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RevisitingtheNaturevs.NurtureDebate:PatternsofCognitiveDevelopmentofFilipinoChildren

S.Quimboa,N.Wagnerb,A.C.Küntayc,d,X.JavieraandJ.Peabodye

aUniversityofthePhilippines,SchoolofEconomics,Diliman,QuezonCity,Philippines,bEconomicsofSustainableDevelopment.InternationalInstituteofSocialStudiesofErasmusUniversity

Rotterdam,TheHague,TheNetherlandscKoçUniversity,CollegeofSocialSciencesandHumanities,İstanbul,Turkey

dUtrechtUniversity,FacultyofSocialandBehavioralSciences,ResearchProgramEducationandLearningeQURE, UCSF and UCLA, California, USA

AbstractUsingtheQIDSpaneldatasetcollectedin2003and2007,weanalyzethedeterminantsofcognitiveabilitiesamongFilipinochildren.Weareinterestedindistinguishingbetweentheeffectsofthelearningandfamilyenvironmentversushealthandnutritioninchildrenaged6months to 7 years. Cognitive abilities aremeasured using the Bayley Scales of InfantDevelopment and the Wechsler Preschool and Primary Scale of Intelligence. The homelearning environment is assessed using the Home Observation for Measurement of theEnvironment. Health and nutrition markers include weight-for-age, height-for-age,hemoglobinandleadlevelsinblood.Employingachildfixedeffectsmodel,wefindthatthehomelearningenvironmenthasasignificantpositiveeffectoncognitiveabilityexplainingmore than one fourth of the standard deviation of cognitive development. Nutritionmarkers,particularly,weight-for-ageandhemoglobinlevelsinbloodalsohavesignificantpositiveeffectsoncognitiveability.Geneticpredispositionandothertime-invarianttraitsexplainroughly40percentof thevariation incognitiveability.Aonestandarddeviationincreaseinthehomelearningenvironmentexplains30.4percentofthestandarddeviationincognitiveabilitywhereasaonestandarddeviationincrease inweight-for-ageexplainsanother8.9percent.Ourresultsindicatethatparentalinputsornurturecancompensateforweakpreconditionsandfostercognitivedevelopmentamongyoungchildren.

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1.IntroductionWerevisit aquestionasked timeandagain: towhatextentare intellectualabilitiesofchildren environmentally determined by early life experiences versus predeterminedgenetically? This is particularly important for disadvantaged children with belowaverage cognitive abilities raising the question whether interventions can beimplemented that either change their genetic predisposition or the learningenvironment.Clearly,cognitiveabilityispartlyinnateandarguesforinterventionsthatcomepriortobirthviaantenatalcareforwomenwiththebenefitsbeingtransmittedtothechildrenintrauterine,e.g.,theprovisionofmicro-nutrientsthatareknowntofosterbrain development (Ashworth and Antipatis, 2001; McArdle and Ashworth, 1999).However, if at least some variation in cognitive abilities is caused by environmentalfactors, thenpolicyhasacritical rolepromotingchildren'scognitivedevelopmentandlong-term socioeconomic success (Heckman 2008). The form and timing ofinterventions could be strongly influenced by what we know about the nature ofdevelopmentofcognitiveabilities.ThispaperexaminestheinteractionofnatureandnurtureintheemergenceofcognitiveskillsinearlychildhoodfromapanelofFilipinochildren.By"nature",werefertotraitsthat are shaped by a child's genetic endowment including innate ability, and otherfactorsthataretime-invariant.Ontheotherhand,by"nurture",werefertotime-varyingparental inputs,whoseoutcomescanbemeasuredintermsof thequalityof thehomelearning environment as well as health and nutrition investments, which can bemeasured with biomarkers. Understanding the differential impact of nature andnurture on child cognitive development is particularly important in developingcountrieswhereresourcesarescarceand investments inhumancapitalcompetewithmanyotherprioritiessuchasinfrastructureandfinancialmarketdevelopment.Fewstudiesoncognitiveoutcomeshavebeenabletosufficientlycontrolforindividualunobserved heterogeneity, including genetic endowment (Rosenzweig and Wolpin,1994). If cognitiveoutcomesaredeterminedbyunobservedgenetic endowment, thenanomittedvariablesproblemisintroducedcausingtheeffectsofotherobservedinputssuch as schooling to be overstated (Todd and Wolpin, 2003). Early investigatorsaddressedthese identificationproblemsusingdata frommonozygotic twins tocontrolfor unmeasured ability and genetic endowment (Behrman and Taubman, 1976;Rosenzweig andWolpin, 1980). This approach, however, is costly and impractical indeveloping countries where twin registries are not available. A less costly approachwouldbetousedatafromsiblings(RosenzweigandWolpin,1994)althoughstudiesonsiblings do not fully deal with unobserved parental preferences, which vary acrosschildren. An eldest son, for example, might be preferred because he is the heir or ayoungestdaughtermightgetspecialattentionbecausesheissocharming.

Socio-economiccharacteristicssuchaspovertyandhouseholdstructurearecorrelatesofcognitiveabilityandmustbeconsidered(Brooks-GunnandDuncan,1997).Ahostofexisting studies has shown that parental nurturing is an important determinant ofcognitive outcomes (Anger et al. 2009; Parker et al., 1999). The home learningenvironmentiswhereparentsmostintimatelyprovidefortheirchildren(Fernaldetal.,2011; Melhuish et al. 2008a; Melhuish et al. 2008b). Thus, a more challengingmethodologicalconstraintthatneedstobeovercomeistofindthepropermetricforthe

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learning environment. Existing studies fail to account for these, possibly, moreimportantenvironmentalfactorssuchaslanguagestimulationandparentalcare.Thesefactorsarenotnecessarilydeterminedbypovertyandcanbeprovidedeveninresource-constrainedhouseholds.Forexample,thetimecaregiversspendtalkingtothechildrenandplayingwith themor theacceptance that isprovided for thechild. Fromapolicyperspective, it is important to assess the impact of such aspects of the learningenvironment to understand how healthy and able children could be raised optimallyeveninrelativelypoorsettings.Inadditiontopositiveandcognitivelystimulatingparenting,childhealthandnutritionare critical links to cognitive outcomes (Behrman, 1993). This link, by contrast to thelearning environment, has been relatively well studied in resource-poor settings. Forexample, several papers use data on Filipino children who were surveyed under theCebu Longitudinal Health and Nutrition Study (CLHNS) (Mendez and Adair, 1999).Analyzing the relationship between early infancy stunting –a long-term nutritionalmarker– and cognitive development at ages 8 and11, researchers found that Filipinochildren,stuntedduringthefirsttwoyearsoflife,hadsignificantlylowertestscoresforcognitive ability than their non-stunted counterparts, while controlling for socio-economic variables such as household income, maternal and paternal education, andplaceof living.The impactwas foundtobemostpronounced forchildrenwithseverestuntingbutthenegativeimpactofstuntingdiminishedovertime.Anotherstudyusingthe CLHNS dataset, examined the effects of the timing of malnutrition on cognitivedevelopment (Glewwe and King, 2001). Based on their OLS estimates, one couldconclude that children’s cognitive development at age eight is negatively affected bypoornutritionduringinfancy.However,inanalternativespecification,theyestimateamodel using instrumental variables that included rainfall and prices, mother’s heightand upper-arm circumference. The instrumental variables estimates indicate thatmalnutritioninthesecondyearoflifehasalargernegativeimpactthanmalnutritioninthe first year of life. They concluded that the evidence of nutrition effects for earlyperiods in childhood (first sixmonths)oncognition in laterperiods isnot robustandpointed to considerable identification challenges (Glewwe and King, 2001). First, thequality of the local educational environment could not fully be observed. Second,parentalpreferences cannotbe controlled for, and third, they cannot fully account forgenotype. Moreover, although they used an instrumental variable approach, theinstruments themselves might also introduce bias and the coefficient estimates arelikely to be imprecise. In another study, also employing the CLHNS dataset, thenutrition-learningnexuswasfurtheranalyzedforpairsofsiblings(Glewweetal.,2001).Controllingforunobservablessuchascommonheredity,environmentandsharedfamilyexperiences of siblings, they demonstrated that well-nourished children enter schoolearlier,andthus,havemoretimetolearnandgreaterlearningproductivityperyearofschooling.Toaddressunobservedchildheterogeneityand theconcomitantbias in theestimatedeffectsofchildhealthandnutritiononcognitiveoutcomes,weusedachildpaneldatasetfromtheQualityImprovementDemonstrationStudy(QIDS)allowingustoderivewithinchild estimates. The panel data were collected from 627 Filipino children whoparticipated in a cognitive development test in 2003/04 and again in 2006/07. Forinfantsandtoddlersaged6monthsto2.5years,cognitivedevelopmentismeasuredbytheBayleyScaleofInfantDevelopment(BSID)andforchildrenabove2.5yearsuptothe

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age of 7 theWechsler Preschool andPrimary Scale of Intelligence (WPPSI)was used.Accordingly,everychildhas twoage-specificmeasurementsofcognitivedevelopment.Byemployingchild-fixedeffectsmodels,weattempttodisentangletheimpactsoftime-invariantunobservedtraitsincludingthosegeneticallytransmittedandthetime-varyingbehavioralpatternsandchoicesmadebyparents.Healthandeducationalinputsaretwoofthemostimportantareasofparentalinterventioninachild'scognitivedevelopment(Melhuish 2010; Nyaradi 2013). The quality of educational inputs in the homeenvironmentisassessedbytheHomeObservationforMeasurementoftheEnvironment(HOME, see Caldwell and Bradley, 2003), and nutrition and health outcomes aremeasuredbyweight-for-age,height-for-age,thebloodlevelofhemoglobinandelevatedlead. Our model estimates provide insights on fundamental questions on childdevelopment: Are there natural limits to a child's cognitive development imposed byone's genetic make-up? And how much can parents do to enhance the cognitivedevelopmentoftheirchildren?

Fourresultsstandout.First,"nature",roughlymeasuredaschild-fixedeffects,explainsmore than 40 percent of the variation in children’s cognitive performance. The jointexplanatorypoweroftheremainingcontrolvariablesisbelow15percent.Thisfindingdemonstratesthelargerolestablepredispositionshaveoncognitiveskillformation.Atthe same time, this shows that even individualswith relatively low levels of intrinsicability have a significant amount of cognitive variation that might be fostered andstimulated.Second,weconfirmed thisobserving that thequalityof thehome learningenvironmenthasalargepositiveimpactoncognitivedevelopment:aonepointincreaseinthescoreassociatedwiththehomelearningenvironmentleadstoroughlyhalfapointincreaseinthecognitivedevelopmentscore.Thissuggeststhatthehomeenvironmentcreatedby theparentshasabig role in facilitating thecognitivedevelopmentof theirchildren. Third, normal levels of hemoglobin are positively associated with cognitiveability. Contemporaneous weight-for-age (wasting) is positively associated withcognitive development whereas contemporaneous height-for-age (stunting) has noeffect.We interpret thebiologic and anthropometric data to reflect the importanceofcontemporaneousnutritionformentalactivityandcognitivedevelopment.Fourth,duetotheuseofchild-fixedeffects,guardiancharacteristicssuchasgenderandthelevelofeducation do not impact the child’s performance in the test because these have littlevariationovertime.Theseguardiancharacteristicswouldvaryonlywhentheguardianchanges, as in the case of parents leaving the home for overseas employment ormarriagesbreakingup.However,wenotethatthepresenceofgrandparentsandotherold people living in the household seems to have a positive and significant role instimulatingthemindsofyoungchildren.

Thepaper isorganizedas follows: Section2outlines the theoretical frameworkwhileSection3presents theempiricalmodel. Section4describes thedata set including themeasuresof cognitivedevelopment and thehome learning environment andprovidesthedescriptivestatistics.TheresultsarepresentedinSection5,andSection6concludes.

2.TheoreticalFrameworkCognitive ability is broadly defined as all mental abilities related to knowledgeformation.Itisphysicallyembodiedincognitiveskills,whichcanbeproducedaccordingtothefollowingfunction:

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Ct=f(Et,Nt,Cht,Ht;C0,η) (1)

where C is a measure of cognitive ability and its initial stock is denoted with asuperscript0.Emeasuresthequalityofthehomelearningenvironment,Nreferstothechild'shealthandnutrition status,Ch areother child-specificobserved traits affectingcognition such as age and gender, and H are household characteristics such ascomposition and size. E,N, Ch, andH capture contemporaneous effects on cognitiveoutcomes, where the subscript t refers to time. On the other hand, η refers tounobservedtraitsaffectingcognition,includingthegeneticendowmentorinnateability.Both the home learning environment and nutritional inputs result from parentalinvestments intermsof timeandresources.Weassumethatall inputshaveapositivemarginalproductoncognitiveperformance:f'E>0,f'N>0,f'Ch>0andf'H>0andallmarginalproducts are subject to diminishing returns. Ourmodel builds on the skill formationtechnologypresented inCunhaandHeckman(2007)andCunhaandHeckman(2010).Figure2schematicallyrepresentsthecognitiveskillformationprocess.ParentalinputsandpreferencesgeneratethehomeenvironmentEandshapethehealthandnutritionstatusNofthechildrenleadingultimatelytotheproductionofcognitiveskills.Yet,theprocessisinfluencedbyobservable(Ch)andunobservable(η)childcharacteristics.Thelatteralsocapturesgeneticmake-up.

Figure2.SchematicDiagramoftheCognitiveSkillsProductionFunction3.EmpiricalModelThe empirical model focuses on the production technology for cognitive skills. Weimposealinearstructureontheskillproductionfunctionforeverychildiattimet:

ParentalInputsand

Preferences

HomeLearning

Environment

HealthandNutritionStatus

Observablechildtraits(e.g.ageandgender)

Unobservablechildtraits(e.g.geneticmake-up)

CognitiveSkills

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Cit=α0+α1Homeit+α2Healthit+α3Childit+α4HHit+λt+ηi+εit

where Cit is a measure for cognitive skills resulting from a standard test. Homeitmeasures the quality of the home learning environment. The health indicators arecollectedinHealthit.Theseincludeweight-for-ageasasuitableproxyfornutritionandheight-for-ageasproxyforaccumulatedchildhealth,saythechildhealthstock.BotharemeasuredintermsofstandarddeviationsrelativetotheWHOreferencepopulation.Inaddition,weincludeadummyvariableequalto1wheneverthechildhasanormallevelofhemoglobinandanotherdummyvariableequal to1wheneverelevated lead(lead>10 micrograms per deciliter) is found in the child’s blood sample. The vector Childitdenotes time varying child characteristics such as age. Although the cognitivedevelopment, the weight and height measurements are age adjusted we include thechild’sageinmonthsascontrolvariabletoensurethatagedynamicsarefullycaptured.Wefurtherincludeanindicatorvariablethat isequalto1inthebaselinesurveyifthechild initially tooktheBayleyScaleof InfantDevelopment(BSID)test.Weincludethisvariable to account for any differences that remain between BSID and the WechslerPreschoolandPrimaryScaleofIntelligence(WPPSI)aftertheage-adjustment.HHit isavector of household characteristics including guardian characteristics such as genderandyearsofeducationoftheprimarycaregiver.Inadditiontoguardiancharacteristics,wealsoincludehouseholddemographicssuchassizeandthedependencyratioforthechildrenwhoareupto14yearsoldandtheelderlywhoare65orolder.Last,wecontrolfor a round effect λt and the child fixed effect ηi. We allow child observations to becorrelatedinanunknownfashionandaccountforitbyclusteringthestandarderror,εit,atthechildlevel.We initially considered specifying our empirical model in first differences instead oflevels. This implies that the bias resulting from the correlation between the time-invariant characteristics - say a child’s genotype - and the nurture inputs, such asnutrition and the creation of a quality home environment, is removed (Kirchberger,2008). With the difference form, however, all child-fixed effects are removed andcoefficients for time-invariant variables such as gender and the initial level of skillscannotbeestimated. Inaddition,adifferencespecificationdoesnotallowus toassessthejointexplanatorypowerofachild'stime-invariantcharacteristicsoncognitiveskills.Wethusresorttoachild-fixedeffectsmodeltoestimatetheextentoftheroleofintrinsicability in cognitive development. In relatedwork, others have used child-fixed effectsestimatorstoaddressthepotentialendogeneityofchildcarechoices(Resuletal.,2010).The fixed effects model allows the estimation of the effects of time-varyingheterogeneity such as that resulting from parenting preferences. This is importantbecause, contrary to existing studies,wehave time-varying information to control forthe home learning environment.We consider the home environment and health andnutritionstatusasaproxyforparentalattitudestowardsthechild.4.DataWe utilized repeated measures of cognitive development and child health from theQualityImprovementDemonstrationStudy(QIDS).QIDS,whichwasimplementedinthethe Philippines from 2003 to 2008 (Shimkhada et al. 2008), was a large randomized

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policyexperimentthattookplacein30publichospitalsandinvolvedover6000childrenthroughoutthecentralregions(themiddleone-third)ofthecountry.ThemainpurposeofQIDSwastoevaluatetheimpactoftwopolicies—onedesignedtoimprovethequalityof clinical care using payment incentives to doctors versus an expanded healthinsurancealternativetoimproveaccesstocare—onthehealthstatusofchildrenage6months up to five years in age. The health status outcomemeasures, alongwith thesocioeconomic covariants, in QIDS were broad ranging from anthropometrics,physiological assessments, blood tests and notably the cognitive development ofchildren(Quimboetal.,2011,Peabodyetal.,2014).Inthisstudy,weusedtheQIDStworoundsofdatacollectedfromthechildrenandtheirparents in the baseline cohort. The children were identified at the time of dischargefrom one of the QIDS hospitals and interviewed in their home 4-6weeks after beingdischarged; theywerere-interviewed2yearsafter the initialsurvey. Duringboththeinitial and follow-up interviews, the children were administered age-appropriatepsychological tests, namely, the Bayley Scale of Infant Development (BSID) andWechsler Preschool and Primary Scale of Intelligence (WPPSI). By using the baselinepanelofchildren,noneofthemdirectlybenefittedfromtheQIDSexperiment.Thus,wearenotabletoassesstheimpactoftheQIDSinterventions,namely,thequalityofcaretheyreceivedinthehospitals,ontheircognitivedevelopment.However,inthestudyathand we can make use of the rich data on cognitive development to assess thedeterminantsofchildren’scognitiveabilitiesindependentlyfromtheQIDSintervention.The baseline cohort of children with tracer conditions pneumonia and diarrhea,consistedofatotalof1,463patientsaged6monthsto6yearsold(roughly50patientsperstudyhospital)whowerepaidaninitialfollow-homevisitin2003.Duringboththefirst and second follow-home interviews, the children were asked to participate inpsychologicaltesting.Twotypesoftestswereadministered:theBSIDforchildrenaged6monthsto2.5yearsandtheWPPSIforchildrenabove2.5yearsuptoabout7years.In2006to2007,thesecondroundofdatacollectiontookplace.Bythenallchildrentooktheage-appropriateWPPSI.Of the1,463patients in thebaselinecohort,only627hadcompletepsychologicaltestscoresandcorrectageinformationinthetworoundsofdatacollection.Thissubsetformstheregressionsample.ConcernswithpossiblebiasesduetoattritionarediscussedinSection5.2.4.1CognitiveAssessmentsTheBayleyScaleofInfantDevelopment(BSID)wasdesignedbyNancyBayley(1993)asassessmenttoolof thecognitivedevelopmentof infantsandtoddlersbetween0and3years.WeuseversionIIofthetool.TheBSIDisamentaldevelopmentindexcombiningscoresoncognitive, languageandpersonaldevelopmentandrepresentsanaccreditedmeasureofthedevelopmentalfunctioningofinfantsandtoddlersusedbypsychologiststo detect developmental delays.1The test is individually administered by specificallytrainedpsychologistsandtakesbetween45and60minutestocomplete.IntheBSID,aseries of test materials are presented to the child by the clinician and the child'sresponsesandbehaviorsareobservedandreported ina standardizedway.2Wemake1TheBayleyScaleofInfantDevelopmentVersionIII(BSID-III)hasbeenmorerecentlyintroduced.2Thefollowingtypesofabilitiesarecapturedbythetest:(i)sensory/perceptualacuities,discriminations,and response; (ii) acquisition of object constancy; (iii) memory learning and problem solving; (iv)

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useofthementaldevelopmentscore,whichisderivedfromthetesting.Inthecontextofthis study, the testwasadministered to childrenbetween theageof6monthand2.5years. We obtained standardized, age-adjusted scores to have measures that arecomparable across age cohorts. The average score in our sample is 88.7 and themaximum score is 149 (see Table 1). According to the BSID manual, cognitivedevelopment is considered normal if scores range between 85 and 114. Thus, thechildren in our sample who took the BSID are normally developed on average. Themaximumscoreof149indicatesthatsomechildren,withscoreshigherthan114,haveacceleratedperformance.For the older children at baseline and all children in the second interview round,weused theWechslerPreschool andPrimaryScaleof Intelligence (WPPSI) tool to assessthecognitivedevelopment,whichisappropriateforchildrenaged2.6to7years.WPPSIis also an individually administered test of cognitive development used for assessingchildrenwhohaveoutgrowntheBSIDtool.TheWPPSIhastwosetsoftools—youngandold—whichareusedforaged2.6to3.11and4.0to7.3,respectively(Wechsler2002).WPPSIhas two component scores, referred to as verbal andperformance.A full-scalescore is derived as a summary measure of cognitive ability encompassing both theverbalandperformancecomponents.Foryoungchildren,theverbalcomponentconsistsof tasks measuring receptive vocabulary and information and the performancecomponentiscomposedoftasksaroundblockdesignandobjectassembly.Fortheolderchildren,theverbalcomponentcombinesinformation,vocabulary,andwordreasoning.The performance score for older children adds tasks ofmatrix reasoning and pictureconcepts to block design (compare Table A1). For comparability across differentversions of the test and over time,weuse age-adjustedWPPSI scores in the analysis.Because the tests were done roughly 2 years apart, all of WPPSI-Young takers atbaselineshifted toWPPSI-Oldat follow-up.TheaverageWPPSIscore inoursample is88.1 and indicates that the average child in the sample is found in the low averageperformance group ranging from a score of 80 to a score of 89. For comparisonpurposes, the classification of average performance starts at 90 points indicating thatthesampledchildrenareslightlylaggingbehindaverageperformancelevels.For the study at hand, we combine the different test scores resulting in a cognitivedevelopmentmeasure composed of an age-adjustedmental development index of theBSID for infants and small children, an age-adjusted WPPSI-Young score for youngchildrenandanage-adjustedWPPSI-Oldscoreforolderchildren.Whilewewouldpreferour analysis to rest on one single test, we are not aware of a single cognitivedevelopmenttestthatcoverstheagerangeof6monthsupto7years.Moreover,ascanbeseenfromFigure1thedistributionofthementaldevelopmentindexoftheBSIDandthefullWPPSIscoreoverlapandtheKolmogorov-Smirnovtestfailstorejecttheequalityofdistribution (p-value=0.130).Becauseweemployawithin-child analysis of the twopathwaysofnatureandnurturetocognitivedevelopment,wearguethatcombiningage-adjusted test scores from different instruments can be a valid approach. Ourcomparison is not across cohorts butwithin-child.At eachpoint ofmeasurement, theage-adjusted score represents the development of the child at that time allowing forsituations inwhich childrenwho performwell during the firstmeasurement, show alowerperformanceduringthesecondtestandviceversa.vocalizationandbeginningofverbalcommunication;(v)basisofabstractthinking;(vi)habituation;(vii)mentalmapping;(viii)complexlanguage;and(ix)mathematicalconceptformation.

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Figure1:Distributionofcognitivedevelopmentscores,bytesttype

Tomeasureachild’shomelearningenvironment,weemploytheHomeObservationforMeasurementoftheEnvironment(HOME)Inventory(CaldwellandBradley2003).Thisisasystematicassessmentof thecaringenvironment inwhichthechild isbroughtupandisintendedtomeasurenotonlythequantitybutalsothequalityofstimulationandsupportavailabletoachildinthehomeenvironment(TotsikaandSlyva2004).HOMEisanestablishedmeasureofchildsimulation in thechild’snormalenvironment thathasbeenused formore than40years as it is easy to administer and score, combines themethodsof interviewandobservationand isnot threatening to the concerned family.TheHOMEinstrumenthas45 itemsfor infantsandtoddlersand55itemsforchildrenaged3to6allowingforincreasingcomplexityaschildrendevelop.Fortheinfantsinoursample(childrenaged6monthsto2.5years)thescaleconsistsofthefollowingitems:(i) The emotional and verbal responsivity of the primary caregiver, (ii) avoidance ofrestrictionsandpunishmentssuchasshoutingatthechild,(iii)theorganizationofthehome environment and the child’s personal space, (iv) the material for play, (v) theparentalinvolvementwiththechild,i.e.whethertheprimarycaregivertendstohaveaneye on the child, and (vi) stimulation by meetings with individuals other than theprimarycaregiver. TheaverageHOMEscaleforyoungchildreninoursampleis29.31out of 45 (age range covered 0-30 months). Thus 65 percent of the possible homestimulusisprovidedtostudyparticipants leavingroomforimprovement.Forchildrenin our sample that are older than 2.5 years the HOME score is clustered into eightsubscales including learning materials, the home environment, vocal and physical

0.0

1.0

2.0

3D

istri

butio

n of

full

scor

e

0 50 100 150

WPPSI BSID

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interactionsandacademicstimulation.3TheaverageHOMEscoreforchildobservationsin the age range of 31-85months is 34.08 out of 55. For the older children only 62percentofthepossiblehomestimulusisachieved.Inassessinghealth,werelyonanthropometricandphysiologicbiomarkers.Allsampledchildrenwereweightedandmeasured,whichallowsustoconstructweight-andheight-for-ageZ-scorescomparingthechildrenunderstudytowell-nourishedchildren intheWHO reference population (World Health Organization, 2007). In addition, bloodsamplesweretakenfromthechildrenandtestedforhemoglobinandlead.Hemoglobinis ameasure of the number of red blood cells and carriesmost of the oxygen in theblood. It was shown that hemoglobin is positively associated with cognitivedevelopment (Ai et al., 2012;PaxsonandSchady,2007). Lead is aknownneurotoxinand adversely affects cognitive development (Dietrich, 2000; Rice, 1988; Aizer et al.,2015). Solon et al. (2008) found, using QIDS data, that a unit increase in blood leadlevelspredictsa3.32pointdropincognitivefunctioningofchildrenaged6monthsto3yearsanda2.47pointdeclineinchildrenaged3to5yearsold.4.2DescriptiveStatisticsTable1showsthedescriptivestatistics.Theaverageage-adjustedfullcognitivescoreis88with baseline scores being slightly higher than follow-up scores. For the cognitivescoresweobserveasimilarnegativecorrelationwithageasfortheanthropometricZ-scores suggesting that the age-adjustment disproportionally impacts older children(Kopp andMcCall, 1982). Themajority of the children, namely 89.8 percent, took theBSID test in the baseline survey.When comparing the baseline scores resulting fromBSIDwiththosefromWPPSIwedonotfindsignificantdifferenceatthe1percentlevelbut reject equality ofmeans at the 5 percent level. The quality of the home learningenvironmentisrepresentedbytheHOMEscore.Theaveragescoreisslightlybelow32and increasesacrosssurveyrounds.This increase intheHOMEscore isbuilt inas thelearning environment of younger children is assessed on 45 items and the learningenvironmentofolderchildrenisassessedon55items.ThehealthstatusofthechildrenisworsecomparedtochildrenfromtheWHOreferencepopulation.Withaweight-for-ageZ-score(WAZ)of-1.22theaveragechildismalnourishedandaccumulatesthisbadhealth as shown by the even lower height-for-age Z-score (HAZ) indicator of -1.59.Despiteaweaklyanthropometriccondition,64.6percentofthechildrenhaveanormalhemoglobinlevelrangingbetween10.5and13.5.Only21.5percentofthechildrenhavean elevated lead level where the cut-off for high lead was set at 10 micrograms perdeciliter,which isratedasa“blood lead levelofconcern”accordingtotheCenters forDiseaseControlandPrevention(2013).

3Thedetailedcategoriesare(i)learningmaterialssuchasageappropriatetoys,(ii)languagestimulationbycommunicatingwiththeprimarycaregiver,(iii)thelargerphysicalenvironment,i.e.thefamilyhouse,(iv)responsivity intheinteractionsbetweencaregiverandchild, i.e. theparentholdsthechildcloseforsometime,(v)academicstimulationthatencouragesthechild’sintellectualabilitysuchaslearningcolorsorpatternedspeech,(vi)modeling,whichreferstotheboundariessetbytheparents(vii)varietybetweendifferent activities such as indoor and outdoor, and (viii) acceptance demonstrated by the way thecaregiverdisciplinesthechild.

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Table1.Summarystatisticsforthefullcognitivedevelopmentscoreandthecovariates Total Baseline Follow-UpVariable Mean Std.Dev. Mean Std.Dev. Mean Std.Dev.Cognitivedevelopmentscores Fullcognitivedevelopmentscore 88.356 13.923 89.297 15.483 87.416 12.104WPPSIfullscoreforallchildrentakingthetest 88.081 13.387 BSIDfullscoreinround1 88.694 14.559 WPPSIfullscoreinround1 94.594 21.436 ChildtookBSIDinround1 0.449 0.498 0.898 0.303 Homelearningenvironment 31.907 7.337 29.903 6.407 33.911 7.657Healthandnutritionindicators WAZ -1.222 1.347 -0.989 1.308 -1.454 1.347HAZ -1.587 1.451 -1.490 1.595 -1.685 1.286Normalhemoglobinlevel 0.646 0.478 0.687 0.464 0.604 0.489Highleadlevel 0.215 0.411 0.300 0.459 0.131 0.337Childcharacteristics Childismale 0.581 0.494 0.581 0.494 0.581 0.494Ageinmonths 33.128 18.021 18.36 10.338 47.896 10.307Guardianandhouseholdcharacteristics Guardianyearsofeducation 7.68 10.347 8.600 3.304 6.761 14.202Femaleguardian 0.904 0.294 0.907 0.290 0.901 0.299Householdsize 6.206 2.372 5.820 2.300 6.592 2.381Ratioofdependentsof0-14years 1.233 0.801 1.170 0.721 1.297 0.871Ratioofdependentsof65+years 0.056 0.229 0.038 0.154 0.075 0.283Percapitahouseholdincome 994.458 993.643 940.311 917.239 1048.605 1062.558

Note:Thetotalnumberofobservationsis1,254correspondingto627observationsperround.Inthefirstroundofdatacollection,563childrentooktheBSIDcognitivetestandtheremaining64tooktheWPPSItest.Amonghouseholdcharacteristics,oursampleconsistsofslightlymoremalethanfemalechildren(58percentaremale).Theaverageageis33monthswithanaverageageof17months atbaseline and roughly4 years in the follow-up survey.Theage-rangeof theparticipating children is from6 to 85months. The control variables include guardianand household characteristics such as the age and gender of the guardian, householdsizeandcomposition.Almostall childrenhavea femaleguardianwhoaveragedabouteight years of education. As can be seen from the slight variations in guardiancharacteristicsacrosssurveyrounds,itispossiblethatachild'sguardianchanges.Inoursample, 47percent of the children changed guardians across survey rounds. This is aparticular feature of the Philippines as many mothers work outside the country asnursesorhousematesandhavetoleavetheirchildrenwithotherfamilymembers.Theaverage household consists of 6 individuals with a considerable share of youngdependentsrelative to theworkingagemembersof thehousehold.Theshareofolderdependentsisonly0.056onaverage.Monthlypercapitaincomerangesatroughly995pesos(22.5US$)indicatingthatthechildrenunderstudyresideinpoorhouseholds.45.Results5.1Mainresults

4Theappliedpeso-US$exchangerateis0,0226.

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Acrossregressions,thefullcognitivedevelopmentscore(thecombinedWPPSIandBSIDscore) is the dependent variable (see Table 2, where the first column presents OLSresults and columns 2 to 3, the child-fixed effects results).5Across specifications, thesignandsignificanceofthekeyexplanatoryvariablesaresimilar.Wehighlightfourkeyresults.First,nature,i.e.thechild-fixedeffects,explainsmorethan40percentofthevariationsin children’s cognitive performance whereas the joint explanatory power of theremaining control variables is below15percentwithin-child (seebottomofTable2).This finding demonstrates the large role nature has for cognitive skill formation.Importantly, italsoshowsthat individualswithrelatively lowlevelsof intrinsicabilitycanbefosteredandstimulated.Oneimportantimplicationofthisresultisthatparentscould potentially respond to the observed ability of their children by compensatinggenetically disadvantaged children with health and nutrition interventions andstimulating them through a conducive home learning environment. At the same time,parentswhoobservethattheirchildrenareverygiftedcouldfurtherfostertheirskillsby nurturing their body and mind. These are specific examples of how parentalpreferences can be captured by changes in the home environment and health andnutritionmarkers.Second,thehomelearningenvironmentishighlystatisticallysignificant(p<0.001)andpositivelyassociatedwithcognitivedevelopment.MovingfromthepooledOLSmodeltothefixedeffectsspecification,wefindthatthereisonlyasmalldropincoefficientsizeof0.032(0.577-0.545).Thusanincreaseof1intheHOMEscoreisassociatedwithroughlyhalfapoint increase in thecognitivedevelopment testscore.Putdifferently,asamplestandarddeviation increase (=7.337) in thequalityof thehome learningenvironmentexplainsmore thanone fourthof thestandarddeviation in thecognitivedevelopmenttest (see Table 1). This finding confirms that parents play an important role instimulatingthecognitivedevelopmentoftheirchildrenandtheycandosobyspendingtime playing with the child, which is not costly. Even resource-poor households canboostthecognitivedevelopmentoftheirchildrenbytalkingtotheirchildandprovidingthemaprotected,encouraginghomeenvironment.Third, among thehealth andnutritionmarkers, theweight-for-ageZ-score (WAZ)hasgreatest impactontestperformance. In thechild-fixedeffectsmodel(Table2,Column2), increasingWAZ by the sample standard deviation (=1.347 fromTable 1) explains8.85percentof the standarddeviation in the cognitiveperformance test scores. If thehomeenvironmentandWAZarejointlyimprovedbytheirrespectivestandarddeviationasmuchas37.57percentinthevariationofcognitivedevelopmentcanbeexplained.Asimilar positive relationship is found for children who have a normal level ofhemoglobin.Theyscore,onaverage,2pointshigheronthecognitivedevelopmenttest.This findingsupportsexistingevidence(Aietal. ,2011andPaxsonandSchady,2007).We further note that the OLSmodel underestimates theWAZ coefficients: Given that

5WealsoemployedanOLSmodeloneachroundofdataseparately toensurethat thepatternswe findwith the panel data are coherently displayed across rounds. The main results about the positiveassociation of the home learning environment and the health indicators hold. We further detect thenegativeimpactofagebutdonotfindanysignificantimpactoftheguardiancharacteristicsoncognitiveskills.We do not present the results as part of ourmain findings as they suffer from ability bias. Theauthorscanmakethemavailableuponrequest.

13

unobservedabilityandcognitiveoutcomestendtobepositivelycorrelated,weinferthatthe unobserved child characteristics and WAZ are negatively correlated. This isconsistentwithacompensationstory:parentscouldbenurturing to"compensate" fornaturebyprovidingmoreorbetterfoodtochildrenwhoarelikelytobeunder-achieversbecauseofperceivedinnatedisadvantages.Thus,inadditiontoqualitytimespentwiththechildren,parentsshouldalsoinvestinpropernutrition.Whileexistingstudieshavealready delineated the importance of early childhood nutrition for cognitivedevelopment(Wisniewski,2010;GlewweandKing,2001;MendezandAdair,1999),thestudyathandunderscoresthenecessityforcontinuedpropernutrition.

Table2.RegressionresultsforthefullcognitivedevelopmentscoreOutcome:Fullcognitivedevelopmentscore OLS FE FE

Basicmodel

withfamilycomposition

Homelearningenvironment 0.577*** 0.545*** 0.554***

(0.056) (0.073) (0.072)Healthindicators

WAZ 0.762** 0.915** 0.861**

(0.296) (0.438) (0.435)HAZ 0.011 -0.600 -0.603

(0.298) (0.399) (0.398)Normalhemoglobinlevel 1.306* 2.019** 2.029**

(0.745) (1.021) (1.027)Highleadlevel -0.779 1.039 1.161

(0.954) (1.285) (1.268)

Childcharacteristics Ageinmonths -0.360*** -0.399*** -0.411*** (0.045) (0.125) (0.124)ChildtookBSIDinround1 -12.115*** -8.535*** -8.620***

(2.624) (2.390) (2.405)Guardianandhouseholdcharacteristics

Guardianyearsofeducation 0.020 -0.016 -0.005

(0.025) (0.045) (0.047)Femaleguardian 0.288 -1.501 -1.629

(1.223) (2.192) (2.181)Householdsize -0.336** 1.387*** 1.193**

(0.159) (0.491) (0.488)Ratioofdependentsof0-14years

1.213

(0.765)

Ratioofdependentsof65+years

4.071*

(2.133)

Percapitahouseholdincome 0.000 -0.000 0.000

(0.000) (0.001) (0.001)ChildFE No Yes YesRoundFE Yes Yes YesWithinvarianceexplainedbycovariates

0.126 0.133

VarianceexplainedbychildFE

0.438 0.440Jointvalidityofhealthindicators(p-value) 0.019 0.037 0.037HausmanFEvsREtest(p-value)

0.003

Note:Acrossspecificationsthetotalnumberofobservationsis1,254correspondingtoatotalnumberof627children.Standarderrorsareclusteredatthechildlevel.***/**/*indicatessignificanceatthe1/5/10

percentlevel,respectively.

14

Height-for-age, however, is not found to have a significant effect on cognitivedevelopment. While the relationship between height and intelligence has beenextensivelystudied(Takietal.,2012;Silventoinenetal.,2006;Sundetetal.,2005),moststudiesdocumentacorrelationforeitheradultsorolderchildrenandmostimportantlyin cross-sectional set-ups. Our results suggest that the height-intelligence link is notmanifested within children over time but rather across children. Furthermore, ourresultsunderscoretheimportanceofcontemporaneousinputsforcurrentperformance,i.e.,nutritionthataffectscurrentweight.ComplementaryevidenceforourresultscomesfromDuc(2011)whoshowedforasampleofVietnamesechildrenthattheeffectofHAZat age one on cognitive skills at age five is not statistically significant once pretermlength is controlled for. It is further suggested by the literature that omitting otherhealthproblemsfromregressionsofchildren’stestscoresonhealthandnutritionleadstoanubwardbiasonstunting(Wisniewski,2010).Wefurthernotethatpreviousstudiesshowedanegativerelationshipbetweenelevatedlead levels in blood and cognitiveperformance (Aizer et al., 2015; Solon et al., 2008).However, these studies rely on cross-sectional datasets and do not control for time-invariant child characteristics. Lead is primarily found in the paint used at the housewalls.6Inthisanalysisitislikelythatbycontrollingforchild-fixedeffectsthatincludesqualityofhousing,whichistypicallyunchangedthroughthesurveyrounds,ismaskingtheleadeffectsonIQthatarereportedincross-sectionalstudies.Fourth,theOLSestimatespointtoanegativerelationshipbetweenhouseholdsizeandcognitive performance in contrast to the child fixed-effects models, which point to apositive impactofhouseholdsizeoncognition.Todisentangle these,we included twomorevariables forhouseholdcomposition,namely theratioofchilddependentsup totheageof14and theratioofelderlydependentsaged65andolder (seeColumn3ofTable2).Wefindthattheratioofchilddependentsdoesnothaveanimpactoncognitiveskillswhereas theratioofelderlydependentshasapositiveandsignificant impactoncognition.Thissuggestsapositiveroleforgrandparentsandotheroldpeoplelivinginthehousehold.Theelderlytendnottohavearegularjobanymoreandwillbeathomeenrichingthelearningenvironment. Theywillhavetimetoplaywiththechildrenandcansingandreadtothechildrenandtellthemstories.Theinteractionofchildrenwitholdermembersrepresentsaninvestmentinthehumancapitaloffuturegenerationsthathassofarnotreachedmuchattentionintheliterature.Whiletheymightnotcontributeto contemporaneous income generation, they appear to build human capital of theyounginthisstudy.InthecontextofFilipinohouseholds,thisfindingisimportantgiventhat an increasing number of household heads and spouses leave their families foremploymentoverseasleavingthechildrentoberaisedbytheirgrandparents.Guardian characteristics such as education and gender do not impact the child’sperformance in thecognitive testing.We find this in thepooledOLSaswellas for thefixed effectsmodel. Onemight argue that this is due to lack of variation in guardiancharacteristicsbut47percentoftheguardianschangeovertimesuggestingthatitisnottheguardianpersewhoisimportantforachild’scognitivedevelopmentbutthehomeenvironment.Whileitseemstobeofnoimportancethatachild’sprimarycaregiveris6Asupplementarysurveyconductedamongchildrenwithelevatedleadlevelsinbloodfoundleadpaintusedontheinteriorandexteriorwallsinhospitalswheretheywereadmittedaswellasinoneschool.

15

themother, household demographics have an impact on child cognitive development.Informationonguardianage,whichmightbeanotherpotentiallyimportantdeterminantofachild’scognitivedevelopment,isnotavailableforallchildrenunderstudy.Forasub-sample of the childrenwith information on guardian agewe also include this controlvariable, which is similarly unrelated to a child’s cognitive performance.7We thusdecidedtoexcludeguardianageinfavorofalargersample.Toaccountforthefactthatsomeofchildrenweretakingadifferenttestatbaseline,weincludeacontrolvariableforallthosechildrenwhotooktheBSIDtestatbaseline.Thecoefficient associated with this indicator variable is negative and highly statisticallysignificant (p>0.001) across specifications indicating the importanceof accounting forthis difference in type of test. For completeness we also control for child age in theanalysis.We find a negative relationship between age and the age-adjusted cognitivedevelopmentscore.Thisrelationshipechoes theone found forageandWAZ/HAZandpointstotheestablishmentofasystematicnegativerelationshipbetweenageandage-adjustedscores. OthershavepreviouslyarguedthatthisisthecasesinceovertimeIQmeasuresbecomemore stableandmorepredictive (KoppandMcCall,1982).Wealsocontrolforpercapitaincometocapturetheimpactofeconomicresources.Controllingfor all the other child and household specific effects, the economic conditions do nothave any additional explanatory power for child cognitive development. Results areunchangednomatterwhetherweincludepercapitaincomeinlevelsorlog.5.2RobustnesschecksandstudylimitationsDuetothehighcostsassociatedwith implementingthecognitivedevelopmenttests,achallengeinworkwithsuchdataissamplesize.Thischallengeiscompoundedinapaneldataset imposing theneed for twoobservationsper included child. Therefore,we areconfronted with sample attrition. The original sample consists of 1,463 children who participated in both survey rounds. However, the psychological assessments were notcompletedorproperly implemented for all children. In addition, thehealth indicatorsweremissingforsomechildren.Consequently,whilewedidnotlosechildrentofollow-up,wedidloseaportionoftheoriginallysurveyedchildrentoincompletedata.Tocheckfor bias in themissing sample,we assess the representativeness of our sample alongsomekeyexplanatoryvariables(Table3).Ourestimationsamplehasasimilargenderdistribution, guardian characteristics, economic conditions and ratio of elderlydependentsasthemissingobservations.Therearesomesmalldifferences:childreninoursampletendtobe7monthsyoungeronaverage;thehouseholdsareslightlybiggerbutthedifferenceisonly0.224;andthereisasmallerratioofyoungdependentswiththe difference being only 0.100. We do not have any evidence from our fieldworksuggestingsystematicattritionbutnotethatsomehouseholdscouldnotbetrackedforthe follow-up survey as they had moved outside the catchment areas. In sensitivityanalysis,whereinweonlyuseasubcomponentoftheWPPSIscore,wecanenlargeourestimation sample by almost 100 children. In this larger analytic sample, the resultsestablishedinthemainpaperarefullysupportedandbalancingholdsforallexceptonevariable,theratioofdependentsof0-14years(seetheappendixTablesA2toA4).

7We do not show the results for the sake of brevity but the authors can make them available uponrequest.

16

Anotherpossible limitationofourstudyisthatthesampledchildrencouldbeasickersub-population as compared to the universe of Filipino children. The children in oursample have all been admitted to a hospital four to six weeks prior to the baselinehouseholdinterviewbecauseofpneumoniaordiarrhea.Whileonehastokeepinmindtheselectionofchildrenintothisstudy,wenotethatforthisanalysis,wedidnothavetorely on data thatwas collected at discharge.Moreover, from a comparison of clinicalindicators at discharge versus baseline (i.e. C-reactive protein level measuringinflammation)wecanconcludethatthechildrenhaverecoveredatthetimeofdischarge(Quimbo et al. 2010). Nonetheless, given this context, we consider our estimates aslower bounds of the possible effects of nurture on cognitive development as healthychildren can potentially even benefitmore from nutrition inputs and stimuli becausetheydonotneedtocompensateorcatchup.Table3:Comparisonofmeansbetweenestimationsampleandattritedobservationsfor

thefullcognitivedevelopmentscore

Estimationsample Attritedobservations Differenceinmeanstest

(p-value)Samplesize Mean Std.Dev. Sample

size Mean Std.Dev.

Childismale 1,254 0.581 0.494 1,032 0.547 0.498 0.103Ageinmonths 1,254 33.128 18.021 1,032 40.371 19.752 0.000***Self-ratedhealthstatusatleastgood 1,254 0.837 0.369 1,032 0.828 0.377 0.573Guardianandhouseholdcharacteristics Guardianyearsofeducation 1,254 7.680 10.347 1,028 8.120 6.635 0.239Femaleguardian 1,254 0.904 0.294 1,030 0.887 0.316 0.186Householdsize 1,254 6.206 2.372 1,027 5.982 2.202 0.020**Ratioofdependentsof0-14years 1,254 1.233 0.801 1,014 1.329 0.905 0.008***Ratioofdependentsof65+years 1,254 0.056 0.229 1,014 0.055 0.198 0.885Percapitahouseholdincome 1,254 994.458 993.643 1,017 1,074.060 1,368.944 0.109

Note:Comparisonofmeansforcovariatesthatareavailableinbothsub-samples.***/**/*indicatessignificanceatthe1/5/10percentlevel,respectively.

A final concern relates to the use of the cognitive development scores themselves. Inmost of the psychological literature these scores are used for cross-sectional cohortstudies.Tocircumventpossibleproblems,wemakeuseofage-adjustedtestscoresandonlyconsiderthewithinchilddevelopment.Moreover,weworkwithacombinationofdifferentscores.Inthemainanalysis,wepresentedresultsforacombinationoftheBSIDmental development index and theWPPSI full score consisting of a combination of averbal test score andaperformance test score (compareSection2.2).Weopt for thiscombinationofscoresasthedistributionsareshowntobestatisticallyidentical(Figure1).Moreover,theexistingliteraturehasshownthatthementaldevelopmentindexoftheBSIDcorrelateswithacorrelationcoefficientof0.73withtheWPPSIfullscore(Bayley,1993).WecouldhavecombinedtheBSIDmentaldevelopmentindex,ofwhichlanguagedevelopmentisoneofthekeycomponents,withonlytheverbalcomponentofWPPSI.Inthesensitivityanalysesfoundintheappendix(TablesA2toA4)wepresentafullsetofresultsforthisexercise.Ourfourkeyfindingsremainrobust.However,thedistributionsoftheBSIDandtheWPPSIverbalcomponentarenolongeridentical(FigureA1).

17

Overall, we are confident that the combined results show that stable predispositionspredetermineachild’spotentialbutenvironmentalfactorscanfosterchilddevelopmentthroughimprovedchildhealthandaqualityhomesurroundingthatstimulatesthechild.Aspartofthishomeenvironment,grandparentscanplayanimportantroleinincreasingthehumancapitalofchildren.6.ConclusionWefindevidencethatachild'stime-invarianttraitsincludinggeneticskillsendowmentexplainasubstantialamountofvariationincognitiveability.Yet,time-varyingparentalinputs affecting the home environment and nutritional status of children alsosignificantly influence young children's cognitive ability. This suggests that parents ofchildren with disadvantaged predispositions can and do compensate for these byappropriate investments in human capital. Some households appear to provide theirchildrenwithabetterhomeenvironmentandothersareabletomakebetternutrition-related decisions. Nature is indeed an important pathway to cognitive ability, butnurtureisapotentialpowerfulcompensatorymechanismandequalizer.Theideaofthe‘NoChildLeftBehindAct’establishedintheUnitedStatesissupportedbythisresearchimplyingthatdisadvantagedchildrenfrompoorfamiliesarenotbeyondhelp.Theycanbenefit from high-quality nutrition and stimulating learning inputs that are notnecessarilycostly.Ourfindingscomplementearlierevidencebyotherswhoarguedthata bundled intervention consisting of early education, nutrition, and health servicestargetedtopoorchildrenachievesimprovementsincognitivedevelopment(Bitleretal.2014).Theevidenceonthepotentialcognitivegainsfromastimulatinghomeenvironmentandbetter health to poor children continues to be scant. Mayer et al. (2015) show thatsimpleinterventionssuchasreminderssenttoparentsabouttheimportanceofreadingtotheirchildrencanhave largeeffects:USparentsofchildren insubsidizedpreschoolprograms considerably increased the time they spent reading to their children. Yet,further research needs to be done to improve our understanding of the role of earlyeducationalinterventionsforthecognitivedevelopmentof(disadvantaged)childreninthelongrun.Moreover,studiesaboutchildren’swellbeingneedtogobeyondassessinghealthstatusandcognitiveabilitiesandalsoconsideremotionalandbehavioralhealthandthedevelopmentofpositivepersonalitytraits(Akeeetal.,2015).ReferencesAi,Yuexian,Zhao,SophieR.,Zhou,Guoping,Ma,XiaoyangandLiu,Jianghong.2012.

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Appendix

FigureA1:Distributionoftheverbalscore,bytesttype

0.0

1.0

2.0

3.0

4D

istri

butio

n of

ver

bal s

core

0 50 100 150x

WPPSI BSID

22

TableA1:WPPSIcomponentsbysub-testMainComposite

ScoresSubtests:WPPSIYoung Subtests:WPPSIOld

Verbal ReceptivevocabularyInformation

InformationVocabularyWordreasoning

Performance BlockdesignObjectassembly

BlockdesignMatrixreasoningPictureconcepts

Source:Wechsler(2002)

TableA2:Summarystatisticsfortheverbalscoreandthecovariates

Total Round1 Round2

Variable Mean Std.Dev. Mean Std.Dev. Mean Std.Dev.Cognitivedevelopmentscores Verbalscore 86.991 13.224 89.165 14.395 84.817 11.545WPPSIfullscoreforallchildrentakingthetest 85.933 12.278 BSIDverbalscoreinround1 88.719 14.483 WPPSIverbalscoreinround1 90.571 14.061 ChildtookBSIDinround1 0.380 0.485 0.759 0.428 Homelearningenvironment 32.231 7.547 30.421 6.902 34.042 7.731Healthindicators WAZ -1.250 1.331 -1.023 1.304 -1.478 1.319HAZ -1.600 1.442 -1.506 1.592 -1.694 1.268Normalhemoglobinlevel 0.637 0.481 0.689 0.463 0.586 0.493Highleadlevel 0.216 0.412 0.314 0.464 0.119 0.324Childcharacteristics Childismale 0.570 0.495 0.570 0.495 0.570 0.495Ageinmonths 36.386 19.254 21.650 12.419 51.123 12.361Guardianandhouseholdcharacteristics Guardianyearsofeducation 7.819 9.478 8.562 3.316 7.076 12.949Femaleguardian 0.893 0.309 0.899 0.301 0.888 0.316Householdsize 6.114 2.341 5.729 2.260 6.499 2.358Ratioofdependentsof0-14years 1.235 0.802 1.170 0.708 1.299 0.881Ratioofdependentsof65+years 0.059 0.229 0.040 0.157 0.078 0.281Percapitahouseholdincome 1,036.597 1,242.240 987.980 1,264.733 1,085.214 1,218.219

Note:Thetotalnumberofobservationsis1,530correspondingto765observationsperround.Inthefirstroundofdatacollection,581childrentooktheBayleycognitivetestandtheremaining184tooktheWechslertest.

23

TableA3:RegressionresultsfortheverbalscoreOutcome:Verbaldevelopmentscore OLS FE FE

Basicmodel

withfamilycomposition

Homelearningenvironment 0.600*** 0.526*** 0.534***

(0.047) (0.062) (0.062)Healthindicators WAZ 0.502* 0.682* 0.637*

(0.274) (0.385) (0.380)HAZ 0.066 -0.227 -0.215

(0.237) (0.349) (0.347)Normalhemoglobinlevel 1.451** 1.745* 1.730*

(0.627) (0.892) (0.891)Highleadlevel -0.155 0.566 0.704

(0.745) (1.008) (0.999)Childcharacteristics Ageinmonths -0.413*** -0.390*** -0.402*** (0.031) (0.103) (0.103)ChildtookBSIDinround1 -9.300*** -7.094*** -7.118*** (1.200) (1.256) (1.242)Guardianandhouseholdcharacteristics Guardianyearsofeducation -0.002 -0.053 -0.047

(0.030) (0.039) (0.041)Femaleguardian 0.041 -1.124 -1.282

(0.924) (1.594) (1.584)Householdsize -0.358*** 0.779 0.628

(0.131) (0.480) (0.476)Ratioofdependentsof0-14years 0.569

(0.685)Ratioofdependentsof65+years 5.274***

(2.030)Percapitahouseholdincome -0.000 0.000 0.001

(0.000) (0.001) (0.001)ChildFE No Yes YesRoundFE Yes Yes YesWithinvarianceexplainedbycovariates 0.186 0.193VarianceexplainedbychildFE 0.395 0.398HausmanFEvsREtest(p-value) 0.014 Note:Acrossspecificationsthetotalnumberofobservationsis1,530correspondingtoatotalnumber

of765children.Standarderrorsareclusteredatthechildlevel.***/**/*indicatessignificanceatthe1/5/10percentlevel,respectively.

24

TableA4:Comparisonofmeansbetweentheestimationsampleandattritedobservationsfortheverbalscore

Estimationsample Attritedobservations Differenceinmeanstest

(p-value)Samplesize Mean Std.Dev. Samplesize Mean Std.Dev.

Childismale 1,530 0.570 0.495 756 0.556 0.497 0.514Ageinmonths 1,530 36.386 19.254 756 36.422 18.981 0.967Guardianandhouseholdcharacteristics Guardianyearsofeducation 1,530 7.819 9.478 752 7.999 7.488 0.649Femaleguardian 1,530 0.893 0.309 754 0.903 0.296 0.473Householdsize 1,530 6.114 2.341 751 6.085 2.213 0.776Ratioofdependentsof0-14years 1,530 1.235 0.802 738 1.361 0.938 0.001***Ratioofdependentsof65+years 1,530 0.059 0.229 738 0.049 0.186 0.319Percapitahouseholdincome 1,530 1,036.597 1,242.240 741 1,016.702 1,029.944 0.706

Note:Comparisonofmeansforcovariatesthatareavailableinbothsub-samples.***/**/*indicatessignificanceatthe1/5/10percentlevel,respectively.