Intergenerational Transmission of Mother-to-Child Health: … · · 2017-10-19transmission of...

Intergenerational Transmission of Mother-to-Child Health: Evidencefrom Cebu, the Philippines

Leah EM Bevis∗and Kira Villa†

August 18, 2017

Using rich longitudinal data from the Philippines, we causally estimate thetransmission of maternal health to child health, from birth through preadoles-cence. To obtain exogenous variation in maternal health we exploit an arrayof data on climate (temperature, precipitation and cyclone) shocks surround-ing the time of the mother’s birth and during her early childhood. This largeset of instruments is weak, with first stage F-statistics hovering around one.We contribute a new machine learning method of choosing/forming optimalinstruments in a setting of many valid but weak instrumental variables, amethod based on singular value analysis (SVA). This method out-performsother options, and results in first stage F-statistics hovering around 50. Us-ing this optimal instrument, we show that mother’s health causally transmitsto child health from birth through childhood and into adolescences. Some ofthis transmission is due to the effect of mother’s health on child health at birthand then the effect of birth health on later health. Yet, the transmission ofmaternal height to child height persists into adolescence beyond its effect onhealth at birth and in early life. Moreover, the transmission of height increasesrather than diminishes as the child ages. This may be, in part, due to a trans-mission of growth velocity that causes the health advantage of having a tallermother to widen with age.

Acknowledgements: The authors gratefully acknowledge aid from HowardDiamond, Cathy Smith and Gil Campo in procuring the geospatial data usedin this paper. All errors are our own.

JEL Codes:

∗Joint first author. Ohio State University. Email: [email protected]†Joint first author. University of New Mexico. Email: [email protected]

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1 Introduction

Intergenerational correlations in socio-economic status characterize an importantdimension of inequality and poverty in a society. They provide evidence on the extentto which children’s future economic well-being is tied to the socio-economic status oftheir parents: is there truly social and economic mobility in a society or are childrenborn to poor families destined to be poor adults? The transmission of health from onegeneration to the next may be an important pathway underlying these correlations.Less healthy children, on average, obtain less schooling, earn lower wages, and own lessassets as adults.1 However, obtaining causal estimates of the transmission of health isdifficult, particularly since causality can run in both directions. The transmission ofpoor health, for example, likely causes a transmission of poverty and, simultaneously,the transmission of poverty likely causes a transmission of poor health. Either or bothdirections of causality are plausible in that health is both genetically andenvironmentally determined.

A large body of literature demonstrates an association between poor maternal healthand poor child health outcomes such as low birth weight (Currie and Moretti, 2005;Victora et al., 2008), infant mortality (Bhalotra and Rawlings, 2011), shorter lifespan(Yashin and Iachine, 1997), and increased morbidity (Eriksson, Bratsberg and Raaum,2005). However, evidence regarding the mother-to-child health transmission is plaguedby omitted variable bias that obscures the causal relationship between mother and childhealth. Causal estimates of the transmission of maternal health to child human capitalare sparse. While nutritionists and economists have in a few cases traced the children ofwomen who participated in supplementation trials, new immunization regimes or otherrandomized, health-related trials, such research measures the intergenerational impactof a particular trial, only.2 It cannot provide evidence regarding the more general,intergenerational transmission of health from mothers who fall within thenormally-observed distribution of health. The best estimates using observational datatend to come from within-mother or within-siblings estimates of maternal-child healthtransmission.3 Understanding the extent that a child’s human capital is determined bythat of his or her mother enables policymakers to better work towards breaking thetransmission of poor human capital outcomes across generations, particularly in thecontext of child poverty. Moreover, improved understanding of the extent that health iscausally transmitted from one generation to the next tells us the degree to which theconsequences of a health shock or the benefits of a health intervention will cross into thenext generation.

Using rich longitudinal data from Cebu, the Philippines, we estimate the causaltransmission of health from mother to child. Specifically we estimate the effect ofmother’s health stock (proxied by height) and health flow (proxied by skinfoldthickness) on her child’s health stock and health flow. We do so across multiplechildhood stages from birth through early childhood and adolescence. To obtainexogenous variation in maternal health, we capitalize on random climate variation inCebu around the time of the mother’s birth and in her early childhood. Specifically, weinstrument mothers baseline health using information on typhoon exposure (proxied by

1For example, see Victora et al. (2008) and ?.2see new lancet paper I am now citing for a good summary of this evidence.3e.g., see the currie paper.

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windspeed) from the year prior to her birth, her birth year, and the first few years ofher life. We also use de-trended information on average monsoon season temperatureand precipitation for that same period.

The far-reaching consequences of early life health shocks to adult human capital andproductivity is well-documented.4 Further, the association between maternal health andchild outcomes (especially child health) is similarly well-documented.5 However, verylittle evidence traces the impact of early childhood health shocks across multiplegenerations. In this paper, we add to the literature on the long-term consequences ofearly life health by demonstrating that the impact of early life climate variationsextends not only to our sample mothers’ adult health but is evident in the health oftheir children through adolescence.

We further provide an important contribution to the limited but growing literature onthe health effects of climate shocks. Much of the previous work in this body ofliterature examines the short- or long-term impact of a climate shock such as ahurricane or earthquake.6 However, our instruments are not necessarily climate shocksbut rather measure climate variation on the intensive margin. Our analysis thereforeestablishes that even simple year-to-year variation in climate can have far-reachingconsequences that can span generations.

The climate variables we employ for our instruments occur during the period from the1930s to the 1960s—two to four decades prior to the birth of our sample children. Wealso de-trend them by time and time squared. It is, therefore, unlikely that ourinstruments affect child health outside of their effect on maternal health. Thus, ourclimate variables likely satisfy the exclusion restriction required for an instrument to bevalid.7 However, our instruments are both many and weak with first-stage F-Statisticshovering around one. We are therefore unlikely to satisfy the relevance condition andmay suffer from two-stage-least-squares (2SLS) bias. To address this problem, wepropose a new method of forming an optimal instrument from many weak instrumentsusing a machine learning method based on singular value analysis (SVA). This methodfar out-performs other methods and results in first-stage F-Statistics ranging fromapproximately 35 to 75.

Finally, we add nuance to previous literature on the intergenerational transmission ofhealth by considering multiple dimensions of both mother’s and child’s health as well asby examining multiple windows of transmission over many stages of childhood. Thisallows us to pinpoint where health transmission is most crucial, and to consider theconditional persistence of transmission. Is transmission of mother-to-child healthstrongest at birth and diminish as the child ages and is exposed to a widening array ofhealth inputs? Are observations of mother-to child health transmission in laterchildhood due to a primary transmission occurring at birth, which in turn, affects laterhealth?

Our results indicate that maternal health exhibits a persistent effect on child health

4e.g., see these papers5see these papers6For example, see this or this.7We provide further evidence below to support this claim.

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years after birth. We find that mother’s health flow measured in her third trimester ofpregnancy significantly predicts her child’s health from birth through childhood.However, this transmission seems to be primarily due to the mother’s health atpregnancy transmitting to her child’s health at birth and then the birth health’s effecton subsequent realizations of child health. Conversely, the transmission of maternalhealth stock to child health stock (proxied by height-for-age z-scores) perists throughoutchildhood and into adolescence even after controlling for health at birth. Not only doesthe transmission persist, but the magnitude of the transmission increases as the childages. One possible explanation for this finding is that increased mother’s height notonly translates into increased height in her child but also into an increased growthvelocity. This results in a widening height advantage as the child gets older. Indeed, theestimated transmission of maternal health stock to child height only reduces to nearzero once we control for all accumulated height.

2 Intergenerational Health Transmission

While a growing literature examines intergenerational transmission of income andeducation in both rich and poor countries, estimating the transmission of health is moredifficult due to the multidimensional nature of health (Strauss and Thomas, 1998). Themajority of evidence concerns the association between maternal health and birthweight, or some other indicator of infant health at birth. For instance, using five cohortstudies from Brazil, Guatemala, India, the Philippines, and South Africa, Victora et al.(2008) find that maternal undernutrition — using a range of indicators — is associatedwith low birthweight. They cannot parse, however, whether this correlation reflects abiological mechanism or a transmission of socioeconomic status.

Using data from California, Currie and Moretti (2005) show that a child is almost 50percent more likely to be low birthweight if his or her mother was low birthweight. Thisis true even controlling for the birthweight of mothers’ siblings and for mothers’socioeconomic status, suggesting that this transmission reflects a biological mechanismrather than a spurious correlation. Bhalotra and Rawlings (2011) examine therelationship between mother’s height and infant survival using a large dataset of 2.24million children born to 600,000 mothers spanning 38 developing countries. They findthat, within mothers, improvements in socioeconomic status and health environmentmitigate the transmission of health between mother and child. Yet they are only able toexamine this transmission along the dimension of infant survival.

A few papers investigate the association between parent health indicators and those fortheir adult children. Estimates of parent-child lifespan correlation fall between 0.15 and0.3 (Yashin and Iachine, 1997). Eriksson, Bratsberg and Raaum (2005) find an averageparent-child morbidity correlation slightly under 0.3.

Another strand of literature examines the cross-capital transmission between parenteducation and child health. In a seminal paper, Thomas, Strauss and Henriques (1991)find that maternal education positively affects child height-for-age, and that thisassociation appears to work through access to information. In Brazil, Ghana and theUS, Thomas (1994) finds that maternal education has a larger positive impact ondaughters’ height than on sons’ height; the opposite is true for paternal education. In

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Pakistan, father’s education is a determinant of child immunization while mother’seducation is the best predictor for child weight-for-age (Aslam and Kingdon, 2012).

In the Philippines particularly, cross-capital transmission between mothers and childrenmay be a complex process. Blau, Guilkey and Popkin (1996) find that both mothers’labor supply and mothers’ wages are negatively associated with breast-feeding in ruralPhilippines. Their results show, however, that increased maternal labor supply actuallyimproves childhood health in the long run, with perhaps questionable impacts in thevery first months. Bevis and Barrett (2015) find that maternal education in thePhilippines is negatively associated with the adult height of their children, evencontrolling for maternal height, paternal education and paternal height. They surmisethat this effect may be due to increased labor supply of taller mothers, as documentedby Blau, Guilkey and Popkin (1996). However, because neither of these papers providecausal estimates, we take care in interpreting their findings.

3 Many Weak IVs

In our analysis we exploint information on climate during the time of our smaplemothers’ birth and in their early childhood to obtain exogenous variation in theirhealth. However, our climate instruments are both many and weak. A growingliterature deals with the asymptotic properties of various estimators in settings withmany weak instrumental variables (IVs). It is well known that parameters estimated viatwo stage least squares are biased towards the parameters that would be estimated viaOLS when instruments are weak. Additionally, instrumental variable estimators makinguse of many, weak instruments can have poor asymptotic properties — when theconcentration parameter is small relative to sample size, neither two stage least squaresnor limited information maximum likelihood estimators are consistent, and the usualstandard errors are too small (Stock and Yogo, 2005; Staiger and Stock, 1997; Chao andSwanson, 2005; Hansen, Hausman and Newey, 2008).

A related literature attempts to choose or formulate a small number of optimal IVs insettings with many weak IVs, thereby avoiding (rather than correcting) the asymptoticproblems associate with many weak IVs. Belloni et al. (2012) propose using the leastabsolute shrinkage and selection operator (Lasso) or the related Post-Lasso estimator insuch a setting. Originally proposed by Tibshirani (1996), Lasso is a shrinkage estimator,designed to reduce model dimensionality by isolating key sources of model variation.Lasso estimates coefficients by minimizing the sum of squares subject to a penalty onlarge coefficients, forcing the majority of coefficients to zero and thereby leaving onlythe strongest predictive covariates in the model.8 Belloni et al. (2012) propose anddemonstrate the use of Lasso in the first stage of a two stage least squares model, as amethod of subsetting the strongest IVs while estimating the first stage equation.

The number of IVs chosen by Lasso depends on the specification of the penaltyfunction, but generally first stage Lasso results in a very small subset of optimal IVs.For instance, Belloni et al. (2012) and Belloni, Chernozhukov and Hansen (2014)

8Because Lasso results in downwardly biased coefficients even for the “chosen” covariates, Post-Lassosimply re-estimates the least squares problem again with only the smaller subset of covariates, usingordinary least squares.

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consider the effects of federal appellate court decisions regarding eminent domain onfour types of housing prices, instrumenting for court decisions with judgecharacteristics. From 138, 143, 147, and 138 potential, price type specific instruments,Lasso chooses only 1, 2, 2, and 4 optimal instruments. Mueller-Smith (2014) similarlyapplies Lasso and Post-Lasso to estimate the effects of incarceration on subsequentbehavior and labor market activity, instrumenting for court decisions with interactionsbetween judge characteristics and defendant characteristics. From likely hundreds ofpotential instruments, Lasso subsets 5 for use in Post-Lasso estimation. Mueller-Smith(2014) points out that that not only does Lasso estimation reduce dimensionality, butexamining the selected covariates lead to insight about the data generating process.

Other machine learning procedures can be used in the first stage with similar effect.Okui (2011) selects IVs in the first stage with a shrinkage procedure that uses a ridgepenalty. This ridge penalty shrinks coefficients towards zero, but doesn’t set them tozero as Lasso does. Ng and Bai (2009) select IVs via boosting, a model selectionprocedure that produces solutions similar to those produced by Lasso.

Rather than choosing an optimal subset of potential IVs, one might instead choseoptimal linear combinations of all potential IVs. Kloek and Mennes (1960) firstproposed principal components as just such a set of linear combinations. Usingprincipal components in the first stage lowers dimentionality while retaining the bulk oforiginal variation. Additionally, the fact that each principal component is orthogonal tothe next increases prediction stability. Amemiya (1966) later supported the propositionby showing that principal components satisfy several desirable properties when it comesto choosing linear combinations of IVs. The use of principal components in the firststage was subsequently embraced in the macroeconomics literature, where observationswere scarce and the accompanying sparsity was particularly useful (Klein, 1969;Mitchell, 1971; Giles and Morgan, 1977).

More recently, Ng and Bai (2009), Kapetanios and Marcellino (2010a), and Kapetaniosand Marcellino (2010b) evaluate the performance of principal component IVs undervarious data generating structures. In a setting where the endogenous variable is trulydriven by a small subset of many potential IVs, subsetting the original IVs results inbetter estimation outcomes than subsetting the principal component IVs (Ng and Bai,2009). This is because the primary principal components will capture variation fromboth the “true” IVs and the less relevant IVs.

Conversely, if the endogenous variable is driven by a small number of unobservable,exogenous common factors, all correlated with the entire set of original IVs, principalcomponent IVs will perform well, as they proxy for the true common factors (Ng andBai, 2009; Bai and Ng, 2010). A factor structure is not necessary for principalcomponents IVs to perform well, however. If the original IVs are themselves imperfectproxies for the true data generating process (i.e., only correlated with the true drivers ofthe endogenous variable), then a subset of the principal component IVs will still beconsistent, and will perform better than a subset of the original IVs (Ng and Bai, 2009;Kapetanios and Marcellino, 2010b),

Most authors subset potential IVs (whether principal component IVs or otherwise)based on explanatory power. Lasso and other shrinkage estimators choose “optimal”

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IVs by minimizing mean squared error, or maximizing explanatory power, but with anadded penalty for high first stage coefficients. Ng and Bai (2009) choose optimal IVs viaboosting as well as two types of “ranking.” Boosting maximizes explanatory power likeLasso, though it works by iteratively selecting predictors that minimize the squares ofthe current residuals. The first ranking procedure simply keeps only IVs with t-valuesover a certain power, thus subsetting on first stage relevance rather than explanatorypower. The second ranking procedure combines both relevance and explanatory power.

Winkelried and Smith (2011) subset principal components based on the magnitude ofassociated eigenvalues, rather than explanatory power, and evaluate two thresholds foreigenvalue magnitude.9 This is similar, though not equivalent, to subsetting on firststage relevance — principal components with greater eigenvalues capture morevariability in the original IV matrix, and thus are likely but not guaranteed to be betterpredictors of the endogenous variable.

We propose a new method of formulating optimal IVs in settings with many weak IVs,using singular value decomposition rather than principal component analysis (PCA) toform optimal linear combinations of the original IVs. Singular value decompositiondecomposes the matrix itself (rather than the covariate matrix as PCA does) into theeigenvectors, or principal axes, and also into “singular values,” which are a directtransformation of eigenvalues. Singular value analysis(SVA) solves a minimizationproblem equivalent to the original ordinary least squares minimization problem, but ina space defined by the new axes, or eigenvectors, obtained by singular valuedecomposition. The parameter estimated by this minimization can be transformed intothe parameter that solves the equivalent ordinary least squares minimization problem.We then apply machine learning procedures to the SVA procedure in order to form one“optimal” IV from our full set of instruments.

4 Data

4.1 Cebu Longitudinal Health and Nutrition Survey

To explore the transmission of mother-to-child human capital we exploit unique datacollected by the Cebu Longitudinal Health and Nutrition Survey (CLHNS). TheCLHNS is a rich longitudinal data set collected from the metropolitan area of Cebu, thePhilippines. Originally designed to study infant feeding patterns and diets, the CLHNSis part of an ongoing study of a cohort of Filipino women who gave birth during the oneyear period between May 1, 1983 and April 30, 1984. The study area encompasses 17urban and 16 rural randomly selected barangays10 in metropolitan Cebu and includesseveral urban, mountainous, and coastal regions.

Within these barangays, all pregnant women due to give birth during the designatedtime frame were canvassed to participate in the study. Women were surveyed in theirthird trimester, at birth, and then every 2 months for the first 24 months of their child’slife. Additional rounds were later added and the mothers, index children, and other

9Doran and Schmidt (2006) do the same when using principal components to solve a GMM estimationproblem, not in an instrumental variable context.

10A barangay is the smallest administrative unit in the Philippines

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caregivers were subsequently surveyed throughout childhood, adolescence and into earlyadulthood. In order to study the transmission of maternal health to child humancapital we exploit information collected at birth and the first two years of the indexchildren’s lives. We also use information from when the index children were 8, 11, and15 years old.11

The CLHNS provide broad and detailed information on numerous dimensions of humancapital. In each round, the surveys collected extensive information on the child, themother, the child’s primary caregiver if that is not his/her mother, the child’shousehold, and his/her mother’s household (if if is not the same as the child’s).Anthropometric measures were taken in each wave on both mother and child. Thereforethese data are uniquely suited towards investigating the relationship between maternaland child health.

Table 1 provides summary statistics on some baseline characteristics and child healthoutcomes for the sample children through age 11. Height-for-age is often considered thegold standard for measuring early life health and health stocks. The children in thissample are relatively short with height-for-age z-scores (HAZ) hovering around twostandard deviations below median height for what is considered healthy for their age.12

Approximately 60% of the sample is stunted13 at some point during the first 6 monthsof life. Stunting rates spike at approximately 75% at age 2 and then reduce to around50% at ages 8 and 11.

Measures related to weight are more sensitive to current nutritional inputs andmorbidity and are thus in part able to capture aspects of current health flows ratherthan stocks. Just over 10% of the sample was born at a low birth weight.14

Weight-for-height (WHZ) and body mass index (BMI) z-scores capture how thin a childis for his/her height and hover around one standard deviation below what is consideredhealthy during all the ages examined. Wasting rates are highest when the samplechildren are young. Approximately 30% of the sample children are wasted at age 1.Wasting rates decrease to around 20%at age 2 and 10% at age 8 but increase again toalmost 20% at age 11.

4.2 Climate Data and Instruments

In order to causally identify the intergenerational transmission of human capital in thissetting we exploit climate information on windspeed, temperature and rainfall aroundthe time of the mother’s birth and during the early years of her childhood. Figure 1plots the distribution of maternal birth year. Mothers in our sample were all bornbetween the years 1936 and 1966, with most born in the 1950s and early 1960s,approximately two to four decades before the birth of our sample children.

To investigate the effect of climatic conditions on maternal health, we use threegeospatial datasets, all stretching back to the 1930s. The majority of geospatialre-analysis data begins in 1979, with the advent of satellites. Re-analysis data

11The first three survey waves conducted after the age two survey were conducted at ages 8, 11, and 15.12World Health Organization reference data was used to standardize all anthropometric z-scores.13A child is considered stunted if his/her height-for-age z-Score is below -2.14A birth weight is considered low if it is below 2500 grams.

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beginning prior to this relies on global records of pressure levels.

We obtain data on windspeed from the 20th Century Re-Analysis project, run by theEarth Science Research Laboratory (ESRL) at the National Oceanic and AtmosphericAdministration. It contains global 10-meter windspeed estimates at a 2 degree spatialresolution in 3 hour intervals (8 observations per day). We average these estimateswithin day, and across the cells that overlay the island of Cebu.

Our temperature data also come from the 20th Century Re-Analysis project, andcontains global surface temperature estimates at a 2 degree spatial resolution in 6 hourintervals (4 observations per day). We procure precipitation data from the GlobalPrecipitation Climatology Centre, also generated by ESRL. This dataset providesmonthly, average precipitation estimates at a 0.5 degree spatial resolution.

4.3 Typhoons in the Philippines

The Philippines is one of the most intensely exposed countries in the world to typhoons(also known as tropical cyclones or hurricanes). Typhoons are characterized by heavyrainfall and intense, damaging winds. Anttila-Hughes and Hsiang (2014) demonstratethe heavy economic and health cost of typhoons in the Philippines. They find thattyphoons increase infant mortality and reduce household income a year after they strikepointing to significant economic losses due to unearned income long after exposure.They further find that households often cope with typhoon shocks by reducingexpenditure on human capital inputs such as medicine, education, and nutritious foods.Salas (2015) further finds that typhoon exposure increases pregnancies post-typhoonevent with the fertility effect concentrated in agricultural areas where child labor ismore prevalent.

Typhoons around the time of an infant’s birth increases his/her risk of morbidity andmortality by destroying household assets and spreading waterborne disease throughcontaminated flood waters due to ocean surges (Anttila-Hughes and Hsiang, 2014;DOH, 2017). We therefore create a monthly measure of typhoon likelihood/windspeedintensity by squaring the maximum windspeed observed in each month, between 1927and 1970. We then use as our first set of instruments these measures of monthlymaximum wind speeds for the month of our sample mothers’ births, each of the 12months preceding their births, and each of the 12 months following their births. Thisallows us to control for windspeed intensity in the months directly before and after thebirths of mothers in our dataset.

Typhoons can also cause significant damage to rice crops. Strong typhoon winds candamage rice crops through logding, striping and injuring plant organs. Rice plants canalso suffer from water stress due to enforced transpiration. Rice crops are mostsusceptible to environmental damage during their reproductive and ripening phasewhich occurs during the two months prior to harvest (Blanc and Stobl, 2016).

Rice is the primary staple in Philippines. The country underwent massive tradeliberalization in agriculture during during the 1990s. Prior to this, however, rice waslargely self-supplied. Therefore shocks to rice production could have severeconsequences for food security and the health of small children or even as-yet-unborn

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children. We therefore create a measure of potential wind destruction to rice crops foreach harvest period (two harvest periods per year) by squaring the period-maximumwindspeed observed for the two months preceding rice harvest. We then use as oursecond set of instruments pre-harvest monthly maximum windspeeds for the two yearsprior to our sample mothers’ birth year, the year of their birth, and the five yearsfollowing their birth.

Similarly, growing conditions (temperature and precipitation) during the monsoonmonths of each year impact rice production, driving local food supply and familyincome in the short run and therefore impacting early life health. We create ayear-specific measure of growing conditions by controlling for average monsoon seasontemperature, average monsoon season rainfall, and interactions between temperatureand rainfall. We use as our third set of instruments these measures for the two yearsprior to, year of, and five years after the births of mothers in our dataset. To avoidcapturing any spurious correlations between time trends in monsoon season temperatureand precipitation (e.g., driven by el nino or by climate change) and time trends inmother’s health, we de-trend monsoon variables by year, allowing for a quadratic shape.Finally, to capture the predictable seasonality of health related to the typhoon andmonsoon seasons we use a vector of maternal month of birth fixed effects as our final setof instruments. Appendix 1 holds greater detail on gridded wind, temperature andprecipitation data, as well as on the variables created with these climate data.

5 Estimation Strategy

We are interested in estimating the transmission of maternal health to child health.Accordingly, we model the health of child i at age a born to mother j living in barangayv, Ca

imv as follows:

H = Caijv = γa1H

ljv + γa2X

aijv + λav + εaijv (1)

,where M l

jv, represents the health measure l of mother j, l ∈ {stock, flow}. Thus, ourestimate, γa1 , captures the intergenerational transmission coefficient of mother-to-childhealth at age a. We estimate 1 separately for ages a ∈ {0, 1, 2, 8, 11, 15}. Xa

ijb is amatrix of individual- and household-level controls and includes household per capitaincome, household size, mother’s age, and mother age cohort dummies that indicatewhether the child’s mother is younger than 20 years, between 20 and 35 years old, orolder than 35. Xa

ijb also includes a vector of barangay of birth fixed effects as well aschild month of birth fixed effects to control for seasonality in health based on month ofbirth. λav is a vector of baranagay of residence fixed effects (which is equivalent tobarangay of birth during the first two years of life).

We consider two dimensions of child health: health stock and health flow. We proxychild health stock by height-for-age z-scores (HAZ) at each age we examine. For agestwo and younger HAZ is calculated using recumbent length rather than height. Weproxy child health flow using weight measures. Specifically, these measures include birth

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weight15, weight-for-height z-scores (WHZ) for ages 1 and 2, andbody-mass-index-for-age z-scores (BMIZ) for ages 8, 11, and 15. All z-scores arecalculated using World Health Organization (WHO) reference data. We similarlyconsider a measure of maternal health stock, M stock

jv , and maternal health flow, M flowjv .

Our measure of maternal health stock is her baseline height measured in centimeters(cm). Our measure of her health flow is her baseline skinfold thickness, also measuredin cm. Both measures are taken at baseline when the women were in their thirdtrimester of pregnancy.16

Estimating 1 using OLS will return a biased estimate of γa1 as mother’s health iscorrelated with numerous, omitted dimensions of socio- economic status that also affectchild health. To obtain a causal estimate of γa1 , we instrument mother’s health usingour climate information from around the time of her birth and in her early childhood.To do so, we would typically instrument for mother’s health using our entire set ofexogenous climate variables: windspeed in the months surrounding birth (WBmv),monsoon conditions (temperature and rainfall) for the years prior to and after birth(MNmv), windspeed during harvest months for the years prior to and after birth(HWmv), and maternal month of birth fixed effect λlmb. The first stage equation of thistwo stage least squares estimation would be specified as follows:

M lmv = βl

1WBmv + βl2MNmv + βl

3HWmv + λlmv + βl4Dmv + ulmv, (2)

where Dmv includes all the individual- and household-level covariates included inEquation (1).

However, this large set of 69 instruments seems to be weak (Stock and Yogo, 2005;Belloni et al., 2012). Indeed, tests on the joint significance of our instruments in (2)return F-statistics that hover around one. Therefore while our instruments likely meetthe exclusion restriction required for an valid instrument, they do not appear to meetthe necessary relevance condition. Therefore rather than estimating (2) with the full setof instruments, we propose a new new method that employs singular value analysis(SVA) and machine learning to choose the optimal linear combination of climatevariables to use as our single instrument.

That is, rather than estimating Equation (2) using ordinary least squares (OLS), whichmaximizes explanatory power at the cost of predictive power, we estimate Equation (2)via singular value analysis (SVA). Singular value analysis minimizes mean squaredforecast error (MSFE) by first decomposing the matrix of exogenous shocks{WBmv;MNmv;HWmv} into a series of orthogonal eigenvectors and matching singularvalues (related to eigenvalues), and then solving a new minimization problem,equivalent to the original OLS minimization problem, in this rotated space. Candidate

15Birth outcomes were measured in the first survey round following the child’s birth. Therefore, mea-surements were not necessarily taken on the day of birth but most were taken withing a few days ofbirth. However, a handful were taken over a month past their day of birth.

16Results do not substantially differ if we use mother’s middle upper arm circumference as her healthflow rather than skinfold thickness. Ideally, we would have liked to have used BMI as our measureof maternal health flow. However, any measure including maternal weight was not an option as oursample mother’s were all pregnant at baseline and their weight thus included the sample child’s weight,by construction.

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solutions to the SVA/OLS minimization are chosen by allowing the number of singularvalues and matching eigenvectors, k, to vary from 1 to kmax, where kmax is the totalnumber of non-zero singular values (Lawson and Hanson, 1974). More informationabout SVA can be found in Appendix 2.

We denote Equation (2) parameters estimated via this SVA process as βl,k1 , βl,k

2 , βl,k3 ,

and λl,kmv. A low k indicates a more parsimonious solution, modeling H lmv with less

dimensions of variance in the original climate shock matrix. Parameter estimates with ahigher k will provide a better fit to the original data, but they will also result in lessstable predictions of M l

mv, and after a point will have lower forecast power as they willbegin to overfit to random noise in our instruments and maternal health measuresrather. Therefore, the choice of k plays a crucial role in shaping our instrumentalvariable.

We choose an optimal k∗ by choosing the k that minimizes MSFE via group-wise crossvalidation. We could choose k in a different manner. For instance, we might choose kaccording to the associated singular values, choosing a cut-off for inclusion similar tothe threshold for eigenvalue magnitude considered by Winkelried and Smith (2011).Theoretically, we could also choose k to maximize explanatory power, rather thanforecast power — but this would simply lead us to choose k = kmax, including alleigenvalues with non-zero singular values in the rotated minimization problem, andwould result in βk

1 , βk2 , βk

3 estimates identical to those estimated under OLS. We mighteven choose k according to some measure of eigenvector relevance, in the rotatedminimization problem.

However, we choose k to minimize MSFE because our goal is to differentiate signal, orcausal associations between climate shocks and mother’s health, from noise, orspurious/accidental associations between climate shocks and mother’s health. Spuriouscorrelations will increase explanatory power, but only meaningful signals will increasepredictive power. Because eigenvectors capture primary directions/axes of the climatedata, in order of decreasing importance, we expect that after some number k∗, allremaining eigenvectors are associated with noise rather than signal, and therefore nolonger increase predictive power.17

Having chosen the MSFE-minimizing k, we then predict our optimal instrumentalvariable as follows:

M l,k∗

m = βl,k∗

1 WBmv + βl,k∗

2 MNmv + βk∗

3 HWm + λl,k∗

mv .

This gives us one optimal instrumental variable, M l,k∗m , for each maternal health

dimension to obtain the causal estimate of the transmission coefficient, γa1 , in Equation(1). This is quite different from the principal component IV approach, where a subset ofprincipal components (that subset being chosen according to some threshold foreigenvalues or relevance) are all included in the first stage of a two stage least squaresestimation. Instead, we use a matrix decomposition approach to choose the optimallinear combination of instrumental variables. We do not use rotated vectors asinstruments themselves.

17Note that if eigenvectors representing signal and eigenvectors representing noise were interspersed, thisordered approach to choosing all eigenvectors of index ≤ k would not work. This approach assumesan approximately smooth transition between “signal eigenvectors” and “noise eigenvectors.”

12

5.1 Persistence and Mechanisms

After obtaining causal estimates of of the mother-to child health transmission at eachobserved age, we additionally examine the persistence of maternal-child healthtransmission by estimating the ongoing, causal impact of baseline maternal health onchild health at later ages, holding earlier child health constant. Ample evidencedemonstrates that early health is an important determinant of later health. Therefore ifwe find in (1) that maternal height significantly impacts child height at birth and at,say, age 11, we do not know if the latter effect is due to the direct transmission ofmaternal health at age 11, an indirect transmission operating through the effect onbirth health, which in turn affects age 11 health, or some combination of the two.

Equation (3) estimates the transmission of mother-to-child health for agesa ∈ {1, 2, 8, 11} in the same manner as Equation (1) except that we also control forlagged child health outcomes.

Caijv = δa1M

kjv + δa2C

laggedijv + δa3X

sijv + λaijv + ηaijv. (3)

We estimate (3) increasing the lagged time period for included child health. Forexample if we estimate (3) for child HAZ at age a = 8, we first include child birthlength as our lagged child health outcome. We then estimate (3) again including age 1child HAZ and then age child HAZ as our lagged child health outcome. If, for example,δa1 = 0 after controlling for birth health, then any transmission found at that age inEquation (1) is due to the effect of mother’s health on birth health. If, on the otherhand, δa1 significantly differs from zero in Equation (3), we know that maternal healthcontinues to causally transmit to child health at age a beyond its indirect effect throughtransmissions that occurred at the age of the lagged health outcome and before.

Equation (3) allows us to pinpoint the windows of mother-to-child health transmission.This, in turn, can elucidate some of the mechanisms underlying this transmission. Forexample, mother’s health may tranmist to child health through a biological mechanism.If this is the case we would expect the coefficient of transmission either to be mostprevalent at birth (suggesting a biological transmission that occurs in utero) or to berelatively constant through all stages (suggesting some sort inherited aspect of healthand growth). There could also be a genetic component to the transmission of health.For example, height has a strong genetic component. However, genetic variation inheight does not typically emerge until the second growth spurt during adolescence.Early growth is primarily determined by nutrition and health status.18 Thus if this isthe case then we would expect the mother-to-child health transmission to be strongestduring adolescence.

Finally, mother’s health may also transmit to child health through parenting ability orsocio-economic status. We identify the mother-to-child transmission of health off ofvariation in the mother’s early life health. As noted previously, early childhood healthaffects a broad range of adult outcomes including economic, demographic, health, andhuman capital outcomes (cite). Therefore, early life health shocks due to climate

18In environments where there are no adverse influences on growth, genetic differences results in aworldwide height variability of about one cm in five-year-old children (cite).

13

variability may result in reduced parenting ability or socio-economic status for thesewomen. While not biological, this still represents a causal transmission of maternalhealth to child health. Only, it is operating through economic mechanisms. If this is theprimary mechanism, then we would expect to find the maternal health transmission tobe strongest for child health flow outcomes, which are more responsive tocontemporaneous inputs and during sensitive periods19 for the production of healthstock (i.e., during early childhood for the production of height).

6 Results

Tables 2—6 report the estimated causal effect of maternal height and skinfold thicknesson child height and weight outcomes from birth through age fifteen. Tables A1 and A2in Appendix 3 compare the transmission coefficients of Equation (1) as estimated byOLS, 2SLS using all 69 instruments in the first stage, and 2SLS using the SVAgenerated instrument in the first stage. What is most notable about the resultsreported here is that the SVA procedure for boosting the power of our instruments inthe first stage was highly successful. Tests on the significance of the SVA generatedinstruments returned F-statistics ranging from 45.41 to 72.65 in the first stagepredicting mother’s height and 35.06 to 66.65 in the first stage predicting mother’sskinfold thickness.20 Conversely, those same F-statistics using the full set of instrumentsin the first stage range from 1.129 to to 1.202 and 0.991 to 1.298, respectively.21

Therefore SVA appears to hold much promise as a way of addressing situations in whichinstruments are many but weak.

6.1 Transmission of Mother-to-Child Health

Table 2 reports the estimated transmission coefficients of mother’s health stock (proxiedher baseline height in cm) and health flow (proxied by her baseline skinfold thickness incm) to child health stock (proxied by height measures). Height-for-age z-scores proxyfor child health stock at each age examined.22 With regard to the child’s height, thetransmission of mother-to-child health does not appear to be statistically orqualitatively significant at birth. However, the transmission of mother’s health stock issignificant at every other observed age. Increasing mother’s height by 1 cm increasesher child’s HAZ by 0.06, 0.08, 0.10, 0.11, and 0.08 standard deviations at ages 1, 2, 8,11, and 15, respectively.

The transmission of mother’s health flow is significant in early childhood but ceases tohave a statistically significant effect after age eight. Increasing mother’s baseline (i.e.,during pregnancy) skinfold thickness by 1 cm increases her child’s HAZ by 0.08standard deviations at ages 1 and 2 and by 0.11 standard deviations at age 8. The effectof mother’s skinfold on child height at ages 11 and 15 is not statistically significant.

19A senstive period for the development of a particular trait or characteristic such as health is a stageof development during which the production of that trait is most sensitive to inputs.

20First-stage F-statistics vary because controls and sample size slightly differs at each childhood stage.Sample size differs due to attrition and interruption.

21The corresponding F-statistics using principal components as instruments hover around 2. Resultsavailable upon request.

22Recumbent length-for-age is used rather than height-for-age at birth, age one, and age two.

14

Table 3 reports the estimated transmission coefficients of mother’s health stock andhealth flow to child health flow (proxied by child weight measures). Child health flow atbirth is proxied by birth weight in grams. Weight-for-height z-scores (WHZ) proxy childhealth flow at ages 1 and 2. Finally, BMI-for-age z-scores (BMIZ) proxy child healthflow at ages 8, 11, and 15. Unlike with birth length, both mother’s health stock andhealth flow are significant determinants of child birth weight. Increasing maternalheight and skinfold thickness by one cm increases birth weight by 26.45 and 30.04grams, respectively. The transmission of maternal health appears to persist throughearly childhood. Increasing maternal height by 1 cm increases child WHZ by 0.04 and0.05 standard deviations at ages 1 and 2, respectively. The corresponding effects ofincreasing skinfold thickness by 1 cm are approximately 0.06 standard deviations ateach age. Increasing maternal height additionally increases child BMIZ at age 8 by 0.06standard deviations. Maternal skinfold thickness does not exhibit a statisticallysignificant effect on child weight at ages 8, 11, and 15.

Figure 4 graphically depicts the results reported in Tables 2 (Panel A of Figure 4) and 3(Panel B of Figure 4). Birth weight is excluded from Panel B of Figure 4 as its scale,grams, differs from that of each of the other child health outcomes, which are measuredin z-score standard deviations. There are a few things worth noting in these results.First, mother’s health flow is a much less precise predictor of child health than herhealth stock. This is intuitive in that health flow, by definition, captures the mother’scontemporaneous health state and is thus a less robust measure of her long-term healthstatus. Given that our measure of health flow is skinfold thickness measured when oursample mothers were in their third trimester, it is a direct input into in utero health.Therefore, we would expect to see, as we do, that mother’s third trimester health flowimpacts health at birth and possibly even early childhood health. What it moresurprising is that the transmission of mother’s health flow at pregnancy continues intoadolescence, particularly with regards to child height.

Second, although the transmission of maternal health to child weight is less preciselyestimated as the child gets older, the transmission point estimate remains relativelystable at around 0.05 standard deviations across each childhood stage we observe. Ameasure of a child’s health flow, such as weight, is sensitive to a wide range of biologicaland environmental inputs including nutrient intake, activity, and disease. The range ofinputs an individual is exposed to increases as that individual ages. We would thereforeexpect this measure to be noisier at older ages. Consequently, it is unclear whether thelack of significance of the effect of maternal health on child weight at older ages is dueto a zero-effect or due to lack of precision. Indeed the confidence bands around theeffect (Panel B in Figure 4) increase substantially in later childhood and adolescence.

Finally, given that an association between maternal health and child health at birth iswidely documented, we expected to find a transmission effect that is strongest at birthand in early childhood which subsequently diminishes as the child ages. However,instead we find a mother-to-child health transmission that persists into adolescence. Wefind a stable estimated transmission effect to child weight across ages (Panel B of Figure4). Moreover, surprisingly, we find that the transmission effect to child height increasesthrough childhood before decreasing slightly in adolescence (Panel A of Figure 4). Inorder to better understand the pattern of health transmission across childhood stages,we must think through some of the mechanisms through which it may operate.

15

While our estimated effects of maternal health on child health are causal, there arenonetheless a number of potential mechanisms underlying them. As noted, mother’shealth may transmit to child health directly through a biological mechanism orindirectly economic mechanisms operating through parenting ability or socio-economicstatus. Examining the persistence of the of the mother-to-child transmission can help toelucidate some of the mechanisms that may be at work.

6.2 Persistence of Mother-to-Child Health Transmissionacross Childhood Stages

In Figure 4 we find that mother’s health transmits to her child not only only at birthbut continues to affect child health through childhood and into adolescence. In thissection we examine the persistence of the mother-to-child health transmission byattempting to pinpoint stages of transmission. For example, the effect of maternalhealth on child health at older ages may be due to a health transmission at birth. Thenestimated effects at later ages may be due to the birth transmission and birth health’ssubsequent effect on later health. Certainly, ample evidence demonstrates that earlyhealth is an important determinant of later health. To explore this explanation, we firstestimate Equation (3), controlling for birth weight—a widely used proxy for birthhealth.

Table 4 reports the estimated transmission coefficients to child health flow (proxied byweight) net of the health transmission at birth and Table 5 reports the correspondingeffects on child health stock (proxied by HAZ). These results are also graphicallydepicted in Figure 5. In Table 4 we see that after controlling for birth weight, thetransmission coefficients on both mother’s height and skinfold thickness decrease inboth significance and magnitude. Only the effect of mother’s height on child WHZ atage two remains weakly significant after controlling for birth health. The second year oflife is the period during which a child begins to transition to the family diet. Therefore,the effect of mother’s health stock on child weight at this age may reflect parentingability or socio-economic contraints on her ability to provide a nutrious diet during thissensitive period. While we do not explicitly test for this explanation, it remainsplausible.23 The lack of significance of the effect of mother’s health on child health flowat all other ages indicates that the maternal health transmission to this dimension ofchild health is largely due to the transmission to child’s health flow at birth.

In Table 5 and Panel A of Figure 5 we see that after controlling for birth weight theeffect of mother’s health flow at pregnancy ceases to have a statistically significanteffect on child height at any age after birth. Thus the primary mechanism throughwhich mother’s health flow during the late stages of pregnancy affects later childhoodhealth appears to be through a transmission to health at birth.

Interestingly, the transmission of mother health stock to child health stock net of thebirth effects (depicted in Table 2 and Panel A of Figure 5) look similar to that reportedin Table 2 and Figure 4. The magnitude of the transmission at each age declinesslightly indicating that the transmission at birth at least partly explains the latertransmission of health stock. However, at each observed age after birth, the

23We will more explicitly test for this explanation in forthcoming analysis.

16

transmission coefficients on health stock remain statistically and clinically significant.Moreover, as previously, rather than observing an transmission that is strongest at birththat gradually diminishes as the child ages, the transmission effect increases inmagnitude throughout childhood and then reduces slightly in adolescence.

To attempt to pinpoint the mechanism underlying this pattern we re-estimate Equation(3) controlling iteratively for each available child health lag. For example, to estimatethe transmission of health stock at age 8, we first estimate (3) controlling for birthlength. We then perform the same estimation controlling for age 1 HAZ rather thanbirth length. We then perform the same estimation controlling for age 2 HAZ ratherthan age 1 HAZ. Assuming that the realization of child HAZ at each age is a sufficientstatistic for all realizations of HAZ that preceded it, this allows us to pinpoint the stageduring which maternal height ceases to have a direct effect on child height. Theseresults are reported in Table 6.

In Table 6 we see that the maternal height effects on age 1 and age 8 HAZ remainsignificant even after controlling any of the preceding child HAZ realizations. At age 1,the effect maternal health stock on child HAZ declines only modestly—from 0.0649 to0.0586—once we control for birth length (the most recent child health lag). The firstyear of life is widely documented to be a sensitive period for linear growth. Thus thepersistence of the health stock transmission at this age may be due to both a directbiological transmission as well as an indirect transmission through socio-economicmechanisms. The transmission effect on age 8 HAZ declines as more recent lags areincluded from a point estimate of 0.0999 with no lags included to 0.0444 with age 2HAZ (the most recent child health lag available) included as a control. The sensitiveperiod for linear growth is thought span from in utero to approximately 3 to 5 years ofage. It is possible that the continued significant transmission we observe aftercontrolling for age 2 HAZ may be due to growth during the latter end of this sensitiveperiod. However, this is still a bit of a puzzle as maternal height does not statisticallysignificantly affect age 2 HAZ when controlling for age 1 HAZ.

At every other observed age, we see that the magnitude of the health stock transmissiondeclines as we control for more recent health lags. However, it does not decline to zerounless the most recent lag is controlled for. Figure 6 graphically depicts this pattern.Panels A and B correspond to Figures 4 and 5 which include no lag and a birth lengthlag, respectively. Panel C of Figure 6 controls for birth length in age 1 HAZ, and age 1HAZ for all other ages. Panel D controls for birth length for age 1 HAZ, age 1 HAZ forHAZ at age 2, and age 2 HAZ for all other ages. Panel E controls for birth length forage 1 HAZ, age 1 HAZ for HAZ at age 2, age 2 HAZ for HAZ at age 8, and age 8 HAZfor all other ages. Finally, Panel F controls for birth length for age 1 HAZ, age 1 HAZfor HAZ at age 2, age 2 HAZ for HAZ at age 8, age 8 HAZ for HAZ at age 11, and age11 HAZ for HAZ at age 15.

In Figure 6 we see that as except for age 1 and age 8 HAZ, the same general patternexists as that depicted in Panel A of Figure 4 of marginal effects across ages but withdeclining magnitude until the most recent child health lag is included. Once the mostrecent lag is included, the marginal effect of maternal height on child height at that ageis no longer statistically different from zero. In Panel F of Figure 6, the most recentavailable lag is included as a predictor for HAZ at each age. At this point maternal

17

height only has a statistically significant effect on child HAZ at age 1 and age 8.

Our findings of increasing marginal effects of maternal height on child height acrossages suggest an accumulating (rather than diminishing) transmission of mother-to-childhealth stock. One possible explanation for the phenomenon is that increased motherheight does not only translate into increased child height (both as genetic height andhealth stock) but also translates into increased growth velocity. This translates into amarginal child height advantage that increases with age. When we control for the mostrecent lagged child health, we are netting out the accumulated height advantage fromprevious periods, leaving only the effect on growth from the last period.

This finding is similar to the idea of dynamic complementarity discussed in Cunha andHeckman (2006). Dynamic complementarity describes the feature of humandevelopment where capabilities or traits produced in one stage of childhood raise theproductivity of inputs into their production at later stages of childhood. For example, achild with higher initial cognitive ability is likely a more able learner and thus canproduce more additional cognitive ability with the same inputs as a child with lowerinitial cognitive ability. Similarly, a healthier child’s body may be better able totranslate health inputs into increased additional health on the margin. Consequently,inputs into human development at different stages of childhood are complementary withearlier inputs making later inputs more productive on the intensive margin. If thetransmission of maternal health stock can be thought of as an input into child healthand continues to transmit throughout childhood, then we would expect to seeaccumulating levels of transmission as we observe in this sample.

7 Conclusion

In the paper, we bring together two important strand of literature. A large body ofdemonstrates the an association between mother and child health. Another substantialbody of literate documents the long-arm of early childhood in that early life healthshocks affect a wide range of adult outcomes including adult health. However, very fewstudies exmaine the effects of an early life health shock on the next generation. In thispaper we bring these two strands of literature together by tracing the impact of climatevariation in the first few years of our sample women’s lives to their adult health to thehealth of their children through adolescence. In doint so, we document the causaltransmission of mother-to-child health across multiple stages of childhood intoadolescence. We do so for measures of health stock and health flow in both our samplemothers and children. To obtain causality we exploit the unique climate conditions ofthe Philippines and use climate information from the time of our sample mothers’ birthand in their early childhood.

We find that mother’s health flow transmits to child health and the effects of thistransmission can be felt into adolsecence, particularly with regards to child height.However, the mechanism of this transmission largely operates through the effect ofmother’s health flow on a child’s health at birth and then the effect of birth health onlater health. More surprisingly, we also find that mother’s health stock transmit to childhealth stock through childhood and adolescence and this is net of its effect on birthweight. This transmission seems to be primarily due to a transmission of growth

18

velocity, which leads to a mother-to-child health stock transmission that increases asthe child ages rather than diminishes.Finally, while our instruments satisfy the necessary exclusion restriction for a validinstrument. The are both many and weak. Therefore 2SLS estimates using our full setof instruments may be biased towards OLS estimates. Therefore, we propose a newmethod for address the problem of many but weak instruments using singular valueanalysis and machine learning methods. This method for boosting instrument powerappears to hold much promise in that it increases first-stage F-statistics from around 1to between 35 and 75.

We find that both mother’s accumulated health stock and her health flow bothsignificantly influence child human capital in the form of health stocks, health flows andcognitive ability. The significant impact of maternal health stocks suggests that thereare biological mechanisms underlying this transmission, while the role of maternalhealth flows implies that mother’s current health status is a direct investment to childhuman capital formation. The transmission of maternal health to child human capitalappears to be strongest in the early years of childhood. Nonetheless, maternal healthcontinues to influence child human capital accumulation years after birth. By ageeleven, however, the transmission of maternal health to child human capital appears toprimarily operate through its impact on earlier health.

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Figure 1: Maternal Year of Birth Distribution

02

46

8Pe

rcen

t

1930 1940 1950 1960 1970Maternal Birth Year

Distribution of Maternal Year of Birth

Figure 2: Maternal Month of Birth Distribution

02

46

810

Perc

ent

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMaternal Birth Month

Distribution of Maternal Month of Birth

22

Figure 3: Mother’s Height by Month ofBirth

Figure 4: Marginal Effect of Maternal Health on Child Health across Ages

-.05

0.0

5.1

.15

Mar

gina

l Effe

ct in

Sta

ndar

d De

viatio

ns

1 2 8 11 15Child Age in Years

Mother Height (cm) Effect Mother Skinfold (cm) Effect

Panel B: Child Weight

Figure 5: Marginal Effect of Maternal Health on Child Health Controlling for BirthWeight

-.10

.1.2

Mar

gina

l Effe

ct in

Sta

ndar

d De

viatio

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0 1 2 8 11 15Child Age in Years


Panel A: Child Height

-.1-.0

50

.05

.1.1

5M

argi

nal E

ffect

in S

tand

ard

Devia

tions

1 2 8 11 15Child Age in Years


Panel B: Child Weight

23

Figure 6: Marginal Effect of Maternal Health on Child Height—Controlling for LaggedChild Health Iteratively

-.05

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5.1

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Mar

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l Effe

ct in

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HAZ Age, Lag Age

No Lags Included

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Birth Lag for All

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Age 1 Lag for Age 2 and Up-.0

50

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Age 2 Lag for Age 8 and Up

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Age 11 Lag for Age 15

24

Table 1: Descritive Statistics

Females Males All

Male 0.00 1.00 0.53(0.00) (0.00) (0.50)

Baseline Household Size 5.70 5.64 5.67(2.87) (2.77) (2.82)

Baseline Per Capita Household Income (Philippine Pesos) 2780.98 2822.49 2803.02(4263.69) (4048) (4149.94)

Health at Birth

Height-for-Age Zscore -0.47 -0.64 -0.56(1.08) (1.08) (1.08)

Birth Weight 2.97 3.03 3.00(0.43) (0.46) (0.45)

Weight-for-Height Zscore -0.83 -0.87 -0.85(1.11) (1.14) (1.13)

Low Birth Weight 0.12 0.10 0.11(0.33) (0.31) (0.32)

Stunted in First 6 Months 0.59 0.63 0.61(0.49) (0.48) (0.49)

Health in First Year



Stunting Incidence in First Year 0.62 0.66 0.64(0.49) (0.47) (0.48)

Wasting Incidence in First Year 0.29 0.34 0.32(0.46) (0.48) (0.47)

Health in Second Year



Stunting Incidence in Second Year 0.73 0.78 0.76(0.44) (0.41) (0.43)

Wasting Incidence in Second Year 0.21 0.22 0.21(0.40) (0.41) (0.41)

Health in Year Eight


Body Mass Index Zscore -0.81 -0.81 -0.81(0.84) (0.94) (0.89)

Skinfold Measurement (cm) 7.27 6.34 6.78(1.95) (1.99) (2.02)

Subscapular Measurement (cm) 6.08 5.30 5.67(1.77) (1.71) (1.78)

Stunted at Age Eight 0.52 0.54 0.53(0.50) (0.50) (0.50)

Wasted at Age Eight 0.08 0.09 0.09(0.27) (0.29) (0.28)

Health in Year Eleven


Body Mass Index Zscore -1.02 -1.14 -1.09(1.05) (1.13) (1.09)

Skinfold Measurement (cm) 10.34 8.58 9.42(3.54) (3.51) (3.63)

Subscapular Measurement (cm) 8.34 6.74 7.50(3.42) (3.04) (3.33)

Stunted at Age Eleven 0.48 0.52 0.50(0.50) (0.50) (0.50)

Wasted at Age Eleven 0.17 0.20 0.18(0.38) (0.40) (0.39)

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Table 2: Transmission of Maternal Health to Child Height—SVA Instrument

Birth Age 1 Age 2

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

Mother’s height 0.0113 0.0649∗∗∗ 0.0802∗∗∗

(0.0234) (0.0215) (0.0246)

Mother’s skin -0.00779 0.0837∗∗ 0.0837∗∗

(0.0340) (0.0380) (0.0410)

IV F-stat 72.28 66.49 66.72 44.34 65.16 48.28

Age 8 Age 11 Age 15

Mother’s height 0.0999∗∗∗ 0.105∗∗∗ 0.0804∗∗∗

(0.0227) (0.0264) (0.0209)

Mother’s skin 0.109∗∗∗ 0.0399 0.0305(0.0410) (0.0439) (0.0372)

IV F-stat 53.31 36.16 48.09 36.35 45.41 35.06

Robust standard errors in parentheses∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1 + p<0.15

Controls include current per capital income and household size, gender, mother’s age, mother age cohorts,and birth month fixed effects, and baseline and current barangay fixed effects (which are equivalent at birth year).

For birth outcomes only an indicator for whether gestational age is in question is also included.Child outcomes are length-for-age z-scores from birth through age 2, and height-for-age z-scores for ages 8, 11, and 15.

Table 3: Transmission of Maternal Health to Child Weight—SVA Instrument

Birth Age 1 Age 2

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

Mother’s height 26.45∗∗∗ 0.0413∗∗ 0.0544∗∗∗

(9.425) (0.0203) (0.0211)

Mother’s skin 30.04∗∗ 0.0627∗ 0.0616∗

(13.54) (0.0335) (0.0333)

IV F-stat 72.65 66.65 66.36 44.38 65.86 48.63

Age 8 Age 11 Age 15

Mother’s height 0.0579∗∗ 0.0355 0.0454(0.0241) (0.0294) (0.0294)

Mother’s skin 0.0206 0.0364 0.0452(0.0392) (0.0473) (0.0469)

IV F-stat 53.31 36.16 48.09 36.35 45.41 35.06



For birth outcomes only an indicator for whether gestational age is in question is also included.Child outcomes are birth weight, weight-for-height z-scores at ages 1 and 2, and BMI z-scores for ages 8, 11, and 15.

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Table 4: Transmission of Maternal Health to Child Height Controlling for Birth Weight

Birth Age 1 Age 2

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

Mother’s height -0.0306 0.0386∗ 0.0619∗∗

(0.0205) (0.0202) (0.0249)

Mother’s skin -0.0553∗ 0.0477 0.0583(0.0288) (0.0343) (0.0403)

Birth Weight (grams) 0.00158∗∗∗ 0.00158∗∗∗ 0.000982∗∗∗ 0.00100∗∗∗ 0.000675∗∗∗ 0.000729∗∗∗

(0.0000553) (0.0000503) (0.0000539) (0.0000556) (0.0000680) (0.0000684)

IV F-stat 66.57 62.98 60.90 42.00 59.65 46.01

Age 8 Age 11 Age 15

Mother’s height 0.0869∗∗∗ 0.0947∗∗∗ 0.0641∗∗∗

(0.0241) (0.0284) (0.0224)

Mother’s skin 0.0860∗∗ 0.0215 0.0114(0.0420) (0.0450) (0.0378)


(0.0000646) (0.0000691) (0.0000764) (0.0000728) (0.0000593) (0.0000625)

IV F-stat 45.79 32.89 41.13 34.52 38.26 32.93




Table 5: Transmission of Maternal Health to Child Weight Controlling for Birth Weight

Birth Age 1 Age 2

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

Mother’s height 26.45∗∗∗ 0.0254 0.0390∗

(9.425) (0.0200) (0.0212)

Mother’s skin 30.04∗∗ 0.0381 0.0405(13.54) (0.0323) (0.0329)

Birth Weight (grams) 0.000619∗∗∗ 0.000624∗∗∗ 0.000552∗∗∗ 0.000580∗∗∗

(0.0000533) (0.0000524) (0.0000580) (0.0000564)

IV F-stat 72.65 66.65 60.62 42.24 60.25 46.10

Age 8 Age 11 Age 15

Mother’s height 0.0411 0.0154 0.0329(0.0253) (0.0314) (0.0318)

Mother’s skin -0.00165 0.0191 0.0282(0.0410) (0.0484) (0.0485)


(0.0000679) (0.0000675) (0.0000845) (0.0000785) (0.0000842) (0.0000802)

IV F-stat 45.79 32.89 41.13 34.52 38.26 32.93



For birth outcomes only an indicator for whether gestational age is in question is also included.Child outcomes are birth weight, weight-for-height z-scores at ages 1 and 2, and BMI z-scores for ages 8, 11, and 15.

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Table 6: Transmission of Maternal Health to Child Height Controlling for Lagged Height

Birth Age 1 Age 2 Age 8

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Mother’s height 0.0113 0.0649∗∗∗ 0.0586∗∗∗ 0.0802∗∗∗ 0.0745∗∗∗ 0.0126 0.0999∗∗∗ 0.0978∗∗∗ 0.0626∗∗∗ 0.0444∗∗

(0.0234) (0.0215) (0.0173) (0.0246) (0.0232) (0.0140) (0.0227) (0.0225) (0.0210) (0.0195)

Birth Length 0.517∗∗∗ 0.358∗∗∗ 0.186∗∗∗

(0.0207) (0.0285) (0.0263)

HAZ at Year 1 1.008∗∗∗ 0.523∗∗∗

(0.0281) (0.0410)

HAZ at Year 2 0.543∗∗∗

(0.0341)

IV F-stat 72.28 66.72 67.00 65.16 65.25 56.11 53.31 52.99 45.28 44.26

Age 11 Age 15

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Mother’s height 0.105∗∗∗ 0.104∗∗∗ 0.0736∗∗∗ 0.0596∗∗ 0.0225 0.0804∗∗∗ 0.0786∗∗∗ 0.0500∗∗ 0.0390∗∗ 0.0308∗∗ 0.0250(0.0264) (0.0263) (0.0255) (0.0246) (0.0171) (0.0209) (0.0204) (0.0194) (0.0191) (0.0152) (0.0165)

Birth Length 0.177∗∗∗ 0.183∗∗∗

(0.0297) (0.0227)

HAZ at Year 1 0.492∗∗∗ 0.468∗∗∗

(0.0491) (0.0386)

HAZ at Year 2 0.516∗∗∗ 0.466∗∗∗

(0.0425) (0.0340)

HAZ at Age 8 0.891∗∗∗ 0.667∗∗∗

(0.0363) (0.0326)

HAZ at Age 11 0.597∗∗∗

(0.0285)

IV F-stat 48.09 47.90 42.10 39.91 39.56 45.41 45.46 39.58 38.16 40.08 35.21

Robust standard errors in parentheses∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1



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Appendix 1 Climate Instruments

Figures A1-A3 illustrate monthly averages for rainfall, temperature, and windspeed, respectively,around Cebu, Philippines. Figure A1 clearly shows that the rainiest months are March-December, orperhaps March-January. In Cebu, rice is grown in two cropping seasons — from various sources andsome data exploration, it seems that planting begins around May, with the first harvest in late Augustor September, and that the second rice crop is planted shortly thereafter, with second harvest in lateJanuary or February.

Accordingly, we define monsoon months as May-December. This is the period when rice is growing,and rice yields most easily impacted by rainfall and temperature shocks. Our year-specific rainfall andtemperature shock variables are therefore given as average rainfall (mm/day) and averagetemperature (degrees Celcius) over the course of May-December in any given year.

Figure A1: Monthly Average Rainfall Figure A2: Monthly Average Temperature

Figure A3: Monthly Average Windspeed

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It is important to note that typically, climate shock variables are formulated as deviations from aregion specific or grid specific mean. This is because most household datasets are spread over a widegeographic area, with varying levels of mean rainfall or temperature. Those varying climate averagesmay be correlated with other regional factors (e.g., high rainfall areas may be wealthier, or warmersareas more dependent on agriculture). If so, regressing a human outcome (e.g., educationalachievement) on realized climate outcomes (e.g., yearly rainfall) may result in a spurious association(e.g., educational achievement may be higher in the region of a country with higher rainfall, simplybecause people are wealthier in that area). Grid-specific or region-specific deviations from long-runclimate means are orthogonal to the long-run means themselves, and thus uncorrelated with otherspatially-varying societal characteristics.

Because all women in our dataset are from Cebu, and because we do not know the specific area ofCebu in which they were born, our estimations do not suffer from this problem. That is, we createCebu-specific rainfall and temperature realizations as area-weighted means of all grid cells that overlapCebu. We cannot, therefore, estimate a spurious correlation between climate realizations and humanoutcomes due to spatially varying, correlated societal characteristics, because women in the north,south, and center of Cebu all receive the same, Cebu-average rainfall or temperature realizations.Otherwise stated, creating deviations from a long-run mean would simply entail subtracting the samemean from all realizations, since long-run means do not vary across mothers in our dataset.

However, we face a different form of spurious correlation. While women in our dataset were all bornin close proximity to one another, they were born over a long period of years between the 1930s andthe 1970s. We therefore de-trend rainfall and temperature realizations within the monsoon season byregressing these realizations on year and year squared, and subtracting non-linear monsoon trends(due perhaps to el nino or la nina) from the year-specific realizations.

Daily windspeed data is used to create two forms of variables. We proxy typhoon incidence in the 12months before and after birth via month-specific maximum windspeeds. We also proxy for high windsor typhoons during the rice “heading” period, or directly before rice harvest, via maximumwindspeeds in January and Febuary (the first harvest of the year) and maximum windspeeds inAugust and September (the second harvest in the year). Notably, maximum windspeeds innon-harvest months (November and December, or even December and January, rather than Januaryand February) do not provide the same power in the first stage.

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Appendix 2 Choosing an Optimal Instrument via Singular

Value Analysis

The ordinary least squares (OSL) estimator minimizes ||Xβ − y||2, where X is an m× n matrix, β isan m× 1 matrix, and y is an m× 1 matrix. Yet singular value analysis (SVA) solves the sameproblem, by minimization an equivalent distance problem in a rotated space.

First, using singular value decomposition (SVD), we may decompose X as follows

X = USVT

where U is an orthogonal m× n matrix, S is a diagonal n× n matrix with successive, positive andnon-decreasing entries, and VT is an orthogonal n× n matrix.

Because U is orthogonal, UT is also orthogonal, and both are therefore distance preserving undermultiplication. Thus,

||Xβ − y||2 = ||UT (Xβ − y)||2

= ||UT (USVTβ − y)||2

= ||SVTβ −UTy)||2

= ||Sγ − g||2

where the third line follows from the fact that U is an orthogonal matrix, and we define γ =VTβ andg =UTy, both n× 1 matrices.

To minimize ||Xβ − y||2, we can therefore choose a γ to minimize ||Sγ − g||2. The original parametervector β is calculated as Vγ.

Predicted outcome y may be equivalently calculated as either Xβ or USγ, since Xβ ' y andSγ ' g= UTy. The residual r may be equivalently calculated as either y −Xβ or U(g − Sγ) sincey −Xβ = Ug −USVTβ = U(g − Sγ).

However, because S holds successively non-increasing diagonal values, the elements of γ becomeincreasingly insignificant to β = Vγ. The candidate solution γ(k) may therefore be considered, whereeach element of γ(k) is identical to that of the full solution γ up until the k’th element, and allsubsequent elements are zero, as below.

γ(k) =

γ1...γk0...0

In many cases the candidate solution γ(k), for some particular k < n, minimizes mean squaredforecasting error (MSFE) better than the full solution γ. This is because the rows of matrix V hold“averages” for the columns of matrix X, but with each row explaining less and less of the variation inX. One might imagine that, past some particular column k, the rows of matrix V hold only

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sample-specific variation in X, rather than variation that can be predicted out of sample. In otherwords, a solution vector β that captures such variation is over-fitting the model, leading to an increasein R2 within sample, but also an increase in MSFE out of sample. A solution vector β that capturesonly the variation within the first k vectors of V will better minimize MSFE.

Often, k is chosen based on the condition number of the implied system, i.e., the instability of thesolution. We instead use group-wise cross-validation to choose k, dividing the sample into 100test/training groups, and measuring MSFE for each k in each of those 100 trials. We then choose thek that minimizes forecast error best, on average. It is worth nothing that this k is larger and resultsin far better predicts and F-statistics in the first stage, than a k based on solution instability.

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Appendix 3 Comparing Transmission Estimates across

Estimation Methods

Table A1: Regression of Child Height Measures on Mother’s Health across Estimators

Birth

(1) (2) (3) (4) (5) (6)OLS OLS 2SLS 2SLS SVA SVA

Mother’s height 0.0333∗∗∗ 0.000732 0.0113(9.22) (0.03) (0.48)

Mother’s skin 0.0217∗∗∗ -0.0469 -0.00779(4.59) (-1.54) (-0.23)

IV F-stat 1.154 1.299 72.28 66.49

Year 1

Mother’s height 0.0583∗∗∗ 0.0586∗∗∗ 0.0649∗∗∗

(16.46) (2.94) (3.02)

Mother’s skin 0.0400∗∗∗ 0.0388 0.0837∗∗

(8.24) (1.27) (2.20)

IV F-stat 1.154 0.992 66.72 44.34

Year 2

Mother’s height 0.0696∗∗∗ 0.0577∗∗ 0.0802∗∗∗

(16.81) (2.55) (3.27)

Mother’s skin 0.0524∗∗∗ 0.0478 0.0837∗∗

(9.16) (1.48) (2.04)

IV F-stat 1.156 1.161 65.16 48.28

Year 8

Mother’s height 0.0647∗∗∗ 0.0716∗∗∗ 0.0999∗∗∗

(16.43) (3.83) (4.40)

Mother’s skin 0.0503∗∗∗ 0.0878∗∗∗ 0.109∗∗∗

(8.40) (3.01) (2.65)

IV F-stat 1.136 1.046 53.31 36.16

Year 11

Mother’s height 0.0623∗∗∗ 0.0663∗∗∗ 0.105∗∗∗

(14.40) (3.30) (3.98)

Mother’s skin 0.0555∗∗∗ 0.0702∗∗ 0.0399(8.33) (2.25) (0.91)

IV F-stat 1.202 1.086 48.09 36.35

Year 15

Mother’s height 0.0685∗∗∗ 0.0609∗∗∗ 0.0804∗∗∗

(19.35) (3.76) (3.86)

Mother’s skin 0.0341∗∗∗ 0.0508∗ 0.0305(6.47) (1.88) (0.82)

IV F-stat 1.129 1.010 45.41 35.06

Controls include household size, per capita income, barangay fixed effects and month of birth fixed effectsRobust standard errors in parentheses

∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1

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Table A2: Regression of Child Weight Measures on Mother’s Health across Estimators

Birth

(1) (2) (3) (4) (5) (6)OLS OLS 2SLS 2SLS SVA SVA

Mother’s height 12.61∗∗∗ 23.97∗∗∗ 26.45∗∗∗

(8.28) (2.65) (2.81)

Mother’s skin 14.13∗∗∗ 18.07 30.04∗∗

(6.93) (1.54) (2.22)

IV F-stat 1.161 1.298 72.65 66.65

Year 1

Mother’s height 0.01000∗∗∗ 0.0321∗ 0.0413∗∗

(3.17) (1.72) (2.04)

Mother’s skin 0.0265∗∗∗ 0.0372 0.0627∗

(5.79) (1.38) (1.87)

IV F-stat 1.144 0.991 66.36 44.38

Year 2

Mother’s height 0.0239∗∗∗ 0.0405∗∗ 0.0544∗∗∗

(6.91) (2.11) (2.58)

Mother’s skin 0.0348∗∗∗ 0.0227 0.0616∗

(6.91) (0.86) (1.85)

IV F-stat 1.170 1.162 65.86 48.63

Year 8

Mother’s height 0.00537 0.0405∗∗ 0.0579∗∗

(1.29) (2.04) (2.40)

Mother’s skin 0.0350∗∗∗ 0.0171 0.0206(5.89) (0.60) (0.53)

IV F-stat 1.136 1.046 53.31 36.16

Year 11

Mother’s height 0.00864∗ 0.0194 0.0355(1.77) (0.85) (1.21)

Mother’s skin 0.0585∗∗∗ 0.0642∗ 0.0364(7.68) (1.91) (0.77)

IV F-stat 1.202 1.086 48.09 36.35

Year 15

Mother’s height 0.00559 0.0143 0.0454(1.11) (0.64) (1.54)

Mother’s skin 0.0371∗∗∗ 0.0268 0.0452(5.10) (0.79) (0.96)

IV F-stat 1.129 1.010 45.41 35.06

Controls include household size, per capita income, barangay fixed effects and month of birth fixed effectsRobust standard errors in parentheses

∗∗∗ p<0.01, ∗∗ p<0.05, ∗ p<0.1

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