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PolicyResearchWorkingPaper10923

ThirstyBusiness

AGlobalAnalysisofExtremeWeatherShocksonFirms

RobertaGatti

AsifM.Islam

CaseyMaue

EshaZaveri

WORLDBANKGROUP

MiddleEastandNorthAfricaRegion&PlanetVicePresidency

September2024

PolicyResearchWorkingPaper10923

Abstract

UsingglobaldatafromtheWorldBank’sEnterpriseSurveysthatincludestheprecisegeo-locationofsurveyedfirms,thispaperexamineshowdryspellsandprecipitationshocksinfluencefirmperformance.Thestudyfindsthatfirmsinareasthatexperiencedryspellshavelowerperformanceintermsofsales.Thisisparticularlytrueforsmallerfirmsandthoseindevelopingeconomies.Ahighernumberofextremedrydaysalsoincreasesthechancesthatafirmwillexitthemarket.Themainchannelsarelargelythroughlaborproductivityandinfrastructureservicedisruptionssuchaswaterandpoweroutages.Thereisalsosomeevidenceoflim-itedaccesstofinanceduetonegativeprecipitationshocks.

Governancemaybeanexacerbatingfactor,withnegativeprecipitationshocksincreasingexposuretocorruption.Yet,thereisalsosomeindicationthatdigitallyconnectedandinnovativefirmsaremoreresilienttonegativeprecipita-tionshocks.Processinnovation,websiteownership,anduseoftechnologylicensedfromforeignfirmsmediatetheeffectsofnegativeprecipitationshocksonfirmperformance.However,thereislittleevidenceofadaptation.Negativeprecipitationshockshavenoeffectonthepresenceofgreenmanagementpracticesorgreeninvestmentsforasubsetoffirmsforwhichsuchdataisavailable.

ThispaperisaproductoftheOfficeoftheChiefEconomist,MiddleEastandNorthAfricaRegionandtheOfficeoftheChiefEconomist,PlanetVicePresidency.ItispartofalargereffortbytheWorldBanktoprovideopenaccesstoitsresearchandmakeacontributiontodevelopmentpolicydiscussionsaroundtheworld.PolicyResearchWorkingPapersarealsopostedontheWebat

/prwp.Theauthorsmaybecontactedataislam@

.

ThePolicyResearchWorkingPaperSeriesdisseminatesthefindingsofworkinprogresstoencouragetheexchangeofideasaboutdevelopmentissues.Anobjectiveoftheseriesistogetthefindingsoutquickly,evenifthepresentationsarelessthanfullypolished.Thepaperscarrythenamesoftheauthorsandshouldbecitedaccordingly.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheInternationalBankforReconstructionandDevelopment/WorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentstheyrepresent.

ProducedbytheResearchSupportTeam

ThirstyBusiness:AGlobalAnalysisofExtremeWeatherShocksonFirms*

RobertaGatti,AsifM.Islam,CaseyMaue,EshaZaveri

JELCodes:Q1,Q5,H54,O14,D73

Keywords:Precipitationshocks,firmproductivity,firm-levelanalysis,climatechange

*RobertaGattiisthechiefeconomistoftheMiddleEastandNorthAfricaRegionoftheWorldBank(email:rgatti@).AsifM.IslamisasenioreconomistintheMiddleEastandNorthAfricaChiefEconomistOffice(aislam@).EshaZaveriisaSeniorEconomistintheOfficeoftheChiefEconomistofthePlanetVice-PresidencyattheWorldBank(ezaveri@).CaseyMaueisapost-doctoralscholarattheUniversityofWashingtonSchoolofEnvironmentalandForestSciences(cmaue@).WewouldliketothankHananJacoby,RichardDamania,DanielLederman,PatrickBehrerandFanZhangforcommentsonanearlierversionofthepaper.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheWorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentstheyrepresent.

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ThirstyBusiness:AGlobalAnalysisofExtremeWeatherShocksonFirms*

1.Introduction

Thevariabilityofrainfall,definedasdeviationsfromitslong-termmean,isagrowingchallenge.Overthepastthreedecades,1.8billionpeople,orapproximately25percentofhumanity,haveenduredabnormalrainfallepisodeseachyear,whetheritwasaparticularlywetorunusuallydryyear(Damaniaetal.,2017).Withclimatechange,deviationsfromtrendsareprojectedtobecomemorepronouncedandfrequent.Droughtsandadversewatersupplyshocksareaparticularconcern,withdroughtfrequencyanddurationrisingbynearlyathirdgloballysince2000(TheUnitedNationsConventiontoCombatDesertification(UNCCD),2022)withlastingnegativeimpactsoneconomicgrowthindevelopingeconomies(Zaverietal.,2023;Russ,2020).

Whiletheeffectsofextremeweathereventsonagricultureandruralareashavereceivedconsiderableattention,therearealsoconsequencesforcitiesthatmayhavesignificantimplications.ThelastfewyearshaveseenseveralmajorcitieslikeCapeTowninSouthAfrica,SãoPaoloinBrazil,andChennaiinIndia,face“dayzero”typeeventsinwhichwatersuppliesbecomethreateninglylow,withcountlessmoremedium-sizeandsmallcitiesexperiencingintermittentwatersupplyandwatershortages(Zaverietal.,2021;Singhetal.,2021).Waterscarcitycansignificantlyimpacthouseholds,publicservices,andcriticalinfrastructuresystems,affectingworkersandentirecommunities(Damaniaetal.,2017;HylandandRuss,2019;IslamandHyland,2019).

Thevariedeffectsofextremeweathereventsontheprivatesectorarenotwellunderstood.Firmsareacriticalengineofeconomicgrowth.Theygeneratejobs,provideessentialproductsandservices,andencourageinnovation.Theylinkcitiesandtownstoglobalmarkets.Thisstudyexplorestheeffectof

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droughts(negativeprecipitationshocks)onaglobalsampleoffirmsinurbancenters.Thestudyfindsthatnegativeprecipitationshockshurtfirmperformanceintermsofsales.Thisisparticularlytrueforsmallerfirmsandthoseindevelopingeconomies.Firmsthatexperiencenegativeprecipitationshocksarealsomorelikelytoexitthemarket.Anadditionalextremedrydayleadstoa0.6percentreductioninsales.Theaveragenumberofextremedrydaysinthesampleis6.7dayssuchthatanincreaseinextremedrydaysofthisamounttranslatestoa3.8percentreductioninsales.Atthesamplemaximumof86daysorabout3monthsofextremedrydays,thelossinsalescanriseto48.6percent.Sinceextremedrydaysalsoleadfirmstoexit,theseestimatesmayrepresentanunderestimateoftheoverallimpact.

Theliteraturehasidentifiedseveralchannelsthroughwhichextremeweathereventscouldaffectfirms.Onechannelisthroughhumancapital.Hotdayscouldleadtomoreabsenteeismorlowerthelaborproductivityofworkers(Somanathanetal.,2021).Anotherchannelisthroughinfrastructure.Negativeprecipitationshocks(droughts)increasetheintensityofwateroutagesthathurtsales(IslamandHyland,2019).Droughtsmayalsoincreasethefrequencyofpoweroutages(DesbureauxandRodella,2019).Accesstofinanceisanotherchannelidentifiedbytheliterature.Frequentclimateshocksmightaffecttheabilityofbankstopredictoutcomes,leadingtoanincreaseininterestratesduetoadditionalriskwhichcould,inturn,increasethecostofcapital(Klingetal.,2021).Extremeweathereventscouldalsoleadtobalancesheeterosionasfirmsthatexperiencemonetarylossesfromshocksbecomemoreleveragedastheyaremorelikelytogettheirloanapplicationsrejectedandbeseenaslesscreditworthy(Benincasaetal.,2024).Weathershockscanalsocreateliquidityshortagesandincreaseloandefaults,deterioratingcreditscoresandaccesstofuturecredit(Aguilar-Gomezetal.,2023).

Theeffectsofextremeweathereventsoninvestment,however,arenotobvious.Ononehand,ifweathershockslimitaccesstofinance,firmsarelesslikelytoinvest.Ontheotherhand,firmsaffectedbyweathershocksaremorelikelytoinvestinfixedassetsastheyreplenishdamagedcapital(Benincasaetal.,2024).Newinvestmentscanalsoleadtovintageeffectswherereplenishmentofcapitalmeansnewerequipment

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withalowerenvironmentalfootprint.Alternatively,firmsmaybecomeenvironmentallyawareandthereforeengageingreeninvestmentsorgreenmanagementpractices.

TheEnterpriseSurveysallowsforthepossibilitytotestsomeofthesechannels.Themainchannelsarelargelyinfrastructureservicedisruptionssuchaswaterandpoweroutages.Thereissomeevidenceofeffectsthroughlaborproductivity(salesperworker)andalsolimitedaccesstofinance–negativeprecipitationshocksdecreasethelikelihoodthanfirmsusebankstofinancingworkingcapitalandhaveaccesstooverdraftfacilities.Anewchanneluncoveredinthisstudyisgovernance.Theintensityofnegativeprecipitationshocksincreasesexposuretocorruption.Whilethereisnoeffectuncoveredregardingweathershocksandinvestmentinmachineryandequipment,thiscouldbeduetothecountervailingeffectsoflimitedaccesstofinanceandtheneedtoreplacedamagedcapital.However,extremedrydaysdoincreasetheprobabilityofinvestinginlandandbuildings.Firmsthatareinnovativeintermsofprocess,havewebsiteownership,andusetechnologylicensedfromforeignfirmsexperiencemoremutedeffectsofextremedrydaysonfirmperformance.Thereisalsosomeevidencethatdigitaltechnologiesandinnovationcanbufferagainstclimateshocks(ZhaoandParhizgari,2024;Liuetal.,2023).Finally,foracross-sectionof2019surveyslargelyconductedintheMiddleEastandNorthAfricaandEuropeandCentralAsia,agreenmoduleincludedinthesurveyinstrumentcapturesgreeninvestmentsandgreenmanagementpractices.Thisstudyleveragesthisnewdataforasubsetoffirmswhereitisavailablebutfindsnocorrelationbetweenprecipitationshocksandgreenmanagementpracticesorgreeninvestments.Oneexplanationcouldbethatfirmsmightadoptsuchpracticesonlyafterrepeateddryspellsovertime.However,thesesurveyquestionsarelimited.Adoptionofgreenmanagementpracticesorinvestmentsarecapturedthroughabinaryvariable–whereavalueof1meansadoptionand0impliesnoadoption-thatpertainsonlytothreeyearspriortothesurvey.Henceafirmthatadoptedanygreenmanagementpracticesormadegreeninvestments4yearspriormaybecodedasazero.Therefore,thesefindingsshouldbeinterpretedwithcare.

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Severalpolicyimplicationscanbedrawnfromthefindingsofthestudy.First,theresultsrevealthatsmallerfirms,andthoseindevelopingeconomiesaremoresusceptibletoclimateshocks.Buildingresilienceamongsmallerfirmsandthoseindevelopingeconomiesisessential.Second,waterandpowerinfrastructurearecentraltothenarrative.Asclimatechangemakesprecipitationpatternsmorevariableandunpredictable,investmentsinpublicwaterandpowerinfrastructuresystemsareanimportantwayinwhichgovernmentscanhelpfirmsadapt.Second,institutionsmatter,andgovernancemayplayacriticalroleinhowfirmsfareafterextremeweatherevents.Third,encouraginginnovationisonewaytobuildresilienceinfirms.Finally,extremeweathereventsmaynotnecessarilyleadfirmstoadapt,andthusotherpolicyinterventionswouldbeneededtoincreasegreenmanagementpracticesandgreeninvestments.

Insummary,thestudymakesthefollowingcontributionstotheliterature.First,thestudyprovidesaglobalanalysisofextremeweathereventsandfirmperformance.Second,ittestsseveralchannelsthroughwhichtheeffectsofnegativeprecipitationshockscanbeidentifiedintheliteraturewhileproposinganewchannelrelatedtogovernance.Third,thestudyexploresthetypesoffirmsthataremoreresilienttoshocks.Andfourth,thestudyusesauniquedatasettodispelthenotionthatfirmsmaybecomeenvironmentallyawareafterclimacticshocks.

Therestofthepaperisstructuredasfollows.Section2describesthedataandtheempiricalapproach.Section3providestheresultswithrobustnesschecks.Section4concludes.

2.EmpiricalApproach

2.1Data

2.1.1EnterpriseSurveys

Themaindatasourceiscross-sectionalfirm-levelsurveysacrosstheworldfromtheWorldBank’sEnterpriseSurveys(ES).TheESarenationallyrepresentativesurveysofprivateformal(registered)firms

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with5ormoreemployeesandcovermanufacturingandservicesfirmslargelycollectedviaface-to-faceinterviewswithbusinessownersortopmanagers.Oursamplefortheanalysisisrestrictedtofirmsthathavegeo-locatedinformation.Thefinalsampleconsistsofabout88,000firms(dependingonthespecification)across174surveysover118economiesinthetimeperiod2009-2019.SummarystatisticsareprovidedintableA1.ThefulllistofcountriesandsurveyyearsarepresentedintableA2.Inaddition,forcountrieswheretherewereseveralroundsofsurveys,wecantrackwhetherthefirmexitedthemarketregardlessofwhethertheywerere-interviewedinsuccessivewaves.Wecanobtaininformationonfirmexitacross55countries.

TheESmethodologyincludesaconsistentdefinitionoftheuniverseofinference,astandardsamplingmethodology,astandardizedsurveyinstrument,andauniformmethodologyofimplementation.Theselectionoffirmsineachcountryisachievedbystratifiedrandomsamplingwiththreelevelsofstratification:sector,size,andlocationwithinthecountry.Samplingweightsareusedtocorrectforunequalprobabilityofselection,ineligibilityandnon-response.ThedataarelargelycollectedusingComputer-AssistedPersonalInterviewing(CAPI)software.TheCAPIsoftwarecollectsgeocoordinatesofthefirm’slocationthatweusetomatchrainfallandtemperaturedatawiththefirm-leveldata.Tomaintainanonymityoftherespondents,thegeo-codesaremaskedarounda2kmradius.

Thesurveysareimplementeduniformlyacrosscountries.Formaltrainingsessionsofsupervisorsandenumeratorsareundertakentoensurethebestpracticesareemployedconsistently.Qualitycontrolchecksareimplementedtoguaranteethequalityofthedatathroughoutthedatacollectionprocess.Consistencychecksareemployedfor10%and50%batchesofthedataduringthesurveytofacilitatecallbackstorespondentstobeundertakenwhennecessarytoverifyinformation.InformationontheEnterpriseSurveysglobalmethodologyandonthesampledesignandweightscomputationisavailableonthewebsite

.Thedatahavebeenwidelyusedby

severalstudiesanalyzingtheprivatesectorindevelopingeconomies(Besley&Mueller,2018;Chauvet&Ehrhar,2018;Hjort&Poulsen,2019).

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2.1.2PrecipitationShocks

Tomeasureprecipitationshocks,weusereanalysisdataproducedbytheEuropeanCentreforMedium-RangeWeatherForecasts(ECMWF).Morespecifically,weusethenewlandcomponentofthefifthgenerationofEuropeanReAnalysis(ERA5),hereafterreferredtoasERA-5Landdataset(Muñoz-Sabateretal.,2021).ThisdatasetisproducedbytheECMWFaspartoftheongoingoperationsoftheCopernicusClimateChangeService(C3S),asubdivisionoftheCopernicusprogram,whichistheEarthObservationarmofthespaceprogramestablishedbytheEuropeanCommission.ERA5-Landisaglobal-scaledatasetthatcontainshourlyrecordsofmorethan50keymeteorologicalvariables(includingprecipitation)ata9kmspatialresolutionovertheperiodfrom1950tothepresent.TheserecordsareproducedbyrunningdownscaledmeteorologicalforcingsobtainedfromtheERA5climatereanalysis

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throughahigh-resolutionlandsurfaceprocessmodeldevelopedbyECMWF.Forourapplication,weusethedailyaggregatedversionofthedataset,whichisfreelyprovidedontheCopernicusClimateDataStore(CDS)andaccessibleviaGoogleEarthEngine.

ThereareseveralkeyfeaturesoftheERA5-Landdata.First,asdetailedbelow,constructingtheprecipitationshockmeasurewefavorinouranalysisinvolvesnormalizingdailyrainfallobservationsagainstaday-of-yearandgrid-cellspecificclimatedistribution.Computingtherelevantmomentsoftheselocalizeddistributionsrequiresalong,consistentlymeasured,timeseriesofdailyrainfalldata.ReanalysisdatasetslikeERA5-Landhaveboththesefeatures.Second,witharesolutionof9km,theERA5-LanddataallowustomeasureprecipitationshockspreciselyintheexactareaswherethefirmsintheEnterpriseSurveysarelocated.Finally,withitscompleteglobalcoverage,usingERA5-Landmeanswecanconstructshockmeasuresforanylocationintheworld,andthusforeverysinglefirmintheESdata.Bycontrast,

1ForanoverviewofERA5,see(Hersbachetal.,2020).

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rainfalldatasetsproducedbylong-runningEarth-observingsatellitesareoftenlowerresolution,havespatialortemporalgapsindatacoverage,andexhibitvariationinthefidelityandmethodologyofmeasurementsovertime.

Inourempiricalanalysis,ourpreferredmeasureofprecipitationshocksisavariablewecall`drydays’,whichvariesattheannualandsecondarysub-nationaladministrativeunit(ADM2)level.Toconstructthisvariable,westartwiththedailytotalprecipitationvaluesobservedinallERA5gridcellsthatarecontainedwithintheADM2unitswhereweobserveatleastonefirmintheESdata.Then,tofocusoncontemporaryclimateandmatchthetemporalcoverageoftheESdata,werestrictthedailydatatotheperiodfrom1990to2021.Wethencomputethelong-run(1990-2021)meanandstandarddeviationforeachdayoftheyearineachgridcell.Usingthesemomentsofthelocalclimatedistribution,wethenclassifywhethertheprecipitationobservedoneachdayismorethan1standarddeviationbelowthelong-runaverageforthatparticulardayoftheyear.Daysthatsatisfythisconditionareconsidered`dry’days.Finally,wesumacrossdayswithingrid-yearsandaverageacrossERA5gridcellswithinADM2unitstoarriveatthefinalmeasureofdrydaysthatweuseinourprimaryempiricalanalysis.Inmanyofourspecifications,wealsoincludeananalogousmeasureof`wet’days,whichcapturesthenumberofdaysinayearwhererainfallwasmorethan1standarddeviationabovethelong-runday-of-yearaverage.

Previousstudieshaveoftenmeasuredrainfallshocksintermsof(normalized)deviationsoftotalcumulativeannualorseasonalrainfallfromlocation-specificlong-runaverages.Standardizeddroughtindices,suchastheSPEIorPDSI,havealsobeenwidelyused.Relativetothesestandards,ourdry(wet)daysmeasurehastwokeyadvantages.First,ourmeasurecapturesextremedryness(wetness)eventsevenwhentheyoccuroveraperiodofjustafewdays(orevenjustasingleday).Thishelpsusidentifynonlineareffectswhich

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canbedilutedwhenweatheroutcomesaremorecoarselyaveragedovertime.

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Second,bynormalizingdailyrainfallvaluesrelativetoalocation-andday-of-yearspecificclimatedistribution,ourmeasureeffectivelysummarizesdeviationsinthetimingofrainfallthroughouttheyear.Forexample,ayearwhereprecipitationisexactlyequaltothelong-rundailyaverageoneverydayoftheyearwouldhavezerodrydaysandzerowetdays.Butayearwiththesametotalannualprecipitation,butwherethetimingofrainfallthroughouttheyearissignificantlyshifted,wouldhavemanydryandwetdays.Asaresult,whenweusedry(wet)daysasourmeasureofprecipitationshocks,wecancaptureeffectsonfirmsthatresultfromdisruptionstothenormaltimingofrainfallthroughouttheyear.

2.2EmpiricalApproach

Theempiricalapproachexploitscross-sectionalvariationintheESdatacombinedwithprecipitation

shocksattheADM2geographicalunit,whileaccountingforsub-nationalADM1fixedeffects.ThemainanalysisestimatestheeffectofprecipitationshocksattheADM2levelonsalesatthefirmlevelwhile

controllingfortemperatureandotherfirm-levelcontrols.Thefollowingregressionisestimated:

yiagst=β1shockgt+yxit+ss+αa+ηt+Eiagst(1)

Where:iindexesfirms,aindexessub-nationalADM1units,gindexesADM2units,sindexessector(2digitISIClevel)andtindexessurvey-years.NotethatADM2ismoregeographicallydisaggregatedunitthanADM1.yiagstisthelogofsales(inUSD).shockstrepresentsmeasuresofprecipitationshocks.ThemainprecipitationshockisnumberofdrydaysforaspecificADM2unit.Averagetemperatureisalsoaccountedforintheestimations.

2Inthisway,ourdry(wet)daysmeasureissimilarintothe`degree-days’temperaturevariablesusedinSchlenkerandRoberts’well-knownanalysisofU.S.maizeyields(Schlenker&Roberts,2009).

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Weemployasmallsetoffirmlevelcontrol(X)intheestimation-whetherthefirmisamulti-firm,ageoffirm(inlogs),sizeoffirm(inlogs),exporterstatus,foreignownership,andwhetherthefirmhasacheckingorsavingsaccount.Sector(ISIC2digitlevel)andsurveyyearfixedeffectsarealsoincludedinthespecification.

Themainconcernoftheestimationsareomittedvariablebiasandselection.Thekeyidentifyingassumptioninthisanalysisisthattheexperienceofextremedrydaysinagivenyearisquasi-randomwithinagivenADM2unit.Abodyofempiricalclimatechangeliteraturehasexploitedrandomvariationsinweathertoestimateareduced-formproductionfunction-styleequation(Delletal.,2012;Felbermayretal.,2022;Kotzetal.,2022).Simultaneitybiasislessofaconcerngiventhatindividualfirmsareunlikelytoinfluenceprecipitationshocks.Toaddressomittedvariablebiasconcerns,wecontrolforavarietyofcontrolsatthefirm-level.Wealsoaccountfortime-invariantomittedvariablesattheADM1geographicallevelthroughADM1fixedeffects.Sincethenumberofdrydayshocksareexogenous,simultaneitybiasislessofaconcern.However,wecannotruleoutthepossibilitythatfirmlocationselectionisendogenoustoprecipitationshocks.Ifproductivefirmsmovetoareaswithfewershocks,thenitmayappearasthoughshocksarenegativelycorrelatedwithfirmperformancealthoughitisdriventosomeextentbyselection.Wetrytoaccountforthisconcernbyincludingcontrolsfordeterminantsoffirmproductivity.Also,giventhatoursampleonlyconsistsofsurvivingfirms,ourestimatesmayunderstatethetrueeffectiffirmsaredrivenoutofbusinessduetothesenegativeshocks.

Weexplorepotentialchannelsthroughwhichprecipitationshocksmayaffectsales.Weachievethisbyregressingvariousvariablesthathavebeenidentifiedintheliteratureasplausiblechannelsofclimaticshocksandotherright-handsidevariablesdefinedinequation(1).Finally,asubsampleoffirmsintheMiddleEastandNorthAfrica,andEuropeandCentralAsiasurveyedaroundtheyear2019wereaskedspecificquestionsongreeninvestmentsandadaptation.Weexploitthisdatatoevaluateiffirmswhoexperienceprecipitationshocksaremorelikelytoadoptvariousgreenmeasures.

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3.Results

3.1MainResults

Table2providesthemainresults.Anincreaseindrydaysleadstoareductioninsales.Thecoefficientisstatisticallysignificantatthe5%level.Intermsofmagnitude,anadditionalextremedrydayleadstoa0.6percentreductioninsales.Theaverageno.ofextremedrydaysinthesampleis6.7dayssuchthatanincreaseinextremedrydaysofthisamounttranslatestoa3.8percentreductioninsales.Aonestandarddeviationincreaseinextremedrydays(12.9days)resultsina7.3percentreductioninsales.Notethattheaveragenumberofextremedrydaysincludesvaluesofzeroesforareasthatdidnotexperienceanyextremedryday.Attheveryextreme,thesamplemaximumforextremedrydaysis86days,about3months.Anincreaseofextremedrydaysofaround3monthsresultsina48.6percentlossinsales.Giventhatwealsofindthatextremedrydaysleadfirmstoexit,theseestimatesreflectthoseofsurvivingfirmsandmay,thus,underestimatetheoverallimpact.

Withregardstoothercovariatesintheestimation,wetdaysarepositivelycorrelatedwithsales,statisticallysignificantatthe1percentlevel.ThisisconsistentwithZaverietal.(2023),whofindthatpositiveprecipitationshockscanbeaboonfortheeconomy.Theestimatesforfirm-levelcovariatesareasexpected:thesizeofthefirm,ageofthefirm,foreignownership,exportingfirmsandthosethathaveaccesstocheckingaccountshavemoresales.Allcoefficientsarestatisticallysignificantatthe1percentlevelofsignificance.

Next,weexploreheterogeneitiesintermsoflevelofdevelopment,region,firmsize,andsector.Thesearereportedintable2.Theresultsshowthatfirmsindevelopingeconomiesaremorevulnerabletoextremedrydaysthanhigh-incomeeconomies.Thecoefficientofextremedrydaysisnegativeandstatisticallysignificantfordevelopingeconomieswhileforhigh-incomeeconomiesthecoefficientisstatistically

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insignificantandalmosthalfthesizeasthatofdevelopingeconomiesinabsoluteterms.FirmsinLatinAmericaandtheCaribbeanaremuchmorevulnerabletoextremedrydaysthanotherregions.Itistheonlyregionforwhichthecoefficientofextremedrydaysisnegativeandstatisticallysignificant,aswellasthelargestamongalltheregionsintermsofmagnitude.ThecoefficientofextremedrydaysisnegativeforEastAsiaandthePacific,Sub-SaharanAfrica,andtheMiddleEastandNorthAfricaalbeitstatisticallyinsignificant.Smallerfirms,andfirmsinservicesectorsarealsomorevulnerabletoextremedrydaysthanlargefirmsandmanufacturingfirms.

3.2Channels

Weexploreanumberofchannelshighlightedintheliteraturethroughwhichnegativeprecipitationshocksmayaffectfirms.Theselargelyincludelaborproductivity,investment,infrastructureserviceinterruptions(powerandwateroutages),andaccesstofinance.Wealsoconsideranadditionalchannelnotmentionedintheliterature–corruption.

Themainfindingsarepresentedintable3.Extremedrydaysleadtolowerlaborproduct