CUNY Brooklyn College Economics Paper


The paper should be about how women’s education affects child health. The actual paper should address a more narrowly defined topic. Was there anything you would have liked to learn on this topic? Engage with at least five readings from outside academic sources (these should be peer reviewed journal articles or similar, not Wikipedia entries, blog posts, or articles in the popular press). Demonstrate your ability to synthesize information from the additional readings you cite along with the class readings. Properly cite your sources. Appropriateness of the topic. Use and citation of at least 5 outside academic sources. Application of economic theory. Style and clarity.You should be looking for peer-reviewed articles from Economics journals for all, or at least most, of your sources. (If you can’t find five relevant sources in economics journals, try to find journals in other social science disciplines.)A few places to look for articles:Demography: of Development Economics: Economics: of Health Economics: and Human Biology: Science and Medicine: reading is provided below:

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Why Does Mother’s Schooling Raise Child Health in Developing Countries? Evidence from
Author(s): Paul Glewwe
Source: The Journal of Human Resources, Vol. 34, No. 1 (Winter, 1999), pp. 124-159
Published by: University of Wisconsin Press
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Why Does Mother’s Schooling
Raise Child Health in Developing
Evidence from Morocco
Paul Glewwe
Mother’s education is often found to be positively correlate
health and nutrition in developing countries, yet the causal
are poorly understood. Three possible mechanisms are: (1)
tion directly teaches health knowledge to future mothers;
numeracy skills acquired in school assist future mothers in
and treating child health problems; and (3) Exposure to mo
from formal schooling makes women more receptive to mo
treatments. This paper uses data from Morocco to assess th
by these different mechanisms. Mother’s health knowledge
to be the crucial skill for raising child health. In Morocco,
edge is primarily obtained outside the classroom, although
using literacy and numeracy skills learned in school; there
that health knowledge is directly taught in schools. This sugg
teaching of health knowledge skills in Moroccan schools co
tially raise child health and nutrition in Morocco.
I. Introduction
Child health is a key indicator of the quality of life in developing
countries. Mother’s years of education is often positively associated with improved
child health and nutritional status (see Behrman, 1990). There are a variety of mechaPaul Glewwe is a Senior Economist in the Development Research Group at the The World Bank. He
would like to thank Hanan Jacoby, Martin Ravallion, and two anonymous reviewers for helpful comments on previous drafts, and Nauman Ilias for excellent computational assistance. This research was
supported by a grant from the World Bank Research Committee (RPO 679-84). The findings, interpretations and conclusions expressed in this paper are entirely those of the author. They do not necessarily
represent the views of the World Bank, its Executive Directors, or the countries they represent. The
data used in this article can be obtained beginning May, 1999, through April, 2002, from Paul
Glewwe, The World Bank, 1818 H Street NW, Washington, DC 20433.
[Submitted October 1996; accepted February 1998]
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Glewwe 125
nisms through which mother’s education could raise child health: (1) Direct
sition of basic health knowledge in school may provide future mothers with in
mation useful for diagnosing and treating child health problems; (2) Literac
numeracy skills learned in school may enhance mothers’ abilities to treat ch
nesses, conditional on health knowledge, and also should help mothers increase
stock of health knowledge after leaving school; and (3) Exposure to modem s
in general via schooling may change women’s attitudes toward traditional me
of raising children and treating their health problems.
This paper attempts to assess the relative importance of these three mechani
using the 1990-91 Moroccan Enquete Nationale des Niveaux de Vie des Men
(ENNVM). Knowledge of the relative importance of these mechanisms can
important policy implications. For example, if the main impact of education
from directly raising mothers’ basic health knowledge, such knowledge shou
taught in schools as early as possible (that is, before girls drop out) and per
should also be taught in special education courses for women of child-beari
who have already left school.
The paper is organized as follows. Section II reviews, in broad terms, the im
of mother’s education on child health and briefly reviews the recent literature
tion III discusses the data and the estimation strategy. Section IV presents the em
cal results. Section V decomposes the total impact of mother’s schooling on
health. Section VI summarizes the results.
II. Mother’s Education and Child Health
A. General Discussion
Figure 1 provides a schematic framework for thinking about the determinants of
child health and nutritional status. As seen at the bottom of that figure, child health
is ultimately determined by three distinct sets of factors: 1. Health and nutritional
inputs provided by the household (arrow i); 2. The local health environment (arrow
f); and 3. The child’s health endowment (arrow h). Health and nutritional inputs
provided by the household include prenatal care, breastmilk, breastmilk substitutes
such as infant formula, calories from adult foods (for weaned children), medicines,
and medical care. The quality of household drinking water sources, toilet facilities,
and other hygienic conditions can also be considered as health and nutritional inputs
provided by the household. The local health environment consists of all community
characteristics that directly affect child health and are generally beyond the control
of the parents, such as prevalence of parasites and the incidence of contagious disease
among the general population. Finally, the child health endowment consists of all
components of the child’s genetic inheritance that have implications for his or her
Household health and nutritional inputs are determined by household decisions
that reflect the characteristics of the household, the local community, and the child,
such as (initial) household assets, parental schooling, community economic and
health-related characteristics (such as the availability and prices of medical services),
and each child’s health endowment. This paper focuses on parental schooling, partic-
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Parental Health Household
Knowledge Income
abc v I / acd
Household Health and
Nutritional Inputs
Child Health
Figure 1
The Determinants of Child Health
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Glewwe 127
ularly mother’s schooling; father’s schooling, apart from its income effect,
likely to be important for maintaining children’s health.
Schultz (1984) argues that mother’s education can influence child health in
ways:1 (1) Education may lead to a more efficient mix of health goods used t
duce child health; (2) Better educated mothers may be more effective at prod
child health for a given amount and mix of health goods; (3) Schooling can
parents’ preferences in systematic ways-for example, educated mothers tend to
for fewer but healthier children; (4) More schooling should raise family inc
either through higher wages or increased productivity in self-employment,
should improve child health status; and (5) Education raises the opportunity
of time, which tends to increase the time mothers spend working outside the
and thus reduce time for child care-this effect of schooling could reduce
health by reducing both maternal time devoted to child care and duration
breastfeeding. In Figure 1, the third and fourth pathways are represented b
arrows a-a’ (and also by a-a”) and acd (via a-a”‘ and c-c”‘), respectively.2 The
two pathways, which reflect the direct effect of the health knowledge and cog
skills that education imparts, have received little attention in the literature. W
it about schooling that makes mothers more efficient in producing child health
Figure 1 presents two mechanisms through which schooling could influenc
choice of health and nutritional inputs via the knowledge and skills it prov
First, schools may directly teach effective health care practices to students
pathway is denoted by b-abc. For example, the impact of diarrhea on child h
can be reduced by oral rehydration therapy (ORT), which can be taught eve
primary schools (see Cash 1983). Second, schooling can influence child health
through the cognitive skills imparted, such as literacy and numeracy. Literate
ers are better able to read written instructions for treating of childhood disease
numeracy enables mothers to better monitor illnesses and apply treatments
direct effect is shown by c-c” in Figure 1. Literacy and numeracy also enable m
to increase their health knowledge by enabling them to gather information
written sources. This indirect effect is path c-c’-abc in Figure 1.
Figure 1 also depicts how factors other than schooling influence child hea
Household physical assets raise household incomes (arrow d), which should ha
a positive effect on both nutritional inputs (such as calories) and environmenta
ditions around the home. The choice of health and nutritional inputs will a
affected by factors associated with the supply of these inputs in the comm
(arrow e). For example, the availability and quality of health and non-health co
nity facilities affects the decisions households make regarding health and nutr
1. Schultz’s framework is primarily concerned with child mortality. Yet broadening it to includ
less severe, aspects of child health does not require significant modification.
2. The fifth pathway, via mother’s time, could be added to Figure 1 but is omitted to reduce
Similarly, the impact of the third pathway via reduced family size could also be made more exp
a box labeled “family size,” another endogenous variable), but this is also omitted to reduce clut
3. The distinction between pathways (1) and (2) in the previous paragraph is that the first conc
efficient mix of physical health inputs (for example, medicines) while the second adds efficien
non-physical inputs (such as care given to the sick child). In this paper, the distinction between phy
and nonphysical inputs is not of primary interest, rather the emphasis is on the different types of know
and skills learned in school, and how they affect efficient use of both types of inputs.
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128 The Journal of Human Resources
inputs. Finally, the child’s health endowment will also affect household health a
nutritional inputs (via arrow g), since more sickly children usually receive lar
amounts of health and nutritional inputs.
B. Recent Empirical Evidence
Many recent studies have examined the impact of mother’s (and father’s) educat
on child health. For comprehensive reviews of the literature see Behrman and D
lalikar (1988), Behrman (1990), and Strauss and Thomas (1995). The discussion
here will be limited to an overview of a few recent studies, focusing on the impa
of mother’s education on height-for-age and weight-for-height.
Studies of the determinants of child height and weight in many countries ha
found positive effects of mother’s education. Most of these studies presented redu
form estimates, but a few went further, examining the pathways by which mothe
education improves child health. In the Philippines, Barrera (1990) found that bett
educated mothers tended to wean their children sooner, but they compensated for t
shortened breastfeeding time with better care; overall, their children were healthier a
measured by higher height-for-age z-scores.4 The only published study that focus
on the “information processing” attributes of schooling is by Thomas, Strauss,
Henriques (1991), which used Brazilian data that included variables for whethe
woman reads a newspaper, listens to the radio, or watches television. Mother
schooling was not significant when dummy variables were included for these “info
mation processing” activities; the newspaper and radio variables were significant
rural areas but only the television variable was significant in urban areas.
Among the most interesting studies are those based on the Cebu Longitudin
Health and Nutrition Survey. Several studies have used these data to model the pat
ways by which exogenous variables influence child nutritional status and morbidi
The Cebu Study Team (1991, 1992) found that mother’s education leads to impro
waste disposal and higher non-breastmilk calorie intake, both of which reduce
incidence of diarrhea. Maternal education also leads to earlier weaning, which c
increase episodes of diarrhea, but the net effect of maternal education is to red
the incidence of diarrhea.
An important critique of findings that mother’s education improves child health
is the hypothesis that education simply reflects unobserved maternal characteristics.
Wolfe and Behrman (1987) used Nicaraguan data on mothers’ siblings to control
for unobserved family fixed effects. They found that applying these controls leaves
no significant effect of mother’s education on child anthropometric status. However,
Strauss (1990) found in Cote d’Ivoire that mother’s education raises child heightfor-age and weight-for-height, even after using family fixed effects estimators.
In summary, there is considerable evidence that mother’s education improves child
health, and some evidence on how this occurs. Still, there are no studies that distin4. Height for age z-scores, which will be used in the empirical work below, are based on fitting a standard
normal distribution to the growth curves of a healthy population of children. A child with a z-score of
zero is exactly at the median in terms of height for age, while children with positive (negative) z-scores
are taller (shorter) than average. Low height for age z-scores indicate stunting due to repeated episodes
of malnutrition over the life of the child, while low weight for height z-scores indicate wasting (weight
loss) due to a current episode of malnutrition (see Gibson 1990).
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Glewwe 129
guish between the literacy and numeracy impacts of schooling and other, m
eral, impacts. Also, there are no studies that attempt to assess directly th
of mother’s health knowledge on child health.
III. Analytical Framework, Data and Estimation
A. Analytical Framework
Estimation of the pathways by which mothers’ schooling affects child health is not
necessarily straightforward. This subsection provides a framework for thinking about
how to estimate these relationships. Recall Figure 1. The bottom of that figure shows
how health and nutritional inputs, the environment and a child’s health endowment
jointly determine child health. This can be expressed in terms of a production function for child health:
(1) Hi = f(HIi, Ei, Ei)
where Hi is the health of child i, HIi is a vector of health inputs chosen by child i’s
household, Ei is a vector summarizing the environmental conditions surrounding
child i, and Ei is the child’s genetic health endowment. Parents take this technological
relationship into account as best they can when making decisions that affect their
children’s health. Although Ei and Ei are outside the household’s control,5 health and
nutritional input choices are chosen by the household.
Estimation of Equation 1 would require detailed information on a large number
of health inputs, which is not feasible with the 1990-91 ENNVM data. However,
as seen in Figure 1, one can substitute out these health inputs and obtain a reduced
form relationship that shows how exogenous variables (those shown at the top of
Figure 1) determine child health:6
(2) Hi = g(FSi, MSi, HAi, Ei, Ei)
where FSi and MSi are father’s and mother’s schooling, respectively, and HAi is the
initial assets of child i’s household.
Although Equation 2 is much easier to estimate, and often has been estimated, it
does not indicate what aspects of mother’s schooling lead to improved child health.
Referring again to Figure 1, one can obtain a better understanding of the impact of
mother’s schooling by replacing it in Equation 2 with the educational outcomes it
directly affects, namely cognitive skills, parental values and health knowledge:
5. The local health environment is not chosen by parents if: a) migration for purposes of finding a better
health environment is rare; and b) households cannot pressure local authorities to improve the local health
environment. The former assumption is supported by migration data from the 1990-91 ENNVM; only
0.5 percent of respondents report that “health reasons” were the main reason for their most recent move.
The latter assumption, while harder to check, is plausible for Morocco because health care provision is
highly centralized, with few funds under the control of local governments (see World Bank 1994).
6. The assumption that parental education is exogenous seems reasonable for Morocco, where average
schooling for men and woman between the ages of 18 and 65 is only 4.7 and 2.3 years, respectively. Even
so, this assumption will be checked in Section IV.
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130 The Journal of Human Resources
(3) Hi = h(FSi, Li, Ni, Vi, HKi, HAi, Ei, ?i)
where Li, Ni, Vi and HKi stand for mother’s literacy, numeracy, values and health
knowledge, respectively. This equation is a conditional demand relationship because,
as explained below, health knowledge, and perhaps literacy and numeracy, may b
Estimation of Equation 3 would clarify the pathways by which mother’s schooling
affects child health, but such estimation is complicated by several problems. First
it is difficult to observe mother’s values (Vi), and indeed there are no such data in th
1990-91 ENNVM survey. Second, a child’s health endowment (?i) is also virtually
impossible to observe. Third, because parents’ treatment of their children’s health
problems often causes them to acquire additional health knowledge, health knowl
edge is likely to be an endogenous variable. In particular, health knowledge is likely
to be negatively correlated with a child’s (unobserved) genetic health endowment
because parents with “healthy” children need not acquire as much health knowledge
as parents with “sickly” children, ceteris paribus.7 Fourth, it is also possible tha
literacy and numeracy are endogenous because actions to acquire additional health
knowledge may lead to greater use of those skills, though the impact of a child’
genetic health endowment on these variables is likely to be considerably smaller
than its impact on health knowledge. The approaches taken to deal with these prob-
lems will be discussed in detail in Subsection IIIC below.
Another relationship of interest is a variation of Equation 3; when attempting to
assess the pathways by which mother’s education affects child health one may wish
to isolate its impact on household income.8 In this case one can add household income
(Yi) to Equation 3 and remove household assets (since their impact on child health
would operate only through their impact on household income). This yields the following conditional demand relationship for estimation:
(4) Hi = h'(FSi, Li, Ni, Vi, HKi, Yi, Ei, ?i).
The relationships of primary interest in this paper are Equations 3 and 4.
A final issue to consider is the possibility that mother’s education improves child
health by reducing the number of children women bear-with fewer children, the
mother should be able to allocate more time and health inputs per child. As mentioned in Section II, the desire for fewer children can be depicted as the impact of
schooling on parental values. Thus, one could modify Equations 3 and 4 by replacing
Vi with the number of children born or, more generally, by adding the latter variable
while retaining Vi (since values may affect child health in other ways). Of course,
since the number of children born is clearly an endogenous variable, estimation re-
7. This could be shown in Figure 1 by an arrow leading from the child’s health endowment to parental
health knowledge.
8. One could go even further. Increased education can raise household income not only by increasing
wage rates but also by increasing the amount of time the mother works outside the home. Moreover,
increased time of the mother away from home may have a direct, negative impact on child health. Thus
one could add both household income and mother’s time spent working to Equation 4. This was tried in
an earlier version of this paper (see Glewwe 1997), but it proved impossible to find instrumental variables
that could plausibly be excluded from Equation 4 and were also good predictors of mother’s time spent
working. In this paper mother’s time spent working has been substituted out of Equation 4.
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Glewwe 131
quires plausible instrumental variables; this will be discussed further in Su
B. The Data
This paper uses data from the 1990-91 Enquete Nationale sur le Niveau de Vie
des Menages (ENNVM), which was implemented by Morocco’s Direction de la
Statistique. The survey, which is based on the World Bank’s LSMS surveys, covered
3,323 households from all areas of Morocco. The survey collected a variety of information from each household, including household expenditures and income, employment, education, assets, agricultural activities, and much more. A key aspect of these
data for this paper is that they contain the height and weight of all household members. Even more important is that a battery of tests was given to household members
in two thirds (2,171) of the sampled households. The tests included: 1. Five questions
on health knowledge; 2. Twelve questions on general knowledge (how to mail a
letter, how to read an electricity bill, and so on); 3. An oral mathematics test of ten
questions; 4. A set of written mathematics tests of varying degrees of difficulty;
5. A set of Arabic reading and writing tests; and 6. A set of French reading and
writing tests. The tests are described in detail in Glewwe (1997). The health knowledge test is of particular interest, since it is rarely a part of any household survey.
It consists of five questions on vaccinations, treating infections, polio, diarrhea and
safe drinking water. The test is fully described in Appendix I.
All persons in the 2,171 selected households between the ages of 9 and 69 were
to be tested except: 1. Individuals with a baccalaureate degree9 or higher level of
education took only the health knowledge test since it was assumed that they could
obtain nearly perfect scores on all other tests; and 2. The health knowledge test was
taken only by individuals between the ages of 20 and 50. The 2,171 households who
participated contained 1,612 children age 5 or younger, of which 81 had mothers
who did not participate in the tests for one reason or another, leaving a sample of
1,531 children. It is assumed that the 39 mothers with a baccalaureate degree would
have received perfect scores on all the tests (except the health test, which they did
take), which boosts the sample size to 1,570. Dropping observations with missing
values leaves a sample of 1,495 children.
Table 1 provides descriptive statistics on all variables used in the analysis. Of
particular interest are the test score variables, which are defined as the number of
questions correctly answered by the respondent. They show substantial variation,
which is necessary to assess the underlying pathways by which mothers’ schooling
raises child health. In addition, these scores should not be highly correlated with
years of schooling, or with each other; if they are, regression analysis is less likely
to identify the underlying mechanisms. Table 2 shows correlation of years in school
with the test scores (the table also includes a test on reading a medicine box-this
will be discussed in Section IV). Mathematics, French and Arabic scores are all
highly correlated with each other and with years in school (correlation coefficients
9. Roughly speaking, a baccalaureate degree lies somewhere between a U.S. high school degree and a
college degree. It is only awarded after passing a rigorous set of examinations.
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132 The Journal of Human Resources
Table 1
Descriptive Statistics of Variables Used
Height for age Z-score -0.94 1.86
Per capita expenditure 5,398.82 5,081.68
Sex of child (female) 0.53 0.50
Age of child (in months) 35.86 20.00
Mother’s height 157.02 6.20
Father’s height 168.49 5.95
Father’s height missing 0.31 0.46
Mother’s years schooling 1.98 2.97
Father’s years schooling 3.14 4.10
Health knowledge 2.89 1.57
General knowledge 1.53 3.18
Reading and writing mathematics 1.18 2.62
Arabic reading 2.33 4.98
Arabic writing 0.57 1.54
French reading 1.48 4.44
French writing 0.26 1.06
Rental income 1,460.79 13,920.17
Irrigated crop land (hectares) 2.36 12.62
Unirrigated crop land (hectares) 30.88 94.59
Tree crop land (hectares) 0.32 1.42
Mother’s married sisters 1.88 1.56
Father’s married sisters 1.51 1.56
Father’s married sisters missing 0.22 0.41
Number of televisions 0.55 0.58
Availability of newspapers 0.21 0.41
Mother’s father’s schooling 0.03 0.16
Mother’s mother’s schooling 0.01 0.10
Sample Size: 1,495.
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Glewwe 133
Table 2
Correlation Among Schooling and Test Score Variables of Mothers
Years Arabic French Health Box of
Schooling Literacy Literacy Numeracy Knowledge Medicine
Years schooling 1.0000
Arabic literacy 0.8938 1.0000
French literacy 0.8869 0.8681 1.0000
Numeracy 0.8665 0.8695 0.8383 1.0000
Health knowledge 0.3343 0.3867 0.3138 0.4356 1.0000
Reading box of medicine 0.8152 0.8553 0.7775 0.8198 0.4387 1.0000
Note: All variables are in logarithms.
from 0.84 to 0.89).10 Health knowledge is less highly correlated with thes
variables (correlation coefficients from 0.31 to 0.44). Whether regression
can distinguish between the impacts of the most highly correlated skills is un
and will become clear only by examining estimation results.
C. Estimation Strategy: General
Consider estimation of equation (4) using the 1990-91 ENNVM.1 Child he
can be measured by the Z-score of child height for age (see endnote 4), whi
cates chronic malnutrition over a child’s lifetime (stunting). In principle
could also use weight for height, but the 1990-91 ENNVM weight data s
from serious measurement error because weight was recorded only to the
Several estimation problems arise concerning the explanatory variables in Equation 4. Begin with the child’s local health environment, Ei. Although the 199091 ENNVM data contain information about local health clinics and other pertinent
variables, some of the data are missing and comparability is a problem. In addition,
the sampling procedure used for choosing the health facilities covered by the survey
is unclear. Because the main interest of this paper focuses on household level variables (the skills and education levels of mothers), a simple community fixed effects
procedure is used to avoid bias caused by omitted community level health environment variables. This is possible because the sampled households were drawn from
140 primary sampling areas.
Of the remaining explanatory variables in Equation 4, two are unobserved: maternal values acquired in school (Vi) and the child’s health endowment (e6). If the effect
10. For simplicity, in the remainder of this paper (the logarithms of) the two mathematics scores are
summed to create a single mathematics variable, and the reading and writing scores are summed for French
and Arabic. For an analysis of the more disaggregated scores, see Glewwe (1997), the findings of which
are basically the same as those in this paper.
11. The following paragraphs also apply to Equation 3, except the discussion on choosing instrumental
variables for household income is irrelevant (household income has been substituted out of Equation 3).
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134 The Journal of Human Resources
of schooling on maternal values is important, one could detect this by adding year
of schooling to Equation 4. A positive effect of years of schooling on height for a
would indicate that values (or perhaps some other aspect of schooling other th
literacy, numeracy, and health knowledge) is an important determinant of ch
health. If the years of schooling variable has no perceptible effect, it is unlike
that values acquired by mothers from schooling is an important pathway by whic
schooling affects child health.
The inability to directly observe a child’s health endowment (E?) could lead t
biased parameter estimates due to its correlation with observed variables. One w
to reduce such bias is to enter the heights of both parents as explanatory variable
since taller parents are likely to have better health endowments, which in turn ar
inherited by their children. In addition, parents (and their children) display variati
in height that is not related to health status-healthy people can vary in heigh
Entering parental height in the regressions controls for this as well, purging t
dependent variable of variation in height that is not indicative of health statu
Note that father’s height is missing for about one third of the children, either be
cause the father did not live with the children or was unavailable for measurement
at the time of the interview. To avoid losing this portion of the sample, which
could lead to sample selectivity biases, a dummy variable is created indicating
that father’s height was missing; in such cases father’s height variable is set equal
to the mean.
Even after adding parental height to reduce bias caused by unobserved child health
endowments, it is still possible that the inability to observe children’s health endowments could bias estimated impacts of observed variables. Health knowledge, household income, and perhaps literacy and