Drivers of Growth Faltering in Children
Matthias Rieger and Sofia Karina Trommlerová∗
ISS, Erasmus University
Graduate Institute, Geneva
October 1, 2014
[Comments welcome]
Abstract.— Growth faltering describes a widespread phenomenon that height and weight-for-age
of children in developing countries collapse rapidly in the first two years of life. We study the
demographic and socio-economic drivers behind growth faltering using Demographic and Health
Surveys from 56 developing countries. Applying non-parametric techniques and exploiting
within mother variation, we find that maternal and household factors are the dominant driver of
the observed dip and of early catch-up in growth. The documented interaction between age and
characteristics of mothers further underlines the need to provide nutritional support during the
first years of life and to improve maternal conditions.
Keywords: height-for-age, weight-for-age, child growth, faltering, gradient, determinants, mother fixed
effects
JEL Classification Codes: I12; I15; I32; O12; D91
∗
Matthias Rieger, Institute of Social Studies of Erasmus University, [email protected]; Sofia Karina Trommlerová,
Department of International Economics, Graduate Institute, Geneva.
1
I.
Introduction
Undernutrition remains a global obstacle to economic development in many countries
around the world. In Africa alone, 165 million children are stunted, i.e. they are too small for
their age (UNICEF, 2012). In this paper, we focus on the determinants of age dynamics in child
growth. In particular, we explore when and under which conditions child growth falters in a
sample of 56 developing countries.
Growth faltering in children was first systematically documented in an influential paper
by Shrimpton et al. (2001). In a cross-sectional study of 39 developing countries and using the
NCHS growth standards, they found that child weight-for-age (WAZ) falters rapidly between 3
and 12 months of life, stabilizing and recovering only after 19 months. Faltering in height-for-age
(HAZ) starts immediately after birth and is particularly strong in the first two years.1 In response
to these findings, several global health policy and information campaigns have been initiated (see
Prentice et al., 2013). 2 More recently, Prentice et al. (2013) detect additional windows of
opportunity for catch-up growth after 24 months, particularly in adolescence.
Studying the drivers of growth faltering in children is crucial because the adverse effects
of poor nutritional status in early childhood are overwhelming. Most importantly, undernutrition
profoundly increases the vulnerability to disease and premature death. An estimated 3.1 million
(or 45%) of annual child deaths can be linked to undernutrition (Black et al., 2008). In the longer
term, early childhood undernutrition impedes cognitive development (Mendez and Adair, 1999),
accumulation of human capital, and educational achievement (Glewwe and Jacoby, 1995;
1
In a follow-up study, Victora et al. (2010) report even more striking age patterns using the new WHO growth
standard and a larger sample of 54 countries. They present evidence of growth faltering in both WAZ and HAZ in
the first 24 months, thereby reconfirming the already established fact that the first two years of life are a “window of
opportunity” for nutrition interventions. Also Maleta et al. (2003) present evidence from Malawi that height (weight)
faltering occurs during the first 36 (3 to 12) months of life, respectively, based on a sample of children under 3 years
of age.
2
See for instance www.thousanddays.org.
2
Glewwe et al., 2001; Victora et al., 2008; Maluccio et al., 2009; Adair et al., 2013; Gandhi et al.,
2011).3 Ultimately, insufficient nutrition during childhood can lower economic success and
productivity in the later stages of life (Hoddinott et al., 2008).
The main objective of this article is to identify possible drivers of growth faltering.
Previous studies have largely focused on age patterns of growth faltering across regions of the
world. We, on the other hand, examine the “usual suspects” linked to low height and weight-forage, and we model their dynamic interaction with child’s age. In other words, we identify
interactive effects between child’s age in months and common demographic and socio-economic
determinants of child growth at the individual (gender, birth order), mother (education, height,
age at marriage, mortality of her children), household (access to electricity, household size), and
country level (GDP per capita, under-5 mortality).4 More importantly, we also investigate which
group of factors (measured at country, household or mother level) is proportionally the biggest
driver of faltering and of early catch-up growth.
Disentangling the determinants of child growth empirically is not an easy task. A recent
conceptual framework by Black et al. (2008) indicates that the underlying causes of household
poverty are lack of various forms of capital (human, physical, financial) and of socio-economic
and political stability in countries and regions. And household poverty is, in turn, an immediate
cause of poor nutritional status. In particular, poor households and their children are more
vulnerable to poor hygiene, diseases, food insecurity, as well as inadequate child care and
3
For instance, height gains during early childhood were positively associated with mathematical ability at the age of
12 years among Malawian children (Gandhi et al., 2011).
4
We study both height and weight because HAZ and WAZ tend to follow different patterns over time, and thus the
drivers of their faltering might also differ (Maleta et al., 2003).
3
nutrition. In other words, the nutritional status is embedded in a complex system where many
variables are not observable, e.g. parental ability, or not easily measurable, e.g. political stability.
Our proposed empirical strategy simplifies this system considerably by evaluating age
heterogeneities within families. More specifically, we make use of a large household dataset in
which some mothers have more than one child under the age of 5 years, and we compare these
children. By comparing siblings, i.e. children born to the same mother in the same household and
country, we account for the main impact of both observable and unobservable characteristics of
mothers, households, and countries. Thus, we are able to identify age-heterogeneities in child
growth conditionally on the main effects of these factors.
The value added of our analysis compared to existing studies is thus fourfold: First, we
explore a broad set of potential determinants of growth faltering in children. Second, our results
can be regarded as more general because they are based on a large sample of 56 developing
countries rather than on one particular country. Third, we find statistically and economically
significant results controlling for mother fixed effects whereas previous studies performed their
analyses using only country or village fixed effects at best.5 Fourth, we study which group of
characteristics is the biggest factor in determining growth faltering.
Overall, our most important finding is that maternal and household characteristics – both
observable and unobservable – are the main drivers of growth faltering and of an eventual catchup growth. All the other results are basically in line with previous studies. For instance, similarly
to Sahn and Alderman (1997) we find that the impact of child, mother, and household factors
5
Although Fernald et al. (2011) do include household effects, they use random effects rather than fixed effects.
Random and fixed effects models are based on different assumptions. In particular, random effects models are more
efficient if the specific effects are uncorrelated with the covariates in the model but inconsistent otherwise. Hausman
test confirms that random effects model would be inconsistent in our case, meaning that the fixed effects model is
more appropriate (p-value 0.000).
4
varies substantially by age groups. Also Fernald et al. (2012) provide evidence from four
developing countries showing that the influence of socio-economic factors on child development
and growth varies across ages.6 Nevertheless, our results indicate that maternal and household
factors matter the most. For instance, the positive correlation between maternal education and
child weight or height increases strongly with age. Moreover, mother fixed effects explain a large
part of growth faltering in height, as well as the eventual catch-up growth after the age of the 36
months.
II.
Data
The data on children, their mothers, and household characteristics stem from the
Demographic and Health Surveys (DHS). Our sample comprises 56 countries in which the DHS
surveys were conducted between 2001 and 2013. Our selection criterion was that at least one
standard DHS survey was conducted in the country since 2000, and we took only the most recent
survey available online. A list of countries and sample sizes for each survey can be found in
Table A1 in the Annex.
Our sample includes all six world regions, as classified by the World Bank: Sub-Saharan
Africa (SSA; 31 countries), Latin America and the Caribbean (LAC; 8 countries), Europe and
Central Asia (ECA; 7 countries), South Asia (SA; 5 countries), Middle East and North Africa
(MENA; 3 countries), and East Asia and Pacific (EAP; 2 countries). The data contain information
on 262,130 mothers along with their 350,152 children aged below 5 years. Table A2 in the Annex
shows descriptive statistics of all variables used in the analysis.
6
Fernald et al. (2012) find that gradients associated with wealth and education tend to grow in importance over the
first two life-years. Comparable wealth-age patterns seem to hold also for a wide array of child development
measures in data from Madagascar (see Fernald et al., 2011).
5
The dependent variables in our analysis are the standard measures of nutritional status of
children, i.e. z-scores of height-for-age (HAZ) and weight-for-age (WAZ). We use the new WHO
2006 child growth standards, which are now widely used and recommended for evaluating
breastfed children. Saha et al. (2009) show that the use of alternative reference populations such
as the NCHS may lead to misclassification of periods of growth faltering. Children in our sample
are, on average, 1.28 standard deviations shorter for their age than the median of the WHO child
growth standard, as can be seen in Table A2. In terms of weight-for-age, the average z-score is 0.85, i.e. these children are, on average, 0.85 standard deviations below the median weight in the
reference population. Age ranges from 0 to 59 months with an average of 28.6 and median of 28
months. The age distribution of children in our sample is roughly uniform.7
Potential explanatory variables of growth faltering are measured at the child, mother,
household, and country level. Children’s characteristics that are identified as potential
determinants of child growth, and possibly faltering, are gender, birth order (e.g. Rutstein, 2005),
and short preceding birth interval (Dewey and Cohen, 2007). Birth interval is considered short if
it is equal to or lower than the median in the overall sample. Mother’s characteristics of
importance are her height (see, for instance, Monden and Smits, 2009), mortality of her children
(to proxy the immediate health environment), age at first marriage, and literacy and education
(see Bicego and Boerma, 1993; Desai and Alva, 1998).
Household characteristics of interest are differences in parental education (Thomas et al.,
1990), household size, access to electricity (Thomas and Strauss, 1992), wealth (Fernald et al.,
2012), and area of residence (Fink et al., 2014). Wealth in the DHS data is measured by an asset
7
Histograms are available on request from the authors.
6
index which is constructed separately for each survey using a principal component analysis.
Based on this index, households in each survey are ranked into five quintiles.
Previous evidence suggests that child health is linked to macroeconomic indicators such
as the GDP (Ravallion, 1990; Smith and Haddad, 2002; Haddad et al., 2003). We use countrylevel GDP per capita, as well as under-5 mortality in the country as proxies of macroeconomic
conditions; both variables are measured at the time of birth. We take GDP per capita in constant
2005 international dollars, adjusted for differences in purchasing power parity (PPP) across
countries. Under-5 mortality in the DHS data is calculated using a synthetic cohort-life table
approach covering 5 years preceding the survey.
The data at hand are cross-sectional, i.e. children are observed only once and they are not
followed over time. Hence, we compare children of different ages within a survey and household.
Similarly to Shrimpton et al. (2001), we currently do not have comparable panel data across
many countries and regions of the world to document growth faltering on a larger scale. Panel
data would allow following individuals over time and accounting more fully for child-specific
heterogeneity or mortality shocks.
Figure 1 in the Annex underlines that the pattern of growth faltering differs substantially
for height and weight. Whereas the standardized height-for-age (HAZ) decreases substantially in
the first 18 months of life, it remains fairly stable afterwards, with only minor fluctuations. Zscores of weight-for-age (WAZ), on the other hand, decrease smoothly and the rate of decrease
slows down with age. Note that HAZ shows bumps around certain age categories. Also Victora et
al. (2010, p. 473) noted “noticeable bumps just after 24, 36, and 48 months”. We hypothesize that
this very likely stems from rounding of age in months done by the enumerators or parents. Since
height changes slower than weight, it is more sensitive to even small inaccuracies in age
7
reporting. Nevertheless, this issue has no noticeable repercussions on the subsequent regression
analysis.
Based on this difference in the observed patterns, the relationship between age and HAZ
is modelled differently from the relationship between age and WAZ in our regression models. In
particular, WAZ is specified as a quadratic function of age, thus allowing the rate of decline to
decrease with age. HAZ, on the other hand, is specified as a linear function of age with a
structural break. This procedure enables estimating different slopes before and after certain age
threshold and is consistent with the observed pattern of a steep negative slope followed by a
horizontal line. The age threshold, at which the structural break is specified in regressions, was
estimated at 18 months of life in a regression model.8
III.
Descriptive Evidence of Growth Faltering
Before moving to the regression analysis, we offer three pieces of descriptive evidence on
the drivers of growth faltering: (i) we explore growth faltering across regions; (ii) we relate the
severity of growth faltering to a region’s and country’s stage of economic development; and (iii)
we plot growth faltering by demographic characteristics and socio-economic conditions. We find
that while regional and country differences do explain a large part of the observed patterns,
household and maternal characteristics have a striking influence.
Growth Faltering Across Regions
There are substantial differences in growth faltering across regions of the world, see
Figure 2. This figure presents a local polynomial smooth9 of HAZ and WAZ across regions. In
general, the shape of growth faltering is roughly similar but its extent differs greatly: whereas
children in MENA, ECA, and LAC regions suffer the least from growth faltering, the situation is
8
Results for the structural break model are available on request from the authors.
9
We specify local polynomials of degree three using « lpoly » in STATA 13.
8
significantly worse in SSA and South and East Asia. And although the division of world regions
into these groups is plausible, the observed differences between SSA and Asia might be less
obvious. In particular, we note that SSA is marginally better off than South Asia in terms of HAZ
and much better off than East Asia. Clearly, economic development varies widely across these
regions, which leads us to the next piece of descriptive evidence.
Growth Faltering Across Stages of Economic Development
Is the severeness of growth faltering related to a country’s GDP? We find that children
living in developing countries with higher GDP p.c. experience less severe growth faltering. In
Figure 3, we plot growth faltering that is experienced between 0-5 and 18-23 months of life at
country level against economic conditions. Note that this relationship is particularly pronounced
for countries in SSA. In a robustness check presented in Figure A1 in the Annex, the intensity of
growth faltering is measured as the predicted increase in HAZ in the first 18 months of life.10 In
both figures, we can clearly see that higher GDP p.c. is associated with a significantly higher zscore, i.e. with less growth faltering. To be more precise, the difference in the predicted growth
faltering in HAZ in the first 18 months is 1.34 between the richest and the poorest country
(Figure A1), which is more than the average HAZ in the sample (1.28, see Table A2). Similarly,
predicted differences between the richest and the poorest country amount to 1.20 in HAZ and
1.25 in WAZ when the increase in growth faltering between the 1st and 3rd half-year of life is
considered (Figure 3).
Demographic and Socio-economic Drivers of Growth Faltering
10
We run country level regressions of HAZ on 3 explanatory variables: age measured in months, a dummy variable
“18 months plus” which takes the value 1 if the child is at least 18 months old and 0 otherwise, and their interaction.
The coefficient of “age” measures the slope of growth faltering in the first 18 months of life. We measure intensity of
growth faltering as coefficient of “age” multiplied by 18 (months).
9
In the last piece of descriptive evidence, we graph possible demographic and socioeconomic drivers of growth faltering. For the following figures, we chose commonly used
proxies for nutrition-relevant characteristics measured at the child (Figure 4), mother (Figure 5),
parental (Figure 6), and household level (Figure 7).11 Across the board, we find that growth
faltering does vary by conditions at the aforementioned levels. In particular, lack of maternal
education, low maternal age at marriage, and lack of access to infrastructure are the leading
socio-economic drivers of growth faltering.
First, does growth faltering vary by gender and birth order? The top panel of Figure 4
suggests that growth faltering does not differ dramatically for female and male children. At first,
girls and boys start off at roughly similar levels in terms of HAZ and WAZ. Afterwards, growth
faltering sets in and it is slightly more pronounced among male children. These gender
differences are, however, small in magnitude and they level out after approximately 40 months,
in particular when focusing on height-for-age. On the other hand, we do see important differences
when considering birth order in the bottom panel of Figure 4. Initially, birth order has no visible
repercussions on height-for-age. However, HAZ of later born children falters more dramatically
than that of their first born siblings. More importantly, these differences seem to persist even after
faltering slows down, at least in our sample’s age range. Additionally, WAZ of first born children
is substantially lower than that of their later born siblings at birth. However, due to more
pronounced growth faltering in WAZ, later born children become worse off after just a few
months.
Second, is growth faltering stronger in children of short mothers and of mothers that
married at a young age? According to the top panel of Figure 5, children of tall mothers are taller
11
Graphs of growth faltering by area and country-level conditions can be found in the Annex, Figure A2.
10
and heavier already at birth and this difference does not substantially increase with age. On the
contrary, we observe no initial differences in stature of newborns in the bottom panel of Figure 5.
However, the height and weight of children born to mothers that married young falls faster with
age. In this context, the age at marriage can be a proxy for maternal experience and mother’s
bargaining power and social standing in the household.
Third, we take a closer look at growth faltering and parental education in Figure 6. In
general, maternal education is often found to be a strong correlate of child growth which is
confirmed also in the top panel of Figure 6: maternal education dampens growth faltering. In the
bottom panel, we explore if differences in parental education matter. We consider three cases: (i)
the mother is more educated, (ii) equally educated, and (iii) less educated than the child’s father.
These variables can proxy, we conjecture, for female empowerment and educational inequality
among parents, both of which are likely correlates of child health. And indeed, faltering is weaker
when mothers have more education than fathers. In contrast, there is no substantial difference
between children whose mothers are less than or equally educated as fathers.
Finally, we are interested in differences in growth faltering by household size and
infrastructure. Figure 7 displays how the correlation between child growth and age varies by
household size and access to electricity. Although household size does not seem to be strongly
associated with growth faltering, access to electricity, as a proxy for wealth and infrastructure,
turns out to be very important. If we compare children living with and without access to
electricity, we find that the latter suffer much more from growth faltering than the former. What
is more, this difference is the largest among all potential factors.
IV.
Regression Model
So far, we have documented important heterogeneities in growth faltering with respect to
observable predictors of child nutrition. However, it is unclear how robust these patterns are to
11
accounting for unobservable factors and a wide array of covariates. To this end, we propose to
use a mother fixed effects model. This model compares two children of different ages “within the
same mother”, which basically means controlling for mother, household, and country
characteristics. At the same time, we can verify how, say, maternal factors such as education and
height correlate with height-for-age across different age groups. The biggest advantage of this
approach is that it enables us to control not only for observable variables such as mother’s
education and household infrastructure, but also – and more importantly – for mother, household,
and country level unobservable characteristics such as maternal ability, social status of the
household, and political stability in the country.
In order to model the interaction between demographic and socio-economic
heterogeneities and growth faltering in height-for-age (haz) of child k, born to mother m, in
household h, and country c, we specify the following model:
haz
= constant + β childageinmonths
childageinmonths
∗
K
H
C
+ β 18monthsplus
#
" β& + 18monthsplus
+ childageinmonths
∗
∗ 18monthsplus
+β) childageinmonths
K
H
C
∗
K
+
#
K
#
" β$ +
H
C
" β'
H
C
#
" β( ∗ 18monthsplus
+ε
+
where the coefficient vector β captures the main effect of growth faltering associated with the
(1)
child’s age in months. The literature has found this coefficient to be negative (see Victora et al.,
2010). As discussed in Section 2, we model a structural break between age and height with the
dummy variable “18 months plus.” The matrices K, M, H, and C collect characteristics that vary
12
at child (child’s gender, birth order, and country GDP per capita in PPP at child’s birth12),
mother (mother’s height, education, mother suffered any child deaths, mother married young),
household (household size, access to electricity), and country (under-5 mortality) level,
respectively. We present regression results based on 10 out of 15 potential explanatory variables.
The final set of variables was selected based on two criteria: (1) the variable exhibits significant
interaction terms in a univariate regression explaining either HAZ or WAZ, and (2) the
interaction terms of the selected variables remain significant also in the full regression on either
HAZ or WAZ.13
The main purpose of this paper is to estimate the effect of interactions between
demographic and socio-economic characteristics measured at the child, mother, household, as
well as country level, and the child’s age in months on growth faltering. To this end, we interact
child age in months with the observable characteristics K, M, H, and C. The possibly dampening
or enhancing effects of the covariates on growth faltering are captured by the coefficient vector
β& . Similarly, we interact the variables with the structural break indicator “18 months plus,”
storing the associated coefficients in the vector β' . Furthermore, we add two additional
interaction terms to complete the model.
So far, Equation (1) represents a simple OLS model in which many relevant factors might
be unobserved and the coefficients might be therefore biased. In order to account for
unobservable characteristics (measured at mother or higher level) that determine child height, we
decompose the error term in Equation (1) into two parts:
12
ε
+
=ν
+
+ϑ
+
(2)
GDP per capita is classified at the child level because we measure it at the time of a child’s birth. Hence, GDP
varies by a child’s age even within a country.
13
Out of the 15 candidate variables, the following 5 did not fulfill these criteria: short preceding birth interval,
maternal literacy, difference in parental education, household wealth, and area of residence (rural/urban).
13
where ν
+
represents a mother fixed effect. Estimation of Equation (1) with the error term as
specified in Equation (2) corresponds to a “within mother” transformation. As a result of such
transformation, all mother, household, and country specific variables M, H, and C drop out and
Equation (1) is reduced to the following expression:
haz
childageinmonths
= β childageinmonths
K
∗
#
" β& + 18monthsplus
H
C
+ childageinmonths
+ β 18monthsplus
∗
∗ 18monthsplus
+β) childageinmonths
Note that the fourth term in Equation (1) is reduced to K
K
H
C
∗
#
+ ./01+
β$ +
#
K
" β'
H
C
∗ 18monthsplus
#
" β( +ν
+
+ϑ
+
(3)
′β$ . Nevertheless, we are still able
to identify the coefficients of interest β& and β' for all variables K, M, H, and C.
While we focus primarily on results for height-for-age, we also present complementary
evidence using weight-for-age (WAZ) as a dependent variable. The corresponding model after
the “within mother” transformation is specified as follows:
waz
= β childageinmonths
childageinmonths
∗
K
H
C
#
+ β childageinmonths
" β& + childageinmonths
∗
#
+ ./01+
β$ +
K
H
C
#
" β' + ν
+
+ϑ
+
(4)
Finally, note that we cluster standard errors at the mother level in the within-transformed
models with mother fixed effects, and at the cluster level in the simple OLS models.
14
V.
Results
What are the drivers of growth faltering in a regression model? This section suggests that
maternal education and age at marriage as well as proxies of country conditions such as GDP per
capita and under-5 mortality explain a nontrivial part of the observed dip in child growth.
In what follows, we present two sets of regression models for both height and weight-forage. First, we will look at the correlates of child health in a simple OLS model. We present both
the main effects of the variables of interest and their interactions with age. To capture the
functional forms suggested by Figure 1, we include a structural break after 18 months in the
height model following Equations (1) and (3), and we add age squared in the weight model as
specified in Equation (4). Second, we introduce mother fixed effects as defined in Equations (2)(4).14 This allows us to account for mother, household, and country level unobservable
characteristics that might otherwise bias our coefficient estimates.
Consider the results for height-for-age in Table 1. The left panel summarizes the simple
OLS model. The first column presents the main effect of the independent variables in the crucial
period of the first 18 months of life. The second column shows the interactive effects with age,
also in the first 18 months of life, while the third column – the interaction with the structural
break variable “18 months plus” – presents the change in the main effect of the independent
variable after the first 18 months of life. 15
14
In other words, we estimate a model on deviations from maternal means, and we identify coefficients solely by
comparing children “within the same mother,” i.e. by comparing siblings. As a result, we cannot estimate the direct
effect of, say, maternal education on child height. Yet, we are not throwing out the baby with the bathwater because
we can still estimate the effect of maternal education as a function of age, i.e. the coefficient of the interaction term
β& . All in all, the mother fixed effects model is both rigorous and well suited for answering our research question
given that we want to investigate the drivers of growth faltering and not the determinants of child health per se.
Due to the specification of our model with a structural break at 18 months and with several interaction terms, see
Equations (1) and (3), the interpretation of coefficients is as follows: (1) the coefficient of each individual variable 4$
measures whether height-for-age differs significantly in levels by this variable in the first 18 months of life; (2) the
coefficient of the interaction term between each variable and age – 4& – measures whether the correlation between
15
15
The OLS model suggests that height-for-age is correlated with gender, birth order,
maternal height, access to electricity, and country conditions (GDP per capita at birth and under-5
mortality) in the first 18 months of life, see the first column of Table 1. The main coefficients of
interest, which measure whether these variables also have differential effects by age during the
first 18 months, are displayed in the second column. Across the board, both demographic and
socio-economic variables interacted with age are significant. For instance, the importance of
maternal education increases as children age. Furthermore, the correlation between maternal
education and height is much more pronounced after the structural break at 18 months, as can be
seen in the third column. Finally, note that all variables exhibiting significant interaction effects
are significant also individually in the first column of Table 1, except for maternal education and
age at marriage. This means that whereas biological and environmental factors (such as gender,
birth order, maternal height, access to electricity, and overall conditions in the country) are
important correlates of the child’s height from the very beginning, the mother’s purely socioeconomic characteristics (such as her education and age at marriage) gain importance in the
course of the child’s early life.
As mentioned earlier, a simple OLS model suffers from problems related to unobserved
heterogeneity. The mother fixed effects model, on the other hand, is superior because it does
account for a wide range of unobserved characteristics. As shown in the right panel of Table 1, in
columns 4 to 6, not all correlates of child height identified in the OLS model remain significant
also in the more rigorous mother fixed effects specification. In particular, we identify five age
interactions that are highly robust and remain significant and sizeable in magnitude: gender,
height-for-age and age changes with the variable of interest in the first 18 months of life, and (3) the coefficient of the
interaction term between each variable and the “18 months plus” variable – 4' – measures whether the difference in
HAZ increases or decreases in levels after the first 18 months of life.
16
maternal age at marriage, maternal education, under-5 mortality, and GDP per capita. We also
detect that these dynamic age effects β& are so strong that the difference in child height becomes
significantly larger after the structural break at 18 months for all of these variables. Note that the
results are not driven by the fixed effects sample definition16 or model specification.17
Having identified five drivers of growth faltering in height, the next step is to establish
their importance, that is, the magnitudes of the estimated age effects. For example, the coefficient
associated with maternal education (0.015) implies that an increase in mother’s educational level
(e.g. from no education to primary education) leads to an increase in the height-for-age z-score by
0.15 for a 10 month old baby. This effect size corresponds to approximately 12% of the sample
mean in height-for-age z-scores. In addition, due to this strong dynamic effect in the first 18
months, the level effect associated with maternal education increases by 0.231 after reaching 18
months, as can be seen in the last column of Table 1. Interestingly, the effect of postponing a
woman’s marriage is very similar in magnitude to the effect of increasing her education.
Apart from maternal characteristics, country-level variables also matter. In countries
where under-5 mortality is high (i.e. above the sample median), the height-for-age z-score
decreases by 0.27 every 10 months. This 10-month-decrease corresponds to a sizeable 21% of the
sample mean of height-for-age. Additionally, this rather strong dynamic effect in the first 18
16
In a robustness check, we examine whether the observed differences between the left and right panels in Tables 1
and 2 are not driven by different samples. To this end, we reestimate the OLS model on the sample from the mother
fixed effects specification (see Table A3 in the Annex) and we find no substantial differences from the results shown
in the left panels of Tables 1 and 2. Note that the sample in mother fixed effects specification is smaller because only
children with at least one sibling contribute to the variance.
17
We conduct several robustness checks in order to test our model specification. First, we add country fixed effects
to the OLS specification and we find no fundamental differences. The only substantial change is that the main effect
of GDP p.c. becomes much larger: -0.109 in case of HAZ and -0.115 in case of WAZ. Second, we add maternal age
at the time of the survey as an additional covariate into the regressions. This addition has no influence on any of the
interaction terms in the OLS regressions, independently of whether we include country fixed effects or not. Results
in mother fixed effects specification do not change at all because maternal age is a mother specific variable and it is
therefore captured by the mother fixed effects.
17
months leads to an increase in the gap of height-for-age z-scores between high and low mortality
countries by 0.508 in children older than 1.5 years, which constitutes 40% of the mean z-score in
the sample.
While weight and height follow different age patterns, we find similar and even more
striking heterogeneities in weight-for-age, see Table 2. Growth faltering is driven by child,
mother, household, and country level variables, both in the OLS and in the rigorous mother fixed
effects specification. For the sake of comparison with height-for-age, we focus on maternal
education in the fixed effects model. The interaction term between maternal education and child’s
age in months is sizeable and significant. Furthermore, the effect is non-linear, as indicated by the
negative and significant coefficient associated with the squared term.18 Columns 5 and 6 indicate
that an increase in mother’s educational level is correlated with a 0.6% increase for a one month
old baby.19 For a 10 months old baby of the same mother, this effect amounts to 4.9%.20 In terms
of the sample mean of weight-for-age z-scores, the effect of an increase in maternal educational
level corresponds to 0.69% of the sample mean for the 1 month old and to 5.8% for the 10
months old baby, respectively. And how big are the age effects for children living in a country
pestered by high under-5 mortality? As a baby ages by 10 months, its weight-for-age will fall by
0.036. This corresponds to 4.2% of the sample mean in weight-for-age.
To sum up, child, mother, household, and country level variables are important drivers of
growth faltering. In particular, maternal education and age at marriage as well as country
variables (GDP p.c. and under-5 mortality) seem important. These variables were identified as
18
Note that we multiplied the square coefficients by 1,000 in order to ease the readability of the table.
19
One month old baby: 0.006 x 1-(0.107/1,000) x 1 x 1 = 0.006
20
Ten month old baby: 0.006 x 10-(0.107/1,000) x 10 x 10 = 0.049
18
strong correlates of child health also in studies that do not take into account age differences.21
Our results, however, underline that age heterogeneities in these correlations should be accounted
for. In other words, there is evidence of important health gradients. The understanding of such
gradients could be crucial for designing better informed policies which can then lead to
improvements in child height and weight.
VI.
The Main Drivers Of Growth Faltering
Which group of factors is the main driver of growth faltering – mother (household) or
country level variables? So far we have shown that growth faltering differs across regions and by
individual characteristics of the child, mother, household, or country (graphs in Section 3) and we
quantified these differences (regression analyses in Section 5).
In this section, we combine approaches from Sections 3 and 5 to show visually that the
inclusion of mother fixed effects is crucial for the analysis of growth faltering. Since graphs in
Section 3 represent merely a bivariate analysis where growth faltering is graphed by one
demographic or socio-economic characteristic at a time, we now move to a multivariate analysis
and include several covariates at once, see Figure 8. We first graph growth faltering without any
covariates (blue line) and then add variables from our regression analyses as controls (red line),
country fixed effects (black line), and finally mother fixed effects (green line).22
Clearly, mother fixed effects have the biggest impact on the shape of growth faltering. In
particular, the comparison of siblings (i.e. including mother fixed effects) mitigates the observed
21
On education, see for instance Bicego and Boerma (1993) and Desai and Alva (1998). On macroeconomic
conditions, see e.g. Ravallion (1990), Smith and Haddad (2002), and Haddad et al. (2003).
22
Given that local polynomial smooths cannot be estimated while controlling for additional covariates in STATA,
we take a different approach. First, we regress HAZ and WAZ on a set of variables. Then we save residuals from this
regression and graph them with a local polynomial smooth in Figure 8. The specific sets of variables are the
following: no variables (blue line), 10 variables from our regression analyses (red line), 10 variables plus country
fixed effects (black line), and 10 variables plus mother fixed effects (green line).
19
severeness of growth faltering in height in the first 18 months. Additionally, we notice a much
stronger reversal of growth faltering in later childhood: after 36 months, the nutritional status in
terms of both HAZ and WAZ improves much more than in specifications without mother fixed
effects.
VII.
Discussion and Conclusion
Undernutrition along with growth faltering still hamper the socio-economic progress of
many developing countries. Although the percentage of stunted children fell from 39.7% in 1990
to 26.7% in 2010 across the globe, and although it is expected to further fall to 21.8% by 2020,
most of this decrease is reportedly driven by improvements in Asia. The proportion of stunted
children in Africa has been, on the other hand, stagnating at approximately 40% and due to the
persistent population growth there, the absolute number of stunted children has been actually
increasing (see de Onis et al., 2012). Despite this recent decline in the fraction of stunted children
in some regions, our paper underlines that growth faltering is still a widespread phenomenon in
many developing countries, and not only an African issue.
Our contribution to the debate on undernutrition consists mainly in identifying the drivers
of growth faltering in early childhood. Notably, we pinpoint a wide range of drivers in a large
sample of developing countries using more rigorous estimation methods than was done in the
past.
Gains in height and weight stagnate during the first years of life among many children in
our sample. As we have shown, this faltering process is most likely driven by observed and
unobserved socio-economic conditions at the mother and household level. And this
heterogeneous faltering during the first “1000 days” of life rightly deserves attention because the
velocity of growth (or of its faltering) will likely impact the speed of human capital accumulation
20
in later life. These results can inform the targeting and timing of policy interventions that aim at
fighting undernutrition and poverty.
Ultimately, our results contribute also to a deeper understanding of widely used
anthropometric measures. On one hand, anthropometric indicators such as HAZ and WAZ are
often used in development economics to measure the distribution of resources within and
between households (Thomas, 1994) or to assess stages of development and standards of living
across the globe (Deaton, 2007). On the other hand, growth charts such as those issued by the
WHO are also “used universally in pediatric care” across the globe (de Onis, 2004, p. 461). This
clearly demonstrates how influential the anthropometric measures are, both in practice and in the
academic arena. Given their importance, it is crucial to understand what these indicators measure
and what they might fail to capture.
That said, it would be interesting (i) to investigate undernutrition patterns in children that
are older than five years, and (ii) to use alternative measurements of health. For instance,
Cameron and Williams (2009) detect no significant age heterogeneities in the correlation between
household income and self-reported health (rather than anthropometric outcomes) for Indonesian
children between 0 and 14 years.
Finally, it is possible that children can recover from spells of faltering. As Crookston et al.
(2013, p. 1555) underline, a boost in “child growth after early faltering might have significant
benefits on schooling and cognitive achievement. Hence, although early interventions remain
critical, interventions to improve the nutrition of preprimary and early primary school-age
children also merit consideration.” Whereas our paper adds to anthropmetric evidence
substantiating the need of early life interventions, future research could further elaborate on the
impact of interventions in late childhood across a wider array of measures.
21
VIII.
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24
Figures
-1.5
Z-score
-1
-.5
0
.5
Growth Faltering
-2
IX.
0
10
20 30 40
Age in months
HAZ
50
60
WAZ
Figure 1: Growth faltering of children in terms of height-for-age (HAZ) and weight-forage (WAZ) z-scores.
Note: Dashed line represents 95% confidence interval.
25
.5
-2
-2.5
-2.5
-2
Height-for-age z-score
-1.5
-1
-.5
0
Weight-for-age z-score
-1.5
-1
-.5
0
.5
Growth Faltering by Region
0
10
20
30
40
Age in months
50
60
0
10
EAP
ECA
LAC
MENA
SA
SSA
20
30
40
Age in months
50
60
Figure 2: Growth faltering in six regions of the world.
Note: Regions are abbreviated as follows: East Asia and Pacific (EAP), Europe and Central Asia (ECA),
Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), South Asia (SA), SubSaharan Africa (SSA).
26
1
0
-2
-3
-3
-2
HAZ
-1
WAZ
-1
0
1
Growth Faltering between 0-5 months and 18-23 months
6
7
8
9
Logarithm of GDP p.c.
ECA
EAP
10
6
LAC
MENA
7
8
9
Logarithm of GDP p.c.
SA
10
SSA
Figure 3: Increase in growth faltering between 0-5 and 18-23 months of life by country
and region.
Notes: Increase in growth faltering is measured as the difference in the average HAZ (or WAZ) score in the
country between children aged 18-23 months and children aged 0-5 months. The line represents a quadratic
fit. See Figure 2 for legend.
27
Growth Faltering by Children's Characteristics
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by gender
0
10
20
30
40
Age in months
50
60
0
Female
10
20
30
40
Age in months
50
60
20
30
40
Age in months
50
60
Male
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by birth order
0
10
20
30
40
Age in months
50
60
0
First born
10
Second born
Later born
Figure 4: Growth faltering of children in terms of height-for-age and weight-for-age by
gender and birth order.
28
Growth Faltering by Mother's Characteristics
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by mother's height
0
10
20
30
40
Age in months
50
60
0
Short mother
10
20
30
40
Age in months
50
60
50
60
Tall mother
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by mother's age at first marriage
0
10
20
30
40
Age in months
50
60
0
Mother married young
10
20
30
40
Age in months
Mother married older
Figure 5: Growth faltering of children in terms of height-for-age and weight-for-age by
maternal height and age at first marriage.
29
Growth Faltering by Parental Education
.5
Weight-for-age z-score
-1
-.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
0
.5
by mother's education
0
10
20
30
40
Age in months
50
60
0
10
20
30
40
Age in months
No education
Primary
Secondary
Higher education
50
60
50
60
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by difference in parental education
0
10
20
30
40
Age in months
50
60
0
Mother less educated
10
20
30
40
Age in months
Mother equally educated
Mother more educated
Figure 6: Growth faltering of children in terms of height-for-age and weight-for-age by
maternal literacy and differences in parental education.
30
Growth Faltering by Household Characteristics
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by household size
0
10
20
30
40
Age in months
50
60
0
Small household
10
20
30
40
Age in months
50
60
50
60
Large household
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by access to electricity
0
10
20
30
40
Age in months
50
60
0
No electricity
10
20
30
40
Age in months
Electricity
Figure 7: Growth faltering of children in terms of height-for-age and weight-for-age by
household size and access to electricity.
31
1.5
1
0
-.5
-.5
0
HAZ Residual
.5
WAZ Residual
.5
1
1.5
Residuals of Z-scores
0
10
20
30
40
Age in months
50
60
0
10
20
30
40
Age in months
1. Unconditional
2. Adding explanatory variables
3. Adding country fixed effects
4. Adding mother fixed effects
50
60
Figure 8: Residuals of height-for-age (HAZ) and weight-for-age (WAZ) z-scores.
Note: Local polynomial smooths of residuals from regressions of HAZ and WAZ on 4 sets of
variables: no explanatory variables (blue line), 10 variables from our regression analyses (red
line), 10 variables plus country fixed effects (black line), and 10 variables plus mother fixed
effects (green line).
32
X.
Tables
Table 1: Regression results for height-for-age
Specification
OLS
Coefficient
MFE
Variable
Age *
Variable
Age 18+ *
Variable
Variable
Age *
Variable
Age 18+ *
Variable
Child is a girl
0.111*
(0.026)
0.009*
(0.002)
0.205*
(0.035)
0.106*
(0.040)
0.011*
(0.004)
0.188*
(0.055)
Birth order
0.018*
(0.007)
-0.002*
(0.001)
-0.033*
(0.010)
-0.788*
(0.025)
0.000
(0.001)
-0.004
(0.015)
Mother is short
-0.457*
(0.027)
-0.011*
(0.003)
-0.066+
(0.035)
-0.005
(0.004)
0.025
(0.050)
No siblings died
0.064+
(0.038)
-0.001
(0.004)
-0.011
(0.050)
0.006
(0.005)
0.037
(0.069)
Mother married young
0.027
(0.028)
-0.006*
(0.003)
-0.127*
(0.036)
-0.015*
(0.004)
-0.251*
(0.053)
Mother's education
0.016
(0.017)
0.009*
(0.002)
0.143*
(0.023)
0.015*
(0.003)
0.231*
(0.033)
Small household
0.036
(0.028)
-0.000
(0.003)
-0.012
(0.037)
0.005
(0.004)
0.021
(0.055)
Access to electricity
0.173*
(0.033)
0.006+
(0.003)
0.095*
(0.044)
0.007
(0.005)
0.113+
(0.060)
Low under-5 mortality
-0.098*
(0.037)
0.016*
(0.003)
0.462*
(0.049)
0.027*
(0.005)
0.508*
(0.066)
GDP p.c. PPP at birth (in 1000$)
-0.038*
(0.007)
0.003*
(0.001)
0.031*
(0.009)
0.002+
(0.001)
0.040*
(0.013)
Variable
Observations
Observations contributing
to variance
R-squared
-0.072*
(0.030)
280,983
280,983
280,983
135,158
0.141
0.206
Notes: OLS and mother fixed effects (MFE) estimations. Dependent variable is z-score of height-for-age. All explanatory
variables except for birth order, mother's education, and GDP p.c. are binary. Mother's education is measured in 4 levels: no
education, primary, secondary, and higher education. "Age 18+" is a dummy variable that takes value 1 if child is at least 18
months old, 0 otherwise. Coefficients of age, "age 18+", "age * age 18+", "age * age 18+ * variable", and constant are estimated
but not shown. Coefficients of variables that do not vary among siblings cannot be estimated in MFE specification. Standard
errors, shown in parentheses, are clustered at the cluster level in OLS and at the mother level in MFE specification. Significance
levels are marked as follows: * p<0.05, + p<0.1.
33
Table 2: Regression results for weight-for-age
Specification
OLS
Coefficient
MFE
Variable
Age *
Variable
Age squared
* Variable
Variable
Age *
Variable
Age squared
* Variable
Child is a girl
0.155*
(0.016)
0.002+
(0.001)
-0.121*
(0.019)
0.195*
(0.024)
-0.001
(0.002)
-0.071*
(0.029)
Birth order
0.030*
(0.005)
-0.002*
(0.000)
0.026*
(0.005)
-0.592*
(0.018)
-0.002*
(0.000)
0.042*
(0.008)
Mother is short
-0.406*
(0.017)
-0.005*
(0.001)
0.060*
(0.019)
-0.005*
(0.002)
0.063*
(0.025)
No siblings died
0.155*
(0.023)
-0.007*
(0.002)
0.083*
(0.026)
-0.009*
(0.002)
0.131*
(0.035)
Mother married young
-0.038*
(0.017)
-0.006*
(0.001)
0.091*
(0.020)
-0.006*
(0.002)
0.089*
(0.026)
Mother's education
0.140*
(0.011)
0.006*
(0.001)
-0.085*
(0.012)
0.006*
(0.001)
-0.107*
(0.016)
Small household
0.136*
(0.017)
-0.004*
(0.001)
0.051*
(0.020)
-0.004*
(0.002)
0.069*
(0.027)
Access to electricity
0.004
(0.021)
0.009*
(0.002)
-0.139*
(0.024)
0.006*
(0.002)
-0.106*
(0.029)
Low under-5 mortality
0.230*
(0.024)
0.005*
(0.002)
-0.132*
(0.025)
0.005*
(0.002)
-0.144*
(0.032)
GDP p.c. PPP at birth (in 1000$)
0.014*
(0.004)
0.002*
(0.000)
-0.016*
(0.005)
0.002*
(0.000)
-0.023*
(0.007)
Variable
Observations
Observations contributing
to variance
R-squared
-0.112*
(0.023)
290,765
290,765
290,765
145,284
0.138
0.094
Notes: OLS and mother fixed effects (MFE) estimations. Dependent variable is z-score of weight-for-age. All explanatory variables
except for birth order, mother's education, and GDP p.c. are binary. Mother's education is measured in 4 levels: no education, primary,
secondary, and higher education. Coefficients of age, age squared, and constant are estimated but not shown. Coefficients and
standard errors of "age squared * variable" are multiplied by 1,000. Coefficients of variables that do not vary among siblings
cannot be estimated in MFE specification. Standard errors, shown in parentheses, are clustered at the cluster level in OLS and at the
mother level in MFE specification. Significance levels are marked as follows: * p<0.05, + p<0.1.
34
Annex
Coefficient of Age * 18 Months
-2
-1
0
Intensity of Growth Faltering in the first 18 months
-3
XI.
6
7
ECA
EAP
8
Logarithm of GDP p.c.
LAC
MENA
9
SA
10
SSA
Figure A1: Intensity of growth faltering by country and region.
Notes: Intensity of growth faltering is measured as the slope of HAZ in the first 18 months of life at country
level multiplied by 18. The line represents a quadratic fit. See Figure 2 for legend.
35
Growth Faltering by Area Characteristics
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by area of residence
0
10
20
30
40
Age in months
50
60
0
Rural area
10
20
30
40
Age in months
50
60
50
60
Urban area
.5
0
-1.5
-2
-2
-1.5
Height-for-age z-score
-1
-.5
Weight-for-age z-score
-1
-.5
0
.5
by GDP p.c. PPP at birth
0
10
20
30
40
Age in months
50
60
Low GDP p.c. PPP at birth
0
10
20
30
40
Age in months
High GDP p.c. PPP at birth
Figure A2: Growth faltering of children in terms of height-for-age and weight-for-age by
residence (rural vs. urban) and GDP p.c..
36
Table A1: Nationally representative DHS surveys included in the analysis
Region
Survey
Year
Albania
ECA
2008-09
1,283
1,524
Armenia
ECA
2010
1,126
1,414
Azerbaijan
ECA
2006
1,585
2,090
Bangladesh
SA
2011
6,890
7,951
Benin
SSA
2011-12
8,304
11,539
Bolivia
LAC
2008
6,061
7,854
Burkina Faso
SSA
2010
4,961
6,736
Burundi
SSA
2010-11
2,346
3,504
Cambodia
EAP
2010-11
3,101
3,824
Cameroon
SSA
2011
3,633
5,195
Chad
SSA
2004
3,202
4,679
Colombia
LAC
2009-10
13,374
16,055
Congo
SSA
2011-12
3,290
4,535
Cote d'Ivoire
SSA
2011-12
2,477
3,306
Democratic Republic of the Congo
SSA
2007
2,482
3,696
Dominican Republic
LAC
2007
7,559
9,540
Egypt
MENA
2008
7,859
10,471
Ethiopia
SSA
2011
7,068
9,941
Gabon
SSA
2012
2,550
3,504
Ghana
SSA
2008
1,951
2,559
Guinea
SSA
2012
2,427
3,215
Guyana
LAC
2009
1,325
1,745
Haiti
LAC
2012
3,190
4,046
Honduras
LAC
2011-12
8,236
10,049
India
SA
2005-06
32,963
43,736
Jordan
MENA
2012
4,286
6,386
Kenya
SSA
2008-09
3,774
5,367
Kyrgyz Republic
ECA
2012
3,037
4,096
Lesotho
SSA
2009-10
1,372
1,673
Liberia
SSA
2006-07
3,464
4,592
Madagascar
SSA
2008-09
3,737
5,043
Malawi
SSA
2010
3,587
4,911
Maldives
SA
2009
2,378
2,693
Country
37
Number of
Mothers
Number of
Children
Mali
SSA
2006
8,243
11,641
Moldova
ECA
2005
1,245
1,392
Morocco
MENA
2003-04
4,515
5,681
Mozambique
SSA
2011
7,034
9,719
Namibia
SSA
2006-07
3,172
3,865
Nepal
SA
2011
1,892
2,363
Nicaragua
LAC
2001
4,686
6,180
Niger
SSA
2012
3,385
5,161
Nigeria
SSA
2008
16,063
23,075
Pakistan
SA
2012-13
2,461
3,636
Peru
LAC
2009
7,884
9,442
Rwanda
SSA
2010-11
3,068
4,128
Sao Tome and Principe
SSA
2008-09
1,328
1,708
Senegal
SSA
2010-11
2,805
3,937
Sierra Leone
SSA
2008
1,766
2,279
Swaziland
SSA
2006-07
1,700
2,112
Tajikistan
ECA
2012
3,397
4,771
Tanzania
SSA
2009-10
4,954
6,969
Timor-Leste
EAP
2009-10
5,571
8,477
Turkey
ECA
2003-04
3,177
4,163
Uganda
SSA
2011
1,418
2,116
Zambia
SSA
2007
3,772
5,429
Zimbabwe
SSA
2010-11
3,716
4,439
262,130
350,152
Total
2001-2013
Note: Regions are labelled as follows: Europe and Central Asia (ECA), East Asia and Pacific (EPA), Latin
America and the Caribbean (LAC), Middle East and North Africa (MENA), South Asia (SA), Sub-Saharan
Africa (SSA).
38
Table A2: Descriptive statistics
Variable
Value
Sample size
Means of dependent variables
Height-for-age z-score
-1.28
330,515
Weight-for-age z-score
-0.85
341,520
Female
49.0%
350,152
First born
26.1%
350,152
Second born
22.6%
350,152
Third or later born
51.3%
350,152
Short preceding birth interval *
50.0%
258,671
Mother is short *
50.6%
336,451
No sibling ever died
79.0%
350,152
Mother married young *
58.6%
337,914
Mother is literate
54.1%
306,376
Mother has no education
33.6%
350,116
Mother has primary education
30.7%
350,116
Mother has secondary education
29.1%
350,116
6.6%
350,116
Mother is less educated than father
26.4%
331,268
Mother is equally educated as father
60.2%
331,268
Mother is more educated than father
13.4%
331,268
Small household *
57.5%
350,152
Household has access to electricity
49.2%
327,373
1st wealth quintile (poorest)
25.0%
343,972
2nd wealth quintile
21.6%
343,972
3rd wealth quintile
19.9%
343,972
4th wealth quintile
18.1%
343,972
5th wealth quintile (richest)
15.4%
343,972
Rural area
64.8%
350,152
Country with low under-5 mortality *
45.1%
350,152
Country with low GDP p.c. PPP at child's birth *
50.1%
345,713
28.6
350,152
3.2
350,152
41.9
258,671
Fraction of children with following characteristics
Mother has tertiary education
Means of continuous and underlying explanatory variables
Child's age in months
Birth order
Preceding birth interval in months
39
Mother's height in meters
Mother's age at marriage in years
Household size (number of household members)
Country-level under-5 mortality per 1,000 live births
Country-level GDP p.c. PPP in international dollars
1.6
336,451
18.3
337,914
6.8
350,152
79.9
350,152
2725.3
345,713
Notes: Variables marked with an asterisk take value 1 if the corresponding underlying variable is
equal to or lower than the overall sample median; 0 otherwise. Variable "country with low under-5
mortality" is an exception: it refers to the median in the sample of 56 countries, not in the overall
sample of children. Descriptive statistics of underlying variables are shown in the third panel.
40
Table A3: Robustness check - OLS estimation on MFE sample
Dependent variable
Height-for-age (HAZ)
Coefficient
Weight-for-age (WAZ)
Variable
Age *
Variable
Age 18+ *
Variable
Variable
Age *
Variable
Age squared
* Variable
Child is a girl
0.108*
(0.035)
0.010*
(0.003)
0.179*
(0.049)
0.169*
(0.022)
0.002
(0.002)
-0.123*
(0.027)
Birth order
-0.001
(0.011)
-0.001
(0.001)
-0.023
(0.015)
0.043*
(0.007)
-0.004*
(0.000)
0.073*
(0.007)
Mother is short
-0.488*
(0.037)
-0.007*
(0.004)
-0.022
(0.049)
-0.404*
(0.022)
-0.005*
(0.002)
0.061*
(0.025)
No siblings died
0.072
(0.050)
0.003
(0.005)
-0.016
(0.067)
0.225*
(0.030)
-0.015*
(0.002)
0.206*
(0.035)
Mother married young
0.043
(0.039)
-0.008*
(0.004)
-0.130*
(0.051)
-0.054*
(0.023)
-0.004*
(0.002)
0.060*
(0.026)
Mother's education
-0.014
(0.025)
0.011*
(0.002)
0.148*
(0.033)
0.154*
(0.015)
0.004*
(0.001)
-0.064*
(0.017)
Small household
0.039
(0.040)
-0.004
(0.004)
-0.074
(0.054)
0.177*
(0.024)
-0.010*
(0.002)
0.155*
(0.028)
Access to electricity
0.163*
(0.046)
0.002
(0.004)
0.055
(0.060)
0.016
(0.028)
0.005*
(0.002)
-0.074*
(0.031)
Low under-5 mortality
-0.066
(0.051)
0.015*
(0.005)
0.411*
(0.067)
0.215*
(0.031)
0.006*
(0.002)
-0.135*
(0.034)
GDP p.c. PPP at birth (in 1000$)
-0.041*
(0.010)
0.003*
(0.001)
0.032*
(0.013)
0.009
(0.006)
0.003*
(0.000)
-0.024*
(0.007)
Variable
Observations
Observations contributing
to variance
R-squared
280,983
290,765
135,158
145,284
0.148
0.123
Notes: OLS estimation. The sample is restricted to observations that contribute to variation in the corresponding mother fixed
effects (MFE) estimation. Model specifications are identical to those from Tables 2 and 3. In the WAZ regression, coefficients
and standard errors of "age squared * variable" are multiplied by 1,000. Standard errors, shown in parentheses, are clustered
at the cluster level. Significance levels are marked as follows: * p<0.05, + p<0.1.
41