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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
PublicDisclosureAuthorized
PublicDisclosureAuthorized
closureAuthorized
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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
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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
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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
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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.
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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
8/8/2019 Indices Mundiales de Goberanza
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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
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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.
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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
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References
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
8/8/2019 Indices Mundiales de Goberanza
25/31
Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2008). GovernanceMattersVII: Aggregateand
IndividualGovernanceIndicatorsfor19962007.WorldBankPolicyResearchWorkingPaperNo.4654.
Washington,
D.C.
Kaufmann,DanielandAartKraay(2008). "GovernanceIndicators: WhereAreWeandWhereShould
WeBeGoing?"WorldBankResearchObserver. Spring2008.
Kaufmann,Daniel,AartKraayandMassimoMastruzzi(2009). GovernanceMattersVIII: Aggregateand
IndividualGovernanceIndicatorsfor19962008.WorldBankPolicyResearchWorkingPaperNo.4978.
Washington,D.C.
8/8/2019 Indices Mundiales de Goberanza
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
8/8/2019 Indices Mundiales de Goberanza
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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
8/8/2019 Indices Mundiales de Goberanza
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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.
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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
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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
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