Natural disasters and human capital accumulation

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  • Policy ReseaRch WoRking PaPeR 4862

    Natural Disasters and Human Capital Accumulation

    Jesus Crespo Cuaresma

    The World BankSustainable Development Network Vice PresidencyGlobal Facility for Disaster Reduction and Recovery UnitMarch 2009

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  • Produced by the Research Support Team

    Abstract

    The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

    Policy ReseaRch WoRking PaPeR 4862

    The author assesses empirically the relationship between natural disaster risk and investment in education. Although the results in the empirical literature tend to be inconclusive, using model averaging methods in the framework of cross-country and panel regressions, this paper finds an extremely robust negative partial correlation between secondary school enrollment and

    This papera product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development Network Vice Presidencyis part of a larger effort in the department to disseminate the emerging findings of the forthcoming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The task manager may be contacted at asanghi@worldbank.org, and the author of this paper at Jesus.Crespo-Cuaresma@uibk.ac.at.

    natural disaster risk. This result is exclusively driven by geological disasters. Natural disaster risk exposure is a robust determinant of differences in secondary school enrollment between countries, but not within countries, which implies that the effect can be interpreted as a long-run phenomenon.

  • Naturaldisastersandhumancapitalaccumulation1

    JesusCrespoCuaresma2

    Keywords:Naturaldisasters,humancapital,education,schoolenrollment,Bayesianmodelaveraging. JELClassifications:Q54,I20,E24,C11.

    1This study was prepared as a background paper to the joint World Bank - UN Assessment on the Economics of Disaster Risk Reduction. Funding from the Global Facility for Disaster Reduction and Recovery is gratefully acknowledged. The author would like to thank Apurva Sanghi, the coordinator of the assessment, for many intellectually challenging discussions that helped shape the form of the paper. The paper has also profited from very helpful comments by Jos Miguel Albala-Bertrand, Jed Friedman, Samir KC, Norman Loayza, Reinhard Mechler, Paul Raschky, Gallina Vincelette and participants at the Brown Bag Lunch Seminar at the World Bank. All remaining errors should be exclusively attributed to the author. 2Department of Economics, University of Innsbruck; World Population Program, International Institute of Applied Systems Analysis (IIASA) and Austrian Institute for Economic Research (WIFO). Postal address: Universittstrasse 15, 6020 Innsbruck, Austria. E-mail address: jesus.crespo-cuaresma@uibk.ac.at

  • 1Introduction

    Inthisstudyweaimatquantifyingtheeffectofnaturaldisasterriskoninvestmentsineducationby exploiting both crosscountry and time differences in school enrollment rates.Given thelarge number of theories explaining differences in the rate of human capital accumulationacross countries, we explicitly take into account model uncertainty by the use of modelaveragingtechniquesinordertoextracttheeffectofcatastrophicriskonschoolenrollment. Traditionally,theempiricalliteratureontheeconomiceffectsofnaturaldisastersconcentratedon the shortruneffectsof catastrophicevents (seee.g.DacyandKunreuther,1969).AlbalaBertrand (1993a, 1993b), Tol and Leek (1999), Rasmussen (2004) or Noy (2009) are somemodern examples of research aimed at measuring the short and mediumrun impacts ofdisasters.On theotherhand,SkidmoreandToya (2002)andCrespoCuaresmaetal. (2008a)concentrateon longruneffectsofdisaster riskon themacroeconomy.3To theknowledgeoftheauthor,withtheexceptionofsomeresultsinSkidmoreandToya(2002),therehasbeennofullyfledged empirical investigation on the effects of natural disasters on human capitalaccumulationhitherto.Thisispreciselytheholethatthispieceofresearchaimstofill. Fromatheoreticalpointofview,theeffectofnaturaldisasterriskoneducationalinvestmentsisnotunambiguous.SkidmoreandToya(2002)arguethattotheextentthatnaturalcatastrophesreducetheexpectedreturntophysicalcapital,rationalindividualswouldshifttheirinvestmenttowardshumancapital.4Althoughthisargumenthighlightsapossiblechannelofimpact,thisisjustoneofthepossibleeffectsofnaturaldisastersonhumancapital.Inprinciple,onecouldalsoargue that, in the framework ofmodels of agentswith finite lives, the potential effect thatnaturaldisasterriskhasonmortalitywould leadtoa lower levelofeducational investment indisasterprone countries. Checchi and GarcaPealosa (2004) present a simple theoreticalmodelwhich assesses the effect of production risk on education. In theirmodel, aggregateproduction riskdetermines theaverage levelofeducation,aswellas itsdistribution.ChecchiandGarcaPealosa(2004)showboththeoreticallyandempiricallythathigheroutputvolatilityleads to lower educational attainment. Interpreting natural disaster risk as a component ofaggregate production risk in the economy, countries which aremore affected by disastersshouldalsopresentlowerlevelsofhumancapitalaccumulation,ceterisparibus.

    The typeofargumentsput forwardabovestem from theoreticalmodelsandaimatunveilingthe role of natural disaster risk as a determinant of crosscountry differences. In this sense,thesetheoreticalexplanationsrefertothe longruneffectsofnaturaldisastersoneducationalinvestments. Shortrun effects on human capital accumulation associated to the actualoccurrenceofthedisastercouldbeextremely importantaswell.Asarepresentativeexample,

    3See Okuyama (2009) for a thorough review of the literature on the assessment and measurement of the economic effects of natural disasters. 4Skidmore (2001) studies investment decisions under catastrophic risk. Unfortunately, the empirical results in Skidmore (2001) are based on a dataset of very reduced size.

  • consider the2005earthquake inPakistan.TheAsianDevelopmentBankand theWorldBank(2005),whenassessing the impactof theearthquake,estimate that853 teachersand18,095studentshadlosttheirlivesasaconsequenceofthedisaster.Theyalsoreportthatthehighestreconstruction costs in termsofmaterial replacement requirements corresponded to schoolsfromprimarytohighersecondarylevel.Over7500schoolswereaffectedbytheearthquakeandthe estimated reconstruction costs for the education sector after the earthquakewere thesecondhighestbysector,afterprivatehousing. Ultimately,thequestionofthehumancapitalacummulationeffectsofnaturaldisasterriskisanempirical one. The fact that we cannot rely on a single theoretical framework for theexplanationofthe linkcallsforanexplicitassessmentofthe issueofmodeluncertaintywhenquantifyingtheeffectofnaturaldisastersoneducational investments. In thiscontributionweuse BayesianModel Averaging (BMA, see Raftery, 1995, and Clyde and George, 2004, forgeneral discussions on BMA and Fernndez et al., 2001b, SalaiMartin et al., 2004, amongothers, forapplications to the issueof the identificationof robustdeterminantsofeconomicgrowth)toobtainrobustestimatesoftheeffectofdisasterriskonsecondaryschoolenrollmentrates. The application of BMA ensures that our results are not specific to the choice of aparticularmodel,and take into theaccountnotonlyuncertaintyof theestimates foragivenmodel,butalsouncertaintyinthechoiceofaspecification. Our results indicate that geological disaster risk is a very robust variable for explainingdifferencesinsecondaryschoolenrollmentratesacrosscountries.Theeffectissizeableandwellestimated. It implies that the school enrollment effect corresponding to themean geologicdisasterriskisaround1.65percentagepointsinsecondaryschoolenrollmentascomparedtoacountrywithzerodisasterrisk.Themaximumdisasterriskdriveneffect inourdataset impliesapproximatelya20percentagepointsdecreaseinsecondaryschoolenrollment. This contribution is structured as follows. Section 2 presents a first descriptive look at theempirical relationship between disaster risk and educational attainment. In section 3 weperform different BMA exercises to assess the robust effect of natural disaster risk as adeterminantofdifferences inschoolenrollmentratesbothbetweenandwithincountries.Wealsoevaluatepotentialdifferences inthenatureoftheeffect intheshortversusthe longrun,andassesstheissueofsubsampleheterogeneityintheresponseofhumancapitalaccumulationtodisasterrisk.Section4concludes.

    2Afirstlookateducationanddisasters

    In this section we present a first exploratory analysis of the relationship between naturaldisastersandhumancapitalaccumulation.Figure1presentsscatterplotsofaveragesecondaryschoolenrollment in theperiod19802000againstdifferentmeasuresofnaturaldisaster riskevaluatedinthesameperiodfor170countries.AsinSkidmoreandToya(2002),weconcentrateforthisgraphonasimplemeasureofnaturaldisasterriskbasedonaveragedisasteroccurrenceby squared kilometer, so that disaster risk is measured as

  • ))]/([1log= icountryofAreaicountryindisastersofNumberdi .5Weavoidusingtheexisting

    dataonquantifiedlossesandreceivedaid,sincethesetypeofmeasuresareusuallyclaimedtobeplaguedwithendogeneityandothermeasurementproblems.Ontheonehand,totheextentthatdisasteraiddecisionsare influencedby reported lossesoraffectedpeople,governmentswouldhavean incentivetooverreportthesefigures.Ontheotherhand,the income levelofacountry(which ishighlycorrelatedwithhumancapitalaccumulation) isabasicdeterminantofthe effectiveness of natural disaster risk management. Since successful risk managementmechanisms will reduce the negative macroeconomic effects of disasters, using estimatedlossescouldspuriouslyleadtoanegativecorrelationbetweendisasterriskandeducationwhichisactuallycausedbytheeffectofeducationonthereductionofnaturaldisasterloss.Skidmoreand Toya (2007), for instance, show that higher levels of education reduce the losses fromnaturaldisasters.We therefore concentratehereonmeasuresbasedondisasteroccurrencefrequency,whichdoesnotcontaininformationonthemagnitudeofthedisaster,butfulfillsthenecessaryconditionofexogeneityfortheanalysis.

    a)

    b)

    5The source of disaster data is EM-DAT: The OFDA/CRED International Disaster Database (www.em-dat.net), Universit Catholique de Louvain, Brussels (Belgium), where catastrophic events are reported which fulfill at least one of the following criteria: a) ten or more people reported killed, b) 100 people reported affected, c) a call for international assistance was issued or d) a state of emergency was declared.

  • c)

    Figure1:Naturaldisasterriskandsecondaryschoolenrollment:totaldisasters(a),

    geologicdisasters(b)andclimaticdisasters(c)

    In Figure 1 we show the raw correlation between disaster risk and education, withoutcontrollingforotherfactorsthatmayaffectschoolenrollment.Wepresentscatterplotsforallcombineddisasters, aswell as for geologic and climaticdisasters.We consider the followingdisastersaspartofthegroupofclimaticcatastrophes:floods,cyclones,hurricanes,icestorms,snowstorms,tornadoes,typhoons,storms,wildfire,drought,andcoldwave,whilethegroupofgeologic disasters includes volcanic eruptions, natural explosions, avalanches, landslides,earthquakesandwave/surge.Sinceatthisstageweexploitexclusivelycrosscountrydifferencesinthesevariables,weaimat interpretingtheresultsas longruneffectsofdisastersonhumancapital accumulation. The results in Figure 1 indicate aweak positive relationship betweendisaster risk and the educational variable,which practically disappears for the subgroup ofgeologicdisasters.Although the relationship isnot statistically significant inanyof the threecasesreported inFigure1,this firstglimpseattherelationshipweare interested inseemstolendsomesupporttotheconclusionsinSkidmoreandToya(2002). Inordertoextractthepureeffectofdisasterriskoneducationalinvestment,however,weneedto control for a series of other variableswhich independently affect educational attainment

  • differences across countries.Alternatively, amore precise picture of the relationship can becapturedbyconcentratingonamorehomogeneousgroupofgeographicalunits.Althoughourstudy aims at a crosscountry analysis, data at the regional level can deliver an interestinginsightintothelinkbetweendisasterriskandhumancapitalaccumulation.Figure2presentsascatterplot where the number of floods (as a roughmeasure of climatic disaster risk, logtransformed) isshownagainstthemeanyearsofschoolingofthepopulationbetween15and29yearsofagefor75districtsinNepalintheyear2001.6Therelationshipbetweeneducationalattainmentanddisaster riskwithin thisgroupofmorehomogeneous regionsappears slightlynegative,butnotstatisticallysignificant.

    Figure2:Floodsversusmeanyearsofschooling(agegroup1529)inNepalesedistricts

    Assessingthe inmpactofnaturaldisastersoneducation impliesformulatingapotentially largemodel,whereahuman capitalaccumulationmeasure ishypothesized todependona setofdeterminantsandnaturaldisaster risk.Obviously, thechoiceofextracontrols to include inamodel linkingdisaster risk tohuman capitalaccumulationdependson the theoretical settingassumed. When assessing empirically the issue of the determinants of human capitalaccumulation,wehavemanycompetingtheoriesandeffectsthathavebeenputforwardintheliteraturetoexplaincrosscountrydifferencesineducationalattainment.Soasnottomakeourempirical results depend on a specific theoretical (and thus econometric) specification ,weinvestigate the issue of the robustness of disaster risk as a determinant of educationalattainmentintheframeworkofmodeluncertaintyusingBMAmethods.

    3Anempiricalanalysisoftheeffectofdisasterriskonhumancapitalaccumulation

    6The data stems from KC and Lutz (2009).

  • 3.1Modeluncertainty

    Theestimationoftheeffectofcatastrophicriskonhumancapitalaccumulation iscarriedoutusinglineareconometricmodelsofthistype,

    (1) ,=1=

    ijj

    k

    jii xde

    where isaproxyofeducationalattainment, is thedisaster riskvariable,

    areotherexplanatoryvariablesandie id )(= 1 KxxX

    isazeromeanerrortermwithvarianceequalto .InSkidmoreandToya (2002), for instance, the initial levelof theeducationalvariableaswellasincome per capita are the only variables in the X set. Since there are numerous variablesaffectingeducationalattainment,weaimatobtainingameasurewhichsummarizestheeffectofnaturaldisasterriskonhumancapitalaccumulationaftertaking intoaccountthedegreeofuncertaintywhichisembodiedinthespecificationgivenby(1)whenthenatureofthevariablesinXwhichactuallybelongtothemodelandthesizeofthemodelitselfareunknown.

    2

    BayesianModelAveraging(BMA)presentsaconsistentframeworkforassessingtheanalysisputforwardabove.7Considera setof K variables, whicharepotentially related (linearly) toeducational attainment in a crosscountry regression framework, so that the stylized

    specification considered is given by (1) for

    X

    KK . In this situation there are K2 possiblecombinationsoftheregressors,eachonedefiningamodel .TheBayesianapproachtothis

    problem implies consideringmodel specification itself as a quantity to be estimated. In thissense,itfollowsimmediatlythat,byBayes'theorem,

    kM

    ,

    )(P)|(P

    )(P)|(P=)|(P2

    1=mm

    K

    m

    kkk

    MM

    MMMY

    YY

    (2)

    whichindicatesthattheposteriorprobabilityofmodel isrelatedtoitsmarginallikelihood,

    ,andpriorprobability, ,ascomparedtotherestofmodelsinthemodelspace.

    FollowingFernndezetal.(2001a),wesetanimproperdiffuseprioron

    kM)|(P kMY )(P kM

    and ,coupledwithZellner's(1986) g prioronthe ( )k,, 1 parametervector,whichimpliesthat

    ,)(0,1)|,,,(P 121 jjkkj XgXNM (3) where isamultivariatenormaldistributionofdimension1kN 1k

    kMand isamatrixwhose

    columnsaregivenby the independent regressors inmodel .This setting implies that thejX

    7Raftery (1995) and Clyde and George (2004) present general discussions of BMA in the setting of linear regressions.

  • Bayesfactor(rationofmarginallikelihoods)fortwocompetingmodels, and ,isgivenby 0M 1M

    11

    1=

    )|(P)|(P=

    (

    20

    21

    )/201(

    0

    11,0

    Nkk

    RgRg

    gg

    MMB

    YY ,

    1)/2

    (4)

    where isthesamplesize, isthedimensionofmodelN jk j and isthestandardcoefficientofdetermination formodel

    2jR

    j .Someparticularvaluesof havebeensystematicallyused intheliterature.For (theUnitInformationPrior,UIP),theBayesianInformationCriterion(BIC)shouldbeusedasabasisofformingBayesfactors(seeforexampleKassandWasserman,1995, and Kass and Raftery, 1995), and thus BMAweights,while theRisk Inflation Criterion(FosterandGeorge,1994)sets .

    gNg 1/=

    21/= Kg 8 Given ,wecanbuildanestimateof thequantityof interest, say)|(P YkM ,asaweightedaverageofallestimatesof wheretheweightsaregivenbytheposteriorprobabilityofeachmodelfromwhichtheestimatewasobtained,

    ).|(P)|(=)|( YY kk

    kMMEE (5)

    Inasimilarfashion,wecancomputemodelaveragedestimatesoftheposteriorvarianceof bycomputing themodelaveragedvarianceof theestimate,which in thissetting summarizesinformationnotonlyabouttheprecisionforagivenmodel,butalsoacrossmodels.

    3.2Theempiricalsetting

    Inourcase,wewilladdaspotentialregressorsin(1)agroupofvariableswhichhavebeenputforward in the literature as important determinants of the differences in human capitalaccumulationacrosscountries.Flugetal.(1998),for instance,assessthe issueoftheeffectofmacroeconomic volatility on investment in education, and presentmodelswhich control forincome inequality,creditmarketdevelopment, initialpercapita incomeand initialeducationallevels.Someauthorshavealsopointedouttheimportanceofsocialandpoliticalinstitutionsasfactorsaffectinghuman capitalaccumulation (Stijns,2006),while infrastructurevariables canalsothoughtofashavinganeffectoneducationalattainment.Thegroupofpotentialregressorsused in the BMA exercise are presented in Table 1.We concentrate on secondary schoolenrollmentasthevariableofinterest.Thefocusonthismeasureisjustifiedontheonehandbythefactthatprimaryschoolingiscompulsoryinmostcountriesofoursample,andontheotherhandbecausethemostimportantresultshithertoontheissueunderstudyhere(SkidmoreandToya, 2002) were obtained using gross secondary school enrollment as the human capitalvariable.

    Table1:Variablesanddefinitions

    8Fernndez et al. (2001a) recommend using a benchmark prior on based on the relative size of the group of

    potential regressors compared to sample size, so that

    g),(Kmax1/= 2 Ng .

  • Variable Description Source

    e Grosssecondaryschoolenrollment,average19802000 WDI

    e0 Initialgrosssecondaryschoolenrollment,1980 WDI

    y0 InitiallevelofGDPpercapita,1980 PWT

    gini Giniindexforincome WDI

    life0 Lifeexpectancy,1980 WDI

    vol VolatilityofGDPpercapitagrowth WDI

    polity Polity2indicator PIV

    pavroad Percentageofpavedroads WDI

    cred Credittoprivatesector(%GDP) WDI

    war Dummyvariableforoccurrenceofwar

    td Disasterrisk,basedontotaldisastersperthousandsquaredkm EMDAT

    cd Disasterrisk,basedonclimaticdisastersperthousandsquaredkm EMDAT

    gd Disasterrisk,basedongeologicdisastersperthousandsquaredkm EMDAT

    WDI:WorldDevelopmentIndicators(WorldBank,2006);PWT:PennWorldTables

    (Summeretal.2006);PIV:PolityIVDatabase(MarshallandJaggers,1995);EMDAT:OFDA/CREDInternationalDisasterDatabase(EMDAT,2004).

    Aspotentialexplanatoryvariables,weconsiderthusproxiesof initial incomeand initialschoolenrollment ( and ) in order to account, on the one hand, forwealthinduced humancapitalaccumulationeffectsand for theobservedpersistenceofhumancapitalaccumulationvariables within countries, as well as for potential convergence in human capital acrosscountries. Inorder tocapturedifferences in incomedistributionacrossstateswe includealsothecountrywideGini index,andthepotentialeffectofmacroeconomic instability isanalyzedusing the standard deviation of annual GDP growth rates as a proxy.We also control fordifferencesinhealthbyincludinglifeexpectancyatbirthintheinitialperiod.Creditconstraintsareincludedintothemodelbyusingaproxyoffinancialdepth:domesticcreditprovidedtothe

    0y 0e

  • privatesector.Thequalityof(political)institutionsiscontrolledforwiththehelpofthePolityIVdatabase, which offers a score variable (polity2) that combines two other score variables(DemocandAutoc)andquantifiesthenatureofthepoliticalsystem inacountry.TheDemocand Autoc scores include information on competitiveness and openness of executiverecruitment, constraints on chief executive, regulation and competitiveness of participation.The polity2measure is defined asDemocminus Autoc and ranges from +10 to10with 10implyingastronglyautocraticregimeand+10astronglydemocraticregime.Wealsocontrolfortheexistenceofwarinagivencountryduringtheperiodunderstudy.Thecrosscountrydatasetcontainsdataonall80countriesforwhichallvariablesinTable1areavailable.9

    Table2:BMAresults

    PIP/PM(PSD) PIP/PM(PSD) PIP/PM(PSD)

    e0 0.999/0.854(0.102) 0.999/0.850(0.102) 0.999/0.842(0.095)y0 0.683/4.722(3.891) 0.694/4.834(3.889) 0.768/5.732(3.923)life0 0.504/0.307(0.361) 0.493/0.298(0.359) 0.514/0.315(0.365)vol 0.242/0.23(0.540) 0.239/0.23(0.536) 0.249/0.24(0.542)

    polity 0.119/0.012(0.078) 0.118/0.011(0.077) 0.120/0.014(0.078)pavroad 0.117/0.00(0.016) 0.117/0.00(0.016) 0.111/0.00(0.015)gini 0.273/0.06(0.126) 0.272/0.06(0.126) 0.256/0.05(0.117)cred 0.215/0.01(0.044) 0.206/0.01(0.043) 0.315/0.03(0.059)war 0.128/0.246(1.204) 0.126/0.232(1.182) 0.164/0.489(1.583)

    td 0.287/3.27(6.344)

    cd 0.173/1.53(4.715)

    gd 0.822/51.9(31.16)g prior BIC BIC BIC

    Priormodelsize 5 5 5Obs. 80 80 80

    Numberofmodels 1024 1024 1024PIPstandsfor``Posteriorinclusionprobability",PMis``PosteriorMean"andPSDis``PosteriorStandardDeviation".FiguresinitalicscorrespondtoentrieswithPIP 0.5. >

    The resultsof theBMAexercise arepresented in Table2.We report theposterior inclusionprobabilityofeachvariable(PIP),whichiscomputedasthesumoftheposteriorprobabilityofthemodels includingthatvariable,togetherwiththemeanoftheposteriordistributionoftheparameter attached to the variable (PM) and its standard deviation (PSD). The PIP can beinterpreted as the probability that a given variable belongs to the truemodel, andwewillclassify explanatory variables as robust if after observing the data the probability that thevariablebelongs to themodel increaseswith respect to theprior inclusionprobabilityof thevariable.FortheBMAresults inTable2,we imposedadiffuseprioroverthemodelspace,so

    9The countries included in the cross-sectional sample are (in abbreviation form): AUS, AUT, BDI, BEL, BFA, BGD, BOL, BRA, BWA, CAF, CAN, CHE, CHN, CIV, CMR, COL, CRI, DNK, DOM, DZA, ECU, EGY, ESP, FIN, FRA, GBR, GHA, GMB, GRC, GTM, HND, IDN, IND, IRL, IRN, ISR, ITA, JAM, JPN, KEN, KOR, LKA, LSO, MAR, MEX, MLI, MRT, MWI, MYS, NER, NGA, NIC, NLD, NOR, NPL, NZL, PAK, PAN, PER, PHL, PNG, PRT, PRY, RWA, SEN, SGP, SLE, SLV, SWE, SWZ, THA, TTO, TUN, TUR, UGA, URY, USA, VEN, ZMB and ZWE.

  • that KfM 21=)(P , implying an average priormodel size off /2K and a prior inclusion

    probabilityof0.5forallregressors.EachoneofthecolumnsinTable2presentstheresultsforagroupofregressors includingadifferentnaturaldisasterriskproxy ineachoneof thesetsofcovariates (all disasters, climatic disasters and geologic disasters, respectively). The initialeducationalattainmentvariableandtheinitialpercapitaincomearehighlyrobustinexplainingsecondaryeducationalenrollment.Theparameterattachedto initialeducationalattainment isestimated very precisely and its posterior distribution has a mean below unity, implying(conditional) convergence in secondary school enrollment levels across countries. The initialincome levelvariablealsoappearsasarobustdeterminantofschoolenrollment,albeitwithavery imprecise estimate of its effect. In two of the three settings presented in Table 2 lifeexpectancy appearsmarginally as a robust determinant of educational attainment,with aneffectwhich ispositivebut alsounpreciselyestimated.Theeffectsof theothernondisastervariablesappearneitherrobustintermsofPIPnorestimatedwithprecision. The results for the natural disaster risk variables shed a light on the nature of the channelsbetween human capital accumulation and catastrophic risk.While the risk levels implied byusing data on all disasters or climatic disasters do not appear robustly linked to schoolenrollment, if we consider exclusively geologic disasters the risk variable is robust andnegatively linked toeducationalattainment.Theeffect is furthermorewellestimated,witharatioofPMtoPSDofaround1.7.Theresultsinthethirdsetofcolumnsimplythatthedecreasein schoolenrollment corresponding to themean country in termsof geologicdisaster risk isaround1.3percentagepoints in secondary schoolenrollmentascompared toacountrywithzero disaster risk. The maximum disaster riskdriven effect implies approximately a 15.9percentagepointsdecreaseinsecondaryschoolenrollment.

    3.3Longrunorshortruneffects?

    Theresultspresented indicatethatnaturaldisasterrisk isaveryrobustvariableforexplainingdifferences in secondary school enrollment across countries. The natural question ariseswhether these are shortrun or longrun effects. Does the occurrence of a disaster reduceschoolingrates immediatly,sothattheeffectcaptured intheeconometricanalysisabove isadirect consequence of the actual disaster realization? Or is the effect a longerrun, morestructuraltypeofchange inthebehaviourofeconomicagentswhenconfrontedwithdisasterrisk? Inordertoanswerthisquestion,weredotheexerciseaboveusingtwopanelsbasedonfiveandtenyearsubperiods.Thissettingallowsustousecountryfixedeffectsandthusextractthepotentialwithincountryeffectofdisasters.Theclassofmodelsweareconsidering inthisexerciseisthus

    (6) ,=1=

    itjtj

    k

    jitit xde

    ,= ittiit (7) wheretheerrorterm cannowbedecomposedintoacountryspecificfixedeffect( i )whichsummarizes unobservable timeinvariant countryspecific characteristics, a fixed time effectcommontoallcountries( t )andtheusualerrortermwithconstantvariance( it ).

  • When implementing the BMA estimatorswe only considermodelswith the twoway fixedeffectstructurein(7),sothattheeffectsaretobeinterpretedasreferingtothewithincountrydimensionandthereforeexplaindifferencesinschoolenrollmentforagivencountryovertime.TheresultsoftheexerciseforthefiveyearandtenyearpanelsarepresentedinTable3,whichhas the same structure as Table 2. The analysis in the panel setting does not include thevariableswar,whichpresents lowvariability in time,andgini, forwhicha singlecomparableobservationwasavailableformostcountriesinthesample.

    Table3:BMAresults

    5yearpanel,withinvariation

    PIP/PM(PSD) PIP/PM(PSD) PIP/PM(PSD)0e 0.974/0.415(0.191) 0.974/0.415(0.191) 0.975/0.414(0.190)0y 0.100/0.435(3.116) 0.100/0.434(3.114) 0.100/0.428(3.102)0life 0.122/0.05(0.313) 0.122/0.05(0.313) 0.121/0.05(0.310)

    vol 0.826/2.17(1.743) 0.826/2.17(1.743) 0.826/2.16(1.740)polity 0.293/0.26(0.603) 0.294/0.26(0.604) 0.288/0.25(0.594)

    pavroad 0.072/0.001(0.061) 0.072/0.001(0.061) 0.072/0.001(0.060)cred 0.109/0.011(0.067) 0.109/0.011(0.067) 0.108/0.010(0.066)

    td 0.079/2.488(33.57)

    cd 0.081/2.844(34.12)

    gd 0.091/60.4(493.9)g prior BIC BIC BIC

    Priormodelsize 4 4 4Obs. 214 214 214

    Numberofmodels 256 256 256

  • 10yearpanel,withinvariation PIP/PM(PSD) PIP/PM(PSD) PIP/PM(PSD)

    0e 0.301/0.058(0.136) 0.299/0.057(0.136) 0.277/0.051(0.130)0y 0.697/9.55(9.934) 0.704/9.707(9.974) 0.697/9.499(9.886)0life 0.092/1.175(15.53) 0.092/1.147(15.48) 0.090/1.039(15.24)

    vol 0.236/0.221(0.636) 0.234/0.218(0.633) 0.238/0.223(0.638)polity 0.082/0.00(0.117) 0.082/0.00(0.117) 0.081/0.000(0.117)

    pavroad 0.085/0.00(0.014) 0.085/0.00(0.014) 0.084/0.00(0.014)cred 0.930/0.156(0.095) 0.931/0.157(0.095) 0.940/0.161(0.095)

    td 0.095/5.126(54.70)

    cd 0.126/12.21(69.85)

    gd 0.293/234.(560.4)g prior BIC BIC BIC

    Priormodelsize 4 4 4Obs. 153 153 153

    Numberofmodels 256 256 256PIPstandsfor``Posteriorinclusionprobability",PMis``PosteriorMean"andPSDis``PosteriorStandardDeviation".FiguresinitalicscorrespondtoentrieswithPIP 0.5. >

    Theresults inTable3revealthattherobustnegativeeffectofnaturaldisasterriskonhumancapital accumulation found in the crosscountry regression setting does not appear if weconcentrate exclusively onwithincountry variation in school enrollment rates. Although thesignoftheparameterattachedtogeologicaldisastersremainsnegative,itisestimatedwithlowprecisionandhasan inclusionprobabilitybelow0.5. It is interesting tosee that the inclusionprobability of the disaster variables, and particularly for the geological disaster variable,increaseswhen thehorizonunderconsiderationmoves towards longruncomparisons.Theseresultsgiveusan interesting insighttothedeterminantsofhumancapitalaccumulation intheshortandmedium run.Theposterior inclusionprobabilitiesof thevariables for the fiveyearpanelshow that,apart from thenaturalpersistenceofhumancapitalaccumulationvariables,income volatility plays the most important role as a determinant of secondary schoolenrollmentratechangesover time.For thecaseof the tenyearpanel, incomedevelopmentsand access to credit appear as themost robust variables. Inparticular theBMA estimateof

    is very precisely estimated and implies that credit constraints play a privileged role indeterminingmediumrunhumancapitalaccumulationdynamics.These resultsare in lineandcomplementthosefoundbyFlugetal.(1998).

    cred

    3.4Parameterheterogeneityandinteractioneffects

    Anobviousquestiontoaskintheframeworkoftheanalysiscarriedoutinthispaperiswhetherthe effect of natural disaster risk on human capital accumulation depends on other countrycharacteristics. The effects of natural disaster risk on severalmacroeconomic variables havebeenshowntobemodulatedbyinstitutionalandeconomicfactors.Noy(2009)showsthatthecostsofdisasters in termsofGDPdependon the strengthof institutions inplace in a givencountry,aswellasonthelevelofincomepercapita.Inasimilarfashion,CrespoCuaresmaetal.

  • (2008a)also foundthatthepotentialpositiveeffectsofdisastersontechnology importswereonlypresentforrelativelydevelopedcountries,anddidnotexistinpooreconomies.Theusualapproach to the assesmentofheterogeneity inelasticities is to include interaction terms, sothatinthiscasetheclassofmodelsconsidered(forthecrosscountrycase)isgivenby

    (8) ,=1=

    ijj

    k

    jiiii xzdde

    wherevariable z (whereinourcase Xz ,althoughitneednotbethecase)isresponsibleforexplainingdifferencesintheelasticityofschoolenrollmenttodisasterrisk. There issomedebate in the literatureonhow to treat interaction terms in the frameworkofvariable selectionandBMA.While someauthors include the corresponding interactionasanextra linear covariate in themodel,without setting anyparticular prior structure onmodelsincludingtheproductofvariables(seeMasanjalaandPapageorgiou,2008),someotherstudiesgiveaspecialtreatmenttomodelsincludinginteractionterms(seeChipman,1996,forageneraldiscussionandCrespoCuaresmaetal.,2008bandCrespoCuaresma,2009,forapplications).Themainproblemwith interpretingBMAresultswith interactionvariables isthat ifthe interactionterm is considered a standard variable and we average over all possible combinations ofvariableswewillbeusingsomeestimatesbasedonmodelswhere the interaction is includedbut the interacted variables (the ``parent variables") arenotpartof the specification. Thesemodelsdonotallowus to interpret the interactioneffectproperly, since theabsenceof theparentvariablesinthespecificationimpliesthattheinteractiontermmaybeactuallycapturingthedirecteffectofoneorbothoftheparentvariables.Inthissense,ifweaimatfulfillingwhatChipman's (1996) strong heredity principle, we should only consider models where theinteractionand theparent termsappear in the specification.For instance, inamoregeneralsetting, ifthereare K variables,where 1K arestandardvariablesandone isan interactionterm (of variables in the former group), standard BMAwould imply averaging over all possible combinations of these variables. On the other hand, the fulfillment of the strongheredity principlewould require excluding from themodel space thosemodel specificationswheretheinteractionwithoutparentvariablesisincluded,whichmeansthat + modelswouldbeactuallyevaluated.

    K2

    12 K 32 K

    Table4:BMAresults:Interactionterms

    Crosscountry

    StandardBMA Strongheredityfulfilled

    PIP/PM(PSD) PIP/PM(PSD)

    0ydt 0.299/0.51(1.676) 0.037/0.21(1.674)

    0ydc 0.181/0.23(1.302) 0.018/0.08(1.167)

    0ydg 0.525/4.15(9.020) 0.094/1.63(10.94)

    0edt 0.205/0.02(0.087) 0.037/0.001(0.048)

    0edc 0.145/0.01(0.070) 0.021/0.001(0.040)

  • 0edg 0.356/0.27(0.550) 0.096/0.010(0.314)

    politydt 0.211/0.20(0.543) 0.009/0.00(0.135)

    politydc 0.145/0.10(0.424) 0.003/0.00(0.077)

    politydg 0.475/3.05(3.883) 0.055/0.31(1.512)

    creddt 0.484/0.16(0.202) 0.014/0.00(0.039)

    creddc 0.304/0.09(0.173) 0.006/0.00(0.029)

    creddg 0.751/1.34(0.946) 0.056/0.04(0.340)5yearpanel

    StandardBMA Strongheredityfulfilled

    PIP/PM(PSD) PIP/PM(PSD)

    0ydt 0.140/0.031(0.147) 0.007/0.003(0.053)

    0ydc 0.118/0.017(0.109) 0.004/0.001(0.036)

    0ydg 0.089/0.010(0.240) 0.001/0.001(0.070)

    0edt 0.077/0.000(0.000) 0.006/0.00(0.000)

    0edc 0.078/0.000(0.001) 0.007/0.00(0.000)

    0edg 0.192/0.016(0.052) 0.141/0.017(0.052)

    politydt 0.084/0.000(0.009) 0.002/0.000(0.005)

    politydc 0.085/0.000(0.009) 0.002/0.000(0.005)

    politydg 0.085/0.00(0.067) 0.002/0.000(0.030)

    creddt 0.095/0.000(0.001) 0.000/0.000(0.000)

    creddc 0.087/0.000(0.001) 0.000/0.000(0.000)

    creddg 0.082/0.000(0.006) 0.001/0.000(0.001)10yearpanel

    StandardBMA Strongheredityfulfilled

    PIP/PM(PSD) PIP/PM(PSD)

    0ydt 0.095/0.00(0.028) 0.009/0.00(0.026)

    0ydc 0.120/0.001(0.027) 0.008/0.00(0.024)

    0ydg 0.280/0.05(0.302) 0.039/0.03(0.320)

    0edt 0.257/0.00(0.005) 0.155/0.00(0.005)

    0edc 0.210/0.00(0.005) 0.119/0.00(0.005)

    0edg 0.355/0.00(0.012) 0.009/0.00(0.003)

    politydt 0.089/0.00(0.005) 0.000/0.00(0.000)

    politydc 0.092/0.00(0.006) 0.000/0.00(0.000)

  • politydg 0.099/0.000(0.028) 0.001/0.00(0.001)

    creddt 0.218/0.00(0.003) 0.038/0.00(0.001)

    creddc 0.090/0.000(0.002) 0.011/0.00(0.000)

    creddg 0.962/0.02(0.014) 0.802/0.02(0.021)PIPstandsfor``Posteriorinclusionprobability",PMis``PosteriorMean"andPSDis``PosteriorStandardDeviation".FiguresinitalicscorrespondtoentrieswithPIP 0.5.Columnslabelled``Strongheredityfulfilled"performBMAusingonlymodelswheretheparentvariablesareincludedwhentheinteractionisactive.

    >

    We apply both approaches to our dataset in order to evaluate the existence of subsampleheterogeneity in theeffectsofnaturaldisaster riskonhuman capital.Weevaluatedifferentmodel spaces,eachone containingpotential interactionsof thedisaster variablewith a) thelevelofincomepercapita,b)theinitiallevelofschoolenrollment,c)thepoliticalregimeandd)thedegreeofcreditconstraint,asmeasuredby .Thus,BMAestimatesareobtained formodelspacesdefinedbythespecifications in(8)with

    credz givenbyeachoneofthesevariables.

    Theresultsarepresented inTable4,wherethePIP,PMandPSDforthe interactiontermsarepresented.10 Several interesting resultsemerge from the analysis.On theonehand, there isbasically no evidence for robust heterogenous effects of natural disasters on educationdependingonthevariablesspecified.Ifweconcentrateontheresultsobtainedbyimposingthestrongheredityprinciple, theonly interactionwithPIPover0.5corresponds to thecombinedeffect of geological disasters and financial depth (cred ) in the tenyear panel. The BMAestimatecorrespondingtothisvariable,althoughonlymarginallyprecise,indicatesthat,ceterisparibus, a countrywhich ismore (geological) disaster prone tends to react less in terms ofschoolenrollment to thealleviationofcreditconstraints.Asimilarnegativeeffect is found inthelongruncrosssectionforthestandardBMAsetting,althoughtheeffectisnotatallrobustifwe consideronlymodelswhere the interacion term appears exclusively togetherwith theparentvariables.Inthecrosssectionsettingtheinteractionofincomeand(geological)disasterriskismarginallyrobust,buttheeffectestimateisextremelyimpreciseand,justasinthecaseofcreditconstraints,itdisappearsifthestrongheredityprincipleisimposedinthedesignoftheBMAestimators.

    3.5Otherrobustnesschecks

    Wealsoperformedother robustness checks toensure that the resultsarenotdrivenby thepriorstructureimposedontheBMAprocedure.TheresultsshownabovearerobusttochangingtheparameterpriorfromtheUnitInformationPriortotheRiskInflationCriterion,aswellastotheuseofahyperprioronmodelsizeasproposedbyLeyandSteel(2009).Forthecrosscountrysetting,wealsoperformedBMAonanalternativesetofcovariateswhichenlargesthegroupofexplanatory variables in Table 1 by an extra variable that measures the percentage ofmountainousterrainintherespectivecountries.Withthisvariable,wecontrolforgeographicaland topographicaleffects thatmaybecorrelatedwith thedisaster riskvariablesbutexertanindependent effect on human capital investment (by, for instance, affecting the return of

    10Complete results for all other variables are available from the author upon request. The results presented in section 3.2 are not affected by the inclusion of the interaction terms as extra variables.

  • infrastructure intermsofschoolenrollment).TheBMAresultsconcerningthe importanceandsizeof theeffectofgeologicaldisaster riskwerebasicallyunchanged,while themountainousterrainvariableonlyachievedaposteriorinclusionprobabilityof0.12.Theposteriordistributionoftheparameterattachedtothevariableis,asexpected,negative,butestimatedwithverylowprecision(theratioofposteriormeantoposteriorstandarddeviationis0.14). Wealsoexplicitlyassessedthe issueof influentialobservationsbyestimatingBMAparametersandinclusionprobabilitiesbasedonsubsamples.Theresultsconcerningthelongruneffectsofgeological disaster risk on secondary school enrollment rates are robust to the followingchangesinthedataset:Trimmingtheobservationscorresponding to thedisasterswith thehighestratioofaffectedindividualsper squarekilometer (severalpercentilesof thedistributionofaffectedpeoplebyareaweretriedasthecuttingpoint,rangingfromthe80thtothe95th,soasnottoreducetheestimationsampledramatically) Trimmingtheobservationscorrespondingtothepoorestcountriesofthesample(thresholdsbasedonobservedincomelevelsranginguptothe30thpercentileweretried) Trimming theobservationscorresponding tozerodisasters,soas toensure that the resultsarenotdrivenexclusivelyby thedifferencesbetweenobservationswith zero versuspositivedisasterrisk Excludingthe4observationswhichare identifiedasoutliersafter inspectionoftheresidualsofthespecificationwhichincludesalltenpotentialvariables.

    4Conclusions Theeffectsofnaturaldisasterriskonhumancapitalaccumulationhavereceivedlittleattentionintheacademicliteraturehitherto.Weofferhereafirstfullyfledgedempiricalstudyassessingtheeffectsofnaturaldisastersonsecondaryschoolenrollmentrates.Inordertoavoidmodelspecific results,we use Bayesianmodel averaging techniques to assess the robustness andquantifytheeffectsofnaturaldisasterriskonhumancapitalaccumulation. Ourresultsgivestrongevidenceofnegative longruneffectsofgeologicalnaturaldisasterriskonsecondaryschoolenrollmentrates.Theeffectsareonlypresentintheshortandmediumruntotheextentthatnaturaldisasterscauseinstabilityandincreaseoutputvolatility.Thelongruneffectstendtobehomogenousacrosscountriesanddonotdependonincomeorthedegreeofhumancapitalaccumulation in therespectivecountry.Furthermore, thepoliticalregimedoesnot affect the disaster risk effect on human capital accumulation. The empirical resultspresentedherearerobusttonumerousvariationsofthesetting.

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