children vulnerability to shocks hurricane mitch in nicaragua as a natural experiment

8/10/2019 Children Vulnerability to Shocks Hurricane Mitch in Nicaragua as a Natural Experiment

1/47

ChildrensVulnerabilitytoWeatherShocks:

ANaturalDisasterasaNaturalExperiment*

JavierE.Baez

MaxwellSchoolofCitizenshipandPublicAffairs

SyracuseUniversity

IndhiraV.Santos

KennedySchoolofGovernment

HarvardUniversity

March2007

In October of 1998 Nicaraguawas hitby HurricaneMitch, one of themost powerful hurricanes of the

Tropical Atlanticbasin in the 20th century. We exploit the exogenous variation in the trajectory of the

hurricane in a quasiexperimental design and show that large and aggregated shocks, such as natural

disasters,haveadversemediumruneffectsonchildrenswellbeing,particularlyintermsofhealth,nutrition

andlaborforceparticipation. Conditionalonbeingsick,childreninaffectedareaswere30%lesslikelytobe

takenformedicalconsultation,eventhoughtherewasnosignificantdifferenceontheprevalenceofillness

between

affected

and

non

affected

children.

Furthermore,

the

probability

of

being

undernourished

among

children in regions hitby Mitch almost quadrupled (an 8.7 percentage point increase) and the overall

distributionof theirnutritionalstatussignificantlyworsenedasa resultof thestorm.Onanothermargin,

whilewe find no significant effect on school enrollment, labor force participation increasedby 58% (an

increase of 8.5 percentage points) among children in areas affected by the hurricane. Similarly, the

proportion of children simultaneously enrolled in school andworkingmore thandoubleddue toMitch,

going from 7.5% to 15.6%. Moreover, further evidence suggests that children were disproportionately

affectedby the shock as thenutritional status ofmothers and adult consumption in affected areaswere

largelyunchangedby the storm.Thisbehavioral responsehas relevant implications for thenatureof the

resourceallocationprocess inthehousehold.Ourresultsdonotseemtobedrivenbyadecliningtrend in

investmentsinchildrenpriortotheshock,andarerobusttodifferentsubsamplesandspecifications,aswell

astoparametricandnonparametric estimationmethods.

___________________*Theauthorsaregraduate students inEconomicsandPublicPolicy, respectively.Special thanksgo toAlbertoAbadie,Farzana

Afridi, Dan Black, Gary Engelhardt, Erica Field, Robert Jensen, Jeffrey Kubik, Jeffrey Liebman and Richard Zeckhauser for

thoughtful comments to previous versions of this document. We also thank participants at the Development Lunch of the

Economics Department (Harvard University), the Eleventh Annual Meeting of the Latin American and Caribbean Economic

Association (LACEA,Mexico), theThirdAnnualGrad StudentConferenceonLatinAmericanStudies (CornellUniversity), the

Seminar on Latin American Studies, Moynihan Institute of the Maxwell School of Public Affairs, and the Workshop of the

EconomicsDepartment(SyracuseUniversity)andtheCEDESeminar(LosAndesUniversity,Bogota).Anyremainingerrorsareour

own.Theauthorscanbecontactedat:[email protected][email protected].


2/47


3/47

3

outflows and these expenditures maybe difficult to make in the face of an income and asset

shortfall.Thismaybeparticularlytrueforruralpopulationsthatlackformalmarketsforcreditand

insurance or, at best, have informal riskbearing arrangements which do not fully protect

consumption and fall apart in the presence of nonidiosyncratic events. If this is the case, a

sufficiently large shock may take children out of school, push them into the labormarket and

worsentheirhealthstatus.Intheliterature,Jacoby&Skoufias(1997)andJensen(2000)haveindeed

foundthatincomevariabilityhadanadverseeffectonchildrensschoolenrollmentandnutritionin

settingswithlimitedaccesstoformalinsurance.

Inthispaper,we investigatethishypothesisforararelystudiednaturalhazard:ahurricane.

Mitch,atropicalstormthatattainedhurricanestatusforthreedaysandreachedsustainedwindsof

180miles

per

hour,

hit

Nicaragua

in

the

last

week

of

October

of

1998.

Hurricane

Mitch

is

one

of

the

mostdestructivestormsevertostrikeCentralAmerica,leavingbehindmorethan50inchesofrain

andmorethan20%ofthepopulation inneedofnewhousing inNicaraguaalone.Approximately

45,000 households in 72municipalitieswere directly affected, nearly 300 schools and dozens of

healthcenterswerelefttemporarilyunusable,andonethirdofcropsinthecountrywasdestroyed

(WorldBank,2001).BecausenotallmunicipalitysegmentsinNicaraguaweredirectlyhitbyMitch,

we exploit this exogenous variation arising from the trajectory of the hurricane as a quasi

experimentaldesign

to

estimate

the

medium

term

effects

of

this

shock

on

the

school

enrollment,

labor supply and health status of children in affected areas. To do so,we implement a double

differenceanalysis(DID)andconstructourexperimentalgroupsusingpaneldatafromtheLiving

StandardsMeasurementStudy(LSMS),ahouseholdlevelsurveycollectedin1998,1999and2001by

theWorldBankand theNational InstituteofStatisticsofNicaragua (INEC).Thus,our treatment

groupiscomprisedofchildreninportionsofmunicipalities(segments)affectedbyMitch,asdefined

bythe1999LSMSanddiscussedbelowinmoredetail.

Our results indicate that largeandaggregateshocks,suchasnaturaldisasters,haveadverse

mediumrun effects on childrenswellbeing, particularly in terms of health, nutrition and labor

forceparticipation.Conditionalonbeingsick,children inaffectedareaswere30% less likelytobe

takenformedicalconsultation,eventhoughtherewasnosignificantdifferenceontheprevalenceof

illness between affected and nonaffected children. Furthermore, the probability of being


4/47

4

undernourishedamongchildreninregionshitbyMitchalmostquadrupled(an8.7percentagepoint

increase)andtheoveralldistributionoftheirnutritionalstatusespeciallyofthoseinthelowertail

significantlyworsenedasa resultof the storm.On anothermargin,whilewe findno significant

effectonschoolenrollment,laborforceparticipationincreasedby58%(anincreaseof8.5percentage

points) among children in areas affectedby the hurricane. Similarly, the proportion of children

simultaneouslyenrolledinschoolandworkingmorethandoubledasaresultofMitch,goingfrom

7.5% to15.6%.Moreover,furtherevidencesuggeststhatchildrenweredisproportionatelyaffected

by the shock as the nutritional status ofmothers and adult consumption in affected areaswere

largelyunchangedbythestorm.Basedonplaceboexperimentestimates,ourresultsdonotseemto

bedrivenbyadecliningtrendininvestmentsinchildrenpriortotheshock.Themainfindingsare

alsorobusttodifferentsubsamplesandspecifications,aswellastoparametricandnonparametric

estimationmethods.

Therestof thepaper isorganizedasfollows.First, in thebackgroundsection,wereview the

existing evidence on the consequences of shocks for childrens wellbeing, discuss a simple

conceptual model to help frame the effects of a hurricane on children in a setting of imperfect

markets and describe the trajectory of Hurricane Mitch and its effects in Nicaragua. Section 3

describes the data, while Section 4 presents our empirical strategy, including the identification

design,main

findings

and

some

robustness

checks

to

our

strategy.

Section

5offers

abrief

discussion

onthetheoreticalimplicationsofourfindings.Finally,Section6concludes.

2.Background

2.1PreviousFindings

Inorderforhouseholdstoachievetheiroptimalconsumptionpath,itisnecessaryforthemto

smoothconsumptionacrosstimeandstatesofnature.However,theliteratureonthistopichaslong

revealedevidenceof imperfect smoothing indevelopingcountrieswhere relevantmarket failures

aremore likely.For instance,Paxson (1992) tested the ideaofperfect smoothing inThaivillages

using crosssectionaldataon incomeand expendituresof farmhouseholds togetherwith rainfall

information.Althoughherestimatesofthemarginalpropensitiestosaveoutoftransitoryshocksare


5/47

5

quitehigh (between0.73and0.83), theyarenotsupportiveoffull insurance.Deaton(1992),using

datafromCtedIvoire,foundthatfluctuationsinhouseholdincomewereonlyweaklyassociated

withcommunityshockswhere therisk isassumed tobeshared.Along thesame lines,Townsend

(1994)examinedtheperformanceofalltheinstitutionsformalandinformalthatcanjointlyinsure

ruralhouseholdsatthevillage levelusing longitudinaldata fromIndia.Hisfindings indicatethat

transitoryidiosyncraticeventswerehighlysmoothedawaybutstillfarfromthefirstbestwithfully

functioningmarketsforcreditandinsurance.

Otherresults inthe literaturego inthesamedirection.1That is,consumptionpartiallytracks

income in lowincome countries, and particularly in rural populations.Natural disasters, rather

aggregate shocks,make it evenharder to isolate consumption from incomepaths. Santos (2006)

foundthat

inter

household

transfers

increased

in

Nicaragua

in

the

aftermath

of

Mitch

but,

for

the

medianhousehold,new transfersonlyoffset15%of the lossesarising from theevent in theeight

months after the storm. The analysis suggests that formal creditmarketswere largely absent in

Nicaraguaandthathouseholdswerenotabletofullysharetherisksarisingfromthestormthrough

otherchannels.

Ifhouseholdscannotperfectlysmoothconsumption,thentheyareforcedtofinanceafraction

oftheircurrentconsumptionandinvestmentoutoftodaysincome.Asaresult,householdscouldbe

forced to reduce the resourcesdirected tobasicexpenditures suchas thoseallocated towards the

educationandhealthofchildren,especiallyconsideringthatsignificantliquidityisneededtocover

thistypeofexpenses.2Eventhoughthesemechanismsofadjustmentcouldbeevident,theexisting

literatureontheeffectsoflargeadverseshocksonchildrenissurprisinglylimited.Still,theavailable

evidence suggests that negative shocks tend to be associated with a decrease in childrens

investmentintermsofeducationandhealth.

Economiccrises,

in

particular,

have

been

found

to

be

associated

with

asignificant

deterioration

ofsocialindicatorsatthemacrolevel.DuringArgentinaseconomicdownturnin1995,forinstance,

1SeeMurdoch(2003)forIndiaandsomeestimatesforLatinAmericalikethoseofGarciaVerd(2001)forMexicoandBarreraand

Perez(2005)forColombiaandNicaragua.2InNicaragua,costsassociatedwithchildrenseducationandhealthcanrepresentanimportantshareofahouseholdsbudget.For

instance,in1998,themedianhouseholdexpendituresforsendingchildrentoschool(tuition,transport,enrollmentfees,uniforms,

booksandotherschoolsupplies)was$2,250crdobas(US$215)peryearhalfoftheirmedianhouseholdincomepercapita(data

from1998LSMS).


6/47


7/47

7

presenceofanaturaldisasterwhichcombinesboththestrongwindsandtorrentialrains.Rarelyhas

therebeen an opportunity to study such a large and exogenous event such asHurricaneMitch.

Second,we explore the effects of the hurricane three years after the episode. The persistence of

adverseeffectsonchildrenhintsatimportantlongrunconsequencesofshocks,especiallyifwetake

intoaccountthateducationandhealthare importantdeterminantsoffutureearningsandwelfare.

Theoretically,thispaperhassignificantimplicationsfortheliteratureonhouseholddecisionmaking

sinceourfindingssuggestthatchildrenarenotonlyworseoffafteranaturaldisaster,butthatthey

are disproportionately affected by these events. More generally, our work contributes to the

understandingofnaturaldisasters.Thisconstitutesvaluableinformationforpolicymakers,aswell

as national and international nongovernmental organizations, interested in designing

comprehensivepoliciestodealwithlargenegativeshocks.

2.2ConceptualFramework

As evidenced from the damages reported by households in the aftermath of the storm,

somethingwewillreturntoinmoredetaillater,naturaldisasterscanadverselyaffectnotonlythe

incomeofthehouseholdbutalsoitsassetsandthephysicalwellbeingofitsmembers.Inaddition,

they can have a negative effect on a countrys infrastructure and overall macroeconomic

environment.More

formally,

we

follow

Skoufias

(2001)

and

develop

asimple

single

period

model

of

household decisionmaking with full information and a unitary household that stresses the

mechanisms throughwhichahurricane canpotentiallyaffect childrensoutcomes. 3We leave the

maindetailsofthismodelforAppendix1butbrieflydiscussitsimplicationsinthissection.

Themediumterm effects of ahurricane on thehouseholds investment in children operate

through threedifferent channels.Firstwe considerwhatwe calldirect effects,which include the

disruptionofschoolandhealthservicesanddamagetocomplementaryinfrastructure,aswellasthe

lossofassetsandinventoriesandthedeathorillnessoffamilymembers.Second,thereareindirect

effects,which reflect the change in the incomeof thehousehold.One theonehand, income can

3Regardingtheunitaryhouseholdassumption,inthisstudyweusepaneldataforagroupofhouseholdsaffectedbyMitchandfor

others not affectedby theHurricane; therefore,byusing aunitarymodel, the assumption is that thebalance of power in the

householddidnotchangeinadifferentiatedmannerforthetwospecifiedgroups.Thisseemstobearealisticassumptionand,thus,

weconsidertheunitarymodelappropriateforourpurposes.


8/47

8

decreaseduetothelossofcrops,jobsorbusiness;ontheother,thenumberofjobsand,thus,income

can rise as a result of reconstruction initiatives and possibly higher levels of public investment.

Finally,wecallsecondaryeffectsthoserelatedtotheslowdownintheeconomyingeneralasaresult

ofinflation,increaseddebt,fallinproduction,etc.

Each of these effects has different implications for the optimal allocation decision of the

household. In ourmodel, disturbances to educational and health services, aswell as the loss of

complementary infrastructure and households assets (i.e. direct effects), ceteris paribus, lead to

familiesinvestinglessinchildrensschoolingandhealth.Theinterruptionordestructionofrelevant

services, including damage to physical facilities and lack of required personnel and supplies,

increasesthemarginalcostofthegoodsandservicesassociatedwithaccumulatinghumancapital,

whileadecrease

in

household

assets

constitutes

adecrease

in

the

households

permanent

income.

Similarly,the incapacityofaproductiveparent,the lossofcrops,jobsorbusiness lead toafall in

incomethatinanenvironmentofincompletecapitalmarketscanfurthertightenthehouseholds

budget constraint.Asnoted above, this drop in income canbe partially or entirely offsetby an

increaseinemploymentduetorebuildingactivities.

Finally,theslowdownoftheeconomyisexpectedtodecreasethedemandforlaborandwages.

Thisdecreaseinwagesmaybereinforcedbyanincreaseinlaborsupplyashouseholdstrytocope

withtheeffectsoftheshock.Thereisanincomeandasubstitutioneffectassociatedwiththischange

inwages.Ontheonehand,thelatterwouldpredictanincreaseinchildrenshumancapitalsincethe

opportunitycostofhumancapital,thewage, isnow lower.However,asthe incomeeffectgoes in

theoppositedirection,formally,thenetchangewouldbeunknown.Onthewhole,asdescribedby

themodel,thetheoreticaleffectsofashocklikeahurricaneontheinvestmentinchildrenshuman

capitalareambiguous.Theendresultis,hence,anempiricalmatter.

2.3HurricaneMitchinNicaragua

Nicaraguais,afterHaiti,thepoorestcountryintheWesternHemisphere.Accordingtothelast

populationcensusin1995,thecountryisdividedinthreemaingeopoliticalregions:Pacific,Central

andAtlantic.Thehighestconcentrationofpovertyisfoundinthetwosemiautonomousregionsof


9/47

9

theAtlantic,whilethelowestisfoundinthecapitalcity(Managua)andtherestofthePacificregion

(WorldBank,2001b).Nicaraguaisfurtherdividedintodepartments(15)andtwoautonomoussub

regionswhichare,inturn,dividedintomunicipalities(147).WhenHurricaneMitchhitthecountry

inOctober1998,NicaraguahadaGDPpercapitaofUS$741(constant2000USdollars),44.71%ofthe

population was living with under US$1 a day, 79.03% with under US$2 a day, and 24% of

individualsaged15andabovewereilliterate.Moreover,in1998,netenrollmentinprimaryschool

wasat74%and12.8%ofchildrenaged1014wereworking,althoughthisproportionhasdecreased

to around 8% in recent years. In terms of health, childmalnutrition is remarkably high as one

quarterofchildrenunderfivewasundernourishedin1998(WorldDevelopmentIndicators,2007).

Among the Latin American and Caribbean countries with the exception of the island of

Montserrat,Nicaragua

was

the

hardest

hit

by

natural

disasters

between

1970

and

1999,

with

cumulativelossesestimatedat338.4%of1997GDP(Charvriat,2000).Duringthelasttwodecades,

thecountryhasbeenaffectedbyearthquakes(1992),droughts(1994,1997,2001),wildfires(1991),

floods(1990,1998)andwindstorms(1993,1998).Ofallofthesenaturaldisasters,HurricaneMitch

wasthestrongestintermsofthenumberofpeopleaffectedanddamagecosts.Mitchstartedasan

ordinarytropicaldepression,butonOctober24itwasclassifiedasahurricane,reachingwindsofup

to90milesperhourand180milesperhourtwodayslater.Thestormchangedcourseseveraltimes;

Cubaand

Jamaica,

at

first,

and

Mexico

and

Belize,

next,

were

alerted.

On

October

27,

Mitch

unexpectedly moved inland in Honduras and steadily weakened, but still its torrential rains

broughtinfivedaysmoreprecipitationsthantheaveragerainfallofafullyear.Following,Northern

andPacificNicaraguareceivedtheheaviestrainsoverfifty inchesofrain infivedays,beforethe

stormloststrengthasitpassedthroughGuatemalaandElSalvador(McKinley&Stevens,1998).

ThehurricanestruckNicaraguafromOctober27toOctober31,1998andmostlyhittheCentral

and Pacific regions of the country.4 Around 19 percent of the population (870,000 people) was

affected (INEC, 1999). The strong winds and the historic amounts of rain that accompanied

Hurricane Mitch, compoundedby the usually weak housing structure in Nicaragua, generated

extensive structural damage. Approximately, 45% of households surveyed in the 1999 LSMS

reported their dwellings havingbeing harmed during the hurricane and, in fact, 29.4% had to

4SeeAppendix2formapsthatdescribethetrajectoryofthehurricaneindetail.


10/47


11/47

11

Bank,2001).Nevertheless,disruptions toschoolingservicesat least inoursampleseemtohave

been fewandbrief. In theareas surveyed in1999,no family cited the schoolbeingdestroyedby

Mitchasareasonnottoenrollachildinclasses,andaveragetraveltimestoschoolwerealsolargely

unchangedafter the storm.Other supply indicators, including thepresenceof teachersand child

space, seem to have actually improvedby 1999 and 2001.7 In addition, the impact ofMitch on

schoolingmayhavebeenlimitedbythefactthatwhenthestormhitNicaraguainNovemberthere

wasonlyonemoremonthleftoftheschoolyear.Theacademicyearwasthencutshortandthestart

ofthenewonewaspushedbackapproximatelyamonth,givingmoretimetorebuildschoolsand

reorganizestudents(Ureta,2005).Similarly,disruptionsinhealthservicesseemtohavebeenlarger

immediatelyafter thestorm,butsignificantly lesssevereby2001. In the1999LSMS,only0.3%of

individualsthatwereillintheweekpriortothesurveyanddidnotwentforconsultationattributed

this to thehealthcenterhavingbeendestroyedby thestormand0.85%tothe lackofappropriate

personnel.Thecorrespondingproportionsin2001arealsonegligible.

Ingeneral,itisestimatedthatHurricaneMitchgeneratedinNicaragualossesgreaterthan$1

billionor51percentofthecountrys1997GDP(GuhaSapiret.al,2003).Tooffsettheselosses,there

was a large influx of emergency relief aid in the aftermath of the storm. Bilateral emergency

assistance forNicaragua fromOECD countriesalonewas$34.8million in1998,$105.7million in

1999and

$17.1

million

the

following

year

(OECD,

2004).

However,

while

monetary

and

in

kind

donations,suchasfood,housing,constructionmaterials,clothing,andmedicineswerewidespread,

muchofthepromisedaidaimedatthereconstructionprocesswasnotdelivered,andtodaymany

roadsandbridgesare stilldownandhousingprojects left incomplete (Hiscock,2005).Moreover,

previousworkhasfoundevidencethataid,althoughwelldirected toaffectedmunicipalities,was

notwelltargetedtowardsthemostvulnerablehouseholdswithinthosecommunities(Ambler,2005;

Lazo,2005).Mosthouseholds inthe1999surveybenefited from foodandhealthprograms(45.3%

and38.1%),followedbydonationsofclothingandemploymentprograms(Table1).Whilein1998

only0.4%ofallhouseholdsreceivedanynonprivatedonations,in199952%oftheaffectedfamilies

werebenefitedbythissortoftransfer.Theaveragesizeofthedonationsperhouseholdwentfrom

$1.08($11.37crdobas)in1998to$40.83($482.30crdobas)in1999.

7 The proportion of households affectedby Mitch reporting these supplyside reasons asjustifications for not enrolling their

childreninschooldecreasedgraduallybetween1998and2001:goingfrom4%in1998to1.3%in1999and1%in2001.


12/47

12

3.Data

For our main analysis, we use data from the Nicaraguan Living Standards Measurement

Studies(LSMS)carriedoutbytheNationalInstituteofStatistics(INEC)in1998,1999and2001,with

the supportof theWorldBank.8TheLSMSarevery richpanel surveyswithmultistage stratified

samples that gather information on a wide range of topics, including income, expenditure,

education, and health at the household and individual levels. In 1999, in the aftermath of the

hurricane,itwasdecidedtodoafollowupofthe1998surveyinaffectedareasinordertoassessthe

effects of Mitch. By November 1998, personnel from LSMS visited the country to identify the

affectedareas.Monthslater,interviewerswentbacktohouseholdsthatmettwoconditions:1)they

were located insegmentsofmunicipalities9affectedby thehurricaneasdeterminedpreviously in

theNovembervisitsand2)surveyed in the1998round.Hence,we identifyahouseholdasbeing

directlyaffectedbyMitchinthe1998and2001surveysifitwaslocatedinamunicipalitysegment

visited in the 1999 LSMS round.10 The availability of panel data provides, therefore, a unique

opportunitytostudytheimpactofanaturaldisasteronhouseholdwelfare.

In addition to the LSMS, in Section 5we use crosssectional data from the 1998 and 2001

Nicaraguan Demographic Health Surveys (DHS) in order to further explore other behavioral

responses

and

their

implications

for

the

process

of

resource

allocation

within

the

household.

The

DHS are nationally representative household surveys, using in Nicaragua the same sample

frameworkastheLSMS,andprovidealargerangeofdataintermsofhouseholdcharacteristicsand

individualshealthandnutritioninformation.11

Our initialworkingsample iscomprisedof2,764households,ofwhich396wereaffectedby

Hurricane Mitch and are part of the treatment group, with the remaining representing the

8

See

INEC,

(2000),

Comparative

Indicators

in

Zones

Affected

by

Hurricane

Mitch,

according

to

Household

Surveys

for

a

detailed

explanationofthemethodologyfollowedintheLSMS.FieldworkfortheEMNV98wascarriedoutbetweenAprilandAugust1998;

fortheEMNV99,inMayandJune1999;finally,forEMNV2001,interviewswerecarriedoutbetweenJuneandAugustof2001.All

monetaryvaluesareexpressedin1998crdobas.9TheLSMSdatainNicaraguawasdividedincensussegments,consistingofamaximumof5060dwellings.10Theinterviewersfollowedallhouseholdsthatin1998hadbeenlocatedinareasthatwerelateraffectedbyMitch evenwhenthey

movedoutofthemunicipality aslongastheystayedinthesameregionasin1998.Only2.25%ofthehouseholdsvisitedin1999

hadpermanentlymovedtoanotherregionaftertheHurricane.Althoughthe1998and2001surveysarerepresentativeofthewhole

Nicaraguanpopulation, the 1999 followup isnot representative of the totalpopulation inNicaragua affectedbyMitch (INEC,

2000).11

FormoreinformationonthemethodologyoftheDHSsurveys,pleaseseehttp://www.measuredhs.com/.


13/47

13

comparison group. The data covers 15 departments and 2 autonomous regions in 126 surveyed

municipalities.Table3presentsselectedpreshockmeancharacteristicsforourtotalsample,aswell

asforasubsampleofruralhouseholds.Bothsamplesarerestrictedtohouseholdswithatleastone

childandthatwerepartofboththe1998and2001surveys.

Householdsinthesamplearelargelypoor(medianannualincomepercapitaofUS$296),with

lowlevelsofeducation(averageof4.1yearsofeducationforheadofhouseholds)andlargefamilies

(averagefamilysizeof6.1members).Householdslivinginareasaffectedbytheadverseshockwere

more rural andhad lower average income per capita ($3,382 crdobas orUS$281.80) than other

households ($6,279 crdobas orUS$523.25).12There are otherdifferences in terms of educational

attainmentoftheparents,theproportionofhouseholdsheadedbywomen,owningadwellingand

thedistance

to

health

centers

and

primary

schools.

These

differences

are

overall

explained

by

a

higherdegreeofruralityintheareasaffectedbythehurricane,andmosteitherdisappearorbecome

verysmallasweconditiononlocation(rightpanelofTable3).Intermsofincomepercapita,even

thoughthedifferencesinmeanspersist,thereisnosignificantdiscrepancyinmedians.Thatis,once

we include geographic controls,households sparedbyMitch are fundamentally similar to those

affectedbythestormand,asaresult,constituteagoodcontrolgroupforourstudyonthebasisof

relevantobservablecharacteristics.

4.EmpiricalStrategyandFindings

4.1Identification

As notedbefore, the objective of this paper is to examinewhether children living in areas

directlyaffectedbyHurricaneMitchexperiencedlowerinvestments inhealthandnutrition, lower

enrollmentrates inschool,andworkedmoreduetotheshock.Ideally,wewouldliketocalculate

theeffectofthestormoneachofthesemeasuresofchildrenswellbeingbycomparingtheactual

outcomeoftheaffectedchildwithwhatthatoutcomewouldhavebeenintheabsenceoftheshock.

12 Our measure of household income results from the addition of in kind and cash earnings from alljobs, net income from

agricultural activities and the nonagricultural familyownedbusiness, the value of all goods and services from agricultural

activitiesandownbusiness thatwereconsumedby thehouseholdandallothersourcesof income (rents,pensions,privateand

publictransfers,etc.).


14/47

14

Obviously,itisimpossibletoobservethesamechildinthesetwoscenariosandthus,werelyonthe

constructionofapropercounterfactualtoassesstheimpactofthehurricane.SinceMitchstrajectory

wasexogenousanditdidnotdirectlyaffectallareasinNicaragua,householdsinregionssparedby

the storm constitute anatural control group.Hence, our approach is to compare the changes in

schooling,childlabor,healthandnutritionaloutcomesexperiencedbetween1998and2001among

childreninregionsdirectlyhitbyMitchtothosethatoccurredamongchildreninthecontrolareas.

Toillustrateourresearchdesignmoreformally,wefollowAngristandKrueger(1998)andset

Cit torepresentaparticularoutcomeofachild type i,where i=1 if thechild is inamunicipality

segment(i.e.sampledistrict)affectedbyMitchandi=0,otherwise.Lettrepresenttheyear,witht=

1998 (preshock)or t=2001 (aftershock).Thereare twopossiblestatesof theworld s: s=1with

Mitch,and

s=0without

the

shock.

The

conditional

mean

function

for

child

outcomes

is:

0 , ,rtC i s t ifthechildisnotaffectedbyMitch;and (1)

1 , ,rtC i s t ifthechildisaffected. (2)

However, asmentioned above, for t = 2001weonly observe (2)when s = 1.Then,weuse

similarvillagesthatwerenotaffectedbyMitchtoestimatethiscounterfactual:

0 0, 1, 2001rtC i s t = = = (3)

In theabsenceoftheshock,assumethatchildrensoutcomescanbewritten inthefollowing

simplifyingmanner:

[ ] XtriC ti +== ,0,0 (4)

Further, lets assume that the effect of the shock (M) canbe capturedby a constant ( )

namely

homogenous

treatment

effects.

For

instance,

any

outcome

of

child

i

in

any

region

can

be

describedasfollows:

XMC ii ++= (5)

Undertheseconditions,andfurtherassumingthattheoutcome variables for households had

the hurricane not taken place, would have had the same growth rate as those of the comparison


15/47

15

group, theaveragecausaleffectof theshockamongaffectedsegments is identifiedby thedouble

differenceestimator:

[ ] [ ]{ ===== 1998,12001,1 1998,12001,1 tiCtiC

[ ] [ ]{ 1998,02001,0 1998,02001,0 ==== tiCtiC (6)

As in Jensen (2000), we are interested in measuring resource flows related to childrens

outcomes in education, child labor and health. For education, our main indicator is school

enrollment,whileforchildlaborforceparticipationwelookattheprobabilityofachildbeingeither

workingorlookingforajob.13Forhealth,weexaminetheutilizationofmedicalservices,conditional

onbeingsickandthenexploresomepotentialeffectsonnutritionalstatusbyanalyzingtheweight

forheightmeasure.14

Inordertoinstrumentfortheshock,weconstructadichotomousindicatorequaltooneifthe

householdwaslocatedin1998inamunicipalitysegmentaffectedbyMitch(asindicatedbyhaving

beingsurveyedin1999)andzerootherwise.Thisdummyvariableallowsustoidentifytheaverage

effectoftheshockamongtheexperimentalgroup.

Our research design allows us to identify only lowerboundestimates of the impact of

HurricaneMitchonchildrensoutcomesforvariousreasons.First,anaturaldisaster,asanaggregate

shock,isalsoexpectedtohaveanindirectnegativeimpactonhouseholdsinmunicipalitysegments

notphysicallyhitby the stormasdescribed inSection2.3.Households living in control regions

were also exposed to themacro effectsof thedisaster andperhaps experienced a lower levelof

transfersaspublicfinancialandphysicalresourcesmayhavebeendivertedtowardsregionsinmore

needofaid.Second,migrants from treatedareaswhichwedonotobserve in thesecondround

maybetheonesmostheavilyhitbythehurricaneand,thus,weobserveonlytheremainingpartof

thedistribution15.

Third,

although

long

distance

migration

does

not

seem

to

be

amajor

issue

in

our

13Thedefinitionusedheretoclassifychildrenasparticipatinginthelabormarketincludesthosewhowereeitherworkingforat

leastonehour(countingalsolaborinfamilybusinesses)orlookingforajobduringtheweekbeforethesurvey.14Weuse theweightforheightZ score in reference to theNCHSmedian recommendedby theWorldHealthOrganization.As

discussedinJensen(2000),thismeasurereflectstheshorttermorcurrentnutritionalstatusofthechild(healthinvestmentflow),as

opposedtotheheightforageindicatorwhichismorerelatedtolongtermnutritionalconditions(healthstock)ofthechild.15As discuses further in the section on robustness checks, there is a possibility thatmigration can lead to an upwardbias ifhouseholdsthatleavearetheleastcreditconstrained.However,wefindnoevidenceforthis.


16/47


17/47


18/47


19/47

19

OursimpleDIDanalysissofarhasshownthatHurricaneMitchhadalargeandnegativeeffect

on treated children by significantly increasing their labor force participation, decreasing the

probabilityofbeingtakenforconsultationifillandworseningtheirnutritionalstatus.However,one

maythink

that

the

control

and

the

treatment

groups

are

different

across

some

key

characteristics,

as

discussedinreferencetoTable3.Therefore,oneneedstoconditiononthosevariablestopickupthe

true causal effect of the shock and increase the precision of the estimates by reducing the

unexplainedvariancefromtherawDIDmodels.Thegeneralformofthebaseregressionequation

fortheseextendedmodelsisthefollowing:

)()( 43221 itddtdtdtitdiitd ZxTreatxTreatxTreatXC ++++++= (6)

where i indexes individuals, t indexesyears (1 ifafter1998,0otherwise) andd indexes the

treatment and control groups (1 if hitby the hurricane, 0 otherwise). C represents any of our

outcomevariablesassociatedwithchildrenswellbeing(e.g.whetherchildisattendingschool), i

isahouseholdfixedeffect,Xisavectorofbothhouseholddemographicsandpreshockobservable

characteristicsatthehouseholdandmunicipalitylevel, isafixedyeareffectandTreatisadummy

fortreatmentgroup(1iftreated,0ifcontrol).Theinteractionassociatedwith 3 ,themaincoefficient

of

interest

in

our

analysis,

captures

all

the

variation

in

childrens

outcomes

between

1998

and

2001

specific to the treatments. The set of covariates includesdemographics of the children and their

parents (e.g. age, sex and schooling), some characteristics of the household (e.g. number of

permanentmembers,locationandhouseownership),productiveassets(businessownership,landto

cultivate),stateeffectsanddummiesforpreshockprogramsofsocialassistanceatthemunicipality

level. Zis a subset of thevectorXwithhouseholddemographics (e.g. sex, age) andhousehold

compositionvariables(e.g.femaleheadedhouseholds)forwhichweestimatepotentialdifferential

treatmenteffects,namely4

.All themodelswere estimatedusing fourdifferentmethods:OLS,

probit (for outcomes of binary nature), fixed effects (conditional logit fixed effects for binary

outcomes)andrandomeffects.Wealsorunseveralspecificationsforeachmethodofestimationin

order to checkboth the sensitivityofour resultsand test for somepotential sourcesofbias.For

simplicity,wereportthecoefficientsoftheparametersofinterestforonlytwoofthesespecifications

(OLSandprobit).


20/47

20

Table5 reports the estimatesof the interactionbetween theyeardummyand the treatment

dummy, namely the causal parameter 3 from the reducedformmodels in (6). Restricting the

analysis and interpretationofour findingsonceagain to the sampleof rural children, all results

obtained from the simple DID estimation remain significant and quantitatively large. The only

qualitative change is related to childrens school enrollment, as discussed above. In particular,

conditioning on the set of covariates describedbefore,we find no significant effect ofMitch on

schoolenrollment.Labor forceparticipation,on theotherhand, increasedby58%amongaffected

childrenduetothestorm.Similarly,theproportionofchildrenthatwassimultaneouslyenrolledin

school andworkingmore than doubled, going from 7.5% to 15.6% In terms of health,we find

parallelresults.Thehurricanemadechildren inaffectedareasover30% less likely tobe taken for

medicalconsultation

conditional

on

being

sick,

even

though

there

was

no

significant

difference

on

diseaseprevalencebetweenaffectedandnonaffectedchildren.Inaddition,duetoMitch,children

whodirectlysufferedtheshockwere8.7percentagepointsmorelikelytobeundernourishedthan

beforemorethanthreetimesthepreshockrate andtheiroveralldistributionofnutritionalstatus

worsenedsignificantly.

4.3RobustnessChecks

Weconductaseriesofempiricalexercisesinordertofurthertesttherobustnessofthecausal

effect identified inourresearchdesign.Asdocumented inSection2,disruptionstoschoolingand

healthservicesafterMitchappearnottohavebeensevere.Inspiteofthat,oneconcernmayremain

intermsofthenonexogenousresponseofthepublicauthoritiesandemergencyreliefgroupstothe

disasteras the flowofaidwas largelycorrelatedwith the treatmentstatusof thehouseholdsand

probablywith some of the outcomes of their children.Actually it is straightforward to sign the

directionofthisbiassincehouseholdswerebetteroffwiththesetransfersastheywouldhavebeen

otherwise.Aswiththeotherpotentialconfoundersdiscussedintheidentificationsection,although

thissortofbias leads toanunderestimationof the realMitcheffect, the coefficientsare stillvery

informative.However, the1998and2001surveyshavea fullsetofquestions that canbeused to

identify those households thatbenefited from several postdisaster programs of assistance (e.g.

constructionorreconstructionofschools,healthcenters,watersanitation,electricity,streets,roads


21/47

21

anddwellings, employmentprograms,health campaigns and inkind transfers such as food and

medicines).Weusethisinformationtoconstructagroupofdichotomousvariablestoaccountforthe

availabilityoftheseprogramsin2001.Althoughtheseindicatorsareoutcomesthemselves,theyare

addedtotherighthandsideofourregressionequationstocheckthesensitivityofourestimatesto

their inclusion.Asexpected, coefficientsare lower,butjust slightlydifferent from thoseobtained

withouttheseadditionalbinaryvariables.19

Themodelsarealsorunonalternativesubsamplesasanattempttosimulateacleanerquasi

experiment:onesuchsubpopulationsareruralhouseholdsgrowingthesamecropsatthepreshock

time.Inprinciple,thisapproachallowsustolookatmorecomparablehouseholdsbycontrollingfor

confoundingfactorssuchasdifferencesinsoil,cropsresiliencetotheshockaswellastheintensity

ofexposure

to

the

storm

arising

from

varying

altitude.

To

implement

this

idea,

we

restrict

the

units

ofanalysis to children in ruralhouseholds thatgrow the threemaincrops in thesample,namely

corn, beans and rice. A potential caveat of this approach stems from the fact that systematic

differential price trends across experimental groups can arise as a result of the hurricane, if the

shortageof theseproducts in treated regions allowed controlhouseholds tobenefit fromhigher

prices.However, imports records fromNicaragua suggest that local scarcity isnotvery likely to

contaminate theseresultsas thedomesticconsumptionofcorn,beansandricedepends toa large

extentfrom

international

markets

and

prices.

Theestimatesfromthemultivariateanalysisonthesesubsamplesareanalogoustotheones

discussedaboveandaresummarizedinTable6.Evidently,thepriceofthisstrategyisasignificant

reductioninthenumberofobservationsand,asaresult,lesspreciseestimates.Inspiteofthat,the

magnitudes of the coefficients remain similar to those previously described, particularly among

children incorngrowinghouseholds (largest samplesize).More importantly, thedirectionof the

effectsofthehurricanestaysthesame:Mitchisassociatedwithasignificantincreaseinchildlabor

forceparticipation,andaworseningofnutritionaloutcomesamongchildreninaffectedareaseven

aftercontrollingforthetypeofcropgrownbyhouseholds.20

19Forinstance,theincreaseofchildlaboridentifiedfortreatedchildrendropsfrom0.085to0.083withthisnewspecification.The

resultsofthesemodelsarenotpresentedinthedocumentforbrevitybutareavailableuponrequest.20Noresultsarepresentedforhealthconsultationsbecausethenumberofobservations,afterconditioningforbeingill,istoosmall

toobtainanymeaningfulinference.


22/47

22

Wealsousenonparametricmethods to testwhetherour findingsareheavilydependenton

the linear specification assumed in the preceding econometric models. More specifically, we

implementDIDpropensityscorematchingtobothrelaxthelinearfromassumptionandmakeour

controland

treatment

groups

more

comparable

by

balancing

their

distributions

of

observables.

Regarding the latter issue, the placement of the hurricane was an exogenous event close tobe

orthogonal to theerror termbutyet correlatedwithobservablesasdiscussed inSection3.Given

this,thepropertiesofthenonparametricdoubledifferenceanalysismakethismethodparticularly

usefultoidentifytheparameterofinterest.

Preshock household covariates in the treatment status logistic regression include location

(rural

or

urban),

municipality,

income

per

capita,

parental

education,

distance

to

closest

school

and

health center, as well as dichotomous variables to identify households owning a dwelling,

businessesandlandtocrop.Severalspecificationsofthepropensityscoreareestimatedjointlywith

differenttypesofkerneltomatchchildrenbetweenthetreatmentandcontrolgroups.21Ingeneral,

the findings hold, as the estimates are again significant and quantitatively large, and similar in

magnitude to those obtained above especially in terms of labor force participation,joint school

attendanceandlaborforceparticipation,aswellasfornutritionalstatus(Table7).

Sofar,

by

using

DID,

we

have

made

the

identifying

assumption

that,

in

the

absence

of

the

weather shock, the control and the experimental groupswould have followed similar trends of

investmentinchildren.Whileonecannotestimatethecounterfactualofwhattheinvestmentwould

havebeenhadtherebeennoHurricaneMitch,weusethe1993LSMStoanalyzewhethertherewas

anydifferentialtrendbetweenthetwogroupsbefore1998.The1993LSMS isnotapanelwiththe

1998,1999and2001surveysandtheconstructionofthesamplesegmentsbetweenthe1993and1998

datasetsdonothaveanymatching correspondence.Consequently, the treatmentstatushad tobe

redefinedfor

these

exercises

and

households

in

municipalities

with

segments

interviewed

in

the

1999 LSMS were classified as part of the treatment group, with the remaining households

comprisingthecontrolgroup.Thisnewassignmentruleisnotexpectedtochangedramaticallythe

21NotesofTable7explainthevariablesandmethodsusedtoestimatethepropensityscoreindetail.


23/47

23

constructionofourexperimentalsamples,especiallyforruralareas,asmostofthesegmentsinthese

municipalitieswerereinterviewedin1999.22

First,we test the pretreatment trendsby looking at somekeydeterminantsof childrens

outcomesusing

the

pseudo

panel

at

the

municipality

level

for

the

period

1993

1998

(Table

8).

Overall, simple DID estimates suggest that there were no differential changes in demographic

characteristics or communitylevel variablesbetween the two groups that couldbe driving the

changesininvestmentinchildrenobservedbetween1998and2001.Althoughthereisanincreasein

thedistance to theclosestschoolandhealthcenter for treatmentsovercontrols, themagnitude is

verysmall(around5and3minutes,respectively)andcanhardlycastdoubtsonourresults.

Next,

we

analyze

whether

there

is

any

indication

of

a

differential

trend

in

the

outcome

variablesbetweenourtwoexperimentalgroupspriorto1998.OurDIDapproach ismorelikelyto

haveidentifiedthecausalityoftheshockifthetreatmenteffectisnotobservedbetween1993and

1998.The results from the rawandextendedplaceboDIDon thepseudopanel (presented in the

lowerpanelofTable 8) support this assertionby revealingno sign of adifferential trend in the

outcomesofinterestsincemostestimatesarenotstatisticallysignificantforeitherthetotalsampleor

fortheruralone.Thecoefficientsinoneofthemodelsofhealthcareutilization(0.204,P>0.235)and

zscoresofweightforheight (0.212,P>0.132),however, revealadifferentially,butnotsignificant,

worsening in themean of the treatedhouseholds over the comparison groupbetween 1993 and

1998. In order to further test these growth rates in the preshock period,we combine the three

surveys (1993, 1998 and 2001) to estimate differenceindifferencedifference models for rural

householdsbetween1993and2001.Theresults,presentedinTable9,indicatethatthelargeandstill

significantadverse effectsofHurricaneMitchparticularlyon child labor forceparticipationand

nutritionremainafterremovinggroupspecificpreshocktrends.

Attritiondoes

not

seem

to

be

acontaminating

issue

for

our

identification

strategy

either.

For

householdswithchildren,there isa20%attritionrateamongtreatedhouseholdsand26%among

22In1993weonlyusethemunicipalitiesincludedinthe1998surveyand,forthepseudoDID,weclassifyahouseholdaspartofthe

treatmentgroup if it is located inamunicipality that in1999was surveyed (and, therefore, consideredaffectedbyMitch).This

definitionof treatmentendsupbeing remarkablyclose to theoneused in themainanalysis, since the1999LSMS contained40

municipalities with an average of 13.65 households and 42 census segments with an average of 13 households (i.e. the

municipalitiesandsegmentssizewasverysimilar),and,asaresult,mosthouseholdswithineachmunicipalityarealsowithinthe

samecensussegment.


24/47

24

control householdsbetween 1998 and 2001; however, forboth groups, households that left the

samplehave similarobservable characteristicsbetween treated and controls except for abigger

proportionofchildrenbetween6and15yearsofageandolderhouseholdheadsamongtheleavers

ofthetreatedgroup.Theunbalanceinthosetwodimensionscanhardlydriveourfindings.Evenin

theextremecaseofsomeendogenousattritionofthissort,thepotentialbiasesmayoffseteachother

ashouseholdswith relatively fewerdependentsmaydobetterwhile,on theotherhand,younger

householdheadsmaydoworse (e.g. lessjobmarketexperience).Incontrast, itmaybe thatcredit

constrainedhouseholdsweremore likely to stayas they lacked the resourcesneeded tomigrate.

Although this can confound our estimates, we do not find evidence that some preshock

determinantsofaccesstocreditandinsurancedifferstatisticallybetweentheleaversandnotleavers

ofthetreatmentandcontrolgroups.Wealsocheckwhetherthosehouseholdsmorestronglyhitby

thehurricaneweremorelikelytomigrate.Wecomparetheratioofagriculturallossestopreshock

wealthamongleaversandnotleaversandfindnoevidencethattheintensityoftheshockwaspart

ofthereasontomigrateamongaffected.23

Inshort,ourresultscanonlybecontested ifthere isanunobservabledifferencebetweenthe

twoexperimentalgroupsoranotherchangenotrelatedtotheshockandnotaccountedforinour

researchdesignthataffectedthegroupsdifferentlyandthatalsohappenedbetween1998and2001.

Since

the

trajectory

of

Mitch

was

quite

random,

largely

exogenous

and

affected

different

and

non

contiguousmunicipalitiesofNicaragua,thisseemsunlikely.

5.IntrahouseholdBehavioralContext

The evidence presented in this paper may have important theoretical implications for the

economicliteratureonhouseholddecisionmakingmodels.Wehaveshownthatchildrenaffectedby

alarge

shock

in

Nicaragua

suffered

significant

negative

consequences

in

terms

of

health

and

child

labor,eventhreeyearsafterthedisaster.Severalreportsonthehurricaneandourowncalculations

suggest that disruptions to the supply of schooling and health services can hardly explain the

observeddeteriorationoftheseoutcomesinthemiddlerun.Thequestionthenarisesofwhywesee

23Amoredetailedanalysisonattritionandmigrationisavailablefromtheauthorsuponrequest.


25/47

25

thisoutcome.Inparticular,didchildrensnutritionworsenbecausetheirparentswereleftwithout

enough income to feed all members of the family? Or was it the case that children were

disproportionatelyaffectedvisvisothermembersofthehousehold?Ifoneweretofindevidence

forthelatter,thenitmaybethecasethatthetraditionalassumptionofparentsbeinggoodagentsfor

childreninthehouseholddecisionmakingprocessissimplynotappropriateinsomesettings.The

aftermathofalargeshockmayleavechildrenatadisadvantagewithinthehousehold,makingthem

morevulnerablethanadultsintheseevents.24Sincechildrenrepresentthefuturehumancapitalofa

country, this resultmay call, in turn, for governments,NGOs and international organizations to

provideinkindaidtargetedtowardschildren(e.g.inschoolfoodprograms)ortocreateincentive

schemes for parents to keep children in school and out of the labor market (e.g. conditional

transfers)afteranaturaldisaster.

Toshed lighton thesebehavioral responses, firstwe lookat the impactof the stormon the

consumption levelsof adultgoods in ruralhouseholds.For this,weuse theLSMS surveysused

through out this paper which contain detailed information on the consumption basket of all

households. In order to define what adults goods are, we compare the composition of the

consumptionbasketofchildlesshouseholdswith thatofhouseholdswithchildren.Thegoods for

whichtheirrepresentationintheconsumptionbasketinchildlesshouseholdsisstatisticallyhigher

thanin

those

with

children

are

considered

adult

goods.

Following

this

definition,

tobacco

and

alcoholareconsideredadultgoods.

Table10summarizesthemainresultsfromthisexercise.Wefindthatthere isnodifferential

change intheconsumptionoftobaccoandalcoholbetweentreatmentandcontrolgroups.That is,

theconsumptionshareofadultgoodsdoesnotseemtohavebeensignificantlyaffectedbyMitch.In

fact, thecoefficients,althoughnotstatisticallysignificant,showan increase in theconsumptionof

tobacco due to the storm. Notwithstanding that adult and children goods may notbe directly

comparablebecauseofdifferentelasticitiesofdemand,thisexercisesuggeststhatchildrenmayhave

beenrelativelyworseoffthanadultswithinfamiliesaffectedbytheshock.

24HoddinottandKinsey(1998,2000),intwodifferentpapers,lookattheimpactofthe199495droughtinZimbabweandfindthat

womenandyoung childrensnutritional statuswasadverselyaffectedby theevent,butnotmens. Inparticular, they find that

childrenages1224monthslostanaverageof1.52.0centimetersoflineargrowthafterthedrought.Forwomen,however,theloss

ofbodymasswasonlytemporary.


26/47

26

WeuseasecondapproachtogetatthedifferentialeffectofHurricaneMitchonhouseholds

membersby focusingonnutrition.TheLSMSdatasetsdonot includeanthropometric information

foradults,andthereforewecannotknowwhathappenedtoadultsnutritionafterHurricaneMitch

inthe

households

we

look

at

in

this

paper.

Instead,

we

use

the

Demographic

Health

Surveys

(DHS)

forNicaragua,acrosssectional survey that includesanthropometric information forchildrenand

theirmothers and thatwas carried out in 1998 and in 2001 to construct a pseudopanel at the

municipalitylevel.25Inthisnewdataset,weanalyzetheeffectofMitchonmothersbodymassindex

andweight/heightindicator,twomeasuresthataresuitabletocapturechangesinadultnutritional

status (Table10).We find that the effectonadultsnutrition is close tozeroandnot statistically

significant.Thatis,usingmeasuresassociatedwithadultsconsumptionandwellbeing,wedonot

findevidence

suggesting

anegative

impact

of

Hurricane

Mitch

contrary

to

our

main

results

in

relationtochildren.

These results have important implications for the nature of the allocation process in the

household.Ontheonehand,modelsof intrahouseholdallocationofresourceshave longstressed

thatunitarymodels ofhousehold preferencesmaynotproperly represent thewaydecisions are

madeinthehouseholdandthat,hence,thesemodelsmayleadtowrongconclusions.Ontheother

hand, collective models try to address some of the shortcomings of the unitary frameworkbut

usuallyassumeparentsarealtruistictowardstheirchildrenwellbeing(eitherbecausetheyhavean

altruistic interest in the children themselvesordue to the idea that childrenshealth is related to

future output and a rent for the parents), still if there is somebargaining processbetween the

parents.However, ifchildrenaredisproportionatelyaffected intheaftermathofashock,eventhe

existingcollectivemodelsmaynottellthewholestory.

Even if it is optimal for thehousehold as awhole, given the existing credit and insurance

constraints,to

take

children

out

of

school,

push

them

into

the

labor

market

and/or

direct

fewer

resourcestowardstheirnutritionandhealthcare,theremaybespaceforpublicactiononbehalfof

the children. This is an interesting and important avenue for research in the future. From a

25TheDHSandtheLSMSfor1998and2001usedthesamesampleframeworkand,therefore,informationfromthetwosamplescan

beusedtogether.Also,bothsurveysusedthesamescalesandmeasuringdevicesfortheanthropometricdata(WorldBank,2002).

Formoreinformationontheresultsdiscussedinthissection,pleasecontacttheauthors.


27/47

27

theoreticalpointofview,itsuggestsaneedtoreviseourassumptionsonhouseholddecisionmodels

and, specifically, the importanceof childrensutilityon theirparentsobjective functions.Froma

policyperspective,ithighlightsthatissuesoftargetingofpostdisasteraiddonotonlyexistatthe

level of communities and householdsbut of individuals since children seem tobe a especially

vulnerablegroupofthepopulation.

6.Conclusions

The existing economic literature strongly suggests that the capacity of households in poor

countries to smooth consumption across time and states of nature is limited. This seems tobe

especiallytrue inthecaseof largegeneralizedshockssuchasnaturaldisasters,whensomeof the

informalmechanismsofrisksharingbecomelesswidespread.Agriculturalpopulations,especially,

are vulnerable to this type of generalized weatherrelated shocks and households are exposed,

therefore,tosignificantuncertainties.Andalthoughsomestudiesexistonthemacroeconomiceffects

andthedirectlossesassociatedwithnaturaldisasters,theliteratureonthewelfareimplicationsof

thesephenomenaatthehouseholdlevelisratherlimited.Inparticular,workontheconsequencesof

these shocks for children, in termsof the investment in theirhealthand their education, isquite

scarce.SinceHurricaneMitchwas largelyunanticipatedandhitonlysomeareasofthecountry,it

constitutesaunique

natural

experiment

setting

for

the

study

of

households

responses

to

this

sort

of

shocks.

Using a quasiexperimental design, this paper has presented evidence on the pervasive

mediumrun effects ofHurricaneMitch on childrenswellbeing inNicaragua.Children in areas

directlyaffectedbythestormwere30%lesslikelytobetakenformedicalconsultationconditionalon

being sick, even though therewas no significant difference on the prevalence of illnessbetween

affected

and

non

affected

children.

Furthermore,

children

in

regions

hit

by

Mitch

were

8.7

percentage

pointsmore likely tobeundernourished and the overall distribution ofnutritional status among

childrenintheseareasespeciallyofthoseinthetailofthedistributionworsenedsignificantlyasa

result of the storm. On the other hand, while we find no significant effect of Mitch on school

enrollment, labor force participation increasedby 58% among children in areas affectedby the


28/47

28

hurricane.Similarly,theproportionofchildrensimultaneouslyenrolledinschoolandworkingmore

thandoubledduetoMitch,goingfrom7.5%to15.6%.Themagnitudeoftheseeffectsisquitelarge.

Wheninterpretingourresults,itisimportanttotakeintoaccounttwoconsiderations.First,

thefindings

represent

lower

bound

estimates

of

the

effects

of

Mitch.

By

using

adifference

in

difference approach,we are attributing all the changes in ourvariablesof interest in the control

grouptononMitchfactors;however,theeffectsofanaturaldisasternodoubtspillovertoallthe

population (especially in a small country), abovealldue to theknownmacroeconomic effectsof

sucha largeshock.Second,sinceweare lookingat theeffectofMitch in2001,almost threeyears

after ithitNicaragua,our resultsare illustrativeof themediumtermeffectsofanaturaldisaster.

Furthermore, since the items thatwe analyze are directly related to the earning potential of the

childrenstudied,

these

results

are

also

very

informative

about

the

existence

of

important

long

term

effectsofnaturaldisastersatthehouseholdandindividuallevels.

Theabsenceofmoreefficientmechanismstodealwithnaturaldisastersputshouseholdsina

situationwheretheyneedtomakedifficultchoices:whichexpendituresorinvestmentstoreduce?

Fromapolicyperspective, it is relevant toknowwhat thesechoicesare.Thisstudy indicates that

childrenmaybe significantlyaffectedafter theneeded reallocationof resourceswhenassetsand

income drastically fall after a disaster. Furthermore, our study suggests that, within affected

households,childrenareleftrelativelyworseoffsinceparentsnutritionandconsumptionpatterns

seem tobe largely unaffected in the mediumterm. This result provides useful insights for the

theoreticalliteratureonthenatureoftheresourceallocationprocessinthehouseholdandparents

prioritieswhenmakingspendingandinvestmentdecisionsafteralargeshock.Furtherstudiesthat

analyzetheagencyroleofparentsvisvistheirchildreninawidercontextareinorder.

Thispaperalsohasimportantimplicationsforexanteandexpostdisasterreliefprograms.To

theextent

that

disruptions

in

school

enrollment

and

health

deficiencies

have

long

term

effects

for

individuals, households and the economy as a whole, our results highlight the need for a

comprehensive agenda when dealing with the consequences of adverse shocks to include the

possibleeffectsonchildren.Aonetimeflowofaidafteralargeshockmaynotdoenoughtoprevent

theadverselongertermeffectsofsuchanevent.Ambler(2005)andLazo(2005)bothfindthatpost


29/47

29

disaster emergency aidafterMitchwasnot targeted at thepooresthouseholds thosewhichhad

sufferedthelargestassetlossesrelativetotheirinitialwealth.Instead,postdisasteraidseemedtobe

directed towards the most affected regions without effective targeting within those areas. They

conclude thatbetter targeting and coordination among relief agencies is needed. Moreover, our

findingssuggest that ifchildrenaredisproportionatelyaffectedafteranaturaldisaster,simpleex

anteorexpostcashtransferstohouseholdsmaynotbeenoughtokeepchildreninschoolandwell

nourished.Expost targetedprogramsdirected specifically towardsmaintaining schoolenrollment

andassuringchildrenarewellfedseemmoreappropriate.Withthis inmind,socialprograms like

Progresaandothersthatprovidefoodforchildreninschoolsmaybeparticularlyadvantageousinthe

aftermathofnaturaldisasterstomitigatethe longertermadverseconsequencesoftheseshocksby

creatingstrongincentivesforparentstolookafterchildrenswellbeing.

Hence,largeshocks,suchasnaturaldisasters,causepopulationstosuffernotonlyintheshort

run. The most destructive consequences of these events in poor countries may well fall on the

countrysfuturehumancapitalstock,children,whomaysufferlongtermeffectsifnaturaldisasters

increasemalnutritionand take themoutof school.And thepicture looks evengrimmer if, in the

absence of appropriate incentives, children are put at a disadvantage even within their own

householdsasinitialevidencediscussedinthispapersuggests.

References

Alderman,H.

and

C.

Paxon,

(1994),

Do

the

Poor

Insure?

A

Synthesis

of

the

Literature

on

Risk

and

Consumption inDevelopingCountries, inEconomics of theChangingWorld, ed.Bacha,NewYork: St.

Martins,pp.4878.

Ambler,C.,(2005),TheDistributionofEmergencyReliefinPostHurricaneMitchinNicaragua,Thesis

Project,WilliamsCollege,Williamstown,MA.

Anderson,M.,(2000),TheImpactofNaturalDisastersonthePoor:ABackgroundNote.,mimeo.


30/47


31/47


32/47

32

Morduch,J.,(1995),IncomeSmoothingandConsumptionSmoothing,JournalofEconomicPerspectives9

(3),pp.103114.

Morduch,J.,(2003),ConsumptionSmoothingAcrossSpace:TestsforVillageLevelResponsestoRisk,

StefanDercon,ed.,InsuranceAgainstPoverty,OxfordUniversityPress.

NicaraguanNational

Institute

of

Statistics

and

Censes

(INEC),

(2000),

Comparative

Indicators

in

Zones

AffectedbyHurricaneMitch,accordingtoHouseholdSurveys.

NicaraguanNational InstituteofStatistics andCenses (INEC), (2002), EncuestaNacionaldeHogares

sobreMedicindeNicelesdeVida2001:ManualdeUsuariodelaBasedeDatos.

Organization forEconomicCooperationandDevelopment (OECD), (2004),GeographicalDistribution

ofFinancialFlowstoAidRecipients,Paris.

Paxson,C.,(1992),Using,WeatherVariabilitytoEstimatetheResponseofSavingstoTransitoryIncome

inThailand,AmericanEconomicReview, 82(1),pp.1533.

Platteau,J.,

(1991),

Traditional

Systems

of

Social

Security

and

Hunger

Insurance:

Past

Achievements

and

ModernChallenges,inAhmad,E.et.al(eds.)SocialSecurityinDevelopingCountries,ClarendonPress,

Oxford.

Rosenzweig, M., (1988), Risk, Implicit Contracts, and the Family in Rural Areas of LowIncome

Countries,EconomicJournal98(393),pp.11481170.

Rosenzweig,M.andO.Stark,(1989),ConsumptionSmoothing,MigrationandMarriage:Evidencefrom

RuralIndia,JournalofPoliticalEconomy,97(4),pp.905927.

Santos, I., (2006), RiskSharing and the Role of InterHousehold Transfers after a Natural Disaster.

Evidence from Hurricane Mitch in Nicaragua, draft, doctoral dissertation, Kennedy School of

Government,Harvard

University.

Skoufias,E.,(2001),ProgresaandItsImpactsontheHumanCapitalandWelfareofHouseholdsinRural

Mexico,ResearchReport139,InternationalFoodpolicyResearchInstitute,Washington,DC.

Townsend,R.,(1994),RiskandInsuranceinVillageIndia,Econometrica62(3),pp.53992.

Udry,C.,(1990),CreditMarketsinRuralNigeria:CreditasInsuranceinaRuralEconomy,WorldBank

EconomicReview,Vol.4,No.3,pp.251269.

Udry,C.,(1994),RiskandInsurance inaRuralCreditMarket:AnEmpiricalInvestigationinNorthern

Nigeria,ReviewofEconomicStudies61,pp.495526.

Ureta,M.,(2005),HurricaneMitch,FamilyBudgetsandSchoolinginNicaragua,draft,CollegeStation,

Texas.

Urroz,A.,M.ArandaandC.Morales, (1999),ElHuracnMitchenNicaraguainCronicasdelDesaster,

document12141,WashingtonD.C.


33/47

33

WorldBank,(2001a),NicaraguaLivingStandardsMeasurementStudySurvey.PostMitchSurvey1999,

SupplementalInformationDocument,Washington,DC.

World Bank, (2001b), Nicaragua Poverty Assessment: Challenges and Opportunities for Poverty

Reduction,ReportNo.20488NI.Washington,DC

WorldBank,

(2001c),

World

Development

Report

2000/2001:

Attacking

Poverty,

Washington,

DC.

World Bank, (2002a), Nicaragua Living Standards Measurement Study Survey 1998, Basic and

SupplementalInformationDocument,Washington,DC.

WorldBank, (2002b),NicaraguaLivingStandardsMeasurementStudySurvey1993Documentation,

Washington,DC.


34/47


35/47

35

dedicatetoinvestmentineachchildsquality,p

Ht .ThemarginaleffectsofX,c

Ht andp

Ht onhuman

capitalareassumedtobepositive.Similarly,humancapitalinvestmentdependsonasetofobserved

characteristics of the child, (including gender, age, birth order, among others); unobserved

characteristics

of

the

child,,

(health

endowment

or

innate

abilities)

and

finally,

,

which

captures

parental education and community characteristics such as the availability of health centers and

schools, prices, environmental factors, among others. The reduced form of the human capital

investmentinthechildcanthenberepresentedasfollows:

),,;,,( p

H

c

H ttXHH= (3)

At the optimum, household expenditures will equal household income and assets.

Expenditures have two components: the consumption of the household (excluding goods and

servicesrelated

to

human

capital),

C

(numeraire),

and

the

consumption

of

goods

and

services

related tohumancapital, XNpx (wherepxdenotes thevectorofpricesofhumancapitalgoods

andNisthenumberofchildreninthehousehold).

Theresourcesofthehouseholdincludeassets(A)andincome.Therearefourdifferentsources

of income: nonlabor income of the household (Ynl), labor income of all children today

( NtTW c

H

c )( ),laborincomeoftheparents( )( pHp

NtTW )andafractionofthelaborincomeof

adultchildren

( AN E ).

Labor

income

of

each

child

is

equal

to

his

wage

(Wc)

multiplied

by

the

numberoftimeunitshededicatestowork(i.e.thedifferencebetweenthetotaltimeendowmentand

thetimespentinschoolormedicalcare).Animportantcharacteristicofthismodelis,therefore,that

itallowsforthepossibilityofachildtobebothenrolled inschoolandworking,arelevantaspect

giventhecharacteristicsofchild labor inNicaragua.The labor incomeof theparents,similarly, is

equal to theirwage (Wp) times the number of time units they dedicate towork. Bringing these

elementstogether,thebudgetconstraintofthehouseholdis:

( ) ( )c c p p

x nl H H AC p XN Y W T t N W T Nt N E A+ = + + + + (4)

Therefore, households maximize utility (1) subject to restrictions (2) (4),by choosing the

appropriatelevelsofconsumption(C),timeallocatedtohumancapital( p

H

c

H tt , ),andconsumptionof


36/47


37/47


38/47

38

Table1.MicroeconomicEffectsofHurricaneMitch1999

Variable (%) Variable (%)

Did the family move to another house? 17.3 After Mitch, this household benefited from assistance programs like:

Was this house temporaly vacant? 29.4 New school/Reconstruction 5.8

During this time, the family moved to? New health center/Reconstruction 2.2

Refugee 35.5 Water provision 1.7

Relatives' home 55.8 Sewage 0.0

Temporary home 7.3 Electricity 1.5

Other municipality 1.5 Latrine 7.2

Other state 1.5 Food 45.3

Other country 0.75 Health programs 38.1

The house/basic services were affected in some way 45.8 Employment programs 17.1

Structure of .. in the household was totally/partially destroyed Donation of clothing 19.7

Walls 56.7 Donation of medicines and/or water 9.2

Floor 19.5 Donation of house 1.7

Roof 58.6 A member of the household died due to Mitch 1.8

Water 41.8 Didn't go to the doctor because health center was destroyed 0.3

Toilet 61.7 Didn't work because source of employment was destroyed 3.3

Electricity 24.0 Distance from your previous house in kilometers 4.1

Notes.%referstothepercentagewithrespecttothetotalnumberofhouseholds(595)includedinthe1999postMitchLSMSsurvey.

Table2. AgriculturalLossesDuetoHurricaneMitch1999

Type of Loss % of hous eholds 1 Average Loss (cordobas) 2 Aver gae Loss 3

A.Agricultural property 9.20% $8,171.67 $406.55

B. Crops 96.63% $4,195.97 $2,191.92

C. Agricultural equipment 2.15% $8,271.43 $96.02

D. Agricultural installations 6.75% $2,669.36 $97.39

E. Animals for work 10.43% $432.15 $24.37

F. Other 7.43% $3,512.35 $116.50

Total agricultural losses $5,418.17 $2,932.75 Notes: (1)Percentageofhouseholdswhoown landandwhowereaffectedbyMitch, (2)Average lossesperhousehold incrdobas,

conditionalonhavingsufferedalossonthespecifiedcategory,(3)Averagelossesperhouseholdincrdobas,notconditionalonhaving

sufferedalossorowningland(losses=0ifnolosses).


39/47

39

Table3.PreShockSummaryStatisticsbyTreatmentStatus

Treatment Control Treatment Control(Mitch=1) (Mitch=0) (Mitch=1) (Mitch=0)

Number of members per household 6.06 6.19 6.04 0.149 6.39 6.56 6.34 0.220

[0.052] [0.144] [0.056] [0.154] [0.079] [0.173] [0.089] [0.195]

Number of children per household 2.60 2.65 2.59 0.058 2.93 2.90 2.94 -0.037

[0.037] [0.100] [0.039] [0.107] 0.06 [0.123] [0.066] [0.140]

Age: 0-6 years 1.11 1.14 1.11 0.032 1.32 1.28 1.32 -0.039

[0.021] [0.059] [0.023] [0.063] [0.033] [0.074] [0.030] [0.083]

Age: 6-15 years 1.49 1.51 1.48 0.025 1.62 1.62 1.62 0.006

[0.025] [0.068] [0.027] [0.073] [0.040] [0.082] [0.045] [0.094]

Proportion of female children 0.501 0.522 0.498 0.024 0.50 0.517 0.491 0.026

[0.006] [0.017] [0.007] [0.019] [0.009] [0.019] [0.010] [0.022]

Age of household head 45.29 45.46 45.27 0.197 44.76 45.46 44.56 0.903

(in years) [0.285] [0.772] [0.307] [0.831] [0.429] [0.929] [0.484] [1.048]

Age of children 7.56 7.70 7.53 0.163 7.30 7.63 7.20 0.425 *

(in years) [0.069] [0.194] [0.074] [0.208] [0.098] [0.215] [0.110] [0.242]

Years of schooling: head of household 4.10 2.93 4.28 -1.359 *** 2.45 2.18 2.52 -0.346 *

[0.082] [0.175] [0.090] [0.197] [0.090] [0.165] [0.105] [0.196]

Years of schooling: spouse 3.99 2.89 4.191 -1.303 *** 2.55 2.40 2.60 -0.202

[0.089] [0.186] [0.099] [0.211] [0.096] [0.187] [0.112] [0.198]

Proportion of households headed 0.272 0.196 0.285 -0.089 *** 0.183 0.153 0.192 -0.039

by women [0.008] [0.02] [0.009] [0.022] [0.010] [0.021] [0.012] [0.024]

Proportion of rural households 0.462 0.722 0.419 0.303 *** ---- ---- ---- ----

[0.009] [0.022] [0.010] [0.024]

Annual income per capita 5,864 3,382 6,279 -2,897 *** 3,217 2,613 3,391 -777.9 *

(Mean in 1998 Cordobas) [418.0] [261.2] [485.4] [551.3] [272.0] [275.1] [341.2] [438.3]

Annual income per capita 3,141 1,836 3,425 -1,588 *** 1,590 1,451 1,615 -164.2

(Median in 1998 Cordobas) [284.9] [185.2] [305.4] [342.3] [226.3] [212.0] [236.1] [185.5]

Proportion of households that 0.496 0.439 0.506 -0.067 *** 0.430 0.390 0.442 -0.052

own dwelling [0.009] [0.024] [0.010] [0.027] [0.013] [0.028] [0.015] [0.032]

Distance to closest health center 13.95 16.58 13.49 3.08 *** 14.68 16.93 14.00 2.93 ***

(minutes) [0.104] [0.315] [0.109] [0.334] [0.177] [0.375] [0.200] [0.426]

Distance to closest primary school 11.23 13.57 10.83 2.74 *** 13.94 14.95 13.65 1.30 ***

(minutes) [0.085] [0.249] [0.090] [0.265] [0.144] [0.301] [0.165] [0.343]

Number of households 2,764 396 2,368 1,279 286 993

Rural Sample

Total TotalVariable Difference Difference

Total Sample

Notes.Standarderrorspresented insquarebrackets.Bootstrappedstandarderrorsofmedian incomeobtained from200 replications.The

symbols***,

(**)

and

[*]

stand

for

significance

at

the

1%,

(5%)

and

[10%]

levels,

respectively.

Approximately

15%

of

the

total

number

of

householdsrepresentstheexperimentalgroup.Seetextfordefinitionsofexperimentalandnonexperimentalhouseholds.


40/47


41/47


42/47


43/47


44/47

44

Table7.DifferenceinDifferencePropensityScoreMatchingEstimatesoftheImpactoftheShockon

Investmentsin

Children

(Reduced

Form

Estimates)

NN(10) G E LL

bw = 0.01 bw = 0.01 bw = 0.01 bw = 0.01

School attendance (children between 0.056 0.052 0.047 0.064 1,641 99.1%

6 and 15 years of age) [0.057] [0.054] [0.056] [0.060]

Child labor force participation (children 0.094 *** 0.091 *** 0.085 ** 0.094 ** 1,641 99.1%

between 6 and 15 years of age) [0.035] [0.034] [0.034] [0.037]

Joint school attendance and child 0.086 *** 0.080 *** 0.075 *** 0.085 *** 1,641 99.1%

labor force participation [0.030] [0.028] [0.029] [0.033]

Health care utilization, conditioned on being -0.146 -0.025 -0.175 ---- 122 96.9%sick (children between 0 and 6 years) [0.096] [0.115] [0.123]

Health care utilization, conditioned on being -0.016 -0.066 -0.140 * ---- 124 97.4%

sick (children between 6 and 15) [0.056] [0.068] [0.075]

Z-score weight-for-height -0.218 ** -0.251 ** -0.243 ** -0.234 ** 681 100%

(children between 0 and 4 years of age) [0.098] [0.096] [0.098] [0.099]

Children with severe undernutrition (


45/47


46/47

46

Table9.DifferenceinDifferenceinDifferenceModelsoftheImpactoftheShockonInvestments

inChildreninRuralHouseholds(MultivariateReducedFormEstimates)

Raw OLS Probit

D-D Pooled Pooled

School attendance (children between 0.020 -0.059 0.003 5,679

6 and 15 years of age) [0.079] [0.062] [0.094]

Child labor force participation (children 0.055 0.147 *** 0.136 * 6,154

between 6 and 15 years of age) [0.046] [0.055] [0.100]

Joint school attendance and child 0.076 ** 0.100 ** 0.081 * 5,679

labor force participation [0.038] [0.048] [0.075]

Health care utilization, conditioned on being -0.298 ** -0.061 0.014 813

sick (children between 0 and 6 years) [0.137] [0.196] [0.276]

Health care utilization, conditioned on being -0.158 * -0.189 ** -0.113 * 2,199

sick (children between 6 and 15) [0.084] [0.083] [0.063]

Z-score weight-for-height -0.334 * -0.391 * -- 2,716

(children between 0 and 4 years of age) [0.191] [0.217]

Children with severe undernutrition (


47/47

Table10.DifferenceinDifferenceEstimatesofAdultConsumptionand

AdultNutritionalStatusinRuralHouseholds

(SimpleandMultivariateReducedFormEstimates)

Treatment Control Raw OLS

(Mitch=1) (Mitch=0) D-D Pooled

Monthly Consumption Share: Alcohol 0.207 0.292 -0.099 -0.024 2,097

[0.072] [0.056] [0.163] [0.160]

Monthly Consumption Share: Tobacco 0.566 0.511 0.040 0.201 2,097

[0.105] [0.041] [0.230] [0.197]

Body Mass Index 27.40 28.83 0.028 0.035 5,186

[0.271] [0.249] [0.443] [0.382]

Weight/Height - Percentage of Reference 161.99 176.63 0.876 1.220 5,186

Median WHO [3.091] [2.848] [3.678] [3.499]

Outcome

Pre-shock mean

N

Notes:

Robust

standard

errors

clustered

at

the

municipality

level

presented

in

square

brackets.

The

units

of

analysis

for

the

first

two

outcomesarehouseholdswithatleastonechildundertheageof15.Monthlyconsumptionsharesofalcoholandtobaccocalculatedasthe

ratioofmonthlyexpenditureontheseitemstototalmonthlyconsumptionpercapitaatthehouseholdlevel.Bothshareswereadjustedfor

differences in geographic prices. Preshock demographic covariates in extended models of consumption shares included number of

children,characteristicsof thehouseholdhead (age,sexandschooling)anddummies to identifysingleheadedhouseholds.Otherpre

shock controls included were the log of income per capita and dummies to identify households owning businesses. Models of

anthropometricoutcomesestimatedfromapseudopanelatthemunicipalitylevelbetween1998and2001.Theunitsofanalysisforthese

modelswerewomenwiththefollowingcharacteristics:wife,headofthehouseholdorgrandmother,withagesbetween18and49years,

nopregnantandlivinginhouseholdswithatleastonechildundertheageof15.Preshockdemographiccovariatesinextendedmodelsof

anthropometricmeasures included totalnumberofbirthsandnumberofhouseholdmembers,womensageandschooling.Otherpre

shockcontrolsincludedawealthindexfromprincipalcomponentscoresanddummiestoidentifysingleheadedhouseholds.Seetextfor

definitionsofoutcomes,experimentalandnonexperimentalhouseholdsandbeforeandafteryears,andforassumptionsusedtoconstruct

thepseudopanelfortheperiod19982001.

children vulnerability to shocks hurricane mitch in nicaragua as a natural experiment

Documents