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PolicyResearchWorkingPaper10995

ImpactsofDisastersinConflictSettingsEvidencefromMozambiqueandNigeria

KarimaBenBih

ChloeDesjonqueres

BramkaJarafino

ElodieBlanc

SoleneMasson

WORLDBANKGROUP

Urban,DisasterRiskManagement,ResilienceandLandGlobalDepartmentDecember2024

PolicyResearchWorkingPaper10995

Abstract

Thispaperestimatesthedifferentiatedeconomicimpactofnaturalhazard-relateddisasters(thespecificdisastersandclimateshocksstudiedherebeingfloods)whentheyoccurinconflictversusnon-conflictaffectedareas.Existinglit-eratureshowsthatdisastersandclimateshockscancausesignificantdistresstocountriesandpeopleonaninstitu-tionalandhouseholdlevel.However,assumptionsaremadethattheirimpacttendstobelargerinconflict-affectedareas,withlittleevidenceavailableonthedifferentiatedextentofthesedamages.Thispaperinvestigateswhether,andtowhatextent,thepresenceofconflictshasamplifiedtheimpactsoffloodsoneconomicactivityandpeople,andhamperedrecovery.Thepaperappliesa“top-down”approachtoesti-matingthedifferentialimpactsofdisastersandclimate

shocksbetweenconflictandnon-conflictaffectedareasusingsatellite-derivedimageryofnightlightradianceasaproxyforeconomicactivity,alongwithgeospatialfoot-printsoffloods.Theanalysisconsiderstwocasestudies:the2019tropicalcyclonesIdaiandKennethandsubsequentfloodsinMozambique,andtheJuly2022floodsinNige-ria.Usingdifference-in-differenceestimations,theanalysisfindsthattherearesignificantdifferencesindisasterandclimateshockimpactsandrecoverybetweenconflictandnon-conflictaffectedareas.Particularly,thereisagreaterdeclineineconomicactivityandalongerrecoverytimeinconflictaffectedareas,asproxiedbythegreaterchangeintheintensityofnightlightradiance.

ThispaperisaproductoftheUrban,DisasterRiskManagement,ResilienceandLandGlobalDepartment.ItispartofalargereffortbytheWorldBanktoprovideopenaccesstoitsresearchandmakeacontributiontodevelopmentpolicydiscussionsaroundtheworld.PolicyResearchWorkingPapersarealsopostedontheWebat

/

prwp.Theauthorsmaybecontactedatkbenbih@.

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

ProducedbytheResearchSupportTeam

ImpactsofDisastersinConflictSettings:EvidencefromMozambiqueandNigeria

Novembre20,2024

KarimaBenBih,WorldBank

ChloeDesjonqueres,WorldBankBramkaJarafino,WorldBank

ElodieBlanc,MotuEconomicandPublicPolicyResearchCenterSoleneMasson,WorldBank

Keywords:EconomicImpactsofDisastersinConflict,Climateshocks;EarthObservations;NPP-VIIRS;Floods;Nigeria;Mozambique;ConflictsinfluenceonDisasterImpactsandRecovery;GDP.

JELClassification:D74;O23;O47;O57;Q34;Q54.

TheauthorsaregratefultoStephaneHallegatte,OscarIshizawa,andJunRentschlerfortheirthoughtfulcomments,suggestions,andguidance.

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Introduction

Theaimofthisstudyistoexaminethedifferentialimpactofdisastersandclimateshocksonpopulationsinconflict-affectedregions,specificallyinvestigatingtherepercussionsoffloodinginconflictversusnon-conflictareas.Usingremotesensingtechnology,weattempttoovercomethechallengeofdatascarcityinconflict-affectedcountries,allowingustoaccountforshort-termimpactsofrecentdisasterandclimateshockevents.Despitetheinherentlimitationsofusingnightlightintensityasaneconomicactivityindicator,itprovidesanempiricalfoundationfortheanalysisandenoughobservationsforanex-postquasi-experimentalimpactevaluation.Weemployadifference-in-differenceeconometricapproach,usingsatelliteimageryofnightlightradiancealongsidegeospatialdataonfloodandconflictevents.ThismethodologicalframeworkisappliedtoassesstheaftermathoftheMarch-April2019FloodsinMozambiquefollowingCyclonesKennethandIdai,aswellasthe2022floodsspanningJulytoOctoberinNigeria.

Resultsshowsignificantdisparitiesintheeffectsofdisastersandclimateshocksbetweenconflict-affectedandnon-conflict-affectedareas.Specifically,weobserveamorepronounceddeclineineconomicactivitiesinconflict-affectedregions.

Thepaperisstructuredasfollows.Thefirstsectionoutlinesthecontextoffloodandconflicts.Itpaysattentiontotheinterconnectednessofconflictanddisastersandclimateshocks,outliningthemethodologyandempiricalstrategyderivedtoestimatesuchex-postimpact.Inthesecondsection,wepresenttheresultsandsupportingdataderivedfromthestudy,includingthecasestudiesonMozambiqueandNigeria.Finally,wediscusslimitationsaswellasbroaderimplicationsbeforeconcluding.

Context:Floodimpactandconflictaffectedpopulation(Literature)

1.Impactofflood

Quantitativeeconomicanalyseshavefrequentlyusednightlightradianceasproxyforeconomicactivity(Chen&Nordhaus,2011;Hendersonetal.,2012).Thesehavealsobeenusedtoestimatetheimpactsofweathervariabilityanddisastersandclimateshocks(Bertinelli&Strobl,2013;Elliottetal.,2015;Felbermayretal.,2022;Heger&Neumayer,2019;MirandaMonteroetal.,2017)and,morespecifically,floods(Kocornik-Minaetal.,2020).Mostanalysesusingnightlightdatausuallydemonstrateanegativeimpactofdisasterandclimateshocksonnightlightsbutwitheffectsresorbingwithintheyearfollowingtheevent(Bertinelli&Strobl,2013;Elliottetal.,2015;Gillespieetal.,2014).Schippers&Botzen(2023)findthatforaseveredisastersuchasHurricaneKatrina,theeffectcanbelongerlasting.

However,thereisadebateabouttheaccuracyofnightlightsasaproxyforeconomicactivity.Criticsarguethatnightlightintensitymaynotcaptureeconomicactivityaccuratelyinallcontexts,suchashighlyruralareas,wherechangesinlightingefficiencycouldaffecttheamountoflightobservedwithoutnecessarilyreflectingchangesineconomicactivity.Possiblyotherculturalandsocialfactorsorgovernmentpoliciesonlightingcouldalsoinfluencetheamountofnightlightobserved.

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Despitetheseconcerns,nightlightshaveseveraladvantagesasadatasource.Theyaregloballyavailable,providingcoverageeveninregionswhereeconomicdatamightbescarceorunreliable.Nightlightsalsohaveastandardspatialresolutionandtimeintervals,whichallowsforconsistentcomparisonsovertimeandacrossdifferentgeographicareas.Whenprocessedandinterpretedcorrectly,takingintoaccountthepotentiallimitationsandbiases,nightlightdatacanindeedserveasausefulproxyfortheintensityofeconomicactivity(Gibsonetal.,2021).

2.Relationshipbetweendisasterandconflict-affectedpopulation

Explicitstudiesoftherelationshipbetweendisasterandclimateriskandconflicthavegainedtractionoverthepastdecade(Siddiqi,2018),specificallyfocusingonco-locationandcausationdebatesassociatedwithclimate-relatedhazards,violentandarmedconflict,andinsecurity(Gemenneetal.,2014;Gleditsch,2012).Often,previousstudieshavefocusedontheimpactsofdisastersonconflicts–whethertheyexacerbateexistingconflicts,ignitenewones,orinsomecaseshaltongoingconflicts(Nel&Regharts,2008;Schleussneretal.,2016;Slettebak,2012;Ghimireetal.,2015;Nardullietal.,2015).Duetosuchuncertainimpactsofdisastersanddisasterrecoveryeffortonconflicts,otherstudiesexplorehowdisasterriskreductionandrecoverymeasuresshouldbedonedifferentlyinconflictcontexts(Brzoska,2018;Petersetal.,2019;WorldBank,2016).

Despitethegrowingbodyofliteraturerelatedtotheintricaciesofdisastersandconflicts,lessattentionhasbeengiventounderstandingandquantifyingtheinfluencesofconflictsondisasterimpacts–theadditionaleconomicimpactsofdisastersshouldtheytakeplaceinconflictareasanditseffectonconflict-affectedpopulation–aswellasthecausalpathwaysandmechanismsbehindsuchadditionalimpacts.Theabsenceofcomprehensiveeconomicdataandgroundtruthdatatovalidatedisasterimpacts,coupledwiththecomplexityofdefiningconflict-affectedpopulationsareamongthescientificchallengesprohibitinganalyzingtheinfluenceofconflictsondisasterimpacts.Thispaperseekstoaddressthisgapandsupportfurtherquantitativeanalysesontheadditionalimpactonhouseholds’welfareandnations’economicgrowthincountriesexperiencingthesecompoundedcrises.

EmpiricalstrategyData

Inthisstudy,weusepixel-levelgeospatialdata,includingnightlights,floodfootprints,populationdensity,andadministrativeboundaries,toeconometricallyanalyzethespecificeffectsoffloodeventsinMozambiqueaswellasNigeria'sconflictandnon-conflictaffectedregions.

Nightlightsdata

Furthermore,weutilizecompositeimagesofnighttimeradiancedatacapturedbytheVisibleInfraredImagingRadiometerSuite(VIIRS)sensoraboardtheNASA-NOAASuomisatellite.Thesemonthlycompositesareavailablesince2012ataresolutionof15arcsecondsby15arcseconds(approximately463metersattheequator).VIIRSDayNightBands(DNB)dataexcludegridcellsaffectedbylightning,straylight,lunarillumination,andcloudcover(Elvidgeetal.,2017).WefavorVIIRSdataovertraditionallyuseddatafromtheDefenseMeteorologicalSatelliteProgram(DMSP)

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duetoseverallimitationsidentifiedinthelatter,includingblurring,lackofcalibration,top-coding,andpoorsuitabilityasaGDPproxyinruralareas(Gibsonetal.,2021).

ToaddresschallengesassociatedwithusingVIIRSnightlightsdataasaproxyforeconomicactivity(Skoufiasetal.,2021),weapplyfilterstoremovepixelswithextremevalues(i.e.,werestrictthesampletovaluescomprisedbetweenthe1stand99thpercentiles)andaccountforthenumberofobservationsavailableperpixel.

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Wecalculatetheaveragenightlightradiancemonthlyspanningfrom1to6monthsbeforeandaftertheoccurrenceofthe2019floodsinMozambiqueandthe2022floodsinNigeria.Twovariablesarecomputed:"avg_rad,"representingthenightlightradianceatthefloodedpixellevel,and"avg_radBuff05"whichaveragesforeachfloodedpixelthenightlightradianceofthepixelitselfandadjacentpixelswithina0.5-kilometerbuffer.

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Thelattervariableispreferredtoensuremaximumobservationavailabilityandtocapturetheimpactonindirectlyaffectedgridcells.

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Wealsoextractedtheassociatedvariables"cf_cvg"and"avg_cvgBuff05",whichindicatethenumberofcloud-freeobservationsinthemonthusedtocalculateaveragenightlightradiance.

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Flooddata

FloodeventsaredeterminedbasedonthemethodologyoutlinedbyDeVriesetal.(2020).WeuseS1GroundRangeDetectedscenesfromtheSyntheticApertureRadarsensorsonboardtheSentinel-1satellite,partoftheEuropeanSpaceAgency'sCopernicusprogram(ESA,2023).ThesescenesprovidedataonZ-scoresderivedfromSARbackscattertimeseriesofsinglebandco-polarizationverticaltransmitverticalreceive(VV)anddualcross-polarizationverticaltransmithorizontalreceive(VH).SinceOctober2014,thisdatahasbeenavailableevery6daysata10-meterresolution.

Floodsaredefinedastheunexpectedpresenceofwaterobservedinanygivenpixel.Todistinguishfloodsfrompermanentorseasonallyoccurringsurfacewater,weutilizethehistoricalLandsat-derivedmonthlywaterprobabilitiesdatasetproducedbytheEuropeanCommission’sJointResearchCentre(Pekeletal.,2016).FloodconfidenceiscategorizedashighifbothVVandVHZ-scoresfallbelowtheidentifiedthresholds,andasmediumifonlyoneofthesepolarizationsisbelowthethresholds.Weclassifyfloodsinareasnotdesignatedaspermanentopenwater(withaprobabilityofwatergreaterthan95%)orwithahistoricalinundationprobabilitylessthanorequalto25%.Foreachcasestudy,wepreselectahistoricalreferenceperiodbasedonexistingknowledgeofpastfloodingeventsintherespectivearea.

Conflictdata

ConflictareasareidentifiedutilizinggeocodeddatasourcedfromtheArmedConflictLocation&EventDataProject(ACLED)database(ACLED,2023),coveringtheperiodfromJanuary2012toDecember2023forNigeriaandfromJanuary2016toDecember2023inMozambique.Forthepurposeofthisstudy,conflict,asdefinedbytheWBG(2024)is,“astateofacuteinsecurityresultingfromtheuseoflethalforcebyagroup—encompassingstateforces,organizednon-stateentities,orotherirregularbodies—drivenbyapoliticalpurposeormotivation.Suchforcemaymanifest

1Pixelswithnocloud-freeobservationsareexcluded.

2Apixelisaround100m2,wetestedwithoutbuffer,500mand1kmandchosea500mbuffertointroducemorevariationofnightlightintensitywithinfloodedpixels.

3Atimeseriesdepictingbothvariablesisprovided

inFigure16

intheappendicesforNigeria.

4Thecorrespondingtimeseriesforispresented

inFigure17for

Nigeria.

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bilaterally—involvingengagementsamongmultipleorganized,armedfactions,occasionallyleadingtocollateralcivilianharm—orunilaterally,whereinagrouptargetsciviliansdeliberately.”Furthermore,forthemostprecisedepictionofareasseverelyaffectedbyconflict,fatalitiesstemmingfromprotests,riots,andstrategicdevelopment(asperACLEDdata)havebeenexcluded,maintainingconsistencywiththeWBGClassificationofFragilityandConflictSituation’s(FCS)objectivesandthescopeofthisstudy.Ouranalysisfocusesonconflictrecordscategorizedas‘Battles’,‘Explosions/Remoteviolence’,and‘Violenceagainstcivilians’.Thesetypesofconflictsareselectedduetotheirviolentnature.

Settlementdata

Todeterminetheurbanizationlevel,weusetheGlobalHumanSettlementLayer(GHSL)whichcombinesgriddedpopulationdataestimatedbyCIESINGPWv4.11GHS-POPR2023andbuilt-upsurfaceinformationfromLandsatandSentinel-2dataGHS-BUILT-SR2023(Schiavinaetal.,2023).

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Thesettlementdataareavailableatthe1kmresolution.Weconsiderthedatafortheyear2020,whichistheclosestavailabletothetimeperiodofinterestforbothcountries.IncaseofNigeria,wedefined‘urban’areasascellsdefinedashigh-densitycluster,

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‘suburban’asmoderate-densitycluster,

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‘rural’asruralandlow-densityclusters

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and‘Uninhabited’asverylowdensityruralandwatercoveredareas

(Figure1)

.

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5InGoogleEarthEngine,thisImagecollectionisaccessiblethrough

/earth

-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_SMOD.

6The‘urban’categoryincludestheclasses30:“UrbanCentregridcell”,23:“DenseUrbanClustergridcell”.

7The‘suburban’categoryincludestheclasses22:“Semi-denseUrbanClustergridcell”and21:“Suburbanorperi-urbangridcell”.

8The‘rural’categoryincludestheclasses13:“Ruralclustergridcell”and12:“LowDensityRuralgridcell”.

9The‘uninhabited’categoryincludestheclasses11:“Verylowdensityruralgridcell”and10:“Watergridcell”.

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Figure1.SettlementcategoriesinNigeria

Populationdata

Tomaintainconsistencywithsettlementdata,populationdensityestimatesatthegridcelllevelaresourcedfromtheHigh-ResolutionSettlementLayer(HRSL)dataset(FacebookConnectivityLabandCenterforInternationalEarthScienceInformationNetwork-CIESIN-ColumbiaUniversity.,2016).

Thesedataareavailableataresolutionof1arc-second(approximately30meters)fortheyear2020.Additionally,alternativepopulationdataareextractedfromtheWorldPopdatabase(Linardetal.,2012;WorldP,2024),availableataresolutionof100metersfortheyear2020.

Table1

belowdescribesthevariablesusedintheanalysisoffloodandconflictimpactsoneconomicactivity,asmeasuredbynightlightchanges.The‘lat’(latitude)and‘lon’(longitude)variablesallowforlocationmappingandsituatingtheanalysisspatially.The'months_EE'variableaidsinunderstandingthetemporaleffectsoffloodsbyindicatingmonthsaftertheeventandnegativevaluesindicatingthemonthspreceding.

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The'flood'variableiscrucialforassessingtheimpactoffloodswithvaryingdegreesofdatareliability.'PopDens'providesinsightsintohowpopulationdensitymightinfluenceorbeinfluencedbyspecificfloodevents.Thevariables'cf_cvgBuff05'and'avg_radBuff05'describedabove,measureeconomicactivitythroughcloudobservationsandnightlightradiance,respectively.Additionally,theyareaveragedoveradjacentpixelstoprovidecontextforeachlocation.

‘Treated'and'Treated_after'distinguishareasaffectedbyconflictbeforeandafterthefloodineachcountry:March-April2019aremonthswherethefloodoccurredinMozambiquewhileJuly2022wasconsideredasthefloodingmonthinthisanalysisforNigeria.Thisisessentialfordeterminingthecausalinferenceofconflictimpact.'Settlement'and'Urban_Suburban'categorizeurbanizationlevelstounderstandhowdifferenttypesofareasareaffectedbyandrespondtobothfloodsandconflictevents.Lastly,'Fatalities'providesadirectmeasureofthehumancostofconflicts.

Thesevariablescollectivelyenableacomprehensiveanalysisoftheeffectsoffloodsandconflictswhentheyco-occurinthesamelocation.Theyareusedtoanalyzetheimpactsoffloodsandconflictonspecificaspectsofeconomicactivity,aspotentiallyinferredbynightlightchanges.

Table1

describesthesevariables,theirunitsofmeasurements,andhowtheywillbeusedintheanalysis.

Table1.Descriptionofvariables

Variables

Description

Unit

lat

latitude

Decimalcoordinates

lon

longitude

Decimalcoordinates

months_EE

Monthsincetheevent

Months(positiveifafterevent,negativeifbeforeevent)

flood

Floodvariable

=1or2ifmediumreliability,=3ifhighreliability

PopDens

HRSLpopulationdensity

Person/km2

cf_cvgBuff05

Totalnumberofcloud-free

observationsthatwentintoeach

pixel(averagedovertheadjacent

pixels)

avg_radBuff05

Averagenightlightradiancevalues(averagedovertheadjacentpixels)

nanoWatts/sr/cm2

Treated

Dummyvariablerepresenting

conflict-affectedarea

=1ifsubjecttoaconflictwithinthebufferareabeforethefloods,=0otherwise

Treated_after

Dummyvariablerepresenting

conflict-affectedarea

=1ifsubjecttoaconflictwithinthebufferareaafterthefloods,=0otherwise

settlement

DegreeofUrbanization

=11ifuninhabited,=12ifrural,=21ifsuburbanand=23ifurban

Urban_Suburban

Urbanizationdummyvariable

=1ifurbanorsuburban,=0otherwise

Fatalities

Totalnumberoffatalitiesassociatedwithconflictswithinthebufferarea

Overallempiricalstrategy

Todifferentiatetheimpactoffloodsoneconomicactivitypriortothefloodbetweenconflict-affected(treatmentgroup),andnon-conflictaffected(controlgroup)areas,wefirstrestrictthesampletoflood-impactedpixels.Wethenapplythedifference-in-differencesregressionmethod,aquasi-experimentaltechniquecommonlyusedforex-postimpactevaluations.Theunderlyingconcept

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involvescomparingtwogroupsovertime.Duetotheirdistinctcharacteristics,weexpectdifferencesinoutcomesbetweenthegroups.However,theevolutionofthesedifferingoutcomesovertime,whileholdinggroupcharacteristicsconstant,shouldfollowasimilartrend(i.e.,thecommontrendassumption)untilanexogenousshockoccurs.Thepresenceofthisparalleltrendiscrucialforestablishingcausalevidenceofimpact.Thedifference-in-differencesresearchdesignisparticularlysuitablefor‘event’studiesandthequantificationoftheimpactofunexpectedshocksoneconomicoutcomes.Thismethodhasbeenextensivelyemployedinthereviewedliterature(Card&Krueger,2000;Galianietal.,2005).Inourcasestudies,weareusingthecanonicaldifferenceindifference,whichmeanstwogroupsandtwotimeperiods(beforeandafter).

Thedifference-in-differenceregressionisspecifiedasfollows:

yi,t=Treatediβ1+postperiodtβ2+Treatmenti,tβ3+covariatesi,tβ4+εit(1)

whereyi,tistheaveragelogofnightlightsdataforeachfloodedpixeliattimet.Theuseofremotesensingdataallowsustoexploreimmediatetoshorttermimpactofthefloodoverourtwogroups.Treatediisadummyvariableequalto1forfloodedpixelilocatedwithintheconflictbufferzonebeforethefloodevent,andto0forfloodedpixelilocatedinanon-conflictaffectedareabeforethefloodevent.postperiodtisadummyvariablethatrepresentstheperiodaftertheexogeneousshock.

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Treatmentitisthetreatmentvariable,i.e.,thevariableofinterestinadifference-in-differencespecificationwhichaccountsfortheinteractionofthetreatedandPostperiodvariable;covariatesitisthesetofadditionalexplanatoryvariablesuspectedtoimpactthelevelofnightlightsradiance;andεitisanindependentandidenticallydistributederrorterm,clusteredattheadministrativelevel2,toavoidspatialautocorrelation.

Oneofthechallengesinouranalysisisthedefinitionofconflict-affectedareas.ConflicteventsinMozambiqueareclusteredgeographicallyassuchthatitisreadilyascertainablewhatregionsaremostimpactedbytheseevents,andthusdefinedasconflict-affectedareasforthepurposeofthisstudy.

Duetothecomplexityandwidegeographicalspanofviolentandnon-violentconflicteventsinNigeria,conflict-affectedareasinNigeriaarenotdefinedbasedonnumberofeventsalonebutarebasedontheWBGFCSconflictclassification.Thisclassificationusespubliclyavailabledatatoannuallyassesscountries,pinpointingthosemostaffectedbyfragilityandconflict.ThismethoddifferentiatesbetweenterritoriesexperiencingFragilityand/orConflictsituations.Alignedwiththisdefinition,thestudyemploysthefollowingconflictindicatorsidentifiedbytheFCSindextodelineateconflict-affectedareasinNigeriaattheLocalGovernmentArea(LGA)scale:

10Thisvariabletakesavalueof1ift=August2020toestimatetheeffect1monthafterthedisaster,t=November2020fortheeffect3monthsafterthedisaster,etc.and0otherwise.

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(1)ForongoingconflictaccordingtoACLED,(a)anabsolutenumberofconflictdeathsabove250,and(b)above2deathsper100,000population.

(2)ForrapidlydecliningsecuritysituationsaccordingtoACLED,(a)anabsolutenumberofconflictdeathsabove250,(b)between1and2deathsper100,000population,and(c)thenumberofcasualtieshasmorethandoubledinthepastyear.

Differenceindifferencemethodologyreliesondifferentassumptionswherethecommontrendisthemostimportantone.Tovalidatethisassumptionofcommontrendsbeforetheflood,indicatingthatthedependentvariableforbothgroupswouldhavecontinuedmovingsimilarlyintheabsenceoftheextremeevent,weconductatestbycomparingchangesinthedependentvariableforthetreatmentandcontrolgroupsovermultipleperiodsprecedingthefloods(i.e.estimatethedifference-in-differencebetweent-2andt-1,thet-3andt-2,etc.).Thisanalysishelpsascertainwhethertheeconomictrajectoriesofthetwogroupswereindeedparallelbeforetheoccurrenceofthefloodevents.Theregressionisspecifiedas:

yi,t=Treatediβ1+postperiodtβ2+Treatmenti,tβ3+covariatesi,tβ4+εit(2)

Variablesarethesameasspecification(1)buttheperiodofinterestisnotthesame.Weprovidestatisticaltestsaswellasthecommontrendvisuallyrepresentedforeachofourcasestudy.

Casestudyselection

Thestudy’sfocusondisasterimpactsinconflictvsnonconflictaffectedareaslimitsthepoolofcountrycasestudies.Thereareseveralaspectsthatdeterminethechoiceofthecasestudycountries.First,theselectedcountriesneedtohavegeographicallylocalizedconflicts,allowingforacontrolledcomparisonwhereconflictistheprimarydifferingfactorofdisasterimpacts.Second,thereisarequirementthattheselectedcountrieswereaffectedbyarapidonsetdisasterinrecentyears,toincreasethepossibilityofavailabledatainassessingthedisaster’simpacts.Ifmultiplecountriesareselected,therapid-onsetdisastereventshadtohappenwithinthesametimeframe.Thiscriterionensuresthatthecasestudiesprovideafocusedexaminationoftheimpactofdisastersoneconomicactivitiesinconflictsettingvsnonconflictsettings,withouttheconfoundingeffectsofdifferentcountryconditionsortimelinesofdisasterevents.Third,thedisasterfootprintsshouldcoverasubstantialgeographicalextentoftheselectedcountries’areaasopposedtolocalizeddisasters.Thiscriterionistoensurethattherearebothconflict-andnon-conflict-affectedareashitbythedisaster.Giventhecriteriaabove,weselectedthe2019TropicalCyclonesIdaiandKennethinMozambiqueandtheJuly2022floodsinNigeriaascasestudies.Furthermore,thetwocountrieshavecomparablecontextsintermsofconflictcharacteristicswhicharecrucialforisolatingthevariableofconflictincomparativeanalysis.

Mozambiquecasestudy:2019TropicalCyclonesIdaiandKenneth

ThefirstcasestudyfocusesonthefloodsinMozambiqueafterTCIdaiandKenneth.In2019,MozambiquewashitbytwoTropicalCyclones(TC),Idai(March4-15)andKenneth(April25-28),bothofwhichhavebeenqualifiedasamongthestrongestTCsonrecordintheSouthernHemisphere(Charruaetal.,2021).Thenortheasternregionofthecountryischaracterizedbyawidespreadlong-

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termhumanitariansituationduetotheongoingconflict,datingbackto2017.Asdepictedin

Figure

2,

TCIdaifirstmadelandfallonMarch4,2019,untilMarch9,beforechangingd