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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.
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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
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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.
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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
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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:
!" = $%&%"'%() (1)
Wherethe‘i’representsthekassetsthatindividual‘j’possessestoachieveawelfarelevel
Wj,whichcouldbecardinalorunitfree(ordinal)dependingonhowthecomponentsenter
thewelfaremeasure.Thelinearityin(1)assumesthatthewelfareisadditiveoverthe
constituents(inourcasetheindividualassets)allowingapossibilityforaperfect
substitutionacrosstheindividualassets.Ifcijwereconsumptionitems,thenWjwouldbe
totalconsumptionexpenditurewithapricevectorai,wherepricesservedasrelative
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weightsforunitcommodity.Herewelfareisassumedtorisewithtotalexpenditureafeature
sharedbyutilitybasedwelfarefunctionstoowellknownineconomics.Inthecaseofassets
ownershipsincetherearenopriceinformationtoaggregatethetotalvalueofassetor
wealthowned,aiwouldhavetobegeneratedfromthedatawithsomeassumptions.The
easiestassumptionwouldbetovalueeachassetequallyasimportanttothehousehold.In
thatcase,$% = )',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
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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
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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
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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
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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
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worldmarket.Allthesesuggestthatspecificandwellimplementedpoliciesarerequiredto
advanceinclusivegrowthinAfricawherethebarriersseemtostemlargelyfrompoor
governanceandfragmentationalongethnicandlinguisticlines.
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Milanovic,B.(2003),“IsinequalityinAfricareallydifferent?”,WorldBank,Washington,mimeo.
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Ravallion,M.andS.Chen(2012).‘MonitoringInequality’.Mimeo.Availableonlineat:https://blogs.worldbank.org/developmenttalk/files/developmenttalk/monitoring_inequality_table_1_.pdf.
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Robinson,B.(2002).IncomeInequalityandEthnicity:AnInternationalView.Paperpresentedatthe27thGeneralConferenceoftheInternationalAssociationforResearchinIncomeandWealth,Stockholm,Sweden.
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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
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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
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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
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TCD
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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.
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