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Corruption,PoliticalInstitutions,andAccountingEnvironment:ACross‐countryStudy

MuhammadNurulHouqeSchoolofAccounting&CommercialLaw

VictoriaUniversityofWellington,NewZealandPhone:+6444636591,Fax+6444635966

noor.houqe@vuw.ac.nz&

RezaMonem*GriffithBusinessSchoolGriffithUniversity

Brisbane,Queensland,AustraliaTel+61(0)737353598,Fax:+61(0)737353719

Email:r.monem@griffith.edu.au

Paperpresentedatthe2013annualTheInternationalJournalofAccountingSymposium,17–20May,Wuhan,P.R.China

*Correspondingauthor.

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Corruption,PoliticalInstitutions,andAccountingEnvironment:ACross‐countryStudy

Abstract:

Using data from 166 countries over the period 1996‐2011, we investigate the role of

accountinginformationinreducingcorruptionaftercontrollingfortheeffectsofpolitical

institutions and economic development. We find strong evidence that accounting

environmentplaysonlyaminorrolerelativetothatofthestrengthofpoliticalinstitutions

inthecontrolofcorruption.Ourresultsholdevenaftercontrollingforvariablesrelatedto

investorprotectionandculturaldimensions.Ourresultschallengetheviewthatcountries

intendingtoreducecorruptionshouldinvestinhigher‐qualityaccountingstandards. Our

findingsalsosuggestthatcountrieswiththestrongestpoliticalinstitutionsstandtobenefit

mostfromIFRSadoption.

JELclassifications:M41,G38,K42

Keywords: Corruption; Accounting environment; Political institutions; IFRS; Economic

development;Investorprotection

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Corruption,PoliticalInstitutions,andAccountingEnvironment:ACross‐countryStudy

1.Introduction

Corruption is a fundamental threat to the viability of any economic system (e.g.,

Blackburn,Bose,&Haque,2008;Mo,2001;Shleifer&Vishny,1993;Wei,2000).Although

avastliteratureexistsonthecausesandconsequencesofcorruptionandhowtocontrolit

(e.g.,Ades&DiTella,1996;Fisman&Svensson,2007;Rock&Bonnett,2004;Tanzi,1998;

Treisman, 2007), research literature linking corruption with accounting is sparse.

However,inarecentstudy,Malagueño,Albrecht,Ainge,andStephens(2010)findthatthe

Big4marketshareandperceivedaccountingqualityareinverselyrelatedtotheperception

ofcorruptioninacountry.Hence,Malagueñoetal.(2010,p.388)conclude,“Countriesmay

beabletodecreasecorruptionbyimprovingthequalityoftheiraccountingandauditing.”

Such a conclusion is premature and misleading given that the weakness of political

institutions is a fundamental andprobably themost crucial element in the economicsof

corruption(Aidt,2003;Lederman,Laoyza,&Soares,2005;Tanzi,1998).Malagueñoetal.

donot fullycontrol for theeffectivenessofpolitical institutions inacountryandmostof

theirproxiesforpoliticalinstitutionsareeitherinsignificantorweaklysignificant.1

In thisstudy,we investigatetheroleofaccounting information inreducingcorruption

aftercontrollingforpoliticalandeconomicfactorsassociatedwithcorruption.Specifically,

wearguethat,althoughtheaccountingenvironmentofacountrymayhavesomeabilityto

1Severalof theproxies forpolitical institutionsused inMalagueñoetal. (2010)arecommon lawsystem,formerBritishcolony,federalsystem,ethnolinguisticdivision,fuelmetalandmineralexports,uninterrupteddemocracy, government intervention, and government turnover. Further, their measure of economicfreedom, following DiRienzo, Das, Cort, and Burbridge (2007), is a mixture of economic and politicalindicators.

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reduce perceived corruption, strengthening of the political institutions has the greatest

potential forreducingcorruption. Given thataccountingquality isdirectly influencedby

thestrengthofpoliticalinstitutions(butnotviceversa),countriesaroundtheworldwould

bemuch better off by improving the quality of political institutions in the fight against

corruption.Furthermore,tryingtoimproveaccountingandauditingenvironmentwithout

any concurrent changes in political institutionsmay have theminimal effect in the fight

againstcorruption.

We exploit country‐level data related to control of corruption (corruption perception

index)andpolitical institutions compiledbyKaufmann,Kraay, andMastruzzi (2012). In

thisstudy,proxiesforpoliticalinstitutionsarevoiceandaccountability,andruleoflaw.We

attempt to capture the accounting environment of a country by identifyingwhether the

countryhasadoptedtheInternationalFinancialReportingstandards(IFRS),sourcedfrom

DeloitteIASPluswebsite(2012),andbyutilizingthefinancialdisclosureindexcompiledby

theWorldBank (2012). Our analysis based on data from166 countries over the period

1996‐2011suggeststhataccountingenvironmentplaysaminorrolesecondarytopolitical

institutions in the control of corruption. Our results hold even after controlling for

variables related to economic development, investor protection, and Hofstede’s (2001)

cultural dimensions. Further, our results are robust to alternative specifications of our

models,alternativesamplespecification,andalternativeestimationtechniques.

Our results imply that the countrieswhich intend to reduce corruptionwill bemuch

better off by investing in the improvement of political institutions. Moreover, countries

with weak political institutions that have adopted the IFRS cannot expect to achieve a

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reduction in corruption (via improved financial reporting) until political institutions are

strengthened.

We make three contributions to the literature on corruption and international

accounting. First,we contribute to the cross‐country studies on corruptionby specifying

theroleofaccountinginthecontrolofcorruption.Specifically,unlikeKimbro(2002)and

DiRienzo et al. (2007), we control for endogenous relation among political institutions,

corruption,andaccountingenvironment.Further,unlikeMalagueñoetal.(2010),wefind

that political institutions have the strongest effect in the control of corruption. Second,

becauseouranalysisencompasses theperiodwhentheIFRShavebeenadopted inmany

countries,we contribute to the literatureon thebenefitsof IFRSadoption. Our analysis

suggests that, countries with weak political institutions (including weak investor

protection)havelittletogainfromtheIFRSadoption.Third,ourresultsmayhelpexplain

themixed evidence in cross‐country studies that address the effect of IFRS adoption on

financial reporting quality (e.g., Ahmed, Neel, &Wang, 2013; Barth, Landsman, & Lang,

2008;Jeanjean&Stolowy,2008;Soderstrom&Sun,2007). Unlessdifferencesinpolitical

institutions across countries are adequately controlled for, evidence from cross‐country

studiesonfinancialreportingqualityandIFRSadoptionwillremaininconclusive.

The rest of the paper is organized as follows. Section 2 provides an overview of the

nature of corruption. Section 3 briefly reviews some key studies on corruption and

develops the theoretical framework linking corruption and accounting environment.

Section4proposestheresearchmodels,andexplainsthedataandthevariablesusedinthe

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study.Empiricalresultsarepresentedinsection5.Section6discussesseveralrobustness

checks.Section7summarizesthepaperandprovidessomeconclusions.

2.Thenatureofcorruption

FollowingShleiferandVishny(1993),wedefinecorruptionbroadlyastheuseofpublic

officeforunauthorizedprivategain.Ourdefinitionofcorruptionisconsistentwiththoseof

Blackburnetal.(2006),andEverett,Neu,andRahaman(2007):corruptionistheabuseof

authority by bureaucratic officials who exploit their discretionary power, delegated to

thembythegovernment,toadvancetheirowninterestsbyengaginginunauthorizedrent‐

seekingactivities.

Aidt (2003)and Jane(2001)argue thatcorruptionhas threeelements.First, someone

must have discretionary power or authority to design regulation or administer policy

outcome. Second, there must be economic rents associated with this power. Third, it

requires a legal or judicial system that decreases the probability to detect and punish

corruptofficials.Hence,opportunities forcorruptionarisewhenever theofficials’actions

involve the exercise of discretion and are impossible to be monitored perfectly (Rose‐

Ackerman,2003).

In the market for corruption, both sides of a transaction have to agree to corrupt

practices(Jane,2001).Forexample,themanagersofprivatefirms,throughtheircontrolof

themanagementandfirmresources,maybribepublicofficialsforsecuringpublicprojects.

Ontheotherhand,publicofficials,actingonself‐interest,mayacceptbribesorkickbacks

from citizens and corporations to maximize their own wealth (Rose‐Ackerman, 2003).

Further, inorder for thecorruption to takeplace,bothpartiesmustagree toconceal the

transactionbecauseitsrevelationcanhavepunitiveconsequencesforbothparties.

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Corruption has devastating effects, especially on the citizens of developing countries.

Corruptionreduceseconomicgrowth(Mauro,1995;Mo,2001;Habib&Zurawicki,2002;

Zhao, Kim, & Du, 2003). An estimate of the effect of corruption on economic growth is

providedbyMo(2001):a1%increaseinthecorruptionlevelreducesthegrowthrateby

about0.72%.Highlycorruptgovernmentsalsospendlessoneducationandhealth(Tanzi,

1998), thereby limiting the potential for economic growth. Mauro (1997) claims that

corruption may reduce the efficiency of domestic and international aid flow through

diversion of funds from intended government projects. While Kehoe (1998) notes that

corruptpracticeselevatethehiddencostofdoinginternationalbusiness,GhosalandMoran

(2005) indicate thatmultinationalcorporationssuffer tarnishedreputations in theworld

marketplace when they forgo their social legitimacy by engaging in corrupt practices.

Furthermore,corruptionreducesrevenuegeneratedthroughtaxationwhenpartiesengage

in tax evasion, contributing to adverse budgetary consequences for the government

(Mauro,1997).

3.Priorstudiesandtheoreticalframework

3.1.Priorstudies:Causesandconsequencesofcorruption

The literature on corruption is enormous and still growing. Besides, several excellent

reviewpapersareavailableonthecausesandconsequencesofcorruption(e.g.,Treisman,

2007;Lambsdorf,2005).Hence,wearegoingtohighlightonlysomeofthekeyfindingsin

thisarea.

SandholtzandKoetzle(2000)arguethat thepolitical‐economicstructureof incentives

andopportunities,andthepeople’sculturalorientationsarethetwoprimaryfactorsthat

determinethelevelofcorruptioninacountry. TheirargumentisreflectedinTreisman’s

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(2007)observations.Basedon a surveyof a decadeof cross‐national empirical studies

since the mid‐1990s, Treisman (2007, p. 211) observes, “[H]ighly developed, long‐

establishedliberaldemocracies,withafreeandwidelyreadpress,ahighshareofwomen

ingovernment,andahistoryofopennesstotrade,areperceivedaslesscorrupt.”Further,

Lederman et al. (2005) document that political institutions in the form of democracy,

parliamentary system, political stability, and freedomof press are associatedwith lower

corruption.TheseviewsareconsistentwiththefindingsofPaldam(2002),andAliandIsse

(2003). In particular, Ali and Isse (2003) find that education, judicial efficiency, and

economic freedom are negatively related to corruption while a country’s foreign aid

dependencyandthesizeofgovernmentarepositivelyrelatedtocorruption.Insum,there

isoverwhelmingevidencetosuggestthatthestrengthofpoliticalinstitutionsinacountry

reallymatters in thecontrolof corruption.Hence, agovernmentwhichwishes to reduce

corruption“shouldsettleforsimpleandstablelegalandadministrativerulesandimprove

ontheinformationprovidedtotheprivatesector”(Lambert‐Mogiliansky,2002,p.48).

In terms of economic determinants of corruption, the most significant (economic)

determinant of corruption is the real gross domestic product (GDP) per capita (Paldam,

2002). Further, within a general equilibrium context, Blackburn et al. (2006, 2010)

demonstrate that corruption and economic development are endogenously determined

with a negative relationship between them. In one of the early studies, Tanzi (1998)

identifieseconomicgrowthasoneof themajorcostsofcorruptionandarguesthatsome

forms of state reforms are required to lower the supply of and demand for corruption.

Further,themostprominentchannelthroughwhichcorruptionaffectseconomicgrowthis

politicalinstability(Mo,2001).Othernegativeeffectsofcorruptionincludeslowingdown

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of foreign direct investment (FDI) in the host country (Shleifer and Vishny, 1993;

Smarzynska & Wei, 2000; Wei, 2000) and misallocation of resources in an economy

because of the necessary secrecy of corruption (Ehrlich & Lui, 1999; Shleifer & Vishny,

1993). Further, corruption in thehost country shifts theownership structure related to

FDItowardsjointventures(Smarzynska&Wei,2000).Evidencealsoexiststhatcorruption

inverselyaffectstherealexchangerateofacountry(Bahmani‐Oskooee&Nasir,2002).

While corruption slows economic growth and investments in most countries, the

economic growth of the large East Asian countries despite high‐level corruption is a

paradox(Rock&Bonnett,2004).However,theEastAsianparadoxcanbeexplainedbythe

highpredictabilityofcorruptionintheregionbecausecorruptionregimeswhicharemore

predictablehavelessnegativeimpactoninvestment(Camposetal.,1999).Priorresearch

hasalsodocumentedfirm‐levelconsequencesofcorruption.Usingdatafromthreeworld‐

wide surveys, Kaufmann andWei (1999) find that firmswhich paymore bribes end up

spendingmoremanagement timewith bureaucrats on negotiating regulations, and face

higher cost of capital. Further, Fisman and Svensson (2007) document that bribery is

negativelyrelatedtofirmgrowth.Insum,corruptionhasmanyadverseconsequencesfor

aneconomy,includinglowereconomicgrowthrate,lowerFDI,misallocationofresources,

and weaker foreign exchange rate. Further, corruption and economic development are

negativelyandendogenouslyrelatedthroughthecompetitionbetweengrowth‐enhancing

andsociallyunproductiveinvestments(EhrlichandLui,1999).

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3.2.Priorstudies:Corruptionandaccounting

Asalreadystated,the literature linkingcorruptionandaccounting issparse. Wewere

abletoidentifyonlythreestudiesinthisarea.TheseareKimbro(2002),Malagueñoetal.

(2010), and Wu (2005a). A fourth study (DiRienzo et al., 2007), although not directly

related to accounting, reports that digital access to information can lower corruption.

Becauseoneoftherolesoffinancialreportingistoreduceinformationasymmetrybetween

managers who control the firms and owners who supply the capital, accounting

environmentinacountryislikelytoplayaroleinthecontrolofcorruption.

Both Kimbro (2002) and Malagueño et al. (2010) conclude that the perception of

corruption is negatively related to accounting quality. Kimbro (2002) employed the

corruption perception index of the years 1995 to 1999 published by the Transparency

International.Shemeasuredaccountingqualityasthenumberofaccountantsper100,000

inhabitants and the CIFAR reporting index based on financial statements in 1990.

Malagueño et al. (2010) used the Big 4 market share and perceived accounting quality

(PAQ),sourcedfromtheWorldEconomicForum(2003),asproxiesforaccountingquality.

Finally,usingcross‐country firm‐leveldata inAsiansetting,Wu(2005) found thatbetter

accounting practices can help reduce both the incidence of bribery and the amounts of

bribe payments, but conforming to high quality accounting standards alone does not

necessarily bring down the incidence of bribery. Wu’s findings support the notion that

accounting can play a role in controlling corruptionwhen other institutional factors are

supportive.

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Arguably,thenumberofaccountantsper100,000inhabitantsusedbyKimbro(2002)is

a proxy for the level of economic development rather than accounting quality. Further,

Kimbro (2002) used (average) GDP growth rate in only one specification of her main

model (seeTable3,p.339)andthecoefficient(=0.126) is insignificant(t=1.517). Her

otherproxyforeconomicgrowth,D74‐80,isadummyvariablewhichtakesavalueof1if

thegrossnationalproduct(GNP)ofacountry isgreaterthanthesamplemedian. AGNP

greaterthanthesamplemediandoesnotcaptureeconomicgrowth,ratheritcapturesthe

level of economic development. Hence, Kimbro’s conclusion that moderate economic

growthisrelatedtolowerlevelofcorruptionisunwarranted.

3.3.Theoreticalframework:Linkbetweencorruptionandaccountingenvironment

Ifeconomics isaboutexpandingthepieandpolitics isaboutdistributing it (Alesina&

Rodrik,1994),accountingisaboutmeasuringthesizeofthepie.Inthissense,economics,

politics,andaccountingareinter‐related.

ShleiferandVishny(1993)articulatethatmaintainingsecrecyofthe corruption ‘deal’

by both parties involved is a necessary condition for the supply of corruption. Further,

DiRienzoetal.(2007)provideevidencethataccesstoinformationisnegativelyrelatedto

corruption. Astheroleofaccountingis toprovideinformationforefficientallocationof

resourcesinaneconomy,non‐disclosureofsecret‘deals’islikelytoperpetuatecorruption.

On the other hand, high-quality accounting is likely to act as a deterrent on the demand side of

corruption. High-quality accounting information is a product of not only the accounting

standards, but also a host of other factors such as managerial incentives, rule of law, and

enforcement (Ball, Robin and Wu, 2003). Hence, firm managers are less likely to engage in

bribery payment in countries with strong rule of law and enforcement. Further, strong internal

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control systems, strong managerial over-sight, and a high degree of accountability are not only

likely to deter bribery payments but also help quick detection of such irregularities. Thus, the

level of perceived corruption and the accounting environment in a country are

endogenously relatedbecause better accounting regime can reduce corruption by better

disclosureofeconomiceventswhilehighercorruptionlevelcanimpedethedevelopment

offinancialreportinganddisclosuretohidecorruption.

4.ResearchDesign

4.1.Data

Fortheprimarydependentvariable,weusecountry‐levelcorruptionperceptionindex

(Low_Corrup) as measured by Kaufmann et al., (2012) in their Worldwide Governance

Indicator (WGI) project. Our initial sample comprises annual observations for 214

countriesovertheyears1996to2011.Fromthisinitialsample,weeliminated31countries

whosecorruptionscoreswerenotavailableinKaufmanetal.(2012).Thenweexcluded17

countries due to missing disclosure index scores, one of our proxies for accounting

environment.Thesetwoexclusionsresultedinasampleof166countries.

Weuseperception‐basedcorruptionindexasameasureofcorruptioninsteadofactual

corruptionexperience.Wemadethischoicebecauseofstrongempiricalevidenceinfavor

of perception‐based measures. Treisman (2007, p. 212) observes, “The more subjective

indexesofperceivedcorruption—basedonevaluationsofexpertsandopinionsofbusiness

people and citizens—turn out to be highly correlated with a variety of factors that are

commonlybelievedtocausecorruption.”Ontheotherhand,measuresofactualcorruption

experience hardly correlate with any of the factors believed to be causes of corruption,

onceonecontrolsforincome(Treisman,2007).

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ThevariableLow_Corruprangesfrom‐1.241to2.374,withhigherscoresindicatingless

corruption. Another popular measure of corruption is Transparency International’s

CorruptionPerception Index (TICPI). It is arguablyone of thebest, andwidelyused in

cross‐countryanalysisofcorruption(Malagueñoetal.,2010).However,weuseKaufmann

et al.’s (2012) corruption indexbecause it incorporatesdata frommore sources thanTI

CPIandattempts to improveonthe treatmentofstatisticaluncertainty inTICPI(Knack,

2007).

Wemeasureaccountingenvironment(Acc_Env)asthesumofthescoreswithregardto

whetheracountryadoptedtheIFRS2forexternalreportingbyitsdomesticfirms,andthe

extent of disclosure requirements. We obtain the IFRS adoption data from the Deloitte

IASPlus website (2012), setting the value of one (1) for countries that have adopted the IFRS

and zero (0) otherwise.

(InsertTable1here)

AshbaughandPincus(2001)andDingetal.,(2007)provideevidencethatIFRSrequires

more comprehensive disclosure than most local accounting standards. Other literature

suggeststhatgreaterdisclosureaftertheadoptionofIFRShasasignificantpositiveeffect

on investors’ confidence, by reducing information asymmetry, agency problems, and

reporting uncertainties (e.g., Barth et al., 2008; Hope et al., 2006; Houqe et al., 2012;

Soderstrom&Sun,2007;Wu,2005a;Zarb,2008).Further,Houqeetal.,(2012)arguethat

adopting a common set of accounting standards, such as the IFRS, vigorously forces

managementtoreportfaithfullyandtruthfully.Thus,managerswillengagelessincorrupt

activities.Inthissense,theadoptionofIFRSreflectsahigh‐qualityaccountingenvironment

2 We interpret adoption of IFRS in a broader sense regardless of the process of how such ‘adoption’ took place.

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bysettingasystemthatallowsfirmstorecognizeaccountinglossesthatarisefrombribery

orillegalpaymentsinanappropriatemanner.

Unfortunately,thereisremarkablylittleliteraturelinkingtheadoptionofIFRSwiththe

levelofcorruptionincountries.Zarb(2008)documentsthattheuseofIFRSisnegatively

correlated with the perception of corruption in developed countries. Hence, we suggest

that the move towards the global use of IFRS appears to be the new platform for

transparency in financial reporting and is potentially another tool in the fight against

corruption. Further, the transparency in accounting information and disclosure can help

reduce the level of corruption by increasing the probability of corrupt practices being

detected(Wu,2005b).

WecollectedtheextentofdisclosurerequirementsdatafromtheWorldBank(2012).It

measurestheextenttowhichinvestorsareprotectedthroughdisclosureofownershipand

financialinformation.Theindexrangesfrom0to10,withhighervaluesindicatinggreater

disclosure.Bushmanet al., (2004)suggest thathigherdisclosurecanhelp reduceagency

problems between management and shareholders, thus preventing the opportunistic

behaviorofmanagers.

We use voice and accountability, and rule of law as twomeasures of the strength of

political institutions (Pol_Ins) in a country, as per Kaufmann et al. (2012). We use two

controlvariablesinthemainmodels:investorprotectionindex(Inv_Pro)publishedbythe

World Bank (2012), and the level of economic development (Eco_Dev)measured as the

natural logarithm of GDP (in US$) as per the World Bank (2010). Our choice of these

controlvariablesismotivatedbytheextantliterature.LaPorta,Lopez‐de‐Silanes,Shleifer,

&Verdy(1998)documentthatweakinvestorprotectionrightscreateagencycostsinthe

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formofexpropriationof shareholder (minority shareholder)wealthby insidermanagers

(majority shareholders). To the extent that, corruption is a form of agency cost, the

corruption level in a country is expected to be inversely related to the level of investor

protection. We include economic development as a control variable because of

overwhelmingevidence,asalreadydiscussedinSection3.1,ofthelinkbetweencorruption

and economic development (e.g., Blackburn et al., 2006, 2010; Paldam, 2002; Treisman,

2007).Table1providesthe listofall thevariables inthisstudy,theirdetaileddefinition,

anddatasources.

4.2.Models

Our objective in this study is to understand the role of accounting environment in

reducing corruption after controlling for variables related to political institutions and

economicdevelopment, rather thanto identifyall thedeterminantsofcorruption.Hence,

weproposeparsimoniousmodels.Ourfirsteconometricmodelisasfollows:

Low_Corrup=α0+α1(Acc_Env)+α2(Inv_Pro)+α3(Eco_Dev)+ε(1)

whereallvariablesareasdefinedearlier.

Inmodel(2)wereplaceAcc_Envwiththestrengthofpoliticalinstitutions(Pol_Ins):

Low_Corrup=ά0+ά1(Pol_Ins)+ά2(Inv_Pro)+ά3(Eco_Dev)+ε(2)

whereallvariablesareasdefinedearlier.

Inmodels(1)and(2),ourintentionistounderstandtheroleofaccountingenvironment

(Acc_Env) and the strength of political institutions (Pol_Ins) separately in the control of

corruption. In model (3), we combine models (1) and (2) to examine the relative

contributionofAcc_EnvandPol_Insinexplainingperceivedcorruption.Thus,model(3)is

asfollows:

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Low_Corrup=ψ0+ψ1(Acc_Env)+ψ2(Pol_Ins)+ψ3(Inv_Pro)+ψ4(Eco_Dev)+ε(3)

whereallvariablesareasdefinedearlier.

We estimate models (1) – (3) using the ordinary least squares (OLS) estimation technique.

However, prior research suggests that (DiRienzo et al., 2007; Kimbro, 2002; Malagueño et al.

2010) that Acc_Env,Pol_Ins,andLow_Corrup could all be endogenously related in the sense that

they are jointly determined. Hence, we treat each of these three variables as an endogenous

variable and employ two-stage least squares (2SLS) technique to estimate the models. Our

empirical models are as follows:

Low_Corrup=α0+α1(Acc_Env)+α2(Pol_Ins)+α3(Inv_Pro)+α4(Eco_Dev)+ε(4)

Acc_Env=ψ0+ψ1(Low_Corrup)+ψ2(Pol_Ins)+ψ3(Inv_Pro)+ψ4(Eco_Dev)+ε(5)

Pol_Ins=ά0+ά1(Low_Corrup)+ά2(Inv_Pro)+ά3(Eco_Dev)+ε(6)

whereallthevariablesareasdefinedearlier.

4.3.Descriptivestatistics

Table 2 provides descriptive statistics of the key variables in this study. As Table 2

reveals, the corruptionperception index (Low_Corrup) ranges from ‐1.598 (most corrupt

country) to 2.441 (least corrupt country). Thus, in our sample, the average country is

highlycorruptwithmean(median)scoreof‐0.046(‐0.304)sittingataround38%(32%)

ofthescale.Themean(median)scoreoftheaccountingenvironment(Acc_Env)is6.301

(6.000) in a scale that ranges from 0.000 (weakest) to 11.000 (strongest) accounting

environment.Intermsofthestrengthofpoliticalinstitutions(Pol_Ins),theaveragecountry

liesaround46%(41%)ofthescalewiththemean(median)scoreof‐0.196(‐0.518)ina

scalethatrangesfrom‐3.299(weakest)to3.516(strongest)politicalinstitutions.

(InsertTable2here)

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Table3presents abivariatecorrelationmatrixwithPearson’s correlationsbelow the

diagonalandSpearman’scorrelationsabovethediagonal.AsTable3shows,amongallthe

variables,corruptionperceptionindex(Low_Corrup)hasthestrongestpositivecorrelation

with thestrengthofpolitical institutions (r=0.923,p<0.001; ρ=0.911,p<0.001) followed

bythelevelofeconomicdevelopment(r=0.752,p<0.001;ρ=0.686,p<0.001).Amongallthe

variables, accounting environment (Acc_Env) has the weakest, albeit positive and

significant, relation with the corruption perception index (r=0.260, p=0.001; ρ=0.223,

p=0.004). These results suggest that countries which invest in strengthening political

institutionswillachievethegreatestresultsinreducingcorruption.

(InsertTable3here)

5.Results

Table4reportstheOLSestimatesofmodels(1)to(3).Inmodel(1),theadjustedR2 is

60.9%.Further,accountingenvironment (Acc_Env) ispositively related to thecorruption

perceptionindexandthecoefficientissignificantatthe5percentlevel(t‐staticofAcc_Env

=2.044).3Among theothervariables inmodel (1), both investorprotection (t‐statisticof

Inv_Pro=3.834)andthelevelofeconomicdevelopment(t‐statisticofEco_Dev=13.818)are

positive and significant at the 1 percent level. These results suggest that stronger

accounting environment (via IFRS adoption and greater disclosure index), stronger

investor protection, and higher economic development are all related to lower level of

corruption.

AsTable4reveals,whenPol_InsreplacesAcc_Envinmodel(2),theadjustedR2improves

to87.4%from60.9%inmodel(1). NowPol_Ins(t‐statistic=18.857)ispositivelyrelated

3Allstatisticaltestsinthisstudyarebasedontwo‐tailedtests.

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to the corruption perception index and statistically significant at the 1 percent level.

Althoughthesignificanceofinvestorprotection(t‐statisticofInv_Pro=1.759)nowweakens

to 10 percent level,Eco_Dev (t‐statistic =5.480) is still significant at the 1 percent level.

Model(3)incorporatesbothAcc_EnvandPol_InsalongwiththecontrolvariablesInv_Pro

and Eco_Dev. In model (3) results, both Acc_Env and Pol_Ins are positively related to

corruptionperception index, butAcc_Env (t‐statistic =1.872) is only significant at the 10

percent levelwhereasPol_Ins (t‐statistic=18.745) is significant at the1percent level. In

model(3)results,investorprotectionisnotsignificantatconventionallevels,althoughthe

levelofeconomicdevelopmentstill retains itssignificanceat the10percent level. Thus,

Table 4 clearly demonstrates that, although stronger accounting environment has some

negative influence on corruption, the strength of the political institutions is the most

dominantvariableinexplainingcorruptionperception.

(InsertTable4here)

InTable5,wereport theestimatesofmodels(4), (5),and(6) using2SLStechnique.4

Theresultsfrom2SLSarelargelyconsistentwiththoseoftheOLSestimatesreportedin

Table 4. As Table 5 reveals, both Acc_Env (coefficient = 0.173, t‐statistic =3.450) and

Pol_INS (coefficient =0.370, t‐statistic =10.470) have positive coefficients and both are

statistically significant at the 1 percent level. However, larger coefficient and larger t‐

statistic forPol_Ins compared to thoseofAcc_Env suggest that the corruptionperception

indexismuchmoresensitivetothestrengthofpoliticalinstitutionsthanitistoaccounting

environment. Further, positive and statistically significant coefficient on Eco_Dev (t‐

statistic=3.440,p=0.001)areconsistentwithpriorresearchthatdevelopedcountrieshave

4TheHausman(1978)test(χ2=24.33,p=0.0002)confirmsendogeneityinthemodels.

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lower levels of corruption (e.g., Kimbro, 2002; Treisman, 2007). In model (5) where

Acc_Env is the dependent variable, the corruption perception index (Low_Corrup) is

positiveandsignificantatthe10percentlevel(t‐statistic=1.790).Thisresultisconsistent

with previous findings (e.g., Kimbro, 2002; Malagueño et al., 2010) that low level of

corruption is associated with improved accounting environment. In model (5), the

strengthofpoliticalinstitutions(Pol_Ins)isnegativebutinsignificant(t‐statistic=‐0.740).

Thus, the strength of political institutions does not directly improve accounting

environment,butpoliticalinstitutionsindirectlyimproveaccountingenvironmentthrough

their effect on corruption. In model (6) where Pol_Ins is the dependent variable, the

corruption perception index (Low_Corrup t‐statistic =17.560) is positive and statistically

significantatthe1percentaaaarriinnnnnnnaaasartgs7xe445ws5confirmingpriorresults

of a strong positive relation between the strength of political institutions and control of

corruption. In sum, results in Table 5 confirm that, after controlling for endogeneity

among control of corruption, political institutions, and accounting environment, the

strengthofpolitical institutionshasthestrongestinfluenceonthecorruptionperception

level.

(InsertTable5here)

6.Robustnesschecks

In this section,we report the resultsof various robustness tests. InTable6, we re‐

estimate the models (1) to (3) on a sample which comprises data from 2002 to 2011.

BecausetheIASBannounceditsfirstprogramoftechnicalprojectstoimprovestandardsin

2001 and a major momentum for global adoption of IFRS standards began with the

European Union’s announcement in 2002 to adopt the IFRS, we modify our sample to

Page | 20  

compriseaccountingenvironmentdatasince2002.Resultsbasedonthismodifiedsample

are reported in Table 6. As Table 6 reveals, Acc_Env (t‐statistic =1.309) now loses its

significance in the control of corruption, although Inv_Pro and Eco_Dev are statistically

significantatthe1percentlevel.Resultsofmodels(2)and(3)arequalitativelysimilarto

thosereportedinTable4.InTable6,althoughthesignificancelevelofAcc_Env(t‐statistic

=2.323)intheresultsofmodel(3)improvesfrom10percentlevelinTable4to5percent

levelinTable6,Pol_Ins(t‐statistic=19.139,p<0.001)isstillthemostdominantdriverin

explainingthecorruptionperceptionindex.

(InsertTable6here)

Resultssofarwerebasedonthemeanmeasuresforeachvariable.Nowwere‐specify

models(1)to(3)usingindividualcomponentsineachmeasureofaccountingenvironment

andthestrengthofpoliticalinstitutions.Thesere‐specifiedmodelsareasfollows:

Low_Corrup=α0+α1(Acc_Env)+α2(Rule_Law)+α3(Inv_Pro)+α4(Eco_Dev)+ε(7)

Low_Corrup=α0+α1(Acc_Env)+α2(Press_Freedom)+α3(Inv_Pro)+α4(Eco_Dev)+ε(8)

Low_Corrup=α0+α1(IFRS)+α2(Pol_Ins)+α3(Inv_Pro)+α4(Eco_Dev)+ε(9)

Low_Corrup=α0+α1(Disclosure)+α2(Pol_Ins)+α3(Inv_Pro)+α4(Eco_Dev)+ε(10)

whereallvariablesareasdefinedinTable1.

As Table 7 reveals, inmodel (7)Acc_Env is not significant at conventional levels (t‐

statistic = 0.516) when only rule of law (Rule_Law) is used as the proxy for political

institutions. In model (7), clearly rule of law (Rule_Law) is the dominant variable (t‐

statistic =28.401, p<0.001). In model (8), the variable Acc_Env (t‐statistic =3.020) is

positive and significant at the 1 percent level, but Press_Freedom (t‐statistic =11.625,

p<0.001), the proxy for the strength of political institutions, is the strongest variable in

Page | 21  

explaining theperceptionof corruption. Inmodel (9), theadoptionof IFRS (t‐statistic=

0.017, p=0.986) is not at all significant, whereas the strength of political institutions

(Pol_Ins t‐statistic=19.568) is significantat the1percent level. Finally, inmodel (10),

although the disclosure of ownership and financial information (Disclosure: coefficient =

0.030, t‐statistic =2.747), the proxy for accounting environment, is significant at the 1

percentlevel,Pol_Ins(coefficient=0.426,t‐statistic=20.088)issignificantatthe1percent

levelaswell.Overall,resultsinTable7re‐assurethatthestrengthofpoliticalinstitutions

isthemostsignificantvariableinexplainingthecorruptionperceptionindex.

(InsertTable7here)

TobeconsistentwithDiRienzoetal.(2007)andMalagueñoetal.(2010),weincorporate

four new variables related to Hofstede’s (2001) cultural dimensions in alternative

specificationsofthemodels.Theseculturaldimensionsareindividualism,powerdistance,

uncertaintyavoidance,andmasculinity. Asaresultof including thesenewvariables, the

modifiedmodelsareasfollows:

Low_Corrup = β0 + β1(Acc_Env) + β2 (Pol_Ins) + β3 (Inv_Pro) + β4(Eco_Dev) + β5 (Indiv) +ε (11)

Low_Corrup = λ0 + λ1(Acc_Env) + λ2 (Pol_Ins) + λ3 (Inv_Pro) + λ4(Eco_Dev) + λ5 (Pow_Dis) + ε (12)

Low_Corrup = γ0 + γ 1(Acc_Env) + γ 2 (Pol_Ins) + γ 3 (Inv_Pro) + γ 4(Eco_Dev) + γ5 (Un_Avoid) + ε (13)

Low_Corrup = α0 + α 1(Acc_Env) + α 2 (Pol_Ins) + α 3 (Inv_Pro) + α 4(Eco_Dev) + α5 (Mascu) +ε (14)

Low_Corrup = δ0 + δ 1(Acc_Env) + δ 2 (Pol_Ins) + δ 3 (Inv_Pro) + δ 4(Eco_Dev)

+ δ5 (Indiv) + δ6 (Pow_Dis) + δ7 (Un_Avoid) + δ8 (Mascu) + ε (15)

where Indiv is the measure of individualism, Pow_Dis is the measure of large power

distanceinasociety,Un_Avoidisthetoleranceforuncertainty,andMascuisthemeasure

of how masculine a society is as per Hofstede (2001). Variable definitions of Indiv,

Pow_Dis,Un_Avoid, andMascu areprovided inTable1.Allothervariablesareasdefined

Page | 22  

earlier.Becauseofmissingdataonculturaldimensions, in thisanalysiswe haveamuch

reduced sample size of 93 countries. As Table 8 reveals, among the newly incorporated

variables, only strong uncertainty avoidance (Un_Avoid) is significant. In model (13),

Un_Avoid(t‐statistic=‐3.031)isnegativeandsignificantatthe1percentlevel.Model(14)

resultssuggestthatmore‐masculinesocietiesaremorecorruptthanless‐masculineones;

thecoefficientofMascu(t‐statistic=‐1.793)isnegativeandsignificant atthe10percent

level. In model (15), where all cultural dimensions are included, only uncertainty

avoidance (t‐statistic = ‐2.880) is significantly negative at the 1 percent level. These

results suggest that countries with strong uncertainty avoidance tend to have higher

corruptionlevelspresumablybecauseinvestorsandindividualsinthesecountriesresolve

uncertainties and delays in administrative procedures by engaging in bribing. Further,

societies that aremoremasculine, and emphasize onmaterial achievement, tend to be

morecorrupt.InTable8,resultsonthekeyvariablesareconsistentwiththosereportedin

previoustables.

(InsertTable8here)

Finally, we incorporate the Big4 market share as a proxy for accounting quality, as

employed by Kimbro (2002) and Malagueño et al. (2010). We find consistent results

(untabulated) for the strength of political institutions and accounting environment. The

strengthofpoliticalinstitutionsisstillthemostdominantvariableinexplainingcorruption

perception.

In sum, all empirical analyses suggest that the strength of political institutions is the

most dominant driver in the control of corruption and the (quality of) accounting

environmentplaysonlysecondaryorsupportiverole. Thus,countrieswishing toreduce

Page | 23  

corruption,andpromoteinvestmentsandgrowthintheeconomywillbemuchbetteroff

by strengthening their political institutions. Adoption of higher quality accounting

standards can deliver higher‐quality financial reporting only in the presence of strong

political institutions in the country. Our results indirectly explain themixedevidenceof

financial reporting quality found in cross‐country studies on the adoption of IFRS.

Countries with weak political institutions cannot expect to have improved financial

reportinginarealsensewiththeadoptionofIFRSbecausetheincentivesemanatingfrom

theinstitutionalsettingofacountryaremorefundamentalthanthestandardsthemselves

indeterminingfinancialreportingquality(Ball,Robin&Wu,2003;Ball,2006).AsCampos

etal.(1999)state,“(C)ountriesthatinvestinitiallyininhibitingcorruptionmayultimately

haveastrongerfoundationforsustaininggrowthoverthelongterm”(p.1065).

7.Conclusion

In this study,we investigated the relationbetween the accounting environment in a

countryandperceptionofcorruption,aftercontrollingfortheroleofpoliticalinstitutions

and economic development in the control of corruption. We were motivated by the

paucityofresearchinthisareaandtheprematureconclusionreachedbyMalagueñoetal.

(2010)ontheroleofaccountingincontrollingcorruption.

Wepooled together datarelatedtocorruptionperceptionindex and thestrengthof

political institutions (voice and accountability, and rule of law) from Kaufmann et al.

(2012), and investorprotectionandeconomicdevelopmentdata from theWorldBank

(2010).Wemeasuredaccountingenvironmentalongtwodimensions:(1)whetherornot

a country has adopted the IFRS; and (2) the extent to which investors are protected

Page | 24  

through disclosure of ownership and financial information. We found that, although

accounting environment has some positive effect in the control of corruption, its role is

relatively minor and secondary to the effect of political institutions. Our results are

robust to alternative specifications of our models. More importantly, our results are

consistent whether we estimated our models using OLS or 2SLS to account for the

endogenousrelationamongpoliticalinstitutions,corruption,andaccountingenvironment.

OurresultsalsoholdwhenweincorporateHofstede’s(2001)culturaldimensionsinour

models.

Our results suggest that the proxies used inMalagueño et al. (2010)may have been

inadequate in capturing the strength of political institutions in a country, and thus, the

optimism they placed on accounting environment in the control of corruption is

unwarranted. In all our estimations, the strength of political institutions, as proxied by

voiceandaccountabilityandruleoflaw,appearstohavethemostsignificantpositiveeffect

onthecorruptionperceptionindex.Thus,strengtheningthepoliticalinstitutionswillhave

thelargestimpactincontrollingcorruptioninacountry.

Ourresultshave implications for thecountries thathaveadopted IFRS inone formor

another.Becauseinterpretationofaccountingstandardsultimatelyrestswiththeauditors,

courts, and judges of a country, those countries that have the strongest political

institutionsstandtobenefitmostfromtheadoptionofIFRS.

Althoughourresultsarestrongandconsistent, theyneedtobe interpretedwithsome

caution. First, like most cross‐country studies, we pooled data from different sources

whichcollectanddisseminatedatafordifferentpurposes.Thus,constructvalidityofsome

Page | 25  

of our proxiesmight be an issue. Second, our results could be sensitive to alternative

proxies for the variables used in the study. Nevertheless, we provide some strong

evidenceoftheroleofaccountingenvironmentincontrollingcorruption.

Page | 26  

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Table1:Descriptionofvariablesandsources

Variable Measure Descriptionofvariable DatasourceDependentvariableCorruption Controlofcorruption

(Low_Corrup)Averageofthecontrolofcorruptionindex for the years 1996, 1998,2000,2002,2003,2004,2005,2006,2007, 2008, 2009, 2010, and 2011.This measure reflects a perceptionof theextent towhichpublicpoweris exercised for private gain,includingbothpettyandgrandformofcorruption,aswell“captureofthestatebyelitesandprivate interest”.It ranges from approximately ‐1.5981 to 2.4411, with a higherscoreindicatingleastcorruptregimeandvice–versa.

Kaufmann etal.,(2012)

IndependentvariablesAccountingEnvironment

Acc_Env1. IFRS2.ExtentofDisclosureIndex

Aggregatescoreoftwomeasures:Adummyvariabletakesthevalueofone (1) if a country has adoptedIFRSandzero(0)otherwise.Disclosure index measures theextent to which investors areprotected through disclosure ofownership and financialinformation. The index ranges from0 to 10, with higher valuesindicatinggreaterdisclosure.

DeloitteIASPluswebsite(2012)World Bank(2012)

Politicalinstitutions

Pol_Ins1.VoiceandAccountability

Aggregatescoreoftwomeasures:Average of the voice andaccountability index for the years1996,1998,2000,2002,2003,2004,2005,2006,2007,2008,2009,2010,and2011.Itmeasures“theextenttowhich a country’s citizens are ableto participate in selecting theirgovernment, as well as freedom ofexpression, freedom of associationand a free media”. It ranges from ‐1.88 to 1.60, with higher scoresindicating greater voice and

Kaufmann etal.,(2012)

Page | 32  

accountabilityaccessandvice‐versa. 2.RuleofLaw Averageof theruleof law index for

the years 1996, 1998, 2000, 2002,2003,2004,2005,2006,2007,2008,2009, 2010, and 2011.. Itmeasuresthe extent to which agents haveconfidenceinandabidebytherulesof society, and in particular thequalityof contractenforcement, thepolice,andthecourts,aswellasthelikelihood of crime and violence. Itranges from ‐1.82 to 1.94, withhigher scores indicating strong ruleoflawandvice‐versa.

Kaufmann etal.,(2012)

ControlvariablesInvestorProtection

Strength of InvestorProtection(Inv_Pro)

Strengthof investorprotection indexmeasures the degree to whichcorporate laws protect the rights ofinvestors,borrowersandlendersandthus facilitate lending. The indexranges from 0 to 10, with higherscores indicating that these laws arebetter designed to protect investorinterest.

World Bank(2012)

EconomicDevelopment

Natural logarithm ofGDPpercapita(US$)(Eco_Dev)

GDP per capita is gross domesticproduct divided by midyearpopulation. GDP is the sum of grossvalueaddedbyallresidentproducersin the economy plus any producttaxes and minus any subsidies notincludedinthevalueoftheproducts.It is calculated without makingdeductions for depreciation offabricated assets or depletion anddegradation of natural resources.DataareincurrentU.S.dollars.

World Bank(2010)

Culture POW_Dis

Power distance: It measures theresponse of people to inequality andtheextenttowhichtheless‐powerfulmembers expect, accept, or evenprefer the fact that power isdistributed unequally. Cultures withan unequal distribution of power

Hofstede(2001)

Page | 33  

IndivUn_Avoid

Mascu

tend to discourage questioningauthority.

Individualism: It refers to the extentthat individuals are integrated intogroups.Itreflectsthatcountrieswithhigh level of individualism places ahigher value of individualachievement and responsibility.Individualistic societies have agreater tolerance of diversity anddifferences of opinion. Theoppositeofindividualismiscollectivismwheregroup or societal norms takeprecedenceoverindividualviews.

Uncertainty avoidance: It measuresthesociety’s toleranceofuncertaintyor unknown situation. Societies thathave high uncertainty avoidance arethose in which people feeluncomfortable in unpredictedsituations which result tounwillingnesstochallengeauthority.

Masculinity index: It measures theextenttowhichasocietyemphasizescompetition and wealth acquisitionover relationship with others andquality of life. Japan is the mostmasculine societywith a score of 95and Sweden is the most femininesocietywithascoreof5.

Hofstede(2001)Hofstede(2001)Hofstede(2001)

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Table2:Descriptivestatistics

Variables Low_Corrup Acc_Env Pol_Ins Inv_Pro Eco_DevMean ‐0.046 6.301 ‐0.196 4.536 8.527Median ‐0.304 6.000 ‐0.518 53.000 8.512SD 0.996 2.499 1.836 2.485 1.513Minimum ‐1.599 0.000 ‐3.299 1.000 5.440Maximum 2.441 11.000 3.516 9.000 11.650N 166 166 166 166 166

AllvariabledefinitionsappearinTable1.

Table3:Correlationmatrix

Variables Low_Corrup Acc_Env Pol_Ins Inv_Pro Eco_DevLow_Corrup 1 0.223***

(0.004)0.911***(<0.001)

0.351***(<0.001)

0.686***(<0.001)

Acc_Env 0.260***(0.001)

1 0.201***(0.009)

0.160**(0.039)

0.191**(0.014)

Pol_Ins 0.923***(<0.001)

0.215***(0.005)

1 0.332***(<0.001)

0.652***(<0.001)

Inv_Pro 0.345***(<0.001)

0.154**(0.047)

0.332***(<0.001)

1 0.186**(0.017)

Eco_Dev 0.752***(<0.001)

0.184**(0.017)

0.699***(<0.001)

0.197**(0.011)

1

AllvariabledefinitionsappearinTable1.

***,**,*Correlationssignificantat0.01,0.05,and0.10levels,(two‐tailedtests),respectively.

Page | 35  

Table4

OLSregressionanalysisofcorruption(dependentvariableislowcorruption)

Low_Corrup=α0+α1(Acc_Env)+α2(Inv_Pro)+α3(Eco_Dev)+ε(1)

Low_Corrup=ά0+ά1(Pol_Ins)+ά2(Inv_Pro)+ά3(Eco_Dev)+ε(2)

Low_Corrup=ψ0+ψ1(Acc_Env+ψ2(Pol_Ins)+ψ3(Inv_Pro)+ψ4(Eco_Dev)+ε(3)

Independentvariables Model1Estimate(p‐value)t‐statistic

Model2Estimate(p‐value)t‐statistic

Model3Estimate(p‐value)t‐statistic

Intercept ‐1.078***(<0.001)‐7.223

‐0.189***(0.006)‐2.798

‐0.311***(0.001)‐3.327

Acc_Env 0.041**(0.043)2.044

0.021*(0.063)1.872

Pol_Ins 0.411***(<0.001)18.857

0.407***(<0.001)18.745

Inv_Pro 0.077***(<0.001)3.834

0.021*(0.080)1.759

0.019(0.113)1.592

Eco_Dev 1.543***(<0.001)13.818

0.470***(<0.001)5.480

0.462***(<0.001)5.415

Adj.R2 0.609 0.874 0.876N 166 166 166

AllvariabledefinitionsappearinTable1.a. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 levels (two‐tailed tests),respectively.b.Dependentvariableiscontrolofcorruption.

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Table5

Simultaneousequationanalysis(2SLS)forthecorruption,politicalinstitutionandaccountingquality

Low_Corrup=α0+α1(Acc_Env)+α2(Pol_Ins)+α3(Inv_Pro)+α4(Eco_Dev)+ε(4)

Acc_Env=ψ0+ψ1(Low_Corrup)+ψ2(Pol_Ins)+ψ3(Inv_Pro)+ψ4(Eco_Dev)+ε(5)

Pol_Ins=ά0+ά1(Acc_Env)+ά2(Inv_Pro)+ά3(Eco_Dev)+ε(6)

Independentvariables

Model(4)Dep.Var.=Low_Corrup

Estimate(p‐value)t‐statistic

Model (5)Dep.Var.=Acc_Env

Estimate(p‐value)t‐statistic

Model(6)Dep.Var.=Pol_Ins

Estimate(p‐value)t‐statistic

Intercept ‐0.1.248***(<0.001)‐3.890

‐6.295***(<0.001)1.790

‐0.129*(0.380)‐0.880

Low_Corrup 1.060*(0.074)1.790

1.699***(<0.001)17.560

Acc_Env 0.1727***(0.001)3.450

Pol_Ins 0.370***(<0.001)10.470

‐0.2150(0.458)‐0.740

Inv_Pro 0.0062***(0.736)0.340

0.046**(0.594)0.530

0.007(0.776)

0.280Eco_Dev 0.472***

(0.001)3.440

‐0.279***(0.699)‐0.390

‐0.016(0.940)‐0.080

Durbinχ2

160.37***

Wu‐HausmanF‐statistic166.740***

R2 0.756 0.074 0.856N 166 166 166

AllvariabledefinitionsappearinTable1.a. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 levels (two‐tailed tests),respectively.

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Table6

OLSregressionanalysisofcorruption:Re‐estimationofmodels(1)to(3)usingdatafrom2002to2011

Low_Corrup=α0+α1(Acc_Env)+α2(Inv_Pro)+α3(Eco_Dev)+ε(1)

Low_Corrup=ά0+ά1(Pol_Ins)+ά2(Inv_Pro)+ά3(Eco_Dev)+ε(2)

Low_Corrup=ψ0+ψ1(Acc_Env+ψ2(Pol_Ins)+ψ3(Inv_Pro)+ψ4(Eco_Dev)+ε(3)

Independentvariables Model (1)Estimate(p‐value)t‐statistic

Model(2)Estimate(p‐value)t‐statistic

Model(3)Estimate(p‐value)t‐statistic

Intercept ‐0.946***(<0.001)‐6.852

‐0.258***(<0.001)‐4.069

‐0.387***(<0.001)‐4.621

Acc_Env 0.025(0.192)1.309

0.025**(0.021)2.323

Pol_Ins 0.394***(<0.001)18.900

0.394***(<0.001)19.139

Inv_Pro 0.078***(<0.001)4.055

0.027**(0.018)2.386

0.023**(0.040)2.069

Eco_Dev 1.501***(<0.001)13.443

0.525***(<0.001)6.353

0.498***(<0.001)6.038

Adj.R2 0.580 0.861 0.864N 166 166 166

AllvariabledefinitionsappearinTable1.a. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 levels (two‐tailed tests),respectively.b.Dependentvariableiscontrolofcorruption.

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Table7

OLSregressionanalysisofcorruption

Low_Corrup=α0+α1(Rule_Law)+α2(Acc_Env)+α3(Inv_Pro)+α4(Eco_Dev)+ε(7)

Low_Corrup=α0+α1(Press_Freedom)+α2(Acc_Env)+α3(Inv_Pro)+α4(Eco_Dev)+ε(8)

Low_Corrup=α0+α1(Pol_Ins)+α2(IFRS)+α3(Inv_Pro)+α4(Eco_Dev)+ε(9)

Low_Corrup=α0+α1(Pol_Ins)+α2(Disclosure)+α3(Inv_Pro)+α4(Eco_Dev)+ε(10)

Variables Model(7)Estimate(p‐value)t‐statistic

Model (8)Estimate(p‐value)t‐statistic

Model ( 9)Estimate(p‐value)t‐statistic

Model(10)Estimate(p‐value)t‐statistic

Intercept 0.073(0.387)(0.867)

‐0.413***(0.004)‐2.885

0.084***(0.386)0.869

‐0.067(0.528)‐0.632

Acc_Env 0.005(0.516)0.651

0.044***(0.003)3.020

IFRS 0.001(0.986)0.017

Disclosure 0.030***(0.007)2.747

Pol_Ins 0.426***(<0.001)19.568

0.426***(<0.001)20.088

Rule_Law 0.941***(<0.001)28.401

Press_Freedom 0.571***(<0.001)11.625

Inv_Pro 0.020*(0.054)1.936

0.021(0.269)1.108

0.036**(0.014)2.492

0.037**(0.010)2.611

Eco_Dev 0.134*(0.052)1.954

0.950***(<0.001)9.554

0.500***(<0.001)5.947

0.473***(0.007)2.747

Adj.R2 0.921 0.860 0.860 0.866N 166 166 166 166

AllvariabledefinitionsappearinTable1.a. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 levels (two‐tailed tests),respectively.b.Dependentvariableiscontrolofcorruption.

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Table8

OLSregressionanalysisofcorruptionwithHofstede’s(2001)measuresofcultureLow_Corrup = β0 + β1(Acc_Env) + β2 (Pol_Ins) + β3 (Inv_Pro) + β4(Eco_Dev) + β5 (Indiv) + ε (11)

Low_Corrup = λ0 + λ1(Acc_Env) + λ2 (Pol_Ins) + λ3 (Inv_Pro) + λ4(Eco_Dev) + λ5 (Pow_Dis) + ε (12)

Low_Corrup = γ0 + γ 1(Acc_Env) + γ 2 (Pol_Ins) + γ 3 (Inv_Pro) + γ 4(Eco_Dev) + γ5 (Un_Avoid) + ε (13)

Low_Corrup = α0 + α 1(Acc_Env) + α 2 (Pol_Ins) + α 3 (Inv_Pro) + α 4(Eco_Dev) + α5 (Mascu) + ε (14)

Low_Corrup = δ0 + δ 1(Acc_Env) + δ 2 (Pol_Ins) + δ 3 (Inv_Pro) + δ 4(Eco_Dev)

+ δ5 (Indiv) + δ6 (Pow_Dis) + δ7 (Un_Avoid) + δ8 (Mascu) + ε (15)

Variables Model(11)Estimate(p‐value)t‐statistic

Model (12)Estimate(p‐value)t‐statistic

Model (13)Estimate(p‐value)t‐statistic

Model (14)Estimate(p‐value)t‐statistic

Model(15)Estimate(p‐value)t‐statistic

Intercept ‐0.178(0.264)‐1.124

0.059(0.784)0.275

0.278(0.159)1.422

0.008(0.964)0.045

0.577**(0.046)2.026

Pol_Ins 0.409***(<0.001)11.731

0.391***(<0.001)10.658

0.416***(<0.001)13.518

0.412***(<0.001)12.881

0.400***(<0.001)10.820

Acc_Env 0.023(0.129)1.532

0.026*(0.079)1.815

0.016(0.263)1.126

0.032**(0.033)2.164

0.024(0.103)1.650

Inv_Pro 0.017(0.378)0.886

0.017(0.370)0.901

0.021(0.233)1.202

0.013(0.481)0.707

0.017(0.322)0.996

Eco_Dev 0.565***(<0.001)4.579

0.571***(<0.001)4.819

0.595***(<0.001)5.222

0.565***(<0.001)4.793

0.576***(<0.001)4.990

Indiv 0.001(0.692)0.398

‐0.000(0.903)‐0.122

Pow_Dis ‐0.003(0.204)‐1.281

‐0.003(0.369)‐0.904

Un_Avoid ‐0.006***(0.003)‐3.031

‐0.005***(0.005)‐2.880

Mascu ‐0.005*(0.077)‐1.793

‐0.004(0.101)‐1.659

AdjR2 0.891 0.899 0.901 0.901 0.910N 93 93 93 93 93

AllvariabledefinitionsappearinTable1.

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a. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 levels (two‐tailed tests),respectively.b.Dependentvariableiscontrolofcorruption.