistributionalimpactofpolicyreformsortheeffectsoninequalityoflong-termtrends,suchasdemographicchange.Dataavailabilityandmodelinghavesignificantlyadvancedsincethen,butdynamicmicrosimulationsremainthemaintooltostudydistributionalchange,andstill,providetheuniqueperspectiveofprojectingsamplesofpopulationforwardintime(simulatingdifferentscenarios).This paper uses the World Bank’s global microsimulation framework labeled GlobalIncomeDistributionDynamics(GIDD)modelincombinationwiththeglobalcomputablegeneralequilibrium(CGE)modelcalledLINKAGE.Bothtoolshavebeendescribedindetailinotherpapers(e.g.Bourguignonet.al.2008andBussoloet.al.2010;thefulltechnicaldocumentationoftheLINKAGEisprovidedbyvanderMensbrugghe,2011),butitisstillusefultobrieflydescribetheirstructureinthissection.Theultimatefocusofanalysisishouseholdwelfare,usinghouseholdpercapitaincomeasitsindicator.ThedistributionDofhouseholdpercapitaincomeyinyeartcanbeexpressed as the product of the joint distribution of all relevant household or individualcharacteristicsXandthedistributionofincomeconditionalonthesecharacteristics:

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( ) = = … ( ) ( | ) ( ) (1)where ( )isthedensityfunctionofthedistributionofincomeandthesummationisoverthedomainC(X)onwhichXisdefined.Householdpercapitaincome(y)canbemodeledasafunctionof:(i)householdmembers’characteristicsorendowments(X),(ii)themarketrewardforthosecharacteristics( ),and(iii)theintensityinhowthoseendowmentsareusedascapturedbyasetofparameters defininglaborforceparticipationandoccupationstatus( | );and,finally,(iv)unobservablecomponents( ):, = , , , , , , (2) TheincomedistributionDforapopulationofNindividualsorhouseholdsinyeartcanberepresentedbyavector , … , … , ,whereeachYitcanbedefinedas in(2) intermsofendowments,prices,laborstatusandunobservables:= , … , = , , , , , , … , , , , , , (3)Howdoesthisdistributionchangedynamically,forexamplefromyearttoyeart+k?ThisframeworkallowsdistinguishingtwosourcesthataffectthedynamicchangeofdistributionD,bothofwhicharerelevantfortheassessmentofthedistributiveimpactoftheeducationwave.Thefirstsourceconsistsofthechangesineithertheparameters or ,namelythemarketrewardstothecharacteristicsXandparametersaffectingoccupationaldecisions.Thismeans,forexample,thatinequalityfordistributionDcangodowniftheskillpremia / isreduced;orifachangeinlabordemandinsectorswithhigherwages(achangein )affectsthedecisiontomovetothesesectorsforsomeindividualsworkinginsectorwithlowerwages.Thesecondsourceofdynamic shift is represented by changes in the distribution of individual and householdcharacteristics(X).Alterationsofthestructureofthepopulationintermsofageandeducation,andchangesinthesizeandcompositionofhouseholds,willallaffectthedistributionofincomeofthatpopulation.11Bothsourcesofdistributionalchangemattertotheimpactoftheeducationwave.Infact,theGIDDallowsgeneratingascenariothatincludestheeducationwave,whichisthencomparedto a counterfactualwhere education achievements remain stable at the levels observed in t.Comparing the distributions _ and _ derived from the two scenarios ineffectisolatesthedistributionalimpactoftheeducationwave.Definingthecontrastingvaluesofendowments,prices,andlaborstatustobuildthetwo scanbequitechallenging,especiallywhendonegloballyformanycountries.Todoso,thefunctionalformofequation(2)isdefinedinasimplefashionusingonlyindependentvariablesavailableforallcountriesinthesample.Inthis

11 Thesetwosourcesofdynamicchangearenotindependentonefromtheotherand,intherealworld,theyaresimultaneouslydetermined.Theproblemofestimatingandrunningafullysimultaneousmicrosimulationframeworkisovercomebymakingsomesimplifyingassumptions.

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paper, the right-handsideof equation (2) includesage, educationendowmentsandsectorofemploymentworkers (countrysubscriptsexcluded for simplicity), soequation(2)canbe re-writtenas:, = + , ( , , ) + , ( , , ) + , ( , , ) + ∑ , , + , (4)where and aredummyvariables identifyingwhetherworkersareskilledorunskilled,respectively; and aredummyvariablestakingthevalueof1iftheworkerisemployedintheagriculturalsectororinthenon-agriculturalsector,respectively; capturestheproportionofhouseholdmembersineachofthekagecohorts.The sarerewards(prices)toeducationendowmentsconditionalonthesectorofemployment,12andthe sarepricesassociatedwithhouseholdcomposition.Finally, includesallotherincomedeterminantsnotincludedinequation(4).Thecounterfactualexpressionto(4)foryeart+kis:, = + , , , , + , , , ,

+ , , , , + ∑ , , + , (5) where thedemographic characteristics, endowments, and returns to these endowmentshavebeenmodified inaccordancewith thecounterfactual scenariosof theeducationwaveandnowave. Using (5) – and the relevant simulated S, E, F, s, and s – the two distributions_ and _ canbegenerated.Toclosethemodel,oneneedstofigureouthowthecharacteristics,representedbythevariousdummies,evolvethroughtimeaswellashowtherewards,representedbythe s,changeinthescenarios.Characteristicsandrewardsshouldbejointlydetermined;however,thisisnotthecasefortheGIDD,norforthemajorityofmicrosimulationmodels.13 Inpractice, theGIDDimplements thesequentialapproachdescribed inFigureA1 in theappendix. In the first step,changesinthenumberofskilledandunskilledworkersareestimatedonthebasisofdemographicchangeandthepipelineeffectdescribedbefore.14Inasecondstep,theseaggregatesareusedas:(i) constraints for the reweighting procedure that is applied to change the distribution ofcharacteristicsat thehousehold level; and(ii)as inputs for theLINKAGEgeneralequilibriummodel.Theequationherebelowsummarizesthereweightingprocedure: 12 Notethatunskilledworkersemployedinagriculture,i.e.whenthesedummies , , areequaltoone,arethereferencecategory,sotheyareexcludedfromtheequation.13SeeBourguignonandBussolo(2011)foradiscussion.14Thesechangesareassumedtobeexogenousandnotaffectedbytherewards.BourguignonandBussolo(2011)describesmorecomplexstructures,wherechangesincharacteristicsaffecttherewards,andthentheseaffectthesupply.[redraft]

13

, = a , , , = , ( ) , and , ,thesupplyofskilledorunskilledworkersinyeart+k,aretheconstraintsofthereweightingprocedurewhichconsistsoffindingmultipliersai,s,t+kthatchangetheoriginal(i.e.foryeart)householdweightswi,s.Oncethesemultipliersaredetermined,thedummies , canberecomputedandusedtocalculatea‘partial’changeinthedistribution.Formally,equation(5)canbere-writtenas:′ , = + , , ′ , , + , , ′ , ,

+ , , ′ , , + ∑ , , + , (5’) Thisnewequation(5’)representsachangeintheincomes,andthusinthedistribution,thatisduetoapuredemographic(orquantity)educationeffect.Othercharacteristicsandrewardshavenotbeenchangedyet.15Thechangeinrewards,inthisspecificcase,thechangeoftheskillpremia,isdeterminedintheLINKAGEgeneralequilibriummodel.Thismodelcalculateswagesofskilledandunskilledworkersbycombiningfirms’demandforlaborwiththeaggregatechangeinsuppliesofthesetwotypes ofworkers. In addition to the changes in rewards, the computable general equilibriummodelalsocalculatesoveralleconomicgrowth,sectoralreallocationoflabor,andanewvectorofconsumerprices.Changesofthesevariables–theLinkageAggregateVariables(LAVs)–areusedasinputsinthefinalstepofthemicrosimulation.Thisstepconsistsofchangingthe sinequation(5’).16 3.2ThemicrodataunderlyingthemodelsandtheanalysisThemethodology combines a large data set from three different sources,whichwe describefurthertohighlighttheinnovation.First,weusepopulationprojectionsfromUN(2015).Second,weuseinput-outputmatricesfromtheGlobalTradeAnalysisProject(GTAP,2011)fortheCGEscenarios.Third,weusetheGIDDdatabase,whichiscomposedbyharmonizedhouseholdsurveys 15 Note that the constraints of the reweighting procedure are in terms of (age cohorts and) education levels of thepopulationinyeart+k,andnotintermsofsectoralemployment.However,becauseskilledworkersmayinitiallybemoreconcentratedinthenon-agriculturalsectors,increasingtheweightofskilledworkersinthetotalpopulationgeneratesalsoanincreaseintheshareofnon-agriculturalemployment.Thisiswhy,inequation(5’),sectoraldummiesarelabelledas′ , ′ , withaprime(’)sign.ThesedonotnecessarilyrepresentthefinalsectoralemploymentwhichisdeterminedbytheCGEmodel.16 In this last step, workers move across sectors to achieve the proportions of employment in agriculture and non-agriculturecalculatedbytheCGEmodel.Notethattheseinter-sectoralmovementsarenetofthesectoralshiftsalreadygeneratedbythereweightingprocedureasmentioninfootnote15.ThemicrosimulationproceduretoselectwhichspecificworkermovesisbasedonaprobabilisticmodeldescribedindetailinBussoloetal(2010).

14

foralargeamongofcountries.TheGIDDdatasetisderivedfromhouseholdsurveysharmonizedbytheWorldBank.17Itprovidesacrosssectionofsurveysusing2011asabase.18Thesamplecovers10.45millionindividualsin127countries,constituting83.4%oftheglobalGDPand86.3%oftheglobalpopulation19(seeTable1).ThecoverageofthedatainGIDDisalsolargeformostspecificregions,bothintermsofpopulationandGDP.Table1:GIDDdatabasecoverageRegion GDPPPP$,B Population,M Microdataobs.Total % Total %Lowandmiddle-incomecountries 13,781.5 90.8 5,788.3 87.8 5,320,925EastAsia&Pacific 5,698.5 96.5 1,991.8 94.6 918,891Europe&CentralAsia 1,287.5 80.9 269.8 67.0 347,714LatinAmerica&Caribbean 3,379.2 98.2 585.6 98.0 1,472,436MiddleEast&NorthAfrica 843.6 19.5 344.1 19.4 70,402SouthAsia 1,703.2 99.3 1,675.0 98.2 782,726Sub-SaharanAfrica 869.6 92.3 922.1 79.4 1,728,756High-incomecountries 40,979.1 81.0 1,277.2 79.6 5,134,448World 54,760.6 83.4 7,065.5 86.3 10,455,373Source:Authors’elaborationusingavailabledatafromGIDD-dataset.From the household surveys,we extract information on individuals’ age, employmentstatus, sector of activity, years of schooling and wage as the starting points to project thepopulationgrowthbyschoolattainmentandthesectoralwagebilldatabase(Cruz,GoandOsorio-Rodarte,2017).ThesepiecesofinformationfeedintotheLINKAGEmodeltoestablishconsistencybetween the skill definition and volume of workers between the CGE model and themicrosimulations.20Table2showstheshareofworkersbybroadsectors(agricultureandnon-agriculture)andskilllevelfor2012.Thetabulationindicatesthattheshareofskilledworkersismuchlargerinhigh-incomecountriesandtheregionofEurope&CentralAsia(ECA).ThethreeregionsofEastAsia&Pacific(EAP),SouthAsia(SAS),andSub-SaharanAfrica(SSA)togetherhadapproximately96%ofthepeoplelivingbelowpovertylinein2015.Inalloftheseregions,35%ormoreofthepaidworkerswereunskilledworkinginagriculture.Animportantchannelthroughwhich changes in the composition of labor supplymay affect income inequality and incomedistribution,inthelongrun,isthemovementofworkersacrossdifferentcategoriesdelineatedbythecells-agriculture,non-agriculture,skilledworkers,andunskilledworkers. 17 The data include household surveys harmonized by the Poverty Group Global Practice of theWorld Bank and theInternational IncomeDistributionDatabase (I2D2). The I2D2 is aWorldBankproject to collate, harmonize andmakeaccessiblecomparable information fromhouseholdsurveysheldby theWorldBank, forpoverty, inequality,education,demographics,andlabormarketanalysis.ForfurtherdetailsonI2D2,seeMontenegroandMaximilian(2008).18Foreachcountry,theclosestsurveyto2011hasbeenchosentobeconsistentwiththedatabaseusedintheLINKAGEmodel(GlobalTradeAnalysisdatabasev.8).FromthisdatasetwederivedtheGIDDSectoralWageBilldatabase(Cruz,Go,andOsório-Rodarte,2017).19 If Japan and Switzerland are included with aggregated information generated from household surveys, using theLuxemburgIncomeStudydataset,theGDPcoverageincreasestoapproximately93%.20Oneissueisthecomparabilityofschoolattainmenttodefineskill-levelbecauseofdifferencesinqualityofeducationacrosscountries.However,thedifferencesofwagesbyskill,sector,andcountryindirectlyaccountforthedifferencesinrespectiveproductivity.

15

Table2:Shareofworkersbyregion,sectorofactivityandskilllevel(percent)Region Agricultural(%) Non-agricultural(%) Workers,millionsSkilled Unskilled Skilled Unskilled Skilled Unskilled TotalLow&middle-incomecountries 3.5 28.0 28.6 39.9 637 1,351 1,989EastAsia&Pacific 2.7 29.1 23.4 44.8 259.8 737.1 997.0Europe&CentralAsia 4.9 6.9 66.4 21.8 48.9 19.7 68.5LatinAmerica&Caribbean 1.7 10.2 51.5 36.5 134.5 118.0 252.5MiddleEast&NorthAfrica 0.2 19.0 19.4 61.5 2.6 10.6 13.2SouthAsia 6.2 36.1 24.4 33.3 145.2 329.9 475.1Sub-SaharanAfrica 3.0 33.9 22.3 40.8 46.1 136.2 182.3High-incomecountries 2.0 0.8 87.4 9.9 416.0 49.4 465.3World 3.2 22.8 39.7 34.2 1,053 1,401 2,454Source:Authors’elaborationusingavailabledatafromGIDD-dataset.

Table3:Averagewageandskillpremiabyregion(2011US$PPP)Region Wages,monthlyUS$(PPP) Labor,millions SkillWagePremiaAgri Non-Agri Agri Non-AgriUnskilled Skilled Unskilled Skilled Unskilled Skilled Unskilled Skilled Agri Non-Agri TotalEastAsia&Pacific 152 244 281 409 2.2 0.4 26.1 9.7 1.6 1.5 1.5Europe&CentralAsia 281 282 380 523 0.2 0.2 0.6 1.8 1.0 1.4 1.4LatinAmerica&Carribean 223 492 359 786 1.1 0.2 3.3 4.9 2.2 2.2 2.4MiddleEast&NorthAfrica 205 296 257 293 0.2 0.0 0.4 0.1 1.4 1.1 1.2SouthAsia 93 124 108 230 7.8 1.5 7.5 4.9 1.3 2.1 2.1Sub-SaharanAfrica 72 155 138 327 4.2 0.5 3.2 2.1 2.2 2.4 2.9High-IncomeCountries 685 1,129 1,037 1,849 0.1 0.3 1.9 14.6 1.6 1.8 1.8Low-IncomeCountries 70 113 107 195 3.2 0.3 3.3 1.3 1.6 1.8 2.0LowerMiddle-IncomeCountries 98 130 158 268 10.5 1.9 11.7 8.2 1.3 1.7 1.9UpperMiddle-IncomeCountries 237 420 303 581 1.8 0.5 26.1 14.1 1.8 1.9 1.9High-IncomeNon-OECD 361 595 678 943 0.0 0.1 0.5 2.6 1.6 1.4 1.4High-IncomeOECD 782 1,311 1,153 2,045 0.1 0.3 1.5 12.0 1.7 1.8 1.8World 114 289 281 987 15.8 3.1 43.0 38.1 2.5 3.5 4.0

Source: Authors’ elaboration based on GIDD-dataset Note: Wage skill premia are calculated by dividing average wages of skilled to unskilled. This table aggregates workers across countries and regions.

16

Table3showsthattherearesignificantdifferencesintheaveragewagesofworkers(conditional on their level of skill) between sectors and regions.21 An average unskilledworkerinnon-agricultureinhigh-incomecountrieshasawagethatissignificantlyhigherthan an average skilledworker in a non-high-income economy. Also, skill premia in thewagesbysectoraresharplycharacterized.223.3ThescenariosUsing the methodology just described, we first define the “education wave” scenario as thebaseline or reference case. On current global trends, the educationwave foretells substantialchangesinthegloballabormarket.Around2012,oneskilledworkerfromahigh-income(OECD)countrywascompeting, intheglobal labormarket,withtwoskilledworkersfromdevelopingcountries.Inlessthanageneration,by2030,thatratiowillbe1to3,confirmingthattheglobalpoolofskilledworkerswilloriginatemostlyindevelopingcountriesasadirectconsequenceoftheeducationanddemographictransitions(asdiscussedinsection2).Thistrendis,inessence,theglobalshockoftheeducationwavethatwilllikelytransformonceagain,asthe‘greatdoubling’didinthefirstwave,theworld’slabormarket.Diggingbeyondthisglobalpicture,theeducationwavewillalsoaffectdistributionwithincountries,alocalshockthatwillvaryfromcountrytocountry.Figure4illustratesthegeographicdispersionoftheeducationwavewiththegrowthratesofskilledlabor(y-axis)againstthoseforunskilledlabor(x-axis).Mostofthedots,whichrepresentcountriesorregions,areabovethe45-degreeline,meaningthatskilledworkerswillgrowfasterthanthatofunskilledworkers.Yet,thereisheterogeneityacrosscountries.First,thegrowthrateofunskilledworkersisoftennegativein‘older’countriesandregion.Compare,forexample,thereductionratesof30%or 15% forunskilledpopulations inEurope or China, versus the expansion rates of 53% forNigeria,or65%fortheSub-Saharanregion.Second,veryfewcountriesorregionsexperiencegrowthratesthataresimilarforthetwogroupsofworkers(thatis,noeducationwave):theonlydotsinthefigurethatare(reasonably)closetothe45-degreelinearethosefortheUS,RussianFederation,andSub-SaharanAfrica.Formostoftheothercountries,however,thedifferencescanbe quite large. In Turkey, the gap between the rates of expansion of skilled and unskilledpopulations is of almost 50 percentage points. For Brazil andNigeria, the gap is close to 40percentagepoints,forIndonesia,India,Chinaisbetween25and33percentagepoints.TheRussianFederationistheonlycountryfoundbelowthe45-degreeline,albeitclosetoit.Inthiscountry,oldergenerationstendtohavemoreeducationthantheyoungerones.Thisfact,combinedwith 21Heterogeneityislargewithinregionsandwithincountries,whichareaccountedforinthecomponentsofglobalincomeinequality.22Thewagesforskilledworkersinagriculturearehigherthanthewagesforunskilledworkersinthesamesector.Thesamehappensinnon-agricultureactivities.

17

anagingpopulation,generatesarateofreductionofskilledworkersslightlyhigherthanthatofunskilledworkers,resultingina(negative)gapof3percentagepoints.Figure 4: Growth rates of labor by skill, sector and country/region

Source:Authors’calculationsbasedonGIDDprojections.Note:thegrowthratesareexpressedasthecumulativegrowthfortheperiod2030-2012.Thereddotsrepresentregions;thebluedotsareindividualcountries. Thesechangesinthelaborsupplyofdifferenteducationlevelsconstitutetheexogenousshockdefinedinthebaselinescenario.Whilelaborsupplyislikelytorespondtowagechanges,the strong assumption that labor supply is exogenous and that it follows demographicdevelopmentsisadoptedhere.Infact,mosteconomicmodelslikewisetreatpopulationgrowthasexogenousand,inthisexercise,laborsupplybyeducationlevelisindirectlyderivedfromtheagingofthepopulationandbyassumingthattheeducationattainmentoftheyoungcohortsinthefuturewill adhere to the same attainment rate observed in the current data. This assumption isconservativebecausesomecountrieswilllikelydobetter.Thatis,theireducationpolicymaybringaboutbettereducationalattainmentamongtheyounggenerationsinthefuturewhencomparedtothoseinthepresentperiod.Theeducationwave,ifsuchimprovementisaccountedfor,wouldbeevenlargerthanwhatiscurrentlydefinedinthebaseline.Table4summarizesthescenariosusedforthisanalysis.Againstthebaseline(educationwave),a“noeducationwave”issetupasthemaincounterfactualsimulation,whichisawhat-ifscenarioinwhichtheeducationtrendofthebaselineisremoved.Twoadditionalscenariosaimtoaddresspotentialconcernsrelatedtotechnologicalchangesthatmaybebiasedtowardsskilled

BRA

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IDN IND

LKA

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ZAF

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MNA

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YHIEUR

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workers. Since thebaselineandwhat-if counterfactual simulations contain similarunderlyingeconomic projections and parameters, their differences minimize the effects of commonassumptionsandisolatetheeffectsofthecritical factorofeachcounterfactualsimulation.Thesimulationperiodisfora20-yearspan,from2011to2030,whichmakesitmorecomparabletothefirstwave.Thistimehorizonalsomakestheanalysismoreconservativebecauseitwillnotcoverthefullimpactoftheeducationwavebeyond2030.Table4:CGEbaselineandcounterfactualscenariosScenario Key features PurposeBaseline – education wave Population projections from UN

(2015) medium fertility variant scenario; economic growth projections from World Bank (2015); the share of skilled workers grows assuming constant education attainment rates.

Establish a business-as-usual or reference case for comparison with counterfactual scenarios.

No education wave Same as baseline except for the fact there are no changes in the share of skilled workers. Provide a counterfactual scenario

without the “education wave.”Higher elasticity of substitution between skilled labor and capital The baseline plus a higher

elasticity of substitution of 3 (previously 2) between skilled labor and capital.

This scenario tests the sensitivity of the results for changes in the substitutability between skilled labor and capital to mimic the consequence of a biased technological change

Alternative nesting structure of labor and capital The baseline with an alternative

nesting structure. Skilled and unskilled labor are substitutable with each other, and together also substitutable with capital. We use elasticities of substitution of 2 for the two relevant nests.

This scenario tests the sensitivity of the results for technological changes that may affect the substitutability between skilled and unskilled workers.

Source:Authors’elaboration.4. Results4.1Resultsatmacrolevel:GrowthdifferentialsandwagepremiaInthesimulations,theproductivityfactorforeachcountryissetexogenouslytoreplicatetheevolutionofeconomicgrowthto2030basedontheWorldBankprojectionsreportedintheGlobalMonitoringReport2015/16(WorldBank,2015).Thecatchingupofproductivityofdevelopingcountriestothelevelsofhigh-incomecountriesgeneratesfastergrowthratesforthedevelopingcountries.Thegrowthdifferentialvis-à-vishigh-incomecountriesisquitepronouncedfortheEastAsiaandthePacificregionaswellasforSouthAsiaregion.Thesedifferentialsbring

19

aboutconvergenceinincomespercapitaandareakeydriverforthereductionofthebetween-countrycomponentofglobalinequality.Theincreasingoftheworkingagepopulationsharesinmany developing countries, vis-à-vis its reduction in high income countries also contributestowardsthisconvergence(CruzandAhmed,2016).Figure 5: Projected economic growth by geographical region

Source:CGEandGIDDsimulationsresults.Is the education wave contributing to the reduction of global inequality from theperspectiveofthisbetween-countryconvergence?Theanswerisyes,butitdoesnotseemmuchintheaggregateasshowninFigure6.Thecontributionoftheeducationwave–vis-à-visascenariowhereskilledandunskilledworkersgrowatthesamerate–comesexclusivelyfromthehigherproductivityofskilledworkerswhoarebecomingmoreabundantinthewavescenario.However,otherdriversofgrowthandconvergence,namelythecatchingupofproductivityandsuppliesofotherfactors(landandcapital),donotchangeacrosssimulations.Forthesereasons,theeducationwaveboostsconvergenceby,atbest,aboutonepercentagepointinEastAsiaandthePacificandLatinAmericaandtheCaribbeanregions,but its impact ismore limited inotherregions.Theconvergencedifferentialsshouldbeconsideredalowerbound,giventhatmoreabundantskilledworkers are likely to generate externalities and an additional productivity uptick that is notsimulatedhere.Inaddition,wehaveveryconservativeeducationprojectionsandwealookingatarelativelyshortperiodoftimetofullycapturetheeffectsoftheeducationwave.

8.7

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Annual average growth rates of income per capita (2012-2030)

20

Figure 6: Incomes convergence - education versus no education wave

Source:CGEandGIDDsimulationsresults.Onefinalpointontheregionalaggregategrowthrates.Theeducationwave,asdiscussedabove,isascenariowherethegrowthoftheglobalpoolofskilledworkersisalmostentirelyduetotheexpansionofeducationindevelopingcountries.Indeed,by2030foralldevelopingcountriesincomespercapitaarehigherinthewaveversusthanintheno-wavescenario,whilefortherichcountries incomes are unchanged. Further to the rising inequality in high-income countriesobservedbyseveralauthors(seefootnote4),themicroresultsbelowsuggestthatincreasingpressureonthemiddle-incomeclassesofhigh-incomecountrieswillcontinue.Changesintheskillandsectorpremiumofwagesaretheothercrucialmacroeconomicresultsofthesimulationsthataffectglobalinequalitythroughitseffectsonincomesdispersionwithineachcountry.Wageswillfollowtheinteractionofthedemandandsupplyoflaborbyskillandsectoraswellastheeconomy-widerepercussionsofotherfactorsingeneralequilibrium.Foreachcountry,ahighersupplyofskilledworkersin2030relativeto2012willleadtoalargerreductionintheskillpremium(Figure7).Theregressionthatrelatestherelativesizeoftheskillpremiumagainsttherelativesupplyofskilledlaborhasawell-definednegativeslope.Thecausalityinthisframeworkrunsfromthechangesinthequantitiesofworkerstothechangesinthewagesbecausealllaborsuppliesareexogenous.Turkey,Brazil,andSouthAfricaareamongstthecountrieswiththelargestdropsintheskillpremium.InTurkey,theskillpremiumby2030isreducedby20percentofwhatitwasin2012.Attheoppositeend,thepremiumincreasesbyabout5percentintheRussianFederation.Ingeneral,theskillpremiumwilltendtodecreaseacrosscountries.

48.1

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Inco

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ita a

s % sh

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of h

igh

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30)

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Figure 7: The skill premium of wages versus supply of skilled labor 2030 relative to 2012, in percent change, all sectors

Source:GIDDsimulations.Note:Relativeskillpremiumchangesarecalculatedas:[(Wage_skill_2030/Wage_unsk_2030)/(W_skill_2012/W_unsk_2012)–1]x100,andthesameformulaisusedforrelativelaborsupplies.Individualresultsarepresentedforselectedcountries:Brazil(BRA),China(CHN),India(IND),Indonesia(IDN),Japan(JPN),Mexico(MEX),Nigeria(NGA),Russia(RUS),SouthAfrica(ZAF),SriLanka(LKA),andtheUnitedStates(USA).Othercountriesareaggregatedbyregions,suchasSub-SaharanAfrica(SSA)andEuropeanUnion(EUR).Forotherregions,wefollowtheWorldBankclassification,includingEastAsiaandPacific(EAP),EastEuropeandCentralAsia(ECA),LatinAmericaandtheCaribbean(LAC),MiddleEastandNorthofAfrica(MENA),andSouthAsia(SAR),combinedtothedemographictypologydescribedinAhmed(2016),whichdividecountriesaccordingtotheirpotentialfordemographicdividendby2030, suchas as: early-dividend (ed), latedivided (lt), andpost-divided (pd).Thisdisaggregation isbasedon theCGEmacro-simulation. Changesinthepremiumofwagesbysectoralsoaffectdistributionalshift.Bysector,werefertourban(non-agriculture)andrural(agriculture)areas. Inhigh-incomecountries,bothskilledandunskilledworkersareperfectlymobileacrosssectorssothatasingle,skill-specific,economy-widewageclearseachlabormarket.Indevelopingcountries,ontheotherhand,skilledworkersareperfectlymobilewhileunskilledworkersaresegmentedbythetwobroadsectors.Asinglewageclearsforskilledworkersclearsacrosssectorsbutnotthecaseforunskilledworkers.Inthelattercase,amigrationfunctionthatallowsunskilledworkerstomovefromtheruraltotheurbansectordefinesthesuppliesofunskilledworkersacrosssectors,inthetraditionofLewis(1954) andHarris andTodaro (1970). Interactionswith the sectoral demands for unskilledworkers then determine the wages, with a premium prevailing for urban (non-agriculture)unskilledworkersrelativetotheircounterpartsintherural(agriculture)sector.Unliketheskillcompositionoflabor,thesectordistributionofunskilledworkersisendogenous.

Skill

pre

mia

in

crea

se

Skill

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de

crea

se

22

Aseachcountryurbanizes,productionand incomegrowth,aswellas theallocationofunskilledworkers,willallshifttowardsnon-agriculturalsectors.23Evenso,theurbanpremiumforunskilledworkerswillstilltendtogodownduetotherelativeintensitiesoffactoruseintheproductionfunctionacrosssectors.Thatis,agriculture,whichismoreintensiveintheunskilledfactor, will release many more unskilled workers than those needs by the expanding non-agriculture sectors, resulting in a relative oversupply of unskilledworkers, driving down thepremiumforunskilledworkersintheurbansector.Figure8depictsthereductionoftheurbanpremiumofwagesin2030relativeto2012againsttherelativesectorsupplyofunskilledworkers.Figure 8: The urban premium of wages and the sector supply of labor

for unskilled workers, 2030 relative to 2012, in percent change

Source:GIDDsimulations.Note: Relative urban premium changes are calculated, just for the unskilled, as: [(Wage_non-agri_2030/Wage_agri_2030) /(Wage_no-agri_2012/Wage_agri_2012)–1]x100,andthesameformulaisusedforrelativeemployment.Individualresultsarepresentedforselectedcountries:Brazil(BRA),China(CHN),India(IND),Indonesia(IDN),Japan(JPN),Mexico(MEX),Nigeria(NGA),Russia(RUS),SouthAfrica(ZAF),SriLanka(LKA),andtheUnitedStates(USA).Othercountriesareaggregatedbyregions,suchasSub-SaharanAfrica(SSA)andEuropeanUnion(EUR).ForotherregionswefollowtheWorldBankclassification,includingEastAsiaandPacific(EAP),EastEuropeandCentralAsia(ECA),LatinAmericaandtheCaribbean(LAC),MiddleEastandNorthofAfrica(MENA),andSouthAsia(SAR),combinedtothedemographictypologydescribedinAhmed(2016),whichdividecountriesaccordingtotheirpotentialfordemographicdividendby2030,suchasas:early-dividend(ed),latedivided(lt),andpost-divided(pd).ThisdisaggregationisbasedontheCGEmacro-simulation. 23 Demand forworkers by the different sectors is in linewith firms’ production planswhich in turn depend onconsumers,exportandotherfinaldemands.Duetoincomegrowth–andanelasticitywithrespecttoincomeaboveoneof consumption of non-agricultural goods – demand formanufacturing goods and services (i.e. demand for non-agricultural goods) increases more rapidly than demand for agricultural commodities. This shift of demand istransmittedtoproduction,andconsequentlydemandforlaborinnon-agriculturalsectorsincreasesfasterthanthatinagriculture.

23

4.2ResultsatthemicrolevelBetween-andwithin-countryevolutionofinequalityUsingthemacroresultsaboutaverageincomeconvergenceandchangesinwagepremia,themicrosimulationpartoftheeconomicframeworkestimatestheimpactoftheeducationwaveatthehouseholdlevel.24Themicrosimulationresultsconfirmthattheworldwillbecomemoreequalby2030asitbecomesmoreeducated(Table5).The(individual-based)Giniindexfallsfrom65.8 in2012 to62.6 in2030,while theTheil-L index is reduced from90.7 to76.6.Comparedtorecentpatterns,theseresultssuggestacontinuationofthereductioninglobalinequality.Duringthegreatdoublingofthegloballaborforce,globalinequalitydecreasedby2.3percentagepoints ina20-year interval from1988 to2008(LaknerandMilanovic2016).Oureducationwavescenarioshowsacomparablereductionof3.2percentagepoint,withtheglobalGini indexfallingduringtheperiod2012to2030. TheTheil-L indexalsosuggestsasimilarreductionofcloseto14percentagepointsby2030.ComparedtoLaknerandMilanovic(2016),ourinitialmeasureofGiniandTheil-Lindicesaresmaller,whichmaybe explained by differences in benchmark year, data sources, country coverage, and theslightimprovementinequalitywhentheeducationwaveismeasuredby2012.25

Table5: Global inequality will go down in a more educated world Inequalitymeasures 2012 2030-EducationWave 2030-NoWaveDemographic FullsimulationGiniindex 65.8 65.5 62.6 63.2Theil-L 90.7 91.0 76.6 78.6TheilDecompositions: Betweenregions(%) 51.7 48.0 41.4 41.0Withinregions(%) 48.3 52.0 58.6 59.0Betweencountries(%) 57.2 53.6 49.1 48.6Withincountries(%) 42.8 46.4 50.9 51.4Percentile75/Percentile25 5.5 5.4 6.7 6.6Mean,$(PPP) 416.9 430.3 835.2 827.4Coeff.ofvariation 3.1 3.3 2.4 2.5Source:Authors’calculationsbasedonGIDDprojections.Whatarethefactorsexplainingtheprojectedreductionininequality?UsingtheTheilindex,itispossibletodecomposeinequalitybetweenandwithingroups.Thecentralpanel 24 The microsimulation exercise is done at the country level using household surveys, but results in this section are aggregated to analyze inequality at the global level. The CGE macro results provide yearly simulations from 2011 to 2030. The microsimulation uses 2012 as a reference year for the harmonized household surveys. Thus, this section uses projections from 2012 to 2030 for the microsimulation exercises. 25 ThelatestyearavailableinLaknerandMilanovic(2016)is2008forwhichtheGiniis70.5.Ourinitialyearis2011,forwhichourGiniindexis65.8.Thus,wefocusthecomparisononrelativechanges.Ferreiraet.al.(2015)describethedifferencesacrossseveralcross-nationalinequalitydatabases.

24

ofTable5showstheresultsofthisdecompositionwhenthegroupsarerepresentedbyeitherregionsorcountries.26Aclearansweremerges:globalinequalitydecreasesmainlybecause,onaverage,poorercountriesarecatchingup.Atthebeginningoftheperiod,thecontributionofthe‘between-groups’componenttototalinequalityismorethan50percent(52percentwhenusingregions,57percentwhenusingcountriesasgroups).However,bytheendoftheperiod,thebetween-countrycomponentdropstothelessthan50percent(duetoincomeconvergence)whilethewithin-countriescomponentshowsalargercontributiontoglobalinequality.Thisresultdoesnotimplythatinequalitywithincountriesisincreasing(moreonthisbelow),but that,at least, inequalitybetweencountries isdecreasingata fasterpace.Poorandlargecountries,suchasIndiaandChina,aregrowingfasttoreshapingtheglobalincomedistributionandcontributingtothereductionofglobalinequality.Theimportanceoftheeducationwaveinthedynamicsofinequalitywithincountriescan be seen by comparing the results of the educationwavewith those of the no-wavescenario inthe lasttwocolumnsofTable5.Thelargerdecreasesoftheskillpremiumintheeducationwavescenariopushesdowninequalitywithincountries,whilethisisnotthecaseintheno-wavescenario.Asaresult,thewithin-groupcomponentintheno-wavescenario,aswellastotalinequality,arehigherthanthoseintheeducationwave.Comparingtheglobalgrowthincidencecurves(GICs)fortheeducationwaveandtheno-wavescenariosisanotherwayofillustratingthechangeintheglobaldistribution(Figure10).TheGICsdifferfromtheelephantcurveofLaknerandMilanovic(2016)inthattheydonothavethetrunkpart.Thereasonsare:themicrosimulationsarebasedonlaborincomes,27andtherearenocorrections for top incomes (which are not recorded in the underlying household surveys).Moreover,our2sectorsby2levelsofskilledworkersdonotprovideenoughheterogeneitytocaptureeffectsonthetopincome.

26Theregionsare:Sub-SaharanAfrica,Middle-EastandNorthAfrica,SouthAsia,EastAsiaandthePacific,EuropeandCentralAsia,LatinAmericaandtheCaribbean,HighIncomecountries.TheseregionscorrespondtotheWorldBankdefinitions.27 Note that household surveys often, if not always, record non-labor incomes. The microsimulations apply the country-specific average growth rate to change these non-labor incomes. The change in the returns of capital from the global CGE could be also used, but this was not done in the current exercise. A main reason is that most of the non-labor incomes are (public and private) transfers and thus do not represents incomes from capital.

25

Figure 9: Global Growth incidence curves The education wave versus the no-wave scenario

Source:Authors’calculationsbasedonGIDDprojections.These GICs highlight several interesting points. First, the educationwave provides itshighestbenefitsforthepopulationwithincomesbetweenthebottom20andtop20;growthratesforthegroupsatthetwoextremesofthedistributionare1to2percentagepointslowerthanforthegroupinthemiddle.Second,theno-educationwaveratesofincomeexpansionarebelowthoseoftheeducationwavescenarioforeveryonewithincomesuptoaboutthe90thpercentile.This is expected as the educationwave ismainly awave in the developingworld.Third, thedistancebetweenthetwolinesappearssmallbut,forthemiddleofthedistributionandthebottom5percent, thedifferenceshouldnotbeunderestimated. In fact,halfapercentagepointgapingrowthratesaccumulatesto10percentlargerincomesafter20years,anon-trivialdifference.Wealsoanalyzethechangesinthecountrycompositionofthebottom20-andthetop-20percent of the global incomedistribution by 2030 (tableA1 of the appendix). In 2012, eightcountriesmadeupthree-quartersofthepopulationinthebottom20oftheglobaldistribution.AndIndiaandChinaaloneaccountedforalmost50percentofit.MostoftheothercountriesinthebottomwerefromSub-SaharanAfrica.Forsomeofthesecountriesalmosttheirentirenationalpopulations(orlargesharesofthem)belongedtotheglobalbottom20.Whileonly15percentofChinesenationalsoccupiedtheglobalbottom20,97percentoftheSouthAfricancitizensresidedinthebottom20,82percentofthosefromCongo,and60percentofNigerians.Thecompositionchangessignificantlyby2030.Notably,ChinadisappearswhileIndiastillcontributesone-quartertothetotalworldpopulationinthebottom20.India’snumbercorrespondsto23percentofthetotalpopulationofIndia,not30percentasin2012.Asimilarevolutioncanbedescribedforthetop20.WiththearrivalofChinain2030,alargepercentageofcitizensfromtheUSandfromWesternEuropeancountrieswilllosetheirtoppositions.

23

45

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SelectcountriesThe educationwave also has strong influences onwithin countries distributions. Forexample,asteepdownwardslopedincidencecurvefortheeducationwaveisobservedinTurkey(Figure 10) while an upward sloping one is registered for other countries, such as Russia.However,thegrowthincidencecurveofTurkeyforthefullshock(thesolidline)seemsratherflat.ThemainreasonisthatthedemographicshockinTurkeyisregressive.28Thisdemographiceffectgenerates the dotted regressive incidence curve shown in Figure 10.29 The fact that thedemographicimpactisregressiveshouldnotbetoosurprising.DeatonandPaxson(1998)showthatthereisaquitestrongageeffectoninequality,meaningthatascohortsagetheirinequalityincreases.Thus,oldersocieties(whichisthecaseforTurkeyin2030vis-à-vis2012,andinfactforallcountries)areexpectedtobemoreunequal.Theeffectofthewagepremia–thatisdrivenbythe education wave – shows, as expected, a progressive GIC. This effect can be inferred byestimatingthedifferencebetweenthedemographicandthefulleffectGICs.Yet,the‘net’GICfortheno-wavescenarioisalsoflat(FigureA2intheappendix). Figure 10: Growth incidence curves for Turkey

28 Wereweightthehouseholdssothatthesimulatedpopulationmatches,intermsofitssharesofageandeducationgroups,theprojected2030demographicstructureisnotdistributionalneutral.Infact,householdswitholderandmoreeducatedmembersgetreweightedmoreheavilythanotherhouseholds.29 Note that the dotted GIC also includes the effect of the inter-sectoral reallocations of unskilled workers and adistributionallyneutralresidualgrowtheffect(sothattheaveragegrowthforthepopulationasawholeistheactualgrowthoftheperiod).ItispossibletodecomposethisGICfurtherandisolatethepuredemographiceffect,buttheobservationinthemaintextwillnotchangesincethesectoralreallocationeffectsareofnegligibleimportance.

27

Figure 11: Growth incidence curve for China

InthecaseofChina,thegrowthincidencecurveisshapedasaninvertedUduetothedemographic effect, and the change in skill and urban premium are jointly and slightlyregressive(Figure11).TheexpansionoftheskilledworkersinChinawilloccurinthemiddleofthedistribution(seeFigureA3intheappendix).Foreachpercentileofthepopulation,wecompare theshareofworkers thatare skilledandunskilledbothat thebeginningof theperiodandattheendoftheperiodduringtheeducationwave(FigureA3a).Thiscomparisontellsusthatmostoftheskilledaretowardsthetopandmostoftheunskilledtowardsthebottom,butalsothattheshiftcausedbytheeducationwaveisconcentratedinthemiddleofthe income distribution. We also find that skilled workers expand in the middle andsubstituteforthecontractionoftheunskilled,alsointhemiddleoftheincomedistribution(FigureA3b).InthecaseofChina,itismainlyastoryofquantity,morethanofprices.A final graph for theUS (Figure 12) illustrateswhatmay happen to high incomecountries.ThegraphshowsthedensitydistributionfortheUSpopulationbyplottingthepercentagesoftheUSpopulationthatarefoundatdifferentlevelsofincome.Theselevelsofincome are shown in the graph not as actual USD amounts, but as the correspondingpercentilesoftheglobalincomedistribution.Soclearly,mostoftheUSpopulationisfoundatthetopoftheworlddistribution.However,itisinterestingtonotethattheUSwillloseitsdominanceatthetopand,by2030,alargershareoftheUScitizenswillbeinlowerpositions.Thisisaresultofbothincomeconvergenceacrosscountries(citizensfromothercountrieswillstartoccupyingthehigherpositionsintheworlddistributionofincome),butalsoofthepressureontheuppermiddle-incomeclassintheUS.

28

Figure 12: Distribution of the US population across the world income distribution, in 2012 and 2030

Source: Authors’ calculations.

Notwithstandingthepotentialpressuresonhighincomecountries,overalltheeducationwavewillbepushingdowninequalitywithincountries.TheGini index is lower foralmostallcountriesintheeducationwavewhencomparedtothecaseofthenoeducationwave.Figure13plotsthedifferenceintheGiniindexby2030betweentheeducationwaveandnoeducation-wave scenarios for each country. A negative number denotes less inequality (animprovement)broughtaboutbytheeducationwave.Formostofthe117countriescovered,changesinthewithin-countryinequalityareclearlymorefavorablewiththeeducationwave.Althoughthe improvementdidnotcompletelyreverse the increase in theoverallwithin-countryinequalityby2030(asshowninTable6),itclearlyamelioratesthedeterioration.30

30 Even if the Gini indices in both scenarios would have deteriorated in 2030 from 2011, as long the index for the education wave is less than the other case, the result would come out as favorable towards reducing inequality.

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29

Figure 13: Difference in the Gini Index between Education and No Wave, by Country, 2030

Source:Authors’calculations.Note:Resultsbasedonmicrosimulationforindividualcountries.There-weightprocedureisbasedonprojectedpopulationin2030,takingintoaccountchangesinthesizeandnumberofskilledworkersbyagecohort,foreachcountrybetween2011and2030.4.3SensitivitytestsThe sensitivity of the results to important factors, such as alternative elasticity ofsubstitutionbetweenlabortypesanddifferentnestingstructurebetweenlaborandcapitalwastested.Ifskilledandunskilledworkersareimperfectsubstitutes,anincreaseintherelativesupplyofskilledworkers,withoutoffsettingskill-biasedchangesindemandorotherfactorsinsupply,willunavoidablyreducethewagepremiumofskill.Inthegrowthliterature,onewaytoaccountforthelackofeffectsofcapitalaccumulationontherental-wageratiosinadvancedeconomiesisto introduce labor-augmenting productivity (due to havingmore capital goods available perworker.)AnotherwayistoraisetheelasticityofsubstitutionbetweenthefactorsofproductionassuggestedbydelaGrandvilleandSolow(2009).Others, likeHouthakker(1955)andJones(2005), distinguished between short-term and long-term production possibilities. Inmoderneconomic growth theory,Acemoglu (2009)emphasized the importanceof directedorbiasedtechnologicalchangeandtheendogenousnatureoftechnologytoexplainthat,inover60yearssince1939, theU.S relative supplyof skillshas increased rapidly,but there, collegepremiumincreasedovertheperiod.31Inthesensitivitytests(table6),theelasticity,alongwithafewotherfactors,waschanged.When theelasticityof substitutionbetweenskilled laborandcapital is raised (relative to thebaseline),themeanincomeincreases.Raisingtheelasticityjustbetweenskilledlaborandcapital 31 See in particular Chapter 15 and figure 15.1 in Acemoglu (2009).

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30

meansthatcapitalaccumulationwouldtendtoraisedemandforskilledlaborinthesamenest.Theeffectwouldtendtomaintainthewagepremiumofskill(similartotheeffectsregardingKandL in thegrowth literature). Inequality inalldimensions (global, regional, andcountry) isrelativelysimilartotheeducationwavescenario.Thefindingssomewhatmimictheeffectsofabiasedtechnologicalchange.Theworst-casescenarioforinequalityundereducationwaveisthealternativenestingstructureofinputs(ST-3).Puttingskilledandunskilledlaborinthesamenesthasthesameeffectasraisingtheelasticityofsubstitutionbetweenthelabortypesagainstcapital.Theeffectwouldmoredirectlydampenanyfallinpremiabetweenskilledandunskilledworkers.Theresultistoraiseglobalinequalitybetweenandwithincountryinallthemeasuresagainstthebaseline,butitisstillslightlybetterthantheno-educationwavescenario.Table6-Effectofeducationwaveonglobalinequalityfordifferentscenarios

Education wave

No education wave

ST-1 -Higher elasticity

ST-2-Alt. nesting

Gini index 62.6 63.2 62.6 63.0 GE(1) (Theil) 76.6 78.6 76.5 77.8 Between - GE(1) (Theil) by region 31.7 32.3 31.7 32.6 Within - GE(1) (Theil) by region 44.9 46.4 44.8 45.1 Between - GE(1) (Theil) by country 37.6 38.2 37.6 38.6 Within - GE(1) (Theil) by country 39.0 40.4 38.9 39.2 Percentile ratio 75 / 25 6.7 6.6 6.8 6.7 Mean, $(PPP) 835.2 827.4 841.7 801.1 Coeff. of variation 2.4 2.5 2.4 2.3 Source:Authors’calculations.Note:ST1: Higherelasticity.Thebaselinewithahigherelasticityofsubstitutionbetweenskilled laborandcapitalof3,insteadof2.Skilledlaborandcapitalarebundledtogetherinthesamenest,andtogethersubstitutabletounskilledlabor;ST2: Alternativenestingstructure.Thebaselinewithanalternativenestingstructureofproduction inputs.Skilledandunskilledaredirectlysubstitutablewithoneanotherinthesamenest,andtogethersubstitutablewithcapital.5. ConclusionThe sudden integration of China, India, and the former Soviet bloc of countries in the globaleconomyhasbroughtaboutthe‘greatdoubling’(Freeman2007)ofthegloballabormarketsandtriggeredsignificantchangesintheglobalincomedistribution.Thispaperanalyzes,inanex-antefashion,whatistheimpactofanother‘greatone-and-a-halftimes’shocktotheglobaleconomy.Becausetheiryounger(andlarger)populationsandthestilllargepositivegapineducationoftheiryoungcohortsvis-à-vistheiroldones,developingcountrieswillsoonbethesolecontributorstotheexpansionoftheglobalpoolofskilledworkers.Evenwithoutanyimprovementineducationeffortby2030,theratioofskilledworkersfromdevelopingcountriestoskilledworkersfromhighincome(OECD)countrieswillbe3to1,upfrom2to1in2012;a‘one-and-a-halftimes’increasethatthispaperlabelstheeducationwave.Themainresultfromtheanalysisoftheimpactofthiseducationwaveisthatglobalinequalitywill

31

likely decrease. This is driven by a reduction of inequality between and within countries.Convergence of incomes per capita between countries, mainly depending on closing up ofproductivity gaps between high income and developing countreis, is further boosted by theeducationwave.Inaddition,despiteitsincreasinglylargerweightindetermingglobalinequality,within-country inequality would descrease in most countries. Education, especially for thedevelopingworld, canstillplay theroleof thegreatequalizer.Theresultsalsoshowthat thepressureonhighincomecountriesmaystillbeoninthefuture.References Acemoglu, D. (2009). Introduction to Modern Economic Growth. Princeton University Press.

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Appendix Table A1 - Change in the composition by country of the global bottom 20 and top 20 groups

Source:Authors’calculationsbasedonGIDDprojections.Figure A1 - GIDD framework

Source:Authors’elaborationbasedonBussolo,DeHoyos,andMedvedev(2010).

% of total

% of country

% of total

% of country

% of total

% of country

% of total

% of country

India 32 30 India 25 23 United States 22 84 China 40 40China 17 15 Nigeria 11 58 China 13 11 United States 18 71

Nigeria 8 57 Congo, Dem. Rep. 7 87 Russian Federation 7 54 Russian Federation 5 52Congo, Dem. Rep. 5 82 Pakistan 7 40 Germany 6 96 Germany 4 76

South Africa 4 97 Ethiopia 6 60 France 5 96 France 4 75Indonesia 4 20 South Africa 4 98 Brazil 5 29 United Kingdom 3 63

Ethiopia 3 45 Tanzania 4 66 United Kingdom 5 89 Brazil 3 19Pakistan 2 16 Philippines 3 30 Italy 4 87

Iraq 2 62 Spain 3 84Uganda 2 53 Turkey 2 32

Mozambique 2 78 Canada 2 64Madagascar 2 89 Mexico 2 17

Tot 76 Tot 75 Tot 76 Tot 76

Bottom 20 Top 20

2012 2030 2012 2030

35

Figure A2 - Net wage skill premia effect in the growth incidence curve for Turkey

Figure A3 - Education wave quantity effects in China

a) Skill level by income decile

b) Difference in Skill level by income decile

Source: Authors’ calculations.

0.2

.4.6

% s

hare

of t

otal

pop

ulat

ion

0 20 40 60 80 100Percentiles of Per Capita Income

Unskilled in Non Agri 2012 Skilled in Non Agri 2012 Unskilled in Non Agri 2030 Skilled in Non Agri 2030

Skill Level by Income Decile, CHN

−.15

−.1

−.05

0.0

5%

sha

re o

f tot

al p

opul

atio

n

0 20 40 60 80 100Percentiles of Per Capita Income

Difference in Unskilled Non Agri Difference in Skilled Non Agri

Difference in Skill Level by Income Decile, CHN