Private health investments under competing risks: Evidence from malaria control in Senegal

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Private health investments under competing risks

Rossi, Pauline; Villar, Paola

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Journal of Health Economics

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10.1016/j.jhealeco.2020.102330

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2020

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Rossi, P & Villar, P 2020, 'Private health investments under competing risks: Evidence from malaria control in

Senegal', Journal of Health Economics, vol. 73, 102330. https://doi.org/10.1016/j.jhealeco.2020.102330

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Private

health

investments

under

competing

risks:

Evidence

from

malaria

control

in

Senegal

Pauline

Rossi

a,∗

_,

_Paola

_Villar

b,1

a_University_of_Amsterdam,_ofﬁce_E6.21,_Roeterstraat_11,_1018WB_Amsterdam,_Netherlands b_University_of_Namur_(UNamur),_CRED,_ofﬁce_633,_Remparts_de_la_Vierge_8,₅₀₀₀_Namur,_Belgium

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received12July2019

Receivedinrevisedform9April2020 Accepted22April2020

Availableonline27April2020 JELclassiﬁcation: D1 H51 I1 J13 O15 Keywords: Healthexpenses Malaria Africa Humancapital Competingrisks

a

b

s

t

r

a

c

t

Thisstudyexploitstheintroductionofhighsubsidiesforanti-malariaproductsinSenegal in2009toinvestigatewhethermalariapreventsparentsfrominvestinginchildhealth.A simplemodelofhealthinvestmentsundercompetingmortalityriskspredictsthatprivate expensestoﬁghtmalariaandotherdiseasesshouldincreaseinresponsetoanti-malaria publicinterventions.Wetestandvalidatethispredictionusingoriginalpaneldatafroma householdexpendituresurveycombinedwithgeographicalinformationonmalaria preva-lence.Weﬁndthathealthexpendituresinmalariousregionscatchupwithnon-malarious regions.Thesameresultholdsforparentalhealth-seekingbehavioragainstotherdiseases likediarrhea.Thesepatternscannotbeexplainedbydifferentialtrendsbetweenregions. Ourresultssuggestthatbehavioralresponsestoanti-malariacampaignsmagnifytheir impactonall-causemortalityforchildren.

∗ Correspondingauthor.

E-mailaddresses:p.rossi@uva.nl(P.Rossi),paola.villar@unamur.be

(P.Villar).

1 _The_authors_are_grateful_to_two_anonymous_referees,_Manisha_Shah, LaurentGobillon,SylvieLambert,PascalineDupas,EricaFieldandAdriana Lleras-Muneyfortheircarefulreadingofthepaperandtheir numer-oussuggestions,andtoKennethHoungbedji,RozennHotte,andKarine Marazyanfortheirinvaluablehelpwithdatamatching.Thepaperwas inspiredbydiscussionswithDenisCogneauandPascalineDupas.The authorswouldliketothankWilliamDow,RachidLaajaj,KarenMacours, PaolaManzini,HesselOosterbeek,YaoPan,ErikPlug,andLiam Wren-Lewisforhelpfulcomments,aswellasparticipantsofseminarsatPSE, UvA,Lille,UNamur,UNU-Wider,LavalandWageningen,andseveral con-ferences:CSAE,JMA, LAGV,DIAL,AFSE,EEA,Oxdev,ESPE,EALE,and NEUDC.PaulineRossigratefullyacknowledgesﬁnancialsupportfromthe NetherlandsOrganisationforScientiﬁcResearch(NWO)throughthe indi-vidualstartinggrantVeni451-17-012.

1. Introduction

Malariahaslongbeentheleadingcauseofchilddeathin Sub-SaharanAfrica,andisexpensivetotreatanddifﬁcult toprevent.Inthiscontext,doesmalariadepressparental investmentinchildhealth?Toanswerthisquestion,we exploitrecentinterventionsthathavemadeanti-malaria productssuddenlyaffordabletomosthouseholds.

Atthebeginningofthe21stcentury,therewasaseries of initiatives coordinated by the international commu-nityundertheRollBackMalariapartnershiptogalvanize theﬁghtagainstmalariainAfrica.Verylarge-scale inter-ventions were implemented to distribute anti-malaria products for free or at highly subsidized prices. On thepreventiveside, 900millioninsecticide-treatednets (henceforth ITNs)have been distributed since theearly 2000s. Nowadays, an estimated two-thirds of children

https://doi.org/10.1016/j.jhealeco.2020.102330

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sleepunderanITNcomparedtovirtuallynonebeforethe distributionstarted.2 _On_the_curative_side,_free_access_to treatmentscalled artemisinin-based combination thera-pies(henceforthACT)hasbeenpromoted.Thescopeofthis interventionismoremodestwithanestimated16%of chil-drentreatedin2015,butthecoverageisincreasingrapidly. Overthepast15years,malariaprevalenceandmortality havebeenhalved(WorldHealthOrganization,2015).

Inthispaper,weexaminehowhealth-seekingbehavior haschangedinresponsetotheseinterventions.Weargue that,beforeRollBackMalaria,poorhouseholdshadfew incentivestoinvestinchildhealthbecausefightingamajor causeofdeathwasunaffordable.Substantiallydecreasing theprice of preventiveand curativeanti-malaria treat-mentsmadeitprofitabletoinvestinhealth,notonlyto avoidmalariabutalsoothercausesofdeath. Dowetal. (1999)werethefirsttoclaimthatsubsidizingtreatments againstonediseasemightboosthouseholds’expensesto preventotherdiseases,becausepeopleallocateeffortsto equalizelifetimesfromallcausesofdeath.Weadapttheir frameworktoshowthatcomplementaritiesalsoariseina stochasticsettingattheextensivemargin.Ourmodel pre-dictsthathouseholdsshouldstartspendingonchildhealth inresponsetoanti-malariapublicinterventions.

Totestthisprediction,weexploitoriginalpaneldata froma Senegalesehousehold survey providing detailed informationonhealthexpenses.Malariacontroleffortsin Senegaltookoffbetweenthetwowavesofthepanel(2006 and2011),providingaperfectsettingtoanalyze house-holds’responses.Ourempiricalstrategyistocomparethe evolutionofchildhealthexpendituresbetweenmalarious andnon-malariousregionsofSenegal.Weﬁndthatchild health expenditures were initially lower and increased more, principallyat theextensive margin,in malarious regions.Thecatch-upwasnotonlyinlevelsbutalsoin com-position,withparentsspendingmoreonpreventativecare. Tripledifferencesexploitingintra-householdcomparisons andvariationinintensityofcampaignsacrosshealth dis-trictssupporttheideathatanti-malariacampaignscaused thechangein spending behavior: thecatch-up(i) does nothappenfor healthspending onadults,who areless atrisk;and(ii)isstrongerin districtswheremore bed-netsweredistributed.Finally,weexploitDemographicand HealthSurveysconductedroughlyatthesametime(2005 and2010)toshowthathealth-seekingbehavioragainst otherdiseases,likediarrhea,increasedmoreinmalarious regions.

Ourresultscouldpotentiallybedrivenbydifferential pre-trendsorchangesinotherdeterminantsof expendi-tures.Weprovideevidencethatthisisnotthecase.When weaccountfortotalincome,distancetohealthfacilities, otherlarge-scalepublichealthcampaigns,rainfallpatterns andregion-speciﬁclineartrends,ourestimatesareeven largerandmoresigniﬁcant.Last,ourresultsare qualita-tivelyunchangedonceweaccountforselectivemigration, attrition,changesinfamilystructure,andusealternative

2 _Another_preventive_intervention_is_to_have_public_agents_spray_the insideofdwellingswithaninsecticide(indoorresidualspraying, hence-forthIRS),butitcoverslessthan5%ofthepopulationatriskinAfrica.

deﬁnitionsofhighprevalence.Weﬁnallydiscussthe rel-ativerolesofthesubsidycomponentandtheinformation componentofanti-malariainterventions.

Broadly speaking, ourpaper ﬁts in the literatureon privateinvestmentsinhumancapitalindeveloping coun-tries. We examine how health investmentsrespond to changes in a speciﬁc mortality risk, while most exist-ingstudieslookateducationalinvestments.Forinstance,

JayachandranandLleras-Muney(2009)showthat

reduc-tions in maternal mortality risk lead to an increase in girls’educationalattainmentinSriLanka.Similarresults havebeenestablishedbytheHIVliterature.Areaswiththe largestincreaseinHIVprevalenceexperiencethelargest declineinschoolinginSub-SaharanAfrica(Fortson,2011). Thisislatermitigatedbyaccesstotherapies,whichreduces perceivedmortalityrisksand promptsparents tospend moreonchildeducation(BaranovandKohler,2018;Lucas et al.,2019).Asforhealth outcomes,Oster(2012)finds that reductions in risky sexual behavior in response to theHIVepidemicinAfricaarelargerinareaswithlower non-HIVmortality.Arelatedstrandoftheliteratureis pri-marilyinterestedinwhetherparentsreinforceorattenuate differences in endowmentsacrosschildren. These stud-ies usepublicinterventionsas anexogenous change in endowmentsandtypicallyfindthatparentalinvestments arehigherforchildrenwhobenefitfromtheintervention (AlmondandMazumder,2013).Ofparticularinterestfor us arethearticlesby AdhvaryuandNyshadham (2014)

andRavindran(2018).Theﬁrstoneshowsthat children

arebreastfedlongerandvaccinatedmoreoftenifthey pre-viouslyreceivediodineinTanzania;andthesecondone arguesthatparentalinvestmentsreinforcetheimpactofan early-childhooddevelopmentprograminIndia.Ourfocus is different: we aim toexplain why complementarities arise, rather thanexploring theconsequences for intra-householdallocations.

Specifically,wearethefirsttousedataonprivatehealth expenditurestovalidateamodelwithcomplementarities betweendisease-specificinvestments.Intheirpaper,Dow

et al.(1999)provideempiricalsupportbyshowingthat

birthoutcomesimproveafterchildvaccinationcampaigns in Sub-Saharan Africa. The evidence is only suggestive, becausewe do not know what additionalinterventions wereembeddedinthecampaigns,andtheymighthave directlyinﬂuenced maternalhealth. Weargue thatdata onhealthoutcomesisnotenoughandthatdataonhealth expensesisnecessarytoimplementaproper,directtestof themodel.Providingempiricalsupportforsuchamodel isusefultoexplainwhypoorpeopleininsalubrious envi-ronmentsinvestlittleintheirchildren’shealth.Treatments toavoidagivendiseasemightbeaffordable,butoncewe recognizethattherearemanydiseases,thetotalcostof ﬁghtingagainstallofthemmaybeprohibitive.

Anothercontributionistoprovideevidenceof behav-ioralresponsestohealthsubsidiesinAfrica.Whetherthese responsesundermineormagnifytheintendedimpactof programsisalong-lastingdiscussion.Alargeliteraturein economicsstudieswhetherpublicinterventionscrowdout privateinvestments.In thecase ofhealthinpoor coun-tries, the debateis not over. On theone hand,Bennett (2012)arguesthatthepublicprovisionofhealthproducts

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therelationshipbetweenaprivate(ITN)andapublic(IRS) investment to ﬁghtmalaria in Eritrea. In line withour results,theyﬁndthathouseholdsweremorelikelytobuy a bednet when public health agents had sprayed their owndwellingswithinsecticide.Inourcontext,weargue thatbehavioralresponsesmagnifytheimpactofRollBack Malariabecausethereisacomplementaritybetween pub-licandprivatehealthexpenditures.

Our paper has strong implications for health poli-cies in Africa. It is often arguedthat horizontal (health system-wide)interventionsshouldbepreferredover verti-cal(disease-speciﬁc)interventions,becausetargetingone diseasemayleadtonegativespillovereffectsinthehealth systembydrawingresourcesawayfromotherconditions. Ourresultssuggestthat,inthecaseofanti-malaria cam-paigns, spilloverscanalsobepositive:peoplereallocate theirresources toﬁght otherdiseases. This mechanism helps explain “one of the surprising results to emerge from large-scale trials of insecticide treated bednets – that the reduction in all-cause mortality with the use ofbednets isconsiderablygreaterthanthereduction in malaria-attributedmortality”(SachsandMalaney,2002, p.684).

The paperisorganized asfollows.Section2 reviews existingevidenceonmalariacontrolandchildhealth. Sec-tion 3 presents a simple model of health investments. Section4 describesthedataand Section5explainsour empiricalstrategy.Section6providesthemainempirical results.Sections7and8dealwithalternativeexplanations androbustnesschecks.Section9discussesthepriceand informationchannelsandconcludes.

2. MalariacontrolandchildhealthinSub-Saharan Africa

Since2000,theevolutionofmalariainAfricahasbeen closelymonitoredbymedicalstudies.3_The_general consen-susisthatRollBackMalariasubstantiallycontributedto reducingmalariaprevalenceandmortalityacrossthe con-tinent(Bhattetal.,2015).InSenegal,threearticlesprovide evidenceofacausalrelationshipbetweenmalariacontrol interventions and progress in child health using differ-ent,complementarymethods:across-sectionalmatching model(Limetal.,2011),alongitudinalstudyofa surveil-lancesite(Trapeetal.,2014)andaplausibilityframework designed by the American Journal of Tropical Medicine

3_See_reviews_by_World_Health_Organization₍₂₀₁₅₎_,_Kesteman_et_al.

(2017)andAppendixAformoredetails.

DemographicandHealthSurveysconductedin19African countries,weestimatethetrendsinchildmortalitybefore andafterthestartofRollBackMalaria,forregionswithlow andhighinitialmalariaprevalence,distinguishingbetween richandpoorhouseholds.Beforetheintervention,overthe period1995–2001,childmortalitywasdecreasingforboth richandpoorhouseholdsinregionswithlowinitial preva-lence.Whereasinregionswithhighinitialprevalence,only therichhouseholdsdisplayadecreasingtrend;therewas noprogress forpoorhouseholds.Aftertheintervention, mortalityalsostartedtodeclinealsoforpoorhouseholds inhighlymalariousregions;theyaretheonlyoneswho exhibitasigniﬁcantbreakintrends.

Thedifferent pre-trendscannot beexplained by dif-ferenthealthinterventionsbetweenregionsbecauserich householdsinmalariousregionswereabletoprogressas muchasrichhouseholdsinnon-malariousregions.They cannot be explained either by a poverty trap, because innon-malarious regions poorhouseholdswere ableto progress as much as rich households. There seems to be some obstacles specific to being poor and living in malariousenvironments.Theexplanationweputforward in this paper is that malaria makes health investments unprofitableforpoorhouseholdsandpreventsthemfrom benefitingfromimprovementsinothercausesofdeath.For richerhouseholds,anti-malariaproductswereaffordable priorto2002,makinghealthexpensesonotherdiseases worthwhile.4

3. Conceptualframework

The main insight from the theoretical literature on healthinvestmentsdecisionsisthatcompetingmortality risksgeneratecomplementaritiesbetweendisease-specific investments.Inaseminalpaper,Dowetal.(1999)argue thattheproductionfunctionofoveralllifetimeunder com-peting risks is Leontief. In a deterministic setting, this impliesthattheoptimalallocationofinvestments equal-izesthetimesofdeathacrossallcauses.Therefore,apublic subsidyrelatedtoaspecificdiseaseraisesprivate incen-tivesto fightall causesof deaths. Chang(2005)further introducesuncertaintyaboutlifetimesin themodeland specifies the conditions under which a disease-specific

4 _An_alternative_explanation_would_be_that_malaria_depresses_adult healththatis,eithermaternalhealthorthebreadwinner’shealth.This wouldlimitpoorhouseholds’abilitytocareandpayfortheirchildren’s health.However,theadulthealthchannelfailstoaccountforthepatterns intotalexpendituresthatwedocumentinSection7.

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Table1

Stylizedfact:trendsinchildmortality.

Highprevalence Highprevalence Lowprevalence Lowprevalence

Poor Rich Poor Rich

LineartrendbeforeRollBackMalaria −0.0014 −0.0038*** −0.0054*** −0.0044***

(0.0015) (0.0013) (0.0007) (0.0010)

LineartrendafterRollBackMalaria −0.0053*** −0.0042*** −0.0057*** −0.0043***

(0.0007) (0.0005) (0.0005) (0.0005)

Observations 134,806 196,943 296,879 317,598

pValueBefore=After 0.033 0.765 0.698 0.950

DHSin:Benin,BurkinaFaso,Cameroon,DRC,Ethiopia,Ghana,Guinea,Kenya,Liberia,Malawi,Mali,Namibia,Nigeria,Rwanda,Senegal,SierraLeone, Uganda,Zambia,Zimbabwe.

Thetablepresentsestimatesofthelineartrendinchildmortalitybefore(1995–2001)andafterthestart(2002–2011)ofanti-malariacampaignsfor differentpopulations:therichesthalfandpooresthalfofhouseholds(accordingtodurablegoodsownership)inregionswithhighandlowinitialmalaria prevalence(≥50%or<50%ofchildrenaged2to10areinfectedbytheparasite).

Wekeptonlychildrenbornatmost10yearsbeforethesurveytoperformtheestimation. Thelastlinereportsthep-valueofatestofequalitybetweenlineartrendsbeforeandafter2002. S.e.in().*p≤0.1,**p≤0.05,***p≤0.01.

reductionin priceis predictedtoincreaseother invest-ments.

Thesemodelsonlydiscussinteriorsolutions,whereas cornersolutionsplayacrucialroleinourcontext.Indeed, themajorityofparents do notspend anythingonchild health.InourSenegalesebaselinedata,lessthan40%report havingsomechildhealthexpendituresduringthe previ-ousyear, and seekingsome medical care when a child issick.Childhealthexpendituresaccountfor3%oftotal expenditures.5_In_Appendix_A.2,_we_propose_a_simple_model explaininghow competing mortalityrisksinfluence the extensivemargini.e.,thedecisiontoinvestornotinchild health.Themainideaisthattheexpectedbenefitof pre-ventingonecauseofdeathdependsontheoverallsurvival function,whichisitselfaproductofcause-specificsurvival functions.Thisgeneratescomplementaritiesbecausesome healthinvestmentsareonlyworthitwhenthemortality riskfromcompetingcauseshasbeensufficientlyreduced. Iftheriskofdyingfromonediseaseishigh,andlittlecan bedonebecausetreatmentsareunaffordable,thismakes otherinvestmentsunprofitable.

Ourmodelpredictsthatprivatehealthexpensesshould increase in response to Roll Back Malaria. Before the intervention,malariawasamajorcauseofdeath.The dis-easeaccounted for 17% of deaths among children aged underﬁveinSub-Saharan Africain 2000(WorldHealth Organization,2015).Ifweexcludeneonataldeaths, result-ingprimarilyfromprematurity,birthasphyxiaandbirth trauma,roughlyonedeathinfourwascausedbymalaria.6 Anti-malaria products were very expensive, represent-ingbetween 20%and 40% of total out-of-pocket health expenditures (Mugisha et al., 2002; Onwujekwe et al., 2000).In acompeting riskframework, this impliesthat those households thatcould not affordto ﬁghtmalaria alsogaveuponsomeotherhealthinvestments.RollBack

5 _To_give_some_perspective,_{out-of-pocket}_health_expenditures_– includ-ingadulthealth–representabout8–10%oftotalhouseholdexpenditures inruralKenyaandurbanIndia(Dupas,2011).

6 _Other_major_causes_were_upper _and_lower_respiratory_infections (inﬂuenza,diphtheria,pneumonia,bronchiolitis,earinfections),diarrheal diseases,andmeasles,accountingtogetherforonehalfofpost-neonatal deaths.

Malariaradicallychangedthesituationbyreducingthecost of preventingandcuringthedisease.Onceanti-malaria treatments becameaffordable,otherhealthinvestments became worthit. Sothedramatic decreasein theprice ofanti-malariaproductsispredictedtoincreasenotonly investmentsagainstmalariabutalsoinvestmentsagainst other diseases.7 _Besides_the_change _in _prices,_Roll _Back Malariamayalsohavechangedbeliefsaboutthemortality riskofmalaria.Thediseaseisclearlyperceivedbyparents asthemain threattotheirchildren –e.g.,Ndiaye etal.

(1994) inSenegal, Tarimoetal. (2000)in Tanzania and

DeressaandAli(2009)inEthiopia.Informationcampaigns arelikelytomaketheriskofdyingfrommalariaevenmore salientandtoincreaseconﬁdenceintreatments(Armand etal.,2017).Inourframework,thiswouldaccentuatethe cross-diseaseeffect.Inthelastsectionofthisarticlewe dis-cusstherelativecontributionofpricesandinformationin explainingthechangesweobserve.

Empirically,asdescribedinthenextsection,weobserve total expenditures on child health and health-seeking behavior against otherdiseases. To formalize, denoteq treatments(curativeandpreventative)boughtby house-holdsatpricep,andQtreatmentsprovidedforfree;malaria is subscriptedby mand other diseases by o. We have data on (pm.qm+po.qo) and Pr((qo+Qo)>0), whereas

ourtheoreticalpredictionsareforqmandqo.Changesin

totalexpenditurescapturebothchangesinquantitiesand changesinprices.Changesinhealth-seekingbehaviorare drivenbychangesinbothcostlyandfreetreatments.To singleoutthevariationinprivateinvestments,wecombine threepredictions:

P1Totalexpendituresonchildhealthshouldincrease. P2The proportion of households with no child health

expendituresshoulddecrease.

P3Health-seekingbehavioragainstotherdiseasesshould increase.

7_Note_that_a_standard_model_of_investment_in_human_capital,_without complementarities,wouldfailtoexplainwhysomeparentsstartspending onotherdiseaseswhenthepriceofadisease-speciﬁctreatmentdecreases.

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wheremalariacontrolendeavorsstartedin2009.We com-binethreedatasetsprovidinginformationbeforeandafter 2009.

4.1. Paneldataonhouseholdexpendituresonchild health

OurmaindatasetisthePovertyandFamilyStructure8 (PauvretéetStructureFamiliale,PSFbyitsFrenchacronym) panelofindividuals.ThePSFdatasetisauniquepanelof individuals,withtheﬁrstwavein2006–2007andthe sec-ondone in2011(DeVreyeretal., 2008).Theﬁrst wave (PSF1)isrepresentativeofthenationalpopulationandwas conductedon1800households.Allindividualsfromthis sampleweretrackeddownduringthesecondwave(PSF2) andinterviewedalongwithallthemembersofthe house-holdtheywerefoundtobelongtoatthatpoint.Thenumber ofhouseholdsplitsissizeableandthesecondwavecovers about3200households.

Oneoriginalfeatureofthisdatasetisthathouseholds weredividedintosub-unitsaccordingtotheirbudgetary arrangements.Thesurveyprovidesinformationon non-healthexpendituresmadeduringthelast12monthsatthe sub-unitlevel.Thisisparticularlyrelevantwhenthe house-holdheadliveswithhisbrother(s)orhasmultiplewives.In thatcase,eachmotherandherdependentchildren9_form onesub-unit,sincethemotherisusuallythemaincaregiver andresponsibleforherchildrenneedsandwell-being.In therestofthepaper,weusetotheterm“household”to refertothesub-unit.Importantlyforourpurpose,the sur-veyregistersinformationonprivatehealthexpenditures paid during thelast 12 monthsbefore theinterview.10 Roughlytwo-thirdsofhealthexpendituresaredevotedto medicationpurchase,followedbyconsultation, hospital-ization,andcommutingtohealthfacilities.Theseexpenses arerecordedattheindividuallevelsowehavetwo poten-tialunitsofobservation:eitherthechildorthesibship(all dependentchildrenofthesamemother).Inthechild-level analysis,wehave moreobservations andwe followthe

8_Momar_Sylla_and_Matar_Gueye_of_the_Agence_Nationale_de_la Statis-tiqueetdelaDémographieofSenegal(ANSD),andPhilippeDeVreyer (UniversityofParis-DauphineandIRD-DIAL),SylvieLambert(ParisSchool ofEconomics-INRA)andAblaSaﬁr(nowwiththeWorldBank)designed thesurvey.ThedatacollectionwasconductedbytheANSD.

9_A_dependent_child_is_a_child_under₁₈_or_an_unmarried_child_living withthemother.Inbothwaves,about17%ofchildrendonotliveinthe samehouseholdastheirmother.

10_Only_1%_of_these_expenditures_are_reimbursed_by_health_insurance schemes.

robustnesstestspresentedinSection8.

The first column of Table2 shows somedescriptive characteristics.Oursampleofinterestismadeupby1594 householdsthatweobserveinbothwaves.Inaddition,789 householdsareonlyobservedinthesecondwave:these mothersdidnothaveanydependentchildreninthefirst wave.Inaddition,573householdsareonlyobservedinthe firstwave:368mothersnolongerhadadependentchild inthesecondwave,and205motherscouldnotbefound. ImplicationsofthisattritionarediscussedinSection8.The mainadvantage ofPSF isto providea panelsowe can estimateregressionswithmotherfixedeffects.Themain disadvantageofthisdatasetisthatonlyexpensesare reg-istered,andnothealth-seekingbehaviorbrokendownby disease.11

4.2. Repeatedcross-sectionsonchildhealthstatus

Togetinformationbydisease,weexploitthe Demo-graphicandHealthSurveys(DHShereafter)conductedin Senegalin2005and2010–2011.Theymeasuretrendsin childmorbidityandhealth-seekingbehavior.DHSreport casesofchildrenagedﬁveandundersufferingfrom diar-rhea,fever,andcoughing.Parentswereaskediftheysought treatmentwhenthechildwassick.DHSalsocollectsdata on vaccination but we do not consider vaccines as an outcome of interest for two reasons. First, they reﬂect changesinpublicratherthanprivatehealthinvestment. AspartoftheExpandedProgramonImmunization, chil-drenarevaccinatedforfreeinpublichealthcarefacilities orduringoutreachactivitieslikemobilevaccinationteams orannualnationalvaccinationcampaigns.Second, cover-agewasalreadyhighin2005:dependingonthevaccine, betweenthreequartersand90%ofchildrenhadbeen vac-cinated(NdiayeandAyad,2006).Themaindrawbackis thatthesurveyisarepeatedcross-section,sochangesover timemaycapturebothchangesinbehaviorandchangesin population.

4.3. Geographicaldataonmalariaprevalence

Ouridentiﬁcationstrategyexploitsthespatialvariation intheinitialexposuretomalaria.WeusetheMalariaAtlas, amapconstructedbyepidemiologists,togetameasure oftheprevalencebeforeanti-malariainterventions(Bhatt

11 _PSF_contains_questions_about_the_health_status_of_children_and health-seekingbehavior,butunfortunatelytheyarenotcomparablebetweenthe twowavesofthepanel.

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Table2

Summarystatistics.

Fullsample Highprevalence Lowprevalence pval(diff)

(1) (2) (3) (4)

Baselinecharacteristicsin2006/2007(PSF1)

Malariaprevalencein2000 0.20 0.37 0.09 0.00

HouseholdinDakarregion 0.30 0.00 0.48 0.00

Householdinotherurbanarea 0.20 0.17 0.22 0.00

Householdinruralarea 0.50 0.83 0.30 0.00

Householdtotalconsumption(thousandsofCFAfrancs) 310.06 185.40 384.33 0.00

Mother’sage 34.99 34.50 35.28 0.11

Panelcharacteristics

NumberofchildreninPSF1 2.60 2.70 2.55 0.02

NumberofchildreninPSF2 2.67 2.74 2.62 0.07

AverageageofchildreninPSF1 7.45 7.42 7.47 0.80

AverageageofchildreninPSF2 7.55 7.22 7.75 0.01

Motherinsamemalariaprevalenceclusterbtw2waves* 0.93 0.93 0.93 0.94

#ofclusters 150 48 102

#ofhouseholdsinPSF1 2167 809 1358

#ofhouseholdsinPSF2 2383 908 1475

#ofhouseholdsinbothwaves 1594 616 978

Data:PSFPanel.PSF1istheﬁrstwavein2006–2007;PSF2isthesecondwavein2011.

*Computedonlyformothersfoundinbothwaves.Wedeﬁneahouseholdasanuclearfamilycomposedofamotherandherdependentchildren. Thelastcolumnshowsthep-valueofthedifferencebetweenhighandlowprevalenceareas.

1euro≈656CFAfrancs.

etal.,2015).Wechose2000asouryearofreferencebecause ofmeasurementissuesinlateryears.12_Both_PSF_and_DHS containGPSinformationaboutthevillageorcityblock(that wecall“cluster”)wheretherespondentlives,makingit possibletomergethemwiththeMalariaAtlas.

5. Empiricalstrategy

Ourmodelpredictshowprivateinvestmentsinchild healthshouldrespondtoanti-malariacampaignsinregions wheremalariaisendemic.Theﬁrstsourceofvariationthat weexploitistemporal,comparinghouseholdexpenditures onchildhealthbeforeandafterthecampaign.However, expenditures may change over time for many reasons unrelatedtotheinterventionof interest.Toaccountfor thetime-varyingdeterminantsofexpenditures,weexploit anothersourceofvariation,comparingmalariousand non-malariousregionsofSenegal.Undertheassumptionthat trendsinthesedeterminantsarethesameinallregions, ourdifference-in-differencesstrategyidentiﬁesresponses toanti-malariacampaigns.

In 2008, the Programme National de Lutte contre le Paludisme (PNLP; National Malaria Control Program) initiatedafour-year-planofmassiveanti-malaria interven-tions.ThePNLP actionswerecoordinatedtoachievethe goalsof theRollBackMalaria partnershipand involved nearly all national and international partners engaged

12 _For_the_year_2000,_estimates_of_prevalence_are_based_on_a_map_of cli-maticsuitabilityformalariatransmission.Forlateryears,estimatesare derivedfromanepidemiologicalmodelcombininginformationoninitial conditionsaswellascoverageandimpactofanti-malariainterventions. Thismodelreliesonstrongassumptionsintermsofexternalvalidity, lin-earityandexogeneity,thatwearenotwillingtomake.Therefore,we prefertousetheestimatesbasedoninitialclimaticconditionsonly.As arobustnesstest,weconsiderestimatesofprevalencein2006andﬁnd comparableresults(cf.TablesA.1andA.2intheappendix).

with malaria preventionand control in thecountry. As shown by Fig. 1, the funds allocated to fight the dis-ease jumped in 2009, once USAID joined the team of donors. The US aid agency coordinates the President’s MalariaInitiative,whichstartedinAngola,Tanzania,and Uganda,andwasgraduallyextendedtootherAfrican coun-tries. Senegal was partof thesecond wave, along with Malawi,Mozambique, and Rwanda.We therefore argue thatlarge-scaleinterventionsweretheresultofan exoge-nouspushbyforeignaiddonors.Beforehand,intheperiod 2002–2008,onlyverytargetedandsmalldistributionsof bednetsandothermalaria-relatedgoodsandservicestook place(President’sMalariaInitiative,2008).Thefirst nation-wideITNsdistributioncampaigntookplaceinJune2009 and targeted specifically children under five and preg-nantwomen.Morethan6millionITNsweredistributed between2008and2010throughoutthecountry,andno specific areawassingled out (PlanNational, 2015). For pregnantwomenandmothersofunder-fivechildren,ITNs couldbeobtainedeitherforfreeorataverysubsidized price:maximumof1euro,insteadofthe10–12euros mar-ketprice(President’sMalaria Initiative,2008).Themain coverage scheme involved a door-to-door approach to deliver a voucher for anITN tobe redeemedlater at a distributionpoint.Thecampaignalsocommunicatedthe importanceofusingITNs.Asaresult,theITNcoverage mea-suredintheDHS-MICSdoubledfrom20%in2006to40% in2010.Moreover,in2010curativetreatments(ACT)were madefreeforallagesinpublichealthfacilities.Tosumup, in2009–2010,thepriceofpreventiveandcurative treat-mentsagainstmalariadecreasedsubstantiallytobecome virtuallyzero.

Beforeanti-malariacampaignsstarted,therewas con-siderablevariationinmalariaprevalenceacrossregionsof Senegal.ThemapinFig.2representstheproportionof chil-dreninfectedbytheparasitein2000.Theproportionranges frombelow2%inthearidregionofLougatoabove60%in

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Fig.1.Temporalvariation:Fundsallocatedtoanti-malariainterventions.

Source:WorldHealthOrganization(2015).Theﬁgureshowstheamountoffundsallocatedtoanti-malariainterventionsinSenegal.Thereisajumpin 2009,whichcoincideswiththeﬁrstnationwidedistributionofbednetsandthefreedeliveryofcurativetreatmentsinpublichealthfacilities.Wehavedata onprivatehealthinvestmentsbefore(in2005–2006)andafter(in2010–2011)thejump.

theareasborderingGuinea.Thenationalaverageis24%. Weusethisthresholdtodeﬁneareaswithalowmalaria prevalence(belowaverage,indarkblueonthemap)and areaswithahighprevalence(aboveaverage,inlightblue andyellowonthemap).Inlowprevalenceareas,the aver-ageprevalencerateisbelow10%,whichisconsideredby epidemiologistsashypoendemic(Bhattetal.,2015).Atthe bottomofFig.2,amapshowsthelocationofthe150 clus-terssurveyedinPSF,eitherinhighprevalenceareas(black triangles)orinlowprevalenceareas(graycircles).Weuse theHigh-Lowcategories inthedescriptivestatistics, for clarityofexposition,whereasintheeconometric speciﬁ-cation,weexploitthecontinuousvariationinprevalence.

Weproceedtoadifference-in-differencesanalysiswith individualﬁxedeffects:

Yi,t=˛0+˛1 Prevalencei+˛2 Postt

+˛3 Postt×Prevalencei+ui+εi,t (1)

Yi,t is theoutcome ofinterest:theannuallevelof child

healthexpenditurespercapitainthehousehold (predic-tion1),adummyvariableequaltooneifthehousehold hasnohealthexpenditure(prediction2),adummy indicat-ingwhetherachildwassickandleftuntreated(prediction 3).Prevalenceiindicatestheinitiallevelofprevalencein

thecityblockorvillage(the“cluster”)wherehouseholdi lives.Posttequalsoneifthesurveytookplaceafter2009.

Standarderrorsareclusteredattheclusterlevelbecause malariaprevalenceismeasuredatthatlevelandwehave enoughclusters.Wetestpredictions1and2usingpanel data,in which casewe include amother ﬁxedeffectui

(notethat˛1cannotbeestimated).Wetestprediction3

usingrepeatedcross-sections,inwhich casewe arenot abletoincludeamotherﬁxedeffect.InSections7and8, weintroducetime-varyingcontrolstoaccountforpotential confounders.

Theidealexperiment would betorandomlyallocate freeanti-malariatreatmentsinendemicareasandto exam-inetheimpactonhouseholds’health-seekingbehavior.13

13_This_experiment_is_run_in_Dupas₍₂₀₁₄₎_with_another_objective:_check whethersubsidizingITNdecreasesthewillingnesstopayforanother healthproduct,waterchlorine.Sheﬁndsnosigniﬁcantimpactand there-forerulesoutcross-productentitlementeffects.Butthesamplesizeis

Inoursetting,anti-malariacampaignswereimplemented inthewholecountryatthesametime.Thereisnoarea excludedortargetedatalaterstagethatcanbeusedasa controlgroup.Instead,weuseareaswherethecampaign couldnotmakeadifferencebecausemalariawasalready undercontrol.Ourcounterfactualisnotwhatwouldhave happenedinmalariousregionsintheabsenceofthe inter-vention, but what would have happened if there was nomalaria in theseregions. Such a strategy is used by

Bleakley(2010),Cutleretal.(2010),andLucas(2010)to assess the impactof childhood exposureto malaria on socio-economicoutcomes.Theyexploitmalaria eradica-tioncampaignsinseveralAmericanandAsiancountries. In the same vein, we exploit Roll Back Malaria to test thehypothesisthat,whenanti-malariatreatmentsarenot heavilysubsidized,thediseasepreventspoorhouseholds frominvestinginchildhealth.

This strategy was recently formalized and named “difference-in-differencesin reverse” in an econometric articlebyKim andLee(2019).In astandard difference-in-differences(DD),thetreatmentandcontrolgroupsare similarinthepre-periodanddifferentinthepost-period. In adifference-in-differences inreverse (DDR),theyare differentat thestart, and becomesimilar.Thischanges the interpretation of the coefficient on the interaction term.DDidentifiespost-periodeffects, whileDDR iden-tifiespre-periodeffects.Inourcontext,wecandefinethe treatmentas“beingaffectedbymalaria.”Inthepre-period, malariaprevalenceishigherinsomeareas:theyformour treatmentgroup.Inthepost-period,malariaprevalenceis loweverywhere,asdocumentedinSection2.The interac-tiontermthereforecapturestheimpactofmalariainthe pre-period,providedotherdriversofhealthexpenditures evolvedinthesamewayforallhouseholds.

Isthisassumptionlikelytohold?Columns2and3of

Table2 provide somedescriptive statistics of our

sam-ple,byinitialmalariaprevalence.Householdstructureis roughlythesameeverywhere: thereare2.6childrenon average,themotherisaround35yearsoldandtheaverage ageofchildrenisseven.Butruralandpoorerareastend

smallandthecoefﬁcientispositiveandlarge,suggestingthatsubsidizing ITNmighthavefosteredtheadoptionofwaterchlorine.

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Fig.2.Spatialvariation:Initialmalariaprevalence.

Notes:TheMalariaAtlasmapshowstheproportionofchildrenbetweenage2and10infectedbyPlasmodiumfalciparumin2000.Itrangesfrombelow 2%toabove60%.Weusethenationalaverage,24%,todeﬁneareaswithalowmalariaprevalence(belowaverage,indarkblue)andareaswithahigh prevalence(aboveaverage,inlightblueandyellow).ThemaponthebottomshowsthelocationofPSFclustersinhigh(blacktriangles)andlow(gray circles)areas.(Forinterpretationofthereferencestocolorinthisﬁgurelegend,thereaderisreferredtothewebversionofthisarticle.).

tobemoreaffectedbythedisease.Wearguethat,despite thesedifferences,comparingareaswithhighandlowinitial prevalencecanbeinformativeforfourreasons.First,weuse previousDHSwavestolookatpre-trendsinhealth-seeking behavior and show that the catch-up had not started beforehand.Second,weexploitthepanelstructureofPSF toaccountforcompositioneffectsinhealthexpenditures.

Indeed,mother ﬁxedeffects allowustodisentanglethe effectofpublicsubsidiesfromthatofchangesinpopulation characteristics.Whatremainstobediscussedarechanges intheenvironmentthatcouldhaveaffectedlowandhigh prevalenceareasdifferentlyduringourperiodofinterest. Thisis ourthirdtest:we examinedifferentialtrendsin otherdeterminantsofchildhealthexpenses:totalincome,

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Fig.3.Childhealthexpenditures,beforeandafteranti-malariainterventions,byinitialprevalenceofmalaria.

Notes:Data:PSFPanel.Theﬁgureshowstheevolutionof(a)annualhealthexpendituresperchild,inthousandsofCFAfrancs(1euro=656CFAfrancs)and (b)thefractionofhouseholdswithzerospendingonchildhealth,inlowandhighmalariaprevalenceareas,before(2006inblack)andafter(2011ingray) anti-malariainterventions.Graph(a):thedifference-in-differencesisequalto3739CFAfrancs(p-value=0.051).Graph(b):thedifference-in-differences isequalto−13p.p.(p-value=0.012).

healthinfrastructure,otherhealthcampaigns,rainfall pat-terns,andgeographicaldynamics.WeshowinSection7

thattheyareunlikelytoexplainourresults.Fourth,weuse twotripledifferencestocircumventtheconcernthat unob-servedtime-varyingheterogeneitybetweenhighandlow prevalenceareasisdrivingthecatch-up.

6. Results

6.1. Healthexpenditures

Fig.3ashowsdescriptivestatisticsonhealth expendi-turesinthetwowavesofPSF,comparingareaswithahigh andlowprevalenceofmalaria.Intheﬁrstwave,households inhighprevalenceareasspentmuchless:onaverage,1720 CFAfrancsperchildperyearagainst7335inlowprevalence areas.Betweenthetwowaves,theytripledtheir consump-tionofhealthcommodities,upto5215,whiletherewas nosigniﬁcantchangeinlowprevalenceareas.Fig.3b dis-playstheextensivemargin.Householdsinhighprevalence

areaswere15.7p.pmorelikelytonotpayexpensestoward healthin2006thantheothers.In2011,theyhadalmost caughtup.BetweenthetwoPSFwaves,theproportionof householdswithzeroexpendituredecreasedby17.7p.p whereasamoremoderatedownturnof4.7p.phappened inlowprevalenceareas.14

InTable3,weincludemotherfixedeffectsandexploit thecontinuousvariationinprevalence.Regardingthe aver-agelevel of expenditures, in column 1, resultsare less strong,suggestingthatpartofthecatch-upisdrivenby compositioneffects.Butthereisalsoevidenceofachange inbehavior:thecoefficientontheinteractiontermisno longersignificantatconventionallevels,butthemagnitude remainslarge.Giventhedistributionofinitialprevalence, it corresponds to a catch-up of 3835 (resp. 1726) CFA

14 _Estimation_of_the_{difference-in-differences}_regression_without_fixed effectscanbefoundinTableA.3intheappendix.Thecoefficientsonthe interactiontermsaresignificantlydifferentfromzero.

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Table3

Difference-in-differences:childhealthexpenditures.

Speciﬁcation Level Withouttop1% Log Ihs Zerospending

(1) (2) (3) (4) (5)

Post×Prevalence 6.392 3.375* 3.092** 0.929** −0.345**

(5.800) (1.885) (1.348) (0.369) (0.168)

Post 0.546 −0.139 0.098 0.014 −0.015

(1.776) (0.589) (0.342) (0.105) (0.040)

MotherFE Yes Yes Yes Yes Yes

N 4550 4504 4550 4550 4550

Meanofdep.var.in2006 5.24 3.22 −3.67 0.88 0.61

Data:PSFPanel.

Difference-in-differencesregressionwithmotherﬁxedeffects.Linearprobabilitymodels.Thelevelofobservationisahousehold,composedofamother andherdependentchildren.

Dependentvariables:healthexpenditurespercapitaforchildreninthehousehold(thousandsofCFAfrancs)incolumns(1)–(4);dummyfornohealth expendituresforanychildinthehouseholdincolumn(5).

Column(1):speciﬁcationinlevel;column(2):excludesthetop1%;column(3):logtransformation–adding0.001todealwithzeros;column(4):inverse hyperbolicsinetransformation.

Standarderrors,in(),areclusteredatthePSFclusterlevel. *p≤0.1,**p≤0.05,***p≤0.01.

francsbetweenareaswiththehighestandthelowest ini-tialprevalence(resp.aboveandbelowtheaverageinitial prevalence).Toputthesenumbersinperspective,Lépine andLeNestour(2012)reportthat,inruralSenegalin2009, healthfacilitieschargedanaverageof200and 100CFA francsfor adultand child outpatient care, respectively. Sohouseholds increasedtheirexpensesonchild health byanamountwhichisnotnegligible.Butthisamountis lowerthanthepriceofa bednetbeforemalariacontrol effortsstarted–around6500–8000CFAfrancs(President’s MalariaInitiative,2008).Thisisconsistentwithourclaim that,intheﬁrstwave,householdscouldnotafford preven-tativemalariameasures.

ResultsattheextensivemarginarereportedinTable3, column5.Thecoefficientontheinteractiontermremains of similar magnitude and significance as without fixed effects.Thefractionofhouseholdsthatstartedinvesting inchildhealthafteranti-malariacampaignswashigherby 21p.p.(resp.9p.p.)inareaswiththehighestinitial preva-lence,comparedtothelowestprevalence(resp.inareas abovethe averageinitialprevalencecompared toareas below).Theextensivemarginspecificationgivesmore pre-cise results than the average spending per capita. The reasonisthathealthexpenditurestypicallydisplaylong righttails.Thereareseveraloptionstodealwiththistype of distributions: excluding outliers (column 2), using a logtransformation (column3) oran inversehyperbolic sinetransformation(column4).Inallspecifications,the difference-in-differencescoefficientisstatistically signifi-cantatconventionallevels.

Wefurtherinspectthedistributionofnon-zerohealth expenditures to understand responses at the intensive margin.Fig.4aplotsthekerneldensityestimatesbefore and after the campaigns, in high and low prevalence areas.Asexpected,distributionsareheavilyskewed, fea-turinglowmodesandlongrighttails.Inlowprevalence areas,thedistributiondoesnotchangebetweenthetwo waves. In high prevalence areas, people switch from very low amounts toslightly higher amounts, converg-ingtoward thedistributionin low prevalenceareas.To formallytestwhetherthechangeinthedistributionof

non-zerohealthexpenditureswasdifferentbetweenhighand lowprevalenceareas,weestimateaquantileregression.

TableA.4intheAppendixreportsthequantile difference-in-differences estimates. Coefficients on Post are never significant, confirming the absence of changes in low prevalenceareas.Coefficientsontheinteractiontermare allpositive,confirmingthatthedistributionshiftedtothe rightinhighprevalenceareas.Importantly,thequantile difference-in-differencesestimateatthemedian(Q50)is 1500CFAfrancs,significantat1%,representinganincrease by75%relativetothebaseline.Asexpected,coefficientsare preciselyestimatedforlowerquantiles,whereasstandard errorsarelargeforupperquantiles.Thus,thereisapositive responseattheintensivemarginaswell.Butitisdifficult todetectduetothelargevarianceinhealthexpenditures, especiallyatthetop.

Last,wediscusschangesinthecompositionofhealth expendituresillustratedbyFig.4b.Inlowprevalenceareas, medicationaccountsfor60%ofexpenses,consultationfor 31%,commutingtohealthfacilitiesfor6%and hospitaliza-tionfor3%.Thebreakdowndoesnotchangeatallbetween thetwowaves.Thepictureisdifferentinhighprevalence areas. Before anti-malaria campaigns, households spent a much larger shareon medication(75%) and hospital-ization(10%)andonly9%onconsultation.Thescopefor preventative care seemed very limited. The breakdown changes markedly aftertheintroductionofanti-malaria subsidiesandconvergestowardthecompositionobserved inlowprevalenceareas:lessonmedicationand hospital-ization,moreonconsultation.Parentsallocatedadditional expensesdifferently,withasmallersharespentoncuring episodesofmalariaandalargersharespentonmedical examination.Anotherwaytodistinguishbetween preven-tativeandcurativetreatmentsistolookatsickandnon-sick childrenseparately(cf.Fig.A.1intheappendix).Thebulk ofhealthexpendituresismadeonsickchildren.Still,we observeaninterestingpatternonnon-sickchildren: spend-inginhighprevalenceareasincreasedfromvirtuallyzero intheﬁrstwavetoaround2000CFAfrancsinthesecond wave,whileremainingstableinlowprevalenceareas.The difference-in-differencesissigniﬁcantat10%.Allinall,we

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Fig.4.Non-zerochildhealthexpenditures,beforeandafteranti-malariainterventions,byinitialprevalenceofmalaria.

Notes:Data:PSFPanel.Figure(a)plotsthekerneldensityestimatesofnon-zeroexpendituresonchildhealthbefore(graphontheleft)andafter(graph ontheright)anti-malariacampaigns,inlow(grayline)andhigh(blackline)malariaprevalenceareas.Figure(b)showsthecompositionofchildhealth expendituresbefore(graphsontheleft)andafter(graphsontheright)anti-malariainterventions,inlow(graphsatthetop)andhigh(graphsatthebottom) malariaprevalenceareas.Example:inlowprevalenceareasin2006,31%ofexpenditureswerespentonconsultation;6%oncommutingtohealthfacilities; 60%onmedication;and3%onhospitalization.

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Table4

Difference-in-differences:health-seekingbehavior.

Sample Allchildren Allchildren Sickchildren

Dependentvariable Childissickanduntreated Childissick Childisuntreated

Diarrhea Feverandcough Diarrhea Fever Cough Feverandcough Diarrhea Feverandcough

(1) (2) (3) (4) (5) (6) (7) (8)

PanelA:Usingalinearprobabilitymodel

Post×Prevalence −0.050 −0.115* −0.000 −0.137** −0.021 −0.092 −0.216* −0.129 (0.054) (0.059) (0.061) (0.064) (0.067) (0.076) (0.113) (0.111) Post −0.038*** −0.029* −0.022 −0.047** −0.053** −0.059** −0.128*** 0.002 (0.014) (0.017) (0.017) (0.021) (0.022) (0.024) (0.036) (0.032) Prevalence 0.067 0.052 0.019 −0.014 −0.195*** −0.130** 0.240*** 0.357*** (0.045) (0.051) (0.048) (0.050) (0.054) (0.061) (0.081) (0.081) Constant 0.148*** 0.206*** 0.205*** 0.293*** 0.299*** 0.399*** 0.726*** 0.512*** (0.011) (0.014) (0.012) (0.015) (0.017) (0.018) (0.026) (0.023) MotherFE No No No No No No No No N 21,251 21,251 21,218 21,225 21,226 21,234 4221 6702

Meanofdep.varin2005 0.17 0.22 0.21 0.29 0.25 0.36 0.79 0.60

PanelB:Usingalogitmodel

Post×Prevalence −0.319 −0.774** 0.009 −0.879** −0.419 −0.566 −1.379** −0.572 (0.429) (0.373) (0.381) (0.349) (0.409) (0.351) (0.602) (0.474) Post −0.349*** −0.185* −0.140 −0.226** −0.242* −0.238** −0.547*** 0.016 (0.122) (0.111) (0.109) (0.111) (0.124) (0.109) (0.180) (0.132) Prevalence 0.483 0.303 0.117 −0.068 −1.062*** −0.563** 1.479*** 1.515*** (0.315) (0.296) (0.286) (0.245) (0.304) (0.265) (0.506) (0.354) Constant −1.746*** −1.347*** −1.354*** −0.881*** −0.839*** −0.406*** 0.946*** 0.036 (0.083) (0.083) (0.075) (0.073) (0.087) (0.077) (0.148) (0.096) MotherFE No No No No No No No No N 21,251 21,251 21,218 21,225 21,226 21,234 4221 6702

Meanofdep.varin2005 0.17 0.22 0.21 0.29 0.25 0.36 0.79 0.60

Data:DHS2005andDHS2010.Samples:Allchildrenunderage5incolumns(1)–(6).Sickchildrenunderage5incolumns(7)–(8). Difference-in-differencesregressionwithoutmotherﬁxedeffects.PanelA:Linearprobabilitymodel.PanelB:Logitmodel. Dependentvariablesin:

Columns(1)–(2)and(7)–(8):Dummyequalto1ifthemotherdidnotseekanymedicaladviceormedicaltreatmentincaseofdiarrhea(columns(1)and (7)),andfeverand/orcough(columns(2)and(8)).

Columns(3)–(6):Dummyequalto1ifthechildsufferedfromdiarrhea(column(3)),fever(column(4)),cough(column(5)),andfeverand/orcough(column (6))inthelasttwoweeks.

Standarderrors,in(),areclusteredattheDHSclusterlevel. *p≤0.1,**p≤0.05,***p≤0.01.

ﬁndevidenceofacatch-upinhealthexpendituresnotonly inlevelsbutalsoincomposition.

6.2. Health-seekingbehavioragainstotherdiseases

Table 4 Panel A presents the estimates of Eq. (1)

for health-seeking behaviors related to diarrhea and a fever/cough.15_In_columns₁_and_2,_we_look_at_the probabil-ityofbeingsickandleftuntreatedamongallchildrenunder ageﬁve.Betweenthetwowaves,thisprobabilitydecreased everywhere,andthedeclinewasstrongerin areaswith higherprevalence.Istheprogressdrivenbyfewerdisease episodesorbymoreremedialcare?Inanutshell,weﬁnd evidenceofchangesinhealth-seekingbehavioronthe pre-ventivesideforfever,andonthecurativesidefordiarrhea. Incolumns3to6,welookattheprobabilityofbeingsick. Thelikelihood of a fever, a major symptomof malaria, decreasedmoreinmoremalariousareas,supportingthe ideathatthedistributionofITNswaseffectivein

prevent-15 _These_are_the_only_diseases_on_which_we_have_information._In_the_data, wecandistinguishbetweenfeverandcoughsfortheprobabilityofbeing sick,butnotfortheprobabilityofbeingtreated.

ingmalariousepisodes.16 _Trends_for_diarrhea_and_cough arenotstatisticallydifferentbetweenareas.Incolumns7 and8,werestrictthesampletosickchildrenandlookat theprobabilityofbeingleftuntreated.Inthecaseof diar-rhea,parentsweresignificantlylesslikelytoseekmedical adviceortreatmentinhigherprevalenceareasin2005.The correlation entirelydisappearsbetweenthetwo waves, suggestingthatparentsstartedactingupondiarrheaonce theyhadbeenrelievedfrommalaria.Inthecaseoffeverand coughs,thecoefficientontheinteractiontermisnegative, butsmallerandnotsignificant.Thismaybeexplainedbya strongattenuationbiasgeneratedbyselectionintoillness, since thepool of children sufferingfromfever changed inmalariousareasafterthecampaigns.Duetodata lim-itations, we cannot run the regression for only coughs.

16_In_levels,_parents_are_not_more_likely_to_report_that_a_child_was_sick_in malariousareas,whichisatoddswiththemedicalliteraturedescribing malaria’stollonchildhealth.Oneexplanationisthatmeasuresof self-reportedhealthareinﬂuencedbysocio-economicstatus.Forinstance,

Sen(2002)showsthatKerala,thestatewiththehighestlifeexpectancyin

India,consistentlydisplaysthehighestratesofreportedmorbidity.This mayexplainwhyreportedmorbidityisnotlowerinlowerprevalence areas,wherepeopleareonaveragebetteroff.

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samepatternholdsinbothcountries:highprevalenceareas catchupwithlowprevalenceareasinthesecondwave.The intensityofthecatch-updependsontheinitialgap–higher inRwanda,lowerinKenya.

6.3. Tripledifferencesexploitingadultsandcampaign intensity

Accordingtothemodel,theperceivedchangeinmalaria mortalityriskisthekeydriveroftheresponse.Holding initialprevalenceconstant,healthexpensesaretherefore predictedtoincreasemoreinsubpopulationswithahigher initialriskorwithastrongerchangeinrisk.Childrenare more likely to diefrom malaria than adults.17 _We _can thereforeusetheevolutionofadulthealthexpendituresto accountforconfoundingchangesinthedemandandsupply ofhealth.Sofar,weonlyexploitedinformationon chil-dren,butweobserveannualhealthexpendituresforeach memberofthehousehold.Wearethusabletoconstruct theaveragehealthspendingperadultineachhousehold.18 Toidentifyacausalimpact,weneedtoassumethatother driversofhealthexpendituresspecifictochildrenevolved inthesamewayinhighandlowinitialprevalenceregions. ResultsarereportedinTableA.6intheappendix.Between thetwowaves,adulthealthexpenditurestendtoincrease, andthechangeisnotcorrelatedwithmalariaprevalence. The coefficient on Post×Prevalence is small and never statisticallysignificant.Bycontrast,childhealth expendi-turesdoincreaseproportionallywithmalariaprevalence. Coefficientsonthetripleinteractionhavethesamesign andmagnitudeasthedifference-in-differencesestimates reportedinTable3.Thissuggeststhatthedemandand

sup-17_In_2000,_91%_of_{malaria-related}_deaths_affected_children_aged_under ﬁve:694,000deathsinchildrenoutof764,000deathsinallagesinAfrica

(WorldHealthOrganization,2015).

18_{Speciﬁcally,}_we_estimate_the_following_equation:

Yi,m,t=

ˇ0+ˇ1 Postt+ˇ2 Postt×Prevalencem+ˇ3 Childreni +ˇ4 Prevalencem×Childreni+ˇ5 Postt×Childreni +ˇ6 Postt×Prevalencem×Childreni+um+εi,m,t

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WhereYi,m,tistheannualhealthspendingperadultifChildren=0andper childifChildren=1inthehouseholdofmotherminwavet.Weincludea motherﬁxedeffectumtocomparetheevolutionofadultsandchildrenin thesamehousehold.ˇ2capturesanychangeinhealthexpenditures cor-relatedwithinitialmalariaprevalence.Thesechangesmaybecausedby reasonsunrelatedtomalariacontrol–theseregionsaredifferenttostart with.Ortheymayhappenbecauseofmalariacontrol,butnotthroughthe competingriskschannelsincemalariamortalityislowamongadults.The competingriskchannel,peculiartochildren,iscapturedbyˇ6.ˇ6 iden-tiﬁesanadditionalchangeinhealthexpendituresonchildren,compared toadults,whichiscorrelatedwithinitialprevalence.

intensecampaigns.EstimatesareshowninTableA.7inthe appendix.Thecatch-uphappenseverywhereand seems tobe faster in districts where more bednets were dis-tributed.CoefficientsonPost×Prevalencearethesameas thedifference-in-differencesestimates,andcoefficientson thetripleinteractionarelarge,butnotsignificant.Bya pre-ponderanceoftheevidence,wethereforearguethatpeople indeedrespondedmorestronglywhentheyexperienceda strongerchangeinmalariamortalityrisk.

7. Alternativehypotheses

Overall,weﬁndthat,afteranti-malariacampaigns, pri-vatehealthinvestments,intotalandagainstotherdiseases, increased more in highly malarious areas than in low prevalenceareas.Thisisconsistentwithourframework ofinvestmentsundercompetingrisks.Inthissection,we discussﬁvealternativehypothesisthatmaygeneratethe sameempiricalpatterns:pre-trends,totalincome,access tohealthcare(infrastructureandcampaigns),rainfall,and geographical dynamics.We explain whythese explana-tionsdonotconfoundourresults,andinfactmakethem stronger.

7.1. Pre-trends

Thefirstconcernweneedtoruleoutisthat malari-ousareasmayhavestartedtocatchupwithnon-malarious areaslongbeforethecampaigns.UsingDHSconductedin 1992and1997,wecanlookattheevolution of health-seekingbehavioragainstotherdiseases. Fig.5plots the proportion of sick and untreated children in the case ofdiarrhea. Therearesomefluctuations,1997 beingan especiallygoodyear.19_But_these_{fluctuations}_are_similar everywhere;inparticular,thecatch-upobservedbetween 2005and2010wasnotunderwayintheearly2000s. 7.2. Totalincome

The second alternative hypothesis argues that total income grew faster in highly malarious areas. It might bedue toconfoundingfactors,orspeciﬁcallybecauseof malariacontrolifhealthimprovementsbeneﬁtedadults,

19 _We_investigated_whether_this_could_be_driven_by_seasonality_in diar-rhoealdiseasesbutitdoesnotseemtobethecase.Allwaveswere conductedinthewinterandamethodologicalreportassessingthe qual-ityofDHShealthdataﬁndsnoevidenceofseasonalityofdiarrheainthe 1997Senegalesesurvey(Pullum,2008).

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P. Rossi and P. Villar / Journal of Health Economics 73 (2020) 102330 Table5

Testsforalternativeexplanations.

Hypothesis Income Healthinfrastructure Othercampaigns Communityhealthworkers Rainfall Dependentvariable Household

total expenditures Distanceto health facilitiesisa concern Childis untreatedin caseof diarrhea Expenditures inlevel Expenditures inlog Zero spending Expenditures inlevel Expenditures inlog Zero spending (1) (2) (3) (4) (5) (6) (7) (8) (9) Post×Prevalence −84.226 0.064 −0.309** 11.253 3.094* −0.325* 8.881 3.940*** −0.442** (68.264) (0.144) (0.133) (7.316) (1.582) (0.194) (7.096) (1.447) (0.177) Post 23.223 −0.041 −0.125*** 0.734 0.098 −0.014 0.588 0.112 −0.017 (22.569) (0.044) (0.036) (1.770) (0.341) (0.040) (1.772) (0.343) (0.041) Prevalence 0.578*** 0.240*** (0.109) (0.081) Constant 0.250*** 0.726*** (0.032) (0.026)

Controls No No Zincdistribution Numberofhealthworkers Rainfallshocks

MotherFE Yes No No Yes Yes Yes Yes Yes Yes

N 4550 19,098 4221 4550 4550 4550 4548 4548 4548

Meanofdep.var.in2006 310.1 0.40 0.79 5.24 −3.67 0.61 5.24 −3.67 0.61 Data:columns(1)and(4)–(9):PSFPanel.Thelevelofobservationisahousehold,composedofamotherandherdependentchildren.Columns(2)–(3):DHS2005andDHS2010.Samples:mothersofchildren under18incolumn(2)andsickchildrenunderage5incolumn(3).

Difference-in-differencesregression.LinearProbabilityModel.

Dependentvariables:column(1):Totalexpendituresforthehouseholdinthelast12monthsinthousandsofCFAfrancs;column(2):Dummyequalto1ifdistancetothenearestfacilityisamajorproblemwhen therespondentissickandwantstogetmedicaladviceortreatment;column(3):Dummyequalto1ifthemotherdidnotseekanymedicaladviceormedicaltreatmentincaseofdiarrhea;columns(4)and(7): Healthexpenditurespercapitaforchildreninmother’shousehold(thousandsofCFAfrancs),inlevel;columns(5)and(8):logtransformation–adding0.001todealwithzeros;columns(6)and(9):Dummyfor nohealthexpendituresforanychildinthemother’shousehold.

Controls:Column(3):Quantityofzinctabletsdeliveredintheregion.Columns(4)–(6):numberofcommunityhealthworkersintheregion.Columns(7)–(9):annualrainfalldeviationfromthecluster-level mean.

*p≤0.1,**p≤0.05,***p≤0.01.

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Fig.5.Pre-trendsinhealth-seekingbehavior.

Notes:Data:DHS1992,DHS1997,DHS2005,andDHS2010.Sample:Allchildrenunderage5.Theﬁgureshowstheproportionofsickanduntreated childreninthecaseofdiarrhea.Barsrepresentthe90%conﬁdenceintervals.

inparticularmothersandbreadwinners.Thiscouldlead toapositiveincomeeffectinhighlymalariousareas,for example,throughanenhancementoflaborproductivity. If health investmentsare normal goods,an increase in incomeshouldtranslateintoanincreaseinhealth expen-ditures. To tackle this issue, we test whether there is a differentialrisein all expenditures.We measuretotal consumption at the household level, including adults. DescriptivestatisticsinTable2indicatethathouseholds inhighlymalariousareaswerepoorerthantheothersto startwith:185vs.384thousandsofCFAfrancs.Column

1 inTable5 showsthat theydidnotcatchupbetween

thetwowaves.Thecoefficientontheinteractiontermis small, insignificant,and ifanything negative.Compared tolow prevalenceareas, households inhighprevalence areasdidnotbecomericher;theymerelyreallocatedpartof theirexpensestochildhealth.Wetriedtoidentifywhich expenseitemsexperienceda decrease,buttheamounts in question are toosmall tobe detected.20 _We specifi-callylookedatadulthealthexpenditurestoseeifexpenses arereallocatedfromparentstochildren. Thisis notthe case: adult health expenditures have increased every-where betweenthe two waves. This is consistentwith thecompetingriskchannelandnotwiththeadulthealth channel.

20_Child_health_expenditures_account_for_3%_of_the_household_total con-sumption.

7.3. Accesstohealthcare

The third alternative hypothesis is about access to healthcare.Wefocusonthreekeyelementsthatchanged duringourperiodofinterest.21_First,_we_exploit informa-tionaboutdistancetohealthfacilitiesrecordedintheDHS. Mothers areasked whetherdistance is a main concern whenseekingmedicaladviceortreatmentforthemselves. AsshownbyTable5,column2,accesswasagreater prob-leminareaswithmoremalariain2005andtherewasno catch-upbetweenthetwowaves.Ifanything,accessseems tohave improvedless in higher prevalenceareas. Sec-ond,therewasaconcomitantcampaignagainstdiarrhea.22 In2010,approximately6millionzincdispersibletablets weredeliveredtoSenegalbyUNICEF,andtheywereonly distributed in a few regions (Derosena, 2011).If these regionswerepredominantlyhighlymalariousareas,this mayexplainthechangeinhealth-seekingbehavioragainst diarrheadescribedinTable4,column7.Infact,the oppo-sitehappened:theinterventionwaspilotedﬁrstinthelow prevalenceregionofThies.Usingdatapublishedina tech-nicalreportfromUNICEF(Derosena,2011),weareableto controlforthequantityoftabletsdistributedineachregion

21 _Ideally,_we_would_need_geo-coded_data_on_health_provision_before_and after2009tocomprehensivelyexaminethismechanism.ServiceProvision Assessmentsurveysprovidethistypeofinformationbuttheﬁrstwave wascollectedin2012–2013,sowecannotmeasurevariations.

22 _Another_campaign_against_measles_was_implemented_between₂₀₀₅ and2010.UsingDHSdata,wecheckedthattheprogressinimmunization coveragewasnotcorrelatedwithinitialmalariaprevalence.

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whentestingprediction3inTable5,column3.Asexpected, thecoefﬁcientontheinteractiontermincreasesinsizeand signiﬁcance.Third,weexaminetheintroductionof com-munityhealthworkersundertheprogramPECADOM(Prise EnChargeADomicile).Healthworkersconduct door-to-doorvisitsintheirvillagetoimprovesurveillance,testing, andtreatment.Thesystemwasintroducedinthreedistricts in2008andgraduallyexpandedto37districtsby2011, coveringbothhighandlowprevalenceareas(PlanNational deLutteContrelePaludismeauSénégal,2012).Duringthat period,healthworkersfocusedexclusivelyonmalaria,so theseeffortstoreachout toeveryonecannot driveour cross-diseaseresult.Still,theylowertransactioncostsand mightexplainwhypeoplestartspendingonmalaria.Plan

NationaldeLutteContrelePaludismeauSénégal(2012)

providesdetailedinformationonthechangeinthe num-berofcommunityhealthworkersineachregionbetween thetwowavesofourpanel.Whenweincludethiscontrol inTable5,columns4to6,ourcoefﬁcientsremainstable. Sotheincreaseinspendinginhighprevalenceareasisnot drivenbyathickernetworkofhealthworkers.

7.4. Rainfall

Thefourthalternativehypothesisemphasizesthe cor-relationbetweenrainfall and malaria transmission.The occurrence and intensity of malaria infection is closely relatedtorainfallpatterns.Thesurgeinhealth expendi-turesinhighlymalariousregionscouldpotentiallyresult fromvariationsintheenvironment.Iftheyear2006was particularlydrywhile2011wasparticularlyrainy,people wouldhavespentmoreoncurativetreatmentsinthe sec-ondwave.Weruleoutthishypothesisusingageo-coded measureofyearlyrainfallprovidedbytheClimateHazards Group23_that_we_were_able_to_merge_with_the_PSF_panel, withtheexceptionofonecluster.Thisallowsusto com-putepositive(flood)andnegative(drought)rainfallshocks foreachPSFclusterandforbothwaves.24_It_turns_out_that theyear2006wasslightlymorerainythanusual(6clusters outof149experiencedaflood)whereastheyear2011was slightlymoredry(12clustersexperiencedadrought).As aconsequence,ourspecificationwouldtendto underesti-matethecausalimpactofanti-malariasubsidiesonhealth expenditures.Whenwecontrolfortheannualrainfall devi-ationfromthehistoricalmean,ourcoefficientsofinterest increaseinmagnitudeandinsignificance(cf.columns7to 9inTable5).

7.5. Geographicaldynamics

Thelast alternativehypothesis is that ourestimates capturedifferentdynamicsbetween geographicalareas,

23 _We_use_the_Climate_Hazards_Group_InfraRed_{Precipitation}_with_Station data(CHIRPS)thatcombinessatelliteimageryandrainfallstationdatato produceannualprecipitationmeasurefrom1981to2015.Formore infor-mationonthisdataset,seehttp://chg.ucsb.edu/data/chirps/index.html.

24 _We_deﬁne_as_positive_(resp._negative)_rainfall_shocks_the_observations whoseannualrainfallmeasureisonestandarddeviationabove(resp. below)theclusterhistoricalmeanofannualrainfallcalculatedoverthe 1981–2015period.

forinstance,betweendifferentadministrativeregionsof Senegalorbetweenruralandurbanareas.Malariaismore prevalent in rural areas, as shown by Table 2. It may bethecasethatruralareascaughtupwithurbanareas during the period of interest, for at least two reasons. First,thereismoreroomforimprovement.Second,food andfuelpricesincreasedsubstantiallybetween2006and 2011,whichmighthaveconstrainedthegrowthofhealth spendinginurbanareas.Toruleoutthisconcern,weﬁrst lookaturbanandruralareasseparately.TableA.8inthe Appendixshowthatthesamespendingpatternisobserved in each subpopulation,although welosea lot ofpower whensplittingthesample.Estimatesarelargerinurban areas,whichisconsistentwiththetheoreticalprediction thatswitchersareinthemiddleoftheincome distribu-tion. In rural areas, the fractionof people for whom it becomesproﬁtabletoinvestinchildhealthislikelytobe small;otherconstraintsremainbinding.Moregenerally, inTableA.9intheAppendix,weaccountforpotentially divergingregionaltrendsbyinteractingPostwithdummies foreachadministrativeregionofSenegal(seebordersin

Fig.2).Bycomparingclusterswithadifferentprevalence locatedin thesameregion,weﬁndstronger resultsfor healthexpenditures(columns1to3)andhealth-seeking behaviors(columns4and5).Thedifference-in-differences coefﬁcientsarelarger,suggestingthatspurious geographi-caldynamicstendtoattenuatethemechanismwewantto highlight.

8. Robustness

Finally, this section presents robustness tests using alternativedeﬁnitionsofthetreatment,unitsof observa-tionsandstandarderrors.Wealsoaccountformigration andattrition.

8.1. Alternativedeﬁnitionoftreatment

Thebaselineeconometricspecificationusesa contin-uousmeasureofprevalence. Alternatively,wecanusea binaryvariableandcheckthatourresultsholdupto dif-ferentcuts.TablesA.1andA.2intheAppendixreportthe estimates of interest using ascutoff points theaverage prevalencein2000andin2006aswellasthethresholds defininglowendemicity(10%)andhighendemicity(40%) in themedicalliterature.Coefficientsontheinteraction termareoftenmorepreciselyestimatedthanwiththe con-tinuoustreatment.Inparticular,theeffectattheextensive marginisstrongerandmoresignificantwhenthecut-off pointislower.Thisisinformativeaboutparentalbeliefs: inthemodel,thefractionofswitchersisrelatedtothe dif-ferencebetweentheperceivedchildsurvivalratewithand withoutinvestinginanti-malariaproducts.Thisfraction shouldbelargerwhentheinitialmalariamortalityriskis larger,buttheorydoesnotpredictwhetherthe relation-ship shouldbelinear,concaveor convex.Our estimates revealthatevensmallprevalencelevelsareperceivedas animportantmortalityriskbyparents.Onlypeople liv-inginamalaria-freeenvironmentdonotrespondtothe

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tialchangeinhouseholdstructure.Forinstance,ifmothers inmalariousregionsaremorelikelytohaveanotherchild betweenthetwowavesandhealthexpendituresarehigher oninfantsthanonolderchildren,thenwewouldobserve arelativeincreaseinhealthexpensesintheseregions.To addressthisconcern,wechangetheunitofanalysisfrom the householdtothe child level.27 _We _include _all chil-drenwhowerebornandlivingwiththemotherinPSF1. We followtheminPSF2,whatevertheresidencestatus, andexaminetheevolutionoftheirhealthexpenditures.In

TableA.10,columns1to3,intheappendix,weconﬁrm

thattestingpredictionsatthechildlevelleadstothesame conclusionasthehousehold-leveltest:individualexpenses increasemuchmoreinhighprevalenceareas.Incolumns4 to9,wesplitthesampleofchildrendependingontheirage atthetimeoftheintervention.Coefficientsarelargerfor childrenyoungerthanfivein2009.Thisisconsistentwith ourmodel:episodesofmalariadogreaterharmtoyounger children,inparticulartheunder-fives,thereforeparental health-seekingbehaviorshouldchangemoreinresponse tothecampaign.

8.3. Alternativestandarderrors

Weconsidertwotypesofadjustments.First,weusea spatialheteroscedasticity and autocorrelationconsistent (HAC)estimator(Conley,1999,2008).The difference-in-differencesp-valuesforvariousdistancecutoffs(from10 to250km)rangefrom0.14to0.18whenlookingat expen-dituresin levels,andfrom0.01to0.03whenlookingat theextensivemargin.Second,weimplementaprocedure toaccountformultiplehypothesistesting (Romano and Wolf,2005a,b).Thosecoefficientsthataresignificantly dif-ferentfromzerowithoutthecorrectionremainsignificant at10%withthecorrection.Wealsoperformajointtestof thethreepredictions,takingtogethercolumns1and5of

Table3andcolumns1and2ofTable4.Thesigniﬁcance levelofthejointtestis2%.

25_We_come_to_the_same_conclusion_if_we_estimate_a_regression_with PrevalenceandPrevalence2(notshown).

26_Mortality_is_potentially_a_concern_but_the_number_of_households expe-riencingachilddeathbetweenthetwowavesistoosmall(below2%)to driveourresults.

27_Another_option_is_to_introduce_some_controls_related_to_the house-holdstructure:averageageofchildren,numberofchildren,andshareof childrenunderfive.Ourcoefficientsremainverystableinmagnitudeand significance,asshownbycolumns1to3inTableA.11intheAppendix.

migrants,resultsareverystable,asshownincolumns4to 6inTableA.11intheAppendix.

8.5. Selectiveattrition

Anotherissuewouldariseiftheattrition observedin thePSFpanelwereselecteddifferentiallybetween malar-iousandnon-malariousareas.Attritionislimited:only5% ofmothersinmalariousareasand12%ofmothersin non-malariousareaswerenotfoundinthesecondwave.Our coefficientofinterestcouldpotentiallybebiasedupwards intwo cases.First, ifattritedmothers innon-malarious areaswerepreciselytheoneswithalargeincreaseinhealth expendituresbetweenthetwowaves.Second,ifattrited mothersinmalariousareaswerepreciselytheoneswith nochangeinhealth expenditures. Thefirstconditionis unlikelytoholdbecauseattritedmothersinnon-malarious areasarericherandspendtwiceasmuchasnon-attrited onesonchildhealthinPSF1.Forthemitisreasonableto supposethattheywerealreadyinvestinginthe preven-tionofalldiseases.Regardingthesecondcondition,attrited mothersinmalariousareaswerealsoricherbuttheyspent relativelylittleonhealthcommoditiesfortheirchildren.If anything,theyseemtobeinasituationwhereswitchingto positivehealthspendingislikely.Allinall,ourcoefficient ofinterestismorelikelytobeunderestimatedratherthan overestimatedbyattrition.

9. Discussionandconclusions

Therearetwopotentialchannelsthroughwhich anti-malariacampaignsmayaffectbehaviors:astrongdecrease inpriceandinformation.28_On_the_one_hand,_Armand_et_al.

(2017) argue that public interventions raise awareness

ofthedangersofmalaria amongthepopulationsothat peoplechange theirbeliefs about thereturns to avoid-ingthedisease.Ontheotherhand,Dupas(2009)provides experimentalevidencethatdemandformalaria preven-tionisverysensitivetoprice,butisnotatallinﬂuenced

28 _A_third_potential_channel_could_be_that_distributing_free_products impactedhealthcareutilizationviaincreasedfamiliaritywithhealth facil-ities.However,inourcontext,theroomforimprovementseemslimited becausethevastmajorityofmotherswerealreadyusedtovisitinglocal healthcenterstogetfreecareforthemselvesandtheirchildren.In2005, 87%ofpregnantwomenhadreceivedpre-natalcareandover95%of childrenhadreceivedatleastonevaccine(NdiayeandAyad,2006).