Finance and income inequality: A review and new evidence · Finance and income inequality: A review...
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KOF Working Papers, No. 410, September 2016
Finance and income inequality: A review and new evidence
Jakob de Haan and Jan-Egbert Sturm
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ETH ZurichKOF Swiss Economic InstituteLEE G 116Leonhardstrasse 218092 Zurich, Switzerland
Phone +41 44 632 42 39Fax +41 44 632 12 [email protected]
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Financeandincomeinequality:Areviewandnewevidence
JakobdeHaana,b,c,dandJan‐EgbertSturmc,d
aDeNederlandscheBank,Amsterdam,TheNetherlands
bUniversityofGroningen,TheNetherlands
cKOFSwissEconomicInstitute,ETHZurich,Switzerland
dCESifoMunich,Germany
Version:September2016
Abstract
Using a panel fixed effectsmodel for a sample of 121 countries covering 1975‐2005,we
examinehowfinancialdevelopment,financialliberalizationandbankingcrisesarerelated
to income inequality. In contrast with most previous work, our results suggest that all
financevariablesincreaseincomeinequality.Theleveloffinancialdevelopmentconditions
the impactoffinancial liberalizationon inequality.Alsothequalityofpolitical institutions
conditions the impact of financial liberalization on income inequality, in contrast to the
quality of economic institutions. Our main findings are robust for using random effects,
cross‐countryregressionsandlegaloriginasinstrumentforfinancialdevelopment.
Keywords:incomeinequality,financialliberalization,financialsectorsize,financialcrises,politicalinstitutionsJEL‐codes:D31,D63,F02,O11,O15
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1. Introduction
We examine the relationship between finance and income inequality using panel fixed
effects regressions for a large sample of countries. To bemore precise, we analyze how
financial development, financial liberalization and financial crises are related to within
country income inequality. As dependent variable we use five‐year averages of Gini
coefficients based on households’ gross income from Solt’s (2009) Standardized World
IncomeInequalityDatabase(SWIID).
There isanextensive literatureon the relationshipbetween financialdevelopment
andincomeinequality.1Theoretically,theimpactoffinancialdevelopmentisambiguous.On
the onehand,more financemaymake it easier for poor individuals toborrow for viable
projects, which may reduce income inequality (Galor and Moav, 2004). Financial
imperfections,suchasinformationandtransactioncosts,maybeespeciallybindingonthe
poorwho lackcollateralandcredithistories so that relaxationof thesecredit constraints
maybenefit thepoor (Beck et al., 2007).On theother hand, improvements in the formal
financial sector could be more likely to benefit the well‐off who rely less on informal
connections for capital (Greenwood and Jovanovic, 1990). As will be discussed in more
detail in section 2, the empirical evidence on the relationship between financial
developmentand income inequality isverymixed. Insteadofprovidingyetanothersetof
regressions that possibly adds to thisdiversity,we examinewhether institutional quality
conditions the relationship between financial development and income inequality, which
mayshedsomelightonthereasonswhystudiesreachdifferentconclusions.Accordingto
Rajan andZingales (2003), inweak institutional environments established interests have
privileged access to finance so that financial development induced by captured direct
controls is likely to hurt the poor. In the presence of strong institutions, financial
development may reduce inequality, allowing the poor to invest in human and physical
capital(Lawetal.,2014).Asimilarargumentcanbemadeforfinancialliberalization(Delis
etal.,2014).2
1SeeClaessensandPerotti(2007)andDemirgüç‐KuntandLevine(2009)forexcellentreviewsoftheliterature.
2Delisetal.(2014,p.1824)arguethat“qualityinstitutionsmightenhancetheimpactofregulationson the distribution of income andweaker institutionsmay undermine such an impact.” However,theydonotexaminethis.Thepresentstudythereforeisthefirsttoexaminewhethertheimpactoffinancialliberalizationonincomeinequalityisconditionedbyinstitutionalquality.
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Inrecentdecadestherehasbeenaglobalpushtoliberalizethefinancialsector. A
small, but growing line of literature examines the impact of financial liberalization on
income inequality. For instance, Beck et al. (2010) assess the impact of U.S. bank
deregulation of the 1970s to the 1990s on the distribution of income and find that
deregulationsignificantlyreduces inequalitybyboosting incomes inthe lowerpartof the
income distribution but has little impact on incomes above the median. Likewise, some
recent studies (Agnello et al., 2012; Delis et al., 2014; Li and Yu, 2014) based on cross‐
country data report that financial liberalization reduces income inequality, but Jaumotte
and Osuorio Buitron (2015) and Naceur and Zhang (2016) conclude that financial
liberalization increases inequality (see section 2 for more details). Bumann and Lensink
(2016) suggest that the impact of financial liberalization on inequality is conditioned by
financial development. In their view, “financial liberalization will improve income
distribution incountrieswherefinancialdepth ishigh.Themainreasonforourfinding is
that in countrieswith high financial depth, the interest rate elasticity of loan demand is
high.Afinancialliberalizationpolicythatimprovesbankefficiencyandreducesborrowing
costswill lead toa sharp increase inaggregate loandemand,requiringan increase in the
depositratetorestoreequilibriuminthefinancialmarket.Theincreaseinthedepositrate
improvestheincomeofsaversand,hence,incomedistribution”(p.144).3Wewillexamine
whetherfinancialdevelopmentconditionsthe impactof financial liberalizationon income
inequality.
Athirdfinancialvariablethatweconsiderarefinancialcrises.Conventionalwisdom
is that the poor suffer disproportionately from recessions following financial crises.
However, Denk and Cournede (2015) do not find a significant effect of banking crisis in
theiranalysisofincomeinequalityinOECDcountries.Onlyfewstudies(e.g.Baldaccietal.,
2002; Agnello and Sousa, 2012 and Li and Yu, 2015) analyze the causal relationship
between financial crises and income inequality for a broader set of countries and report
mixedfindings.
Our contribution to the literature is threefold. First, we include financial
development, financial liberalization and financial crises in our empirical analysis of the
relationshipbetweenfinanceandincomeinequality.Previousstudiesincludeatbesttwoof
3In their model, agents with the best investment skills become investors, and earn the highestamountofmoneyandagentswithfewerinvestmentskillsbecomesavers,andearnlessmoney.
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these variables at the same time. Second, we use different indicators of financial
liberalization.Likepreviousstudiesweuse the financial liberalizationdataofAbiadetal.
(2010), but also construct an alternative indicator based on some components of the
economicfreedomindexoftheFraserInstitute(Gwartneyetal.,2015).Thisenablesusto
checkhowsensitiveresultsareforthewayfinancial liberalizationismeasured.Third,we
examinewhethertheimpactoffinanceonincomeinequalityisconditionedby:(1)thelevel
offinancialdevelopmentassuggestedbyBumannandLensink(2016);and(2)institutional
qualityassuggestedbyRajanandZingales(2003).
Our results suggest that a higher level of financial development, financial
liberalization and the occurrence of a banking crisis all increase income inequality in a
country. Incontrast to thepredictionbyBumannandLensink(2016),ourresultssuggest
that with high levels of financial development, financial liberalization increases income
inequality. We also find evidence that, unlike the quality of economic institutions, the
quality of political institutions conditions the impact of financial liberalization on income
inequality: with higher levels of democratic accountability the positive effect of financial
liberalizationoninequalityincreases.Institutionalqualitydoesnotconditiontheimpactof
financial development on income inequality, in contrast to the prediction by Rajan and
Zingales(2003).
The remainder of the paper is structured as follows. Section 2 discusses related
studiesinmoredetail.Section3describesourmethodologyanddataused,whilesection4
presentsthemainresults.Section5offersasensitivityanalysisandsection6concludes.
2. Literature review
As pointed out by Demirgüç‐Kunt and Levine (2009), theory provides ambiguous
predictions for the impact of finance on income distribution. A distinction can be made
between the effects of finance on the extensive and the intensivemargin. The extensive
margin isabout theuseof financialservicesby individualswhohadnotbeenusing those
services.Forexample, financialdevelopmentmayhelppoor families toborrowtopay for
education. Inequality falls in models with this mechanism (Galor and Moav, 2004).4The
4However, thequestion iswhether financialdevelopmentassuchreducesthese financial frictions.Perhapsthesefrictionscanbereducedbyotherfactors,suchastechnology,withoutalargerfinancialsector (Demirgüç‐Kunt and Levine, 2009). This suggests that other financial sector characteristics
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effect of financial development on income inequality on the intensivemargin is different.
Improvementsinthequalityandrangeoffinancialservicesdonottendtobroadenaccess
tofinancialservices,butinsteadimprovethequalityoffinancialservicesenjoyedbythose
already purchasing financial services (Greenwood and Jovanovic, 1990). The benefits of
these intensive margin effects accrue primarily to the rich, widening the distribution of
income.
Theextensiveempiricalliteratureontherelationshipbetweenfinancialdevelopment
andincomeinequality provides verymixed findings.5Although several studies report that
countrieswithhigherlevelsoffinancialdevelopmenthavelessincomeinequality(seee.g.Li
etal.1998,Clarkeetal.,2006,Becketal.,2007,Kappel,2010,HamoriandHashiguchi,2012
andNaceurandZhang,2016),6otherstudiesreportanon‐linearrelationship(e.g.Kimand
Lin,2011andLawetal.,2014),7mixedresults(Bahmani‐OskooeeandZhang,2015),8ora
thansizeshouldbeexamined.Mostempiricalresearchfocuses,however,on financialsectorsize,arecentexceptionbeingthestudybyNaceurandZhang(2016).
5Herewe only discuss research usingmacro data for a large set of countries. For a discussion ofothertypesofresearchwerefertoDemirgüç‐KuntandLevine(2009).
6Lietal.(1998)usedatafor49countriesoverthe1947‐94periodandreportastrongrelationshipbetween income inequality and their measure for financial development (M2/GDP). Beck et al.(2007)reportanegativerelationshipbetweenfinancialdevelopment(proxiedbyprivatecredit‐to‐GDP)andthegrowthrateoftheGinicoefficient,whichholdswhencontrollingforrealpercapitaGDPgrowth,laggedvaluesoftheGinicoefficient,andawidearrayofothercountry‐specificfactors.Theirsampleconsistsof65countriesovertheperiod1960‐2005.Usingasimilarmodelforalargergroupof countries (83) but a shorter sample period (1960‐1995), Clarke et al. (2006) also find thatfinancialdevelopment reduces inequality.Kappel (2010),whousesa sampleof59 countries for across‐countryanalysisand78countriesforapanelanalysisovertheperiod1960to2006,concludesthat financial development reduces income inequality for high‐income countries, but is notsignificant for low‐income countries. Hamori and Hashiguchi (2012) use annual panel data for asampleof126countriesoverthe1963‐2002periodandfindthatbothM2/GDPandprivatecredit‐to‐GDP reduce estimated household income inequality when they use panel fixed effects and GMM.Naceur andZhang (2016)use a sample of143 countries from1961 to2011 and find that severaldimensions of financial development considered (access, efficiency, deepening and stability) cansignificantlyreduceincomeinequalityandpoverty,whilefinancialliberalizationtendstoexacerbatethem.
7Based on a sample of 65 countries for 1960‐2005, Kim and Lin (2011) find that the benefits offinancialdevelopmentonincomedistributionoccuronlywhenthecountryhasreachedathresholdlevel of financial development. Below this critical threshold, financial development exacerbatesincomeinequality.Usingdatafor81countriesovertheperiod1985‐2010inacross‐sectionmodel,Lawetal.(2014)concludethatfinancialdevelopmenttendstoreduceincomeinequalityonlyafteracertain threshold level of institutional quality hasbeen achieved.Until then, the effect of financialdevelopmentonincomeinequalityisnonexistent.
8Usingtimeseriesregressionsfor17countries,Bahmani‐OskooeeandZhang(2015)reportthatonlyin three out of the 10 countries where finance has a short‐term equalizing effect on income
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positive relationship between financial development and income equality. For instance,
JauchandWatzka(2012),whouseapanelof138countriesfortheyears1960‐2008,find
that financial development increases income inequality when they use fixed effects and
controlforGDPpercapita.Jaumotteetal.(2013)investigateincomeinequalitywithafocus
ontradeandfinancialglobalization.Intheirsampleof51countriesfrom1981to2003,they
includeprivatecreditoverGDPasacontrolvariableandobtainapositiveandsignificant
coefficient for financial development. Similar results are reported by Dabla‐Norris et al.
(2015),whoconcludethatfinancialdeepeningisoneofthemaindriversoftheincreasein
income inequality in their sample covering 97 countries and five‐year panels over the
period1980–2012.
In thepanel regressions for theGini coefficient ina sampleof18Asiancountries
overthe1996‐2005reportedbyLiandYu(2015)thecoefficientofcredit‐to‐GDPispositive
and significant. Likewise, Denk and Cournède (2015) conclude that more finance is
associated with higher income inequality in their sample of 33 OECD countries. This
relationshipholdswhen intermediatedcredit andstockmarket capitalizationareused to
measure thesizeof finance.Financial sectoremployeesareverystronglyconcentratedat
the top of the income distribution, and their earnings exceed those of employees with
similarprofiles(suchasage,genderoreducation)inothersectors(Denk,2015).
Whereasmost studies discussed do not explore the transmission from finance to
inequality, Gimet and Lagoarde‐Segot (2011) examine specific channels linking banks,
capitalmarketsandincomeinequality.Theyconstructasetofannualindicatorsofbanking
and capital market size, robustness, efficiency and international integration and then
estimate the determinants of income distribution using a panel structural vector
autoregressivemodelfor49countriesoverthe1994–2002period.9Theseauthorsconclude
thatfinancialsectordevelopmentincreasesincomeinequalityandthatthisimpactseemsto
runprimarilyviathebankingsector;NaceurandZhang(2016)reachthesameconclusion.
Finally, some studies suggest that the impact of financial development on income
inequalitymaybeconditionedbythequalityofinstitutions(cf.Delisetal.,2014andLawet
distributiontheimprovementlastsinthelongrun.
9Inviewof thequalityand frequencyofdataon income inequality,wehave seriousdoubtsaboutusingannualdataonincomeinequality.ThiscritiquealsoappliestootherstudiesusingannualdatasuchasLiandYu(2014),Bahmani-Oskooee
and Zhang (2015) and Naceur and Zhang (2016).
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al., 2014). For instance, under lowquality of economic institutions financialdevelopment
(or financial liberalization)maynot affect inequalitydue to lackof judicialprotection for
thepoor(ChongandGradstein2007).Likewise,RajanandZingales(2003)arguethatunder
weakpoliticalinstitutionsdejurepoliticalrepresentationisdominatedbydefactopolitical
influenceallowingestablished interests to influenceaccess to financeso that theybenefit
morefromfinancialdevelopmentthanthepoor.
Severalargumentshavebeenputforwardintheliteraturesuggestingthatfinancial
sector liberalization may affect income distribution. First, imperfections in the credit
market prevent the poor frommaking productive investment, in for instance, education
(Banerjee and Newman, 1991). If financial liberalization reduces these credit market
imperfections, income inequality may be reduced. Second, financial reformsmay lead to
more equal access to credit thereby improving the efficiency of the domestic financial
system(Abiadetal.,2008).
A few studies examine the relationshipbetween financial sector liberalizationand
incomeinequalityusingcross‐countrydata(mostofthesestudiesusethedatabaseofAbiad
et al. (2010) for measuring liberalization; see section 3 for further details). Das and
Mohapatra (2003) find that liberalization of equity markets benefits people in the top
quintileoftheincomedistributionattheexpenseofthe‘middleclass’,whilepeopleinthe
lowestincomesharesarenotaffected.Usingapanelof62countriesfor1973–2005,Agnello
etal. (2012)analyzethe impactof financialreformson income inequality.Theirevidence
suggests that removal of policies towards directed credit and excessively high reserve
requirements, and improvements in the securities market reduce income inequality.
Likewise,Delisetal.(2014)concludethathigherliberalizationofbankinggenerallyleadsto
narrower incomedistribution.Yet, theyalso find that this effect is not uniformacross all
liberalization policies, nor is it the same across countries with different levels of
developmentordifferenttypesoffinancialenvironments.Inparticular,theabolishmentof
creditcontrolsdecreases income inequalitysubstantially,andthiseffect is long lasting.Li
and Yu (2014) report for 18 countries in Asia for the 1996‐2005 period that financial
reformiseffectiveinreducingincomeinequality,butthattheeffect ismoreprofoundina
country with higher human capital. Jaumotte and Osuorio Buitron (2015) investigate
income inequality in 20 advanced economies during 1980–2010 with a focus on labor
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market institutions and include the indexofAbiad et al. (2010) as control variable. They
findthatitscoefficientissignificantlypositive.10AlsoNaceurandZhang(2016)reportthat
financialliberalizationincreasesinequality.
ChristopoulosandMcAdam(2015)examinethelinkbetweenfinancialreformsand
thestabilizationof incomeinequalityusingpanelunitroottestsextendedtoallowforthe
presence of some covariates. Their results suggest that although both gross and net Gini
indicesfollowaunitrootprocessthispicturechangeswhenthevariousfinancialreforms
indicesareconsideredasadditionalcovariatesinthestandardpanelunitrootapproach.In
particularwhilstgrossGinicoefficientsaregenerallynotstabilizedbyfinancialreforms,net
measuresaremorelikelytobestabilized.
Bumann and Lensink (2016) argue that the impact of financial liberalization on
inequality is conditioned by financial development. Their theoreticalmodel suggests that
financialliberalizationwillimproveincomedistributionincountrieswherefinancialdepth
ishigh.Theauthors’empiricalresultssuggestthattheirmeasureoffinancialliberalization,
i.e. capital account liberalization, only tends to lower income inequality if the level of
financialdepth,asmeasuredbyprivatecreditoverGDP,exceeds25percent.Theseresults
stand incontrast to the findingsofFurceriandLoungani (2015).Basedonpaneldata for
149countriesfrom1970to2010,theseauthorsconcludethat,onaverage,capitalaccount
liberalization increases inequality. In addition, their results suggest that capital account
liberalization leads to larger increases in inequality in countries with a weak level of
financialinstitutionsandwhentheyarefollowedbyepisodesoffinancialcrises.
Finally,weconsidertheimpactoffinancialcrisesonincomeinequality.Wealthlosses
duetoafinancialcrisisprobablywillhitthetopoftheincomedistribution.However,low‐
income individuals will be hit more if the financial crisis is followed by an economic
downturn(whichisnotalwaysthecase).Indeed,accordingtotheOECD(2013),duringthe
global financial crisis the average market income inequality across OECD countries
increasedby1.4percentagepoints. Looking at the17OECDcountries forwhichdataare
available over a long time period,market income inequality increased bymore between
10ThisfindingisconsistentwiththeresultsreportedbyPhillipponandReshef(2013)whoexaminelong‐run trends in finance in a few advanced economies. They find that financial deregulationincreasedthedemandforskillsinthefinancialsectorandthatrelativewagesinthefinancialsectorarerelatedtoskill‐intensity.
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2007 and 2010 than what was observed in the previous 12 years. However, Denk and
Cournede(2015)donotfindasignificanteffectofbankingcrisiscrisesintheiranalysisof
income inequality in 33OECD countries during 1970‐2011. As far aswe know, only few
studies have examined the causal relationship between financial crises and income
inequality forabroadersetofcountries.Baldaccietal.(2002)reportthatcurrencycrises
haveapositiveimpactontheGinicoefficient.IntheiranalysisofincomeinequalityinAsian
countries,LiandYu(2015)includeabankingcrisisdummyandfindthat ithasapositive
relationshipwith the Gini coefficient (crises lead tomore inequality). Also Atkinson and
Morelli (2011) find that income inequality is likely to increase after a banking crisis. In
contrast, Agnello and Sousa (2012), who use annual data for 62 OECD and non‐OECD
countriesforthe1980‐2006periodfindmixedresults.WhileforOECDcountriesabanking
crisisreducesinequality,fornon‐OECDtheauthorsobserveasignificantriseininequality
beforetheonsetofthecrisisbutnoeffectthereafter.Incontrast,forasampleofdeveloping
countries,Honohan(2005)doesnotfindevidenceforasignificantdifferencebetweenGini
coefficients before and after a banking crisis. Likewise, Jaumotte and Osuorio Buitron
(2015)donotreportasignificantimpactofbankingcrisesonincomeinequality.
Whilethereislimitedresearchonacausalrelationshipbetweenfinancialcrisisand
inequality,thecausalityintheotherdirection,i.e.from(increasesin)incomeinequalityto
financial crises, has received substantial attention. High or rising income inequality may
cause low‐incomegroupsto leverage inorderto increaseormaintainconsumption levels
which,inturn,mayincreasethelikelihoodofafinancialcrisis.Therelativeincometheory,
habit formations and a ''keeping up with the Joneses'' phenomenon may explain such
behavior (see Atkinson and Morelli, 2011 for a further discussion). For instance, in the
modelofKumhofandRancière(2011)risingincomeinequalityandstagnantincomesinthe
lower deciles lead workers to borrow to maintain consumption growth. This increases
leverage,andeventuallyashocktotheeconomyleadstoafinancialcrisis.Indeed,thereis
much evidence that financial crises are often preceded by credit booms (Schularick and
Taylor,2012).
However,theempiricalevidenceinsupportofcausalityrunningfrominequalityto
financial crises isweak at best. Cross‐country data indicate that banking crises have not
systematicallybeenprecededbyrising inequality(AtkinsonandMorelli,2011;Bordoand
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Meissner,2012),althoughGuandHuang(2014)reportsomesupportingevidence.11
3. Data and method
3.1Incomeinequality
Ourleft‐handsidevariable istheGinicoefficientbasedonhouseholds’ incomefromSolt’s
(2009) StandardizedWorld Income Inequality Database (SWIID). We use the index that
represents household income before taxes, as this shows inequality exclusive of fiscal
policy.12As pointed out byDelis et al. (2014) and Solt (2015), the SWIIDdatabase is the
most comprehensive database and allows comparison across countries, because it
standardizes income.13The Gini coefficient is derived from the Lorenz curve and ranges
between 0 (perfect equality) and 100 (perfect inequality).We acknowledge that the Gini
coefficientislessthanperfectandthatothermeasures,suchastheshareofincomeofthe
lowest quintile,may sometimes bemore appropriate.Data availability, however, dictates
our choice.We construct averages of the Gini coefficients across 5 yearswhere the Gini
coefficientsarecenteredatthemiddleofthefive‐yearperiod.
Weusefive‐yearnon‐overlappingaveragesforthreereasons(seealsoDabla‐Norris
etal.,2015).First,annualmacroeconomicdataarenoisy,andthisappliesespeciallyfordata
on income inequality (Delis et al., 2014). Second, the annual income inequality data in
SWIID are imputed for years for which no information was available in the underlying
databases (there are only infrequent measures of inequality for much of Africa, Latin
America,andAsia).Third,someoftheexplanatoryvariablesusedareonlyavailableforfive‐
11AtkinsonandMorelli(2011)examinetherelationshipbetweencrisesandincomeinequalityusingcase studiesofbankingcrisesovera100‐yearperiod (1911‐2010) in25countries.Theyconcludethat “bankingcriseswereprecededby falling inequality asmany timesasby rising inequality” (p.47). They also report that there “is more evidence that financial crises are followed by risinginequality” (p. 49). Using data from 14 advanced countries between 1920 and 2000, Bordo andMeissner(2012)reportthatcreditboomsheightentheprobabilityofabankingcrisis,butthereisnoevidencethatariseintopincomesharesleadstocreditbooms.GuandHuang(2014)challengetheseresults on econometric grounds.Using a similar dataset, they “establish strongevidence for risinginequalityasasignificantdeterminantofcreditboomsandthereforefinancialcrisesinAnglo‐Saxoncountriesandothersimilareconomies”(p.513).However, forothercountriestheirevidenceisnotsupportiveforapositivecausallinkfrominequalitytocrises.
12Using Gini coefficients for net income, as some studies do (e.g. Agnello et al., 2012) wouldcomplicateidentificationoftheeffectoffinanceonincomeinequality.
13Still,itisnotwithoutproblems;seeGalbraith(2012;chapter2)foranextensivediscussion.
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year intervals. Fourth, we are not so much interested in short‐term, i.e. business cycle,
driveneffects.
WehaveconsideredusingtheWorldIncomeInequalityDatabase(WIID)insteadof
SWIID as source for data on income inequality. SWIID is based on WIID, but is
supplementedbyother sources andhas allof itsobservationsmultiply‐imputed (Jenkins,
2015).14WIIDoftenprovidesmorethanoneGinicoefficientforthesamecountry/year.To
dealwiththisproblem,weproceedasfollows.Wefirsttakeaveragesofcountry/yearpairs
thathavethesamequalitylabel(high,average,low,notknown).Thisreducesthenumber
ofpotentialduplicatestoatmost4(high,average,low,notknown)percountry/yearpair.
Next,wetaketheaverageGinicoefficientthatbelongstothehighestqualitygroupsothat
wehaveoneobservationpercountry/year,whichcanbeanaverageofseveralobservations
ofthehighestqualityavailable.Aswearenotinterestedinshort‐rundynamics,ouranalysis
isbasedon5‐yearaverages.AsSWIIDprovidesobservationsforeachyear,theseaverages
canbeeasilycomputed.InWIIDtherearemanymissingobservations.Weconsideredtwo
alternative approaches to construct proxies for theGini coefficientmeasuredover5‐year
intervals.First,wetaketheGinicoefficientsinthemiddleofthis5‐yearperiod,ifavailable
(Gini(WIID)).Thedownsideofthisapproachisthatwearenotaveragingoutofacoupleof
adjacentyears.Asanalternative,wetakea5‐yearsaverage(Gini5yearsavg(WIID));this
results in a non available in case at least one of these years is not available, thereby
reducingthesamplesubstantially.
[Table1here]
Table1presentssomesummarystatisticsforthegrossandnetGinicoefficientsdrawn
fromSWIIDandtheGinicoefficientsconstructedonthebasisofWIID.Asthetableshows,
thenumberofobservationsavailablefortheincomeinequalitymeasuresbasedonWIIDis
much lower than for those based on SWID. When using data from WIID we cannot
distinguishbetweenmarketandnetGinicoefficients.AspointedoutbySolt(2015,p.685)
thisisproblematic:“mixinggross‐anddisposable‐incomeobservations…suggestsasimple
failuretoconsiderwhatisthetheoreticallyrelevantvariable…”However,suchadistinction
14WethankStephenJenkinsforprovidingthisdatabase.
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wouldreducethesampleevenfurther.Itappearsthataboutaquarteroftheobservationsin
WIIDfollowsomekindofgrossincomeconcept.
The correlations reported in Table 1 support this and suggest thatmost of theWIID
dataareindeedbasedonanetincomeconcept:thecorrelationoftheWIIDvariableswith
our(preferred)grossGinicoefficientfromSWIIDisrelativelylow(0.6or0.5),whereasitis
muchhigherwiththenetGinicoefficientsfromSWIID(around0.9).
As we are interested in the impact of finance on income inequality before income
redistributionanddowanttobeabletoworkwitharespectableandrepresentativesample,
wedecidedagainstusingtheWIIDdata.
3.2Explanatoryvariables
WemeasurefinancialdevelopmentbyprivatecreditdividedbyGDP.Thismeasureexcludes
credit to the central bank, development banks, the public sector, credit to state‐owned
enterprises,andcrossclaimsofonegroupof intermediariesonanother.Thus, itcaptures
the amount of credit channeled from savers, through financial intermediaries, to private
firms. It has advantages over alternativemeasures of financial development, such asM2
overGDP,whichdoesnotmeasureakeyfunctionoffinancial intermediaries,whichisthe
channelingofsociety’ssavingstoprivatesectorprojects(Becketal.,2007).Inaddition,the
evidenceofGimetandLagoarde‐Segot(2011)andNaceurandZhang(2016)suggeststhat
theimpactoffinanceonincomeinequalityrunsviathebankingsectorratherthancapital
marketcapitalization.15
Figure1showstwoscatterplotsofourmeasuresforincomeinequalityandfinancial
development.The graphon the left‐hand side shows the relationshipusing the rawdata.
This graph does not suggest that there is a relationship between the two variables. The
graph on the right‐hand side shows the relationship controlling for country‐fixed effects.
Thisgraphsuggeststhatmorefinancialdevelopmentincreasesincomeinequality.
[Figure1here]
15Using the data as described in Čihák et al. (2012), we also investigate whether the results aresensitive to using stock market capitalization as percentage of GDP as measure of financialdevelopment.Althoughthisisreducingthesamplesubstantially,thequalitativeresultstendtogointhesamedirection.
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We use twomeasures for financial sector liberalization. First, following previous
studies we employ the data of Abiad et al. (2010) that is based on several sub‐indices
mostlypertaining tobanking regulatorypracticesmeasuredon a scale from0 to3 (fully
repressed to fully liberalized). The database covers 91 economies over the 1973–2005
periodandconsistsofseven indicesof financialsector liberalization.Our firstmeasureof
financial liberalization is the sum of six sub‐indices. As the sub‐index on banking
supervisionisnotaboutfinancialsectorliberalizationweexcludeit.Oursampleforwhich
weusethisproxyforfinancialliberalizationconsistsof89countries(listedinTableA1of
theAppendix)andrunsfrom1975to2005.
Asanalternative,weemploydata from theFraser Instituteoneconomic freedom
thathasabroadercoverageofthefinancialsector(aspointedoutbyDelisetal.(2014),the
indexofAbiadetal.(2010)primarilyreflectspoliciesrelatedtothebankingsector)16andis
availableformorecountriesandmorerecentyears.Theeconomicfreedomindexcoversup
to 157 countries with data relevant for this paper being available for approximately 70
countries as far back as 1975.17We use the sum of four sub‐indices from the economic
freedomdatabase,namelythesub‐indices3D,4C,4Dand5A.Theseindicesrangebetween
0(not free) to10(totally free).Thefirst indexrefers to freedomtoownforeigncurrency
bankaccountsandmeasurestheeasewithwhichothercurrenciescanbeusedviadomestic
andforeignbankaccounts.Thesecondindexisbasedonthepercentagedifferencebetween
the official and the parallel (black) market exchange rate. Countries with a domestic
currencythatisfullyconvertiblewithoutrestrictionsreceiveascoreoften.Whenexchange
ratecontrolsarepresentandablackmarketexists,theratingswilldeclinetowardzeroas
theblack‐marketpremiumincreasestowardmorethan50%.Inthelattercase,azerorating
isgiven.The third indexmeasurescontrolsof themovementof capital. The fourth index
measurestheextenttowhichthebankingindustryisprivatelyowned,theextenttowhich
creditissuppliedtothegovernmentsectorandwhethercontrolsoninterestratesinterfere
withthemarketincredit.Oursampleforwhichweusethisproxyforfinancialliberalization
16The sub‐indices of the index of Abiad et al. (2010) refer to credit controls and reserverequirements, interest rate controls, banking‐sector entry, capital‐account transactions,privatizations of banks, liberalization of securities markets, and banking‐sector supervision andcapitalregulation.
17The data go back to 1970 but cover only 53 countries. Several studies have examined therelationshipbetweentheoveralleconomicfreedomindexandincomeinequality,see,e.g.BerghandNilsson(2010)andSturmanddeHaan(2015).
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consistsof121countries(listedinTableA1oftheAppendix)andrunsfrom1975to2005.
Figures2and3showtherelationshipbetweenourmeasuresforincomeinequality
andfinancialliberalization,againwithandwithoutcontrollingforfixedeffects.Thegraphs
withoutfixedeffectsdonotsuggestthatthereisarelationshipbetweenincomeinequality
and financial liberalization, while those with fixed effects suggest that financial
liberalizationleadstomoreinequality.
[Figures2and3here]
OurcrisisdatacomefromLaevenandValencia(2013)whoprovideinformationon
the timing of systemic banking crises. Chaudron and de Haan (2014) show that this
database ismore reliable than competing financial crises databases. Crises are identified
based on several criteria. First, there should be signs of financial distress in the banking
system. Banking crises are also identified by “significant banking policy intervention
measures”ofwhichtheyidentifysix(suchasadepositfreezeornationalizations).Atleast
three of these measures need to have been implemented for a crisis to be classified as
systemic.Thisconditionissupplementedwiththreeothercriteria,namelythattheshareof
nonperforming loans exceed 20 percent, bank closures make up at least 20 percent of
bankingassetsandfiscalrestructuringcostsexceed5percentofGDP.Ourcrisisvariableis
onewhenabankingcrisisstartedinthefive‐yearperiodbeforeandiszerootherwise.
3.3Method
As we are interested in the within country relationship between finance and income
inequality,weuseadynamicpanelmodel insteadofOLScross‐sectionregressions inour
main analysis. As pointed out by Beck et al. (2007), a dynamic panelmodel has several
advantages compared to cross‐country regressions as the latter do not fully control for
unobserved country‐specific effects and do not exploit the time‐series dimension of the
data.Themodelestimatedis:
, , , , , ,
WhereIneq isincomeinequality,FD isfinancialdevelopment,FLisfinancialliberalization,
BCdenotestheoccurrenceofabankingcrisisandXisavectorofcontrolvariables,whileu
denotestheerrorterm.Timelagsareusedtoavoidendogeneityissues(butthismaynotbe
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sufficientandthereforeweconsideralternativeapproachesbelow).ForFDandFLwetake
valuesattheendofthefive‐yearperiodprecedingtheperiodcoveredbytheGinicoefficient
(whichisafive‐yearaverage),whilethebankingcrisisdummyisonewhenabankingcrisis
startedinanyofthefiveyearsprecedingthefive‐yearperiodusedforcalculatingtheGini
coefficient.We have used a very long list of control variables based on previous studies
(showninTableA2intheAppendix;TablesA3andA4providessummarystatisticsanda
correlationmatrix).18
AspointedoutintheIntroduction,wefocusontwointeractionsthat,accordingto
insightsfromtheliterature,mayconditiontheimpactoffinanceonincomeinequality.First,
weexaminewhethertheimpactoffinancialliberalizationonincomeinequalitydependson
the level of financial sector development. Second, we examine whether the impact of
financialliberalizationand/orfinancialdevelopmentonincomeinequalityisconditionedby
institutionalquality.
We have constructed two institutional quality variables using the ICRG database
measuring the quality of political institutions and the quality of economic institutions,
respectively.Onascalefromzero(lowquality)tosix(highquality),thevariabledemocratic
accountabilitymeasures not just whether there are free and fair elections, but also how
responsive government is to its people. This variable comes directly from the ICRG
database. It measures precisely what e.g. Acemoglu and Robinson (2013, p. 36) have in
mindwhen theyexplainwhy thequalityofpolitical institutionsmatters inexplaining the
different economic fates of Mexico and the US and the role of access to finance therein:
“Unlike inMexico, intheUnitedStatesthecitizenscouldkeeppoliticians incheckandget
ridofoneswhowouldusetheirofficestoenrichthemselvesorcreatemonopoliesfortheir
cronies. ... The broad distribution of political rights in theUnited States, especiallywhen
compared toMexico, guaranteed equal access to finance and loans.” Our indicator of the
quality of economic institutions is the sum of three ICRG variables, namely bureaucratic
quality,corruptionandlawandorder(takingdifferencesinscalingoftheseindicatorsinto
account)whereahighernumberindicatesbetterquality.
18Duetodataavailabilitysomevariablesthathavebeensuggestedtoberelatedtoincomeinequality,suchastechnology(seee.g.Dabla‐Norrisetal.,2015),couldnotbeincluded.
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4.Mainresults
Tables2and3presenttheresultswhereweproceedasfollows.First,weshowtheresults
whenwedonot include control variables.Asour three financemeasuresmaybe related
(e.g. more financial development may lead to more banking crises and a low level of
financial development may be an incentive for countries to introduce financial
liberalization),we first showsimplebivariate regressionsbefore includingallour finance
measures. In the next step we add the interactions outlined above. To interpret the
interactioneffects,weusegraphsassuggestedbyBramboretal.(2006).19Finally,weadd
controlvariablesinTable2thatturnouttobesignificant(inTable3weincludethesame
controls).InTable2themeasureforfinancialliberalizationbasedonAbiadetal.(2008)is
used, while in Table 3 financial liberalization is proxied by the index based on several
components of the Fraser Institute’s economic freedom index, which captures more
dimensionsofthefinancialsystemthantheindexofAbiadetal.(2010).
[Tables2and3here]
In the first three columns of Tables 2 and 3 the financial sector variables are
included separately, while column (4) shows the results when all finance measures are
included. In the regressions in these columns we do not include interaction terms and
control variables. The results suggest that financial development, financial liberalization
andbankingcrisesincreaseincomeinequality,alsowhentheyareincludedsimultaneously.
Nextweturntotheinteractionoffinancialliberalizationandfinancialdevelopment
toexaminewhetherfinancialdevelopmentconditionstheimpactoffinancialliberalization
on income inequality, as suggested by Bumann and Lensink (2016). The line in Figure 4
shows the marginal impact of financial liberalization on income inequality for different
levelsoffinancialdevelopment.Thewhiskersshowtheconfidencebandandthegreybars
showthedistributionoftheobservations.Thegraphsarebasedontheestimatesreported
in column (5) of both tables. The graphs in Figure 4 suggest that the impact of financial
liberalization is conditioned by the level of financial development: the positive impact of19Moststudiesdiscussedinsection2thatconsiderinteractionsdrawconclusionsonthebasisofthesignificanceoftheinteractionterm,whichgenerallyisnottheproperwaytodealwithinteractionsasshownbyBramboretal.(2006).
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financial liberalization on the Gini coefficient is higher when financial development is
higher. This conclusion holds for bothmeasures of financial liberalization.20Adding time
fixedeffectsdoesnotchangeourconclusion(notshown;resultsavailableonrequest).So
these resultsdo not support thepredictionofBumannandLensink (2015) that financial
liberalizationwilldecreaseincomeinequalityathighlevelsoffinancialdevelopment.
[Figure4here]
In thenextstepweconsider institutionalquality.We firstaddourproxies for the
qualityofpoliticalandeconomic institutionstothemodelshownincolumn(4).Including
thesevariablesmayshedsomelightontherelevanceofapotentialcriticismofourresults,
namely that inequalityand financialdevelopmentarebothdrivenby institutional factors.
For instance,according toClaessensandPerotti (2007,p.749), “economic inequalityand
(financial) underdevelopment are jointly determined by institutional factorswhich cause
unequal access topoliticalandcontractual rights.” If true,addingproxies for institutional
qualityshouldaffectourresults.Itturnsoutthatdemocraticaccountabilityissignificantin
contrasttoourproxyforthequalityofeconomicinstitutionswhichisthereforenotshown
incolumn(6)ofTables2and3.Ourresultssuggestthatbetterpoliticalinstitutionsreduce
income inequality. Importantly, adding the quality of institutions does not change our
previousfindingthatfinanceincreasesincomeinequality.
Figure5showsthemarginaleffectsoffinancialliberalizationonincomeinequality
fordifferent levelsofdemocraticaccountability.Thegraphs arebasedon the regressions
shown incolumn(7)ofTables2and3.Theysuggest that thepositive impactof financial
liberalizationontheGinicoefficientishigherincountrieswithahigherqualityofpolitical
institutions. In fact,at low levelsofdemocraticaccountability financial liberalizationdoes
not significantly affect income inequality. In these regressions we do not include the
20We have also examined the interaction of financial development and the Chin‐Ito index forfinancialopenness.Kuniedaetal.(2014)arguethattherelationshipbetweenfinancialdevelopmentand income inequality is conditioned by financial openness. Their evidence, based on a sample ofmore than 100 countries for the period 1985‐2009, suggests that in financially open countries(wherefinancialopennessiscomputedfromthedatasetofLaneandMilesi‐Ferretti,2007),financialdevelopment (measuredasprivate credit toGDP) increases income inequality,while in financiallyclosed economies financial development decreases income inequality. Our results (available onrequest)donotprovideevidenceforthisview.
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interaction between financial liberalization and financial development as financial
development has been shown to be dependent on institutional quality (see e.g. Law and
Azman‐Saini,2012).
Figure 6 presents the marginal effects of financial development on income
inequality for different levels of democratic accountability. The graphs are based on the
regressions shown in column(8)ofTables2and3.Theydonotprovidestrongevidence
thattheimpactoffinancialdevelopmentonincomeinequalityisconditionedbythequality
ofpoliticalinstitutions,incontrasttothepredictionofRajanandZingales(2003)thatunder
high‐qualityinstitutionsfinancialdevelopmentwillreduceinequality.
Theinteractionsofourfinancevariablesandourproxyforthequalityofeconomic
institutionsdonotsuggestthattheimpactoffinanceonincomeinequalityisconditionedby
the quality of economic institutions. For instance, Figure A1 in the Appendix shows the
marginaleffectsof financial liberalizationon theGinicoefficient fordifferent levelsof the
qualityofeconomicinstitutions.Althoughmostlysignificantlypositive,themarginaleffects
of financial liberalization on inequality for different levels of institutional quality are not
significantlydifferentfordifferentvaluesofinstitutionalquality(thewhiskersoverlap).
[Figures5and6here]
The next column in both tables shows the results when we add economic
globalization to themodel shown in column(7)ofTables2and3.Assaid,weconsidera
long listofpotentialcontrols,butmostofthemarenotsignificant. Inlinewithfindingsof
Sturm and de Haan (2015), globalization turns out to be significant in Tables 2 and 3
(column9).Addingcontrolsdoesnotchangeourconclusionsasshownbythemarginalplot
graphs(availableonrequest).
5. Sensitivity analysis
Inthissectionwepresenttheoutcomesofseveralsensitivityteststhathavetwopurposes.
First, as our results deviate from those of several previous studies,we examine towhat
extentour findings changewhendifferent empirical set‐ups areused. Second,we further
analyzewhetherourresultsarerobustforendogeneity,whichisakeyissueinthistypeof
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analysis.
5.1Randomeffectsmodels
So far,our resultsarebasedonpanel fixedeffectsmodels. In this sectionwepresent the
outcomesofrandomeffectsmodelsfollowingClarkeetal.(2006)whouserandomeffects
arguing that using fixed effects takes away much (cross‐country) variation. Since the
Hausmantestsoftendonotclearlyindicatethatfixedeffectsneedtobeused,itmakessense
toalsoestimaterandomeffectsmodels.Thishasanadditionaladvantage,namelythatwe
can followseveralpreviouspapers(Clarkeetal.,2006;Kappel,2010;Kaniedaetal.2014
and Law et al., 2014) and use legal origin dummies as instruments for financial
development.AccordingtoLaPortaetal.(1997,1998),theintroductionofcommonorcivil
law into a country via conquest or colonization not only affected the legal rules but also
institutions.Forinstance,theprotectionofpropertyrightsincommonlawcountries,which
impacts thedevelopmentof financialmarkets, is stronger thanthat incivil lawcountries,
notably incountrieswithFrenchcivil law.Therefore, legalorigindummiesare frequently
usedasinstrumentalvariables(cf.AcemogluandJohnson,2005).
Table4showstheoutcomes.Columns(1)‐(4)presenttheresultswhenweusethe
measureforfinancialliberalizationbasedonthedataofAbiadetal.,whilecolumns(5)‐(8)
contain the results for the financial liberalization measure based on components of the
economicfreedomindex.
Columns(1)and(5)showtheresultswhenweestimatethemodelshownincolumn
(5) of Tables 2 and 3 which includes our finance variables and the interaction between
financial liberalizationandfinancialdevelopmentallowingforrandomeffects. Itturnsout
that the results are very similar. Next, in columns (3) and (7) we include democratic
accountability in the model containing our three finance measures together with its
interactionwithfinancial liberalization(cf.column(7) inTables2and3).Likebefore,the
results suggest that finance increases inequality, while institutional quality decreases
inequality. In countries where democratic accountability is high, the effect of financial
liberalization turns significantly positive. Hence, moving to a random effects framework
doesnotleadtodifferentresults.
Finally,columns(2),(4),(6)and(8)showtheIVresults.Incolumns(2)and(4)our
measuresforfinancialdevelopmentandfinancialliberalizationaretakenupandallowedto
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interact.(Thiscorrespondstothespecificationincolumn(5)ofTables2and3).Incolumns
(4) and (8) the interaction between the quality of political institutions and financial
liberalization is included.(Thiscorresponds to thespecification incolumn(7)ofTables2
and3).Theoutcomessuggestthatinstrumentingfinancialdevelopmentbylegalorigindoes
notleadtodifferentoutcomes(seealsoFigureA2intheAppendix).
[Table4here]
5.2Cross‐countryregressions
Next, we present cross‐country regressions results in Table 5. Even though we feel that
panelmodelsaremostappropriateforourpurpose,wewanttocheckwhetherourresults
aredifferentwhenwefocusoncross‐countrydifferencesinincomeinequalityratherthan
within‐country income inequality. We only show the outcomes for the financial
liberalization measure based on the data of Abiad et al., as this is the variable used in
previous studies. We use the specification with the three finance variables, democratic
accountability and the interaction between financial liberalization and democratic
accountability for different cross‐sections (1991‐95, 1991‐2000, 1991‐2005, 1996‐2000,
1996‐2005,and1996‐2010).This corresponds to column(7) inTables2and3.The final
three columns show the outcomes in casewe again instrument financial developmentby
legal origin using the latter time periods. The results for banking crises and financial
liberalizationarebroadly in linewithour findingsbasedonpanel estimates,butwenow
find some evidence that financial development reduces income inequality (although the
estimatedcoefficientisnotsignificantinmostregressions).Thissuggeststhatourfocuson
within‐country income inequality explains to some extent the difference between the
resultsofourstudyfortheimpactoffinancialdevelopmentonincomeinequalityandthose
of previous studies focusing on cross‐country income inequality. Another difference
betweenthepanelandthecross‐countryregressionsisthatthecoefficientofthequalityof
politicalinstitutionsisneversignificantinthelatter.
[Table5here]
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5.3OECDcountries
In this sectionwe report the resultswhenwe estimate somemodels forOECD countries
only.Table6showsfixedeffectspanelregressionsforthespecificationsshownincolumns
(4),(5)and(7)ofTables2and3.Ourprioristhattheinteractionswillnotbesignificant,as
the countries in this subsample aremuchmorehomogeneouswhen it comes to financial
developmentandinstitutionalqualitythanisthecaseinourfullsample.Thisindeedturns
out to be the case. Still, ourmain result that finance increases income inequality is also
confirmed for OECD countries, also when we use our alternative measure for financial
liberalization(lastthreecolumnsofTable6).
[Table6here]
6. Conclusion
Our results suggest that financialdevelopment, financial liberalization andbanking crises
increase income inequality. Inaddition, the impactof financial liberalizationon inequality
seems tobe conditionedby the levelof financialdevelopmentand thequalityofpolitical
institutions. Our findings are in contrast to several previous studies that examined the
relationshipbetweenfinancialdevelopmentandincomeinequality.
As explained in section 2, theory is not clear whether financial developmentwill
increase or decrease income inequality. Our results suggest that financial development
increasesinequality,whichisinlinewiththemodelofGreenwoodandJovanovic(1990).It
is important, however, to stress that our results donot imply that financial development
and financial liberalization are necessarily bad for the poor. There is a large literature
showingthatfinanceplaysapositiveroleinpromotingeconomicdevelopment(atleastup
to a point),21whichwill benefit the poor. An interesting avenue for future research is to
21Somerecent studies suggest that this relationshipmaybenon‐linear.For instance,Arcandetal.(2012)reportthatatintermediatelevelsoffinancialdepth,thereisapositiverelationshipbetweenthe size of the financial system and economic growth, but at high levels of financial depth, morefinanceisassociatedwithlessgrowth.Infact,themarginaleffectoffinancialdepthonoutputgrowthbecomes negative when credit to the private sector reaches 80‐100 per cent of GDP. Likewise,Cecchetti and Kharroubi (2012) report that financial development has a non‐linear impact onaggregateproductivitygrowth.Basedonasampleofdevelopedandemergingeconomies,theyshowthatthelevelof financialdevelopmentisgoodonlyuptoapoint,afterwhichitbecomesadragon
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modeltheeffectsoffinancialdevelopmentandfinancialliberalizationonincomeinequality
andeconomicgrowthsimultaneously.
Our finding that financialdevelopmenton income inequality isnotconditionedby
democraticaccountabilityisincontrasttothepredictionofRajanandZingales(2003)that
underhigh‐quality institutions financialdevelopmentwill reduce inequality.However,we
find evidence that the impact of financial liberalization is conditioned by the quality of
politicalinstitutions.OurfindingsdoalsonotsupportthetheoreticalpredictionofBumann
and Lensink (2016) that financial liberalization will improve income distribution in
countrieswhere financialdepth ishigh.Tothecontrary,ourresultssuggest that financial
developmentenhancestheincomeinequalityincreasingeffectoffinancialliberalization.To
explainthisfindingisbeyondthescopeofthecurrentpaperandisleftforfutureresearch.22
Finally, we like to stress that our results are based on Gini coefficients for gross
income, thereby ignoring (onpurpose)government redistributionpolicies.An interesting
issue for future research is to examine whether countries that have higher income
inequality due to finance, have decided to redress this inequality by more income
redistribution. Likewise, itwould be interesting to examinewhether our results hold for
other measures for income inequality. This requires, however, that such data become
availableforalargersetofcountriesthaniscurrentlythecase.
Acknowledgements
TheviewsexpressedarethoseoftheauthorsanddonotnecessarilyreflectthoseofDNB.
We like to thank participants in the DNB Annual Research Conference (November 20,
2015), research seminars at Deakin University (January 20, 2016), the National Bank of
Serbia (February 26, 2016), IdEP, Lugano (April 12, 2016), and ISEG, Lisbon (June 23,
2016), as well as the European Public Choice conference (March, 30‐April 2, 2016), the
SUERFconference“Centralbankingandmonetarypolicy:Whichwillbethenewnormal?”
growth.22 But their model can be easily be adjusted and made in line with our results by allowing high-income agents to have a higher saving rate. That would make them benefit more from the increased loan demand – that is more pronounced in financially developed countries – that is caused by financial liberalization than those with lower income levels.
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inMilan(April14,2016),theCESifoVeniceSummerInstitute(July20,2016),especiallyour
discussantStephanieArmbruster, theSilvaplanaWorkshoponPoliticalEconomy(July31,
2016)andtheVfS‐AnnualConference(September5,2016)fortheircommentsonprevious
versionsofthispaper.
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Tables
Table1.SummarystatisticsofdifferentGinicoefficients–SWIIDvs.WIID
Variable Obs Mean St. Dev. Min Max 1 2 3 4
1 Gross Gini (SWIID) 530 45.37 7.26 22.66 69.85 1
2 Net Gini (SWIID) 530 38.20 9.45 19.43 66.20 0.70 1
3 Gini (WIID) 335 39.26 9.72 20.10 74.30 0.63 0.89 1
4 Gini 5 years avg. (WIID) 184 36.98 8.92 21.32 58.40 0.52 0.90 0.97 1
Correlation with
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Table2.Financeandincomeinequality:panelestimates(dependentvariable:Ginicoefficient;Abiadetal.dataforfinancialliberalization)
Table3.Financeandincomeinequality:panelestimates(dependentvariable:Ginicoefficient;economicfreedomdataforfinancialliberalization)
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Table4.RandomeffectsGLSandG2SLSestimates
Table5.Cross‐countryregressions
(1) (2) (3) (4) (5) (6) (7) (8)
VARIABLES FD +IV PI +IV FD +IV PI +IV
Start of a Systemic Banking Crisis during t‐7 and t‐3 1.012** 0.954*** 1.017*** 1.023*** 1.436*** 1.221*** 1.010*** 1.000***
(2.513) (2.687) (2.862) (3.559) (3.441) (2.979) (2.720) (3.098)
Domestic credit to private sector (% of GDP) ‐0.0188 ‐0.0872* 0.0283*** 0.0124 ‐0.0358 ‐0.138** 0.0277*** 0.00508
(‐0.578) (‐1.790) (3.426) (0.560) (‐0.900) (‐2.362) (3.613) (0.191)
Financial liberalisation 0.0338 ‐0.00983 ‐0.109 ‐0.179 ‐0.0401 ‐0.0317 ‐0.618* ‐0.712**
(0.455) (‐0.137) (‐0.924) (‐1.565) (‐0.180) (‐0.128) (‐1.860) (‐2.374)
c.domcredgdp#c.finlib 0.00391** 0.00708*** 0.00919** 0.0167***
(2.202) (3.688) (2.087) (2.696)
ICRG: Democratic Accountability ‐1.456*** ‐1.706*** ‐2.020***‐2.236***
(‐3.092) (‐3.569) (‐3.257) (‐4.265)
c.democ#c.finlib 0.0817***0.105*** 0.217*** 0.258***
(2.722) (3.640) (2.831) (3.987)
Observations 426 426 345 345 518 518 410 410
Number of cntid 89 89 86 86 121 121 110 110
F‐test on domcredgdp (p‐value) 8.57e‐08 3.80e‐06
F‐test on finlib (p‐value) 0.000673 0.000638 4.49e‐05 1.77e‐05 0.00761 0.00141 0.00187 0.000113
F‐test on democ (p‐value) 0.00836 0.000754 0.00436 9.09e‐05
Notes: Country‐random effects are included. Standard errors are clustered at the country level in columns (1), (3), (5) and (7). In the "+IV"
columns dom. credit is instrumented using legal origin dummies and bootstrapped standard errors are shown.*** p<0.01, ** p<0.05, * p<0.1.
Abiad et al. index (corrected) Avg.of EFW‐areas 3D, 4C, 4D and 5A
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Table6.RegressionsincludingonlyOECDcountries
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Figures
Figure1.Financialdevelopment(credit/GDP)andincomeinequality(Ginicoefficients)
Figure2.Financialliberalization(Abiadetal.measure)andincomeinequality(Ginicoefficients)
2030
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Figure3.Financialliberalization(economicfreedommeasure)andincomeinequality(Ginicoefficients)
Figure4.Marginalimpactoffinancialliberalizationonincomeinequalityfordifferentlevelsoffinancialdevelopment
Figure5.Marginalimpactoffinancialliberalizationonincomeinequalityfordifferentlevelsofdemocraticaccountability
2030
4050
6070
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0.00
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0.0
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ini (
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Figure6.Marginalimpactoffinancialdevelopmentonincomeinequalityfordifferentlevelsofdemocraticaccountability
-0.0
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ini (
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0 1 2 3 4 5 6ICRG: Democratic Accountability
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ini (
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Appendix
TableA1.Countriesincluded
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TableA2.Variables:Descriptionandsources
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TableA3.Summarystatistics
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TableA4.Correlationmatrix
FigureA1.MarginaleffectsoffinancialdevelopmentontheGinicoefficientfordifferentvaluesofthequalityofeconomicinstitutions
Table2–finreform_cor Table3 – ffw_avg
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ini (
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FigureA2.MarginaleffectsoffinancialliberalizationontheGinicoefficientfordifferentvaluesoffinancialdevelopmentandpoliticalinstitutionalqualityestimatedwithG2SLS
0.00
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