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Effect of typhoons on economic activities in Vietnam: Evidence using satellite imagery
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Titre du chapitreEffect of typhoons on economic activities in Vietnam: Evidence using satellite imagery
Abstract
AUTHORS
Etienne Espagne Agence Française de Développement Paris (France)
Yen Boi HA
École polytechnique fédérale de Zurich (ETH) Zurich (Switzerland)
Kenneth Houngbedji IRD DIAL, Laboratoire d'Economie de Dauphine (LEDa), Université Paris Dauphine PSL Paris ( France)
Thanh Ngo Duc
LOTUS Laboratory, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi (Vietnam)
This paper investigates the effect of typhoons on economic activities in Vietnam. During the period covered by our analysis, 1992 2013, we observed 63 typhoons affecting different locations of the country in different years with varying intensity. Using measures of the intensity of nightlight from satellite imagery as a proxy for the level of economic activity, we study how the nighttime light brightness varies across locations that were variably affected by the tropical cyclones. The results suggest that typhoons have on average dimmed nighttime luminosity of the places hit by 5 ± 5.8% or 8 ± 7.8% depending on the specifications we made.
Keywords
Natural disasters, economic growth, Vietnam
Acknowledgments
The authors gratefully acknowledge funding and support from the French Development Agency (AFD), the French National Institute for Sustainable Development (IRD) and the University of Science and Technology Hanoï (USTH).
The study benefited from comments and insights from Rémi Bazillier, Denis Cogneau, Philippe Delacote, Liam Wren Lewis and seminar participants at the ICDE conference in Clermont Ferrand and the GEMMES Webinar series. Any remaining errors or omissions are ours.
JEL Classification Q54, Q52, C21, O53
Original version English Accepted September, 2022
COORDINATION
Etienne Espagne (AFD)
Résumé
Cet article étudie l’effet des typhons sur les activités économiques au Vietnam. Au cours de la période couverte par notre analyse, 1992 2013, nous avons observé 63 typhons affectant différents endroits du pays à différents moments avec une intensité variable. En utilisant des mesures de luminosité nocturne provenant de l’observation satellitaire comme indicateur du niveau d’activité économique, nous étudions comment varie l’intensité lumineuse dans les endroits qui ont été affectés par les typhons. Les résultats suggèrent que les typhons ont en moyenne réduit l’intensité de la luminosité nocturne des localités touchées de 5 ± 5.8% ou 8 ± 7.8% en fonction des hypothèses de nos modèles.
Mots clés
Catastrophes naturelles, croissance économique, Vietnam
Remerciements
Les auteurs remercient l’Agence française de développement (AFD), l’Institut de recherche pour le développement (IRD) et l’Université des sciences et technologies de Hanoï (USTH) pour leur financement et leur soutien. L’étude a bénéficié des commentaires et des idées de Rémi Bazillier, Denis Cogneau, Philippe Delacote, Liam Wren Lewis et des participants de la conférence ICDE de Clermont Ferrand et de la série de webinaires GEMMES. Toute erreur ou omission est de notre fait.
Codes JEL Q54, Q52, C21, O53
Titre du chapitre
Introduction
Globaldamagescausedbynaturaldisastershavesteadilyincreasedintherecentdecades duetoacombinationofacceleratedeconomicdevelopmentandclimatechange(seeEMDAT,2010;Stocker,2014;Takagi,2019).Ascoastalregionsaroundtheworldattracteconomic activitiesandfosterpopulationgrowth,assessingtheirresiliencetonaturaldisastersiskeyto identifyingtheplaceswhicharemostvulnerable,andtodevisingandtestingtheefficiency ofthemostadequatepublicpolicies.Witharapidlydevelopingeconomyandprojected positivepopulationgrowth,Vietnamepitomizesthechallengesandopportunitiesposedby naturaldisastersinawarmingclimate.Thecountryhasanextensivecoastlinelocatedin theNorthwesternPacificBasin,wheremosttropicalcycloneactivitiesworldwidearelocated. Between1992and2013,wehavecountedthatintensestormswithwindspeedsabove64 knotsstruckthecountry63times.Whenmakinglandfallincoastalregions,wherealarge shareofthepopulationconcentrates,thestormsareusuallyaccompaniedwithheavyrainfall, andcauselandslidesandsevereflooding.Theyleaveintheirwakedeaths,lossoflivelihoods andinfrastructures,populationdisplacements,anddisruptionofeconomicactivities(see Elliottetal.,2015;EM-DAT,2010;GrögerandZylberberg,2016;Nguyenetal.,2019).
TolaythegroundforidentifyingpublicpoliciesthatmayhelpVietnam,andhopefullyother countriesinsimilarcontexts,adapttoincreasedcyclonesactivitiesinawarmingclimate,this papersetsouttodocumenthoweconomicactivitiesatthelocalandcountrylevelreactto localshocksinducedbytropicalcyclones.Existingstudiesdocumentingtheeffectoftyphoons inVietnamhavefoundlargenegativeimpactsoftropicalcyclonesonhouseholdwelfare measuredbyincomeandexpenditure(Arourietal.,2015;Thomasetal.,2010;Trung,2015), whereasfirmsexperienceashort-termreductioninretailsales,andanincreaseininvestment inlargecities,whichcanbeduetoashort-termboostindemandforreconstruction(Vuand Noy,2018). NoyandVu(2010)findthatdisastersthatresultinhigherdeathsleadtolower outputgrowth,whileoneswithhigherinfrastructuraldestructionleadtohighereconomic growthintheshortrun.
Thispapercontributestothisgrowingliteraturebyprovidingestimatesoftheaggregateeffect oftyphoononeconomicactivitiesatthelocallevel.Weinnovateintwocentraldirections (seeFelbermayrandGröschl,2014,fordetailedaccountsofthelimitationsofstudiesthat usedselfreportedoutcomesorex-postdatacollectedondamagestocharacterizeexposure tonaturaldisasters).
First,weusesatelliteimagerythatdocumentsthevicinityofimpactoftyphoonstoidentifythe locationsaffected,whichwedefineasanyarealocatedwithintheradiusof30-ktwinds(see Knappetal.,2018).Thisapproachallowsustoovercometheselectionbiasofex-postdata collectedinlocationswithdamagesduetonaturaldisasters.Alternativeapproacheshave reliedonwindfieldmodelstoestimatetheintensityoftyphoonexposureatanydistanceina giventime,usingvariousclimateparameters(Geigeretal.,2018;Strobl,2019).Theestimates areusedasinputstoanimpactfunctiontoproducepercentageofdamagetoinfrastructure. However,thereareseveralconcernswhenitcomestoVietnam.Windfieldmodelsdonot accountfortopography.Thisleadstolargemeasurementerrorswhenwecomparethree differentwindfieldmodelstoweatherstationobservations.Inaddition,theimpactfunction
thatiscommonlyusedreliesonparameterscalibratedusingdataobservedinhigh-income countrieswherelocalinfrastructurecansustainhigh-levelofwindspeeds(seeEmanuel,2011). Inalllikelihood,theresultingthresholdwillunderestimatethedamagecausedbytyphoons inVietnamwheretherearenumerouscasesof “relativelyweak” stormsthatcausedsevere damages.Forinstance,Koniisacategory-onetyphoonthatmadelandfallinVietnamin 2003.Twohourspriortoimpact,itreached1-minutemaximumsustainedwindspeedof55 knotsandweakenedafterwards.However,whileRanaetal.(2022)usedawindfieldmodel andimpactfunctionthatassignedminimalimpactofthetyphoonKonionhousing,EM-DAT (2010)reportedthatthetyphoonmadefivethousandspeoplehomeless.
Second,weuseaveragenighttimelightintensityperyearavailableat1kilometersquared resolutiontoproxytheintensityofeconomicactivitiesatthelocallevel.Nighttimelight intensityhavebeenwidelyexaminedandshownastrongcorrelationwitheconomicactivity bothatthecountrylevel(ChenandNordhaus,2011;Hendersonetal.,2012),andsub-national levels(DonaldsonandStoreygard,2016;HodlerandRaschky,2014).Withagrowingnumber ofstudiesdocumentingtheconsequencesofextremeeventsusingnighttimelightluminosity (seeBertinelliandStrobl,2013;Elliottetal.,2015;Felbermayretal.,2018;Strobl,2012),proxying economicactivitiesatthelocallevelinVietnamwithnighttimelightintensityprovidesa benchmarkagainstwhichtheresultsinVietnamcanbeinterpreted.
Combiningdetailedtrackingoftyphoonsandaveragelevelofnighttimelightintensity,we usedatwo-wayfixedeffectsregressionframework(seedeChaisemartinandD’Haultfœuille, 2020)toestimatehownighttimelightintensityhasvariedatthelocationhitbytyphoons between1992and2013.Comparingtherelativechangesinnighttimelightovertimeacross locationsthatexperiencedatyphoon,wefindthatnighttimelightintensityatlocationshitby typhoon(s)reducedonaverageby8%theyearwhenthetyphoon(s)madelandfall.Wefind weakevidencethatthetyphoon(s)havelastingeffectstwoyearslater.However,theseresults arelimitedtothelocationsthathadstablenighttimelightforatleastoneyearbetween1992 and2013andcannotbeextrapolatedatthecountrylevel.
Theremainingofthepaperproceedsasfollows.In Section2 wedescribethemaindata andtheresearchdesignusedtoestimatethelocaleffectoftyphooninVietnam. Section3 presentsthemainresultsofthestudyandSection4concludes.
2. Dataandmethods
2.1. Night-timeluminosity
Thispaperusesmeasuresofannualnighttimelightintensityproducedusingsatelliteimagery fromtheDefenseMeteorologicalSatelliteProgram(DMSP).Theimagesacquiredhavebeen processedforthegeneralpublicbytheNationalOceanicandAtmosphericAdministration (NOAA)withrecordsdatingfrom1992upto2013.Theavailableinformationcontainsannual averagenightlightintensitywithnormalizedvaluesrangingfrom0indicatingnolightto63 digitalnumber(DN).Theycapturenocturnallightingfromhumanactivityforgridpixelsof around30arcsecondsize,approximately1kilometersquaredattheequator(seeElvidge
etal.,2022,fordetails).Aggregatingthemeasuresofnighttimelightintensityatthelocal levelthroughoutthecountryandcomparingittoofficialestimatesofyearlyGDP,wefind thatnighttimelightintensityisstronglycorrelatedwiththenationalGDPofVietnambetween 1992and2013(seeFigureA-1).With402,852pixelscovered,174,960havedisplayedstable nighttimeoveratleastoneyearbetween1992and2013(seeFigure1foramapofpixelslitin 1992and2013).1
(a) Nighttimelightin1992
(b) Nighttimelightin2013
Figure1:NighttimelightintensitythroughoutVietnamasmeasuredin1992and2013. Thisfigure describesthevariationofnighttimelightintensitythroughoutVietnamin1992and2013.Thecolor intensifiesasnighttimelightluminositymovesfrom0(nocolor)to63(darkblue).Source:Authors’own elaborationusingdataonnighttimelightfromtheDMSP-OLSprogram.
2.2. TyphoonData
Todetecttheplaceshitbyatyphooninagivenyear,thispaperusesdataprovidedbythe InternationalBestTrackArchiveforClimateStewardship(IBTrACS)programwhichreports informationon3-hourlycentrallocationsoftheWesternNorthPacific’sstormswiththeir respectivemaximumsustainedwindspeedinconjunctionwithdetailssuchasdirection, forwardvelocity,centralpressure,etc.Outof754stormsrecordedintheWesternNorthPacific during1992to2013,werestricttheanalysistotyphoon-leveleventsthatreach1-minute sustainedwindspeedof64knotsaccordingtointernationalstandards.Usingthatdefinition, wefindthatVietnamwasexposedto63typhoonsthathavecrossedregionsofthecountry overtheperiodcoveredbytheanalysis.Then,wecombinedthedataproducedbyIBTraCS
1Asnewsatellitesarelaunchedapproximatelyeveryfiveyears,theyproduceoverlappingobservations.Whenever needed,wechoosethesatellitethathasthehighernumberofcloud-freeobservationsbeingusedtoproducethe annualaverage.
withauxiliaryinformationprovidedbytheJapanMeteorologicalAgency(JMA)–oneofthe mainagenciesthatmonitorstormsinthewesternNorthPacific(WNP)andtheSouthChina Sea–toidentifytheradiuscoveredbywindwithspeedgreaterorequalto30knots.This allowsustoidentifyapixelbeinghitwhenitislocatedwithinaradiusof30-knotswind(see Figure2foragraphicalrepresentationofspatialdistributionofplacesvisitedbytyphoons between1992and2013).
Figure2:NumberoftyphoonsmakinglandfallthroughoutVietnambetween1992and2013. This figurerepresentshowthenumberoftyphoonsthatmadelandfallatthelocallevelthroughoutVietnam between1992and2013.Thecolorintensifiesasthenumberoftyphoonsdetectedmovesfrom0(no color)to35(red).Source:Authors’ownelaborationusingdataontyphoonscollectedbyIBTrACS.
2.3.
Estimatingtheeffectoftyphoonsonnight-timelightluminosity
Toestimatetheeffectoftyphoonatthelocallevel,weconsiderthefollowingspecification:
(
(1)
IHS ( ) standsforinversehyperbolicsinusfunction.Theprimaryvariableofinterestis, Nit , theaveragenighttimelightintensityatthelocationofpixel i inyear t.Sincenighttimelight intensityremainsat0throughouttheperiodforalargenumberofpixels,weconsideras unitofanalysisthepixelsthatshowedstablelightoveratleastoneyearbetween1992and 2013.Hence,ourestimatesareonlyvalidforthelocationthathadstablelightduringthe period1992-2013.Nonetheless,alargefractionofpixel-timeshad0asvalueofnighttime lightintensityatdifferentpointsoftime,andweusetheinversehyperbolicsinusfunctionto accountfortheskeweddistributionofnighttimelightintensity.
Weseektoestimatetheimpactoftheincidenceofatleastonetyphoon, Sit ,forpixel i inyear t.Thespecificationincludeslocationfixedeffect, µi ,toaccountforunobservedpixel-specific time-invarianteffectsthatcanbecorrelatedwithtyphoondamageandeconomicactivity levelssuchasformsofresilienceorinvestmentinadaptationtotyphoonseffects.Years fixedeffects, πt ,arealsoincludedtoaccountforcorrelatedeffectscommonlysharedacross locationinagivenyear.Theunobservedheterogeneity, i ,isassumedtobecorrelatedacross pixelslocatedinthesameprovince.
Weassumethatthepathfollowedbytyphoonsareexogenousanduseatwo-wayfixed effectsregressionframeworktoestimatetheparameter β.Wenote Nit (1) thelevelofnight timelightintensitywhenatyphoonmakesalandfallatthelocationofpixel i inyear t and Nit (0) thelevelofnighttimelightintensityhadnotyphoonmadealandfallatthelocationof pixel i inyear t.Ourparameterofinterestistherelativeexpectedchangeinnighttimelights intheyearwhenapixelishitbyatleastonetyphoon,orthesemi-elasticity:
(2)
Since Sit isabinaryvariable,thesemi-elasticityofnight-timelightintensitytoincidence ofatleastontyphooninayearcanalsobeexpressedasfollows
= exp (β) 1 (see BellemareandWichman,2020).Recentadvancesineconometricsshowthatthefixedeffect estimatorof β willlikelyproducemisleadingestimatesoftheaverageeffectoftyphoons (seede ChaisemartinandD’Haultfœuille,2022).2 Toproducerobustestimateoftheeffects oftyphoons,weconsideralternativeapproachesandusedtheestimatorsuggestedby deChaisemartinandD’Haultfœuille(2020)andreportestimatesofthedynamiceffectsof incidenceoftyphoon(s)atthelocallevel.Weaccountforauto-correlationofobservations (location×year)locatedinthesameprovince.Likewise,wereporthowtheestimatevarywhen weaccountforspatialauto-correlationofunobservableheterogeneityacrossneighboring locationsandrestricttheestimatestothelocationsthatdisplayedstablenightlightoverat leastoneyearandwereatleast10kmapartfromoneanother.
2Inourcase,thetwo-wayfixedeffectsregressionsislikelytoproducebiasedestimatesbecauselocationsswitch onandofffrombeinghitbytyphoonsandtheeffectsoftyphoon(s)incidenceinagivenyearislikelytovary acrosslocationsandovertime.Incidentally,weobservedthattheestimatesproducedbythetwo-wayfixedeffects estimatoristheaverageofseveralaveragetreatmenteffectswithpositiveandnegativeweights.
Results
Wefindthatexpectednightlightintensityatthelocalleveldimmedonaverageby 5 ± 5.8% or 8 ± 7.8% whenalocationisaffectedbyatyphoon(seeFig.3).Moreover,theestimates suggestthattheeffectisdynamicandmaypersistuptotwoyearsatthelocationshitby typhoon(s).Thoughtheestimatesarenotcomparable,previousstudiesthatdocumentedthe economiccostsofextremeweatherhavefoundnegativeandshort-livedmacroeconomic impactoftropicalcyclones.Morespecifically,BertinelliandStrobl(2013)findthatanaverage hurricaneleadstoareductioningrowthofnightlightsby3.4%intheCaribbean.Regardingthe neighboringcountryofVietnam,DelValleetal.(2018)showsthatinGuangdongprovinceof China,theimpactisnegativeandsignificantonlywithinthemonthoftheevent.Onaverage, nightlightsisreducedby1%asaresultoftyphoondamage.Theauthorsalsoemphasizehow institutionalcontextcanplayanimportantrole.
Figure3:Expecteddynamicchangeofnightlightintensityassociatedtotyphoonatlocallevel. The figurerepresentsthepercentagevariationofnightlightintensityovertimeinthelocationsthatwerehit byatleastonetyphooninagivenyear(incomparisontothelocationsthatwerenothit).Thepoint estimatesarereportedalongwiththe95andthe90%confidenceintervals.Theestimatesreportedin redwithathinconfidenceintervalslinesareproducedusingasamplethatincludesallthepixelswith stablenightlightoveratleastoneyearbetween1992and2013.Theestimatesreportedinbluewith thickerconfidenceintervalslinesareproducedwithasampleoflocationswithstablenightlightthat areatleast10kmapartfromoneanother.Source:Authors’calculation.
Toexplorethedifferenceofpoliciesacrosscountriesthatmayhelpreducethecostof typhoons,futurestudiesshouldreporthowthepointestimatescomparewhenthesame methodologicalapproachisusedacrosscountries.Inthemeantime,ourapproachmay underestimatethelevelofnightlightintensityintheabsenceoftyphoonsatthelocations wheretyphoonsmadelandfallsinVietnam.Indeed,detailedanalyses(seetheplacebo
estimatesreportedinTableA-1)indicatethatlocallevelofnightlightintensityisonaverage relativelyhighertwoyearspriortotheincidenceoftyphooncomparedtothecounterfactual levelspredicted.Thissuggeststhattheparalleltrendassumptionthatunderliethecausal interpretationoftheresultsmightnotholdandleadstounderestimatetherelativelossof nightlightintensityexperiencedintheaftermathoftyphoonsinVietnam.
4. Conclusions
Thisstudyusesdatafromsatelliteobservationsoftyphoonsandrecentadvancesin econometricsoftwo-wayfixedeffectsmodelstoestimatetheimpactsoftyphoonatthelocal levelinthecontextofVietnam.Theresultssuggestthatexpectedyearlynightlightintensity atthelocationshitbytyphoonsdimmedby 8 ± 7.8% whenthetyphoonsmadelandfall. Theeffectsarelikelytolingerthefollowingyearormoredependingonthemethodological approaches.However,wealsofindthatthevariationofnightlightintensityovertimediffers acrossthelocationsthatexperiencedtyphoonslandfallandthecounterfactualsuggested bythemodellingapproach.Thedirectionofthediscrepancysuggeststhatourapproach underestimatesnightlightintensityhadthelocationshitnotexperiencedatyphoonina givenyear.Thereby,theestimatesreportedarelikelyunderestimatingtheeffectoftyphoons andinvitecaution.
Sinceallthelocationswithstablenightlightwereaffectedbytyphoonsatleastonceover theperiodofanalysis,andtheeffectoftyphoonspersistedoveratleastoneyear,alternative estimatorsthataremoreparsimoniouswhencreatingthecounterfactualcouldhelpprovide betterestimatesoftheeffectoftyphoons.Toaccountforthelastingeffectsoftyphoon,these estimatorswilllikelyrequirelongertimeseries.Futurestudiesshouldalsoexpandtheanalyses toothercountries.Thiswillhelpdocumenthowtheeffectoftyphoonsvaryacrosscountries andinvestigatethepotentialroleofthecross-countryvariationofpublicpoliciesrelatedto naturaldisastermanagement.
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Supplementarymaterials
TableA-1:Expectedchangeofnightlightintensityassociatedtotyphoons
Coefficient β Semi-elasticity ξ ns (1)(2)(3)(4)
Dynamicchange: -oneyearbefore(reference)0.0000.0000.0000.000 (.)(.)(.)(.) -yearwhentyphoon(s)hit-0.080-0.050-0.077-0.048 (0.04)(0.03)(0.04)(0.03) -oneyearafter-0.091-0.076-0.087-0.073 (0.13)(0.05)(0.12)(0.05) -twoyearsafter-0.0880.065-0.0840.067 (0.23)(0.08)(0.21)(0.09) -threeyearsafter0.051-0.0850.053-0.082 (0.46)(0.09)(0.48)(0.08) -twoyearsbefore(placebo)0.1310.1240.1400.132 (0.05)(0.03)(0.06)(0.03)
Numberofobservations30,9763,849,12030,9763,849,120 Numberofpixelswithstablelight1,408174,9601,408174,960 Numberofclusters63636363
Note:Thetableshowsestimatesof β and ξ ns from Eq.(1) and Eq.(2).Column “(1)” reportstheestimatesof β usingasampleofpixelswithstablelightthatareatleast11 kmapartfromoneanother.Column “(2)” reportstheestimatesof β whenthesample includesallthepixelswithstablelight.Column “(3)” and “(4)” reportestimatesof ξ ns : theexpectedchangeovertimeofrelativenightlightintensityacrosslocationsaffected bytyphoon(s)comparedtolocationswithnotyphooninagivenyear t.Incolumn “(3)” , theestimationisrestrictedtopixelswithstablelightthatare11kmapart.Column “(4)” reportstheestimatesusingallthepixelswithstablelight. Thestandarderrorsareclusteredattheprovincelevelandarereportedinparentheses.
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