Division of Economics and Business
Working Paper Series
Bribes, Bureaucracies and Blackouts:
Towards Understanding How Corruption at
the Firm Level Impacts Electricity Reliability
Jacquelyn Pless
Harrison Fell
Working Paper 2015-10
http://econbus.mines.edu/working-papers/wp201510.pdf
Colorado School of Mines
Division of Economics and Business
1500 Illinois Street
Golden, CO 80401
December 2015
c 2015 by the listed authors. All rights reserved.
Colorado School of Mines
Division of Economics and Business
Working Paper No. 2015-10
December 2015
Title:
Bribes, Bureaucracies and Blackouts: Towards Understanding How Corruption at the Firm
Level Impacts Electricity Reliability
Author(s):
Jacquelyn Pless
Division of Economics and Business
Colorado School of Mines
Golden, CO 80401-1887
japless@mymail.mines.edu
Harrison Fell
Division of Economics and Business
Colorado School of Mines
Golden, CO 80401-1887
hfell@mines.edu
ABSTRACT
This paper looks at whether bribes for electricity connections affect electricity reliability. Using detailed firmlevel data, we estimate various specifications based upon repeated cross-sections and means-based pseudopanels to show that bribes are closely related to poorer electricity reliability. We find that the propensity to
bribe for an electricity connection is associated with an increase of 20 power outages per month and a 28%
increase in annual sales lost due to power outages on average. The results parallel a tragedy of the commons story: electricity, which exhibits common-pool resource characteristics, suffers from overexploitation
as self-interested individual firms rationally bribe for electricity, creating negative impacts in aggregate on
the overall quality of the resource. Given the importance of electricity reliability for economic growth and
development, the findings imply that improving oversight and enforcement measures at the consumer level
targeting the reduction of bribery for electricity connections could contribute to growth and development.
JEL classifications: O1, Q4
Keywords: Corruption, Electricty, Reliability, Quality of Government, Institutions, Common-pool Resource
IIntroduction
Morethan1.2billionpeoplearoundtheworldarewithoutelectricityand1billionmorehaveaccessto
onlyunreliablepowernetworks(UN,2015).Unreliableandinadequatepowercanhinderor
completelyhaltenterpriseproductivity,creatingsignificantconstraintsoneconomicactivity,growth,
andhumandevelopment.Ahandfulofpapersempiricallyillustratetheseeffects.Forexample,
AndersenandDalgaard(2013)demonstratehowweakpowerinfrastructureleadstoasubstantial
growthdrag,Fisher-Vandenetal.(2015)showthatelectricityshortagessignificantlylimitfirm
productivity,Rud(2012)studieselectricityprovisionandindustrialdevelopmentandfindsastrong
relationshipbetweenelectrificationandmanufacturingoutput,andDollaretal.(2005)showthat
powerlosseshaveastatisticallysignificantnegativeeffectonproductivity.1Poorelectricityreliability
alsoimpedestheabilityofhouseholdstoconducteverydayactivities,rangingfromrevenuegenerating
andcapacitybuildingactivitiestosocialengagements.Humansrelycriticallyonasecureandstable,
high-qualitysupplyofpower,howeverimprovingreliabilityischaracterizedbyvastcomplexityandis
notstrictlyanissuerelatedtoinvestinginphysicalelectricalinfrastructureexpansionsand
improvements.Theproblemsareoftensymptomsofmuchdeeperissuesthattranscendtheboundaries
oftheelectricitysectorandareintimatelytiedtoareassuchasgovernance,corruption,fiscalpolicy,
socialequity,andpoliticalinstitutions.
Assuch,theunderlyingcausesofpoorelectricityreliabilityarecomplexandcriticallyrelevant
topolicymakers,revenue-generatingfirms,andultimatelyeverymemberofsociety.Inthispaper,we
focusoncorruptionattheconsumerlevelandshowhowbriberyforelectricityconnectionsisrelated
topoorerelectricityreliabilityasmeasuredbypoweroutagesandtheirrelatedcommerciallosses.
Bribesmadebyconsumersreflectrationalself-interestedbehaviorasfirmsseektosecureelectricity
connectionsinordertooperate.However,inaggregate,wepostulatethatthisbribingbehavior
overexploitstheelectricalgrid,creatingaweakersystemthatismorevulnerabletopoweroutages.In
lightofthis,thesefirmsactuallyexperiencemorepoweroutagesandincurgreatercommerciallosses,
whichiscontrarytotheintuitiveresultofthebribetransactionresultinginmoresecureandreliable
serviceprovision.
Usingdetailedfirm-levelbriberyandelectricityreliabilitydata,weformadatasetof
repeatedcross-sectionsincluding72,617manufacturingandservicesfirmsacross118countries
from2006to2012.Wealsocreateameans-basedpseudo-panelforanadditionalsetof
specifications.Instrumentingfortheendogeneityofbriberywithfivefirm-levelinstruments,our
resultsacrossnumerousrobustnesschecksconsistentlyshowthatbribesforelectricity
connectionshaveastatisticallysignificantcorrelationwithmoremonthlypoweroutagesandtheir
1Furthermore,Eberhardetal.(2008)findthatgrossdomesticproduct(GDP)lossesduetopoweroutagescanbeashighas6
percentKessides(1993)providesacomprehensivereviewofanoldersetofliteratureontheimpactsofinfrastructureon
economicdevelopment,focusingmostlyontheimplicationsforeconomicgrowthbutalsohighlightingtheimportanceof
infrastructureforimprovementsinotherdevelopmentindicatorsthatcapturequalityoflife.
2
relatedcommerciallosses.Inthepreferredspecifications,wefindthatthepropensitytobribefor
anelectricityconnectionisassociatedwithanincreaseof20poweroutagespermonthanda28%
increaseinannualsaleslostduetopoweroutagesonaverage.2Interpreteddifferently,aone
standarddeviationincreaseinthepropensitytobribeisassociatedwithexperiencing7more
poweroutagespermonthanda10%increaseinannualsaleslostduetopoweroutages.
Ourempiricalsettingismotivatedbytheobservationthatelectricitynetworksexhibitcommonpoolresource(CPR)characteristicsinwhichtheresource(thesharedelectricitygrid)facestheriskof
beingover-exploitedduetoatensionbetweenindividualrationalityandsocialefficiency.Itmaybe
rationalforindividualfirmstoofferbribestosecureelectricityconnectionsforbusinessoperations,
butinaggregate,suchconnectionsthatcreate‘unmanageddemand’mayoverloadtheelectricalgrid.In
thiscontext,bribingservesasaproxyforover-use.Considerthecaseofafirmbribingapowersupplier
employeeforanelectricityconnection.Thisemployeemaynotdisclosetheactivitytomanagement,
andthusthepowerthatissuppliedtothefirmconditionalonthebribeisnotaccountedforinthe
powersupplymodel.Withoutknowledgeofthisbribe,managementisleftwithoutatrueestimateof
thequantityofelectricityitneedstoprovidetothegridandasupply-demandimbalancecanoccur,
negativelyimpactingoperationalefficiency.3Ifthereisahighincidenceofthisactivityacrossfirms
sharingthesameelectricalwires,thesystemmaybecomeoverburdenedbythe‘unmanageddemand’.
Thisinefficiencycanleadtosystemfailure.Essentially,whileonemayreasonablyexpectthatbribesfor
electricityensureitsprovision,anabundanceofthisactivityonasharedsystemmayadverselyimpact
theresourcequalityinaggregate.Ifwebelievethatbribingisagoodproxyforover-useofelectricity,
thenourresultssuggestthatthisoverexploitationofelectricity,dueinparttoconsumerbehavior,
weakensthegridandmakesitmorevulnerabletopoweroutages.ThismirrorsoutcomesofthewellknownCPRproblem,constitutingatypeofsocialdilemmainwhichrationalityattheindividuallevel
leadstoanoutcomethatisnotoptimalfromtheperspectiveofthegroup.SeeAppendixAformore
detailsonhowelectricityasaserviceexhibitsCPRcharacteristics,withaparticularfocusonhow
electricityisrivalandnon-excludable.
Toourknowledge,thispaperisthefirsttostudyhowcorruptbehaviorattheconsumerlevel
impactsthedemandsideoftheelectricitysector.Abodyofempiricalresearchspecificallyfocusingon
corruption’simplicationsforinfrastructuresectorsandgrowthhasemergedoverthepastfew
2AccordingtotheWorldBankEnterpriseSurveys,thedatasourceforouroutagedata,apoweroutageoccurswhenthereis
equipmentmalfunctionfromthefailureofadequatesupplyofpower.Brownoutsarealsoconsideredpoweroutages.
Respondentswereaskedtocalculatethenumberofoutagesinatypicalmonth,soifoutagesareseasonal,thisdoesnotinclude
monthsinwhichoutagesaremostfrequentorwhentheyaremostinfrequent.
3Expandinguponthetraditionalframeworkofthinkingofnaturalresources,similarCPRstoriesareapplicabletocongestion
andinfrastructuresuchastrafficonhighways.InthegeneralCPRframework,individualscanconsumeacommonresourceto
theindividual’sbenefitbutwithanassociatedsocialcost:ifaggregateconsumptionexceedsthatwhichissupplied,thequality
oftheresourcedeterioratesorperhapsdiminishesentirely.Ifoverconsumptionoccursintheformofbribesforelectricity
connectionsthatarenotaccountedforinelectricityproviders’supplymodels,totaldemandcanexceedthatwhichisexpected
andsupplied.
3
decades,4someofwhichfocusesonelectricity.Whilethequestionofhowcorruption,initsbroadsense,
impactsthepowersectoranditsperformanceisnotnew,veryfewpapersaddresstheissueof
reliability.5Mostoftheexistingliteraturemeasurescorruptionatthecountrylevel(ratherthan
consumerlevel)andfocusesontheelectricitysectorsupplysideimpacts(i.e.,generation,transmission,
anddistribution)ratherthandemandsideimpacts(i.e.,howend-usersexperienceandconsume
electricityasaservicethroughreliability).Forexample,afewstudieshaveshowntheadverseeffectsof
corruptionmeasuredatthecountrylevelonelectricitydistributionefficiencyandtransmissionand
distribution(T&D)losses.Smith(2004)usescross-sectionaldatatoanalyzehowT&Dlossesvary
accordingtocountry-levelcorruptionperceptionindices.Similarly,Estache,Goicoechea,andTrujillo
(2009)studytheimpactofcountry-levelcorruptionandutilityreformsonelectricitysupplyefficiency.
WhiletheauthorsrefertoT&Dlossesasaproxyforqualityofservice,T&Dlossesdonotcapturehow
theservicereliabilityisactuallyexperiencedbyend-users,suchasthroughpoweroutagesorsome
otherdirectmeasureofreliabilityasfeltbyconsumers.Lastly,DalBóandRossi(2007)andWrenLewis(2013)considerpoliticaleconomyfactorsaffectingtheelectricitysectoratamoremicrolevel,
buttheyfocusonhowfirmsonthesupplyside(distribution)areimpactedbycorruptionratherthan
thoseonthedemandside.
Overall,ourstudymakesthreemaincontributions.Primarily,weofferthefirstempirical
studyofhowconsumerlevelcorruptbehavioronthedemandsideoftheelectricitysector
negativelyimpactsthereliabilityofpowerreceived,whichiscontrarytotheintuitionthatbribing
securestheintendedrewardsofreliableservice.Offeringinsightsintothenatureoftheelectricity
gridasaCPR,wemotivatetheimportanceofstudyingcorruptbehaviorattheconsumerleveland
itsimpactinaggregatetoofferinsightsintooneangleforpolicyandgovernanceinterventions
aimingtoimprovereliability.Second,wecontributetothesmallbutgrowingliteratureonlarge
socio-technicalsystemsexhibitingCPRcharacteristics.6Theproblemofsharingcommonresources
underliesmanylarge-scaleconflictsthatarecriticaltoahighfunctioningeconomy,fromchallenges
relatedtoglobalwarmingandthereductionofgreenhousegasemissionstotheuseofman-made
resourcesystemslikebridgesandirrigationcanals.First-bestconsumptionofCPRsisoften
difficult,andthusconsiderationofthesecomplexsocio-technicalsystemsfromthisperspective
shedslightoninstitutionaldesignandregulatorymechanismsforfosteringsecond-best
consumption.Lastly,giventheimportanceofinfrastructureandthereliabilityofitsservicesfor
economicgrowth,thispapercontributesanewinsightforfosteringdevelopmentandgrowthmore
broadly.
4Forinstance,FismanandSvensson(2007)showthatbriberyisnegativelycorrelatedwithfirmgrowthandBahand
Fang(2015)showthatbusinessenvironment—whichincludesameasureofcorruption—isnegativelyassociatedwith
productivityandoutputinSub-SaharanAfrica.
5Infrastructureoperations—suchaselectricityprovision—areparticularlyvulnerabletocorruption(Bergara,Henisz,and
Spiller,1998;DalBó,2006;EstacheandTrujillo,2009)thusdrawingincreasinginterestfromresearchers,decision-makers,
andpolicymakerstoexploremechanismsforreducingitsimpactsoninfrastructureperformance(EstacheandWren-Lewis,
2011).
6Forinstance,seeKünnekeandFinger(2009)andKiesling(2009)fordiscussionsofinfrastructuresystemsasCPRs.
4
Theremainderofthispaperisorganizedasfollows.InSectionII,wedescribeourempirical
modelforthepreferredspecifications.WedescribethedatainSectionIII,resultsinSectionIV,and
robustnesschecksinSectionV.WeconcludewithpolicyimplicationsinSectionVI.
IIEmpiricalModel
Weestimatetheimpactofthepropensitytobribeforelectricityconnectionsonpoweroutagesand
commerciallossesduetopoweroutagesusingrepeatedcross-sections,followingthesimplelinear
modelforifirms:
𝑦! = 𝛽𝑏𝑟𝑖𝑏𝑒𝑠! + 𝛾𝑋!! + 𝜐! + 𝑢! + 𝑠𝑧! + 𝑠𝑐! + 𝜀! (1)
where𝑦! iselectricityreliabilityforfirmi(eithertheaveragenumberofpoweroutages
experiencedbythefirmmonthlyorthepercentageoftotalsaleslostduetopoweroutages
annually)and𝑏𝑟𝑖𝑏𝑒𝑠! ispropensitytobribeforanelectricityconnection.𝑋! isamatrixoffirm-and
country-levelcontrolvariablesdescribedinSectionIIIand𝜐! areyearfixedeffectstoaccountfor
macroeconomicfluctuations,technologychanges,andenergypriceshocks.Wecontrolfor
unobservablesacrosscountrieswithcountryfixedeffects,𝑢! ,capturinginherentdifferencesin
powersystemcharacteristics.7Firmsizedummies,𝑠𝑧! ,controlfordifferencesinpoweroutages
experiencedacrossfirmsofdifferentsizes,sectorfixedeffects,𝑠𝑐! ,capturetheimportanceof
electricityasaninputtooperations,and𝜀! isthedisturbanceterm.
TherearethreeidentificationconcernsthatcouldleadOLSestimationofEquation1to
producebiasedestimatesof𝛽.First,briberycouldbeendogenousduetosimultaneitybias.Onecan
imaginethatfirmshaveanincentivetobribeforelectricityconnectionsoramoresecureserviceif
theelectricityinfrastructureisinpoorconditionalready.Inotherwords,perhapsitisnotthecase
thatbriberyforelectricityconnectionsleadstomorepoweroutagesbutratherthattheexistenceof
aweakelectricityinfrastructurethatsuffersfromahighincidenceofpoweroutagesprovidesfirms
withanincentivetobribeforamoresecureservice.Ifthisisthecase,thenourspecificationsuffers
fromsimultaneitybias.Weuseaninstrumentalvariables(IV)strategytoaccountforthe
endogeneityofbriberybehaviorwiththeinstrumentsdescribedinSectionIII.
Second,weareunabletoseparatelyidentifytheeffectofbriberyandcountryfixedeffects
withoutintra-countryvariationinbribery.Inoursample,weincludeonlyfirmslocatedincountries
withintra-countryvariationinthepropensitytobribe.Ideally,toexaminethisCPRsetting,we
wouldhavegrid-leveldatatoidentifytheaggregatedeffectoffirmbribesongridreliability.Given
ourfirm-leveldatathatdonotincludelocationidentifiers,werelyontheassumptionthat
7Wealsoestimatingspecificationsthatinteractedyearandcountryfixedeffectstocapturecountrywidetrendsandtheresults
werethesame.
5
electricitygridsandtheirterritoriesaredefinedsub-nationallyandthatafirm’spropensitytobribe
isrepresentativeofbribepropensityofotherfirmsextractingpowerfromthesamegrid.8
Third,electricityreliabilitycouldbecorrelatedwithshocksatthefirmleveloverthetime
period,andourrepeatedcross-sectionsdonotallowustoincludefirm-levelfixedeffectsaswe
wouldinatraditionalpanelsetting.Assuch,wecreateameans-basedpseudopanelinwhich
cohortsoffirmswithsimilarcharacteristicsaretrackedovertimetostrengthenouridentification
strategy.SectionIIIdescribesthecharacteristicsonwhichwegroupobservations.The
observationsforeachcohortconsistofaveragesofthevariablevaluessothatwhatresultsisa
pseudopanelwithrepeatedobservationsforCcohortsoverTperiods.Themodelspecificationfor
pseudo-panelregressionsiswrittengenerallyas
!
𝑦!" = 𝛽𝑏𝑟𝚤𝑏𝑒𝑠!" + 𝛾𝑥!"
+ 𝑢! + 𝑣! + 𝜀!" , 𝑐 = 1, … , 𝐶; 𝑡 = 1, … , 𝑇,
(2)
where𝑦!" istheaveragevalueofallobserved𝑦!" ’swithincohortcinperiodt,andlikewise,the
othervariablesarealsoaveragesofobservedvalueswithineachcohortcinperiodt.Here,𝑢! are
cohort-levelfixedeffects(theaverageoffirmfixedeffectsincohortc).Identificationrestsupon
withincohortvariationforagivenyearandvariationovertimeforagivencohort.Yearfixedeffects
allowfortimeeffectstobeaccountedforinaflexibleway,measuringtheimpactofsectortrends,
andtime-invariantunobservablesatthecohortlevelarecontrolledforwithcohortfixedeffects.
Thismethodofaveragingwithincohortsalsohelpstoremovepotentialmeasurementerroratthe
firmlevel(AntmanandMcKenzie,2007).Onelastchallengetobearinmindisthatthenumberof
firmsineachcohortandtimeperiodisnotthesame,whichcouldinduceheteroskedasticity.We
followDargay(2007)andHuang(2007)tocorrectforthisbyweighingallcohortvariablesbythe
squarerootofthenumberoffirmsineachcohort.Becausebriberyisstillendogenous,we
implementthesameinstrumentalvariableapproachasintherepeatedcross-sectionregressions.
IIIData
Ourdatasetincludesbothfirm-levelandcountry-levelvariablesthatcomefromthreedifferent
publicdatabases.Wecollectfirm-leveldatafromtheWorldBankEnterpriseSurveys,including
informationontopicsthatspanelectricityinfrastructure,corruption,andperformancemeasures.9
Wematchfirmstocountry-leveldatafromtheWorldBank’sDevelopmentIndicatorsDatabase10
forvariouscontrolvariablesandtheWorldBank’sGovernanceIndicatorsDatabase11forcountrylevelcorruptionandgovernancecontrols.Ourfulldatasetcovers72,617firmsacross118countries
from2006to2012.Onceweomitfirmsthatareincountrieswithoutintra-countryvariationin
8Iffirmsextractingpowerfromthesamegridprovidedifferentanswersregardingtheneedtobribe,thenboththemagnitude
andsignificanceofourparameterestimateswillbelower,makingourestimatesupperbounds.
9Availableatenterprisesurveys.org.
10Availableathttp://data.worldbank.org/data-catalog/world-development-indicators.
11Availableathttp://info.worldbank.org/governance/wgi/index.aspx#home.
6
briberypropensity,oursampleincludes69,283observationsacross104countries.Table1
providesthesummarystatisticsforthispreferreddataset,whichiswhatweusethroughoutour
mainanalysis.Thereissubstantialvariationineachofthevariablesacrossfirmsaswellasacross
countries.TableB1inAppendixBprovidesthesamesummaryforthefulldataset.
Table1:SummaryStatisticsofKeyVariables
Numberof
Observations
ReliabilityMeasures
Outages(monthlyavg.)
38,753
Mean
23,199
Losses(%oftotalsales)
12.52
Standard
Deviation
7.667
26.6
Min
11.73
0
Max
0
600
100
BriberyinElectricitySector
PropensitytoBribe
11,858
0.14
0.347
0
1
57,919
69.43
34.89
0
100
Firm-LevelControls
WorkingCapital(%internal)
Public(indicator)
68,493
0.0601
0.238
0
1
PercentPrivate
67,800
89.13
29.09
0
100
GeneratorOwnership(indicator)
49,307
0.288
0.453
0
1
Sales(annual)(LCUs)
61,483
7.76E+10
1.15E+13
0
2.70E+15
Country-LevelControls
GDPperCapita
67,166
10,024
6,929
568.6
29,321
PopulationDensity
69,283
104
175.8
1.72
1,125
Inflation(%)
64,684
6.416
5.78
-2.41
34.7
GovernmentIndicatorAverage
69,283
-0.0369
0.536
-1.584
1.77
Source:WorldBankDatabases
Ourprimaryvariableofinterest,bribery,ismeasuredbywhetherthefirmreportedinthe
EnterpriseSurveysthatinformalgiftsorpaymentsaregenerallyexpectedorrequiredinorderto
obtainanelectricalconnection(1=yesor0=no).12Althoughthismeasureisnotanexplicit
indicationofexecutedbribes,weassumethatahigherpropensitytobribeforelectricity
connections(ortheperceptionofitsnecessityforobtaininganelectricityconnection)isreflective
ofhigherincidenceofbriberyforelectricityconnectionsintheregionthatthefirmisoperating.13In
12Somemayquestionwhetherreliablefirm-leveldataoncorruptioncanbecollected—itisoftentheviewthatitisnear
impossibletocollectreliablequantitativeinformationoncorruptiongiventhesecretivenatureofcorruptactivities
(ReinekkaandSvensson,2002).However,Kaufmann(1997)arguesthatwithappropriatesurveymethodsandinterview
techniques,firmmanagersarewillingtodiscussitwithrelativecandor,andfirmmanagerscanbegiventheright
incentivestocooperateandtruthfullyreporttheirexperiences.Whilesurveydataareneverperfect,theEnterprise
Surveysarethemostrobustresourceforthefirm-levelvariableswewishtostudy.
13Thewayinwhichweobservebriberydoesnotallowustoidentifytheeffectivenessofbribesinobtainingelectrical
connectionsorsecuringreliablepowersincethedatadonotexplicitlytrackwhetherabribewassuccessfulin
guaranteeingservice.Infact,thebriberymeasuredoesnotevencapturewhetherbribeswereexecutedorbribesfor
turningpoweronandoff—itjustproxiesforthepotentialinitialconnectionitselfandwhetherfirmsaremorelikelyto
needtobribeforelectricity.Ourobjectiveisnottoaskwhetherbribesareeffectivemechanismsforobtainingsecure
powerbutrathertounderstandwhethermorebriberyincidenceoverburdensthegridenoughtocontributetopower
outages.
7
thiscontext,ahigherincidenceorlikelihoodofbribingatthefirmlevelislikelyassociatedwitha
higherincidenceofbriberyonthatfirm’selectricalgridinaggregate.Asaproxyforgridfailure,we
considertwofirm-levelvariablesthatreflectthequalityofservicereceived:averagenumberof
monthlypoweroutagesandpercentageoftotalannualsaleslostspecificallyduetopower
outages.14Weassumethatmorefrequentpoweroutagesatthefirmlevelarerelatedtomore
frequentoutagesonthebroadernetworkfromwhichthatfirmextractspower.15Wediscusshow
weaddressthispotentialsensitivitywithrobustnesschecksinSectionV.
Onealsomaybeconcernedthatapropensitytobribeiscorrelatedwithpoorcountry
managementorinfrastructure,whichimpactstheelectricitysectorandreliability.TheWorldBank
DevelopmentIndicatorsDatabaseandtheWorldBankGovernanceIndicatorsDatabaseprovide
country-leveldataonimportanteconomicandgovernancevariablesthatunderlietheoverall
infrastructureconditionswithinaregion,providinguswithcontrolsforinherentweaknessesand
vulnerabilitiesintheelectricitysystemthatcouldbecontributingtopoweroutages.Specifically,we
includethreecountry-levelmacroeconomicvariablesthatarerelatedtothepotentialqualityof
infrastructure:GDPpercapita(PPPconstant2005internationaldollars),populationdensity
(peoplepersquarekmoflandarea),andtheinflationrate(basedontheconsumerpriceindex).We
assumethatwealthiercountriesgenerallyhavemoreresourcesavailableforinvestingin
infrastructure,whichcouldenhancethebaselinequalityandstabilityoftheelectricitysystem.
PopulationdensityisincludedsinceitissometimessuggestedthatT&Dlosses,aproxyforthe
stabilityofapowersystem,arerelatedtopopulationdensity.16Inflationrateisincludedtoproxy
forgeneralmacroeconomicinstabilityanditsrelationtoinfrastructureconditions.
Additionally,theWorldBankGovernanceIndicatorsDatabaseprovidesdatathatcapture
sixbroadaspectsofgovernanceandcorruptionatthecountry-level.17Eachindexrangesfrom-2.5
(weakgovernance)to2.5(stronggovernance).Includingtheseinouranalysishelpsustocontrol
fortheimpactofbroaderinstitutionalweaknessesandvulnerabilitiesthatcontributetothequality
andmanagementofinfrastructureservices.Theenergysectorisaprimetargetfor,andsourceof,
corruption,anditisoftenriddledwithothergovernanceissues.Forinstance,afewattributesthat
contributetothisvulnerabilityincludethepotentialforgeneratingconsiderableeconomicrents,
theneedforlargecapitalinvestments,andthecentralroleofgovernmentagencies.Thereisoftena
lackoftransparencyofdecision-makingaswellasadearthofeffectivelegalsystemsforreducing
theriskofdecision-makersabusingtheirpower.Becausethegovernanceindicatorsarehighly
14Bothofthesevariablesareself-reportedestimatesprovidedbyfirmmanagementintheEnterpriseSurveys.
15Althoughsomeoftheseoutagescouldbeplanned,ourdatadonotdifferentiatebetweenplannedoutagesand
unplannedoutages,andthuswetakethemonthlyestimatesasbeingproxiesfortotaloutages.Ifthereisbiasdueto
unplannedoutages,itislikelyrelativelyconsistentacrossentitiesoncewecontrolforthefirm’scountrylocation.
16SeeMIT(2011)fordiscussion.
17Themeasuresincludevoiceandaccountability,politicalstabilityandabsenceofviolence,governmenteffectiveness,
regulatoryquality,ruleoflaw,andcontrolofcorruption.Thesemeasurescombinetheviewsofcitizens,enterprises,and
expertsurveyrespondentsbasedon32individualdatasourcesfromnon-governmentalorganization,international
organizations,privatesectorfirms,andthinktanks.
8
correlatedwitheachotherandcapturesimilaruncertaintiesthatimpactinvestmentandoperating
conditions,weaveragethesixindicatorstocreateasingle‘governancequality’controlvariable.18
Weincludeonefirm-levelcontrolvariablethatisintendedtocaptureabroader
countrywidecharacteristicthatisrelatedtoinfrastructurequality:accesstofinance.Provisionof
end-userfinanceisoftenneededtoovercomethebarriersrelatedtothehighinitialcapitalcosts
associatedwithgainingaccesstoenergyservices.Moreworkingcapitalfrominternalsources
ratherthanbanksormicrofinancearrangementsmayimplyaccesstofinancebarriersinthe
economy,whicharecommonlycitedasamajorobstacletoinvestinginandimprovingelectricity
infrastructureandthusthequalityofenergyservices(UNEP2012).Weproxyforthisbarrierwith
thepercentageofthefirm’stotaltheworkingcapitalthatthefirmfinanceswithinternalsources.
Wealsomaybeconcernedwithfirmsreceivingpreferentialtreatmentinwaysthatcould
impactthequalityofelectricityprovidedtothefirm.Forinstance,ifafirm(orfirmswithinacertain
industry)contributessignificantlytotheeconomy,itmayhavemorebargainingpowertoensure
thatitreceiveshighqualityelectricity.Weincludetotalannualsales(logged)tocontrolforthis,
assumingthathighsalefiguresreflectalargecontributiontotheeconomyandthepotentiallyfor
receivingpreferentialtreatment.Twovariablesrelatedtofirmownershiparealsoincludedwith
theintentionofcapturingfactorscorrespondingtoheightenedperceptionsofcorruption(Galanget
al.,2013)anddealingswithpublicofficialsinacquiringcertainservices(RienikkaandSvensson,
2002):publicownership(asanindicatorvariable)andthepercentofthefirmthatisprivately
owned.Someevidencehasshownthatstate-ownedfirmshavealoweredsensitivitytocorruption,
andthefusionoffirmownershipwiththestatecanmakefirmslesssensitivetothenegative
impactsofcorruption,potentiallyinfluencingtherelationshipbetweenthefirmandservice
providerinawaythatimpactsthequalityofserviceprovided.Furthermore,privatefirmsoften
bearthebruntofcorruptionwhereasstate-ownedfirmsthatbenefitfromthestatecouldbemore
likelytoturnablindeyetotheactivity(Galangetal.,2013).Otherworkhasshownthatfirms
dealingwithpublicofficialswhoseactionsdirectlyaffectthefirm’sbusinessoperationsusually
havetopaybribes,suchaswhenacquiringpublicinfrastructureservices(ReinikkaandSvensson,
2002).Eachofthesefactorscouldcontributetoafirmreceivingpreferentialtreatmentinelectricity
serviceprovision.
Lastly,weincludegeneratorownershipasacontrolvariablesinceitmayindicatethatthe
firmwillnotneedtobribeforanelectricityconnectionifpowerislostgivenitsbackupsourceof
electricity.Ifafirmownsagenerator,thenthereislittletononeedforelectricityconnectionbribes
sinceonsitegenerationispossibleinthefaceofpoorreliability.Furthermore,firmsowning
generatorsinthefirstplacemayindicateananticipationofpoorservice,whichmaybereflectiveof
thebaselineelectricitysystemquality.
18Wealsoranregressionswitheachindicatorincludedseparatelyandourresultsremainedstable.
9
Weproposeasetoffivefirm-levelinstrumentsforbribery:femaleownership,foreign
ownership,obtainmentofaninternationallyrecognizedqualitycertification,ageofthefirm,and
thepracticesofcompetitorsintheinformalsectorbeingamajorobstacletooperations.Ourchoice
ofinstrumentswasmotivatedbyareviewoftheliteraturethatempiricallyexploresthe
determinantsofbribery.Femaleownershipandforeignownershipbothhavebeenshowntobe
associatedwithalowerpropensitytobribe(Swamyetal.,2001;Dollaretal.,2001;Clarke,2014).
Whenwomenarebetterrepresentedingovernmentandtheworkforce,lesscorruptionpervades.
Foreign-ownedfirmsmayhaveastrongerincentivethandomesticfirmsnottopaybribesbecause
foreigninvestorsindevelopedeconomiesmightbeconcernedaboutthelawsintheirhome
countries(Leeetal.,2007;Clarke,2014).Wemeasurefemaleownershipasadummyvariable
(equaltoonewhenfirmshaveanyfemaleparticipationinownership).Wemeasureforeign
ownershipasthepercentageofthefirmthatisforeign-ownedbecausefirms’bargainingpower
increasesasitspercentageofforeignownershipincreases,andafirm’sbargainingpowerimpacts
whetherbribesaredemandedofthem.Weexpecthigherpercentagesofforeignownershiptobe
associatedwithalowervulnerabilitytocorruptionandlesspropensitytobribe(Leeetal.,2007).
Similarly,wesuspectthatfirmsthathaveobtainedinternationalqualitycertificatesmaybe
lesslikelytobribe.WemeasurethisasanindicatorvariablebasedontheEnterpriseSurvey
questionaboutwhetherthefirmhasreceivedacertificationrelatedtoqualitymanagementfrom
theInternationalOrganizationforStandardization(ISO).Weassumethatthemanagementofthese
firmsmaintainstransparencyandenforcementguidelinesthathelptoreducetheirtendencyto
fulfillbribedemandsortoofferbribes.Furthermore,obtaininginternationalqualitycertifications
couldimprovethemotivation,awareness,andmoraleofemployees(WorldBank,2014),
potentiallyreducingthefirm’spropensitytobribe.
Ageofthefirmisusedasaninstrumentbecauseofthenotionfoundintheliteraturethat
olderfirmsmighthavedifferentexperiencesthanyoungerfirmswheninteractingwithgovernment
officials.Newfirmsmaybelessvisibleandincurfewerbribedemands.Ontheotherhand,new
firmsmayrequiremorepermitandlicenseapplications,possiblydemandmorebribesbecauseof
morefrequentinteractionswithgovernmentofficials(Clarke,2014).Nonetheless,theageofthe
firmhasbeencitedasbeingrelatedtobriberypropensity,asdemonstratedinLeeetal.(2007).We
followLeeetal.(2007)andmeasureageofthefirmbythenumberofyearssincefoundation.
Lastly,weinstrumentwithameasureofhowsevereofanobstacletheinformalsectoristo
firmoperations.Inmanydevelopingcountries,theinformalsectorisamajorenginefor
employmentandgrowth(Schneider,2002).Therearenumerousmotivationsforfirmstooperate
underground(Johnsonetal.,1997;Johnsonetal.,1998;Johnsonetal.,2000;Johnsonetal.,2001;
Mayetal.,2002),andoperatingunofficiallyissometimesconsideredtobeamechanismfor
avoidingthepredatorybehaviorofgovernmentofficialsthatseekbribesfromthosewithofficially
registeredactivities(LavalléeandRoubaud,2009).Atthesametime,entrepreneursmaybribe
publicofficialsinanattempttosecuretheirinformalactivities,andindeed,informalsectorfirms
10
maybeexposedtodemandsforbribesevenmoresothanformalfirms(LavalléeandRoubaud,
2009).Iffirmsbelievetheyarecompetingwithotherfirmsthatarebribing,suchasthoseinthe
informalsector,theremaybeagreaterincentivetoalsosupplybribestosecureservices.Inother
words,iffirmmanagementbelievestheinformalsectorisanobstacletooperationsandthatfirms
intheinformalsectorarebribing,itmaybelieveitalsoneedstobribeinordertomaintaina
competitiveadvantage.WeusetheresponsetooneofthequestionsontheEnterpriseSurveysto
proxyforthisthataskedfirmstoranktheseverityoftheobstacleonascalefrom1(notsevere)to
4(verysevere).
Itispossiblethattheseinstrumentscouldbecorrelatedwithcountry-levelinstitutionsand
unobservedconditionsthatwecannotcontrolforinouranalysis.Forinstance,theremaybesome
unobservedcharacteristicofacountry’sculturalenvironmentthatimpactsthelikelihoodoffemale
participationinownership,orperhapsfewerfirmsincertaincountriesobtainqualitycertifications
becausethebusinessclimatemakesitnearlyimpossibletomeetthestandards.Furthermore,
perhapsfirmsviewtheinformalsectorasasevereobstaclebecauseitisindeedamoresignificant
componentoftheeconomyincountriescharacterizedbyalessstablebusinessclimate.Itisnot
possibletotestwhethertheseinstrumentsindeedarecorrelatedwithunobserveddeterminantsof
poweroutages,howeverwecanexaminethecorrelationsbetweentheinstrumentsandother
covariates,whichareintendedtocapturetheunderlyingmacroeconomicfabricwithinwhichthe
firmsareoperatingandcouldbecorrelatedwithsimilarunobservables,toprovideevidencethat
ourchoiceofinstrumentshavestrongtheoreticalgrounding.
TableB2inAppendixBpresentsthecorrelationmatrix.Wecanseethattheinstruments
exhibitextremelylowcorrelationswithnearlyallofourothercovariates,mostlywithcorrelations
under0.10.Thisgivesusmoreconfidencethatourinstrumentsarenotcorrelatedwithunobserved
determinantsofpoweroutages.Thereareafewexceptions,however.Forinstance,foreign
ownershipishighlycorrelatedwithprivateownership(𝜌 = −0.90).Toaccountforthis,werun
regressionswithvariouscombinationsoftheinstrumentsetasarobustnesscheck.
Finally,designingandsettinguppseudopanelsisnottrivial.Cohortsmustbegroupsof
firmsthatsharecommoncharacteristics,whereeachfirmisamemberofonlyonecohort,and
cohortsmusthavefixedmembershipbasedoncharacteristicsobservedforallobservationswithin
thesample.Onenaturalcharacteristictobasecohortgroupingsuponisregionofoperation.
Unfortunately,becausewedonothaveenoughcountriesthatarerepeatedconsecutivelythrough
thesampleperiod,groupingoncountryisnoteffective.Assuch,wegroupbasedongeographic
regionfollowingtheWorldBankclassifications.Groupingonlyonregion,however,isextremely
limiting.Therefore,wegroupontwootherfirmattributesthatlikelyaffectafirm’sbribery
behavior—firmsizeandsector—andoneothercountry-levelvariablethatcapturesthebroader
corruptionclimateasaproxyforthepropensitytoengageincorruptactivities,the‘controlof
corruption’WorldBankGovernanceIndicator.
11
Ourselectionoffirmsizewasmotivatedbyempiricalresearchshowingthatitmaybe
relatedtobribingbehavior(AbedandGupta,2002;AndersonandGray,2006;Leeetal.,2007).19
Firmsizeisdefinedbythenumberofemployees,where5-19employeesindicatesthefirmissmall,
20-99ismedium,and100+islarge.Becausesizeisstillarelativelycrudeinstrumentonwhichto
group,wealsogrouponsectorasacharacteristicaffectingtheincentivetobribe.Thechoiceof
sectorismotivatedbyitbeingaproxyforelectricitydemandorthefirm’srelianceonelectricityfor
operationsandrevenuegeneration.Forexample,bothAppleandFordarelargecompanies,but
theirenergyconsumptionprofilesareunlikelytobesimilar.Therefore,theirpropensitytobribefor
powerisprobablydifferentsincetheirbusinessmodelsrelyuponitdifferently.Essentially,
groupingbysectoraimstocapturethefirm’srelianceuponelectricity,supportedbystudiesthat
haveshownthatfirmsindifferentindustriesexhibitdifferentbribingbehavior.20
Groupingfirmsbasedon‘controlofcorruption’,whichcharacterizestheconditionsofthe
countryinwhicheachfirmoperates,helpsustoovercomethechallengeofnotbeingabletogroup
oncountryitself.Perhapsmoreimportantly,itcapturesthemacro-levelconditionsthatweexpect
wouldimpactafirm’spropensitytobribe.TheWorldBankdefines‘controlofcorruption’as‘the
extenttowhichpublicpowerisexercisedforprivategain,includingbothpettyandgrandformsof
corruption,aswellas‘capture’ofthestatebyelitesandprivateinterests’.Themetricalsomeasures
thestrengthandeffectivenessofacountry’spolicyandinstitutionalframeworktoprevent
corruption.Onecanimaginethataweakercontrolofcorruptionasdefinedinthiswaymayenable
morebribery.Furthermore,briberyisnotjustaproblemrelatedtocorruptbehaviorbyfirms
offeringbribes.Justasinanytransaction,briberytransactionshavebothasupplysideanda
demandside.Officialswiththepowertooffergovernmentcontracts,issuealicense,orallocate
somescarceresourcecandemandbribes,aswell(Dixit,2013).Thisdirectlyrelatesto‘controlof
corruption’,whichcapturesbriberydemand.
Thecontrolofcorruptionmeasureisanindexcombiningupto22differentassessments
andsurveys,eachofwhichreceivesadifferentweightbaseduponestimatedprecisionandcountry
coverage.Theindicatorrangesfrom-2.5(weak)to2.5(strong).Wegroupfirmsbasedonwhich
quartiletheyfallwithinbasedontherangeofthismeasureinoursample.Again,wefacethe
concernthatthismeasurecouldchangeforfirmsovertime,howeverwearenottooconcerned
sincethepaceofchangecanbequiteslowdependinganddependonbuildingthefoundationsfor
reform(Johnston,2014).
19Thesestudiesshowthatsmallerfirmspaybribesmorefrequentlyandpaymorebribesasashareofrevenuesrelative
tolargerfirms.
20Somestudieshaveshownthatthereareindustryleveldifferencesinbribery(Clarke,2014).Forinstance,Herreraand
Rodriguez(2003)showthatmanufacturingfirmsarelesspronetobribethanservicesectorfirms.Leeetal.(2010)show
thatfirmsizeisnegativelyassociatedwiththelikelihoodofbribery.
12
IVResults
Table2presentsourmainresults.21Theestimatesconsistentlysuggestthatahigherpropensityto
bribeforanelectricityconnectionisassociatedwithmorepoweroutagesandtheirrelatedlosses,
orinotherwords,alessreliableelectricitysystem.Thecoefficientestimatesforbriberycanbe
interpretedasthechangeinmonthlypoweroutagesandthechangeinfinanciallossesasa
percentageoftotalsales,onaverage,associatedwiththepropensitytobribeforelectricity
connections.WhenusinganIVapproachandincludingallcontrols,thepropensitytobribeis
associatedwithanincreaseof20poweroutagespermonthonaverage,whichisstatistically
significantatthe5%level.Interpretedanotherway,anincreaseinthepropensitytobribebyone
standarddeviationisassociatedwithabout7morepoweroutagespermonth.Furthermore,the
propensitytobribeisassociatedwitha28%increaseinfinanciallossesduetopoweroutages,ora
onestandarddeviationincreaseinthepropensitytobribeisassociatedwitha9.8%increasein
losses(significantatthe5%level).Forrobustnesspurposes,TablesB6andB8inAppendixB
providesetsofregressionresultswhereweaddonecontrolvariableatatimetotheIV
specificationsforoutagesandlosses,respectively,inordertobemoreconfidentthattheresultsare
notdrivenbysomeunobservablesrelatedtoourcontrolvariables.Weseethattheresultsare
statisticallysignificantinallcasesandthemagnitudesofthecoefficientestimatesarerelatively
stable.
Table2:IVRegressionsforMonthlyPowerOutagesandFinancialLossesDuetoOutages
Outages
Outages
Losses
Losses
PropensitytoBribeforanElectricityConnection
18.13**
20.51**
14.03*
28.29**
(8.46)
(9.29)
(8.33)
(12.41)
No
Yes
No
Yes
Country-levelcontrols
No
Yes
No
Yes
CountryDummies
Yes
Yes
Yes
Yes
SectorDummies
Yes
Yes
Yes
Yes
FirmSizeDummies
Yes
Yes
Yes
Yes
Firm-levelcontrols
Time(year)Dummies
Yes
Yes
Yes
Yes
Observations
3567
2106
1638
968
F-statistic
4.89
6.05
4.43
4.02
Underidentificationtest(p-value)
0.0097
0.004
0.007
0.036
Overidentificationtest(p-value)
0.546
0.878
0.606
0.745
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
21Wedonotincludenaïve(non-instrumented)regressionresultssincethesimultaneitybiasrenderssuchresultsinvalid.
However,wehaveconfirmedthatthedirectionofthebiasiscorrect.Whenwedonotinstrument,theeffectofbribeson
outagesiszero,implyinganegativebias.WeregressedbribepropensityonoutagesalongwithallcontrolsandIVsas
regressorsandwefoundanegativecoefficientestimateforoutages.Thiscanbeexplainedbythelackofincentivetobribefor
anelectricityconnectiononceoutagesaresofrequentthatpurchasingabackupgeneratorisamorereasonablesubstitute.The
sameexerciseforfinanciallossesconfirmsthedirectionofbiasforthoseregressionsaswell.
13
Thefindingsfromthepseudo-panelregressionsalsoconsistentlysupporttheconclusionthat
briberyadverselyaffectselectricityreliabilityhoweverthemagnitudesoftheparameterestimates
aremuchhigher.WhenincludingbothcohortandtimefixedeffectsinIVregressions,the
propensitytobribeisassociatedwithhigherlevelsofbothoutcomevariables.Table3summarizes
theseresults,showinghowwefindpositiveandstatisticallysignificantcoefficientestimates.
Specifications1and2includeallcontrols,andwedropthegovernanceindicatoraveragecontrol
variableinspecifications3and4sincethisvariableencapsulatesoneofthevariablesonwhichwe
formedthecohorts(‘controlofcorruption’).Wepresentthefirststageresultsforspecifications1
and2inTablesB10andB11ofAppendixB,respectively.Examinationofthefirst-stageregression
resultsrevealsthattheinstrumentsarestrongerforthepooledOLSregressions.Assuch,weprefer
thepooledOLSIVregressionresultspresentedinTable2giventheirslightlystrongerfirst-stage
resultsandthelossofstatisticalpowerinthepseudo-panelregressions.
Table3:Pseudo-PanelRegressionResults,InstrumentalVariablesApproach
Groupingonregion,sector,firmsize,and‘controlofcorruption’inhostcountry
Outages
Losses
(1)
(2)
Outages
(3)
Losses
(4)
PropensitytoBribeforanElectricityConnection
46.65*
64.57***
52.04*
48.71**
(26.96)
(20.24)
(26.87)
(21.80)
Firm-levelcontrols
Yes
Yes
Country-levelcontrols
Yes
Yes
Yes
w/oGov.
Avg
Yes
w/oGov.
Avg
CohortFixedEffects
Yes
Yes
Yes
Yes
Time(year)FixedEffects
Yes
Yes
Yes
Yes
Observations
302
239
302
239
Robuststandarderrorsinparentheses;errorsareclusteredoncountry
***p<0.01,**p<0.05,*p<0.1
VRobustnessChecks
Tobesureourresultsholdwhenincludingallfirms,werunIVregressionsonthefulldatasetand
showthatthepropensitytobribeforelectricityconnectionsisstillstatisticallysignificantforboth
outcomemeasuresandstableinmagnitudewithourpreviousfindings(seeTable4).Wealso
conductasimilarexerciseaswiththepreferredspecificationstodemonstratethatthemagnitude
andsignificanceofcoefficientestimatesarestabledespitewhichcontrolvariablesareincluded,and
theseresultsareprovidedinTablesB12andB13inAppendixBforoutagesandcommerciallosses,
respectively.Theestimatesfallwithinasimilarrangeastheydidinthepreferredspecifications(15
to22forpoweroutagesand15%to30%forlosses).
14
Table4:IVPooledOLSRegressionResultsforFullDataset
Outages
Losses
PropensitytoBribeforanElectricityConnection
22.01**
30.40**
(9.75)
(13.15)
Observations
1032
2183
F-statistic
6.003
3.69
Underidentificationtest(p-value)
0.004
0.043
Overidentificationtest(p-value)
0.885
0.746
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
Note:Bothspecificationsincludefirm-levelandcountry-levelcontrols,andcountry,sector,firm
size,andyeardummies.
Thesecondbiggestconcernisthateventhoughthereexistswithin-countryvariationin
bribery,thedatadonotallowustoattributeindividualfirmstospecificgridsinordertocapture
someaggregatedgrid-levelimpact.Thiscreatesuncertaintyastowhetherspatialheterogeneity
actuallyexists.Therefore,werunregressionsondatafromfirmsonlylocatedingeographically
largecountriestoseeiftheresultsholdwhenwearemoreconfidentthatrespondentsareindeed
spatiallydispersed.Wefirstdefine‘geographicallylarge’ascountriesthataregeographicallylarger
thantheQ3sizefoundinourdataset,whichis1,280,000squarekilometers.Ourresultsforpower
outagesandfinanciallossesarepresentedinspecifications1and2,respectively,ofTable5.The
findingsholdforpoweroutages,howeversignificanceislostinthefinanciallossesregressions.We
considertwoadditionaldefinitionsof‘geographicallylarge’:countriesthatare2millionsquare
kilometersorlargerandthosethatare4millionsquarekilometersorlarger,whichweretwo
cutoffsthatappearedtobenaturalbreakingpointswhenplottingthedata.Wepresenttheseresults
inTable5aswell.Columns3and4presenttheresultsforcountrieslargerthan2millionsquare
kilometers.Columns5and6presenttheresultsforcountrieslargerthan4millionsquare
kilometers.Forcountriesgreaterthan2millionsquarekilometers,theresultsmaintain
significanceforpoweroutagesbutitislostagainforfinanciallosses.Forcountriesgreaterthan4
millionsquarekilometers,significanceismaintainedforbothoutcomemeasures.
Apositivecoefficientestimateforeachregressionandsignificanceforeachregressionis
maintainedonoutages.WhiletheF-statisticsindicatethattheseregressionssufferfromweak
instrumentation,specification1hasgreatestpowerwhilealsoexhibitingarelativelyhighFstatistic,andthusitisourpreferredspecificationofthisset.Demonstratingthatbriberypropensity
maintainsapositivecoefficientthatissignificantatthe5%levelwhileinstrumentingandwithall
controlsonthissetofgeographicallylargecountriesprovidesuswithmoreconfidencethatour
dataexhibitspatialheterogeneityinbriberypropensity.
15
Table5:IVPooledOLSRegressionResultsforGeographicallyLargeCountries
Outages
Losses
Outages
Losses
Outages
Losses
(1)
(2)
(3)
(4)
(5)
(6)
PropensitytoBribe
5.40**
0.411
3.22***
5.45
2.61**
6.82*
(2.54)
(8.59)
(0.99)
(3.60)
(1.09)
(3.77)
646
224
322
105
322
105
Observations
F-statistic
12.34
0.551
2.21
0.565
2.48
0.861
Robuststandarderrorsinparentheses;***p<0.01,**p<0.05,*p<0.1
Note:Eachspecificationisinstrumented.Allspecificationsincludefirm-levelandcountry-levelcontrols,and
country,sector,firmsize,andyeardummies.Specificationsdifferaccordingtothecut-offincountrygeographic
sizefordefining‘geographicallylarge’.Columns1&2considercountriesthataregeographicallylargerthantheQ3
sizeinourdatasetas‘geographicallylarge’;Columns3&4definegeographicallylargeaslargerthan2million
squarekilometers;Columns5&6definegeographicallylargeaslargerthan4millionsquarekilometers.
Athirdconcernthatarisesisthatnotallfiveoftheinstrumentsaresignificantdeterminants
ofbriberyforelectricityconnections,asdemonstratedbythefirst-stageregressionresults.22While
theinformalsectorobstaclevariableandageoffirmaresignificantpredictorsatthe1%levelinthe
first-stageregressionsforbothoutcomevariables,theotherthreeIVsarenot.Therefore,wewantto
besurethatourresultsarenotsensitivetowhichinstrumentsweinclude.Werunfiveadditional
instrumentalvariableregressionsforeachoutcomemeasure,addingoneinstrumentatatime,and
findthattheresultsarerelativelyconsistentacrossspecifications.Whenregressingonpower
outages,inallspecificationsexceptforone,wemaintainsignificanceandfindthatbriberyis
associatedwith19to21morepoweroutagespermonth(seeTable6).Similarly,whenregressingon
commerciallosses,inallspecificationsexceptforone,wemaintainsignificanceandfindthatbribery
isassociatedwithanincreaseinlosses20%to40%ofannualsales(seeTable7).Forthetwo
specificationswheresignificanceislost,thecoefficientestimatesremainpositive.Thesefindingsare
consistentwiththoseinourpreferredspecifications.Ourresultspasstheunder-andoveridentificationtests,andthuswefavorinclusionoftheentiresetofIVsinthepreferredspecifications
givenourreviewoftheliteratureandthetheoreticaljustificationfortheirinclusion.
22Also,ourF-statisticsarelow,whichmaybeproblematic.
16
Table6:IVRobustnessChecksforPooledOLSRegressions-Outages
13.77
19.45*
21.45**
21.26**
20.51**
(16.11)
(10.83)
(10.23)
(9.74)
(9.29)
X
X
X
X
X
X
X
X
X
X
X
X
PropensitytoBribe
IncludedInstruments
InformalSectorObstacle
AgeofFirm
QualityCertification
X
X
X
Observations
4215
2413
2322
2106
2106
F-statistic
6.82
8.82
7.58
5.26
6.05
FemaleOwnership
PercentForeign
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
Note:Allspecificationsincludefirm-levelandcountry-levelcontrols,andcountry,sector,firmsize,and
yeardummies.
Table7:IVRobustnessChecksforPooledOLSRegressions-CommercialLosses
PropensitytoBribe
40.32*
13.52
20.65*
24.45*
28.29**
(20.87)
(12.95)
(12.18)
(12.88)
(12.41)
X
X
X
X
X
X
X
X
X
X
X
X
IncludedInstruments
InformalSectorObstacle
AgeofFirm
QualityCertification
FemaleOwnership
X
X
PercentForeign
X
2455
1155
1117
968
968
Observations
F-statistic
6.05
5.78
5.29
3.61
4.02
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
Note:Allspecificationsincludefirm-levelandcountry-levelcontrols,andcountry,sector,firmsize,and
yeardummies.
Lastly,itispossiblethatthewayinwhichwechosetogroupfirmsforformingthepseudo-panel
introducedbiassincetheselectionofattributesonwhichtogroupwerelargelymotivatedbyexisting
studiesintheliterature.Oneattributethatmightbeparticularlytroublingisourchoiceofgrouping
firmsbasedontheregioninwhichtheyarelocated.Eachregionincludesnumerouscountriesof
varyingmacroeconomicconditions,institutionalchallenges,cultures,andmore.Weconsideromitting
thisvariableasonethatwegroupuponandgroupfirmsbasedononlyfirmsize,sector,and‘controlof
corruption’.Whilefirmsizeandsectorarebothcitedaspotentialdeterminantsoffirm-levelbribing
behavior,‘controlofcorruption’stillcapturesthebroadercountry-levelstabilitythatmayinfluencea
firm’sbehavior.Theresultsfromtheseregressions,wheninstrumentingwiththefullsetofinstruments
17
andincludingallcontrols,areprovidedinB14ofAppendixB.Onceagain,thepositivecoefficient
estimateismaintainedforpoweroutagesatthe5%level.Whilesignificanceislostwhenregressingon
commerciallosses,thesignremainspositive.
Overall,therobustnesschecksdemonstratethatthepositivecoefficientfindingsare
maintainedwithsignificanceinallcaseswhenregressingonmonthlypoweroutages,butthe
significanceofresultswhenregressingoncommerciallossesismoresensitivetotheselectionof
dataandinstruments.Nonetheless,thepositivecoefficientestimateismaintainedacrossall
robustnesschecks.Furthermore,thepoweroutagesoutcomeisthemeasurethatmoredirectly
capturesthephenomenonthatweaimtomeasureinthisanalysis.Thegoalistoidentifywhether
moreillegalconsumptionofelectricitycontributestoalessreliableelectricitysystemasmeasured
byitsfailure.Monthlypoweroutagesareadirectreflectionofpowersystemfailures,while
commerciallossesduetopoweroutagesarenormalizedtototalfirmsalesandcouldbeinfluenced
bythefirm’sdependenceonelectricityforoperationsandprofitabilityasopposedtostrictly
capturingtheoccurrenceofpoweroutages.Itispossiblethatthefirm-levelcontrolsandvarious
setsoffixedeffectsdidnotfullycontrolforthisinfluence.
Thepositivecoefficientestimatesofconsistentmagnitudemaintainedacrossallrobustness
checkspecificationswhenregressingonpoweroutagesprovidesuswithmoreconfidenceinour
findings.Thatis,ourresultssuggestthatbribesforelectricityconnectionsarecloselyrelatedto
poorelectricityreliability.
VIConclusion
Previousworkhasdemonstratedtheimportanceofelectricityreliabilityforeconomicgrowthand
development,howevertheunderlyingcausesofpoorelectricityreliabilityindevelopingcountrieshave
gonerelativelyunstudied.Weaimedtonarrowthisgapintheliteraturebyfocusingontheroleof
corruptionanditsimpactonreliability,offeringthefirstempiricalstudytoourknowledgeofhow
consumer-levelcorruptbehavioronthedemandsideoftheelectricitysectornegativelyimpactsthe
serviceprovided.Inouranalysis,thepropensitytobribeforanelectricityconnectionisassociatedwith
anincreaseof20poweroutagespermonthanda28%increaseinannualsaleslostduetopower
outagesonaverage.Ourresultsarerobustacrossarangeofspecificationsbaseduponrepeatedcrosssectionsandameans-basedpseudo-panel,showingthatbribesarecloselyrelatedtopoorerreliability.
Thisimpliesthatoneeffectivegovernanceinterventionforimprovingreliabilitymaybetofocuson
reducingbribesforelectricityconnections,perhapsthroughenhancedtransparency,oversight,and
enforcement.
Furthermore,wearguethatourfindingscanbeexplainedinthecontextofthewell-knownCPR
problem,whererationalityattheindividuallevelleadstoanoutcomethatisnotoptimalfromthe
perspectiveofthegroup.Studyingcorruptionatthefirmlevelallowedustoobservewhatappearsto
berationalself-interestedbehavior—bribesforelectricityconnections—andcorrelateitwithan
outcomethatproxiesforreducedqualityofservice:poweroutages.Poweroutagesoccurmore
18
frequentlywhenasystemisweak,andifwebelievethatbriberyforelectricityconnectionsisa
reasonableproxyforover-consumption,thenthesebribesfacilitateaweakerstateoftheelectrical
systemthatismorevulnerabletofailure.Thisphenomenonparallelsatragedyofthecommonsstory:
whenusersfailtointernalizethecongestioncoststhattheyimposeonothers,inefficientconsumption
occursandresourcequalitydiminishes.
Ensuringadequateelectricityreliabilityiscomplex,particularlyinadevelopingcountry
context.Thechallengesextendfarbeyondthecapacitytoinvestinthephysicalinfrastructure.Itwas
theaimofthispapertohighlighttherelevanceofjustoneoftheseunderlyingissues—corruption—and
shedlightonthepotentialofpoliciesandgovernanceinterventionsthatfocusonreducingcorruption
attheconsumerlevelinthequestforimprovingelectricityreliability.
19
Acknowledgments
Forhelpfulcommentsandfeedback,theauthorsthankEdBalistreri,IanLange,andPeter
ManiloffoftheColoradoSchoolofMines,DougArentoftheJointInstituteforStrategic
EnergyAnalysis,MorganBazilianoftheWorldBank,AdrienneOhlerofIllinoisState
University,andtherefereesandparticipantsfromthe2014InternationalAssociationfor
EnergyEconomicsBestStudentPaperCompetition.Theauthorsalsothanktheattendeesof
numerouspresentationsfortheirfeedback,includingthosewhoparticipatedinthe2015
AssociationofEnvironmentalandResourceEconomistsSummerConference,2015Front
RangeEnergyCamp,aseminarattheEconomicsDepartmentattheUniversityofVermont,
twobrownbagpresentationsattheColoradoSchoolofMines,andabrownbagpresentation
attheNationalRenewableEnergyLaboratory.Forpartialfinancialsupport,theauthors
thanktheJointInstituteforStrategicEnergyAnalysis.
20
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23
AppendixA:ElectricityasaCommonPoolResource(CPR)
ACPRisaresourcesystem(naturalorman-made)thatissufficientlylargetomakeitcostly(butnot
impossible)toexcludeconsumersfromobtainingbenefitsfromitsuse,exhibitingbothrivalandnonexcludablecharacteristics(Ostrom,1990).Suchgoodsarerival(orsubtractable)inthesensethat
multipleindividualscanusethesystemwhileeachindividual’sconsumptionsubtractsfromthetotal
quantityavailabletoothers.CPRsarenon-excludableinthesensethatitisdifficulttokeepthosewho
havenotpaidforthegoodfromconsumingit.WhileCPRclassificationsaretraditionallyusedto
describenaturalresourcesystemssuchasriverbasins,lakes,andforests,largesocio-technicalsystems
alsofitwithinthisframework,receivinglimitedattentionintheliteraturetodatedespitetheir
increasinglyfrequentCPR-relatedproblemssuchascongestionandoveruse,accessregulationthat
preventsexcessiveappropriation,andpoorinvestmentincentives(KünnekeandFinger,2009).
Electricpoweritself—theactualelectronsconsumed—isapureprivategood.Asindividuals
extractelectronsfromthegrid,theysubtractfromthetotalquantityavailabletoothers.23However,
viewingelectricityasstrictly‘electricpower’ignoresthephysicalrealitiesofelectricityasitisactually
transmitted,delivered,andconsumedbyend-users.Thatis,whenindividuals,households,andfirms
consumeelectricity,theyareactuallyconsumingabundleofvaluedgoods—electricpoweritself(the
electrons),aswellasreliability,voltage,andfrequency(Toomeyetal.,2005).Becauseeachofthese
componentsreliesupontheothers,thisbundlecannotbedisaggregated.Wecannotconsumeelectric
powerwithoutitsassociatedvoltage,andreliablepowercannotbedeliveredwithoutadequatevoltage
andfrequencyconditionsinitsassociatedwirednetwork.Forexample,whenthereexistdeviationsin
networkfrequencyorvoltage,generatorscantripandtheentiresystemcanfail,leavingthose
connectedtothesystemwithoutelectricpower(JoskowandTirole,2007).
Whileitistheelectricpowercomponentofthebundlethatmakeselectricitysubtractable,itis
thevoltage,frequency,andreliabilitycomponentsthatmakeitnon-excludable.Regardlessofwhatis
paidbyindividualcustomers,andevendespitedifferencesintheactualpowerdemandedandreceived,
consumersonthesamenetworksharethesamevoltage,frequency,andprobabilityofapoweroutage.
Toelaborate,thereareatleasttwoexplanationsforelectricitybeingnon-excludablethatare
relatedtotheinterdependenceofthesebundlecomponents.First,systemsareoftenspreadacross
largegeographicalareaswithdifficulttomonitoraccesspoints.Whileitispossibletotrackpower
consumptionandidentifyirregularitieswithsomeprecisiongivenexistingmeteringtechnology,not
every“leak”inthedistributiongridcanbetrackedbecauseoftheexistenceofnon-technicallosses
(NTLs).Therearetwodifferenttypesofelectricitylosses24:technicallosses,whicharecausedbylosses
intransmissionordeficienciesinoperationsandphysicalinfrastructure,25andNTLs,whichreflect
inefficienciesresultingfromactionsoutsideofthephysicalpowersystem.CommoncausesofNTLs
23Theonlycontextinwhichmultiplepeoplecanconsumethesameelectronsisinasharedlightingspace.
24Electricitylossesrefertoelectricityinjectedintoatransmissionanddistributiongridthatisnotpaidforbyfinalend-users.
25Theseareinherenttothecurrenttransportationandassociatedwiththeinfrastructurecharacteristicsofthepowersystem
itself,consistingmainlyofpowerdissipationinelectricalsystemcomponentssuchastransmissionlinesandtransformers
(Suriyamongkol,2009).
24
includeelectricitytheft,non-payment,andpoorrecordkeepingandoversight(WorldBank,2009).Any
typeofillegalconnectiontothepowergridcanbeclassifiedaselectricitytheft,andNTLsaremost
significantincountriescharacterizedbyrelativelyhigheconomic,social,andpoliticalrisk(Smith,
2004).26
Althoughtechnicallossescanbetrackedandoptimized,asthisissimplyanengineeringissue
thatinvolvespowersystemplanning,NTLsaremuchmoredifficulttomeasuresinceactionsexternalto
thesystemareunaccountedforbysystemoperators(Suriyamongkol,2009).EvenintheU.S.,where
thereexistsalegacyinfrastructureandstronginstitutions,lossesfromelectricitytheftaresignificant.27
Thissuggeststhataccesspointsareindeeddifficulttomonitor,eveninwell-establishedsystemsandin
well-developedcountries.28TheseNTLscanleadunstablepowersystemstofailoroperatesuboptimallyastheyoverloadgenerationunitsandleadtoover-voltage,whichthencanleaveutilities
withoutatrueestimateofthequantityofelectricityitneedstoprovidebothlegalandillegalcustomers
(Depuruetal.,2011).29
Whilethefirstargumentforelectricitybeingnon-excludableappliestoallfourcomponentsof
theelectricityservicebundle,similarlogicrelatedtotrackingvoltageandfrequencyrequirements
supportsthesecondreasonthatelectricityisnon-excludable:itisdifficulttomonitorappropriated
serviceseventolegalcustomers,whereserviceshererefertovoltageandfrequencyrequirements.
Voltageandfrequencyareimpacteddirectlybychangesinloadcausedbyothers’consumption,and
thusNTLsthatareimpossibletotrackyetimpactthevoltageandfrequencyconditionsontheshared
networkmakeitdifficulttoensureappropriatevoltageandfrequencylevelstoallconsumersinrealtime.Sinceelectricitycannotbestoredeconomicallytoday,supplyanddemandmustalwaysbeinrealtimebalanceinordertofunctionappropriately,andrelyingupononlydatafromend-usemetersand
substationsleavessystemoperatorsblindtotheactualoperatingconditionsondistributionlines.As
26NTLs,however,existinmostregionsoftheworldandarenotstrictlyafunctionofcountrywealth.LowerGDPpercapita
appearstobeassociatedwithhigherelectricitylosses,butthereareexceptions.Forinstance,therearecaseswhereGDPper
capitaislowbutlossesrelatedtopoorreliabilityarealsolow(MillardandEmmerton,2009).
27IntheU.S.,electricitythefthasbeenestimatedtocostbillionsofdollarsannually(Nesbit,2000).AmericanElectricPower
(AEP),whichcoversabout200,000squaremilesacrosselevenstatesasthelargestinvestor-ownedutilityintheU.S.,has
indicatedthatitsrevenueprotection-relatedbillings(whicharedeterminedbasedontheftestimatesfrompowersystem
analysissoftware)exceed$3.2millionannually(Suriyamongkol,2009).
28Suriyamongkol(2009)showshowtoday’spowersystemanalysestoolscannotevencaptureknownNTLsletalonethose
thatarenotknown,whichisnecessaryforbalancingloadsandensuringadequatevoltagelevelsandfrequencyforproviding
reliablepower.Meterreadingscanhelptodetectsometamperingwithequipmentandperhapspreventsomeelectricitytheft,
butmeterdataanalyticstechniquesaloneareonlyeffectiveinidentifyingroughly30%ofpowertheft(EPRI,2001).
29WhenNTLsareespeciallyhigh,theycan“trip”generationunitsandinterruptpowersupply(Sullivan,2002).Inorderto
maintainthesystem’svoltageneardesignlevel,generatorsmustprovidereactivepowerorelseelectricityequipmentowned
bycustomers,suchascomputersandrefrigerators,willbedamaged(Toomeyetal.,2005).Withmultiplecustomersserved
simultaneouslybyawirednetworkandreceivingthesamevoltage,anyindividual’sactionhasanimpactonvoltagelevels.
Whenvoltagedrops,generatorsproduceexcesspower,whichleadsallgeneratorsconnectedtothesystemtospinfaster.This
increasesthefrequencyofthenetworkandexcessenergyisabsorbedbytherotationalenergycontainedingeneratorsand
turbines.Deviationsfromthedesignfrequencyofgeneratorscancauseextremelyexpensivedamage,suchasturbineblades
spinningofftheirshafts(Toomeyetal.,2005).Thiscanleadsystemstoshutdown.
25
such,theelectricpowernetworkcreatesasystemwherecustomersconsumeasharedoveralllevelof
reliabilityinwhichitisimpossibletoexcludeconsumersfromitsbenefits.30
Assuch,thesharednatureoffrequencyandvoltageservices,whicharerequiredfor
maintainingsystemreliability,makethemsusceptibletothecollectiveactionofindividuals.Thisisthe
underlyingmotivationforourstudyofhowindividualfirmbehaviorcould,inaggregate,impactthe
largersharedsystem.
30Thisisbecausethetransmissionofelectricitythroughnetworksfollowsthewayofleaseelectricalresistance(i.e.,loopflows
ofelectricpower),imposingmutualrestrictionstousers(KünnekeandFinger,2009).
26
AppendixB:SupportingTables
TableB1:SummaryStatisticsofKeyVariables(FullDataset)
NumberofObs.
Standard
Deviation
Mean
Min
Max
ReliabilityMeasures
Outages(monthlyavg.)
Losses(%oftotalsales)
40,522
12.15
24,424
7.375
26.1
11.56
0
0
600
100
BriberyinElectricitySector
PropensitytoBribe
0.137
Firm-LevelControls
WorkingCapital(%internal)
60,272
69.33
Public(indicator)
71,820
0.0585
0.235
0
1
PercentPrivate
71,110
89
29.22
0
100
GeneratorOwnership(indicator)
51,495
0.291
0.454
0
1
Sales(annual)(LCUs)
64,499
7.41E+10
1.12E+13
0
2.70E+15
12,154
0.343
0
34.67
1
0
100
Country-LevelControls
GDPperCapita
70,500
10,066
6,977
568.6
29,321
PopulationDensity
72,617
107.9
176.4
1.72
1,125
Inflation(%)
67,839
6.37
5.691
-2.41
34.7
GovernmentIndicatorAverage
72,617
-0.02
0.544
-1.584
1.77
Source:WorldBankDatabases
27
TableB2:CorrelationsBetweenExcludedInstrumentsandOtherRegressors
InformalSector
Obstacle
Quality
Certification
Ageof
Firm
Female
Ownership
Percent
Foreign
InformalSectorObstacle
1.000
QualityCertification
-0.124
1.000
AgeofFirm
0.105
0.115
1.000
FemaleOwnership
0.002
0.052
0.027
1.000
PercentForeign
-0.070
0.192
0.038
-0.083
1.000
WorkingCapital(%internal)
-0.152
0.015
-0.127
-0.008
0.016
Public
-0.011
0.087
0.091
-0.002
0.053
PercentPrivate
0.076
-0.200
-0.040
0.061
-0.900
GeneratorOwnership
0.026
0.071
0.098
-0.057
0.116
Log(totalannualsales)
-0.057
0.203
0.152
-0.042
0.172
Log(GDPpercapita)
-0.004
0.009
0.083
-0.043
-0.078
Log(populationdensity)
-0.042
0.138
-0.013
0.114
0.055
Inflation
-0.049
-0.113
-0.095
-0.058
-0.049
GovernmentIndicatorAverage
0.224
-0.008
0.321
-0.010
0.045
28
TableB3:Within-CountryVariationofPropensitytoBribeforElectricityConnections
Afghanistan
Albania
Angola
Argentina
Azerbaijan
Bahamas
Bangladesh
Belarus
Benin
Bhutan
Bolivia
BosniaandHerzegovina
Botswana
Brazil
Bulgaria
BurkinaFaso
Burundi
Cameroon
CentralAfricanRepublic
Chad
Chile
China
Colombia
Congo
CostaRica
Croatia
CzechRepublic
DRC
DominicanRepublic
Ecuador
ElSalvador
Estonia
Ethiopia
Fiji
FyrMacedonia
Gabon
Gambia
Georgia
Ghana
Guatemala
Guinea
GuineaBissau
Guyana
Honduras
Mean
0.357
0.197
0.259
0.025
0.300
0.133
0.510
0.079
0.478
0.022
0.027
0.093
0.025
0.078
0.094
0.192
0.148
0.235
0.214
0.472
0.013
0.058
0.035
0.156
0.038
0.034
0.055
0.534
0.059
0.097
0.020
0.068
0.071
0.194
0.118
0.200
0.313
0.029
0.333
0.049
0.571
0.089
0.146
0.094
StandardDeviation
0.481
0.401
0.440
0.157
0.464
0.352
0.505
0.273
0.511
0.147
0.163
0.292
0.158
0.268
0.293
0.398
0.359
0.428
0.415
0.506
0.115
0.233
0.183
0.369
0.192
0.183
0.229
0.503
0.237
0.297
0.140
0.255
0.260
0.402
0.325
0.410
0.468
0.171
0.476
0.217
0.499
0.288
0.358
0.293
Minimum
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Maximum
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
29
Indonesia
Iraq
Kazakhstan
Kenya
Kosovo
KyrgyzRepublic
LaoPDR
Latvia
Lesotho
Liberia
Lithuania
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mexico
Moldova
Mongolia
Montenegro
Mozambique
Nepal
Nicaragua
Niger
Nigeria
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Romania
Russia
Samoa
Senegal
Serbia
SierraLeone
SlovakRepubic
SouthAfrica
SriLanka
St.KittsandNevis
St.VincentandGrenadines
Swaziland
Tajikistan
Tanzania
TimorLeste
0.286
0.319
0.171
0.277
0.091
0.208
0.283
0.057
0.150
0.444
0.026
0.132
0.125
0.326
0.420
0.029
0.079
0.044
0.161
0.154
0.135
0.313
0.069
0.148
0.384
0.683
0.023
0.132
0.042
0.161
0.077
0.053
0.187
0.208
0.063
0.137
0.063
0.045
0.055
0.139
0.053
0.037
0.087
0.329
0.217
0.178
0.454
0.468
0.379
0.449
0.292
0.415
0.455
0.233
0.362
0.527
0.162
0.343
0.342
0.471
0.499
0.169
0.270
0.208
0.369
0.376
0.347
0.479
0.254
0.362
0.487
0.469
0.149
0.340
0.200
0.369
0.270
0.224
0.390
0.415
0.243
0.348
0.250
0.213
0.229
0.351
0.229
0.192
0.288
0.473
0.415
0.387
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
30
Togo
Tonga
TrinidadandTobago
Turkey
Uganda
Ukraine
Uruguay
Uzbekistan
Vanuatu
Venezuela
Vietnam
Yemen
Zambia
0.261
0.588
0.047
0.051
0.176
0.209
0.017
0.154
0.149
0.052
0.233
0.410
0.024
0.449
0.507
0.213
0.220
0.384
0.409
0.130
0.376
0.360
0.222
0.424
0.498
0.156
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
Zimbabwe
0.222
0.428
0
1
31
TableB4:Pseudo-PanelSummaryStatistics,OutagesSpecifications
Observations Mean St.Deviation Min
Max
PropensitytoBribe
302
0.0837
0.200
0
1
MonthlyOutages
302
5.863
14.23
0
120
Log(GDPpercapita)
302
5.259
2.502
1.143 10.06
Log(populationdensity)
302
2.372
1.364
0.488 6.113
Inflation
302
3.795
4.349
0.273 30.55
GovernmentIndicatorAverage
302
-0.252
0.379
-1.765 1.215
GeneratorOwnership
302
0.229
0.309
0
1
InternalWorkingCapital(%oftotal)
302
39.62
28.57
0
100
PublicOwnership
302
0.0422
0.166
0
1
PercentPrivate
302
52.42
30.65
0
100
Log(annualsales)
302
10.25
5.220
2.187 26.50
TableB5:Pseudo-PanelSummaryStatistics,CommercialLossesSpecifications
PropensitytoBribe
Losses(%oftotalsales)
Log(GDPpercapita)
Log(population
density)
Inflation
GovernmentIndicator
Average
GeneratorOwnership
InternalWorking
Capital(%oftotal)
PublicOwnership
PercentPrivate
Log(annualsales)
Observations
Mean
St.Deviation
Min
Max
239
239
239
239
0.122
4.962
6.293
2.776
0.271
9.960
2.538
1.415
0
0
1.415
0.511
1
80
10.06
6.113
239
239
4.130
-0.266
4.104
0.435
0.299
-1.326
28.19
1.215
239
239
0.291
46.62
0.362
31.92
0
0
1
100
239
239
239
0.0486
61.29
12.37
0.186
32.14
5.452
0
0
2.360
1
100
26.50
32
TableB6:PooledOLSInstrumentalVariablesRegressionResults–MonthlyPowerOutages
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Propensityto
18.13** 19.49*** 15.31** 15.68** 16.64**
14.27**
14.83**
14.79**
15.05**
20.51**
Bribe
(8.461) (7.131) (6.407) (6.416) (7.544)
(6.086)
(6.623)
(6.627)
(6.813)
(9.294)
WorkingCapital
-0.00912 -0.0103 -0.00978 -0.00975 -0.0123
-0.0155** -0.0155** -0.0156** -0.0157*
(%internal)
(0.008)
(0.007) (0.007)
(0.007)
(0.008)
(0.008)
(0.008)
(0.008)
(0.008)
Generator
0.871*
0.851*
0.800*
1.093*
1.092*
1.094*
1.189*
1.065
Ownership
(dummy)
(0.497) (0.492)
(0.441)
(0.629)
(0.662)
(0.662)
(0.670)
(0.701)
PublicOwnership
1.230
1.172
1.354
1.337
1.336
1.326
1.286
(dummy)
(1.556)
(1.603)
(1.846)
(1.854)
(1.855)
(1.869)
(1.941)
Private
-0.00654 -0.00676 -0.00795 -0.00794 -0.00735 -0.00573
Ownership(%)
(0.0100) (0.00945) (0.00999) (0.00999) (0.0100) (0.0105)
Log(totalannual
-0.181
-0.155
-0.156
-0.163
-0.151
sales)
(0.166)
(0.171)
(0.172)
(0.173)
(0.177)
Log(GDPper
-6.546
-4.230
-3.247
-8.223
capita)
(14.87)
(11.97)
(11.61)
(13.49)
Log(population
-19.43
-21.88
-16.98
density)
(19.36)
(18.91)
(24.86)
Inflation
0.0815
0.0376
(0.362)
(0.465)
Government
-5.949*
IndicatorAverage
(3.266)
Observations
3,567
2,813
2,504
2,499
2,499
2,234
2,118
2,118
2,106
2,106
CountryFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
YearFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
SectorFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
FirmSizeFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
TableB7:FirstStageRegressionResults-PooledOLS-MonthlyPowerOutages
WorkingCapital(%internal)
GeneratorOwnership
Coefficient
St.Error
p-value
0.000
0.000
0.580
0.034*
0.018
0.067
Public
0.009
0.036
0.810
PercentPrivate
0.000
0.001
0.437
Log(totalannualsales
-0.001
0.004
0.861
Log(GDPpercapita)
0.277
0.508
0.588
Log(populationdensity)
-0.947
0.940
0.318
Inflation
0.000
0.019
0.993
GovernmentIndicatorAverage
0.168**
0.078
0.035
InformalSectorObstacle
0.019***
0.006
0.002
-0.004
0.020
0.840
-0.001***
0.000
0.007
FemaleOwnership
0.000
0.018
0.992
PercentForeign
0.000
0.001
0.492
No.ofObservations
2106
F-testofexcludedinstruments
6.05
QualityCertification
AgeofFirm
***p<0.01,**p<0.05,*p<0.1
34
TableB8:PooledOLSInstrumentalVariablesRegressionResults–CommercialLossesDuetoPowerOutages(%oftotalsales)
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Propensityto
14.03*
20.32*
25.16** 26.27**
29.43**
28.90*** 27.83*** 28.06*** 29.03*** 28.29**
Bribe
(8.334) (11.42)
(10.50) (10.69)
(11.71)
(10.04)
(10.10)
(10.19)
(10.64)
(12.41)
WorkingCapital
-0.0156
-0.0202 -0.0165
-0.0159
-0.0222
-0.0262
-0.0265
-0.0260
-0.0264
(%internal)
(0.010)
(0.013)
(0.013)
(0.013)
(0.016)
(0.016)
(0.016)
(0.017)
(0.016)
Generator
-2.708*** -2.541*** -2.666***
-1.577*
-1.793*
-1.801*
-1.573*
-1.564*
Ownership
(dummy)
(0.884)
(0.873)
(0.951)
(0.902)
(0.915)
(0.919)
(0.890)
(0.873)
PublicOwnership
4.260
4.239
4.616
4.673
4.653
4.714
4.720
(dummy)
(3.004)
(2.971)
(2.899)
(2.877)
(2.882)
(2.947)
(2.957)
Private
-0.0145
-0.0104
-0.00933
-0.00889 -0.00858 -0.00830
Ownership(%)
(0.0168)
(0.0143) (0.0147)
(0.0147) (0.0152) (0.0151)
Log(totalannual
-1.017*** -1.045*** -1.040*** -1.040** -1.047**
sales)
(0.373)
(0.394)
(0.395)
(0.409)
(0.409)
Log(GDPper
-21.29
-9.429
-2.918
-3.259
capita)
(49.27)
(53.44)
(57.10)
(57.96)
Log(population
-113.8
-112.4
-112.0
density)
(105.3)
(104.9)
(104.8)
Inflation
-0.679
-0.675
(1.966)
(2.080)
Government
-0.628
IndicatorAverage
(4.773)
Observations
1,638
1,321
1,160
1,156
1,156
1,028
980
980
968
968
CountryFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
YearFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
SectorFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
FirmSizeFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
35
TableB9:FirstStageRegressionResults-PooledOLS–CommercialLossesas%ofTotalSales
Coefficient
St.Error
p-value
WorkingCapital(%internal)
0.000
0.000
0.597
GeneratorOwnership
0.007
0.022
0.764
Public
0.000
0.030
0.992
PercentPrivate
-0.001
0.001
0.656
Log(totalannualsales
0.000
0.007
0.953
Log(GDPpercapita)
-0.693
0.650
0.292
Log(populationdensity)
Inflation
GovernmentIndicatorAverage
InformalSectorObstacle
QualityCertification
AgeofFirm
-0.205
0.902
0.821
0.042**
0.019
0.034
0.237
0.145
0.109
0.020***
0.007
0.006
0.010
0.026
0.689
-0.001**
0.001
0.018
FemaleOwnership
-0.017
0.024
0.476
PercentForeign
-0.001
0.001
0.664
No.ofObservations
968
F-testofexcludedinstruments
4.02
***p<0.01,**p<0.05,*p<0.1
36
TableB10:FirstStageRegressionResults-Pseudo-PanelIVRegressionsonOutages
Coefficient
St.Error
p-value
WorkingCapital(%internal)
0.002
0.001
0.074
Public
-0.217
0.130
0.106
PercentPrivate
-0.011
0.005
0.034
GeneratorOwnership
0.357
0.116
0.005
Log(totalsales)
-0.036
0.017
0.045
Log(GDPpercapita)
0.153
0.057
0.012
Log(populationdensity)
-0.019
0.040
0.650
Inflation
-0.003
0.029
0.930
GovernmentIndicatorAverage
-0.146
0.111
0.201
InformalSectorObstacle
0.028
0.029
0.352
QualityCertification
0.053
0.098
0.591
AgeofFirm
-0.003
0.003
0.371
FemaleOwnership
0.078
0.087
0.379
PercentForeign
-0.011
0.005
0.029
No.ofObservations
302
F-testofexcludedinstruments
3.64
***p<0.01,**p<0.05,*p<0.1
37
TableB11:FirstStageRegressionResults-Pseudo-PanelIVRegressionsonCommercialLosses
Coefficient
St.Error
p-value
WorkingCapital(%internal)
0.002
0.001
0.269
Public
-0.112
0.194
0.572
PercentPrivate
-0.003
0.008
0.719
GeneratorOwnership
0.079
0.151
0.609
Log(totalsales)
-0.051
0.021
0.029
Log(GDPpercapita)
0.146
0.091
0.130
Log(populationdensity)
-0.055
0.048
0.267
Inflation
-0.070
0.035
0.062
GovernmentIndicatorAverage
-0.432
0.122
0.003
InformalSectorObstacle
0.041
0.030
0.193
QualityCertification
0.275
0.123
0.041
AgeofFirm
-0.001
0.004
0.880
FemaleOwnership
0.202
0.088
0.036
PercentForeign
-0.003
0.008
0.681
No.ofObservations
239
F-testofexcludedinstruments
3.6
***p<0.01,**p<0.05,*p<0.1
38
TableB12:FullDatasetVersionofPooledOLSIVResults–MonthlyPowerOutages
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
PropensitytoBribe
18.36** 20.17*** 15.93**
16.29**
17.27**
15.16**
(8.536) (7.303)
(6.538)
(6.548)
(7.715)
(6.295)
WorkingCapital(%
-0.00854 -0.00974 -0.00923
-0.00920 -0.0116
internal)
(0.008)
(0.007)
(0.007)
(0.007)
(0.008)
GeneratorOwnership
0.845*
0.826*
0.775*
1.058*
(dummy)
(0.485)
(0.480)
(0.432)
(0.615)
PublicOwnership
1.244
1.182
1.350
(dummy)
(1.569)
(1.616)
(1.864)
PrivateOwnership
-0.00696 -0.00714
(%)
(0.0097) (0.0092)
Log(totalannual
-0.167
sales)
(0.165)
Log(GDPpercapita)
Log(population
density)
Inflation
GovernmentIndicator
Average
Observations
3,704
2,899
2,590
2,585
2,585
2,311
CountryFEs
Yes
Yes
Yes
Yes
Yes
Yes
YearFEs
Yes
Yes
Yes
Yes
Yes
Yes
SectorFEs
Yes
Yes
Yes
Yes
Yes
Yes
FirmSizeFEs
Yes
Yes
Yes
Yes
Yes
Yes
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
(7)
15.77**
(6.863)
-0.0147*
(8)
15.73**
(6.867)
-0.0148*
(9)
16.05**
(7.071)
-0.0148*
(10)
22.01**
(9.753)
-0.0148*
(0.008)
1.053
(0.008)
1.055
(0.008)
1.149*
(0.00810)
1.005
(0.647)
1.334
(0.647)
1.332
(0.655)
1.321
(0.689)
1.279
(1.874)
-0.00830
(1.874)
-0.00830
(1.889)
-0.00772
(1.973)
-0.00614
(0.0097)
-0.139
(0.0097)
-0.140
(0.0097)
-0.147
(0.0102)
-0.136
(0.170)
-6.535
(15.05)
(0.171)
-4.341
(12.21)
-18.40
(0.172)
-3.372
(11.90)
-20.74
(0.178)
-8.534
(14.08)
-15.38
(19.75)
(19.26)
0.0824
(0.372)
(25.74)
0.0381
(0.483)
-6.104*
2,195
Yes
Yes
Yes
Yes
2,195
Yes
Yes
Yes
Yes
2,183
Yes
Yes
Yes
Yes
(3.301)
2,183
Yes
Yes
Yes
Yes
39
TableB13:FullDatasetVersionofPooledOLSIVResults–CommercialLossesas%ofTotalFirmSales
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Propensityto
15.10* 21.17* 25.88** 26.97**
30.11** 29.92*** 28.93*** 29.16***
Bribe
(8.339) (11.58) (10.66) (10.83)
(11.88)
(10.18) (10.26)
(10.35)
WorkingCapital
-0.0145 -0.0190
-0.0153
-0.0146
-0.0206
-0.0246
-0.0249
(%internal)
(0.010) (0.013)
(0.013)
(0.013)
(0.016)
(0.016)
(0.016)
Generator
-2.645*** -2.481*** -2.590*** -1.572* -1.777**
-1.785**
Ownership
(dummy)
(0.856)
(0.845)
(0.916)
(0.880)
(0.892)
(0.896)
PublicOwnership
4.263
4.263
4.605
4.663
4.642
(dummy)
(3.016)
(2.986)
(2.916)
(2.893)
(2.899)
Private
-0.0122
-0.00772 -0.00657 -0.00619
Ownership(%)
(0.0159) (0.0136) (0.0139) (0.0139)
Log(totalannual
-0.992*** -1.018*** -1.013***
sales)
(0.368)
(0.389)
(0.391)
Log(GDPper
-19.86
-7.797
capita)
(48.97)
(53.22)
Log(population
-115.9
density)
(105.1)
Inflation
Government
IndicatorAverage
Observations
1,726
1,390
1,229
1,225
1,225
1,092
1,044
1,044
CountryFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
YearFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
SectorFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
FirmSizeFEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Robuststandarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
(9)
30.32***
(10)
30.40**
(10.81)
-0.0243
(13.15)
-0.0245
(0.017)
-1.558*
(0.017)
-1.558*
(0.869)
4.699
(0.872)
4.711
(2.966)
-0.00584
(2.990)
-0.00541
(0.0144)
-1.012**
(0.0145)
-1.017**
(0.405)
-0.932
(0.406)
-0.705
(56.92)
-114.3
(57.96)
-113.6
(104.5)
-0.710
(1.967)
(104.1)
-0.748
(2.101)
-1.091
1,032
Yes
Yes
Yes
Yes
(5.014)
1,032
Yes
Yes
Yes
Yes
40
TableB14:IVPseudo-PanelRegressions–Robustnesscheckon
grouping
Outages
Losses
PropensitytoBribeforaElectricity
Connection
45.51**
1.87
(20.67)
(7.39)
281
226
Observations
Robuststandarderrorsinparentheses;errorsareclusteredoncountry
***p<0.01,**p<0.05,*p<0.1
Note:Bothspecificationsareinstrumentedandincludefirm-leveland
country-levelcontrols,aswellascohortandyearfixedeffects.
Intheseregressions,cohortsaregroupedbasedonsector,firmsize,
and‘controlofcorruption’inhostcountry.
41