全球大趋势和消除贫困的追求报告

UNITEDNATIONSCONFERENCEONTRADEANDDEVELOPMENT

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May2024

UNCTAD/WP/2024/2

Globalmegatrendsandthequestfor

povertyeradication

Abstract

PatrickN.Osakwe

Trade,PovertyandInequalitiesBranch,ALDC,UNCTAD

patrick.osakwe@

OlgaSolleder

Trade,PovertyandInequalitiesBranch,ALDC,UNCTAD

olga.solleder@

Globalmegatrendssuchasincomeinequality,climatechange,demographicshifts,technologicalprogress,andurbanisationareshapingthefutureofsocieties.Yet,theirquantitativeimpactsondevelopmentareneitherwellunderstoodnorestablished.Thispaperexaminestheindividualandcombinedeffectsoftheseglobalforcesonpoverty,usingbothcross-sectionandpanelestimationtechniquesonaglobaldatasetcoveringtheperiodfrom1995to2019.Regardingthedirecteffects,itfindsthatinequality,urbanization,andtechnologyarethemegatrendswitharobustimpactonpovertyinboththelongandmediumterms.Demographicshiftsandclimatechangehavesomeimpactonpoverty,buttheresultsdependonthesamplesandspecificationsconsidered.Furthermore,thepaperfindsthatinadditiontotheirdirecteffects,technology,urbanization,anddemographicshiftsaffectpovertythroughtheirinteractionswithincomeinequality.Amongthecontrols,percapitaincome,education,andprivatecreditaresignificantdriversinthemediumterm,whilepercapitaincomeistheonlycontrolvariablethatmattersinthelongrun.

Keywords

Poverty,megatrends,inequality,technology,climatechange,demography,urbanization.

Thefindings,interpretationsandconclusionsexpressedhereinarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheUnitedNationsoritsofficialsorMemberStates.ThedesignationsemployedandthepresentationofmaterialonanymapinthisworkdonotimplytheexpressionofanyopinionwhatsoeveronthepartoftheUnitedNationsconcerningthelegalstatusofanycountry,territory,city,orareaorofitsauthorities,orconcerningthedelimitationofitsfrontiersandboundaries.Thispaperhasnotbeenformallyedited.

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Contents

Acknowledgements 2

1.Introduction 3

2.Transmissionmechanismslinkingglobalmegatrends

topoverty 6

3.Estimationapproach 10

4.Regressionresults 13

5.Conclusions 21

References 24

Annex 26

Acknowledgements

Theauthorsthanktwoanonymousreferees,aswellasUNCTADcolleagues,AnidaYupariAguadoandPaulAkiwumi,forcommentsonanearlierversionofthepaper.

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1.

Introduction

Theeradicationofpovertyisthefundamentalpublicpolicychallengeofourtime.Itiscentraltofosteringsustainabledevelopmentatboththenationalandgloballevels.Itisalsoanimperativetoachievetheglobalmantrathatnooneshouldbeleftbehindinthedevelopmentprocess.Thisimportantfacthasbeenacknowledgedbytheinternationalcommunityasreflectedinthedecisionofworldleadersin2015todevotethefirstoftheseventeensustainabledevelopmentgoals(SDG)toendingpovertyinallitsformseverywhere.TherecognitionofthepivotalroleofpovertyeradicationinpromotingsustainedandsharedprosperityisalsoadrivingfactorinthedecisionoftheinternationalcommunitytodevotethefirstofthesixfocusareasoftheDohaProgrammeofActionforleastdevelopedcountriesto“Investinginpeople,eradicatingpovertyandbuildingcapacity.”

Overthepastfewdecades,significantprogresshasbeenmadeinreducingglobalpovertylargelyduetopositiveeconomicdevelopmentsinChinaandIndia.Usingthepovertyheadcountratiobasedonthe$2.15adaythreshold,theglobalpovertyratefellfrom37.8percentin1990to8.4percentin2019.1Despitethisprogressextremepovertyremainshighandtherearesignificantchallengestoaddressinseveralareas.Forexample,theprogressachievedtodatehasbeenunevenandpovertyisincreasinglyconcentratedinAfrica(Table1).2Inaddition,theeconomicandsocialenvironmentsinwhichgovernmentsmustdesignandimplementpoliciestocombatpovertyhavebecomemoreuncertainduetothefollowing“globalmegatrends”3:climatechange,demographicshifts,technologicalprogress,incomeinequalityandurbanisation.Theseglobalforcesposeseriousriskstothequestforsustainabledevelopment.Forexample,iftheseforcesarenotwellmanagedandifpresenttrendscontinue,itisunlikelythatgoal1oftheSDGoneradicatingpovertywillbeachievedbythe2030targetdate(UnitedNations2020).Notwithstandingtherisksposedbythesemegatrends,tothebestofourknowledge,thereisnoeconometricstudyexaminingtheindividualeffectsofmegatrendsonglobalpoverty(apartfromstudiesonincomeinequality).

1TheCovid-19pandemichasreversedsomeofthegainsachievedinthepastfewdecades.Asaresultofthepandemictheglobalpovertyraterosefrom8.4percentin2019to9.3percentin

2020andthendeclinedto8.4percentin2022(WorldBank2022).

2In1990,EastAsiaandPacificaccountedforabout52.9percentofglobalextremepoverty,SouthAsiafor28.2percentandSub-SaharanAfricafor13.6percent.In2019,thatisafterthreedecades,EastAsiaandPacificaccountedforonly3.6percentofglobalextremepoverty,SouthAsiafor24.1percentandSub-SaharanAfricafor60percent.Interestingly,unliketheotherregionswherethenumberofpoorpeopledeclinedbetween1990and2019,inSub-Saharan

Africathenumberofpoorpeopleincreasedfrom271.5millionto389million.

3Globalmegatrendsrefertomacroeconomic,social,andpoliticalforcesshapingthefutureofsocietieswithprofoundimpactsoneconomies.

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Thereisalsonostudythatinvestigatesquantitativelyhowtheseforcesinteractandtheeffectoftheseinteractionsonpoverty.Againstthisbackdrop,ourpaperattemptstoaddressthecurrentlacunaintheliterature.

Theliteratureonthedriversofpovertyisvastandgrowing(Cerraetal.2021;Fosu2017;Epaulard2003;AliandThorbecke2000).Oneclassofthisliteratureusesaggregatemacroeconomicandsocialdatatoexaminethedeterminantsofpoverty.Forexample,LeGoffandSingh(2014)examinedtherelationshipbetweentradeopennessandpovertyusingpaneldataforAfricancountries.Theyfoundthattheeffectoftradeopennessonpovertydependsonthedepthofthefinancialsector,thelevelofeducationandthestrengthofinstitutions.Similarly,KpodarandSingh(2011)investigatedthelinkbetweenfinancialstructureandpovertyandfoundthatinanenvironmentwhereinstitutionsareweakbank-basedfinancialsystemscontributetopovertyreduction.Furthermore,asinstitutionsgetstrongermarket-basedfinancialsystemsbecomebeneficialforthepoor.Anotherclassoftheexistingliteraturefocusesontheroleofsectoralgrowthinunderstandingpovertyusingdisaggregateddata.BerardiandMarzo(2017)provideamethodologytostudytheelasticityofpovertywithrespecttosectoralgrowthatthecountrylevel.Theyarguethatboththecompositionofgrowthanditsoverallintensitymatterfortherelationshipbetweengrowthandpoverty.Inarelatedpaper,ErumbananddeVries(2021)examinetheroleofstructuralchangeingrowthandpovertyreduction.Theyfoundanassociationbetweenaggregatelabourproductivitygrowthandpovertyreductionindevelopingcountries.Theyalsofoundthatpovertyreductionwasassociatedwithstructuralchangeandmanufacturingproductivitygrowth.

Whilethepapersdiscussedabovehavemadeimportantcontributionstotheliteratureonthedriversofpoverty,theydonotinvestigatetheeffectsofglobalmegatrendsandtheirimplicationsforpovertyreduction.Inthisregard,ourpapercomplementsandaddsvaluetotheextantliteraturebyexaminingtheindividualimpactofeachoftheseglobalforcesonpoverty.Anothercontributionofthepaperisthatinadditiontoexaminingtheindividualeffectsofglobalmegatrendsonpoverty,wealsoinvestigatehowtheyinteractwithincomeinequality,andhowtheseinteractionsaffectpoverty.Thisisimportantbecausetheconfluenceoftheseglobalforcesmayhaveanimpactthatisquitedifferentfromtheirindividualeffects(Poloz2022).Thethirdcontributionofourpaperisthatitexaminesboththemediumandlong-rundriversofpovertywithcontrolsforotherpotentialcorrelatesofpovertyidentifiedintheliterature,namely:incomepercapita,education,infrastructure,institutions,trade,macroeconomicinstability,andfinancialdevelopment.

Therestofthepaperisorganisedasfollows.Section2discussesthetransmissionmechanismslinkingthefiveglobalmegatrendswithpovertyandexaminesthebilateralcorrelationbetweentheseforcesandameasureofpoverty:thepovertyheadcountratio.Insection3,wediscusstheempiricalstrategyadoptedinourpapertogetherwiththevariablesanddataused.Insection4,wepresentandanalysetheresultsforthebaselineregressions,conductrobustnesschecks,andexaminewhetherthereareinteractioneffectsamongmegatrends.Section5containssomeconcludingremarks.

4

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Table1

PovertyratesandnumberofpooratUS$2.15perdaypovertyline

(Byregion)

Povertyheadcountratio(%)

Numberofpoorpeople

(millions)

1990201919902019

EastAsiaandPacific

65.8

1.1

1055.5

23.6

EuropeandCentralAsia

3.2

2.4

15.0

11.8

LatinAmericaandtheCaribbean

16.7

4.3

73.2

27.8

MiddleEastandNorthAfrica

6.1

-

14.0

-

SouthAsia

49.7

8.5

563

156.3

Sub-SaharanAfrica

53.3

35.1

271.5

389.0

Restoftheworld

0.5

0.6

4.1

6.7

World

37.8

8.4

1996.2

648.1

Source:CompiledbasedondatafromonlineannexofWorldBank(2022).

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2.

Transmissionmechanismslinkingglobalmegatrendsto

poverty

Incomeinequality

Theeconomicliteraturesuggeststhatinequalityhasbothdirectandindirectconsequencesforpovertyreduction(Bourguignon2004;MarreroandServén2022).Thedirecteffectemanatesfromthefactthatforanygivengrowthrate,aworseningofincomedistributionwillincreasepoverty.Andtheindirecteffectarisesfromtheideathatinequalitycanincreasepovertybyinhibitinggrowththroughthefollowingmechanisms:creditmarketimperfections;socialconflicts;andredistributivedemocracy.Whentherearecreditmarketimperfectionsinaneconomypoorpeoplecannotborrowtoeitherexploitinvestmentopportunitiesoroffertheirchildrenagoodeducation.Inthiscontext,inequalityresultsinunderutilizationofacountry’spotentialandretardsgrowth(Bourguignon2004).Anotherchannelthroughwhichinequalityharmsgrowthisthatitfosterssocialandpoliticalinstabilitywhichisnotconducivetoinvestmentandgrowthinaneconomy(Ferreiraetal.2022).Inequalitycanalsoreducegrowthinademocraticsocietybecauseitincreasesthelikelihoodofadoptionofredistributionpolicieswhichwouldhavetobefinancedthroughhighertaxestherebyreducinggrowth(AlesinaandRodrik1994).Whilethemechanismsdiscussedaboveimplythatinequalityisbadforgrowth,itisworthnotingthatthereisalsoarelatedliteraturesuggestingthatinequalitycanfostergrowthbasedonthenotionthattherichhaveahighermarginalpropensitytosavethanthepoorandsoinequalityincreasessavingstherebyfacilitatinginvestmentandgrowth(Ferreiraetal.2022).

Climatechange

Amajorchannelthroughwhichclimatechangeaffectspovertyisbyreducingagriculturalproductionandgrowth(Hallegatteetal.2016).Byincreasingthefrequencyofextremeweathereventsandnaturalhazards(suchasheatwaves,drought,andflooding)climatechangehasanegativeeffectonagriculturalproductivityandproductionwithdireconsequencesforvulnerablepopulationswhodependonagriculturefortheirlivelihoods(UnitedNations2020).Climateinducedincreasesinprices,naturaldisastersandhealthproblemscanalsopushpeopleintopovertyaswellasreducetheabilityofthepoortoescapepovertytherebyincreasingthepovertyrate.Consequently,climatechangecanhaveasignificantnegativeimpactonpoverty,particularlyindevelopingeconomiesthatdonothavetheresourcesandcapacitytomitigateandadapttotheassociatedrisks.

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Demographicshifts

Theworldisexperiencingasignificantslowdowninpopulationgrowthratesandshiftsinpopulationagestructures(WorldBank2016;UnitedNations2020).Inthedevelopedcountries,anincreaseinlifeexpectancycoupledwithlowfertilityrateshaveresultedinanincreaseintheproportionofolderpeople(aged65andabove)inthepopulation.Itiswell-knownthataspeoplegetoldertheirabilitytocarryoutnormaldailyactivitiesdeclines,andtheywillhavetorelyontheworkingagepopulationforcareandotherneeds.Consequently,achangingagestructurethatincreasestheproportionofolderpeopleinthepopulationwillincreasethedependencyratio,raisetheburdenontheworkingpopulation,andincreasepoverty.Incontrasttothedevelopedcountries,inthedevelopingcountriesthedemographictransitionisassociatedwithanincreaseintheworkingagepopulation,whichrepresentsademographicdividendandanopportunitytoraiselivingstandardstherebyreducingpoverty.

Technologicalprogress

Technologicalinnovationisanimportantsourceofproductivitygrowthandjobcreation,particularlyinnewsectorsandindustries.Butitalsocreateswinnersandlosersinaneconomyandsocouldhavebothpositiveandnegativeimpactsonpoverty.Forexample,iftechnologicalchangeislabour-augmentingtheoverallimpactislikelytobepoverty-reducingbutifitislabour-savingtheoverallimpactislikelytobepoverty-increasing.Inadditiontothefactor-biasoftechnologicalchange,thespeedoftechnologicalchangealsomattersindeterminingtheultimateimpactitcouldhaveonpovertyinaneconomy(KorinekandStiglitz2017).Whentechnologicalchangeoccursataslowpace,thepotentialdisruptioninthelabourmarketwillbelessbutwhenthepaceoftechnologicalchangeisfast,ittransformslabourmarketsrapidlyandinducesstructuralshiftsineconomiesthatcouldresultinsignificantjoblossesinoldsectorsandindustries.Inthiscontext,theimpactonpovertywilldependonhowexposedthepooraretoindustriesandsectorsthatarecontractingaswellasonhoweasyitisforthepoortotransitionintonewgrowthsectorsandindustriesresultingfromtechnologicalchanges.Itwillalsodependonwhethertechnologicalchangeisaccompaniedwithskillsdevelopmentandtrainingmeasuresaswellasredistributionpoliciesgearedtowardscushioningthepotentialnegativeimpactonthepoor.

Urbanization

Urbanizationinvolvesanincreaseintheurbanshareofthetotalpopulationofacountryandarisesprincipallyfromfoursources:anaturalincreaseinurbanpopulation,rural-urbanmigration,reclassificationofcities,andinternationalmigration(UnitedNations2020).Urbanizationcanhaveanimpactonpovertythroughdifferentmechanisms.Forexample,migrationofpeoplefromruraltourbanareascanincreaseboththeurbanwageintheformalsectorandtheruralwage(duetohigheragriculturalproductivity)therebyreducingpoverty.Itcanalsocontributetotheaccumulationofhumanandphysicalcapitaltherebyfosteringgrowthandcreatingthebasisforpovertyreduction(Haetal.2021).Historically,urbanizationintheadvancedeconomieswastriggeredanddrivenbyindustrializationwhichisanimportantengineofgrowth,jobcreationandpoverty

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reduction(Gollinetal.2016).However,insomedevelopingcountries,particularlyinAfrica,urbanizationisassociatedwithrapidgrowthofinformalsector,slumformationsandhomelessness.Inthiscontext,althoughurbanizationcanplayapositiveroleinpovertyreductionandthedevelopmentprocess,itcanalsobeasourceofincreasesinpovertyifittakesplacewithoutindustrialdevelopmentandcreationofdecentjobs.

Havingdiscussedthetransmissionmechanismsthroughwhichtheglobalmegatrendscouldbelinkedtopoverty,itwouldbeinterestingtoexaminewhetherthereisanybilateralassociationbetweeneachofthemegatrendsandpovertyindicatorsinthedata,notingthatcorrelationsinthemselvesdonotimplycausality.Figure1presentsthebilateralcorrelationsbetweenthepovertyheadcountratioandselectedvariablesofinterestinthecross-sectionofcountries,withdataaveragedovertheperiod1995to2019.Thedataindicatesthatincomeinequality,theagedependencyratio(ameasureofdemographicshifts)andtheshareofpopulationaffectedbyclimate-relatednaturaldisasters(ameasureofclimatechange)arepositivelyassociatedwiththepovertyheadcountratio.Thecorrelationcoefficientsare0.64(inequality),0.74(demographicshifts),and0.09(climatechange).4Bycontrast,theshareoftheurbanpopulationintotalpopulation(aproxyforurbanization)andthepercentageofthepopulationusingtheinternet(aproxyfortechnologicalchange)arenegativelyassociatedwiththepovertyheadcountratio,withcorrelationcoefficientsequalto-0.61and-0.54respectively.Inaddition,thepovertyheadcountisstronglynegativelycorrelatedwithincomepercapita,withacorrelationcoefficientof-0.76.

4OnepossiblereasonforthislowcorrelationbetweenclimatechangeandpovertyinthedataisthatalthoughglobalextremepovertyisheavilyconcentratedinAfrica,mostofthoseaffectedbynaturaldisastersareinAsiaandthePacific(UNFPA2018).Furthermore,climatechangehasmultipledimensions(UnitedNations2020),anditisdifficulttocapturethedifferentdimensionsinoneindicator.

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Figure1

Bivariatecorrelationsofpovertywithselectedvariables

Povertyheadcount

204060

0

PovertyheadcountPovertyheadcountPovertyheadcount

60

0204060020406002040

3040506070

Incomeinequality

Povertyheadcount

0204060

20406080100

Urbanization

Povertyheadcount

60

02040

020406080

Internetusers

406080100

Agedependency

01234

Climatechange(disaster−affectedpopulation)

7891011

Incomepercapita(ln)

Note:Eachdotisthemedianforacountryovertheperiod1995-2019.

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3.

Estimationapproach

Theempiricalstrategyweadoptistwo-fold.First,weexaminetheempiricalrelationshipbetweenglobalmegatrendsandpovertyusingcross-sectiondata,whichcanbeinterpretedasrepresentingthelong-runeffectsofthesemegatrendsonpoverty.Second,wetakeadvantageofthepanelstructureofthedatabyestimatingpanelregressionswhichprovideinsightsintothemedium-runimpactsofthesemegatrendsonpoverty.Weestimatethepanelregressionsusingthefixedeffectsapproach,whichpermitsustocontrolfortimeinvariantcountrycharacteristicsandtimeeffectstherebymitigatingomittedvariablebias.Whilethefixedeffectsapproachaccountsforomittedvariablebias,itdoesnotcontrolforpotentialreversecausality.Tomitigatetheriskofreversecausalityinthefixed-effectsmodel,wealsoconductestimationsusinglagged,ratherthancontemporaneous,valuesofallregressors(seeBlotevogeletal.2022).5

Empiricalspecification

Webeginourempiricalinvestigationoftherelationshipbetweenglobalmegatrendsandpovertybyestimatingacross-sectionpovertyregressionasspecifiedinEquation(1).

Povertyi=λ+α’Mi+β’Xi+εi(1)

wheresubscriptidenotescountry,Povertyiisameasureofpovertyincountryi,Miisavectorcontainingcountry-levelindicatorsofthefivemegatrendsofinterestinthisstudy(inequality,urbanization,demographicshift,climatechange,andtechnologicalprogress),andαisavectorofrespectivecoefficientsonthemegatrends.Xiisavectorofcontrolvariables(income,education,tradepolicy,accesstocredit,macroeconomicinstability,andinstitutions),andβisavectorofcoefficientsonthecontrols.λisaconstantandεiisanerrorterm.

InadditiontoEquation(1),wealsoestimatethefollowingpanelregression6byfixedeffects:

Povertyit=α’Mit+β’Xit+μi+γt+εit(2)

5WealsotriedestimationbySystemGMM,buttheestimateswerehighlyunstableandimprecise,particularlywhenthevariablesareinnon-logform,reflectinginparttheweakinstrumentproblem.

6SeeforexampleDollarandKraay(2004).

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wheresubscriptsiandtindicate,respectively,countryandtime(5-yearperiods).Povertyitisanindicatorofpovertyincountryiattimet.ThevectorMitcapturesfiveglobalmegatrends(inequality,urbanization,demographicshift,climatechange,andtechnologicalprogress)andαisavectoroftheirrespectivecoefficients.containscontrolvariables(income,education,tradepolicy,accesstocredit,macroeconomicinstability,andinstitutions)andβisthevectoroftheircoefficients.Timeinvariantcountrycharacteristics(orcountryfixedeffects)arecapturedbyμi,γtisatimeeffectandεitisanerrorterm.Wefirstuseacontemporaneousspecification,andthenaspecificationwithallregressorslaggedbyoneperiod(representing5years)tomitigatetheriskofreversecausality(Blotevogeletal.2022).

Insection2weprovidedanexplanationofthemechanismsthroughwhichthefiveglobalmegatrendscouldaffectpovertyanddiscussedtheexpectedsigns.Consequently,inthissectionwesimplydiscussthechoiceofthecontrolvariablesincludedintheregressionsandtheirexpectedsigns.Ourchoiceofthecontrolvariablesisguidedbytheliteratureanddataavailability.Incomepercapitaisoneofthevariablesweincludetocontrolforthelevelofeconomicdevelopmentwiththeexpectationthatahigherlevelofdevelopmentisassociatedwithlesspoverty.Incomeisalsoanimportantcontrolvariablebecausetheliteraturesuggeststhateconomicgrowthisamajordriverofchangesinpoverty,withhighergrowthexpectedtodecreasepovertyforagivenincomedistribution(Bourguignon2004).Theliteraturealsosuggeststhatanincreaseinhumancapitaloreducationdecreasestheincidenceofpovertythrough,forexample,enhancingjobprospectsandmakingiteasiertoearndecentwages(Rahman2013).Tradeisanothervariablethathasbeenwidelydiscussedasapotentialdriverofpovertyalthoughtheoreticallyitsimpactisambiguous(LeGoffandSingh2014):ontheonehandgreateropennessincreasesconsumerchoiceandprovidesaccesstolargermarketsforagriculturalgoodsproducedinsectorswherethepoorareheavilyconcentrated.Ontheotherhand,moreopennessincreasescompetitionandreducesthebargainingpowerofunskilledlabourrelativetoskilledlabourandcapital.Furthermore,tariffliberalizationmayresultinlossoftariffrevenuesimpactingpovertythroughthischannel.Macroeconomicinstabilityasreflectedininflationorinflationvolatilityisexpectedtoincreasepovertybyreducingtherealwageandincomeofthepoor(Epaulard2003).Financialdevelopmentisexpectedtoreducepovertyby,forexample,makingitpossibleforthepoortoborrowagainstfutureearningsandtoinvest.Itcanalsoreducepovertybymakingiteasierforhouseholdstomanagerisks(KpodarandSingh2011).Institutionsarealsoconsideredtoplayanimportantroleinpovertyalleviation,withpoorqualityinstitutionsexpectedtoincreasepovertythrough,forexample,reducinglabourandcapitalproductivityandcreatingpovertytraps(TebaldiandMohan2010).

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Datasourcesandvariabledefinitions

Themainmeasureofpoverty,thedependentvariable,usedinourempiricalanalysesisthepovertyheadcountratio.However,wealsousedthepovertygapinthesectionwhereweconductedrobustnesschecks.ThetwopovertyindicatorsaresourcedfromtheWorldDevelopmentIndicators(WDI)databaseoftheWorldBank,andtheindicatorsarebasedonthelatestpovertythresholdof2.15$aday(2017PPP).7Asiscommonintheempiricalliterature,wemeasureincomeinequalitybytheGinicoefficientofpre-taxincomesourcedfromthecross-countrycomparablecompaniondatasetdevelopedbyUNU-WIDER(2022).Intherobustnesschecks,wealsousethePalmaratiofromUNU-WIDERasanalternativetotheGinicoefficient,whilerecognisingthatitonlycapturesthetailsratherthantheentireincomedistribution.OurincomemeasureisGDPpercapitaobtainedfromtheWDI.UrbanizationismeasuredbytheshareofurbanpopulationintotalpopulationsourcedfromtheWDIandbytheshareofurbansurfaceinthetotalsurfaceobtainedfromFAO(2022).Demographicshiftiscapturedbytheagedependencyratio,i.e.theratioofpeopleyoungerthan15andolderthan64totheworkingagepopulation(thoseaged15-64).ThedemographicshiftvariableisfromtheWDI.Climatechangeismeasuredbytheshareofpopulationaffectedbyclimate-relatednaturaldisasters(includingdroughts,floodsandextremetemperatureevents)andvalueofalleconomiclossesduetosuchdisasters,withbothmeasuresbeingobtainedfromtheEmergencyEventsDatabaseEM-DAT(CRED2023).Tech