DP2013-33

Does Wom enʼs Em powerment Redu ce
Preva lence of Stunted and UnderweightChild ren in Rural India?
Katsushi S. IMAI
Samuel Kobina ANNIM
Veena S. KULKARNI
Raghav GA IHA
October 22, 2013

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Does Women’s Empowerment Reduce Prevalence of Stunted andUnderweight Children in Rural India? *Katsushi S. Imai *Economics, School of Social Sciences, University of Manchester, Arthur Lewis Building, Oxford
Road, Manchester M13 9PL, UK & RIEB, Kobe University, Japan; Email:Katsushi.Imai@manchester.ac.uk
Samuel Kobina Annim
Department of Economics, University of Cape Coast, Cape Coast, Ghana; Email:skannim@gmail.com
Veena S. Kulkarni
Department of Criminology, Sociology & Geography, Arkansas State University, P.O. Box 2410, AR
72467, USA; Email: vkulkarni@astate.edu
Raghav Gaiha
Faculty of Management Studies, University of Delhi, Kamla Nagar
New Delhi, DL India; Email: gaiha@MIT.EDU
First Draft: 10th March 2012
Second Draft: 26th August 2012
This Draft: 22nd October 2013
Abstract
This study investigates whether mother’s empowerment as measured by her bargaining
power relative to father’s affects children’s nutritional status using the three rounds of
NFHS data in India. First, the relative educational attainment of mothers significantly
contributes to z scores pertaining to the short-term measures of nutritional status ofchildren. Besides, the quantile regression results show strong associations between
women’s bargaining power and better nutritional status of children in terms of the longterm measure of nutrition at the low end of its conditional distribution. Finally, we findthe relation between access to health schemes and better nutritional measures of children.
Key Words: Child Nutrition, Malnutrition, Empowerment, Bargaining, NFHS, NCAER, Quantile
Regressions, Pseudo Panel, India
JEL Codes: C21, C23, C26, I14*Corresponding Author: Katsushi S. Imai (Dr) Economics, School of Social Sciences, University ofManchester, Arthur Lewis Building, Oxford Road, Manchester M13 9PL, UK
Phone: +44-(0)161-275-4827; Fax: +44-(0)161-275-4928; E-mail: Katsushi.Imai@manchester.ac.uk
Acknowledgements
This study is funded by ESRC research grant entitled ‘On the Change of Poverty and Undernutritionin Rural India’ (RES-000-22-4028). We appreciate useful comments from the participants in
Workshop on Poverty and Inequality in China and India, the University of Manchester in March 2012,
the 15th Kansai Development Microeconomics (KDME) Workshop at Osaka, Japan, July 2012, and a
research seminar at Dosisha University in August 2012. We thank Md Azam, David Bloom.
Subhasish Dey, Per Eklund, Raghbendra Jha, and Takahiro Sato for their valuable advice at various
stages of the project. The views expressed here are those of the author and the usual disclaimer
applies.

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Does Women’s Empowerment Reduce Prevalence of Stunted andUnderweight Children in Rural India?
1.

Introduction

Malnutrition remains a major concern in India despite the country’s impressive economic
growth. India has one of the worst levels of low birth weight, underweight and wasting
among children in BRIC and SAARC 1 countries (IAMR, 2011). While 43% (38%) ofchildren under age five are moderately underweight (stunted) in India, the corresponding
figures are 6% (11%) in China, 23% (14%) in Sri Lanka, 31% (37%) in Pakistan, 39% (43%)
in Nepal and 41% (36%) in Bangladesh in 2000-7 (ibid., 2011).
However, this is not to deny the gradual decline in the rates of moderately underweightand stunted children in India experienced over the past three decades (Deaton and Drèze,2009). A recent study employing the first two rounds of National Family Health Survey(NFHS) data dated 1992-93 and 1998-99, documents that notwithstanding the rise in ruralurban disparity favouring urban children and in gender disparity favouring girls, there was an
improvement in overall nutritional status of children under three during the 1990s (Tarozzi &Mahajan, 2007). Another piece of research based on the survey conducted in 2010-11
covering 112 rural districts of nine relatively poor states, reports that (i) the prevalence ofunderweight children declined from 53 % in 2004 to 42 % in 2010-11 with an average annual
rate of reduction of 2.9%; (ii) the nutritional advantage of girls over boys aged 0-3 years isreversed in the 3-5 age category (in both height-for-age and weight-for-age); and (iii)
underweight children are more prevalent among mothers with low levels of education(Naandi Foundation, 2011).
In spite of the positive trends in the reduction of malnutrition levels, the rates are highenough to cause concern, especially since they persist amidst a phase of impressive economic
growth. The high levels of child malnutrition potentially impact country’s prospects for2

continued economic growth as child malnutrition in early years may result in malnutrition,
lower cognitive skills and lower productivity in adult years. Additionally, high rates ofprevalence of nutritional deprivation is a humanitarian concern requiring policy-makers andinternational communities to refocus their policy priorities on direct and indirectinterventions to reduce children’s malnutrition.
The extant literature on determinants of child health and malnutrition in developing
countries in general and in India in particular highlights the significance of economic, social,
cultural, and/or infrastructural factors impacting at multiple levels, such as individual,
household and community (Ackerson & Subramanian, 2008; Allendorf, 2007; Kandpal &McNamara, 2009; Kravdal, 2004; Smith et al., 2003). Recent empirical studies on childmalnutrition in India tend to focus on one of the following three factors as determinants ofchildren’s nutritional or health status: (i) mothers’ education, empowerment, and/or relative
bargaining power vis-a-vis fathers’ (e.g. Ackerson & Subramanian, 2008; Gaiha & Kulkarni
2005; Kandpal & McNamara, 2009), to which the present study has a close link, (ii) social
capital at community levels (e.g. Borooah, 2005; Kravdal

2004); and (iii) policy

interventions such as the Integrated Child Development Services (ICDS) (e.g. Das Gupta etal., 2005). Though the results are context specific, mother’s characteristics have consistently
emerged as significant when examining child malnutrition status (Gaiha & Kulkarni 2005;
Kravdal 2004; Luke & Xu 2011; Shroff et al., 2009).
The findings on women’s role in reducing the prevalence of malnutrition are corroborated
by the opinions of the policymakers. For instance, Olivier de Schutter presented to the UN in
March 2013 and argued that “(s)haring power with women is a shortcut to reducing hunger
and malnutrition, and is the single most effective step to realizing the right to food,” andurged “world governments to adopt transformative food security strategies that address
cultural constraints and redistribute roles between women and men” (de Schutter, 2013).

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IFAD, UN also emphasised earlier the important role of women’s empowerment at
community levels in reducing child malnutrition in Asia (Eklund et al., 2007; Imai andEklund, 2008). However, rigorous studies to estimate the role of women’ autonomy inreducing child malnutrition based on the large household datasets are scarce for developing
countries in general and for India in particular. The present investigation attempts to fill thegap.
This study estimates the determinants of nutritional measures of children under age three
in rural India during the period 1992-2006 with the focus on the role of mother’s
empowerment as measured by mother’s relative (to father’s) bargaining power. The findings
are therefore expected to contribute to our understanding of the factors related to childnutrition in India. We address the following two questions: (i) Whether the mother’s relative
bargaining power is associated with to the nutritional status of her children? and (ii) What are
the household level, infrastructural and policy variables that are related to children’s nutrition
status?. We employ all the three rounds of National Family Health Survey data (NFHS
henceforth) conducted in 1992-93, 1998-99 and 2005-06 and construct pseudo panel data to
obviate the shortcomings stemming from the cross-sectional nature that majority of theprevious studies suffer from.
The rest of the paper proceeds as follows. The next section briefly reviews the existing
body of literature on child malnutrition in the context of developing countries in general andin the context of India in particular. Section 3 outlines the theoretical and the basic analyticalframework that underlie our econometric analysis. After describing the data in Section 4, we
discuss the econometric models and specifications in Section 5. Section 6 reports the
econometric results. The final section offers concluding remarks.

4

2.

Literature Review

We briefly review the following two strands of literature that are pertinent to the present
study. The first dimension pertains to the definitions and measurement of women’s
empowerment or relative bargaining power. The second strand relates to the more empiricalquestion of how women’s empowerment or relative bargaining power affects children’s
nutritional status.

(1)

Definitions and measurement of women’s empowerment

On the first part, it is important to acknowledge that “women’s empowerment” is a broadconcept usually employed in multi-disciplinary studies on development. In contrast,“women’s bargaining power” is typically modelled and empirically tested by economists
within the literature of intra-household resource allocations. While women’s empowerment
can encompass women’s inherent ability or capability to make strategic choices in society(e.g. Kabeer, 1999; Malhotra et al., 2002), it can be more narrowly defined as “women’s
relative position or exercise of power within the gender system” (Williams, 2005, p.7) where‘gender system’ is defined as the ‘socially constructed expectations for male and female
behaviour that are found (in variable from) in every known society’ (Mason, 1995, p.1, citedby Williams, 2005, p.7). This is close to the concept of “women’s bargaining power” in theeconomics literature and the present study adopts the narrow definition of “women’s
empowerment”.
It is not straightforward to measure women’s empowerment or their bargaining power
given that it is a somewhat ambiguous concept. Due to the multidimensionality of women’s
empowerment and bargaining power, different measures have been used in the empiricalliterature (Doss, 2013). A basic issue is whether it should be measured in terms of variables
that drive the bargaining process or bargaining strength (e.g. access to employment, credit) or5

in terms of outcomes of bargaining (household allocation of expenditure on health andeducation of children) (ibid., 2013). However, it should be noted that women’s preferences
are taken as revealed in the outcomes.2
The empirical literature of intra-household resource typically assumes that the wives’economic positions or education/knowledge in the household affect their bargaining power
vis-a-vis the husbands. Typical proxies for women’s empowerment include inheritance orassets at marriage (Quisumbing and Malluccio, 2003; Fafchamps et al., 2009), male andfemale non-labour income (Thomas et al., 1997) and couple’s education differences (Smith etal., 2003; Thomas, 1994). Using only the macro data and without relying on the locallyavailable micro data, Smith and Haddad (2002) defined “women’s status” as “the ratio of
female life expectancy at birth to male life expectancy at birth” or “female gross secondary
school enrolment rates” to show the role of women’s empowerment in improving childnutrition. Fafchamps et al.’s (2009) study is unique in employing new variables, such as,violence or cognitive ability as proxies of women’s empowerment or bargaining.
Additionally, research apart from the intra-household resource allocations, has focused on
non-economic dimensions of women’s empowerment. Hashemi et al. (1996) analyses various
dimensions of women’s empowerment in the context of microcredit programmes in
Bangladesh, such as, decision-making power, political and legal awareness, and participationin public protest and political campaign. Bloom et al. (2001) in their study focus on control
over finances, decision-making power, and freedom of movement.
For the purpose of the current analyses, given the data constraints, we conceptualize
women’s empowerment in a more narrow way and adopt the one which is closer to theconcept of women’s bargaining power used in economics. In order to capture non-economic
dimensions in women’s empowerment we employ the following measures; (a) relative
achievement in education in terms of the ratio of mother’s and father’s schooling years; (b)6presence of domestic violence (whether a husband beats a wife if she is unfaithful to him);and (c) whether the wife needs permission from the husband when she goes to the market.
The first variable captures the relative educational background of father or mother which
affects the bargaining process. On the other hand, the second is a proxy for the extent towhich a wife is threatened by physical violence in her decision making or bargaining with her
husband. This is, in our view, largely a domestic affair and thus more closely linked to
women’s bargaining power. The third variable proxies the extent to which a wife is restricted
in her autonomous decision-making. While the third proxy reflects social norms of societies,
there is often the close interplay of social norms and women’s autonomy (Doss, 2013) and weassume that the latter is likely to be a significant element in this interplay.

(2)

The relationship between women’s empowerment and child nutrition

On the second strand of the literature, the empirical findings on the relationship between
women’s empowerment and bargaining power and children’s nutritional status in the context
of India present a mixed picture. For instance, drawing upon the 1994 NCAER data, Gaiha
and Kulkarni (2005) find that reduction in wage gap between men and women reduces severe
stunting in terms of the number of stunted children in a household. Also, their analysis
indicates that household income, composition of household and children and caste are
significant variables in reducing stunting. Using the NFHS-2 data, Maitra (2004) shows thatchild health is affected only indirectly through the improved usage of health care, which isdetermined by women’s education and control over household resources or bargaining power.
Kravdal’s (2004) study, again, employing the NFHS-2 data finds that women’s
empowerment index as measured by i) sum of binary response variables, such as, whether ahusband is justified beating his wife in certain situations and ii) average women’s educationin the census enumeration area, have significant association with child mortality levels.

7

Ackerson et al. (2008) based on NFHS-2 data find that domestic violence, an indication of
weak bargaining power of wives against husbands increases the prevalence of stunted
children and of underweight adult women.
Outside India, most studies have found that increased women’s empowerment orbargaining power has a positive effect on children’s nutritional status. For example, Duflo(2003) studied the impact of the cash transfer programme in South Africa on children’s
nutritional status and found that pensions received by women has a large impact on theanthropometric status of only girls. Smith et al. (2003) used cross-country data and found apositive effect of women’s empowerment on child nutrition indicators. Using various
measures of women’s bargaining and household data on rural Ethiopia, Fafchamps et al.
(2009) concluded that female bargaining power improved child nutritional status.

3.

Theoretical Considerations

This section discusses theoretical arguments on how women’s bargaining power affects childnutritional status. In conceptualizing the determinants of child nutritional status, researchers
choose to either specify a structural health production function (Thomas, 1994) or to model
the intrahousehold bargaining between a mother and a father and then derive the reduced
form equation for child health or nutrition where the bargaining or empowerment index isused as a determinant together with household characteristics. Thomas (1994) combines ahealth production function where child health as an output is a function of a number of inputs(e.g. nutrient intakes and the quantity and quality of child/ health care and individual andhousehold characteristics) with a standard utility function of the household member under abudget constraint for the household. This process can be typically done in the framework of
unitary household models in which the household head makes a decision on behalf of8household members, all the household resources are pooled, and a mother and a father havean identical taste (Becker, 1974, see Park, 2007 for the application to child nutrition)3.
In the non-unitary model framework, personal preference and bargaining power matter.
This consists of cooperative bargaining models and non-cooperative bargaining models. In
the cooperative bargaining models, a mother or a father derives his or her utility from own
consumption of commodities and public goods (e.g. health or nutritional status of children)and the bargaining process is affected by an outside option or the extra-household
environmental parameters (EEP) which are, for example, conditional on the threat of martial
dissolution or on other environmental factors (McElroy & Horney, 1981; McElroy, 1990). In
case of bargaining over child health or nutritional status, a mother and a father are assumed tomake decisions over the quality of health, nutritional conditions of children or the spending
on child health care independently as a part of his or her utility maximization problem(Maitra, 2004, Park, 2007, Fafchamps et al. 2009) 4 . Non-cooperative bargaining models
include Lundberg and Pollak’s (1993) model which specifies the threat point as a noncooperative equilibrium within marriage and define it in terms of traditional gender roles andgender role expectations.
Because the health production approach requires detailed data on health inputs (e.g. healthcare, nutritional intakes, and prices), our conceptual framework is based on the cooperative
bargaining model following most of the empirical studies of child health and nutrition. We
assume that a household consists of a mother, m , a father, f , and a certain number ofchildren, k, considered to be ‘a public good’ for both parents. It is assumed that children are
not decision-makers and for simplicity parents care about the health quality or nutritional
status of children. 5 Let x j be the jth person’s consumption (j = m, f), and q be the (average)

health quality of children. The jth person’s utility is defined as U j x j , q j A j . Here we defineA j , EEP, a vector consisting of exogenous factors that determine the preferences of the9individual j. A j may depends on the factors determined outside the household, e.g. unearned
income for j, as well as his or her individual characteristics.
Each individual is assumed to choose x j (own consumption) and q (child health quality)tomaximise.

In

this

setting,

thehouseholdutilityfunctionisdefinedasU m xm , q ; Am   1   U f x f , q; Af  where γ represents the “bargaining power” of themother (wife) in a household (0    1) . The household’s utility maximization problem is
specified as follows:
Max U H  U m xm , q ; Am   1   U f x f , q; A f (1)xm , x f , q

subject toI  p m x m  p f x f  pc q(2)

where I is a household’s income, pi is the price of the private goods for the mother or thefather, and pc is the shadow price of public goods, that is, children in this case. In general, theoptimal q* (health quality of child) will depend on parameters such as γ, pc, I, pi, and Ai as
follows:

q *  q *  , I , pm , p f , pc , Am , A f (3)This model sheds light on the household decision on child health. For example,“bargaining power” γ may reflect women’s empowerment represented by female educationand female labour force participation. Given that a female is more likely than a male, to valueq, the quality of children’s health, the stronger bargaining power of a female reflected inhigher γ leads to a better nutritional outcome. In this framework, a higher level of education is
likely to improve the nutritional status of children through higher γ. Ai represents eachhousehold member’s attitude toward health care, which may be different in various classes orsocial groups. Economic growth increases a household’s income level I and improves thehealth of children.
10However, the above conceptual framework is limited in the sense that the “bargaining
power” γ is treated as an exogenous variable and determined by, for instance, female
education or cultural factors, in other words, γ affects the household decision on the numberof children, but not the other way around. It is assumed here that the bargaining coefficient, γ,
is exogenously determined by, e.g., female education or cultural factors: in other words, γaffects the household decision on the number of children, but not the other way around.
However, the bargaining coefficient, γ, can be endogenous in reality, that is, the household
decision on the health quality of children in turn affects γ, as modelled by Basu (2006) who
assumed the endogeneity of γ in the collective-bargaining model. This endogeneity is nottaken into account in the above framework, but the endogeneity of γ is econometricallyaddressed by the IV estimation in the empirical section.

4.

Data

This study draws upon three rounds of NFHS data, NFHS-1 (year 1992-3), NFHS-2 (year1998-9) and NFHS-3 (year 2005-06). The NFHS is a major nationwide, large multi-roundsurvey conducted in a representative sample of households in India with a focus on healthand nutrition of household members, especially of women and young children.6 The survey
covers the issues including fertility, family planning, maternal and child health, gender,
HIV/AIDS, nutrition and malaria. Data were collected at the individual level (children,
mothers and fathers in NFHS-3) as well as household and community level. This study uses
the data on children aged zero to three years in rural areas for NFHS-1, 2 and 3. This isbecause children below age four are covered in NFHS-1, below age three in NFHS-2, andbelow age five in NFHS-3. It is also well known that nutritional conditions from zero to three
years have the most fundamental effect on stunting in later life (Maluccio et al., 2007).

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We measure the nutritional status as z scores of ‘height-for-age’ (stunting), ‘weight-forage’ (underweight) as well as ‘weight-for-height’ for children below three years. We follow
the z score measure based on ‘children from a diverse set of countries such as Brazil, Ghana,
India, Norway, Oman and the USA’ (WHO 2006, p.1) put forward by WHO (2006).
Following WHO (1997), we define z score as:z  score xi  xmedian   x(4)where x i is, for example, height of child i, xmedian is the median height from the reference
population of the same age and gender, and  x is the standard deviation from the mean of thereference population. The z-score for the reference population has a standard normal
distribution in the limit. Thus, there is a less than 2.3% probability that a healthy child will
have a z score less than -2 (WHO, 1997). We classify, as per the common practice, children
with a z score below -3 as ‘severely stunted’, and those with a z score between -3 and -2 as‘moderately stunted’. Underweight or wasting is defined in a similar manner. In this study,
however, we define children with z score below -4 as ‘acutely malnourished’ given the large
number of children severely or moderately malnourished. Such a classification would help us
examine the determinants of acute malnutrition at the tail end of the distribution. Although
there is no clear biological justification for “-4” as a threshold, yet given that WHO defines
children with “z score below -3” as “severely stunted”, the level of malnutrition for thosebelow “-4” should be acutely severe and is likely to have serious health consequences in theirlater life.7 Also, as the factors influencing underweight and overweight children are likely to
be different, we consider the factors affecting those in other appropriate ranges.

5.

Econometric Specifications

Our main objective of the econometric analyses is to identify determinants of childmalnutrition in rural India to test (i) ‘Whether the mother’s empowerment as measured by12mother’s relative (to father’s) bargaining power is associated with the nutritional status of herchildren?’ and (ii) ‘Which factors (including those associated with children, households,
infrastructure and policy) are correlated with children’s nutritional status?’.
Methodologically, we apply multiple techniques which make the present study distinct
from extant empirical studies on child malnutrition in India. First, following Borooah (2005)
and Kandpal and McNamara (2009), we apply QR (in addition to OLS) to estimate different
coefficient estimates at different points in the conditional distribution of nutritional status,
rather than at the mean. Second, IV estimation has been also applied to take into
consideration the endogeneity of (i) bargaining power of women and (ii) access to healthinsurance schemes. Third, we use pseudo panel data models by combining two or threerounds of the NFHS data.

OLS and IV
We presented a simple version of the bargaining model in Section 3, but it is not easy to findthe variables which would exactly capture different factors specified in the theoretical model(e.g. the extrahousehold environmental parameters and the bargaining coefficient,  ). We
therefore use the reduced form equation approach in which the child nutritional condition is afunction of the bargaining indicators and household characteristics since the NFHS data do
not contain the variables, such as prices specific to father’s or mother’s consumption or theindividual unearned income.
We consider the reduced form equation which estimates the effect of (a proxy of) the
bargaining power on child nutritional status. Here we distinguish three units, child, household
and community.8 We denote i for the ith child (or an ID number identifying a particular child)and h for the hth household (a household ID number) in a total sample at time t (year). We

13

estimate qih , a nutritional status indicator (namely, z score of height-for-age, weight-for-age,
or weight-for-height) as:

qih  qih  h , Bi , X h , Zh , H h , R, P (5)It is assumed here that Am and A f (or Am A f ) in the equation (3) can be captured by asingle variable  h representing the mother’s relative (to father’s) bargaining power. The
variable,  h is our measure of women’s empowerment and comprises our central independent
variable. As we discussed in Section 2, we proxy  h by (i) the proportion of mother’s yearsof schooling to father’s years of schooling ( [schooling years of mother]/ [schooling years offather]) (after controlling for average schooling years of mother and father); (ii) a dummy
variable on whether the father (husband) is justified in hitting or beating the mother (wife)
when the mother (wife) is unfaithful to the father (husband) (1 for Yes; 0 for No); (iii) a
dummy variable on whether the mother (wife) needs permission from the father (husband)
when she goes to market (1 for Yes; 0 for No).
In case of the IV estimation which has been tried for NFHS-39,  h is instrumented by the(proportional) difference of father’s age and mother’s age on the grounds that the relatively
older father tends to have a greater bargaining power, but it does not have a separate anddirect impact on their child’s nutritional conditions. Also, we use the village-level average ofthe ratio of predicted wage rates for women and those of men as an additional instrument.10
The idea is that the gender difference of implied aggregate wages would determine the extent
to which a woman is disadvantaged in her village, yet as it based on village level aggregate, it
is unlikely to have a direct effect on child nutrition at the individual level. The instrument isvalidated by the specification tests as will be shown in the next section.

Bi is a vector of characteristic of the ith child: whether male or not; age and its square; andwhether the second, third or fourth child.
14

X h is a vector of household specific variables, such as household characteristics andcompositions, such as, household size; share of children under the age of five in total numberof household members; the average schooling years of the mother and the father; mother’sage; its square; and whether a household has access to electricity; whether a household has aradio (or a TV; bicycle; a flush toilet).

Z h is a vector of variables capturing the social, environmental or infrastructural factors
specific to the hth household: time necessary for getting water; whether a household belongs
to scheduled castes (SCs) (or scheduled tribes (STs), other backward groups); religion
dummies (e.g. Hindu, Muslim, Christians).

H h is a policy variable that would affect child’s health: whether any member of thehousehold to which a child belongs has access to a health insurance or a healthcare scheme.
This is a household level variable. Health insurance or a healthcare scheme is broadly defined
as an aggregate category that includes e.g. government sponsored health insurance schemesor private medical insurance schemes. This is instrumented by two instruments in the IV
regression, (i) the infrastructure variables to capture the availability of information, as
indicated by the number of households in the village that have access to a telephone11 and (ii)the village-level need for health care which is proxied by the village-level average of theaccess, both of which are likely to have only weak correlations with child malnutrition at theindividual level. One may criticise that both of these may affect child malnutrition eventhough the village-level averages are taken, but these are validated by specification tests aswill be shown in the next section. It should be also noted that the coefficient estimate for

H h is at best, Intent-to-Treat (ITT) estimates (not ATT, Average Treatment Effects on
Treated) and that the estimate does not imply causality.
R is a vector of regional dummies (BIMARU 12, South, East, and West) as well as state

dummies to take account of state fixed effects. P is a price vector (for sugar, egg, and cereal).
15

Quantile Regressions
As discussed by Aturupane et al. (2008) and Borooah (2005), it is important to estimate theeffect of various variables on child nutritional status on different points in its conditional
distributions because behavioral response to predictors (e.g. mother’s bargaining power) is
likely to be different between a malnourished child and an overweight child. As in Koenker
and Bassett Jr. (1978), quantile regression for the  th percentile takes the form:Min   qi   i b    1    qi   i b b Ri i: qi  X t bi i: qi  X t b(6)where 0    1 , qi is a dependent variable (z score of child nutritional status), and  i is avector of all the explanatory variables in Equation (5). For example, if  =0.5, this is amedian regression. Most of the studies show the results  =0.05, 0.1, 0.25, 0.75 and so on,
but we have chosen the median of each nutritional group for  to estimate the (approximate)determinants of nutritional conditions for each group. For example, if we find that 12% ofchildren are severely undernourished ( z  3.0 ), we have used 0.06 as  . Also, because theerror terms in each group are likely to be heteroscedastic, bootstrap estimates of theasymptotic variances are calculated with 100 repetitions.

Pseudo Panel Data Model
One of the limitations of the above model is that each round of the NFHS data is used
separately for the cross-sectional estimations. To overcome this and to identify thedeterminants of child nutritional status over the years, we also apply the pseudo panel model
which aggregates micro-level data by any cohort that is commonly observed across crosssectional data sets in different years. We apply the pseudo panel for the cohort k based on the
combination of states and mother’s age groups (15-19 years, 20-24 years, … , 45-49 years).13
The cohort is denoted as k in the equation (7) below.
16

qih kt  qih kt  h kt , Bi kt , X h kt , Z h kt , P kt

(7)

where k denotes cohort and t stands for survey years for three rounds of NFHS data, 1992,
1998 and 2005. The upper bar means that the average of each variable is taken for eachcohort, k for each round, t . Regional variables do not have time variation and have been
dropped. A variable on health scheme or health insurance has been also dropped as this isavailable only in NFHS-3.
Equation (7) can be estimated by the standard static panel model, such as fixed effects or
random effects model.
lqi kt    lw1 i kt  l  Dt    kt  ekt(8)lwhere qi kt is a dependent variable,  i kt represents explanatory variables in Equation (7),

Dt is a vector of year dummies,  kt is the unobservable individual effect specific to cohort k(e.g. cultural effects which are not captured by explanatory variables), and ekt is an error term.
The issue is whether equation (8) is a good approximation to the underlying household panel
models for household i in equation (8)’ below. It is not straightforward to check this as we
do not have ‘real’ panel data.

qit     lw1 l it  l  Dt   i  eit(8)’However, as shown by Verbeek and Nijman (1992) and Verbeek (1996), if the number ofobservations in cohort k tends to infinity,  kt   k and the estimator is consistent. In our*case, the average number of observations in each cohort (combination of states and mother’s
age groups) is 73.6 for NFHS data. This is not ideal, but reasonably large reflecting the hugesample size of our datasets covering all parts of India and thus the estimator is close to being
consistent. It may be noted that, we, as is usually done interpret the results of pseudo panel
estimations with caution. Once we take account of the cohort population, Equation (8) will17

become the model developed by Deaton (1985) whereby qi kt and  i kt are considered to beerror-ridden measurements of unobservable cohort means, which leads to so-called ‘error-invariables estimator’ (see Fuller, 1987, for more details).

6.

Results

This section discusses the central results emerging from the models presented in Section 5.
Table 1 summarizes the coefficient estimates of bargaining indicators estimated by NFHS
data, namely, a) the ratio of mother’s schooling years to father’s schooling years (for NFHS-1,
2 and 3); b) whether a husband is justified in beating his wife when she is unfaithful; or c)whether a wife is allowed to go to the market without permission from a husband (NFHS-2
and 3 only). Each variable is included one at a time. The average education of a father and amother is considered as a control variable for the ratio of schooling years. Table 2 gives asummary of the signs and statistical significance of coefficient estimates for all the
explanatory variables based on OLS, IV and quantile regressions applied to the three rounds
of NFHS data. Table 3 reports the results of pseudo panel model based on NFHS data. As thevariables available for NFHS-1 are limited, we present two our results for the three different
child nutrition measures in two parts. The first part is based on NFHS 1, 2 and 3 and thesecond part is based on NFHS 2 and 3. These are shown in columns (1) to (6) in Table 3. The
choice between fixed effects model and random effects model is based on the Hausman test
and except one case for ‘weight-for-age’ (column (2), Table 3), we have chosen the fixed
effects model. In the interest of space, we discuss the key findings for several representative
explanatory variables categorized as, ‘Women’s empowerment or bargaining measures’,‘Health insurance or health care schemes’ and ‘Other selected covariates’.
(Tables 1, 2, and 3 to be inserted)

18

(1)

Women’s empowerment or bargaining measures

Relative educational attainment of mothers
First, we discuss the results based on the three bargaining variables, (a) the ratio of mother’s
to father’s schooling years, (b) presence of domestic violence (whether a husband beats awife if she is unfaithful) and (c) autonomy in the wife’s decision-making in everyday life
(proxied by whether she is allowed to go to market without her husband’s permission).
It should be noted that in all the regressions, we have controlled for the average schoolingyears of a father and a mother to see the conditional correlation between their relative
difference in educational attainments and child nutritional status. The average schooling years
are positive and significant in most cases - irrespective of years or estimation methods (OLS,
IV, or quantile regressions) except Table 3 where the pseudo panel model is applied. Our
results underscore the importance of parental education in improving child nutrition14, though
the estimates need to be interpreted cautiously since statistical significance does notnecessarily imply causality between parental education and child nutrition. The same caveat
applies to all the regression results in Tables 1-3.
Our results on “the ratio of mother’s and father’s schooling years” show that, after
controlling for the average level of parental education, a child whose mother is relatively
better educated tends to have a better nutritional status in some cases. For the NFHS-1 data,
the conditional correlation between the two variables is statistically significant for thestunting measure (which relies mainly on the statistical correlation for relatively stunted
children, as suggested by the QR results) and for the underweight measure (on average based on OLS). In case of NFHS-2, the statistical relation between the two is significant for
the underweight measure (on average - based on OLS as well as for relatively underweightchildren - based on QR). It is also significant, in case of NFHS-3, for the stunting measure(for relatively stunted children, as suggested by QR) as well as for the wasting measure (on19average - based on OLS and IV, and for relatively under-nourished or normal children - basedon QR) (Table 1). A significant correlation is also found in the second column of Table 3 inwhich the random-effects model is applied for pseudo panel. While the results vary according
to the specifications, we can conclude that the empowerment of women (proxied by theirrelative education) is associated with better nourishment of children at the lower conditional
distribution of nutritional measures. This suggests that children of mothers with little
education tend to be undernourished and they have to be supported by government
interventions.
Domestic Violence
Domestic violence (whether a husband beats a wife if she is unfaithful) is not statisticallysignificant in OLS for any of the three child undernutrition measures in 1998-99, but it isnoted that it is statistically significant at the tail end of distribution (for the acutely
undernourished children with Z score -4.0) for ‘weight-for-age’ and ‘weight-for-height’
measures. That is, the lack of women’s empowerment, which is represented by domestic
violence, is statistically associated with the short-term measures of children’s
undernourishment in 1998-99.
In 2005-06, domestic violence is positively associated with “height-for-age” measure,though this is a counter-intuitive result that seems to be driven by adequately or overnourished children (with Z score 1 to 2), as suggested by QR. On the contrary, it is negatively
associated with “weight-for-height” mainly for overweight children (with Z score 2 to 3).
Overall, the violence variable is negative and significant at 5 % level (OLS). As a larger
value in height tends to increase “height-for-age” and decrease “weight for height”, this is not
surprising and more emphasis should be generally placed on the results of the former. It issafe to conclude that the relation of women’s empowerment on child nutrition has become
weaker over time.

20However, domestic violence in general appears to be positively correlated with “weightfor-age” as suggested by column (5) of Table 3, the case of pseudo panel analysis. It does notsuggest any causality relationships and our policy implications about the importance of
reducing domestic violence, which can be derived from the data in 1998-99, will not beaffected.
Autonomy in wife’s decision-making in everyday life
In 1998-99 autonomy in the wife’s decision-making in everyday life which is proxied by thevariable on “whether she is allowed to go to market without her husband’s permission”, ispositive and statistically significant in the case of OLS for “weight-for-age” and “weight-forheight”. It is not significant for “height-for-age”. The results of QR suggest that the positive
association for “weight-for-age” and “weight-for-height” are more clearly observed forchildren undernourished or ‘adequately’ nourished than those over-nourished. Taking thecase of “weight-for-age”, the coefficient estimate is positive and significant for those with zscore ranging from -4 to -2 with the estimate larger for more malnourished children. The sizeof coefficient implies that having autonomy (changing the value from 0 to 1) is associatedwith improvement in z score for underweight by 0.067 to 0.142 for undernourished children.
With the caveat that the results show conditional statistical correlations rather than causality,
they suggest that wife’s autonomy could play a potentially important role in reducing theprevalence of underweight children.
On the other hand, the autonomy in everyday decision-making is positively andsignificantly associated with “height-for-age”, chronic measure of child nutrition in 2005-06,
but not with “weight-for-age” or “weight-for-height” (except one case for the latter, z scoreof -2.0, where a negative and significant correlation is found). On the results of QR for“height-for-age”, significant coefficient estimates are found for z scores -3, -1 and 1. Herewife’s autonomy could reduce the prevalence of stunted children given the same caveat. It is21not easy to generalize the pattern of the results, but the pseudo panel analysis in Table 3
support the significant and positive association between the proxy for wife’s autonomy andstunting and underweight measures. This posits the overall significant correlation between
women’s empowerment and child undernutrition.
(2)

Household access to health insurance or healthcare schemes

The variable on whether a household has access to health scheme or health insurance isavailable for only NFHS-3. When we apply IV to “height-for-age” or “weight-for-age”,
where it is instrumented by the availability of telephone in the region and village-level accessto vaccination to take account of the endogeneity problem, the result suggests that household
access to health insurance or healthcare schemes is associated with better nutritional status ofchildren. Specification test results for IV estimations are shown in the last panel of Table 1.
In the first stage, the availability of landline telephone as an instrument is statisticallysignificant at the 1% level and the village-level access to vaccination is either significant at
the 10% level (in the case of “weight-for-height”) or insignificant with t values ranging from
1.52 to 1.55 in the other two cases. The result of under-identification test implies that theexcluded instruments are correlated with the endogenous regressors and the correlation is not
weak. Also, Hansen’s over-identification test shows that the joint null hypothesis that the
instruments are uncorrelated with the error term is not rejected, which supports the validity of
the instruments. Apart from the need for cautious interpretations for the over-identification
test, Deaton (2010) argued that the IV result should not be interpreted as guidance for policy
because of the underlying heterogeneity of the impact across different agents - which in ourview is partly addressed by QR - and the difficulty in establishing the case for “exogeneity”,
which is often confused with “externality” by researchers. While IV results cannot be used as
evidence for the causal relationship between the health insurance scheme and child nutritional
status, it is safe to mention the conditional correlation between the two, that is, the household

22access to health insurance scheme is statistically associated with better nutritional status ofchildren. The actual “impact” of health insurance scheme would have to be evaluated byusing real panel data or by carrying out carefully designed experimental studies.
(3)

Other Covariates

In the interest of brevity, we only summarise the results based on the cross-sectional
regressions for three rounds of NFHS data in Table 2. 15 The first column of each panel
summarizes the results of OLS and IV where ‘+’ or ‘-’ are shown in case the coefficientestimate is significant. If it is significant only for OLS or IV, it is shown as, e.g., “+(ols)” or“+(iv)”. If the variables are not available, it is shown as ‘(NA)’. In case of QR, while ‘+’ and‘-’ signs indicate statistically significant cases, we show, e.g. ‘+M’ for the case where apositive and significant coefficient is found in one of the categories ‘malnourished’ (z score<-2.0), ‘+N’ (or ‘-N’) for significant cases for ‘normal’ (-2.0<z < 2.0) and ‘+O’ (or ‘-O’) for
significant cases for ‘over-nourished’. In an exceptional with both negative and positive
significant coefficient estimates observed in different categories, the results are summarized,
for example, as “-MN; +O”.
It is important to note that the coefficient estimates of OLS based on the mean of theconditional distribution of a dependent variable do not necessarily reflect the coefficientestimates of each group derived by QR, though as expected the results of OLS by and large
reflect the results for the ‘mean’ group (which is normally close to the median). The results of
QR are useful to check whether those of OLS will hold for all the nutritional groups acrossthe entire conditional distribution of z. In a few cases, the results of QR are not only different
from those of OLS, but change the signs at different points of conditional distributions. For
example, a child’s age is positive and statistically significant for ‘weight-for-height’(wasting) in OLS and IV for NFHS-3 (see the final column of Table 2), but is positive andsignificant up to the group with z score <-1.0, not significant for the group with z between -1

23and negative and significant for those with z score >1.0. This implies that the change of‘weight-for-height’ is in the direction of being equalized as the child gets older, but OLS is
not able to capture that. This is the point emphasized by Borooah (2005). However, suchcases are few and far between and we get results mostly consistent across different estimation
methods. Again, given the space limitations, we highlight estimates provided in Tables 2 and3 for select covariates in addition to the women’s empowerment variables.
Environment‘Time necessary for getting water’ has an expected negative and significant sign in somecases, in particular, for underweight and wasting (Table 2). Pseudo panel analysis confirmsthat it is negatively and significantly associated with “weight-for-height”. As women are
responsible for fetching water, there is an unavoidable trade-off between this activity andchildcare. Access to electricity has a positive and significant coefficient for underweight andwasting measures in Table 2. It is negative and significant in column (3) of Table 3, “weightfor-height” for pseudo panel applied to all the three rounds.
Child characteristics
Consistent with previous studies (Borooah, 2005; Kandpal & McNamara, 2009), whether achild is male is negative and significant in most cases in Table 2 and the case of “weight forheight” in Table 3. However, given that previous research suggests by, that the sign of a sex
dummy of a child over the years can differ across countries (Charmarbagwala et al. 2004),
our results are likely to be context specific. Age of a child is negative and significant with its
square positive and significant in both Tables 2 and 3, implying that z score is decreasing as achild grows but a marginal change will be smaller as he or she grows. Consistent with Gaiha
and Kulkarni (2005), the present estimates in Table 2 show that irrespective of whichmeasure is used higher birth order negatively affects nutritional status.
Household Compositions or Characteristics

24Mother’s age is positive and significant with its square negative and mostly significant,
implying that older mothers tend to have better nourished children with a non-linear effect(Table 2 and Columns (2), (3) and (6) of Table 3). Having more children under the age of fiveis associated with lower levels of nutrition mainly for short-term measures of undernutrition,
namely “weight-for-age” and “weight-for-height” (see Table 2). However, it is negative andsignificant in Column (4) of Table 3 where height-for-age is estimated by the pseudo panel
method covering NFHS-2 and 3. Owning a TV is associated with better child nutritional
status across different years and for different measures, particularly for the children
undernourished (see Table 2), which has been broadly confirmed by the pseudo panel
estimates (see Table 3). This result implies that TV may help households access theinformation on nutrition. There is some evidence that having a flush toilet at home isassociated with better child nutrition. Further, children belonging to Scheduled Castes, Tribes
or other backward groups tend to have lower nutritional levels than the rest (see Table 2).
Food PriceAs hypothesised, in Table 2 we obtained negative and significant coefficient estimates forfood price for NFHS-1 in 1992-93. Further, food price is positive and significant for ‘weight
for age’ for the pseudo panel for NFHS 2 and 3 (see Column 5, Table 3). Price of sugar isnegative and significant for ‘weight for age’ in 2005-06. These inconsistencies across years
call for further examination, for example, in terms of whether a household is a net foodconsumer or a net food producer (Ivanic & Martin, 2008). Moreover, the commodity
disaggregation has to be more detailed to reflect changing compositions of different foodcommodities (e.g. sweetened beverages, and fried and processed foods need to be taken
account as their intake has increased). Finally, cross-price effects on complements andsubstitutes are often significant and not captured here.

25

7.

Concluding Observations

This study investigates whether mother’s empowerment measured as mother’s relative
bargaining power affects children’s nutritional status using three rounds of NFHS data for theyears 1992-93, 1998-99, and 2005-06. 16 OLS, IV, quantile regressions (QR) and pseudo
panel models are applied to these data sets. We summarise our central findings in thefollowing paragraphs.
First, the relative bargaining index defined as the share of mother’s schooling years overfather’s schooling years is found to be positively and significantly associated with z scores
pertaining to the short-term measures of nutritional status of children, namely, “weight-forage” and “weight-for-height”. The results of QR suggest, however, that the bargaining poweris statistically correlated with a chronic measure of nutritional status, ‘height-for-age’, at thelow end of conditional distribution of z score. Second, the result of IV estimation indicatesthat access to health scheme or health insurance is statistically associated with higher valuesof ‘weight- for- age’ in 2005-06. Third, health-related facility, infrastructure and environment
are related to lower prevalence of child malnutrition. It is implied, for instance, that wider
access to a flush toilet is likely to improve nutritional status of children in terms of stunting
and underweight. Easier access to water seems to be important in reducing ‘wasting’. The
results of QR imply that access to radio and TV is likely to be important for improving themeasures of ‘stunting’ and ‘underweight’ particularly at the lower distribution. Also, children
belonging to scheduled caste are more likely to be undernourished.
Despite the steady decline in the prevalence of undernourished children, India is still oneof the few countries with the worst levels of low birth weight, underweight and wasting
among children in BRIC and SAARC countries. In terms of policy implications, more
provisions of healthcare or health insurance schemes are likely to be effective in reducing theshort-term nutritional deprivation of children. Policies to empower women in poor

26

households that tend to have more malnourished children are also required for reducing not
only short-term nutritional deprivations but also chronic deprivations. As children of womenwith low educational levels are more likely to experience poor nutritional outcomes,
concerted efforts to disseminate information on children’s nutrition and health to women withlow education levels would help. In a similar vein targeted programs to enhance access toenhance educational and employment opportunities for the scheduled caste/scheduled tribe(ST/ST) population and in particular to the SC/ST women would potentially positively
impact children’s nutritional levels. Further, improvement in infrastructure facilities in formsof improved access to safe drinking water, sanitation and electricity are likely to besignificant in alleviating nutritional deprivation.
If we go by the predictions of household models, both Beckerian and bargaining,
expanding outside employment options for women is key to their empowerment. However,there are many other factors, such as women’s own asset holding, income, consumption or
production skills, which would lead to women's empowerment (e.g. Doss, 2013; Kabeer,
1999, 2005). Future works should carry out rigorous evaluations of policy interventions orpoverty alleviation measures, such as microfinance, in terms of whether they would affectthese outside options that empower women and thus reduce the prevalence of childmalnutrition in developing countries.

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Table 1. A summary of Effects of Bargaining Power of Mother on child malnutrition in Rural India (NFHS 1, 2 and 3)
NFHS-1 Rural 1992-3
OLS

Quantile Regression
Under-nourished(1)
Z score-4.0

Height for Age *1 *2(2)
Z score-3.0

Normal(3)
Z score-2.0(4)
Z score-1.0(5)
Z score-0Over-nourished(6)
Z score1.0(7)
Z score2.0(8)
Z score3.0

Height for Age[Mother’s Schooling Yrs/

0.05080.000238

0.122**

0.0750*0.04420.00342-0.0315-0.0740-0.179Father’s Schooling Yrs]

(1.504)(0.00364)(2.755)(2.127)(1.235)(0.0637)(-0.397)(-0.643)(-1.523)

Average Schooling Years

0.0343+0.01100.04020.0490*0.0415+0.0465+0.02270.05760.0866(1.784)(0.354)(1.118)(2.265)(1.847)(1.750)(0.525)(0.672)(0.756)Weight for Age

Weight for Age[Mother’s Schooling Yrs/

0.0400+0.0741

0.0672*0.0406

0.0591*0.00499-0.00794-0.0656-0.127

Father’s Schooling Yrs]

(1.901)(1.384)(2.170)(1.494)(2.284)(0.188)(-0.241)(-1.327)(-0.550)

Average Schooling Years

0.0446**0.02140.0435**0.0505**0.0544**0.0613**0.0597**0.0684*0.00818(3.676)(0.768)(2.598)(3.337)(3.692)(4.005)(2.994)(2.334)(0.0689)

Weight for Height

Weight for Height[Mother’s Schooling Yrs/

-0.00508-0.0830-0.0387

0.06300.01734.13e-06-0.0427-0.0349-0.0708

Father’s Schooling Yrs](-0.177)(-0.919)(-0.499)(1.540)(0.449)(0.000145)(-0.936)(-0.431)(-0.509)

Average Schooling Years

0.0381*

0.06610.0476

0.0548+0.03190.0376+

0.03100.05650.0283(1.158)(1.737)(1.546)(1.936)(1.583)(1.455)(0.417)(2.356)(1.106)

Table 1. A summary of Effects of Bargaining Power of Mother on child malnutrition in Rural India (NFHS 1, 2 and 3)(cont.)
NFHS-2 Rural 1998-9
OLS

Quantile Regression
Under-nourished(1)
Z score-4.0[Mother’s Schooling Yrs/

(2)
Z score-3.0

Height for Age *1 *2

Height for Age-0.00176

0.02770.0183

NormalOver-nourished(3)
Z score-2.0(4)
Z score-1.0(5)
Z score-0(6)
Z score1.0(7)
Z score2.0(8)
Z score3.0-0.0016-0.00940

0.004070.00550-0.0177-0.0284(-0.632)

Father’s Schooling Yrs](-0.147)(1.035)(1.189)(-0.126)(-0.677)(0.259)(0.228)(-0.762)

Average Schooling Years

0.0421**

0.0416**

0.0551**0.0490**0.0470**0.0398**0.0310**0.01750.0303(7.865)(3.768)(8.449)(8.125)(7.934)(5.328)(2.904)(1.073)(1.215)

Whether a husband beats

0.000834

0.02900.01610.0128-0.0168

0.03550.0620-0.139-0.474**

if a wife is unfaithful(0.0243)(0.464)(0.369)(0.319)(-0.416)(0.688)(0.844)(-1.341)(-2.693)33

Whether a wife is allowed
to go to market without
permission from a husband

0.03530.06070.07010.0318-0.00761

0.07250.0794-0.09394.70e-05(0.937)(0.918)(1.425)(0.800)(-0.170)(1.302)(1.001)(-0.798)(0.000256)

Weight for Age

Weight for Age[Mother’s Schooling Yrs/

0.0222*0.03680.0231*

0.0649**0.0364*0.0272**0.006540.02210.01180.00631-0.0423

Father’s Schooling Yrs]

(2.327)(0.483)(2.263)(3.157)(2.384)(3.150)(0.637)(1.422)(0.612)(0.197)(-0.826)

Average Schooling Years

0.0448**0.05490.0478**0.0420**0.0603**0.0505**0.0396**

0.0383**0.0276**0.02520.0291(10.39)(1.075)(3.616)(3.826)(8.692)(7.981)(8.392)(7.278)(3.249)(1.639)(1.197)

Whether a husband beats-0.03691.670-0.141+-0.0290-0.0317-0.01240.00586-0.0400-0.0283-0.0248

if a wife is unfaithful(-1.322)(0.190)(-1.835)(-0.678)(-0.896)(-0.352)(0.175)(-0.750)(-0.267)(-0.124)

Whether a wife is allowed
to go to market without
permission from a husband

0.0633*-0.8060.142+

0.124*

0.0672+0.03820.0878*0.01070.126

0.0989(2.119)(-0.208)(1.810)(2.474)(1.803)(1.068)(2.323)(0.182)(1.205)(0.532)

Weight for Height

Weight for Height *3[Mother’s Schooling Yrs/

0.00592

0.175

0.02790.03240.0623**0.0401*0.01360.02010.00560-0.0321-

Father’s Schooling Yrs]

(0.415)(0.289)(0.648)(0.863)(2.655)(2.394)(1.140)(1.412)(0.260)-

Average Schooling Years

0.0194**0.161

0.05570.0384**0.0531**0.0376**0.0307**0.0170**0.00883(-0.858)
0.00674(2.890)(0.319)(1.120)(3.708)(4.920)(5.234)(5.587)(2.750)(0.834)(-0.651)-

Whether a husband beats-0.0698

24.81-0.156+-0.0631-0.0380-0.0782*-0.0461-0.0553-0.0458-

if a wife is unfaithful(-1.617)(0.280)(-1.832)(-0.815)(-0.710)(-2.508)(-1.436)(-0.773)(-0.586)-

Whether a wife is allowed
to go to market without
permission from a husband

0.126**

0.105*

0.162*

0.104-(2.645)(2.331)(2.405)(1.471)--9.1180.188*-0.0183

0.0746

0.0663+(-0.738)(2.381)(-0.243)(1.328)(1.710)-

Table 1. A summary of Effects of Bargaining Power of Mother on child malnutrition in Rural India (NFHS 1, 2 and 3) (cont.)
NFHS-3 Rural 2005-6
OLS

IV

Quantile Regression
Under-nourished(1)
Z score-4.0

Height for Age(2)
Z score-3.0

Normal(3)
Z score-2.0(4)
Z score-1.0(5)
Z score-0Over-nourished(6)
Z score1.0(7)
Z score2.0(8)
Z score3.0

Height for Age

Case 1Case 2

Case 3[Mother’s Schooling Yrs/

-0.0188-0.00608-0.0183-0.0237

0.0635**0.0359**0.0111-0.00050-0.0104

0.00210-0.0041-0.0619

Father’s Schooling Yrs](-1.302)(-0.381)(-0.342)(-0.982)(3.379)(2.796)(1.014)(-0.0461)(-0.622)(0.0799)(-0.123)(-0.832)

Average Schooling Years

0.0418**0.0225+0.03950.0384**0.0416**0.0492**0.0502**0.0449**0.0350**0.0208*0.0144-0.0286(6.888)(1.896)(0.570)(3.636)(3.045)(7.515)(9.498)(9.363)(5.258)(1.998)(0.936)(-0.897)-0.0192-0.0532-0.03200.04200.08400.183**

0.310**

0.330

Whether a husband beats

0.0800+-0.29734

if a wife is unfaithful(1.782)

Whether a wife is allowed
to go to market without
permission from a husband

0.0944*

Whether a household hasaccess to Health Insurance
or Health case schemes-0.01414.729+(-0.131)(1.897)(-0.280)(-0.921)(-0.819)(1.011)(1.422)(2.846)(2.658)(1.339)0.571

0.000545

0.0910+0.03350.0564+0.02320.106*

0.152

0.124

(0.365)(0.00809)(1.841)(0.909)(1.762)(0.469)(2.018)(0.918)(0.613)-0.1150.222+-0.02410.0820-0.09540.03890.180-0.207(-0.031)(2.189)(1.683)(-0.432)(-0.195)(0.868)(-0.588)(0.136)(0.649)(-0.473)*Specification Tests for IV (the cases in favour of valid instruments are shown in bold)First Stage IV
Case 1*
Village-level average land-

0.0796line phone access(3.06)**

Case 2*

Case 3**Case 1: Underidentification test (Kleibergen-Paap rk LM statistic): 11.237** (Chi-sq(2) P-val = 0.0036)
Hansen J statistic (overidentification test of all instruments): 2.590

0.016

Village-level access to

0.9273)

Hansen J statistic (overidentification test of all instruments): 0.291 (Chi-sq(1) P-val =(1.52)

vaccination(Chi-sq(1) P-val = 0.1076)*Case 2: Underidentification test (Kleibergen-Paap rk LM statistic): 0.151 (Chi-sq(2) P-val =0.5895)Age difference of mother and

0.000390.0007*Case 3: Underidentification test (Kleibergen-Paap rk LM statistic): 5.592+ (Chi-sq(2) P-val = 0.0611)

father(0.33)(0.55)

Hansen J statistic (overidentification test of all instruments): 0.155 (Chi-sq(1) P-val =Village-level the ratio of men’s-0.013-0.157and women’s implied wage rates(-0.19)(-2.29)*Weight for Age
OLS

0.6936)Weight for Age
IV[Mother’s Schooling Yrs/

-0.00424

0.00374

0.0185-0.0218-0.03020.02370.01020.004220.00904

0.0212-0.0111-0.0228

Father’s Schooling Yrs](-0.360)(0.285)(0.488)(-1.092)(-1.193)(1.135)(1.354)(0.392)(0.823)(1.325)(-0.594)(-0.377)

Average Schooling Years

0.0439**0.0267**0.07460.0323**0.0537**

0.0615**0.0469**0.0392**0.0370**0.0395**0.0208-0.00315(9.386)(2.816)(1.485)(3.260)(3.123)(13.32)(14.27)(15.67)(7.928)(5.052)(1.397)(-0.145)

Whether a husband beats-0.01423.880-0.00647-0.0492-0.0175

0.01420.01230.06380.108-0.0430

if a wife is unfaithful(-0.429)(0.604)(-0.0703)(-1.104)(-0.429)(0.459)(0.293)(1.196)(1.352)(-0.272)

Whether a wife is allowed
to go to market without
permission from a husband

0.03162.103

0.02900.03810.0202-0.0147-0.0384-0.04650.04240.210(1.508)(0.245)(0.835)(0.483)(-0.477)(-1.110)(-0.976)(0.444)(1.516)

0.06970.08880.01530.03280.05070.184

0.666+

0.176

Whether a household hasaccess to Health Insurance
or Health case schemes(1.000)0.000801

4.621*(0.00881)(2.263)(1.651)(0.403)(0.578)(0.107)(0.482)(0.502)(0.920)(0.404)*Specification Tests for IV (the cases in favour of valid instruments are shown in bold)First Stage IV
Case 1*
Village-level average land-

0.0783line phone access(3.10)**

Village-level access to

0.016

vaccination(1.55)

Case 2*

Case 3**Case 1: Underidentification test (Kleibergen-Paap rk LM statistic): 11.633** (Chi-sq(2) P-val = 0.0030)
Hansen J statistic (overidentification test of all instruments): 0.163(Chi-sq(1) P-val = 0.6867)*Case 2: Underidentification test (Kleibergen-Paap rk LM statistic): 0.584 (Chi-sq(2) P-val =0.7468)

Hansen J statistic (overidentification test of all instruments): 0.990 (Chi-sq(1) P-val =0.3199)Age difference of mother and

0.00088

0.00010*Case 3: Underidentification test (Kleibergen-Paap rk LM statistic): 6.109* (Chi-sq(2) P-val = 0.0471)

father

(0.76)(0.09)

Hansen J statistic (overidentification test of all instruments): 0.155 (Chi-sq(1) P-val =35

0.6936)

Village-level the ratio of men’s-0.0039-0.168(-0.06)(-2.47)*0.0300*0.04510.00609

and women’s implied wage rates

Weight for Height[Mother’s Schooling Yrs/

0.0286*Weight for Height0.03250.0297+0.00530

0.005270.00188

0.0219+0.0217-0.00318(-0.118)

Father’s Schooling Yrs]

(2.284)(2.311)(0.935)(0.270)(1.020)(1.946)(0.406)(0.456)(0.214)(1.959)(0.948)Average Schooling Years

0.0284**0.0266**

0.05450.01600.0404**0.0583**0.0399**0.0245**0.0208**0.0204**0.0255**0.0114(5.648)(3.411)(0.748)(1.485)(2.947)(4.221)(7.180)(5.634)(6.766)(3.094)(3.100)(0.673)0.144-0.0801-0.0667-0.0712-0.0384-0.0358-0.123*-0.327*(-2.545)

Whether a husband beats-0.0847*

if a wife is unfaithful(-2.369)(1.545)(-1.409)(-1.623)(-1.512)(-1.330)(-0.770)(-2.384)

Whether a wife is allowed
to go to market without
permission from a husband-0.0388

2.041

0.0531-0.109-0.067+-0.0449-0.0275-0.0381-0.01190.0642(-1.142)(1.368)(0.550)(-1.498)(-1.676)(-1.228)(-0.757)(-0.935)(-0.182)(0.509)Whether a household hasaccess to Health Insurance
or Health case schemes

3.531(0.361)-0.00355

0.695

0.591**

0.0590-0.0382

0.02410.0577-0.0024

0.153-0.321(-0.0382)(0.449)(2.747)(0.400)(-0.355)(0.255)(0.520)(-0.016)(0.863)(-0.964)*Specification Tests for IV (the cases in favour of valid instruments are shown in bold)First Stage IV
Case 1*
Village-level average land-

0.0868line phone access(3.31)**

Village-level access to

0.017

vaccination(1.66)+

Case 2*

Case 3**Case 1: Underidentification test (Kleibergen-Paap rk LM statistic): 13.261** (Chi-sq(2) P-val = 0.0013)
Hansen J statistic (overidentification test of all instruments): 0.162(Chi-sq(1) P-val = 0.6873)*Case 2: Underidentification test (Kleibergen-Paap rk LM statistic): 0.243 (Chi-sq(2) P-val =0.8854)

Hansen J statistic (overidentification test of all instruments): 1.263 (Chi-sq(1) P-val = 0.2611)

0.000558

0.00040*Case 3: Underidentification test (Kleibergen-Paap rk LM statistic): 6.0109* (Chi-sq(2) P-val = 0.0496)

father

(0.48)(0.33)

Hansen J statistic (overidentification test of all instruments): 0.351 (Chi-sq(1) P-val =Village-level the ratio of men’s

0.0093-0.168Age difference of mother andand women’s implied wage rates

0.5534)(-2.42)*(0.14)
Notes: *1. t-statistics in parentheses (** p<0.01, * p<0.05, + p<0.1). Statistically significant coefficients are shown in bold. *2. Coefficient estimates cannot be obtained in the case of “Z score 3.0”.

36

Table 2. Summary of Results of OLS, IV and Quantile Regressions (QR) based on NFHS Data
VARIABLES

HAZ*1
OLS
QR

NFHS-1 (1992/3)
WAZ*1
OLS
QR

Bargaining/ Women’s Empowerment
Ratio ofschoolingyears+M(mother/father)
Average++MN
schooling years
Whether a husband
beats his wife if she isunfaithful
Whether a wife isallowed to go tomarket withouthusband’s permission

+

+M

+

+

HWZ*1
OLS/IV
QR

+

+N

HAZ*1
OLS
QR

+

(Data ‘Not Available’ or NA)

NFHS-2 (1998/9)
WAZ*1
OLS
QR

+MN

+

+M

+

+MN

HWZ*1
OLS
QR

HAZ*1
OLS/IV
QR

+M

+M

+

-O

+MN

+(ols)

-M
+M

(NA)

+

+MN

+

NFHS-3 (2005/6)
WAZ*1
OLS/IV
QR

+

+

+NO
+(ols)+N+(ols)+M

HWZ*1
OLS/IV
QR

+

+MN

+

+

-(ols)

-O

+(iv)

-M

PolicyWhether a household
has access to healthinsurance/healthcare
scheme

(NA)

(NA)

+M

Environment
Time necessary forgetting water
Whether a household
has access toelectricity

-N

-N
+

Child Characteristics
Whether child is
Male

-M

Child’s Age

-

-

-

Age squared
Whether second
child

+

+

+

-N
-O

Third child
Fourth more

-

-NO

+MN

+

-MN

+
-

+N
-MN

+

-

-M

+

+M

-MN
+M

+MN

-N

-M

-NO

-

-O
-MN;

-

-

-

-M

-

-MN

-

-MN

-

-M

-

-

-

-

-

-

-M

-

-

-

-

+

+

+

+

+

-(ols)

-M
-MN
-MN

+
-

+MN
-MN

+

+NO

+

+

+

+

+

+MN

-O
-NO

-

+O
-N
-NO

-

-MN
-MN
-MN

-

-N
-MN
-MN

-

-N
-N
-N

-

-MN
-MN

+
-

+
-

+N

+
-

+MN
-MN

+
-

+MN
-MN

+
-

+N
-N

+
-

+MN
-MN

+M;
-N

-

-

+M;
-NO;
-M;
+O

-NO
-N
-N

Household Composition & Characteristics
Mother’s age
Mother’s age
squaredHousehold size

+

+NO
+NO

+
-

-MN;
+O

-MO
37

-O
+O
+

+MN

Share of children
under 5
Whether a household
holds Radio
-- TV

+

+M
+M

+

-O
+MN
+M

+

-NO
+MN

+O
+

+MN

+

+MN
+M

-- Fridge

+MN

+

+M
+N

-

-

-M

+

+O

Hindu

-O

+

-N

-

-N
+MO
-M

-MN
+
+MN

-

+MN

+

-NO
+

-NO
+N

-

-MN

+NO

-MN

-

-MN

-M

-

-

-

-

-NO

-

-MN

-MN

-

-MN

-

-MN

-

+N

-

-MN
-MN

-MN

+

-(iv)

-N

-

-M

+

+O
+NO

-MN
+NO
+NO

-MN

+NO

-

+O
-

Sikh

+N
-M

+(iv)
+
+

+

MuslimChristian

-NO
+MN
+MN
+
+M;
-O

-N

-- Bicycle-- Flush toilet
Whether a householdbelongs to Scheduled
Caste
Whether a householdbelongs to Scheduled
Tribe
Whether a householdbelongs to Other
Disadvantaged
Groups

+

+
+M

+M
+O

Regional Dummies-M;+O

BIMARU

+

+N

South

-O

-

+

+MO

-

-

-M

East
West

-

-

-NO

+M;
-O

-

-(iv)
-(ols)

-N
+
+NO

-

-(ols)

-(iv)

-(iv)

-MN

-(ols)

+
-NO

+ols

+MO

-MN
+N

-N

-O

+

+N
-NO
+M

Food Price
Food Price

-N

+M;
-NO

+

+NO

+

+

(NA)

Sugar Price

(NA)

(NA)

-

Egg Price

(NA)

(NA)

-(iv)

Cereal Price

(NA)
(NA)
*1 HAZ: Z score for Height for Age; WAZ: Z score for Weight for Age; WHZ: Z score for Weight for Height. *2 “+” or “-“ is shown in the case where the coefficient estimates are statistically significant. In the case of Quantile Regression (QR), M stands for‘Malnourished’(shown as Italics to emphasise the factors associated with the nutritional changes of under-nourished children). That is, “+M” means “positive and statistically significant only for malnourished children. Similarly, N stands for Normal and O stands for
Over-nourished. We put M (or N, O) if we find any sub-group for which a coefficient estimate is statistically significant. A full set of results are furnished on request.

38

Table 3. Pseudo Panel for Z Score of Children based on the NFHSdata(1)
Height for
Age

VARIABLES
Fixed or Random(2).
Weight for
Age

(3).
Weight for
Height(4).
Height for
Age

Based o NFHS 1, 2 and 3
FE*1

Effects Model(5).
Weight for
Age

(6).
Weight for
HeightBased on NFHS 2 and 3

RE*1

FE

0.197** *3(3.372)

FE

FE

FE

0.280**

0.06010.243**

0.281*(3.451)(0.497)(3.047)(2.484)

Bargaining/ Women’s Empowerment[Mother’s Schooling Yrs/
Father’s Schooling Yrs]
Average schooling years
Whether a husband beats his

0.0432(0.476)*2

0.01430.00645-0.03540.03530.0231-0.0298(0.367)(0.316)(-1.012)(0.814)(0.809)(-0.736)---

wife if she is unfaithful
Whether a wife is allowed to go
to market without husband’s
permission

-

-

-

0.388

0.376*

0.260(1.451)(2.140)(1.040)0.453+

0.342*

0.136(1.946)(2.230)(0.627)

Environment
0.00188-0.00105-0.01000**

0.00276

0.00300-0.00894*(0.468)(-0.404)(-2.772)(0.627)(1.035)(-2.175)

Whether a household has access

0.349-0.0861-0.463+0.296-0.169-0.447

to electricity(1.273)(-0.638)(-1.890)(0.988)(-0.858)(-1.595)

Time necessary for getting waterChild Characteristics
Whether child is maleChild’s age
Age squared
Whether second child
Third child
Fourth more

-0.0312-0.156-0.400*-0.174-0.292+-0.468*(-0.145)(-1.043)(-2.076)(-0.735)(-1.876)(-2.123)-0.218**-0.0760*-0.0785+-0.149**-0.0675+-0.0722(-4.153)(-2.103)(-1.670)(-2.654)(-1.823)(-1.374)

0.00357**0.00182*

0.00208+0.001940.001330.00198(2.752)(2.030)(1.792)(1.396)(1.454)(1.531)

0.566-0.213-0.4720.829+

0.0624-0.890*(1.401)(-0.776)(-1.306)(1.813)(0.207)(-2.084)0.354-0.368-0.4271.072+-0.167-0.909+(0.781)(-1.194)(-1.053)(1.920)(-0.454)(-1.745)0.480-0.721**-0.844*

1.122*-0.0291-1.041*(1.132)(-2.694)(-2.222)(2.347)(-0.0923)(-2.331)

Household Composition & Characteristics
Mother’s age
Mother’s age squared
Share of children under 50.02590.124**

0.115*-0.0730

0.02630.113+(0.412)(2.979)(2.040)(-1.029)(0.564)(1.710)-0.000351-0.0017**-0.00158*

0.000809-0.000455-0.00148(-0.405)(-2.982)(-2.030)(0.824)(-0.705)(-1.614)-0.485-0.381+

0.0672-3.348**0.398

2.541*(2.448)(-1.521)(-1.885)(0.235)(-3.012)(0.544)

Whether a household holds

0.400

0.237

-0.324-0.05360.0240-0.251Radio(1.533)(1.492)(-1.390)(-0.181)(0.123)(-0.907)0.004820.165

0.767**

0.752*

0.675**

0.914**(0.0163)(0.905)(2.893)(2.155)(2.939)(2.803)- TV- Fridge- Bicycle

0.311

0.0471

0.06490.0581

0.371-0.0460(0.814)(0.203)(0.190)(0.119)(1.155)(-0.101)

0.363

0.161

-0.112-0.0972

0.215-0.137(1.534)(1.302)(-0.528)(-0.354)(1.187)(-0.535)- Flush Toilet

0.949**

0.654**-0.647*0.374-0.0266-0.688*(3.274)(4.184)(-2.495)(1.024)(-0.111)(-2.017)

Whether a household belongs to-1.191**-0.699**-0.499+-1.402**-1.036**-1.000**(-2.937)

Scheduled Caste(-3.884)(-3.731)(-1.819)(-3.845)(-4.318)

Whether a household belongs to

0.378-0.00342-0.0479-0.217-0.382-0.427

Scheduled Tribe(1.078)(-0.0197)(-0.153)(-0.502)(-1.344)(-1.059)39

Whether a household belongs to-0.1370.129

0.388+-0.129

0.112

0.110

Other Backward Groups(-0.522)(0.895)(1.658)(-0.422)(0.556)(0.385)

Hindu
MuslimChristian
Sikh
rural

-0.389-0.431+

0.157-0.592

0.118

0.185

(-0.538)(-1.648)(0.243)(-0.740)(0.224)(0.248)-1.140-0.3800.111-1.674*-0.350-0.0777(-1.529)(-1.406)(0.166)(-1.983)(-0.630)(-0.0986)-1.190+-0.2450.161-1.242+

0.309

0.385

(0.723)(0.634)(-1.925)(-0.999)(0.292)(-1.910)-2.442*-0.3751.871*-2.605*0.02852.176*(-2.344)(-1.033)(2.007)(-2.377)(0.0396)(2.125)-0.629----(-0.906)
Regional Dummies
BIMARU
South
East

0.392

-0.310**1.169**

0.526

0.716*

1.316**(0.968)(-3.831)(3.229)(1.229)(2.544)(3.295)-0.819*-0.199*-0.00629

0.329(-2.463)(-2.537)(-0.0212)(0.960)-0.440*-0.296**-0.201-0.505*-0.581**-0.286(-2.447)(-3.893)(-1.249)(-2.553)(-4.468)(-1.551)

West
Food price-0.255**0.570

0.0563(-3.078)(1.551)(0.233)-0.000239-0.00124

0.002420.004200.00625*

0.00640(-0.0941)(-1.377)(1.065)(0.875)(1.978)(1.427)0.08090.461+

2.054+(0.0631)(1.736)(1.791)Time Dummies
Whether 1998
Whether 2005
Constant

0.523

1.900

1.688

1.917*

1.039(0.258)(1.050)(1.210)(2.089)(0.798)-0.211-1.728-3.8960.706-4.204-4.172(-0.0939)(-1.852)(-1.935)(0.344)(-3.118)(-2.191)

Observations

390

419

R-squared

0.377

390

338

338

338

0.486

0.408

0.404

0.498

Number of state

29

29

29

29

29

29

Hausman Test

Chi2(29)=

Chi2(30)=

Chi2(29)=

Chi2(30)=

Chi2(30)=

Chi2(31)=93.17**

19.23

280.85**59.79**

138.07**66.55**

2

Prob>chi
0
0.935
0
0.001
0
0.0002Chosen Model (fixed-effects (FE)*1*1or random-effects (RE) model)
FE
RE
FE
FE
FE
FE
Notes: *1. FE stands for Fixed-Effects Model and RE random effects model. *2. t-statistics in parentheses (** p<0.01, *p<0.05, + p<0.1). *3. Statistically significant coefficients are shown in bold.

40

NOTES
1

BRIC comprises the fast growing countries of Brazil, Russia, India and China. SAARC

stands for The South Asian Association for Regional Cooperation.
2

Doss (2013, p.35) argued that “(g)iven the convincing evidence that bargaining power isimportant in some specific cases, we should be more willing to accept the findings of lessrigorous studies as well as those that simply demonstrate correlations.”
3

The health production approach could be incorporated in non-unitary or bargaining

household models (Thomas, 1994).
4

Maitra (2004) assumes that parents bargain over the use of health care (e.g. prenatal careand hospital delivery) and examines the effects of health care on child mortality. To avoid
complication in the empirical model, we assume that parents can directly bargain over childhealth and nutritional status where the bargaining coefficient captures both direct effects ofbargaining and indirect effects through the use of health care.
5

An underlying assumption is that parents care about the average health quality of theirchildren without their preferences over boys or girls in improving their health following thetheoretical literature (e.g. Maitra, 2004) as inclusion of different preferences for mothers andmothers will unnecessarily complicate the model. Further, the nutritional advantage of girls
over boys aged 0-3 years has been found in India (e.g. Naandi Foundation, 2011) and it is notentirely clear the extent to which son’s preference exists among parents and how it results indifferent nutritional outcomes between boys and girls. We have included the interaction ofchild’s age and various women’s empowerment measures to see how its or their effects onchild’s nutritional status differ according to child’s sex, but they are not statisticallysignificant. This implies that there is no significant interacted effect between women’s
empowerment and child’s sex. This does not necessarily imply the lack of son’s preference,but it indirectly supports our assumption in the model.

41

6

See http://www.nfhsindia.org/index.html for the detailed description of NFHS.

7

Sachdev et al. (1992) reported that 37 died among 382 children under 5 due to fatal

diarrhoea in India had a mean z score of -4.3 (with s.d. 1.2) for weight- for- age and of -3.8
(s.d. 1.3) “for height- for- age”, which implies the acuteness of malnutrition corresponding toz score under -4.
8

Variance should be clustered at the household level, but as Stata 11.0 does not allow

clustering for QR or IV regressions, we take account of the clustering effects only for OLS.
But in case of OLS, once we introduce the heteroscedasticity-robust estimator, ‘clustering’cannot be corrected. However, we find (in case of OLS) that clustering at household or atcommunity level does not change the results significantly and so given the large sample size,
we present the case where only heteroscedasticity is adjusted by a robust estimator.
9

IV estimations were tried for the NFHS-2 as well, but no plausible results were obtained

due to the lack of valid instruments.
10

As the NFHS-3 data do not include wage rates, we have estimated men’s wage rates andwomen’s wage rates separately using the NCAER data in 2005 and applied Two Sample Two
Stage Least Squares to obtain the implied wage rates for men and women separately using the
NFHS-3 data and then have taken village-level averages. Details of the results of wage
equations will be furnished on request. This method is limited as the wage levels are derived
as implied values, but will be useful in obtaining a valid instrument for bargaining variables
under the data constraints. This method is not possible with NFHS-2 due to the data
limitations (e.g. lack of the data for adult men or unavailability of NCAER data in the sameyear).
11

Ideally, the variable on mobile phone access should be also used, but the survey did notcover such data.
12

BIMARU stands for the states of Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh.
42

13

It is assumed here that the regional effects are supposed to be constant. Taking account oftime-variant regional factors, such as regional health initiatives, is important, but our data do
not include such information.
14

The positive and significant coefficient estimate of the average education could imply theimportance of knowing about appropriate parenting practices, knowing where to access
additional information (e.g. health clinic, ICDS center, TV, newspaper) and being able to use
these sources of information. We thank one of the referees for pointing this out.
15

A full set of results will be furnished on request.

16

The National Council of Applied Economic Research (NCAER) data in 2005 were also

used to derive the implied values of wage rates for women and men which have been used toconstruct an instrument. In fact, we have used NCAER data in 1994 and 2005 and attempted
all the cases (OLS, IV, QR and pseudo panel) to cross-check the results. While there are a
few inconsistencies (e.g. the bargaining variables are not statistically significant), the overall
patterns of the results are similar. The results will be furnished on request.

43