Financial development and poverty reduction in developing countries: New evidence from banks and microfinance institutions

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ScienceDirect

ReviewofDevelopmentFinance6(2016)82–90

Financial

development

and

poverty

reduction

in

developing

countries:

New

evidence

from

banks

and

microfinance

institutions

Ficawoyi

Donou-Adonsou

a,∗

,

Kevin

Sylwester

b

a_Department_of_Economics_and_Finance,_John_Carroll_University,₁_John_Carroll_Boulevard,_University_Heights,_OH_44118,_United_States b_Southern_Illinois_University,_Carbondale,_United_States

Availableonline3August2016

Abstract

Theliteratureonfinancialdevelopmentandgrowthhasreceivedalotofattentionoverthe pasttwodecades.Unlikegrowth,notmuchof considerationhasbeengiventopovertyreduction.Moreover,mostofthepaststudiesfocusonbankandstockmarketdevelopment.Theadventof microfinanceinstitutions(MFIs)letstothinkaboutthepotentialroleMFIscanplayinacountrywideeconomy.Inthisstudy,weconsidertowhat extentbanksandMFIsreducepoverty.Weapplytheinstrumentalvariablesapproach,namelythefixed-effectstwo-stageleastsquares,toapanel of71developingcountriesovertheperiod2002–2011.UsingcredittoGDPasthemainfinancialdevelopmentindicator,theresultsindicatethat banksreducepovertywhenpovertyismeasuredbytheheadcountratioandpovertygap.Asforthesquaredpovertygap,thereisnosignificant effectofbanks.Ontheotherhand,MFIsdonotappeartohaveanyimpactonpovertyregardlessofthemeasureofpovertyemployed.Theseresults implythatwhilebankshavesomeabilitytoreducepoverty,MFIsdonot,atleastattheaggregatelevel.Ourresultsarerobusttotheuseofassets toGDPasanalternativemeasureoffinancialdevelopment.

JELclassiﬁcation: O11;G21

Keywords: Banks;Microfinance;Poverty;Developingcountries

1. Introduction

Numerous studies have considered interactions between

financial development and economic growth, including the

directionofcausalitybetweenthetwo.Someresearchhasalso

considered poverty and financial development. These studies

includeHonohan(2004),Becketal.(2007),Odhiambo(2010),

and Jeanneney and Kpodar (2011). Studies such as Jalilian and Kirkpatrick (2002) consider a “trickle-down” approach.

BuildingupontheframeworkofBecketal.(2000)andDollar

andKraay(2002),JalilianandKirkpatrick(2002)firstconsider

how financialdevelopmentaffects economicgrowthandthen

examine to what extent growth reduces poverty. A common

elementofthesestudiesisthattheyhaveconsideredeconomy

∗_{Corresponding}_author._Tel.:₊₁₂₁₆₃₉₇_4970;_fax:₊₁₂₁₆₃₉₇_1728.

E-mailaddresses:[email protected](F.Donou-Adonsou), [email protected](K.Sylwester).

PeerreviewunderresponsibilityofAfricagrowthInstitute.

wide measures of financial development, such as money and

quasi money, market capitalization, or private credit in their

empirical work. Suchindicators failtocapture how different

institutionswithinthefinancialsectorinfluencepoverty.

Thisomissioncouldbeespeciallyimportantwhenexamining

microfinanceinstitutions(MFIs)sincetheywerepromotedfor

thespecificpurposeofprovidingfinancialservicestothepoor,

especiallycredit,inordertoalleviatepoverty.Microfinancefirst

gained prominenceduring the1970s withorganizations such

as the GrameenBank in Bangladesh and the work of

advo-cates like Mohammad Yunus. The scope of MFIs has since

grownmanifold.From 2002to2011,thegrossloanportfolio

ofMFIsindevelopingcountriesincreasedbymorethan1700%

anditsnumberofactiveborrowersincreasedby400%.1MFIs

havebeenmostprevalentinSouthAsia,especiallycomparedto

otherpoorregionssuchasAfricaandLatinAmericaalthough

theyhavegrownintheseregionsaswell.Theyalsooftenserve

1_MIX_Market_data_from_{www.mixmarket.org}_.

http://dx.doi.org/10.1016/j.rdf.2016.06.002

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ruralcommunitieswherebanksoftencannotbefound.Although

MFIs also make loansfrom their deposits as do banks,their

financingcanalsocomefrominvestorborrowing,fromequity,

andfromgrants.Sincetheyoftenservepoorcommunities,loans

toindividualsaregenerallymuchsmallerthantypicalbankloans

andoften not collateralized. Therefore,given that banks and

MFIsservedifferentclienteles,makedifferenttypesofloans,

andare financedfromdifferent sources,theycapturedistinct

aspects of financial developmentand so could havedifferent

impactsuponpovertyalleviation.

Theobjectiveofthispaperistocomparetraditionalbanks

tomicrofinanceinstitutionsastowhatextenteachcontributes

topovertyreduction.Inthissense,wecoincidewithJeanneney

andKpodar(2011)inthatwecharacterizefinancialdevelopment

as access tofinancial services inthe “banking system”

com-posedoftraditionalbanksandMFIs.StudiessuchasKhandker

(2005)andMahjabeen(2008)haveconsideredtowhatextent

microfinance has lowered poverty at the local level. More

recenttheoreticalwork(AhlinandJiang,2008;Yusupov,2012;

Bueraetal.,2012)hassuggestedthepotentialfor

macroecono-miceffectsofmicrofinance.Forexample,Donou-Adonsouand

Sylwester(2015)findthatmicrofinanceloangrowthincreases

economic growth and total factor productivity indeveloping

countries.Inthispaper,wetakethepotentialforMFIstohave

macroeconomic impacts seriously, especially giventhe rapid

growthinMFIs,andexaminetowhatextentthisrisehasbeen

abletoreducepovertyatanationallevel.Comparingthiseffect

tothatfromtraditionalbankshelpstoplaceanyimpactinbetter

contextaswellastoseeifMFIsdo,indeed,playanenhanced

roleinreducingpoverty.

AnexampleofsuchacomparisoncomesfromThanvi(2010)

for the Cooch Behar District of WestBengal, India. Thanvi

describesashiftfrombankstoMFIsduetotheunavailability

(inpartortotal)ofloansfrombanks.ForThanvi,MFIs

supple-menttheroleofbanksbyreachingtheunreached.Inthisway,

onemightinferthatMFIsshouldbeabletoreducepovertytoa

largerextentthanbanks.However,Thanvialsodocumentsthat

MFIschargehigherinterestrates.OneMFI,Bardhan,charges

aneffective rate of 24%,twice that chargedby banks.These

higherratesraisequestionsregardinghoweffectiveMFIsareat

reducingpoverty.

Thisstudyemploysaninstrumentalvariablesapproachtoa

panelof 71developing countriesoverthe period2002–2011.

The results indicate that banks reduce poverty when poverty

is measured by the headcount ratio or the poverty gap but

not when poverty is measured by the squared poverty gap.

On the other hand, MFIs do not appear to have any impact

on poverty regardless of the measure employed. While the

results suggest that banks play a role in reducing poverty,

MFIsdonotappeartohavedoneso,atleastatthe aggregate

level.

Thepaperisstructuredasfollows:Section2providesamore

detaileddescriptionoftheliterature.Section3describesthedata

andoutlinesthemethodology.Section4presentsandexplains

theresults.InSection5,weprovidearobustnesscheckusing

analternativemeasureoffinancialdevelopment,andSection6

offersconcludingdiscussion.

2. Literaturereview

Greenwoodand Jovanovic(1990) develop amodel where

financial intermediaries analyze imperfect information and

channelfundsfromsaverstoborrowers.Theirmodelincludesa

participationcost,alump-sumfeethatagentsmustpayto

partic-ipateinthefinancialsector.Thisfeeeffectivelykeepsthepoor

fromtakingadvantageof opportunitiesinthefinancialsector.

Notonlywouldthepoornotbenefit,buttheincomedistribution

could evenwidenbetweenlowandhighincomeagents.This

implicationisendorsedbyStiglitz (1993)for whomfinancial

marketfailureisthefundamentalcauseofpovertyin

develop-ingcountries.Applyingthismodeltoourcase,theparticipation

feewouldlikelybelowerforMFIsandsotheywouldbe

bet-terabletoobtaincredit,invest,andescapepoverty.Ofcourse,

whetheritissufficientlylowsoastobenefitthepoorisanother

question.

Such considerations have not been examined at a

macro-economic level where, as stated,economy wide measuresof

financialdevelopmentareused.JalilianandKirkpatrick(2002),

Beck et al. (2008), and Jeanneney and Kpodar (2011) have

usedthetrickle-downapproach–anindirecteffectoffinancial

developmentonpovertyreductionthrougheconomicgrowth–

toinvestigate financialdevelopment andpovertyreduction in

developingcountriesandfindthatfinancialdevelopmentfosters

growthwhichthenreducespoverty.Forinstance,Jalilianand

Kirkpatrick (2002) argue that by widening financial services

access to the poor,their income will grow, whicheventually

will reduce poverty. For example, an insurance service

pro-videdtothepoorcanbetterprotectthemagainstincomeshocks.

Otherstudieshaveinvestigatedthedirectrelationshipbetween

financialdevelopmentandpovertyreductionortheincome

dis-tribution.Thesestudiesinclude Honohan(2004),Jalilianand

Kirkpatrick(2005),Becketal.(2007),Perez-Moreno(2011),

JeanneneyandKpodar(2011),andSehrawat andGiri(2015)

although theydiffer bothinterms of what proxies for

finan-cialdevelopmenttheyuseaswellasintheiroutcomevariable

(headcountratio,povertygap,Ginicoefficient,etc.).

GiventhepurportedroleofMFIsinassistinglowerincome

households,variousstudieshavefocusedupontheseinstitutions

andexaminedtowhatextenttheycanhelpraiselivingstandards

amongthepoor. Severalstudieshavefoundbeneficialeffects

uponconsumption or income(Khandker,2005; Kondoet al.,

2008;Berhane,2009;Collinsetal.,2009;ImaiandAzam,2011; BerhaneandGardebroek,2011),housingconditions(Berhane, 2009;BerhaneandGardebroek,2011),village-levelwagesand

investmentinagriculture(KaboskiandTowsend,2012),savings

(Kondo etal., 2008; Dupas andRobinson, 2009),andhealth

andfoodsecurity (Stewartetal.,2010).Otherstudiesremain

skeptical.Forinstance,Chowdhury(2009)castsdoubt onthe

effectivenessofmicrofinanceasapovertyalleviationtoolgiven

theprofit-seekingnatureoffinancialinstitutions.Hearguesthat

microfinance,thoughitprovidesasafetynetandcanhelpsmooth

consumption, needsits borrowers tohavebusiness skillsand

marketinginformationforloanstoexpandbusinessesandcreate

jobs.Likewise,CopestakeandWilliams(2011)arguethatMFIs

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povertyasobstaclessuchasselectionbiasweakenthepositive

effectsofMFIsonhouseholds’welfare.

Theseempirical studiesexaminetheeffectsof MFIsatthe

local level, butto what extent might MFIs influencepoverty

at the national level? Even if the effects of any one MFI

are only felt locally, countrywide effects could still arise if

MFIswerelocatedinmanycommunitiesasinmany

develop-ing countries.Another possibility for macroeconomic effects

could occur through spillovers, especially if increasing

con-sumption or investment spurs additional job creation. Buera

et al.(2012) build an economy-wide model of

entrepreneur-shipwhereinMFIsservicethepoorwhocannotborrowinthe

“traditional”financialsector.Theythenexploretowhatextent

MFIsinfluenceoutput,capital,totalfactorproductivity,wages,

andinterestrates,findinginsomecasesthatMFIsnotonlyraise

outputbutdecreasedisparitiesbetweenrichandpoor.Ahlinand

Jiang(2008)andYusupov(2012)alsofindthatMFIspromote

developmentonawiderscale.

3. Dataandmethodology

3.1. Data

Banksaredefinedasfinancialinstitutionsthatacceptdeposits

andmakeloans.Inthisstudy,weconsiderdepositmoneybanks

commonlystudiedinthefinance-growthliterature.Thesebanks

accept demand deposits, saving deposits, and time deposits

(Beck et al., 2013), andare composedof commercial banks

and otherfinancial institutions such as thrift institutions and

credit unions.Microfinance, on the other hand, isdefined as

institutionsthatprimarilyprovidefinancialservicestothepoor.

They maybecomparedtocreditunionsinterms ofstructure

andactivities. However, microfinance institutions differ from

banks in that they receivemost of their funding from

exter-nal loans, grants, or investors. They also differ from banks

in that they mostly make small loans called “microcredit”

tothe poor. Referringto MicroﬁnanceInformation Exchange

(MIX),amicrofinanceinstitution“can be anonprofit

organi-zation,regulatedfinancial institutionor commercialbankthat

provides microfinance products and services to low-income

clients.” Microfinancedatacomesfrom theMFIProﬁlesand

Reports from MIX. MIX recognizes six legal statuses for

microfinanceinstitutions:banks,credits/cooperatives,non-bank

financial institutions, non-governmental organizations, rural

banks,andothers.Dataregardingtraditionalbankscomesfrom

Becketal.(2013).

Our mainmeasure capturing the rolesof banks andMFIs

aretheirrespectiveprivatecreditasapercentageofGDPsince

loansrepresentthekeyfinancialserviceofferedbymost

insti-tutions,especiallyindevelopingcountries.Thus,higherlevels

ofcreditimplyhigherlevelsoffinancialservices,andtherefore

higher levelsof financial intermediation. LookingatTable1,

bankcreditaverages27.34%ofGDPwhereasthatfor

micro-finance credit is 1.12%. The reason for this largedifference

liesinthe differencebetweenthe twoinstitutions’customers.

Banksgenerallyfundlargerenterpriseswithmuchhigher

lever-ageabilities, whileMFIs fund smallerenterpriseswithmuch

lower leverage abilities. Although we focus upon credit, we

willalsoconsideranotherfinancialmeasure,namelytheratioof

assetstoGDP,asarobustnesscheck.AssetstoGDP,contraryto

privatecredittoGDP,includecredittothepublicsectoraswell.

Alltwofinancialdevelopmentvariableshavebeenextensively

usedintheliterature.2Foreachofthesevariables,weconsider

onemeasureforbanksandanothermeasureforMFIs.

Compar-ingresultsbetweenthetwomeasurescanprovidebettercontext

forwhatextentMFIsinfluencepovertycomparedtotraditional

banks.Giventhatbanks’dataaredeflated,wealsodeflatethe

microfinancedataasdescribedinBecketal.(2013).3

Aweaknessofourapproachisthatloansfromwhatone

gen-erallylabelsasmicrocredit–and,hence,–loansfromMFIs–are

combinedwithloansfromtraditionalbanksasMIXreliesupon

thecharacteristicsoftheborrowertodenotealoanas

“micro-credit”andnotthelender.Thisshortcomingisnotfatalforour

purposes becausewe wanttoexamine towhatextent finance

directedtowardthepoor(regardlessfromwhatsource)lowers

poverty.

Asforvariablescapturingtheextentofpoverty,weconsider

the povertyheadcount ratio,thepovertygap, andthe squared

poverty gap. The first two variables come from Poverty and

Equity Database published by the World Bank and the last

variable comes fromPovcalNet,also published bythe World

Bank.TheWorldDevelopmentIndicatorsprovidesdataforthe

GinicoefficientandrealGDPpercapita($PPP).InTable3,we

providemoredetailsonallthesevariables.

Thedatacovers71developingcountriesfrom2002to2011.

Theinitialyearischosenduetodataavailability.Thesummary

statisticsareprovidedinTable1,thecorrelationcoefficientsin

Table2,andthecountriesinthesamplearelistedinappendix.

An important feature of these correlation coefficientsis that

thepovertymeasuresareweaklynegativelycorrelatedwiththe

financialdevelopmentvariables.Itisalsoimportanttopointout

thatthemagnitudesofthecorrelationsarehigherforthe

meas-uresof bankactivity. Onelastfeatureof thecorrelationtable

istheweaknegativecorrelationbetweenbanksandMFIswith

respecttotheirrespectivecreditandassets.

3.2. Theempiricalmodelandmethodology

We usethe growth-poverty model suggested by Ravallion

(1997)andRavallionandChen(1997).AdamsandPage(2005)

haveusedthismodeltoinvestigatetheimpactofinternational

migrationandremittancesonpovertyindevelopingcountries.

Weadopt asimilarapproachthatcontrols for incomeandits

distributiontoinvestigatetheimpactoffinancialdevelopment

onpoverty.Theempiricalmodelisgivenby:

logpovit=αi+β1 logμit+β2loggit+β3logxit+εit (1)

2_See_for_instance_Levine_et_al.₍₂₀₀₀₎_and_Beck_et_al.₍₂₀₁₃₎_.

3_Beck _et _al. ₍₂₀₁₃₎ _provide _the _following _deflation _method:

{(0.5)*[Ft/Pet+Ft−1/Pet−1]}/[GDPt/Pat], where F is credit or assets; Pe

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Table1

Descriptivestatistics.

Variable Obs. Mean Std.dev. Min Max

Povertyheadcountat$1.25adayPPP(%) 324 14.30 18.59 0.00 87.72

Povertygapat$1.25adayPPP(%) 324 5.22 8.26 0.00 52.76

Squaredpovertygapat$1.25adayPPP(%) 314 3.67 6.97 0.00 52.76

PercapitaGDP($PPP-constant2011) 700 6088.77 4794.86 492.61 22,569.81

Giniindex 325 42.05 9.71 16.23 67.40 Creditbank(%GDP) 710 27.34 22.51 0.55 121.49 CreditMFI(%GDP) 710 1.12 1.87 0.00 13.70 Assetbank(%GDP) 687 35.24 25.98 0.63 131.49 AssetMFI(%GDP) 709 2.09 4.22 0.00 48.58 Ruleoflaw 620 3.14 1.08 1.00 6.00 Ethnictensions 620 3.72 1.24 0.00 6.00 Table2 Correlationcoefficients.

Creditbank Assetbank CreditMFI AssetMFI Headcount Pov.gap Sq.pov.gap Gini index P.c.GDP Ruleof law Ethnic tensions Creditbank 1 Assetbank 0.93 1.00 CreditMFI −0.03 −0.12 1.00 AssetMFI −0.04 −0.11 0.97 1.00 Headcount −0.10 −0.12 −0.04 −0.08 1.00 Povertygap −0.10 −0.12 −0.02 −0.05 0.96 1.00

Squaredpovertygap −0.23 −0.24 −0.06 −0.06 0.86 0.93 1.00

Giniindex 0.08 0.11 0.02 −0.05 0.39 0.43 0.19 1.00

PercapitaGDP 0.11 0.19 −0.33 −0.28 −0.52 −0.44 −0.47 −0.15 1.00

Ruleoflaw 0.05 0.02 −0.04 −0.02 −0.14 −0.14 −0.16 −0.67 0.18 1.00 Ethnictensions 0.09 −0.02 0.00 0.01 0.26 0.23 −0.05 0.08 −0.19 −0.26 1

Table3

Variabledescriptionandsource.

Variable Description Source

Povertyheadcount Povertyheadcountratioat$1.25aday2005$PPP.Itisthepercentageofthepopulationlivingonless than$1.25adayat2005internationalprices

Povertyandequitydatabase Povertygap Povertygapat$1.25aday2005$PPP.Itisthemeanshortfallfromthepovertylineexpressedasa

percentageofthepovertyline

Povertyandequitydatabase Squaredpovertygap Squaredpovertygapat$1.25aday2005$PPP,definedasa%ofpovertyline.Itisanindicatorof

povertyseverity.

PovcalNet

PercapitaGDP PPP(constant2011international$) Worlddevelopmentindicators Giniindex Measuresincomeinequality.Anindexof0representsperfectequality,whileanindexof100implies

perfectinequality

Worlddevelopmentindicators Creditbank Depositmoneybankscredit,definedasa%ofGDP Becketal.(2013)

CreditMFI Measuredbythegrossloanportfolio,definedasa%ofGDP Marketinformationexchange Assetbank Depositmoneybanksassets,definedasa%ofGDP Becketal.(2013)

AssetMFI Definedasa%ofGDP Marketinformationexchange

Ruleoflaw Measureslawandordertraditionofthecountry.Itrangesfrom0(weaktradition)to6(strong tradition)

Internationalcountryriskguide Ethnictensions Measuresthedegreeoftensionwithinacountryattributabletoracial,nationality,orlanguage

divisions.Itrangesfrom0(hightensions)to6(minimaltensions)

Internationalcountryriskguide

Note: We use the deflation method proposed by Beck et al. (2013) to deflate MFI credit and assets. The deflation formula is given by {(0.5)*[Ft/Pet+Ft−1/Pet−1]}/[GDPt/Pat],whereFiscreditorassets;Peisend-of-periodCPI,andPaisaverageannualCPI.

where pov is the measure of poverty in countryi at time t;

μ represents the mean per capita income measured by per

capitaGDP ($PPP);g is incomeinequality measured by the

Ginicoefficient;xdenotesafinancialdevelopmentindicatoras

measuredbybankdevelopment(bankcredit)andmicrofinance

development (MFI credit); αi denotes country fixed-effects;

and ε is the error term. In Eq. (1), the coefficients (βi) are

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Table4

OLSestimatesoftheeffectsoffinancialdevelopment(measuredbyprivatecredit/GDP)onpoverty.

Povertyheadcount Povertygap Squaredpovertygap

1 2 3 1 2 3 1 2 3 PercapitaGDP −1.732*** −2.396*** −1.707*** −2.444*** −2.907*** −2.449*** −2.006*** −1.753** −1.992** (−4.17) (−4.32) (−3.46) (−6.45) (−5.70) (−5.49) (−3.77) (−2.17) (−2.63) Giniindex 4.593*** 4.516*** 4.581*** 4.245*** 4.098*** 4.247*** 3.374*** 3.444*** 3.366*** (4.24) (3.88) (4.18) (4.87) (4.63) (4.84) (3.60) (3.63) (3.60) Creditbank −0.531** – −0.529** −0.326* – −0.326* 0.151 – 0.154 (−2.61) (−2.57) (−1.67) (−1.66) (0.61) (0.64) CreditMFI – −0.026 −0.006 – −0.009 0.001 – 0.010 −0.005 (−0.45) (−0.12) (−0.18) (0.03) (0.09) (−0.04) Constant 1.157 5.498 0.974 6.787 10.308* 6.827 4.277 2.301 4.170 (0.19) (0.8) (0.16) (1.42) (1.75) (1.32) (0.72) (0.28) (0.56) WithinR2 0.49 0.46 0.49 0.53 0.51 0.53 0.22 0.22 0.22 #ofcountries 68 68 68 68 68 68 66 66 66 #ofobs. 309 309 309 299 299 299 270 270 270

Note:Allvariablesareexpressedinlogs.Theestimationisbasedonthefixed-effectsmethodforwhichwereportthewithinR-squared.t-statisticsinparentheses arebasedonstandarderrorsthatarerobusttoheteroskedasticity.***,**,and*denotesignificanceat1%,5%,and10%,respectively.Thenumberofobservations isreducedinthetablebecauseofmissingvaluesforpovertyvariables(seesummarystatisticsfordetails).

InEq.(1),andconsistentwithRavallion(1997),percapita

incomeoreconomicgrowthisexpectedtolowerpovertywhile

income inequality is expected to have a positive effect on

poverty. Asfor the financial developmentindicator, which is

theadditiontothismodel,itsrelationshipwithpoverty isnot

clear-cutinthe literature.Nevertheless,followingJalilianand

Kirkpatrick (2002), Beck et al. (2008), and Jeanneney and Kpodar (2011), who find that financial development fosters

growthwhichthenreducespoverty, weexpect financial

indi-catorstoreducepoverty.

Tomitigateendogeneityconcerns,onesometimestakesfirst

differencesasinRavallionandChen(1997)sincedifferences

arelesspersistentovertimethanarelevels.However,werefrain

fromdoingsoas needingtwoobservationsfor povertyorthe

Ginicoefficientwouldgreatlyreducethesamplesize.Despite

thisconcern,wefirstestimateequation(1)usingthefixed-effects

technique(OLS),whichassumesthatourindependentvariables

areexogenous.Tothenaddressendogeneityconcerns,wealso

useinstrumentsandestimateviatwo-stageleastsquares(2SLS).

Thefirstinstrumentweuseisethnictensions.4Becketal.(2003)

haveusedthisvariableandfindsignificantnegativecorrelation

betweenethnicfractionalizationandprivatecredit.This

corre-lationcouldbeexplainedbythefactthatgreaterethnicdiversity

impliestheadoptionofpoliciesandinstitutionsgearedtoward

powerandcontrolandnottowardcreatinganopenand

compet-itivefinancialsystem.Thesecondinstrumentweuseistherule

oflawusedbyLevineetal.(2000).Bankssignalotofcontracts,

andacountryhavingatraditionofestablishinglawandorderis

likelytoboostitsfinancialsectordevelopment.Bothethnic

ten-sionsandruleoflawrangefromzerotosixpointsaccordingto

theInternationalCountryRiskGuidedatabase.Ethnictensions

measurethedegreeoftensionwithinacountryattributableto

4 _Ethnic_tensions_differs_from_ethnic_{heterogeneity}_in_that_the_former_takes_into

accountactualeventsanddisturbanceswithinthecountryarisingfromethnic heterogeneity.

racial,nationality,or languagedivisions.Lowerratingsimply

hightensions,whilehigherratingsaregiventocountrieswhere

tensions are minimal.Higher values for therule of law

vari-abledenoteagreateradherencetolawandorder.Inadditionto

thesetwoinstruments,wealsoincludethefirstandsecondlags

of financialdevelopmentindicatorasinstruments.Singhetal.

(2011)usethefirstandsecondlagsofmoneyandquasimoney

measuredas(M2)/GDPanddomesticcredit/GDP,respectively,

whenanalyzingthedeterminantsandmacroeconomiceffectsof

remittances.

To gauge the validity of the aforementioned instruments,

we runtheSargan-Hansentestofoveridentifyingrestrictions.

Underthenullhypothesis,theinstrumentsarevalid.Wewillalso

includethesesixinstrumentsintheregressionmodeland

esti-matebyOLS.Iftheinstrumentsarevalid,thentheircoefficient

estimatesshouldbezero.

4. Results

Webeginwiththefixed-effectsresultswithoutusing

instru-ments.TheresultsarereportedinTable4.Column(1)reports

the resultswithbankcreditonly,column(2) withMFIcredit

only,andcolumn(3)withbothvariablesincludedinthesame

model. Controlling for per capitaincome andthe Giniindex

that arenegativelysignificantasexpected,theresultsindicate

bankcreditreducespovertyasfarasthepovertyheadcountand

povertygapareconcerned.Asforthesquaredpovertygap,we

donotseeanysignificanteffectofbankcredit.MFIcredit,on

the otherhand,does notappeartohavesignificanteffects on

povertyreductionregardlessofwhatpovertymeasureisused.

However, giventhe potential for financial development tobe

endogenous,theseOLSestimatesmaybebiased.

Table5reportsthefixed-effects2SLSresultswhenusingrule

oflaw,ethnictensions,andthefirsttwolagsofbothbankcredit

andMFIcredittoinstrumentfor bankcredit andMFIcredit.

TheresultsinTable5indicatethatbankcreditreducespoverty

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Table5

2SLSestimatesoftheeffectsoffinancialdevelopment(measuredbyprivatecredit/GDP)onpoverty.

1 2 3 1 2 3 1 2 3 PercapitaGDP −2.052*** −2.842*** −2.265*** −2.327*** −2.971*** −2.547*** −0.987 −1.410** −1.155* (−4.13) (−5.03) (−3.94) (−5.07) (−5.68) (−4.81) (−1.60) (−2.10) (−1.68) Giniindex 3.398*** 3.809*** 3.472*** 4.112*** 4.508*** 4.193*** 3.74*** 3.966*** 3.860*** (4.04) (4.41) (4.07) (5.19) (5.56) (5.22) (4.18) (4.30) (4.16) Creditbank(1) ₋_0.648*** _– ₋_0.685*** ₋_0.552*** _– ₋_0.585*** ₋_0.256 _– ₋_0.312 (−3.30) (−3.31) (−2.88) (−2.91) (−1.08) (−1.13) CreditMFI(2) _– ₋_0.003 _0.053 _– _0.007 _0.054 _– _0.019 _0.075 (−0.05) (0.73) (0.10) (0.80) (0.14) (0.51) Constant 8.775 12.038* 10.559* 7.014 9.360 8.806 −4.648 −2.558 −3.341 (1.51) (1.87) (1.67) (1.29) (1.56) (1.49) (−0.68) (−0.35) (−0.46) WithinR2 0.42 0.39 0.41 0.50 0.47 0.50 0.26 0.26 0.25 #ofcountries 58 58 58 58 58 58 57 57 57 #ofobs. 214 214 214 206 206 206 185 185 185 Sargan–Hansenp-value 0.178 0.703 0.271 0.061 0.721 0.177 0.386 0.623 0.503

Note:Allvariablesareexpressedinlogs.Theestimationisbasedonthefixed-effects2SLSmethodforwhichwereportthewithinR-squared.t-statisticsinparentheses arebasedonstandarderrorsthatarerobusttoheteroskedasticity.***,**,and*denotesignificanceat1%,5%,and10%,respectively.Thenumberofobservations isreducedinthetablebecauseofmissingvaluesforpovertyvariables(seesummarystatisticsfordetails).ThesampleisfurtherreducedbecauseICRGdoesnot reportdataforsomecountrieslikeBenin,Cambodia,Nepal,etc.(1)CreditbankisinstrumentedusingRuleoflaw,Ethnictensions,1stand2ndlagsofcreditbank, PercapitaGDP,andGiniindexasinstruments.(2)CreditMFIisinstrumentedusingRuleoflaw,Ethnictensions,1stand2ndlagsofcreditMFI,PercapitaGDP, andGiniindexasinstruments.

naturallogarithmsforbothpovertyandbankcreditimpliesthat

thecoefficientonbankcreditcanbeinterpretedasanelasticity.

Fortheheadcountindex,a10%increaseinbankcreditreduces

povertybyabout6.5–6.9%,whereasthesame10%increasein

bankcreditreducespoverty by5.5–5.9% asmeasured bythe

povertygap.Fortheformer,thismeansthata10%increasein

bankcreditreducesthefractionofthepopulationlivingonless

than$1.25adaybyabout6.7%.Forthelatter,thedifferencein

incomebetweenthe$1.25perpersonthresholdandtheactual

incomeofthepoordiminishesbyroughly5.7%.

ComparedtotheOLSestimates,theIVelasticitiesare

sta-tisticallystronger(1%significanceversus5%and10%forthe

OLSestimates)andlargerinmagnitude.JustliketheOLS

esti-mates,thecoefficientsfromtheIVestimationarenotsignificant

when considering the squared poverty gap although the sign

goesfrompositivetonegative.AsforMFIcredit,theIVresults

confirmthosefromTable4inthatMFIcreditdoesnothaveany

significanteffectonpovertyreduction.Percapitaincomeand

GinicoefficienthavetheexpectedsignsasinRavallion(1997)

andAdamsandPage(2005).Moreimportantly,themagnitudes

oftheirelasticitiesareconsistentwiththoseinRavallion(1997)

andAdamsandPage(2005).Onlyforthesquaredpovertygap

isincomepercapitanotstatisticallysignificantwhenthemodel

includesbankcreditonlyas itsmeasureoffinancial

develop-ment.

Onthevalidityoftheinstruments,theoveridentificationtests

generallyindicatethat theinstrumentsare valid.Weobserve,

however,arejectionatthe10%significancelevelwhen

regress-ingthepovertygaponthemeasureofbankcreditbyitself.As

thismaycastsomedoubtonhowvalidourinstrumentsare,we

takeastepfurtherandregressthethreepovertymeasuresonthe

proposedinstruments.TheresultsreportedinTable6indicate

thatonlythefirstlagofbankcreditissignificantlyassociated

withthesquaredpovertygap.Intheory,thisinstrumentshould

be dropped.However,we keep itin theinstrument listwhen

consideringthesquaredpoverty gapbecausetheresults– not

reportedherebutavailableuponrequest–donotqualitatively

change when excluding the first lag of bank credit from the

instrumentlist.

To sum up, our results indicate that bank credit reduces

poverty when poverty is measured by the headcount ratio

and poverty gap. These results support those from Honohan

(2004), Jalilian and Kirkpatrick (2005), Beck et al. (2007),

JeanneneyandKpodar(2011),andSehrawatandGiri(2015),

whoalsofindthatfinancialdevelopmentlowerspoverty.

How-ever,theseconclusionsaretemperedinthatwefindnosignificant

effectwhenusingthesquaredpovertygaptomeasurepoverty.

MFI credit, on the other hand, does not appear tohave any

impact on poverty regardless of the measure we consider,

suggesting that any effectupon poverty reduction is at most

small. We are not the first to be skeptical of the ability of

MFIs to lower poverty as Chowdhury (2009) raises similar

doubts.

From our results, one can thus wonder how banks show

poverty reductioneffects (atleastas shownbytheheadcount

and poverty gap measures)whereas MFIs do not, especially

sinceMFIsfocusuponhelpingthepoor.Onepossiblewayto

explaintheeffectofbankcreditonpovertyisthrough

invest-mentsininfrastructure.Infact,manydevelopingcountriesstill

havealotofroomforimprovementsininfrastructureandsuch

projects could create variousspillovers. Aconstruction

com-pany,forinstance,mayseekfundingfrombankstobuildroads.

Thisroadconstructionwillrequirethehiringofmanypeople,

including the poor, whichwill lead toadecrease in poverty.

Therecouldbealsoanindirecteffectonpovertyreductionas

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Table6

OLSestimatesoftheeffectsofinstrumentsonpoverty.

1 2 3 1 2 3 1 2 3 PercapitaGDP −2.194*** −2.691*** −2.360*** −2.431*** −2.961*** −2.757*** −0.965 −1.526** −1.168 (−4.97) (−4.90) (−4.61) (−4.53) (−4.49) (−4.19) (−1.32) (−2.08) (−1.44) Giniindex 3.490*** 3.636*** 3.516*** 4.149*** 4.467*** 4.298*** 3.699*** 3.856*** 3.774*** (2.74) (2.98) (2.65) (3.31) (3.55) (3.23) (3.40) (3.44) (3.42) Ruleoflaw 0.323 0.289 0.328 0.264 0.187 0.253 0.429 0.442 0.402 (1.37) (1.12) (1.42) (0.82) (0.68) (0.87) (1.17) (1.23) (1.04) Ethnictensions 0.075 0.162 0.106 0.224 0.334 0.250 0.292 0.214 0.293 (0.38) (0.62) (0.52) (0.99) (1.16) (1.06) (0.90) (0.60) (0.90)

1stlagcreditbank −0.119 – −0.139 0.059 – 0.056 −0.887* – −0.923**

(−0.31) (−0.37) (0.16) (0.15) (−1.94) (−2.09)

2ndlagcreditbank −0.608 – −0.613 −0.739 – −0.791 0.758 – 0.735

(−1.21) (−1.18) (−1.35) (−1.42) (1.30) (1.27)

1stlagcreditMFI – 0.035 0.054 – 0.014 0.032 – −0.035 0.019

(0.62) (1.36) (0.21) (0.71) (−0.34) (0.17)

2ndlagcreditMFI – −0.059 −0.019 – −0.001 0.032 – 0.053 0.038

(−1.07) (−0.40) (−0.03) (0.74) (0.79) (0.55) Constant 9.420 10.807 10.862 7.561 8.821 10.099 −5.859 −1.849 −4.058 (1.37) (1.74) (1.55) (0.99) (0.248) (1.23) (−0.74) (−0.22) (−0.46) WithinR2 _0.45 _0.39 _0.45 _0.53 _0.48 _0.53 _0.28 _0.27 _0.29 #ofcountries 58 58 58 58 58 58 57 57 57 #ofobs. 214 214 214 206 206 206 185 185 185

Note:Allvariablesareexpressedinlogs.Theestimationisbasedonthefixed-effectsmethodforwhichwereportthewithinR-squared.t-statisticsinparentheses arebasedonstandarderrorsthatarerobusttoheteroskedasticity.***,**,and*denotesignificanceat1%,5%,and10%,respectively.Thenumberofobservations isreducedinthetablebecauseofmissingvalues(seesummarystatisticsfordetails).

facilitatemarketaccessbyreducingtransportationcosts.

More-over,roadscanshortendistancestofeederroadsandincrease

laborproductivity inagricultureas shown inUganda byFan

andZhang(2008).Itisobviousthatlargecompanies,andtoa

lesserextent mediumcompanies,will notgo tomicrofinance

institutionstoseekfundinggiventhesizeofMFIs’portfolio.

Asecond possibleexplanationastowhytraditional banks

could bemoresuccessful ineliminatingpoverty isthat some

banksfacingcompetitionfromMFIshavealsoexpanded

lend-ingtothepoor.Infact,somebanksoffermicrocreditservices,

andsomesmallscalebusinessesmayfindbankloanscheaperas

MFIs are believedtochargeon averagehigher interest rates.

Thanvi (2010) finds that an MFI in India charges twice the

effectiveratechargedbybanks.Finally,fromthesupplyside,

Holden andProkopenko (2001) argue that the quality of the

loanportfolioofMFIsissometimespoorbecauseofinadequate

managementanddeficienciesincontroloftheiractivities.Asa

consequence,itisdifficultfor MFIstoreachefficiencylevels

tocovertheircosts.Asforthedemandside,Chowdhury(2009)

pointsout that borrowers maybe lacking business skillsand

marketinginformationforloanstoexpandbusinessesand

cre-atejobs.Borrowerspossessingtheseskillsmightthenbemore

successfulatobtainingfinancingfromtraditionalbanks.

Afinalreasonisthemuchgreatersizeofbanks.Asshown

inTable1,bank creditandassetsaremanytimes largerthan

thatofmicrofinanceinstitutions,providingmuchgreater

poten-tialfor their changesinlendingtoaffectthepoor. Moreover,

lendingfromMFIsisalmostalways“small”,implyingthatany

effectscouldbecontainedlocallyandsonotimpactpovertyat

thecountrylevel.

5. Robustnesscheck

Inthissection,wecheckhowrobustourresultsarebyusingan

alternativemeasureoffinancialdevelopment,namelyassetsasa

percentageofGDP.Thisvariableisalsopopularamongfinancial

developmentindicatorsintheliterature(seeforinstance,Beck

etal.,2013).Asmentionedearlier,theassetstoGDPvariable

has the advantage toincludecredit tothe public sector,

con-trarytocredittoGDP,whichonlyincludescredittotheprivate

sectorbybanksandotherfinancialintermediaries.For

conve-nience,weskiptheOLSestimatestofocusontheIVestimates.

Table7reportsthefixed-effects2SLSestimateswhenfinancial

developmentismeasuredbytheassetstoGDPratio.

The results are qualitatively similar to those of credit in

Table5.However,themagnitudesoftheelasticitiesarehigher.

An increaseinbankassetsby10% reducestheproportion of

peoplelivingonlessthan$1.25adaybyabout8%(7.8–8.4%).

Likewise,thesameincreaseof10%inbankassetsinducesabout

an8%(7.6–8.4%)declineintheamountofincomeseparating

thoseinpovertyfromthe$1.25threshold.Asbefore,estimates

arenotsignificantwhenusingthesquaredpovertygap.Turning

tomicrofinance,MFIassetsdonotappeartohaveanyimpact

onanyofthethreemeasuresofpoverty.Asbefore,incomeper

capitaandtheGinicoefficienthavetheexpectedsignsandare

statistically significant. Also, it is important tohighlight that

inallregressions,wefailtorejectthenullhypothesisof

over-identifying restrictions at conventional levels of significance.

Alltogether,theseresultsdonotsystematicallydifferfromthe

creditresultsintheprevioussection,althoughtheelasticitiesof

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Table7

2SLSestimatesoftheeffectsoffinancialdevelopment(measuredbyassets/GDP)onpoverty.

1 2 3 1 2 3 1 2 3 PercapitaGDP −2.211*** −2.699*** −2.515*** −2.437*** −2.921*** −2.826*** −1.312** −1.515* −1.653* (−4.36) (−4.26) (−3.77) (−3.27) (−5.01) (−4.68) (−2.08) (−1.86) (−1.87) Giniindex 3.111*** 3.758*** 3.161*** 3.719*** 4.487*** 3.798*** 3.33*** 4.009*** 3.421*** (3.54) (4.33) (3.55) (4.53) (5.49) (4.56) (3.56) (4.28) (3.57) Assetsbank(1) ₋_0.778*** _– ₋_0.839*** ₋_0.762*** _– ₋_0.835*** ₋_0.295 _– ₋_0.383 (−3.30) (−3.30) (−3.27) (−3.32) (−1.08) (−1.13) AssetsMFI(2) _– ₋_0.026 _0.055 _– ₋_0.003 _0.070 _– _0.035 _0.086 (−0.35) (0.71) (−0.05) (0.99) (0.27) (0.53) Constant 11.839* 10.968 14.568** 10.290* 8.989 13.695** −0.095 −1.799 2.908 (1.95) (1.62) (2.01) (1.82) (1.43) (2.05) (−0.01) (−0.23) (0.32) WithinR2 0.42 0.39 0.41 0.51 0.47 0.50 0.26 0.25 0.25 #ofcountries 57 58 57 57 58 57 56 57 56 #ofobs. 210 214 210 202 206 202 181 185 181 Sargan–Hansenp-value 0.478 0.438 0.651 0.296 0.712 0.315 0.147 0.691 0.221

Note:Allvariablesareexpressedinlogs.Theestimationisbasedonthefixed-effects2SLSmethodforwhichwereportthewithinR-squared.t-statisticsinparentheses arebasedonstandarderrorsthatarerobusttoheteroskedasticity.***,**,and*denotesignificanceat1%,5%,and10%,respectively.Thenumberofobservations isreducedinthetablebecauseofmissingvaluesforpovertyvariables(seesummarystatisticsfordetails).ThesampleisfurtherreducedbecauseICRGdoesnot reportdataforsomecountrieslikeBenin,Cambodia,Nepal,etc.(1)AssetsbankisinstrumentedusingRuleoflaw,Ethnictensions,1stand2ndlagsofassetsbank, PercapitaGDP,andGiniindexasinstruments.(2)AssetsMFIisinstrumentedusingRuleoflaw,Ethnictensions,1stand2ndlagsofassetsMFI,PercapitaGDP, andGiniindexasinstruments.

6. Conclusion

Thispaperexaminestherelationshipbetweenfinancial

devel-opment as measured by the size of either traditional banks

or MFIs andpovertyreduction. Specifically, we compare the

extent to which each one of thesefinancial institutions

con-tributestoalleviatepoverty.Tothateffect,weemployapoverty

modeldevelopedbyRavallion(1997)thatregressespovertyon

incomepercapitaandtheGiniindex,inwhichweinclude

finan-cialdevelopmentindicators.Applying the fixed-effects 2SLS

techniquetoapanelof 71developingcountriesfrom2002to

2011,ourresultsindicate thatbankloanshavepoverty

reduc-tioneffectswhenpovertyismeasuredbypovertyheadcountand

povertygap.Whenpovertyismeasuredbysquaredpovertygap,

bankloansdonothavesignificantimpacts.MFIloans,onthe

otherhand,donotappeartosignificantlyreducepovertyacross

allthethreemeasuresofpoverty.Theseresultsarerobustwhen

financialdevelopmentismeasuredbybankassetsorMFIassets

asapercentage ofGDP. Moreimportantly,banks’impacton

povertyishigher.Ourresultssuggestthatbankingdevelopment

canhelpcombatextremepoverty,butmayfailtoreachthe

poo-rest,whilemicrofinancedevelopmentisstillatinfantstageand

hascertainlyalongwaytogotoalleviatepoverty.Manystudies

havereportedpoverty reduction effects of MFIsat themicro

level,buttheseeffectsareyettoappearattheaggregatelevel.

A few studies have reported that financial development

reducespoverty(Honohan,2004;JalilianandKirkpatrick,2005;

Becketal.,2007;JeanneneyandKpodar,2011;Sehrawatand Giri,2015),andourbankresultsareconsistentwiththe

liter-ature,atleastwhenpovertyismeasuredbypovertyheadcount

andpoverty gap. In the model of Greenwoodand Jovanovic

(1990),thereisafeethepoorneedtopaytoparticipateinthe

formalfinancialmarket.Failingtopaythisfeeexcludesthepoor

fromtakingadvantageofopportunitiesinthefinancialsector.A

possiblepolicyrecommendationistolowersuchfeesinorderto

allowmorepeopletotakeadvantageoffinancialopportunities

thatbankscanprovide.Suchfeescouldbeacauseofamarket

failure,whichaccordingtoStiglitz (1993)isthefundamental

causeofpovertyindevelopingcountries.

The microfinance results seem to be consistent with

Chowdhury (2009), who casts doubt on the effectiveness of

microfinance as a means to alleviate poverty. Even if

theo-ries(Bueraetal.,2012)andevidence(Donou-Adonsou,2014; Donou-AdonsouandSylwester,2015)suggestthatMFIshave

macroeconomiceffectssuchasgrowtheffect,ourstudyclearly

implies that the so-called trickle-down effect inthe financial

developmentliterature maynot workfor MFIs as itdoes for

banks. We do not suggest that the international community

shouldnotsupportMFIs,butourresultsdosuggestthatevenif

theyarebeneficial,MFIsarenotpanaceasforreducingpoverty.

Weurgefurtherworkinthisareatobetterunderstand

find-ings.The dataonlyextendback to2002,preventinguseof a

longtimeseries.Perhapsoursampleperiodisnotlongenough

toadequatelymeasuretheeffectofMFIsonpoverty.Asstated

previously,duetodataavailabilityourmeasuresofthesizeof

MFIsrelyuponthecharacteristicsoftheborrower.Ideally,one

wouldlike toexamine towhatextentresultswouldchange if

MFIs weredefinedbased onthecharacteristics of the lender.

Finally,weconsiderthepotentialforbanksandMFIstohave

countrywideeffects, butmacroeffects couldarise atregional

(i.e.provinciallevels)eveniftheyarenotcountrywide.These

limitationswarrantcontinuedstudyofthisissue.

Appendix.

The countries in the sample include: Albania, Armenia,

Azerbaijan,Bangladesh,Benin,Bolivia,Brazil,BurkinaFaso,

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(Dem.), Congo (Rep.), Costa Rica, Cote d’Ivoire,

Domini-canRepublic,Ecuador,Egypt,ElSalvador,Ethiopia,Georgia,

Ghana, Guatemala, Haiti, Honduras, India, Indonesia,

Jor-dan,Kazakhstan,Kenya,Kyrgyzstan,Macedonia,Madagascar,

Malawi,Mali,Mexico,Moldova,Mongolia,Morocco,

Mozam-bique, Namibia, Nepal, Nicaragua, Niger, Pakistan, Panama,

Peru, Philippines, Romania, Russia, Senegal, Serbia, Sierra

Leone, South Africa, Sri Lanka, Swaziland, Syria, Tanzania,

Thailand,Togo,Tunisia,Turkey,Uganda,Ukraine,Venezuela,

Vietnam,Yemen,Zambia.

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