1

WhyisinequalityhighinAfrica?

AbebeShimelesa.shimeles@afdb.org

andTigueneNabassaga

t.nabassaga@afdb.orgPaperpresentedatthe10thAfricanEconomicConference

November2,2015

Kinshasa

DRC

2

Abstract

Wecomputeasset-basedinequalityfor44Africancountriesinmultiplewavesusingoveramillionhouseholdhistoriesanddecomposewithincountryinequalityintospatialcomponentsandthoseattributedtohouseholdspecificcharacteristicssuchaseducation,occupationandexperience.Ourresultssuggestthatcloseto40%ofassetinequalityarespatialwithsignificantdifferenceacrosscountries.Politicalgovernanceandethnicfractionalizationexplain25%ofspatialinequalitywhilelevelofdevelopmentisuncorrelatedwithit.Inaddition,spatialinequalityisstronglycorrelatedwithchildandmaternalmortalityandothermeasuresofhumanopportunity.Thebetweencountryinequalityislowerincountrieswithrelativelyhighproportionofhouseholdscompletedtertiaryeducation.Countrieswithhighremittanceflowsalsohadlowerinequality.Finally,goodsorassetmarketdistortionsplayanimportantroleindrivinginequalityinAfrica.

3

1. Introduction

AvailableevidencesuggeststhatAfricaisthesecondmostunequalcontinentinthe

worldnexttoLatinAmerica(e.g.RavallionandChen,2012).Highinequalityalsoseemsto

havepersistedforovertimewithnovisiblesignofdeclining(Bigsten,2014;Milanovic,

2003).Paucityofdataatthehouseholdlevelinrepeatedwavesformanycountries

preventedanysystematicanalysisontheunderlyingdeterminantsofinequalityinAfrica.

Previousattemptsbasedoncross-countrypaneldataindicateethnicfractionalizationasa

robustdeterminantofincomeinequalityinAfrica(Milanovic,2003).Whiletheremaybe

enoughjustifiablepoliticaleconomyreasonsforethnicallyfragmentedcountriesto

experiencehighinequality,itisalsopossiblethattheethnicityvariablemaybepickingup

otherunobservedfactorsrelevantforpolicy.Inaddition,themainchallengeresearchers

commonlyfacewhileworkingoninequalitydataforAfricancountriesisitsqualityand

availabilityinreasonablysufficientwaves.Householdincomeandconsumptionsurveys,the

sourceofmostincomeinequalitydataarecollectedinfrequentlyandinirregulartime

intervalsinmanycasesmakingcontemporaneouscomparisonsdifficult(Deverajan,2012).

ThisstudyutilizesunitrecorddatafromDemographicandHealthSurveys(DHS)for

44countriesin102wavescoveringtheperiod1989-2011andapproximatelyoveramillion

householdstoanalyzethedriversofwealth/assetinequalityinAfrica.Thisapproach,

besideshavingtheadvantageofutilizinghouseholdlevelinformation,itallowsfor

consistentcomparisonofinequalityacrosscountriesandtime.Thefocusismainlyto

understandtherolesofinequalityinopportunitiesthatappealtopublicpolicysuchasthose

thatoperatethroughinterventionsinlabormarkets,particularlyskillacquisitionsand

migration,andpricedistortionsaffectingassetmarkets.

Weundertooktheanalysisattwolevels:inequalitywithincountriesandbetween

countries.The‘within’countryinequalityanalysisdecomposestheGini-coefficientforassets

intospatialandindividual/householdspecificcomponentsusinghouseholdlevelunitrecord

data.Ourfindingindicatesthatspatialinequalityontheaveragecontributescloseto35%-

40%ofoverallassetinequalitywithsignificantvariationacrosscountries.Thefindingsfrom

‘between’countriesanalysissuggestthatconditionalonotherimportantcovariates,suchas

initialpercapitaGDP,sizeofgovernment,etc.,assetorwealthinequalityisnegatively

4

correlatedwithhigherproportionofthelaborforcewithtertiaryeducation,sizeof

remittancesasashareofGDPandpricedistortionsinthemarketforkeyassets.Someof

thekeydriversofinequalityconsideredinthepaperarepotentiallyendogenous.For

example,migrantstendtosendoutmoreremittancesinplaceswherehouseholdassetsare

scarceorexpensivesothatowningthemisvaluabletorecipientsimplyingthathighasset

inequalitymayleadtohigherremittances.Weuseethnicfractionalizationasinstrumenton

theassumptionthatitaffectsassetinequalityonlythroughitseffectonremittances.

Statisticaltestsperformedsuggestethnicitytobeavalidinstrument.Policyimplicationsof

thekeydriversofinequalityarediscussedinlightofthecurrentdebateonindustrialpolicy

andstructuralchange.

2. Analyticalframeworkanddata

2.1. Analyticalframework

Developmenteconomicshastackledandunderstoodinequalityfromtwodifferent

perspectives.Thepersonalorsizedistributionofincome,whichmapsagivenpopulation

withincomeearnedorassetowned.Thisisoftenstatisticalsummarythatprovides

informationonhowequitableasocietyoracountryisatapointintime.Thefocusofthis

paperandmanyothersinthedevelopmenteconomicsdisciplineismainlyonthisaspectof

inequality.Theotherdimensionexaminesthefactorsofproduction,suchaslabor,capital,

landandotherresourcesandprovidesatheoryfordeterminationoftheirreturns,suchas

wages,profit,rentandotherformsofpayments.Thisaspectofinequality,commonlycalled

thefunctionaldistributionofincomehasbeenthebasisofmosteconomictheorieson

inequalitywhichdatesasfarbackastheclassicaleconomistssuchasAdamSmith,David

Ricardo,FrançoisQuesnay,KarlMarx,andotherswhopostulatedinherentconflictamong

the‘classes’becauseofunfairappropriationinthesharingofthenationalpie.Theadventof

themarginalistsinthe1990s‘justified’inequalityasanoutcomeofthefunctioningof

marketforceswheretheearningofeconomicagentsiscommensuratewithits(marginal)

productivity.Itfollowsthatwage,rents,profitsarereflectionsoftheirmarginalproductivity

inproductionwhenmarketsoperatefreelyandunencumbered(e.g.KnutWicksell).In

pursuitofperfectcompetition,theissueofincomeinequalityhasbeenrelegatedtothe

backgrounduntildevelopmenteconomicsinthelate20thcenturyreintroduceditintothe

realmofpublicpolicy.

5

Theearlyliteratureindevelopmenteconomics,includingthatofLewis(1954)andKaldor

(1956)viewedincomeinequalityfromtheprismofeconomicgrowthwheretheyargued

thattherich,becausetheytendtohavehighermarginalsavingratethanthepoor,could

spurgrowtharguingthatinitialinequalitymaybegoodforgrowth.Recentworkbasedon

thenewgrowththeory(e.g.GalorandZeira,1993)showedindeedthathighinitial

inequalitycouldbebadforgrowth.ThestylizedfactdocumentedbyKuznetswhere

inequalitytendstorisewithpercapitaGDPatinitialstageofdevelopmentandlatertends

todecline(betterknownasKuznets’curve)attractedenormousattentionintheempirical

literatureregardingthelinkbetweeninequalityandgrowth.Thisliteratureisvastandno

attemptwillbemadeheretoreviewtheevidence.Forourpurposeswerelyonthesomeof

thehypothesisputforwardinpreviousliteratureonthemechanismsinwhichinequality

persistorincreasesovertimetounderstandwithinandbetweeninequalitypatterns.

Particularly,ofimportancearesuchasinitialdistributionofendowments(education,etc.),

politicaleconomyfactors(elitecapture)/institutionsandredistributivepolicies(e.g.

Acemoglu,etal,2001;Easterly,2007).

AparticularlyusefulwaytounderstandbetterissuesofinequalityinAfricanistothink

oftheroleofdifferentprocessesthatshapeitspatternovertimeandacrossregions,such

asstructuralfactorsandmarketforces(e.g.Easterly,2007).Itisplausibletothinkthatin

mostAfricancountrieswheremarketsarenascentforcesandhavenottakendeeprootsin

resourceallocation,theroleofstructuralfactorstendtobestrong.Someofthestructural

factorsincludethelegaciesofslavery,colonialisminlargeswathofAfricaandthatof

apartheidinSouthAfricahaveleftdeepmarksinthedistributionofland,politicalpower

andotherrelatedprocessesthatimpactdirectlyinequality.Theinequalitiesinducedby

marketforceshavealsodifferentialimpactonhouseholds,firms,regions,etc.This

distinctionisusefulbothforpublicpolicyaswellasidentifyinglongtermcorrelatesor

driversofinequality.Arelatedbutpowerfuldevelopmentintherecentliteratureisthe

decomposingofinequalityinducedbycircumstancesbeyondthecontroloftheindividual

(calledinequalityofopportunities)andthatwithintheboundsofhis/herchoices,suchas

effort.Thisliteratureisimportantinthatitmakesacleardistinctionbetweeninequalities

thatare‘unacceptable’bothongroundsofmoralityandefficiency.Inequalityarisingfrom

circumstancesbeyondone’scontrolincludethatarisesbecauseonebelongstoaparticular

6

race,gender,ethnicity,religionorothergroup,henceearnedlowerforthesamelevelof

effortandability.Whiletheempiricaldistinctionbetweeninequalityofopportunitiesand

thatofeffortischallengingduetothedatarequirements,someestimateshaveprovided

interestinginsightsthatcouldbeinvokedtounderstandsomeofourresults.

2.2. Dataandmethodsofestimation

ThedataweusedforthisstudyisbasedonunitrecorddatafromtheDHSfor44African

countriesinmultiplewavesforatleast30countriescoveringtheperiod1990-2013(see

AppendixTable1).Foreaseofanalysis,wegroupedtheperiodsintopre1995,1996-2000,

2001-2005,2006-2013.Thedataconsistsofhistoriesofoveramillionhouseholdsover

theseperiods.Thedatacoversawiderangeofvariablesincludingdemographic

characteristics,assetownership;accesstoutilitiesandbasicsocialservices,educationand

occupationofhead,awiderangeofhealthoutcomes(stunting,wasting,diseasesburden)

anditisnationallyrepresentative.Sincethesurveyinstrumentsandmethodsaregenerally

standardized,ittheyarecomparablespatiallyandtemporally.Toconstructourmeasureof

assetinequality,weresortedtenitemsforwhichdataisavailableinallwavesforall

countries.Theseare,typeofhousing(numberofrooms,floormaterial-perke,cement,

ceramic,earth-,roofmaterial-bricks,tin,grass,earth,etc.);),sourcesofaccesstowater(tap,

waterkiosk,well,etc),accesstoelectricity,andownershipofdurablehouseholdassetssuch

asradio,TV,refrigeratorandcar.Thechallengeistogenerateasingleassetindexthat

wouldallowustocomputetheGinicoefficientforassets.

FollowingShimelesandNcube(2015),wedefinedawelfaremeasureforeachhousehold

Wj,overindividualconstituentscijsuchthat:

!" = $%&%"'%() (1)

Wherethe‘i’representsthekassetsthatindividual‘j’possessestoachieveawelfarelevel

Wj,whichcouldbecardinalorunitfree(ordinal)dependingonhowthecomponentsenter

thewelfaremeasure.Thelinearityin(1)assumesthatthewelfareisadditiveoverthe

constituents(inourcasetheindividualassets)allowingapossibilityforaperfect

substitutionacrosstheindividualassets.Ifcijwereconsumptionitems,thenWjwouldbe

totalconsumptionexpenditurewithapricevectorai,wherepricesservedasrelative

7

weightsforunitcommodity.Herewelfareisassumedtorisewithtotalexpenditureafeature

sharedbyutilitybasedwelfarefunctionstoowellknownineconomics.Inthecaseofassets

ownershipsincetherearenopriceinformationtoaggregatethetotalvalueofassetor

wealthowned,aiwouldhavetobegeneratedfromthedatawithsomeassumptions.The

easiestassumptionwouldbetovalueeachassetequallyasimportanttothehousehold.In

thatcase,$% = )',sothatmeanassetownershipvaluewouldbegeneratedwithcijasa

binaryvariable(whetherornotahouseholdownstheasset).Thisassumptioncomesat

greatcostwhereeachassetwouldcontributeequallytothewellbeingofthehousehold

bothinvalueandutility.Forinstance,owningaradioisconsideredasvaluableasowninga

car,etcwhichessentiallydistortssignificantlytheinequityunderlyingownershipofassetsof

differentvalueandutility.Thecommonapproachintheempiricalliteratureistousedata

reductionmethodstogeneratetheindividualweightsaswellasasingleindexthathasthe

potentialtoreflecttheintrinsicvalueofeachoftheassetsandthedifficultyofowning

them.Inthisstudy,weuseMultipleCorrespondenceAnalysis(MCA)whichiscloselyrelated

withfactoranalysisorprincipalcomponentsanalysis.TheonlydifferenceisthattheMCAis

suitableforcategoricalvariables(forexample,Booyseen,etal,2008).iFormally,ifwe

denote$" theweightofcategoryjand*%" theanswerofhousehold+tocategory,,thentheassetindexscoreofhousehold+is:

-./% = $"0"() *%" (2)

This indexcanthenbenormalizedbetween0and1 toallowfor inter-temporalandcross

countrycomparisonsbythefollowingformula

1234$5+678_-./% = :;<=>?@A(:;<)?EF :;< >?@A(:;<) (3)

3. Resultsanddiscussion

HowunequalisAfrica?Thisisapointwetakeupbrieflybeforewepresentourresultsfrom

theDHSdata.Figure1showsthelevelofGinicoefficientbasedonhouseholdsurveysas

8

reportedinWorldBank’spovcalnetdatafortheperiod1982-2011.Thefigurecomparesthe

GinicoefficientforAfricaandOtherDevelopingregions(LatinAmericaandAsia).

<Figure1here>

Whatemergesisthatdespitethelevelof‘development‘ascapturedbypercaptiaincomes,

Africancountriesgenerallytendtoexhibithigherinequalitythantherestofthedeveloping

world.TheresultremainsunchangedevenafterweremovedfromtheAfricansamplethe

toptenmostunequalcountriestoreducetheirinfluenceindrivingtherestofthe

continent’sinequalitypattern.GiventhattheOtherDevelopingcountriesaremadeupof

mainlyLatinAmerica,highlyunequalcontinent,andAsia(withtherelativelylowincome

inequality)theresultmaynotbesurprising.Toseetheeffectofmergingthesetwo

continents,wealsoplottedthesamegraphforthethreeregions(LatinAmerica,Asiaand

Africa).Stillthepicturewegot(notreported)isthatwhileLatinAmericatendtohavethe

highestGiniforhigherlevelofpercapitaGDP,atthelowerend,itisAfricancountrieswho

exhibitedthehighestinequalityofallregions.Figure2belowplotsthetrendintheGini

coefficientforAfricancountrieswhichindicatedasteadyraiseinthe1980sand1990s.It

levelledoffinthe2000decade.StilltheaverageGinicoefficientisintherangeof40%that

impliesthetop20%ownalmost60%ofincome.Thus,itbegsaquestionthatwhydowesee

sohighinequalityinAfrica?Thenextsectionattemptstotackletheseissues.

<Figure2here>

3.1. Inequalitywithincountries

Theuseofmicro-datathatcoversoveramillionobservationofferauniqueopportunityto

constructapatternthatcouldshadeinsightintotheevolutionofinequalityinAfrica.In

decomposingthecomponents,weappealedasindicatedinsection2ofthepaperthe

recentliteraturethatattributesthesourcesofinequalitytostructuralandmarketfactorsas

inEasterly(2007)orinequalityofopportunitiesandeffortasin(xxx).Thishelpstoorganize

thethinkinginlininguptherelevantvariables.Assuchtherefore,wegroupedhousehold

specificvariables,suchaseducation,occupation,age(proxyforexperience)asrepresenting

typesofinequalitythatcouldbeattributedtomarketforcesoreffort.Thestructural

barriersarerepresentedbygender,butalsogeography.Herethelatterisabitcontroversial

9

asmarketsalsocreatewedgeinincomesorassetownershipbetweenregions.However,

onecouldarguethatthenascentnatureofmarketforcesinmostAfricancountriesandthe

patternofsettlementsthatoftenfollowethnicorreligiousidentity,thegeographicor

spatialcomponenthasthepotentialtocapturemainlyelementsofinequalitydrivenby

factorsbeyondthecontrolofindividuals(politicaleconomyfactors,history,linguistic

barriers,ethnicity,etc).

Table1belowreportstheasset-basedGinicoefficientfor44Africancountriesthatcover

atleast65%ofAfrica’spopulationineachperiod.Asindicatedabove,notall44African

countriesweresurveyedinallperiods.But,inanyoneoftheperiods,thenumberof

countriescoveredwasmorethan25allowingforreasonableestimateofasset-based

inequalityforAfrica.Thekeymessageisthatasset-basedinequalityhasbeenhighinAfrica

intherangebetween40-45%.Thisisasignificantlyhighnumber.Itcouldeasilyimplythat

thetop1%owned35to40%ofthehouseholdassetandamenitiesinacountry.Theother

aspectisthatithasbeenpersistentlyhighovertwodecades,nosignofdeclining.Thisis

indeedalsoquiteworrisome.Aninteresting,nosomuchsurprising,aspectoftheasset-

basedinequalityisthatthecontributionofspatialinequalityisquitesignificant,hovering

around35%inallperiods,whilethatofhouseholdeducation,occupationorage(proxyfor

experience)explainonlycloseto10%oftheoverallinequality,therestbyotherfactors

(unobservedfactors).

<Table1>

Whenwelookcloseratthespatialdimensionofinequality,wealsonotethatthereisa

widedifferenceacrosscountriesrangingfromahighofaround61%inplaceslike

Madagascar,AngolaorNigerandlowestrangingaround10%insmallcountrieslike

Comoros,orwelldevelopedplaceslikeEgypt.Thespatialcomponentofassetinequality

stronglyhasallthemarksofwhatweidentifiedasstructuralinequalityoronethatcaused

bycircumstancesbeyondthecontrolofindividualsasinmoralphilosophyofRomer(xx).

Figure3forinstancesuggeststhatthereisastrongcorrelationbetweengovernance

(aggregateMoi-Ibrahimindex)andethnicfractionalization(notreported),yetnosystematic

correlationwithpercapitaGDP.Table3illustratestherelationship.Closeto25%ofthe

variationinspatialinequalityisduetoeconomicgovernanceandethnicfractionalization.In

10

theformer,highervaluesorbettergovernancewascorrelatedwithlowerspatialinequality

andethnicallydiverseorfractionalizedcountriesexhibitedhighspatialinequality.This

suggeststhatthisportionofinequalityechoEasterly’s(2007)structuralinequalityorthe

inequalityofopportunitydiscussedinprecedingparagraphs.Anotherinterestingfindingwe

presentisthatspatialinequalityishighlycorrelatedwithincidenceofchildandmaternal

mortalityaswellasotherindicatorsofhumanopportunity.Thisisquiteausefulinsightinto

theseriousnessofspatialinequalityinaffectinglivingstandardsaswellindependentlyof

percapitaincome.

3.2. Inequalitybetweencountries

Thelongtermrelationshipbetweeninequalityandasetofotherpolicyrelevantfactors

couldbeinferredthroughcross-countrycomparisons.Table2providesthedescriptive

statisticspertainingtoourattempttoestablishsomelevelofcorrelationbetweeninequality

andotherconditioningvariablessuchasinitialpercapitaGDP(aproxyforinitial

endowments),sizeofgovernment,educationparticularlytertiaryeducation,market

distortionsbothforassetandcommodities.Animportantdimensionthathasalsobecome

increasinglyrelevantwhendiscussinginequalityisinterpersonalincometransferssuchas

remittancesintheabsenceofredistributivepoliciesandpracticesintheAfricancontext.

Table(4)reportsasetofregressionresults(allcorrectedforheteroscedasticity)for

thepooleddatausingtheassetbasedinequalityfromtheDHS.Theresultsareenlightening.

Tertiaryeducationturnsouttobeanimportantpredictoroflowerinequalitywithlarge

coefficient.Countrieswithonestandarddeviationhigherproportionofhouseholdswith

tertiaryeducationexperiencedadeclineinassetinequalityofabout17%.Similarly,we

foundremittancestobeanimportantpartofthestoryinreducinginequality.Giventhe

strongemphasisinpreviousliteratureonethnicfractionalizationasimportantdriverof

inequality,weexaminedthepossibilitythatethnicitymaybepickinguptheeffectsof

remittances.First,remittancesandethnicfractionalizationarehighlycorrelated.Barring

spuriouscorrelation,themechanismcouldbethroughmigration.Ethnicallyhomogenous

societiestendtohavestrongernetworkswhichfacilitatesmobilitywithinandoutsideofa

country.ThefirststageregressionwereportedinTable4atteststothispossibility.

Furthermore,theethnicityvariablewithallitsproblemsofmeasurementishardlyan

11

endogenousvariablethatvarieswithcharacteristicsofcountries,particularlythosethat

potentiallyaffectbothremittancesandethnicityatthesametime.Withtheseassumptions,

wefoundthatremittancesaffectinequalitysignificantly.Asarobustnesstestwerand

similarregressionsforconsumptionbasedinequalitygeneratedfromacompletelydifferent

dataset.Stillremittancesbeartherightsignandsignificanceastheassetbasedinequality

(Table5).Inbothcasesthetestofexogenityalsosuggestsethnicitytobeavalidinstrument

forremittances.WealsonoteinTable5thatmarketdistortionsparticularlywithrespectto

consumptioninequalityplayanimportantrole.Thehigherthedistortionfromtheworld

market,thehigherthelevelofincomeinequality.

4. Conclusions

WedocumentedthatAfricahashadhighinequalityinthelasttwodecadesthathas

persistedovertime.Inthispaperattemptwasmadetogivesomeinsightonthepossible

driversofinequalityusingaconsistentlyconstructedassetbasedinequalityfromtheDHS

datasetusingunitrecorddataofoveramillionhouseholds.

Weapproachedinequalityfromtheperspectiveofitstwomainsourcesemphasizedinthe

recentliterature:structuralandmarketdrivenwhichmayalsobeviewedfromthe

perspectiveofinequalityofopportunitiesandindividualeffort.Theinequality

decompositionthatemergedshowedthatspatialinequalitytohaveastrongerrolein

drivingoverallassetinequalityinAfrica,whichinturnisdrivenmainlybygovernance

conditionsandethnicfractionalization.Interestingly,thespatialdimensionofinequality

wasuncorrelatedwithpercapitaincome.Inaddition,spatialinequalityseemtohavean

independenteffectoninfantandmaternalmortality,diseaseburdenaswellashuman

opportunity.Thisisaninterestingfindingthatneedstobefurtherstudied.Highspatial

inequalityisafettertohighstandardoflivingandessentiallyunaffectedbyhowhighthe

averagelevelofdevelopmentofacountryis.

Ourstudyalsoidentifiedimportantcorrelatesofinequalityusefulforpolicy.Thisinclude

tertiaryeducationandremittancesasimportantfactorsthatmaylowerinequalitybeitof

assetorincome.Ofparticularimportancetoincomeinequalityisalsopricedistortion

whichgenerallycapturestherelativescarcityofconsumptiongoodsincomparisontothe

12

worldmarket.Allthesesuggestthatspecificandwellimplementedpoliciesarerequiredto

advanceinclusivegrowthinAfricawherethebarriersseemtostemlargelyfrompoor

governanceandfragmentationalongethnicandlinguisticlines.

13

References

Acemoglu,Daron,Johnson,Simon,Robinson,James,2002.ReversalofFortune:GeographyandInstitutionsintheMakingoftheModernWorldIncomeDistribution.QuarterlyJournalofEconomics,Vol.117,1231–1294.

Bigsten,A(2014),“DimensionsofincomeinequalityinAfrica”,WIDERWorkingPaper2014/050

Booysen,F.,vanderBerg,S.,Burger,R.,Maltitz,M.v.,&Rand,G.d.(2008).UsinganAssetIndextoAssessTrendsinPovertyinSevenSub-SaharanAfricanCountries.WorldDevelopment,36(6),1113-1130.

Brunori,Paolo,FranciscoH.G.Ferreira,VitoPeragine(2013),“InequalityofOpportunity,IncomeInequalityandEconomicMobility:SomeInternationalComparisons”IZADiscussionPaper7155

Devarajan,S.(2013).Africa'sStatisticalTragedy.ReviewofIncomeandWealth,59,S9-S15.

Easterly,W(2007),“Inequalitydoescauseunderdevelopment:Insightsfromanewinstrument”,JournalofDevelopmentEconomics,84(2007)755–776

Fearon,J.D.,&Laitin,D.D.(2003).Ethnicity,Insurgency,andCivilWar.AmericanPoliticalScienceReview,97(01),75-90.

Kaldor,Nicholas,1961.Capitalaccumulationandeconomicgrowth.In:Lutz,F.A.,Hague,D.C.(Eds.),TheTheoryofCapital.St.Martin'sPress,NewYork.

Galor,Oded,Zeira,Joseph,1993.Incomedistributionandmacroeconomics.ReviewofEconomicStudies60,35–52.

Glaeser,E.L.(2005).Inequality.NBERWorkingPaperSeries(11511).

Milanovic,B.(2003),“IsinequalityinAfricareallydifferent?”,WorldBank,Washington,mimeo.

Shimeles,AandM.Ncube(2015),“Themakingofthemiddle-classinAfrica:evidencefromhouseholdsurveys”,JournalofDevelopmentStudies,51(2)

Ravallion,M.andS.Chen(2012).‘MonitoringInequality’.Mimeo.Availableonlineat:https://blogs.worldbank.org/developmenttalk/files/developmenttalk/monitoring_inequality_table_1_.pdf.

14

Robinson,B.(2002).IncomeInequalityandEthnicity:AnInternationalView.Paperpresentedatthe27thGeneralConferenceoftheInternationalAssociationforResearchinIncomeandWealth,Stockholm,Sweden.

15

Table1.Inequalitylevelsin44AfricancountriesPeriod AverageGini

coefficientforassets

Componentduetospatialinequality

Componentduetoinequalityofopportunities1

Componentduetoother

factors

Before1995 0.42 0.37 0.11 0.521996-2000 0.43 0.34 0.13 0.532001-2005 0.38 0.32 0.13 0.542006-2009 0.40 0.34 0.14 0.512010-2013 0.44 0.39 0.13 0.47

Table2:DescriptiveStatistics

Variable Obs Mean Std.Dev. Min MaxAssetGini 109 0.461 0.136 0.081 0.758Moi-IbrahimGovernanceindex 93 49.622 9.826 28.800 71.500Ethnic-fractionalization 92 0.664 0.229 0.000 0.930Plural 93 0.434 0.233 0.120 0.910highereducationcoefficient 92 0.892 0.428 0.123 2.418Highereducationcoefficientfrompooledsampleindex 89 0.745 0.268 0.066 1.383Tradeopenness 102 0.482 0.735 0.096 4.539MeanyearofSchooling 82 4.037 1.952 0.700 10.800remittances(ratioofGDP) 84 0.025 0.028 0.000 0.105Assetpricedistortion 97 0.115 0.671 -0.735 3.134Governmentexpenditurein1995(%GDP) 98 24.93 8.571 14.54 56.34logof1985GDP 98 7.116 0.699 5.742 9.712BankCredittoPrivatesector(%ofGDP) 93 18.19 16.180 2.414 118.15Urbanization 102 34.99 11.689 11.72 66.060

1Componentoftheinequalityduetohouseholdheadeducationlevel,occupationandage

16

Table3:spatialinequality,ethnicityandgovernance(heteroscedasticitycorrectedregression)Dependentvariable:spatialinequality Ethnicfractionalization 0.191** (0.058)Moi-Ibrahimgovernanceindex -0.00355* (0.00153)LogpercapitaGDPin2000prices 0.00354 -0.0174Constant 0.379** (0.136)N 51R2 0.26Standarderrorsinparentheses*p<0.05,**p<0.01,***p<0.001

Table4:correlatesofassetinequality(regressioncorrectedforheteroscedasticity)Dependentvariable:Ginicoefficientforasset OLS OLS IVEthnicfractionalization 0.187*** 0.0847

(0.000) (0.139) Skillgap(tertiaryeducation) 0.141*** 0.165*** 0.159***

(0.000) (0.000) (0.000)Assetpricedistortion 0.0411** 0.0295 0.0289

(0.005) (0.129) (0.122)Sizeofgovernment -0.00111 -0.00397 -0.0070**

(0.564) (0.11) (0.003)InitialpercapitaGDP -0.0559* -0.0443 -0.0458**

(0.015) (0.057) (0.006)Remittances -1.008 -2.377**

(0.054) (0.003)Timedummies Yes Yes YesTestsofExogenity Durbin(score)chi2(1) 2.62689 (p=0.105)Wu-HausmanF(1,52) 2.19002 (p=0.145)F-valueFirstStageRegression 15.46N 78 65 65P-values in parenthesis. *p<0.1,**p<0.05,***p<0.01

17

Table 5: correlates of consumption based Gini

OLS OLS OLS IVEthnicfractionalization 0.368*** 0.382*** (0.0709) (0.0790) Highereducationenrollment -0.00367 -0.00350 -0.0111*** -0.00828*** (0.00229) (0.00234) (0.00217) (0.00239)Householdconspricelevel2 0.107*** 0.1000*** 0.181*** 0.206*** (0.0341) (0.0357) (0.0413) (0.0483)Sizeofgovernment 0.0252 0.0262 0.0451** 0.0650** (0.0197) (0.0206) (0.0204) (0.0279)Agriculturevalueadded(%GDP) -0.00339** -0.00359** -0.00345** -0.000751 (0.00154) (0.00166) (0.00170) (0.00305)Urbanizationrate 0.00702*** 0.00719*** 0.00705*** 0.00832** (0.00165) (0.00173) (0.00198) (0.00379)Remittances 0.000278 -0.00955 -0.0850*** (0.0104) (0.0116) (0.0218)_cons 2.988*** 2.945*** 2.938*** 2.454*** (0.407) (0.415) (0.452) (0.713)R-sq 0.673 0.690 0.564 0.345N 107 95 100 95TestsofExogenity Durbin(score)chi2(1) 0.632125 (p=0.2897)Wu-HausmanF(1,60) 0.611246 (p=0.3164)F-value(1,87)FirstStageRegression 13.7933 (p=0.0000)

Figure1:InequalityinAfrica&OtherDevelopingregionsatdifferentlevelofdevelopment(1980-2011)

2Thepricelevelofhouseholdconsumptionisthepriceleveloftheshareofoutput-basedGDP(thehouseholdconsumptionpart)relativetotheUSone.

18

Figure2:IncomeinequalitytrendsinAfrica

Figure3:spatialinequalityandgovernance

2030

4050

60

Gin

i coe

ffici

ent (

cons

umpt

ion)

3 4 5 6 7Log of per capita consumption in PPP

Africa Africa without the top 10 inequals countriesNon African developing countries

3840

4244

46

Gini co

efficie

nt (con

sumptio

n)

1980 1990 2000 2010Survey year

Africa Africa without the top 10 inequals

19

Figure4:spatialinequalityandaccesstoimprovedwater

AGO

BDI

BEN

BFA

CMR

EGY

ETH

GHA

KEN LSO

MAR

MDG

MOZ

MWI

NAM

RWA

SWZ

TCD

TZA

UGA

ZMBZWE

0.2

.4.6

Spat

ial in

equa

lity

30 40 50 60 70Mo Ibrahim Governance Index

AGO

BDI

BEN

BFA

CMR

EGY

ETH

GHA

KEN LSO

MAR

MDG

MOZ

MWI

NAM

RWA

SWZ

TCD

TZA

UGA

ZMBZWE

0.2

.4.6

Spat

ial in

equa

lity

40 60 80 100Access to Improved Water

20

Figure5a-5b:Spatialinequalityanddiseaseburden(mortality,tuberculosis,etc)

Figure5b

AGO

BDI

BEN

BFA

CMR

EGY

ETH

GHA

KEN LSO

MAR

MDG

MOZ

MWI

NAM

RWA

SWZ

TCD

TZA

UGA

ZMBZWE

0.2

.4.6

Spat

ial i

nequ

ality

0 500 1000Maternal Mortality

AGO

BDI

BEN

BFA

CMR

EGY

ETH

GHA

KENLSO

MAR

MDG

MOZ

MWI

NAM

RWA

SWZ

TCD

TZA

UGA

ZMBZWE

0.2

.4.6

Spati

al ine

quali

ty

0 20 40 60Incidence of Tuberculosis

21

AppendixTable1Country NumberofhouseholdsAngola 9,950Benin 27,257BurkinaFaso 32,925Burundi 8,596Cameroon 31,615CentralAfricanRepublic 5,485Chad 11,556Comoros 2,066Comoros 4,482Congo 11,767CongoBrazzavil 11,632CongoDRC 18,171Coted'ivoire 9,686Côted'Ivoire 10,606Dem.Rep.oftheCongo 8,728Egypt 81,218Ethiopia 43,761GABON 9,755Gabon 5,882Ghana 28,144Guinea 17,907Kenya 24,556Lesotho 17,562Liberia 24,003Madagascar 38,020Malawi 55,327Mali 41,651Morocco 32,065Mozambique 19,819Namibia 18,371Niger 24,580Nigeria 86,078Rwanda 36,569Senegal 30,748Senegal 4,175SierraLeone 19,639SouthAfrica 11,708Sudan 5,125Swaziland 4,602Tanzania 34,624Togo 7,072Uganda 35,743Zambia 26,617Zimbabwe 29,419Total 1,019,262

22

iSeeSahnandStifel(2000)forapplicationoffactoranalysistoassetpovertyinselectedAfricancountries.