Assessing the Impact of Birth Spacing on Child Health ...€¦ · years. Birth order is top coded at 3+ older siblings. A number of household and child characteristics were also used

Assessing the Impact of Birth Spacing on Child

Health Trajectories

Mahesh KarraHarvard T.H. Chan School of Public Health

[email protected]

Ray MillerHarvard Center for Population and Development Studies

[email protected]

April 7, 2017

Abstract

Using longitudinal data from the Young Lives Study (YLS), we investigatethe effect of birth spacing between siblings on child health outcomes. We poolYLS data from a birth cohort of 8,000 children and 4,600 of their siblings between2002 and 2013 from four low-and middle-income countries. We assess the impactof birth intervals on child height-for-age and weight-for-age for the pooled siblingsample as well as for the single birth cohort over time. We find increased childHAZ and WAZ scores among children who are more widely spaced relativeto children who are narrowly spaced (within two years from an older sibling).However, we also find evidence of catch-up growth (converging HAZ and WAZ

scores) for closely spaced children as they age. We find a similar pattern ofresults for children who are of lower birth order.

JEL classifications: I10, O57Keywords: birth spacing, health, siblings

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1 Introduction

The importance of birth spacing for maternal and child health has been a long-standingsource of interest to researchers and policymakers alike (World Health Organization,2005). Evidence from systematic reviews and other empirical studies have suggestedthat short birth intervals (less than two years between births) may be associated withincreased risk of maternal and child morbidity, including pregnancy-related complica-tions (high blood pressure, pre-eclampsia), preterm birth, low birthweight, and smallfor gestational age, as well as increased risk of mortality for both women and children(Conde-Agudelo et al., 2006; DaVanzo et al., 2004; Winikoff, 1983). Some studies havealso examined the relationship between birth spacing and longer-term cognitive andeducational outcomes in children and have found longer intrapartum spacing to beassociated with improved school test scores in older siblings, while the effects of longerspacing were found to be minimal for younger siblings (Broman et al., 1975; Bucklesand Munnich, 2012). By the same token, studies have also begun to explore the roleof birth order on child health and socioemotional development and have found thatolder siblings are likely to be more socially outgoing and persistent than their youngersiblings (Black et al., 2016).

When pooling the evidence together, however, results from this body of researchhave concluded that many of the findings on both birth spacing and birth order, par-ticularly those related to child morbidity and adverse health outcomes, are either weakor mixed (Dewey and Cohen, 2007). Moreover, to our knowledge, no studies have in-vestigated whether adverse health outcomes associated with short intrapartum spacingpersist in older children, especially as they transition into adolescence. In this study,we investigate the effect of birth spacing between siblings on child health outcomes(height and weight) using longitudinal data that was collected on a cohort of childrenand their sibling in four low-and middle-income countries. As part of our analysis, weassess the impact of short and long birth intervals for the pooled sample of childrenand separately by country, and we also assess whether and how this estimated impactchanges for the sample as children aged.

2 Background

The relationship between short birth intervals and high infant and child mortalityhas been well established in a wide range of populations (DaVanzo et al., 2004). Onthe other hand, there is very little empirical evidence that directly assesses the linksbetween birth intervals and child morbidity, which is surprising considering the mech-anisms through which birth intervals may operate to influence child health and well-being have been extensively discussed in the literature (DaVanzo et al., 1983; Miller,1991). In particular, the consequences of a short birth interval for child health out-comes, particularly at younger ages, have often been attributed to the physiologicaleffects related to the “maternal depletion syndrome,” which postulates that the woman

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may not have fully recuperated from one pregnancy before supporting the next one(Conde-Agudelo et al., 2012; Dewey and Cohen, 2007). Other mechanisms that havebeen hypothesized to contribute to a detrimental effect of a short preceding intervalinclude: 1) behavioral effects that are associated with competition between siblings,which may include competition for parental time or resources; 2) depleted parentalinvestments or household resources that were used for the earlier birth, which mayinclude a lack of physical resources or even a psychological or emotional inability toprovide the later child with adequate attention if its birth came sooner than desired;and 3) larger morbidities through higher disease transmission among closely spacedsiblings, particularly at younger ages (DaVanzo et al., 2004; Conde-Agudelo et al.,2012).

Several recent studies have examined the extent to which birth order may contributeto child height and weight, nutritional status, and other measures of child developmentand attribute differences in health outcomes between siblings to sibling competition,gender bias (son preference), and resource constraints (Jayachandran and Pande, 2015;Black et al., 2016; Nuttall and Nuttall, 1979). However, none of these studies directlyestimate the extent to which birth intervals play a role and, in the best of analyses,only control for birth interval effects by examining the effects of birth order amongpopulations in which siblings are similarly spaced. The closest approximation of po-tential child morbidity effects from birth spacing is provided by studies that examinethe relationship between indicators of childhood malnutrition (stunting, wasting, un-derweight) and family formation patterns, but evidence of the effect from this literaturewas found to be mixed (Winikoff, 1983). A more recent systematic review by Deweyand Cohen (2007) assessed the evidence on the effects of birth spacing on child nutri-tional status from 52 studies and noted that approximately half (25) of these studiesfound that a previous birth interval of at least 36 months was associated with a 10 to50 percent reduction in childhood stunting (with similar findings for wasting), whereasthe remaining studies either found no association or were inconclusive. A study byRutstein (2008), which pooled birth history data from 52 Demographic and HealthSurveys (DHS) that were conducted from 2000 to 2005, observed a positive associationbetween birth interval length and child nutritional status outcomes. Similarly, a morerecent study by Fink et al. (2014), which pooled 153 cross-sectional DHS surveys across61 countries conducted between 1990 and 2011, found that birth intervals of less than12 months and between 12 and 23 months were associated with higher relative risks forstunting (relative risks of 1.09 and 1.06, respectively) as compared to a 24–35 monthinter-pregnancy interval. Due to the cross-sectional nature of the data, however, boththe Rutstein (2008) and the Fink et al. (2014) studies were limited in their ability tomake inferences on the persistence of these associations in children over time.

Our study aims to address the methodological shortcomings in the literature in twoways. Firstly, we improve on prior methodologies that have almost exclusively reliedon cross-sectional data by exploiting a longitudinal dataset that allows us to effectivelyobserve trends of the estimated birth spacing effect across cohorts and over time. Sec-ondly, we employ statistical models that rely on within-family sibling comparisons of

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birth spacing for identification. This approach serves to minimize residual confoundingby adjusting for all time-invariant factors that remain constant within the family.

The rest of the paper is divided as follows: in Section 3, we describe the setting forthe study and the data and empirical methods that were used for analysis; Section 4presents and discusses the results from our analysis; and Section 5 concludes.

3 Data and Methods

For our analysis, we use longitudinal data from the Young Lives Study (YLS), which isan international study that aims to investigate the determinants of childhood povertyand well-being (Oxford Department of International Development, 2017). As partof the YLS, detailed health, nutrition, education, and other sociodemographic datais collected on a cohort of children born between 2001 and 2002 from four low- andmiddle-income countries—Ethiopia, India, Peru, and Vietnam. The sampling designof the YLS included selecting 20 communities in each country and randomly selecting100 children from each. Currently, data is available on approximately 8,000 children(2,000 from each country) over four survey waves that were conducted in 2002, 2006,2009, and 2013, when children were approximately one, five, eight, and twelve yearsold. The study also collects information on household and child characteristics in eachsurvey wave, including the anthropometric markers height and weight. Beginning inthe third survey wave, anthropometric markers were also collected for a sibling of theprimary cohort of children.

We derive birth spacing and order by combining the household rosters collectedduring each wave of the survey. Date of birth was collected directly for the primarycohort and for siblings with anthropometric data. For the remaining siblings reportedon the household rosters, reported age was combined with the date of interview tocalculate approximate date of birth. For those with an older sibling, we group spacingto next oldest sibling into four categories: <2 years, 3-4 years, 4-7 years, and 7+years. Birth order is top coded at 3+ older siblings. A number of household andchild characteristics were also used in analyses to help control for demographic andsocioeconomic effects on child health outcomes. These included sex, age in monthsat measurement, age squared, mother’s age at birth, mother’s age at birth squared, awealth index, total number of siblings, and caregiver’s education.

To construct the analytic sample, we first pool observations from all four surveywaves of the YLS from each of the four countries for the primary cohort and theirsiblings with anthropometric data. This gives us a total of 41,596 observations. Ofthese, 1.7% were dropped due to missing data on birth spacing and another 6.1%dropped due to missing household or child characteristics. This left us with a pooledsample of 38,368 observations—29,352 from the primary cohort of YLS children and9,016 from their siblings.

We focus on two physical health outcomes, child height-for-age and weight-for-age.Height-for-age, which is measured by a child’s height-for-age z-score (HAZ), captures

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a child’s restricted growth potential associated with the chronic or long-term effects ofundernourishment. In contrast, weight-for-age, measured by a child’s weight-for-agez-score (WAZ), is more sensitive to short-term health and environmental shocks as itcaptures weight loss associated with acute malnutrition.

3.1 Pooled Analysis

We begin our analyses by running a simple OLS model on our pooled sample. Thebenchmark specification is given by:

Yis =K∑

k=2

δkI (Spacei = k) +L∑

l=2

αlI (Olderi = l) + ζFBi +N∑

n=2

γnI (Comi = n)

+M∑

m=2

λmI (Y OBi = m) +Z∑

z=2

ηzI (Seai = z) + βXis + µs + uis, (1)

where Yis is an outcome for individual i measured in survey round s; Spacei is years tonext oldest sibling (<2 years is the reference group); Olderi is number of older siblings(one older sibling is the reference group); FBi is a dummy for first-born child; Comi iscommunity of residence; Y OBi is year of birth; Seai is season of birth1; Xis are childcharacteristics; µs is a survey round fixed effect; and uis is the random error term. Thecoefficients of interest are those on birth spacing δ and birth order α.

Interpretation of the coefficients of interest requires careful consideration. Effectsestimated from this model can only be interpreted as causal if birth spacing and or-der are uncorrelated with any unobserved determinants of examined outcomes. It isclearly the case that geographic residence is likely to be correlated with both healthoutcomes and birth spacing, as access to family planning and other health servicesvary considerably across countries and locales. However, effects associated with geo-graphic area are controlled for with the inclusion of community dummy variables. Anadditional concern is the existence of seasonal patterns of fertility that correlate withour independent variables of interest. If, for example, the pregnancies associated withshorter birth intervals were correlated with times of the year when food was relativelyscarce, then results could be attributed to season of birth as opposed to birth spacing(e.g. Moore et al., 1999, 2004; Rayco-Solon et al., 2005; McEniry, 2011; Lokshin andRadyakin, 2012; Miller, 2017). Moreover, studies have documented seasonal patternsof fertility across a variety of countries (e.g. Rajagopalan et al., 1981; Panter-Brick,1996; Buckles and Hungerman, 2013). However, the inclusion of month by country ofbirth dummies controls for seasonal effects that occurred at the country level and wereindependent of birth spacing.

Nonetheless, it is still conceivable that fertility patterns could correlate with ad-ditional unobserved characteristics of children or their families. To address this con-cern we examine the robustness of results to a family fixed effects model specification.

1Season of birth is controlled for with a month of birth by country dummy.

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Specifically, we estimate the following model:

Yifs =K∑

k=2

δkI (Spaceif = k) +L∑

l=2

αlI (Olderif = l) + ζFBi +M∑

m=2

λmI (Y OBif = m)

+Z∑

z=2

ηzI (Seaif = z) + βXifs + µs + θf + uifs, (2)

where Yifs is an outcome for individual i from family f measured in survey round s;θf is a family fixed effect; and other independent variables are as previously defined.Due to colinearity with the family fixed effect, we drop the household wealth index,total number of siblings, and caregiver’s education from individual characteristics Xifs.This approach controls for any remaining permanent unobserved correlation between achild’s family and the spacing measures by comparing children within the same family.The primary cost of this approach is the loss of precision.

3.2 Panel Analysis

We are also interested in examining if the effects of birth spacing and order change aschildren age. We exploit the panel structure of the YLS in addressing this question.First, we limit the sample to the primary cohort of children and estimate the followingOLS model separately for each wave of the survey:

Yi =K∑

k=2

δkI (Spacei = k) +L∑

l=2

αlI (Olderi = l) + ζFBi +N∑

n=2

γnI (Comi = n)

+M∑

m=2

λmI (Y OBi = m) +Z∑

z=2

ηzI (Seai = z) + βXi + ui, (3)

where Yi is an outcome for individual i and other independent variables are as previ-ously defined in equation (1). This approach allows for comparison of effects at agesone, five, eight, and twelve estimated longitudinally for a single birth cohort.

As an alternate approach to examining trends over ages we also use the fully pooledsample and re-estimated equation (2) while interacting all independent variables withchild’s age and age squared.2 We then use the estimated coefficients to predict themarginal effects of increased spacing or birth order over ages. The advantage of thisapproach is again the inclusion of family fixed effects to control for permanent unob-served heterogeneity within a family.

4 Results

Table 1 provides descriptive statistics for the sample used in analyses. The sampleincluded approximately 12,000 individuals nested in 7,400 families with data available

2All independent variables except for the family fixed effect were interacted.

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for 4,600 unique sibling pairs. Average HAZ and WAZ scores were quite low withapproximately 28% of the sample classified as stunted and 27% underweight.3 About11% of the sample were spaced within two years of an older sibling and 6% spacedseven or more years. Total number of siblings average 2.2 and 36% of the sample werefirst-born children.

Table 1: Descriptive Statistics

N Mean SD Min Max

HAZ 37788 -1.35 1.18 -6.49 3.00WAZ 37945 -1.22 1.26 -6.53 4.54Stunted 37788 0.28 0.45 0.00 1.00Underweight 37945 0.27 0.44 0.00 1.00Preceding birth interval

<2 years 38368 0.11 0.32 0.00 1.002-3 years 38368 0.17 0.38 0.00 1.003-4 years 38368 0.13 0.33 0.00 1.004-7 years 38368 0.16 0.37 0.00 1.007+ years 38368 0.06 0.24 0.00 1.00

First-born child 38368 0.36 0.48 0.00 1.00Older siblings

1 38368 0.32 0.47 0.00 1.002 38368 0.15 0.36 0.00 1.003+ 38368 0.17 0.38 0.00 1.00

Male 38368 0.52 0.50 0.00 1.00Mom’s age at birth 38368 25.58 6.03 11.00 53.00Age (months) 38368 87.02 50.73 0.16 353.28Wealth index 38368 0.53 0.21 0.00 1.01Total siblings 38368 2.19 1.78 0.00 11.00Caregiver’s edu. 38368 4.77 4.51 0.00 17.00Ethiopia 38368 0.26 0.44 0.00 1.00India 38368 0.27 0.45 0.00 1.00Vietnam 38368 0.24 0.43 0.00 1.00Peru 38368 0.23 0.42 0.00 1.00

Observations 38368Individuals 12069Families 7462Sibling Pairs 4694

Source: Young Lives Study, young cohort. Sample of children with non-missingchild characteristic covariates.

4.1 Pooled OLS and Family Fixed Effects

Results from the pooled OLS analysis are presented in the first two columns of Table2 and in Figures 1 and 2. Relative to being spaced within 2 years of their next oldest

3Stunted (underweight) refers to a HAZ (WAZ) score below 2 standard deviations from the WHOMGRS reference median height (weight).

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sibling, being spaced 2 to 3 years is associated with a 0.08 increase in a child’s HAZ

score, while being spaced at least 7 years is associated with a 0.29 increase. By thesame token, being spaced 2 to 3 years or at least 7 years of an older sibling is associatedwith a 0.13 increase and 0.27 increase in child WAZ scores.

Table 2: Pooled Sample: OLS vs. Family FE

HAZ WAZ HAZ WAZ

Space 2-3 0.084∗∗ 0.131∗∗∗ 0.115∗∗∗ 0.109∗∗∗

(0.034) (0.031) (0.041) (0.039)Space 3-4 0.195∗∗∗ 0.185∗∗∗ 0.247∗∗∗ 0.177∗∗∗

(0.037) (0.035) (0.051) (0.046)Space 4-7 0.172∗∗∗ 0.200∗∗∗ 0.221∗∗∗ 0.190∗∗∗

(0.037) (0.035) (0.057) (0.053)Space 7+ 0.293∗∗∗ 0.275∗∗∗ 0.347∗∗∗ 0.153∗

(0.048) (0.045) (0.094) (0.087)Older 2 -0.082∗∗∗ -0.073∗∗∗ -0.098∗ -0.092∗

(0.028) (0.026) (0.054) (0.050)Older 3+ -0.009 -0.004 -0.158 -0.163∗

(0.042) (0.039) (0.100) (0.089)

Sibling FE No No Yes YesClusters 7460 7460 7460 7460Obs 37788 37945 37788 37945

Robust standard errors (clusted at the family level) in paren-theses, p-values—*** p<0.01, ** p<0.05, * p<0.1. Additionalindependent variables in all regressions: mother’s age at birth,mother’s age at birth squared, age (months), age squared, anddummies for first-born, sex, survey round, and year and seasonof birth. OLS regressions also include wealth index, number ofsiblings, caregiver’s education, and community dummies.

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Figure 1: Effects of Birth Spacing

When examining the results from the family fixed effects analysis, the magnitudesof the estimated associations between birth spacing and child anthropometric outcomes(both HAZ and WAZ) are generally similar to the estimates from the pooled OLSanalysis. For HAZ there were moderate increases when moving from the simple OLS

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Figure 2: Effects of Birth Order

to the family fixed effect specification. For example, being spaced at least 7 years of anolder sibling is associated with a 0.34 increase in a child’s HAZ score under the familyfixed effects analysis. In contrast, there were small declines for WAZ. However, theconfidence intervals around these estimates are also wider than those from the OLS,particularly for larger birth intervals where there are generally fewer observations.

Figure 2 plots the estimated birth order effects for both the pooled OLS and familyfixed effects model. Relative to second born children, those with two older siblingshad significantly lower HAZ and WAZ scores regardless of model specification witheffect sizes ranging from -0.07 to -0.09. For those with three or more older siblings, theeffects are statistically insignificant but imprecisely estimated.

4.2 Effects Over Ages

We exploit the panel structure of the YLS dataset by estimating the evolution of childhealth outcomes for the primary birth cohort over time. The estimates from OLSregressions at each age are presented for birth spacing in Figure 3 and birth order inFigure 4 (point estimates are shown in appendix Table A1). By plotting the estimatesfrom each of the four time periods in sequence, we can observe how the associationsbetween child health and birth spacing / order change for the cohort over time.

As Figure 3 shows, the magnitude of the associations between birth spacing andchild health, both in terms of height and weight, declines over time and most signifi-cantly between the ages of 1 and 5. Moreover, the effect of birth spacing only persiststo age 12 for children who are very widely spaced (at least 7 years for HAZ and at least4 years for WAZ). Similarly, Figure 4 shows that the magnitude of the associationbetween birth order and both child health outcomes also declines over time. Takentogether, the observed attenuation of birth spacing and birth order effects providesevidence of potential catch-up growth among more narrowly spaced children and thoseof higher birth order.

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Figure 4: Effects of Birth Order

The decline in the impact of birth spacing over the 12-year observation periodis further confirmed by Figure 5, which plot marginal effects (and their respectiveconfidence bounds) that are predicted from a family fixed effects specification in whichindependent variables are interacted with age. Unlike the cohort analysis, however,we observe that the marginal effects for birth spacing, though small in magnitude,continue to be significant at older ages across both health outcomes (Figure 5). Thesame is not true for birth order, which show an effectively null association over time(see appendix Figure A1).

4.3 Heterogeneity and Additional Outcomes

We provide additional results by country in the Appendix. Table A2 presents thepooled OLS analysis for each of the four countries separately, while Tables A3 to A6

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present the cohort analysis for each country. Findings from these analyses indicate thatthere is tremendous heterogeneity across countries, both in terms of the magnitude ofthe effect of birth spacing and birth order as well as the persistence of the effect inthe country-specific cohorts over time. As expected, the confidence intervals aroundmost of the country-specific coefficient estimates are wider, particularly among thosechildren who are more widely spaced or who are of higher birth order.

Appendix Tables A7 to A10 presents results when conducting analyses on sex-specific sub-samples. Overall, spacing effects were stronger for females while malesexhibited faster catch-up growth.

We also run analysis using child stunting and underweight as outcomes, whichallows us to compare findings from our cohort analysis with the existing estimatesfrom the literature. Specifically, we re-estimate equations (1) and (3) using a logitregression (results are presented in appendix Table A11 and Table A12). In the pooledOLS analysis we estimate that for children spaced within two years of an older siblings,odds of stunting are about 19% higher than those spaced 2-3 years and 60% higher thanthose spaced 7+ years. Analogous numbers for underweight are about 22% and 70%.When limiting the sample to the primary cohort and estimating the model separatelyby age, we again find that spacing effects attenuate as children become older. However,while differences are not statistically significant by age twelve for stunting, the effectsof less than two years spacing remains quite strong for underweight (odds are 22% and55% higher than 2-3 and 7+ years).

Finally, we run a secondary analysis using prenatal investments in the child andsubsequent birth outcomes as dependent variables (see appendix Table A13).4 Thisanalysis provides some suggestive evidence on if observed effects are operating entirelythough biological channels (e.g. “maternal depletion syndrome”) or if there are behav-ioral aspects at play as well (e.g. competition for resources). We find that wider spaced

4This data was only available for the primary cohort of children.

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children received more prenatal care5, were less likely to born at home, and were morelikely to have a medical professional present at birth. Moreover, they were larger atbirth (both in terms of birth weight and parent’s perception of size), less likely to bepremature, and more likely to be from a reportedly wanted pregnancy.

5 Discussion and Conclusions

In this study, we use longitudinal data from the Young Lives Study on 8,000 individualsand 4,600 of their sibling between 2002 and 2013 from four low-and middle-incomecountries. We assess the impact of birth spacing on height-for-age and weight-for-agefor the pooled sample as well as for the primary cohort over time. We find increasedchild HAZ and WAZ scores among children who are more widely spaced and those whoare of lower birth order. However, we also find evidence of catch-up growth (estimatedHAZ and WAZ scores that converge to the null) for closely spaced children as well asfor children of higher birth order as they age.

Cross-country heterogeneity and the finding of stronger effects and slower catch-up growth for girls suggests the effects of birth spacing could be partially driven byparental investment behavior. The differences in prenatal care further supports thishypothesis, however additional research is needed to further disentangle the biologicaland behavioral channels.

There are several limitations to our study. While we find considerable evidence ofcatch-up growth over time, we are presently unable to say for certain whether childrenwho are closely spaced or of high birth order are able to completely make up any initialgrowth differences that they may experience from these effects. We see considerable at-tenuation over time in both height- and weight-for-age growth gaps across birth spacingand birth order groups, and we observe the trajectory over time to indicate a potentialconvergences of growth towards the null. Given the relatively short (12-year) periodover which our sample is observed, however, we are unable to say whether or not aconvergence is assured in the long run, particularly as children enter into periods ofpotentially rapid growth and development during adolescence. A common methodolog-ical criticism in this literature is the inability to adequately account for within-familyheterogeneity in unobservable characteristics, which in turn could bias the estimates ofthe effects of birth spacing (Rosenzweig and Wolpin, 1988; Rosenzweig, 1986). Morespecifically, the impact of birth spacing on child health outcomes is likely to be drivenby a wide number of individual, social, and contextual determinants, and these de-terminants may vary from birth to birth within the same family as parents choose todifferentially invest in children who may be differentially spaced (Fink et al., 2014).

5We used the level of antenatal care variable provided in the first round data set. The YLS studyteam constructed the variable as follows: a mother that reported no antenatal care was given a zero.For those who had antenatal visits, one was added if the first visit was when they were four monthspregnant or before, one was added if the mother had five or more visits in total, and one was addedif the mother was given tetanus injections. This gave a value between zero and three for all mothers.

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For these reasons, our use of a family fixed effect would not be sufficient in adjustingfor any residual confounding that is driven by differential birth timing decisions.

When taken together, our results suggest that short birth intervals and high fertilityare likely to contribute to poor child growth and development. Interventions that aimto lower fertility and increase birth intervals, including family planning and reproduc-tive health services that reinforce healthy timing and spacing of pregnancy, thereforehave the potential to substantially reduce the number of stunted and underweight chil-dren. Finally, it is important that we continue to investigate the biological, social,and behavioral mechanisms by which adequate birth spacing might improve health forchildren - a thorough understanding of these pathways is essential for the developmentof effective policies, programs, and evidence-based interventions that seek to addresskey determinants of poor child health.

6 Ethical Considerations

Ethical approval for the evaluation was granted by the Harvard T.H. Chan School ofPublic Health Institutional Review Board (IRB), Protocol No. IRB17-0028.

7 Acknowledgments

The authors thank David Canning, Guenther Fink, and seminar participants at theHarvard Center for Population and Development Studies for their helpful commentsand suggestions on the analysis.

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16

Appendix A: Tables and Figures

Table A1: 2001-02 Birth Cohort by Age

HAZ WAZ

Age 1 Age 5 Age 8 Age 12 Age 1 Age 5 Age 8 Age 12

Space 2-3 0.105 0.028 0.048 0.013 0.155∗∗∗ 0.099∗∗∗ 0.122∗∗∗ 0.077∗

(0.063) (0.044) (0.046) (0.047) (0.055) (0.037) (0.042) (0.040)Space 3-4 0.343∗∗∗ 0.130∗∗ 0.132∗∗ 0.063 0.279∗∗∗ 0.103∗∗ 0.141∗∗∗ 0.081∗

(0.078) (0.056) (0.055) (0.052) (0.057) (0.044) (0.046) (0.048)Space 4-7 0.327∗∗∗ 0.128∗∗∗ 0.062 0.037 0.281∗∗∗ 0.125∗∗∗ 0.138∗∗∗ 0.115∗∗∗

(0.069) (0.046) (0.047) (0.043) (0.052) (0.042) (0.044) (0.041)Space 7+ 0.374∗∗∗ 0.214∗∗∗ 0.205∗∗∗ 0.183∗∗∗ 0.345∗∗∗ 0.187∗∗∗ 0.200∗∗∗ 0.184∗∗∗

(0.085) (0.059) (0.063) (0.059) (0.063) (0.047) (0.055) (0.054)Older 2 -0.183∗∗∗ -0.075∗ -0.070∗ -0.087∗∗ -0.137∗∗∗ -0.096∗∗∗ -0.114∗∗∗ -0.046

(0.053) (0.039) (0.036) (0.036) (0.048) (0.035) (0.037) (0.037)Older 3+ -0.116 -0.057 0.007 0.058 -0.136∗∗ -0.053 -0.037 0.088

(0.079) (0.061) (0.061) (0.059) (0.066) (0.057) (0.056) (0.058)

Obs 7197 7305 7299 7285 7215 7327 7315 7293

Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01, ** p<0.05,* p<0.1. Additional independent variables in all regressions: mother’s age at birth, mother’s age at birthsquared, age (months), age squared, wealth index, number of siblings, caregiver’s education, and dummies forfirst-born, sex, survey round, year and season of birth, and community.

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Figure A1: Effects of Birth Order

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Table A2: Pooled Sample by Country

HAZ WAZ

India Ethiopia Vietnam Peru India Ethiopia Vietnam Peru

Space 2-3 0.178∗∗ 0.170∗∗ 0.044 0.010 0.120∗ 0.168∗∗∗ 0.040 0.060(0.076) (0.076) (0.091) (0.073) (0.071) (0.065) (0.108) (0.080)

Space 3-4 0.357∗∗∗ 0.339∗∗∗ 0.192∗ -0.063 0.273∗∗∗ 0.287∗∗∗ 0.080 -0.142(0.102) (0.085) (0.112) (0.102) (0.095) (0.071) (0.125) (0.107)

Space 4-7 0.150 0.389∗∗∗ 0.284∗∗ -0.170 0.179∗ 0.278∗∗∗ 0.234 -0.103(0.111) (0.095) (0.120) (0.116) (0.103) (0.081) (0.144) (0.118)

Space 7+ 0.415∗∗ 0.650∗∗∗ 0.150 -0.130 0.192 0.231 0.048 -0.109(0.184) (0.184) (0.190) (0.187) (0.148) (0.159) (0.217) (0.203)

Older 2 -0.100 -0.041 0.032 -0.059 0.001 -0.048 0.081 -0.086(0.115) (0.090) (0.150) (0.114) (0.107) (0.077) (0.145) (0.110)

Older 3+ -0.213 -0.055 0.020 -0.010 -0.069 -0.055 0.022 -0.052(0.228) (0.152) (0.297) (0.201) (0.207) (0.126) (0.270) (0.202)

Sibling FE Yes Yes Yes Yes Yes Yes Yes YesClusters 1885 1819 1929 1827 1885 1819 1929 1827Obs 10271 9660 9246 8611 10349 9673 9275 8648

Robust standard errors (clusted at the family level) in parentheses, p-values—*** p<0.01, ** p<0.05, *p<0.1. Additional independent variables in all regressions: mother’s age at birth, mother’s age at birthsquared, age (months), age squared, and dummies for first-born, sex, survey round, and year and seasonof birth.

Table A3: 2001-02 Birth Cohort by Age: India

HAZ WAZ


Space 2-3 0.107 -0.039 0.005 -0.049 0.127 0.022 0.070 -0.006(0.110) (0.071) (0.069) (0.075) (0.086) (0.057) (0.065) (0.062)

Space 3-4 0.423∗∗∗ 0.131 0.185∗∗ 0.109 0.365∗∗∗ 0.120 0.153 0.072(0.125) (0.089) (0.081) (0.090) (0.092) (0.091) (0.096) (0.106)

Space 4-7 0.257∗ 0.040 -0.008 -0.042 0.199∗ 0.022 0.081 0.032(0.128) (0.085) (0.083) (0.080) (0.097) (0.084) (0.079) (0.077)

Space 7+ 0.187 -0.033 0.040 -0.070 0.243 0.016 0.141 -0.000(0.169) (0.110) (0.139) (0.126) (0.146) (0.091) (0.095) (0.110)

Older 2 -0.357∗∗∗ -0.187∗∗ -0.172∗∗ -0.147∗∗ -0.184∗ -0.172∗∗ -0.221∗∗∗ -0.073(0.121) (0.072) (0.071) (0.057) (0.088) (0.062) (0.062) (0.066)

Older 3+ -0.365∗∗ -0.306∗∗ -0.146 -0.079 -0.389∗∗∗ -0.330∗∗ -0.261∗∗ -0.013(0.136) (0.121) (0.124) (0.125) (0.111) (0.130) (0.120) (0.132)

Obs 1800 1829 1833 1837 1828 1837 1839 1839

Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01, ** p<0.05,* p<0.1. Additional independent variables in all regressions: mother’s age at birth, mother’s age at birthsquared, age (months), age squared, wealth index, number of siblings, caregiver’s education, and dummiesfor first-born, sex, survey round, year and season of birth, and community.

18

Table A4: 2001-02 Birth Cohort by Age: Ethiopia

HAZ WAZ


Space 2-3 0.248 0.054 0.162∗ 0.056 0.279∗ 0.184∗∗ 0.229∗∗∗ 0.065(0.146) (0.112) (0.092) (0.095) (0.148) (0.087) (0.077) (0.068)

Space 3-4 0.574∗∗∗ 0.182 0.212 0.115 0.398∗∗ 0.166∗ 0.187∗∗ 0.033(0.200) (0.137) (0.133) (0.114) (0.163) (0.095) (0.078) (0.078)

Space 4-7 0.713∗∗∗ 0.238∗∗ 0.297∗∗∗ 0.121∗ 0.489∗∗∗ 0.243∗∗ 0.330∗∗∗ 0.123∗∗

(0.164) (0.107) (0.100) (0.070) (0.143) (0.086) (0.062) (0.057)Space 7+ 0.874∗∗∗ 0.399∗∗ 0.446∗∗ 0.198 0.689∗∗∗ 0.383∗∗∗ 0.436∗∗∗ 0.224∗∗

(0.234) (0.169) (0.177) (0.143) (0.181) (0.092) (0.097) (0.085)Older 2 -0.251∗ 0.030 -0.080 -0.103 -0.254∗ -0.078 -0.113 -0.045

(0.143) (0.085) (0.090) (0.088) (0.132) (0.080) (0.077) (0.058)Older 3+ -0.093 0.074 0.062 0.134 -0.262∗ -0.012 -0.014 0.115

(0.166) (0.102) (0.104) (0.114) (0.133) (0.090) (0.092) (0.091)

Obs 1687 1772 1760 1772 1655 1775 1771 1771

Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01, **p<0.05, * p<0.1. Additional independent variables in all regressions: mother’s age at birth, mother’sage at birth squared, age (months), age squared, wealth index, number of siblings, caregiver’s education,and dummies for first-born, sex, survey round, year and season of birth, and community.

Table A5: 2001-02 Birth Cohort by Age: Vietnam

HAZ WAZ


Space 2-3 0.032 0.079 0.073 0.057 -0.134 0.058 0.070 0.148(0.119) (0.103) (0.119) (0.144) (0.112) (0.116) (0.128) (0.126)

Space 3-4 0.278∗∗ 0.122 0.194 0.034 0.028 0.108 0.171 0.123(0.125) (0.114) (0.122) (0.125) (0.106) (0.103) (0.121) (0.121)

Space 4-7 0.251∗∗ 0.201∗∗ 0.065 0.108 0.052 0.189∗ 0.156 0.204∗

(0.095) (0.078) (0.092) (0.116) (0.098) (0.098) (0.108) (0.110)Space 7+ 0.299∗∗ 0.238∗∗ 0.169 0.257∗ 0.057 0.162∗ 0.101 0.284∗∗

(0.113) (0.083) (0.107) (0.123) (0.112) (0.089) (0.120) (0.124)Older 2 -0.086 -0.070 0.025 -0.085 -0.042 -0.125 -0.119 -0.102

(0.070) (0.075) (0.071) (0.069) (0.065) (0.072) (0.081) (0.092)Older 3+ 0.135 0.163 0.181 0.101 0.186∗ -0.028 -0.096 -0.003

(0.128) (0.121) (0.157) (0.117) (0.091) (0.135) (0.139) (0.143)

Obs 1904 1910 1902 1867 1919 1915 1901 1870

Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01,** p<0.05, * p<0.1. Additional independent variables in all regressions: mother’s age at birth,mother’s age at birth squared, age (months), age squared, wealth index, number of siblings,caregiver’s education, and dummies for first-born, sex, survey round, year and season of birth,and community.

19

Table A6: 2001-02 Birth Cohort by Age: Peru

HAZ WAZ


Space 2-3 0.021 0.039 -0.023 0.023 0.248∗∗∗ 0.099 0.090 0.131(0.127) (0.075) (0.106) (0.097) (0.082) (0.073) (0.079) (0.080)

Space 3-4 0.011 0.072 -0.048 -0.059 0.204∗∗ -0.002 0.100 0.136(0.108) (0.116) (0.095) (0.093) (0.081) (0.080) (0.078) (0.090)

Space 4-7 0.045 0.018 -0.104 -0.054 0.277∗∗∗ -0.026 -0.064 0.101(0.116) (0.090) (0.084) (0.097) (0.074) (0.079) (0.077) (0.089)

Space 7+ 0.168 0.298∗ 0.250∗ 0.293∗∗ 0.459∗∗∗ 0.213∗∗ 0.205∗∗ 0.257∗∗

(0.189) (0.145) (0.122) (0.122) (0.090) (0.095) (0.095) (0.106)Older 2 0.010 0.000 -0.008 0.022 -0.010 -0.033 -0.004 0.066

(0.103) (0.082) (0.062) (0.077) (0.096) (0.069) (0.075) (0.076)Older 3+ -0.132 -0.134 -0.076 0.027 -0.016 0.035 0.102 0.162

(0.138) (0.130) (0.111) (0.115) (0.133) (0.120) (0.117) (0.136)

Obs 1806 1794 1804 1809 1813 1800 1804 1813

Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01,** p<0.05, * p<0.1. Additional independent variables in all regressions: mother’s age at birth,mother’s age at birth squared, age (months), age squared, wealth index, number of siblings, care-giver’s education, and dummies for first-born, sex, survey round, year and season of birth, andcommunity.

Table A7: Pooled Sample: OLS vs. FamilyFE, Females Only

HAZ WAZ HAZ WAZ

Space 2-3 0.137∗∗∗ 0.141∗∗∗ 0.098 0.127∗

(0.048) (0.044) (0.079) (0.075)Space 3-4 0.205∗∗∗ 0.199∗∗∗ 0.167∗ 0.221∗∗

(0.054) (0.048) (0.098) (0.092)Space 4-7 0.223∗∗∗ 0.233∗∗∗ 0.153 0.148

(0.052) (0.048) (0.115) (0.105)Space 7+ 0.379∗∗∗ 0.309∗∗∗ 0.371∗ 0.164

(0.068) (0.061) (0.193) (0.174)Older 2 -0.099∗∗ -0.078∗∗ -0.073 -0.050

(0.039) (0.036) (0.099) (0.097)Older 3+ -0.010 -0.010 -0.133 -0.139

(0.058) (0.054) (0.192) (0.183)


Robust standard errors (clusted at the family level)in parentheses, p-values—*** p<0.01, ** p<0.05, *p<0.1. Additional independent variables in all regressions:mother’s age at birth, mother’s age at birth squared, age(months), age squared, and dummies for first-born, sur-vey round, and year and season of birth. OLS regressionsalso include wealth index, number of siblings, caregiver’seducation, and community dummies.

20

Table A8: Pooled Sample: OLS vs. FamilyFE, Males Only

HAZ WAZ HAZ WAZ

Space 2-3 0.036 0.117∗∗∗ 0.095 0.181∗∗

(0.047) (0.045) (0.086) (0.077)Space 3-4 0.186∗∗∗ 0.171∗∗∗ 0.237∗∗ 0.234∗∗∗

(0.051) (0.050) (0.104) (0.090)Space 4-7 0.125∗∗ 0.159∗∗∗ 0.320∗∗∗ 0.403∗∗∗

(0.051) (0.050) (0.122) (0.107)Space 7+ 0.226∗∗∗ 0.250∗∗∗ 0.388∗∗ 0.269∗

(0.067) (0.065) (0.185) (0.150)Older 2 -0.067∗ -0.066∗ -0.043 0.012

(0.040) (0.039) (0.114) (0.100)Older 3+ -0.018 -0.006 0.002 0.074

(0.059) (0.054) (0.208) (0.177)


Robust standard errors (clusted at the family level) in paren-theses, p-values—*** p<0.01, ** p<0.05, * p<0.1. Addi-tional independent variables in all regressions: mother’s ageat birth, mother’s age at birth squared, age (months), agesquared, and dummies for first-born, survey round, and yearand season of birth. OLS regressions also include wealth in-dex, number of siblings, caregiver’s education, and commu-nity dummies.

Table A9: 2001-02 Birth Cohort by Age, Females Only

HAZ WAZ


Space 2-3 0.205∗ 0.055 0.094 0.022 0.142∗ 0.122∗∗ 0.150∗∗∗ 0.109∗

(0.105) (0.059) (0.066) (0.073) (0.084) (0.051) (0.055) (0.059)Space 3-4 0.312∗∗ 0.115 0.180∗∗ 0.109 0.240∗∗∗ 0.152∗∗ 0.208∗∗∗ 0.122∗

(0.119) (0.081) (0.079) (0.081) (0.087) (0.063) (0.071) (0.069)Space 4-7 0.393∗∗∗ 0.191∗∗∗ 0.135∗∗ 0.114∗ 0.310∗∗∗ 0.187∗∗∗ 0.232∗∗∗ 0.159∗∗

(0.098) (0.063) (0.062) (0.066) (0.074) (0.055) (0.062) (0.061)Space 7+ 0.460∗∗∗ 0.289∗∗∗ 0.337∗∗∗ 0.271∗∗∗ 0.426∗∗∗ 0.259∗∗∗ 0.266∗∗∗ 0.202∗∗

(0.139) (0.088) (0.092) (0.094) (0.103) (0.076) (0.079) (0.083)Older 2 -0.188∗∗∗ -0.122∗∗ -0.104∗ -0.153∗∗ -0.153∗∗ -0.079∗ -0.090∗ -0.059

(0.068) (0.056) (0.056) (0.068) (0.061) (0.042) (0.053) (0.057)Older 3+ -0.226∗∗ -0.122 0.061 0.118 -0.152∗ -0.039 0.009 0.130

(0.107) (0.083) (0.080) (0.092) (0.088) (0.069) (0.075) (0.082)

Obs 3438 3483 3485 3475 3449 3493 3492 3480

Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01, ** p<0.05,* p<0.1. Additional independent variables in all regressions: mother’s age at birth, mother’s age at birthsquared, age (months), age squared, wealth index, number of siblings, caregiver’s education, and dummiesfor first-born, survey round, year and season of birth, and community.

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Table A10: 2001-02 Birth Cohort by Age, Males Only

HAZ WAZ


Space 2-3 0.033 0.013 0.019 0.023 0.170∗∗ 0.076 0.104 0.056(0.080) (0.058) (0.061) (0.069) (0.078) (0.060) (0.069) (0.064)

Space 3-4 0.397∗∗∗ 0.131∗ 0.082 0.021 0.323∗∗∗ 0.047 0.078 0.052(0.092) (0.069) (0.074) (0.069) (0.085) (0.065) (0.073) (0.071)

Space 4-7 0.285∗∗∗ 0.065 0.002 -0.023 0.269∗∗∗ 0.044 0.045 0.058(0.088) (0.060) (0.072) (0.069) (0.084) (0.067) (0.073) (0.067)

Space 7+ 0.325∗∗∗ 0.146∗ 0.107 0.129 0.283∗∗∗ 0.115 0.153∗ 0.177∗∗

(0.096) (0.076) (0.085) (0.082) (0.094) (0.074) (0.088) (0.082)Older 2 -0.208∗∗∗ -0.035 -0.029 -0.002 -0.158∗∗ -0.098∗ -0.120∗ -0.022

(0.077) (0.058) (0.055) (0.050) (0.073) (0.059) (0.064) (0.060)Older 3+ -0.059 -0.020 -0.048 0.020 -0.170∗∗ -0.080 -0.082 0.050

(0.100) (0.075) (0.083) (0.066) (0.084) (0.067) (0.074) (0.078)

Obs 3759 3822 3814 3810 3766 3834 3823 3813

Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01, **p<0.05, * p<0.1. Additional independent variables in all regressions: mother’s age at birth, mother’sage at birth squared, age (months), age squared, wealth index, number of siblings, caregiver’seducation, and dummies for first-born, survey round, year and season of birth, and community.

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Figure A2: OLS Effects of Birth Spacing by Sex

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Figure A3: Effects of Birth Spacing: Females Only

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Figure A4: Effects of Birth Spacing: Males Only

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Table A11: Pooled Sample:Stunting and Underweight

Stunted Underweight

Space 2-3 0.839∗∗∗ 0.818∗∗∗

(0.055) (0.056)Space 3-4 0.727∗∗∗ 0.703∗∗∗

(0.053) (0.053)Space 4-7 0.746∗∗∗ 0.706∗∗∗

(0.054) (0.054)Space 7+ 0.615∗∗∗ 0.582∗∗∗

(0.063) (0.063)Older 2 1.174∗∗∗ 1.110∗

(0.067) (0.066)Older 3+ 1.044 0.990

(0.087) (0.087)

Sibling FE No NoClusters 7460 7460Obs 37788 37945

Odds ratios reported. Robust stan-dard errors (clusted at the family level)in parentheses, p-values—*** p<0.01, **p<0.05, * p<0.1. Additional independentvariables in all regressions: mother’s ageat birth, mother’s age at birth squared,age (months), age squared, wealth index,number of siblings, caregiver’s education,and dummies for first-born, sex, surveyround, year and season of birth, and com-munity.

Table A12: 2001-02 Birth Cohort by Age: Stunting and Underweight

Stunted Underweight


Space 2-3 0.815∗∗ 0.929 0.974 1.100 0.797∗ 0.880 0.895 0.815∗

(0.081) (0.096) (0.124) (0.123) (0.097) (0.097) (0.094) (0.099)Space 3-4 0.639∗∗∗ 0.756∗∗ 0.775∗ 0.905 0.664∗∗∗ 0.784∗∗ 0.772∗∗ 0.762∗∗

(0.074) (0.097) (0.112) (0.114) (0.083) (0.097) (0.090) (0.092)Space 4-7 0.592∗∗∗ 0.778∗∗ 0.961 0.979 0.698∗∗∗ 0.733∗∗ 0.828 0.713∗∗∗

(0.069) (0.090) (0.127) (0.111) (0.076) (0.090) (0.101) (0.079)Space 7+ 0.571∗∗∗ 0.676∗∗∗ 0.672∗∗ 0.869 0.533∗∗∗ 0.663∗∗∗ 0.662∗∗∗ 0.643∗∗∗

(0.089) (0.101) (0.114) (0.128) (0.092) (0.104) (0.100) (0.095)Older 2 1.276∗∗∗ 1.200∗ 1.190∗ 1.222∗∗ 1.377∗∗∗ 1.297∗∗∗ 1.220∗∗ 0.998

(0.101) (0.122) (0.121) (0.115) (0.155) (0.128) (0.117) (0.091)Older 3+ 1.164 1.058 1.122 0.960 1.593∗∗∗ 1.178 0.958 0.747∗

(0.156) (0.136) (0.170) (0.124) (0.204) (0.175) (0.133) (0.115)

Obs 7193 7305 7299 7285 7038 7149 7051 7030

Odds ratios reported. Robust standard errors (clusted at the community level) in parentheses, p-values—*** p<0.01, ** p<0.05, * p<0.1. Additional independent variables in all regressions: mother’s age atbirth, mother’s age at birth squared, age (months), age squared, wealth index, number of siblings,caregiver’s education, and dummies for first-born, sex, survey round, year and season of birth, andcommunity.

24

Table A13: Prenatal and Birth Outcomes

PreCare Wanted C-sect Premature ProBirth HomeBirth Birthweight Birthsize

Space 2-3 0.104 0.207∗ -0.247 -0.170 0.018 -0.201 73.377∗ 0.144(0.090) (0.121) (0.201) (0.158) (0.133) (0.132) (40.820) (0.101)

Space 3-4 0.165 0.569∗∗∗ -0.242 -0.280∗ -0.010 -0.157 83.531∗∗ 0.101(0.103) (0.145) (0.250) (0.165) (0.165) (0.159) (41.056) (0.101)

Space 4-7 0.210∗∗ 0.811∗∗∗ -0.251 -0.188 0.507∗∗∗ -0.624∗∗∗ 63.252∗ 0.195∗∗

(0.100) (0.153) (0.227) (0.169) (0.156) (0.141) (37.087) (0.094)Space 7+ 0.361∗∗∗ 0.972∗∗∗ -0.050 0.193 0.638∗∗∗ -0.737∗∗∗ 57.134 0.291∗∗∗

(0.117) (0.169) (0.232) (0.173) (0.188) (0.173) (40.292) (0.111)Older 2 -0.089 -0.788∗∗∗ -0.128 -0.128 -0.204 0.179 16.490 -0.119

(0.095) (0.116) (0.173) (0.122) (0.139) (0.137) (31.028) (0.078)Older 3+ -0.265∗∗ -1.164∗∗∗ -0.275 -0.054 0.122 -0.245 148.918∗∗∗ 0.043

(0.108) (0.161) (0.299) (0.191) (0.195) (0.188) (53.790) (0.114)

Obs 7104 7153 4024 7074 6586 7092 4410 7270

Odds ratios reported (except for birthweight). Robust standard errors (clusted at the community level) in paren-theses, p-values—*** p<0.01, ** p<0.05, * p<0.1. Additional independent variables in all regressions: mother’sage at birth, mother’s age at birth squared, age (months), age squared, and dummies for first-born, sex, surveyround, year and season of birth, and community.

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Assessing the Impact of Birth Spacing on Child Health ...€¦ · years. Birth order is top coded at 3+ older siblings. A number of household and child characteristics were also used