indices mundiales de goberanza

8/8/2019 Indices Mundiales de Goberanza

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Policy Research Working Paper 5430

T Wdwd Gv Idt

Mtd d At Iu

Daniel Kaumann

Aart Kraay

Massimo Mastruzzi

WPS5430

PublicDisclosureAuthorized



closureAuthorized


2/31

Abstract

Policy Research Working Paper 5430

T ummz t mtd tWdwd Gv Idt (WGI) jt,d td t u. T WGI v v 200ut d tt, mu x dm v tt 1996: V d Autbt,Pt Stbt d Ab V/m,Gvmt Etv, Rut Qut, Ru Lw, d Ct Cut. T tdt bd v udd dvduud vb, t m wd vt xt dt u. T dt t t vw

T dut t Mm d Gwt m, Dvmt R Gu t t t dtmt t tud t u d qu d v dvmt. P R WP td t Wb t tt://.wdb.. T ut m b ttd t @wdb..

v uv dt d ub, vt,d NGO t xt wdwd. T WGI xt t m m ut tmt. T t t t dfut mu v u d dt. Evt t t m t ut, t

WGI mt mu -ut d v-tmm. T t dt, tt wtt dtd ud u dt, vb t

www.vdt..


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TheWorldwideGovernanceIndicators:MethodologyandAnalyticalIssues

DanielKaufmann,BrookingsInstitution

AartKraay

and

Massimo

Mastruzzi,

World

Bank

AccesstheWGIdataatwww.govindicators.org

_____________________________

[email protected],[email protected],[email protected]. The findings,interpretations,and

conclusionsexpressedinthispaperareentirelythose oftheauthors.Theydonotnecessarilyrepresenttheviewsofthe


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1. IntroductionThe

Worldwide

Governance

Indicators

(WGI)

are

along

standing

research

project

to

develop

crosscountryindicatorsofgovernance. TheWGIconsistofsixcompositeindicatorsofbroad

dimensionsofgovernancecoveringover200countriessince1996: VoiceandAccountability,Political

StabilityandAbsenceofViolence/Terrorism,GovernmentEffectiveness,RegulatoryQuality,RuleofLaw,

andControlofCorruption. Theseindicatorsarebasedonseveralhundredvariablesobtainedfrom31

differentdatasources,capturinggovernanceperceptionsasreportedbysurveyrespondents,non

governmentalorganizations,commercialbusinessinformationproviders,andpublicsector

organizationsworldwide.

ThispapersummarizesthemethodologyandkeyanalyticalissuesrelevanttotheoverallWGI

project.Theupdateddataforthesixindicators,togetherwiththeunderlyingsourcedataandthedetails

ofthe

2010

update

of

the

WGI,

are

not

discussed

in

this

paper

but

are

available

online

at

www.govindicators.org. WealsoplantoreleaseanddocumentsubsequentupdatesoftheWGIpurely

online,withthispaperservingasaguidetotheoverallmethodologicalissuesrelevanttotheWGI

projectandfutureupdates.

IntheWGIwedrawtogetherdataonperceptionsofgovernancefromawidevarietyofsources,

andorganize

them

into

six

clusters

corresponding

to

the

six

broad

dimensions

of

governance

listed

above. ForeachoftheseclusterswethenuseastatisticalmethodologyknownasanUnobserved

ComponentsModelto(i)standardizethedatafromtheseverydiversesourcesintocomparableunits,

(ii)constructanaggregateindicatorofgovernanceasaweightedaverageoftheunderlyingsource

variables,and(iii)constructmarginsoferrorthatreflecttheunavoidableimprecisioninmeasuring

governance.

Webelievethistobeausefulwayoforganizingandsummarizingtheverylargeanddisparate

setofindividualperceptionsbasedindicatorsofgovernancethathavebecomeavailablesincethelate

1990swhenwebeganthisproject. Moreover,byconstructingandreportingexplicitmarginsoferror


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Thisemphasisonexplicitreportingofuncertaintyaboutestimatesofgovernancehasbeennotably

lackinginmostothergovernancedatasets.1

WhilethesixaggregateWGImeasuresareausefulsummaryoftheunderlyingsourcedata,we

recognizethatformanypurposes,theindividualunderlyingdatasourcesarealsoofinterestforusersof

theWGIdata. Manyoftheseindicatorsprovidehighlyspecificanddisaggregatedinformationabout

particulardimensionsofgovernancethatareofgreatindependentinterest. Forthisreasonwemake

theunderlyingsourcedataavailabletogetherwiththesixaggregateindicatorsthroughtheWGI

website.

Therestofthispaperisorganizedasfollows. Inthenextsectionwediscussthedefinitionof

governancethatmotivatesthesixbroadindicatorsthatweconstruct. Section3describesthesource

dataongovernanceperceptionsonwhichtheWGIprojectisbased. Section4providesdetailsonthe

statisticalmethodology

used

to

construct

the

aggregate

indicators,

and

Section

5offers

aguide

to

interpretingthedata. Section6containsareviewofsomeofthemainanalyticissuesinthe

constructionanduseoftheWGI,andSection7concludes.

2. DefiningGovernanceAlthoughtheconceptofgovernanceiswidelydiscussedamongpolicymakersandscholars,there

isasyetnostrongconsensusaroundasingledefinitionofgovernanceorinstitutionalquality. Various

authorsandorganizationshaveproducedawidearrayofdefinitions. Somearesobroadthattheycover

almostanything,suchasthedefinitionof"rules,enforcementmechanisms,andorganizations"offeredbytheWorldBank's2002WorldDevelopmentReport"BuildingInstitutionsforMarkets". Othersmore

narrowlyfocusonpublicsectormanagementissues,includingthedefinitionproposedbytheWorld

Bankin1992asthemannerinwhichpowerisexercisedinthemanagementofacountry'seconomicandsocialresourcesfordevelopment". Inspecificareasofgovernancesuchas theruleoflaw,thereareextensive debates among scholars over thin versus thick definitions where the former focus


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Wedrawonexistingnotionsofgovernance,andseektonavigatebetweenoverlybroadand

narrowdefinitions,todefinegovernanceasthetraditionsandinstitutionsbywhichauthorityinacountryisexercised. Thisincludes(a)theprocessbywhichgovernmentsareselected,monitoredandreplaced;(b)thecapacityofthegovernmenttoeffectivelyformulateandimplementsoundpolicies;and(c) therespectofcitizensandthestatefortheinstitutionsthatgoverneconomicandsocialinteractionsamongthem. Weconstructtwomeasuresofgovernancecorrespondingtoeachofthesethreeareas,resultinginatotalofsixdimensionsofgovernance:

(a) Theprocessbywhichgovernmentsareselected,monitored,andreplaced:1. VoiceandAccountability(VA)capturingperceptionsoftheextenttowhichacountry'scitizensareabletoparticipateinselectingtheirgovernment,aswellasfreedomofexpression,freedomof

association,andafreemedia.

2. PoliticalStabilityandAbsenceofViolence/Terrorism(PV)capturingperceptionsofthelikelihoodthatthegovernmentwillbedestabilizedoroverthrownbyunconstitutionalorviolentmeans,including

politicallymotivatedviolenceandterrorism.

(b) Thecapacityofthegovernmenttoeffectivelyformulateandimplementsoundpolicies:3. GovernmentEffectiveness(GE)capturingperceptionsofthequalityofpublicservices,thequalityofthecivilserviceandthedegreeofitsindependencefrompoliticalpressures,thequalityofpolicy

formulationandimplementation,andthecredibilityofthegovernment'scommitmenttosuchpolicies.

4. RegulatoryQuality(RQ)capturingperceptionsoftheabilityofthegovernmenttoformulateandimplementsoundpoliciesandregulationsthatpermitandpromoteprivatesectordevelopment.

(c) Therespectofcitizensandthestatefortheinstitutionsthatgoverneconomicandsocialinteractionsamongthem:5. RuleofLaw(RL)capturingperceptionsoftheextenttowhichagentshaveconfidenceinandabide


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Webelievethatthisdefinitionprovidesausefulwayofthinkingaboutgovernanceissuesaswell

asausefulwayoforganizingtheavailableempiricalmeasuresofgovernanceasdescribedbelow. Yet

werecognizethatforotherpurposes,otherdefinitionsofgovernancemayofcoursealsoberelevant. In

thisspiritwemakethesourcedataunderlyingourindicatorspubliclyavailableat

www.govindicators.org,andencourageuserswithdifferentobjectivestocombinethedataindifferent

waysmoresuitedtotheirneeds. Inthenextsectionofthepaperwedescribehowweuseour

definitionstoorganizealargenumberofempiricalproxiesintothesixcategoriesmentionedabove.

Wealsonotethatthesesixdimensionsofgovernanceshouldnotbethoughtofasbeing

somehowindependentofoneanother. Onemightreasonablythinkforexamplethatbetter

accountabilitymechanismsleadtolesscorruption,orthatamoreeffectivegovernmentcanprovidea

betterregulatoryenvironment,orthatrespectfortheruleoflawleadstofairerprocessesforselecting

andreplacinggovernmentsandlessabuseofpublicofficeforprivategain. Inlightofsuchinter

relationships,itisnotverysurprisingthatoursixcompositemeasuresofgovernancearestrongly

positivelycorrelatedacrosscountries. Theseinterrelationshipsalsomeanthatthetaskofassigning

individualvariablesmeasuringvariousaspectsofgovernancetooursixbroadcategoriesisnotclearcut.

Whilewehavetakenconsiderablecaretomaketheseassignmentsreasonablyinourjudgment,insome

casesthereisalsoroomfordebate. Forthisreasonaswell,theavailabilityoftheunderlyingsource

datais

auseful

feature

of

the

WGI

as

it

allows

users

with

other

objectives,

or

other

conceptions

of

governance,toorganizethedatainwayssuitedtotheirneeds.

3. GovernanceDataSourcesfortheWGIIn

the

WGI

project

we

rely

exclusively

on

perceptions

based

governance

data

sources.

In

Section6belowwediscussinmoredetailtherationaleforrelyingonthisparticulartypeofdata. Our

datasourcesincludesurveysoffirmsandhouseholds,aswellasthesubjectiveassessmentsofavariety

ofcommercialbusinessinformationproviders,nongovernmentalorganizations,andanumberof


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assessmentsofcorruptionbasedonitsnetworkofrespondents. Asdiscussedinthefollowingsection,

wethencombinethesemanydifferentmeasuresofcorruptionintoacompositeindicatorthat

summarizestheircommoncomponent. Wefollowthesameprocessfortheotherfivebroadindicators.

ComplementingTable1inthispaper,acompletedescriptionofeachofthesedatasources,

includingadescriptionofhoweachoftheindividualvariablesfromthemisassignedtooneofthesix

broadWGImeasures,isavailableontheDocumentationtabofwww.govindicators.org. Almostallof

ourdatasourcesareavailableannually,andwealigntheseannualobservationswiththeyearsforthe

WGImeasures. Inafewcasesdatasourcesareupdatedonlyonceeverytwoorthreeyears. Inthis

case,weusedatalaggedbyoneortwoyearsfromthesesourcestoconstructtheestimatesformore

recentaggregateWGImeasures. Detailsontheseissuesoftimingcanalsobefoundinthefull

descriptionsoftheindividualdatasources.

We

note

also

that

there

are

small

changes

from

year

to

year

in

the

set

of

sources

on

which

the

WGIscoresarebased. Thesetooaredocumentedonline,andreflecttherealitythatweintroducenew

datasourcesastheybecomeavailable,andifnecessaryonoccasiondropexistingsourcesthatstop

publicationorundergoothersignificantchangesthatpreventusfromcontinuingtheiruseintheWGI.

Whereverpossiblewemakethesechangesconsistentlyforallyearsinthehistoricaldataaswell,in

ordertoensuremaximumovertimecomparabilityintheWGI. UsersoftheWGIshouldthereforebe

awarethateachannualupdateoftheWGIsupersedespreviousyearsversionsofthedatafortheentire

timeperiodcoveredbytheindicators.

TheWGIdatasourcesreflecttheperceptionsofaverydiversegroupofrespondents. Several

aresurveysofindividualsordomesticfirmswithfirsthandknowledgeofthegovernancesituationinthecountry. TheseincludetheWorldEconomicForumsGlobalCompetitivenessReport,theInstitutefor

ManagementDevelopmentsWorldCompetitivenessYearbook,theWorldBank/EBRDsBusiness

EnvironmentandEnterprisePerformancesurveys,theGallupWorldPoll,Latinobarometro,

Afrobarometro,andtheAmericasBarometer.Werefertotheseas"Surveys"inTable1.


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DepartmentofStateandFrancesMinistryofFinance,IndustryandEmployment,weclassifytheseas

"PublicSectorDataProviders"inTable1.Anumberofdatasourcesprovidedbyvariousnongovernmentalorganizations,suchas

ReportersWithoutBorders,FreedomHouse,andtheBertelsmannFoundation,arealsoincluded.

Finally,animportantcategoryofdatasourcesforusarecommercialbusinessinformationproviders,suchastheEconomistIntelligenceUnit,GlobalInsight,andPoliticalRiskServices. Theselasttwotypes

ofdataproviderstypicallybasetheirassessmentsonaglobalnetworkofcorrespondentswithextensive

experienceinthecountriestheyarerating.

ThedatasourcesinTable1arefairlyevenlydividedamongthesefourcategories. Ofthe31

datasourcesusedin2009,5arefromcommercialbusinessinformationproviders;surveysandNGOs

contribute9sourceseach;andtheremaining8sourcesarefrompublicsectorproviders. Animportant

qualification

however

is

that

the

commercial

business

information

providers

typically

report

data

for

largercountrysamplesthanourothertypesofsources. AnextremeexampleistheGlobalInsight

BusinessConditionsandRiskIndicators,whichprovidesinformationonover200countriesineachofour

sixaggregateindicators. Primarilyforreasonsofcost,householdandfirmsurveystypicallyhavemuch

smallercountrycoverage,althoughthecoverageofsomeisstillsubstantial. Ourlargestsurveys,the

GlobalCompetitivenessReportsurveyandtheGallupWorldPolleachcoveraround130countries,but

severalregionalsurveyscovernecessarilysmallersetsofcountries. Someoftheexpertassessments

providedbyNGOsandpublicsectororganizationshavequitesubstantialcountrycoverage,butothers,

particularlyregionallyfocusedoneshavemuchsmallercountrycoverage. In2009forexample,data

fromcommercialbusinessinformationprovidersaccountforaround34percentofthecountryyear

datapointsinourunderlyingsourcedata,whilesurveysandNGOscontribute20percenteach,and

publicsector

providers

contribute

the

remaining

26

percent

of

data

points.

AsavitalcomplementtotheaggregateWGImeasures,wealsomakeavailablethroughtheWGI

websitetheunderlyingdatafromvirtuallyalloftheindividualdatasourcesthatgointoouraggregate

indicators The majority of our data sources such as Freedom House and Reporters Without Borders


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TheonlydatasourcesweareunabletomakefullypublicaretheWorldBank'sCountryPolicy

andInstitutionalAssessment(CPIA),andthecorrespondingassessmentsproducedbytheAfrican

DevelopmentBankandtheAsianDevelopmentBank. Thisreflectsthedisclosurepolicyofthese

organizationsandnotachoiceonourpart.Wedonotehoweverthatstartingin2002theWorldBank

beganpublishinglimitedinformationonitsCPIAassessmentsonitsexternalwebsite. Fortheyears

20022004theoverallCPIAratingsarereportedbyquintileforcountrieseligibletoborrowfromthe

InternationalDevelopmentAssociation(IDA),theconcessionallendingwindowoftheWorldBank. Since

2005,the

individual

country

scores

for

the

IDA

resource

allocation

index,

arating

that

reflects

the

CPIA

aswellasotherconsiderations,havebeenpubliclyavailableforIDAeligiblecountries. TheAfrican

DevelopmentBank'sCPIAratingshavealsobeenpubliclyavailablebyquintilesince2004,andhavebeen

fullypublicsince2005, whiletheAsianDevelopmentBank'sscoreshavebeenfullypublicforits

concessionalborrowerssince2005. ThoseCPIAscoresmadepublicbythesemultilateraldevelopment

banks

are

also

available

through

our

website.

Alltheindividualvariableshavebeenrescaledtorunfromzerotoone,withhighervalues

indicatingbetteroutcomes. Theseindividualindicatorscanbeusedtomakecomparisonsofcountries

overtime,asallofourunderlyingsourcesusereasonablycomparablemethodologiesfromoneyearto

thenext. Theyalsocanbeusedtocomparethescoresofdifferentcountriesoneachoftheindividual

indicators,recognizing

however

that

these

types

of

comparisons

too

are

subject

to

margins

of

error.

We

cautionusershowevernottocomparedirectlythescoresfromdifferentindividualsourcesforasingle

country,asthesearenotcomparable.Forexample,adevelopingcountrymightreceiveascoreof0.7on

a01scalefromonedatasourcecoveringonlydevelopingcountries,butmightreceivealowerscoreof

0.5onthesame01scalefromadifferentdatasourcethatcoversbothdevelopedanddeveloping

countries. Thisdifferenceinscorescouldsimplybeduetothefactthatthereferencegroupof

comparatorcountriesisdifferentforthetwodatasources,ratherthanreflectinganymeaningful

differenceintheassessmentofthecountrybythetwosources. Asdiscussedindetailinthefollowing

section,ourprocedureforconstructingthesixaggregateWGImeasuresprovidesawayofadjustedfor

such differences in units that allows for meaningful aggregation across sources


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4. ConstructingtheAggregateWGIMeasuresWe

combine

the

many

individual

data

sources

into

six

aggregate

governance

indicators,

correspondingtothesixdimensionsofgovernancedescribedabove.Wedothisusingastatisticaltool

knownasanunobservedcomponentsmodel(UCM).2 Thepremiseunderlyingthisstatisticalapproachis

straightforwardeachoftheindividualdatasourcesprovidesanimperfectsignalofsomedeeper

underlyingnotionofgovernancethatisdifficulttoobservedirectly. Thismeansthat,asusersofthe

individualsources,wefaceasignalextractionproblemhowdoweisolateaninformativesignalabout

theunobservedgovernancecomponentcommontoeachindividualdatasource,andhowdowe

optimallycombinethemanydatasourcestogetthebestpossiblesignalofgovernanceinacountry

basedonalltheavailabledata? TheUCMprovidesasolutiontothissignalextractionproblem.

Foreachofthesixcomponentsofgovernancedefinedabove,weassumethatwecanwritethe

observed

score

of

country

on

indicator

,

,

as

a

linear

function

of

unobserved

governance

in

countryj,,andadisturbanceterm,,asfollows:(1) whereand areparameterswhichmapunobservedgovernanceincountry, ,intothetheobserveddatafromsource,. Asaninnocuouschoiceofunits,weassumethatisanormallydistributed

random

variable

with

mean

zero

and

variance

one.3

This

means

that

the

units

of

our

aggregategovernanceindicatorswillalsobethoseofastandardnormalrandomvariable,i.e.withzero

mean,unitstandarddeviation,andrangingapproximatelyfrom 2.5to2.5. Theparameters andreflectthefactthatdifferentsourcesusedifferentunitstomeasuregovernance. Forexample,onedata

sourcemightmeasurecorruptionperceptionsonascalefromzerotothree,whileanothermightdoso

onascale

from

one

to

ten.

Or

more

subtly,

two

data

source

might

both

use

ascale

notionally

running

fromzerotoone,buttheconventionofonesourcemightbetousetheentirescale,whileonanother

sourcescoresareclusteredbetween0.3and0.7. Thesedifferencesinexplicitandimplicitchoiceof

unitsintheobserveddatafromeachsourcearecapturedbydifferencesacrosssourcesinthe


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Weassumethattheerrortermisalsonormallydistributed,withzeromeanandavariancethat

isthesameacrosscountries,butdiffersacrossindicators,i.e.

.Wealsoassumethatthe

errorsareindependentacrosssources,i.e. 0 forsourcedifferentfromsource.Thisidentifyingassumptionassertsthattheonlyreasonwhytwosourcesmightbecorrelatedwitheach

otherisbecausetheyarebothmeasuringthesameunderlyingunobservedgovernancedimension. In

Section6belowwediscussthelikelihoodandconsequencesofpotentialviolationsofthisidentifying

assumptioninmoredetail.

Theerrortermcapturestwosourcesofuncertaintyintherelationshipbetweentruegovernanceandtheobservedindicators.First,theparticularaspectofgovernancecoveredbyindicator

couldbeimperfectlymeasuredineachcountry,reflectingeitherperceptionerrorsonthepartofexperts(inthecaseofpollsofexperts),orsamplingvariation(inthecaseofsurveysofcitizensor

entrepreneurs).Second,therelationshipbetweentheparticularconceptmeasuredbyindicatorkand

thecorrespondingbroaderaspectofgovernancemaybeimperfect.Forexample,eveniftheparticular

aspectofcorruptioncoveredbysomeindicator,(suchastheprevalenceofimproperpractices)isperfectlymeasured,itmayneverthelessbeanoisyindicatorofcorruptioniftherearedifferencesacross

countriesinwhatimproperpracticesareconsideredtobe.Bothofthesesourcesofuncertaintyare

reflectedintheindicatorspecificvarianceoftheerrorterm,

. Thesmalleristhisvariance,themore

preciseasignal

of

governance

is

provided

by

the

corresponding

data

source.

Givenestimatesoftheparametersofthemodel,,,and,wecannowconstructestimatesofunobservedgovernance,giventheobserveddataforeachcountry. Inparticular,theunobservedcomponentsmodelallowsustosummarizeourknowledgeaboutunobservedgovernance

incountry

usingthedistributionof

conditionalontheobserveddata

. Thisdistributionisalso

normal,withthefollowingmean:

(2) |, ,


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aregivenby ,andarelargerthesmallerthevarianceoftheerrortermofthesource.In

other

words,

sources

that

provide

amore

informative

signal

of

governance

receive

higher

weight.

Acrucialobservationhoweveristhatthereisunavoidableuncertaintyaroundthisestimateof

governance. Thisuncertaintyiscapturedbythestandarddeviationofthedistributionofgovernance

conditionalontheobserveddata:

(3) |, , 1 /

Thisstandarddeviationissmallerthemoredatasourcesareavailableforacountry,i.e.thelargeris,andthemoreprecisethoseindividualdatasourcesare,i.e.thesmalleris.Werefertothisnumberasthestandarderrorofourestimateofgovernanceforeachcountry. Thesestandarderrorsare

essentialtothecorrectinterpretationofourestimatesofgovernance,astheycapturetheinherent

uncertaintyismeasuringgovernance. Forexample,wheneverwecompareestimatesofgovernancefor

twocountries,orforasinglecountryovertime,wealwaysreportthe90percentconfidenceinterval

associatedwithbothestimatesofgovernance,i.e.theestimateofgovernance+/ 1.64timesits

standarddeviation. Thisrange,whichwerefertoasthemarginoferrorforthegovernancescore,has

thefollowinginterpretation: basedontheobserveddata,wecanbe90percentconfidentthatthetrue,

butunobserved,

level

of

governance

for

the

countries

lies

in

this

range.

A

useful

(and

conservative)

rule

ofthumbisthatwhenthesemarginsoferroroverlapfortwocountries,orfortwopointsintime,then

theestimateddifferencesingovernancearetoosmalltobestatisticallysignificant.4

Thepresenceofmarginsoferrorinourgovernanceestimatesisnotaconsequenceofouruseof

subjectiveorperceptionsbaseddatatomeasuregovernance. Rather,itsimplyreflectstherealitythat

availabledataareimperfectproxiesfortheconceptsthatwearetryingtomeasure. Justasalternative

surveybasedmeasuresareimperfectproxiesfortheoveralllevelofcorruptioninacountry,factbased

descriptionofthelegalregulatoryframeworkarealsoonlyimperfectproxiesfortheoverallbusiness

environment facing firms. A key strength of the WGI is that we explicitly recognize this imprecision, and


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Inordertoconstructtheseestimatesofgovernanceandtheiraccompanyingstandarderrors,

werequireestimatesofalloftheunknownsurveyspecificparameters,

,

,and

.Weobtainthese

inamodifiedmaximumlikelihoodproceduredetailedintheAppendix. Weestimateanewsetof

parametersforeachyear,andalloftheparameterestimatesforeachdatasourceineachyear,together

withtheresultingweights,arereportedonlineintheDocumentationtabofwww.govindicators.org. In

addition,foreachcountryandforeachofthesixaggregateindicators,wereporttheestimateof

governance,i.e.theconditionalmeaninEquation(2),theaccompanyingstandarderror,i.e.the

conditionalstandard

deviation

in

Equation

(3),

and

the

number

of

data

sources

on

which

the

estimate

is

based.

Oneimportantfeatureofourchoiceofunitsforgovernanceisthatwehaveassumedthatthe

worldaverageisthesameineachyear. Whileourindicatorscanbemeaningfullyusedtocompare

countriesrelativepositionsinagivenyear,andtheirrelativepositionsovertime,theindicatorsarenot

informativeabout

trends

in

global

averages

of

governance.

While

at

first

glance

this

may

appear

restrictive,byreviewingthetimeseriesoftheindividualsourcesoverthepastseveralupdatesofthe

WGI,wehavedocumentedthatthereisverylittleevidenceoftrendsovertimeinglobalaveragesofour

individualunderlyingdatasources. Asaresult,ourchoiceofunitsforgovernancewhichfixestheglobal

averagetobethesameineachperioddoesnotappearunreasonable. Moreover,thisimpliesthat

changesin

countries

relative

positions

are

unlikely

to

be

very

different

from

changes

over

time

in

countriesabsolutepositions. Andfinally,fixingtheglobalaveragetoequalzerodoesnotpreventthe

analysisoftrendsovertimeinregionalorothergroupaveragesofcountries.

5. UsingandInterpretingtheWGIDataWereporttheaggregateWGImeasuresintwoways: inthestandardnormalunitsofthe

governanceindicator,rangingfromaround 2.5to2.5,andinpercentileranktermsrangingfrom0

(lowest)to100(highest)amongallcountriesworldwide.5 Figure1showsthedatainthesetwoways,


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plottheirpercentileranksonthehorizontalaxis,andtheirestimatesofgovernanceandassociated90%

confidenceintervalsontheverticalaxis.

Forillustrativepurposeswehavelabeled20countriesequallyspacedthroughthedistributionof

governance(i.e.atthe5th,10th,15th,etc.percentilesofthedistribution). Thesizeoftheconfidence

intervalsvariesacrosscountries,asdifferentcountriesarecoveredbyadifferentnumbersofsources,

withdifferentlevelsofprecision. Akeyobservationisthattheresultingconfidenceintervalsare

substantialrelativetotheunitsinwhichgovernanceismeasured. FromFigure1itshouldalsobe

evidentthatmanyofthesmalldifferencesinestimatesofgovernanceacrosscountriesarenotlikelyto

bestatisticallysignificantatreasonableconfidencelevels,sincetheassociated90percentconfidence

intervalsarelikelytooverlap.

Forexample,whileacountrysuchasPeruranksaheadofacountrysuchasJamaicaonControl

ofCorruption,theconfidenceintervalsforthetwocountriesoverlapsubstantially,andsooneshould

notinterprettheWGIdataassignalingastatisticallysignificantdifferencebetweenthetwocountries.

Formanyapplications,insteadofmerelyobservingthepointestimates,itisoftenmoreusefultofocus

ontherangeofpossiblegovernancevaluesforeachcountry(assummarizedinthe90%confidence

intervalsshowninFigure1),recognizingthattheselikelyrangesmayoverlapforcountriesthatarebeing

compared.

Thisisnottosayhoweverthattheaggregateindicatorscannotbeusedtomakecrosscountry

comparisons. Tothecontrary,thereareagreatmanypairwisecountrycomparisonsthatdopointto

statisticallysignificant,andlikelyalsopracticallymeaningful,differencesacrosscountries. Forexample,

the2009ControlofCorruptionindicatorcovers211countries,sothatitispossibletomakeatotalof

22,155pairwisecomparisonsofcorruptionacrosscountriesusingthismeasure. For63percentof

thesecomparisons,90%confidenceintervalsdonotoverlap,signalingstatisticallysignificantdifferences

intheindicatoracrosscountries. Andifwelowerourstatisticalconfidencelevelto75percent,which

maybequiteadequateforsomeapplications,wefindthat73percentofallpairwisecomparisons

identify statistically significant differences


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thislinecorrespondtoimprovementsintheWGIestimatesofgovernance,whilecountriesbelowthe

linecorrespondtodeteriorations. Thefirstfeatureofthisgraphisthatmostcountriesareclustered

closetothe45degreeline,indicatingthatchangesinourestimatesofgovernanceinmostcountriesare

relativelysmallevenoverthedecadecoveredbythegraph. Asimilarpatternemergesfortheother

fourdimensionsofgovernance(notshowninFigure2),and,notsurprisingly,thecorrelationbetween

currentandlaggedestimatesofgovernanceisevenhigherwhenweconsidershortertimeperiodsthan

thedecadeshownhere.

Nevertheless,asubstantialnumberofcountriesdoshowsignificantchangesingovernance. In

ordertoassesswhetherthechangeovertimeinanindicatorforagivencountryoveracertainperiodis

significant,ausefulruleofthumbistocheckwhetherthe90percentconfidenceintervalsforthetwo

periodsdonotoverlap. WehighlightandlabelthesecasesinFigure2. Moregenerally,overthedecade

20002009coveredinFigure2,wefindthatforeachofoursixindicators,onaverage8percentof

countriesexperience

changes

that

are

significant

at

the

90

percent

confidence

level.

Looking

across

all

sixindicators,28percentofcountriesexperienceasignificantchangeinatleastoneofthesix

dimensionsofgovernanceoverthisperiod. SincetheworldaveragesoftheWGIareconstantovertime,

thesechangesarenecessarilyroughlyequallydividedbetweenimprovementsanddeteriorations.

Wealsonotethatthe90percentconfidencelevelisquitehigh,andforsomepurposesalower

confidencelevel,say75percent,wouldbeappropriateforidentifyingchangesingovernancethatmay

bepracticallyimportant. Notsurprisinglythislowerconfidencelevelidentifiessubstantiallymorecases

ofsignificantchanges: fortheperiod20002009,andlookingacrossthesixaggregateWGImeasures,on

average18percentofcountriesexperienceasignificantchangeintheWGI. Overthesameperiod,54

percentofcountriesexperienceasignificantchangeonatleastoneofthesixWGImeasures.

WheninterpretingdifferencesbetweencountriesandovertimeinthesixaggregateWGI

measures,itisimportanttoalsoconsulttheunderlyingsourcedata. Thisisbecausedifferencesacross

countriesorovertimeintheaggregateWGImeasuresreflectnotonlydifferencesincountriesscores

on the underlying source data but also differences in the set of underlying data sources on which the


17/31

originalsourcestorunfromzerotoone,withhighervaluescorrespondingtobettergovernance

outcomes. Sinceallofoursourcesusereasonablycomparablemethodologiesovertime,thedatafrom

theindividualindicatorscanusefullybecomparedbothacrosscountrieswithinagiventimeperiod,and

overtimeforindividualcountries. However,wecautionusersoftheWGIdatanottocomparethe

individualindicatordatafromonesourcewithanother. Asnotedintheprevioussection,different

indicatorsusedifferentimplicitaswellasexplicitchoiceofunitsinmeasuringgovernance. Whilethe

processofaggregationcorrectsforthesedifferences,theunderlyingsourcedata,evenwhenrescaled

torun

from

zero

to

one,

still

reflects

these

differences

in

units

and

so

is

not

comparable

across

sources.

6. AnalyticalIssuesInthissectionwereviewanumberofmethodologicalandinterpretationissuesinthe

constructionand

use

of

the

WGI.

Our

objective

is

to

concisely

summarize

anumber

of

key

points

that

havecomeupoverthepastdecadeintheWGIproject,andthatwehaveaddressedindetailinour

earlierpapers,asreferencedbelow. Wefirstdiscussanumberofissuesrelatedtoourchoiceof

aggregationmethodology.Wethendiscussthestrengthsandpotentialdrawbacksofthesubjectiveor

perceptionsbaseddataonwhichwerelytoconstructtheWGI.

6.1 AggregationMethodologyAfirstbasicquestiononemightaskiswhywehavechosentousetheunobservedcomponents

(UCM)methodologytoconstructtheWGI,asopposedtoother,possiblymorestraightforward,

methods. Forexampleasimplealternativemethodwouldbetoaveragetogetherthepercentileranks

ofcountriesontheindividualindicators,ashasbeendonebyTransparencyInternationalinthe

constructionoftheCorruptionPerceptionsIndexorbytheDoingBusinessProjectintheconstructionof

theEaseofDoingBusinessrankings.6 Anotheralternativewouldbetodoaminmaxrescalingofthe

sourcedataandthenaveragetherescaleddata,asforexampleisdonebytheIbrahimIndexofAfrican


18/31

Governance. WhiletheUCMmethodologyweuseissomewhatmorecomplex,relativetothese

alternativeswefinditoffersthreemainadvantages.

First,theUCMsapproachtoplacingdataincommonunits,asdescribedinSection4,hasthe

advantageofmaintainingsomeofthecardinalinformationintheunderlyingdata. Incontrast,methods

basedoncountryrankingsbydefinitionretainonlyinformationoncountriesrelativeranks,butnoton

thesizeofthegapsbetweencountries. Andincontrastwiththeminmaxmethod,theUCMapproach

hastheadvantageofbeinglesssensitivetoextremeoutliersinthedata.

Second,theUCMapproachprovidesanaturalframeworkforweightingtherescaledindicators

bytheirrelativeprecision,asopposedtosimplyconstructingunweightedaveragesasisdonebymost

othercrosscountrycompositemeasuresofgovernanceandtheinvestmentclimate. Thedatadriven

precisionweightingapproachintheUCMhastheadvantageofimprovingtheprecisionoftheoverall

aggregateindicators. However,wedonotwanttooverstatetheimportanceofthisbenefit. Wehave

foundthatprecisionweightingreducesthemarginsoferrorofouraggregateindicatorsbyonlyabout

20percentrelativetounweightedaverages. Similarly,wefindthatthechoiceofweightingschemehas

forthemostpartrathersmalleffectsontherankingofcountries.7Thisislargelyduetothefactthatthe

variousindividualindicatorsunderlyingtheWGIarequitehighlycorrelated,andsothereislimited

scopeforchangesincountryrankingsduetoreweightingofsources.

ThethirdadvantageoftheUCMmethodologyisthatitnaturallyemphasizestheuncertainty

associatedwithaggregateindicatorsofgovernance. TheUCMusefullyformalizestheissueof

aggregationasasignalextractionproblem: sincetruegovernanceisdifficulttoobserveandwecan

observeonlyimperfectindicatorsofit,howcanwebestextractasignalofunobservedgovernance

fromtheobserveddata? Underthisview,allindividualindicatorsofcorruption,forexample,shouldbe

viewedasnoisyorimperfectproxiesforcorruption. Aggregatingthesetogethercanresultinamore

informativesignalofcorruption. Buteventheseaggregatemeasuresareimperfectandthis

imperfectionisusefullysummarizedbythestandarderrorsandconfidenceintervalsgeneratedbythe

UCM


19/31

getthingsdoneandanexpertassessmentofpublicsectorcorruption,couldbemeasuringquite

differentthings. Butourinterpretation,andrationaleforcombiningthetwowithothersinacomposite

measure,issimplythatbothprovidenoisyorimperfectsignalsoftheprevalenceofcorruption,andby

combininginformationfromthetwo,wecangetabetterestimateofoverallcorruption. Ofcoursethis

comesatthecostoflosingthespecificnuancesoftheindividualsourcesandtheirdefinitions. Butthisis

notaneither/ordecision,asboththecompositesummaryindicatorsaswellastheunderlying

individualmeasuresareavailableinthecaseoftheWGI.

Afurtherquestionrelatedtoaggregationiswhetheritmakessensetouseallavailabledata

sourcesforallcountries,asopposedtousingonlythosedatasourcesthatcoverallcountriesandinall

timeperiods. Theadvantageofthelatterapproachisthatallcomparisons,bothacrosscountriesand

overtime,wouldbefullybalancedinthesenseofrelyingonthesamesetofdatasources. Incontrast,

intheWGImostcomparisonsovertimeandacrosscountriesareunbalancedinthesensethatthey

arebased

on

the

potentially

different

sets

of

source

data

available

for

the

two

comparators.

This

runs

theriskthatchangesinscoresontheaggregateindicatorsreflectnotonlychangesintheunderlying

sourcedataavailableforthecountries,butalsochangesinthesetofavailabledatasources.While

relyingonapurelybalancedsetofcomparisonshastheappealofeasyinterpretation,thismustbeset

againstaveryimportantpracticaldrawbackthatthisrestrictiongreatlylimitsthesetofcountriesand

datasources

that

could

be

covered

by

the

aggregate

indicators.

For

example,

the

2009

Control

of

Corruptionindicatorcovers211countriesandisbasedonatotalof22datasources. However,ifwe

weretorestrictattentiononlytoabalancedsampleofcountriescoveredinthefivedatasourceswith

thelargestcountrycoverage,oursampleofcountrieswouldfallnearlybyhalfto114. Sincethe22

individualdatasourcesusedbytheWGItogetherprovide1950country/indicatordatapoints,restricting

attentiontothisbalancedsamplewouldalsoinvolvediscarding morethanthreequartersofdata

sources,andnearlythreequartersoftheavailabledatapoints(i.e.15x114/1950=0.71). Doingsowould

alsoreducethediversityofsources,asfourofthefivetopdatasourcesbycountrycoveragecomejust

fromonetype,commercialbusinessinformationproviders.


20/31

comparisons. Themedianpairwisecomparisonwouldbebasedon6commondatasources,and60

percentofallpairwisecomparisonswouldbebasedonatleast5commondatasources. Turningtoover

timecomparisons,inpastupdatesoftheWGIwehavedocumentedthatthelargemajorityof

statisticallysignificantchangesovertimeintheWGIarelargelyduetochangesintheunderlyingsource

data,ratherthantochangesinthecompositionofdatasourcesinthetwoperiods.8

6.2 UseofPerceptionsDataAs

noted

in

the

introduction,

the

WGI

project

is

based

exclusively

on

subjective

or

perceptions

basedmeasuresofgovernance,takefromsurveysofhouseholdsandfirmsaswellasexpert

assessmentsproducedbyvariousorganizations. Thisdecisionisbasedonourviewthatperceptions

datahaveparticularvalueinthemeasurementofgovernance.9 First,perceptionsmatterbecause

agentsbasetheiractionsontheirperceptions,impression,andviews. Ifcitizensbelievethatthecourts

areinefficientorthepolicearecorrupt,theyareunlikelytoavailthemselvesoftheirservices. Similarly,

enterprisesbasetheirinvestmentdecisions andcitizenstheirvotingdecisions ontheirperceivedview

oftheinvestmentclimateandthegovernment'sperformance. Second,inmanyareasofgovernance,

therearefewalternativestorelyingonperceptionsdata. Forinstance,thishasbeenparticularlythe

caseforcorruption,whichalmostbydefinitionleavesnopapertrailthatcanbecapturedbypurely

objectivemeasures.

Third,wenotethatevenwhenobjectiveorfactbaseddataareavailable,oftensuchdatamay

capturethedejurenotionoflawsonthebooks,whichoftendifferssubstantiallyfromthedefactorealitythatexistsontheground. Infact,inKaufmann,KraayandMastruzzi(2005)wedocument

sharpdivergencesbetweendejureanddefactomeasuresofbusinessentryregulationandfindthatcorruptionisimportantinexplainingtheextenttowhichtheformerdifferfromthelatter.Similarly,

HallwardDriemeier,KhunJush,andPritchett(2010)documentthatthereislittlecorrespondence

betweenfirmsactualexperienceswiththeregulatoryenvironmentandtheformaldejureregulationsthatfirmsfaceinasampleofAfricancountries. Ortotakeanevenstarkerexample,ineveryoneofthe

70 countries covered in the 2007 and 2008 waves of the Global Integrity Index it is formally illegal for a


21/31

publicofficialtoacceptabribe. Yet,despitethembeingidenticalwhenmeasureddejure,therearelargedifferencesacrossthesecountriesinperceptionsofthefrequencywithwhichbribesareinfact

acceptedbypublicofficials.

Despitetheseadvantages,onemightneverthelessreasonablybeconcernedaboutvarious

potentialproblemsintheinterpretationofthesubjectivedatawerelyonintheWGI. Broadlysuch

concernsquestiontheextenttowhichperceptionsdataadequatelycapturetherelevantreality. Afirst

basicissueissimplythatperceptionsdataongovernanceareimprecise. Thisbyitselfisnotsurprising

aswehavearguedabove,allmeasuresofgovernanceandtheinvestmentclimatearenecessarily

impreciseproxiesforthebroaderconceptstheyareintendedtomeasure. Imprecisionalonedoesnot

disqualifytheuseofperceptionsbaseddataongovernanceratheritunderscorestheimportanceof

usingempiricalmethodstoquantifyandtakeseriouslytheextentofimprecision,aswedowiththe

marginsoferrorreportedintheWGI.

Apotentiallymoreseriousconcernisthattherearevarioussystematicbiasesinperceptions

dataongovernance. Onepossibilityisthatdifferenttypesofrespondentsdiffersystematicallyintheir

perceptionsofthesameunderlyingreality. Forexample,itcouldbethecasethatbusinesspeople,

representedbyownersofthebusinessescoveredinasurvey,ortheexpertassessmentsprovidedby

commercialbusinessinformationproviders,havedifferentviewsofwhatconstitutesgoodgovernance

thanothertypesofrespondents,suchashouseholdsorpublicsectoragencies. Weaddressedthis

concerndirectlyinKaufmann,KraayandMastruzzi(2007a,b),wherewecomparedtheresponsesof

businesspeopletoothertypesofrespondentsandfoundlittleinthewayofsignificantdifferencesin

crosscountrycomparisonsbasedonthesetwotypesofresponses. Anotherpossibilityisthatbiasesare

introducedbytheideologicalorientationoftheorganizationprovidingthesubjectiveassessmentsof

governance.We

investigated

this

possibility

in

Kaufmann,

Kraay

and

Mastruzzi

(2004)

by

asking

whetherexpertassessmentsprovidedbyanumberofratingagenciesweresystematicallydifferentin

countrieswithleft orrightwinggovernments. Heretoowefoundlittleevidenceofsuchbiases.

Another type of bias in perceptions data might be the possibility that subjective assessments of


22/31

Yetanotherpotentialsourceofbiascomesfromthepossibilitythatdifferentprovidersof

governanceperceptionsdatarelyoneachothersassessments,andasaresultmakecorrelated

perceptionserrors. Thiswouldunderminetheinformationcontentinsuchindicators. Andmoresubtly,

itwouldalsounderminethevalidityofourweightingschemeintheWGI,whichisbasedonthe

observedcorrelationsamongsources. Ifdatasourcesarecorrelatedmerelybecausetheymake

correlatedperceptionserrors,itwouldnotbeappropriatetoassignhigherweighttosuchmeasures.10

Assessingthepracticalimportanceofthisconcernisdifficultbecausethehighcorrelationbetween

governanceperceptions

rankings

from

different

sources

could

be

due

either

to

perception

errors,

or

due

tothefactthatthesesourcesareinfactaccuratelymeasuringcrosscountrycorruptiondifferencesand

sonecessarilyagreewitheachother. InKaufmann,KraayandMastruzzi(2007c)weproposedanovel

waytoisolatethesetwopotentialsourcesofcorrelation,bycomparingtheratingsproducedby

commercialriskratingagencies(thatareoftenthoughttobemostpronetosuchgroupthink)with

crosscountryfirmsurveyresponses. Ourstrikingfindingwasthatthesedatasourceswerenomore

correlatedamongthemselvesthantheywerewiththefirmsurveyresponses,castingdoubtonthe

practicalimportanceofthissortofbias.

7. ConclusionsInthispaperwehavesummarizedthekeyfeaturesoftheWorldwideGovernanceIndicators

project. TheWGIprojectreportscompositeindicatorsofsixdimensionsofgovernance,coveringover

200countriesandterritoriessince1996,andisupdatedannually. Thesixaggregategovernance

indicatorsarebasedonhundredsofindividualunderlyingvariablesfromdozensofdifferentdata

sources. ThesourcedataunderlyingtheWGIcomefromalargenumberofindividualsources,and

reflecttheviewsongovernanceofthousandsofsurveyrespondentsandpublic,private,andNGOsector

expertsworldwide. Theunderlyingsourcedatacapturingthiswidediversityofviewsandexperiencesis

availabletogetherwiththesixaggregateWGImeasuresatwww.govindicators.org.


23/31

consequenceofthisisthatourestimatesofgovernancearesubjecttonontrivialmarginsoferror. Since

thebeginningofthisresearchprojectinthe1990s,wehaveemphasizedtheimportanceofthe

estimation,disclosure,anduseofthemarginsoferrorininterpretingcountryscores. Inparticular,users

oftheWGIshouldnotoverinterpretsmalldifferencesinperformance(acrosscountriesorovertime)in

thesixaggregateWGImeasuresinparticular,andallgovernancemeasuresingeneral. Careful

interpretationofthisdatawithdueregardtomarginsoferrorisimportant,asdatabasedmonitoring

governanceperformancearoundtheworldhasbecome morecommon,andasempiricalmeasuresof

governancebecome

more

widely

used

by

policy

makers,

analysts,

journalists,

risk

rating

agencies,

and

multilateralandbilateraldonoraidagencies

ThepresenceofmarginsoferrordoesnotimplythattheWGIcannotbeusedtomake

meaningfulcomparisonsofgovernanceacrosscountriesorovertime. Rather,ourestimationof,and

emphasison,suchmarginsoferrorisintendedtoenableuserstomakemoresophisticateduseof

imperfectinformation.

Using

the

WGI,

we

find

that

even

after

taking

margins

of

error

into

account,

it

is

possibletomakemanymeaningfulcrosscountryandovertimecomparisons:almosttwothirdsofall

crosscountrycomparisonsin2009resultinhighlysignificantdifferences(at90percentconfidence

levels),andmorethanonequarterofcountriesshowasignificantchangeinatleastoneofthesixWGI

measuresduringthedecade20002009.

Thispaperhasalsoofferedaconcisesummaryofsomeofthekeymethodologicalandanalytical

issuesthatcomeupintheconstructionandinterpretationofcompositegovernanceindicatorsbasedon

perceptionsdata.WereferreaderstopreviousyearsversionsintheGovernanceMattersseriesof

workingpapersformoredetailoneachoftheseissues. Finally,andasinpastyears,wecontinueto

cautionusersthataggregateindicatorssuchasthesixWGImeasuresareoftenablunttoolforpolicy

adviceatthecountrylevel. Usersoftheaggregateindicatorscanusefullycomplementtheiranalysis

withanindepthexaminationofthethedetaileddisaggregateddatasourcesunderlyingtheWGI,

togetherwithawealthofpossiblemoredetailedandnuancedsourcesofcountryleveldataand

diagnostics on governance issues


24/31

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Efron,BradleyandCarlMorris(1971). LimitingtheRiskofBayesandEmpiricalBayesEstimatorsPart

1: TheBayesCase. JournaloftheAmericanStatisticalAssociation. 66:807815.

Efron,BradleyandCarlMorris(1972).LimitingtheRiskofBayesandEmpiricalBayesEstimatorsPart

1: TheEmpiricalBayesCase. JournaloftheAmericanStatisticalAssociation. 67:13039.

Goldberger,A.(1972). MaximumLikelihoodEstimationofRegressionsContainingUnobservable

IndependentVariables. InternationalEconomicReview. 13:115.

HallwardDriemeier,Mary,GitaKhunJush,andLantPritchett(2010). DealsVersusRules: Policy

ImplementationUncertaintyandWhyFirmsHateIt. NBERWorkingPaperNo.16001.

Kaufmann,Daniel,AartKraayandPabloZoidoLobatn(1999a).AggregatingGovernanceIndicators.

WorldBankPolicyResearchWorkingPaperNo.2195,Washington,D.C.

Kaufmann,Daniel,AartKraayandPabloZoidoLobatn(1999b).GovernanceMatters.WorldBank

Policy

Research

Working

Paper

No.

2196,

Washington,

D.C.

Kaufmann,Daniel,AartKraayandPabloZoidoLobatn(2002).GovernanceMattersIIUpdated

Indicatorsfor2000/01.WorldBankPolicyResearchWorkingPaperNo.2772,Washington,D.C.

Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2004). GovernanceMattersIII: Governance

Indicatorsfor1996,1998,2000,and2002.WorldBankEconomicReview. 18:253287.

Kaufmann,

Daniel,

Aart

Kraay

and

Massimo

Mastruzzi

(2005).

Governance

Matters

IV:

Governance

Indicatorsfor19962004.WorldBankPolicyResearchWorkingPaperNo.3630.Washington,D.C.

Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2006a). MeasuringGovernanceUsing

PerceptionsData",inSusanRoseAckerman,ed. HandbookofEconomicCorruption. EdwardElgar.

Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2006b). GovernanceMattersV: Aggregateand

IndividualGovernanceIndicatorsfor19962005.WorldBankPolicyResearchWorkingPaperNo.4012.

Washington,D.C.

Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2007a). TheWorldwideGovernanceIndicators

Project: AnsweringtheCritics". WorldBankPolicyResearchWorkingPaperNo.4149.Washington,D.C.

Kaufmann Daniel Aart Kraay and MassimoMastruzzi (2007b) Growth and Governance: A


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Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2008). GovernanceMattersVII: Aggregateand


Washington,

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Kaufmann,DanielandAartKraay(2008). "GovernanceIndicators: WhereAreWeandWhereShould

WeBeGoing?"WorldBankResearchObserver. Spring2008.

Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2009). GovernanceMattersVIII: Aggregateand


Washington,D.C.


26/31

Appendix: EstimatingtheParametersoftheUnobservedComponentsModelInordertoimplementEquations(2)and(3)inthemaintext,weneedtofirstestimatethe

unknownparameters,,andforeveryindicator. Thisinturnrequiresustodistinguishbetween"representative"and"nonrepresentative"indicators,whichwetreatdifferentlyinthe

estimationprocess. Representativeindicatorsareindicatorsthatcoverasetofcountriesinwhichthe

distributionofgovernanceislikelytobesimilartothatintheworldasawhole. Practicallytheseinclude

allofourindicatorswithlargecrosscountrycoverageofdevelopedanddevelopingindicators.

Incontrastnonrepresentativeindicatorscovereitherspecificregions(forexampletheBEEPS

surveyoftransitioneconomiesortheLatinobarometersurveyofLatinAmericancountries),orparticular

incomelevels(forexampletheWorldBankCPIAratingsthatcoveronlydevelopingcountries). Our

classificationof"representative"and"nonrepresentative"indicatorsisgiveninTable1ofthispaper.

Forthesetofrepresentativeindicators,weusetheassumptionofthejointnormalityofand

writedownthelikelihoodfunctionoftheobserveddata. Theassumptionofrepresentativenessis

crucialherebecauseitjustifiesourassumptionofacommondistributionforgovernanceacrossthese

differentsources. Asusefulnotation,let , , , , , ,and , , ,andletandbediagonalmatriceswith andonthediagonal. Usingthisnotation,themeanofthevectorofobserveddataforeachcountryj,,isandthevarianceis . Thecontributiontotheloglikelihoodofcountryjthereforegivenby:

(4)

ln L,, ln||

Summingtheseoverallcountriesandthenmaximizingovertheunknownparametersdeliversourmaximumlikelihoodestimatesof,,andforeveryrepresentativeindicator. Identificationrequiresthatwehaveaminimumofthreerepresentativeindicators. Notethatthenumberofdata

sourcesavailableforeachcountryvaries,andsothedimensionofandis 1,andconformably,,and are . Thiswayweareabletocompilethelikelihoodfunctioneventhoughtherepotentiallyaremissingobservationsforeachcountryevenamongtherepresentativeindicators.

Wecannotapplythismethodtononrepresentativeindicators. Toseewhy,considerthe

maximumlikelihoodestimateofforsomesource. Unsurprisinglythisisthemeanscoreacrosscountriescoveredbyindicator. ItisstraightforwardtoseefromEquation(1)thattheexpectedvalueofthesamplemeanofscoresonindicatoris ,where denotestheaveragelevelof


27/31

Wecanneverthelessobtainconsistentestimatesoftheunknownparametersofthenon

representativeindicatorsbyusingthefollowingsimpleargument. Ifwereobservable,wecouldestimate

,,andforanyindicatorsimplybyregressingtheobservedscoreson. Althoughisitselfnotobservable,wedohaveanestimateofbasedontherepresentativeindicators. Inparticular,letdenotethispreliminaryestimateofbasedononlyonthedatafromtherepresentativeindicators. Wecandecomposethisconditionalmeanintoobservedgovernanceplusits

deviationfromthemean,i.e. . Sinceisindependentof,wecanviewasmeasuringwithclassicalmeasurementerror. ItiswellknownthatOLSestimatesoffromaregressionofon

willproducedownwardbiasedestimatesduetotheusualattenuationbias.Inparticular,the

probabilitylimit

of

the

OLS

slope

coefficient

is

1/. Sincethevarianceofissimplythevarianceoftheconditionalmeanof giveninEquation(3),andsinceisobservable,wecancorrecttheOLScoefficientsforthisattenuationbiastoarriveatconsistentestimatesoftheparameters

ofthenonrepresentativeindicators.Wecollectalloftheparameterestimatesfromtherepresentative

andnonrepresentativesurveys,andinsertthemintotheexpressionsinEquations(2)and(3)toarrive

atestimatesofgovernanceandstandarderrorsforeachcountry.

Finally,there

are

two

further

rescaling

steps

before

we

arrive

at

the

final

estimates

that

we

report.Wefirstrescalethedatatosetthemeanofthegovernanceestimatestozero,andtheir

standarddeviationtoone. TheestimatesofgovernanceobtainedfromtheUCMtheoreticallyhavea

meanofzero,andastandarddeviationslightlylessthanone. Inanyparticularsample,however,the

meancouldbeslightlydifferent. Toavoidconfusionininterpretingthedata,webeginbysettingthe

meanofthegovernanceestimatesforeachindicatorandyeartozero,andthestandarddeviationto

one. Inparticular,foreachindicatorandyear,wesubtractthesamplemean(acrosscountries)from

eachcountry,

and

divide

by

the

sample

standard

deviation

(across

countries).

We

then

also

divide

the

standarderrorsofthegovernanceestimatesforeachcountrybythesamplestandarddeviationofthe

governanceestimates.

Thisfirstrescalingisjustarenormalizationofthescores,andofcoursehasnoimpacton

countries'relativepositionsonthegovernanceindicators. Itisalsoconsistentwithourchoiceofunits

forgovernancenotedabove,andnotablythatithasameanofzeroandastandarddeviationofonein

eachperiod.

If

there

were

trends

in

global

averages

of

governance

over

time,

this

choice

of

units

would

notbeappropriate. However,aswehavedocumentedinKaufmann,KraayandMastruzzi(2004,2005,

2006b,2007c,2008,and2009),wedonotfindstrongevidenceofsignificanttrendsinworldaveragesof

governanceinourunderlyingindicators.Wethereforethinkthischoiceofunitsisappropriate.

Moreover,absentanychangesinglobalaveragesofgovernance,changesovertimeincountries'relative


28/31

countriesthatwerecontinuouslyinthesample. Thisinturnmeansthatitwouldbeinappropriateto

imposeaglobalaveragegovernancescoreofzeroinearlierperiodsforthesmallersetofcountriesfor

which

data

is

available

in

the

earlier

periods,

since

our

earlier

estimates

did

not

include

the

better

than

averageperformersaddedlater. Italsomeansthatsomecountriesinouraggregateindicatorsinthe

earlieryearswouldhaveshowedsmalldeclinesinsomedimensionsofgovernanceovertimethatwere

drivenbytheadditionofbetterperformingcountriesinlateryears.

Weaddressthisissuewithasecondsimplerescalingoftheaggregategovernanceindicators.

Wetakethemostrecentyearofourindicatorsasabenchmark. Inparticular,theindicatorsfrom2005

onwardsindicatorscoverbetween206and213countries,whichareclearlyrepresentativeoftheworld

asawhole.

Consistent

with

our

choice

of

units

for

governance,

the

estimates

for

these

years

have

zero

meanandstandarddeviationofoneacrosscountriesduetothefirststandardizationnotedabove. We

nextconsiderthecountriesthatwereaddedin2005relativeto2004.Wethenadjusttheworldwide

averagescorein2004sothatitwouldhaveameanofzerohadweincludedthe2005scoresforthosecountriesaddedin2005relativeto2004.11 Wethencontinuebackwardsintimeinthesamewaytoadjustthedataforeachpreviousyearbackto1996. Inparticular,toarriveatthefinalscorereported

foreachcountry,weneedtosubtracttheadjustedmeananddividebytheadjustedstandarddeviation.

Thestandarderrorofthegovernanceestimatealsoneedstobedividedbytheindicatedadjusted

standarddeviation. Foryearsafter2005,wedonotrescalethedatainthiswayasthesampleof

countriescoveredbytheaggregateindicatorschangesonlyminimally.

11Theadjustmentfactorforthemeanissimply /,where isthenumberofcountrieswith

datainperiodTand istheaveragescoreoftheadditionalcountriesinperiod. Thehigheristheaveragescoreofthenewentrantsand/orthemorenewentrantsthereare,themorewelowerthemeaninthepreviousperiod.


29/31

Figure 1: Governance Estimates and Margins of Error for the WGI, 2009

Government Effectiveness

Control of Corruption

ANGOL

A

AUSTRALIA

BELGIUM

BRUNEI

C

ENTRALAFRICAN

REPUBLIC

CAPEVERDE

MICRONESIA

GABON

CROATIAM

EXICO

NIUE

PHILIPPINES

PORTUGAL

REUNION

SIE

RRALEONE

TUNISIA

TUVALU

VANUATU

ZAMBIA

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 10 20 30 40 50 60 70 80 90 100

Governance

Rating

Percentile Rank

ANGOLA

BAHRAIN

BHUTAN

DJIBOUTI

DOMINICANREPUBL

SPAIN

FRANCE

MICRONESIA

GUINEA-BISS

GUYANA

JAMAICA

LUXEMBOURG

NEWCALEDONIA

NEPAL

OMAN

PERU

UNITEDSTATES

YEMEN

S

OUTHAFRICA

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Governance

Rating


30/31

Figure 2: Changes Over Time in Governance Estimates, 2000-2009

Voice and Accountability

Rule of Law

BDI

COG

ERI

GABIRQ

JOR

LBR

MDG

SGPSLE

THA

TUN

UGA

VEN

YUG

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

2

009

2000

ALBARG

BOL

COL

ECU ERI

EST

GEO

Hong Kong SAR, China

IRN

LBN

LBR

LVA

RWASLB

TCD

THATTO

VEN

YUG

ZWE

2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

2009

2000


31/31

indices mundiales de goberanza

Documents