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The Journal of Development Studies
ISSN: 0022-0388 (Print) 1743-9140 (Online) Journal homepage: http://www.tandfonline.com/loi/fjds20
Educated Mothers, Well-Fed and Healthy Children?
Assessing the Impact of the 1980 School Reform
on Dietary Diversity and Nutrition Outcomes of
Zimbabwean Children
Marshall Makate & Clifton Makate
To cite this article: Marshall Makate & Clifton Makate (2017): Educated Mothers, Well-Fed
and Healthy Children? Assessing the Impact of the 1980 School Reform on Dietary Diversity
and Nutrition Outcomes of Zimbabwean Children, The Journal of Development Studies, DOI:
10.1080/00220388.2017.1380796
To link to this article: http://dx.doi.org/10.1080/00220388.2017.1380796
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Published online: 25 Oct 2017.
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Download by: [UAE University]
Date: 26 October 2017, At: 22:38
The Journal of Development Studies, 2017
https://doi.org/10.1080/00220388.2017.1380796
ARTICLE
Downloaded by [UAE University] at 22:38 26 October 2017
Educated Mothers, Well-Fed and Healthy
Children? Assessing the Impact of the 1980 School
Reform on Dietary Diversity and Nutrition
Outcomes of Zimbabwean Children
MARSHALL MAKATE
* & CLIFTON MAKATE
**
*Department of Economics, University at Albany State University of New York, Albany, NY, USA, **UNEP-Tongji Institute of
Environment for Sustainable Development, Tongji University, Shanghai, China
(Original version submitted January/2017; Final version accepted August/2017)
ABSTRACT We scrutinise the causal influence of schooling on child dietary diversity and nutrition in Zimbabwe
using the exogenous variability in schooling prompted by the 1980 education policy, a natural trial fitting a fuzzy
regression discontinuity design. We established that a one-year of learning promotes dietary diversity and
nutrition even after accounting for plausible mediating factors. Also, education is more liable to impact dietary
practices and nutrition through improvements in health knowledge, literacy, wealth and prenatal care utilisation.
These findings suggest that promoting schooling access to girls in resource-poor nations might have far-reaching
implications on feeding practices and consequently child nutrition.
1. Introduction
Theoretical and empirical research has strongly linked education to nonmarket outcomes (Grossman,
2006). Nevertheless, much of the already established evidence pointing to a positive association
between education and health (Chou, Liu, Grossman, & Joyce, 2010; De Walque, 2007;
Lindeboom, Llena-Nozal, & van der Klaauw, 2009; Lleras-Muney, 2005; Lundborg, Nilsson, &
Rooth, 2014; Mccrary & Royer, 2011) has largely focused on developed countries with less emphasis
on less-industrialised nations. Moreover, there is less agreement in the empirical literature on the
causal nature of this fundamental relationship (Chou et al., 2010; Cutler & Lleras-Muney, 2010). A fair
portion of the previous literature has concentrated on uncovering the plausible connections between
educational accomplishments and child health outcomes (Chou et al., 2010; Currie & Moretti, 2003;
Grossman, 2006; Lindeboom et al., 2009; Silles, 2015). Policy efforts in several low-income nations
has revolved around ensuring a well-schooled and nourished population with specific focus on the
underprivileged segments. Thus, it becomes essential to evaluate whether there exist any plausible and
positive externalities of parental schooling on child health outcomes.
Correspondence Address: Marshall Makate, Department of Economics, University at Albany State University of New York,
1400 Washington Avenue, Albany, NY 12222, USA. Email: mmakate@albany.edu
© 2017 Informa UK Limited, trading as Taylor & Francis Group
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2 M. Makate & C. Makate
This study examines the fundamental link between mother’s schooling and child dietary diversity – an
important correlate of child nutritional status that has received little attention in the empirical literature. We
also assess the possible influence of education on child nutrition outcomes – one of the primary targets of
the Sustainable Development Goals (SDG) (United Nations, 2015). A lucid appreciation of the plausible
links through which education influences dietary diversification and child nutrition outcomes is of
particular importance to Zimbabwe, a country currently struggling to meet its public health obligations
(Who, 2015). According to the 2015 Zimbabwe Demographic and Health Survey (ZDHS), nearly 27 per
cent of children under five years were considered short for their age (that is, stunted), 3 per cent were
wasted (that is, thin relative to their height), 8 per cent were underweight (that is, considered thin for their
age), and approximately 6 per cent were considered overweight. Given the already established connections
between malnutrition and child mortality (Nangalu, Pooni, Bhargav, & Bains, 2016; Pelletier and Frongillo
Jr, 1995) and between poor infant and child feeding practices with chronic malnutrition (Reinbott et al.,
2016), it becomes imperative for us to comprehend the correlates of dietary diversity. Besides, dietary
diversity reflects on dietary quality, nutrient adequacy and food security (Mango, Zamasiya, Makate,
Nyikahadzoi, & Siziba, 2014; Mirmiran, Azadbakht, & Azizi, 2006). Theoretically, it is likely to be
inversely correlated with child malnutrition (Frost, Forste, & Haas, 2005).
The health economics literature offers several explanations of why education might impact health
(Grossman, 2006). Two leading accounts point to the productive and allocative efficiency explanations.
The latter contends that highly educated individuals have the ability to choose the right mix of health inputs
that maximises their health utility and are therefore more likely to accommodate and process new information
faster than their less educated equals (Deaton, 2002; Grossman, 1972). Alternatively, the former proposition
postulates that improved schooling enhances the marginal product of health, making educated individuals
efficient producers of health (Grossman, 1972). In the context of child dietary diversity and nutrition, suppose
the school curriculum incorporates lessons on diet and nutrition, then we expect the highly educated to
possess superior health knowledge (Johnston, Lordan, Shields, & Suziedelyte, 2015) which consequently
contributes to better feeding practices and overall nutrition. Moreover, educated individuals are more likely to
be intellectually curious and literate (Cascio & Lewis, 2006), qualities that empower them to understand
health information posted through the media, and even the internet, without much difficulty (Bundorf,
Wagner, Singer, & Baker, 2006; Wagner, Hu, & Hibbard, 2001).
In this paper, we examine the impact of mother’s schooling on child dietary diversity and nutrition
outcomes in Zimbabwe using the 1980 education reform that improved secondary school opportunities for
children as an identification strategy. This policy provides an ideal setting for us to evaluate the influence of
mother’s education on child dietary diversity and nutrition outcomes within a fuzzy regression discontinuity
design (RDD). Following previous studies in developing countries (Aguero & Bharadwaj, 2014; Behrman,
2015b; Grépin & Bharadwaj, 2015; Makate & Makate, 2016; Tsai & Venkataramani, 2015), we adopt an
identification technique that exploits the exogenous variability in schooling prompted by the reform which
augmented secondary schooling prospects for individuals who were 13 years or below in 1980 compared to
their counterparts whose age in 1980 fell outside the secondary school starting age (that is, above 13 years).
The differences in schooling opportunities created a disconnect in the likelihood of attending secondary
school, which we use as an instrumental variable in the empirical framework discussed later.
Our contributions to the literature, especially for developing countries, are twofold. First, we furnish
new evidence on the causal influence of mother’s schooling on child dietary diversity using a natural trial
fitting a fuzzy RDD in which estimates are local average treatment effects and closely resemble the
estimates often provided in the programme evaluation literature (Imbens & Angrist, 1994). We examine
these issues in the context of a resource-poor country where health care amenities are deficient including
deteriorating child health and nutrition outcomes (WHO, 2015). The empirical approach uses more
recent and nationally representative data collected by the ZDHS in 2005/2006, 2010/2011 and 2015.
Second, we explore the potential alleyways through which mother’s education might influence child
feeding and nutrition outcomes. The rest of the paper is structured as follows: Section two gives the
background to the study including the related literature and an overview of the 1980 school reform in
Zimbabwe; Section three describes the methods; Section four furnishes the results; Section five provides
a discussion of the findings; while Section six concludes.
Education, child dietary diversity and nutrition 3
2. Background
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2.1 Related literature
The nexus between education and nonmarket outcomes has received huge attention in the empirical
literature (Grossman, 2015). While the focus of early work has mainly been on the establishment of
plausible correlations linking education and nonmarket outcomes, many recent studies have credibly
uncovered plausible causal effects of schooling on numerous nonmarket outcomes in the developed
world (Grossman, 2006). Even though previous studies agree on the positive association between
schooling and overall wellbeing in general, empirical evidence on the causal connections is far from
conclusive (de Walque, 2007; Lindeboom et al., 2009; Lleras-Muney, 2005). Moreover, caveats still
remain in the context of low-income countries where evidence on the causal impact of schooling is
somewhat still less developed (Lochner, 2011).
Education influences child health and nutrition outcomes through various pathways including
improved health knowledge, health investments and social economic status among others
(Grossman, 2006). It’s plausible that higher educational attainments are more likely to enhance ageappropriate and quality diets essential for growth, nurturing and feeding practices. Parental education
is widely regarded as an important determinant of child nutritional status regardless of the food
security status of the population (Shi & Zhang, 2011), as inadequate knowledge on proper feeding
practices may result in poor nutritional intakes and consequently to malnutrition (Reinbott et al.,
2016). Alternatively, food insecurity can be a major constraint for caregivers to make use of gained
knowledge, as food availability, affordability and utilisation within the household are directly linked to
diets of younger children (Black et al., 2008; Mcdonald et al., 2015).
Studies for developing countries probing the causal influence of schooling on health outcomes
are not only scarce, but have generally focused on explaining the plausible links with HIV/AIDS
knowledge, fertility preferences, child nutrition and child mortality outcomes (Aguero &
Bharadwaj, 2014; Behrman 2015a, b; Desai & Alva, 1998; Duflo, Dupas, & Kremer, 2015;
Emina, Kandala, & Inugu, 2009; Grépin & Bharadwaj, 2015; Makate & Makate, 2016; Makoka,
2013; Osili & Long, 2008). Since studies have established that the education of the mother (not the
father) is likely to impact child health outcomes (Chou et al., 2010; Lindeboom et al., 2009), this
study focuses on the role played by mother’s schooling on altering child feeding and nutrition
outcomes. Highly schooled mothers are more likely to have longer birth intervals (Cleland & Van
Ginneken, 1988; Mukuria, Cushing, & Sangha., 2005., Makate & Makate, 2016) and have wellnourished children (Makoka, 2013). Also, schooling improves the mother’s knowledge regarding
child health (Lindeboom et al., 2009; Makoka, 2013), including the causes, prevention and
treatment of diseases (Frost et al., 2005) and promotes health-seeking behaviour during pregnancy
(Makate & Makate, 2016, Ruel, Habicht, Pinstrup-Andersen, & Gröhn, 1992). Moreover, children
of educated mothers are more likely to be fully immunised and live in hygienic environments
(Emina et al., 2009; Makoka, 2013). In the case of developing countries, it is often the case that
immunisation programmes are provided through large scale state-funded initiatives which might
subdue the overall influence of schooling. Other channels include access to economic resources
brought about by a higher education (Adler & Newman, 2002; Glewwe, 1999; Grossman, 2006;
Lindeboom et al., 2009). Furthermore, educated mothers especially in less-industrialised nations are
more likely to reside in metropolitan areas where they have better access to improved health and
sanitation services compared to their rural counterparts (Makoka, 2013). Additionally, Alderman,
Hentschel, and Sabates (2003) and Desai and Alva (1998) contend that average years of schooling
at the community level might yield positive externalities which helps improve sanitation and
medical services and consequently child nutrition within those communities.
The biggest challenge faced by previous studies has been on the provision of credible estimates reflecting
the causal effects of schooling. Notable studies have adopted instrumental variable methods that use, for
example, college openings as an instrumental variable for education (Currie & Moretti, 2003), mandatory
schooling laws (Chou et al., 2010; Gathmann, Jürges, & Reinhold, 2015; Grytten, Skau, & Sørensen, 2014;
Güneş, 2016; Lundborg et al., 2014; Mccrary & Royer, 2011; Parinduri, 2017) and universal primary
4 M. Makate & C. Makate
education policies (Behrman, 2015b; Makate & Makate, 2016; Osili & Long, 2008; Tsai & Venkataramani,
2015). We follow the latter strand of literature in our empirical approximation of the causal impact of
schooling on child dietary diversity and nutrition outcomes including the possible mechanisms through
which schooling might propagate in Zimbabwe.
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2.2 The 1980 education policy in Zimbabwe
Zimbabwe conquered British oppression to end several years of misery and bondage, becoming a fully
independent state in 1980. Independence meant a step into an unknown yet hopeful future as the native
Zimbabwean people embarked on a new chapter which saw the rise of their first and only president to
date – Robert Mugabe. The pre-independence period was characterised by huge educational imbalances that Mugabe had vowed to correct if voted into power (Gordon, 1994). Before independence,
education was deliberately biased in favour of white children who enjoyed free and compulsory
schooling up to the age of 15 years while, for blacks, many bottlenecks were put in place with very
few children transitioning to secondary school (Nhundu, 1992). In some cases, children from black
families even failed to secure places to enrol in primary school due to a deliberate shortage of
enrolment places (Riddell, 1980).
Several intellectuals have written about Zimbabwe’s 1980 educational restructuring (Dorsey, 1989;
Kanyongo, 2005; Nhundu, 1992). Independence ushered in a new era in the learning system as the
newly elected government implemented a wide array of reforms including making primary school free
and compulsory for all Zimbabwean children regardless of race or ethnicity. Also, the government
implemented policies that ensured automatic grade transitioning from primary to secondary school,
removed any barriers prohibiting older children that delayed school enrolment to attend if they coveted
to do so (Nhundu, 1992). While it is fair to note that enrolment at both secondary and primary levels
increased, the most impressive increase was witnessed in secondary schools (Nhundu, 1992). As
enrolment soared, the government embarked on a mega school construction operation to fulfil the
rising demand for schools which saw primary (secondary) schools doubling from 2401 to 4291 (177 to
1276) between 1980 and 1986 (Dorsey, 1989). Several of the secondary schools were constructed in
the countryside (Ansell, 2002) where approximately 70 per cent of Zimbabwe’s population resides
(Zimstat, 2016). The upsurge in schools contributed to a rise in secondary school enrolment from
nearly 69,686 to 482,000 representing a staggering 592 per cent increase in student enrolment between
1979 and 1986 (World Bank, 2007).
Children in Zimbabwe officially enter primary school at the age of six years and spend seven years
of classroom-led instruction. On-time and successful completion of the primary school stage (at about
age 13 years) guarantees a smooth transition to secondary learning where they spend an additional four
years of intellectual growth and development. Fulfilling the requirements of the secondary school level
(form four) guarantees the receipt of the ordinary level (O-level) certificate and ensures eligibility for
advanced level studies for about two years. Students who successfully complete the A-level phase and
receive appropriate grade or subject points smoothly transition to college or university education.
3. Methods
3.1 Data source
We use data sourced from the nationally representative ZDHS conducted in 2005–2006, 2010–2011
and 2015. This data relates to the child nutrition outcomes for women, several years after the 1980
education policy in Zimbabwe was implemented. This data is ideal for our purposes as it is a rich
source of reliable and comparable information on the household structure, health, labour market,
household wealth and educational attainment for household members and is available for many
developing countries including Zimbabwe. This is a cross-sectional survey undertaken by Inner City
Fund (ICF) International in partnership with local governmental agencies to primarily collect health
information for women of reproductive ages 15–49 and their children. The ZDHS, which incorporates
Education, child dietary diversity and nutrition 5
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a nationally representative sample of about 11,000 households or more, uses a two-stage stratified
sampling technique grounded in the Zimbabwe population censuses as the sampling frames. The
2005–2006 and 2010–2011 surveys used the 2002 population census while the 2015 used the 2012
population census as the primary sampling frame. We specifically chose the three most recent ZDHS
datasets since they contain similar and complete information regarding the specific foods given to
children within the 24-hour period before each survey and the anthropometric measurements for the
most recent child born in the five years preceding each survey. For an excellent description of the
ZDHS, the reader is referred to the most recent DHS final report for Zimbabwe (Zimstat, 2016). To
lessen the possibility of bias associated with pooling across multiple surveys, we adopted a similar
strategy to adjust the survey probability weights as implemented in Makate and Makate (2016). The
analysis here uses the birth recode component file of the ZDHS which contains both mother and childlevel features. We are primarily interested in the data for children who are alive at the time of each
survey and whose birth mother was born between 1961 and 1971.
3.2 Child dietary diversity and nutrition outcomes
The analysis here considers five outcome variables related to child dietary diversity and nutrition.
First, as in Amugsi, Mittelmark, and Oduro (2015), we computed an index to measure child dietary
diversity for children six months and older based on a series of questions that each respondent was
asked during the ZDHS. These questions solicited information regarding the feeding practices of
women for their surviving children as observed at each survey date. Respondents were asked
whether they had given their children any of the foods mentioned in the groups (a–n) below within
the 24 hours before each survey. These food groups include, as recorded in the ZDHS individual
questionnaire (Zimstat, 2016): (a) juice or juice drinks; (b) milk such as tinned, powdered or fresh
animal milk; (c) bread, rice, noodles, porridge or other foods made from grains; (d) pumpkin,
carrots, squas or sweet potatoes that are yellow or orange inside; (e) white potatoes, white yams,
manioc, cassava, or any other foods made from roots; (f) any dark green, leafy vegetables; (g)
vitamin A fruits (for example, mangoes, papayas and any other vitamin A fruits); (h) any other fruits
or vegetables; (i) liver, kidney, heart or other organ meats; (j) any meat, such as beef, pork, lamb,
goat, chicken or duck; (k) eggs; (l) fresh or dried fish or shellfish; (m) any foods made from beans,
peas, lentils or nuts; and (n) cheese or other food made from milk. Each question had a response of
either ‘yes, gave child’ or ‘no, did not give child. We computed an additive index in which each
‘yes’ (‘no’) response received a score of one (zero) to create a dietary diversity score representing
food consumption within the recommended 24-hour reference time period for each child (Kennedy,
Ballard, & Dop, 2011; Steyn, Nel, Nantel, Kennedy, & Labadarios, 2006). This score ranged from
0–14, with a score of zero indicating that the child did not receive any of the specific foods
mentioned and 14 suggesting that the child received all the mentioned foods (complete diversification). The higher score indicates how varied the foods typically consumed by the child are in a 24hour period, as well as the parent’s economic ability to acquire and consume a variety of such foods
(Hoddinott & Yohannes, 2002), holding all other things constant. Since a dietary diversity score of
zero is less likely to be plausible, we provided additional analysis presented as Supplementary
Materials (see Table A1) to show that our estimates for dietary diversity shown in Table 3 are robust
to the exclusion of children with a diversity score of zero.
Second, child nutrition is measured using anthropometric indicators: child’s height-for-age (H/A)
and weight-for-age (W/A), all expressed in terms of standard deviations as compared to the
standard median (z-scores) of the World Health Organization (WHO) reference populations
(WHO, 2006). The H/A z-score (henceforth HAZ) is a cumulative linear growth measure which
can indicate past chronic inadequacies of nutrition or frequent illness in the case of deficiencies.
We computed a binary indicator to reflect ‘stunting’ (that is, HAZ < 2Þ. The W/A z-score
(henceforth WAZ), a common yardstick of assessing the magnitude of malnutrition over time, is
a measure of the body mass relative to the age (WHO, 2006). We also include a binary indicator to
measure underweight (that is, WAZ < 2Þ.
6 M. Makate & C. Makate
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3.3 Explanatory variables
The explanatory variable of interest, education, is measured using the years of completed schooling and a
binary indicator for secondary school completion as observed at each survey date. We particularly focus on
the schooling attainments of the primary child care giver (the mother in this case) and not the father since
father’s schooling is more likely to be highly correlated with mother’s education as highlighted in Chou
et al. (2010). Our models account for several explanatory variables believed to influence child dietary
diversity and nutrition outcomes. In later discussions, we consider father’s education as a potential
mechanism through which mother’s education might impact child dietary diversity and nutrition. We
included controls for the mother’s age including its square and the child’s age as well as its square, all
observed at the survey date. Since the extent of dietary diversity is likely to depend on the available
financial resources and competition for food within the household, we included controls for household
wealth, employment status of the respondent and the proportion of children under the age of five years in
each household including a binary indicator for female head of household. Including the indicator for
female headed households allows us to account for the potential challenges faced by such families in the
quest to balance work and life responsibilities. Also, we included controls for the province of birth of the
child as well as an urban residence dummy to account for any possible unobserved geographic heterogeneity in birth environments. Additionally, we included region-specific age fixed effects to control for the
intensity of the schooling programme which likely varied across Zimbabwe’s 10 provinces.
3.4 Identification approach
Identifying the causal influence of the education policy on the potential outcomes (child dietary
diversity and nutrition) ðYi Þis calculated as the difference between the individual’s child health outcome when the person is susceptible to the education programme Yi ð1Þ and the child health outcome
of the individual when not susceptible to the education policyYi ð0Þ. In other words, we examine the
influence of mother’s education on the dietary diversity and nutrition of her offspring. The main
complication in causal inference studies is the fact that we do not have the liberty to observe the two
states of the world at the same instance, instead we only observe the outcome variables linked with the
receipt of treatment by the individual. Let’s define Pi 2 f0; 1g where Pi ¼ 0 defines non-exposure or
non-participation and Pi ¼ 1 susceptibility to the programme. Then, we can define the outcomes of
each individual as a function of programme eligibility as follows:
Yi ¼ ð1 Pi Þ Yi ð0Þ þ Pi Yi ð1Þ ¼
Yi ð0Þ if Pi ¼ 0
Yi ð1Þ if Pi ¼ 1
(1)
The ultimate challenge lies in the construction of a suitable comparison group that captures the most
probable outcome in the different treatment states. This study adopts a fuzzy RDD to identify the causal
influence of the 1980 education policy on dietary diversity and nutrition outcomes for Zimbabwean
children. The underlying idea in a RDD is the realisation that susceptibility to the policy depends on
whether an individual lies (by chance) above or below an assignment variable Zi (in this case, the
respondent’s age at time of policy enactment). Comparing the individuals on each side of the cut-point
allows us to compute the causal influence of schooling free of selection distortion. As noted in Imbens and
Lemieux (2008), it’s possible that Zi can be somewhat associated to the outcome variables of the study but
this correlation is presumed to be smooth. The identification strategy we adopt in this study is borrowed
from several related studies in the case of low-income nations (Aguero & Bharadwaj, 2014; Behrman
2015a, b; Makate & Makate, 2016; Tsai & Venkataramani, 2015). The crux of this strategy lies in the
realisation that susceptibility to the policy was determined (at least in large part) by whether the
respondent’s age was within the secondary school starting range or otherwise.
Thus, we can spell out an individual’s susceptibility to this schooling policy as follows:
P i ¼ I ½ Zi c (2)
Education, child dietary diversity and nutrition 7
where Zi is the respondent’s age at policy enactment. In the case where we have complete programme
compliance, the likelihood of susceptibility to the education policy Pi takes either zero (non-exposure)
or one (complete exposure) and is a function of the running variable Zi (that is, the individual’s age at
policy enactment). However, since grade repetition is a common phenomenon in many African
countries including Zimbabwe (Alderman, Gilligan, & Lehrer, 2012; Nishimura, Yamano, &
Sasaoka, 2008), we maintain the imperfect compliance hypothesis throughout the paper. This imperfect compliance allows us to adopt a ‘fuzzy’ and not ‘sharp’ RDD (Angrist, Pischke, & Pischke,
2009). In the case of the fuzzy RDD, we have the following:
lim ProbðPi ¼ 1jZi ¼ zÞÞ lim ProbðPi ¼ 1jZi ¼ zÞ
z#c
z"c
(3)
where the term lim ProbðPi ¼ 1jZi ¼ zÞ represents the limit in the likelihood of exposure to the policy
z#c
when the value of z nears c from the right side and likewise lim ProbðPi ¼ 1jZi ¼ zÞ as the value of z
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z"c
approaches c from the left side. According to Hahn, Todd, and van der Klaauw (2001), the causal
impact of schooling can thus be defined as follows:
lim E ðY j Z ¼ zÞ lim E ðY j Z ¼ zÞ
β1 ¼
z#c
z"c
lim E ðP j Z ¼ zÞ lim E ðP j Z ¼ zÞ
z#c
(4)
z"c
Equation (4) expresses the discontinuity in the outcome variable as a ratio of the discontinuity in the
policy exposure variable (Hahn et al., 2001). To interpret β1 as causal, several assumptions outlined
here (Jacob, Zhu, Somers, & Bloom, 2012; Van Der Klaauw, 2002) must be satisfied. First, it should
be the case that the assignment variable is known and may not be manipulated by the individuals. In
our case, this assumption is satisfied since exposure is defined well before the education policy is
implemented. Second, it must be the case that there is a random assignment of individuals around the
age cut-off to ensure comparability of the younger and older cohorts. Although this assumption is
rather difficult to directly test, Mccrary (2008) recommends checking the density of the assignment
variable using a histogram or kernel density function. We tested this assumption and provide evidence
to suggest that it does hold with our analysis data. Third, other variables relevant for the analysis
should not be showing any discontinuities within the analysis interval (Hahn et al., 2001; Van Der
Klaauw, 2002). We formally test this assumption through smoothness tests and results show no
evidence of any violation.
3.5 Empirical specification
Our empirical formulation abstracts from a basic linear regression model in which child dietary
diversity or nutrition ðYi Þ is a function of mother’s schoolingðSi Þ, other explanatory variables ðXi Þ,
and a random disturbance term ðui Þ and specified as follows:
Yi ¼ a0 þ β1 Si þ X 0i β2 þ ui
(5)
Identifying the causal influence of schooling on dietary diversity and nutrition outcomes is convoluted
by the endogeneity nature of schooling (Si ) which if ignored results in misleading inferences (Grossman,
2006). Thus, an ordinary least squares (OLS) estimation of Equation (5) is likely to be biased. To
minimise this concern, the empirical approach adopts a two-stage least squares (2SLS) method to
compute the local average treatment effect (LATE). As noted in Van Der Klaauw (2002), the 2SLS
model uses the dummy indicator for susceptibility to the education policy as an instrumental variable for
schooling. Our specification is of the type estimated in previous-related studies (Aguero & Bharadwaj,
2014; Behrman, 2015b; Makate & Makate, 2016; Tsai & Venkataramani, 2015). In the first-stage, we
8 M. Makate & C. Makate
regressed years of schooling, Si on the instrumental variable Pi representing exposure to the policy, and
other background characteristics. This model is formulated as follows:
Si ¼ π 0 þ π 1 Pi þ π 2 Pi ðZ1980 13Þ þ π 3 ð1 Pi Þ ðZ1980 13Þ þ X 0i π 4 þ εi
(6)
where P ¼ 1j½Z1980 13 is the instrumental variable for education, Z1980 as before, is the age of the
individual at the time of implementing the education policy, Z1980 is a vector of controls and includes
survey year fixed effects, child’s year of birth fixed effects, province fixed effects, province-specific
age fixed effects and an urban residence dummy among other relevant variables and εi is an
idiosyncratic disturbance term. Equation (6) is estimated using OLS to compute the predicted years
of schooling to be used in the second stage regression taking the following form:
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Yi ¼ β0 þ β1 S^i þ β2 Pi ðZ1980 13Þ þ β3 ð1 Pi Þ ðZ1980 13Þ þ X 0i β4 þ i
(7)
where β2 and β3 are the coefficients for the linear approximations on either the right or left side of the age
cut-point. The coefficient of interest in our main model shown in Equation (7) is β1 , a reasonable estimate
of the causal impact of schooling if all the assumptions mentioned earlier are satisfied. The crux of the
identification strategy lies in the observation that individuals in the younger cohorts (that is, those aged 13
and below in 1980) should be comparable in their observable and unobservable characteristics apart from
their education levels. Even though Zimbabwe’s independence in 1980 ushered in other policy initiatives
apart from the secondary school expansions (Thomas & Maluccio, 1996), these were less likely to have
influenced our outcomes in exactly the same way as the education reform as noted here (Grépin &
Bharadwaj, 2015). Given that the further we drift from the age cut-point the more precise but biased our
estimates become and the narrower this age bandwidth, the more imprecise the estimates become (Lee &
Lemieuxa, 2010), we carefully selected our comparison groups bearing this fact in mind.
3.6 Robustness tests
To assess the robustness of our estimates, we conducted several specification checks. First, given that
there is always a trade-off between bias and precision in selecting the age bandwidth with shorter age
bandwidths linked to imprecise but less biased estimates and wider bandwidths allied to precise but
more biased estimates (Lee & Lemieuxa, 2010), we considered wider and narrower age bandwidths of
8–20 and 10–18 years in 1980. Second, given that the 1980 education policy was intended to raise
secondary school enrolment, we replicated the analysis using a binary indicator for secondary school
completion as the instrumented variable. Third, given the potential differences between boys and girls
as well as urban and rural communities where many schools were built (Ansell, 2002), we fitted our
models disaggregated by the child’s gender and rural/urban residence. Fourth, to minimise potential
confounding due to the inclusion of the dummy variable for urban residence, we estimated our models
excluding this variable to test the sensitivity of our estimates.
4. Results
4.1 Descriptive statistics
Table 1 furnishes the summary statistics of select variables used in the study. We show the descriptive
statistics for the control, treatment and partly treated cohorts. The average time spent in school by
women in our analysis sample was about 7.49 years with those in the relatively younger (older)
cohorts 1967–1971 (1961–1965) having spent an average of 8.42 (7.60) years. Almost 52.60 per cent
of the families were headed by females whose average age revolved around 41.71 years as observed at
the survey date. Approximately 39.40 per cent of these women read newspapers at least once a week,
36.90 per cent lived in urban communities and came from families averaging about 5.36 people.
Education, child dietary diversity and nutrition 9
Table 1. Summary statistics for selected variables used in the analysis
Overall
Treatment
Control
Partly treated
cohorts
cohorts
cohorts
cohorts
(1961–1971)) (1967–1971)) (1961–1965))
(1966)
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Variables
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Characteristics for mothers
Years of completed schooling
7.492 3.870 8.420 3.353 5.926 4.101 7.600 4.113
Female head of household
0.526 0.499 0.518 0.500 0.532 0.499 0.550 0.498
Age at survey date
41.710 4.310 39.950 4.228 44.261 2.922 43.130 3.857
Age in 1980
13.212 3.090 10.862 1.366 16.907 1.339 14.000 0.000
Employed
0.511 0.500 0.525 0.499 0.480 0.500 0.544 0.499
Frequently read newspapers/magazines
0.394 0.489 0.440 0.496 0.313 0.464 0.418 0.494
General health knowledge
Ever heard of oral rehydration therapy
0.550 0.498 0.588 0.492 0.452 0.498 0.607 0.490
Ever heard of any modern family planning
0.465 0.499 0.512 0.500 0.392 0.488 0.439 0.497
method
Ever heard of HIV/AIDS
0.947 0.223 0.928 0.258 0.980 0.140 0.944 0.230
Knows when in the ovulatory cycle woman can 0.816 0.388 0.823 0.382 0.793 0.405 0.852 0.356
be pregnant
General health knowledge index (1–4)
2.387 0.877 2.473 0.890 2.226 0.823 2.455 0.913
Proportion of children under age five
0.117 0.137 0.123 0.136 0.107 0.136 0.113 0.151
Household size
5.359 2.527 5.250 2.360 5.521 2.718 5.434 2.758
Household wealth (quintiles 1–5)
3.202 1.435 3.226 1.429 3.124 1.445 3.351 1.419
Urban resident
0.369 0.483 0.380 0.485 0.338 0.473 0.417 0.494
Number of mothers
3886
2215
1318
353
Characteristics for children
Child is female
0.498 0.500 0.503 0.500 0.491 0.500 0.496 0.500
Any prenatal care
0.934 0.248 0.936 0.245 0.930 0.256 0.931 0.256
Four or more prenatal care visits
0.716 0.451 0.745 0.436 0.644 0.480 0.655 0.479
Number of children
3873
2492
1087
294
Notes: We adjusted all the estimates to be nationally representative. SD = Standard deviation. Estimates for
children are based on those children who are 10 years and younger as observed at the survey date.
Source: The Zimbabwe Demographic and Health Survey (ZDHS) 2005–2006; 2010–2011; 2015.
Many of the interviewed women appeared to be well informed about oral rehydration therapy, modern
family planning methods, HIV/AIDS and about their ovulation cycle. The children in our analysis sample
are equally split by gender and nearly 93.40 per cent of them were born to mothers who sought prenatal
care during their pregnancies. Overall, the younger and older cohorts seem to be somewhat comparable in
terms of their features such as family size, child gender, urban residence, proportion of under-five children,
women’s age at interview date and female headed households.
4.2 The effects of the schooling reform on schooling
The immediate influence of the 1980 education reform in Zimbabwe was a burst in overall secondary
school enrolment rates. Since the influence of this reform largely hinged upon the individual’s age at
enactment, women in younger cohorts (that is, aged 13 years and younger in 1980) appear to have
acquired more years of schooling than their relatively older counterparts at policy enactment, as depicted
in Figure 1. The gap in education at age 13 years is quite evident. The two dotted lines demarcate the
treatment cohorts (to the left of the first dotted vertical line) and the control cohorts on the right side of
the dotted vertical line. As mentioned earlier, we consider girls aged 14 years in 1980 (that is, born in
1966) to have partially profited from the education policy and thus excluded from the analytical sample.
In the fuzzy RDD, we use the dummy indicator for age 13 years and below in 1980 as the instrumental
variable. Disaggregating the results by rural/urban residence or high/low household wealth shows that
women born 1967–1971 were more liable to have acquired more years of schooling than their
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10 M. Makate & C. Makate
Figure 1. The effect of the 1980 education reform in Zimbabwe on maternal education for the overall sample and
subgroups (urban/rural and high/low wealth).
Notes: Each dot represents the average years of completed education for each age cohort with a local polynomial
fitted for the treatment (age 13 years and younger in 1980) and control (above 13 years in 1980). The vertical
dotted lines represent the discontinuity in years of schooling around the age 13 (in 1980) threshold.
Source: The Zimbabwe Demographic and Health Surveys, 2005–2006, 2010–2011 and 2015.
counterparts born in 1961–1965. Figure 2 also suggests that the 1980 education policy was indeed a
nationwide undertaking which impacted girls in the younger cohorts relative to their older counterparts.
To better comprehend the influence of the school reform on maternal education, the top section of
Table 2 reports the coefficient estimates from regression models with years of schooling as the
dependent variable. The results show that women aged 9–19 years in 1980 but excluding those
aged 14 years were more liable to have increased their average education by about 1.06 years.
Those residing in rural communities and from low-wealth households were liable to have acquired
an average of 1.60 and 1.69 years respectively and the results are statistically significant at the 1 per
cent significance level. The bottom section of Table 2 reveals that the 1980 education policy enhanced
the prospects of completing secondary school. In all the regressions, we included linear age trends
below and above the age cut-off, indicators for the survey year, province of residence, urban residence
and household wealth (where applicable). Overall, the results suggest that the education policy
significantly impacted females living in the countryside and from mostly poor backgrounds. A
quick check of the instrumental variable reveals that our chosen instrument performs reasonably
well. The F-test statistics reported in Table 3 varied from 11.75 to 14.80 and were all statistically
significant with p-values ranging from 0.014 to 0.017 suggesting a fairly strong instrumental variable
as noted in Staiger and Stock (1997).
4.3 The effect of maternal schooling on child dietary diversity and nutrition
We illustrate in Figure 3 the average child outcomes for women in the 1967–1971 and
1961–1965 cohorts. Figure 3 shows that women born in 1967–1971 appear to have relatively
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Education, child dietary diversity and nutrition 11
Figure 2. Secondary school completion (or higher) by province of residence.
Notes: 1 = Manicaland, 2 = Mashonaland Central, 3 = Mashonaland East, 4 = Mashonaland West,
5 = Matabeleland North, 6 = Matabeleland South, 7 = Midlands, 8 = Masvingo, 9 = Harare, 10 = Bulawayo.
As before, the x-axis represents the age of the respondent in 1980.
higher dietary diversity scores than their counterparts born in 1961–1965. Also, children born to
mothers in the younger cohorts appear to have relatively higher height-for-age and weight-forage z-scores compared to their older counterparts with the disconnect in the outcomes coinciding
with the age 13 threshold. The impact on stunting and underweight rates seem to be less clearcut. One limitation of the graph is the fact that we cannot ascertain the extent to which the
influence of schooling was causal. Thus, we proceed to Table 3 which shows the 2SLS
regression estimates.
The main results of our analysis are presented in Table 3. The top-most part of Table 3 shows the
baseline OLS regression estimates (without addressing endogeneity bias) while the 2SLS estimates are
furnished in the middle and bottom sections of the table. The baseline estimates reveal a positive
correlation between maternal schooling and child dietary diversity, height-for-age (HAZ) and weightfor-age (WAZ) z-scores and negatively correlates with stunting and underweight. Specifically, we
noted that a one-year rise in education implies a 0.05-unit increase in dietary diversity and increases
HAZ and WAZ by 0.04 and 0.03 standard deviations, respectively. Also, increasing schooling by one
year is allied with decreasing stunting rates by nearly 1.20 percentage points and statistically
significant at the 5 per cent level. Comparing the OLS to the 2SLS estimates reveals that the 2SLS
estimates are much larger. This observed discrepancy can first be attributed to the fact that the OLS
estimates did not account for potential endogeneity of education. Also, the fact that the 2SLS estimator
measures the local average treatment effect (LATE) as contrasted with the average treatment effect
(ATE), the focus of the OLS estimator might account for this discrepancy (Angrist, Imbens, & Rubin,
1996). The standard tests for the first stage instrument strength suggest that our instrumental variable is
less likely to be weak since the F statistics range from 11.75 to 14.80, above the minimum standard of
10 (Staiger & Stock, 1997). Also, the corresponding p-values ranging from 0.014 to 0.015 all support
our claim of a strong instrumental variable.
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12 M. Makate & C. Makate
Figure 3. The 1980 education reform in Zimbabwe and its effects on child dietary diversity and nutrition
outcomes.
Notes: The vertical dotted lines represent the age 13 (left) and 14 (right) thresholds. To the left (right) of the age 13
threshold lies the treatment (control) cohorts.
The 2SLS estimates presented in the bottom section of Table 3 and calculated after accounting for
potential endogeneity bias reveal a strong and significant influence of maternal schooling on child dietary
diversity and nutrition. Specifically, we found that a one-year increase in school increases the dietary
diversity score by about 0.43 units and statistically significant at the 5 per cent
level. The 0.43-unit increase
100
rise in dietary diversity scores
in the dietary diversity score translates into an 18.09 per cent 0:429
2:371
given that the average score for children in our analysis sample was about 2.371. A one-year increase in
schooling improves HAZ by about 0.20 which translates into a 14.90 per cent improvement in HAZ
knowing that the average HAZ for the youngsters in our analytical sample was -1.37. Also, mothers who
spent an added year in school experience a 0.19-unit improvement in their children WAZ and statistically
significant at the 5 per cent level. This 0.19-unit improvement in WAZ roughly represents a 26.28
improvement in WAZ. Our results also show that increasing schooling by one year lowers the plausibility
of stunting and being underweight by nearly 5.60 and 3.80 percentage points. Given that nearly 32.80 and
12.40 per cent of the children in our sample were stunted and underweight, the noted changes translate to
an approximate 17.07 and 31.67 per cent reduction in stunting and being underweight, respectively. These
estimates are all statistically significant at the 1 per cent level. The last panel of Table 3 shows the estimates
computed when we consider the binary indicator for secondary school completion as the instrumented
variable. The results also indicate that our 2SLS estimates are statistically significant (with the exception
for stunting) and weakly robust to a different measure of education. Given that our first-stage F-statistics for
these estimates are lower (ranging from 4.89 to 15.09) we concentrate on the estimates in the middle
section of Table 3.
We also conducted several tests to check the robustness of our main estimates provided in
Table 3. The results (only the 2SLS estimates) from these robustness checks are furnished in the
Supplementary Materials (see Table A2). First, since the precision of the RDD estimates hinges
upon the choice of the age bandwidth (Van Der Klaauw, 2002), with shorter bandwidths reflecting
(0.291)
(0.106)
1.063***
7.477
3532
0.421***
0.502
3528
Overall (age 9–19
in 1980)
0.299*
0.754
1232
(0.151)
0.467***
0.365
2287
1.598***
6.364
2293
−0.206
9.535
1239
(0.567)
Rural sample
Urban
sample
(0.134)
(0.285)
0.472*
0.257
1224
1.689***
5.342
1262
Low wealth
(0.231)
(0.315)
0.418***
0.640
2264
0.519
8.663
2270
High wealth
(0.124)
(0.293)
Notes: ***Significant at 1 per cent level; *significant at 10 per cent level. In parentheses are robust standard errors clustered at two different dimensions, first by the province
of residence and the age of the woman in 1980. The analysis sample consists of women aged between 9 and 19 years (inclusive) in 1980 but excludes those aged 14 years.
We included controls for the province of residence, survey fixed effects, linear age trends above and below the age 13 threshold and an indicator for urban residence.
Binary indicator for age 13
Mean of the dependent variable
Observations
Completed secondary education
Binary indicator for age 13
Mean of the dependent variable
Observations
2SLS estimates
Table 2. First stage results: the impact of the 1980 education reform on schooling in Zimbabwe
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Education, child dietary diversity and nutrition 13
(0.131)
0.429**
11.747
0.014
2.371
8.742***
2538
15.095
0.002
2.370
(2.294)
(0.021)
(0.132)
0.053**
0.211
2538
0.204**
13.935
0.015
−1.369
2.151**
978
4.890
0.006
−1.369
0.036*
0.165
978
(2)
(0.797)
(0.070)
(0.016)
(0.104)
Child height-for-age z-score
0.190**
13.935
0.015
−0.723
2.049*
978
4.890
0.006
−0.718
0.030**
0.038
978
(3)
(1.015)
(0.062)
(0.012)
(0.050)
Child weight-for-age z-score
−0.038*
14.802
0.017
0.124
−0.406*
978
4.890
0.006
0.124
−0.056*
13.935
0.015
0.328
−0.567
978
4.890
0.006
0.327
(0.339)
(0.027)
−0.004
−0.005
978
(5)
(0.197)
(0.016)
(0.003)
(0.019)
Child is underweight
−0.012** (0.005)
−0.074** (0.027)
978
(4)
Child is stunted
Notes: ***Significant at 1 per cent level; **significant at 5 per cent level; *significant at 10 per cent level. The standard errors for coefficient estimates are shown in
parentheses and clustered at two different dimensions (that is, by the mother identifier and her age in 1980). The reported estimates are based on the sample women aged
9–19 years in 1980 but excludes women who were aged 14 years in 1980. In every specification, we included linear age slopes on either side of the age 13 threshold, year of
child’s birth fixed effects, child’s gender, survey fixed effects, region fixed effects, region-specific age fixed effects, information access indicator (read newspapers/
magazines), household size, fraction of children under age five in the household, household wealth, mother’s age and its square, indicator for female head of household,
employment status and a binary variable for urban residence.
OLS estimates
Years of completed schooling
Completed secondary school
Observations
2SLS estimates
Years of completed schooling
First stage F-statistic
P-value
Mean of the dependent variable
Completed secondary school
Observations
First stage F-statistic
P-value
Mean of the dependent variable
(1)
Child dietary diversity score
Table 3. Two stage least squares estimates of the impact of maternal education on child dietary diversity and nutrition outcomes
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14 M. Makate & C. Makate
Education, child dietary diversity and nutrition 15
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imprecise but less biased estimates, panel A in Table A2 shows the estimates from a specification
(Equation [7]) that uses the cohort of mothers who were aged 10–18 years old in 1980. The results
indicate a much stronger influence of maternal schooling on child dietary diversity, HAZ, WAZ,
stunting and being underweight. Also, considering a wider age bandwidth of 8–20 years in 1980
reveals a robust impact of education on dietary diversity, HAZ and WAZ. The estimates found
when we consider secondary school completion reveals a robust effect on HAZ only.
Second, restricting the analytical sample to children aged 6–36 months shows that education only impacts
WAZ and stunting with statistical significance with expected but insignificant effects on dietary diversity,
HAZ and being underweight. Third, since the influence of schooling is likely to differ by place of residence,
we split our sample into rural and urban. The results shown in panels C and D show that education had a much
stronger and significant influence on dietary diversity and nutrition outcomes for children living in the
countryside. Lastly, disaggregating the analysis by the child’s gender reveals that mother’s education
significantly impacts child feeding and nutrition outcomes regardless of the child’s gender. Overall, the results
from the sensitivity checks are consistent with our main findings presented in Table 3 and appear to show a
mildly robust causal influence of mother’s schooling on child dietary diversity and nutrition outcomes.
4.4 What are the potential mechanisms linking education to child feeding practices and nutrition
outcomes?
The influence of education on child feeding practices and nutrition outcomes is likely to propagate through
several channels. We considered several mechanisms connected to the general health knowledge of the
mother, literacy, access to information, father’s education, maternal healthcare utilisation, family size,
household wealth and the respondent’s position in the family. The 2SLS estimates for these analyses are
furnished in Table 4. The results show that schooling improves general health knowledge regarding the oral
rehydration method used for treating diarrhoea in children, modern contraceptive methods, transmission of
HIV/AIDS and about when in the ovulation cycle a woman can get pregnant. Also, a year spent in school is
more likely to promote newspaper readership and general literacy with statistical significance at the 5 and 1
per cent levels, respectively. Furthermore, we noted that highly educated mothers are more likely to come
from relatively affluent families and engage in health-seeking behaviour such as prenatal care utilisation
and keeping health cards for their children. We failed to establish any evidence to support pathways
connected to female headed families, modern contraceptive usage, paternal schooling and share of children
under age five years within each household.
The credibility of the RDD estimates lie in the legitimacy of the assumptions we noted earlier (see subsection 3.4) in the study. To enhance the credibility of our estimates, we conducted additional tests to rule
out the possibility for spurious associations. We present the results for these analyses in the Supplementary
Materials. We first checked whether the respondent’s age in 1980 exhibits any discontinuities and that there
are no manipulations. A visual inspection of the histogram in Figure A1 shows no evidence of any
manipulation or bunching at the discontinuity point.
We also checked for potential discontinuities in the assignment variable (that is, the respondent’s age at
policy enactment in 1980) to rule out any discontinuities in other explanatory variables included in our
regression models. This important check of the covariates allows us to consistently identify the influence of
the school reform free of any plausible breaks in the other variables. If the fuzzy RD estimates are valid, we
expect to see smoothness or continuity in these variables as well. The results displayed in Figure A2 show
no evidence of any discontinuities in other variables other than the assignment variable.
5. Discussion
This study examines the influence of maternal educational attainments on child feeding practices and
nutrition outcomes using nationally representative data from Zimbabwe. Using the 1980 education
reform as a source of exogenous variability in schooling for identification of the causal effects, we
found that additional years of schooling by the mother significantly enhances child dietary diversity
3529
12.057
0.091**
(0.029)
3532
12.162
0.071
Female head of
household
(0.041)
3507
11.638
0.083***
Able to read and
write
(0.008)
3532
12.162
0.012
Family
planning
(0.023)
1042
44.471
0.011
0.004
0.011
930
10.436
0.004
3532
12.162
0.004
3532
11.799
0.004
0.004
0.004
0.004
Any prenatal
Has health card for
Proportion of children under
Household wealth
care
child
age five
(high)
(0.237)
0.026**
(0.009)
0.075*
(0.034)
0.003
(0.004)
0.064***
(0.013)
(0.043)
Read
newspapers
3182
8.386
0.004
Father’s years of
education
0.438
3532
12.162
0.131**
Health knowledge
Notes: ***Significant at 1 per cent level; **significant at 5 per cent level; *significant at 10 per cent level. Shown in parentheses are standard errors clustered at two different
dimensions (that is, the region of residence of the woman and her age in 1980). Reported estimates are based on the sample of women aged 9–19 years in 1980 but excluding
those aged 14 years in 1980.
Years of completed
schooling
Observations
First stage
F-statistic
P-value
Years of completed
schooling
Observations
First stage
F-statistic
P-value
2SLS estimates
Table 4. Two stage least squares estimates: pathways through which education might impact child dietary diversity and nutrition outcomes
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16 M. Makate & C. Makate
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Education, child dietary diversity and nutrition 17
and nutrition outcomes. These findings are weakly robust to several sensitivity checks and corroborate
the findings in the burgeoning literature for less-industrialised countries that has assessed education’s
impact on numerous outcomes including fertility (Behrman, 2015a; Duflo et al., 2015), knowledge
related to HIV/AIDS (Aguero & Bharadwaj, 2014; Behrman, 2015b; Tsai & Venkataramani, 2015),
child mortality (Grépin & Bharadwaj, 2015; Makate & Makate, 2016) and nutrition (Desai & Alva,
1998; Glewwe, 1999; Thomas, Strauss, & Henriques, 1991; Vollmer, Bommer, Krishna, Harttgen, &
Subramanian, 2016).
The empirical literature has discussed several ways through which education might impact child
health and nutrition outcomes. Highly educated individuals may possess an upper hand in their
information processing capability and thus become more efficient in producing good health or rather
better at discerning and engaging in healthy behaviours (Grossman, 1972). Additionally, we explored
other ways through which education might propagate. The finding that increased educational attainments improves child feeding practices and nutrition is not surprising (Desai & Alva, 1998;
Kabahenda, Andress, Nickols, Kabonesa, & Mullis, 2014; Thomas et al., 1991). As we have
established, mother’s education is likely to improve general health knowledge (Glewwe, 1999;
Johnston et al., 2015), literacy and numeracy skills acquired through classroom learning which
ultimately help future mothers to comprehend the benefits of balanced diets to their children. Thus,
incorporating school-based health and nutrition education programmes (Tamiru et al., 2016) in
Zimbabwean schools might also help enhance general health knowledge, which might consequently
improve child feeding practices and nutrition outcomes in the country.
Furthermore, we found that the influence of schooling on child dietary diversity and nutrition is more
likely to work through prenatal care utilisation during pregnancy. This result is not surprising since
pregnant women are more likely to receive extensive and appropriate educational advice on healthy eating,
living, rest, exclusive early breastfeeding, advice on the dangers of smoking and alcohol consumption
during pregnancy, parenting skills, family planning, birth spacing, feeding practices and where to seek
additional care if needed (Lincetto, Mothebesoane-Anoh, Gomez, & Munjanja, 2006).
The findings in this paper revealed a discrepancy between the OLS and 2SLS estimates as
shown in Table 3. While this observation is consistent with previous empirical studies, it remains a
puzzle yet to be explored further. We speculate that the observed discrepancy in the context of
Zimbabwe might be attributable to the realisation that the school reforms here were non-compulsory in nature and thus would result in a LATE that is different from that calculated when
schooling is mandatory. In this case, it’s possible that other children with high ability could not
have access to secondary schooling prior to the reforms. Thus, with the lowered costs of schooling
these children might self-select into schooling while those of low abilities might have opted to
stay away from school which might be the possible reason why the 2SLS estimates are larger than
the OLS estimates measuring the ATE.
Our study is not without its limitations. First, we certainly acknowledge the possibility that
other post-independence changes that have since taken place in Zimbabwe (Thomas & Maluccio,
1996) might drive our estimates. However, we argue that these changes are less likely to have
exerted similar cohort-specific effects as the education policy in Zimbabwe and hence less likely
to influence our empirical estimates. Moreover, the last few decades in Zimbabwe have been
characterised by poor public policies which saw economic progress in the country decline to
intolerable levels. Second, our reliance on cross-sectional data makes it difficult to explore any
plausible dynamic or intergenerational aspects of schooling on child wellbeing. Third, we could
not assess the extent to which paternal health knowledge might influence maternal knowledge
which might impact feeding practices and nutrition outcomes for children. Given that the ZDHS
does not capture information on paternal health knowledge, we could not explore this possibility.
Nevertheless, we make a vital contribution to the literature on the causal effects of mother’s
schooling in low-income regions especially sub-Saharan Africa. Fourth, our analysis is only based
on the group of children who are alive as observed at the survey date. Thus, we are unable to
consider the nutrition outcomes for those children who are deceased, which might be a source of
18 M. Makate & C. Makate
selection bias. Since the ZDHS does not provide this information, we are unable to do address this
concern. Lastly, our estimates are only limited to one context – Zimbabwe. Future studies might
extend to other contexts and explore other pathways through which mother’s education might
influence feeding and nutrition outcomes.
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6. Conclusion
This study provides empirical evidence that mother’s education improves child dietary diversity and
nutrition in Zimbabwe – a nation currently struggling to meet the health and wellbeing of its younger
generation. The finding that maternal schooling might propagate through its influence on prenatal care
utilisation suggests that policies that promote schooling might have far reaching implications to the overall
health and wellbeing of the population in developing countries. The nutritional standing of children in
Zimbabwe is projected to worsen (Unicef, 2016), hence the need for extensive research to establish ways
through which the trend might be reversed. Our analysis suggests that policies that promote secondary
school education in Zimbabwe, especially amongst girls, might have far reaching implications on child
feeding practices and nutrition outcomes and consequently improve child health in the country.
Acknowledgements
This paper is adapted from the PhD dissertation of Marshall Makate, completed at the University
at Albany, State University of New York in Albany New York, USA under the guidance of
Professor Pinka Chatterji. The authors are extremely grateful to the comments received from
Econometrics seminar participants at the University at Albany including from the Editor and
anonymous reviewers of the journal who helped improve the quality of this paper.All the data and
codes used to generate tables and figures of this paper will be made available upon request. The
views expressed in this paper are those of the authors and do not necessarily reflect the views of
the institutions of affiliation. All remaining errors are ours.
Disclosure statement
No potential conflict of interest was reported by the authors. The disclosure statement has been inserted. Please
correct if this is inaccurate.
Supplementary Materials
Supplementary Materials are available for this article which can be accessed via the online version of this journal
at https://doi.org/10.1080/00220388.2017.1380796
ORCID
Marshall Makate
Clifton Makate
http://orcid.org/0000-0002-2005-2970
http://orcid.org/0000-0002-6061-6638
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